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
24 th February 2009
Approx number of words (12,000)
1 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
3 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 I of each peat sample.
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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 II 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.
6 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.
8
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.
9 Introduction
10 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.
11 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).
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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
13 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
14 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.
15 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
16 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.
17
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
20 enumeration data including, basal area per plot, reveals that the mixed swamp forest has a significantly higher basal area (75,054 cm 2) 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.
21 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).
22 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).
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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
24 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).
25 Methodology
26 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).
27
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.
28 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).
29
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
30
Table 1 – Locations of transects and sampling points for soil samples. A stratified random method was used .
Distance along Width of the phenology Sample Transect phenology plot plot (0-300 m) (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.
31
Figures 12– 16 show flow diagrams of methodologies used to determine peat chemistry. pH
10g peat sample Centrifuged pH measured and calibrated against known solutions of pH 4 and 7. + 30 minutes 5ml distilled water
Figure 12 - Flow diagram showing the methodology for determining pH of each peat sample.
Calcium, Magnesium and Potassium
5g peat sample Centrifuged Spectrometer Process repeated four 20ml peat/acetate analysis to + solution times to make a total determine 30 minutes 80ml solution Acetate concentrations of nutrients
Figure 13 – Flow Diagram showing the methodology for determining Calcium, Magnesium and Potassium content of each peat sample.
Organic Carbon
5g peat sample Sample is Sample is the placed Sample is oven dried for cooled, and 0 into an oven at 900 C cooled and 24 hours. mass was for a further 5 hours mass is (105 0C) recorded. to form ash. recorded.
% organic carbon= ash mass/dry mass x 100%
Figure 14 –Flow Diagram showing the methodology for determining organic carbon content of each peat sample
32
Nitrogen
0.05ml NaOH Solution made up to Solution is heated 50ml by addition of Solution is using an ‘autoklab’ + K2O8S2. which is a pressure then cooled. (Potassium steam sterilizer. For 2 0.05g peat peroxydisulfate ) hours. sample
Solution is then made up to 20ml by Add 0.8ml of H 2SO 9 2ml of solution addition of NaOH. (Salycilic acid) pipetted into a test tube.
Post reaction the solution is analysed by a Add 0.8ml of H 2SO 9 spectrometer. (Salycilic acid) (Wavelength = 410nm) Nitrogen concentrations were determined
Figure 15 – Flow Diagram showing the methodology for determining nitrogen content of each peat sample
Phosphorous v0.25g peat sample
Topped up with 15ml of solution is 15ml of solution is + distilled water filtered with filter filtered with filter to make a 1l paper (hole size paper (hole size 42). 257.2 ml of Perchloric Acid
The ‘Scheel’ method
was used for the 2ml of this solution is A 25 ml solution is then made up in
spectrometer used in the spectrometer. a conical flask by addition of distilled water.
‘Scheel 1’ method was used then ‘Scheel 2’ method then add a further added 2ml of solution 2ml of sample and left to settle for 15 minutes
Wavelength of 700nm used in the spectrometer ‘Scheel 3’ method 4ml of sample calibrated against standardised solutions of added to 5 ml of distilled water and known phosporous concentrations. left to settle for 15 minutes. ’ Figure 16 – Flow Diagram showing the methodology for determining phosphorous content of each peat sample.
33
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
34 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
35 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 X 100 Total Number of individuals
Relative Dominance* = Dominance of a species X 100 Dominance of all species
Relative Frequency = Frequency of a species (%) X 100 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.
36
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.
37
Analysis
38 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 R 2 and p value (p<0.05; r 2<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.
39 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 cm 2) and lowest at transect 3.5 km (167.04 cm 2). 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 Mean Basal Basal Area Tree Per Plot Area per per hectare Density (cm 2) plot * (m -2 ha -1) (ha-1) (cm 2) 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
40
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.
41
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.
42 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 Numbe Species Name Relative Type r of Importance Species 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, 39.54 elateriospermum / tokbrai
2.25 MSF 68 Sapotaceae, Palaquium, Leiocarpum 123.92 Myrtaceae, Syzygium, havilandii 37.39 Euphorbiaceae, Blumeodendron, 33.44 elateriospermum / tokbrai 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
43 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; r 2<0.57). There is no significant relationship between diversity of vegetation and organic carbon content, (Figure 21 (b)), (p >0.05; r 2<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; r 20.05).
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; r 2<0.16)
There is not a significant relationship between vegetation species diversity and phosphorous content. (Figure 23 (b)), (p>0.05; r 2<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).
44 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; r 2<0.42). There is not a significant relationship between vegetation diversity and magnesium content, (Figure 26 (b)), (p>0.05; r 2<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 Standard n=30 Error of means Ph 3.58 3.54 3.64 3.65 3.87 3.90 3.69 ±0.16 Organic 57.456 57.40 57.526 57.438 57.12 57.176 57.35 ±0.24 Carbon 6 4 (%) N 1.694 1.39 1.06 1.274 1.7 1.296 1.40 ±0.56 (%) P 373.25 386.6 365.75 277.69 478.3 444.69 387.77 ±80.44 (ppm) K 0.428 0.292 0.194 0.232 0.202 0.254 0.26 ±0.15 (mg/100g) Ca 1.502 0.864 0.898 0.824 0.674 2.496 1.20 ±0.93 (mg/100g) Mg 0.994 0.968 0.93 0.96 0.92 0.95 0.95 ±0.05 (mg/100g)
45
(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)
46
(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.
47
(a)
(b)
Figure 23- (a) P content of peat samples against distance into forest (b) P content against species diversity
(a)
48
(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
49
(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; r 2<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.
50
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.
51 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).
52
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%).
53
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’.
54
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)
55 Discussion
56 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
57 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
58 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
59 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
60 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
61 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
62 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).
63 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
64 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.
65
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.
66
Conclusion
67
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.
68 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.
References
Abrams, P.A. (1995) Monotonic or Unimodal Diversity Productivity Gradients: What Does Competition Theory Predict? Ecology , Vol. 76, No. 7 pp. 2019 2027.
Aerts, R. & Chapin, F.S. (2000) The mineral nutrition of wild plants revisited: a re evaluation of processes and patterns. Advances in Ecological Research , 30, 1– 67.
Anderson, J.A.R (1964) The structure and development of the peat swamps of Sarawak and Brunei. Journal of Tropical Geography , 18:7 16 69
Anderson, J.A.R (1983) The tropical peat swamps of Western Malesia. In Mires: Swamp, Bog, Fen and Moor . 4B Regional Studies. Ed.: Gore, A.J.P. Elsevier, Amsterdam, The Netherlands.
Augspurger, C. K. 1981. Reproductive synchrony of a tropical shrub:experimental studies on effects of pollinator and seed predators on Hybanthus prunifolius (Violaceae). Ecology 62: 775– 788.
Berendse, F. (1994) Litter decomposability – a neglected component of plant fitness . Journal of Ecology , 82, 187–190.
Boetler, D.H (1965) Hydraulic Conductivity of peat. Soil Science , 100:227 231.
Bridgham, S.D., Pastor, J, Janssens, J.A., Chapin,C. and Malterer, T.J. (1996). Multiple limiting gradients in peatlands – a call for a new paradigm. Wetlands . 16, 45 65.
Chapin, F.S. (2003) Effects of plant traits on ecosystem and regional processes: a conceptual framework for predicting the consequences of global change. Annuals of Botany , 91, 455–463
Cortlett R.T. & Lafrankie J.V. (1998). Potential Impacts of climate change on Tropical Asian Forests through influence on phenology. Climatic Change . 39: 2 3. pp. 439 453.
Crews, T.E., Kitayama, K., Fownes, J.H., Riley, R.H., Herbert, D.A., Mueller Dombois, D. & Vitousek, P.M. (1995) Changes in soil phosphorus fractions and ecosystem dynamics across a long chronosequence in Hawaii. Ecology , 76, 1407–1424.
Driessen,P.M. (1977) Peat Soils. In: Soils and Rice . Ed.:IRRI. International Rice Research Institute, Los Banos, Philippines. Pp. 763 779
Etherington, J.R. (1976) Environment and Plant Ecology. John Wiley and Sons, London, Chapter 8. Henry, M. Stevens, H. and Carson, W.P. (1999) Plant Density Determines Species Richness along an Experimental Fertility Gradient. Ecology, Vol. 80, No. 2 pp. 455 465 Hobbie, S.E. (1992) Effects of plant species on nutrient cycling. Trends in Ecology and Evolution , 7, 336–339
70
Hooper , D.U. and Vitouesk, P.M. (1997) The effects of plant composition and diversity on ecosystem processes. Science. Vol 27 Pages : 1302 1305.
Husson, S.J. D'Arcy, L.J, Nesbit, M., McLardy, C.S., Morrgh Benard. (2002). An investigation into the relationship of fruit availability and diversity, as a food source relating to Orang utan distribution during the extended dry season of 2001, within the Tropical Peat Swamp Forest of the Sungai Sebangau Catchment, Central Kalimantan, Indonesia Draft p resented at the International Symposium on land management and biodiversity.
Ingram, H.A.P. (1983) Hydrology. In : Ecosystems of the world. 4A Mires:Swamp, bog, fen and moor. Gneral Studies. (Ed Gore A.J.P). PP 67 158. Elsevier, Amsterdam.
Kanapathy K (1975) Growing crops on peat soils. Ministry of Agriculture and Rural Development, Malaysia
Kent, M. and Coker, P. (1992) Vegetation Description and Analysis: A practical Approach . John Wiley & Sons Ltd. Sussex, England. Pages 167 169
Kitchell J.F, Schindler D.E. Herwig B.R , Post D.M., and. Olson, M.H.(1999). Nutrient Cylcling at the Landscape Scale: The role of di el foraging migrations by geese at the Bosque del Apache National Wildlife Reguge, New Mexico . Limonology and Ocenography. 44 pp 828 836
Marage, D. and Gégout, J. PImportance of soil nutrients in the distribution of forest communities on a large geographical scale. Global Ecology and Biogeography , (Global Ecol. Biogeogr.) (2009)18, 88–97
Melillo, J.M., Aber, J.D. & Muratore, J.F. (1982) Nitrogen and lignin control of hardwood leaf litter decomposition dynamics. Ecology , 63, 621–626.
Moore, P.D. (1974) Peatlands. Unwin Brothers Limited. Surrey. Pages 7 8 & 84 87.
Mori, T. (2000). Effects of droughts and forest fires on dipterocarp forest in East Kalimantan. In E. Guhardja, M. Fatawi, M. Sutisna, T. Mori, and S. Ohta (Eds.). Ecological Studies 140,
71 Rainforest ecosystems of East Kalimantan: El Niiio, drought, fire and human impacts , pp. 29 45. Springer Verlag, Tokyo, Tokyo, Japan
Page, S. E., Rieley J.O, Shotyk O.W, (1999). Interdependence of peat and vegetation in a tropical peat swamp forest. PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY OF LONDON SERIES B-BIOLOGICAL SCIENCES Volume : 354 ( 1391) Pages 1885 1897 1999
Page, S. E., Wösten, R. A. J., Weiss, D., Rieley, J. O., Shotyk, W. and Limin, S. H. (2004). A record of Late Pleistocene and Holocene carbon accumulation and climate change from an equatorial peat bog (Kalimantan, Indonesia): implications for past, present and future carbon dynamics. J. Quaternary Sci ., Vol. 19 pp. 625–635.
Parkyn, L. Stoneman R.E. and Ingram, H.A.P Eds. (1997) Conserving Peatlands. CAB International . Pages 35 42.
Proctor J, Anderson J.M; Chai P, Vallack H.W.(1983) Ecological Studies in Four Contrasting Lowland Rain Forests in Gunung Mulu National Park, Sarawak: I. Forest Environment, Structure and Floristics. The Journal of Ecology , Vol. 71, No. 1. pp. 237 260.
Proctor M.C.F. Maltby E. (1998). Relations between acid atmospheric deposition and the surface pH of some ombrotrophic bogs in Britain. Journal of Ecology 86: 2 pp 329 340.
Rieley, J.O. & Page S.E. (1990). Ecology of Plant Communities: A phytosociological Account of the British Vegetation . Longman Scientific and Technical. Harlow. Pages
Rieley, J.O. Sieffermann, R.G. & Page S.E. (1993).The origin, development, present status and importance of the lowland peat swamp forest of Borneo. Suo , 43:241 244.
Rieley, J.O. & Page S.E. (1995) The Ecology and Environmental Importance of the Peat Swamp forest in the Sg.Sebangau Catchment, Central Kalimantan, Indonesia. Report of Fieldwork, 1993 1995. University of Nottingham (Unpublished report).
Rieley et al 1996. Distribution of peatlands in Indonesia. Global Peat Resources .
72
Rowell, D. (1994) Soil Science: Methods and Applications , Prentice Hall, London.
Russon A . E. Erman A. & Dennis R. (2001) .The population and distribution of orangutans (Pongo pygmaeus pygmaeus ) in and around the Danau Sentarum Wildlife Reserve, West Kalimantan, Indonesia . Biological Conservation . 97 : 1 pp. 21 28.
Ryden H. & Jeglum J. (2006). The Biology of Peatlands . Oxford University Press. Chapter 8.
Shimada, S., Takahashi, H., Haraguchi, A. & Kaneko, H. (2001) The carbon content characteristics of tropical peats in Central Kalimantan, Indonesia: Estimating their spatial variability in density Biogeochemistry 53: 249–267, 2001.
Shepherd, P.A. , Rieley, J.O., Page S.E. (1997) The relationship between forest structure and peat characteristics in the upper catchment of the Sungai Sebangau, Central Kalimantan, Indonesia. In: Biodiversity and Sustainability of tropical peatlands , Eds: J.O. Rieley & S.E. Page. pp 191 210
Silivius, M.J., Simons, H.W. and Verhheught, W.J.M. (1984) Soils, Vegetation and Nature Conservation for the Berbak Game Reserve, Sumatra, Indonesia . Arnhem Research Institute for Nature Management. The Netherlands.
Slik J, Poulsen A.D, Ashton A, Cannon C.H., Eichhorn K.A.,Kartawinata,K .Lanniari I., Nagamasu H., Nakagawa, M van Nieuwstadt G.L., Payne J., Purwaningsih, A. Saridan, K. Sidiyasa R.,W.Verburg R.W, Webb C.O. and Wilkie. P. A floristic analysis of the lowland dipterocarp forests of Borneo. Journal of Biogeography , 30, 1517–1531
Sakai. S. (2002).General flowering in lowland mixed dipterocarp forests of South east Asia. Biological Journal of the Linnean Society .75:2 pp. 233 247
Stholgren, T. (2007) Measuring Plant Diversity: Lessons from the Field . Oxford University Press. Page 28.
Sulistiyanto, Y., (2004). Nutrient Dynamics in different sub types of peat swamp forest in Central Kalimantan, Indonesia. Ph.D. Thesis, Department of Geography, University of Nottingham, UK.
73 Verry, E.S. (1997) Hydrological Processes of natural, northern forested wetlands. In: Northern forested wetlands : Ecology and Management. (Ed. Trettin C.C.) pp. 163 88. CRC Press, Boca Raton.
Vijarnsorn.P. (1996) Peatlands in Southeast Asia: a regional perspective. In: Maltby E, Immirzi CP, Safford RJ (eds) Tropical lowland peatlands of Southeast Asia, proceedings of a workshop on integrated planning and management of tropical lowland peatlands. Cisarua,Indonesia, 3–8 July 1992. IUCN, Gland, Switzerland.
Wantanabe, M. and Suzuki, E. (2007) Species Diversity, abundance and vertical size structure of rattans in Borneo and Java. Biodiversity and Conservation. 5 pp 351 359
Wösten, J. H. M. ; Van Den Berg, J. ;. Van Eijk , P; Gevers, G. J. M. ;. Giesen, W. B. J. T ; Hooijer, A. ; Idris, A. ; Leenman, P. H. ; Rais, D.S. ; Siderius, C. ; Silvius, M. J. ; Suryadiputra, N. ; Wibisono, I.T.(2006) Interrelationships between Hydrology and Ecology .in Fire Degraded Tropical Peat Swamp Forests. International Journal of water resources and development. 22(1) 157 174
Wösten, J.H. Clymans, E. Page S.E, Rieley, J.O. and Limin,S.H. (2008) Peat water interrelationships in a tropical peatland ecosystem in Southeast Asia. Catena. Vol 73 (2) Pages 212 224
Yule. C.M. & Gomez .L.N. (2008). Leaf Litter Decomposition in tropical peat swamp forest in Penninsula Malaysia . Wetlands Ecology Management . Vol 4 (1) Pages 255 263.
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75 Appendix
76
Appendix A
Soil Analysis
Tra No. Sa pH (%) N P K Ca Mg nse mpl Org ct e ani c Car bon
0.4 1 3.4 57. 1.2 341 0.8 0.6 1.0 1 35 8 .59 8 1
0.4 2 3.7 57. 2.5 427 0.1 0.6 0.9 9 83 7 .06 7 8 7
0.4 3 3.5 57. 1.5 370 0.4 1.4 0.9 9 36 8 .4 6 8 6
0.4 4 3.4 57. 1.1 384 0.3 3.7 1.0 1 4 5 .49 6 1 5
0.4 5 3.7 57. 1.8 342 0.2 1.0 0.9 2 34 9 .72 7 4 8
Me 3.5 57. 1.6 373 0.4 1.5 0.9 an 84 456 94 .25 28 02 94 2
1 6 3.5 57. 1.5 355 0.4 0.8 1.0 9 37 2 .14 5 3 3
1 7 3.5 57. 0.6 351 0.4 0.7 1 7 43 7 .43 5 4
1 8 3.3 57. 1.6 376 0.2 0.6 0.9 1 43 7 .1 2 1 3
1 9 3.5 57. 0.9 378 0.2 1.3 1.0 4 48 3 .39 4 1
1 10 3.6 57. 2.1 472 0.1 0.8 0.8 9 32 6 .34 4 7
Me 3.5 57. 1.3 386 0.2 0.8 0.9 an 4406 9 .68 92 64 68
1.6 11 3.6 57. 0.8 383 0.1 1.2 0.9 5 56 7 .92 3 6 4
1.6 12 3.5 57. 0.6 314 0.3 0.9 0.9 5 48 2 .36 8 9
77 1.6 13 3.6 57. 1.0 419 0.2 0.7 0.9 4 41 6 .65 5
1.6 14 3.6 57. 1.5 408 0.1 0.7 0.8 7 59 8 8 7 9
1.6 15 3.757. 1.1 302 0.1 0.7 0.9 59 7 .86 6 3 3
Me 3.6 57. 1.0 365 0.1 0.8 0.9 an 42 526 6 .75 94 98 3 8
2.2 16 3.6 57. 0.9 308 0.1 0.7 0.9 5 7 56 8 .13 4 5 2
2.2 17 3.6 57. 0.9 296 0.1 0.8 0.9 5 4 48 4 .78 9 5 8
2.2 18 3.6 57. 1.7 262 0.3 0.7 0.9 5 6 55 3 .19 4 9 4
2.2 19 3.6 57. 1.3 278 0.2 0.8 1 5 8 2 .84 6 8
2.2 20 3.6 57. 1.4 242 0.2 0.8 0.9 5 3 4 2 .53 3 5 6
Me 3.6 57. 1.2 277 0.2 0.8 0.9 an 56 438 74 .69 32 24 6 4
2.7 21 4 57. 1.6 505 0.1 0.6 0.8 5 4 9 .03 1 9 6
2.7 22 3.9 57. 1.2 382 0.2 0.6 0.9 5 3 21 3 .68 9 1
2.7 23 3.7 57. 1.1 483 0.2 0.5 0.9 5 4 08 5 .53 9 6
2.7 24 3.8 57. 1.1 478 0.1 0.8 0.9 5 9 18 2 .8 9 7 2
2.7 25 3.8 56. 3.3 541 0.3 0.5 0.9 5 1 75 1 .51 1 3 5
Me 3.8 57. 1.7 478 0.2 0.6 0.9 an 74 124 .31 02 74 2
3.5 26 3.9 57. 1.2 455 0.3 2.0 0.9 3 24 3 .78 8 9 5
3.5 27 3.8 57. 0.8 353 0.2 1.9 0.9 1 4 3 .24 4 4 7
78 3.5 28 3.8 57. 1.8 467 0.1 1.3 0.9 8 3 1 .02 9 2 3
3.5 29 3.8 57. 1.5 395 0.2 4.5 0.8 6 44 3 .27 4 5 6
3.5 30 3.9 56. 1.0 553 0.2 2.5 1.0 9 5 8 .51 2 8 4
Me 3.8 57. 1.2 444 0.2 2.4 0.9 an 94 176 96 .96 54 96 5 4
79
Appendix B
Summary outputs of Regression Analysis
1 – Carbon Nitrogen Ratio vs Distance (km)
SUMMARY OUTPUT
Regression Statistics Multiple0.144089 R
R Square 0.020762 Adj uste 0.22 d R 405 Squ are
Standard Error 8.582349 Obs 6 erva tion s
AN OV A
df SS MS F Sign ifica nce F
80 Reg 16.24 6.24 0.08 0.78 ress 663 663 480 536 ion 8 8 7 2
Resi 4294. 73.6 dual 626 567 9 2
Total 5 300.8735
Coe Stan t P- Low Upp Low Upp ffici dar Stat valu er er er er ents d e 95 95 95.0 95.0 Err % % % % or
Inte 40.1 7.31 5.48 0.00 19.8 60.4 19.8 60.4 rcep 772 887 953 536 567 976 567 976 t 7 5 4 6 4 6
X 0.97 3.35 0.29 0.78 10.2 10.2 Vari 632 254 121 536 8.33 844 8.33 844 able 8 7 2 185 9 185 9 1
2- Magnesium vs Distance (km )
SUMMARY OUTPUT
81 Regression Statistics Multiple 0.643573 R
R 0.41 Squ 418 are 6
Adj 0.26 uste 773 d R 3 Squ are
Stan 0.02 dar 289 d 6 Erro r
Obs 6 erva tion s
AN OV A
df SS MS F Sign ifica nce F
Reg 10.00 0.00 2.82 0.16 ress 148 148 810 792 ion 3 3 9
Resi 40.00 0.00 dual 209 052 7 4
Tot 50.00 al 357 9
Coe Sta t P- Low Upp Low Upp ffici nda Stat valu er er er er ents rd e 95% 95% 95.0 95.0 Erro % % r
Inte 0.98 0.01 50.3 9.33 0.92 1.03 0.92 1.03 rcep 249 952 200 E- 828 670 828 670 t 5 5 5 07 5 5 5 5
82
X -0.00 -0.16 -0.00 -0.00 Vari 0.01 894 1.68 792 0.03 979 0.03 979 abl e 504 4 17 987 1 987 1 1
3 – Magnesium vs Simpson Diversity Index
SUMMARY OUTPUT
Regression Statistics Multiple 0.303069 R
R 0.09 Squ 185 are 1
Adj - uste 0.13 d R 519 Squ are
Stan 0.02 dar 554 d Erro r
Obs 6 erva tion s
AN OV A
df SS MS F Sign ifica nce F
Reg 10.00 0.00 0.40 0.55 ress 026 026 456 931 ion 4 4 3 5
Resi 40.00 0.00 dual 260 065 9 2
Tot 50.00 al 287 3 83
Coe Sta t P- Low Upp Low Upp ffici nda Stat valu er er er er ents rd e 95% 95% 95.0 95.0 Erro % % r
Inte 0.30 0.40 0.74 0.49 -1.43 -1.43 rcep 438 724 741 635 0.82 507 0.82 507 t 2 6 7 5 631 8 631 8
X -0.42 -0.55 -0.91 -0.91 Vari 0.27 689 0.63 931 1.45 371 1.45 371 able 153 2 605 5 677 6 677 6 1
84 4- Calcium vs Distance (km)
Regression Statistics Multiple 0.362143 R
R 0.13 Squ 114 are 7
Adj - uste 0.08 d R 607 Squ are
Stan 0.72 dar 087 d 7 Erro r
Obs 6 erva tion s
AN OV A
df SS MS F Sign ifica nce F
Reg 10.31 0.31 0.60 0.48 ress 375 375 377 053 ion 9 9 3 3
Resi 42.07 0.51 dual 865 966 3 3
Tot 52.39 al 241 1
Coe Sta t P- Low Upp Low Upp ffici nda Stat valu er er er er ents rd e 95% 95% 95.0 95.0 Erro % % r
Inte 0.79 0.61 1.28 0.26 -2.49 -2.49 85 rcep 028 475 553 798 0.91 710 0.91 710 t 1 1 1 5 654 4 654 4
X 0.21 0.28 0.77 0.48 -1.00 -1.00 Vari 881 159 702 053 0.56 065 0.56 065 able 8 8 3 303 2 303 2 1
5- Calcium vs Simpson Diversity Index
SUMMARY OUTPUT
Regression Statistics Multiple 0.303069 R
R 0.09 Squ 185 are 1
Adj - uste 0.13 d R 519 Squ are
Stan 0.02 dar 554 d Erro r
Obs 6 erva tion s
AN OV A
df SS MS F Sign ifica nce F
Reg 10.00 0.00 0.40 0.55 ress 026 026 456 931 ion 4 4 3 5
Resi 40.00 0.00 dual 260 065 9 2
86
Tot 50.00 al 287 3
Coe Sta t P- Low Upp Low Upp ffici nda Stat valu er er er er ents rd e 95% 95% 95.0 95.0 Erro % % r
Inte 0.30 0.40 0.74 0.49 -1.43 -1.43 rcep 438 724 741 635 0.82 507 0.82 507 t 2 6 7 5 631 8 631 8
X -0.42 -0.55 -0.91 -0.91 Vari 0.27 689 0.63 931 1.45 371 1.45 371 able 153 2 605 5 677 6 677 6 1
87 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 Signific df SS MS F ance F 12.78 0.0021885 0.002 8974 0.0232 Regression 1 25 189 4 47158 0.0006845 0.000 Residual 4 04 171 0.0028730 Total 5 29
Standard P- Lower Uppe Lower Upper Coefficients Error t Stat value 95% r 95% 95.0% 95.0% 0.403 - 0.041 - 0.0111556 0.933 5040 0.0205 3846 0.0205 0.0413 Intercept 0.010411498 85 291 3 61648 44 6 85 0.023 0.032 0.0051100 3.576 2471 0.0040 4622 0.0040 0.0324 X Variable 1 0.018274465 69 168 6 86637 92 87 62
7 – Percentage of trees in flower vs Distance (km)
SUMMARY OUTPUT
Regression Statistics Multi 0.30 ple 9406 R 5
R 0.09 Squ 5732 are 4
Adju - sted 0.13 R 0335 Squ are
Stan 4.15 dard 2726 Error 5
Obs 6 ervat ions
ANO VA
df SS MS F Signi fican ce F
88 Regr 1 7.30 7.30 0.42 0.55 essi 2783 2783 3469 0700 on 1 4
Resi 4 68.9 17.2 dual 8055 4513 8
Total 5 76 .2 8333 3
Coef Stan t P- Low Upp Low Upp ficie dard Stat valu er er er er nts Error e 95% 95% 95.0 95.0 % %
Inter 15.9 3.54 4.50 0.01 6.11 25.7 6.11 25.7 cept 4996 1372 3893 0790 7537 8239 7537 8239 4 6 1 3
X - 1.62 - 0.55 - 3.44 - 3.44 Vari 1.05 2191 0.65 0700 5.55 8293 5.55 8293 able 5633 80745 4 956 2 956 2 1
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 1 0.01 0.01 3.19 0.14 essi 665 665 786 825 on 8 8 1
Resi 4 0.02 0.00 dual 083 520 89 6 9
Tota 5 0.03 l 749 4
Coef Stan t P- Low Upp Low Upp ficie dard Stat valu er er er er nts Erro e 95% 95% 95.0 95.0 r % %
Inter 0.36 0.06 5.90 0.00 0.19 0.53 0.19 0.53 cept 363 154 806 410 274 451 274 451 3 9 8 8 7 9 7 9
X - 0.02 - 0.14 - 0.02 - 0.02 Vari 0.05 819 1.78 825 0.12 786 0.12 786 able 042 3 826 1 869 869 1
9- Potassium vs Simpson Diversity Index
Regr essio n Stati stics
Mult 0.30 iple 802 R 7
R 0.09 Squa 488 re 1
Adju - sted 0.13 R 14 Squa re
Stan 0.02 dard 549 Erro 7 r
Obs 6 erva tion s
ANO VA
90 df SS MS F Signi fican ce F
Regr 1 0.00 0.00 0.41 0.55 essi 027 027 930 257 on 3 3 7 2
Resi 4 0.00 0.00 dual 26 065
Tota 5 0.00 l 287 3
Coef Stan t P- Low Upp Low Upp ficie dard Stat valu er er er er nts Erro e 95% 95% 95.0 95.0 r % %
Inter 0.06 0.03 1.86 0.13 - 0.17 - 0.17 cept 820 666 011 638 0.03 000 0.03 000 4 6 3 36 6 36 6
X - 0.13 - 0.55 - 0.28 - 0.28 Vari 0.08 167 0.64 257 0.45 032 0.45 032 able 527 8 754 2 086 9 086 9 1
10 – Phosporus vs Distance (km)
Regression Statistics Multiple 0.398532 R
R 0.15 Squa 882 re 7
Adju - sted 0.05 R 147 Squa re
Stan 71.4 dard 130 Erro 8 r
Observations 6
ANO VA
Significance df SS MS F F Regression13851.73 3851.73 0.755267 0.433852 91
Resi 4 203 509 dual 99.3 9.82 1 7
Total 5 24251.04
Coefficients Standard Error t Stat P-value Lower 95% Upper Lower 95% 95.0% Intercept 341.2645 60.89982 5.603702 0.00498172.1795 510.3495 172.1795 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 0.155617 Square
Adju - sted 0.05 R 548 Squa re
Stan 0.02 dard 462 Erro 7 r
Obs 6 erva tion s
ANO VA
df SS MS F Signi fican ce F
Regr 1 0.00 0.00 0.73 0.43 essi 044 044 718 897 on 7 7 5
Resi 4 0.00 0.00 dual 242 060 6 6
Tota 5 0.00 l 287 3
92
Coef Stan t P- Low Upp Low Upp ficie dard Stat valu er er er er nts Erro e 95% 95% 95.0 95.0 r % %
Inter - 0.06 - 0.91 - 0.16 - 0.16 cept 0.00 213 0.11 323 0.17 530 0.17 530 721 5 601 6 972 6 972 6
X 0.00 0.00 0.85 0.43 - 0.00 - 0.00 Vari 013 015 859 897 0.00 057 0.00 057 able 6 8 5 03 5 03 5 1
12- Nitrogen vs Distance (km)
Regression Statistics Multiple R 0.197685 R 0.039079 Square
Adju - sted 0.20 R 115 Squa re
Stan 0.27 dard 673 Erro 9 r
Obs 6 erva tion s
ANO VA
df SS MS F Signi fican ce F
Regr 1 0.01 0.01 0.16 0.70 essi 245 245 267 733 on 8 8 5 5
Resi 4 0.30 0.07 dual 633 658 7 4
93 Tota 5 0.31 l 879 5
Coef Stan t P- Low Upp Low Upp ficie dard Stat valu er er er er nts Erro e 95% 95% 95.0 95.0 r % %
Inter 1.48 0.23 6.29 0.00 0.83 2.14 0.83 2.14 cept 590 599 625 325 066 113 066 113 2 8 4 2 7 7 7 7
X - 0.10 - 0.70 - 0.25 - 0.25 Vari 0.04 810 0.40 733 0.34 654 0.34 654 able 36 3 333 5 374 1 374 1 1
13- Nitrogen vs Simpson Diversity Index
SUMMARY OUTPUT
Regression Statistics
Mult 0.20 iple 308 R 2
R 0.04 Squa 124 re 2
Adju - sted 0.19 R 845 Squa re
Stan 0.02 dard 624 Erro 2 r
Observations 6
ANO VA
Significance df SS MS F F Regression10.000118 0.000118 0.1720650.699565
Resi 4 0.00 0.00 dual 275 068 5 9
94 Total 5 0.002873
Coefficients Standard Error t Stat P-value Lower 95% Upper Lower 95% 95.0% Intercept 0.072473 0.066051 1.097232 0.334158 -0.25586 -0.25586 0.11091 0.11091
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 0.74 iple 855 R 5
R 0.56 Squa 033 re 4
Adju 0.45 sted 041 R 8 Squa re
Stan 0.12 dard 151 Erro 2 r
Obs 6 erva tion s
ANO VA
df SS MS F Signi fican ce F
Regr 1 0.07 0.07 5.09 0.08 essi 527 527 781 688 on 5 8
Resi 4 0.05 0.01 dual 906 476 1 5
Tota 5 0.13 l 433 1
95
Coef Stan t P- Low Upp Low Upp ficie dard Stat valu er er er er nts Erro e 95% 95% 95.0 95.0 r % %
Inter 57.5 0.10 555. 6.3E 57.2 57.8 57.2 57.8 cept 597 362 469 -11 720 474 720 474 5 4 8 4 5 4 5
X - 0.04 - 0.08 - 0.02 - 0.02 Vari 0.10 746 2.25 688 0.23 461 0.23 461 able 717 7 783 8 896 7 896 7 1
15 – Carbon vs Simpson Diversity Index
Regression Statistics Multiple 0.648348 R
R 0.42 Squa 035 re 6
Adju 0.27 sted 544 R 4 Squa re
Stan 0.02 dard 040 Erro 4 r
Obs 6 erva tion s
ANO VA
df SS MS F Signi fican ce F
Regr 1 0.00 0.00 2.90 0.16 essi 120 120 078 374 on 8 8 1 6
Resi 4 0.00 0.00 dual 166 041 5 6
Tota 5 0.00 l 287
96 3
Coef Stan t P- Low Upp Low Upp ficie dard Stat valu er er er er nts Erro e 95% 95% 95.0 95.0 r % %
Inter 5.48 3.19 1.71 0.16 - 14.3 - 14.3 cept 364 300 739 104 3.38 488 3.38 488 7 7 3 1 156 6 156 6
X - 0.05 - 0.16 - 0.05 - 0.05 Vari 0.09 567 1.70 374 0.24 975 0.24 975 able 482 1 317 6 939 1 939 1 1
97