Ecology, 83(10), 2002, pp. 2782–2797
q 2002 by the Ecological Society of America

HABITAT PATTERNS IN TROPICAL RAIN FORESTS: A COMPARISON OF
105 PLOTS IN NORTHWEST BORNEO
MATTHEW D. POTTS,1,5 PETER S. ASHTON,2 LES S. KAUFMAN,3 AND JOSHUA B. PLOTKIN4
1Division of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts 02138 USA
Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, Massachusetts 02138 USA
3Department of Biology, Boston University, Boston, Massachusetts 02215 USA
4Institute for Advanced Study and Princeton University, Princeton, New Jersey 08540 USA

2

Abstract. Understanding the maintenance of high tropical tree species diversity requires
disentangling the effects of habitat vs. geographic distance. Using floristic, topographic,
and soil nutrient data from 105 0.6-ha plots in mixed dipterocarp forest throughout Sarawak,
Malaysian Borneo, we explore the degree to which floristic patterns are habitat-driven fromlocal to landscape scales. We assess how the floristic influence of geographic distance vs.
abiotic factors varies from local to regional scales. We employ several multivariate analyticaltechniques and perform a hierarchical clustering of the research plots using the Steinhaus
index of floristic dissimilarity, as well as Mantel analyses on matrices of floristic, habitat,and geographic distance. These analyses indicate that floristic variation is more strongly
correlated with habitat than with geographic distance on the regional scale. On the locallandscape to community scale, we find evidence of a resource threshold above which habitat
effects weaken; that is, below the resource threshold floristic similarity between sites isdominated by habitat effects, while above the threshold floristic similarity between sites isdominated by geographic-distance effects. We also find evidence that topography and soil
nutrients correlate in part independently with floristics. These results, together with previous
studies in the Neotropics, emphasize that tree species distribution and community composition are variously influenced by the interplay of both habitat and dispersal-driven effects.
Key words: Borneo, lowland ever-wet rain forest; cluster analysis; deterministic vs. stochastic
effects; diversity; habitat effects vs. dispersal-driven effects; Mantel analysis; ordination; Sarawak,
Malaysian Borneo; soil nutrients; tropical rain forest.

INTRODUCTION
Most theories of the maintenance of high tropicaltree species diversity rely to some degree on habitatand distance effects. Niche-assembly theories (Lieberman et al. 1985, Hubbell and Foster 1986, Denslow
1987, Kohyama 1994, Terborgh et al. 1996, Clark et
al. 1998) stress the importance of environmental heterogeneity while dispersal-assembly theories (Tilman
and Pacala 1993, Hurtt and Pacala 1995, Hubbell et al.
1999) emphasize the effects of spatial isolation generated by dispersal limitation. Disentangling the effectsof habitat vs. geographic distance is essential to understanding the maintenance of high tropical tree species diversity.
The evidence for habitat-driven vs. historically driven floristic patterns in species-rich tropical tree communities is equivocal. A large number of scientists haveclaimed to show habitat-driven patterns at geographic
and landscape scales (Ashton [1964, 1976] and Austin
et al. [1972] in Borneo; Gentry [1988], Tuomisto andRuokolainen [1994], Tuomisto et al. [1995], Duivenvoorden [1995] and Duivenvoorden and Lips [1995],
in the Upper Amazon; Clark et al. [1995] in Costa
Manuscript received 21 March 2001; revised 4 February 2002;
accepted 4 March 2002.
5 E-mail: potts@deas.harvard.edu

Rica). But many others have claimed that such patterns
are weak and subordinate to the gradual floristic change
incurred by limited seed-dispersal distance (Wong andWhitmore 1970, Hubbell 1979, 2001, Hubbell and Foster 1986) and mortality and speciation rates (Hubbell
1997, 2001). To date, only a few studies have investigated the relative influence of habitat factors and geographical distance on floristic composition at a rangeof spatial scales (Gentry 1988, Terborgh et al. 1996,
Pyke et al. 2002).
In this study, we systematically explore the degree
to which floristic patterns are habitat driven on the local
and landscape scales. We focus our analyses on thelowland ever-wet rainforests of northwest Borneo, a
region of uniform climate but extremely rich species
diversity and high habitat heterogeneity (Ashton 1964,
1984, Whitmore 1984, Wong 1998).
The floristic and structural diversity of the lowland
forests of northwest Borneo have long been documented (Beccari 1902). The zonal forest on lowland
red-yellow ultisolic soils is mixed dipterocarp forest(MDF), so called on account of family dominance ofthe emergent canopy and ectotrophic mycorrhizal Dipterocarpaceae, but absence of species dominance. MDF
is distinguished by exceptional albeit variable species
richness, and tall stature of 35–75 m Ashton (1964).
Previous studies in Borneo have to some extent doc-

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HABITAT EFFECTS IN TROPICAL FORESTS

umented relationships between habitat and floristic
composition. Austin et al. (1972) showed that tree species composition correlated with topography and/or
soil nutrients at two MDFs on contrasting soils .100
km apart in Brunei, each with 50 plots. Newbery and
Proctor (1984) compared forests and soils between andwithin single plots, in Mulu National Park, Sarawak,
including MDF, heath forest on podsols, swamp forest,and forest over karst limestone. They found that therelationships among soil nutrients, floristics, and habitat, if any, were complex. This was due in part to thefact that the soils in their samples were derived primarily from organic matter and had higher soil nutrient
concentrations than soils derived from rock substrates.
Finally, Baillie et al. (1987) analyzed 5000 clusteredpoint samples in MDF throughout a region in Sarawakof ;4000 km2 overlying a single sedimentary geological formation; samples from mineral soil were analyzed using similar methods to ours. They found thatspecies’ distributions differed in relation to soils’ mineral nutrient concentrations, notably phosphorus andmagnesium.
In contrast, studies in the Neotropics have reported
conflicting results concerning the relationship between
habitat and floristic composition. In plots .40 km apartin mature floodplain forests of the Peruvian Amazon,
Terborgh et al. (1996) found that mature-forest samples
floristically resembled one another more than they did
adjacent successional and upland-forest samples. Pitman et al. (1999) compared eight 1-ha plots in 29 sites
throughout the Amazon valley, including upland andfloodplain forests. They found that forests of the samebroad habitat type but in different regions were lesssimilar than forests in different habitats within the same
region. Because historical geologic and edaphic variation were apparently correlated at geographical scale,the relative influence of habitat- and history-driven
forces could not be disentangled. Terborgh and Andersen (1998) documented the distribution of 825 tree
species in plots with an overall area .36 ha in a varietyof habitats in 400 km2 of the Upper Amazon. There,
they found that about 15% were habitat specialists, andconcluded that most were generalists; most were also
geographically widespread. Pitman et al. (2001) haveconfirmed that most upland species are widespread in
the Peruvian and Ecuadorian Amazon; one third werecommon to plots 1400 km apart and most generally
occurred in very low densities but with nonrandom
local abundance. Finally, Pyke et al. (2002) comparedsamples of mixed lowland semideciduous forest, at
known distances apart. They demonstrated that floristicchange is strongly correlated with distance betweenplots, and not, apparently, with abiotic factors. In noneof these Neotropical studies were soils samples taken,
nor was rainfall seasonality taken into account though
the length of the dry season varies by up to four months
in the larger scale studies.
Duivenvoorden (1995) and Duivenvoorden and Lips

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(1995), however, documented similar patterns of species richness to those on low-nutrient soils in northwest
Borneo, from the low-nutrient sands of the Guyana
Shield rocks of the aseasonal Columbian Amazon.
Soils samples were analyzed, but distance relationships
were not studied. The main floristic gradient in speciesrich mixed forest on red-yellow upland soils over sandstone was correlated with mineral soils nutrients. Species turnover appeared to be lower than reported, forinstance by Ashton (1964) in Borneo. Two main species
associations were recognized, on soils differing in theform of their surface humus in a manner analogous totemperate mull and mor.
The following questions therefore still remain, andprovide the focus of this paper:
1) How does the relative influence of geographic
distance vs. abiotic factors on tree species composition
vary from local to regional scales? This question addresses the fundamental issue, as yet untested by field
data, of the relative role of historical factors vs. abiotic
factors in structuring rain-forest floristics at landscapeand larger scales. The existence of predictable floristic
spatial variation is of more than theoretical interest. If
species assemblages occur as a mosaic of islands
throughout the landscape, then predictions from The
Theory of Island Biogeography (MacArthur and Wilson
1967) will in part be applicable at this smaller scale.
That would imply that plant species extinctions would
be substantially delayed if widespread forest fragments
are conserved, though this prediction would be invalidated were mobile links lost.
2) At the local community scale, are the influencesof abiotic factors, specifically soils nutrients and topography, stronger in some habitats than others? (a)Is there a nutrient threshold above which any correlations between nutrient concentrations are relaxed, andovertaken by correlation with between-plot distances?
(b) Do the relative strengths of correlations between
floristic composition and different abiotic factors varywith habitat? Specifically, do soil nutrient concentrations correlate with floristic composition independently
from topography?
3) Do the geographical and floristic patterns in the
Neotropics generalize to other parts of the tropics?
MATERIALS AND METHODS

Environmental setting
Northwest Borneo experiences an unusually equable
climate with .2500 mm of rain. In general, every
month receives on average more rainfall than the expected evapotranspiration. Occasional droughts do occur (Brunig 1969, Baillie 1976). However, the unprecedented El Niño-related drought of 1982–1983 failed
to penetrate this region, while the yet more intense
1997–1998 drought only reached the northeast down
to Bintulu (Fig. 1), and at relatively low intensities (M.
D. Potts, unpublished manuscript). Geological evi-

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Ecology, Vol. 83, No. 10

FIG. 1. Map of northwest Borneo, indicating the 12 site locations (see Table 1 for site abbreviations). Numbers inparentheses indicate the number of plots at each site.

dence suggests that northwest Borneo has escaped major changes in rainfall seasonality since the mid-Miocene (Morley and Flenley 1987, Morley 1999).
The lowlands of northwest Borneo are now recognized as an extension of the Riau Pocket (Corner 1960,
Ashton 1984, Wong 1998), a floristically distinct andremarkably species-rich plant region (Plotkin et al.
2000b) in the center of the Sunda Shelf, also encompassing the Riau Archipelago and eastern peninsular
Malaysia. Northwest Borneo consists of late Cretaceous and Tertiary sediments predominated by sandstone, and intercalated with mainly Miocene volcanic
intrusions. Intermittent tectonic uplift, which continues, has created a largely rugged landscape but forundulating coastal hills on soft Neogene sediments andextensive peat swamps. Variable susceptibility to erosion of thin sediments and high rainfall characterize
the landscape, resulting in generally shallow, often
highly leached soils of varying nutrient concentration(Baillie et al. 1987).

Field sampling
Our investigation relies on a set of data collected in
the 1960s throughout Sarawak, East Malaysia, the major state in northwest Borneo. One hundred and five0.6-ha (1.5-acre) plots were laid out throughout apparently mature lowland mixed dipterocarp forest inSarawak, East Malaysia (see Plate 1), over a distanceof 500 3 150 km at 1.58–4.58 N latitude. The plots
were clustered at fourteen localities, here termed‘‘sites’’ (Fig. 1). The fourteen sites chosen sample thefull range of upland yellow/red soils and geologicalformations found in the region (Table 1). At three sites,
judged on existing evidence to be on relatively highnutrient mineral soils (C), medium-nutrient soils (H),and low-nutrient soils (G), at least 15 plots were established to provide data for independent analysis (Fig.
1, Table 1).
A different number of plots was established at eachsite (Fig. 1). Plots were placed at least 200 m apart,and were positioned so as to include, as far as possible,
a single topographic element: plateaus and convex
slopes, concave slopes, and flat alluvium, here termedridges, slopes, and valley bottoms. Locations with recent or large canopy gaps were avoided. A plot areaof 0.6 ha (1.5 acres) was chosen in light of some deficiencies associated with previous 1-acre plot sizes in
Brunei (Ashton 1964). As in that study, ridge plots werelinear, 300 m long, and slope and valley-bottom plots
were rectangular 60 3 100 m. In each plot, all trees.9.8 cm in diameter at breast height (dbh) (12 inches
girth) were measured for girth and identified to species.
In each plot, soils were augured to 30 cm at two
random points, and a pit dug to 1 m at a third random

HABITAT EFFECTS IN TROPICAL FORESTS

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PLATE 1. Canopy of a mixed Dipterocarp forest located in Lambir Hills National Park (see Site L in Fig. 1), Sarawak,
Malaysia. Photograph by Matthew D. Potts.

TABLE 1.

Summary site descriptions. Soils are listed in order of predominance at each site.
Site

Altitude (m)†Substrata

A1) Carapa Pila (Kajang Hill)

870

Tertiary basalt

A2) Ulu Temiai, Mujong Hose
Mountains
B) Santubong Mountain840

Tertiary dacite

260–340

Paleocene sandstoneC) Mount Mersing‡

140–880

Tertiary basaltD) Ulu Dapoi, Tinjar

150–220

Tertiary daciteE) Bok-Tisam Forest Reserve

30–150Miocene shaleF) Bako National Park‡

30–75

Paleocene sandstoneG) Iju Hill, Ulu Arip

75–300Tertiary rhyoliteH) Raya Hill, Kapit

120–460

Eocene shale, sandstoneJ) Ulu Bakong, Miri

30–90

Miocene shaleK) Northern Lambir Hills

60–120Miocene sandstone, clay

L) Southern Lambir Hills NationalPark‡

70–160Miocene sandstone, shaleM) Nyabau Forest Reserve

45–160

Oligocene sandstoneN) Segan Forest Reserve

30–210

Oligocene sandstone, shale† Above sea level.
‡ Site includes permanent plots (Ashton and Hall 1992).

Soil
udult ultisol; red-brown friableloam, thin patchy mor
udult ultisol; gray-brown friableloam; thin mor
humult ultisols; yellow sand;
4–6 cm mor
udult ultisols, inceptisols; yellow-brown friable clay; thinmor (high ridges)
humult, (D1) udult (D2) ultisols; yellow clay; thin mor(D1)
udult ultisols; yellow brown,
friable loam; no mor
humult ultisols; yellow sand;
6–9 cm mor
skeletal humult ultisols, inceptisols; yellow loam; thin
patchy mor
skeletal humult ultisols, inceptisols; yellow loam; thin
patchy mor
udult ultisols; yellow-brown friable loam; no mor
humult ultisols; yellow sand,sandy clay; 5–10 cm mor
humult (L2 udult) ultisols; yellow sand, sandy clay; 5–10
cm morhumult ultisols; yellow-red
sandy clay; 5–12 cm mor
humult (udult) ultisols; yellow
loam, thin patchy mor

MATTHEW D. POTTS ET AL.

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point. Depths of litter, raw humus free of mineral soil
(if present), humus-discolored mineral soil, mineralsoil above bedrock, mottling (if present), rooting, substrate fragments, and substrate (when reached) wererecorded in the pit. Samples were collected from themineral soil at two depths (20–30 cm; 70–80 cm) inthe deep pit, and at one depth (20–30 cm) at the augured
points.
The soil samples were analyzed at the Semongok
Research Center of the Sarawak Department of Agriculture, using the methods given by Sim (1965). The
samples were separately analyzed for pH (in water).
Samples were then air dried. ‘‘Reserve’’ contents ofphosphorus and the cationic nutrients were extractedwith hot concentrated hydrochloric acid (Bailey 1967).
The iron and the aluminum contents of this extract were
determined by alkaline precipitation and, taken together, are referred to here as ‘‘Group III elements.’’ Mechanical analysis was by the pipette method after treatment with hydrogen peroxide and dispersion with sodium hexametaphosphate.
The following topographic variables were also recorded at each plot: altitude, ‘‘steepness’’ (estimatedas the change in altitude averaged over all the 20 3
20 m subplots), and land formation (ridge or slope).

Data analysis
To determine the scale and relative importance of
abiotic factors in shaping community structure, we employed four distinct methodological approaches. All
methods relied upon the same basic measure of floristic
dissimilarity between sites. Alternative floristic metrics(which we also tested) yielded similar results.
Indices of floristic similarity.—Throughout the paper, we utilize the Steinhaus index of floristic dissimilarity. The Steinhaus is equivalent to the Bray-Curtis
index of dissimilarity on the number of stems by species (Legendre and Legendre 1998).
The Steinhaus dissimilarity index, DS, between plots
A and B is defined as follows:

DS (A, B) 5 1 2

2

O min(n , n ) .
O (n 1 n )
A
i

A
i

B
i

B
i

(1)

The sum is taken over all species, and nAi denotes
the abundance of the ith species at plot A. Therefore,
the denominator of DS is simply the total number ofindividuals at both sites.
Habitat factors.—The abiotic variables that we analyzed were altitude, steepness, ridge vs. slope, pH,

Ecology, Vol. 83, No. 10

and percentage clay. Soil chemistry is expressed asconcentrated HCl-extractable P, Ca, Mg, K, and GroupIII elements (Al and Fe) at 20–30 cm. We also analyzed
the Group III ratio between the two sample depths (20–30 cm and 70–80 cm) as a measure of leaching (Fig.
2).
Cluster analysis.—We performed a cluster analysis
on the floristic composition of our 105 study plots. We
used a standard, hierarchical average-linkage clustering
algorithm. This algorithm initially assigns each plot toa separate group; at each iteration, the clustering routine unites the two groups that have the smallest meandissimilarity (i.e., floristic dissimilarity, measured viaDS) between them. The algorithm is complete when all
the plots are united into one group. The results of suchan analysis are easily visualized in a dendrogram. The
average-linkage clustering makes no a priori assumptions about underlying structure in the data.
At each iteration in the cluster analysis, we determined whether any habitat variables were significantly
correlated with the clustering. In other words, at eachbifurcation, a boot-strapping permutation test (Good1994) determined whether the mean values of each habitat factor differed significantly between the two clustergroups.
Mantel analyses.—We used a Mantel analysis (Mantel 1967, Legendre and Legendre 1998) to investigate
the relative strengths of geographic distance vs. abiotic
habitat factors as determinants of floristic composition.
A complete Mantel analysis entails computing distancematrices and then computing simple and partial Mantel
statistics that are identical to those of simple and partial
Pearson correlation coefficients, except the Mantel statistics take into account the natural dependence foundin the distance matrices. Significance of results is usually judged (as it was in this case) by empirically deriving a test statistic by bootstrapping the original data.
It should be noted that results of a Mantel analysis arecorrelations between matrices that measure the extent
to which the variation in the distances of one matrix
corresponds to the variation in the distances of the othermatrix.
We performed the Mantel test on the data set as awhole as well as those plots from Mount Mersing (C),Raya Hill (H), and Iju Hill (G) in which at least 15
plots were sited, separately. At the Mersing site weonly used the 15 low-altitude plots (,500 m). Owing
to concerns of significant floristic dissimilarity betweenplots in different sites, the Mantel test on the data set→

FIG. 2. Box plots of habitat factor data, by site. The height of the box is equal to the interquartile distance (IQD) whichis the difference between the third and first quartile of the data. The whiskers extend to the extreme values of the data or
1.5 3 IQD from the center, whichever is less. A1 and A2 are in different sites (see Fig. 1); E1, K1, and L2 are plots thatclustered far from the other plots in their respective sites. (see Fig. 3). The ‘‘a’’ after the soil habitat factor indicates the
sample was taken at 20–30 cm depth; ‘‘ppm’’ means parts per million, i.e., micrograms soil residual element per gramuniversal soil.

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FIG. 3. Biplot of results of principal components analysis (PCA) for soil habitat factors across all 105 plots. The x-axis
represents the scores for the first principal component, the y-axis the scores for the second principal component; in the axis
label the percentage of variance explained is given in parentheses. The original variables are represented by arrows thatgraphically indicate the proportion of the original variance explained by the first two principal components. The directionof the arrows indicates the relative loadings on the first and second principal components. After the variable names, ‘‘a’’means samples were taken at a depth of 20–30 cm, while ‘‘b’’ means 70–80 cm; ppm 5 parts per million (i.e., mg element/g mineral soil).

as a whole was performed by pooling four of the plots(,2 km) from each of the sites with at least four plots(C, E, F, G, H, J, K, L, M, N) and using these combinedplots to calculate the floristic dissimilarity matrix (asimilar procedure was used to calculate the habitat matrix). In this way we hoped to minimize the effects ofhigh species turnover on dissimilarity indices.
The spatial distance matrix was calculated by tabulating the pairwise geographic distances betweenplots, while the floristic distance matrix was calculatedusing the Steinhaus index.
The calculation of the abiotic-factor distance matrices was somewhat more complicated. Preliminary analyses of the soil chemistry data indicated a high degreeof correlation among the various soil variables. To distill the key edaphic differences between plots, we performed a principal components analysis (PCA) on thecorrelation matrix of edaphic factors, and used projections onto the first component as a single measure ofedaphic distance between two plots (Fig. 3). Before the
PCA was performed the soil nutrient data were nor-malized by the use of the Box-Cox transformation andthen standardized. The correlation matrix was used so
as to equally weight all soil nutrient factors. For habitat
distance matrices involving a single habitat variable(residual Mg and residual P each in parts per million[micrograms of soil residual element per gram of universal soil]), interplot distances were calculated by simply taking the absolute difference in nutrient levels
between the sites.
We also computed matrices that summarized the
pairwise topographic distances between the plots. The
presence of both discrete (ridge vs. slope) and continuous (altitude and steepness) topographic data required
the use of Gower’s (1971) dissimilarity index. Gower’s
dissimilarity index, DG (Legendre and Legendre 1998),
between plots A and B with n descriptors is defined asfollows:

DG (A, B) 5 1 2

O

1 p
s.
n j51 j(2)

In this equation, for a discrete variable j, sj 5 0 for

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FIG. 4. Plot similarity as a function of distance. An ‘‘C’’ indicates pairs of plots differing in soil surface characteristics(humult vs. udult) with different surface lithologies, while a ‘‘1’’ indicates pairs of plots with similar soil surface characteristics.

agreement between plots A and B, and sj 5 1 for disagreement. For a continuous descriptor j, sj 5 1 2 z Aj
2 Bj z /Rj where Rj is the maximum difference between
all plots under consideration. The Gower index conveniently compiles both discrete and continuous topographic variables into a single topographic metric.
The use of a PCA to distill the soil nutrient data andthe use of Gower’s index to combine continuous andordinal data have both advantages and disadvantages.
Preliminary analysis of the soil nutrient data indicateda high degree of covariation among variables. Using a
PCA allowed us to account for this high covarianceamong soil nutrient variables and create a few combined measures of soil nutrient variability among theplots. We do admit though that PCAs can be noisy andmay lead to some loss of information. We believe thatits ability to distill the soil nutrient data outweighs any
loss of power its use creates. The main advantage ofGower’s index was that it allowed us to combine continuous data on altitude and slope with landform data.
While the index is sensitive to scale of measurement
we have no reason to believe that it drastically altered
the results. Analyses using a few plots indicated thatthe difference between a slope and ridge could be justas significant as a large change in altitude.
Given all the pairwise matrices defined above, theMantel tests based on Kendall’s t nonparametric rankorder statistic were performed. According to Dietz(1983), Kendall’s t has greater power than the standard
Z statistic, often used in Mantel analyses. Partial Mantel statistics, whose calculation are identical to firstorder Kendall partial correlation statistics, are also re-ported (Legendre and Legendre 1998). The partial
Mantel test measures whether two factors (e.g., floristics and habitat) are correlated across the plots aboveand beyond cross correlation through a third factor(e.g., distance).
Plot similarity 3 distance relationships.—We also
plot the relationship between floristic similarity andgeographic distance, ignoring all other habitat variables. These results permit comparison with recent
work by Pyke et al. (2002) in the Neotropics, where
quantitative abiotic data were not available. We havelabelled this graph (Fig. 4) according to the surface soil
characteristics of the sites, however, to emphasize an
abiotic factor-driven trend that explains much of theobserved pattern.
RESULTS

Floristic patterns and species habitat preferences
We sampled 47 786 individuals representing 1762
species within the 105 0.6-ha plots. There was significant variation in species diversity, stem density, andFisher’s alpha (Fisher et al. 1943) among the plots (Fig.
5). While 16% of the species sampled only occurred
once, ,6% of the pairwise comparisons of plots had
no species in common (Fig. 5d). Thus, while high,species turnover does not lead to an inordinate numberof pairs of plots having floristic dissimilarity of 1.
However, species did show a strong geographic affiliation. Of the 436 species that occurred in 10 or moreof the plots, 85% of them were geographically associated with a particular site or group of sites (Fisher’s

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FIG. 5. Floristic summary of sample plots: histograms of (a) stem density (median: 459); (b) species diversity (median:150); (c) Fisher’s alpha (median: 74.2); and (d) species turnover as measured by the number of species shared between twoplots (median: 21).

exact test [Zar 1984] on species site preference). Thishigh degree of species spatial ‘‘clumping’’ by site precluded us from being able to statistically analyze individual species’ habitat preference. However, if we
restrict ourselves to the species that were among the
10 most abundant species in at least five of the plots
we do find some suggestive evidence for habitat preferences among species (Table 2). Sixty species occurred among the 10 most abundant species in at least
5 plots, 18 species in at least 10 plots, and one in 25
plots. Of these, 51 occurred among the 10 most abundant at more than one site. Of the nine species confined
to one site, six were in the Mount Mersing basalt plots(C), and one species each were confined to the G, H,
and M plots. Forty-three of the 60 species were confined to one of the two major plot clusters: 23 species
to the plot group on siliceous soils and 20 species tothe plot group on clay-rich rocks.

Relative importance of geographic distance vs.
abiotic habitat factors on the landscape scale
The Mantel test of plots from Sarawak (northwest
Borneo) as a whole (Table 3) indicates that the association between floristic composition and edaphic factors (0.651), as measured by the first axis of the soil
chemistry variables PCA, is significantly stronger thanthe correlation between floristics and geographic dis-

tance (0.058). The first axis of the PCA (Fig. 3) isstrongly correlated with Mg, K, and Group III elements.
The results of the cluster analysis echo those of theMantel test. Recall that the cluster dendrogram wascalculated from floristic information alone. The firstdivision in the dendrogram (Fig. 6) neatly coincides
with two major habitat-related groups: those plots withclay-rich udult soils lacking surface raw humus except.600 m (sites A, C, E, and J) from those plots withsandy or sandy-clay humult soils with a surface raw
humus horizon. The two clusters also differ significantly in the majority of the soil mineral variables. The
next few bifurcations in the dendrogram are also correlated strongly with habitat characteristics, despite
substantial geographic distances between co-clusteredsites. For example, the third division breaks off thehigh-altitude Mount Mersing (C plots) and Carapa Pila
(A1) and Ulu Temiai (A2) plots from the rest of udultgroup; the fourth division breaks off the Raya Hill (H)plots despite their closer geographic proximity to the
Iju Hill (G) plots. Nevertheless, the dendrogram doessuggest a positive effect of geographic distance on floristics. For example, the second division breaks off the
B and F plots, which are the furthest away, spatially,
from the rest of the plots, though their soils also havethe lowest clay and Group III element content, andrepresent an extreme on at least one soil gradient (Fig.

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TABLE 2. Species rank-order abundance at 14 sites in northwest Borneo. Reported are species occurring among the 10 mostabundant species in at least five of the 105 0.6-ha plots.
Type†

No. of plots‡

Plots species occurs in§

Shorea macroptera ssp. baillonii
Koilodepas longifolium
Vatica micrantha
Allantospermum borneense
Shorea balanocarpoides
Dryobalanops aromatica
Dryobalanops lanceolata
Millettia nieuwenhuisii
Mangifera parvifolia
Diospyros curraniopsis

Genus and species

humultgeneralist
humulthumultgeneralist
humultudult
generalist
humult
udult

25
24
22
22
19
17
17
16
15
15

G(13) H(10) N(2)C(5) E(2) G(3) H(7) J(4) L(1) N(2)G(9) H(3) K(1) L(3) M(4) N(2)G(8) K(3) L(3) M(5) N(3)G(12) H(3) J(2) M(1) N(1)G(9) K(3) L(4) M(1)C(9) E(8)E(9) H(3) J(4)F(1) G(3) H(3) L(6) M(2)C(6) E(7) J(2)Teijsmanniodendron sarawakanum
Dipterocarpus caudiferus
Dacryodes expansa
Dryobalanops beccarii
Elateriospermum tapos
Mallotus wrayi
Gonocaryum longiracemosum
Diospyros sumatrana
Mesua myrtifolia
Hopea andersonii

udult
udult
humulthumultgeneralist
generalist
humultudult
humult
udult

15
13
12
12
12
11
11
10
9
9

C(14) D(1)
C(11) J(2)G(5) K(3) L(4)B(2) D(1) F(3) H(4) N(2)E(7) G(1) K(2) L(1) N(1)C(8) H(3)F(2) H(2) K(2) M(3) N(2)C(4) D(1) E(5)G(6) M(2) N(1)C(9)

Drypetes microphylla
Eugenia valdevenosa
Alangium javanicum
Polyalthia cauliflora
Lophopetalum globrum
Dipterocarpus globosus
Pimeleodendron griffithianum
Gymnacranthera contracta
Shorea macroptera ssp. macropteraefolia
Shorea pinanga

udult
udult
udult
udult
generalist
humultgeneralist
humultgeneralist
generalist

9
9
8
8
8
8
8
8
7
7

C(9)
A(2) C(7)C(2) E(6)C(8)C(1) G(4) H(3)K(2) L(5) M(1)E(1) F(2) G(3) H(2)G(2) K(3) M(1) N(2)D(1) G(3) J(1) N(2)H(6) J(1)Shorea faguetiana
Mallotus leptophyllus
Hydnocarpus polypetala
Hydnocarpus woodii
Eusideroxylon zwageri
Bouea oppositifolia
Hopea dryobalanoides
Shorea polyandra
Hopea mesuoides
Shorea faguetoides

humultgeneralist
generalist
udult
generalist
humultudult
udult
generalist
generalist

7
7
7
7
7
6
6
6
6
6

G(1) H(5) N(1)A(1) E(1) H(2) J(2) L(1)C(4) E(2) H(1)C(5) E(2)C(4) E(2) H(1)L(1) M(5)C(4) G(2)E(5) J(1)J(2) N(4)G(2) H(2) J(2)Shorea parvifolia
Shorea pauciflora
Ptychopyxis arborea
Mallotus leucocalyx
Ganua pierrei
Teijsmanniodendron holophyllum
Melanorrhoea wallichii
Gluta laxiflora
Orophea sp.
Popowia pisocarpa

generalist
humultudult
udult
humulthumulthumulthumult
udult
udult

6
6
6
6
6
6
5
5
5

D(1) E(3) J(1) K(1)G(2) H(3) K(1)C(5) E(1)C(6)B(1) F(1) G(4)H(4) N(2)B(2) G(1) H(2)L(1) M(4)A(1) C(3) E(1)C(5)

Dipterocarpus rigidus
Shorea pilosa
Shorea rubra
Shorea xanthophylla
Vatica odorata
Vatica vinosa
Mallotus griffithianus
Cryptocarya densiflora
Nephelium mutabile
Teijsmanniodendron sinclairii

humulthumulthumultudult
generalist
generalist
humultudult
udult
humult

5
5
5
5
5
5
5
5
5
5
5

M(5)
G(1) H(4)H(5)C(4) L(1)C(1) H(4)A(1) C(3) N(1)G(3) M(1) N(1)C(5)C(4) E(1)G(5)† Types of occurrence, i.e., whether the species is confined to a particular plot group in the cluster analysis (udult, highnutrient; humult, low nutrient) or whether the species is a generalist.
‡ Total number of plot occurrences.
§ Numbers in parentheses are the number of plot occurrences by site. See Fig. 1 for site codes and locations.

MATTHEW D. POTTS ET AL.

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Ecology, Vol. 83, No. 10

TABLE 3. Mantel tests of correlation between floristic variation, soils (‘‘habitat’’), and topographic and geographic distance in four groups of 0.6-ha plots in Sarawak, East Malaysia.
Data

Floristics

HabitatDistance

Floristics

HabitatDistancea) All log plots
Habitat PCA Axis 2

Habitat PCA Axis 1
Floristics
HabitatDistance

0.651**
0.651**
20.068**

0.058
0.168

0.172

0.089**
0.083**
0.047020.1230.058
20.127b) Low-altitude Mount Mersing (site C) plots, high-nutrient mineral soil
Habitat PCA Axis 1
Floristics
HabitatDistance

0.191**
0.190*
0.269**

20.022

Topography

0.269**
0.306

0.059**
0.030**
0.265**

Residual Mg (ppm)
Floristics
HabitatDistance

0.184*
0.200*
0.280**

20.0850.269**
0.113

0.101
Residual P (ppm)

0.269**
20.031**0.090*
0.115*
0.278**

20.106**

0.269**
20.077**c) Iju Hill (site G) plots, medium-nutrient soils
Habitat PCA Axis 1
Floristics
HabitatDistance

0.223*
0.221*
0.269**

20.020Topography

0.271**
0.041

0.106**
0.121**
0.277**

Residual Mg (ppm)
Floristics
HabitatDistance

0.420**
0.405**
0.244*

20.0700.271**
20.038Residual P (ppm)

0.271**
0.121*

0.009

0.130**
0.122**
0.267**

0.271**
0.049*

0.014d) Raya Hill (site H) plots, low-nutrients soils
Topography

Habitat PCA Axis 1
Floristics
HabitatDistance

0.041*
0.004*
0.395**

0.396**
0.096

0.086

0.265**
0.242**
0.383**

20.020**0.397**

0.010**
0.027

0.007
Residual P (ppm)

Residual Mg (ppm)
Floristics
HabitatDistance

0.396**
0.111

0.140**
0.396**
0.021*

0.014**
0.375**

0.396**
0.322**

0.294**

Notes: Mantel statistics are above the diagonal, and partial Mantel statistics are below thediagonal (partial Mantel statistics were calculated with respect to habitat, distance, and floristics). See Fig. 1 for locations of the plots.
* P , 0.05; ** P , 0.01.

6). In general, the cohesion of the plots at each site in
the final clusters notwithstanding within-site habitat
variability is also striking (Fig. 2).
The placement of the Bukit Lambir L2 plot and the
Ulu Dapoi (D) plots provide the best evidence for theimportance of habitat over distance. The L2 plot is on
udult clay-loam while the rest of the L plots are on
humult sandy soils. The L2 plot is of the same shale
geologic formation as the E and J plots and is placedwith the rest of the clay plots (sites A, C, E, and J)
while the remainder of the humult L plots, which areover an adjacent sandstone geological formation, areplaced with the humult cluster. Likewise, D2, which ison a dacite scarp, is placed with the other udults while
D1, nearby on the plateau with humult soils, clusters
with the remainder of the humult plots.

Finally, the graph of plot similarity by distance (Fig.
4) suggests that sites with the same surface soil characteristics are more similar than sites that do not sharethe same surface soil characteristics. However, we do
not observe any clear relationship between floristic
similarity and distance for plots within 200 km of eachother. In this range floristic similarity varies from 0 to
0.5.

Resource threshold for habitat effects
Both the results of the Mantel test and the cluster
analysis indicate that soil resource levels play an important role in the balance between spatial distance vs.
habitat as determinants of floristic similarity—especially for plots within the same local area (site). Thisconclusion is reached by comparing the differential ef-

October 2002

HABITAT EFFECTS IN TROPICAL FORESTS

2793

FIG. 6. Cluster analysis, average-linkage clustering, of 105 0.6-ha plots from mixed dipterocarp forests of Sarawak, East
Malaysia. Capital letters of plots in dendrogram indicate sites (see Fig. 1, Table 1). Habitat factors significantly (*P , 0.05;**P , 0.01) related to numbered divisions are listed in boxes. For a full explanation of habitat factors see Fig. 2 and Materials
and methods: Field sampling.

fects of distance and habitat on floristic similarity in
15 plots at each of sites C (low-altitude plots), G,
and H.
The fifteen plots at each of these three sites sample
similar ranges of altitude (200–500 m) and topography,
although spurs rather than ridges were available atMount Mersing. Within each site, all plots are a similar
distance apart with a maximum of 4 km between plots.
Particulate organic matter is concentrated in the surface horizons of undisturbed low-altitude upland tropical soils (I. H. Baillie in Richards 1996:256–286). By
sampling mineral soil below the zone of humic discoloration we minimized the possible influence on nutrient concentration of the litter of individual tree species, whose presence might in turn have been influ-

enced by dispersal and geographic constraints, andthereby increased the validity of cross-site comparisons. The lower mineral ion concentrations are similar
at the three sites (Fig. 2), but the highest levels at eachof the sites increase from the rhyolite G site throughthe sedimentary H site to the basic volcanic C site.
Maximum concentrations at the C site are sometimes
an order of magnitude greater than at the other sites.
Variability in mineral ion concentrations was generally
lowest at site G and highest at site C.
The results from the Mantel test (Table 3) imply thata threshold exists above which differences in nutrient
levels are no longer associated with floristic differences. In the C plots, which have the highest nutrient concentrations but also the highest nutrient variability, theMATTHEW D. POTTS ET AL.

2794

Ecology, Vol. 83, No. 10

TABLE 4. Correlations between topographic variables in three groups of plots in Sarawak,
East Malaysia.
Residual
Mg(ppm)

ResidualK(ppm)a) Mount Mersing (site C) plots, low altitude
NS
NS
NS
Slope or ridge
20.352
20.162
20.219Altitude (m)20.1140.019
0.019
Steepness

NS

NS

NS

0.105
0.057

0.257
0.019

0.010
0.152

**
20.34320.010*20.457*20.04820.248
0.295

*
0.057
0.295

NS

NS

NS

0.171
0.086

0.095
0.010

0.114
0.143

Variable

ResidualP(ppm)

ResidualCa(ppm)b) Iju Hill (site G) plots
NS
Slope or ridge
20.486*
Altitude (m)0.314
Steepness

0.371*
0.048c) Raya Hill (site H) plots
Slope or ridgeNS
20.114
Altitude (m)20.190
Steepness

20.200
0.000

NS

NS

Residual
GrIII
(ppm)

NS

GrIII-a/
GrIII-bNS

20.09520.162

Notes: ‘‘GrIII’’ indicates Group III elements, here iron and aluminum; ‘‘a’’ means soil sample
was taken at 20–30 cm depth, ‘‘b’’ at 70–80 cm, and ‘‘ppm’’ means parts per million, i.e., mg
element per g soil. For discrete data (landform) results of a permutation test (Good 1994) are
reported, while for continuous data (altitude and steepness) the results of a Spearman rankorder correlation are reported.
* P , 0.05; ** P , 0.01; NS 5 not statistically significant.

association between floristic similarity and spatial distance (0.269) is much higher than that of edaphic(0.190) or topographic (0.030). Habitat–floristic comparisons using single habitat factors yielded similarly
low association levels. Yet in the G plots, with boththe lowest levels and lowest variability in nutrient levels, the association between floristic similarity and nutrient levels (0.221) is on par with that of spatial distance (0.269). In addition, among the G plots, when
we restrict the habitat variable to the soil mineral Mg,
we find that the association between habitat and floristics (0.405) is nearly twice that of association between spatial distance and floristics (0.244). The cluster
analysis confirms this trend. The C plots unite quite
early in the clustering hierarchy, where divisions correlate with few habitat variables. The G plots, on theother hand, and to a lesser extent the H plots, unite
quite late in the cluster analysis and differ in a substantial number of habitat variables.

Edaphic vs. physiographic factors
Few significant correlations exist between edaphic
soil mineral variables and physiographic variables (Table 4). However, there is only a consistent but unexplained negative correlation of Mg with ridges vs.
slopes. Thus, differences between physiographic andedaphic effects can be compared without concern about
autocorrelation.
The results of the Mantel analysis at individual sites(C, G, H) clearly illustrate that edaphic factors aremuch more important than physiographic factors in explaining floristic similarity between plots. Edaphic factors associate much more strongly with floristic similarity than physiographic effects (0.190 vs. 0.030

across the low-altitude C plots; 0.221 vs. 0.121 at siteG).
The exception to this rule is the Raya Hill (H) plots,
where the strength of physiographic effects (0.242) arestronger than edaphic effects (20.011). At the Raya
Hill site (H), unlike the Mount Mersing (C plots) and
Iju Hills (G plots) that are composed of a uniform volcanic substrate, the sedimentary rocks consist of interbedded sandstones and shales with freely draining
erodible sandstones supporting the ridges, while clayrich landslip-prone soils cover the slopes. These are,in fact, the geomorphological characteristics most often
found in the region.
DISCUSSION

Determinism in forest composition
In spite of marked local endemism within these putative northwest Borneo refugium, particularly on the
humult ultisols of the coastal hills (Ashton 1992, 1995,
Wong 1998), the cluster and Mantel analyses show that
habitat is more strongly associated with floristic similarity than is geography on a regional scale. Cluster
analysis showed that mixed dipterocarp forest (MDF)
is primarily divided into two main groups of species
associations, which correlate with the presence of acidsurface raw humus analogous to temperate mor, and its
absence on soils in which organic matter is relatively
sparse. The scale of species spatial patterning on thecommunity level (;50 ha) observed at the Lambir (L)site (Fig. 1) is substantially larger than that which is
captured by the sample plot size of 0.6 ha (Plotkin etal. 2000a). Despite the small signal-to-noise ratio in
our data, we find significant habitat effects, indicatingthat habitat effects are strong indeed.

October 2002

HABITAT EFFECTS IN TROPICAL FORESTS

Predictability in composition of the more abundantspecies on the major soil types provides further evidence that soils mediate the presence of floristicallydistinct communities, as in temperate forests. Only 17
out of the 60 species that were among the 10 mostabundant species in at least five plots were edaphic
generalists. All other 43 species were confined to soils
with, or without, surface raw humus (humult and udult,
respectively, in Table 3). Among these 60 species, theectotrophically mycorrhizal Dipterocarpaceae weredisproportionately represented with 24 species whereas,10% of the species sampled were dipterocarps.
These results are quite surprising when we consider
the limitations of both our floristic and soil chemical
data. Nevertheless, our nutrient measures correlatedwith maximum growth rates and the soils range of species with maximum growth rates per plot (Ashton andHall 1992). We believe the data provide a reliable quantitative measure of edaphic similarity between plots andalso an approximation of the long-term nutrient inputs
to the forest ecosystem from the substrate.
The observed consistency in composition provides
further, albeit circumstantial, evidence that deterministic forces play as strong a role as random forces, drift,and dispersal limitation in shaping forest composition
across Northwest Sarawak. In addition, palaeontological evidence suggests that these forests have remainedlargely unchanged since the mid-Miocene. Thus, using
spatial distance as a proxy for time, our results suggestthat the landscape is composed of a mosaic of equilibrium forest types. Therefore, for the conservation planner it will be important to ensure adequate conservationof MDF types on the various surface lithologies, but
it is also necessary to ensure a geographical spread ofconservation areas. In any case, survival of floristic
diversity will additionally depend on the survival ofpollinators and fruit dispersers.

Resource thresholds in tropical forests
Besides addressing the balance between deterministic and stochastic forces in shaping community structure at local and landscape scales, our data provide
evidence of resource thresholds above which thestrength of edaphic effects weakens significantly. We
have seen that edaphic similarity plays very little orno role in explaining floristic similarity between plotsat the high-nutrient C site, while at the low-nutrient G
site edaphic similarity overall is as important as distance in explaining floristic similarity between plots.
At the G site, similarity in magnesium level betweenplots is almost twice as correlated with floristic structure than is distance; in fact, at the G site, Mg is asstrongly correlated with floristics as any other factor
in all the Mantel analyses. The strength of correlation
between species composition and Mg in low-nutrient
ultisols of northern Borneo has been previously documented (Baillie and Ashton 1983).

2795

Comparison with the Neotropics
Our results amplify but also in part differ from thosefrom the Neotropics. Terborgh et al. (1996), Terborghand Andersen (1998), and Pitman et al. (1999), Pitman
et al. (2001) found that most species in mature forest
within a major habitat and geographical region (comparable in nature and extent to our dataset), the uplandsof the Upper Amazon, had wide geographic and ecological amplitudes. Local abundance of generally
sparsely distributed species does occur, but in the absence of either climate or soils data no habitat-correlated gradients in species composition were demonstrated. The relationships between floristic compositionand soils that we have shown in Borneo quite strikingly
resemble those recorded by Duivenvoorden (1995) on
the Guyana Shield lowlands of the Colombian Amazon,
which show apparently similar landscape and soil associations to ours. He too found that the major overall
floristic gradient on low-nutrient soils was correlatedwith mineral soil nutrients. Duivenvoorden provided
limited evidence of habitat specificity among tree species on these low-nutrient upland soils. He did not examine the effects of distance on floristic similarity.
Pyke et al. (2002) also found little evidence of habitatrelated floristic variation, locally or at distances ,50
km in Panama, and concluded that pattern was driven
by inter-plot distances. Consistency of composition
among the most abundant species was markedly lower
than in our data sets from Northwest Borneo, and soil
analytical data were also unavailable in their case, but
their soils were udult clays overlying shale and limestone influenced by basic vulcanicity, and probably
contained high relative nutrient concentrations. Our results suggest that geographic distance dominates floristic variation in such habitats in Borneo also.
High-nutrient volcanic and other soils are restricted
to a small overall area of the humid tropical lowlands(Weischet and Caviedes 1993). Much research in tropical-forest ecology has been focused on habitats suchas those in Central America, but it is unclear at present
whether most upland rain forest soils carry nutrient
concentrations above or below the threshold observedin our results, below which soil resources more influence species composition do than distance effects. Results from the Amazon suggest that soils there may
mostly be above the threshold, though more studies areneeded from the low-nutrient Guyana and Brazilian
Shields. The absence of high species turnover along anutrient gradient on low-nutrient soils of the aseasonal
Colombian Amazon, documented by Duivenvoorden(1995), nevertheless remains unexplained though hesuggested that this might be an artefact of the small
size of his plots (0.1 ha).
The regions of the Neotropics investigated so far
appear to have an almost ubiquitous history of slashand-burn agriculture. In Borneo, by contrast, traditionalslash-and-burn was confined to the most fertile soils

MATTHEW D. POTTS ET AL.

2796

and less steep slopes (M. Dove, personal communication). Such land use may increase stochasticity inlocal floristic composition of communities with limited
vegetative regeneration and limited seed dispersal.

Conclusions
Our results, though preliminary, indicate that abiotic
factors are more influential in differentiating species
associations in primary tropical rainforest than has recently been claimed, particularly in the Neotropics.
They suggest important avenues for further research(currently underway in Center for Tropical Science 50ha plots) to identify and quantify the scale and variationof resource levels in local communities. In the future,
we must couple an analytical understanding of suchvariation to species distributions and performance. In
addition, direct experimental studies will be useful totest this hypothesis. Such controlled experiments
should further clarify the key functional roles andscales of influence of deterministic and stochastic effects on floristic composition.
ACKNOWLEDGMENTS
These data were collected as part of a program of forest
botany sponsored by the Sarawak, Malaysia, Forest Department during 1963–1966. Particular thanks are due the lateIlias bin Pa’ie, who led the field teams, and Eric Macklin
who was instrumental in developing the analysis. Fundingfor data management and analyses was provided by the Arnold Arboretum and the Dean’s fund of the Faculty of Arts
and Science, Harvard University. William Bossert, Rick Condit, Stuart Davies, Peter Greig-Smith, Michelle Koh, David
Newbery, John Terborgh, and Campbell Webb offered valuable insights to the draft. In addition we thank our managing
editor, David Burslem, and an anonymous reviewer for suggestions that greatly improved this paper. M. D. Potts was
supported in part by an EPA STAR Graduate Fellowship. J.
B. Plotkin was supported by a graduate fellowship from the
National Science Foundation, the Teresa and H. John Heinz
III Foundation, and the Burroughs Wellcome Fund.
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