Christopher Legg

EXTRAPOLATING TREE SPECIES ABUNDANCESFROM SAMPLE PLOTS TO THE WHOLE FOREST:

A CASE HISTORY FROM WESTERN JAMBI PROVINCE,SUMATRA, INDONESIA

Christopher LeggEU Forest Inventory and Monitoring Project

Jakarta Indonesia

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

The Forest Inventory and Monitoring Project, funded by the European Unionand working with the Ministry of Forestry and Plantation Crops,

has carried out a detailed inventory of 18 clusters of forest sample plotsin the four provinces of southern Sumatera

This map shows thelocations of plot clusters(black stars) against thebackground of an elevationmap.The western Jambi studyarea has the highestdensity of plots, and wasselected for this study

of species distribution.

Sample plot locations were chosenbased 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 atelevations less than 850 m in the western Jambistudy area, together with roads, settlementsand the locations of sample plot clusters. Contourswith an interval of 500m are also shown. The purple line is the boundary of the Kerinci National Park.

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

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

Topographic preferences oftree species in the Batang Ule

plot

View from NE

View from NW

3D plots of trees and topographyillustrate distribution of threedifferent species

Paranephelium xestophyllum(green leaves) is concentratedin the flat valley area, Pouteriamalaccensis (yellow leaves) prefersridges, while Parashorea lucida(blue leaves) shows no topographic preference.

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 beinvestigated using data from all 7 plots

Distribution of Species and Individuals in Jambi Sample Plots

0102030405060708090

100

1 2 3 4 5 6 7

Number of Plots

cum % species

cum % trees

Plots Species Individuals Cum Speciescum % speciesCum trees cum % trees7 9 1188 9 1.1 1188 10.26 22 1342 31 3.8 2530 21.85 28 1524 59 7.2 4054 35.04 57 1828 116 14.1 5882 50.73 80 1722 196 23.8 7604 65.62 159 1749 355 43.2 9353 80.71 467 2244 822 100.0 11597 100.0

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 betweennumber of species and number ofplots containing that species.Linear relationship between total number of individuals and number of plotswith that species.

Tebo Pandak Pemunyin

Sungai Pinang Pangkalan Jambu

Distribution of common species within plot clusters

To interpolate speciesabundance between plotclusters, the distribution

of individuals of that species within a cluster

must be relatively uniform.

The distribution of thethree most commontree species in four

clusters is shown here.

Details of distribution in a single plot cluster

Elateriospermum tapos - black crossesShorea parvifolia - red circlesShorea gibbosa - blue circles

Although there is some visiblelocalised clumping of individuals,the overall pattern is of a fairlyuniform regional distribution

Pangkalan Jambu cluster

Shorea conica

0

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0 50 100 150 200 250

density

dis

tan

ce

Shorea gibbosa

0

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0 5 10 15 20

density

dis

tan

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Dacryodes costata

0

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0 2 4 6 8

density

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Interpolation of abundance surfaces between plot clusters

Interpolation of abundances of individualtree species between sample plot clustersis based on the observation that, for somespecies, abundance changes linearly with distance.

Shorea conica occursin only 3 plots, andthe abundance/distancerelationship is linear

Shorea gibbosa showsa linear abundance/distancerelationship for 4 sites,decaying at lower abundances

Dacryodes costatashows no relationshipbetween abundanceand distance betweesample plots.

Plot Shorea gibbosa Pouteria malaccensis Dacryodes costata Payena accuminataObserved calculated observed calculated observed calculated observed calculated

BTU 1.67 2.00 9.33 1.70 1.33 3.77 2.67 1.59PKJ 13.78 13.73 4.00 0.46 5.33 0.68 0.00 0.91PMY 0.89 2.44 0.89 5.36 6.22 0.72 1.56 1.84SDR 1.56 2.90 2.44 0.41 2.67 0.41 2.22 1.46SPI 16.89 9.50 0.22 3.14 0.44 4.08 0.67 0.61TBK 2.44 1.38 4.44 1.93 0.44 5.10 1.56 1.70TLT 1.33 3.06 0.00 2.44 0.00 2.66 1.56 1.98correlation 0.91 -0.43 -0.79 0.65

Accuracy Assessment of Interpolation

The accuracy of interpolation of species densities canbe checked by calculating the species abundance at oneplot cluster from values at the other six clusters, repeatingthis process for all seven clusters, and then comparing thecalculated species densities with the observed densitiesat the clusters.This has been done for all species occuringat six or more clusters.

Results for 4 representative species are shown below. For Shorea gibbosa the correlationbetween 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. Interpolatedvalues must be seen graphically in order to understand these differences.

Examples of Abundance Surfaces

Shorea gibbosa Payena accuminata

Pouteria malaccensis Dacryodes costata

R=0.91T=165

R=0.65T=42

R=-0.43T=83

R=-0.78T=72

Abundance surfaces calculated forthe four species shown in the tableon the previous slide, showingdifferent types of distribution anddifferent degrees of correlationbetween observed and calculatedabundances

Correlation is highest when species distributionis unimodal, with a single area of greatest abundanceand a systematic decrease away from the peak

Correlation increases with increasing totalnumbers of individuals

Correlation is lowest when species distributionis multi-modal or random, with multiple peaksof abundance, and little or no similarity betweenadjacent plot clusters

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 studyarea, erupted massively in geologically recent times(probably less than 10,000 years ago), covering the wholearea with thick ash-flow tuffs and other debris

There was massive destruction of the forest

Present distribution patterns may reflect re-colonisationafter the eruption, with species spreading outwards fromtheir original establishment points by gradual seeddispersal

Multi-modal distributions could result from multiplere-establishment of more resistant species, or couldreflect control of underlying geology

Geological Control of Species Distribution?

Previous work in western Jambi suggested a strong geological control onspecies abundance

Granite - redMetamorphic - orangeYoung volcanics - greenClastic sediments - brown

Dacryodescostata

The current study does not support geological control

Old sample plots were separated by about 30km, and it is possible that spatial trendsin species abundance produced the effects interpreted as being due to geology

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

Genus Species Interpolate IndividualsShorea conica YYY 285Shorea gibbosa YYY 165Koompassia malaccensis YY 135Scaphium macropodum YY 129Santiria laevigata YY 101Elateriospermum tapos Y 326Shorea parvifolia Y 207Pimelodendron griffithianum Y 182Macaranga hypoleuca Y 81Diallium platysepalum Y 53Knema latifolia N 71Macaranga gigantea N 61Archidendron bubalinum N 60Pometia pinnata NN 94Shorea leprosula NN 58Pouteria malaccensis NNN 83Dacryodes costata NNN 72

• 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