Download LOCAL RICHNESS-SPECIES POOL RATIO: A CONSEQUENCE OF

Folia Geobotanica 36: 9-23, 2001
LOCAL RICHNESS-SPECIES POOL RATIO:
A CONSEQUENCE OF THE SPECIES-AREA RELATIONSHIP
Sándor Bartha & Péter Ittzés
Institute of Ecology and Botany of the Hungarian Academy of Sciences, H-2163 Vácrátót, Hungary;
e-mail [email protected], [email protected]
Keywords: Abundance distribution, Coexistence mechanisms, Dispersal limitation, Individual based null
models, Methodology, Niche limitation, Spatially explicit simulations, Spatial pattern, Species saturation
Abstract: A constant ratio between species richnesses estimated at the local and regional scale is interpreted
as a proof of quasi-neutral unsaturated communities. Based on Zobel’s model of plant community (ZOBEL,
Folia Geobot. 36: 3−8, 2001) we tested the methodology of the species-pool concept by comparing the saturated
and unsaturated communities generated by spatially-explicit mechanistic simulations with known assembly
rules. Tests show that local-regional species plots can be applied to distinguish saturated vs. unsaturated
communities, however, the outcome of tests, i.e. the relationship between local and regional richness depends
on the size of the areas compared. Independently from the mechanisms controlling diversity, trivial saturation
will appear if one of the scales is either too small or too broad because species-area curves are bound at these
extreme scales. Similarly, trivial unsaturation will appear if the two scales compared are close to each other.
The application of species-area curves is useful because they help to find scales for non-trivial relationships.
Field tests reporting quasi-neutrality and unsaturated plant communities were performed at the intermediate
scales of the corresponding species-area curves, and they were estimated from heterogeneous samples.
Therefore, this field evidence might be biased by scaling artefacts. We propose to reanalyze the field evidence
with solid scaling conventions and to restrict the concept of quasi-neutrality to subordinated functional groups
based on the following hypotheses: (1) neutrality will appear within subordinated guilds as a consequence of
the hierarchical structure of plant communities; (2) the lower a guild in the hierarchy the higher neutrality of
within-layer processes detected; (3) quasi-neutrality found at the community level is not a proof of
community-level neutrality but it is due to the higher number of subordinated species in the samples.
INTRODUCTION
The emerging non-equilibrium paradigm in ecology (PICKETT et al. 1992, WU & LOUCKS
1995) has renewed the interest in regional and historical processes. While traditional
non-spatial models assumed that communities are saturated with species, the new paradigm
considers local communities as open, unsaturated systems significantly constrained by the
limited availability of species. Little is known about the magnitude of this constraint, i.e. to
what extent communities are unsaturated. Since species richness at a finer scale might be
influenced by the coarser-scale species richness (called “species pool”, ERIKSSON 1993, ZOBEL
1997) it becomes important to measure and compare species richness at different scales.
In related studies the local richness of a community is plotted against the regional richness
of different biogeographical regions. A proportional relationship between local and regional
richness is interpreted as evidence for an unsaturated community, while in a saturated
community there is no such correlation (RICKLEFS 1987, CORNELL & LAWTON 1992, CALEY
Forum: Species-pool hypothesis
10
S. Bartha & P. Ittzés
& SCHLUTER 1997, CORNELL & KARLSON 1997). Most of the authors believe that saturation
indicates fine-scale limitation of species richness due to niche limitation. On the contrary,
a proportional relationship is caused by broad-scale evolutionary constraints such as the
limited biogeographical distribution of species.
Recently ZOBEL (2001, this volume) reviewed the species-pool concept and discussed its
relevance in plant communities. He formulated new definitions and a new individual-based
quasi-neutral model concluding that competition is much less important in plant communities
than it is assumed by other studies of assembly rules (cf. WILSON 1999).
The methodology of the species-pool hypothesis is attractive because of its simplicity. The
species-pool concept has recently received considerable interest. Few papers, however, have
dealt with the methodological aspects (CRESSWELL et al. 1995, SRIVASTAVA 1999). Although
the essence of the test is the comparison of species richness detected at different scales, it
remains undefined as to what are the appropriate scales for such comparisons. In our
contribution we will test the scale-dependence of this methodology by means of simulations
with known mechanisms and contrasting types of patterns.
LOCAL VS. REGIONAL RICHNESS AND THE SPECIES-AREA RELATIONSHIP
The present theory offers no help to select particular scales in a study of the species-pool
hypothesis. We suggest that species-area curves provide a potential tool for working out
standard scaling conventions for comparative studies by local-regional richness plots.
SRIVASTAVA (1999) connected the two concepts and used species-area curves to interpret
local-regional richness plots. Accepting that the species area relationship is described by the
equation: logS = z(logA) + c, where S refers to species richness, A the area, while z and c are
the parameters of the curve (ARRHENIUS 1921), we can interpret local-regional richness plots
by the variation of the z and c parameters. SRIVASTAVA (1999) suggested that variation of the
z parameter yields in a curvilinear local-regional plot typical of saturated communities. On
the other hand, when species-area curves are parallel in log-log space (z parameter is constant),
the ratio of local to regional richness will be similar between regions, yielding an unsaturated
local-regional plot (cf. Fig. 1 in SRIVASTAVA 1999).
The limitation of Srivastava’s approach is that we should understand first what z and c
parameters mean. The related reasoning is a ground for long and unsolved discussions in
community ecology (cf. GOTELLI & GRAVES 1996: 207−238). In our opinion, the problems
and debates related to the interpretation of species-area curves essentially come from the lack
or limitations of models behind the theories.
In his review, ZOBEL (2001, this volume) proposed a new individual-based spatially-explicit
model for understanding the species-pool hypothesis. We believe that this model provides
a solid foundation and framework for the species-area relationships and the related
species-pool concept. The model assumes that vegetation consists of discrete sessile individuals
(or ramets) of similar size, it appears within a finite geographic area (region), and that the
individuals are assigned to different species, and their sizes are considerably smaller than the
total area of the region. We will show that the species-area relationship is a direct consequence
of these assumptions, and the variation of species-area curves can be explained by the variation
of the parameters of this individual-based spatially-explicit model.
If individuals are discrete and sessile, the probability of finding at least one individual in
a randomly-positioned sampling unit will depend on the size of the sampling unit. Comparing
populations of different abundances, a more abundant population will have a greater probability
Species-pool and species-area relationship
11
Fig. 1. Examples of the single species probability-area curves. (a) Effect of abundance on the probability of
finding a species in a sampling unit. All patterns have random spatial dispersion. (b) Effect of spatial pattern
on the probability of finding a species in a sampling unit. All patterns have constant 50% frequency.
of appearance in a sampling unit of given size (Fig. 1a). Comparing populations with the
same abundance but of different patterns, aggregated species will appear at a lower probability
in a sampling unit, while a species with regular distribution will appear at a higher probability
than the corresponding species of random distribution (Fig. 1b).
The shape of these curves are similar to the curves of the species-area plots. In fact, the
probability-area curves of single species are the components of the traditional species-area
curves. Species-area curves can be estimated by summing up the probability-area curves of
each species, i.e. a species-area curve can be understood by decomposing it to the
probability-area curves of individual species. The variation between species-area curves can
be interpreted by the variation in the component populations, i.e. by differences in abundances,
relative abundances, and spatial patterns. We simulated static spatial patterns of discrete sessile
individuals of similar size according to Zobel’s model (Fig. 2). We demonstrate that the shape
of the species-area curves depends on the total number of individuals (density), the spatial
pattern of individuals (random, regular, or patchy), and the way how these individuals are
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S. Bartha & P. Ittzés
Fig. 2. Variation of species-area curves. (a) Variation due to changing species pool. All patterns have random
spatial dispersion, lognormal abundance distribution, total density is 0.5. (b) Variation due to changing total
density. d is the average number of individuals in a grid cell. All patterns have random spatial dispersion,
lognormal abundance distribution, species pool = 64 species. (c) Variation due to changing spatial pattern or
changing relative abundances. Total density is 0.5 for all patterns, species pool = 64 species.
Species-pool and species-area relationship
13
assigned
to
species
(species/abundance relations).
If other parameters are
constant (individuals are
randomly-distributed in space,
and relative abundances follow
a lognormal distribution), the
plateau of the curves varies
according to the total number
of species (species pool) (Fig.
2a). It is clear that at an
intermediate
sampling-unit
size the species richness is
positively related to the size of
Fig. 3. Generalized interpretation of species-area curves: variation due
to changing densities, species pool, relative abundances and spatial the species pool. If the size of
the species pool is constant
patterns.
(and individuals have random
spatial dispersion and their relative abundances follow a lognormal distribution), the intercept
of the curves varies with the total abundance (density) of individuals (Fig. 2b). At an
intermediate sampling-unit size the species richness is positively related to the total abundance
of individuals. If both total abundance and species pool are constant, i.e. the plateau and
intercept are relatively fixed, the slopes of the curves will change if we change the relative
abundances and spatial patterns of component species (Fig. 2c). At an intermediate
sampling-unit size the species richness is lower if the equitability of relative abundances is
lower or if some species have aggregated spatial patterns. Species with lower abundance
and/or a more aggregated pattern have a lower chance to appear in a sampling unit (cf. Fig. 1).
If a multi-species assemblages is composed of species of lower abundances or a more clumped
patterns then its species richness will be relatively lower at an intermediate scale.
Zobel’s individual-based model and the related reference patterns with contrasting relations
of individuals offers a simple and effective way to interpret species-area curves (Fig. 3).
Similarly clear relationships are expected when local species richness is related to regional
richness in the local-regional richness plots. Fig. 4 shows the local-regional richness plots
created from the patterns of Fig. 2c. Recall that the density of individuals was kept constant
here, but abundance relations and spatial patterns have been changed. Slight variation of the
species pool was generated as well. Therefore, we can investigate the nature and variation of
a local-regional richness plot within and between pattern types as well. The regular spatial
pattern used to be the consequence of competitive interactions between species, i.e. saturation
expected for this case. In the case of other patterns, unsaturation is expected because no
spatial segregation between individuals occurred, and some species will always be missing
from local samples due to their lower rank in the dominance hierarchies or due to their spatial
patchiness.
The correlation between the species richnesses of different scales are strongly dependent
on the scales considered, i.e. on the way how local and regional scales have been defined
(Fig. 4). Clearly, no correlation has been found in Fig. 4a, while Fig. 4b suggests proportional
relationships. The change in the results was independent of the type of the reference patterns.
The slope of individual patterns are slightly different in Fig. 4b, and these slopes also change
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S. Bartha & P. Ittzés
with scale. When all types of
patterns
are
considered
together, i.e. the sample is
heterogeneous, the proportional
character is even stronger. The
trend of scale-dependence
found can be interpreted by the
corresponding
species-area
curves (Fig. 5). There is no
correlation found when the local
scale is close to the size of
individuals or the regional scale
is close to the minimum area,
i.e. to the plateau of the
species-area curve (Fig. 5.).
Note that lack of correlation is
not proof of local saturation in
a
community.
Saturation
appeared in our example but at
regional scale because we are
close to the scale of the
minimum area. The universal
character
of
species-area
relationship involves inherent
correlation between richnesses
at different scales. The closer
the scales compared are the
stronger this relationship is.
Therefore, we have a great
chance to have a proportional
relationship at intermediate
scales.
Fig. 4. Relationship between local and regional species richness at
Our results showed that there
different spatial patterns and different relative abundances. (a), (b) The are
simple
relationships
effect of scale. Fitted linear regressions indicate significant between the type of patterns and
proportional relationships. Total density is 0.5 for all patterns, total
the shape of species-area
number of species varies (53, 55, 57, 59, 61, 63 species). 1 × 1, 45 ×
45; 10 × 10, 21 × 21 denote the scales (plot sizes in artificial units). curves. However, contrary to
the suggestions of SRIVASTAVA
(1999: Fig. 1), there are no
simple relationships between local and regional richness and the parameters of species-area
curves. The relationship between local and regional richness depends on the areas compared.
When testing for saturation or unsaturation, we should keep in mind that trivial saturation
will appear if one of the scales are either too small or too broad because species-area curves
are bound at these extreme scales. Similarly, trivial unsaturation will appear if the two scales
compared are close to each other. Species-area curves are useful because they help to find
scales for non-trivial relationships.
Species-pool and species-area relationship
15
In practice, field work is
performed at intermediate scales
because the local (community)
scale is generally broader than the
finest scale of the plant
individuals, and the regional
scale is usually finer than the
minimum area of the regional
pool (except RYDIN & BARBER
2001, this volume). Furthermore,
field
samples
are
often
heterogeneous in respect to the
total density of individuals,
Fig. 5. Generalized scale effects on local-regional species plots: species/abundance relationships,
interpretation based on species-area curves.
and
spatial
pattern.
The
consequence is that field results
show a proportional relationship between local and regional species richness (PÄRTEL et al.
1996). However, this constancy of the richness ratio should be considered very carefully.
A constant ratio involves scale invariance suggesting that the size of the areas compared is
not important. However, our results show that areas compared are of great importance and
should be standardized according to the characteristics of the community. Standardization by
the number of individuals (ZOBEL & LIIRA 1997) seems to be a promising opportunity.
However, the problem with scaling by the number of individuals is that it standardizes only
the local scale. The number of individuals should be counted at the regional scale as well but
it is not feasible.
WHAT CAN WE INFER FROM LOCAL SPECIES SATURATION?
Recently, it has been demonstrated (HERBEN 2000) that local competition can generate
a correlation between richness per unit area and species pool. Therefore, the existence of
a correlation betweeen local and regional richness cannot be used to distinguish between the
local (bottom-up) and regional (top-down) control of diversity. HERBEN (2000: 125) recognized
that the number of quadrats within the region is a critical parameter affecting the correlation.
In the previous section we concluded that the correlation found between local and regional
richness depends on the shape of the species-area curve and the size of areas selected to
represent the local and regional scales. This effect is due to the species-area relationship, i.e.
it can be interpreted by a phenomenon, disregarding the mechanisms in the background.
Our previous discussion offers an interpretation in terms of reference patterns only.
Therefore, we are presenting some tests based on spatially-explicit mechanistic simulations.
We use simulation because it provides a tool for testing patterns generated by known rules.
Our approach will be similar to HERBEN’s (2000) approach, however, we will extend his
simulations with explicit attention being paid to the scales relevant to the simulated
spatio-temporal dynamics.
We performed simulation experiments with different roles of competition and dispersal.
The model was a cellular automata of a 220 × 220 rectangular grid of cells, where each cell
can carry one individual. Survivorship and fecundity of a focal individual depend on the
number of individuals n in the Moore neighbourhood (CZÁRÁN 1998).
16
S. Bartha & P. Ittzés
Table 1. Parameter values used in simulation.
Initial abundance
Initial distribution
Dispersal (in cells)
Competition (ci)
Per capita reproduction
Psurv > 0.5
Psurv < 0.5
Grid size
Number of time steps
Saturated
Competitive
Dispersal limited
Non saturated
High reproduction
Low reproduction
high (4000)
lognormal
long (400)
strong (0.2)
low (800)
lognormal
short (60)
low (1.0)
low (800)
lognormal
long (400)
low (1.0)
low (800)
lognormal
long (400)
low (1.0)
2
1
2
1
4
2
2
1
220 × 220
30
s
Psurv (i) = ∏ cni
i=1
where ci is an indicator of the intensity of competition, and s is the number of species.
Competitive effects are multiplicative. Survivorship decreases exponentially with the
increasing number of individuals in the neighbourhood. Following the assumptions of Z OBEL
(2001) the size of individuals and their competitive effects were equal disregarding the identity
of species. Fecundity depends on survivorship. If Psurv < 0.5 then fecundity decreases to half
of the maximum. The program is different from the usual cellular automata algorithms in the
dispersion of propagules. The distance of offspring from their parent individual is a stochastic
variable with a Gaussian distribution. Colonizing individuals appear in random positions
within the grid. The initial relative abundances follow a lognormal distribution.
Experiment 1 represents a predominant local control of diversity. Competitive effects are
strong, initial abundances are high, while dispersal limitation is low, i.e. propagules could
reach any position within the grid (see Tab. 1 for parameters).
Experiment 2 represents a regional control of local diversity. Initial density is low. Species
have a low dispersal ability, i.e. the majority of offsprings appear close to the parent individuals.
Abundance is controlled only by the local carrying capacity of grid cells, i.e. competition is
minimized. Limited dispersal produces a patchy spatial pattern of species.
Experiment 3 starts with a low initial density, and competition is minimized. Parameters
are the same as in the Experiment 2, except dispersal, which is unlimited, as in the first
experiment.
Experiment 4 is the same as the third experiment except that there is a higher fecundity
of individuals.
Due to the low initial abundances, Experiments 2−4 remained unsaturated during the first
10 generations. On the contrary, Experiment 1 started with high abundances. It reached an
equilibrium and become saturated after 5 generations (Fig. 6a,b). Twelve replicates were
performed in each experiment with varying total species pools (ranging from 94 to 105).
Further variation between replicates was caused by the inherent stochastic spatio-temporal
dynamics of simulations.
The relationship between local and regional richness was calculated from data of the 6th
generation because Experiment 1 had already reached saturation here while other versions
Species-pool and species-area relationship
Fig. 6. Simulated patterns with contrasting assembly
mechanisms. For other parameters see Tab. 1. (a) Temporal
pattern of local abundances. (b) Temporal pattern of local
species richness. (c) Species-area curves in the 6th
generation. A, B, C refer to the areas selected for creating
local-regional richness plots in Fig. 7.
17
were still unsaturated. According to the
species-pool hypothesis, a proportional
relationship is expected in Experiments
2, 3, and 4, while local and regional
richness should be uncorrelated in
Experiment 1.
In the previous section we concluded
that the relationship between local and
regional richness is scale dependent. If
any scale of the local-regional pair is
bound, it can result in a trivial
non-correlation (Fig. 5). Therefore we
calculated the species-area curve in each
case in order to select appropriate scales
for the local-regional richness plots
(Fig. 6c). A plot size of 11 × 11 cells
was selected as the local scale. Recall
that species richness saturated around 55
species at this scale (Fig. 6b). This plot
size can be accepted as the community
scale because even in the 6th generation
richness varies between 14 and 35
(Fig. 7a). A plot size of 51 × 51 cells
was considered as the corresponding
regional scale because richness varies
between 55 and ca. 100 at this scale, i.e.
between the maximum of the community
scale and the global maximum of
richness used in the simulation. Larger
scales would probably produce trivial
non-correlation because three of the
simulated patterns were getting close to
the minimum area, i.e to the upper
boundary of the curves. For comparison,
another intermediate scale of 31 × 31
cells was also considered as
a community scale (Fig. 7b).
When comparing 11 × 11 cells as a
local scale and 51 × 51 cells as a regional
scale,
saturated
communities
(Experiment 1) show no correlation,
while there is significant correlation in
one of the unsaturated experiments
(Experiment 4). This supports the
species-pool hypothesis and the related
theory and methodology. Surprisingly,
18
Fig. 7. Simulated patterns with contrasting assembly
mechanisms. (a), (b), (c) Scale effects on the relationship
between local and regional species richness. Fitted linear
regressions indicate significant proportional relationships. For
other parameters see Tab. 1.
S. Bartha & P. Ittzés
two of the unsaturated experiments
show no correlation between regional
and local richness at these scales.
When comparing 31 × 31 cells as
a local scale and 51 × 51 cells as
a regional scale, local and regional
richness is proportional in three
experiments. One of these cases is
Experiment 1 that should be saturated
according to the generative rules of
the simulations. The unsaturatedness
detected for Experiment 1 is an
obvious scaling artefact. We expected
correlation in the case of Experiment
2 because of the simulated regional
control of local species richness due
to the limited dispersal. In our
experimental design, Experiment 2
was considered as a clear case of
dispersal
limited
unsaturated
community. On the contrary,
Experiment 2 remains uncorrelated,
i.e. it shows saturation at this scale.
The saturation detected by the
local-regional richness plots is
confirmed by the temporal patterns as
well (Fig. 6b). Experiment 2, 3, and
4, which had low competition,
saturated at similar abundances, but
not at similar richness (Fig. 6a,b). The
upper boundaries for local richness in
Experiment 3 and 4 are around 55
species, while in Experiment 2 local
species become saturated at around 24
species. It means that dispersal
limitation itself can constrain local
richness
similarly
to
local
competition. Recall that the mortality
of individuals in these simulations
were the function of the density of
individuals in the neighbourhood and
the competition parameters ci. In
Experiments 2−4 individuals were
long-lived due to the lack of
competition from the neighbourhood.
A long life-span combined with
Species-pool and species-area relationship
19
limited dispersal resulted in founder controlled communities (YODZIS 1978, HERBEN 1995).
Consequently, the saturation detected in Experiment 2 can be explained by competition, i.e.
by competition due to space preemption.
Our results based on dynamic mechanistic simulations confirm the conclusions about scale
dependence that we received from analyzing the static reference pattern. At certain scales, if
local and regional scales are too close to each other, the extreme scale effects appear and
artefact linear correlations can be detected (Fig. 7c).
The results from testing the species-pool hypothesis are clearly scale-dependent. Our
suggestion from this experiment is that the range of local richness should not overlap with
the range of regional richness to avoid the type of artefacts shown in Fig. 7c. It is common
in field studies testing the species-pool hypothesis that local and regional scales were artificially
selected. Unfortunately, cumulative curves like the species-area curve offer little help to select
some standard scales for comparative purposes. A potential solution would be to perform
comparative analyses at the characteristic maximum scales of communities (JUHÁSZ-NAGY
1967, 1984, 1993, JUHÁSZ-NAGY & PODANI 1983). These are the spatial scales where
within-community diversity and the spatial dependence of species combinations reach a
maximum. However, that line needs to be explored in the future.
ON THE EVIDENCE AND MODELS SUPPORTING THE QUASI-NEUTRAL
CONCEPT OF COMMUNITIES
Evidence from animal (CORNELL & KARLSON 1997) and plant communities (PÄRTEL et al.
1996) suggest that the unsaturation of species richness is common in nature and competition
is less important in community organization (ZOBEL 2001, statement 5.1.). However, our
simulation results raise a doubt about the reliability of field evidence which can be biased by
inappropriate scales or by the heterogeneity of a sample.
Aside from testing regression in local-regional richness plots, ZOBEL (2001) proposed
interesting definitions and hypotheses about the components and interactions in plant
communities. He states that asymmetric competition for light is the prevalent mechanism of
competitive exclusion in plant communities, while (according to his opinion) belowground
competition can never result in competitive exclusion (but see TILMAN 1988, BURKE et al.
1998). Therefore, he distinguishes between vertical layers in vegetation, where each layer is
composed of plant individuals of the same size and identical resource use, and often also is
similar in related life-history characteristics. He states that “in contrast to animals, populations
of maximally similar plant species are the least likely to compete as strongly as to result in
a competitive exclusion of one population” (ZOBEL 2001, 2.1.1.). Further he defines plant
community as identical plant individuals of the same vertical layer (ZOBEL 2001, 2.1.1.).
Biotic interactions (light competition) between adjacent plant individuals belonging to different
layers are not considered as community interaction in this definition.
These definitions generate a neutral model of plant community with no ecological
differences between species. As a consequence of this definition “species” can coexist and
their number and relative abundances will be driven only by stochastic drifts (see PALMER
2001, this volume) or by landscape-scale constraints according to the species-pool hypothesis.
If there is some competition within a layer, it happens between identical individuals, where
“species” identity is only a taxonomic assignment and not a relevant ecological category. If
someone accepts Zobel’s model in this strict form, the neutrality will be a direct consequence
20
S. Bartha & P. Ittzés
of definitions. In our opinion we should test first the assumptions, i.e. the reality of this model
rather than test its expectations (cf. ZOBEL 2001, 1.14).
We prefer another definition of plant community (PALMER & WHITE 1994) and do not agree
with Zobel’s view that between-guild relations are unimportant. However, we emphasize that
Zobel’s model represents an important step in the development of the methodology of
community organization and assembly rules because his model explicitly considers individuals,
guilds, and related scales.
Our previous studies on the spatial organization of plant communitites (SZOLLÁT & BARTHA
1991, BARTHA 1992, BARTHA et al. 1995a, MUCINA & BARTHA 1999, GOSZ et al. 2000) do
not support the general neutrality suggested by Zobel. However, there is evidence which
supports neutrality or quasi-neutrality within certain functional groups. For example, analyzing
the spatial pattern of the herb layer in a Mediterranean wood, Campetella and his co-workers
(CAMPETELLA et al. 1999) found that clonal herbs and shrubs form large patches and these
patches are spatially independent from each other. BARTHA et al. (2000) analyzed the spatial
variation of total cover of guilds in old field succession using the method of WILSON & GITAY
(1995) and did not find significant deviation from randomness in case of the subordinated
guilds.
BARTHA et al. (2000) found quasi-neutrality within a subordinated guild. Meanwhile, the
subordinated guilds were not independent from the dominant species. In fact, the
quasi-neutrality detected in the case of subordinated species can be interpreted by the indirect
effect of dominants. Therefore, we propose to restrict the concept of quasi-neutrality for the
groups of subordinated species.
When we assume the existence of guilds (i.e. species with similar resource use, including
the similar spatio-temporal exploration of resources) together with a competitive hierarchy
of guilds (e.g. the vertical layers of plant species in a forest) then we can propose the following
hypotheses:
Hypothesis 1: Neutrality will appear within subordinated guilds and it is a consequence of
the hierarchical structure of plant communities. Competitive exclusion does appear but it is
restricted to the exclusion of some species from the dominant guild or to the exclusion of
entire subordinated guilds from the community.
Explanation 1: Individuals within a guild are similar but between guilds there are significant
differences in the exploration and use of resources and in the tolerance to conditional factors.
Species exclusion might need longer time but its existence is supported by the fact that the
number of species within dominant guilds are lower than the number of species in subordinated
guilds. Also there is a broad variation in relative abundance of guilds between communities.
Hypothesis 2: The lower is a guild in the hierarchy the higher neutrality of within-layer
processes are detected.
Explanation 2: The effects of the individuals of dominant guilds are cumulative, therefore,
members of a more subordinated guild experience more and stronger control by dominant
individuals. Being subordinated in space and time involves being isolated in certain
spatio-temporal resource gaps. If members of subordinated guilds are isolated, they have less
chance to interact with each other.
Hypothesis 3: If quasi-neutrality is found at the community level, it is due to the higher
number of subordinated species in the samples.
Explanation 3: Tests based on species richness are usually overdominated by the effects
of subordinated species. Therefore, several aspects of community organization which would
Species-pool and species-area relationship
21
appear between guilds or between dominant species are masked and remain hidden in tests
based on the local-regional richness plots. Decomposing community-level patterns into
components representing within-, and between-guild relationships of individuals would clarify
the related debates.
The species-pool hypothesis was born on the grounds of the non-equilibrium theory. It
involves a new, spatially explicit view of ecology. Theoretical studies have proven that
conditions for coexistence change dramatically if we use more realistic spatially-explicit
individual-based models of plant communities. For example, assuming that space is finite
and individuals are discrete (CZÁRÁN 1989, DURETT & LEVIN 1994) or populations are not
perfectly mixed (CZÁRÁN & BARTHA 1989, SILVERTOWN et al. 1992), or there is some variation
in the size, shape and mobility of individuals (HARA 1993, HERBEN 1995) the conditions of
coexistence will be different from the conditions predicted by classical non-spatial models.
These results underline the importance of the reality of models used for understanding
community organization. Zobel’s individual-based model has such potential. However, his
model should incorporate the known properties of the spatio-temporal structure of populations
and communities.
NEED FOR A MORE ADVANCED METHODOLOGY
The classical non-spatial equilibrium paradigm of species coexistence had the advantage
of simplicity. The most favourable consequences of the above-mentioned simplifying
assumptions were that communities could be represented by a small number of state variables
(species richness, abundances), spatio-temporal scales could be ignored, and coexistence
conditions could be expressed by simple parameters (e.g. relations of pairwise competitive
coefficients of populations).
Present community ecology is characterized by a transitional stage. Verbal theories
emphasize the importance of non-equilibrium processes, historical and landscape effects, and
the effects of scales. However, the related methodology is still in fact based on a non-spatial
equilibrium ecology.
The non-equilibrium paradigm involves the importance of spatial and temporal
contingencies (LAWTON 1999). A consequence of the paradigm is that individuals are not
equal because individuals have different spatio-temporal neighbourhoods, i.e. different
spatio-temporal contingencies. The community-scale dynamics depend on the
within-community diversity and heterogeneity (BARTHA et al. 1998). Therefore, these relations
must be measured explicitly, i.e. relevant state variables should refer to the within community
diversity and heterogeneity. Abundances and spatial dependencies of all species combinations
within a community should be measured in detail to be able to refer to the dynamically-relevant
microstates (BARTHA et al. 1998). Because we never know “a priori” what are the
dynamically-relevant effective spatio-temporal neighbourhoods, a series of scales should be
applied in each particular study. Increasing evidence suggests that species coexistence can
be understood by using spatially-explicit individual-based non-equilibrium models (e.g.
PACALA 1986, CZÁRÁN & BARTHA 1992, HERBEN 1995, WINKLER & SCHMID 1995, BARTHA
et al. 1995b). However, the price of realism is the complexity of both descriptive and generative
models (cf. JUHÁSZ-NAGY 1984, CZÁRÁN 1998).
The species-pool hypothesis is stimulating because it emphasizes the regional control of
local diversity. However, up to now, the details of regional control and the mechanisms behind
it remained unexplored. We argue that future development in this area needs a far more
22
S. Bartha & P. Ittzés
advanced methodology. Taking species-area relationship into account and selecting scales in
an intermediate but not too narrow range of areas could be the first step in developing
appropriate solid scaling conventions for studying the species-pool hypothesis.
Acknowledgements: We are grateful to Tomáš Herben for inviting us to contribute to this discussion and to
Miklós Kertész for comments on an earlier draft. We thank Dóra Krasser and Zoltán Botta-Dukát for their
help in statistical analyses. This study was supported by the Hungarian National Science Foundation (OTKA
F 026458 and T 032630).
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