Journal of
Ecology 2001
89, 947– 959
Habitat associations of trees and shrubs in a 50-ha
neotropical forest plot
Blackwell Science Ltd
KYLE E. HARMS‡, RICHARD CONDIT, STEPHEN P. HUBBELL* and
ROBIN B. FOSTER†
Smithsonian Tropical Research Institute, Apdo 2072, Balboa, Republic of Panama, *Department of Botany,
University of Georgia, Athens, Georgia 30602, USA, †Department of Botany, Field Museum, Chicago, Illinois
60605, USA
Summary
1 Tests of habitat association among species of tropical trees and shrubs often assume
that individual stems can be treated as independent sample units, even though limited
dispersal conflicts with this assumption by causing new recruits to occur near maternal
parents and siblings.
2 We developed methods for assessing patterns of association between mapped plants
and mapped habitat types that explicitly incorporate spatial structure, thereby eliminating
the need to assume independence among stems.
3 We used these methods to determine habitat-association patterns for 171 species of
trees and shrubs within the permanent 50-ha Forest Dynamics Project plot on Barro
Colorado Island, Panama.
4 Many fewer significant habitat associations result from the new methods than from
traditional, but inappropriate, chi-square tests. The low-lying plateau, the most extensive
habitat on the 50-ha plot, had nine species positively associated with it and 19 species
negatively associated, leaving 143 species whose distributions were not biased with
respect to this habitat. A small swamp in the plot was the most distinct habitat, with 32
species positively and 20 species negatively associated, leaving more than two-thirds of
the species neither positively nor negatively associated.
5 To the extent that habitat association reflects habitat specialization, our results suggest
that local habitat specialization plays a limited role in the maintenance of species diversity
in this forest.
Key-words: environmental heterogeneity, maintenance of species diversity, niche
differentiation, spatial autocorrelation, specialization
Journal of Ecology (2001) 89, 947–959
Introduction
Niche differentiation with respect to resources remains
a prominent hypothesis to account for the maintenance
of tree species diversity in tropical forests (Ashton
1969; Connell 1978; Leigh 1999). One manifestation of
resource-based niche differentiation consists of habitat
specialization, such that different species of trees are
best suited to different habitats, where they are competitively dominant and relatively more abundant
(Hubbell & Foster 1983; Tilman & Pacala 1993). To
© 2001 British
Ecological Society
‡Present address and correspondence: Kyle E. Harms,
Department of Natural Resources, Fernow Hall, Cornell University, Ithaca, New York 14853, USA (tel. +607 255 1067;
fax +607 255 0349; e-mail keh27@cornell.edu).
determine the relative contribution of habitat specialization to the maintenance of diversity in tropical forests
requires rigorous quantification of the relationships
between species’ distributions and habitat variables, as
well as the identification of the causes underlying those
patterns (Hubbell & Foster 1986a; Burslem et al. 1995;
D.B. Clark et al. 1999; Webb & Peart 2000).
The distribution of individuals within a population
of plants is rarely random across a landscape, especially
as the scale of focus increases from the local neighbourhood outwards (Greig-Smith 1979; Levin 1992). For
example, the dispersion patterns of tropical trees and
shrubs are generally more clumped, or aggregated, than
random (Hubbell 1979; Condit et al. 2000; Plotkin et al.
2000). Furthermore, tropical trees and shrubs often display distributional biases with respect to environmental
948
K. E. Harms et al.
© 2001 British
Ecological Society,
Journal of Ecology,
89, 947–959
variables, across spatial scales of several ha to many
km2 (Hall & Swaine 1981; Gentry 1992). Nevertheless,
a common assumption required by many of the statistical
tests used in studies of habitat association is that trees
are independently distributed with respect to conspecifics
(Greig-Smith 1952; Condit 1996; Clark et al. 1998).
The independence assumption is often violated by
the patterns produced by dispersal and recruitment.
Most tree seeds fail to disperse far from the maternal
parent ( Janzen 1970; Connell 1971; Clark J.S. et al. 1999;
Hubbell et al. 1999), giving rise to seedlings found near
conspecifics (Hubbell et al. 1999; Connell & Green 2000;
Harms et al. 2000). The aggregating influence of limited
dispersal may create or contribute to spatially autocorrelated patterns of distribution (Condit 1996; Clark et al.
1998; Plotkin et al. 2000).
Hubbell & Foster (1983, 1986c) described the striking
degree to which the distributions of several species
matched particular topographic features of the 50-ha
Forest Dynamics Project (FDP) plot of Barro Colorado
Island (BCI), Panama. Although they acknowledged
that trees are not distributed independently, they used
chi-square goodness-of-fit tests, which rely on the
assumption that each stem is an independent sample
unit (Snedecor & Cochran 1980; Clark et al. 1998). Chisquare tests of independence (Clark et al. 1995, Clark
et al. 1998), as well as the ordination methods employed
by Lieberman et al. (1985), the canonical correspondence analysis of Oliveira-Filho et al. (1994) and the
Komolgorov-Smirnov tests of Pacheco & Henderson
(1996), similarly require independence among stems
or among the contiguous quadrats within which trees
were counted. However, if spatial structure exists within
the tree populations, at the scale of the study, then neither
individual trees nor contiguous quadrats can be treated
as independent sample units and standard statistical
tests are not appropriate (Legendre & Legendre 1998).
Multiple, non-contiguous plots were used by D. B. Clark
et al. (1999), Pitman et al. (1999) and Svenning (1999)
to examine habitat-association patterns among neotropical trees and palms, and by Webb & Peart (2000) to
compare and contrast the habitat association patterns of
seedlings and trees in a Bornean forest. When plots are
widely spaced across a range of habitat types, spatial
autocorrelation may be weak or absent among plots.
However, before using tests that assume independence
among sample units, the degree of spatial autocorrelation within the data should be shown to be consistent
with the requirements of the tests (Cressie 1991).
Our objective in this study is to re-assess patterns
of habitat association for species inhabiting the 50-ha
FDP plot on BCI, knowing that spatial autocorrelation exists for both the plants and the habitats in question. We asked whether the distributions of species with
respect to habitat variables were likely to have arisen by
chance, given the spatially autocorrelated habitat map
and the short-distance seed-dispersal and recruitment
patterns of the trees and shrubs. In order to improve
upon previous investigations of these patterns, a
procedure was required for generating appropriate null
models of the distributions of stems with respect to
habitats (Gotelli & Graves 1996). Spatial autocorrelation
could not be ignored (Legendre 1993) and we developed
procedures for testing patterns of association that
incorporate critical properties of the spatial structure
observed in both the plant and habitat data sets.
Methods
We examined the habitat-association patterns of trees
and shrubs within the 50-ha Forest Dynamics Project
(FDP) plot of Barro Colorado Island (BCI), Panama
(Hubbell & Foster 1983, 1986a,b,c). Detailed descriptions of the climate, geology, flora and fauna of BCI can
be found in Croat (1978), Leigh et al. (1982) and Gentry
(1990).
The FDP plot was established in 1980, when a topographic survey was completed to provide elevations
for each intersection of a 20-m grid throughout the
plot. All stems of free-standing trees and shrubs ≥ 1-cm
diameter at breast height (d.b.h.) have since been mapped,
identified, tagged and measured on five separate occasions (Hubbell & Foster 1983, 1992; Condit 1998). The
median time that a 1-cm d.b.h. sapling has spent growing to that size and becoming established in the plot is
16.6 years (Hubbell 1998). To determine overall patterns
of habitat association of established individuals, we used
all stems ≥ 1-cm d.b.h. in our analyses.
The site for the FDP plot was originally chosen for
its relative uniformity of relief and other physical conditions; the elevational range of the plot is only 38 m
(Condit et al. 2000). Nevertheless, variation in topography, edaphic conditions, species composition and
forest age exists across the plot (Hubbell & Foster 1983,
1986c; Condit 1998) and we focused on small-scale topographic heterogeneity. All but 66 of the 1250 20 × 20-m
quadrats of the FDP plot could be unambiguously
assigned to one of six habitat categories (young forest,
high plateau, low plateau, slope, streamside, and swamp;
see below, Table 1 and Fig. 1). The 66 remaining quadrats
were designated as mixed habitat and were excluded
from tests of association.
The north-eastern edge of the FDP plot is bordered
by secondary forest (c. 100 years old) which extends
into just under 2 ha of the plot (Hubbell & Foster 1983,
1986c; Condit 1998). The remaining 48 ha have never
been clearcut for agriculture (Piperno 1992). We chose
to examine only patterns evident within the old growth
forest, thereby eliminating forest age as a habitat variable.
BCI consists almost entirely of well-drained upland
soils (Dietrich et al. 1982; Hubbell & Foster 1986c;
Condit 1998), although a seasonally inundated swamp
is present on the FDP plot. The 1.5-ha swamp, defined
by the extent of standing water at the end of the wet season
in 1992, was considered as a separate habitat type
because swamps are often floristically distinct from the
949
Habitat
associations of
trees and shrubs
Table 1 Areas of each habitat, total numbers of stems ≥ 1-cm d.b.h. in the 1990 census, and total stem densities by habitat for the
50-ha Forest Dynamics Project plot of Barro Colorado Island, Panama
Habitat
Slope (degrees)
Elevation (m)
Total area (ha)
Total number of stems
[density (no. ha–1) ]
Old forest – Low plateau
Old forest – High plateau
Old forest – Slope
Old forest – Swamp
Old forest – Streamside
Young forest
Mixed habitats
<7
<7
≥7
All
All
All
All
< 152
≥ 152
All
All
All
All
All
24.80
6.80
11.36
1.20
1.28
1.92
2.64
126,417 (5097.46)
31,156 (4581.76)
55,419 (4878.43)
3355 (2795.83)
5679 (4436.72)
9629 (5015.10)
12,359 (4681.44)
Fig. 1 The 50-ha Forest Dynamics Project plot of Barro Colorado Island, Panama, divided into habitats assigned to 20 × 20-m quadrats.
© 2001 British
Ecological Society,
Journal of Ecology,
89, 947–959
surrounding vegetation, primarily due to physiological
requirements to tolerate water-logged soils (Kwan &
Whitmore 1970; Lieberman et al. 1985).
Streams, usually flanked by relatively steep ravines,
are found in the north-east and the south-west corners
of the plot, the latter draining the swamp. Although
seasonal, the streams usually contain water well into
each dry season (Hubbell & Foster 1986c; Condit 1998).
Without direct moisture estimates in the 20 × 20-m
quadrats that include streams, we make the a priori
assumption that they are among the areas with the
highest soil-water availability in the 50-ha plot (excluding
the swamp). We identify these streamside quadrats as a
distinct habitat type based on the common observation, at other sites, that some tree species are restricted
to riparian habitats (Oliveira-Filho et al. 1994).
Due to the geology and hydrology of BCI, the sloping
areas of the FDP plot provide more moisture later into
the dry season than plateau sites (Hubbell & Foster
1986c; Condit 1998), as shown, for instance, in direct soil
moisture estimates along two transects covering slope
and plateau sites (Becker et al. 1988). A cap of andesite
underlies the central plateau (Johnsson & Stallard
1989; Leigh 1996) and water drains via the slopes at its
edges, which form the slopes of the 50-ha plot.
For each 20 × 20-m quadrat, elevation was calculated as the mean of values at its four corners and slope
as the mean angular deviation from horizontal of each
of the four triangular planes formed by connecting
three of its corners. We chose a slope of 7° as the criterion
for distinguishing slope from plateau quadrats, since
this criterion included most of the southern and eastern
950
K. E. Harms et al.
slopes that fall away from the main plateau of the FDP
plot. Using a steeper angle as the criterion eliminated
sections of those slopes, while shallower angles included
many more quadrats away from those main slopes. We
chose 152 m above mean sea level to separate high plateau
from low plateau, since even small changes in elevation
can result in changes in vegetation (e.g. Lieberman et al.
1985) and 152 m is the approximate mid-elevation of
the principal sloping regions of the plot.
-
We calculated chi-square goodness-of-fit statistics for
patterns of habitat association to provide a comparison with our alternative methods. Each habitat provided a chi-square deviation of observed relative to
expected numbers of stems for each species. To assess
the goodness-of-fit for each species in each habitat,
we used the conservative test that the single-habitat
chi-square deviation be equal to or greater than the
critical chi-square value for the full five-habitat test, i.e.
χ2df=4 = 9.488.
To account for differences in total stem density
among habitats, we calculated expected values on a
density, rather than area, basis. The expected value for
a given species-habitat combination was calculated as
the total number of stems of the focal species summed
over all five habitats, then multiplied by the proportion
of stems of all species in the five habitats accounted for
by the focal habitat.
A chi-square test using five habitats requires that
each of the five expected values be > 1 (Snedecor &
Cochran 1980 p. 77). An expected value < 1 was obtained
for the least extensive habitat (the swamp) when a species
had ≤ 65 stems. We therefore restricted our analyses to
the 171 most abundant species in the five focal habitats on
the FDP plot in 1990, all with > 65 stems ≥ 1-cm d.b.h.
Chi-square goodness-of-fit tests are two-tailed, since
deviations of equal magnitude of observed values away
from their corresponding expected values result in
the same test statistics irrespective of the direction of
the deviation. We used α = 0.05 to determine statistical
significance throughout this study.
-
-
© 2001 British
Ecological Society,
Journal of Ecology,
89, 947–959
Our alternative methods for testing patterns of habitat
association involve developing a null hypothesis that
allows us to determine how strongly associated with
each habitat a species would be if associations were
caused by coincidental similarity between the spatial
structure of the species’ population and the arrangement of habitats. We compared the observed relative
densities of stems when they were superimposed on the
true habitat map with expected, or null, distributions of
expected relative densities generated using many simu-
lated habitat maps. Relative densities were calculated
as the proportions of stems of all species belonging to
each species in each habitat. Simulated habitat maps
were generated by both an agglomerative randomization algorithm (hereafter referred to as randomized
habitats) and by moving the true habitat map about a
two-dimensional, or flat, torus (hereafter referred to as
torus translations). Critical properties of the spatial
structure of both the habitats and plant populations
were thus maintained, while altering the positions of
habitats with respect to the trees (see Clifford et al. 1989
for a criticism of fully randomizing a single variable
when assessing the association between two spatially
autocorrelated variables). Our methods are complementary to those used by Plotkin et al. (2000), who
developed Poisson-cluster modelling to redistribute
trees with respect to habitats in their tests of habitat
association.
The randomized-habitats procedure created a series
of simulated habitat maps in which non-overlapping
areas corresponded in extent to the five principal habitats
of the true map. Each simulated map therefore included
exactly 620 low plateau, 170 high plateau, 284 slope, 32
streamside and 30 swamp quadrats, as well as 114 unused
quadrats (corresponding to the young forest and mixed
habitats).
Each simulated habitat map was initiated by assigning the swamp quadrats. A single quadrat, chosen at
random from the 1250 comprising the 50-ha plot, was
designated as the ‘seed’ quadrat for the swamp habitat,
a second quadrat was chosen from the seed’s neighbours, and subsequent quadrats from the neighbours
of any already allocated until the simulated swamp was
complete (i.e. 30 quadrats in size). The streamside
habitat was then initiated by chosing a quadrat at random
from the remaining 1220 unassigned quadrats and
completed by progressively adding a further 31
random quadrats from those bordering the growing
habitat. We always started simulations with the least
extensive habitat and proceeded to the most extensive;
high plateau, slope and low plateau habitats were
constructed in the same way as the swamp and
stream habitats. The entire procedure was repeated
1000 times.
In most simulations, the habitats were in five separate
but internally contiguous blocks of quadrats, i.e. all
quadrats within each habitat could be traced to all
other quadrats of the habitat through shared edges or
corners of quadrats of only that habitat. However, one
unused corner of the plot occasionally became isolated
during creation of earlier habitats. If a subsequent
habitat’s seed quadrat was chosen in this corner and
the habitat filled the available space before completion, a second seed quadrat was chosen at random from
the remaining unassigned quadrats and the growing
habitat was finished around that second seed, resulting
in a divided habitat. Divided habitats were rare and we
chose to allow them because slope, stream and low
plateau habitats are divided in the true map (Fig. 1).
951
Habitat
associations of
trees and shrubs
level of significance for a two-tailed test), then it was
considered to be statistically associated (either positively
or negatively) with the habitat. In other words, a species
was determined to be positively associated with a
particular habitat if and only if: Proportion {simulated
map relative density < observed map relative density} ≥
0.975; a species was negatively associated if and only if:
Proportion {simulated map relative density > observed
map relative density} ≥ 0.975.
We illustrate our tests for significance with torustranslation results for tree species that have contrasting
distribution patterns with respect to the slope of the
50-ha plot (Fig. 2; Chamguava shippii [Myrtaceae], negatively associated; Chrysoclamys eclipes [Guttiferae],
positively associated; Pouteria reticulata [Sapotaceae],
neutrally associated). Figure 3 shows the frequency
distributions of expected values for relative stem densities
and the corresponding observed values.
The torus-translation procedure consists of moving
the true habitat map about a two-dimensional, or flat,
torus by 20-m increments in the four cardinal directions (Harms 1997). Imagine a map of the habitats of
the FDP plot lying beneath a map of the trees and being
moved, i.e. translated, by 20-m increments. As strips of
quadrats are moved beyond a border of the plot, they
are placed inside the opposite border. Related toroidal
randomizations and restricted permutations have been
used to incorporate horizontal spatial structure into
tests of spatial association, as between two spatial
point processes, e.g. interspecific spatial associations
(Bailey & Gatrell 1995; Palmer & van der Maarel
1995; Roxburgh & Chesson 1998), and the spatial
co-occurrence of ecological boundaries (Fortin et al.
1996).
The FDP plot consists of a 50 (N-S) × 25 (E-W) grid
of 20 × 20-m quadrats and 1250 unique torus translations of the habitat map are therefore possible (including the 0,0 translation). A further three maps can be
generated from each translation: 180° rotation, mirror
image and 180° rotation of the mirror image. Together,
these procedures provide 4999 unique habitat maps,
each differing from the true, untranslated habitat map
(the original 0,0 translation).
For the tests of association, each simulated map was
overlain by the true distribution of trees and the relative
density of each species (see above) was calculated for
each habitat. Evaluation of all randomly seeded maps
(n = 1000) and all torus-translated maps (n = 4999)
gave frequency distributions of relative-density estimates for each species in each of the five principal
habitats, one set of distributions for the randomizedhabitats tests and one set for the torus-translation tests,
respectively.
If the relative density of a species determined from
the true habitat map was more extreme than at least
97.5% of the simulated relative densities (i.e. α = 0.05
Results
We found many fewer significant habitat associations
using our alternative methods than we did using chisquare tests. Chi-square tests resulted in 317 significant
positive and negative associations out of a potential
855 species-habitat combinations, compared with 124
from randomized-habitat tests and 171 from torustranslation tests (Table 2). In addition, there were more
species (128 out of 171; 75%) significantly positively or
negatively associated with at least one habitat type
using the chi-square tests than there were using the
randomized-habitats tests (87 out of 171; 51%) or the
torus-translation tests (110 out of 171; 64%) (Appendix 1).
Nevertheless, most significant associations according
to the randomized-habitats tests (95% of positive
associations and 77% of negative) and torus-translation
tests (95% of positive and 77% of negative) were also
significant using chi-square tests (Appendix 1).
Table 2 Chi-square, randomized-habitats and torus-translation tests for habitat associations on the 50-ha Forest Dynamics
Project plot of Barro Colorado Island, Panama. The first column for each test contains results for 171 species for which there were
> 65 stems in the five focal habitats in the 1990 census. The second column for each test contains results for the 50 most common
species, all of which had ≥ 855 stems in the five focal habitats in the 1990 census. For each habitat, ‘+’ indicates significant positive
association and ‘−’ indicates significant negative association (α = 0.05 for all three tests)
Chi-square
© 2001 British
Ecological Society,
Journal of Ecology,
89, 947–959
Randomized habitats
Torus translation
Habitat association
171 species
50 species
171 species
50 species
171 species
50 species
High plateau +
Low plateau +
Slope +
Swamp +
Streamside +
Total +
High plateau −
Low plateau −
Slope −
Swamp −
Streamside −
Total −
22
26
43
39
31
161
55
36
31
22
12
156
13
15
18
9
11
66
25
15
18
19
11
88
6
3
26
32
9
76
15
10
8
15
0
48
4
1
7
0
5
17
7
0
3
0
11
21
4
9
33
32
19
97
14
19
18
20
3
74
3
7
11
5
4
30
5
5
7
15
2
34
952
K. E. Harms et al.
Fig. 2 Stem distributions on the 50-ha Forest Dynamics Project plot of Barro Colorado Island, Panama, for: (a) Chamguava shippii [Myrtaceae],
(b) Chrysoclamys eclipes [Guttiferae], and (c) Pouteria reticulata [Sapotaceae].
© 2001 British
Ecological Society,
Journal of Ecology,
89, 947–959
Discrepancies between the chi-square tallies and
those for the other two methods were increased by
excluding species that were significantly positively or
negatively associated only with the swamp. We found
fewer such species using chi-square tests (16 compared
with 27 from randomized-habitats tests and 25 from
torus-translation tests), so the proportion of species
significantly associated with at least one other habitat
was more reduced for randomized-habitats tests (to
42% of species) and torus-translation tests (to 58%)
than for chi-square tests (to 72%).
The discrepancies between chi-square results and the
other tests were more pronounced for some of the habitats (Table 2). In particular, the majority of plateau
associations indicated by the chi-square tests vanished
under the alternative tests, so that the fraction observed
was not much different from the 5% expected by chance
alone. In contrast, for slope and swamp, and to a lesser
953
Habitat
associations of
trees and shrubs
Fig. 2 Continued
extent for the streamsides, although many associations
were lost, a majority of associations indicated by chisquare persisted under the more conservative tests.
The 50 most abundant species within the old growth
forest on the BCI 50-ha plot accounted for 83.8% of all
stems in the 1990 census. Although many of these species
show at least one positive or negative habitat association, a substantial proportion of the abundant species
are neutrally associated with each habitat (Table 2).
© 2001 British
Ecological Society,
Journal of Ecology,
89, 947–959
Our tests of habitat association are designed to identify
habitats in which species are disproportionately overor under-represented relative to all other species. In
theory, a very abundant species could be statistically
associated with a habitat with only a slight difference in
density among habitats. To determine the strength of
associations, we therefore examined density differences
among habitats by calculating the ratio of each species’
density in a habitat relative to its density in the low
plateau, the most extensive habitat on the FDP plot.
Condit et al. (1996) chose ratios of > 1.5 to define
slope- and swamp-‘specialists’ (meaning a 50% higher
density on the slope or swamp, respectively, than on the
low plateau), and these criteria appear to conform well
with our results for patterns of habitat association. In
both the slope and swamp habitats, almost all species
with density ratios > 1.5 were significantly positively
associated with the slope or swamp, respectively. Species with ratios < 1.5 were never positively associated
with either the slope or swamp (Fig. 4).
Significant negative association with the slope
occurred when the density ratio for the slope was
< 0.77, but never when the ratio was > 0.77 (Fig. 4).
Significant negative association with the swamp only
occurred when the ratio of density in the swamp was
< 0.25 of the density on the low plateau (Fig. 4). Thus,
only fairly strong associations resulted in statistical
significance using the torus-translation method.
Upon comparing habitats in a pair-wise manner, there
were surprisingly few species with significant associations, either positive or negative, in more than one habitat
(Table 3; only torus-translation tests are shown, but
randomized-habitats results were similar). Although
we might anticipate species to be distributed similarly
with respect to the wetter, albeit well-drained habitats,
only seven species were significantly positively associated
the slope; the remaining 32 overall patterns of association each characterized six or fewer species.
954
K. E. Harms et al.
Discussion
A primary assumption of many traditional tests of
habitat association is that the sample units can be treated
as independent (Snedecor & Cochran 1980; Legendre
1993; Clark et al. 1998). However, this assumption is
often in direct conflict with the recruitment processes
of plants (Condit 1996; Hubbell et al. 1999; Connell &
Green 2000; Harms et al. 2000). Anaxagorea panamensis,
a shrub with balistically dispersed seeds, provides an
extreme example. All 588 of its stems on the FDP plot
were found in the most north-western hectare (see map
in Condit 1998 p. 187) and limited seed dispersal has
probably played a dominant role in creating this pattern.
Since most of the stems of A. panamensis are found
within slope habitat, a chi-square test results in a strongly
significant association with the slope, even though this
species is absent from the extensive slopes along the
eastern and southern portions of the FDP plot. In
contrast, A. panamensis is not significantly associated
with the slope according to our alternative statistical
methods. Our alternative methods are more conservative than chi-square tests, since species are unlikely
to show significant associations unless habitat biases
are consistent over large portions of the study area.
50-
Fig. 3 Results of the torus-translation test for slope association
applied to: (a) Chamguava shippii [ Myrtaceae], (b) Chrysoclamys
eclipes [Guttiferae], and (c) Pouteria reticulata [Sapotaceae].
The frequency histogram represents the distribution of
expected relative stem densities obtained from the torustranslated habitat maps, while the arrow indicates the observed
relative stem density on the true habitat map.
© 2001 British
Ecological Society,
Journal of Ecology,
89, 947–959
with both the slope and streamsides. No other pair of
habitats shared more than two species that were
significantly and congruently associated (Table 3).
With five principal habitat types and three possible
association patterns with respect to each habitat
(positive, negative or neutral) there are 35 = 243 different possible ways for species to be distributed with
respect to the five focal habitats. Therefore, if differences
in overall patterns of association were maximized, each
of the 171 species could show a different set of habitat
associations. Chi-square tests, however, produced only
64 of the possible sets, and the randomized-habitats
method and torus-translation methods produced even
fewer (25 and 36 sets, respectively; Appendix 1). Using
torus-translation tests, 64 species exhibited the most
common set of associations (neutral associations with
all five habitats). Twenty-five species were associated
only with the swamp (15 positively and 10 negatively),
while 13 species were associated (positively) only with
In agreement with Hubbell & Foster (1983, 1986c),
many species show no apparent distributional biases
with respect to habitat boundaries. Nevertheless, several
species are strongly positively or negatively associated
with specific habitats.
A group of species is strongly associated with the
slopes, including Beilschmiedia pendula (see map in
Hubbell & Foster 1986a p. 328), Chrysochlamys eclipes
(Fig. 2), Poulsenia armata (see map in Hubbell &
Foster 1983 p. 30), Unonopsis pittieri (see map in
Hubbell & Foster 1986c p. 213) and Virola surinamensis.
Unlike most species in this forest, the autecology of
V. surinamensis has been studied extensively. Howe and
colleagues (Howe 1986, 1990; Fisher et al. 1991) have
shown that V. surinamensis is associated with slopes
and streamsides on BCI, and that seedling survivorship
is enhanced in these habitats. Increased water potentials during the dry season on the slope, relative to
plateau sites, may impose an ecological filter that prevents V. surinamensis and other drought-sensitive species
from occurring off the slope. The hypothesis that some
species are associated with the slopes due to greater
water availability in otherwise well-drained soils is
supported by seven species being positively associated
with both slope and streamside habitats, while only two
species are positively associated with both slope and
swamp (Table 3).
955
Habitat
associations of
trees and shrubs
Fig. 4 Strength of associations for 171 species using density ratios: (a) slope vs. low plateau, and (b) swamp vs. low plateau. Each
density ratio was calculated as the density in the first habitat over the density in the second. Species are divided into three
categories according to the results of the torus-translation tests, i.e. species significantly positively, neutrally or negatively
associated with the first habitat.
Table 3 Cross-tabulations of habitat associations according to the torus-translation method. Each subtable is a single pair-wise
comparison for two habitats of the distribution of 171 species among three association categories (‘+’ = significant positive
association; ‘−’ = significant negative association; N = neutral association): HiP = High plateau; LoP = Low plateau;
Slp = Slope; Str = Stream; Swp = Swamp
© 2001 British
Ecological Society,
Journal of Ecology,
89, 947–959
LoP +
LoP N
LoP −
HiP +
0
3
1
HiP N
8
128
17
HiP −
1
12
1
HiP +
HiP N
HiP −
Swp +
1
27
4
Swp N
3
108
8
Swp −
0
18
2
LoP +
LoP N
LoP −
Slp +
0
26
7
Slp N
4
104
12
Slp −
5
13
0
HiP +
HiP N
HiP −
Str +
0
18
1
Str N
3
133
13
Str −
1
2
0
LoP +
LoP N
LoP −
Swp +
0
26
6
Swp N
6
101
12
Swp −
3
16
1
Slp +
Slp N
Slp −
Swp +
2
25
5
Swp N
26
81
12
Swp −
5
14
1
LoP +
LoP N
LoP −
Str +
0
15
4
Str N
9
125
15
Str −
0
3
0
Slp +
Slp N
Slp −
Str +
7
11
1
Str N
25
108
16
Str −
1
1
1
HiP +
HiP N
HiP −
Slp +
0
27
6
Slp N
3
110
7
Slp −
1
16
1
Swp +
Swp N
Swp −
Str +
2
15
2
Str N
30
101
18
Str −
0
3
0
956
K. E. Harms et al.
Several species appear to avoid the swamp, while
species positively associated with the swamp include
several species of figs (Ficus spp.) and palms, which are
often important floristic components of Neotropical
swamps (Henderson 1995). Species positively associated
with the swamp may be extremely drought intolerant,
light demanding (light levels may be higher in the
swamp due to reduced stem densities compared with
other habitats, Table 1), or capable of surviving prolonged
periods in water-logged soils and standing water.
Some of the species that were excluded from our
analyses may be locally rare due to specialized requirements for relatively uncommon habitat types, such as
the swamp and streamsides. For example, Eleais oleifera
[Arecaceae], the American oil palm, had too few stems
on the FDP plot to test for associations, even though all
16 of its stems were found in the swamp (see map in
Condit 1998 p. 195). However, when we examined the
habitat associations of rarer species within the plot, we
found no compelling differences between rarer and commoner species in the degree to which they were associated
with habitats. Among the 56 species with ≥ 15 and ≤ 65
individuals, 32 (57%) showed at least one significant association according to the torus-translation method, similar
to the 64% obtained for the 171 species with > 65 stems.
Habitat associations in this paper are based on all
stems ≥ 1-cm d.b.h. Nevertheless, habitat associations
may be size-class dependent (Webb & Peart 2000),
either due to ontogenetic niche shifts (sensu Clark & Clark
1992) or due to processes unrelated to habitat variables,
e.g. invasion history (Hubbell & Foster 1986a).
Our habitat map was created from a relatively coarse
set of habitat variables (see Methods) and associations
between trees and finer-scale environmental variables may
become apparent once finer-scale data become available.
-
© 2001 British
Ecological Society,
Journal of Ecology,
89, 947–959
There are several potential mechanisms that could
cause, or contribute to, an observed match between the
distribution of a sessile organism and a particular
environmental variable (Pickett & Bazzaz 1978; Goldberg
1985; Wesser & Armbruster 1991; Thomson et al. 1996),
including: (i) historical patterns of dispersal, colonization or previous physical conditions (Hubbell & Foster
1986a); (ii) anthropogenic causes (Clark et al. 1995);
(iii) the influence of competitors or other biological
enemies (Connell 1961; Paine 1966); and (iv) habitat
specialization or habitat-related competitive superiority
(Hubbell & Foster 1986a).
Limited, or habitat-biased, distribution patterns may
be the ephemeral or transient result of a population’s
history of seed dispersal and immigration (Primack &
Miao 1992; Losos 1995). Species, such as A. panamensis,
that show significant patterns of association according
to chi-square tests, but not according to more conservative tests, may be typical examples. A contrasting example
may be provided by Drypetes standleyi, a species that is
significantly positively associated with the slope habitat
according to both the chi-square and torus-translation
tests (Appendix 1). Nevertheless, this association may
reflect a transient, coincidental match between the
current distribution and the slope habitat. D. standleyi
appears to be invading the plot from the east, the slopes
are primarily found in the eastern third of the plot and the
significant association with the slope may disappear if
D. standleyi continues to spread across the FDP plot.
For a given species, different carrying capacities
across a landscape, e.g. in different habitats, may result
from inherent physiological differences, or norms of
reaction, or may be imposed by competitors or pests.
At least since Hutchinson (1957) it has been recognized
that the fundamental niche of an organism cannot be
inferred from measuring the realized niche as interactions with other species, e.g. competitors, predators or
pathogens, can result in a limited range of conditions,
habitats or locations in which a species is found (Connell
1961; Paine 1966). Realized patterns of habitat association could therefore be partially, or wholly, caused by
interactions with other species.
The relative contributions of source-sink population
dynamics (sensu Pulliam 1988) and mass effects (sensu
Shmida & Wilson 1985) to habitat associations cannot
be determined by analysing static patterns alone. For
example, distributions that are widespread among
habitats may result from either habitat generalization
or from source-sink population dynamics in which
recruitment subsidies from favourable habitats maintain sink subpopulations in less favourable habitats
(Pulliam 1988). Nevertheless, in practice, sink locations
may often be characterized by lower stem densities as a
consequence of decreased demographic performance
relative to source locations.
Since a given pattern of habitat associations could
have been produced by a variety of alternative causes,
experimental studies are generally required to determine the relative efficacy of potential mechanisms to
produce the patterns evident in any static study of habitat
association (Burslem et al. 1995; Clark et al. 1995; Clark
et al. 1998; D. B. Clark et al. 1999).
If realized habitat associations can be used as estimates
of the degree to which species are specialized to particular habitats, the torus-translation procedure clearly
shows more ‘slope-specialists’ than ‘plateau-specialists’,
despite the fact that slope sites represent a substantially
smaller percentage of the FDP plot than do plateau sites.
If negative associations can be used to identify sink
subpopulations within the FDP plot, then the list of
species neutrally or positively associated with a particular
habitat type would be the number capable of sustaining
populations if the plot were composed of only that
habitat type. Out of 171 species with > 65 stems on the
957
Habitat
associations of
trees and shrubs
FDP plot in 1990, there were 161 species neutrally or
positively associated with the low plateau according to
the randomized-habitats tests and 152 species according to the torus-translation tests. The habitat with the
largest number of negative associations, the swamp,
was avoided by 15 species according to the randomizedhabitats tests and 20 according to the torus-translation
tests, leaving 156 and 151 species, respectively, neutrally or
positively associated. This exercise demonstrates that,
if we were to assume that static patterns of association
reflect source and sink subpopulations, the vast majority
of species might still be found in the FDP plot if it were
composed of a single habitat type. This suggests that
very little of the plot’s diversity (> 300 species total) can
be attributed to local habitat variation.
Our results contribute to the growing body of evidence
suggesting that many species of tropical trees are differentially distributed with respect to habitat variables
at both local and regional scales (Clark et al. 1995;
Clark et al. 1998; D. B. Clark et al. 1999; Pitman et al.
1999; Svenning 1999; Webb & Peart 2000). Nevertheless, and in accord with recent studies in other tropical
forests (Pitman et al. 1999; Webb & Peart 2000), our
results do not support the hypothesis that habitat
specialization is among the principal mechanisms of
coexistence maintaining a large fraction of the alpha
diversity within communities of tropical trees.
Acknowledgements
We thank J. Barone, B. Bolker, D. & D. Clark, L. Curran,
J. Dalling, D. Deutschman, J. Dushoff, J. Eberhard,
J. Franklin, G. Gilbert, P. Green, T. Gullison, D. Hilbert,
H. Horn, G. Hurtt, R. John, E. Leigh, H. MullerLandau, S. Levin, E. Losos, S. O’Brien, S. Pacala,
J. Plotkin, D. Stratton, J.-C. Svenning, G. Webb and
J. Wright for helpful discussions during the conceptualization and completion of this project. D. Clark,
L. Haddon and an anonymous reviewer provided
helpful suggestions for improving the manuscript.
KEH acknowledges support from Sigma Xi, Princeton
University and the Smithsonian Tropical Research
Institute. We also thank the field workers and data
managers who contributed to the 1990 census of the
50-ha Forest Dynamics Project (FDP) plot, especially
R. Perez and S. Loo de Lao. The FDP has been supported
by grants from the National Science Foundation, the
Smithsonian Scholarly Studies Program, the Smithsonian
Tropical Research Institute, the John D. and Catherine
T. MacArthur Foundation, the World Wildlife Fund,
the Earthwatch Center for Field Studies, the National
Geographic Society, the Geraldine R. Dodge Foundation
and the W. Alton Jones Foundation.
Supplementary material
© 2001 British
Ecological Society,
Journal of Ecology,
89, 947–959
The following material is available from http://
www.blackwell-science.com/production/journals/
suppmat/jec/jec641/jec641.sm.htm
Appendix 1 Tree and shrub habitat associations within
the BCI 50-ha forest plot.
References
Ashton, P.S. (1969) Speciation among tropical forest trees:
some deductions in light of recent evidence. Biological Journal
of the Linnean Society, 1, 155–196.
Bailey, T.C. & Gatrell, A.C. (1995) Interactive Spatial Data
Analysis. Longman Scientific and Technical, Harlow,
UK.
Becker, P., Rabenold, P.E., Idol, J.R. & Smith, A.P. (1988)
Water potential gradients for gaps and slopes in a Panamanian
tropical moist forest’s dry season. Journal of Tropical Ecology,
4, 173–184.
Burslem, D.F.R.P., Grubb, P.J. & Turner, I.M. (1995)
Responses to nutrient addition among shade-tolerant tree
seedlings of lowland tropical rain forest in Singapore. Journal
of Ecology, 83, 113–122.
Clark, D.A. & Clark, D.B. (1992) Life history diversity of
canopy and emergent trees in a Neotropical rain forest.
Ecological Monographs, 62, 315–344.
Clark, D.A., Clark, D.B., Sandoval, R. & Castro, M.V. (1995)
Edaphic and human effects on landscape-scale distributions of tropical rain forest palms. Ecology, 76, 2581–2594.
Clark, D.B., Clark, D.A. & Read, J.M. (1998) Edaphic variation and the mesoscale distribution of tree species in a
neotropical rain forest. Journal of Ecology, 86, 101–112.
Clark, D.B., Palmer, M.W. & Clark, D.A. (1999) Edaphic factors
and the landscape-scale distributions of tropical rain forest
treess. Ecology, 80, 2662–2675.
Clark, J.S., Silman, M., Kern, R., Macklin, E. & HilleRisLambers, J. (1999) Seed dispersal near and far: patterns
across temperate and tropical forests. Ecology, 80, 1475–
1494.
Clifford, P., Richardson, S. & Hémon, D. (1989) Assessing the
significance of the correlation between two spatial processes.
Biometrics, 45, 123–134.
Condit, R. (1996) Defining and mapping vegetation types
in mega-diverse tropical forests. Trends in Ecology and
Evolution, 11, 4–5.
Condit, R. (1998) Tropical Forest Census Plots: Methods and
Results from Barro Colorado Island, Panama and a Comparison with Other Plots. Springer-Verlag, Berlin.
Condit, R., Ashton, P.S., Baker, P., Bunyavejchewin, S.,
Gunatilleke, S., Gunatilleke, N., Hubbell, S.P., Foster, R.B.,
Itoh, A., LaFrankie, J.V., Lee, H.S., Losos, E., Manokaran,
N., Sukumar, R. & Yamakura, T. (2000) Spatial patterns in
the distribution of tropical tree species. Science, 288, 1414 –
1418.
Condit, R., Hubbell, S.P. & Foster, R.B. (1996) Changes in
tree species abundance in a neotropical forest: impact of
climate change. Journal of Tropical Ecology, 12, 231–256.
Connell, J.H. (1961) The influence of interspecific competition and other factors on the distribution of the barnacle
Chthamalus stellatus. Ecology, 42, 710–723.
Connell, J.H. (1971) On the role of enemies in preventing
competitive exclusion in some marine animals and in rain
forest trees. Dynamics of Populations (eds P.J. den Boer &
G.R. Gradwell), pp. 298 –312. Centre for Agricultural Publication and Documentation, Wageningen, The Netherlands.
Connell, J.H. (1978) Diversity in tropical rain forest and coral
reefs. Science, 199, 1302–1309.
Connell, J.H. & Green, P.T. (2000) Seedling dynamics over
thirty-two years in a tropical rain forest tree. Ecology, 81,
568 – 584.
Cressie, N.A.C. (1991) Statistics for Spatial Data. Wiley, New
York, USA.
Croat, T.B. (1978) Flora of Barro Colorado Island. Stanford
University Press, Stanford, USA.
958
K. E. Harms et al.
© 2001 British
Ecological Society,
Journal of Ecology,
89, 947–959
Dietrich, W.E., Windsor, D.M. & Dunne, T. (1982) Geology,
climate, and hydrology of Barro Colorado Island. The Ecology
of a Tropical Forest: Seasonal Rhythms and Long-Term
Changes (eds E.G. Leigh Jr, A.S. Rand & D.M. Windsor),
pp. 21– 46. Smithsonian Institution Press, Washington,
District of Columbia, USA.
Fisher, B.L., Howe, H.F. & S.J.Wright. (1991) Survival and
growth of Virola surinamensis yearlings: water augmentation in gap and understory. Oecologia, 86, 292– 297.
Fortin, M.-J., Drapeau, P. & Jacquez, G.M. (1996) Quantification of the spatial co-occurrences of ecological boundaries.
Oikos, 77, 51–60.
Gentry, A.H. (1990) Four Neotropical Rainforests. Yale University Press, New Haven, Connecticutt, USA.
Gentry, A.H. (1992) Tropical forest biodiversity: distributional patterns and their conservational significance. Oikos,
63, 19– 28.
Goldberg, D.E. (1985) Effects of soil pH, competition, and
seed predation on the distributions of two tree species.
Ecology, 66, 503–511.
Gotelli, N.J. & Graves, G.R. (1996) Null Models in Ecology.
Smithsonian Institution Press, Washington, District of
Columbia, USA.
Greig-Smith, P. (1952) Ecological observations on degraded
and secondary forest in Trinidad, British West Indies. II.
Structure of the communities. Journal of Ecology, 40, 316–
330.
Greig-Smith, P. (1979) Pattern in vegetation. Journal of Ecology, 67, 755–779.
Hall, J.B. & Swaine, M.D. (1981) Distribution and Ecology of
Vascular Plants in a Tropical Rain Forest. Dr W. Junk
Publishers, The Hague, The Netherlands.
Harms, K.E. (1997) Habitat-specialization and seed dispersallimitation in a Neotropical forest. PhD thesis, Princeton
University, Princeton, New Jersey, USA.
Harms, K.E., Wright, S.J., Calderón, O., Hernández, A. &
Herre. E.A. (2000) Pervasive density-dependent recruitment
enhances seedling diversity in a tropical forest. Nature, 404,
493 – 495.
Henderson, A. (1995) The Palms of the Amazon. Oxford
University Press, Oxford, UK.
Howe, H.F. (1986) Consequences of seed dispersal by birds: a
case study from Central America. Journal of the Bombay
Natural History Society, 83 (Suppl.), 19 – 42.
Howe, H.F. (1990) Survival and growth of juvenile Virola
surinamensis in Panama: effects of herbivory and canopy
closure. Journal of Tropical Ecology, 6, 259– 280.
Hubbell, S.P. (1979) Tree dispersion, abundance, and diversity
in a tropical dry forest. Science, 203, 1299–1309.
Hubbell, S.P. (1998) The maintenance of diversity in a neotropical tree community: conceptual issues, current evidence, and challenges ahead. Forest Biodiversity: Research,
Monitoring and Modeling (eds F. Dallmeier & J.A. Comiskey),
pp. 17– 44. UNESCO, Paris, France.
Hubbell, S.P. & Foster, R.B. (1983) Diversity of canopy trees
in a neotropical forest and implications for conservation. Tropical Rain Forest: Ecology and Management (eds
S.J. Sutton, T.C. Whitmore & A.C. Chadwick), pp. 25 – 41.
Blackwell Science, Oxford, UK.
Hubbell, S.P. & Foster, R.B. (1986a) Biology, chance, and history and the structure of tropical rain forest tree communities.
Community Ecology (eds J. Diamond & T.J. Case), pp. 314 –
329. Harper & Row, New York, USA.
Hubbell, S.P. & Foster, R.B. (1986b) Canopy gaps and the
dynamics of a neotropical forest. Plant Ecology (ed. M.J.
Crawley), pp. 77–96. Blackwell Science, Oxford, UK.
Hubbell, S.P. & Foster, R.B. (1986c) Commonness and rarity
in a neotropical forest: implications for tropical tree conservation. Conservation Biology: the Science of Scarcity and
Diversity (ed. M.E. Soule), pp. 205 – 231. Sinauer Associates, Sunderland, Massachusetts, USA.
Hubbell, S.P. & Foster, R.B. (1992) Short-term dynamics of a
neotropical forest: why ecological research matters to tropical conservation and management. Oikos, 63, 48–61.
Hubbell, S.P., Foster, R.B., O’Brien, S.T., Harms, K.E.,
Condit, R., Wechsler, B., Wright, S.J. & Loo de Lao, S.
(1999) Light gap disturbances, recruitment limitation, and
tree diversity in a neotropical forest. Science, 283, 554–557.
Hutchinson, G.E. (1957) Concluding remarks. Cold Spring
Harbor Symposium on Quantitative Biology, 22, 415–427.
Janzen, D.H. (1970) Herbivores and the number of tree species
in tropical forests. American Naturalist, 104, 501–528.
Johnsson, M.J. & Stallard, R.F. (1989) Physiographic controls
on the composition of sediments derived from volcanic and
sedimentary terrains on Barro Colorado Island, Panama.
Journal of Sedimentary Petrology, 59, 768–781.
Kwan, W.Y. & Whitmore, T.C. (1970) On the influence of soil
properties on species distribution in a Malayan lowland
dipterocarp rain forest. Malayan Forester, 33, 42–54.
Legendre, P. (1993) Spatial autocorrelation: trouble or new
paradigm? Ecology, 74, 1659–1673.
Legendre, P. & Legendre, L. (1998) Numerical Ecology, second
English edition. Elsevier, Amsterdam, The Netherlands.
Leigh, E.G. Jr (1996) Epilogue: research on Barro Colorado
Island, 1980 – 1994. The Ecology of a Tropical Forest:
Seasonal Rhythms and Long-Term Changes, 2nd edn (eds
E.G. Leigh Jr, A.S. Rand & D.M. Windsor), pp. 469–503.
Smithsonian Institution Press, Washington, District of
Columbia, USA.
Leigh, E.G. Jr (1999) Tropical Forest Ecology: a View from Barro
Colorado Island. Oxford University Press, Oxford, UK.
Leigh, E.G. Jr, Rand, A.S. & Windsor, D.M. (1982) The Ecology
of a Tropical Forest: Seasonal Rhythms and Long-Term
Changes. Smithsonian Institution Press, Washington,
District of Columbia, USA.
Levin, S.A. (1992) The problem of pattern and scale in ecology.
Ecology, 73, 1943–1967.
Lieberman, M., Lieberman, D., Hartshorn, G.S. & Peralta, R.
(1985) Small-scale altitudinal variation in lowland wet
tropical forest vegetation. Journal of Ecology, 73, 505 –516.
Losos, E.C. (1995) Habitat specificity of two palm species:
experimental transplantation in Amazonian successional
forests. Ecology, 76, 2595– 2606.
Oliveira-Filho, A.T., Vilela, E.A., Carvalho, D.A. & Gavilanes,
M.L. (1994) Effects of soil and topography on the distribution of tree species in a tropical riverine forest in south-eastern
Brazil. Journal of Tropical Ecology, 10, 483–508.
Pacheco, M.A.W. & Henderson, A. (1996) Testing association between species abundance and a continuous variable
with Kolmogorov-Smirnov statistics. Vegetatio, 124, 95 –
99.
Paine, R.T. (1966) Food web complexity and species diversity.
American Naturalist, 100, 65–75.
Palmer, M.W. & van der Maarel, E. (1995) Variance in species
richness, species association, and niche limitation. Oikos,
73, 203– 213.
Pickett, S.T.A. & Bazzaz, F.A. (1978) Organization of an
assemblage of early successional species on a soil moisture
gradient. Ecology, 59, 1248–1255.
Piperno, D.R. (1992) Fitolitos, arqueología y cambios prehistóricos de la vegetación de un lote de cincuenta hectáreas
de la isla de Barro Colorado. Ecología de un Bosque Tropical:
Ciclos Estacionales Y Cambios a Largo Plazo (eds E.G. Leigh
Jr, A.S. Rand & D.M. Windsor), pp. 153–156. Smithsonian
Institution Press, Washington, District of Columbia, USA.
Pitman, N.C.A., Terborgh, J., Silman, M.R. & Nuñez, P.
(1999) Tree species distributions in an upper Amazonian
forest. Ecology, 80, 2651–2661.
Plotkin, J.B., Potts, M.D., Leslie, N., Manokaran, N.,
LaFrankie, J. & Ashton, P.S. (2000) Species-area curves,
spatial aggregation, and habitat specialization in tropical
forests. Journal of Theoretical Biology, 207, 81–99.
959
Habitat
associations of
trees and shrubs
© 2001 British
Ecological Society,
Journal of Ecology,
89, 947–959
Primack, R.B. & Miao, S.L. (1992) Dispersal can limit local
plant distribution. Conservation Biology, 6, 513–519.
Pulliam, H.R. (1988) Sources, sinks, and population regulation. American Naturalist, 132, 652– 661.
Roxburgh, S.H. & Chesson, P. (1998) A new method for
detecting species associations with spatially autocorrelated
data. Ecology, 79, 2180– 2192.
Shmida, A. & Wilson, M.V. (1985) Biological determinants of
species diversity. Journal of Biogeography, 12, 1–20.
Snedecor, G.W. & Cochran, W.G. (1980) Statistical Methods,
7th edn. Iowa State University Press, Ames, Iowa, USA.
Svenning, J.-C. (1999) Microhabitat specialization in a speciesrich palm community in Amazonian Ecuador. Journal of
Ecology, 87, 55– 65.
Thomson, J.D., Weiblen, G., Thomson, B.A., Alfaro, S. &
Legendre, P. (1996) Untangling multiple factors in spatial
distributions: lilies, gophers, and rocks. Ecology, 77, 1698–
1715.
Tilman, D. & Pacala, S. (1993) The maintenance of species
richness in plant communities. Species Diversity in Ecological
Communities (eds R.E. Ricklefs & D. Schluter), pp. 13–25.
Univeristy of Chicago Press, Chicago, Illinois, USA.
Webb, C.O. & Peart, D.R. (2000) Habitat associations of trees
and seedlings in a Bornean rain forest. Journal of Ecology,
88, 464– 478.
Wesser, S.D. & Armbruster, W.S. (1991) Species distribution
controls across a forest-steppe transition: a causal model
and experimental test. Ecological Monographs, 61, 323 –
342.
Received 17 October 2000
revision accepted 3 May 2001