1. EXTRAPOLATING TREE SPECIES ABUNDANCES FROM SAMPLE PLOTS TO THE WHOLE FOREST: A CASE HISTORY FROM WESTERN JAMBI PROVINCE, SUMATRA, INDONESIA Christopher Legg EU Forest Inventory and Monitoring Project Jakarta Indonesia Christopher Legg

2. It is important to know the species composition of natural tropical forest • to conserve rare species • to plan timber extraction • to assess resources of non-timber forest products • to manage wildlife BUT • tropical forests are extremely diverse • many species are very rare • some species have a very restricted range • distribution rules for tropical trees are poorly understood

3. The Forest Inventory and Monitoring Project, funded by the European Union and working with the Ministry of Forestry and Plantation Crops, has carried out a detailed inventory of 18 clusters of forest sample plots in the four provinces of southern Sumatera This map shows the locations of plot clusters (black stars) against the background of an elevation map. The western Jambi study area has the highest density of plots, and was selected for this study of species distribution.

4. Sample plot locations were chosen based on these criteria:- • relatively undisturbed primary forest • elevations less than 850 m asl • representative of the main lithologies • good geographic distribution Within plot clusters:- • Transects (3 or 4) normal to regional topography • 45 plots in each cluster • Plots spaced at about 100 metres along transects • Plots orientated normal to local topography • Plot dimensions 100*10 metres • All trees >10 cm DBH sampled and measured The map shows the extent of natural forest at elevations less than 850 m in the western Jambi study area, together with roads, settlements and the locations of sample plot clusters. Contours with an interval of 500m are also shown. The purple line is the boundary of the Kerinci National Park.

5. Extrapolation from sample plots to larger areas • total area of sample plots in a cluster is 4.5 hectares • average area of cluster is 440 hectares • total area of forest <850m in Jambi study area is 72,200 hectares • plots represent 1% of surrounding area and 0.025% of forest Is extrapolation possible? Possibility 1 • recognise associations between species and environment • topography classified as land facets • soils, possibly from combination of geology and land facets • extrapolate species based on known topography and geology Possibility 2 • assume that species counts from plots indicate averages for cluster • recognise regional trends in abundances of species • apply interpolation techniques to construct abundance surfaces

6. Relating Species to Environment • FIMP sample plots not ideally suited for this analysis • scattered 0.1 ha plots without continuity • uncertainty about surrounding topography • differences in scale between plot data and topographic maps Statistical studies have shown little correlation between species and slopes, land-facets and soils. • Batang Ule plot better suited for analysis • one plot of 3 hectares • detailed topographic mapping BUT • only one lithology (granite) • restricted area (3 ha vs 27 ha for FIMP plots) Batang Ule data used for initial tests in relating species to environment

7. Topographic preferences of tree species in the Batang Ule plot 3D plots of trees and topography illustrate distribution of three different species View from NE Paranephelium xestophyllum (green leaves) is concentrated in the flat valley area, Pouteria malaccensis (yellow leaves) prefers ridges, while Parashorea lucida (blue leaves) shows no topographic preference. View from NW

8. Conclusions from Batang Ule study • only 10 species have enough individuals to establish preferences • of these species, 4 are concentrated on ridges, 3 on lower slopes, 1 in the valley • less than 2% of species, and 25% of individuals, could be studied • Batang Ule is a relatively small and possibly unrepresentative plot • forest structure parameters such as mean basal area and tree height, correlate with topography Possibility of regular regional variations in species abundance must be investigated using data from all 7 plots

9. Distribution of tree species between plots • 8 species out of 822 occur in all plots • 22 species occur in 6 out of 7 plots • 56.8% of all species occur in only one plot • species in all plots are 10.2% of all individuals • species in 6 plots are 11.6% of all individuals • species in only one plot are 19.3% of individuals Exponential relationship between number of species and number of plots containing that species. Linear relationship between total number of individuals and number of plots with that species.

10. Distribution of common species within plot clusters To interpolate species abundance between plot clusters, the distribution of individuals of that species within a cluster must be relatively uniform. The distribution of the three most common tree species in four clusters is shown here. Pemunyin Tebo Pandak Sungai Pinang Pangkalan Jambu

11. Details of distribution in a single plot cluster Elateriospermum tapos - black crosses Shorea parvifolia - red circles Shorea gibbosa - blue circles Although there is some visible localised clumping of individuals, the overall pattern is of a fairly uniform regional distribution Pangkalan Jambu cluster

12. Interpolation of abundance surfaces between plot clusters Interpolation of abundances of individual tree species between sample plot clusters is based on the observation that, for some species, abundance changes linearly with distance. Shorea conica occurs in only 3 plots, and the abundance/distance relationship is linear Shorea gibbosa shows a linear abundance/distance relationship for 4 sites, decaying at lower abundances Dacryodes costata shows no relationship between abundance and distance betwee sample plots.

13. Accuracy Assessment of Interpolation The accuracy of interpolation of species densities can be checked by calculating the species abundance at one plot cluster from values at the other six clusters, repeating this process for all seven clusters, and then comparing the calculated species densities with the observed densities at the clusters.This has been done for all species occuring at six or more clusters. Results for 4 representative species are shown below. For Shorea gibbosa the correlation between observed and calculated densities is very high, 0.91, lower at 0.66 for Payena accuminata, and strongly negative for Pouteria malaccensis and Dacryodes costata. Interpolated values must be seen graphically in order to understand these differences.

14. Examples of Abundance Surfaces Abundance surfaces calculated for the four species shown in the table on the previous slide, showing different types of distribution and different degrees of correlation between observed and calculated abundances R=0.91 T=165 R=0.65 T=42 Payena accuminata Shorea gibbosa Correlation is highest when species distribution is unimodal, with a single area of greatest abundance and a systematic decrease away from the peak Correlation increases with increasing total numbers of individuals R=-0.78 T=72 R=-0.43 T=83 Correlation is lowest when species distribution is multi-modal or random, with multiple peaks of abundance, and little or no similarity between adjacent plot clusters Pouteria malaccensis Dacryodes costata

15. Discussion of Observed Distributions • some species show a smooth variation in abundance across the study area • the abundance of these species can be mapped by interpolation between plots • other common species show no correlation in abundance between adjacent plots • no extrapolation based on simple surfaces can be done for these species Gunung Tujuh, a volcano immediately west of the study area, erupted massively in geologically recent times (probably less than 10,000 years ago), covering the whole area with thick ash-flow tuffs and other debris There was massive destruction of the forest Present distribution patterns may reflect re-colonisation after the eruption, with species spreading outwards from their original establishment points by gradual seed dispersal Multi-modal distributions could result from multiple re-establishment of more resistant species, or could reflect control of underlying geology

17. Geological Control of Species Distribution? Previous work in western Jambi suggested a strong geological control on species abundance The current study does not support geological control Old sample plots were separated by about 30km, and it is possible that spatial trends in species abundance produced the effects interpreted as being due to geology Dacryodes costata Granite - red Metamorphic - orange Young volcanics - green Clastic sediments - brown

18. CONCLUSIONS • 5 species with 815 individuals can be interpolated with confidence • 5 species with 849 individuals can be interpolated but with less confidence • 1.2% of total species and 14% of individual trees can be interpolated • topographic preferences of 10 species are known • double the number of plot clusters • better topographic control of plots • more information on distribution rules • increased number of species extrapolated • improved knowledge of topographic controls