Journal of Animal
Ecology 2004
73, 841– 851
The importance of habitat heterogeneity, biotic
interactions and dispersal in abundance–occupancy
relationships
Blackwell Publishing, Ltd.
ALISON R. HOLT, PHILIP H. WARREN* and KEVIN J. GASTON*
Environment Department, University of York, Heslington, York YO10 5DD, UK; and *Department of Animal and
Plant Sciences, University of Sheffield, Sheffield S10 2TN, UK
Summary
1. In simple microcosm systems, the form of interspecific abundance–occupancy relationships has been found to be dependent on biotic interactions. However, in more complex
systems such effects may be obscured by those of habitat heterogeneity. Here we test this
proposal using laboratory microcosms of protists and bacteria.
2. The independent effects of species interactions and heterogeneity were tested by
comparison of the abundance–occupancy relationship formed in multiple habitat patch
systems containing all species together, with that relationship formed by combining
data from equivalent systems containing each protist species alone and between homogeneous and heterogeneous environments.
3. There was more residual variation about positive interspecific abundance–occupancy
relationships formed in heterogeneous environments in interacting and non-interacting
communities as the majority of species were more restricted in the number of patches
they could occupy compared to homogeneous landscapes.
4. Abundance–occupancy relationships in interacting communities were better defined
than those in non-interacting communities. The inclusion of interspecific interactions
caused a reduction in the abundance and occupancy of the majority of species and
changed the position of species within the relationship.
5. Our results show that biotic interactions influence the abundance–occupancy relationships even with imposed environmental heterogeneity. However, in heterogeneous
environments, for some species these processes occurred in fewer patches, causing increased
residual variation about positive interspecific abundance–occupancy relationships compared to homogeneous environments.
Key-words: interspecific abundance–occupancy relationship, biotic interactions, habitat
heterogeneity, microcosms.
Journal of Animal Ecology (2004) 73, 841–851
Introduction
Positive interspecific relationships between the abundance and occupancy of species in a given taxonomic
assemblage is one of the most general patterns in
macroecology (Hanski 1982; Brown 1984; Gaston &
Lawton 1990; Hanski, Kouki & Halkka 1993; Lawton
1993; Gaston 1994, 1996; Gaston, Blackburn & Lawton
1997; Gaston & Blackburn 1999). Species with low local
abundance tend to be restricted in distribution, whereas
those with high local abundance occur more widely.
© 2004 British
Ecological Society
Correspondence: Alison R. Holt, Environment Department,
University of York, Heslington, York YO10 5DD, UK (tel.
+01904 434789, e-mail [email protected]).
Several hypotheses have been proposed to explain this
pattern, but thus far attempts to understand its mechanistic basis have achieved only limited success (Gaston
et al. 1997; Gaston et al. 2000).
The majority of mechanisms suggested to bring about
positive interspecific abundance–occupancy relationships
are concerned with ecological processes (for review see
Gaston et al. 1997). These include (i) the metapopulation dynamic hypotheses (Hanski 1991a,b; Nee, Gregory
& May 1991), where abundance–occupancy relationships are predicted to result from two particular patterns of local extinction and colonization. The carrying
capacity hypothesis assumes interspecific variation in
species’ carrying capacities (local abundance), and that
locally abundant species have lower extinction rates and
842
A. R. Holt,
P. H. Warren &
K. J. Gaston
© 2004 British
Ecological Society,
Journal of Animal
Ecology, 73,
841–851
higher colonization rates than the less abundant species,
leading in turn to higher occupancy (Nee et al. 1991). The
rescue effect hypothesis assumes that emigration and
immigration affect local dynamics. The rate of immigration in the system increases as the number of patches
occupied increases, and immigration decreases the
probability of a local population becoming extinct (the
‘rescue effect’) (Hanski 1991a). (ii) The niche-breadth
hypothesis (Brown 1984), predicts that the relationship
arises owing to interspecific variation in the realized niche
breadth of species and positive relationships between
niche breadth and both local abundance and regional
occurrence. (iii) The resource-availability hypothesis
( Hanski et al. 1993; Gaston 1994), assumes variation in
the abundance and occupancy of the resources on which
different species depend, with those exploiting resources
that are locally common and widespread tending themselves to become abundant and widespread.
None of the above ideas includes (at least explicitly)
the possible effects of biotic interactions on abundance–
occupancy relationships. In a previous experiment (Holt,
Warren & Gaston 2002), building on the work of
Warren & Gaston (1997), we tested the importance
of biotic interactions and dispersal in the formation of
interspecific abundance–occupancy relationships using
aquatic microcosms of protists. We found that the
inclusion of biotic interactions altered both the abundance and occupancy of individual species, changing
their position within the relationship, and that clearer
abundance–occupancy relationships were generated with
biotic interactions than by the same set of species in
equivalent systems but in monocultures. As in Warren
& Gaston (1997) we concluded that the mechanism
causing the observed abundance–occupancy relationships
was closely related to the carrying capacity hypothesis
in the presence of dispersal, but would operate just
through extinction in the absence of dispersal. The
relationship would be expected to arise in virtually any
system in which species either go extinct or colonize new
patches, and where these processes are related to the
density of species within patches in which they occur.
That experiment (Holt et al. 2002) used uniform
patches of habitat. As a result it is possible that the role
of biotic interactions may have been over-emphasized.
In real landscapes the environment is spatially heterogeneous, offering a range of habitats to each species.
The presence of such heterogeneity may alter interspecific interactions, making their effects less pronounced.
Environmental heterogeneity may also strengthen the
effects central to some of the hypotheses proposed to
cause the abundance–occupancy relationship, such as the
niche-breadth (Brown 1984) and resource-availability
hypotheses (Hanski et al. 1993; Gaston 1994), lessening
the relative importance of biotic interactions.
In this paper we report the results of an experiment
where we include heterogeneous environments as well as
homogeneous ones to test both how such heterogeneity,
interactions between species, and dispersal influence
the formation of abundance–occupancy relationships.
Methods
Laboratory microcosms make it possible to carry out
experimental investigations of these mechanisms as
they allow the manipulation of processes at spatial and
temporal scales that are large relative to the organism
(e.g. Luckinbill 1974; Dickerson & Robinson 1985;
Drake et al. 1993; Lawler & Morin 1993; Warren &
Gaston 1997; Petchey et al. 1999). The experiment
involved systems of multiple habitat patches, with the
manipulation of three factors: habitat heterogeneity,
species interactions and between-patch dispersal. Each
experimental unit consisted of a block of 12 habitat
patches. Each patch was a single 20 mL Petri dish, containing 15 mL of medium (90% Chalkley’s solution:
5 mL of each of the following stock solutions in
985 mL of distilled water and autoclaved: NaCl 2·00 g per
100 mL, KCl 0·08 g per 100 mL, CaCl2 0·12 g per
100 mL (Thompson, Rhodes & Pettman 1988), 5% hay
infusion: 6 g chopped hay autoclaved in 600 mL distilled
water and 5% soil water: 100 mL loam-based potting
soil autoclaved in 700 mL distilled water).
The habitat heterogeneity treatment involved two
levels of heterogeneity, one in which a block of patches
had identical environmental conditions, and one in
which the block comprised a mixture of three different
patch environments. In homogeneous environments
each patch in a block offered the same resource level of
one wheat grain in addition to the 15 mL of medium.
Heterogeneous environments were created by manipulating the available energy per patch by varying the
number of wheat grains. Here, patches 1– 6 of each block
contained medium and no wheat grain (low resource
patches), patches 7–9 had medium and 1 wheat grain
(intermediate resource patches) and patches 10–12
contained medium and 3 wheat grains (high resource
patches). The total number of wheat grains in both
homogeneous blocks and heterogeneous blocks was
therefore the same (12 grains). To control for the effects
that uneven physical structure may bring, mimics of
wheat grains made from potters’ clay (fired to 1260 °C)
were used so that every patch in all blocks contained 3
‘wheat grains’ either real, clay or a mixture.
Within homogeneous and heterogeneous environments the species interaction treatment involved either
blocks of patches into which all 12 species of protist
were introduced, or blocks into which each of the 12
species was introduced alone (with the exceptions of
the predator species Amoeba and Euplotes, which were
each inoculated along with a food source, Tetrahymena).
This created one multi-species landscape occupied by
12 species (the interacting community), and 12 monoculture landscapes (the non-interacting community).
This assembly procedure created 100% occupancy at
the beginning of the experiment. Protist species were
chosen to constitute a community consisting of three
major trophic groupings: predators: Amoeba proteus
(Pallas) Leidy (300 µm), Euplotes patella Ehrenberg
(120 µm); omnivores: Blepharisma japonicum Suzuki
843
Abundance–
occupancy
relationships
© 2004 British
Ecological Society,
Journal of Animal
Ecology, 73,
841– 851
(200 µm); bacterivores: Chilomonas paramecium Ehrenberg (20 µm), Colpidium striatum Stokes (60 µm), Colpoda
sp. Muller (50 µm), Entosiphon sp. Dujardin (30 µm),
Halteria sp. Dujardin (30 µm), Paramecium caudatum
Ehrenberg (150 µm), Spirostomum teres Claparède
& Lachmann (300 µm), Tetrahymena pyriformis
(Ehrenberg) Lwoff (30 µm) and Tillina sp. Gruber
(175 µm). The inoculum for each patch was 1 mL of
medium from each protist species stock culture (after
mixing). This allowed from 200 to 1300 individuals of
the small to medium species to be transferred and at least
20 individuals of the larger species.
The dispersal treatment was either no between-patch
dispersal or a low between-patch dispersal rate. The low
dispersal treatment was a one-way transfer of c. 2% of
the medium (containing protists) between six randomly chosen pairs of patches within the same landscape, carried out once every seven days. All of the
microcosms (including those with no dispersal) were
gently mixed before dispersal on each dispersal occasion.
The same random pattern of transfers was used for
interacting communities and non-interacting communities, but this pattern was changed on each dispersal
occasion.
Heterogeneous and homogeneous environments
therefore consisted of interacting communities (1 block)
and non-interacting communities (12 blocks), which
were replicated three times for each of the two dispersal treatments (giving 6 interacting community
blocks, 6 blocks of 12 non-interacting communities,
and a total of 936 patches). The microcosms were
maintained in the dark at a constant temperature of
15 °C.
Sampling took place after 4, 8 and 12 weeks. The
larger species (Amoeba, Blepharisma, Euplotes, Colpidium,
Paramecium, Spirostomum and Tillina) were counted
directly in a patch when they occurred at low densities,
and at medium and high densities a subsampling approach
was used. After gentle mixing of a patch, four 0·1 mL
subsamples were taken using a Gilson pipette and the
number of individuals in each sample counted to estimate mean density per patch. The smaller species were
subsampled using the same technique. Fresh medium
was added to each patch to replace what had been
removed after each sampling event. After sampling,
each patch was scanned under a dissecting microscope
at 15 –20 times magnification to ensure that species
occurring at very low abundances had not been missed.
It is important to note that Colpoda and Tillina may
form resting cysts after a short period of rapid population growth. If cysts are present it is debatable whether
these species constitute real extinctions. However,
during the sampling of the experiment no cysts were
observed.
Occupancy was the number of patches a species
occupied (0 –12) in each microcosm block and abundance
was the mean density of the species across all patches in
which it occurred. Both were log-transformed for the
analysis.
Results
( )
    
  
Heterogeneity
The interspecific relationships between abundance and
occupancy in heterogeneous environments appeared to
show more scatter than in homogeneous environments
(Fig. 1). This scatter increased from week 4 to week 12.
To examine whether the strength of the homogeneous
and heterogeneous relationships varied, we tested for
differences in the values of the rank correlations, with
or without between-patch dispersal for non-interacting
communities and interacting communities individually
using a two-way . In non-interacting communities
after 4 weeks, correlation coefficients of the relationship
between abundance and occupancy in homogeneous
environments were significantly higher than in heterogeneous environments (heterogeneity treatment F1,8 =
11·3, P = 0·01; dispersal treatment F1,8 = 10, P > 0·05),
but after 8 weeks there were no significant differences
( heterogeneity F1,8 = 0·67, P > 0·05; dispersal F1,8 = 0·09,
P > 0·05). After 12 weeks those systems with betweenpatch dispersal showed significantly higher correlation
coefficients (heterogeneity F1,8 = 1·58, P > 0·05; dispersal F1,8 = 12·2, P < 0·01). However, a significant
interaction between the heterogeneity and dispersal
treatments showed that rank correlation coefficients were
higher in heterogeneous environments with betweenpatch dispersal (F1,8 = 5·76 P < 0·05; Tukey multiple
comparisons test P < 0·05). In interacting communities
homogeneous environments also showed a tendency
towards higher correlation coefficients than heterogeneous environments (4 weeks: heterogeneity F1,8 = 0·05,
P > 0·05; dispersal F 1,8 = 1·07, P > 0·05; 8 weeks:
heterogeneity F1,8 = 8·3, P < 0·01; dispersal F1,8 = 0,
P > 0·05; 12 weeks: heterogeneity F1,8 = 5·1, P = 0·05;
dispersal F1,8 = 0·04, P > 0·05).
Biotic interactions
As in Holt et al. (2002), the main difference between
the interspecific abundance–occupancy relationships
of interacting and non-interacting communities is the
way in which the data points are distributed in the
plots. In the non-interacting communities species are
spread across the abundance axis but are clustered
along the occupancy axis. On the other hand, interacting community species are spread well across both the
x and y axes. Although this pattern seems clear graphically, and to be an important difference biologically, it
is rather ineffectively captured by a parametric or nonparametric correlation.
The proportion of species at full occupancy tended to
be higher in non-interacting communities than interacting ones in homogeneous environments (Mann–Whitney
U-test, 4 weeks: n1+2 = 6, U = 5, P < 0·05; 8 weeks:
844
A. R. Holt,
P. H. Warren &
K. J. Gaston
Fig. 1. The relationship between within patch abundance (estimated density mL −1) and occupancy (number of patches occupied)
for each treatment in both non-interacting and interacting communities for homogeneous and heterogeneous environments after
(a) 4 weeks, (b) 8 weeks and (c) 12 weeks. Each graph represents the mean over the three replicates. Labels indicate the species
present: Am: Amoeba proteus, Bl: Blepharisma japonicum, Ca: Colpoda sp., Ch: Chilomonus paramecium, Co: Colpidium stiatum,
En: Entosiphon sp., Eu: Euplotes patella, Ha: Halteria sp., Pa: Paramecium caudatum, Sp: Spirostomum teres, Te: Tetrahymena
pyriformis, Ti: Tillina sp. The Spearman’s rank correlation coefficient for each relationship is shown underneath the bottom left
of each plot (*P < 0·05; **P < 0·01; ***P < 0·001).
© 2004 British
Ecological Society,
Journal of Animal
Ecology, 73,
841–851
n1+2 = 6, U = 12, P > 0·05; 12 weeks: n1+2 = 6, U = 4·5,
P < 0·05). The proportion of species at full occupancy
was also higher in non-interacting communities in
heterogeneous environments (Mann–Whitney U-tests,
4 weeks: n1+2 = 6, U = 4·5, P < 0·05; 8 weeks: n1+2 = 6,
U = 5, P < 0·05; 12 weeks: n1+2 = 6, U = 8, P > 0·05).
As a consequence of this difference in the number
of species at full occupancy interspecific abundance–
occupancy relationships appeared to be better defined
with higher correlation coefficients in interacting communities than non-interacting communities after 4 weeks
whether in homogeneous or heterogeneous environments
(Fig. 1). After 8 and 12 weeks this was still the case in
homogeneous environments. In heterogeneous envi-
ronments there was a more pronounced residual scatter
about the interacting community relationships, although
they still appeared better defined than non-interacting
communities. However, the correlation coefficients in
non-interacting communities were higher (Fig. 1).
(  )
   
     
The average abundance over occupied sites and occupancy of each species separately was compared between
homogeneous and heterogeneous environments,
non-interacting and interacting communities, with and
without between-patch dispersal after 4, 8 and 12 weeks
845
Abundance–
occupancy
relationships
Fig. 1. Continued
(Tables 1 and 2 show the results after 8 weeks only as
these are representative of the general trends).
Heterogeneity
© 2004 British
Ecological Society,
Journal of Animal
Ecology, 73,
841– 851
Habitat heterogeneity had a more marked effect on
species occupancy than abundance and in general the
number of patches a species occupied decreased (Table
1a,b; Table 2). After 8 weeks Colpidium showed a significant increase in abundance with habitat heterogeneity (Table 1a). In heterogeneous environments the
abundance of Tillina decreased in the presence of interspecific interactions whether between-patch dispersal
occurred or not (Table 1b). Habitat heterogeneity imposed
no significant effects on the abundance of the remaining species (Table 1a,b).
Colpoda and Spirostomum significantly decreased in
occupancy with habitat heterogeneity in non-interacting
communities with or without dispersal, whereas Paramecium increased (Table 2a). Entosiphon in non-interacting
communities increased in occupancy in heterogene-
ous environments only with between-patch dispersal
(Table 2a). The occupancy of Euplotes and Halteria
declined in heterogeneous environments in non-interacting
communities when dispersal did not occur. Tillina significantly decreased in occupancy in heterogeneous
environments in non-interacting communities with
dispersal (Table 2a).
Amoeba decreased in occupancy with habitat heterogeneity in interacting communities with or without
dispersal (Table 2a). The occupancy of Halteria and
Tillina significantly decreased when there was no betweenpatch dispersal, and the occupancy of Paramecium significantly increased (Table 2a). The remaining species
in interacting communities showed no significant
difference in occupancy between heterogeneous and
homogeneous environments, with or without dispersal.
Biotic interactions
The majority of species decreased in abundance significantly in both heterogeneous and homogeneous
846
A. R. Holt,
P. H. Warren &
K. J. Gaston
Fig. 1. Continued
© 2004 British
Ecological Society,
Journal of Animal
Ecology, 73,
841–851
environments with the inclusion of biotic interactions,
although there were some exceptions (Table 1a,b). The
abundance of Amoeba was significantly higher in interacting communities than non-interacting communities
( Table 1a). The level of abundance that Blepharisma
and Halteria attained was not significantly different
between interacting and non-interacting communities.
Tillina decreased in abundance in interacting communities in the presence of habitat heterogeneity, but not
in homogeneous environments ( Table 1b). There was
no difference in the abundance that Chilomonas attained
between interacting and non-interacting communities
in homogenous environments with no between-patch
dispersal ( Table 1b).
Although the abundance of Colpidium decreased
significantly in interacting communities (Table 1a), the
significant interaction between environment and communities with or without interspecific interactions showed
that its abundance was lowest in interacting communities
with an homogeneous environment (Tukey multiple
comparison test P < 0·05).
The majority of species also experienced a decrease
in occupancy with the inclusion of biotic interactions
in both homogeneous and heterogeneous environments
(Table 2b). However, the number of patches occupied
by Amoeba in homogeneous environments increased
with biotic interactions, but there was no difference in
occupancy between interacting and non-interacting
communities in heterogeneous environments ( Table 2b).
The occupancy of Blepharisma and Paramecium also
did not change between non-interacting or interacting
communities in heterogeneous environments. In the
absence of dispersal in heterogeneous environments
the occupancy of Entosiphon and Euplotes did not differ
between interacting and non-interacting communities
(Table 2b). Entosiphon also showed no significant
difference in occupancy between interacting and noninteracting communities when in homogeneous landscapes, where in the same environment Tillina showed
no difference in occupancy between interacting and
non-interacting communities with no between-patch
dispersal.
847
Abundance–
occupancy
relationships
© 2004 British
Ecological Society,
Journal of Animal
Ecology, 73,
841– 851
Table 1. Summary table (for results at 8 weeks) showing the general trends in the results of (a) three-way s for the effects of habitat heterogeneity, species interactions and dispersal on log-transformed
abundance of species (n = 3, d.f. = 1,8) (*P < 0·05; **P < 0·01; ***P < 0·001). Arrows indicate higher or lower abundance in heterogeneous environments, interacting communities or with dispersal. (b) The effects
of habitat heterogeneity (HE: heterogeneous environment; HO: homogeneous environment), on non-interacting and interacting communities between no dispersal (ND) and dispersal (D) on log-transformed
abundance using t-tests assuming unequal variance (n = 3) (*P < 0·05; **P < 0·01; ***P < 0·001). Three-way s were not used for these species as there was zero variance in some treatment combinations
mainly due to their extinction in interacting communities. Arrows indicate higher or lower abundance in heterogeneous environments, interacting communities or with dispersal, when statistically significant or when
statistical tests could not be used
(a)
Species
Habitat
heterogeneity
F
Abundance
higher (↑) or
lower (↓) in
heterogeneous
environment?
Amoeba
Blepharisma
Colpoda
Colpidium
0·27
0·08
0·26
12·2**
Entosiphon
Euplotes
Halteria
Paramecium
2·5
0·9
0·1
0·5
(b)
Biotic
interactions
F
Abundance
higher (↑) or
lower (↓) in
interacting
communities?
Dispersal
F
Abundance
higher (↑) or
lower (↓) with
dispersal?
–
–
–
↑
301·73***
1·0
167·4***
162·0***
↑
–
↓
↓
0·0
0·4
0·4
9·1**
–
–
–
↑
–
–
–
–
4·5**
22·4***
1·2
7·0*
↓
↓
–
↓
0·8
0·8
8·4*
0·0
–
–
↑
–
Habitat heterogeneity
Non-interacting
Interactions
Heterogeneity
*biotic interactions
Biotic interactions*
dispersal
F
–
–
–
26·9***
8·6*
–
–
–
–
Dispersal
Interacting (arrows
indicate higher (↑) or
lower (↓) abundance
in heterogeneous
environments
Homogeneous
environments (arrows
indicate higher (↑) or
lower (↓) abundance in
interacting communities)
Heterogeneous
environments (arrows
indicate higher (↑) or
lower (↓) abundance in
interacting communities)
Non-interacting
Interacting (arrows
indicate higher (↑) or
lower (↓)abundance
with dispersal)
Species
D
ND
D
ND
D
ND
D
ND
HO
HE
HO
HE
Chilomonas
Spirostomum
Tetrahymena
Tillina
t3 = 0·17
t3 = 0·12
t3 = 0·84
t3 = 1·23
t3 = 0·07
t3 = 0·23
t3 = 0·36
t3 = 0·22
–
–
–
↓
t2 = 1·54
–
–
↓
↓
↓
↓
t2 = 0·24
t2 = 3·49
↓
↓
t2 = 0·54
↓
↓
↓
↓
↓t2 = 32·75***
↓
↓
↓
t3 = 0·01
t3 = 0·23
t3 = 0·27
t3 = 0·12
t3 = 0·27
t3 = 0·19
t3 = 0·0
t3 = 0·05
↓
–
–
t2 = 0·45
↓
–
–
–
848
A. R. Holt,
P. H. Warren &
K. J. Gaston
Table 2. Summary table (for results at 8 weeks) showing comparisons of mean log occupancy (+ 1), for treatment combinations
(n = 3) where variance was not zero, using t-tests assuming unequal variance (*P < 0·05; **P < 0·01; ***P < 0·001). (a) Each
species between homogeneous and heterogeneous environments in biotic interaction and dispersal treatments ( ND: no dispersal,
D: dispersal). Arrows indicate higher or lower abundance in heterogeneous environments, interacting communities or with
dispersal, when statistically significant and when statistical tests could not be used. † species occupy all patches in non-interacting
communities and no patches in interacting communities. (b) Each species between interacting and non-interacting communities
in the heterogeneity and dispersal treatments. (c) Each species between systems with dispersal and no dispersal in heterogeneity
(HO: homogeneous environments, HE: heterogeneous environments) and biotic interaction treatments
(a)
© 2004 British
Ecological Society,
Journal of Animal
Ecology, 73,
841–851
Habitat heterogeneity
Non-interacting
(arrows indicating higher (↑)
or lower (↓) occupancy in
heterogeneous environments)
Interacting
(arrows indicating higher (↑) or
lower (↓) occupancy in
heterogeneous environments)
Species
D
ND
D
ND
Amoeba
Blepharisma
Colpoda
Chilomonas
Colpidium
Entosiphon
Euplotes
Halteria
Paramecium
Spirostomum
Tetrahymena
Tillina
t2 = 0·31
t2 = 2·06
↓
–
–
↑
–
–
↑
↓
†
↓t3 = 3·81*
t2 = 2·45
t3 = 2·02
↓
–
–
t4 = 0
↓
↓
↑
↓
†
t3 = 1·13
↓
t3 = 0·09
t3 = 0·31
–
t3 = 1·27
t2 = 2·04
t3 = 0·58
t2 = 3·48
t2 = 2·05
–
†
–
↓
t2 = 1·00
t3 = 0·73
t3 = 1·24
t2 = 0·43
t2 = 1·56
t3 = 1·00
↓t2 = 5·96*
↑t2 = 6·17*
–
†
↓
(b)
Interactions
Homogeneous environments
(arrows indicating higher (↑)
or lower (↓) occupancy in
interacting communities)
Heterogeneous environments
(arrows indicating higher (↑) or
lower (↓) occupancy
ininteracting communities)
Species
D
ND
D
ND
Amoeba
Blepharisma
Colpoda
Chilomonas
Colpidium
Entosiphon
Euplotes
Halteria
Paramecium
Spirostomum
Tetrahymena
Tillina
↑
t2 = 2·62
↓
↓
↓
t2 = 0·85
↓
↓
↓
↓
↓†
↓t2 = 5·30*
↑
↓t2 = 4·83*
↓
↓
↓
t3 = 2·83
↓
↓
↓
↓
↓†
t3 = 1·13
t2 = 1·86
t2 = 2·84
↓t2 = 5·38*
↓
↓
↓
↓
↓
t3 = 0·95
↓
↓†
↓
t2 = 2·96
t2 = 1·33
↓
↓
↓
t2 = 1·91
t2 = 2·32
↓t2 = 6·33*
t2 = 2·96
↓
↓†
↓
(c)
Dispersal
Non-interacting (arrows indicating
higher (↑) or lower (↓) occupancy
with dispersal)
Interacting
(arrows indicating higher (↑)
or lower (↓) occupancy with dispersal)
Species
HO
HE
HO
HE
Amoeba
Blepharisma
Colpoda
Chilomonas
Colpidium
Entosiphon
Euplotes
Halteria
Paramecium
Spirostomum
Tetrahymena
Tillina
t2 = 1·97
t2 = 0·21
–
–
–
t2 = 0·93
–
–
–
–
†
t2 = 1·17
t2 = 0·01
t3 = 1·92
↑
–
–
↑
↑
–
t2 = 1·27
t3 = 032
†
t2 = 3·2
–
t3 = 0·53
t2 = 1·05
↓
t2 = 0·29
t3 = 1·31
t3 = 0·39
t3 = 0·79
t2 = 0·97
–
†
t3 = 0·47
t2 = 0·94
t3 = 0·46
t4 = 0
↓
t2 = 0·33
t3 = 0·26
t3 = 1·10
t3 = 1·35
t3 = 0·12
–
†
–
849
Abundance–
occupancy
relationships
Dispersal
There were only three species that showed a difference
in abundance as a result of the dispersal treatments.
Colpidium and Halteria both significantly increased in
abundance with dispersal ( Table 1a), although the significant interaction between community type and dispersal treatment for Colpidium showed that abundance
was higher with dispersal in interacting communities
(Tukey multiple comparison test P < 0·05). Chilomonas
also had a tendency to decrease in abundance with dispersal but only in interacting communities (Table 1b).
The number of patches Colpoda, Entosiphon and
Euplotes occupied increased in the presence of betweenpatch dispersal in heterogeneous environments with no
interspecific interactions (Table 2c). Chilomonas decreased
in occupancy with dispersal in both homogeneous and
heterogeneous environments in interacting communities
(Table 2c).
These results suggest that species interactions are
a strong influence on the abundance and occupancy
levels that each species attains, causing species to occupy
very different positions in the abundance–occupancy
relationship from those in the non-interacting communities. The most abundant species in non-interacting
communities are not necessarily the most abundant in
interacting communities ( Fig. 1). Habitat heterogeneity has a less marked effect on species abundance and
occupancy. Only a small proportion of species show
systematic variation in abundance levels but more
species show variation in occupancy due to habitat
heterogeneity ( Tables 1 and 2). Habitat heterogeneity
seems to have more effect on occupancy in non-interacting
communities. Dispersal rarely caused systematic differences in species abundance or occupancy (Tables 1
and 2).
(  )
   ’  
      

Thus far the results have shown that biotic interactions
are an important influence on the structure of interspecific abundance–occupancy relationships generated in
microcosm systems even with environmental heterogeneity. We illustrate this further by showing the response
of each individual species’ abundance in non-interacting
and interacting communities to the resource levels within
a heterogeneous environment. This clearly shows (Fig. 2)
that biotic interactions greatly influence the abundance
of individual species despite the imposed environmental heterogeneity.
Discussion
© 2004 British
Ecological Society,
Journal of Animal
Ecology, 73,
841– 851
Positive interspecific abundance–occupancy relationships occurred in both homogeneous and heterogeneous
environments, in non-interacting and interacting communities with and without between-patch dispersal.
Abundance and occupancy of the majority of species
were affected by biotic interactions (Tables 1 and 2).
Amoeba appears to be particularly strongly affected
by interactions, as it becomes more common and widespread in interacting communities. This is likely to
reflect greater prey availability in interacting communities. The remaining species generally decreased in
abundance and occupancy in interacting communities.
The predator Euplotes did not share the benefits of an
increased prey availability as Amoeba, perhaps demonstrating a narrower diet breadth. All species show marked
differences in their position within the abundance–
occupancy relationship due to biotic interactions (Fig. 1),
many of the species with a high abundance and occupancy in non-interacting communities are the rarer or
extinct species in the interacting communities, for
example Colpoda and Tetrahymena. Biotic interactions
still strongly dictate the position of species in the relationship despite the presence of environmental variation.
For each species individually there were differences
between the abundances attained in non-interacting
and interacting communities which overrode the pattern
of heterogeneity in the landscape (Fig. 2). This suggests
that species in interacting communities are not simply
responding to the pattern of heterogeneity in the environment, but biotic interactions are a stronger influence
on the abundance levels a species can attain in each of the
resource levels. However, there are no species that occur
in totally different resource patches in non-interacting
compared with interacting communities. This suggests
that there are still detectable effects of environmental
variation across non-interacting and interacting
communities.
The strengths of the relationships between abundance
and occupancy were generally significantly lower in
heterogeneous environments whether in interacting
or non-interacting communities (Fig. 1). Species in
homogeneous environments tended to maintain full
occupancy for longer than those in heterogeneous ones.
This may simply be due to the inclusion of poor quality
patches in the heterogeneous landscapes, which reduces
the likelihood of a species being able to occur in all of
the existing patches.
Habitat heterogeneity caused more variation in the
number of patches a species occupied than it did in species abundance. Heterogeneity in the resource levels
available meant that the number of patches a species
could occupy varied more than in homogeneous environments (Tables 1 and 2). This number of patches
occupied reflected a species’ ability to occupy all resource
levels (generalist) or be restricted to one or two resource levels (specialist). For example, Tillina was the
only species that showed a tendency to decrease in both
abundance and occupancy in heterogeneous environments, all other species decreased only in occupancy
with the exception of Colpidium that increased only in
abundance (Table 1b). The majority of species were
able to maintain similar abundances in heterogeneous
environments as in homogeneous environments despite
850
A. R. Holt,
P. H. Warren &
K. J. Gaston
Fig. 2. The mean log abundance (+ 1) of each species over all replicates in a treatment (+ 1 SE) from non-interacting communities
(black bars) and interacting communities (open bars) over the three resource levels (0, low resource level; 1, medium resource level;
3, high resource level ) of the heterogeneous environments with no dispersal after 8 weeks. Am: Amoeba proteus, Bl: Blepharisma
japonicum, Ca: Colpoda sp., Ch: Chilomonus paramecium, Co: Colpidium stiatum, En: Entosiphon sp., Eu: Euplotes patella, Ha:
Halteria sp., Pa: Paramecium caudatum, Sp: Spirostomum teres, Te: Tetrahymena pyriformis, Ti: Tillina sp.
© 2004 British
Ecological Society,
Journal of Animal
Ecology, 73,
841–851
either being restricted by the environment or being able to
occupy more patches. This explains the scatter about the
abundance–occupancy relationships in heterogeneous
environments (Fig. 1), where species are spread more
widely along the occupancy axis than the abundance
axis compared to in homogeneous environments. In turn,
this may cause the weaker relationships in heterogeneous environments than homogeneous environments.
For example, in interacting communities with betweenpatch dispersal in an heterogeneous environment after
8 weeks the scatter is caused by the reduction in occupancy of Halteria, the increase in occupancy of Paramecium and the increase in abundance of Colpidium.
This is also likely to explain why there is a negative
abundance–occupancy relationship in heterogeneous
environments after 12 weeks in interacting communities (Fig. 1c). This is because generalist species such as
Amoeba, Entosiphon and Paramecium are able to main-
tain a reasonable abundance across all resource levels
whereas the more specialist Blepharisma is restricted
to a small number of patches (intermediate to high
resource level) where it can maintain high abundances
(Fig. 2).
These results show that the presence of biotic interactions is a strong influence on the abundance and
occupancy levels of each species and in turn affects
the positions of species in the resultant abundance–
occupancy relationship. They show no evidence for the
rescue effect hypothesis as there is largely no significant
difference in each species abundance or occupancy
levels between treatments with or without dispersal
within each environment ( Tables 1 and 2). Therefore, the
mechanism driving the abundance–occupancy relationship in the presence of habitat heterogeneity is likely
to be due to the carrying capacity hypothesis (Nee et al.
1991) and variants upon it (Warren & Gaston 1997).
851
Abundance–
occupancy
relationships
Colonization and/or extinction still seem to be related
to abundance, the effect of which is increased by the
additional variance in abundance produced by biotic
interactions. However, these processes now appear to
occur for some species over a more restricted number of
patches than in homogeneous environments. The combination of maintaining a high abundance level in a
restricted number of patches may cause more variation
about abundance–occupancy relationships.
This experiment suggests that biotic interactions are
important in influencing abundance–occupancy relationships in natural communities even in the presence
of environmental heterogeneity. This is especially
important as all proposed explanations of interspecific
abundance occupancy relationships do not explicitly
consider species interactions. However, environmental
heterogeneity in the multiple patch systems still influenced the form of the relationships, in this case mainly
though variation in occupancy. This suggests that
proposed environmentally based mechanisms such as
niche breadth (Brown 1984) and resource availability
(Hanski et al. 1993; Gaston 1994) are likely to influence
species occupancy levels, but it is unlikely that they will
entirely explain the cause of the positive interspecific
abundance–occupancy relationship, as they seemed to
alter the form of a relationship that already existed as a
result of biotic interactions.
Acknowledgements
A.R.H. is supported by a NERC studentship, and
K.J.G. is a Royal Society University Research Fellow.
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Received 29 July 2003; accepted 15 December 2003