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Does soil fertility influence the vegetation diversity of a tropical peat swamp
forest in Central Kalimantan, Indonesia?
By Leanne Elizabeth Milner
Dissertation presented for the Honours degree of BSc Geography
Department of Geography
University of Leicester
24th February 2009
Approx number of words (12,000)
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Contents Page
LIST OF FIGURES I
LIST OF TABLES II
ABSTRACT III
ACKNOWLEGEMENTS IV
Chapter 1: Introduction 1
1.1 Aim 2
1.2 Objectives 2
1.3 Hypotheses 2
1.4 Scientific Background and Justification 3
1.5 Literature Review 7
1.5.1 Soil Fertility and Vegetation Species Diversity 7
1.5.2 Tropical Peatlands 7
1.5.3 Vegetation and Soil in tropical peatlands 8
1.5.4 Hydrology 14
1.5.5 Phenology and Rainfall 15
Chapter 2: Methodology 17
2.1 Study Site and Transects 18
2.2 Soil Analysis 21
2.3 Chemical Analysis 22
2.4 Tree Data 25
2.5 Phenology Data 25
2.6 Rainfall Data 26
2.7 Data Analysis 26
2.7.1 Soil Data Analysis 26
2.7.2 Tree Data Analysis 26
2.7.3 Phenology Data Analysis 28
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2.7.4 Rainfall Data Analysis 28
Chapter 3: Analysis 29
3.1 Tree and Liana Analysis 30
3.1.1 Basal Area and Density 31
3.1.2 Relative Importance Values 33
3.2 Peat Chemistry Analysis 35
3.3 Tree Phenology Analysis 43
3.4 Rainfall Analysis 46
Chapter 4: Discussion 47
4.1 Overall Findings 48
4.2 Peat Chemistry 48
4.3 Vegetation and Phenology 51
4.4 Peat Depth and Gradient 53
4.5 Significance of the Water Table 54
4.6 Limitations and Areas for further Research 56
Chapter 5: Conclusion 59
5.0 Conclusion 60
REFERENCES 62
APPENDICES 67
Appendix A: Soil Nutrient Analysis 68
Appendix B: Regression Outputs 70
Appendix C: Tree Data ON CD
Appendix D: Phenology Data ON CD
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List of Figures
Figure 1 – Distribution of tropical peatlands in South East Asia and location of the study
area.
Figure 2 – Photograph of pnueumatophores (breathing roots).
Figure 3- Photograph of Riverine type vegetation.
Figure 4 – Photograph of Mixed Swamp forest vegetation
Figure 5 - Data table taken from Page et al (1999) outlining the changes in peat thickness,
surface elevation, gradient and corresponding forest type in the Sungai Sebangau
catchment.
Figure 6 - Peat surface elevation, peat thickness and mineral ground topography along a
24.5km transect from Sungai Sebangau. Source – Page et al (1999).
Figure 7 - Peat water table levels recorded at the end of the 1993 dry season in study plots
located in peat swamp forest in the upper catchment of the Sungai Sebangau.
Source- Page et al (1999).
Figure 8 - Map of Indonesia (Kalimantan circled in red).
Figure 9- Map of Setia Alam base camp in relation to Palangkaraya
Figure 10 - Remote Sensing image (false colour composite) of Sebangau field area.
Figure 11 - Transects and phenology plots at the Setia Alam field station.
Figure 12 - Flow diagram showing the methodology for determining pH of each peat sample.
Figure 13 - Flow Diagram showing the methodology for determining Calcium, Magnesium
and Potassium content of each peat sample.
Figure 14 - Flow Diagram showing the methodology for determining organic carbon content
of each peat sample. I
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Figure 15- Flow Diagram showing the methodology for determining nitrogen content of each
peat sample.
Figure 16 – Flow diagram showing the methodology for determining phosphorous content of
each sample.
Figure 16 - Formulae for Simpson Diversity Index.
Figure 17 - Results of Simpson Diversity Index of tree and liana data against distance into the
forest. The graph shows the average diversity within each phenology plot.
Figure 18 - Basal area (cm3) of all trees > 6cm dbh of each vegetation plot (0.15ha) against
distance into forest.
Figure 19 - Tree density (ha-1
) of all trees >6cm dbh extrapolated from all vegetation plots
against distance into forest.
Figure 20 - (a) Average Ph of peat samples at each transect location against distance into the
forest. (b) Average pH of peat samples at each transect location against tree and
liana diversity using the Simpson Diversity Index at each phenology plot.
Figure 21 - (a) Percent organic carbon in peat samples against distance into forest. (b) %
organic carbon against diversity.
Figure 22- (a) Nitrogen in peat samples against distance into the forest. (b) Nitrogen content
of peat samples against diversity.
Figure 23 - (a) Phosphorous content of peat samples against distance into forest (b)
Phosphorous content against species diversity.
Figure 24 - (a) Potassium content against distance into the forest. (b) Potassium content
against species diversity.
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Figure 25 - (a) Calcium content against distance into the forest. (b) Calcium content against
species diversity
Figure 26 - (a) Magnesium content against distance into the forest. (b) Magnesium content
against species diversity.
Figure 27- Carbon Nitrogen ratio of peat samples at each transect location against distance
into the forest.
Figure 28 - Percent of trees in flower in July in all phenology plots from 0.4 – 3.5 km.
Figure 29 - Average percent trees in flower for all months and years (01/07/2004 – 1/07/07)
plotted against distance into the forest (km).
Figure 30 - A scatter plot, percent of trees in flower in the month of July from 2004 to 2007
against the Simpson diversity index generated for each corresponding phenology plot.
Figure 31 - Percentage of trees in flower in 2007 across all vegetation plots.
Figure 32 - Percentage of trees in flower using data from 2004-2007.
Figure 33 - Line graph comparing average monthly rainfall for Palangkaraya with mean
percent of trees in flower (2004-2007).
Figure 34 – Photographs of (a) Transition forest (b) Mixed Swamp forest.
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List of Tables
Table 1 - Locations of transects and sampling points for soil samples.
Table 2 - The tree and liana diversity at 6 phenology plot locations across their
corresponding transect locations in the Sebangau forest. The Simpson diversity
index determines the diversity of species on a scale of 0-1. (0=most diverse, 1 =
least diverse.)
Table 3 - Chemical Analysis Data for surface peat at 6 transect locations in the Sebangau
forest. The results show an average of 5 samples collected at each transect
location. The mean calculated is the mean of all the values, n=30.
Table 4 - The tree and liana diversity at 6 phenology plot locations across their
corresponding transect locations in the Sebangau forest. The Simpson diversity
index determines the diversity of species on a scale of 0-1. (0=most diverse, 1 =
least diverse.)
Table 5 - Location, vegetation type, basal area and tree density for selected plots (0.15ha)
(Basal area for all trees < 6cm dbh), in the Sg, Sebangau Catchment, Central
Kalimantan.
Table 6 - Forest type, Number of Species and Relative Importance Values (RIV’s) for the
most dominant species across transects 0.4 km to 3.5km.
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Acknowledgments
In the writing of this dissertation I would like to thank various people for their help and support
during the decision making process, collection of data and final write up. Firstly Dr Susan Page
who initially suggested I complete the study in Kalimantan and for her excellent advice and
support throughout the whole process. This research would not have been possible without the
help from OUTROP especially Laura Graham and Simon Husson who helped me to design the
project and ensured all people involved were safe when in the forest. A special thank you to my
friend Sarah Read who travelled with me to Kalimantan to help collect my data and for keeping
me sane when the novelty of cold rice for breakfast began to wear off! Last but by no means least
thank you to my boyfriend Lee Shepherd for helping with the important job of proof reading and
general moral support.
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Abstract
There is a clear uniformity in peat nutrient status across the ‘Mixed Swamp Forest’ and
‘Transition Forest’ types. It is clear from vegetation analysis that there are distinct changes in
species diversity, basal area and density although justification by peat chemistry alone is not
sufficient. Other ecological and hydrological factors must be considered as the ombrotrophic
peatland system is far more complex than can be explained solely by peat nutrient status.
Analysis of peat chemistry was conducted across six transect locations in the upper catchment of
the Sungai Sebangau peat swamp forest, Central Kalimantan, Indonesia. There were no
statistically significant relationships between any one nutrient content and distance therefore
providing evidence for homogeneity. Vegetation data was used to calculate species diversity and
it was apparent that there are differences in floristic structure as vegetation diversity decreases
with distance.
Percentage of trees in flower decreases slightly with distance, thus providing further evidence for
changes in vegetation across the peat dome. It can be deduced that peat nutrient content is not a
significant causal factor in determining flowering, due to the presence of comparatively constant
concentrations of nutrients.
Consideration of peat depth, gradient and level of water table was applied to the discussion
therefore facilitating a more comprehensive justification for the controls on vegetation structure.
The presence of nutrients is critical for vegetation growth, however it is the actual uptake which
is likely to be a more significant factor. Characteristics of the peat dome influence the delivery
and hence the uptake of nutrients that are subsequently used in vegetation nutrition.
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Introduction
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1.0 Introduction
Title:
Does soil fertility control vegetation species diversity in tropical peatlands of Central
Kalimantan?
1.1 Aim:
To compare soil chemical properties, species diversity and productivity across the Setia Alam
field station, and to explore other ecological factors that may control vegetation species diversity.
1.2 Objectives:
(1) To determine any differences in chemical properties of peat across the study site.
(2) To determine any differences in species diversity and productivity across the site.
(3) To determine any differences in soil nutrient availability that may account for differences
in vegetation richness and/or productivity across the study site.
(4) To determine any differences in soil nutrient availability that may account for differences
in biomass across the study site.
1.3 Hypotheses:
H0 – There is not a significant relationship between soil fertility and vegetation species diversity
H1 – There is a significant relationship between soil fertility and vegetation species diversity.
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1.4 Scientific Background and Justification
‘Soil nutrient content plays a key role in plant growth through mineral nutrition and toxicity’,
(Marage & Gegout, 2009). This study investigates the role of peat nutrient content its control on
the vegetation species composition and flowering patterns in the tropical peatlands of Central
Kalimantan, Indonesia.
Tropical peatlands occur in areas of East and South East Asia (Figure 1), Africa, the Carribean,
Central and South America, although Indonesia contains the worlds largest area of peatland
estimated at 15.6 Mha (Nugroho et al, 1992) although larger figures of 16-27 Mha have been
identified, with 6.8 Mha in Kalimantan, (Rieley et al 1996). Tropical peatlands are of scientific
interest due to their unique and dynamic nature. They are unbalanced systems, as they are
accumulating peat due to the rate of production of organic material, exceeding rate of
decomposition. ‘As the peat blanket thickens, the surface vegetation becomes insulated from
underlying soils and rocks resulting in floristic changes which reflect the altered hydrology and
chemistry of the peat surface’, (Moore, 1974).
‘Tropical peat consists of partially decomposed trunks, branches and roots within a matrix of
structureless, dark brown, amorphous, organic material’, (Anderson,1964). The peat soils
(histosols) are formed when organic material is unable to fully decompose due to acidic and
anaerobic conditions in the soil. Most of the peatlands in Kalimantan are fibric in nature (Rieley
& Page 2005) and have low mineral content, these are the least decomposed of all the peatlands.
Peatlands in this area are ‘ombrogeneous’ meaning that ‘water and nutrient supplies are derived
entirely from aerial deposition, in the form of rain, aerosols and dust’, (Page et al 1999).
13
Figure 1 – Distribution of tropical peatlands in South East Asia and location of the study area.
(Source Wösten et al 2008)
The histosols have a low bulk density owing to their high organic content in the (more than 30%)
upper profile of the peat, (FAO-UNESCO, 1990). These peatlands therefore have a high porosity
and hence their high water holding capacity. This is important for the control of hydraulic
conductivity and the delivery of nutrients to the vegetation. Larger pore spaces result in a greater
hydraulic conductivity or increased ease at which water can flow through the peat.
Analysing patterns of vegetation variation can infer spatial variations in environmental factors.
The variation of species abundance in response to an environmental factor is known as an
environmental gradient, Kent & Coker (1992). There are many biotic and abiotic factors that
control the structure and composition of vegetation including soil fertility, water availability,
light availability as well as competition between species. Although this study is primarily
concerned with the nutrient content of peat soils as an ecological factor for vegetation, other
factors will be taken into account when describing the overall ecosystem processes.
There has been relatively little research undertaken on the ecology of the tropical peat swamp
forest ecosystem in Central Kalimantan. The earliest studies were undertaken in northern Borneo
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by Anderson (1961, 1963 & 1964) who determined relationships between peat thickness and tree
species composition in Brunei and Sarawak. More recently (1993 onwards) fieldwork has been
undertaken in the Sebangau peat forest in Central Kalimantan, southern Borneo.
There have been limited studies on soil fertility and its effects on vegetation diversity. Shepherd
et al (1997) completed a study on the forest structure and peat characteristics in the upper Sungai
Sebangau catchment, focusing on the differences in forest structure and tree species composition
across the whole peat swamp forest catchment area. Chemical analysis of surface peat was
undertaken in the different forest sub-types in the study area. There has been more emphasis on
understanding the physical and chemical characteristics of peat soils which are being used for
agriculture and a number of papers have been published on these aspects (Rieley & Page, 2005).
Peat soils provide an inimicable environment for crop growth since they are acidic and very low
in available nutrients. The proposed study fills a gap in the current knowledge as there has not
been a detailed study of the mixed swamp forest and transition forest. Other studies (Shepherd et
al, 1997 & Page et al, 1999) explain how vegetation composition changes over all forest types.
‘The ecological aspects of tropical peat swamps, in particular decomposition processes and
nutrient dynamics, remain poorly understood’, (Yule & Gomez, 2008). ‘Tropical peatland
systems remain relatively unknown among the wider scientific community and recognition of
their biological, environmental and economic importance has been a slow process’, (Page et al
2006).
The nutrient analysis of peat samples will be combined with vegetation diversity, biomass and
phenology data to gain an overall understanding of primary forest characteristics. There have
been limited studies on the patterns of flowering data across the Sebangau. This study is also
important in terms of the wider ecosystem and for many centuries ecologists have been fascinated
by peatlands, (Moore, 1974). Ecologists have found this ecosystem to be abundant in flora and
fauna, hosting many endemic species include the flagship endangered Orangutan. Not only
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diverse, this forest is unique and often described as a ‘dual ecosystem’.(Rieley et al ,1996) It is a
tropical rainforest and a tropical peatland acting together as one complex system of which
relatively little research has been undertaken. A deeper understanding of this fragile ecosystem
can help ensure it is protected for future generations.
The overall aim of this study is ascertain if soil fertility is a controlling factor in determining
vegetation diversity in the tropical peat swamp forest of Central Kalimantan, Indonesia. This
research looks at a detailed nutrient analysis of peat samples along transects in the Sebangau
forest. This chemical analysis is then compared to vegetation data to determine any relationship
between fertility of the peat and vegetation diversity. Other ecological factors are then taken into
account as controlling factors in an attempt to explain the peat swamp system regarding its
vegetation structure and composition.
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1.5 Literature Review
1.5.1 Soil Fertility and Vegetation Species Diversity
‘The availability of soil nutrients is one of the main factors in determining the species
composition of plant communities’, (Ordoñez, 2009). In very fertile environments species
diversity may be ‘driven down by the Interspecific Exclusion Hypotheses’, (Stevens & Carson,
1999). As fertility rises dominant species may suppress the growth of subordinate species.
Nutrients also have a considerable effect on the ‘quantity, rate and form of plant growth’, (Moore
& Chapman, 1986).
1.5.2 Tropical Peatlands
(Rieley et al, 1997) states that peat is distributed worldwide from polar to tropical regions in both
hemispheres wherever suitable climatic and edaphic conditions prevail. ‘Tropical peatlands are
very different from the temperate systems on which much peatland research is based’, (Charman,
2002). ‘The largest ombrotrophic (only receives water from precipitation) peatlands are in the
tropics of Indonesia and Malaysia’, (Anderson, 1983). Page et al, (2006), states that lowland
ombrogeneous peatlands support peat swamp forest of which some vegetation species are
endemic.
Original studies of lowland peat swamp forests of Indoneisa and Malaysia were completed by
Anderson, (1961, 1963 & 1964) who recorded up to 927 species of vegetation from peat swamps
in Borneo. Anderson originally addressed that there are two types of swamp forest and this
particular site at the Sungai Sabangau catchment is classed as ‘a true peat swamp’ due to it
17
characteristically having a pH of less than 4.0 and a markedly convex surface. The natural
vegetation of lowland peat swamp varies from a mixed swamp near the margins (with up to 240
spp ha-1
) to pole forest in the interior (lower tree diversity, dominated by one or a few species,
such as Shorea albida), (Anderson ,1963; Silvius et al 1984).
The vegetation of tropical peat swamps is dominated by trees of which many are adapted to the
waterlogged conditions. Trees often display buttress of stilt roots (Figure 2) that provide
improved stability and breathing roots (pneumatophores) that protrude above the peat surface,
(Page et al 2006).
Figure 2 – Photograph of pnueumatophores (breathing roots) in the mixed swamp forest in Sebangau Forest,
Central Kalimatan, Indonesia. © Milner 2008.
1.5.3 Vegetation and soils in peatlands
‘It has long been considered that the ionic composition of wetlands varies considerably and that
such variations are ususually accompanied by floristic changes in the surface vegetation,’ (Moore
& Bellamy 1974). This forms the basis and the overall aim of this research.
18
There have been detailed studies of vegetation in Indonesia (Shepherd et al, 1997, Sieffrmann et
al, 1992, Rieley & Ahmad-Shah 1996) although very little data has been collected in terms of the
chemical properties of soils, (Brearley et al, 2004). Research by Radjagukguk, (1992) explained
that thick peats (> 2m) have different physical and chemical properties compared to thin peats
(< 2m) and the surface layer (0-50cm) of thick peats is poorer in plant nutrient elements than the
surface of thin peats. Sulistiyanto, (2004) analysed the role of leaf litter fall and found that
vegetation growing on thin peat results in a higher nutrient return than those on thicker peat.
Research funded by The Darwin Initiative (1998) was one of the first projects undertaken in this
area with a focussed understanding on land-use changes and their effects on biodiversity.
Subsequently, there have been limited studies in terms of soil nutrient content and its effects on
vegetation. There has been more emphasis on understanding the physical and chemical
characteristics of peat soils which are being used for agriculture, (Kanapathy 1975; Andriesse
1988; Vijarnsorn 1996).
Proctor et al, (1984) discussed forest environment structure and floristics in contrasting lowland
forests in the Gunung Mulu National Park, Sarawak, Indonesia. Above Ground Biomass was
calculated within four different forest types, ranging from 250 t ha-1 in alluvial forest to 650 t ha-1
in dipterocarp forest. Chemical analysis of soil samples was undertaken and their differences in
relation to forest type discussed. The species rich dipteropcarp forest grows on soils which were
found to be acidic and high in calcium. It was explained by Proctor et al, (1984) that there was no
clear relationship between soil nutrient content and vegetation diversity or above ground biomass.
‘Many factors are probably involved in controlling these attributes’, Proctor et al, (1984). If a
statistically significant relationship is not present between soil nutrient content and vegetation
species diversity other factors may need to be taken into account.
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Silivius et al, (1984) also researched tropical peat forest, although the study was focussed in
Sumatra, Indonesia. An analysis of soils and vegetation was undertaken and it was found that,
‘the peat soils were chemically poor and naturally infertile’, Silvius et al, (1984). The diversity of
vegetation species was found to be relatively low compared to dryland forest although they are
important reservoirs of biodiversity.
Microbe populations are important in peatland ecology for the release of nutrients available for
uptake by vegetation. Microbes aid in the process of decomposition; a process of which is
evidently low due to the accumulation of peat. Deomposition rates are low due to the constant
waterlogging and low pH, (Moore, 1974). ‘There has long been a presumption that the slow rate
of decomposition of leaf litter, and hence the build up of peat, is caused by the inhibition of
microbial and fungal activity’, (Yule & Gomez, 2008). If microbe activity is inhibited then this
could suggest that nutrients will be limited.
The relationships between vegetation and soil characteristics of the peat swamp forest in the Sg,
Sebangau catchments were explored by Shepherd et al, (1997). Vegetation analysis was
undertaken in relatively undisturbed forest to determine how the structure and composition of the
forest changed with distance across the peat dome. Peat depth was measured at 500 metre
intervals as well as depth of the water table during the 1993 dry season. The overall findings were
that there was evidence for a sequence of forest types moving across the peat dome. ‘The
sequence of change is from riverine forest (floodplain forest) through marginal mixed swamp
forest, low pole forest to tall forest in the interior’, (Shepherd et al, 1997).
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Figure 3 - Riverine Sedge Swamp Vegetation. © Milner 2008.
Figure 4 – Mixed Swamp forest vegetation. © Milner 2008.
Chemical analysis showed that all samples were very acidic and low in some ions that were
analysed, namely Ca, K, Mg, N and P. There was a slight decrease in nitrogen and potassium
content moving from mixed swamp forest to transition, although there were higher levels of
calcium, magnesium and phosphorus in the transition forest than the mixed swamp forest. Tree
21
enumeration data including, basal area per plot, reveals that the mixed swamp forest has a
significantly higher basal area (75,054 cm2) than that of the low pole forest (46,987 cm
2). In
general there is an increase in the number of trees per plot from the riverine forest to the low pole
forest. This study will include basal area and tree density calculations for five plots within the
MSF and one in the transition forest to show variations which were not included in Shepherd et
al, (1997).
More recent studies, (Sarjarwan et al, 2002; Weiss et al, 2002) for example Page et al, (1999),
also details characteristics of the peat soils in the Sebangau. Soil nutrient analysis was undertaken
across the peat dome and four different forest types where identified similar to that discussed by
Shepherd et al, (1997). Vegetation analysis is compared to the underlying peat surface. The main
findings were that there are distinct changes in forest structure from the rivers edge to the
watershed. The forest types identified in this paper were ‘Riverine forest’, ‘Mixed Swamp forest’
(MSF) through to ‘Low Pole Forest’ and finally ‘Tall Pole Forest’. There are also some
transitional types for example between the MSF and Low Pole. Figure 5, below, shows the
locations and distances of these forest types.
Figure 5 – Data table taken from Page et al, (1999) outlining the changes in peat thickness, surface elevation,
gradient and corresponding forest type in the Sungai Sebangau catchment.
22
From this study evidence was found for presentation of an ombrotrophic peat dome system as
mean peat thickness generally increases moving in from the forest edge resulting in an increasing
surface elevation to the centre of the dome. ‘The gradient of the surface decreases moving into
the centre with the steepest surfaces at the edges of the peat dome (see Figure 6). In the upper Sg.
Sebangau catchment there are extensive areas where the peat reaches a thickness of 10m and
more towards the centre of the domes. Peat thickness decreases towards the major rivers where it
is absent from the alluvial levees’, (Page et al, 1999). Analysis of peat chemistry also found that
the peat has a low nutrient content which is a feature of most ombrotrophic peatlands, (Shotyk,
1988). This data was collected in the same study area as to be undertaken for this research;
therefore it can be used as secondary supporting data.
Figure 6 – Peat surface elevation, peat thickness and mineral ground topography along a 24.5km transect from
Sungai Sebangau. Source – Page et al, (1999).
23
There have been recent studies on the above ground biomass between peat swamp forest sub-
types. Sulistiyanto, (2004) calculated values ranging from 314 t ha-1
in marginal mixed swamp
forest to 252 t ha-1
for low canopy pole forest.
1.2.4 Hydrology
‘Water table depth is one of the most important ecological factors in most mire systems
particularly in raised bogs’, (Hakan, 2006). Ingram (1983) points out that change in the water
table depth may occur in relation to the surrounding mineral ground. This study focuses on small-
scale patterns of variability and therefore depth of the water table could influence these patterns,
Wheeler (1995).
Verry (1997) found that with increasing depth to the water table in a wetland, the maximum
height of plants increases and the lowest water tables will allow plants to grow. Changing depths
of the water table create periods of anaerobic conditions within the peat soils when there is a
sufficiently high water table. These fluctuations of the water table create ‘mire breathing’
(Ingram, 1983).The study by Page et al, (1999) as previously mentioned also contains
measurements of water table depth. Figure 5 shows the convex nature of the peat dome and the
increasing thickness of peat moving away from the Sungai Sebangau. The depth of the water
table drops moving away from the Sungai Sebangau (see Figure 7).
Figure 7 – Peat water table levels recorded at the end of the 1993 dry season in study plots located in peat swamp
forest in the upper catchment of the Sungai Sebangau. Source- Page et al, (1999).
24
A study by Wösten et al, (2008) discussed the relationships between peat and water in degraded
peatland in Central Kalimantan. The groundwater levels were recorded and the depths of the
water table was found to change depending on rainfall events, for example in ‘July 1997 which
was a dry El Niño year, areas for which deep groundwater levels were calculated coincided with
areas that were on fire as detected from radar images’, Wösten et al, (2008). Therefore depth of
the water table will be affected by amount of rainfall which varies both seasonally and between
years.
1.2.5 Phenology
Phenology data has been analysed in Central Kalimantan although most of the literature uses
either flowering or fruiting data in terms of accounting for food supplies for the gibbon and
orangutan population, (Russon, 2001; Mckonney, 2005 & Husson et al, 2002). Cortlett &
Lafrankie, (1998) analysed flowering patterns of vegetation in tropical Asian forests including
that of Kalimantan. Phenology patterns were analysed and their relationship with climatic
seasonality. In this study phenology patterns between vegetation plots and their relationship with
precipitation will be analysed. The majority of tropical plants show some periodicity although
this may not be annual, (Longman & Jenik, 1987). Rainfall can initiate flowering of vegetation
therefore it would be expected that after an El Nino event percentage trees in flower would be
relatively low, (Sakai, 2006)
‘Masting is the intermittent production of large seed crops by a plant species within a
population’, (Kelly, 1994). Sakai (2002), analysed the flowering patterns in lowland mixed
dipterocarp forest of South East Asia. Results showed that most trees come into general flowering
25
and set fruit massively. There is a relationship between the occurrence of El Niño and flowering
events.
The timing of general flowering however, appears to be generally unpredictable, (Sakai, 2006).
‘The tropical climate of Central Kalimantan is characterised by ‘wet’ and ‘dry’ seasons’,
(Inubushi, 2003). The timing of the wet and dry seasons experienced in Central Kalimantan may
be identified by flowering events. ‘In seasonal climates, rainfall triggers flowering, which can
signify the start of a new climatic condition or season’, (Augspurger, 1981).
26
Methodology
27
2.0 Methodology
2.1 Study Site and Transects
The study site is located in Central Kalimantan, Southern Borneo, Indonesia at co-ordinates
113°57"E, 2°17"S. (Figure 8). The Sebangau forest is centred on the black water Sg. Sebangau
River. This forest is part of a vast area of tropical peatlands that covers most of the lowland river
plains of Southern Borneo. Indonesia occupies the largest area of peatland in the tropical zone,
Rieley et al, (1996). The focus for this study is within the Setia Alam Field Station within the
Sebangau National Park, located approximately 20 km south west of the settlement of
Palangkaraya, (Figure 9).
Figure 8. Map of Indonesia (Central Kalimantan circled in red). Source: Multimap (6.05.08).
The study area contains a wide variety of forest types that are recovering from different forest
fires and the effects of logging. Four distinct zones of vegetation have been identified namely,
‘Sedge Swamp’, ‘Mixed Swamp Forest (MSF)’, ‘Low Pole Forest’ and ‘Very Low Canopy
Forest’, Page et al, (1999). The vegetation that is supported is reputed to be less diverse than
dryland forest but, nevertheless they provide important reservoirs of biodiversity, (Anderson,
1964; Silvius et al, 1984). This peatland forest is a lowland, ombrotrophic system, in which water
and nutrient supplies to the peat surface are derived entirely from precipitation (rainfall, dusts and
aerosols).
28
Figure 9 - Setia Alam Base camp, Natural Laboratory of Peat Forest, Sebangau National Park, Central Kalimantan,
Indonesia.
Source:http://www.restorpeat.alterra.wur.nl/download/SUSAN+CHEYNE+ET+AL+WORKSHOP+23+SEPT+2005
.pdf (20/12/08).
A series of twelve transects have been set up at the Setia Alam Field Station which run adjacent
to the former logging railway. The 2.5 km by 2.5 km grid system has been set up with transects
laid out at approximately every 600 metre intervals, running east to west. Within this grid system
members of the Centre for International Cooperation in Tropical Peatland, (CIMTROP) have set
up various phenology plots parallel to the transects, within what is relatively undisturbed forest.
29
The plots are 0.15ha, (5 m x 300 m) and all trees with a diameter at breast height (DBH) greater
than 6cm, lianas greater than 3cm and some figs have been tagged and their species identified.
Tree local names were provided by staff of The Orang-utan Tropical Peatland Project
(OUTROP).
Figure 10 - Remote Sensing image (false colour composite) of Sebangau field area.
Source: © Agata Hosclio (University of Leicester).
30
Figure 11: Transects and phonology plots at the Setia Alam field station. Source : http://www.orangutantrop.com/MapofLAHG.pdf (02/05/2008)
Data was collected along transects 0.4, 1.0A, 1.6, 2.25, 2.75 and 3.5 and their respective
phenology plots, (see Figure 11). Data collected from transects 0.4, 1.0A, 1.6, 2.25 and 2.75 km
are within the MSF and 3.5 km is moving into a transition zone between the MSF and the Low
Pole forest.
2.2 Soil Analysis
Peat samples were taken from transects (0.4, 1.0A, 1.6, 2.25, 2.75 and 3.5 km) as per Table 1.
Soil was collected from the top 30 cm under the letter layer. A stratified random sampling
method was implemented to collect five peat samples in line with the phenology plots of each
transect. Each plot was divided into five blocks of 60 metre intervals; samples were then
collected at random locations within each block. Random locations were selected by generating
random numbers from a calculator. Table 1 below shows the sampling locations along the six
transects
31
Table 1 – Locations of transects and sampling points for soil samples. A stratified random method was used.
Sample
Transect
Distance along
phenology plot
(0-300 m)
Width of the phenology
plot
(0-5m) 1 0.4 5 1.4
2 0.4 84 1.1
3 0.4 145 2.5
4 0.4 230 2.8
5 0.4 266 0.2
6 1A 15 0.5
7 1A 104 3.4
8 1A 129 2.3
9 1A 198 1.1
10 1A 257 2.7
11 1.6 39 2.7
12 1.6 113 0.8
13 1.6 166 0.5
14 1.6 231 4.1
15 1.6 284 3.4
16 2.25 54 2.6
17 2.25 118 4.1
18 2.25 148 3.5
19 2.25 196 1.0
20 2.25 267 1.6
21 2.75 12 0.1
22 2.75 62 4.6
23 2.75 149 3.3
24 2.75 203 4.4
25 2.75 248 0.3
26 3.5 30 4.6
27 3.5 82 2.5
28 3.5 155 3.4
29 3.5 212 0.2
30 3.5 286 2.8
The peat samples were stored and then analysed within the Geography Department at the
University of Palangkaraya.
2.3 Chemical Analysis
The peat samples were analysed for the following chemical components; pH, Organic carbon
content (C), Nitrogen (N), Phosphorus (P), Potassium (K), Calcium (Ca) and Magnesium (Mg).
From this data the C:N ratios can be calculated and used as a proxy for the quality of the organic
matter, (Heal et al, 1997) There was an initial drying of samples under natural conditions for one
week, then a 2 mm sieve was used to test for dryness.
32
Figures 12– 16 show flow diagrams of methodologies used to determine peat chemistry.
pH
Centrifuged
+ 30 minutes
Figure 12 - Flow diagram showing the methodology for determining pH of each peat sample.
Calcium, Magnesium and Potassium
Centrifuged
+ 30 minutes
Figure 13 – Flow Diagram showing the methodology for determining Calcium, Magnesium and Potassium content
of each peat sample.
Organic Carbon
Figure 14 –Flow Diagram showing the methodology for determining organic carbon content of each peat sample
10g peat sample
5ml distilled water
pH measured and calibrated against
known solutions of pH 4 and 7.
5g peat sample
Acetate
20ml peat/acetate
solution
Process repeated four
times to make a total
80ml solution
Spectrometer
analysis to
determine
concentrations of
nutrients
5g peat sample
oven dried for
24 hours.
(1050C)
Sample is
cooled, and
mass was
recorded.
Sample is the placed
into an oven at 9000C
for a further 5 hours
to form ash.
Sample is
cooled and
mass is
recorded.
% organic carbon= ash mass/dry mass x 100%
33
Nitrogen
+
Figure 15 – Flow Diagram showing the methodology for determining nitrogen content of each peat sample
Phosphorous
v
+
Figure 16 – Flow Diagram showing the methodology for determining phosphorous content of each peat sample.
0.05ml NaOH
0.05g peat
sample
Solution made up to
50ml by addition of
K2O8S2.
(Potassium
peroxydisulfate)
Solution is heated
using an ‘autoklab’
which is a pressure
steam sterilizer. For 2
hours.
Solution is
then cooled.
2ml of solution
pipetted into a test
tube.
Add 0.8ml of H2SO9
(Salycilic acid)
Solution is then made
up to 20ml by
addition of NaOH.
Add 0.8ml of H2SO9
(Salycilic acid)
Post reaction the solution
is analysed by a
spectrometer.
(Wavelength = 410nm)
Nitrogen concentrations
were determined
0.25g peat sample
257.2 ml of
Perchloric Acid
Topped up with
distilled water
to make a 1l
15ml of solution is
filtered with filter
paper (hole size
15ml of solution is
filtered with filter
paper (hole size 42).
A 25 ml solution is then made up in
a conical flask by addition of
distilled water.
2ml of this solution is
used in the spectrometer.
The ‘Scheel’ method
was used for the
spectrometer
‘Scheel 1’ method was used then
added 2ml of solution
‘Scheel 2’ method then add a further
2ml of sample and left to settle for 15
minutes
‘Scheel 3’ method 4ml of sample
added to 5 ml of distilled water and
left to settle for 15 minutes.
’
Wavelength of 700nm used in the spectrometer
calibrated against standardised solutions of
known phosporous concentrations.
34
2.4 Tree Data
The phenology plots within the grid system contain trees, lianas and figs of DBH greater than 6
cm for trees and 3 cm for lianas have been tagged and species identified. The data collected
includes; species name, tag number, type of vegetation (tree, liana or fig), diameter at breast
height (DBH) and basal circumference. This data was collected by members of The Orang-utan
Tropical Peatland Project (OUTROP) and is a source of secondary data to supplement the
research. The data has been collected on a monthly basis from April 2003 to May 2008, for the
purposes of this study the data will be used to calculate vegetation diversity indices and the
results will be compared between phenology plots.
The Simpson diversity index was used to calculate vegetation diversity, of which the formulae is
included below;
Figure 17 – Formulae for Simpson Diversity Index
The application of this index will result in a value between 0 and 1 with 0 being the most diverse
vegetation.
2.5 Phenology Data
The phenology plot data have also been collected to gain an insight into the fruiting and
flowering patterns of the aforementioned tree data, although only trees with DBH greater than
20cm have been recorded. ‘The percentage of stems in fruit’ and ‘the percentage of stems in
flower’ have been calculated to give an overview of the productivity of each phenology plot. The
35
data was collected from April 2003 to May 2008. This data will be compared between plots and
between months. This data was also of a secondary source, it was also collected by members of
OUTROP.
2.6 Rainfall Data
Monthly rainfall data was a secondary data source taken from, ‘The Technical Report: Hydrology
of EMRP area and water management implications for peatlands’ which forms part of the
‘Master plan for the Conservation and Development of the ex-Mega Rice Project Area in Central
Kalimantan’, (Hooijer et al, 2008). Monthly rainfall figures for Palangkaraya are used to reveal if
there are any relationships between monthly flowering patterns and quantity of rainfall. A
regression analysis is performed to determine whether any relationships are present.
2.7 Data Analysis
2.7.1 Soil Analysis
All of the data collected will be analysed in order to meet the overall aims and objectives of the
research. The soil data will used to compare changes in each soil nutrient across the study site. A
regression analysis will be performed on each nutrient, against distance as well as how it changes
with vegetation species diversity. The carbon – nitrogen (C:N) ratios will be calculated for each
sampling location. The C:N ratio will be compared against distance into the forest and vegetation
species diversity.
2.7.2 Tree Data
The tree data will be used to calculate diversity of plant species using the Simpson diversity
index, (Figure 16). These diversity figures will be compared across the site to determine any
trends in vegetation species diversity across different vegetation zones. The soil nutrient contents
36
will be compared against diversity to test for any significant relationship that may explain; if and
how soil fertility is a controlling factor in determining vegetation species diversity. Diversity of
the phenology plots will also be regressed against flowering of the same plots, to establish if
diversity is a factor in controlling the percentage of trees that are in flower.
Relative importance values (RIV’s) are calculated for a number of vegetation species to see how
their importance changed in different plots. The ‘Importance Value’ can be between 0-300 and a
leading dominant can be deduced within each vegetation plot, this is simply the species with the
highest importance value. ‘The importance values attempt to relative contribution (or dominance)
of a species in a plant community’, Stholgren (2007). Each importance value is calculated using
the following components, developed by Kent and Coker, (1992).
Relative Density = Number of Individuals of species
Total Number of individuals
Relative Dominance* = Dominance of a species
Dominance of all species
Relative Frequency = Frequency of a species (%)
Frequency of all species
IMPORTANCE VALUE = Relative Density + Relative Dominance + Relative Frequency
* Dominance is defined as the mean basal area per tree multiplied by the number of trees of the species.
X 100
X 100
X 100
37
1.7.3 Phenology Analysis
Phenology data will be used to determine any trends in flowering across the study site and if and
how this changes with soil nutrient content and plant diversity. By performing a regression
analysis these relationships can be revealed. Percentage trees in flower will be plotted against
mean average rainfall so to deduce any relationship. Phenology patterns will analysed between
months to appreciate how flowering changes throughout the year.
1.7.4 Rainfall Analysis
Mean monthly rainfall figures for Palangkaraya will be plotted against phenology data as
previously stated above. Any relationship between rainfall and flowering will then be able to be
identified.
38
Analysis
39
3.0 Analysis
3.1 Tree and Liana Diversity
The Simpson Diversity (SD) Index was used to determine the diversity of plant species, (trees of
a dbh >6cm and lianas of dbh >3cm). Table 4 shows the results of the diversity index. Plotting
the SD index value against distance into the forest (Figure 17) shows that diversity decreases with
increasing distance. The R2
and p-value (p<0.05; r2<0.77) suggest a statistically significant
relationship between diversity and distance into the forest. The results show that the most diverse
plots were at 0.4 and 1.0 km transects which are contained within the MSF, (Page et al, 1999).
Table 4- The tree and liana diversity at 6 phenology plot locations across their corresponding transect locations in
the Sebangau forest. The Simpson Diversity Index determines the diversity of species on a scale of 0-1. (0=most
diverse, 1 = least diverse.)
Transect Number Simpson Diversity Index
0.4 0.028
1.0 0.025
1.6 0.036
2.25 0.044
2.75 0.046
3.5 0.091
Figure 17. Results of Simpson Diversity Index of tree and liana data against distance into the forest. The graph
shows the average diversity within each phenology plot.
40
The lowest tree and liana diversity was at the phenology plot at transect 3.5 km in the transition
area to ‘Low Pole Forest’, the Simpson Diversity Index indicates a value of 0.091 which is
significantly less diverse than all other values.
3.1.1 Basal Area and Tree Density
Basal area per plot is calculated as per Shepherd et al (1997), in Table 5. Figure 15 shows that
mean basal area per 0.15 hectare plot generally increases up to transect 2.75 km before
decreasing at transect 3.5 km. The mean basal area is highest at transect 2.75 km (215.43 cm2)
and lowest at transect 3.5 km (167.04 cm2). There is a decrease in basal area of approximately
18% moving from transect 2.75 km in the MSF to transect 3.5km in the TF.
Table (5) Location, vegetation type, basal area and tree density for selected plots (0.15ha) (Basal area for all trees <
6cm dbh), in the Sg. Sebangau Catchment, Central Kalimantan.
Transect Forest Type Basal Area
Per Plot
(cm2)
Mean Basal
Area per
plot *
(cm2)
Basal Area
per hectare
(m-2 ha
-1)
Tree
Density
(ha-1)
0.4 km Mixed swamp 50,548 178.61 33.69 1886.47
1.0 km Mixed swamp 57,812 173.60 38.53 2219.53
1.6 km Mixed Swamp 66,112 172.16 44.07 2559.75
2.25 km Mixed Swamp 63.818 148.75 42.54 2859.65
2.75 km Mixed Swamp 73,680 215.43 49.11 2279.32
3.5 km Transition 60,308 167.04 40.20 2406.58
* Basal Area per plot/number of trees
41
Figure 18 – Basal area (cm3) of all trees > 6cm dbh of each vegetation plot (0.15ha) against distance into forest.
Figure 19 shows the tree density increases moving from transect 0.4 km (1886.47 trees ha-1
) to
2.25 km (2859.65 trees ha-1
) although density then decreases at transect 2.75 km (2279.32 trees
ha-1
) before increasing again at transect 3.5 km, (2406.58 trees ha-1
).
Figure 19- Tree density (ha
-1) of all trees >6cm dbh extrapolated from all vegetation plots (0.15 ha) against distance
into forest.
Tree enumeration data generally shows that there is an overall increase in basal area and tree
density with distance. Basal area and tree density are generally lower in the MSF when compared
to the Transition forest type.
42
3.1.2 Relative Importance Values (RIV’s)
Relative importance values, (Table 6) highlight the leading dominant species at each transect
location. There were at least 50 trees and liana species identified within each plot and analysis of
this vegetation data shows that there are differences in floristic structure across the study site.
Although many of the same species exist across all transects the RIV’s vary considerably. In
transect 0.4 km, the trees with the highest RIV of any one species was Clausicace, Mesua, Sp.1
(RIV = 69.57). In transect 3.5km the highest RIV of any one species was Sapotaceae,
Palaquium, Leiocarpum (RIV = 193.08). RIV’s show that the vegetation composition becomes
more heavily dominated by certain species in the transition forest compared to the MSF. These
changes in species composition identify the changes in forest type.
43
Table 6 – Forest type, Number of Species and Relative Importance Values (RIV’s) for the most dominant species
across transects 0.4 km to 3.5km.
Transect Forest
Type
Numbe
r of
Species
Species Name
Relative
Importance
Value
0.4 MSF 68 Clusiaceae Mesua Sp 1. 69.57
Ebenaceae, Dipspyros, Bantamensis 53.94
Myristicasceae, Horsfielda, Crassifolia 49.62
Euphorbiaceae, Neoscortechinia, Kingii 43.74
1.0 MSF 71 Clusiaceae, Calophyllum, Hosei 71.25
Dipterocarpaceae,Shorea, Teysmanniana 55.98
Myristicaceae, Horsfielda, Crassifolia 41.05
Sapotaceae, Palaquium, Leiocarpum 35.25
1.6 MSF 65 Myristicaceae, Horsfielda, Crassifolia 85.56
Sapotaceae, Palaquium, Leiocarpum 42.15
Clusiaceae, Calophyllum, Hosei 39.65
Euphorbiaceae, Blumeodendron,
elateriospermum / tokbrai 39.54
2.25 MSF 68 Sapotaceae, Palaquium, Leiocarpum 123.92
Myrtaceae, Syzygium, havilandii 37.39
Euphorbiaceae, Blumeodendron, elateriospermum / tokbrai
33.44
Hypericaceae, Cratoxylon, glaucum 33.43
2.75 MSF 60 Sapotaceae, Palaquium, Leiocarpum 118.23
Dipterocarpaceae,Shorea, Teysmanniana 60.35
Myristicaceae, Horsfielda, Crassifolia 42.07
Meliaceae, Sandoricum, beccanarium 38.64
3.5 Transition 50 Sapotaceae, Palaquium, Leiocarpum 193.08
Dipterocarpaceae,Shorea, Teysmanniana 93.89
Clusiaceae, Calophyllum, Hosei 46.96
Clusiaceae, Calophyllum, Soulattri 41.56
44
3.2 Peat Chemistry
Chemical analysis of peat samples collected at five locations, along six transects in the study
area, show that the peat is acidic (Mean pH = 3.69, SE ±0.16). The pH increases with increasing
distance, (Figure 20 (a)), (p<0.05; r2 <0.83). Diversity of vegetation species increases with
increasing pH, regression analysis shows a statistically significant relationship, (Figure 20 (b)),
(p<0.05; r2<0.68).
Percent organic carbon content does not significantly vary across the study area, (Figure 21 (a))
(p>0.05; r2<0.57). There is no significant relationship between diversity of vegetation and
organic carbon content, (Figure 21 (b)), (p >0.05; r2<0.42).
Nitrogen content also does not significantly vary across the study site (Figure 22 (a)), (p>0.05;
r2<0.04). There is no significant relationship between diversity of vegetation and nitrogen
content, (Figure 22 (a)), (p>0.05; r20.05).
There is some variation in phosphorous content although there is no trend across the study site
just variation between transects i.e. there is a considerably higher content at transect 3.5 km
(mean = 444.694, SE = ±80.44 ) than at all other test sites.(Figure 23 (a)), (p>0.05; r2<0.16)
There is not a significant relationship between vegetation species diversity and phosphorous
content. (Figure 23 (b)), (p>0.05; r2<0.16).
Potassium content generally does not vary across the study site, (Figure 24 (a)), (p>0.05; r2<
0.44) although there is a higher concentration at transect 0.4 km, (0.428g), compared to all other
transects. There is not a significant relationship between vegetation species diversity and
potassium content, (Figure 24 (b)), (p>0.05; r2<0.10).
45
The calcium content does not vary significantly across the study site, (Figure 25 (a)), (p>0.05;
r2<0.13), although there is a marked increase in calcium at transect 3.5 km in the transition forest.
The standard error (±0.93) suggests a variation between samples. There is not a significant
relationship between vegetation species diversity and calcium content, (Figure 25 (b)), (p > 0.05
r2= 0.09).
The magnesium contents of peat samples do not vary significantly across the study site, (Figure
26 (a)), (p>0.05; r2<0.42). There is not a significant relationship between vegetation diversity and
magnesium content, (Figure 26 (b)), (p>0.05; r2<0.10). The standard error of the means show
there is a marked variation between transects (±80.44) the variation is highly erratic and no
statistical relationship is present.
Table 2 – Chemical Analysis Data for surface peat at 6 transect locations in the Sebangau forest. The results show
an average of 5 samples collected at each transect location. The mean calculated is the mean of all the values, n=30.
0.4 1.0 1.6 2.25 2.75 3.5 Mean
n=30
Standard
Error of
means
Ph 3.58 3.54 3.64 3.65 3.87 3.90 3.69 ±0.16
Organic
Carbon
(%)
57.456 57.40
6
57.526 57.438 57.12
4
57.176 57.35 ±0.24
N
(%)
1.694 1.39 1.06 1.274 1.7 1.296 1.40 ±0.56
P
(ppm)
373.25 386.6 365.75 277.69 478.3 444.69 387.77 ±80.44
K
(mg/100g)
0.428 0.292 0.194 0.232 0.202 0.254 0.26 ±0.15
Ca
(mg/100g)
1.502 0.864 0.898 0.824 0.674 2.496 1.20 ±0.93
Mg
(mg/100g)
0.994 0.968 0.93 0.96 0.92 0.95 0.95 ±0.05
46
(a)
(b)
Figure 20 – (a) Average pH of peat samples at each transect location against distance into the forest. (b) Average
pH of peat samples at each transect location against tree and liana diversity using the Simpson Diversity Index at
each phenology plot.
(a)
47
(b)
Figure 21- (a) Percent organic carbon in peat samples against distance into forest. (b) Percent organic carbon
against diversity.
(a)
(b)
Figure 22- (a) Nitrogen in peat samples against distance into the forest. (b) Nitrogen content of peat samples
against diversity.
48
(a)
(b)
Figure 23- (a) P content of peat samples against distance into forest (b) P content against species diversity
(a)
49
(b)
Figure 24(a) – Potassium content against distance into the forest. (b) Potassium content against vegetation species
diversity.
(a)
(b)
Figure 25 (a) Calcium content against distance into the forest. (b) Calcium content against vegetation species
diversity
50
(a)
(b)
Figure 26– (a) Magnesium content against distance into the forest. (b) Magnesium content against vegetation
species diversity.
The carbon-nitrogen ratio values are presented in Figure 27. There is no relationship between
carbon nitrogen ratios and distance into the forest, (p>0.05; r2<0.02
) at the 95 % confidence
level, therefore it can deduce that there is a relatively uniform spatial concentration of organic
carbon and nitrogen across the Sebangau study site up to 3.5 km.
51
Figure 27 – Carbon Nitrogen ratio of peat samples at each transect location against distance into the forest.
The C:N ratios are generally high this is a reflection of the nitrogen that is available in the peat
substrate, it is in an unavailable form for use by vegetation due to immobilistation by the soil
microbial population (Rowell, 1994). There is variation in C:N ratios between transects although
all display values greater than 30. The variations could be explained by differences in uptake
rates of nitrogen at different locations. Alternatively the slight variation in gradient would result
in differing moisture levels between hummocks and pools on the sampling surface.
52
3.3 Tree Phenology
Figure 28 is an analysis of phenology data collected in each July since 2004, as to be consistent
with all other data sets, i.e. soil samples. Phenology data shows that there is a general trend of a
decreasing percentage trees in flower from 0.4 km to 1.6 km before levelling off at 2.75 km and
then there is a marked increase in flowering at the 3.5 km transition zone.
Figure 28. Percent of trees in flower in all phenology plots from 0.4 – 3.5 km. Each point plotted represents an
average percentage flowering for each plot for the month of July over a 4 year period from 1/07/2004 to 1/07/07.
Data had not been collected for 1/07/2008 when completing this study.
There is a general trend of decreasing percentage flowering with distance into mixed swamp
forest, although there is a marked increase in percentage flowering in the transition forest
(distance = 3.5 km)
Figure 29 – Average percent trees in flower for all months and years (01/07/2004 – 1/07/07) plotted against distance
into the forest (km).
53
Phenology is directly compared with the species diversity of the same vegetation plots, (Figure
30). There is not a significant relationship between flowering and vegetation diversity (p>0.05;
r2<0.13). There are some discrepancies between timing of dataset collection as vegetation data
used to calculate SD index was wholly collected in 2007 but the phenology data collected each
month over a four year period between 2004 and 2007.
Figure 30. A scatter plot, percent of trees in flower in the month of July from 2004 to 2007 against the Simpson
diversity index generated for each corresponding phenology plot.
There is a change in phenology between months, Figure 31 shows the highest percentage
flowering during the months of October and November 2007 (22.9 %, 23.9%) and the lowest
percentage flowering months are February and March 2007. There is a marked increase of
flowering in June 2007 (22.3%).
54
Figure 31 – Percentage of trees in flower in 2007 across all vegetation plots.
Figure 32 – Percentage of trees in flower using data from 2004-2007.
Figure 32 shows the percentage trees in flower across all years and months during the years 2004
to 2007. The highest percentage flowering was during June corresponding to the ‘dry season’ and
the lowest in February, corresponding to the ‘wet season’.
55
3.6 Rainfall Data
Figure 33 shows on two vertical axis evidence for a distinct dry season during the months of June
through October, this is indicated by the trough of monthly mean rainfall.. The ‘wet season’ can
be explained by the higher rainfall values during the months of November through April. From
this analysis it can be derived that there is a noticeable increase in flowering during this ‘dry
season’ as well as a reduction in flowering during the ‘wet season’.
Figure 33 – Line graph comparing average monthly rainfall for Palangkaraya with mean percent of trees in flower
(2004-2007)
56
Discussion
57
4.0 Discussion
4.1 Overall Findings
Results of this study accept the null hypothesis as there is clear uniformity of nutrient content in
peat soils of the upper catchment of the Sungai Sebangau. Homogeny of nutrients masks the
distinct changes in floristic structure of the lowland peat swamp forest (Table 4.). Although peat
geochemistry is relatively uniform across the site, data from secondary sources (e.g. Shepherd et
al, 1997; Page et al, 1999) suggest that peat characteristics such as peat thickness and hydrology
could be factors in determining vegetation structure.
Figure 34. Forest types under investigation, (a) Transition forest 3.5 km (b) Mixed Swamp Forest, 1.0 km.
Photographs © Anna Marzec.
4.1 Peat Chemistry
Review of the literature and analysis of peat chemistry as per Figures 20 to 27, show that the
tropical peatlands within the Setia Alam field station are nutrient poor. ‘Plants in nutrient poor
environments produce small amounts of litter and conserve large amounts of nutrients in
recalcitrant tissues, thus reinforcing the infertile environment’, (Melillo et al., 1982; Hobbie,
1992; Berendse, 1994; Crews et al., 1995; Aerts & Chapin, 2000).
Water supply is entirely derived from atmospheric units due to the convex surface of the
ombrotrophic peatland; therefore vegetation is dependent upon nutrients from precipitation, dust
and aerosols. Charman (2002), explains that precipitation is usually relatively dilute in solutes
hence the low nutrient status of the Sg. Sebangau catchment. Rainfall across the study site would
58
have been comparatively uniform thus providing an explanation for the similarities in nutrient
content across transects.
All nutrient analysis with the exception of pH resulted in evidence for no statistically significant
relationship between ‘nutrient content’ and ‘distance’ as well as ‘vegetation species diversity’
and ‘nutrient content’. These results are comparable to that of Shepherd et a,l (1997) and Page et
al, (1999) There were some variations between transects, possible justification for these are now
explained in turn.
The peat is very acidic (mean pH of 3.69 (±0.16) and there appears to be a weak acidity gradient
with increasing acidity in the MSF at transect 0.4 km. Chemical analysis of peat samples show
that pH increases with distance into the forest. A low standard error suggests that although there
is an increase in pH the increase is significantly low as the variation between means is small.
‘Acidity itself is not a limiting factor in plant growth although low nutrient availability can
reduce growth’, (Bridgham et al, 1996) the overall acidic conditions and a lack of calcium (mean
= 1.20, ±0.93) may inhibit metabolic rates of microbial populations, (Etherington, 1976). The
presence of this acidity gradient would not be the causal factor for any changes in vegetation
diversity.
There is a marked increase in calcium content moving from the MSF at transect 2.75 km to the
transition forest at 3.5 km. Results are consistent with Shepherd et al (1997), although
contrasting results were obtained by Page et al (1999), whereby calcium concentrations were
found to be lower in the transition forest than in the MSF. A possible explaination for higher
concentrations further up the peat dome could relate to calcium being more strongly held to soil
particles than other nutrients such as potassium. ‘Calcium is a divalent cation therefore it is more
59
strongly held by soil particles than monovalent cations such as potassium’, Rowell (1994).
Calcium inputs from precipitation could therefore attach to soil particles further up the peat dome
where other nutrients may be carried downslope in solution.
The irratic nature of phosporus content across transects could be explained by the presence of
guano deposits on the peat surface. Kitchell et al (1999), explored the nutrient status of guano
deposits in New Mexico. Significant concentrations of phopsporus were revealed in many
samples. Although the peat itself may contain little variation in phosphorus concentrations,
random guano deposits could be controlling the results presented in Figure 23. Significant
increases in potassium content were observed in pine forest in Chartley Moss, Staffordshire, UK.
‘Part of this nutrient input is derived from the droppings of Corvus frugilegus (rook), which roost
over winter in the pine trees’, (Rieley & Page, 1990). Differences in potassium content could be
explained by random inputs from animal or bird species. Variations in other nutrient content
between transects, i.e. nitrogen or phosporus, could just be due to different degrees of nutrient
uptake by the vegetation.
Carbon nitrogen ratios are indicative of overall soil fertility and the amount of decomposition of
the organic material. The ratios displayed in Figure 27 are well above 30 indicating high organic
carbon content, this is due to low levels of decomposition. Most soils have a C:N ratio between 9
and 12 therefore ratios above 30 would suggest a limiting supply of nitrogen. Mineralisation rates
decline at high C:N ratios consequently vegetation lacks available nitrogen, (Rowell, 1994). The
variations between transects could be explained by the differences in uptake of nitrogen by
different species at different locations.
The absence of any relationship between magnesium concentration and distance indicates that
concentrations are relatively uniform across the site (SE ±0.05). ‘Magnesium plays an important
60
role in photosynthesis as it is the central atom in a chlorophyll molecule’, (Kent & Coker, 1992).
As the peat has a low nutrient status, vegetation may be adapted to low magnesium
concentrations. The small variations between transects could be explained by the differences in
uptake of magnesium by different species at different locations.
Microbe populations are important in peatland ecology for the release of nutrients available for
uptake by vegetation. Microbes aid in the process of decomposition a process of which is
evidently low due to the accumulation of peat. Deomposition rates are low due to the constant
waterlogging and low pH. This may provide an explanation for differences in vegetation
structure, as waterlogging increases in the Transition forest, there is a lower rate of
decomposition and therefore less nutrients are released by microbes for uptake by vegetation.
4.3 Vegetation and Phenology Data
The acidic, nutrient poor peat surface, subject to seasonal waterlogging supports a less diverse
array of vegetation than that of dryland forest, (Anderson, 1964). The increasing value of the
Simpson Diversity Index illustrates this decreasing diversity gradient moving into the forest.
There is not a significant relationship that associates vegetation species diversity with any one
nutrient content therefore there is a need to account for the distinct changes in floristic structure if
peat chemistry cannot be used for justification.
The changes in RIV’s exemplify how the transition forest is dominated by few tree species
namely, Sapotaceae, Palaquium, Leiocarpum (RIV = 193.08) and Dipterocarpaceae, Shorea,
Teysmanniana, (RIV = 93.89). There are many explanations for the dominance of these species
at this location. These particular species may just be better adapted to the characteristics of the
underlying peat surface such as the depth of peat and hydrology. There could also be an argument
61
for dominant species having excluded other subordinate species by the ‘Interspecific Exclusion
Hypothesis’. Sapotaceae, Palaquium, Leiocarpum and Dipterocarpaceae, Shorea, Teysmanniana
may have been able to take advantage of the resources presented at transect 3.5 km.
Not only does the floristic structure change across the peat dome the basal area increases with
distance into the MSF although there is a distinct reduction in basal area per plot in the Transition
forest. This highlights the change in forest type as tree density increases, the mean basal area
decreases. This is evidence for the presence of narrower trees, growing at a higher density, at
transects 3.5 km. There is a need to account for the distinct changes in forest type if again peat
chemistry cannot be used for justification.
Flowering patterns do not significantly change with distance (Figure 29) although there are a
slightly lower percentage of trees in flower in the Transition forest than that of the MSF.
Explanation for this decrease could simply be a reduction in the number of potentially flowering
tree species with distance, therefore ultimately a lesser degree of flowering in the Transition
forest. As the diversity of vegetation species decreases with distance it would suggest that some
species have become more dominant over others. It may also be that some species are able to
survive and flower in the MSF cannot do so in the Transition forest. During the month of July
however, there is a greater degree of flowering in the Transition forest, (Figure 28). It is difficult
to explain why this is occurring although certain species vegetation present in the transition forest
may display more flowering during the dry season than in both the dry and wet seasons
combined.
It has been recognized that there were differences in flowering pattern in 2007 and the mean
averages for 2004 to 2007. (Figures 31 and 32). Flowering peaks during June for both data sets
although there is a high percentage flowering during the ‘wet season’ in 2007 than that of the
62
average of all years. ‘2007 was an unusually wet year with a quite wet “dry” season’, (Page
pers.comm, 2008). Augspurger (1981) recognised that rainfall can trigger flowering. The
amplified rainfall could provide justification for evidently greater percentage flowering in 2007.
A wetter “dry” season could have triggered flowering that perhaps would not have occurred
under average rainfall levels.
4.4 Peat Depth and Gradient
Previous studies have highlighted the importance of the peat depth and its associated level of the
water table. Across the convex dome the peat layer increases in depth therefore the peat in the
MSF (approx 4 metres, Page et al, 1999) is considerably deeper than that at the Transition forest,
(approx 6.25 metres, Page et al, 1999). ‘There is no doubt that on ombrotrophic peatlands the
gradient is strongly associated with vegetation change’, (Phillips, 1998). The increasing gradient
of the peat surface with distance at Setia Alam (Shepherd et al,1997) would influence the flow of
water, therefore the delivery of nutrients available for uptake by vegetation.
The flow of water outwards from the peat dome brings a constant flow of nutrients in solution to
the mixed swamp forest. The vegetation in the MSF can then use these nutrients for growth,
which would be a further explanation for increased basal area here than in the transition forest.
Although peat chemistry does not show any significant relationship with distance it may be that
the nutrient content of surface and groundwater would display these trends. It would be expected
that nutrient contents in the Riverine type forest on the edge of the peat dome would receive yet
even greater concentrations of nutrients.
Due to the gradient of the peat surface, precipitation inputs to the peat dome drain outwards
towards the area of low pole and transition forest types which are closer to the edges. There is a
63
decrease in gradient between the low pole and tall interior forest (Page et al 1999). Water collects
here and stangnates due to a lower gradient. This creates anoxic conditions for a longer time
period; therefore particularly during the wet season the water table can be high above the peat
surface consequently only few species can survive. This provides further justification for an
evident reduction in vegetation diversity in the transition forest. Some of the flowering species
that are present in the MSF may not be adapted to these anoxic conditions therefore this goes
someway to explaining why there is a decrease in mean decrease in flowering in the Transition
forest.
4.5 Significance of the Water Table
The fluctuations in depth of the water table are critical in the control of vegetation growth. In
both forest types the water table frequently rises above the mean peat surface level, Shimada et al
(2001). The fluctuations in depth of the water table, creates a ‘moisture – aeration regime’,
Moore (1974). At high water table levels the conditions become more anoxic therefore making it
more difficult for oxygen to diffuse through pore spaces in the peat. The conditions are said to be
anaerobic and it is under these conditions that various chemical process can be inhibited.
Nitrogen concentrations are low because they are largely influenced by soil conditions, whether
they are aerobic or anaerobic. Bacteria require oxygen that would usually occupy upper aerobic
layers of peat therefore nitrification may be limited. This leads to a reduction in available
nitrogen, for uptake by vegetation, which then becomes a limiting factor for plant growth. As
anaerobic conditions are created across all transects nutrient availability would also be limited
across all transects which could explain the generally lower vegetation diversity than that of
dryland forest. ‘A pH < 5 also severly hampers nitrification’, (Rydin & Jeglum, 2006).
64
During the 1993 dry season the water table was higher in the transition forest (34.3 cm below the
surface) than the MSF, (39.0 cm below the surface), (Shepherd et al, 1997). As the water table is
lower in the MSF, the peat would be more aerated during the dry season, when compared to that
of the transition forest. Oxygen in aerated soils can diffuse through pore spaces more readily.
‘The redox potential is a measure of the tendancy of peat/water to oxidise or reduce substances’,
(Hobbie, 1992). In anaerobic conditions the redox potential decreases and a series of chemical
transformations can take place as a result of bacterial activity, (Gerrard, 1985). Minerals become
reduced, reduction of nitrate means nitrogen is lost as nitrous oxide, plants need this nutrient to
grow, and this could be a limiting factor for certain species.
During the dry season the water table would typically be below the surface although it is lower at
the edges, leading to more aerated soils. Oxygen can therefore diffuse more easily leading to a
greater availability of nutrients to vegetation. An increased availability of nutrients would result
in a greater basal area (see table 5) due to higher growth rates. Basal area is greater at transect
2.75 km in the MSF than at transect 3.5 km in the Transition forest.
4.6 Limitations and Areas for Further Research
Analysis of peat chemistry alone would not necessarily lead to a comprehensive reflection of the
nutrient content in the ecosystem. Peat nutrient concentrations are only rough approximations of
nutrient supply to plants, as most of the soil nutrient stocks can be occluded in relictriant forms
(Aerts & Chapin, 2000). The net nitrogen mineralisation rate as used by Ordoñez (2009) would
be a more accurate way of determining if nitrogen was a limiting nutrient in terms of the
65
composition of vegetation species. The main limitation of the research would be the number of
variables under investigation. An improvement would be to include data on chemistry of surface
water and nutrient content of biomass which would have provided a more definitive depiction of
nutrient uptake by plants.
A further limitation was that due to constraints on time only trees of dbh ≥ 6cm were recorded as
part of the vegetation data. For a more accurate analysis of vegetation diversity all species would
need to be recorded. Visual inspection of the field site revealed evidence for Pandans
(Freycinetia and Pandanus spp.) present with especially high density at 3.5 km these were not
included in the vegetation analysis. Further research could investigate the degree to which
pandans dominate Transition forest and account for their location.
The site history needs to be taken into account as in the past the area was used for logging
purposes. ‘Peat swamp forests have been logged intensively through the official concession
system, although most of this has now stopped because licenses have expired, but logging now
threatens the long term stability of the ecosystem’, (Böhm & Siegert, 2002). Selectively logging
trees may have altered the dynamics of the vegetation therefore causing inaccuracies with
analysis. Detailed information on the species that were logged would need to be provided in order
to account for any discrepancies in results.
In order to provide a justification for changes in vegetation diversity more ecological factors
would need to be taken into consideration. Soil nutrient analysis alone is not comprehensive
enough and even when taking into account changes in peat characteristics there are many other
biotic and abiotic factors that could be linked to vegetation diversity. Light availability,
competiton between species, soil temperature and moisture content could all be considered as
part of future research.
66
Further analysis could be undertaken to analyse the concentrations of toxic elements (Fe, Mn and
S) that may be contained with the peat. Waterlogged conditions can lead to more available forms
of metallic ions, Charman (2002). Accumulation of these toxic elements can limit plant growth.
Data may reveal relationships between vegetation species composition and the concentrations of
toxic levels on nutrients.
When considering nutrient uptake by vegetation there is a need to consider how rooting depth
may change across the site. Research into rooting systems and depth could reveal the depth at
which different species uptake nutrients from the peat. As the depth of the peat becomes
shallower it may be possible that roots are no longer uptaking nutrients from the peat but that of
the underlying mineral substrate. ‘Where peat is sufficiently shallow, some plants may be able to
root into underlying mineral soils to obtain additional nutrients to those present in the peat’,
(Smith, 2002). The peat is thicker with distance across the peat dome therefore with further
investigation it may be possible to identify if vegetation in the MSF is obtaining some nutrients
from the mineral soil.
Moore (1974) explains how the hydraulic conductivity can change given the bulk density of the
peat soil. In denser peat, water moves more slowly than through a matrix of looser peat thus
resulting in a different nutrient status. Further research could investigate if and how the bulk
density changes across the peat dome and how this affects delivery of nutrients.
67
Conclusion
68
5.0 Conclusion
The overall aim of this research was to investigate the role of soil fertility on the diversity of
vegetation in the upper catchment of the Sg.Sebangau tropical peat swamp forest, Central
Kalimantan. This aim has been addressed and results show that there is relative homogeneity of
peat nutrients across the MSF and transition forest types. Although there are distinct changes in
vegetation diversity across the peat dome, peat chemistry alone cannot alone be a causal factor.
69
From analysis of peat depth, surface gradient and depth of the water table it can be inferred that
topographic and hydrological factors play a critical role in determing vegetation type.
Due to the nature of the ombrotrophic peat dome, peat depth increases with distance and depth of
the water table increases with distance. Within the area of low pole and transition forest types
however there is a depression in the surface of the peat leading to stagnation of water and
creating anaerobic conditions for a longer period of time than that of the mixed swamp forest.
The anaerobic conditions inhibit uptake of nutrients by vegetation therefore only few species can
survive. In order to gain a more comprehensive illustration of how peat nutrient content may
influence vegetation there is a need to analyse the actual uptake of nutrients. The nutrient content
of the biomass may prove more valuable.
Research in the Sebangau National Park is important because of its significance with regard to
biodiversity. Whilst peat swamp forest vegetation is less diverse than dry land forest, it has been
recognised as an important reservoir of plant diversity in South East Asia (Anderson, 1963 &
Silvius et al, 1984). Peat swamps are a large terrestrial store of carbon, therefore protection of the
forest is important as disturbance would have implications for global climate change. Forest
clearing and drainage can lead to increased risk of fire therefore release of carbon emissions from
the system into the atmosphere.
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Appendix
77
Appendix A
Soil Analysis
Tra
nse
ct
No. Sa
mpl
e
pH (%)
Org
ani
c
Car
bon
N P K Ca Mg
0.4 1 3.4
1
57.
35
1.2
8
341
.59
0.8
8
0.6 1.0
1
0.4 2 3.7
9
57.
83
2.5
7
427
.06
0.1
7
0.6
8
0.9
7
0.4 3 3.5
9
57.
36
1.5
8
370
.4
0.4
6
1.4
8
0.9
6
0.4 4 3.4
1
57.
4
1.1
5
384
.49
0.3
6
3.7
1
1.0
5
0.4 5 3.7
2
57.
34
1.8
9
342
.72
0.2
7
1.0
4
0.9
8
Me
an
3.5
84
57.
456
1.6
94
373
.25
2
0.4
28
1.5
02
0.9
94
1 6 3.5
9
57.
37
1.5
2
355
.14
0.4
5
0.8
3
1.0
3
1 7 3.5
7
57.
43
0.6
7
351
.43
0.4
5
0.7
4
1
1 8 3.3
1
57.
43
1.6
7
376
.1
0.2
2
0.6
1
0.9
3
1 9 3.5
4
57.
48
0.9
3
378
.39
0.2 1.3
4
1.0
1
1 10 3.6
9
57.
32
2.1
6
472
.34
0.1
4
0.8 0.8
7
Me
an
3.5
4
57.
406
1.3
9
386
.68
0.2
92
0.8
64
0.9
68
1.6 11 3.6
5
57.
56
0.8
7
383
.92
0.1
3
1.2
6
0.9
4
1.6 12 3.5
5
57.
48
0.6
2
314
.36
0.3 0.9
8
0.9
9
78
1.6 13 3.6
4
57.
41
1.0
6
419
.65
0.2 0.7
5
0.9
1.6 14 3.6
7
57.
59
1.5
8
408 0.1
8
0.7
7
0.8
9
1.6 15 3.7 57.
59
1.1
7
302
.86
0.1
6
0.7
3
0.9
3
Me
an
3.6
42
57.
526
1.0
6
365
.75
8
0.1
94
0.8
98
0.9
3
2.2
5
16 3.6
7
57.
56
0.9
8
308
.13
0.1
4
0.7
5
0.9
2
2.2
5
17 3.6
4
57.
48
0.9
4
296
.78
0.1
9
0.8
5
0.9
8
2.2
5
18 3.6
6
57.
55
1.7
3
262
.19
0.3
4
0.7
9
0.9
4
2.2
5
19 3.6
8
57.
2
1.3 278
.84
0.2
6
0.8
8
1
2.2
5
20 3.6
3
57.
4
1.4
2
242
.53
0.2
3
0.8
5
0.9
6
Me
an
3.6
56
57.
438
1.2
74
277
.69
4
0.2
32
0.8
24
0.9
6
2.7
5
21 4 57.
4
1.6
9
505
.03
0.1
1
0.6
9
0.8
6
2.7
5
22 3.9
3
57.
21
1.2
3
382
.68
0.2 0.6
9
0.9
1
2.7
5
23 3.7
4
57.
08
1.1
5
483
.53
0.2 0.5
9
0.9
6
2.7
5
24 3.8
9
57.
18
1.1
2
478
.8
0.1
9
0.8
7
0.9
2
2.7
5
25 3.8
1
56.
75
3.3
1
541
.51
0.3
1
0.5
3
0.9
5
Me
an
3.8
74
57.
124
1.7 478
.31
0.2
02
0.6
74
0.9
2
3.5 26 3.9
3
57.
24
1.2
3
455
.78
0.3
8
2.0
9
0.9
5
3.5 27 3.8
1
57.
4
0.8
3
353
.24
0.2
4
1.9
4
0.9
7
79
3.5 28 3.8
8
57.
3
1.8
1
467
.02
0.1
9
1.3
2
0.9
3
3.5 29 3.8
6
57.
44
1.5
3
395
.27
0.2
4
4.5
5
0.8
6
3.5 30 3.9
9
56.
5
1.0
8
553
.51
0.2
2
2.5
8
1.0
4
Me
an
3.8
94
57.
176
1.2
96
444
.96
4
0.2
54
2.4
96
0.9
5
80
Appendix B
Summary outputs of Regression Analysis
1 – Carbon Nitrogen Ratio vs Distance (km)
SUMMARY OUTPUT
Regression Statistics
Multiple
R
0.144089
R Square 0.020762
Adj
uste
d R
Squ
are
-
0.22
405
Standard Error 8.582349
Obs
erva
tion
s
6
AN
OV
A
df SS MS F Sign
ifica
nce
F
81
Reg
ress
ion
16.24
663
8
6.24
663
8
0.08
480
7
0.78
536
2
Resi
dual
4294.
626
9
73.6
567
2
Total 5 300.8735
Coe
ffici
ents
Stan
dar
d
Err
or
t
Stat
P-
valu
e
Low
er
95
%
Upp
er
95
%
Low
er
95.0
%
Upp
er
95.0
%
Inte
rcep
t
40.1
772
7.31
887
7
5.48
953
0.00
536
5
19.8
567
4
60.4
976
6
19.8
567
4
60.4
976
6
X
Vari
able
1
0.97
632
3.35
254
8
0.29
121
7
0.78
536
2
-
8.33
185
10.2
844
9
-
8.33
185
10.2
844
9
2- Magnesium vs Distance (km)
SUMMARY OUTPUT
82
Regression Statistics
Multiple
R
0.643573
R
Squ
are
0.41
418
6
Adj
uste
d R
Squ
are
0.26
773
3
Stan
dar
d
Erro
r
0.02
289
6
Obs
erva
tion
s
6
AN
OV
A
df SS MS F Sign
ifica
nce
F
Reg
ress
ion
10.00
148
3
0.00
148
3
2.82
810
9
0.16
792
Resi
dual
40.00
209
7
0.00
052
4
Tot
al
50.00
357
9
Coe
ffici
ents
Sta
nda
rd
Erro
r
t
Stat
P-
valu
e
Low
er
95%
Upp
er
95%
Low
er
95.0
%
Upp
er
95.0
%
Inte
rcep
t
0.98
249
5
0.01
952
5
50.3
200
5
9.33
E-
07
0.92
828
5
1.03
670
5
0.92
828
5
1.03
670
5
83
X
Vari
able
1
-
0.01
504
0.00
894
4
-
1.68
17
0.16
792
-
0.03
987
0.00
979
1
-
0.03
987
0.00
979
1
3 – Magnesium vs Simpson Diversity Index
SUMMARY OUTPUT
Regression Statistics
Multiple
R
0.303069
R
Squ
are
0.09
185
1
Adj
uste
d R
Squ
are
-
0.13
519
Stan
dar
d
Erro
r
0.02
554
Obs
erva
tion
s
6
AN
OV
A
df SS MS F Sign
ifica
nce
F
Reg
ress
ion
10.00
026
4
0.00
026
4
0.40
456
3
0.55
931
5
Resi
dual
40.00
260
9
0.00
065
2
Tot
al
50.00
287
3
84
Coe
ffici
ents
Sta
nda
rd
Erro
r
t
Stat
P-
valu
e
Low
er
95%
Upp
er
95%
Low
er
95.0
%
Upp
er
95.0
%
Inte
rcep
t
0.30
438
2
0.40
724
6
0.74
741
7
0.49
635
5
-
0.82
631
1.43
507
8
-
0.82
631
1.43
507
8
X
Vari
able
1
-
0.27
153
0.42
689
2
-
0.63
605
0.55
931
5
-
1.45
677
0.91
371
6
-
1.45
677
0.91
371
6
85
4- Calcium vs Distance (km)
Regression Statistics
Multiple
R
0.362143
R
Squ
are
0.13
114
7
Adj
uste
d R
Squ
are
-
0.08
607
Stan
dar
d
Erro
r
0.72
087
7
Obs
erva
tion
s
6
AN
OV
A
df SS MS F Sign
ifica
nce
F
Reg
ress
ion
10.31
375
9
0.31
375
9
0.60
377
3
0.48
053
3
Resi
dual
42.07
865
3
0.51
966
3
Tot
al
52.39
241
1
Coe
ffici
ents
Sta
nda
rd
Erro
r
t
Stat
P-
valu
e
Low
er
95%
Upp
er
95%
Low
er
95.0
%
Upp
er
95.0
%
Inte 0.790.611.280.26 -2.49 -2.49
86
rcep
t
028
1
475
1
553
1
798
5
0.91
654
710
4
0.91
654
710
4
X
Vari
able
1
0.21
881
0.28
159
8
0.77
702
8
0.48
053
3
-
0.56
303
1.00
065
2
-
0.56
303
1.00
065
2
5- Calcium vs Simpson Diversity Index
SUMMARY OUTPUT
Regression Statistics
Multiple
R
0.303069
R
Squ
are
0.09
185
1
Adj
uste
d R
Squ
are
-
0.13
519
Stan
dar
d
Erro
r
0.02
554
Obs
erva
tion
s
6
AN
OV
A
df SS MS F Sign
ifica
nce
F
Reg
ress
ion
10.00
026
4
0.00
026
4
0.40
456
3
0.55
931
5
Resi
dual
40.00
260
9
0.00
065
2
87
Tot
al
50.00
287
3
Coe
ffici
ents
Sta
nda
rd
Erro
r
t
Stat
P-
valu
e
Low
er
95%
Upp
er
95%
Low
er
95.0
%
Upp
er
95.0
%
Inte
rcep
t
0.30
438
2
0.40
724
6
0.74
741
7
0.49
635
5
-
0.82
631
1.43
507
8
-
0.82
631
1.43
507
8
X
Vari
able
1
-
0.27
153
0.42
689
2
-
0.63
605
0.55
931
5
-
1.45
677
0.91
371
6
-
1.45
677
0.91
371
6
88
6- Simpson Diversity Index vs Distance (km)
SUMMARY OUTPUT
Regression Statistics
Multiple R 0.8727
81989
R Square 0.761748401
Adjusted R Square 0.702185501
Standard Error 0.013081512
Observations 6
ANOVA
df SS MS F
Signific
ance F
Regression 1
0.0021885
25
0.002
189
12.78
8974
4
0.0232
47158
Residual 4
0.0006845
04
0.000
171
Total 5
0.0028730
29
Coefficients
Standard
Error t Stat
P-
value
Lower
95%
Uppe
r 95%
Lower
95.0%
Upper
95.0%
Intercept 0.010411498
0.0111556
85
0.933
291
0.403
5040
3
-
0.0205
61648
0.041
3846
44
-
0.0205
6
0.0413
85
X Variable 1 0.018274465
0.0051100
69
3.576
168
0.023
2471
6
0.0040
86637
0.032
4622
92
0.0040
87
0.0324
62
7 – Percentage of trees in flower vs Distance (km)
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.309406
5
R Square
0.095732
4
Adjusted R Square
-0.130335
Standard Error
4.152726
5
Observations
6
ANOVA
df SS MS F Significance F
89
Regression
1 7.302783
7.302783
0.423469
1
0.550700
4
Residual
4 68.98055
17.24513
8
Total 5 76.28333
3
Coefficients
Standard Error
t Stat
P-valu
e
Lower
95%
Upper
95%
Lower
95.0%
Upper
95.0%
Intercept
15.94996
4
3.541372
6
4.503893
1
0.010790
3
6.117537
25.78239
6.117537
25.78239
X Variable 1
-1.055633
1.622191
8
-0.650745
0.550700
4
-5.55956
3.448293
2
-5.55956
3.448293
2
8 – Potassium vs Distance (km)
Regression Statistics
Multiple R 0.666543
R Square 0.444279
Adjusted R Square 0.305349
Standard Error 0.072174
Observations 6
ANO
VA
df SS MS F Signi
fican
ce F
Regr
essi
on
1 0.01
665
8
0.01
665
8
3.19
786
0.14
825
1
Resi
dual
4 0.02
083
0.00
520
90
6 9
Tota
l
5 0.03
749
4
Coef
ficie
nts
Stan
dard
Erro
r
t
Stat
P-
valu
e
Low
er
95%
Upp
er
95%
Low
er
95.0
%
Upp
er
95.0
%
Inter
cept
0.36
363
3
0.06
154
9
5.90
806
8
0.00
410
8
0.19
274
7
0.53
451
9
0.19
274
7
0.53
451
9
X
Vari
able
1
-
0.05
042
0.02
819
3
-
1.78
826
0.14
825
1
-
0.12
869
0.02
786
-
0.12
869
0.02
786
9- Potassium vs Simpson Diversity Index
Regr
essio
n
Stati
stics
Mult
iple
R
0.30
802
7
R
Squa
re
0.09
488
1
Adju
sted
R
Squa
re
-
0.13
14
Stan
dard
Erro
r
0.02
549
7
Obs
erva
tion
s
6
ANO
VA
91
df SS MS F Signi
fican
ce F
Regr
essi
on
1 0.00
027
3
0.00
027
3
0.41
930
7
0.55
257
2
Resi
dual
4 0.00
26
0.00
065
Tota
l
5 0.00
287
3
Coef
ficie
nts
Stan
dard
Erro
r
t
Stat
P-
valu
e
Low
er
95%
Upp
er
95%
Low
er
95.0
%
Upp
er
95.0
%
Inter
cept
0.06
820
4
0.03
666
6
1.86
011
0.13
638
3
-
0.03
36
0.17
000
6
-
0.03
36
0.17
000
6
X
Vari
able
1
-
0.08
527
0.13
167
8
-
0.64
754
0.55
257
2
-
0.45
086
0.28
032
9
-
0.45
086
0.28
032
9
10 – Phosporus vs Distance (km)
Regression Statistics
Multiple
R
0.398532
R
Squa
re
0.15
882
7
Adju
sted
R
Squa
re
-
0.05
147
Stan
dard
Erro
r
71.4
130
8
Observations 6
ANO
VA
df SS MS F
Significance
F
Regression13851.733851.730.755267 0.433852
92
Resi
dual
4 203
99.3
1
509
9.82
7
Total 5 24251.04
Coefficients Standard Error t Stat P-value Lower 95% Upper
95%
Lower
95.0%
Intercept 341.264560.89982 5.6037020.00498172.1795 510.3495172.1795 510.3495
X Variable 1 24.24358 27.8963 0.869061 0.433852 -53.209 101.6961 -53.209 101.6961
11- Phosporus vs Simpson Diversity Index
Regr
essio
n
Stati
stics
Multiple R 0.394483
R
Square
0.155617
Adju
sted
R
Squa
re
-
0.05
548
Stan
dard
Erro
r
0.02
462
7
Obs
erva
tion
s
6
ANO
VA
df SS MS F Signi
fican
ce F
Regr
essi
on
1 0.00
044
7
0.00
044
7
0.73
718
5
0.43
897
Resi
dual
4 0.00
242
6
0.00
060
6
Tota
l
5 0.00
287
3
93
Coef
ficie
nts
Stan
dard
Erro
r
t
Stat
P-
valu
e
Low
er
95%
Upp
er
95%
Low
er
95.0
%
Upp
er
95.0
%
Inter
cept
-
0.00
721
0.06
213
5
-
0.11
601
0.91
323
6
-
0.17
972
0.16
530
6
-
0.17
972
0.16
530
6
X
Vari
able
1
0.00
013
6
0.00
015
8
0.85
859
5
0.43
897
-
0.00
03
0.00
057
5
-
0.00
03
0.00
057
5
12- Nitrogen vs Distance (km)
Regression Statistics
Multiple R 0.197685
R
Square
0.039079
Adju
sted
R
Squa
re
-
0.20
115
Stan
dard
Erro
r
0.27
673
9
Obs
erva
tion
s
6
ANO
VA
df SS MS F Signi
fican
ce F
Regr
essi
on
1 0.01
245
8
0.01
245
8
0.16
267
5
0.70
733
5
Resi
dual
4 0.30
633
7
0.07
658
4
94
Tota
l
5 0.31
879
5
Coef
ficie
nts
Stan
dard
Erro
r
t
Stat
P-
valu
e
Low
er
95%
Upp
er
95%
Low
er
95.0
%
Upp
er
95.0
%
Inter
cept
1.48
590
2
0.23
599
8
6.29
625
4
0.00
325
2
0.83
066
7
2.14
113
7
0.83
066
7
2.14
113
7
X
Vari
able
1
-
0.04
36
0.10
810
3
-
0.40
333
0.70
733
5
-
0.34
374
0.25
654
1
-
0.34
374
0.25
654
1
13- Nitrogen vs Simpson Diversity Index
SUMMARY OUTPUT
Regression
Statistics
Mult
iple
R
0.20
308
2
R
Squa
re
0.04
124
2
Adju
sted
R
Squa
re
-
0.19
845
Stan
dard
Erro
r
0.02
624
2
Observations 6
ANO
VA
df SS MS F
Significance
F
Regression10.000118 0.0001180.1720650.699565
Resi
dual
4 0.00
275
5
0.00
068
9
95
Total 5 0.002873
Coefficients Standard Error t Stat P-value Lower 95% Upper
95%
Lower
95.0%
Intercept 0.0724730.066051 1.0972320.334158 -
0.11091
0.25586 -
0.11091
0.25586
X Variable 1 -0.01928 0.046477 -0.41481 0.699565 -0.14832 0.109762 -0.14832 0.109762
14 – Carbon vs Distance (km)
Regr
essio
n
Stati
stics
Mult
iple
R
0.74
855
5
R
Squa
re
0.56
033
4
Adju
sted
R
Squa
re
0.45
041
8
Stan
dard
Erro
r
0.12
151
2
Obs
erva
tion
s
6
ANO
VA
df SS MS F Signi
fican
ce F
Regr
essi
on
1 0.07
527
0.07
527
5.09
781
5
0.08
688
8
Resi
dual
4 0.05
906
1
0.01
476
5
Tota
l
5 0.13
433
1
96
Coef
ficie
nts
Stan
dard
Erro
r
t
Stat
P-
valu
e
Low
er
95%
Upp
er
95%
Low
er
95.0
%
Upp
er
95.0
%
Inter
cept
57.5
597
5
0.10
362
4
555.
469
8
6.3E
-11
57.2
720
4
57.8
474
5
57.2
720
4
57.8
474
5
X
Vari
able
1
-
0.10
717
0.04
746
7
-
2.25
783
0.08
688
8
-
0.23
896
0.02
461
7
-
0.23
896
0.02
461
7
15 – Carbon vs Simpson Diversity Index
Regression Statistics
Multiple
R
0.648348
R
Squa
re
0.42
035
6
Adju
sted
R
Squa
re
0.27
544
4
Stan
dard
Erro
r
0.02
040
4
Obs
erva
tion
s
6
ANO
VA
df SS MS F Signi
fican
ce F
Regr
essi
on
1 0.00
120
8
0.00
120
8
2.90
078
1
0.16
374
6
Resi
dual
4 0.00
166
5
0.00
041
6
Tota
l
5 0.00
287
97
3
Coef
ficie
nts
Stan
dard
Erro
r
t
Stat
P-
valu
e
Low
er
95%
Upp
er
95%
Low
er
95.0
%
Upp
er
95.0
%
Inter
cept
5.48
364
7
3.19
300
7
1.71
739
3
0.16
104
1
-
3.38
156
14.3
488
6
-
3.38
156
14.3
488
6
X
Vari
able
1
-
0.09
482
0.05
567
1
-
1.70
317
0.16
374
6
-
0.24
939
0.05
975
1
-
0.24
939
0.05
975
1
