Week 2: Some more Theory, then Rudimentary Species Distribution Modeling: Habitat Suitability Models...
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Week 2: Some more Theory, then Rudimentary Species Distribution Modeling: Habitat Suitability Models and Logistic Regression GIS 5306 GIS in Environmental
Week 2: Some more Theory, then Rudimentary Species Distribution
Modeling: Habitat Suitability Models and Logistic Regression GIS
5306 GIS in Environmental Systems
Slide 2
Last Time Intro and Logistics Main Questions How are organisms
distributed on the surface of the Earth, and why? As Earths
environment changes (climate, land use, geomorphologic, geologic,
biologic), how will the distribution of organisms change? Species
Distribution Maps: Basic unit of Biogeography Dot maps Outline maps
Contour maps Hybrids Physical Factors (Abiotic Environment) of
species distributions Ecological Foundations of species
distributions Population growth intraspecies interactions
Interspecies interactions Dispersal, extinction not dealt with
Slide 3
First: Project Options 1.Either a) the class or b) each student
(or student team) will develop a research question (or questions).
2.Each student will argue for a taxon or group of taxa to study,
including the study area (Florida gets highest priority?). 3.When
1&2 are decided, Each student will then learn, evaluate, and
teach one or more methods for studying the taxa to answer the
research question to the class.
Slide 4
Option b When the methods have been examined, the class will
decide the best methods and data required to answer the original
research question. Then the whole class will conduct various
aspects of the analysis (comparison of multiple methods?). Then we
will write the paper, and submit it to an appropriate journal.
Slide 5
Pre-project exploration Each student (or team of 2 depending on
the final size of the class) will become familiar with one or two
of the methods for species distribution modeling, then present the
theory of the method, the software for the method, and an example
laboratory exercise to the class, including providing data for the
exercise. This can be the tutorial produced by the developer of the
software, or an exercise developed by the student(s).
Slide 6
Work Assignments Instructor will conduct next weeks class:
Theory of predictive vegetation mapping (subset of Species
Distribution Modeling) Selection and description of one method for
predictive vegetation mapping (Habitat Suitability) Tutorial for
working through a Habitat Suitability example. Provision of data
for working up several examples. Read the assigned papers linked on
the syllabus.
Slide 7
Methods from which to Choose (See Class References) Habitat
Suitability Modeling (Binford) Gradient Analysis (Ordination)
methods (surprisingly not a major part of the literature) Logistic
Regression Fuzzy Set Approaches (also more seen as part of habitat
suitability modeling) Biodiversity Informatics Facility compilation
of multiple software sites:
http://biodiversityinformatics.amnh.org/index.php?section_id=105&content_id=345
http://biodiversityinformatics.amnh.org/index.php?section_id=105&content_id=345
DMAP distribution mapping software http://www.dmap.co.uk/
http://www.dmap.co.uk/ MAXENT (Tutorial Available)
http://sourceforge.net/projects/maxent/,
http://www.cs.princeton.edu/~schapire/maxent/
http://sourceforge.net/projects/maxent/http://www.cs.princeton.edu/~schapire/maxent/
GARP http://www.nhm.ku.edu/desktopgarp/
http://www.nhm.ku.edu/desktopgarp/ Openmodeller (multiple methods)
http://openmodeller.sourceforge.net/index.php?option=com_frontpage&Itemid=1
http://openmodeller.sourceforge.net/index.php?option=com_frontpage&Itemid=1
http://www.springerlink.com/content/n805714x26265573/
http://www.springerlink.com/content/n805714x26265573/
http://mac.downloadatoz.com/openmodeller-desktop/
http://mac.downloadatoz.com/openmodeller-desktop/ R-forge
contributions for Species Distribution Modeling
https://r-forge.r-project.org/R/?group_id=697
https://r-forge.r-project.org/R/?group_id=697 Land-change Modeler
for IDRISI and ArcGIS
http://clarklabs.netkeepers.com/products/Land-Change-Modeling-IDRISI.cfm
Free GIS: DIVA-GIS:
http://www.diva-gis.org/http://www.diva-gis.org/
Slide 8
Distribution of Species Main Points 1.Individuals of each
species have ecological requirements and limits that, along with
historical factors, determine distribution. 2.Physical
environmental factors create gradients of tolerance and optimality
for organisms. 1.E.g. water, light, pH, temperature, salinity, etc.
3.Biotic environment (other organisms) also create gradients of
tolerance and optimality. 1.Population growth (intraspecific
interactions) 2.External (predation, competition, mutualism)
3.Internal (pathogens, parasites)
Slide 9
New Topic: Distribution of Species Secondary Points
1.Interactions of physical and biotic factors always occur. 2.Very
difficult to demonstrate mechanisms of distribution limitation, but
straightforward to show correlations among physical and biotic
factors and distributions.
Slide 10
The Niche Also implies that there can be multiple, or
combinations of limiting factors
Slide 11
All Models Follow the Same Principle Mapping from species and
environmental factor distribution (Geographic Space), modeled in
Environmental Space, and re-mapped into Geographic Space Figures
from Elith and Leathwick 2009
Slide 12
Another Way to Look at it. Theory Modeling Framework Map of
predicted occurrence Species occurrence data Environmental Data
Validation Candidate variables Scale Sample Design Redrawn from
Franklin, J. 2009. Mapping Species Distributions. Cambridge.
Slide 13
This Time A little more general theory Binford: Rudimentary
SDM. Habitat Suitability/Cartographic Overlay National Fish and
Wildlife Service/Agency Wildlife-habitat relationship (WHR) models
GAP Analysis Project Workshop: Swallow-tailed Kite in North-central
and northwest Florida.
Slide 14
Predictive vegetation mapping: Franklin 1995 Fundamental paper
reviewing practice to 1994; 30 papers! Principles of Predictive
Species Distribution Modeling (PSDM) Predictive Vegetation Mapping
Habitat Modeling Dependence of predictive vegetation mapping on
ecological niche theory and gradient analysis Cited by 363 more
recent papers as of 30 August 2010
Slide 15
http://www.nrs.fs.fed.us/atlas/
Slide 16
Definition: Predictive vegetation mapping by Climate Envelopes
Predicting the vegetation composition across a landscape from
mapped environmental variables. Species-by-species Longleaf Pine
Bald Cypress Live Oak
Slide 17
Longleaf Pine Niche Graph and Importance Value Map
http://www.nrs.fs.fed.us/atlas/tree/niche_121.html
Slide 18
Gradient Analysis and Continuum Concept (Whittaker 1951)
Whittaker 1973 continuum concept more explicitly puts forth
hypotheses about species response functions (curves) to
environmental gradients, e.g., that they are Gaussian.
Slide 19
Predictive vegetation mapping: Modeling Always starts with the
development of some type of model, followed by the application of
that model to a geographic database to produce the predictive map,
a realization of the model. Franklin 1995
Slide 20
Foundations and Premises Predictive vegetation mapping is
founded in ecological niche theory and vegetation gradient
analysis. Premise: vegetation distribution can be predicted from
the spatial distribution of environmental variables that correlate
with or control plant distributions. Franklin 1995
Slide 21
Pragmatism Further, maps of the environmental variables or
their surrogates must be available, or easier to map than the
vegetation itself, in order for predictive vegetation mapping to be
a practical or informative exercise. Franklin 1995
Slide 22
Model Foundations In order to extrapolate over space
(predictive vegetation mapping) or time (vegetation change
modeling), direct gradients or their surrogates must be mapped
(temperature, potential solar radiation, precipitation, soil-
moisture availability, geology or soil chemistry).
Slide 23
Spatial Focus Focus on the prediction of plant species
distributions or vegetation patterns at the 'regional' scale, e.g.,
where the mapped extent of the predictions are generally at or
within the biogeographic range of the dominant plant species.
Franklin 1995
Slide 24
Models from Franklin 1995 Boolean Statistical Methods
Regression GLM GAM Logistic (Logit) Baysian MONOMAX Maximum
Likelihood Classification Rule-based Methods Multivariate Methods
Discriminant analysis Canonical correlation Classification Trees
Neural Networks Decision-tree Classification Genetic
Algorithms
Slide 25
Potential Vegetation vs. Actual Vegetation Figure 1 Conceptual
model showing the relationship between direct gradients (nutrients,
moisture, temperature), their environmental determinants (climate,
geology, topography) and potential natural vegetation, and the
processes that mediate between the potential and actual vegetation
cover (the latter is sensed by a remote sensing device). Franklin
1995 page 479
Slide 26
Austin, M.P. 2002. Spatial prediction of species distribution:
an interface between ecological theory and statistical modelling.
Ecological Modelling 157:101-118. Cited by 495
Slide 27
Rudimentary SDM: Habitat Suitability/Cartographic Overlay Goes
back to McHarg 1969: Design with Nature
Slide 28
Suitability for Ski Areas Not drinking water Slope between 5%
and 75%; Aspect N, W, E Soils not erodible, not high runoff
Vegetation Vegetation: grass, degraded forest Biodiversity
Biodiversity low Areas suitable for ski areas
Slide 29
Site Suitability Analysis, or Multi-Criteria Evaluation Areas
Suitable for Ski Resort Development
http://gis.ncsu.edu/research/tourism/GIS_Tourism_Projects.htm This
project aimed at identifying the best area suitable for development
of a ski resort in Mitchell and Yancey Counties in NC. The
following factors were included in the suitability analysis: Land
cover, access, snow precipitation, land ownership, elevation,
aspect and slope.
Slide 30
McHargian analysis: Woodlands
Slide 31
Works for SDM/Habitat Modeling, too What are habitat
requirements for Single species Multiple species Vegetation
communities, assemblages Biodiversity SCALE ISSUE Dot maps vs. area
maps. Fine vs. coarse scale Depends on question!
Slide 32
Animal Species Distribution Modeling Habitat
Suitability/Wildlife-Habitat Relationship (WHR) Models GAP Analysis
Project: Keeping Common Species Common Where are the gaps in
biodiversity protection?
http://gapanalysis.nbii.gov/portal/server.pt
Slide 33
Research/Management Question How well are we protecting common
plants and animals? Corollary: if we are not protecting them well,
what can we do about it? Land-use planning Conservation purchases
and easements Originally not common species but biodiversity.
Slide 34
GAP Procedure 1. Map LAND COVER of the dominant ecological
systems 2. Map and model SPECIES ranges and distributions 3. Map
land STEWARDSHIP 4. Conduct the ANALYSIS All from
http://gap.greeninfo.org/
Slide 35
GAP Procedures Note WHRM is an associational database: what
animals would be expected to be found in vegetation alliance
(habitat). Qualitative! From Complete GAP Handbook available from
ftp://ftp.gap.uidaho.edu/products/handbookpdf/ CompleteHandbook.pdf
ftp://ftp.gap.uidaho.edu/products/handbookpdf/
CompleteHandbook.pdf
Slide 36
Florida GAP http://www.wec.ufl.edu/coop/gap/lcmapping.htm Data
available at FGDL data repository www.fgdl.orgwww.fgdl.org
Slide 37
Definition: Habitat Modeling Habitat where and animal lives the
living and non-living characteristics of a landscape that an animal
uses what animals need to survive and reproduce Different kinds of
habitat: Food, water, hiding cover (prey) or ambush cover
(predators), thermal cover (against heat or cold or both), and nest
sites (or other special needs for reproduction), the minimum
amounts and spatial arrangement of the first 5 components
Slide 38
Habitat Modeling Expert opinion Literature Compilation into
database What should occur where Absences difficult to model
Database query What collection of vertebrates should be in what
vegetation alliances? Calculate biodiversity hotspots Stewardship
determination What is not already protected? Considers management
objectives of public agencies
Slide 39
Gaps Identified! Now what?
http://gapanalysis.nbii.gov/portal/community/GAP_Analysis_Program/Communities/Maps,_Data,_&_Reports/Find_Upd
ated_GAP_Regional_Data/
Slide 40
Animal Species Distribution Models Habitat Suitability Indices
US Fish and Wildlife Service
http://www.nwrc.usgs.gov/wdb/pub/hsi/hsiintro.htm
Slide 41
Habitat Suitability Modeling; Wildlife- Habitat Relationship
Models: Theory Habitat suitability index (HSI) model is intended
for use with the habitat evaluation procedures (HEP) developed by
the U.S. Fish and Wildlife Service (1980) for impact assessment and
habitat management. The model was developed from a review and
synthesis of existing information, and includes unpublished
information that reflects the opinions of persons familiar with
black- shouldered kite ecology. It is scaled to produce an index of
habitat suitability between 0 (unsuitable habitat) and 1.0
(optimally suitable habitat). This model is a hypothesis of
species-habitat relations, not a statement of proven cause and
effect. The model has not been field tested.
Slide 42
HIS: Theory All from literature or experts Distribution and
commercial importance Life history overview Habitat requirements
Food for both adults and immatures (and associated habitat) Cover
for both adults and immatures Breeding habitat (e.g. nesting) if
different Geographic area and season Minimum habitat area
Slide 43
Example Habitat Suitability Index: American Alligator
(Alligator mississippiensis) The American alligator is
characteristically a resident of river swamps, lakes, bayous, and
marshes of the Gulf and Lower Atlantic Coastal Plains from Texas to
North Carolina. HSI publications have standard format: INTRODUCTION
Distribution and Commercial Importance Life History Overview
HABITAT REQUIREMENTS food Cover HABITAT SUITABILITY INDEX (HSI)
MODEL Model Applicability Model Description Suitability Index (SI)
Graphs for Model Variables Component Index (CI) Equations and HSI
Determination Field Use of Model Interpreting Model Outputs
REFERENCES
http://www.nwrc.usgs.gov/wdb/pub/hsi/hsiindex_byauthor.htm
Slide 44
American Alligator HSI Model
Slide 45
American Alligator HSI Model: Data
Slide 46
Slide 47
Slide 48
American Alligator HSI Model If all of these components can be
mapped in a GIS, then the map of habitat suitable for alligators
can be produced.
Slide 49
American Alligator HSI Model
Slide 50
If all of these components can be mapped in a GIS, then the map
of habitat suitable for alligators can be produced.
Slide 51
Fuzzy?
Slide 52
Several Classification Approaches Source: Hill, K.E. 1997. The
Representation of Categorical Ambiguity: A Comparison of Fuzzy,
Probabilistic, Boolean, and Index Approaches in Suitability
Analysis. Dissertation, Harvard University Standard Classification:
An area either is or is not a member of the set of xxxxx land
cover. Probabilistic and Fuzzy Classification: An area has a XX%
probability of belonging to the xxxxx set of land cover, or a
membership value of XX (fuzzy).
Slide 53
Workshop Model the distribution of an interesting animal using
a habitat-suitability/WHR approach. Swallow-Tailed Kite Elanoides
forficatus Seen occasionally in Florida Data and ArcGIS project
available at S:\geog\geoglab\GEO5306_GIS_Applications_Enviro
nment\Swallow-Tailed_Kite_distribution_modeling Download the folder
to your computer. It is big!
Slide 54
Swallow-Tailed Kite Elanoides forficatus Diminishing population
Habitat loss, fragmentation Formerly in 21 States Possibly fewer
than 5000 remain, with 60-65% of the population breeding in Florida
during the summer months. Habitat Modeling is said to be a major
long- term goal of STK conservancy
Slide 55
Swallow-Tailed Kite Conservation Status Listed as imperiled by
The Nature Conservancy. The predominant identified threat is the
loss of suitable nesting habitat. National Audubon Societys
Watchlist as a species of critical concern Designated by the U. S.
Geological Surveys Biological Resources Division as a Species at
Risk IUCN Species of Least Concern FL Freshwater Fish and Wildlife
Conservation Commission: one of Florida's most vulnerable and
poorly understood species Habitat Modeling is said to be a major
long-term goal of STK conservancy
Slide 56
Swallow-Tailed Kite Conservation Status The current challenges
to kite conservation include wetland loss and drainage, extensive
clear-cutting, short rotation timber harvesting, and significant
land use changes along migration routes and wintering habitats in
South America (Gruber 2009).
Slide 57
Swallow-Tailed Kite Elanoides forficatus The disappearance of
this species from three-fourths of its breeding range between 1880
and 1910 (Figure 1) was one of the most dramatic range contractions
of any bird species before the highly publicized post-WWII
Peregrine falcon crash (Cely 2005). From Gruber, 2009
Slide 58
Swallow-Tailed Kite Elanoides forficatus: Natural History
Predator Insects (airborne) Herps Small Mammals Small birds
(nests!) Neo-tropical Migrant Breeds in SE US, winters in South
America Gruber 2009
Slide 59
Habitat Requirements (Gruber 2009): Different Scale than Area
Map Riparian and bottomland forest Mixed pine Strong preference for
nesting in dominant or co-dominant loblolly pine (Pinus taeda)
stands growing near or within wetlands (Cely 2005). The physical
structure of the forest stand is perhaps more important than the
specific vegetation communities. Tall, easily accessible nest trees
near open areas that provide sufficient prey (Meyer 1995). Nests
are typically built at the top of the tallest trees in the stand;
the preferred surrounding stand is usually low density and has an
uneven height/age structure (Meyer 1995). (Continued)
Slide 60
Habitat Requirements (Gruber 2009) (continued) Kites have large
home ranges encompassing thousands of acres, and will often commute
long distances, up to 24 km, from the nest site to forage. The main
food source of kites is large insects caught on the wing; a variety
of other prey such as snakes, anoles, frogs, nestling birds, and
wasps nests, are gleaned from vegetation. Large communal roosts
near nesting areas are common. Social nesting behavior: clustered
distribution. May restrict dispersal into unused habitat.
Slide 61
Data http://www.natureserve.org/ Ridgely, R. S., T. F. Allnutt,
T. Brooks, D. K. McNicol, D. W. Mehlman, B. E. Young, and J. R.
Zook. 2007. Digital Distribution Maps of the Birds of the Western
Hemisphere, version 3.0. NatureServe, Arlington, Virginia,
USA.
Slide 62
Data
Slide 63
Data: Local Data www.fgdl.gov and others www.fgdl.gov Habitat
(vegetation, land-cover) Topography (elevation, slope, aspect)
Soils (SSURGO) Hydrography (all water bodies including wetlands)
Species data: difficult to come by. Found sightings in
SPECIES_OBS_APR10, data available from FGDL
Slide 64
Slide 65
Habitat Model The first component of the habitat model was to
identify land cover types most likely to be used as nesting sites
(Beyeler 2008). Nest points were buffered by 1000 m, and all land
use/land cover types found within those 3.14 km2 buffers were
identified as potentially suitable for nesting (Table 1) (Gruber
2009)
Slide 66
Water Management District Classification: FLUCCS Code Suitable
Habitat
Slide 67
Literature Cely, J.E. 2005. Swallow-tailed Kite (Elanoides
forficatus) Fact Sheet. South Carolina Department of Natural
Resources. http://www.dnr.sc.gov/cwcs/pdf/Swallowtailedkite.pdf.
Gruber, J. 2009. Targeting Potential Conservation Sites for
Swallow-tailed Kites (Elanoides forficatus) in Levy County,
Florida. Unpublished M.E.M. thesls. Duke University Meyer, K. D,
and M. W Collopy. 1990. Status, distribution, and habitat
requirements of the American Swallow-tailed Kite (Elanoides
forficatus) in Florida. Final report. Florida Nongame Wildlife
Program, Florida Game and Fresh Water Fish Commission, Tallahassee.
Meyer, K.D. 1995. Swallow-tailed Kite (Elanoides forficatus). In A.
Poole and F. Gill, editors. The Birds of North America, Number 138.
Academy of Natural Science, Philadelphia, PA, and American
Ornithologists Union, Washington, D.C., USA. Ridgely, R. S., T. F.
Allnutt, T. Brooks, D. K. McNicol, D. W. Mehlman, B. E. Young, and
J. R. Zook. 2007. Digital Distribution Maps of the Birds of the
Western Hemisphere, version 3.0. NatureServe, Arlington, Virginia,
USA. Sykes Jr, P. W, C. B Kepler, K. L Litzenberger, H. R Sansing,
E. T.R Lewis, and J. S Hatfield. 1999. Density and Habitat of
Breeding Swallow-Tailed Kites in the Lower Suwannee Ecosystem,
Florida (Densidad y Habitat Reproductivo de Elanoides forficatus en
la Parte Inferior del Ecosistema Suwanee, Florida). Journal of
Field Ornithology 70, no. 3: 321336. Wright, M. H, R. O Green, and
N. D Reed. 1970. A collection of observations and field notes on
the nesting activities of the swallow-tailed kite (Elanoides
forficatus) in the Everglades National Park. ZIMMERMAN, G.
PRIORITIES FOR RESEARCH AND MONITORING, MANAGEMENT, AND OUTREACH AS
DETERMINED BY THE SWALLOW-TAILED KITE CONSERVATION ALLIANCEA
PARTNERSHIP TO ADVANCE CONSERVATION OF A VULNERABLE SPECIES.
Zimmerman, G. M. 2004. STUDIES OF THE ANNUAL CYCLE OF THE
SWALLOW-TAILED KITE (ELANOIDES FORFICATUS): MIGRATION, HABITAT USE,
AND PARASITES. Georgia Southern University.