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ASSESSING LANDSCAPE CONNECTIVITY USING BUTTERFLY DISTRIBUTION
AND DISSEMINATING AGROFORESTRY TECHNOLOGIES IN AGRARIAN SETTLEMENTS IN BRAZIL
By
WENDY FRANCESCONI
A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY
UNIVERSITY OF FLORIDA
2011
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© 2011 Wendy Francesconi
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To my parents and Mason’s recovery
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ACKNOWLEDGMENTS
I would like to thank my parents for their patience. I would also like to thank my
aunt and uncle Horacio and Aurora Amado, also for their patience and unconditional
support during my years in New York while working and studying. I would also like to
thank my dear friend and undergraduate mentor Dr. Martin Gluck for pushing me to
believe in myself and in my dreams. Also, this dissertation would have not been
possible without the generous and genius oversight of Dr. Thorsten Fischer.
I would also like to thank Dr. Laury Cullen and the Institute for Ecological
Research (IPE) for making the field work of this dissertation possible. Among IPE’s
staff, I am so grateful to my field assistant Wellington Lacerna for being so professional
and smart, but above all for being a friend. I also would like to extend my thanks to the
rest of the staff and my roommates in Teodoro Sampaio. They were all like a family
during my time in Brazil. I hope the products of this dissertation will help IPE continue to
thrive.
Very specially, I would like to acknowledge my dissertation advising committee
who has given me the confidence to grow professionally. This dissertation would not be
possible without the thoughtful advice and detail oversight of Dr. Douglas Levey, Dr.
Jaret Daniels, Dr. Taylor Stein and Dr. Youlian Qiu. I hope the publications that will
come out of this dissertation will be as successful as the relation we developed over the
years. I would like to specially thank my friend and advisor Dr. P.K. Nair, who took a
leap of faith when he decided to serve as my adviser even though my dissertation ideas
were slightly outside his realm of expertise. I appreciate his kind and always positive
support.
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I would also like to acknowledge my funding sources and the School of Forest
Resources and Conservation at the University of Florida, who have been very generous
supporting my education and research. I am very grateful to the Sourth Eastern Allience
of Graduate students and Professoriate (SEAGEP), the National Security Education
Program Boren Fellowship, the Tropical Conservation and Development Program
(TCD), and the Working Forest in the Tropics Program (WFT). I am also deeply
appreciative of my teaching fellowship institutions. I would like to thank the Science
Partners in Inquiry-based Collaborative Education (SPICE) and the National Science
Foundation GK-12 program. These organizations have provided me with numerous
teaching and communication skills, which have become very useful during the last
couple of years. Finnally I would like to thank Middle Tennessee State University and
the Agribusiness and Agriscience department for the wonderful opportunity of teaching
while finishing my dissertation.
To all the rest whom in one way or another contributed to this achievement,
nothing but good wishes and a big thank you sent your way.
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TABLE OF CONTENTS page
ACKNOWLEDGMENTS .................................................................................................. 4
LIST OF TABLES ............................................................................................................ 9
LIST OF FIGURES ........................................................................................................ 12
LIST OF ABBREVIATIONS ........................................................................................... 18
CHAPTER
1 INTRODUCTION .................................................................................................... 21
2 THE HISTORY AND APPLICATION OF TWO THEORIES AT PONTAL DO PARANAPANEMA, BRAZIL ................................................................................... 27
The History of Pontal do Paranapanema ................................................................ 27 The Rise of the Movement of the Landless Rural Workers (Movimento dos
Trabalhadores Rurais Sem Terra - MST ....................................................... 27
Brazil’s Agrarian Reform and the Environment ................................................. 29 MST in the Pontal Region ................................................................................. 31
Landscape Ecology Theory and Pontal do Paranapanema .................................... 33 The Landscape at Pontal do Paranapanema ................................................... 34
Landscape Ecology and Agriculture ................................................................. 38 Ecological Stepping Stones as Conservation Strategies .................................. 39 Agroforestry as a Landscape Connectivity Strategy ......................................... 40
Landscape Ecology, Agroforestry and GIS....................................................... 43 Fruit Feeding Butterflies as Indicators of Landscape Connectivity ................... 45
Social Learning Theory and Pontal do Paranapanema .......................................... 46 Social Learning Theory Research .................................................................... 47 Social Learning Theory, Diffusion of Innovation, and Agroforestry ................... 49
3 BUTTERFLY DISTRIBUTION IN FRAGMENTED AGRICULTURAL LANDSCAPES OF EASTERN BRAZIL .................................................................. 52
Methodology ........................................................................................................... 54
Site Description ................................................................................................ 54
Data Collection ................................................................................................. 57 Data Analysis ................................................................................................... 60
Results .................................................................................................................... 62 Mean Number of Individuals and Species ........................................................ 62 Butterfly Differences between Land-use and Habitats ...................................... 63
Riberão Bonito ........................................................................................... 63 Agua Sumida ............................................................................................. 64
Linear Discriminant Analysis ............................................................................ 65
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Species Similarities (ANOSIM Analysis)........................................................... 66
Environmental Factors Analysis ....................................................................... 67 Discussion .............................................................................................................. 70
Fruit Feeding Butterflies in Fragmented Agricultural Landscapes .................... 70 ANOVA Land-use Comparisons based on butterfly abundance and species
richness ......................................................................................................... 74 Species Composition in Agricultural Land-use Systems .................................. 76 Effect of Abiotic Parameters on Fruit Feeding Butterfly Diversity ..................... 79
Concluding Remarks............................................................................................... 81
4 AGROFORESTRY AS ECOLOGICAL STEPPING STONES: PREDICTING BUTTERFLY MOVEMENT USING LEAST-COST ANALYSES IN FRAGMENTED AGRICULTURAL LANDSCAPES. .............................................. 107
Methodology ......................................................................................................... 110 Data Collection ............................................................................................... 112
Statistical Data Analysis ................................................................................. 114 GIS Methodology ............................................................................................ 116
Landscape Image Georeferencing and land-use polygon digitization ...... 116 Trap points ............................................................................................... 117 GIS analysis ............................................................................................. 118
Results .................................................................................................................. 121 Regression Analysis of Abiotic Factors .......................................................... 121
GIS Analysis ................................................................................................... 122 Recapture Data Analysis ................................................................................ 123 ANOVA Comparisons of Recapture Data ....................................................... 127
Discussion ............................................................................................................ 127 GIS Model Development for Movement Data using Abiotic Factors ............... 127
Abiotic and Recapture Data Least-Cost Path Analysis ................................... 134 Recapture Data .............................................................................................. 136
Concluding Remarks............................................................................................. 138
5 AGROFORESTRY INFORMATION DISSEMINATION AT PONTAL DO PARANAPANEMA, SÃO PAULO, BRAZIL ........................................................... 173
Methodology ......................................................................................................... 178 Study Site ....................................................................................................... 178 Data Analysis ................................................................................................. 179
Results .................................................................................................................. 181
Discussion ............................................................................................................ 186
6 DISSERTATION CONCLUSIONS ........................................................................ 204
Agroforestry and Biodiversity Conservation .......................................................... 204 Butterflies in the Pontal Landscape ................................................................ 205 GIS Analysis and Agroforestry as Stepping Stones ....................................... 207
Social Learning Study ........................................................................................... 209
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APPENDIX
A LIST OF BUTTERFLY SPECIES AT RIBERÃO BONITO ..................................... 212
B LIST OF BUTTERFLY SPECIES AT AGUA SUMIDA .......................................... 215
C RAREFRACTION CURVES FOR EACH LAND-USE AT RIBERÃO BONITO ...... 217
D RAREFRACTION CURVES FOR EACH LAND-USE AT AGUA SUMIDA ............ 221
E REGRESSION ANALYSIS OF ABIOTIC FACTORS AT RIBERÃO BONITO ....... 225
F REGRESSION ANALYSIS OF ABIOTIC FACTORS AT AGUA SUMIDA............. 226
G REGRESSION ANALYSIS OF ABIOTIC FACTORS AT RIBERÃO BONITO FOR THE GIS MODEL DEVELOPMENT ............................................................. 227
H REGRESSION ANALYSIS OF ABIOTIC FACTORS AT AGUA SUMIDA FOR THE GIS MODEL DEVELOPMENT ...................................................................... 228
I QUESTIONNAIRE ................................................................................................ 229
LIST OF REFERENCES ............................................................................................. 236
BIOGRAPHICAL SKETCH .......................................................................................... 249
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LIST OF TABLES
Table page 3-1 Summary of butterfly distribution (numbers of species and individuals) in
various land-use systems at an agricultural settlement in Riberäo Bonito, São Paulo state, Brazil. .............................................................................................. 92
3-2 Summary of butterfly data collected per land-use system at AS. Table contains the number of traps, total and average numbers of individuals and species at each land-use system. ....................................................................... 93
3-3 ANOVA multiple pairwise Tukey-Kramer comparison of number of butterfly individuals between land-use systems at Riberão Bonito. Significance set at p-value = 0.05 ..................................................................................................... 94
3-4 ANOVA multiple pairwise Tukey-Kramer comparison of number of butterfly species between land-use systems at Riberão Bonito. Significance set at p-value = 0.05 ........................................................................................................ 95
3-5 ANOVA pairwise Tukey-Kramer comparison of number of butterfly individuals between land-use systems at Agua Sumida. Significance set at p-value = 0.05 .................................................................................................................... 96
3-6 ANOVA pairwise Tukey-Kramer comparison of number of butterfly species between land-use systems at Agua Sumida. Significance set at p-value = 0.05 .................................................................................................................... 97
3-7 Species similarities analysis (ANOSIM) between land-use systems at Riberão Bonito. To correct for multiple comparisons, Boferroni correction set the significant level to 0.05/n was applied where n was the number of comparisons between the land-use practices (28). ............................................ 98
3-8 Species similarities analysis (ANOSIM) between land-use systems at Agua Sumida. To correct for multiple comparisons, Boferroni correction set the significant level to 0.05/n was applied where n was the number of comparisons between the land-use practices (21). ............................................ 99
3-9 Mean values and standard deviation for environmental factors per land-use system at Riberão Bonito ................................................................................. 100
3-10 Mean values for environmental factors per land-use system at Agua Sumida . 100
3-11 Multiple pairwise GLM ANOVA of temperature between land-use systems at Riberão Bonito. Significance set at p-value = 0.05 ........................................... 101
3-12 Multiple pairwise GLM ANOVA of wind speed between land-use systems at Riberão Bonito. Significance set at p-value = 0.05 ........................................... 102
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3-13 Multiple pairwise GLM ANOVA of percentage relative humidity between land-use at Riberão Bonito. Significance set at p-value = 0.05 ................................ 103
3-14 Multiple pairwise GLM ANOVA of temperature between land-use systems at Agua Sumida .................................................................................................... 104
3-15 Multiple pairwise GLM ANOVA of wind speed between land-use systems at Agua Sumida .................................................................................................... 105
3-16 Multiple pairwise GLM ANOVA of percentage relative humidity between land-use at Agua Sumida ......................................................................................... 106
4-1 Number of times a distinct path crossed over different land-use practices at Riberão Bonito. Predicted paths proposed by abiotic factors GIS model (HG-homegarden, SC-shaded coffee, E-eucalyptus, C-cassava, SG-secondary growth, S-sugarcane). ...................................................................................... 170
4-2 Number of times a distinct path crossed over different land-use practices at Agua Sumida. Predicted paths proposed by abiotic factors GIS model (HG-homegarden, SC-shaded coffee, E-eucalyptus, C-cassava, SG-secondary growth, S-sugarcane). ...................................................................................... 170
4-3 Number of times a distinct path crossed over different land-use practices at Riberão Bonito. Predicted paths proposed by the recaptured data GIS model (HG-homegarden, SC-shaded coffee, E-eucalyptus, C-cassava, SG-secondary growth, S-sugarcane). ..................................................................... 171
4-4 Number of times a distinct path crossed over different land-use practices at Riberão Bonito. Predicted paths proposed by the recaptured data GIS model (HG-homegarden, SC-shaded coffee, E-eucalyptus, C-cassava, SG-secondary growth, S-sugarcane). ..................................................................... 171
4-5 ANOVA Fisher (LSD) pair-wise comparisons between land-use practices at Agua Sumida. ................................................................................................... 172
5-1 Social Learning Theory Constructs and survey questions to measure the model. ............................................................................................................... 203
5-2 Stepwise regression results: Number of agroforestry practices incorporated ... 203
5-3 Correlations between Social Learning Theory Constructs and adoption of Agroforestry Practices ...................................................................................... 203
A-1 List of butterfly species at Riberão Bonito ........................................................ 212
B-1 List of butterfly species at Agua Sumida ........................................................... 215
E-1 Regression analysis for the number of butterfly individuals at Riberão Bonito . 225
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E-2 Regression analysis for the number of butterfly species at Riberão Bonito ...... 225
F-1 Regression analysis for the number of butterfly individuals at Agua Sumida ... 226
F-2 Regression analysis for the number of butterfly species at Agua Sumida ........ 226
G-1 Regression analysis for the number of butterfly individuals at Riberão Bonito . 227
G-2 Regression analysis for the number of butterfly species at Riberão Bonito ...... 227
H-1 Regression analysis for the number of butterfly individuals at Agua Sumida ... 228
H-2 Regression analysis for the number of butterfly species at Agua Sumida ........ 228
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LIST OF FIGURES
Figure page 3-1 Satellite image and spatial distribution of land-use practices at Riberão
Bonito. The different color shades represent each of the land-use practice and forest habitats in the landscape. Red quadrant shows the experimental area. ................................................................................................................... 86
3-2 Satellite image and spatial distribution of land-use practices at Agua Sumida. The different color shades represent each of the land-use practice and forest habitats in the landscape. Red quadrant shows the experimental area. ............. 87
3-3 Visual representation of land-use systems in ArcScene (ArcGIS 9.2). The height of each land-use system represents the relative abundance of butterfly individuals and species at Riberão Bonito. ......................................................... 88
3-4 Visual representation of land-use systems in ArcScene (ArcGIS 9.2). The height of each land-use practice represents the relative abundance of butterfly individuals and species at Agua Sumida............................................... 89
3-5 Linear Discriminant Analysis Canonical Plot of species similarities between land-use systems at Riberão Bonito. Ellipses identify the relative spatial distribution of the different land-use practices and habitats based on butterfly subfamily data. Axes “x” explains 85% of the data variability, whereas axes “y” explain only 13%. .......................................................................................... 90
3-6 Liner Discriminant Analysis Canonical Plot of species similarities between land-use systems at Agua Sumida. Ellipses identify the relative spatial distribution of the different land-use practices and habitats based on butterfly subfamily data. Axes “x” explains 85% of the data variability, whereas axes “y” explain only 13%. .......................................................................................... 91
4-1 Google Earth satellite image of Riberão Bonito Settlement. Colored polygons show the different land-use systems, Homegardens, shaded coffee, forest edge, eucalyptus, cassava, and secondary growth, pasture (landscape matrix) and forest (eucalyptus trees were cut prior to when this satellite image was taken so are not visible in the figure) (© Google). .......................... 140
4-2 Google Earth satellite image of a section of Agua Sumida Settlement. Colored polygons show the different land-use systems, Homegardens, shaded coffee, forest edge, sugar cane and secondary growth, pastures and forest interior are not outlined as they can be depicted from the image. .......... 141
4-3 Polygons depicting the forest fragment areas in each experimental landscape. Top row images represent a fraction of forest patch b and a (left to right) at Riberão Bonito, and bottom row represent a fraction of forest patch b and a (left to right) at Agua Sumida. .................................................... 142
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4-4 Analytical Framework for GIS methodology in ArcGIS. Step by step GIS model procedure. Rectangles represent information layers and ovals represent ArcGIS operations. ........................................................................... 143
4-5 Regression equation for butterfly species and individuals as a function of the average abiotic factors a teach trap point. The coefficient values were added to the “regression” GIS analysis giving weight to the different variables for the butterfly movement trajectory (Only significant variables were included in the analyses). ......................................................................................................... 144
4-6 Cost raster layers of abiotic factors. The individual abiotic factors layers were combined through a “mean” operation using the Cell Statistics tool in Spatial Analyst. ............................................................................................................. 145
4-7 Example of Cost Weighted Distance analysis for one of the forest interior patches in combination with the calculated Cost Raster layer. The output layers are the Cost Distance and the Cost Direction layers. ............................. 146
4-8 Best Single path (green line) from the release point to the selected forest patch at Riberão Bonito. ................................................................................... 147
4-9 Each Cell path analysis (green lines) from the release point to the forest patches at Riberão Bonito. ............................................................................... 148
4-10 Best Single path (green line) from the release point to the forest patches at Riberão Bonito. ................................................................................................. 149
4-11 Each Cell path (green lines) from the release point to the forest patches at Riberão Bonito. ................................................................................................. 150
4-12 Best Single path (green lines) from the release point to the forest patches at Agua Sumida. ................................................................................................... 151
4-13 Each Cell path (green line) from the release point to the forest patches at Agua Sumida. ................................................................................................... 152
4-14 Best Single path (green lines) from forest patch to forest patch at Agua Sumida. ............................................................................................................ 153
4-15 Each Cell path (green lines) from forest patch to forest patch at Agua Sumida. ............................................................................................................ 154
4-16 Number of butterfly individuals recaptured, after being released, in the different land-use systems at an agricultural settlement in Riberão Bonito, Sao Paulo state, Brazil (C-coffee, E-eucalyptus, F-forest interior, FE-forest edge, HG-homegarden, P-pasture, SC-shaded coffee, SG-secondary growth). ............................................................................................................ 155
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4-17 Comparison of butterflies recaptured in forest habitats (interior and edge) and homegardens at RB (F-forest, FE-forest edge, H-homegardens) ..................... 155
4-18 Second recapture events for individuals previously recaptured. Table shows a three step movement behavior of butterflies from the release point, to “x” land-use system, to “y” land-use system. ......................................................... 156
4-19 Recapture events not from the Release Point at RB (results do not include consecutive recaptures in the same butterfly trap point) .................................. 156
4-20 Sum of butterfly individuals recaptured in the different land-use systems at AS (C-coffee, E-eucalyptus, F-forest interior, FE-forest edge, HG-homegarden, P-pasture, SC-shaded coffee, SG-secondary growth). ............... 157
4-21 Comparison of butterflies recaptured in forest habitats (interior and edge) and homegardens at AB .......................................................................................... 157
4-22 Second recapture events at Agua Sumida (results do not include recaptures at the same trap point) ...................................................................................... 158
4-23 Recapture events not from the release point at Agua Sumida ......................... 158
4-24 Visual images of recapture movements at Riberão Bonito in ArcGIS. Recaptures between the release point a Van Someren - Rydon traps was represented with straight lines. Greater thickness and darker color of the lines signifies greater number of recaptures. .................................................... 159
4-25 Visual images of recapture movements at Agua Sumida in ArcGIS. Recaptures between the release point a Van Someren - Rydon traps was represented with straight lines. Greater thickness and darker color of the lines signifies greater number of recaptures. .................................................... 160
4-26 Best Single path from the release point to the forest patches at Riberão Bonito. Least-Cost path analysis using the recapture butterfly data GIS model. ............................................................................................................... 161
4-27 Each Cell path analysis (green lines) from the release point to the forest patches at Riberão Bonito. Least-Cost path analysis using the recapture butterfly data GIS model. .................................................................................. 162
4-28 Best Single path (green line) from the release point to the forest patches at Riberão Bonito. Least-Cost path analysis using the recapture butterfly data GIS model......................................................................................................... 163
4-29 Each Cell path (green lines) from the release point to the forest patches at Riberão Bonito. Least-Cost path analysis using the recapture butterfly data GIS model......................................................................................................... 164
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4-30 Best Single path from the release point to the forest patches at Agua Sumida. Least-Cost path analysis using the recapture butterfly data GIS model. ............................................................................................................... 165
4-31 Each Cell path (green line) from the release point to the forest patches at Agua Sumida. Least-Cost path analysis using the recapture butterfly data GIS model......................................................................................................... 166
4-32 Best Single path (green lines) from forest patch to forest patch at Agua Sumida. Least-Cost path analysis using the recapture butterfly data GIS model. ............................................................................................................... 167
4-33 Best Single path (green lines) from forest patch to forest patch at Agua Sumida. Least-Cost path analysis using the recapture butterfly data GIS model. ............................................................................................................... 168
4-34 Standardize residuals distribution plot. Observed vs. predicted values of butterflies per trap point at Agua Sumida. Notice outlier trap point belonging to trap “4g”. ....................................................................................................... 169
5-1 Main source of income at Riberão Bonito and Agua Sumida farming settlements (“y” axes represents the number of responses and “x” represents the different income sources). .......................................................................... 194
5-2 Main type of agriculture products produce at the farming settlements (“Y” axes represents the number of responses and “x” represents the different farming activities). ............................................................................................. 194
5-3 Main sources of agricultural knowledge listed by the farmers intervied (“Y” axes represents the number of responses and “x” represents the different information sources). ........................................................................................ 195
5-4 Percentage of farmers interviewed that have incorporated agroforestry practices in their farmland ................................................................................ 196
5-5 Social group that first mentioned the term “agroforestry” to the farmers interviewed (“Y” axes represents the number of responses and “x” axes represents the different social groups). ............................................................ 196
5-6 Main sources of information on agroforestry technologies (“Y” axes represents the number of responses and “x” represents the different information sources). ........................................................................................ 197
5-7 Percentage of the farmer’s friends and neighbors that practice agriculture and/or agroforestry. .......................................................................................... 197
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5-8 The farmer’s opinion for each of the most common agroforestry practices in the Pontal region (“y” represents the number of responses and the “x” axes represent an opinion scale from “very good” to “very bad”). ............................. 198
5-9 Groups with whom farmer’s more often discuss agricultural topics .................. 198
5-10 Frequency in which farmers discuss agricultural issues with family, friends and neighbors. .................................................................................................. 199
5-11 The farmer’s perceived advantages of discussing agricultural issues with family, friends and neighbors. ........................................................................... 200
5-12 Number of farmers that have requested advice on agricultural topics to neighbor farmers in the Pontal Region (this questioned was skipped by many of the respondents and so the total number of responces was low). ................ 200
5-13 Two MST homesteads at Pontal do Paranapanema. A) depicts a homestead on a farm with four different types of agroforestry practices. B) depicts a farm without agroforestry practices. .......................................................................... 201
5-14 Geographical distribution of farms with different number of agroforestry practices at Riberão Bonito. ............................................................................. 201
5-11 Geographical distribution of farms with different number of agroforestry practices at Agua Sumida. ................................................................................ 202
C-1 Butterfly species richness in Forest at Riberão Bonito ..................................... 217
C-2 Butterfly species richness in forest edge at Riberão Bonito .............................. 217
C-3 Butterfly species richness in eucalyptus at Riberão Bonito .............................. 218
C-4 Butterfly species richness in shaded coffee at Riberão Bonito ......................... 218
C-5 Butterfly species richness in homegardens at Riberão Bonito .......................... 219
C-6 Butterfly species richeness in secondary growth at Riberão Bonito ................. 219
C-7 Butterfly species richness in cassava at Riberão Bonito .................................. 220
C-8 Butterfly species richness in pastures at Riberao Bonito .................................. 220
D-1 Butterfly species richness in forest at Agua Sumida ......................................... 221
D-2 Butterfly species richness in forest edge at Agua Sumida ................................ 221
D-3 Butterfly species richness in shaded coffee at Agua Sumida ........................... 222
D-4 Butterfly species richness in homegardens at Agua Sumida ............................ 222
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D-5 Butterfly species richness in secondary growth at Agua Sumida...................... 223
D-6 Butterfly species richness in sugarcane at Agua Sumida ................................. 223
D-7 Butterfly species richness in pastures at Agua Sumida .................................... 224
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LIST OF ABBREVIATIONS
AHP Analytical Hierarchy Process
ANOSIM Analysis of Similarity
AS Agua Sumida
GIS Geographic Information Systems
INCRA National Institute for Colonization and Agrarian Reform
IPE Institute for Ecological Research
ITESP Land Institute of the State of São Paulo
LDA Linear Discriminant Analysis
MASTER Movement of Landless Farmers
MRR Mark-Release-Recapture
MST Movement of Landless Rural Workers
NGO Non-Government Organization
PNRA National Agrarian Reform Plan
RB Riberão Bonito
SP São Paulo
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Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy
ASSESSING LANDSCAPE CONNECTIVITY USING BUTTERFLY DISTRIBUTION
AND DISSEMINATING AGROFORESTRY TECHNOLOGIES IN AGRARIAN SETTLEMENTS IN BRAZIL
By
Wendy Francesconi
December 2011
Chair: P.K. Ramachandran Nair Major: Forest Resources and Conservation
The intensification of agriculture continues to threaten the survival of wildlife in
agricultural landscapes. The adoption of environmentally friendly agricultural practices
such as agroforestry –trees planted with crops and/or animals - could contribute to
biodiversity conservation. Motivated by this premise, this dissertation evaluated
ecological and social aspects of agroforestry adoption in Brazil. The objectives were to
describe the distribution of butterflies in two agricultural landscapes, to examine the
effectiveness of agroforestry as ecological stepping stones – small patches of
vegetation that can be used by wildlife for dispersal-, and to evaluate whether social
learning theory can explain agriculture information dissemination.
The distribution and dispersal patterns of fruit feeding butterflies and of abiotic
variables were used to compare the effectiveness of different land-use practices in
attracting forest butterflies, and to test if agroforestry can provide increased functional
connectivity- the landscape’s ability to facilitate or impede the movement of organisms.
Van Someren-Rydon butterfly traps were distributed in a grid in agroforestry areas
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bordered by forest, and capture data were analyzed using Geographic Information
Systems (GIS).
The study also addressed the dissemination and acquisition of information on
sustainable agricultural practices that promote wildlife conservation by agrarian reform
settlements in Brazil. A total of 94 heads-of-household were interviewed about farming
practices and sources for agriculture information. In addition, some of the questions
were developed to determine if whether Social Learning Theory was useful in
understanding dissemination of agricultural information among famers.
Forest butterflies were more frequently associated with agroforestry plots than with
monocultural agricultural systems. Spatial analyses showed that these butterflies
preferred to disperse through agroforestry plots when crossing the agricultural
landscape. Results from the social study indicate that farmers with greater social
networks and agricultural experience were more likely to adopt agroforestry practices.
However, the results for the application of Social Learning Theory were inconclusive.
While two of the theory’s constructs (imitation and differential reinforcement) were
significantly correlated with agroforestry adoption, the other two (differential association
and definitions) were not.By understanding how to motivate farmers to adopt
agroforestry practices, the ecological connectivity of the landscape could be improved,
making agricultural areas more conducive for wildlife conservation.
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CHAPTER 1 INTRODUCTION
The conversion of natural habitats into agricultural land poses an increasing threat
to the conservation of wildlife. Problems such as low soil fertility, land tenure conflicts,
climate change induced droughts and floods will continue to drive farming activities
towards land degradation and result in cutting of forests and overall expansion of
agricultural frontiers. Efforts to create conservation units and forest sanctuaries are not
enough to effectively protect biodiversity, because the natural world is interconnected;
no ecosystem stands alone. Therefore it is necessary to look beyond the spectrum of
the forest reserve to successfully protect wildlife. Specifically, changes in the agricultural
landscape could contribute substantially to biodiversity conservation in landscapes
comprised of both forest and agricultural practices. This could be achieved through the
adoption of environmentally friendly agricultural practices such as agroforestry - the
intentional planting of trees or woody perennials on the same unit of land being used for
raising agricultural crops or animals, either concurrently or sequentially. The multiple-
species managed in agroforestry makes them ecologically more complex than
monocultures, and therefore more similar to natural habitats. Agroforestry systems also
allow farmers to diversify their income sources and thus enhance their financial stability.
Hence, by ensuring the wellbeing of the farming community, the incorporation of
agroforestry systems in agricultural areas is expected to bring greater social and
ecological stability.
Motivated by this premise, I evaluate in this dissertation ecological and social
aspects of agroforestry adoption in agricultural landscapes dominated by cattle ranching
activities. I was interested in testing the assumption that agroforestry practices increase
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functional connectivity, and in understanding how farmers obtained agricultural
information that leads to the incorporation and dissemination of agroforestry
practices.To address these interests I approached them in two different ways. First, I
examined the distribution and dispersal of fruit feeding butterflies, which are primarily
associated with forest habitats in the region. Second, I conducted a survey with the
heads of households of farms in the Pontal do Paranapanema region, State of São
Paulo, Brazil. I used the survey to examine the flow of agricultural information and to
determine significant factors influencing the adoption of agroforestry.
Prior to the agrarian reform in 1942 the Pontal region was declared a 246,840 ha
reserve (Grande Reserva do Pontal – The Great Pontal Reserve) (Cullen et al., 2005).
Given its ecological and environmental characteristics, the reserve was part of Brazil’s
Atlantic forest. Today, the remaining forest patches of the Atlantic Forest are considered
to be of great biological importance as one of the world’s biodiversity hotspots (Carnaval
et al., 2009). Unfortunately, soon after the reserve was established in the Pontal region,
permits were issued for the construction of a railroad in 1946. This action opened the
area to illegal forest clearing and fraudulent land speculation. The deforestation of the
reserve continued over the following fifty years until the late 1990s, when the Land
Institute for the State of São Paulo (ITESP) along with the National Institute of
Colonization and Agrarian Reform (INCRA) began regulating and legalizing land
ownership in the Pontal. By this period, a total of 50,000 ha of forest were still
remaining. From this area, two ecological reserves were created (Moro do Diabo State
Park and Mico-Leão Preto Ecological Reserve) while 7,000 ha of smaller scattered
forest patches remained on private and agrarian reform lands (Cullen et al., 2005).
23
The characteristics of the landscape at Pontal do Paranapanema made it an ideal
location for evaluating the effectiveness of agroforestry for conservation purposes. The
scattered forest fragments, some of which still contained much of the vegetative
structure and composition of the original landscape, were embedded in a pasture
matrix. In addition, the creation of farming settlements has contributed to the
reclamation of land by planting native and exotic trees. These small woodlots are
considered agroforestry practices and serve multiple purposes. Among the most
obvious of these benefits for farmers in the region are that they produce food and
timber, and provide shade. Yet, they do much more: they help protect the soil from
further degradation and they increase the total amount of area covered by vegetation in
the landscape. It is not realistic for the landscape at the Pontal to be restored to its
natural state. However, the way in which the current setting is managed, and with the
continued protection of the remaining patches, the survival of the species that still
persist in remaining forest patches may be ensured. Determining if agroforestry
woodlots serve the ecological functions and contribute to the conservation of species is
of value towards understanding landscape dynamics where rural communities exist
adjacent to natural environments. The combination of farms, agroforestry and forest
patches at Pontal do Paranapanema make the region a natural experiment for
landscape ecology.
Data on the distribution of forest butterflies were collected to compare the
attractiveness of different land-use practices and to test whether areas of agroforestry
can provide ecological stepping stones for forest butterflies dispersing between patches
of forest. Butterflies were chosen as the study taxon because they were abundant at the
24
research site, relatively well known, and easy to manipulate. They are also recognized
as environmental indicators of landscape connectivity and are associated with other
biological groups (Fleishman and Murphy, 2009; Uehara-Prado et al., 2007; DeVries et
al., 1999).
At two farming settlements in the Pontal region, a landscape-scale research study
was carried out using butterflies to measure species richness and to test for dispersal.
Two quadrant areas (2.5 and 2.2 km²) were selected within each farming settlement.
These areas were landscape mosaics, consisting of agroforestry and conventional
agricultural plots situated between large fragments of Atlantic forest. Butterfly presence
and movement were monitored through Mark Release Recapture (MRR) techniques
using Van Someren-Rydon traps (DeVries, 1987) distributed in the research areas as a
grid. The traps were situated every 150 meters. Temperature, wind speed, wind
direction, percentage of shade, and relative humidity, were collected during the butterfly
monitoring period.
The two farming settlements where the study was conducted were created by
agrarian reform initiatives. At pontal do Paranapanema the issue of land distribution has
been the cause of multiple conflicts between large landlords and landless rural workers.
According to the 1995/1996 agricultural census of the municipality of Teodoro Sampaio,
where this study took place, twelve properties were larger than 2,000 ha, while almost
500 properties were between 10 and 100 ha (PEMD, 2006). With the support of the
Catholic Church and the rural workers’ unions, the agrarian reform in Pontal do
Paranapanema was a victory for the Landless Workers Movement (Movimento dos
Trabalhadores Rurais Sem Terra - MST), which resulted in more than 3,000 families
25
receiving land previously held by fraudulent owners. The families were distributed in
farming settlements, and among them the research areas where located at: Riberão
Bonito and Agua Sumida. At the Riberão Bonito settlement there were 192 families,
while at Agua Sumida there were 121. Although the farming lots from the settlements
vary in shape and size, on average each farm was about 20 ha, and together the
setlements in the Pontal cover 8% of the land in the region (Ditt, 2002).
A questionnaire survey was undertaken in order to understand the pattern of
dissemination and acquisition of knowledge of agricultural practices. A total of 94
heads-of-household were interviewed. The questionnaire consisted of open and closed
questions that focused on sources of income, interaction with different social groups,
agricultural practices and preferences, and sources of agricultural and agroforestry
information. In addition, some of the questions in the questionnaire were developed to
determine if Social Learning was an applicable learning theory of agricultural practices
and information dissemination among the farming community in this region.
This dissertation is organized into six chapters. This introductory chapter is
followed by a historical and sociopolitical description of the study area and a literature
review of the theories evaluated. Chapter 3 examines the distribution of butterflies and
changes in microclimatic conditions in the fragmented agricultural landscape, and
determines the significance of the abiotic factors measured in relation to butterfly
presence and abundance. In this chapter, repeated measures ANOVA and regression
analyses were conducted to compare the different land-use practices and identify
significant abiotic variables. Chapter 4 focuses on the spatial analysis of the data, using
Geographic Information Systems (GIS). The capture and recapture data on butterflies
26
were used to perform spatial and geostatistical analyses. Functions such as inverse-
distance interpolations as well as Least-cost Path were used to create an analytical
framework that predicts butterfly dispersal behavior in fragmented agricultural
landscapes containing agroforestry plots. Chapter 5 uses the data collected from the
social study to understand the flow of agricultural information and to determine if Social
Learning theory is an applicable conceptual framework to understand agroforestry
adoption. To test the applicability of this theory, a Spearman Rho correlation tests was
run to determine the relationship between the observations of farming practices as
models for the adoption of those practices among settlement farmers in the Pontal
region. The sixth and final chapter summarizes the results from the three experimental
chapters, and provides some general conclusions and recommendations for future
conservation efforts in the agricultural landscape at the Pontal.
27
CHAPTER 2 THE HISTORY AND APPLICATION OF TWO THEORIES AT PONTAL DO
PARANAPANEMA, BRAZIL
The History of Pontal do Paranapanema
One of the most pervasive problems in Brazil is social inequality. This inequality is
the foundation of the many socioeconomic contradictions that can be found in the
nation. Since the early colonial period of Brazil, the Portuguese monarchy shaped
society into a feudal social system. With the objective of securing as much land as
possible in the new continent, the monarchs granted royal favorites complete authority
over large territories to use and secure under Portugal’s reign. It was under the
administration of the royal landlords that the first deforestation episodes began. The
land principle of “use it or lose it” was the driver of this period, and it persisted after
being adopted by Brazil’s legal system. First it was Portuguese officials and later the
Brazilian government that allowed the indiscriminate conversion of land for resource
extraction and agriculture. If landowners were not using the territories under their
domain, newcomers had the right to “squat” and make use of the land. Policies of this
sort were used as potential solutions to the problems of inequality in the country. Yet, it
is easy to imagine why these policies did little to resolve the problem. Powerful
landlords manipulated the law to their advantage and easily kicked powerless and
isolated squatters off their land (Wright and Wolford, 2003)
The Rise of the Movement of the Landless Rural Workers (Movimento dos Trabalhadores Rurais Sem Terra - MST
Since the beginning of the colonial period in (1500 – 1815) and after Brazil’s
independence from Portugal (1821-1823), continued social inequality and injustice led
to the first signs of uprising by the poor in the middle of the 19th century. In many rural
28
regions of Brazil, families that had acquired land lost their farms to overseas syndicates
that were granted permits to modernize agriculture production in the country. The
actions by large landowners and the powerful commercial groups did not mix well with
the civil right, socialist and even communist sentiments of the times. Rural unions and
peasant coalitions began organizing in the late 1940s, 1950s and early 1960s. Their
agenda included (1) Fighting against large landowners who wanted to expel them (2)
Establishing political alliances, and most importantly, (3) Demanding land reforms and
greater rights for rural workers. The three main peasant groups that became organized
were: the Ligas Campesinas (Peasant Leagues), the Ultabs (Unions of Peasants and
Agricultural Workers of Brazil) and the Master (Movement of Landless Farmers).
However, the groups and the ideals they represented were repressed with the harsh fist
of Brazil’s military dictatorship regime in 1964. The peasant groups were seen by the
military as a communist menace, to the point that many of their leaders went into exile,
were imprisoned, or killed. The steps towards agrarian reform taken by these farming
organizations were crushed during these early dictatorship years during the late 1960’s
(Harnecker, 2003).
With the dictatorship, Brazil began what is known as the period of “painful
modernization”. The mechanization of the countryside was accompanied by the creation
of large monoculture plantations of soybean (Glycine max). Transnational corporations
often formally requested lands that were occupied by rural workers who had no land
titles. During this period, peasants were again displaced from their homes, and those
that worked as sharecroppers were replaced by machinery. According to Dean (1997)
the modernization of agriculture in Brazil was a setback to the country’s struggle against
29
inequality. Many farmers migrated to large cities, which in turn became crowded within a
short period of time and jobs became scarcer. During the 1970s unemployment became
widespread in cities and rural areas.
During the following decades, the rural poor found some support through the
Catholic Church and other sympathizers which saw the need for national land reform
(Wright and Wolfold, 2003). Landless farmers began to take actions in different regions
throughout the country. By creating peasant encampments, landless farmers became
organized creating the commissions and sectors, which evolved into what became
Brazil’s Movement of the Landless Rural Workers (MST) in 1984 (www.mst.org.br).
Brazil’s Agrarian Reform and the Environment
Brazil’s agrarian reform has been a complex socioeconomic process with direct
effects on the environment. The land redistribution procedures such as locating
contestable areas that were misappropriated, and the design and dimensions of the
parcels to be assigned (also called modules), were done without any regard to the sites’
environmental conditions, such as water availability and topography, nor did they
showed any concerns about the protection of the natural environment (Romeiro et al.,
1994). During the early stages of the agrarian reform there were no environmental
assessment tools in place to monitor the agricultural practices and the long term
productivity of the settlement lands.
The agricultural policies and incentives created by the military government during
the dictatorship (1964 – 1985) influenced the agricultural attitudes and behaviors in all
of the agricultural sectors in the country including the settlements. Most farmers who
received land through the agrarian reform adopted conventional agriculture methods. In
doing so they became dependent on the government’s financial and technical
30
assistance, which the new farming technologies required. However, the farmers
received little support from the government in terms of knowledge on how to sustainably
manage their acquired land. The settlements in the state of São Paulo in particular,
became a broken replica of large scale homogeneous production systems. There was
no agricultural diversity among or within farms. Much of the extension support offered
by the government was focused on improving the production of commercial crops and
little has been done to promote sustainable practices. Other than providing the settlers
with land, the reform had done little to improve their livelihoods.
There is no question that agrarian reform in Brazil was long overdue. However, for
this process to be a realistic long-term solution towards poverty alleviation and social
inequality, the existing and future settlements need to be properly planned and
managed (Esterci, 2003). The increasing social pressures demanding more land to be
redistributed has resulted in settlements being created not only from contestable
properties, but also from public lands. This has prompted some settlements to be
established in lands that were previously protected areas, and the current land
management practices of the settlements in public or private areas do not guarantee
long term productivity of the land or of the farmers’ well-being. According to Domingues-
Dulley and Caravalho (1994), the agrarian reform settlements, in spite of the
environmental problems associated with the creation of some of them such as water
and soil degradation and biodiversity loss, have the potential to symbolize and promote
good land stewardship in Brazil. The bottom line is that it is in the interest of small-size
landholders to adopt sustainable agricultural practices that do not exhaust the limited
resources within their assigned parcels.
31
MST in the Pontal Region
Pontal do Paranapanema has been called the heart of the MST struggle in Brazil.
The Pontal is located in the Southwestern region of the State of Sao Paulo, and
comprises an area of about 18,441.60 km² (http://sit.mda.gov.br). Many confrontations
have taken place between large landowners and rural workers, which has increased the
discontent of the landless movement and brought publicity to the land reform issue in
the region. During the 1990s a total of 17,940 families occupied 335 fazendas (large
ranches), and were able to obtain 100,000 ha of land (DATALUTA, cited by Mancano
and Barbosa, 2001). During this decade the Pontal was one of the regions with the
largest number of land conflicts in the country. According to Freitas (2008), currently
there are 102 agrarian reform settlements in the Pontal, and about 6,000 families have
received land. More than half (56%) of the agrarian reform settlements in the state of
São Paulo have been granted legal status in the Pontal region. Yet, they represent only
19.5% of the land in the Pontal; an additional 40% is still being contested (Ditt, 2002).
Pontal do Paranapanema was once a continous semi-deciduous forest. However,
like other legal forest reserves, it could not escape deforestation that was experienced
throughout the state of Sao Paulo or the country. The 260,000 ha that made up the
reserve was declared a national park in 1942 by the governor of the state at that time,
Fernando Costa (Leite, 1998). Yet, soon after in 1946, changes in political powers
changed the history of the region and its inhabitants. Plans to construct a railroad, along
with corruption and false land ownership titles common throughout the country
(grileiros), led to the illegal burning of the reserve. Since then, the forest cover has been
reduced to a small fraction (1.8%) of its original size (Valladares-Padua, 2002). The
Pontal was quickly converted from forest to farm land in order to stop any actions by
32
conservation groups towards its protection. The history of occupation and dubious
landownership of this region made the region an excellent target for the MST farmers to
contest for redistribution. The fact that the land was once recognized by law as a forest
reserve was the main argument for the landless movement to dispute the illegitimacy of
its landowners.
While the land was cleared and prior to being used as pastureland, the Pontal first
experience a short-lived timber extraction period of valuable tree species such as
peroba (Aspidosperma spp.), jatobá (Hymenaea courbaril L.), ipê (Tabebuia spp.) and
angico (Anadenanthera spp.). Then, there were two different agricultural cycles, first
coffee (Coffea arabica) and then cotton (Gossypium barbadense). These crop-centered
industries brought several waves of rural workers seeking employment and a better
livelihood. However, the characteristics of the soil did not mix well with the simplified
agricultural systems being practiced, and declines in production resulted in
unemployment (PEMD, 2006). Despite the decline of jobs in agriculture, other industries
were arriving to the region, and the construction of two hydroelectric plants brought
feelings of prosperity to the growing towns during the1970s and 1980s (PEMD, 2006). It
was during this period that the MST ideals also arrived to the region.
Farming settlements in the State of Sao Paulo have usually been created on lands
previously used for cattle grazing (Dulley and Caravalho, 1994). This was the case with
the farming settlements in Pontal do Paranapanema. Once the forest vegetation was
cleared, the organic matter that supplied the soil with nutrients was removed. The
predominant soils are classified in the Brazilian method as dark-red latossolos and
podzolicos (oxisols and spodosols in US soil taxonomy); they are characterized as
33
sandy, highly drainable, acidic, and with low clay content (Ronconi-Rodrigues et al.,
2007). According to Dulley and Caravalho (1994), the land at Agua Sumida, which is
one of the farming settlements included in the present study, is representative of the soil
and water conditions found in other settlements in the region and in the state. The types
of soil found in this region have low nutrient content and are highly susceptible to wind
and water erosion. Also, water is usually a limiting factor during the dry season.
Because of past land management practices, currently the land distributed to the MST
farmers in the Pontal is of poor agricultural quality. In general, the current soil conditions
at the Pontal are not adequate for monocultures or annual cultures, yet these
agricultural practices are constantly introduced in these lands.
Landscape Ecology Theory and Pontal do Paranapanema
Conversions of forests to farmland and consequent changes in land cover have
altered the structure and composition of the landscape at Pontal do Paranapanema
during the past 50 years (Ditt, 2002). Within this period, the dynamics of the natural
ecosystems found at the Pontal were transformed from a continuous forest landscape to
an isolated one (Bjorklund et al., 1999). Presently, pastures constitute the dominant
land-use system, and other agricultural practices and forest fragments are part of the
landscape as embedded patches. Some of the original fauna and flora can be found in
the remaining forest fragments. Yet, only in the larger patches such as Moro do Diabo
State Park, it is possible to find primary forest areas and a greater diversity of species.
Most of the smaller forest fragments in the region have been in one way or another
disturbed resulting in secondary forest (Ditt, 2002). The historical events at Pontal do
Paranapanema have made this region a natural experiment for the study of island
biogeography theory (MacArthur and Wilson, 1963, 1967), however, on land instead of
34
water. The biological diversity found in remnant forest fragments can be explained
based on the principals of island biogeography (patch size and immigration). In addition,
the theory helps explain the vulnerability of the remaining forest fragments to further
changes in the environment, given that smaller and more isolated fragments would
result in a lower number of species. Island biogeography in the Pontal provides
scientific argument for the incorporation of agricultural practices that could increase
landscape connectivity as explained in the following section.
The Landscape at Pontal do Paranapanema
The theory of island biogeography states there is a direct relationship between the
area and the isolation of a natural habitat (to the mainland) and the number of species it
contains (MacArthur and Wilson, 1963, 1967). As the area increases, the number of
species increases, and as the area becomes more isolated, the number of species that
can migrate to it decreases. Comparisons between habitats for the total number of
species is usually considered an indicator of the degree of disturbance, in addition the
composition of species gives scientists and land-use managers a better understanding
of the community changes that take place (Huston, 1995 in Ditt, 2006). A study by Ditt
(2006) analyzed and compared 13 forest fragments in the Pontal and found there were
positive correlations between the size of the fragment and their degree of conservation
(based on the forest structure variables), and between the degree of conservation and
the inverse distance to other forest fragments. These findings confirm the importance of
fragment size and landscape connectivity for the conservation of remnant forest habitats
in the Pontal landscape.
In contrast to other areas in the region, the largest forest fragment, now Moro do
Diabo State Park, was spared from being cleared. With the exception of a few thousand
35
hectares, which were cleared for agriculture and later abandoned, the park has
maintained its original size and vegetation. The area the park occupies was declared
government land in 1934. The legal documentation that established the area as a park
was filed prior to the deforestation of other locations in the region. This legal status
ensured that the land was kept protected for the conservation of the existing fauna and
flora. Land speculators and squatters who came to the region during the land invasions
of the1950s were thrown out of the park by the Environmental and Civil Police (PEMD,
2006). These agencies assumed responsibility for the protection of the park until 1965,
when the Brazilian Forest Institute came to the region to provide administrative support..
Since then, the perimeter of Moro do Diabo state park has been delineated and
maintained.
With the exception of Moro do Diabo, most remnant forest fragments reflect
changes in their species communities. In addition to remnant populations of native
plants and animal species in the forest fragments, invasive species are also present and
have come into the region following the changes in vegetation. According to
metapopulation theory, some fragments may be sources of native species and some
may be sinks (Hanski, 1994,1998). The persistence of native species in the region is
dependent on the quantity and quality of suitable habitat, and of the permeability of the
landscape between fragments. According to Cullen et al. (1997) because Moro do
Diabo State Park is the largest and least disturbed forest habitat in the Pontal, it may be
a source of native species, supplying individuals to other forest fragments in the
landscape. However, given the high degree of fragmentation in the region, the distances
between remnant forest patches may be too great for many species to be able to
36
disperse between patches, in which case the park would not function as a source of
colonists for most other forested areas (Viana in DItt, 2006).
Selected reforestation efforts have taken place in the Pontal to restore some of the
landscape’s degraded ecosystem functions. Many of these efforts have been led by
IPE. Along with the Land Institute of the State of Sao Paulo (Instituto de Terras do
Estado de Sao Paulo-ITESP), IPE arranged the planting of green belts 50 m wide
around some of the forest fragments. This was done as a method to prevent forest fires,
invasive species, and other edge-related threats to the remaining patches of forest.
Also, according to the Brazilian forest code, rural properties must have 20% of their land
covered by trees (the law is known as Legal Reserve). However, this law has not been
widely implemented nor enforced, especially among owners of small holdings. The
amount of land received by individual settlers through the agrarian reform is relatively
small (15 – 20 ha), and for them to set aside 3 – 4 ha of that land without any financial
returns, is difficult (Ronconi-Rodrigues et al., 2007). However, IPE has used this
stipulation to promote agroforestry systems that could meet both legal and financial
expectations. Agroforestry systems are beneficial to small-scale landholders as they
can produce income while conserving the environment (Montagnini, 1992). Other
restoration strategies promoted by IPE include a local environmental education
program, and promotion of tree nurseries to provide the farmers with easy access to a
variety of native species. All these initiatives represent efforts to restore the degraded
landscape and conserve its natural areas.
The landscape mosaic at the Pontal consists of a few main agricultural practices.
Pastures, as mentioned above, are the main agricultural land use system in the region.
37
Both large and small property owners have pastures for cattle ranching and/or milk
production. Large ranches usually have Bhrama and Zebu breeds for meat production,
while smallholder farmers produce milk from a mixture breeds. Other important
practices include cultivation of sugarcane (Saccharum officinarum L.) and cassava
(Manihot esculenta Crantz). Cassava is a labor intensive crop and small-scale farmers
will typically only grow small plots for home consumption. In contrast, sugarcane can be
found in the region in large and small scale plantings. Large plantations are produced
by sugarcane mills for ethanol production in agreement with large land owners, and
recently with agrarian reform settlers as well (Freitas, 2008). Farmers who cultivate
sugarcane in small plots without mill contracts do so for home consumption and as feed
for animals.
Other land-use systems in the Pontal landscape include agroforestry, the most
common being homegardes. The trees planted around the house by the farmers have
been purchased at the community nurseries promoted by IPE, or grown from seeds
acquired by other means. Other agroforestry practices include shaded coffee systems,
living fences (usually of eucalyptus), alley cropping systems of cassava, banana, or
other fruit trees combined with corn (Zea mays), and taungya systems of corn and
beans with native trees (Cullen et al., 2004 Lima, 2007; Rodriguez et al., 2007). Other
elements in the landscape not necessarily in farmland are riparian buffers (Uezu, 2008).
By law these areas must be protected and therefore are not incorporated into the
farming settlement lands. The presence of trees in these agroforestry practices may be
increasing the connectivity of the landscape, therefore increasing the resilience of the
region to further environmental degradation.
38
Landscape Ecology and Agriculture
Given that rural landscapes are a combination of farm land and natural habitats,
the application of landscape ecology concepts can help develop species conserving
strategies on agricultural productive lands. Agriculture is the main cause of
deforestation in tropical forests (Miyamoto, 2009; Brown and Pearce, 1994). The
expansion of agriculture has largely been caused by the increasing human population
which is now reaching seven billion. Habitat conversion for agricultural purposes
represents a real threat to the preservation of natural areas and the species they
contain. According to global estimates 57,000 ha of tropical forest are destroyed every
day (Miller and Tangley, 1991). Brazil has become the focus of many conservation
efforts as it contains about 30% of earth’s tropical forest. However, much of it is being
converted to agriculture and being leased to commercial agricultural syndicates.
Often, the creation of new agricultural land requires habitat loss and/or habitat
fragmentation. Much of the work done within the scope of landscape ecology has
focused on the effects of human induced habitat fragmentation and loss of natural areas
(Fahrig, 2003). The agricultural landscape at Pontal do Paranapanema experienced
habitat loss. The percentage of total natural habitat in the landscape was reduced,
which necessarily resulted in the size reduction of natural patches, and their increased
isolation. What can be deduced from the conmcepts in landscape ecology is the idea
that landscapes can be manipulated in ways that minimize the negative effects of
habitat conversion process over the remaining plant and animal species and
ecosystems processes. The application of landscape ecology principles to agricultural
settings for the purpose of biodiversity conservation, could help reduce the negative
39
effects produced by the isolation and size reduction of remaining natural areas, and in
turn reduce the negative effects of habitat loss.
Ecological Stepping Stones as Conservation Strategies
Ecological stepping stones are usually smaller and less complex habitats
compared to the patches they are associated. The concept of ecological stepping
stones in landscape ecology is little studied as a strategy for biodiversity conservation.
Early research on stepping stones to increase movement in landscapes has usually
been done to explain special cases of island colonization (Gilpin, 1980; MacArthur and
Wilson, 1967). In theory the movement of propagules between remaining patches could
be facilitated with the incorporation of stepping stones, given that the presence of
intermediate stepping stone can increase the rate of colonization and affect the species
composition (Gilpin, 1980; Diamiond, 1975). The smaller size of steppings stones in
comparison to the larger adjacent areas, results in their reduce capacity to host the
same number of animal and plant species found in the source area. The main function
of stepping stones is to increase the number of species by decreasing the dispersal
distance that would otherwise deter species with lower dispersal capacity. In this way,
the presence of stepping stones can indirectly determine the species composition in
target islands or patches by promoting, or in some cases inhibiting the movement of
organisms (Thornton, 2007).
Landscape ecology stepping stones could be incorporated as conservation
strategies. It is undisputable that the incorporation of these structures can increase
land-use heterogeneity, and heterogeneous landscapes are considered more diverse
(Lefeuvre and Barnaud, 1988). Due to the intensification of agriculture, which has lead
to the loss and isolation of natural habitat, western european governments have taken
40
actions to tackle the problems associated with biodiversity decline. Countries such as
France, Belgium, Germany, and Denmark have adopted policies and incorporated
landscape ecology strategies to reduce erosion and biodiversity loss. Burel and Baudry
(2003) discuss the application of landscape ecology concepts in the management and
design of landscapes. They describe the European Ecological Network (ECONET)
initiative by European countries to link parks and reserves with ecological corridors.
Other examples from Britain and the United States include hedgerows, forest belts,
greenbelts (Harris and Scheck, 1991) and greenways (Little 1990), which function not
only to restore ecosystem processes and landscape dynamics, but to improve society’s
well-being by providing a space for recreation, culture, education and aesthetics. In
agricultural landscapes, conservationists now understand that whatever efforts carried
out to protect biodiversity in these areas will require an integrated approach that
includes landscape conservation structures.
Agroforestry as a Landscape Connectivity Strategy
The structure and composition of landscape ecology strategies would ideally be
very similar to the natural habitat. In this way, the incorporation of agroforestry patches
as ecological steppings stones would not fit the definition. One of the goals of
agroforestry systems is to generate income through the production of crops or other
commercial products, the selected species in these agroecosystems cannot replace the
ecosystem dynamics of the intact habitat. The value of agroforestry systems as a
landscape ecology strategy to restore connectivity comes from their comparison with
more intensive land-use options, such as monocultures which are thought of as modern
agriculture. Götz et al. (2004) explores three main hypothesis about how agroforestry
can help conserve biodiversity, among them the “agroforestry-habitat hypothesis” states
41
that agroforestry systems can provide habitat to species that are partially forest-
dependent, and the “agroforestry-matrix hypothesis” refers to the contributions of
agroforestry facilitating the movement of forest species. Both these hypotheses address
the issue of landscape connectivity in agricultural areas and how agroforestry can help
increase it.
In agreement with the Brazil’s Legal Forest Reserve, farmers should maintain
areas of natural vegetation in their lands. Yet, actions towards making this effective are
few on behalf of landowners at implementing the law and of the government at
enforcing it. This rule of law is even more difficult to be observed by subsistence
farmers, who are naturally hesitant about making part of their small landholding non-
remunerative (Ramos Filho and Francisco, 2004). For subsistence farmers, land
stewardships is an issue of economics. The study by Rodrigues et al. (2004) evaluated
the financial benefits of agroforestry system as an alternative for farmers to comply with
the Legal Forest Reserve requirement, while also maintaining the land productive. They
concluded that even though the financial benefits to farmers will depend on the
characteristics and intensification of the system, agroforestry incorporation had positive
returns for families in the Pontal region. Agroforestry adoption helped diversity income
and the products consumed by the farm.
In agricultural settings, the incorporation of agroforestry practices could function as
ecological stepping stones. The tree component of agroforestry practices can serve
many ecosystem restoration functions, among them increase landscape connectivity.
However, evidence of agroforestry systems as a landscape ecology strategy to increase
funtional connectivity is currently limited. In addition, agroforestry systems may not be
42
effective connectivity structures for many forest species. On the contrary, they may
even act as population sinks exposing forest animals to hunters, predators or disease
(Laurance, 2004; Naughton-Treves and Salafsky (2004). Yet, some support for the
potential role of agroforestry systems increasing landscape connectivity has emerged,
as well as the experimental data on these topic. The work by Guillen et al. (2006), for
example, followed groups of howler monkeys (Alouatta palliata) inside a forest reserve
and in the farming landscape (which included shaded coffee agroforestry). Their
findings indicate that howler monkeys utilize trees in shaded coffee plantations and the
resources within them in a similar way as old secondary and early regeneration forest.
They concluded that shaded coffee agroforestry systems were important as habitat for
howler monkeys and were used as corridors to move between forest areas.
However, the potential of agroforestry to serve as ecological stepping stones has
been poorly researched. Some scientist speculated about the value of agroforestry
woodlots as stepping stones for butterflies and other forest fauna (Cullen et al.,
2004).Yet, without data to support these assumptions, the role of agroforestry as
stepping stones remains largely inconclusive. The study by Uezu et al. (2008) provides
an experimental approach to evaluating this assumption: they assessed the influence of
agroforestry plots on bird distribution and abundance at Pontal do Paranapanema, and
found that agroforestry woodlots contained a higher number of generalist bird species
compared with the pasture matrix. They interpreted these results to indicate
agroforestry systems as being able to increase landscape connectivity by acting as
stepping stones for generalist species. They also suggested that the use of agroforestry
systems as stepping stones would not only be dependent on the size and isolation of
43
the plot, but also on the degree of resistance or permeability of the matrix analyzed. The
authors concluded that agroforestry stepping stones would be more useful in situations
were the landscape has intermediate levels of permeability-“ where it is not too resistant
as to prevent some individual’s movement and neither is it so permeable as to allow
unrestricted movement throughout the matrix” (Uezu et al., 2008).
Landscape Ecology, Agroforestry and GIS
The lack of powerful analytical tools used to be a major difficulty in studying
landscape scale patterns in ecology. This changed with the development of Geographic
Information Systems (GIS). GIS computer-based systems analyze spatial data as a
series of layers describing a particular aspect of the geography of an area (Haines-
Young et al., 1993). By combining layers of information in GIS, it is possible to identify
or answer spatially explicit questions. Given that landscape ecology is interested in
spatially distributed environmental data, GIS software proves to be an efficient way of
storing data and analyzing it.
Since the assimilation of GIS in the study of environmental phenomena, the
number of publications that have incorporated this technology has been increasing.
Different functions within the different software have been implemented, different
models have been proposed and various tool boxes have been developed to address
specific environmental questions (Hooge and Eichenlaud, 1997; Majka, 2007). Among
the software available ArcGIS from ESRI is perhaps the most well-known and widely
used. Within ArcMap, one of the packages of ArcGIS, the Spatial Analyst extension has
been key in the production and manipulation of map information. Because of its
practical and relative ease at integrating geographical and environmental data, spatial
analyst is perhaps the preferred GIS tool for the analysis of data in landscape ecology.
44
The emerging questions in the field of agroforestry and conservation address the
issue of their influence on wildlife at the agricultural landscape (Schroth et al., 2004).
The potential of agroforestry to increase landscape connectivity and serve as
transitional habitat for animal dispersal in the fragmented agricultural landscapes is still
speculative. Research on agricultural settings using GIS has been conducted to
determine the degree of connectivity provided by different landscape elements
(Adriaensen et al., 2003; Schippers et al., 1996). Following a similar research approach,
agroforestry plots can be analyzed in ArcGIS to determine their role at increasing the
permeability of the landscape. Different algorithms such as “Cost Distance”, “Least-Cost
Path (also called Shortest Path)” and “Corridor” can be used to calculate the optimum
traveling trajectories between locations in a landscape.
The study by Larkin et al. (2004) used least-cost path analysis to simulate
dispersal pathways of black bears (Ursus americanus) in Florida. Using various layers
to represent the different land-cover/land-use systems, along with their resistance
values either preventing or facilitating movement, their resulting pathways display
landscape connectivity scenarios for black bears moving between national forest parks
in Florida. The study by Sutcliffe et al. (2003) also examined animal dispersal in a
fragemented landscape with least-cost analysis in ArcGIS. In contrast to the work by
Larkin et al., this analysis used an experimental approach and included empirical data
on inter-patch movement of two species of butterflies to validate the predicted pathways
in GIS. In their landscape scenario, grassy banks were important dispersal habitats for
the butterflies. They conclude that the restoration of these habitats would greatly
increase landscape connectivity for the butterfly species examined. The pathways
45
predicted by the models developed in these studies are a reflection of the potential use
of least-cost analyses and ArcGIS in general as tools that can help design landscapes
for biodiversity conservation purposes.
Least-cost path analysis trace paths of minimum resistance cost between a
designated source and target area. In fragmented agricultural landscapes, forest bound
species need to move from one forest fragment to the next through the agricultural
matrix. If agroforestry practices are capable of increasing landscape connectivity for the
movement of forest species, then the predicted least-cost pathways will cross the
agroforestry plots.
Fruit Feeding Butterflies as Indicators of Landscape Connectivity
In human modified environments the presence or absence of forest specialist
species such as butterflies, can be interpreted as a measure of tolerance or intolerance
to the biotic and abiotic conditions found in the particular land-use system (Pomeroy
and Service 1986). Butterflies are often used as indicators of changes in the
environment and recently been used as indicators of functional connectivity (Murphy et
al., 1990; Brown, 1991; Kremen, 1992; Fermon et al., 2000; Sutcliffe et al.,2003;
Schulze et al., 2004). In neotropical environments the positive association between
butterflies and tree species richness makes them good indicators of changes in
vegetation (Bobo et al., 2006; Schulze et al., 2004; Erlich and Raven, 1964). The
assessment of butterflies as indicators by Kremen (1992) identified them as excellent
when comparing habitat heterogeneity due to anthropogenic disturbance. This is
because butterflies are sensitive to changes in microclimate (Kremen, 1992; Murphy et
al. 1990; DeVries, 1987).
46
Neotropical fruit feeding butterflies in particular are practical indicator species.
Many researchers have expressed the value of using frugivorous adult butterflies in
programs intended to monitor changes in tropical ecosystems (Daily and Ehrlich, 1995;
Sparrow et al., 1994; Kremmen, 1992; Brown, 1991; Lovejoy et al., 1986; Holloway,
1980). The assessment of fruit feeding butterflies is relatively easy and efficient
(Sparrow et al., 1994), and given their applicability as indicators of other taxa, in
addition to the direct impact that forest fragmentation has on butterfly diversity (Daily
and Ehrlich, 1995; Singer and Ehrlinch, 1991), they can be considered an appropriate
group to monitor for the evaluation of the functional connectivity potential of agroforestry
practices. Given that maintaining connectivity between isolated forest fragments is
critical for the continuation of forest bound species, and that a strong correlation exists
between butterflies and land cover (Milder et al., 2010), comparing fruit feeding butterfly
distribution in different land-use systems can be interpreted to assess the relative value
of agroforestry systems in butterfly conservation, and as a parameter to determine the
efficiency of agroforestry practices as ecological stepping stones.
Social Learning Theory and Pontal do Paranapanema
Understanding the decision-making process that leads a farmer to adopt or not a
given agricultural practice could facilitate the dissemination of agroforestry in order to
increase landscape connectivity and improve the environment, while contributing to the
long-term well being of the farming community. Social Learning may be an applicable
theory to explain agroforestry adoption in the Pontal do Paranapanema region. Social
learning as described by Albert Bandura (1988) is basically a behavioral theory that
describes the central role of models in human learning processes. According to
Bandura (1977), there are four steps or principals within this leaning process: attention,
47
retention, motor reproduction and motivation. Even though this learning process could
be applied to many learning situations or behaviors (positive or negative), Bandura
demonstrated the validity of his theory in the adoption of deviant behavior. His
experiments of children behaving violently with a bobo doll after watching videos were
adults acted violently, were able to capture the essence of the theory. Yet learning
through models is not exclusive to learning criminal behavior, and not necessarily
results in engaging in the observed behavior. Many factors in the individual and the
environment could trigger a different type of response. In situations where formal
education is mostly lacking, learning can occur from the observation of others within a
community. In Pontal do Paranapanema, settles from the landless movement had to act
as a collective to pressure the government and demand land reform. In this way the
settlers became conditioned to observe and learn from each other’s behavior that at the
time was defiant against large landowner and government institutions. These learning
mechanisms may have transcended once land was granted, and continue to play a role
within the community in learning agricultural practices.
Social Learning Theory Research
Social learning theory has been primarily used in the understanding of deviant
behavior. According to Akers (1998) there were many relevant aspects of behavioral
psychology found in social learning theory and theories in criminology, both linking the
individual’s learning capacity with the social environment. Burges and Akers (1966)
combined aspects of Sutherland’s Differential Association theory in criminology with
aspects of Bandura’s Social Learning theory in social psychology. Differential
Association theory by Sutherland describes a process were the subject learns criminal
behavior through observation or interaction with others within a social context. The work
48
of Burgers and Akers expanded Sutherland’s theory to incorporate not only the
principles of social learning by Bandura, but also the punishment and reward ideas of
Differential Reinforcement by Skinner (1953).
Akers (1977) developed an applicable methodology to test for Differential
Association-Reinforcement theory, which came to be known as social learning theory in
social criminology. Akers et al. (1979) tested social learning theory by conducting an
experiment with teenagers. Survey data were collected on alcohol and drug abuse. The
proposed methodology by Arker (1977) established four parameters: Imitation,
Definitions, Differential Association and Differential Reinforcement. These parameters
addressed Bandura’s social learning principals. Differential association refers to the
interactions and identity with different groups, and includes both normative and
behavioral dimension. In other words it refers to a subject’s exposure to a behavior and
what is thought about the behavior. Definitions refers to the subject’s ability to retain
information about the behavior, rationalize it and determine their personal meaning as
either desirable or undesirable, justified or unjustified, good or bad, right or wrong.
Imitation denotes following behavior that has been modeled or observed, and finally,
differential reinforcement addresses the expected consequences of engaging in a
behavior.
The application of these parameters has been successful in various studies testing
deviant behavior. The work by Fisk (2006) proved the applicability of the social learning
theory for illegitimate music downloading. However, the use of this theory to explain
non-criminal behavior has been less explored. There are some studies using social
learning theory applied to media and advertising (Lunz, 1983), and others in health care
49
and health education fields (Sorensen, 2005). However the use of social learning theory
to explain the adoption of agricultural technology has seldom been explored. The study
by Burger et al. (1993) presents an attempt to apply social learning theory from an
economics perspective and try to understand coffee production adoption in Kenya. Their
study did not use Akers’ proposed parameters, but instead used weights of coffee
production popularity among farmers. They found that without information on
consequences, farmers tend to make decision to adopt or not to adopt coffee based on
their observation of farmers that are more similar to themselves. Other research linking
agricultural technology adoption and social learning have not been found in the
literature.
Social Learning Theory, Diffusion of Innovation, and Agroforestry
Research on the adoption of agricultural technologies such as agroforestry has
usually been analyzed based on diffusion of innovation theory by Everett M. Roger
(1962). The theory began within rural sociology, but given its broader applicability it has
become a communications theory that tries to explain social change. In rural sociology,
diffusion of innovations provided a framework on which to evaluate development
programs in agriculture, and so, it has been used extensively for studying the
incorporation of sustainable agriculture practices in developing countries.
In researching the concepts in diffusion of innovation and social learning there
seem to be various parallels between the theories. To begin, there are four main
elements (innovation, communication channels, time and social system) in diffusion of
innovation theory that affect the rate of adoption of the innovation. According to the
proponents of the theory, each of these elements can be identified on the diffusion
process of any type of innovation. Within the characteristics of the innovation, Roger
50
(1962) describes how the diffusion process will be different based on the characteristics
of the innovation perceived by the individual. In describing how the individual perceived
the innovation, diffusion of innovations theory uses explanatory concepts similar to
those in social learning (though with different labels). Among the attributes of the
innovation, adoption depends on: observability, complexity, trialability, compatibility and
relative advantage. These characteristics are analogous to differential association,
definitions, imitation, and differential reinforcement in social learning respectively.
Another parallel between diffusion of innovation and the four social constructs of
social learning are the adoption steps. Under the “time” element of diffusion of
innovation, describes the process in which the decision is made to adopt the
technology or not. This process goes through a series of steps conceptualized as the
following: knowledge, persuasion, decision, implementation and confirmation. Again,
these are different labels that described Akers’ parameters of social learning.
Knowledge is gained by the individuals (which could be through observation of the
innovation behavior), persuasion takes place when the individual forms an opinion of
the innovation (as definition in social learning), decision and implementation as imitation
indicate that the individual engages in the innovation behavior, and confirmation occurs
when after engaging in the behavior there is either a positively or negatively feedback
that reinforces the behavior or not.
Furthermore, Rogers (2003) acknowledges that the diffusion of an innovation,
especially in isolated rural communities, “is a social process more so than a technical
matter”. In understanding the social structure of a diffusion of innovation scenario,
Roger points out the existence of a formal and informal arrangement pattern among the
51
individuals of a group. This “structure” gives rise to communication networks, and at the
center of these communication networks are opinion leaders. Opinion leaders are
influential people in the community because they are more exposed to external forms of
communication. And opinion leaders often act as models and their innovative behavior
can be imitated by other members in the systems. The opinion leaders in diffusion of
innovation theory can be synonymous of the behavior models described in social
learning theory.
Evaluating the applicability of social learning theory for agroforestry adoption is an
initiative supported by several arguments. The social structure of the farming settlers at
Pontal do Paranapanema has and is based on social leaders who acted as behavioral
models. According to Wright and Wolford (2003), after MST farmers receive their land,
the social structures that existed prior the redistribution persist. In addition, the theory
that is usually implemented to understand agricultural innovation adoption has many
parallels with social learning theory. Yet, social learning theory is more interested in the
cognitive process that takes place in the individual, than in the actual spread of the
technology. Understanding the learning process may be useful for taping into the cycle
in order to promote more sustainable agricultural practices that benefit both the farmer’s
well-being and the environment.
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CHAPTER 3 BUTTERFLY DISTRIBUTION IN FRAGMENTED AGRICULTURAL LANDSCAPES OF
EASTERN BRAZIL
Fragmentation of tropical forests due to agricultural expansion is a leading cause
for loss of terrestrial biodiversity (Bobo et al., 2005; Sala et al., 2000). Conversion of
natural habitats into anthropogenic landscapes has numerous consequences for
remaining populations of plants and animals. In particular, isolation of populations in
forest fragments leads to shifts in ecosystem, community and population dynamics that
often result in the local extinction of species (Damschen et al., 2006; Ricketts et al.,
2001; Gascon et al., 1999). In areas where forests have been cleared for agriculture,
maintaining landscape connectivity, which has been defined by Talor et al. (1993) as
“the degree to which the landscape facilitates or impedes movement among resource
patches”, is fundamental for preventing such extinctions (Laurance, 2004). Given that
restoration of agricultural areas back to their natural state is unlikely due to rapid
population growth, increasing demands of agricultural products and increasing land
scarcity, biodiversity-friendly agricultural practices such as agroforestry have been
suggested as a means to overcome the decreasing connectivity in fragmented
agricultural landscapes (Bhagwat et al., 2008).
Agroforestry – the intentional planting of trees or woody perennials on the same
unit of land being used for raising agricultural crops or animals, either concurrently or
sequentially – is a collective term for integrated farming practices where two or more
components are managed simultaneously (ICRAF, 2000). Agroforestry practices are
considered sustainable, promoting biodiversity and presumably increases the functional
connectivity -the species specific movement of organisms in non-habitat- of the
agricultural landscape (Goulart, et al., 2011; Schroth et al, 2004; Tischendorf and
53
Fahrig, 2000). The high vegetative complexity intrinsic to agroforestry practices makes
them more environmentally friendly compared to monocultures (Nair, 1993). The tree
components modify the microenvironment so that biotic and abiotic conditions are more
similar to those found in the original forested habitat, which helps restore ecosystem
functions (McNeely and Schroth 2006; Schroth et al., 2004). Research on the effect of
agroforestry on biodiversity have confirmed higher species diversity in agroforestry
systems compared with adjacent conventional agricultural systems (Harvey and
Villalobos, 2007; Mendez et al. 2006; Harvey et al., 2006; Perfecto, 2005; Wezel and
Bender, 2003; Estrada, 2000), thus supporting the potential of agroforestry in providing
habitat to some species of the area’s original habitat. The inclusion of agroforestry
practices in agricultural landscapes is thought to help protect primary forest species by
facilitating movement between remaining natural areas (Uezu et al., 2008; Boshier,
2004; Cullen et al., 2004; Laurance and Vasconcelos, 2004; Naughton-Treves and
Salafsky, 2004; Rice and Greenberg, 2004).
However, the relation between agroforestry practices and landscape connectivity
is not yet well understood. The overall purpose of this study was to examine the
potential of agroforestry to increase functional connectivity, and its effects on
microclimate in agricultural landscapes. Given the difficulty in measuring functional
connectivity, the presence or absence of fruit feeding butterflies was used as an index
of habitat preference outside suitable forest areas as suggested by Wiens et al. (2008)
and Fleishman and Murphy (2009), and therefore as a means to infer information about
the role of agroforestry at increasing functional connectivity in relation to other land-use
practices and forest habitats in the agricultural matrix.
54
The objectives of this study were to (1) identify the spatial distribution (abundance
and species richness) of fruit feeding butterflies in a continuous landscape containing
forest patches, agroforestry and non-agroforestry practices, as an indirect measure of
functional landscape connectivity; (2) compare the composition of fruit feeding butterfly
species among the different land-use practices and forest habitats; and (3) examine the
influence of abiotic factors (temperature, wind speed and humidity) on the spatial
distribution of fruit feeding butterflies.
Methodology
Site Description
The landscape of Pontal do Paranapanema in São Paulo, Brazil, where the study
was conducted, has experienced large scale deforestation, intensive agricultural
practices, and more recently, some efforts towards the reclamation of the land through
agroforestry. Until the 1940s, the region was covered by a continuous semi-deciduous
forest. The subsequent clearing of the forest for agriculture resulted in the extinction of
many plant and animal species (PEMD, 2006). Small clusters of species-rich forest
fragments were left in the pasture-dominated landscape (Cullen, 2005). In spite of their
relatively small size (< 5% combined of the original forested area), forest fragments in
the region are of great biological value because they contain many endemic and
endangered species (Padua et al. 2002). Even though the fragments have been
recognized as ecologically important, their future is uncertain because most of them
have no legal status in terms of ownership or protection. From a landscape perspective,
the incorporation of homegardens, shaded coffee plots, and other agroforestry
practices, results in tree-covered islets embedded in the pastures.
55
The study was conducted in two farming settlements (Riberão Bonito and Agua
Sumida) within the Pontal do Paranapanema (hereafter “Pontal”) region of Brazil, which
is located in the western-most section of the state of Säo Paulo, between the Parana
and Paranapanema rivers. The weather is dry subtropical with an average annual
temperature of 21°C, the rainy season lasts from January to March with a maximum
average temperature of 32°C, and the dry season extends from May to August with a
minimum average temperature of 13°C. Annual precipitation ranges from 1,100 to 1,300
mm, and elevation is between 250 and 500 m a.s.l. (PEMD, 2006; Diegues, 1990).
The settlements are associated with forest fragments and contain small
subsistence farms where the dominant land cover is pasture and milk production is the
main agricultural activity. Since purchase or appropriation of their government-assigned
lots, a common practice by local farmers has been to plant trees, shrubs and
vegetables, primarily around houses to create homegardens, but also to plant trees in
the pasture land as to create “living fences” and to provide shade in agricultural plots.
Some of the most desired trees species used include native and non-native fruit trees
such as Bananas (Musa sp.), Pêssego (Prunus persica), Papaya (Carica papaya),
Coconut (Cocos nucifera) and Mango (Mangifera sp.). Other common trees planted
include Grevília (Grevillea robusta) and Ipe (Tabebuia sp.). However, the most common
tree planted is Eucalyptus (Eucalyptus sp.), which is grown for its timber, usually as
living fences or in tree plantations. Homegardens, living fences and shaded crops
constitute agroforestry practices, and have resulted in a complex mosaic of partially
forested habitats between the remaining fragments of primary and secondary forest.
56
Homegardens and shaded coffee plots are the major agroforestry practices
contained within the study site. Other agroforestry practices found in the region include
living fences, tree/livestock combinations (silvopastures) and alley cropping. Among all
of these, homegardens are the most common and widespread (Cullen et al., 2001). The
homegardens in the two settlements vary in shape, size, and management. In general,
the homegardens range in size from 0.5 to 1 ha; approximately half have more than 30
trees (Menegario, 2006). In addition to fruit produced by their trees, the homegardens
provided a space for rearing small animals such as chickens, ducks, and pigs, as well
as other agricultural products included in the vegetable gardens (medicinal plants,
condiments, tubers and ornamentals). Shaded coffee agroforestry in the Pontal
however, is less common and generally occurs in larger operations than homegardens,
with an average size of 1 ha, 4000 coffee plants, and 400-800 shade trees (Lima,
2007). Shade trees in coffee plantations in the Pontal are often planted in rows (2.5 x 5
m apart); two coffee plants generally occur between trees in alleys.
The non-agroforestry land-use practices in the study area include sole stands
(monoculture) of eucalyptus, sugarcane, and cassava, as well as fallow pastures
(Figures 3-1 and 3-2). Eucalyptus is a very common and preferred tree in the region
because it is fast growing and provides a cash crop. Sugarcane plots are also common
to the region and the state of São Paulo in general. Large plantations of sugarcane
have been expanding in the Pontal region through signed contracts with large
landowners and agrarian reform settlers (Freitas and Sparovek, 2008). Among the
farming settlements, small plots of sugarcane can be found; they are used mainly for
household consumption or feed for livestock. Cassava monoculture plots are also
57
common in the study area. Many of the farmers in the region have experience planting
this crop, which is sold in local markets and used for household consumption.
The largest forest fragment in the Pontal region (Moro do Diabo State Park) has a
rich diversity of butterfly species. An inventory by Mielke and Casagrande (1997)
reported a total of 426 species in 48 subfamilies, most in the families Nymphalidea and
Hesperiidae. The high butterfly diversity in the Pontal is typical of Brazil’s remaining
Atlantic forests (Brown 2000, Uehara-Prado et al., 2007). Even though an assessment
of forest butterflies has not been conducted outside the forest fragments in the region,
all fruit feeding butterflies captured were recognized as indicators of functional
landscape connectivity between forest patches. The distribution of fruit-feeding
butterflies in the agricultural landscape will provide information about habitat preference
and the contribution of each land-use practice at enhancing the connectivity of the
landscape.
Data Collection
Data on butterfly distribution were collected in two experimental quadrants that
included the area of several farms at Ribearo Bonito and Agua Sumida farming
settlements (Figure 3-1 and 3-2). The quadrants were located between two forest
patches that were several times larger than the farming area between them and
encompassed different land-use practices and a small fraction of the adjacent forest
patches. The quadrant at Riberão Bonito was slightly larger than at Agua Sumida with
total areas of 2.4 and 2.2 km2, respectively. The agricultural landscape mosaics
included both agroforestry and conventional agricultural practices, as typical for the
area. Butterfly abundance was quantified through captures in Van Someren-Rydon
butterfly traps (DeVries, 1987). These types of traps have widely been used in the
58
literature for diversity studies and for the comparison of different land-use systems
(Pozo et al, 2008; DeVries 2001; Brown and Freitas, 2000; Pinhero et al, 1992; DeVries,
1987; Rydon, 1964). For the purposes of this study, the function of baited traps was not
to inventory the entire butterfly community, but to target the same butterfly guild (low
strata fruit-feeding butterflies) present in the fragmented agricultural landscape.
Positioning of the butterfly traps in the landscape formed a rectangular grid. Within
this grid, each trap was identified with an alphanumeric code. A geographic positioning
system device was used to determine the location for each trap, and the traps were
suspended between 1 to 1.5 m above the ground. The total number of traps at Riberão
Bonito was 112 and at Agua Sumida was 96. The traps were baited with a mixture of
fruits, sugar and rum (Dolia et al., 2008). After a period of trial and error, a mixture
composed of 2 bananas, ½ cup of rum, ½ cup of sugar, and 1/4 cup of fruit juice
(usually orange) was standardized. Once the traps were baited, they were checked
every other day, and butterflies were marked and released, and the bait mix
replenished. Recaptures were not counted when determining the butterfly abundance
and diversity of the different habitats. The sampling period was 24 days at each
settlement.
The sampling period was not intended as a complete inventory of the fruit feeding
butterflies in the farming landscape. Given that the bait was consistent throughout the
trap grid in the experimental landscape, the targeted butterfly fruit-feeding guild inside
the forest was also targeted outside the forest patch area. Species were identified
through use of butterfly inventories at the Moro do Diabo State Park (Parque Estadual
do Moro do Diabo- PEMD) museum, the guide by Uehara-Prado et al.(2007), and the
59
butterfly inventory for Brazil’s Atlantic forest by Brown et al., (2000). One moth species
(Ascalapha odorata) was commonly captured and included in analysis because it is
ecologically equivalent in behavior and habitat use to the common butterfly species
attracted to the traps. The experiment was conducted from August through November of
2008. Data collection at Riberão Bonito began on August 19 and concluded on
September 11. Data collection at Agua Sumida took place from October 10 to
November 5.
The total number of butterfly traps placed at each land-use plot was proportional to
the area of the system in the quadrant. The size of each quadrant was intended to
depict different land-use components characteristic of the region. Within the
experimental quadrant at Riberão Bonito, there were eight different land-use practices.
Of these, 14 butterfly traps were inside the forest patch, 12 at the forest edge, 64 in
pastures, three in shaded coffee plots, nine in homegardens, three in cassava plots,
three in abandoned pasture areas (designated as secondary growth), and three in
eucalyptus plantation plots. Within the quadrant at Agua Sumida there were seven land-
use practices. In contrast to Riberão Bonito, there were no cassava or eucalyptus plots;
instead, there was a sugarcane plot that fit 10 butterfly traps. The other traps were
distributed as follows: 15 inside the forest fragments, eight in the forest patch edge,
three in shaded coffee, five in homegardens, four in secondary growth, and 51 in the
pasture.
At the end of the butterfly data collection period, data for abiotic factors were
collected. Temperature, wind speed and relative humidity were measured during six
consecutive days at each trap site using a Windmate (accuracy +/- 3%). In addition,
60
because weather fluctuations were relatively small in the region during the sampling
period (+/- 1°C between the temperature mean values of August through November
according to metereological records), we assumed the overall regional weather to be
stable and for the microclimatic differences to be directly related the different land-use
and management practices (http://www.defesacivil.sp.gov.br/v2010/meteorologia3.asp).
Abiotic data were measured once each day around noon (from 11 am until 2pm). After
waiting a couple of minutes for the Windmate to adjust to the new location, data were
recorded for temperature, wind speed and humidity. The order in which the traps were
visited was changed every day to avoid any bias due to daily variation in data collection
time.
Data Analysis
Given that the trapping effort was proportional to each type of land-use, the
average values of individuals and species were calculated for all traps within each land-
use. Differences in statistical power due to the uneven number of traps per land-use did
not affect the measurement because the data reached the habitat’s species asymptote
after 24 sampling days (Wilhm, 1970)(APPENDIX A and B). The average values were
described between the different land use systems. In addition, a repeated measures
statistical analysis of variance (ANOVA) was performed using the SAS Generalized
Linear Mix Models (GLIMIX) procedure (Von Ende, 2001). The data were log (In)-
transformed for normality and the least squared means were used because of unequal
group sizes (Dunnet, 1980). Multiple pairwise comparisons using the Tukey–Kramer
method were applied to control Type I error. Significant differences between land-use
types were set to less than or equal to 0.05.
61
Differences in species composition were further compared using Linear
Discriminant Analysis and the nonparametric statistical analysis of similarities (ANOSIM:
Cleary, 2004). A pair-wise ANOSIM analysis with zero adjusted Bray-Curtis distance
was run in R for butterfly species similarities between the different land-use practices at
each experimental site. To correct for multiple comparisons, a significant level of 0.05/n
was applied (Bonferroni correction) where n was the number of comparisons between
the land-use practices (28 at Riberao Bonito and 20 at Agua Sumida). In addition, a
Linear Discriminant Analysis (LDA) was conducted for each landscape to identify
subfamily differences between land-use practices. Discriminant analyses have long
been applied in ecology for comparisons of habitat structural characteristics based on
species data (Williams, 1983; Conner and Adkinsson; 1976; Anderson and Shugart,
1974). Because the number of species was too large for the LDA comparison, the
number of butterflies per species was grouped by subfamily prior to conducting the
analysis. A stepwise procedure was first done to identify which butterfly subfamily
groups were significantly separated between land-use systems. This process also
removed subfamily groups that had extremely low counts (less than 2 individuals). The
ANOSIM matrix was run in R, while the Linear Discrepant Analysis was run in JUMP 8.
Finally, the abiotic factors were analyzed in relation to the butterfly and land-use
data in SAS. To standardize the variables, data were log (In)-transformed for normality.
A simple linear regression was conducted to determine the statistical correlation
between the abiotic factors and the presence of fruit feeding butterflies. In addition,
GLIMIX ANOVA’s were run to compare the different land-use systems based on each
abiotic factor (temperature, wind speed and humidity). Differences between land-use
62
systems were analyzed in conjunction with the butterfly results to identify distribution
patterns in the landscape.
Results
Mean Number of Individuals and Species
At Riberão Bonito, 2648 butterflies belonging to 77 species were captured, while at
Agua Sumida 4900 individuals and 47 species were captured (Table 3-1, 3-2, Figure 3-
3, 3-4). Among the fruit feeding species captured, Memphis ryphea (Cramer, 1775) and
Adelpha cytherea (Fruhstorfer, 1915) were the most abundant within the Riberão Bonito
agricultural landscape (491 and 499 individuals, respectively). At Agua Sumida the most
common species were Eunica tatila (Herrich-Schaeffer, 1855), Hamadryas februa
(Huebner1823), and Memhis ryphea, with 2086, 891 and 780 individuals, respectively.
At each site, some species were captured only a single time. At Riberao Bonito
these were Archeoprepona demophon (Linnaeus, [1758]), Catonephele aconticu
(Linnaeus, [1771]), Chlosine lasinia (Geyer, [1837]), Pareuptychia interjeta, Pyrrhogira
neaerea (Linneaus, [1758]), and Taygetis acuta (Weymer [1910]). At Agua Sumida
species such as Callicore hydaspes (Drury, [1782]), Nica flavilla (Godart [1824]), and
Smyrna blomfildia (Fabricius, 1781) were rare, yet these same species at Riberão
Bonito were more frequent. However, there were rare species at Agua Sumida that
were also rare at Riberão Bonito. Only one individual each of the following species was
captured at the Agua Sumida landscape and rarely or never captured at Riberao Bonito:
Caligo beltrao (Illiger, [1801]), Morpho catenarius (Perry), and Taygetis acuta (Weymer,
[1910]).
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Butterfly Differences between Land-use and Habitats
Riberão Bonito
Differences based on the number of butterfly individuals captured showed that
none of the land-use practices was comparable to forest habitat at Riberão Bonito. The
abundance of fruit feeding butterflies found inside the forest patches was significantly
different from the forest edge habitat and the rest of the land–use practices in the
experimental agricultural landscape. In contrast, the ANOVA pairwise comparisons
between forest edge, eucalyptus and shaded coffee, were significantly different from the
rest of the land-use systems, but not between each other (forest edge and eucalyptus p
= 0.995, forest edge and shaded coffee p = 0.999, and shaded coffee and eucalyptus p-
= 1.000) (Table 3-3). A second group of land-use practices that were not significantly
different from each other were secondary growth, homegardens and cassava plots
(secondary growth and homegardens p = 0.948, secondary growth and cassava p =
0.214, and cassava and homegardens p = 0.601). In addition, cassava plots were not
significantly different from pastures (p= 0.304). Finally, pastures were significantly
different from all the other land-use practices.
With respect to number of species (Figure 3-4), forest interior and forest edge
were significantly different from all other land-use practices except for eucalyptus plots
(p= 0.086 and 0.999, respectively), and shaded coffee systems (p = 0.425 and 1.000,
respectively) (Table 2-4). Furthermore, eucalyptus plots were not significantly different
from shaded coffee (p = 0.999), and these two were significantly different from the other
land-use practices except for secondary growth areas (p = 0.259 and 0.072,
respectively). However, the pairwise comparison between secondary growth and forest
edge was significantly different (p = 0.004). In addition to shaded coffee and eucalyptus
64
plots, secondary growth was not significantly different from cassava (p = 0.272) or
homegarden plots (p = 0.989), and homegardens were not different from the cassava
plots (p = 0.645). Similar to the results for the number of butterfly individuals, the
number of species in pastures were significantly different from all the land-use
practices, except cassava plots (p = 0.321).
Agua Sumida
The ANOVA analysis results for Agua Sumida were similar to those at Riberão
Bonito. Pairwise comparisons between land-use systems based on the number of
individuals showed that forest interior was significantly different from all land-use
practices except for shaded coffee and forest edge areas (p = 0.654 and p = 0.978
respectively) (Table 3-5), and these two were not significantly different from each other
(p= 0.360). Shaded coffee plots were also not significantly different from homegardens
(p =0.166), secondary growth (p = 0.176) and sugarcane plots (p = 0.338), and these
last three systems were not significantly different from each other. Pasture was the only
land-use system that was significantly different from the rest of the landscape
components in the experimental site at Agua Sumida (p = <.0001 for all comparisons).
Pairwise comparisons for the number of butterfly species between land-use
systems were the same as with the number of individuals at Agua Sumida. According to
the ANOVA analysis, forest interior was not significantly different from forest edge or
shaded coffee systems (p = 0.987 and p = 0.654 respectively)(Table 3-6), but was
significantly different from the other land-use practices. Also, forest edge and shaded
coffee were not significantly different from each other (p = 0.360). Furthermore, there
were no significant differences between shaded coffee, homegardens, secondary
65
growth areas and sugarcane. In contrast, pastures were significantly different from all
other land-use systems.
Linear Discriminant Analysis
To visually represent the differences in species composition among the land-use
systems, a canonical plot was created using Linear Discriminant Analysis (LDA). Out of
the nine subfamilies found at Riberão Bonito, only five were selected by the stepwise
procedure in LDA (Biblininae, Charaxinae, Ithomiinae, Limnitidae and Morphinae), and
from the seven subfamilies in Agua Sumida, six were selected (Biblininae, Brassolinae,
Charaxinae, Morphinae, Nymphalinae and Satyrinae). The Discriminant Analysis
resulted in uncorrelated linear combinations of the subfamilies (canonical variates) and
can be visualized in the canonical plots (Figure 3-5, and 3-6). At Riberão Bonito some of
the ellipses were concentric with pastures starting at the center followed by cassava,
homegardens and secondary growth. This spatial distribution suggest that the species
found in the pasture were also found in the the other habitats.The eucalyptus ellipse is
located below the previous land-use practices, and the ellipse for shaded coffee is
located towards the right where the ellipses for forest and forest edge are located. This
indicates that the species composition in shaded coffee was more similar to the forest
habitats than any of the other agricultural land-use practices. At Agua Sumida, pasture,
secondary growth, homegardens, shaded coffee and sugarcane are also semi-
concentric, and similar to the ellipse spatial distribution at Riberäo Bonito, shaded coffee
is the outer most land-use moving towards the right and closer to the ellipse for forest
habitats.
At Riberão Bonito the first and second canonical axes explained 94% of variation
in in the number of butterfly indidivudals and subfamilies between the land-use
66
practices. The first canonical axes had an eigenvalue of 3.08 and described 81% of
butterfly variability. The second canonical axes had an eigenvalue of 0.51 and
described 13% of the butterfly variability. The first canonical axes explained most of the
observations and can be interpreted as an increase in the number of butterfly
individuals from the different subfamilies from left to right. In addition and according to
the coefficient scores, the subfamily Biblininae and Charaxinae are more common and
found throughout the landscape, while Ithomiinae, Limenitidae and Morphinae are less
common and are associated to forest and forest edge areas. At Agua Sumida the first
and second canonical axes also describe a large percentage of the data variability
(89%). However, the first canonical axes explained a lower percentage of the data
variability (eigenvalue 3.6 and 69%), while the second canonical axes explained more
compared to the axes for Riberão Bonito (eigenvalue 1.03 and 19.9%). At Agua Sumida
Biblininae and Brassolinae are the most common subfamilies throughout the landscape,
while Morphinae, Nymphalinae and Satyrinae are less common and more likely
associated with forest habitats.
Species Similarities (ANOSIM Analysis)
A species similarities analysis was conducted to statistically compare the species
composition between the land-use practices. The results from the ANOSIM models
showed significant differences in the butterfly species composition among some of the
land-use components. In contrast to the spatial organization of the ellipses suggested
by the LDA models, the similarity analysis of the species composition variability
indicated a different pattern of relatedness among the land-use practices. At Riberão
Bonito, the ANOSIM analysis found significant differences between the pasture matrix
and all the other land-use practices and forest habitats except for cassava
67
monocultures. In addition, the pairwise comparisons between homegardens and casava
was significantly different (p = 0.001). The ANOSIM results for Agua Sumida were
similar from those for Riberão Bonito. Pastures were again significantly different to all
the other lands-use practices and habitats. In addition, forest and forest edge, were
significantly different to secondary growth (p= 0.001 and p= 0.001, respectively) and
sugarcane (p= 0.001 and 0.002, respectively). The rest of the pairwise comparisons
were not significantly different from each other.
Environmental Factors Analysis
Overall the results from the abiotic factors ANOVA among land-use practices
indicate that the microclimate conditions found in agroforestry were more similar to the
conditions found in other agricultural practices than to forest habitats. As far as
temperature values, agricultural land-use practices were similar. Temperatures in
shaded coffee and homegardens were consistently not significantly different from those
in forest edges, eucalyptus, secondary growth, cassava, sugarcane and pastures. At
Riberão Bonito, both agroforestry systems were significantly different from forest, but
that was not the case at Agua Sumida.
An initial comparison of the average values per trap point for the abiotic factors
indicated that forest interior sites had lower temperatures, wind speed, and higher
humidity than the other land-use practices. Temperature variability at Riberão Bonito
was greater than at Agua Sumida (Tables 3-9 and 3-10). In comparison, the data
ranges for average values between land-use practices for wind speed and humidity
were similar at both landscapes. Humidity levels, however, were lower at Riberão
Bonito than at Agua Sumida.
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The results from the multiple linear regression analysis showed a significant
relationship between the abiotic variables measured and the presence of butterflies at
each trap point (F value= <.0001). The adjusted R-squared values for the regression
model at Riberão Bonito explained almost half the variation in the number of individuals
and number of species (0.47 and 0.50, respectively). The R-squared values at Agua
Sumida were much higher than at Riberão Bonito (0.80 for both the number of
individuals and number of species).
In the regression model using the number of butterfly individuals as the response
variable, at Riberão Bonito, the p-values for temperature, wind speed and relative
humidity were significant (p-values: 0.0207, <.0001, and 0.0151, respectively). Areas
were temperatures were lower (within the temperature range measured), with low wind
speeds and higher relative humidity values, were associated with higher number of
butterfly individuals. Similarly, the regression model using the number of butterfly
species as the response variable also indicated statistical significance of the measured
environmental factors (p-values: 0.0371, <.0001, and 0.0013, respectively).
At Agua Sumida the adjusted R-square value in the regression model for the
number of individuals and species was high (0.80). In this landscape, only the effect of
wind speed and humidity were significant (p = <.0001 and 0.0003, respectively). In
contrast to Riberão Bonito, temperature was not a significant predictor of the number of
butterfly individuals and species (p = 0.458 and 0.665, respectively).
The ANOVA pairwise comparisons of the land-use practices based on the
measured abiotic factors showed no clear patterns of differences at Riberão Bonito.
Temperature values in forest fragments were significantly lower (3.5 to 4.1°C) from
69
those of other land-use practices except for forest edge and eucalyptus plots (Table 3-
11). Temperature values in eucalyptus on the other hand were not significantly different
from any of the other land-use practices, including pastures. Temperature variability in
shaded coffee, homegardens, and secondary growth were only significantly different to
forest interior (p = 0.001, <.001, 0.002). Pastures and cassava plots were significantly
different from forest and forest edge (pastures p = <.001, <.001 and cassava p = 0.018,
<.001, respectively).
Wind speed in the forest interior at Riberão bonito had low values (average 0.6
m/s). Wind conditions in this habitat were comparable to those in several land-use
practices including shaded coffee, eucalyptus, homegardens and secondary growth
(Table 3-12). In contrast, wind speed in forests interior was significantly different from
forest edge, cassava plots and pastures (p = 0.015, 0.049, <.001), which had higher
wind speed values (averages 0.68, 0.95 and 1.47 mp/s, respectively). In addition to
forest interior, wind speed variability in forest edge, was different from that of pastures
(p = 0.0322). Shaded coffee was only significantly different from pastures (p = 0.0016),
and wind speed variability in homegardens was not significantly different from any other
land-use system.
Humidity levels in forest interior were (9.2 to 39.9%) higher than the other land-use
practices and similar only to forest edge and secondary growth areas at Riberão Bonito
(Table 3-13). In shaded coffee, humidity conditions were significantly different from
forest interiors and forest edge habitats (p = 0.002, and 0.044), but differences were not
obtained for the rest of the land-use practices. Homegardens, however, were only
70
significantly different from forest interiors (p = 0.005). Moisture in pastures was different
from both forest interior and forest edge habitats (p = <.001 and <.001)
Except for temperature, there were greater differences in microclimate conditions
between forest and agricultural habitats at Agua Sumida. Temperature was not
significantly different among all land-use systems except between forest interior and
pasture (p = 0.006) (Table 3-14). Pairwise comparisons for wind speed showed that
forest interior was significantly different from all other land-use practices except for
forest edge habitats. These results differed from the ANOVA results for wind values at
Riberão Bonito, where wind speed variability was not significantly different between the
forest interior and several land-use practices. Moreover, wind speed in shaded coffee
was not significantly different from that in homegardens and vice versa; and pastures
were significantly different from all land-use practices except for secondary growth.
Humidity in forest interiors and forest edge areas were similar to each other and
significantly different from the agricultural land-use practices (Table 3-15). Land-use
systems such as shaded coffee, homegardens, secondary growth and pastures had
similar variability and were significantly different from forest, forest edge and sugarcane.
Sugarcane was the only agricultural land-use practice with significantly higher humidity
values compared with all other practices (shaded coffee p = 0.003, homegarden p =
<.001, secondary growth p = <.001 and pasture p = <.001), yet significantly lower
compared to that of forest habitats (forest interior p = <001 and forest edge p = <.001).
Discussion
Fruit Feeding Butterflies in Fragmented Agricultural Landscapes
The relative abundance of butterfly individuals and species provides in the
agricultural landscape serves as an initial means to compare the potential of the
71
different land-use practices to increase landscape connectivity. Forest traps captured
the highest number of individuals and species, whereas pastures captured the lowest
(Riberão Bonito) and second lowest (Agua Sumida) number of individuals (Table 3-1
and 3-2). These results were expected because these habitats were radically different in
terms of vegetative structure and composition, and are at opposite ends in terms of
land-use intensity and anthropogentic disturbance (Klein et al., 2002). At both sites, the
averages for pastures, cassava and secondary growth areas were lower than for
agroforestry practices (except for secondary growth at Riberão Bonito). These findings
validate the view that agroforestry practices are biodiversity-friendly compared to
monocultures or un-shaded agricultural systems, as suggested by Schroth et al. (2004).
The sugarcane plot at Agua Sumida, however, captured a higher number of
individuals than shaded coffee and homegardens, and a higher number of species
compared to homegardens. Given that sugarcane is an intense monoculture land-use
system, these results contradict the commonly held notion about the conservation
benefits of monocultures compared to to more sustainable agricultural practices such as
agroforestry. Yet, they can be explained by examining the nature and spatial
arrangement of the sugarcane plot in the landscape. At Agua Sumida, sugarcane was
immediately adjacent to one of the forest patches (Figure 3-3). The proximity of the
sugarcane plot to the forest patch most likely influenced the abundance and
composition of butterflies in this land-use practice. Distance between patches is
considered an important factor for the dispersal and colonization of individuals and
species (Bowler, 2009; Dolia et al., 2008). In addition, fruit- feeding butterflies may find
sugarcane plots attractive as a source of food. Sugarcane juice is sometimes used as
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one of the ingredients for the bait in butterfly traps (Uehara-Prado et al., 2007; Dolia et
al., 2008), therefore, any broken stem within a mature sugarcane plantation (like the
one studied), would attract fruit and nectar feeding butterflies (pers. Obs.). The nature
and location of this land-use may have led to a greater number of individuals and
species compared to agroforestry plots. Yet, the general characteristics of sugarcane
management and production make this agricultural system an intense and
environmentally harmful land-use practice (Ranta et al., 1998; Petit and Petit, 2003).
The overall findings for the average abundance and species richness of fruit-
feeding butterflies in agroforestry practices agree with those of similar studies (Bhagwat
et al., 2008). Different insect groups have been studied along the vegetative gradient
that often exists in modified agricultural landscapes (Perfecto et al., 1996; Perfecto et
al., 2003; Bobo et al., 2006; Harvey et al., 2006; Tobar et al., 2007). By collecting
butterfly data using a systematic approach, the distribution of butterflies in the
agricultural landscape was estimated. The virtual representation of the landscape in
terms of average number of individuals and species shows how the values for
agroforestry practices fall between those of the forest habitats and the pasture matrix.
Given the abrupt modification induced by the conversion of natural habitats into
pastures, agroforestry plots are used by butterflies to a greater extent than the
surrounding matrix. The visual representation of species richness and abundance
suggest that shaded coffee (in particular) and homegardens serve as landscape
components that increase permeability, and therefore functional connectivity in the
agricultural landscape.
73
Tobar et al., (2007) also found butterflies in agroforestry practices to have
intermediate values compared with secondary forest areas and pastureland in Costa
Rica. Yet, while riparian forest (agroforestry practice) were not significantly different to
secondary forest habitats, single row living fences (agroforestry practice) were not
significantly different from pastures. Depending on the type of practice, butterfly
species richness and diversity values in agroforestry practices will be skewed to either
side of the vegetation gradient. Differences in management also apply when comparing
the same type of agroforestry practice. Perfecto et al., (2003) examined the effect of
different shade cover intensities on the presence of butterfly species in shaded coffee
agroforestry. They found a positive correlation between the percentage of shade cover
and the number of butterfly species. Furthermore, tree density has been proposed as
the vegetation parameter that best predicts fruit-feeding butterfly species richness in
agricultural landscapes (Bobo et al., 2006). The results of the present study agree with
these findings. Eucalyptus and shaded coffee plots had the highest butterfly species
richness values as well as the highest tree density among the agricultural practices. In
general, agroforestry practices that structurally resemble forest areas are usually more
species-rich than simplified practices with fewer interacting plant and animal
components. The presence of trees and shrubs in agroforestry increases the vegetative
coverage of the otherwise intensively managed single-species agricultural system
(Chacon, 2005). In turn, the increased vegetative coverage can provide permanent or
temporary habitat to dispersing organisms in the landscape.
74
ANOVA Land-use Comparisons based on butterfly abundance and species richness
The repeated measures ANOVA suggest a similar pattern of landscape
components that were not significantly different from each in terms of butterfly
abundance and species richness, and can be described into three groups.
The first group is composed by Eucalyptus plantations, shaded coffee plots, forest
interior and forest edge habitats. The comparative studies by Schulze et al. (2004) and
Bos et al. (2007) found shaded agroforestry systems (shaded cacao (Threobroma
cacao L.) to be similar to forest areas in terms of ant- and beetle-species richness. The
lack of significant difference between eucalyptus and shaded coffee with the forest
habitats may be due to their high tree density, which has been identified as a predictor
of high species richness of fruit-feeding-butterflies (Bobo et al., 2006).
Homegardens, secondary growth, and monoculture plots (cassava at Riberão
Bonito and sugarcane at Agua Sumida) make up a second group of land-use practices
that were not significantly different from each other in terms of butterfly species
richness. A study by Kunte et al. (1999) on butterfly diversity between vegetation types
also found homegardens, shrub/savanna habitats, monoculture and plantings to have
similar mean species richness values. However, the vegetative complexity of these
land-use systems is very different. Plant diversity in homegardens is much higher
compared to monocultures and secondary growth (Hemp,2006; Mohan, 2007). The lack
of significant differences among these land-uses suggests that factors other than high
values of plant diversity typical of homegardens may be responsible for their lower
butterfly presence (Bobo et al., 2006). The study by Schulze et al. (2004) supports this
argument as they did not find positive correlations between the species richness of fruit-
75
feeding butterflies and species richness of understory plants in agroforestry, forest
areas and annual cultures.
It is possible that the high number of plant species characteristic of homegardens
would be more attractive to forest butterfly species if the area they occupied in the
landscape was larger. According to global inventories, the average size of
homegardens in the Pontal is typical of homegardens elsewhere (0.3 ha) (Das and Das,
2005; Fernandes and Nair, 1986). In addition, the size of the homegardens were
smaller compared to the areas occupied by the monoculture crops and secondary
growth areas in the landscape. Given that one of the main functions of homegardens in
the Pontal is the production of fruits for household consumption, and that fruit and palm
trees such as mango (Mangifera sp.), papaya (Carica papaya), guayaba (Psidium
guajava), banana and coconut are frequently present in these systems, the butterfly-
species-richness values are expected to be comparable to those of other tree-shaded
systems. Yet, the ANOVA analysis did not identify significant differences among
homegardens, monocultures plots and secondary growth areas. If the principles of
island biogeography theory are applied to the agricultural landscape, and it is assumed
that homegardens can serve as habitat by providing food and shelter to forest butterflies
as the literature suggests, butterfly species richness would increase as a function of
increasing the area of homegardens. The small size of homegardens may be
insufficient for them to have a significant role in increasing the functional connectivity in
the landscape to conserve wildlife (Steffan-Dewenter and Tscharntke, 2004).
Pastures constitute the third group of land-use practices. Pastures were
significantly different from all the other land-use systems in terms of species richness
76
and abundance (except for cassava plots at Riberão Bonito) (Tables 3-3 and 3-4).
Pastures in the Pontal region were the dominant land-use practice and were
characterized by having very few scattered trees and shrubs (Cullen, 2005). This land-
use practice had the lowest species richness values. Due to their low plant-diversity,
pastures are an unsuitable environment for forest-specialist butterflies (Tobar et al.,
2007), and are used only temporarily by butterflies while they are moving between
forest patches (Marin et al., 2009). The intensive grazing practices at the Pontal result in
pastures stripped of hospitable vegetation (for feeding, resting, perching, ovipositioning
and hiding), which resulted in the low presence of fruit-feeding butterflies (Kruess and
Tscharntke, 2002). Butterflies captured in this land-use were most likely moving across
the matrix, but were attracted by the bait in the traps (Marin et al., 2009).
Species Composition in Agricultural Land-use Systems
The context was that although most of the available studies that compared
biodiversity in agroforestry to other habitats used species richness values (Bhagwat et
al., 2008), only a few had statistically compared the species composition of the targeted
populations among land-use systems. A study by Uehara-Prado et al. (2007) discussed
how species richness values alone did not reflect the effect of anthropogenic
disturbances in the Atlantic forest of Brazil. Moreover, when they compared the species
composition of fruit-feeding butterflies in disturbed versus undisturbed landscapes, they
found similarities in terms of species richness, but also differences in terms of
community structure.
The separation of land-use systems in the canonical plots using the discriminant
analysis showed a similar spatial association at both landscapes. The ellipses that
represented the different agricultural land-use practices were located around the (0,0)
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coordinate intersection point of the canonical axes scale. Pastures were at the center,
and the rest of the agricultural land-use practices formed semi-concentric ellipses
surrounding that point. This could indicate that the composition of species was low and
similar among these practices, or that a clear separation of the groups was not possible
due to too many zero values in the data. Either way, a similar pattern of separation for
the different land-use practices is apparent for both landscapes. In the present study,
shaded coffee was clearly the agricultural land-use practice that most closely resembled
the composition of species found in forest and forest edge habitats, and was followed by
eucalyptus at Riberão Bonito.
As far as the butterfly distributions in the landscape are concerned, some
subfamilies were more restricted to forest habitats. At Riberão Bonito, the scoring
coefficient value for Morphinae, Chasaxinae and Limenitidae suggests these
subfamilies are found more often in forest habitats, Morphinae in particular. In contrast,
although Biblidinae and Ithominiinae were also found in forest habitats, in comparison to
the other subfamilies they were more common in the agricultural landscape. At Agua
Sumida, the coefficient score values for the second canonical axes for Morphinae,
Nymphalinae and Charaxinae suggests that in this landscape butterflies of these
subfamilies were more frequently found in forest edge habitats. Satyrinae, in contrast,
was found in forest interior, and Biblidinae and and Brassolinae were frequent in
agricultural landscapes (in general a move from left to right on the X axis of the
canonical plots represents a move towards greater species richness). These findings
are consistent with those from Brown and Freitas (2000), who report that the species
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richness of forest-restricted subfamilies was associated with environmental parameters
(vegetation, temperature and topography) particular to Brazil’s Atlantic forest habitats.
The results from the pairwise ANOSIM species similarity analysis were more
explicit identifying significant differences between land-use practices than the LDA
canonical plots. At Riberão Bonito the analysis of variability within and between groups
showed significant differences in species composition between pastures and all the
other habitats (Table 3-7). Even though forest interior and forest edge contained a
larger number of species compared to the agricultural practices, the abundance of rare
species was low (one or two individuals per species). Because the ANOSIM similarity
analysis compares the data variability within and between land-use practices, these
results indicate the presence of rare species in forest habitats was not large enough for
it to account for significant differences between agricultural practices and forest
fragments (except for pastures). Excluding pastures (the matrix), the ANOSIM results at
Riberao Bonito could be interpreted to suggest that eucalyptus, shaded coffee,
homegardens, secondary growth areas and cassava plots, contribute to the
permeability of the landscape, or that their presence is not as disruptive for the
persistence of butterflies.
At Agua Sumida, the ANOSIM analysis also resulted in significant differences
among the land-use practices. These groups are the same as the ones suggested by
the species richness ANOVA analysis (Table 3-7). By having an overall less diverse
butterfly community composition at Agua Sumida (37 species less than at Riberão
Bonito), there was less within-group variability (rare species added “noise” to the data
analysis). The results for this analysis, in addition to those for species richness ANOVA,
79
identify shaded coffee as a land-use practice that can contribute to the conservation of
forest-bound butterfly species in fragmented agricultural landscapes.
Effect of Abiotic Parameters on Fruit Feeding Butterfly Diversity
Wind speed and relative humidity were found to be reliable microclimatic
predictors of fruit-feeding butterfly occurance in the landscape. The effects of these
parameters were significant in terms of butterfly species-richness and abundance at
both research sites. Temperature, however, was significant only at Riberão Bonito. The
fit of the regression model was good for Agua Sumida, but moderate for Riberão Bonito.
The relatively low fit of the regression model at Riberão Bonito is most likely due to
influential parameters that were not measured in that particular landscape that affected
the distribution of fruit feeding butterflies. Brown et al. (2000) examined numerous
environmental factors to explain butterfly variability in Brazil’s Atlantic forests. They
found temperature, topography, vegetation and soils to have major influence on butterfly
species richness and community structure. Schulze and Fiedler (1998) also found forest
butterflies to be sensitive to changes in temperature and humidity. Among biotic factors,
Dolia et al., (2007) found that the percentage of canopy cover negatively affected
butterfly abundance and richness. However, they sampled butterfly diversity across
families, so their results are not specific to Nymphalidae. Indeed several biotic and
abiotic factors influence the presence and distribution of organisms. In this study abiotic
factors were generalized into three (temperature, wind speed and humidity). The results
agreed with the literature, and forest butterflies were more often present in sheltered
areas away from high wind conditions, where temperature and relative humidity had
lower daily fluctuations (Denis, 2004).
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At Agua Sumida temperature was not a significant predictor of forest butterfly
occurrence. According to Kingsolver (1985), butterflies require thorax temperatures
between 28 and 40°C to sustain continuous flight, and between 33 and 38°C for
vigorous flight. For butterflies to move between land-use systems, temperatures must
be within the latter limits. The average temperature at Agua Sumida was 5.5°C higher
than that at Riberão Bonito (32.0 ± 1.43 °C and 26.5 ± 1.72 °C respectively). In addition,
temperature variability between land-use systems was lower at Agua Sumida.
According to the ANOVA for temperature between land-use systems, forest and
pastures were the only two habitats that were significantly different at Agua Sumida
(Table 3-16). The higher overall temperature in the landscape in addition to the lower
temperature variability among land-use practices suggest that butterfly distribution in the
landscape at Agua Sumida was less restricted by temperature variability between
habitats.
The lack of significance of temperature for butterfly distribution at Agua Sumida
could be the result of regional changes in temperature due to seasonality, which would
lead to more uniform temperature variability between the different land-use components
in the landscape during the daily intervals when these measurements were collected.
While at Riberão Bonito the data for abiotic factors were collected between the dry and
rainy seasons (October, 2008) when temperatures are cooler; at Agua Sumida the
abiotic data were collected during the rainy season (January 2009) when temperatures
were higher.
According to the regression model, wind speed was negatively correlated with fruit
feeding butterfly richness and abundance (APPENDIX E and F). However, a clear
81
pattern of differences between land-use practices based on wind speed was not evident
from the ANOVA pairwise comparisons. At Agua Sumida, wind speed in agroforestry
systems was significantly different from almost all the other land-use systems, except
for homegardens with shaded coffee, secondary growth and pastures. In contrast, at
Riberão Bonito, agroforestry systems were not significantly different from other land-use
systems (including forest areas), with the exception of pastures being different from
shaded coffee. Similar wind speed values for pastures were recorded by Cant et al
(2005). According to these results it could be argued that under higher wind speed
conditions, differences in land-use preference by butterflies would be more evident.
Dover et al. (1997) found population densities of Satyrids to increase in shelter-
providing features in farmland; they concluded that butterflies would tend to seek shelter
under unfavorable wind speed conditions. Further research is required on the effects of
wind speed on butterfly habitat selection.
The successful development and reproduction of fruit feeding butterflies requires
high humidity levels (around 70%) (Bauerfeind et al., 2007). Studies by Schulze and
Fiedler (1998) and by Hill (1999) found butterflies to be sensitive to changes in humidity.
Based on their findings and the present results, none of the agricultural land-use
practices in the landscape (except forest habitats) would be suitable as permanent
habitat for forest butterflies. Humidity conditions found in agroforestry systems were not
significantly different from those in secondary growth and pastures, and these were too
low and unstable for long-term butterfly survival.
Concluding Remarks
Given the worldwide trend to convert unprotected forest remnants into farmland, it
is vital to understand the value of landscape elements for forest species conservation.
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Agroforestry practices have been proposed as land-use systems with biodiversity refuge
potential. Studies comparing biodiversdity in agroforestry, including the present one,
have been able to provide evidence that areas where agroforestry is practiced are used
by forest specialist species. However, the potential contributions to butterfly
conservation by agroforestry systems will vary depending on changes in seasonality,
which would affect abiotic conditions and the butterfly species composition at the
landscape level, which will depend on the remaining natural habitats, and of course on
the characteristics of the land-use practice (type, structure, and composition).
The distribution of butterflies in the landscape suggests that agroforestry plots may
function as stepping stones. The butterfly data and abiotic values of shaded coffee in
particular, were often comparable to those of forest habitats. Shaded coffee plantations
may fulfill an important role in the conservation of fruit feeding forest butterflies
compared to other land-use practice (except for eucalyptus plots). Given the reduced
vegetative diversity of shaded agricultural practices compared to forest habitats, it is
unlikely that the presence of shaded coffee increases diversity (acts as a source of
individuals and species) in the landscape (Faria and Baumgarten, 2007). Nevertheless,
the consistent lack of significant differences between shaded coffee and forest habitats
in several of the analyses indicates that this agroforestry practice provides an important
service increasing permeability between forest fragments.
Comparisons of land-use systems based on the abiotic factors were different for
each parameter. The abiotic factos measured (temperature, wind speed, humidity) had
significant influence on the presence of fruit-feeding butterflies in the landscape (except
for temperature at Agua Sumida). In conclusion, the microclimate conditions found in
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forest interiors, which are suitable to forest butterflies, were not present to the same
extent in any of the agricultural practices examined. Abiotic factors in most land-use
practices were significantly different to those in forest areas. Forest edge was the only
habitat that shared microclimatic characteristics with forest interiors across the three
parameters.
Among the agricultural practices, while conditions for one abiotic factor might have
been not significantly different from those in the forest (e.g., suitable wind speed in
sugarcane field), the other factors measured would not (e.g., unsuitable high
temperatures in sugarcane field). A clear pattern of differences or lack thereof between
practices did not emerge from the present study. However, the results suggest that
abiotic factors in homegardens shared more characteristics with other agricultural
practices than with forest habitats. Butterfly responses to microclimatic changes are
usually guild or species specific, which were confirmed by the significant differences in
butterfly species richness, composition and abundance between forest habitats and
homegardens. These results provide evidence as to why homegardens in the Pontal
cannot be considered suitable long-term habitats for forest dependent butterflies.
Pastures were the most extensive land-use practice and they represent the matrix
in the landscape mosaic at Pontal do Paranapanema. Pastures contained the highest
number of butterfly traps, yet captured the lowest number of butterfly individuals and
species at Riberão Bonito and second to lowest at Agua Sumida. These results confirm
the matrix limitations at promoting inter-patch dispersal in agricultural landscapes, and
so, for the need of agricultural practices that can act as connecting elements that
increase functional landscape connectivity. Even if forest species highly dependent on
84
the presence of natural habitats and high levels of landscape permeability (such as
Morphinae) do not benefit from the incorporation of scattered connecting elements in
the landscape (such as shaded coffee or homegardens), the results suggest that the
incorporation of agroforestry practices encourages the presence of a subset of fruit
feeding butterfly species outside the forest, and could promote their movement through
the pasture matrix and serve as transient habitat between forest patches.
Promoting landscape conservation in agricultural landscapes needs to be
accompanied by initiatives that conserve the remaining natural habitats. Forest patch
ecosystems (forest and forest edge) contained many times (two to 10) the number of
butterflies and butterfly species compared to the different agricultural practices
analyzed. The remaining natural patches in the Pontal are the source for fruit-feeding
butterflies in the landscape, and their preservation is vital to the conservation of many
naturally occurring species. Land-use change from forest habitats to farmland causes a
reduction in the diversity and number of organisms present in the landscape. Improving
the agricultural matrix by incorporating more sustainable agricultural practices will not
be enough if the protection of these forest patches cannot be guaranteed.
Unfortunately, many of the remaining patches in the Pontal region are not under
protection and their future and the future of the many species they support is still
uncertain.
Agroforestry plots cannot replace natural habitats. Isolation between remaining
forests patches due to the abrupt changes in land cover (from forest to pasture or from
forest to monoculture) is the trend of conventional agricultural landscapes. This reality is
what gives meaning to agroforestry plots as landscape biodiversity conservation
85
components. The biophysical characteristics of agroforestry practices improve the
microenvironment relative to the matrix making these land-use components more
hospitable for species that may require “pit-stops” while dispersing through unfamiliar
agricultural patches. Even though the relative diversity conservation value of
agroforestry may vary, their incorporation should continue to be promoted as their
adoption represents positive socioeconomic, as well as other environmental changes in
farmland and the farming community. In the meantime, more research is required to
determine how to improve and utilize the different characteristics of these land-use
practices for diversity conservation purposes, according to different landscape
scenarios. Doing this would result in the strategic use of agroforestry for landscape
management projects with biodiversity conservation goals. The role of agroforestry
practices for butterfly conservation in highly modified agricultural areas such as Pontal
do Paranapanema is to serve as provisional habitats assisting the movement of some
populations of forest specialist species.
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Figure 3-1. Satellite image and spatial distribution of land-use practices at Riberão Bonito. The different color shades represent each of the land-use practice and forest habitats in the landscape. Red quadrant shows the experimental area.
87
Figure 3-2. Satellite image and spatial distribution of land-use practices at Agua Sumida. The different color shades represent each of the land-use practice and forest habitats in the landscape. Red quadrant shows the experimental area.
88
Figure 3-3. Visual representation of land-use systems in ArcScene (ArcGIS 9.2). The height of each land-use system represents the relative abundance of butterfly individuals and species at Riberão Bonito.
89
Figure 3-4. Visual representation of land-use systems in ArcScene (ArcGIS 9.2). The height of each land-use practice represents the relative abundance of butterfly individuals and species at Agua Sumida.
90
Figure 3-5. Linear Discriminant Analysis Canonical Plot of species similarities between land-use systems at Riberão Bonito. Ellipses identify the relative spatial distribution of the different land-use practices and habitats based on butterfly subfamily data. Axes “x” explains 85% of the data variability, whereas axes “y” explain only 13%.
91
Figure 3-6. Liner Discriminant Analysis Canonical Plot of species similarities between land-use systems at Agua Sumida. Ellipses identify the relative spatial distribution of the different land-use practices and habitats based on butterfly subfamily data. Axes “x” explains 85% of the data variability, whereas axes “y” explain only 13%.
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Table 3-1. Summary of butterfly distribution (numbers of species and individuals) in various land-use systems at an agricultural settlement in Riberäo Bonito, São Paulo state, Brazil.
Land-use System Number of Traps Number of Individuals
Number of Species
Average number of Butterfly Individuals/Trap (every other day) (Standard deviation)
Average Number of Butterfly Species/Trap (every other day) (Standard deviation)
Forest 14 1282 59 12.87 (10.57) 4.24 (1.82)
Forest edge 12 722 42 6.73 (3.58) 2.81 (0.92)
Eucalyptus 3 111 18 3.08 (0.63) 2.08 (0.71)
Shaded Coffee 3 114 22 3.17 (0.98) 2.19 (0.22)
Homegarden 9 113 28 1.05 (0.63) 0.99 (0.64)
Cassava 3 20 8 0.55 (0.89) 0.50 (0.68)
Secondary growth 5 86 18 1.44 (1.26) 1.17 (0.87)
Pasture 63 200 24 0.27 (0.34) 0.22 (0.22)
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Table 3-2. Summary of butterfly data collected per land-use system at AS. Table contains the number of traps, total and average numbers of individuals and species at each land-use system.
Land-use System Number of Traps/Land-use System
Number of Individuals
Number of Species Average number of Butterfly Individuals/Trap (every other day)
Average Number of Butterfly Species/trap every other day
Forest 16 2011 38 12.50 (4.96) 4.59 (1.05)
Forest edge 9 1317 35 13.09 (4.12) 4.46 (0.63)
Shaded Coffee 3 161 9 4.76 (1.08) 2.32 (0.23)
Homegarden 5 202 9 3.41 (1.10) 1.48 (0.36)
Sugarcane 10 650 15 5.88 (3.45) 1.63 (0.81)
Secondary growth 4 106 8 2.25 (0.96) 1.20 (0.23)
Pasture 50 453 10 0.77 (0.58) 0.49 (0.30)
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Table 3-3. ANOVA multiple pairwise Tukey-Kramer comparison of number of butterfly individuals between land-use systems at Riberão Bonito. Significance set at p-value = 0.05
Land-use Forest Forest Edge
Eucalyptus Shaded Coffee
Homegarden Secondary Growth
Cassava Pasture
Forest -
Forest Edge 0.013 -
Eucalyptus 0.016 0.995 -
Shaded Coffee 0.064 0.999 1.000 -
Homegarden <.001 <.001 0.007 0.002 - Secondary Growth <.001 0.001 0.041 0.016 0.985 -
Cassava <.001 <.001 <.001 <.001 0.601 0.214 -
Pasture <.001 <.001 <.001 <.001 <.001 <.001 0.304 -
95
Table 3-4. ANOVA multiple pairwise Tukey-Kramer comparison of number of butterfly species between land-use systems at Riberão Bonito. Significance set at p-value = 0.05
Land-use Forest Forest Edge
Eucalyptus Shaded Coffee
Homegarden Secondary Growth
Cassava Pasture
Forest -
Forest Edge 0.012 -
Eucalyptus 0.086 0.999 -
Shaded Coffee 0.425 1.000 0.999 -
Homegarden <.001 <.001 0.018 0.002 -
Secondary Growth <.001 0.004 0.259 0.072 0.999 -
Cassava <.001 <.001 <.001 <.001 0.645 0.27 -
Pasture <.001 <.001 <.001 <.001 <.001 <.001 0.321 -
96
Table 3-5. ANOVA pairwise Tukey-Kramer comparison of number of butterfly individuals between land-use systems at Agua Sumida. Significance set at p-value = 0.05
Land-use Forest Forest Edge
Shaded Coffee
Homegarden Secondary Growth
Sugarcane Pasture
Forest -
Forest Edge 1.000 -
Shaded Coffee 0.146 0.222 -
Homegarden <.001 <.001 0.167 -
Secondary Growth <.001 <.001 0.176 1.000 -
Sugarcane <.001 <.001 0.338 0.998 0.999 -
Pasture <.001 <.001 <.001 <.001 <.001 <.001 -
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Table 3-6. ANOVA pairwise Tukey-Kramer comparison of number of butterfly species between land-use systems at Agua Sumida. Significance set at p-value = 0.05
Land-use Forest Forest Edge Shaded Coffee
Homegarden Secondary Growth
Sugarcane Pasture
Forest -
Forest Edge 0.987 -
Shaded Coffee 0.654 0.360 -
Homegarden <.001 <.001 0.231 -
Secondary Growth <.001 <.001 0.252 1.000 -
Sugarcane 0.003 0.001 0.906 0.735 0.788 -
Pasture <.001 <.001 <.001 0.002 0.008 <.001 -
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Table 3-7. Species similarities analysis (ANOSIM) between land-use systems at Riberão Bonito. To correct for multiple comparisons, Boferroni correction set the significant level to 0.05/n was applied where n was the number of comparisons between the land-use practices (28).
Land-use Forest Forest Edge Eucalyptus Shaded Coffee
Homegardens Secondary Growth
Cassava Pasture
Forest -
Forest Border 0.188 -
Eucalyptus 0.320 0.163 -
Shaded Coffee 0.619 0.287 0.082 -
Homegardens 0.005 0.005 0.006 0.008 -
Secondary Growth 0.309 0.227 0.108 0.087 0.323 -
Cassava 0.260 0.208 0.097 0.100 0.001* 0.640 -
Pasture 0.001* 0.001* 0.001* 0.001* 0.001* 0.001* 0.276 -
*Significant at 0.002
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Table 3-8. Species similarities analysis (ANOSIM) between land-use systems at Agua Sumida. To correct for multiple comparisons, Boferroni correction set the significant level to 0.05/n was applied where n was the number of comparisons between the land-use practices (21).
Land-use Forest Forest Edge
Shaded Coffee
Homegardens Secondary Growth
sugarcane Pasture
Forest -
Forest Border 0.349 -
Shaded Coffee 0.170 0.025 -
Homegardens 0.005 0.001 0.594 -
Secondary Growth 0.001* 0.002* 0.038 0.348 -
sugarcane 0.001* 0.001* 0.138 0.365 0.411 -
Pasture 0.001* 0.001* 0.001* 0.001* 0.001* 0.001* - *Significant 0.0023
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Table 3-9. Mean values and standard deviation for environmental factors per land-use system at Riberão Bonito
Land-use Temperature (°C)
(Standard Deviation) Wind Speed (m/s)
(Standard Deviation)
Relative Humidity(%) (StandardDeviation)
Forest 23.89 (1.59) 0.06 (0.09) 51.67 (17.01)
Forest Border 25.10 (0.87) 0.68 (0.58) 46.94 (7.43)
Eucalyptus 26.09 (2.02) 0.25 (0.06) 32.02 (1.01)
Shaded Coffee 27.39 (0.37) 0.03 (0.02) 31.03 (2.05)
Homegarden 26.60 (1.33) 0.65 (0.54) 38.86 (3.57)
Secondary Growth 26.65 (0.94) 0.61 (0.41) 42.00 (1.81)
Cassava 27.94 (0.42) 0.95 (0.12) 33.14 (6.48)
Pasture 27.19 (1.28) 1.47 (0.49) 35.92 (5.42)
Table 3-10. Mean values for environmental factors per land-use system at Agua
Sumida
Land-use Temperature (°C)
(Standard Deviation)
Wind Speed (m/s) (Standard
Deveiation)
%Relative Humidity (Standard Deviation)
Forest 31.04 (1.05) 0.01 (0.02) 69.32 (1.40)
Forest Border 31.34 (1.11) 0.03 (0.45) 68.39 (2.35)
Shaded Coffee 30.76 (0.16) 0.74 (0.12) 50.62 (0.89)
Homegarden 31.27 (1.17) 1.24 (0.19) 51.10 (3.64)
Secondary growth 32.41 (1.42) 1.46 (0.25) 47.92 (4.29)
Sugarcane 32.22 (2.10) 0.20 (0.20) 59.43 (2.62)
Pasture 32.50 (1.26) 1.59 (0.29) 47.71 (3.95)
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Table 3-11. Multiple pairwise GLM ANOVA of temperature between land-use systems at Riberão Bonito. Significance set at p-value = 0.05 Land-use Forest Forest
Edge Eucalyptus Shaded
Coffee Homegarden Secondary
Growth Cassava Pasture
Forest -
Forest Edge 0.245 -
Eucalyptus 0.129 0.929 -
Shaded Coffee 0.001 0.111 0.916 -
Homegarden <.001 0.148 0.999 0.982 -
Secondary Growth
0.002 0.315 0.999 0.993 1.000 -
Cassava <.001 0.018 0.642 0.999 0.764 0.863 -
Pasture <.001 <.001 0.828 1.000 0.893 0.984 0.975 -
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Table 3-12. Multiple pairwise GLM ANOVA of wind speed between land-use systems at Riberão Bonito. Significance set at p-value = 0.05
Land-use Forest Forest Edge Eucalyptus Shaded Coffee
Homegarden Secondary Growth
Cassava Pasture
Forest -
Forest Edge 0.015 -
Eucalyptus 0.998 0.816 -
Shaded Coffee 1.000 0.346 0.999 -
Homegarden 0.052 1.000 0.880 0.446 -
Secondary Growth
0.275 1.000 0.955 0.648 1.000 -
Cassava 0.049 0.984 0.556 0.214 0.976 0.971 -
Pasture <.001 0.032 0.024 0.002 0.057 0.197 0.996 -
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Table 3-13. Multiple pairwise GLM ANOVA of percentage relative humidity between land-use at Riberão Bonito. Significance set at p-value = 0.05
Land-use Forest Forest Edge Eucalyptus Shaded Coffee
Homegarden Secondary Growth
Cassava Pasture
Forest -
Forest Edge 0.788 -
Eucalyptus 0.004 0.074 -
Shaded Coffee 0.002 0.044 1.000 -
Homegarden 0.005 0.286 0.894 0.804 -
Secondary Growth
0.269 0.935 0.660 0.545 0.996 -
Cassava 0.008 0.127 1.000 1.000 0.957 0.781 -
Pasture <.001 0.001 0.990 0.965 0.965 0.708 0.999 -
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Table 3-14. Multiple pairwise GLM ANOVA of temperature between land-use systems at Agua Sumida Land-use Forest Forest Edge Shaded
Coffee Homegarden Secondary
Growth Sugarcane Pasture
Forest -
Forest Edge 0.998 -
Shaded Coffee 0.999 0.994 -
Homegarden 0.999 1.000 0.998 -
Secondary Growth 0.529 0.830 0.661 0.858 -
Sugarcane 0.312 0.772 0.629 0.842 1.000 -
Pasture 0.006 0.200 0.295 0.429 1.000 0.996 -
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Table 3-15. Multiple pairwise GLM ANOVA of wind speed between land-use systems at Agua Sumida Land-use Forest Forest Edge Shaded
Coffee Homegarden Secondary
Growth Sugarcane Pasture
Forest -
Forest Edge 1.000 -
Shaded Coffee <.001 0.004 -
Homegarden <.001 <.001 0.069 -
Secondary Growth
<.001 <.001 0.003 0.822 -
Sugarcane 0.422 0.678 0.015 <.001 <.001 -
Pasture <.001 <.001 <.001 0.035 0.929 <.001 -
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Table 3-16. Multiple pairwise GLM ANOVA of percentage relative humidity between land-use at Agua Sumida Land-use Forest Forest
Edge Shaded Coffee
Homegarden Secondary Growth
Sugarcane Pasture
Forest -
Forest Edge 0.995 -
Shaded Coffee <.001 <.001
Homegarden <.001 <.001 1.000 -
Secondary Growth <.001 <.001 0.941 0.796 -
Sugarcane <.001 <.001 0.003 <.001 <.001 -
Pasture <.001 <.001 0.771 0.336 1.000 <.001 -
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CHAPTER 4 AGROFORESTRY AS ECOLOGICAL STEPPING STONES: PREDICTING
BUTTERFLY MOVEMENT USING LEAST-COST ANALYSES IN FRAGMENTED AGRICULTURAL LANDSCAPES.
Given the rapid conversion of natural areas for agricultural purposes, there is a
growing concern for the protection of forest-bound species located in remnant forest
patches in agricultural landscapes (Harvey and Villalobos, 2007; Harvey et al., 2005;
McNeely and Scherr, 2003; Daily, 2001; Hobbs and Saunders, 1994). Agroforestry, the
practice of growing trees or woody perenials in combination with crops and/or animals,
can help maintain biodiversity in agricultural landscapes (Bhagwat et al., 2008; Uezu et
al., 2008, Leon and Harvey, 2006; Estrada, 2000). By increasing the vegetative cover,
agroforestry practices are assumed to promote the movement of organisms, therefore
increasing the functional connectivity of the landscape (Uezu, 2008). Given the
importance of landscape connectivity – the attribute of the landscape to facilitate or
impede the movement of organims - for the conservation of wildlife in agricultural areas,
actions to reduce the current levels of fragmentation of forest have increased, along
with the development of technologies that can analyzed spatial data and evaluate
changes in the landscape (Bunn et al., 2000; Jensen et al., 1996; Iverson et al., 1989).
Because Geographic Information Systems (GIS) can be used to analyze landscapes
and predict movement, their application to evaluating agroforestry practices as
landscape elements that can be used as temporary habitat for dispersing forest
organisms should be explored.
The concept of agroforestry as ecological stepping stones – small patches of
vegetation embedded in a disimmilar matrix and that can be used by wildlife for
dispersal, developed from landscape ecology, and is founded on island biogeography
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and metapopulation theory (MacArthur and Wilson, 1967; Hanski, 1999; Lindenmayer et
al., 2005). Assuming that conventional agricultural practices such as treeless pastures
or monocultures represent an inhospitable habitat or the “ocean” in island
biogeography, agroforestry plots could act as islands in the landscape matrix, and the
dispersal of organisms between remaining natural habitats could be facilitated through
agroforestry plots. The greater plant diversity characteristic of agroforestry practices,
makes them more complex agroecosystems in terms of structure and composition,
which in turn may be conditions preferred by forest organisims given their greater
similarity with natural habitats. Furthermore and according to metapopulation theory, if
migration to these agroforestry “islands” occurs, individuals of forest specialist species
could either stay in the agroforestry plot (if resources such as food, shelter, mates, and
abiotic factors are satisfactory), emigrate (if resources are not satisfactory), or perish
(Lennon et al., 1997). Depending on the matrix conditions and given that agroforestry
practices are not natural habitats they may not be able to support the long-term survival
of forest specialist species (Uezu et al., 2008; Baum et al., 2004). However, they may
provide temporary shelter to some forest and generalist species moving between
natural habitats.
The development of technologies such as GIS has made modeling of spatial
phenomena more approachable. Field data based on the distribution of organisms and
abiotic variables can be analyzed within a spatial context to determine the pathways
and areas in the landscape that are more favorable for the movement of forest species
(Majka, 2007). Landscapes in GIS can be displayed as a surface grid made of
contiguous cells (squared areas), and movement can be measured as a function of the
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characteristics at each cell (or cell value) (Drielsma et al., 2007). Least Cost Path
analysis is a set of operations in GIS that can be used to identify paths where the lowest
cell values are present (Douglas, 1994). This type of analysis in GIS computes the
fastest, shortest, less impact route(s), based on the characteristics of the landscape.
Furthermore, depending on the type of analysis, movement between cells can be
measured in terms of traditional distance units such as meters, kilometers or miles, or
be measured as units relevant to the type of analysis pursued such as time, difficulty,
money, etc. (Douglas, 1994; Adriaesen et al., 2003). Significant environmental variables
that predict the presence of butterflies for example, can be used as parameters to
calculate the cell values in the landscape surface grid, and use this information
toidentify the preferred traveling routes of butterflies when moving between forest
patches in agricultural landscapes containing agroforestry practices.
Among the potential environmental variables influencing the movement of forest
specialist butterflies, tree density, temperature, humidity, and wind are likely among the
most important (Dolia et al., 2007; Dennis, 2004; Sutcliffe et al., 2003). Whether
searching for mates, oviposition sites, or food, butterflies sometimes leave their natural
habitat to explore and colonize new areas (Lennon, et al., 1997). By mapping how
environmental variables are distributed in agricultural landscapes, it may be possible to
predict dispersal into and through unsuitable areas. In order to achieve this, a model
was developed using the microclimatic characteristics of different land-uses practices
and habitats in agricultural landcapes. The developed GIS Least Cost Path model was
intended to evaluate if the presence of agroforestry practices can serve as stepping
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stones for forest butterfly species moving between remnant forest habitat in
fragemented agricultural landscapes (Uezu et al., 2008; Cullen et al., 2004).
The objectives of this project were: (1) to study butterfly movement by developing
a GIS analytical framework based on abiotic parameters, (2) to compare the predicted
paths produced by the Least Cost models in GIS with the butterflies data collected in
the field, and (3) to evaluate the assumption that agroforestry systems can serve as
stepping stones (described in chapter 2) for forest butterflies moving between forest
habitats.
Methodology
Two agricultural research areas representative of the landscape were selected
within the Pontal do Paranapanema region, in the western-most section of the state of
Säo Paulo, Brazil. The regions topography is a combination of slopes and shallow hills,
where cattle grazing pastures are the dominant land-use. The Pontal was covered by a
contiguous semi-deciduous forest before it was converted into agriculture land, and only
5% of the original vegetation remains (PEMD, 2006; Veloso et al., 1991). The average
annual temperature is 21°C. The rainy season lasts from January to March with a
maximum average temperature of 32°C, and the dry season extends from May to
August with a minimum average temperature of 13°C. The annual precipitation ranges
from 1,100 to 1,300 mm, and the region’s elevation is between 250 and 500 m a.s.l.
(PEMD, 2006; Diegues, 1990).
The study was conducted in two farming settlements: Riberão Bonito and Agua
Sumida. Riberão Bonito is located at 22°32’19.62” S and 52°22’35.67” W (Figure 4-1),
while Agua Sumida is located at 22°18’29.13” S and 52°20’ 17.64” W (Figure 4-2). The
dominant land cover is pasture, and milk production is the main agricultural activity
111
practice by farmers in the region. Farmers in agrarian reform settlement in the Pontal
have acted as land stewards by planting trees, shrubs and vegetables in the farm land
as agroforestry homegardens, living fences, and as shade trees in agricultural plots.
Homegardens and shaded coffe were the agroforestry practices evaluated for ther
contributions as butterfly stepping stones. These land-use practices resemble small
vegetation islands embedded in the pasture matrix. Homegardens range in size from
0.5 to 1 ha and contained an average of 30 trees (Menegario, 2006). Shaded coffee
plots on the other hand, were larger with and average size of 1 ha, and contain
approximately 400 – 800 trees (Lima, 2007). Homegardens are multistory combinations
of trees, crops and sometimes animals around the homestead (Kumar and Nair, 2004).
Shaded coffee systems are also important ecological and economical agro-ecosystem.
While the coffee constitutes a major source of household income, the trees that shade
the coffee plants provide food and wood products, as well as soil, wind and sun
protection and serve as a biodiversity refuge (Perfecto et al., 1996).
Biodiversity at the pontal is associated with the original natural ecosystem, and the
largest forest fragment (Moro do Diabo State Park) is thought to act as a source of
species and individuals in the region (Cullen, 2003). Butterfly species richness at Moro
do Diabo State Park was reported by Mielke and Casagrande (1997) who conducted an
inventory an identified a total of 426 species distributed in 48 subfamilies, most of them
of the Nymphalidea and Hesperiidae families. The high number of butterfly species in
the largest forest remnant at the Pontal is typical of Brazil’s remaining Atlantic forests
(Brown 2000, Uehara-Prado et al., 2007). The assessment of forest butterflies outside
the forest fragments in the region will be used as an indicator of functional landscape
112
connectivity between forest patches. By mapping the distribution of butterflies in the
agricultural landscape, information about movement preference can be predicted, and
so, the contribution of each land-use practice at enhancing or not the connectivity of the
landscape can be infered.
Data Collection
Within each settlement a quadrant was created to carry out the butterfly data
collection (Figure 3-1, and 3-2). The quadrants areas were 2.4 and 2.2 km2 and
encompassed different land-use practices. Among them were agroforestry and
conventional agricultural practices in-between two forest patches. Within each quadrant
Van Sommer Ryan butterfly traps were placed every 150 meters from each other in a
grid pattern (DeVries, 1987). A geographic positioning system device was used to
determine the location for each trap, and the traps were suspended between 1 to 1.5 m
above the ground. The total number of traps at Riberão Bonito was 112; while at Agua
Sumida it was 96. At Riberão Bonito there were 8 different habitats. Of these, 14
butterfly traps were inside the forest patch, 12 were at the forest edge, 64 in pastures, 3
in shaded coffee plots, 9 in homegardens, 3 in cassava plots, 3 in abandoned pasture
areas, which we will denominate secondary growth patches, and 3 in eucalyptus
plantation plots. At Agua Sumida there were 7 habitats. In contrast to Riberão Bonito,
there were no cassava or eucalyptus plots; instead, there was a sugar cane plot that fit
10 butterfly traps. The other traps were distributed as follows, 15 inside the forest
fragments, 8 in the forest edge, 3 in shaded coffee systems, 5 in homegardens, 4 in
secondary growth, and 51 in the pasture. The number of butterfly traps at each land-use
plot was proportional to the area of the system in the landscape quadrant.
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The butterfly traps were ment to target the same fruit-feedign butterfly community
composed mostly of Nymphalid subfamilies.Traps were baited with a mixture of fruits,
sugar and rum (Dolia et al., 2008). After trial and error, a mixture composed of 2
bananas, 1/2 cup of rum, 1/2 cup of sugar, and 1/2 cup of fruit juice (usually orange)
was used. Once the traps were set up, they were checked every other day for butterfly
data collection and to replenish the bait mix during a period of 24 days at each
experimental site (from August 19 to September 11, 2008 at Riberão Bonito, and from
October 10 to November 5, 2008 at Agua Sumida. The present study and its
methodology represent a snap shot of the distribution of a targeted butterfly guild in the
fragmented agricultural landscapes.
Data for abiotic factors was measured at the end of the butterfly data collection
period. Information on temperature, wind speed and relative humidity was collected
during six consecutive days at each trap using a Windmate (accuracy +/- 3%). Abiotic
data was measured once at each trap point around noon (from 11 am until 2pm). In
order to avoid any bias due to the time of day the data was collected, the order in which
the traps were visited was changed every day. Given that weather fluctuations were
relatively small in the region (+/- 1°C between the temperature mean values of August,
September, October and November), the overall regional weather was assumed to be
stable and for the microclimatic differences to be directly related the different land-use
and management practices,
(http://www.defesacivil.sp.gov.br/v2010/meteorologia3.asp).
In addition to the abitoc factors measured with the Windmate, the light
environment at each trap in the different land-use practices was measured as the
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percentage of shade provided by the existing vegetation. Shade was calculated by
averaging the amount of canopy at the butterfly trap points within the same land-use
practices. The percent of shade was measured using a spherical densitometer. Four
readings were taken at each trap point, and the unshaded area in the densitometer was
multiplied by 4.17 (according to the instrument’s procedures). The resulting values were
subtracted from 100 to get the percentage of shaded area as shown in Equation 4-1
below (Lemmon, 1957).
100% - (Densiometer Reading x 4017) = Shade% (Equation 4-1)
Butterflies trapped in the forest edge or forest interior were individually marked and
released at the release point in the middle of the experimental landscape. The release
point was the geographical point located midway from the edges of the rectangular area
covered by the traps and hereafter called the “experimental area”. With the purpose of
stimulating dispersal through the agricultural matrix, forest caught butterflies were
individually marked with a Sharpie® pen on their hindwings and taken to the release
point and set free (Desrochers et al., 2011). It was assumed that forest butterflies
released in the middle of the experimental landscape, would try to fly back to their
natural habitat. In order to do so, they may or may not use the standing homegardens
and shaded coffee plot as stepping stones prior to flying back to the forest habitat.
Statistical Data Analysis
The environmental factors (temperature, wind speed, relative humidity and
percentage of shade) were analyzed in relation to the butterfly data (species and
abundance). A multiple regression was conducted using Statistical Analysis Software
(SAS) 9.2 (Copyright © 2011 SAS Institute Inc.). The purpose of this was to provide the
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data to construct the regression equation using the Raster Calculator tool in Spatial
Analyst, ArcGIS. This operation produced a raster layer that was later used as the
friction layer for the Least Cost path analsysis, which predicted the butterfly movement
paths. The predicted paths were then compared against the Least Cost paths created
using the interpolated butterfly recapture data as the friction layer.
Prior to conducting the multiple regression analysis, and given that some of the
observations had a value of zero, a constant (one) was added to all the variables before
log-transforming for normality (Sokar andRolf, 1995; Zar 1999). Then the data were
analyzed using SAS Multiple Regression procedure and significance was set to less
than or equal to 0.05 (SAS Institute, 2002). At each landscape, two regression analyses
were conducted, with the number of butterfly species and the number of butterfly
individuals as the dependent variables, respectively. The regression equations were
subsequently used to calculate the values for each cell in the output raster layer. In
theory, the partical regression coefficients produced by the statistical analysis in the
regression equation describe the effect of each parameter on the dependent variables,
assuming the other parameters in the model are held constant (DeVeaux et al., 2005).
The raster layers were or the dispersal or butterflies in ArcGIS.
Data on recaptured butterflies were compared between land-use systems. From
the butterfly release point at the center of the experimental landscape, the first and
second recapture events were compiled and reported for each land-use practice. Many
individuals were recaptured several times. However, the statistical analysis was only
conducted for the first recapture event. An ANOVA Fisher (LSD) pair-wise comparison
with a confidence interval set at 95% was performed between the land-use practices.
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Butterflies that were recaptured independent from the release points were reported but
not analyzed. The ANOVA for the recaptured butterfly data was used to compare
habitat selection after the butterflies were released in the middle of the agricultural
landscape matrix. Significant differences, or lack there of in the number of recaptured
butterflies between land-use systems would help determine the value of agroforestry
plots as stepping stones. The ANOVA operations were not modified to correct for
proximity or area of the different land-use practices.
The butterfly data for first recapture events were represented visually in ArcGIS
9.3.1 (ESRI, 2009) and analyzed using Least-Cost Path. Starting at the release point in
the middle of the experimental landscape, a straight line was digitized towards the trap
point where each given butterfly was recaptured. The thickness and color of the line
represents the number of recaptures relative to other trap points. Increased thickness
and darker color (from yellow to red) means higher of butterfly recaptures at that trap
point within a particular land-use.
GIS Methodology
Landscape Image Georeferencing and land-use polygon digitization
Satellite images for the experimental landscapes were obtained from Google Earth
(Image©2010 TerraMetrics). These were converted to TIFF files and were later
georeferenced and rectified in ArcMap. The World Geodetic System 1984 (WGS 1984)
was used as the geographic coordinate system to georeference ten ground control
points. Once the image was correctly projected onto the geographic system, the
different land-use areas in the agricultural landscape were represented by digitizing
polygons using the Editing tool in ArcMarp. In order for the vertexes to align perfectly
between polygons, the snapping tool in the editing toolbar was activated. Both at
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Riberão Bonito and Agua Sumida there were a total of 19 different polygons. These
polygons depict the different habitats found at each experimental landscape. Last, the
vector polygon layer was converted into a raster layer and an information field with the
name of the land-use system was added to the attributes table of each polygon (land-
use system).
Using the existing polygon raster layers, four new layers were created depicting
each forest interior polygon. In both experimental landscapes there were two forest
interior patches. In order to predict the movement of butterflies from one forest patch to
the other, each forest interior polygon was depicted in a new layer by its own (forest
interior patch = 0, landscape=No Data). Each of these forests represents a source of
butterflies and a destination or target for butterfly movement (Figure 4-3). For the
purpose of distinguishing each forest interior polygon, the southern most patch at each
landscape was considered Forest A, and the northern most patch was considered forest
B. In addition to the forest polygons, two other layers were created to depict the areas
where the butterflies were release in the middle of the experimental landscape after
being collected in the forest bound traps. A small polygon area (10 m x 10 m) was
digitized at the geographic center of the experimental areas. As with the forest interior
layers, the cells within the butterfly release polygon areas were reclassified (release
area =0, Landscape = No Data).
Trap points
Geographic data points were collected using a geographic positioning unit
(Garming) at the ground location for each butterfly trap in the field. These trap location
points were downloaded and projected using the same geographical coordinate system
as the landscape image (WGS, 1984). Along with the geographical coordinates, the
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butterfly and environmental data were associated with each trap point. In the Attribute
Table for the trap point layer, 10 data fields were added using the Editor function. The
fields of information contained for each trap point were the following: trap point
identification code, latitudinal coordinate, longitudinal coordinate (in decimal degrees),
average temperature, average relative humidity, average wind speed, percentage of
shade, and recapture data (first time recapture). Interpolation of the point values for the
environmental variables produced the friction layers on which the analysis was based.
GIS analysis
The analytical framework of the GIS model can be described as a series of
consecutive operations in ArcGIS 9.3.1 (Figure 4-4). The log-transformed environmental
variables (percentage of shade, temperature, wind and relative humidity) were
incorporated in ArcGIS’s geospatial processing program (ArcMap) by adding the GPS
coordinates for each butterfly trap point. The butterfly trap points were added to
ArcMap using the “Add XY Data” feature under Tools. In addition, the Spatial Analyst
extension program was enabled for the subsequent analysis of the layers. Once the trap
points were georeferenced and added to the virtual landscape in ArcMap, each
environmental factor was interpolated using the Inverse Distance Weighing operation in
Spatial Analyst. Cell size was set at 1 x 1 meters in order to increase landscape
heterogeneity. The interpolated raster (continuous data) layers described the variability
for each environmental factor throughout the experimental landscape.
The interpolated layers were combined using Raster Calculator. The result of the
raster calculation was a single layer combining the environmental factors according to
their influence.
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In order to subsequently integrate the output layers from the raster calculation the
values were normalized (Radiarta et al., 2011; Saaty, 1977). The output layers from the
raster calculation describe the presence of butterfly species, or of individuals, as a
single layer combining the abiotic factors according with their relative influence. High
cell values indicated areas where the environmental conditions were suitable for
butterflies, while low values indicated unsuitable areas. Given that these layers have
different scales and units (number of individuals and number of species), they were
normalized using the Reclassified tool in Spatial Analyst.
Once the Raster Calculator output layers were normalized into a comparative
scale, they were combined to produce the Cost Raster layer. The layers were integrated
through a geometric “Mean” operation using the Cell Statistics tool in Spatial Analyst
(Figure 4-6). The high abundance and species richness values of butterflies are both
indicators of butterfly habitat suitability were movement is assumed to be prefered. By
merging the Raster Calculator layers, the output layer predicts the presence of
butterflies based on the combined microclimatic characteristics at each cell area (1m²),
and can be used to predict the movement of butterflies in the landscape.
The subsequent spatial analyst operations necessary to conduct the Least Cost
Path analysis was Cost-Weighted Distance. The products of the Cost Weighted
Distance operation were two output layers: the Cost Distance and Cost Direction raster
layers. These are in turn input layers required for the Shortest Path operation, which
results in the Least Cost Path analysis of the landscape (Adriaensen et al., 2003;
Rothley, 2005).
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By using Shortest-Path tool in Spatial Analyst it is possible to predict the most
likely trajectories for movement in the landscape (Richard and Armstrong, 2010). The
Shortest-Path operation allows the user to choose between three different types of path
calculation methods (Best Single, Each Zone, Each Cell). Best Single analysis
identifies the best single and most cost efficient path between two areas (source and
target) in the landscape. Each Cell analysis on the other hand, identifies the cost
efficient paths for each cell that makes up the area from which movement is iniciated
(source). For the purposes of this study, “Best-Single” and “Each-Cell” were the
methods in Shortest-Path used to predict butterfly dispersal in the landscape.
The Shortest Path tool (which produces the paths in Least-Cost Path analysis)
was used to predict the dispersal trajectories among three types of scenarios between a
source and target areas. The source can be defined as the starting point were
movement is initiated, such as the forest interior were butterflies naturally occur, or at
the release point where the forest captured butterflies were set free as a means to
encourage dispersal. The target however, is the ending point were movement is
completed such as when the butterflies returns to the forest interior. The proposed
Least Cost Path model was developed to predict the trajectories where movement is
facilitated for butterflies traveling according to the following three scenarios: from forest
A to forest B, from Forest B to forest A, and from the release point to either forest
habitat.
Evaluating the results from the Least Cost Path analysis was done in a descriptive
and comparative manner. The pathways that resulted from the Best Single and Each
Cell output layers by performing the Shortest-Path operation were summarized and
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compared to the results from the Lest Cost Path using the recapture data. Each
pathway covered different land-use practices from the source to the target area. The
number of times a type of land-use practice was crossed by a path was compared to the
number of times that land-use practice was croseed using the results from Least Cost
Paths developed with the recapture data. The overall similarity and differences in the
paths by the abiotic model with the recapture data model were used as a method to
evaluate the effectiveness of the proposed analytical framework as a method to predict
butterfly movement.
Results
Regression Analysis of Abiotic Factors
The results from the multiple regression analyses identified the significant
variables that were included in the GIS model. At Riberão Bonito, the initial regression
analysis identified temperature, wind speed and percentage of shade as significant
predictor variables for butterfly abundance and species richness. However, relative
humidity was not significant predicting the number of butterfly species, and therefore
was not included in the GIS analysis at Riberão Bonito. The regression was re-run to
obtain the partial coefficient estimate values for each environmental factor (APPENDIX
G). At Agua Sumida, wind speed, relative humidity and shade were significant
variables. However, temperature was not a significant explanatory variable predicting
the number species and number of individual butterflies, and therefore was excluded
from the GIS analysis. As in Riberão Bonito, the regression analysis was run again only
with significant variables (APPENDIX H).
The regression models for both landscapes were significant and had a relatively
high fit. The models at Riberão Bonito explained 45% and 47% of the variation in
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species richness and individual captures, respectively. At Agua Sumida perecent
variation explained in the same two dependent variations was 71% and 75%. In
addition, the regression models were significant in both landscapes (p-value <.0001).
These results provided a good starting point for predicting butterfly movement through
the experimental landscapes.
GIS Analysis
Predicted Least-Cost Paths Using the Proposed Abiotic Factors Model. A
total of 14 layers were created in ArcMap. Three layers were produced for the scenario
where movements started at the release point, and four layers were produced for the
scenario where movement took place between forest patches at each landscape. The
estimated number of routes (also called linkages and pathways in the literature) that
could be counted independently originating from the source area (which could be a
forest patch or release point area) to the target area (always a forest path) at Riberão
Bonito was 16; while at Agua Sumida it was 9. Of these, the number of times a route
crossed – entered and exited- through one of the agroforestry plots in the experimental
landscapes was higher than the number of times a route crossed through a different
land-use practice (except for the pasture matrix and forest edge habitat). At Riberão
Bonito, from a total of 35 crossings by Least-Cost paths over the different land-use plots
in the agricultural mosaic, 20 occurred in agroforestry practices (15 for homegardens
and 5 for shaded coffee) (Table 4-1). At Agua Sumida, from a total of 16 land-use
crossings, 10 took place across agroforestry systems (5 for homegardens and 5 for
shaded coffee) (Table 4-2). According to the predicted Least Cost Paths produced by
the abiotic factors model the number of times a path crossed an agroforestry plot was
relatively high compared to the other habitats (excluding the pasture matrix and the
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forest edge areas). In addition, crossings through the secondary growth strip at Riberão
Bonito or the sugarcane at Agua Sumida were necessary in order for the paths to reach
the targeted forest areas. These land-use systems received high crossing values in the
predicted models. However, the Best Single paths from the release point avoid these
areas and predicted trajectories towards the forest patch that did not include these land-
use practices.
Recapture Data Analysis
The butterfly recapture data were analyzed using Least Cost Path analysis to
compare the predicted paths from the abiotic data results. At Riberão Bonito of the total
number of butterflies captured in forest habitats was 2004 individuals. Of these, a total
of 110 were recaptured in in different land-use practices after released at the release
point. In addition, another 34 recaptures took place for butterflies previously recaptured.
The recaptures of previously caught butterflies were considered second, third and so on
recaptures. At Agua Sumida the total number of butterflies captured in forest habitats
was 3328 and a total of 443 were recaptured. Among the recaptured individuals, 111
additional recapturing events were recorded. Secondary recapturing events included a
few individuals that were trapped up to six times during the sampling period. The total
number of single and multiple recapture events was 143 at Riberão Bonito and 554 at
Agua Sumida.
The total number of species captured at Riberão Bonito was 77 while at Agua
Sumida it was 47. At Riberão Bonito 23 species were recaptured at least one time.
However, Memphis ryphea (Cramer, 1775) and Hamadryas februa (Hübner, 1823) were
the species with the highest frequency of recapture events with 51 (35%) and 40 (28%)
- sometimes more than once for the same individual. At Agua Sumida 18 species were
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recaptured, and as in Riberão Bonito, the most frequent species were Memphis ryphea
and Hamadryas februa with 104 (19%) and 272 (49%) recapture events. Other
frequently recaptured species include Hamadryas epinome (C. Felder & R. Felder,
1867), Memphis morvus (Fabricius, 1775), Biblis hyperia (Cramer, 1779), Eunica tatila
(Herrich-Schäffer, 1855), and Ascalapha odorata (Linnaeus, 1758).
After the trapped butterflies were released at the release point in the middle of the
experimental landscape, most recaptures took place in traps located inside
homegardens plots. The number of butterflies recaptured in homegardens at Riberão
Bonito was 24 while at Agua Sumida it was 130 (Figure 4-16 and 4-20). However, if the
butterflies recaptured in the traps located in the forest edge and the traps inside the
forest are combined, then the number of butterflies recaptured in the forest fragments is
larger than the number of butterflies recaptured in homegardens. The number of
butterflies recaptured in traps in the forest patches at Riberão Bonito would be 27, and
the number of butterflies recaptured at Agua Sumida would be 161 (Figure 4-17 and 4-
21). Yet if the forest edge and the forest interior are consider discrete habitats, then
homegardens would be the preferred land-use practice by butterflies released at the
release point.
Given that some butterflies were recaptured more than once after being released,
their movement paths can be described. At Riberão Bonito, five butterflies were
recorded moving from the release point in the middle of the experimental landscape to
one of the homegarden plots and then to the shaded coffee plot (Figure 4-18). Four
butterflies were recorded traveling from a homegarden plot to another after being
released. At Agua Sumida seven butterflies released at the release point were first
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recaptured in a homegarden plot and later again in the forest edge habitat (Figure 4-22).
Following that, six butterflies were recaptured in homegardens after being released at
the release point, and then were recaptured in the shaded coffee plot. Even though the
number of observations for these recorded movements is low, they provide preliminary
information of the butterflies’ trajectories in the agricultural matrix.
In addition to the recapture events for butterflies released at the release point,
other butterflies were captured and recorded moving between two different points. At
Riberão Bonito, a total of 16 butterflies were captured and later recaptured again at the
same or different land-use systems (Figure 4-19). Of this total, two butterflies were
captured moving from the pasture to a cassava plot, two were captured moving
between traps within eucalyptus plots, two were captured moving between the shaded
coffee plot and forest interior traps, and two individuals were captured moving between
traps inside the shaded coffee plot. Traveling butterflies not released at the release
point were also trapped at Agua Sumida (Figure 4-23). A total of 81 butterflies were
trapped in two separate occasions. The most frequent movement observed were
butterflies flying between traps in the sugarcane plot with a total of 28 recapture events.
Eunica tatila was the primary species engaging in this type of movement behavior.
Other landscape movements include 13 butterflies dispersing between traps inside the
shaded coffee plot, nine butterflies moving between homegardens, and six butterflies
moving from the shaded coffee plot to the forest.
The data for first recapture events were represented in ArcGIS to visualize with
Euclidean distances between pairs of trap points (Figure 4-24 and 4-25). At Riberão
Bonito, the number of recaptured butterflies was low, and therefore a clear pattern of
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movement is not obvious. However, the most frequent butterfly movements from the
release point occurred at traps located in the eucalyptus plot and in the shaded coffee
plot. Other frequent movements show butterflies flying towards the homegardens
parallel to the forest, and butterflies flying back to the forest towards trap 6a. At Agua
Sumida, the most common movements occurred from the release point to forest path B,
and from the release point towards the shaded coffee plot parallel to the forest.
Predicted least-cost paths using the recapture data model. To compare and
evaluate the results from the Least-Cost Path analysis using the abiotic factors to
construct the model, the recapture data were also analyzed using Least-Cost Path to
predict butterfly movement patterns. A total of 14 layers were also created in ArcMap to
predict the paths based on the observed movements from the release point and
between forest patches. The estimated number of independent routes originating from
the forest patch or release point area at Riberão Bonito was 13; while at Agua Sumida
was 15 (Figure 4-26, 4-27, 4-28, 4-29, 4-30, 4-31, 4-32, and 4-33). Of these, at Riberão
Bonito the paths crossed through agroforestry plots more often than they passed
through other types of land-use practices. However, at Agua Sumida, the number of
times the paths crossed the sugarcane was greater than the total number of times the
paths crossed the agroforestry plots. At Riberão Bonito, from a total of 46 crossings by
the predicted Least-Cost paths over the different land-use plots in the agricultural
mosaic, most of them (23) occurred in agroforestry practices (17 for homegardens and
six for shaded coffee) (Table 4-3). At Agua Sumida, from a total of 26 land-use
crossings, most of them occurred in the sugar cane plot (12), and were followed by
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crossings though agroforestry systems (nine in homegardens and two in shaded coffee)
(Table 4-4).
ANOVA Comparisons of Recapture Data
The ANOVA results for the recapture data showed significant differences between
the land-use practices at one of the experimental landscapes. While the ANOVA
analysis model was not significant for the recapture data at Riberão Bonito (p = 0.196),
the model was significant at Agua Sumida (p = 0.035). Initially the analysis for Agua
Sumida was not significant either. However, after examining the data, an outlier data
point was removed. The data point belonged to trap “4g”, which was located close to the
release site and had recaptured a number of butterflies several times higher than most
other traps (Figure 4-27). Yet, once this trap point was removed, the model became
significant. At Agua Sumida the results from the ANOVA Fisher (LSD) pair-wise
comparisons indicated that shaded coffee and forest edge were not significantly
different, and both were significantly different from pastures and sugarcane (Table 4-5).
Discussion
GIS Model Development for Movement Data using Abiotic Factors
The coefficient values from the regression analyses were used to assign weight to each
environmental variable. By incorporating the parameter estimates in the construction of
the analytical framework, the proposed GIS movement predicting model is statistically
robust and offers a realistic analysis of the spatial environment to determine the
traveling trajectories suitable for fruit feed butterflies. Much of the application of GIS in
environmental science has been focused on the identification of areas that are
appropriate for a given conservation objective. This is because the software offers
multiple operations capable of integrating landscape information (Haines-Young et al.,
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1993) .Only recently has the identification of habitat in GIS been used to describe the
influence of the various factors determining the presence of organisms (Beier et al.,
2008). Habitat preference and area priority studies typically use area suitability
methodologies to identify locations with desired characteristics based on specific
criteria. Suitability studies in GIS determine priority by different methods, some more
subjective than others (Sutcliffe et al., 2003). Beier et al. (2008) called the lack of
consistency in habitat suitability models in GIS “the subjective translation problem”.
According to their review on this subject, out of the 24 articles analyzed, 15 used expert
opinions and previous research to estimate the resistance values for the environmental
factors used in models predicting corridors, routes, or cost raster surfaces. Studies of
this sort, though not incorrect, lack the scientific robustness necessary for replication of
the results.
In the present study, the analytical framework used to develop the Cost Raster (or
resistance) layer is comprehensive and simple. The cell values in the Cost Raster were
derived from the regression equation and are a function of the significant environmental
factors that predict butterfly presence. In addition, the proposed field work and GIS
methodlogy to study butterfly movement using GIS are reproducible, and the analytical
framework allows for the combination of a large number of significant factors with their
respective relative influence. This approach may further our understanding of model
development for the purpose of predicting butterfly movement more accurately. Other
available approaches, like the one by Xiaofend et al. (2011) apply an analytic hierarchy
process (AHP). In their reearch, the authors used AHP to identify habitat for the
conservation of Amur tigers in northeastern China. The AHP method was developed by
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Saaty (1977) to derive the weights assigned to the different variables in suitability
models. The method involves conducting a pair-wise comparison between the different
variables by applying a scale of importance between factors. The product of the matrix
(which is a ratio) is used as an index that is later multiplied by the value of the
environmental parameter at each point, and therefore modifying the weight of the
variables in relation to the others. Even though this method has been used with
empirical data on a few studies to identify potential habitat for species conservation
(Xiaofend et al., 2011; Radiarta et al., 2011), there is still room for the development of
more straight forward methodologies that determine the influence excerted within and
between variables. The development of simple and statistically sound methods for
defining the cost of environmental variables in GIS has been perhaps the biggest
constraint to the widespread use of Least-Cost Path analysis (Adriaensen et al., 2003).
Regardless of the methods used to weigh the variables in the analytical models, a
common operation in the development of a Cost Raster (resistance) layer is the process
of normalization. In the present study, the output layers from the Raster Calculator
operation had different units (butterfly individuals and species). Normalization of the
data allowed for their combination using a geometric mean, which is also a common
method for integrating resistance layers (Beier et al., 2008). The GIS analytical
framework proposed by the present study for the prediction of butterfly movement is
based on the same concepts and methods of studies previously conducted in this field,
yet it provides a simplified application of the tools.
Least cost paths using abiotic factors. The effective application of Spatial
Analyst tools in ArcGIS to real world landscapes have had significant impact in the
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planning and managing of natural resources for conservation purposes (Carvalho and
Gomes, 2003). Among its multiple potential applications in different fields, Least Cost
analyses have been designed in part to predict areas within a landscape where
movement (of animals, human activities, etc.) is favored (Desrochers, et al. 2011; Pinto
and Keitt, 2009; Larkin et al., 2004; Yu et al., 2001). However, the value of these tools in
the field of landscape ecology is currently being put to the test (Eury et al., 2011;
Etherington, 2011; Maren et al., 2011; Gurrutxaga et al., 2010)
The study by Sutcliffe et al. (2003) developed a simple model for predicting the
Least-Cost Path for butterflies moving in agricultural landscapes. In their analytical
framework, cost for the different land-use practices was based on tree density. Their
findings suggest that the use of Least-Cost Path analysis was a better predictor of
butterfly movement than Euclidean distance. As in Sutcliffe et al. (2003) study, the
present analysis tests the application of the Least-Cost Path to predict butterfly
movement, but it also compares the usage of agroforestry practices with conventional
agricultural practices. In contrast to Sutcliffe et al. (2003) model, the proposed butterfly
movement predictor model has a greater number of relevant landscape factors making
it more comprehensive. Furthermore, besides Best Single, Each Cell analyses were
conducted as a way to simulate multiple movement scenarios between the source and
target areas.
The criterion used to compare the usage of the different land-use practices, was
the number of times the different agricultural plots in the landscape were crossed by the
predicted paths. According to this criterion, and if forest edge and the pasture matrix are
disregarded, homegardens were the land-use practice with the highest number of path
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crossings at Riberão Bonito. However at Agua Sumida the results for crossings
between the land-use practices were tied between homegardens, shaded coffee and
sugarcane. Given the configuration of the experimental landscapes, the plots for
secondary growth and sugarcane covered the studied landscape from side to side, and
were therefore necessarily crossed by the paths when predicting movement from forest
path to forest patch, which suggests they facilitate dispersal. However, these results can
be misleading. In particular, the size and the shape of the different land-use plots likely
affect the number of times they are crossed by the Least-Cost Paths. For this reason
the criterion used should be used mainly as a starting point of comparisons between the
predicted paths. Therefore numbers of crossings by the predicted paths is a criterion
that needs to be explained within the context of the landscape examined.
When comparing the studied landscapes, there were some differences and
similarities between the results. These are mainly due to the spatial structure and
composition of the landscapes. At Riberão Bonito, the Best Single path from the release
point to forest A included more land-use practice as there was more surface
heterogeneity between the source and target area compared to Agua Sumida.
Desrochers et al. (2011) discuss how almost straight Least-Cost paths are evidence of
low landscape variability, in which case there would be no difference between a Least-
Cost analysis and measuring Euclidean distances. Greater landscape heterogeneity
results in Least-Cost paths that reflect functional distances, and for the purpose of this
study, paths that reflect the functional connectivity of the landscape for traveling forest
butterflies.
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Among the similarities, homegardens were frequently crossed at both landscapes.
Homegardens in the Pontal region were small in terms of the area they occupy, though
numerous in terms of plots. The homegardens at the farming settlements contained
high tree diversity, but lower tree density compared to shaded coffee and eucalyptus
plots. In addition, the average size of homegardens was smaller (~ 0.5 ha) than that of
other land-use practices (twice to several times larger except for secondary growth
areas in Agua Sumida). However, nearly every predicted path using Each Cell analysis
for movements between forest A to B, and B to A had routes that crossed a
homegarden. The results for the Least-Cost Path predictor model using the abiotic
factors suggest that homegardens are valuable land-use management practices
contributing to the dispersal of forest butterflies in the agricultural landscapes. According
to the predictor paths, homegardens have lower resistance values compared to the
pasture matrix and can serve as intermediate habitats or stepping stones increasing the
functional connectivity of the landscape.
Another similar result between the experimental landscapes was that the Best
Single path for movement between forests was located in the areas that represented
the shortest Euclidean (straight line) distance. Least-Cost analysis measures the
functional or effective distance between two points (Sutcliffe et al., 2003). As the
algorithms of the Least-Cost models add the cell values from the cost distance layers,
areas between the source and target points with the shortest Euclidean distance would
contain fewer cells, and therefore fewer values to be added (Adriaensen et al., 2003).
Unless there are areas in the landscape where cells have lower values than the cells
located in the area that would correspond to the Euclidean distance, the Best Single
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path will be located in those areas. These results emphasize the environmental
differences between the forest and the agricultural land-use mosaic (so that the shortest
functional distance between forests is located in the region that corresponds to the
shortest overall distance) and attest to the significance of isolation for the colonization
and dispersal of individuals and species in island biogeography theory (MacArthur and
Wilson, 1969)
In contrast, the predicted paths from the Each Cell analysis identified functional
distances other than the Euclidean distance. By identifying the shortest path for “each
cell” contained in the polygon of the targeted area, the Each Cell function runs the
analysis multiple times; as if it was a multiple simulation (ArcGIS 9.3 Help). The results
from the Each Cell analysis may provide a more realist approach to predicting butterfly
dispersal behavior as it takes into consideration that moving from one forest patch to the
next can begin from any point within the source and target areas. In addition, organisms
not always travel in the most cost efficient way as the Best Single path implies, but
butterflies may also engage in non-directed flight behaviors (Conradt et al., 2000; Van
Dyck and Baguette, 2005). In this way, the results by the Each Cell analysis
complement those by the Best Single path. The overall assessment of the proposed
paths by the abiotic predictor model is that they support the argument that the majority
of butterflies would include agroforestry systems as part of the traveling routes. This
means the combined environmental conditions found in agroforestry systems were
distinct (in terms of cost) from those in the pasture matrix. Movement resistance was
minimized by agroforestry land-use practices favoring butterfly dispersal through them,
therefore according to the Least-Cost Paths proposed by the abiotic factors model in
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GIS, the frequency in which homegardens and shaded coffee are crossed over by the
predictor paths suggest these systems serve as stepping stones.
Abiotic and Recapture Data Least-Cost Path Analysis
By plotting the recapture data per trap point in ArcGIS it is possible to obtain a
visual representation of the butterfly recapture events in the landscape (Figure 4-24 and
4-25). Two patterns of movements stand out from the images. One is the movement
from the release point back to one of the forest patches, which happened to be the
same patch as the one predicted by the abiotic factors Least-Cost Path model, and the
second is the movement from the release point to the adjacent homegardens (at
Riberão Bonito) or shaded coffee plot (at Agua Sumida). The results by the abiotic
factors Least-Cost model did not predict movements parallel to the forest target areas.
This is because the model only assumed the forest interior habitats as the target
destinations. However, based on the butterfly recapture results, other habitats in
addition to the forest patches could be set as secondary target destinations in order to
improve the predictive capability of the GIS model. In addition, longer or more intensive
sampling effort would perhaps have provided more information to help differentiate
patterns of movement more clearly. In the absence of that, however, the available data
provide some evidence of dispersal behavior by forest butterflies, and for the use of
agroforestry practices as ecological stepping stones.
There are several parallels when comparing the Least-Cost Paths from the
proposed abiotic GIS model with the Least-Costs Path results using the recapture data.
The majority of the recapture events took place in traps located in homegardens.
Similarly, and as discussed above, homegardens were the land-use practice most likely
included in the routes by the abiotic Least-Cost Path analysis. Furthermore, after
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released in the middle of the landscape at Riberão Bonito, the recapture data suggested
that 67% of the butterflies moved into a homegarden, forest interior or the shaded
coffee plot. These were the land-use practices present in the traveling route proposed
by the Best Single path of the Least-Cost models for both the abiotic and recapture data
analysis. Under the different landscape scenario at Agua Sumida, the Best Single path
using the recapture data suggested that after release in the middle of the landscape the
butterflies first moved into the adjacent homegarden before returning in an almost
straight line back to one of the forest patches. In contrast, the Best Single path by the
proposed abiotic GIS model showed a straight movement back into one of the forest
patches. However, the Each Cell analysis from the release point to the same forest
patch showed the route predicted by the recapture data analysis. When comparing the
predicted abiotic and recapture Least-Cost Paths the results were very similar for the
analyses from the release point to the forest patches. These results may not completely
validate the abiotic GIS model, but they support the development of movement models
following the proposed analytical framework in ArcGIS.
The predicted Least-Cost paths for movements between forest patches were
different between the proposed abiotic model and the recapture data analyses. While
the proposed Best Single paths in the abiotic model were located at the shortest
Euclidean distance between the forest patches, the Best Single paths using the
recapture data were located near the release site area. In contrast, the Each Cell results
for the recapture data were more similar to the paths proposed by the abiotic GIS
model. Given that the forest captured butterflies were released at the release point, the
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GIS model developed using the recapture data is bias towards this area and should only
be compared to the movements by the abiotic GIS model starting at this source area.
Not being able to replicate the conditions between landscapes is a constraint when
doing research using Least-Cost models (Sutcliffe et al., 2003) and is likely the reason
so many studies have used virtual landscapes (Adriaensen et al., 2003; Sutcliffe et al.,
2001; Pinto and Keitt, 2009). However, the main limitation of the present study was
interpreting the GIS results and comparing these with the recapture data. Given that
one of the objectives of this study was to evaluate the potential function of agroforestry
systems as stepping stones, interpreting the paths produced by the Least-Cost
analyses proved to be a challenge. Other studies that have used Least-Cost models to
predict dispersal behavior for species have interpreted their results in a descriptive
manner (Walker and Craighead, 1997; Pinto and Keitt, 2009), or have identified criteria
as in the present study (Larkin et al., 2004). Jenness et al. (2011) created a set of tools
in ArcGIS for the evaluation of Least Cost Corridors, yet they are mostly descriptive as
well. A clear consensus on how to test the functionality of GIS predicted paths still
needs to be developed.
Recapture Data
The results for the recapture data provide evidence that agroforestry systems were
used as habitat by traveling forest butterflies in agricultural landscapes. The ANOVA
comparison for recaptured butterflies between land-use practices showed that forest
edge and shaded coffee habitats were significantly different to pastures and sugarcane
practices at Agua Sumida. The literature suggests that shaded coffee is a land
management practice that has the capability to host more species than other
agricultural practices (Tejada-Cruz and Sutherland, 2004; Gleffe et al., 2006, Komar,
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2006; Raman et al., 2006). The ANOVA results agree with the literature and suggest
that shaded coffee plots act in a similar way as forest edge habitats, attracting
butterflies moving between forest fragments; both habitats are different than the pasture
matrix. Therefore, shaded coffee systems can provide refuge to dispersing butterflies in
agricultural landscapes, and these results provide statistical evidence validating the
assumption that shaded coffee can serve as stepping stones.
In addition, after the butterflies were released at the middle of the experimental
landscape, most of the recaptures were in homegardens (if forest edge and forest
interior are considered two discrete habitats). These observations confirm that the forest
butterflies use homegarden plots as their refuge outside their natural habitat. However,
there was great variability in the number of recaptured butterflies between homegarden
plots at Agua Sumida. This variability made homegardens not significantly different from
forest habitats or pasture.
At Agua Sumida, the trap point “4g” recaptured a larger number of butterflies
compared to other traps in homegarden plots (78 individuals). When this trap point was
removed as an outlier from the ANOVA analysis, the model became significant. This
trap point was located in the homegarden plot closest to the release site, and its
proximity could have resulted in the higher number of trapping events. However, there
were three other traps located within the same distance; yet, these were located in other
land-use practices, which support the idea that butterflies in the matrix seek refuge in
homegardens. Geographic distance is an important factor influencing butterfly dispersal
(Dolia et al., 2007). However, it is the combination of the different site specific
environmental characteristics, such as the ones in homegardens versus pastures, that
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determines dispersal decision-making process by forest specialist butterflies (Van Dyck
and Baguette, 2005).
Concluding Remarks
The experimental design and GIS model developed in the present study were
intended to recreate the most important characteristics of the landscape to predict
movement behavior of dispersing fruit feeding butterflies. Even though the recaptured
data collected was informative, it was not sufficient to validate the Least Cost models
developed in ArcGIS 9.3. A longer collection period of recapture data and a better
criterion method to compare the images may be required to trace butterfly movement
more precisely and to evaluate the model’s predicted paths. Regardless, the value of
the proposed model lays in its simplicity -- it can serve as a framework for future studies
interested in identifying the dispersal trajectories of organisms. In addition, the Least
Cost analysis was site specific as it incorporated relevant predictor parameters at each
experimental site in relation to the butterfly data collected. Furthermore, given that fruit
feeding butterflies are environmental indicators of functional connectivity, the
development of a model capable of predicting dispersal behavior can be useful when
planning and designing agricultural landscapes for conservation purposes.
The proposal of agroforestry practices as biological stepping stones may be
thought of as an overstatement that deviates from the conventional definition of these
conservation strategies in landscape ecology. The reality is that, in fragmented
agricultural landscapes where land is privately owned and allocated for agriculture, the
establishment of actual stepping stones composed of natural habitat for the purpose of
biological conservation is challenging. The concept of stepping stones and of corridors
has been redefined by ArcGIS. In the evaluation of landscapes and land-use practices
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using GIS technologies, the identification of movement is based on a functional
definition more so than a physical one. In this way, these tools can help identify areas in
landscapes where movement is preferred by dispersing organisms. Within this context,
agroforestry systems can serve as stepping stones if they can facilitate dispersal in
fragmented agricultural landscapes. The present study examined the use of GIS tools to
evaluate this premise; according to the predictive models, the use of agroforestry
systems (homegardens and shaded coffee) is frequently included in the optimal paths
trajectories. Furthermore, the recapture data proved statistical evidence that supports
the use of shaded coffee as stepping stones for forest-bound fruit-feeding butterflies
moving in agricultural landscapes.
The application of GIS technologies for conservation purposes has usually been
focused on allocating suitable land for a given objective. However, GIS tools can also
be useful evaluating landscape attributes such as functional connectivity. The present
study used field data to produce new insights on the application and effectiveness of
Least-Cost Path for the dispersal of butterflies in fragmented agricultural landscapes.
According to the results we can conclude that (1) geographical distance had precedent
over the different land-use practices present in the agricultural mosaic for fruit feeding
butterflies moving between natural habitats (forest to forest), (2) agroforestry systems
(homegardens and shaded coffee) may act as stepping stones if they are located in the
direction of the butterfly’s traveling trajectory, and (3) the presence of different
agricultural practices increases landscape heterogeneity, which in turn increases the
irregularity of the predicted Least-Cost paths.
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Figure 4-1. Google Earth satellite image of Riberão Bonito Settlement. Colored
polygons show the different land-use systems, Homegardens, shaded coffee, forest edge, eucalyptus, cassava, and secondary growth, pasture (landscape matrix) and forest (eucalyptus trees were cut prior to when this satellite image was taken so are not visible in the figure) (© Google).
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Figure 4-2. Google Earth satellite image of a section of Agua Sumida Settlement.
Colored polygons show the different land-use systems, Homegardens, shaded coffee, forest edge, sugar cane and secondary growth, pastures and forest interior are not outlined as they can be depicted from the image.
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Figure 4-3. Polygons depicting the forest fragment areas in each experimental
landscape. Top row images represent a fraction of forest patch b and a (left to right) at Riberão Bonito, and bottom row represent a fraction of forest patch b and a (left to right) at Agua Sumida.
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Figure 4-4. Analytical Framework for GIS methodology in ArcGIS. Step by step GIS model procedure. Rectangles represent information layers and ovals represent ArcGIS operations.
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Figure 4-5. Regression equation for butterfly species and individuals as a function of the average abiotic factors a teach trap point. The coefficient values were added to the “regression” GIS analysis giving weight to the different variables for the butterfly movement trajectory (Only significant variables were included in the analyses).
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Figure 4-6. Cost raster layers of abiotic factors. The individual abiotic factors layers were combined through a “mean” operation using the Cell Statistics tool in Spatial Analyst.
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Figure 4-7. Example of Cost Weighted Distance analysis for one of the forest interior patches in combination with the calculated Cost Raster layer. The output layers are the Cost Distance and the Cost Direction layers.
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Figure 4-8. Best Single path (green line) from the release point to the selected forest patch at Riberão Bonito.
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Figure 4-9. Each Cell path analysis (green lines) from the release point to the forest patches at Riberão Bonito.
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Figure 4-10. Best Single path (green line) from the release point to the forest patches at Riberão Bonito.
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Figure 4-11. Each Cell path (green lines) from the release point to the forest patches at Riberão Bonito.
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Figure 4-12. Best Single path (green lines) from the release point to the forest patches at Agua Sumida.
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Figure 4-13. Each Cell path (green line) from the release point to the forest patches at Agua Sumida.
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Figure 4-14. Best Single path (green lines) from forest patch to forest patch at Agua Sumida.
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Figure 4-15. Each Cell path (green lines) from forest patch to forest patch at Agua Sumida.
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Figure 4-16. Number of butterfly individuals recaptured, after being released, in the different land-use systems at an agricultural settlement in Riberão Bonito, Sao Paulo state, Brazil (C-coffee, E-eucalyptus, F-forest interior, FE-forest edge, HG-homegarden, P-pasture, SC-shaded coffee, SG-secondary growth).
Figure 4-17. Comparison of butterflies recaptured in forest habitats (interior and edge) and homegardens at RB (F-forest, FE-forest edge, H-homegardens)
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Figure 4-18. Second recapture events for individuals previously recaptured. Table shows a three step movement behavior of butterflies from the release point, to “x” land-use system, to “y” land-use system.
Figure 4-19. Recapture events not from the Release Point at RB (results do not include consecutive recaptures in the same butterfly trap point)
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Figure 4-20. Sum of butterfly individuals recaptured in the different land-use systems at AS (C-coffee, E-eucalyptus, F-forest interior, FE-forest edge, HG-homegarden, P-pasture, SC-shaded coffee, SG-secondary growth).
Figure 4-21. Comparison of butterflies recaptured in forest habitats (interior and edge) and homegardens at AB
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Figure 4-22. Second recapture events at Agua Sumida (results do not include recaptures at the same trap point)
Figure 4-23. Recapture events not from the release point at Agua Sumida
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Figure 4-24. Visual images of recapture movements at Riberão Bonito in ArcGIS. Recaptures between the release point a Van Someren - Rydon traps was represented with straight lines. Greater thickness and darker color of the lines signifies greater number of recaptures.
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Figure 4-25. Visual images of recapture movements at Agua Sumida in ArcGIS. Recaptures between the release point a Van Someren - Rydon traps was represented with straight lines. Greater thickness and darker color of the lines signifies greater number of recaptures.
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Figure 4-26. Best Single path from the release point to the forest patches at Riberão Bonito. Least-Cost path analysis using the recapture butterfly data GIS model.
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Figure 4-27. Each Cell path analysis (green lines) from the release point to the forest patches at Riberão Bonito. Least-Cost path analysis using the recapture butterfly data GIS model.
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Figure 4-28. Best Single path (green line) from the release point to the forest patches at Riberão Bonito. Least-Cost path analysis using the recapture butterfly data GIS model.
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Figure 4-29. Each Cell path (green lines) from the release point to the forest patches at Riberão Bonito. Least-Cost path analysis using the recapture butterfly data GIS model.
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Figure 4-30. Best Single path from the release point to the forest patches at Agua Sumida. Least-Cost path analysis using the recapture butterfly data GIS model.
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Figure 4-31. Each Cell path (green line) from the release point to the forest patches at Agua Sumida. Least-Cost path analysis using the recapture butterfly data GIS model.
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Figure 4-32. Best Single path (green lines) from forest patch to forest patch at Agua Sumida. Least-Cost path analysis using the recapture butterfly data GIS model.
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Figure 4-33. Best Single path (green lines) from forest patch to forest patch at Agua Sumida. Least-Cost path analysis using the recapture butterfly data GIS model.
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Figure 4-34. Standardize residuals distribution plot. Observed vs. predicted values of butterflies per trap point at Agua Sumida. Notice outlier trap point belonging to trap “4g”.
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Table 4-1. Number of times a distinct path crossed over different land-use practices at Riberão Bonito. Predicted paths proposed by abiotic factors GIS model (HG-homegarden, SC-shaded coffee, E-eucalyptus, C-cassava, SG-secondary growth, S-sugarcane).
Path Type Movement Land-use Practice
HG SC E C SG S
Best Single Release point to Forests 1 1 0 0 0 na
Each Cell Release point to Forests 5 1 1 1 3 na
Best Single Forest A to B 0 0 0 0 0 na
Each Cell Forest A to B 5 2 1 2 4 na
Best Single Forest B to A 0 0 0 0 0 na
Each Cell Forest B to A 4 1 1 0 2 na
Total Predicted Paths 15 5 3 3 9 na
Table 4-2. Number of times a distinct path crossed over different land-use practices at Agua Sumida. Predicted paths proposed by abiotic factors GIS model (HG-homegarden, SC-shaded coffee, E-eucalyptus, C-cassava, SG-secondary growth, S-sugarcane).
Path Type Movement Land-use Practice
HG SC E C SG S
Best Single Release point to Forests 0 0 na na 0 0
Each Cell Release point to Forests 1 0 na na 0 1
Best Single Forest A to B 1 1 na na 0 1
Each Cell Forest A to B 1 2 na na 0 1
Best Single Forest B to A 1 1 na na 0 1
Each Cell Forest B to A 1 1 na na 1 1
Total Predicted Paths 5 5 na na 1 5
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Table 4-3. Number of times a distinct path crossed over different land-use practices at Riberão Bonito. Predicted paths proposed by the recaptured data GIS model (HG-homegarden, SC-shaded coffee, E-eucalyptus, C-cassava, SG-secondary growth, S-sugarcane).
Path Type Movement Land-use Practice
HG SC E C SG S
Best Single Release point to Forests 2 1 0 1 0 na
Each Cell Release point to Forests 3 1 2 1 0 na
Best Single Forest A to B 2 1 0 1 1 na
Each Cell Forest A to B 3 1 0 2 2 na
Best Single Forest B to A 2 1 0 1 1 na
Each Cell Forest B to A 5 1 4 3 4 na
Total Predicted Paths 17 6 6 9 8 na
Table 4-4. Number of times a distinct path crossed over different land-use practices at Riberão Bonito. Predicted paths proposed by the recaptured data GIS model (HG-homegarden, SC-shaded coffee, E-eucalyptus, C-cassava, SG-secondary growth, S-sugarcane).
Path Type Movement Land-use Practice
HG SC E C SG S
Best Single Release point to Forests 1 0 na na 0 1
Each Cell Release point to Forests 2 0 na na 0 1
Best Single Forest A to B 1 0 na na 0 1
Each Cell Forest A to B 1 1 na na 2 3
Best Single Forest B to A 1 0 na na 0 1
Each Cell Forest B to A 3 1 na na 1 4
Total Predicted Paths 9 2 na na 3 12
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Table 4-5. ANOVA Fisher (LSD) pair-wise comparisons between land-use practices at Agua Sumida.
Category LS means Groups
Shaded Coffee 9.000 A
Forest Edge 8.300 A Forest Interior 5.938 A B
Homegardens 5.571 A B
Secondary Growth 5.500 A B
Pastures 2.385
B
Sugarcane 1.250 B
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CHAPTER 5 AGROFORESTRY INFORMATION DISSEMINATION AT PONTAL DO
PARANAPANEMA, SÃO PAULO, BRAZIL
The creation of farming settlements as a consequence of agrarian reform in Pontal
do Paranapanema has resulted in degradation of the remaining natural areas in some
regions and the integration of conservation efforts and sustainable development in other
regions (Cullen et al., 2005). Many of the appropriated and government re-distributed
lands were marginal natural areas unsuitable for agriculrture and therefore had not been
used for that purpose (Romeiro et al., 1994). The official recognition of land reform
settlements in these areas resulted in their inevitable conversion to agriculture and to
the deforestation, fragmentation and isolation of nearby forest remnants (Heredia et al.,
2003). However, in some areas where previous landowners had practiced poor land
stewardship, the settlers have tried to ameliorate the environmental conditions by
planting trees (both natives and exotic). According to the official position of the
Movement of Landless Rural Workers (MST), the settlements, considered as a social
movement that has helped propel Brazil’s Agrarian reform, contribute to the restoration
of the rural landscape. Furthermore, MST states that their environmental interests are
compatible with the concept of sustainable development (www.mst.org.br).
At Pontal do Paranapanema, efforts towards the restoration of the degraded
landscape have been achieved in large part due to the efforts of the Institute for
Ecological Research (IPE), which is a local conservation organization (Cullen et al.,
2005). The adoption of agroforestry practices in combination with the establishment and
protection of forest reserves and other landscape ecology strategies, may be improving
the chances for native and endemic tree species to survive in this region (Cullen et al.,
2004; Cullen et al., 2001). Upon arrival, agrarian reform settlers had to build their
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houses and work the land for agricultural purposes. Given the ecological importance of
the remaining forest fragments in the Pontal region, IPE became heavily involved in
integrating the farmers into landscape conservation initiatives (Mielke and Cassagrande
1997; Albernaz, 1997). Through the development of nurseries, workshops, tree planting
events and the settlers’ active participation as stakeholders in decisions for regional
conservation measures, IPE has conducted a large share of the dissemination of
sustainable agricultural practices such as agroforestry.
Research on diffusion of innovation has been widely conducted in rural sociology
to determine the spread of technical information and practices within a social system
(Rogers, 2003). However, MST settlers are a special type of farming community that
deserves to be a specific case study. They are not traditional farmers of several
generations working the same land and having a strong attachment to the region
(Stedman, 1999). Instead, they emerged in the 1970s from a social movement that
contested the land tenure of large ranches with dubious titles (Harnercker, 2003). MST
farmers have proven to act as a social entity with a specific sociopolitical agenda, which
has led to their rise and establishment as a new form of farming community. The
grassroots movement is organized in cascading units that are represented by a
centralized body of members. Consequently the channels of communication and
information flow back and forth through local, regional and national representatives. At
the base of the movement, in each settlement there is a set of 10 to 15 families in
charge of making decisions and communicating these to the rest of the community
(Harnecker, 2003). In spite of their participatory decision-making process, a pattern
towards hierarchical associations still exists among the members (Wright and Wolford,
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2003). Due to the social structure and organizational characteristics of MTS farming
settlements, the conceptual framework of Social Learning Theory learning may apply to
the adoption of agroforestry, and agricultural practices in general, in the Pontal do
Paranapanema region. Given that formal sources for agricultural information are limited
in the region, learning through observation, imitation, and reinforcement may be an
important mechanisims for the dissemination of sustainable and non-sustainable
practices.
In psychology and criminology, Social Learning Theory has been used to explain
learning of deviant behavior (Akers, 1973, Bandura, 1978). However, the theory, as
originally presented by Miller and Dollard (1941) was an attempt to understand cognitive
learning of any kind of behavior as long as the learning occurs within a specific social
context. Bandura (1977) further developed these ideas by describing the observational
learning and modeling process as a series of steps: attention, retention, reproduction,
and motivation. Another version of these theoretical concepts, in combination with
Sutherland’s (1947) differential association theory, has been operationalized by Akers
(1979) (Fisk, 2006). Akers et al. (1979) was the first empirical study that used the Social
Learning concepts to explain learned (deviant) behavior. In contrast to Bandura,
Burgess and Akers (1966) called these steps the four major explanatory concepts of
Social Learning Theory, and described them as: differential association, definitions,
differential reinforcement and imitation.
Regardless of the large volume of research applying Social Learning Theory to
criminal/deviant behavior (Pratt et al., 2010; Fisk, 2006; Skinner and Fream, 1997); it
has also been used to explain observational learning processes in other fields. For
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example, the theory has been applied to research studies on media and advertising
(Lunz, 1983), and it has also been applied to health education fields. Yet, Social
Learning, or variations of the theory have received little attention in the field of natural
resources (Pahl-Wostl, 2002; Pahl-Wostl et al, 2007), and much less in learning
behavior in rural settings to explain the adoption of agricultural practices (Burger et al.,
1993). In natural resources, Social Learning Theory has been used to describe the
decision making process of communities acting as collectives.
Studies on agroforestry adoption have usually focused on the practice itself in
terms of its diffusion spatially and temporarily (Feder and Umali, 1993). The review
paper by Mercer (2004) on agroforestry adoption presents a summary of the different
diffusion theories researched. According to his analysis, most of the work studying
agroforestry adoption has been conducted in economics, geography, and sociology. Of
these, sociologists have concentrated on the social rewards and the communication
channels associated with the adoption of agroforestry. Understanding agroforestry
adoption has proven to be a complex process with multiple dimensions of decision-
making factors to be taken into consideration (Pattanayak et al., 2003).
However, among the various approaches used in adoption-diffusion research, the
core elements of Social Learning Theory can be found. Throughout the different
research models within innovation diffusion, there are many parallels with social
learning theory: (1)The focus on the degree of interaction with others is comparable to
the so-called differential association in social learning theory (Feder and Umali, 1993);
(2)The cognitive process of hearing about the innovation and deciding to make use of
the new idea is similar to defining the behavior from the individuals’ perspective in social
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learning (Roger and Shoemaker, 1971; Feder et al., 1985); (3) The analysis of the
perceived financial contributions of the innovation can be interpreted as the differential
reinforcement concept (Franzel and Scherr, 2002; Vosti et al., 1998, Arnold and
Dewees, 1995); and (4) Implementing the innovation and learning by doing or by
spillover is analogous to imitation in social learning theory (Forster and Rosenzweig,
1995). The application of Social Learning Theory to explain agroforestry adoption as an
experimental approach is worth exploring due to the many similarities with innovation
diffusion type research, also because of the perceived agricultural information flow from
core to periphery in MST farming settlements (Isaac et al., 2007). The potential
implication of validating this type of learning behavior process could be useful for the
propagation of environmentally friendly agricultural practices that also promote
sustainable development.
With the purpose of understanding the learning mechanisms among MST farming
settlements at the Pontal, the present study evaluated agroforestry adoption from a
social learning perspective. If the adoption of agroforestry practices is related to the
farmer’s interaction and observation of other farmers practicing various forms of
agroforestry, then learning through the observation of models as described by social
learning theory may be valid to explain the transfer of agricultural knowledge. This
information could be used as a means to promote sustainable agricultural practices in
MST communities. The specific objectives were (1) to describe the main characteristics
and sources of information of two MTS farming communities at Pontal do
Paranapanama, (2) to identify parameters that can describe farmers that more readily
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engage in agroforestry practices (3) and to test the applicability of the Social Learning
constructs by Burgess and Akers (1966).
Methodology
Study Site
The Pontal used to be a continuous semi-deciduous forest prior to its conversion
to agriculture (Veloso et al., 1991). Today, scattered forest fragments are evidence of
the pre-anthropogenic landscape; they constitute about 5% of the original vegetation
that made up what was once known as the Pontal do Paranapanema Great Reserve
(PEMD, 2006). The region is located in the western-most section of the state of Säo
Paulo, where the Parana and Paranapanema rivers meet. The landscape is
characterized by a combination of shallow hills and slopes, and elevation is between
250 and 500 m a.s.l. (PEMD, 2006). The weather is dry subtropical with an average
temperature of 21°C and annual precipitation ranges from 1,100 to 1,300 mm (Diegues,
1990).
A survey of closed- and open-ended questions was conducted to obtain
quantitative and qualitative contextual information about farming practices (APPENDIX
I). Within the Pontal do Paranapanema region of Brazil, the study was conducted in two
farming settlements (Riberão Bonito and Agua Sumida).Riberão Bonito and Agua
Sumida farming settlements were created during the 1990s through the government’s
agrarian reform led by Brazil’s landless social movement. The settlements are similar in
size, population, and location in terms of distance to markets and towns. One adult per
farm, usually the head of household was interviewed and asked questions about their
relations with neighbors and about sources of agricultural information. The interviews
were conducted during the months February and March, 2009, and consisted of 40
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questions that included personal information such as age, gender and years living in the
settlement, as well as questions design to measure the four constructs of social learning
theory (Fisk, 2006).
To test for the four constructs of Social Learning Theory, questions related to
differential association, definitions, imitation and differential reinforcement in relation to
information acquisition and adoption of agroforestry practices were incorporated in the
survey (Table 5-1). The relationships between these constructs and the number of
agroforestry practices and farm products were analyzed using Spearman’s Rho
correlation test. The format of the social construct questions was based on the survey
instruments developed by Akers (1977) and Fisk (2006). The questionnaires in these
surveys were created to validate the theory of Social Learning for criminal or deviant
behavior. In order to apply this theory to agriculture behavior modeling, the questions
were modified to obtain similar relevant information. The responses were measured as
categorical data. Some questions used a percentage (5%, 25%, 50%, 75% and 95%),
others a series of options (Excellent, Good, Fair, Bad, Awful), and some provided a
semistructured list of items to choose from (as in the question that asked for the
perceived benefits and costs of agroforestry). For subsequent analysis the responses
were coded on a 5 point Likert-like scale ranging from 1 to 5.
Data Analysis
Some of the questions in the survey instrument were analyzed with descriptive
statistics to understand the basic structure of the populations studied and the flow of
agricultural information. The descriptive statistics data included the mean and standard
deviations of demographic variables such as age, level of education, years living in the
Pontal region, etc. Other data such as the main sources of income and principal
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agricultural practice were described, though not compared statistically. In addition, the
Social Learning Theory constructs were compared with the number of agroforestry
practices per farm using a Spearman’s Rho correlation procedure.
A regression analysis was also run in SAS 9.2A to identify parameters that could
explain agroforestry adoption in the Pontal region. With the purposes of providing
support for the applicability of Social Learning Theory, the regression analysis was
conducted to determine if greater social interactions and exposure to agroforestry
practices increases their adoption. The number of agroforestry practices a farm had was
set as the dependent variable and a backward elimination model was used to test
different potential variables. Among them, age, gender, level of education etc., were
some of the farmers’ personal characteristics evaluated in the analyses. The other
parameters addressed aspects of the farmer’s interaction and exposure to agroforestry.
They included: the social groups with whom the farmers most frequently discussed
agricultural practices, the number of families they knew and interacted with, the number
of families they knew that practiced agroforestry, the frequency of agroforestry system
incorporation, and the distance to the nearest farm with diverse agroforestry practices.
The regression model was built with a total of 12 independent variables. Distance
to the nearest farm containing four agroforestry practices, and number of
friends/neighbors practicing agroforestry had to be estimated. Distance between farms
was measured in meters using ArcGIS 9.3 (farms with four agroforestry practices were
the target farms as four was the highest number of different agroforestry practices
adopted by farms in the region). The number of friends/neighbors the respondents knew
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that practiced agroforestry was calculated from the percentage of friends/neighbors that
practice agroforestry, and the total number of families they interacted within the region.
ArcMap in ESRI ArcGIS 9.3 was used to measure the distance between farms. A
map was created to geographically locate all the farm areas in the settlements
landscapes. This was done using Google Earth images for each settlement, 20
geographical control points, and two map sketches of the farm areas. The landscape
images were georeferenced and superimposed with the map sketches to digitize the
polygon for each farm in Vector GIS. Once the farm polygons were identified using the
Editor tools, these were converted to raster images (1 cell = 10 m²). The surveyed
farms were selected and labeled based on the number of agroforestry systems
practiced. Distance to the nearest farm with four different agroforestry practices was
measured as the Euclidean distance from the center of the farms. The center or
“centroid” of the farms was estimated using Zonal Geography in Spatial Analysis, and
the Euclidean distance was calculated using the ruler option in the Measure tool.
Results
A total of 94 households were surveyed: 49 at Agua Sumida and 45 at Riberão
Bonito. Analysis of the demographic data indicated that 38% of the interviewees were
female and 62% were male. The average age of the respondents was 51 years, and the
average number of years attending school was 4.4, though the distribution for this
variable was slightly skewed toward the lower end. Among the farmers interviewed,
migration to their government assigned or purchased land began in 1984, and
continued until the year prior to when this study was conducted in 2008. For 41% of the
farmers interviewed, 1998 was the year when they acquired their property. Yet, the
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number of years the respondents had been living in the Pontal region precedes this
date by an average of 30 years.
All the landowners interviewed were engaged in agricultural activities and for most
respondents their farms were their main livelihood. When asked about their most
important sources of income, 59% of the respondents mentioned a type of agricultural
activity; other sources of income included government pensions or food assistance
programs, non-farm jobs, and financial support from family members outside the farm.
Among the income-producing agricultural practices, milk production was the most
profitable and widespread (Figure 5-1). Among the surveyed farmers, milk production
was mentioned 67 times, and was followed by cassava, which was mentioned 49 times.
In the last decade many farmers have switched from producing cassava as their main
agricultural practice, to producing milk (Figure 5-2).
The majority of the settlers in the Pontal region have worked in agriculture for most
of their lives. The average number of years the respondents have been working in
agriculture is 36. Many of them had been engaged in this activity since childhood.
Among the sources of knowledge of agricultural practices, family members were the
group most frequently mentioned; other sources included friends or neighbors in the
Pontal region, their own experience through trial and error, and extension agents
(Figure 5-3). According to their responses, the settlers came from many different
regions in the country, from far away states in the Northwest, to neighboring farming
communities and towns. When asked if they were planning to move somewhere else in
the future, all but a couple of respondents said they were planning to stay in their land
and continue farming for the rest of their lives.
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When asked about agroforestry, 93% said they had heard about the concept.
Furthermore, without being asked to name an example, many of the farmers mentioned
shaded coffee. Out of the total number of farmers, 88% said they have incorporated
agroforestry practices in their farms (Figure 5-4). Of the total number of agroforestry
practices inventoried on the 94 farms, 46% were homegardens (Figure 5- 13), 21%
living fences, 16% silvopastures, 9% shaded coffee systems, and 8% alley-copping
systems (usually cassava intercropped with corn). When asked about who first
mentioned the term “agroforestry” to them, 49% said they heard about them from an
agricultural organization or institution such as IPE or ITESP (Figure 5-5). Closely
following, 39% said they first heard about agroforestry from a friend or neighbor. A small
group of farmers also mentioned family or other sources of information such as
television or off-farm work environments. A different question addressed who taught the
farmers the agroforestry practices they currently have in their farms (Figure 5-6). The
most frequent answer was family membersm, closely followed by extension agents from
IPE, alone through trial and error, and by friends or neighbors from the Pontal region.
To compare the perceived influence exerted by friends or neighbors that practice
agroforestry, the farmers were asked to approximate the percentages of their neighbors
who engaged in agroforestry practices (Figure 5-7). More farmers thought a lower
proportion of their friends and neighbors were engaged in agroforestry than what the
data indicated. Only 40% thought almost all their friends and neighbors had agroforestry
practices in their farms. In contrast 80% thought that almost all of their friends and
neighbors were agricultural producers. None of the respondents said they did not have
friends or neighbors engaged in agriculture, yet 1% said they did not know anyone that
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practice agroforestry. Even though most of the farmers had a positive attitude towards
the different agroforestry practices (good or very good), shaded coffee was the
exception (Figure 5-8). Almost half (45%) the farmers had a neutral or negative opinion
on shaded coffee. In contrast, homegardens were only viewed as positive or neutral,
and as far as silvopastures and living fences, most thought of these practices as
positive, but there were a few that perceived them as not being worth incorporating.
The farmers were also asked about the frequency of their agricultural interactions
with other farmers, and their opinion about exchanging agricultural knowledge. When
asked to select the group with whom they usually discuss agricultural practices, most
answered family (39%) and neighbors (37%), and a smaller percentage (24) answered
friends (Figure 5-9). As far as the frequency in which these interactions take place, the
most common answer was once a week, closely followed by less than once a month
(Figure 5-10). The third most common answer was between the latter two: once a
month. These responses appear to have a bimodal distribution. In addition to the
frequency of these interactions, the respondents were asked what would be the
advantages of discussing agricultural practices with friends, neighbors, and family
members. The most common response was that they were a source of information that
was easily accessible, while others explained that they could provide examples, and
that they were a source of new ideas (Figure 5-11).
The stepwise regression resulted in a model with three significant parameters
(Table 5-2). The regression analysis was initially built with a total of 12 candidate
variables; however through the backward elimination procedure only three were
significant in explaining the number of agroforestry practices adopted. The total number
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of families known by the famers (p = 0.0120), along with the total number of years
working in agriculture (p = 0.0059), and the frequency of agroforestry system
incorporation (p = 0.0013), were parameters that explained some of the variability in
agroforestry practice adoption. The regression model was significant (p-value <0.0001)
and the parameters explained 28% of the data variability (adjusted R2 0.2793). The
regression procedure had nine steps and the initial model with all the candidate
variables had a model fit of 31% (adjusted R2= 0.3179).
Distance to the nearest farm with four agroforestry practices was further analyzed.
As the last parameter eliminated in the backward elimination stepwise regression
procedure, and given that this parameter is a function of the spatial arrangement of the
landscapes, it was analyzed separately for each settlement (Figure 5-14, 5-15). The
results for the Spearman Rho correlation between the number of agroforestry practices
per farm and the distance of these to the nearest farm with four agroforestry practices,
showed that distance was significant at Agua Sumida farming settlement (p-value=
0.0173), but not for Riberão Bonito (p-value=0.7660).
The results from the correlation analysis between the Social Learning Theory
constructs and the number of agroforestry practices a farmer has adopted, provide
some evidence of the use of this theory as an explanatory framework (Table 5-3). Of the
total 15 possible correlations, five were significant. The relation between the number of
agroforestry practices and the frequency in which these were incorporated (r=0.436, p
value<0.0001), and the number of agroforestry practices and the benefits thought to be
obtained by incorporating them (r= 0.363, p value= 0.0003) were low, yet significant.
Also, there were significant relations between the percentage of friends/neighbors
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practicing agroforestry and the frequency in which these were incorporated by farmers
(r=0.224, p value=0.0297), and between the frequency in which agroforestry practices
are incorporated by farmers and the farmer’s perception of agroforestry systems.
Finally, the relation between the frequency in which farmer’s incorporate agroforestry
systems and the benefits thought to be obtained was significant (r=0.216, p
value=0.0375).
The correlation analysis provides support for two of the four social constructs.
Imitation and the rewards (benefits) aspect of Differential Reinforcement were positively
correlated with the adoption of higher number of agroforestry practices. However, the
correlation analysis also resulted in several relations that were not significant. The
questionnaire items used to test Differential Association and Definitions were not able to
identify positive correlations with the number of agroforestry practices in farms. In
addition, negative correlations were expected for the associations between the different
variables and the negative aspects (cost) of Differential Reinforcement. Yet, there was
only one negative relation with the farmer’s perception of agroforestry (Definitions), and
it was not significant.
Discussion
The results from the questionnaire portray the MST farmers of the Pontal region as
a very diverse community. Even though most heads of household and property owners
were male, females constituted a large percentage (38%). The settlers also varied
greatly in terms of level of education, age, place of origin, and years of experience in
agriculture. The range of values for these variables was for the most part normally
distributed. The apparent heterogeneous community that exists in the Pontal
settlements is contrasted by a homogeneous form of agriculture. There was a high
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degree of consensus in terms of the main agriculture and agroforestry practices in the
region. With only a handful of exceptions, all the farmers in the studied settlements
produced milk as their main source of income, and had homegardens surrounding their
homestead. Even if they did not acknowledge the practice of planting trees and
vegetable gardens around the house as an agroforestry practice (12% said they were
not practicing any agroforestry), 92% of the farms had homegardens around the house.
The various processes of knowledge acquisition of agricultural practices were as
diverse as the farming community. However, some generalizations can be made. Most
of the farmers have been raised in rural areas and been involved in agriculture since a
young age. Yet, their experience has usually been farming illegally in areas without land
tenure, or as hired rural workers in commercial agriculture (Wright and Wolford, 2003).
According to Cullen et al. (2005), the knowledge gained as an employee in a company
or a laborer in a large ranch does not guarantee knowledge on land stewardship or
sustainable agricultural practices appropriate for small land holdings. In addition,
according to Valladares-Padua (2002) a small percentage of farmers in agrarian reform
settlements have spent most of their lives in urban areas (20%), which can be
interpreted as having no previous farming experience. Irrespective of the farmers’
experience in agriculture, the results indicate that they seek family members as their
primary source of agricultural information, and friends and neighbors as a secondary
source. These results confirm that farmer to farmer communication is an important
source of agricultural knowledge dissemination in these farming communities, and that
the flow of information is not only top down (as would be the case through extension
agents and institutions), but also horizontal within the community.
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Isaac et al. (2007) examined the communication patterns in the transfer of
knowledge and adoption of agroforestry, using Social Network Theory instead of Social
Learning Theory. They evaluated formal (i.e. extension agent) and informal (i.e. farmer
to farmer) sources of agroforestry information. The authors were interested in identifying
core-periphery spatial relations of information flow based on social ties (interactions with
relatives, friends and with successful farmers), and found evidence that the community
was organized in a core-periphery structure. Core members were highly sought farmers
that acted as information centrals for the transfer and implementation of knowledge on
agroforestry practices. Social Learning Theory also implies a core-periphery information
dissemination framework, where the core farmers are the models of the behavior and
the observers are the farmers at the periphery. According to the results, farmers with
few agroforestry practices sought advice from family, friends and neighbors more
readily than did farmers with various agroforestry practices (Figure 5-12). These
findings suggest a core-periphery structure in which farmers with few agroforestry
practices are advice seekers, and farmers with various agroforestry practices are advice
givers. Even though farmers in the Pontal region have access to formal sources of
agricultural information (government extension agents) in nearby towns, they also rely
on their family, friends and neighbors for knowledge and advice.
Determining potential variables that can explain agroforestry adoption has been
studied using diffusion theories (Mercer, 2004). Some of the explanatory variables used
in the regression analysis of the current study have also been used in adoption-diffusion
studies for agroforestry (Matthews et al., 1993; Mercer, 2004; Akpabio and Ibok, 2009).
However, these studies were not very successful at linking the empirical findings with
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the underlying diffusion innovation theory (Lindner, 1987; Abadi Ghadim and Pannell,
1999; Pattanayak et al., 2003), which opens a window of opportunity to explore other
theories and approaches to explain agroforestry adoption-decision-making processes.
Demographic parameters such as gender, age, and level of education are commonly
used because they serve as proxy variables to evaluate farmer specific influences
(Mercer, 2004). According to the meta-analysis by Pattanayak et al. (2003),
demographic parameters, which they called household preferences, were significant in
about 40% of the studies, and gender tended to be more significant than age and
education, with male heads of households being more likely to adopt agroforestry
practices. The gender disparity is often interpreted as a reflection of a difference in
household resources for adopting agroforestry. In the present study, these parameters
were not significant explaining agroforestry adoption at the Agua Sumida and Riberão
Bonito farming settlements. Demographic parameters provide valuable information for
understanding the structure and composition of a population, which in turn can help
describe innovation adoption processes. However, generalization of these do not
contribute much to generalizations about agroforestry adoption as they are site specific,
and will vary depending on the socioeconomic and cultural characteristics of the
communities in question.
The non-demographic parameters in the regression analysis were included to
address the concepts within Social Learning Theory. Most empirical studies on
agroforestry adoption have used biophysical and socioeconomic data (Pattanayak et al.
2003; Mercer, 2004). Recurrent socioeconomic determinant factors (such as household
preferences, resource endowment, market incentives, biophysical factors, and risk and
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uncertainty) try to determine whether and how the innovations contribute to the farms’
welfare and economic development. In contrast, the factors analyzed in the present
study fall within a cognitive social learning framework and try to determine whether and
how greater interactions and exposure to agroforestry practices influence adoption of
those practices. Given that the transfer of agroforestry knowledge between farms is not
yet well understood (Isaac et al., 2007), several variables were included into the
regression model as an initial experimental approach to this subject.
The results for the regression model suggest that the number of families, the years
of experience working in the agricultural sector, and previous experience with
agroforestry were significant parameters when adopting agroforestry. These parameters
reflect the concepts of Differential Association and Differential Reinforcement in Social
Learning Theory. It can be assumed that the more people a farmer knows, the greater
the number of social interactions. Farmers who had a broader social circle may be
exposed to a wider variety of agricultural practices, among them agroforestry. The
results suggest that farmers who had incorporated several agroforestry practices in their
farms usually knew more families in the region. The number of years working in
agriculture was also a significant parameter explaining agroforestry adoption. The
mixing of plant and animal components in agroforestry is a traditional form of agriculture
that promotes ecosystem processes and is less dependent of conventionally used agro-
chemical technologies. Farmers with greater experience in agriculture may be more
familiar with these practices as the incorporation of monocultures is frequently
associated with modern agriculture. The last significant parameter of the regression
model, the frequency of incorporating agroforestry practices, also implied accumulated
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personal experience. The significance of this parameter, however, suggests that the
previous experience in agroforestry was positive, which reinforced the behavior
providing incentives for the farmer to continue to incorporate agroforestry practices.
Among the potential variables used in the regression analysis, the number of
families the farmer knew that had agroforestry practices did not significantly influence
their decision to adopt those practices. Yet, as mentioned above, the variable for
number of families the farmers knew in total, was significant. These results seem
counterintuitive as one would assume that the more families the farmer knows, the more
families they would know that have agroforestry practices. However this was not the
case at the Pontal. In particular, there appears to be confusion among the farmers
about what constitutes an agroforestry practice and what does not. Some farmers
associate the term agroforestry with some of the practices that have been promoted by
IPE, among which the most well-known are shaded coffee and alley cropping systems.
Homegardens, for example, are sometimes not thought of as an agroforestry practice.
Five of the interviewed farmers said they had not practiced agroforestry on their farms,
when in fact each of them had a homegarden system surrounding their house. In
addition, the answers to the questions about their general opinion on agroforestry
practices (94% of the farmers had a favorable opinion) did not reflect the variability of
agroforestry practice adoption. This discrepancy could be interpreted as not really
having a clear position about the matter.
The applicability of Social Learning Theory to explain agroforestry information
dissemination and adoption was partially supported by this study. In trying to explain
deviant and criminal behavior, the validity of Social Learning Theory has been
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supported by ample empirical research (Skinner, 1953; Bandura 1969; Staats, 1975).
Many of these experimental studies have been conducted in laboratory settings (Akers
et al., 1979). However, there has been little research on the application of the principles
of Social Learning Theory to non-criminal behavior, and even less research on the
theory in natural settings. Adapting the social constructs and testing for them to explain
agroforestry adoption may not be intuitive. The lack of consistency on the correct
identification of what constitutes agroforestry may have jeopardized the evaluation of
the theory’s Differential Association and Definitions constructs. Still, the positive results
of the other two components are encouraging and suggest that learning through model
observation might be an appropriate conceptual framework to understand learning
processes in agricultural settings.
The strongest relationship between the number of agroforestry practices adopted
and the social constructs was with the beneficial aspects of the Differential
Reinforcement component. This indicates that if farmers in the Pontal region were more
aware of the benefits of agroforestry incorporation, they would be more willing to adopt
these sustainable agricultural practices. Imitation was also significantly correlated with
agroforestry adoption. The interview question to test for imitation was the frequency in
which the farmers had adopted agroforestry practices. This question, as true for the rest
of the social construct questions, was adapted from studies on Social Learning on
deviant behavior. However, when applied to adoption of agroforestry it should have
been phrased to be more specific about observing the behavior and imitating it
afterwards (Isaac et al., 2007). Regardless, this construct was supplemented by other
items in the survey instrument, and given that this is an initial application of the theory to
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agroforestry adoption, it is suggested that future research on this topic should develop
questions that link the observation with the imitation more directly.
The adoption of agricultural practices such as agroforestry is a slow and dynamic
process. Even if the present study was not able to fully confirm Social Learning Theory
as a mechanism for agriculture and agroforestry learning, it provides partial evidence for
its potential application and as a starting point. There are many characteristics within
the Pontal farming settlements that suggest to some degree the farmers rely on other
farmers as a source of information on agricultural practices. The fact that most of the
farms in both farming settlements have incorporated homegardens is an example of
observing and imitating behavior as this practice has not been formally taught to the
farmers. Also, the fact that farmers have slowly moved from crops to dairy production by
observing what their friends, family and neighbors in the region are doing, suggest a
active learning process. The variables used to evaluate this theory and the survey
questionnaire may need to be revised to be able to obtain more conclusive results either
for or against the fit of Social Learning Theory. Adopting agroforestry practices is a
multidimensional subject and many factors can be involved in the decision-making
process. Understanding a farmer’s learning process seems like a logical place to start if
the objective is to promote the dissemination of environmentally friendly technologies. In
the absence (or with limited presence) of educational institutions, learning through
modeling as it is described in Social Learning Theory may be the standard learning
mechanism for agricultural and agroforestry knowledge.
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Figure 5-1. Main source of income at Riberão Bonito and Agua Sumida farming settlements (“y” axes represents the number of responses and “x” represents the different income sources).
Figure 5-2. Main type of agriculture products produce at the farming settlements (“Y” axes represents the number of responses and “x” represents the different farming activities).
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Figure 5-3. Main sources of agricultural knowledge listed by the farmers intervied (“Y” axes represents the number of responses and “x” represents the different information sources).
0
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90
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Figure 5-4. Percentage of farmers interviewed that have incorporated agroforestry practices in their farmland
Figure 5-5. Social group that first mentioned the term “agroforestry” to the farmers interviewed (“Y” axes represents the number of responses and “x” axes represents the different social groups).
Yes 88%
No 12%
0
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60
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Figure 5-6. Main sources of information on agroforestry technologies (“Y” axes represents the number of responses and “x” represents the different information sources).
Figure 5-7. Percentage of the farmer’s friends and neighbors that practice agriculture and/or agroforestry.
0
10
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0
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90
0 25% 50% 75% Almost all Don’t know
Practice Agriculture
Practice Agroforestry
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Figure 5-8. The farmer’s opinion for each of the most common agroforestry practices in the Pontal region (“y” represents the number of responses and the “x” axes represent an opinion scale from “very good” to “very bad”).
Figure 5-9. Groups with whom farmer’s more often discuss agricultural topics
0
10
20
30
40
50
60
70
80
very good good neutral bad very bad
Homegarden
Silvopasture
Living Fence
Shaded Coffee
Neighbors 37%
Friends 24%
Family members
39%
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Figure 5-10. Frequency in which farmers discuss agricultural issues with family, friends and neighbors.
0
5
10
15
20
25
30
35
More thanonce per day
Once per day 2 - 3 timesper week
once perweek
Once permonth
Less thanonce amonth
Never
200
Figure 5-11. The farmer’s perceived advantages of discussing agricultural issues with family, friends and neighbors.
Figure 5-12. Number of farmers that have requested advice on agricultural topics to neighbor farmers in the Pontal Region (this questioned was skipped by many of the respondents and so the total number of responces was low).
0
10
20
30
40
50
60
70
Noadvantages
Exchangeinformation
A source youcan trust
ProvideExamples
Learn newthings
Easy access
0
2
4
6
8
10
12
14
16
0 1 2 3 4
Number of AF Practices
Sought Advice
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A) B)
Figure 5-13. Two MST homesteads at Pontal do Paranapanema. A) depicts a homestead on a farm with four different types of agroforestry practices. B) depicts a farm without agroforestry practices.
Figure 5-14. Geographical distribution of farms with different number of agroforestry practices at Riberão Bonito.
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Figure 5-11. Geographical distribution of farms with different number of agroforestry practices at Agua Sumida.
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Table 5-1. Social Learning Theory Constructs and survey questions to measure the model.
Social Learning Theory Construct
Question asked by Survey Instrument
Differential Association What percentage of your friends/relatives/neighbors has agroforestry practices in their farms?
Definition What do you think about Agroforestry practices?
Imitation With what frequency have you incorporated different agroforestry practices in your farm?
Differential Reinforcement (Benefits)
What types of benefits do you think incorporating Agroforestry Practices have?
Differential Reinforcement (Cost)
What types of disadvantages do you think incorporating Agroforestry practices have?
Table 5-2. Stepwise regression results: Number of agroforestry practices incorporated R-squared 0.2793
df Sum of Squares Mean Squared F Significance
Regression 3 28.13334 9.3778 11.11 <0.0001 Residual 86 72.58888 0.84406 Model Variable B S.E. of B t Significance of t Intercept 0.00165 0.36149 0.000 0.9964 No. of Families 0.00377 0.00147 6.59 0.0120 Years Farming 0.01818 0.00644 7.97 0.0059 AF Adoption in the Past
0.33576 0.10087 11.08 0.0013
Table 5-3. Correlations between Social Learning Theory Constructs and adoption of
Agroforestry Practices
No.AF Practices
Differential Association
Definitions Imitation Differential Reinforcement
(Benefits)
Differential Reinforcement
(Cost)
No.AF Practices - 0.179 0.145 0.436** 0.363** 0.181
Differential Association - 0.004 0.224* 0.0591 0.089
Definitions - 0.169 0.216* -0.075
Imitation - 0.347** 0.059
Differential Reinforcement - 0.042
Differential Reinforcement -
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CHAPTER 6 DISSERTATION CONCLUSIONS
Agroforestry and Biodiversity Conservation
For the last 70 years, the region of Pontal do Paranapamena has experienced
forest fragmentation and habitat loss. The rapid deforestation of the Pontal is a
reflection of what has also taken place in other regions in Brazil. The lack of knowledge,
vision, and concern for the intrinsic value of natural resources and their functions within
ecosystems, have resulted in the destruction of natural resources and the local
extinction of many plant and animal species. Further, the replacement of these natural
habitats for the incorporation of unsustainable agricultural practices has led to resource
depleted soils for future agricultural activities. Much of the land given out through the
agrarian reform process in Brazil has been previously degraded by the use of
unsustainable practices. Landless communities, who have struggled for government
recognition and social justice, will have to continue struggling to restore the ill effects
caused on the environment if they expect to increase the value of their adquired land.
Agrarian reform farmers will need to practice sustainable agricultural techniques if
they intend to make their farms their long-term primary income source. Unlike large farm
owners who have the financial means to incorporate industrialized agricultural practices
on their properties, farmers in settlements lack the resources to turn their small
landholding into competitive and profitable monoculture practices. Industrialized
agricultural practices have oversimplified agricultural systems in exchange for greater
short-term high yields and financial returns. Large scale monocultures usually require
chemical inputs (fertilizers and pesticides) and expensive machinery. However, the
continued use of agricultural practices that do not take into account the natural balance
205
of the ecosystem, eventually become less resilient and collapse. This was observed in
the agricultural history of Pontal do Paranapanema. After the destruction of the forest,
monocultures of coffee and cotton quickly depleted the soil’s nutrients content rendering
them useful only for less nutrient demanding activities such as cattle ranching.
With the redistribution of large land areas to farmers of the Movement of Landless
Rural Workers (MST), some landscape restoration has taken place. The new property
owners have planted trees around their houses primarily to provide services such as
shade, to protect the homestead and farm land from heavy rains and wind, and to
produce products such as fruits and biomass for compost. These small and scattered
woodlots in the landscape, which are considered agroforestry practices, may be
providing more services than the ones originally intended to by the farmers. The trees
planted may be helping to restore the soil’s structure and fertility, contribute to climate
change mitigation by sequestering carbon, and increase the connectivity of the
landscape by providing habitat to dispersing wildlife. The present study was particularly
interested in evaluating the role of these woodlots at increasing the landscape
connectivity and acting as ecological stepping stones, and in understanding the
decision-making process for farmers to adopt these practices.
Butterflies in the Pontal Landscape
The values for fruit feeding butterflies in agroforestry systems coincided with the
findings by other authors on the influence of agroforestry on wildlife. Because
agroforestry systems can host a larger number of species compared to monoculture
systems, they are promoted as ecologically friendly agricultural practices. The present
study found similar results as previous studies for the number of species and individuals
of butterflies. Agroforestry systems host intermediate values of fruit feeding butterflies
206
compared to forest fragments and pastures. However, landscapes are composed of
various land-use systems and their combined effect is what makes a landscape more or
less permeable for the presence of butterflies and, presumably, other taxa.
The different land-use practices examined create a gradient of attractiveness for
fruit feeding butterflies. Tree shaded systems had more butterfly species and individuals
than treeless systems. In addition, the number of trees may be more important than the
diversity of trees as eucalyptus plantations had more butterflies than homegardens,
which were rich in terms of tree species. Eucalyptus plantations and shaded coffee
were valuable land-use systems for the conservation of butterflies as they were the
land-use systems that most closely resembled forest edge habitats. These three land-
use systems are characterized by a high density of trees. In contrast, lower tree density
practices, including homegardens, were more closely related to the pasture matrix.
However, given that homegardens are much more complex land-use systems than
monocultures or pastures, it is possible that the smaller areas occupied by
homegardens (0.5- 1 acres) compared to shaded coffee or eucalyptus plantations (4 -8
acres) rendered them less attractive to butterflies moving in the landscape.
The number of species and their abundance provides insight on the distribution of
fruit feeding butterflies in the landscape. However, the analysis of the species
composition helped determined the actual potential of the different land-use systems in
the conservation of forest butterflies. The different analysis (Discrete Analysis, and
ANOSIM) used to compare species composition provided different information. What
can be generalized is that both agroforestry systems examined had relatively high
diversity values, and were not statistically different to other land-use components except
207
for the pasture matrix. Yet, the discrete analysis, which compared differences in
butterfly subfamily population between land-use systems, separated out anthropogenic
habitats from natural habitats, while the ANOVA analysis of similarity, which focused on
the species resemblances, found no differences between the agroforestry systems and
forests habitats. In conclusion, in terms of species composition, eucalyptus plantations
and shaded coffee systems had the highest butterfly conservation potential as their
species composition was not significantly different to forest habitats (ANOSIM Analysis),
and because when separated out through the canonical plot these land-use systems
had subfamily assemblages closer to forest habitats than the other land-use systems.
The distribution of butterflies in the landscape was highly dependent on the abiotic
factors. Wind speed and humidity were significant predictors of butterfly presence at
both experimental landscapes, while the significance of temperature may be seasonal.
Fruit-feeding butterfly presence declined in areas exposed to high wind and low
humidity conditions, favoring areas were wind speed equaled zero and relative humidity
ranged between 46 and 70%. These were the abiotic conditions found in forest habitats.
In contrast, temperature was only significant for fruit feeding butterflies during the cooler
months prior to the rainy and warmer summer season. When comparing the different
land-use systems based on the abiotic factors, the results were not consistent and
generalizations were not possible. This may be because the sampling periods and
geographical orientation of the landscapes were different.
GIS Analysis and Agroforestry as Stepping Stones
The experimental design and GIS model in the present study were intended to
recreate the characteristics of the landscape and to predict movement behavior of
dispersing butterflies. The effective application of Spatial Analyst tools in ArcGIS to real
208
world landscapes have had significant impact in the planning and managing of natural
resources for conservation purposes.However, methodologies to make these tools
applicable to the field of landscape ecology still need to be developed. The
management of the field data in GIS was conducted using two different approaches.
One consisted of including the regression analysis coefficients for the abiotic data
(regression approach), and the second in comparing the average values for each point
in the landscape with the average values for the butterflies natural habitat (suitability
approach). Interestingly, the application of these different data management analysis
led to similar cost raster layers that simulated the characteristics of the landscape based
on the data collected. The subsequent analysis of the layers using these two
approaches resulted in the same least cost paths and corridors.
The use of the Least Cost path model predicted forest butterflies moving from one
forest patch to the next through the use of agroforestry systems. Most of the least cost
paths crossed an agroforestry system to reach the second forest patch. Even though
further research needs to be conducted, the recapture data showed that second to
direct movements from one forest to the next, butterflies frequently moved into
homegardens and shaded coffee after being released in the landscape. This confirms
the effectiveness of agroforestry systems at increasing landscape connectivity in
fragmented agricultural areas. Furthermore, the number of recaptured butterflies in
forest edge areas and shaded coffee systems was significantly different to pastures and
sugarcane at one of the experimental landscapes. These results validate the
assumption that agroforestry systems (shaded coffee) can serve as stepping stones for
butterflies moving between forest fragments.
209
Social Learning Study
The valuable role of agroforestry practices for the conservation of wildlife support
any efforts to propagate these systems among farming communities. Understanding
farmers’ perspective on agroforestry and how they learn about agricultural practices in
general, is essential for the dissemination of land stewardship and sustainable
agricultural practices. Given the few formal education opportunities among the farming
settlements in the Pontal do Paranapanema, Social Leaning theory was evaluated as
an informal leaning process in which farmers learn from observations of farming
behavior and interactions with others. The results for the evaluation of the parameters
commonly used to test for this theory in criminology, neither confirmed nor rejected the
application of the theory. However, the various parallels between Social Learning
Theory and Diffusion of Innovations Theory, suggest the mechanisms used to examine
the former for deviant behavior, are implicit in the study of the later. A better application
of the social learning framework may be required to better understand the learning
mechanisms and information dissemination of agricultural behavior.
The analysis of the farming communities at the Pontal revealed valuable
information about their interests, agricultural behaviors and perspectives of agroforestry.
Most of the farming families intend to stay on their properties for the rest of their lives,
and also intend to earn an income for the land. Given the degraded condition of the
region, the farmers are faced with the challenge of restoring the environment. Many of
the farmers have planted trees in their properties and incorporated agroforestry
systems. Those who have more diverse farming activities, tend to have greater diversity
of income sources and well-being. The main motivation for the farmers to plant trees is
to provide shade and collect fruits. However, if greater emphasis could be made on the
210
financial benefits of agroforestry adoption, the incorporation of these systems would be
facilitated. Even if the present study was unable to validate the application of Social
Learning Theory for the dissemination agroforestry, the information collected can serve
as preliminary data for future studies evaluating learning mechanisms among agrarian
reform settles, or as background information for sustainable development initiatives.
Land-use, Landscapes and Learning. The conversion or incorporation of
different land-use systems in previously forested habitats has different effects on the
ecosystem and environment. In deforested and degraded areas, agroforestry
incorporation is a productive reforestation alternative that meets human requirements
while helping conserve the environment (more so than other practices). Based on the
present study, shaded coffee systems have many characteristics that make them similar
to forest (edge) habitats. Among the different land-use systems examined, shaded
coffee plots provided habitat to fruit feeding butterfly species and individuals in a similar
way as forest habitats, and served as stepping stones for butterflies moving in the
landscape. To increase landscape connectivity and ensure the conservation of species
in remnant forest fragments in the Pontal region, the incorporation of shade coffee
agroforestry systems should be promote among the farming community.
However, the farmers at the Pontal do not believe engaging in shade coffee is a
prudent agricultural venture. The socioeconomic incentives for incorporating this
practice are not sound in the Pontal. Because of the lack of financial security and
incentives, farmers are skeptical of the benefits of shaded coffee. Despite the confirmed
ecological benefits of these tree shaded agricultural systems, the dissemination of this
practice in the Pontal is halted. Sustainable development agencies may need to
211
consider practices that are more in tune with the perspectives and behaviors of the
farmers in the region. Given the popularity of milk production among the farmers, the
incorporation of agroforestry systems such as silvopastures or permanent living fences
may be more appropriate and may also contribute to increasing connectivity of the
landscape. Even though silvopastures and living fences are less tree dense and can’t
provide the same microclimatic conditions of shade coffee plots, these systems could
prove to be a more widely accepted land-use practice that works well with the farmers’
main agricultural interest and activities.
212
APPENDIX A LIST OF BUTTERFLY SPECIES AT RIBERÃO BONITO
Table A-1. List of butterfly species at Riberão Bonito
Forest Interior
Forest Edge Pasture Homegardens
Shaded Coffee Cassava Eucalyptus
Secondary Growth Total
Adelpha spp 204 262 20 2 6 0 2 3 499
Amphidecta reynoldsi 3 1 0 0 0 0 0 0 4
Archeoprepona demophom 0 0 0 1 0 0 0 0 1
Ascalapha odorata 48 33 72 10 8 3 10 17 201
Biblis Hyperia 81 34 6 7 3 1 12 5 149
Caligo beltrao 0 0 0 0 0 0 0 0 0
Callicore affine 0 0 1 0 0 0 0 0 1
Callicore cynosura 2 0 0 0 0 2 0 0 4
Callicore hydaspes 21 29 11 2 4 0 3 7 77
Callicore pyas 13 0 1 3 0 0 0 0 17
Callicore sorana 5 11 7 15 1 4 11 6 60
Capronnieira abretia 12 14 4 3 2 0 6 0 41
Catonephele aconticus 0 0 1 0 0 0 0 0 1
Catonephele numilia 6 8 2 1 0 0 0 1 18
Chlosine lasinia 0 0 0 1 0 0 0 0 1
Diaetria eluina 0 2 0 1 0 0 0 1 4
Dynamine milita 1 0 0 1 0 0 0 0 2
Eryphanes reevesi 0 0 0 0 0 0 0 0 0
Eryphanis polixena 0 0 0 0 0 0 0 0 0
Eunica foto 3 1 0 0 0 0 0 0 4
Eunica maja 10 10 2 2 0 0 0 0 24
Eunica malvina 0 3 0 0 0 0 0 0 3
Eunica margarita 1 0 0 0 0 0 0 0 1
Eunica tatila 78 34 14 7 3 2 7 9 154
Euptychia ernestina 17 7 0 2 15 0 7 3 51
Eutoita hegisia 0 0 0 2 0 0 0 0 2
Hamadryas amphinome 4 2 1 0 0 0 0 0 7
Hamadryas arete 3 0 0 0 0 0 0 0 3
Hamadryas epinome 4 1 0 2 1 0 7 0 15
Hamadryas februa 85 41 2 8 8 1 25 4 174
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Table A-1. Continued
Forest Interior
Forest Edge Pasture Homegardens
Shaded Coffee Cassava Eucalyptus
Secondary Growth Total
Hamadryas ferona 7 4 0 0 0 0 0 0 11
Hamadryas grimon 1 0 0 0 0 0 0 0 1
Hamadryas ithmoides 13 6 0 0 2 0 0 0 21
Heraclides thoas 0 0 0 1 0 0 0 0 1
Historis odius 0 0 0 0 0 0 0 0 0
Hypna clytemnestra 2 0 0 0 0 0 0 0 2
Mcclungia Salonia 3 0 0 0 0 0 0 0 3
Memphis appias 13 5 0 0 0 0 0 2 20
Memphis arginossa 4 6 1 0 0 0 0 0 11
Memphis morvus 39 18 1 5 2 1 2 2 70
Memphis oretre 0 1 1 0 0 0 0 0 2
Memphis philumena 2 0 0 0 0 0 0 0 2
Memphis ryphea 271 86 37 26 34 6 10 18 488
Moneuptychia soter 7 1 0 0 1 0 0 0 9
Morpho debidia 10 6 0 0 0 0 0 0 16
Morpho granadensis 0 1 0 0 0 0 0 0 1
Morpho rara 1 0 0 0 0 0 0 0 1
Morpholike with white 2 0 0 1 0 0 0 0 3
Nica flavia 1 2 0 0 0 0 0 0 3
Opsiphanes invirae 0 0 0 0 0 0 0 0 0
Opsiphones quiteria 1 1 0 0 0 0 0 0 2
Otilia velica 0 0 2 0 0 0 0 0 2
Pareuptychia interjecta 0 0 0 1 0 0 0 0 1
Paryphthimoides phronius 74 20 5 2 5 0 0 4 110
Phoebis senna 0 0 0 2 0 0 0 0 2
Phoebis statira 0 0 1 0 0 0 0 0 1
Prepona laertes 0 0 0 0 0 0 0 0 0
Pyrhocira neacrea 0 1 0 0 0 0 0 0 1
Smyrna Blomfildia 2 2 1 0 0 0 0 0 5
Splen deptychia doxes 2 0 0 0 0 0 0 0 2
Taydebis peculiares 1 2 0 1 0 0 0 0 4
Taygetis acuta 0 1 0 0 0 0 0 0 1
Taygetis andromeda 9 0 0 0 0 0 0 0 9
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Table A-1. Continued
Forest Interior
Forest Edge Pasture Homegardens
Shaded Coffee Cassava Eucalyptus
Secondary Growth Total
Taygetis laches 21 5 0 1 2 0 2 0 31
Taygetis roja 5 0 0 0 0 0 0 0 5
Taygetis virgilia 10 1 0 0 0 0 0 0 11
Taygetis yphthima 1 0 0 0 0 0 0 0 1
Temis laothoe 67 20 4 1 1 0 1 1 95
Vanesa brasilensis 1 0 0 0 0 0 0 0 1
Yphthimoides affinis 8 0 1 0 2 0 3 0 14
Yphthimoides angularis 72 28 0 0 3 0 1 1 105
Yphthimoides castrensis 20 8 0 2 8 0 2 1 41
Yphthimoides disaffecta 4 1 1 0 0 0 0 0 6
Yphthimoides grimon 4 0 0 0 0 0 0 0 4
Yphthimoides ochracea 1 0 0 0 0 0 0 0 1
Zaretis itys 5 2 0 0 3 0 0 1 11
Unknown 1 (pink and white) 0 1 0 0 0 0 0 0 1
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APPENDIX B LIST OF BUTTERFLY SPECIES AT AGUA SUMIDA
Table B-1. List of butterfly species at Agua Sumida
Forest Interior
Forest Edge Pasture Sugarcane Homegardens
Shaded Coffee
Secondary Growth Total
Archeoprepona demophom 11 4 0 0 0 1 0 16
Biblis Hyperia 156 117 8 18 5 14 2 320
Caligo beltrao 1 0 0 0 0 0 0 1
Callicore hydaspes 1 0 0 0 0 0 0 1
Callicore pyas 2 3 0 0 0 0 0 5
Callicore sorana 11 21 13 12 2 0 1 60
Catonephele numilia 1 2 0 0 0 0 0 3
Dynamine milita 1 6 0 3 0 0 0 10
Eryphanes reevesi 3 0 0 0 0 0 0 3
Eryphanis polixena 1 1 0 0 0 0 0 2
Eunica spp (unknown) 0 2 0 0 0 0 0 2
Eunica junonia 0 0 0 1 0 0 0 1
Eunica tatila 747 365 235 494 109 61 74 2085
Euptychia ernestina 2 0 0 0 0 0 0 2
Giant moth 74 34 130 38 16 7 19 318
Hamadryas amphinome 5 0 0 1 0 0 0 6
Hamadryas arete 3 0 0 0 0 0 0 3
Hamadryas epinome 97 65 3 8 12 9 0 194
Hamadryas februa 418 321 15 38 33 49 2 876
Hamadryas ferona 10 3 0 0 1 0 0 14
Hamadryas ithmoides 2 1 0 0 0 0 0 3
Historis odius 3 3 0 1 0 0 0 7
Hypna clytemnestra 9 2 0 0 0 1 0 12
Memphis appias 3 2 0 0 0 0 0 5
Memphis morvus 13 9 2 2 0 0 0 26
Memphis ryphea 364 302 37 29 22 18 5 777
Memphis with one red spot 0 1 0 0 0 0 0 1
Morpho debidia 14 8 0 1 0 0 0 23
Morpholike with white 0 1 0 0 0 0 0 1
Nica flavia 1 0 0 0 0 0 0 1
Opsiphanes invirae 14 6 0 2 0 0 0 22
216
Table B-1. Continued
Forest Interior
Forest Edge Pasture Sugarcane Homegardens
Shaded Coffee
Secondary Growth Total
Paryphthimoides phronius 7 0 0 0 0 0 0 7
Prepona laertes 1 1 0 0 0 0 0 2
Prepona pylene 0 1 0 0 0 1 0 2
Smyrna Blomfildia 0 1 0 0 0 0 0 1
Taygetis andromeda 2 0 0 0 0 0 0 2
Taygetis angularis 0 1 0 0 0 0 0 1
Taygetis laches 5 4 0 0 0 0 0 9
Taygetis virgilia 5 3 0 0 0 0 0 8
Temis laothoe 4 1 7 0 2 0 3 17
Yphthimoides angularis 2 2 0 0 0 0 0 4
Yphthimoides castrensis 7 1 0 1 0 0 0 9
Yphthimoides grimon 1 0 0 0 0 0 0 1
Zaretis itys 9 3 1 0 0 0 0 13
Unknown (large yellow/blue)) 1 0 0 0 0 0 0 1
Unknown 2 0 1 0 0 0 0 0 1
Unknown 3 0 1 0 0 0 0 0 1
217
APPENDIX C RAREFRACTION CURVES FOR EACH LAND-USE AT RIBERÃO BONITO
Figure C-1. Butterfly species richness in Forest at Riberão Bonito
Figure C-2. Butterfly species richness in forest edge at Riberão Bonito
218
Figure C-3. Butterfly species richness in eucalyptus at Riberão Bonito
Figure C-4. Butterfly species richness in shaded coffee at Riberão Bonito
219
Figure C-5. Butterfly species richness in homegardens at Riberão Bonito
Figure C-6. Butterfly species richeness in secondary growth at Riberão Bonito
220
Figure C-7. Butterfly species richness in cassava at Riberão Bonito
Figure C-8. Butterfly species richness in pastures at Riberao Bonito
221
APPENDIX D RAREFRACTION CURVES FOR EACH LAND-USE AT AGUA SUMIDA
Figure D-1. Butterfly species richness in forest at Agua Sumida
Figure D-2. Butterfly species richness in forest edge at Agua Sumida
222
Figure D-3. Butterfly species richness in shaded coffee at Agua Sumida
Figure D-4. Butterfly species richness in homegardens at Agua Sumida
223
Figure D-5. Butterfly species richness in secondary growth at Agua Sumida
Figure D-6. Butterfly species richness in sugarcane at Agua Sumida
224
Figure D-7. Butterfly species richness in pastures at Agua Sumida
225
APPENDIX E REGRESSION ANALYSIS OF ABIOTIC FACTORS AT RIBERÃO BONITO
Table E-1. Regression analysis for the number of butterfly individuals at Riberão Bonito
R-squared 0.4707
df Sum of Squares
Mean Squared F Significance
Regression 3 42.26052 14.08684 32.01 <0.0001 Residual 108 47.52718 0.44007 Model Variable B S.E. of B t Significance of t Intercept 3.73448 1.47685 2.52 0.0129 Temperature -0.11795 0.05023 -2.35 0.0207 WindSpeed -0.34585 0.06277 -5.51 <.0001 Humidity 0.01933 0.00783 2.47 0.0151
Table E-2. Regression analysis for the number of butterfly species at Riberão Bonito
R-squared 0.5078
df Sum of Squares
Mean Squared F Significance
Regression 3 19.39930 6.46643 37.14 <0.0001 Residual 108 18.80183 0.17409 Model Variable B S.E. of B t Significance of t Intercept 2.10722 0.92889 2.27 0.0253 Temperature -0.06669 0.03160 -2.11 0.0371 WindSpeed -0.23432 0.03948 -5.93 <.0001 Humidity 0.01628 0.00492 3.31 0.0013
226
APPENDIX F REGRESSION ANALYSIS OF ABIOTIC FACTORS AT AGUA SUMIDA
Table F-1. Regression analysis for the number of butterfly individuals at Agua Sumida
R-squared 0.7987
df Sum of Squares
Mean Squared F Significance
Regression 3 61.59463 20.53154 121.65 <0.0001 Residual 92 15.52672 0.16877 Model Variable B S.E. of B t Significance of t Intercept -0.83603 1.43149 -0.58 0.5606 Temperature 0.02541 0.03409 0.75 0.4579 WindSpeed -0.34498 0.05757 -5.99 <.0001 Humidity 0.03561 0.00936 3.31 0.0003
Table F-2. Regression analysis for the number of butterfly species at Agua Sumida
R-squared 0.7970
df Sum of Squares
Mean Squared F Significance
Regression 3 25.14857 8.38286 120.43 <0.0001 Residual 92 6.40382 0.06961 Model Variable B S.E. of B t Significance of t Intercept -0.97168 0.91932 -1.06 0.2933 Temperature 0.00951 0.02189 0.43 0.6649 WindSpeed -0.15098 0.03697 -4.08 <.0001 Humidity 0.03293 0.00601 5.48 <.0001
227
APPENDIX G REGRESSION ANALYSIS OF ABIOTIC FACTORS AT RIBERÃO BONITO FOR THE
GIS MODEL DEVELOPMENT
Table G-1. Regression analysis for the number of butterfly individuals at Riberão Bonito
R-squared 0.4523
df Sum of Squares
Mean Squared F Significance
Regression 3 24.39987 8.13329 29.73 <0.0001 Residual 108 29.54332 0.27355 Model Variable B S.E. of B t Significance of t Intercept 11.87227 2.86130 4.15 <.0001 Temperature -8.48136 2.01526 -4.21 <.0001 WindSpeed -0.48184 0.10575 -4.56 <.0001 Shade 0.14333 0.05542 2.59 0.0110
Table G-2. Regression analysis for the number of butterfly species at Riberão Bonito
R-squared 0.4756
df Sum of Squares
Mean Squared F Significance
Regression 3 16.25612 5.41871 32.04 <0.0001 Residual 106 17.92514 0.16911 Model Variable B S.E. of B t Significance of t Intercept 10.73027 2.27888 4.71 <.0001 Temperature -7.75852 1.60522 -4.83 <.0001 WindSpeed -0.33199 0.08376 -3.96 0.0001 Shade 0.15206 0.04433 3.43 0.0009
228
APPENDIX H REGRESSION ANALYSIS OF ABIOTIC FACTORS AT AGUA SUMIDA FOR THE GIS
MODEL DEVELOPMENT
Table H-1. Regression analysis for the number of butterfly individuals at Agua Sumida
R-squared 0.7067
df Sum of Squares
Mean Squared F Significance
Regression 3 24.68077 8.22692 71.48 <0.0001 Residual 89 10.24284 0.11509 Model Variable B S.E. of B t Significance of t Intercept -4.46717 1.74420 -2.56 0.0121 Wind -0.31600 0.10423 -3.03 0.0032 Humidity 2.71310 1.01138 2.68 0.0087 Shade 0.10663 0.03011 3.54 0.0006
Table H-2. Regression analysis for the number of butterfly species at Agua Sumida
R-squared 0.7482
df Sum of Squares
Mean Squared F Significance
Regression 3 15.32723 5.10908 89.13 <0.0001 Residual 90 5.15889 0.05732 Model Variable B S.E. of B t Significance of t Intercept -3.45140 1.23030 -2.81 0.0062 Wind -0.22752 0.07354 -3.09 0.0026 Humidity 1.98171 0.71335 2.78 0.0067 Shade 0.10278 0.02123 4.84 <.0001
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APPENDIX I QUESTIONNAIRE
Date
Lot Number
Settlement
1. What is your name?
_______________________________________________________
2. How old are you? __________________________________________________
3. Gender ___
4. Where were you born?
______________________________________________________
5. How many people live in your household? ____________________________________
6. How long do you live in the Pontal Region? ___________________________________
7. When were you assigned a land allotment?
_____________________________________________________________________
8. How long did you have to camp (with the Landless Movement) before receiving land
from the government?____________________________________________________
9. Before receiving land, how long did you worked in agriculture?
_____________________________________________________________________
10. How long do you plan to live in the Pontal region?
_____________________________________________________________________
11. Where did you live before coming here?
_____________________________________________________________________
12. What is your highest level of education?
_____________________________________________________________________
13. Do you have friends and/or family living in this region? If yes, more less how many
families do you know in the region?
______________________________________________________________________
______________________________________________________________________
14. How many families do you know the families living your settlement?
__________________________________________________________________
230
15. What type of a relation do you have with your neighbors? Positive, negative and why?
_____________________________________________________________________
_____________________________________________________________________
16. What is the main agricultural product in your farm and why you choose it?
_____________________________________________________________________
_____________________________________________________________________
17. Is this product your main income source? If not, what is it? Este produto é sua principal
fonte de renda?
_____________________________________________________________________
_____________________________________________________________________
_____________________________________________________________________
18. What other products do you produce either for profit or consumption?
_____________________________________________________________________
_____________________________________________________________________
19. More or less what proportion of your friends and neighbors produce farming goods in
their settlement lot?
0
25%
50%
75%
100%
Don't know
20. With what frequency do you speak with your farming friends about agricultural
practices?
More than once per day
Daily
2 -3 times a week
Once a week
Once a month
Less than once a month
Never
231
21. How did you learn the agricultural practices that you use today? (Check all that
apply)
Trial and error (alone)
Friends from the Pontal region (neighbors)
Friends from a different region
Took courses
From educational media
Some organization or institute
Family members
Other______________________________________________________________
21. With what frequency do you see other agricultural producers share information about
agricultural practices?
More than once per day
Daily
2 -3 times a week
Once a week
Once a month
Less than once a month
Never
22. With which of these groups do you discuss about agricultural practices?
Neighbors ( )
Friends ( )
Family ( )
23. In your opinion, what is the biggest advantage about sharing agricultural information
with friends, family and neighbors?
You trust their opinion on these topics
Easy access
They teach you new things
Other__________________________________________________________________
______________________________________________________________________
24. Have you ever followed the advice or recommendations regarding agricultural practices
that any of your neighbors have given you?
_____________________________________________________________________
25. Have you heard about agroforestry systems (agricultured mixed with trees such as
living fences, windbreaks, shaded pastures, shaded coffee, homegardens)?
232
Yes
No
26. Who has talked to you about agroforestry?
Friends, neighbors
Institutions (IBAMA, IPE, other)
Family member
Other_______________________________________________________
27. What types of agroforestry systems you practice in your farm?
______________________________
______________________________
_______________________________
28. What proportion of your friends, neighbors or relatives practice afroforestry in their
farms?
None
25%
50%
75%
Almost all
29. How often do you see farmers in the region planting trees to increment their áreas
under agroforestry??
Never
Once or twice
Rarely
Sometimes
Often
30. Háve you tried to incorporate agroforestry practices in your farm?
31. How often have you tried to incorporate agroforestry practices
in your farm?
Yes
No
233
Never
A few times
Often
32. Who taught you the agroforestry practices that you have incorporated in your far?
Select all that apply
Trial and error (alone)
Friends/neighbors in the Pontal region
Friends from another region
Took courses
Technicias from IBAMA
Technicias from IPê
Media
Government programs
Relatives
Other__________________________________________________________________
31. What do you think about agroforestry practices?
Great
Good
No opinion
Bad
Awful
32. What do you think about Exchange of information between farmers?
Great
Good
No opinion
Bad
Awful
33. What do you think about planting trees of different species around your house?
Great
Good
No opinion
Bad
Awful
34. What do you think about planting trees in cattle grazing pastures?
234
Great
Good
No opinion
Bad
Awful
35. What do you think about planting trees as living fences?
Great
Good
No opinion
Bad
Awful
36. What do you think about planting trees mixed with coffee plantations?
Great
Good
No opinion
Bad
Awful
37. What type of benefits do you think agroforestry systems have? Select all that apply.
Source of extra income
Improves soil conditions
Offers refuge to animals
Protects you house and farm from from wind, rain, and sun
Produces fruits and products to be use in the household
Other______________________________________________________________
38. What negative effects can agroforestry systems have? Select all that apply
Time consumption
Need money to invest in agroforestry
Benefits take time
Tree planting takes up space that otherwise would be used for something else
Other___________________________________________________________________
39. In your opinion, how long do the benefits of agroforestry practices take?
235
They are slow
Medium
Fast
40. If you were to have positive results from an agroforestry practice would you continue that
practice?
Yes
No
Maybe
Don’t know
41. If you were to have negative results from na agroforestry practice, you would try to
improve on it, continue with the practice as it is, or you would change the agricultural
practice completely?
Improve
Continue
Change
Don't know
42. What do you expect when you plant trees in you farm?
________________________________________________________________________
________________________________________________________________________
________________________________________________________________________
________________________________________________________________________
________________________________________________________________________
236
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BIOGRAPHICAL SKETCH
Wendy Francesconi received her PhD.from the School of Forest Resources and
Conservation at the University of Florida, U.S.A., and holds a master’s degree in
Environmental Science from Yale University, School of Forestry and Environmental
Studies. Her doctoral research was focused on evaluating different agricultural practices
for their potential to increase functional connectivity, and on examining the
dissemination of agroforestry practices among farmers in eastern Brazil. Dr.
Francesconi’s professional interests include the application of sustainable agricultural
practices and the design of rural landscapes for biodiversity conservation purposes.
