ECOSYSTEMS
Ecosystems (2001) 4: 797– 806
DOI: 10.1007/s10021-001-0047-7
© 2001 Springer-Verlag
Survival, Gap Formation, and
Recovery Dynamics in Grassland
Ecosystems Exposed to Heat
Extremes: The Role of Species
Richness
Liesbeth Van Peer,1* Ivan Nijs,1 Jan Bogaert,1 Iris Verelst,2 and
Dirk Reheul2
1
Research Group of Plant and Vegetation Ecology, Department of Biology, University of Antwerp, Universiteitsplein 1,
B-2610 Wilrijk, Belgium; and 2Department of Plant Production, Faculty of Agricultural and Applied Biological Sciences,
University of Ghent, Coupure Links 653, B-9000 Ghent, Belgium
ABSTRACT
A field experiment was performed in which the
richness of perennial grasses (S) was varied in
model ecosystems exposed to a simulated heat
wave (free air temperature increase and drought).
The proportion of individuals that survived the heat
wave decreased with S, which could be ascribed to
higher water consumption in the species-rich systems. Higher transpiration at high diversity was also
observed in other studies using functional groups
and could have originated from increased leaf area,
less intense stomatal closure, or a combination of
both. The increased tiller number per plant that we
observed, while leaf area per tiller remained constant, suggests that an enhanced leaf area index was
most likely responsible. However, competitive interactions also seemed to play a role in the influ-
ence of S on survival. Regrowth of the surviving
individuals, expressed as leaf area per living plant
after a recovery period following the heat wave,
increased with S, most likely due to the dominance
of productive species, which was facilitated by the
additional space yielded by more intense gap formation at higher S (due to higher plant mortality).
Species richness affected both the size and density
of the gaps. Mean size increased exponentially with
S, while density increased at low S but decreased at
higher S when connectance of the gaps occurred.
Size distribution of the gaps was not affected.
INTRODUCTION
cipation of these problems renewed interest in the
relationship between species diversity and the stability of ecosystems. Simple communities are generally believed to be less stable than complex ones,
based on Elton’s (1958) mathematical and experimental results. Elton’s predator–prey and host–parasite models suggested that vulnerability to invading species is high in the natural habitats of small
islands (characterized by few species) and that insect invasions or pest outbreaks are more frequent
Key words: community stability; gap formation;
heat wave; perennial grasses; regrowth; resistance;
species richness.
In the coming century, the rapid loss of species in
response to human pressures on the global environment and more frequent and intense climate
extremes (Watson and others 1998) are expected to
become major environmental problems. The anti-
Received 18 January 2000; accepted 31 May 2001.
*Corresponding author: e-mail: [email protected].
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L. Van Peer and others
in communities that have been greatly simplified by
human intervention. Since then, many models (for
example, Gardner and Ashby 1970; May 1972;
DeAngelis 1975; King and Pimm 1983) but only a
few field studies (Ewel 1986; Berish and Ewel 1988;
Tilman and Downing 1994; Tilman 1996) have focused on the relationship between diversity and
stability in natural ecosystems, often arriving at opposite conclusions (for example, see Frank and McNaughton 1991; Rodriguez and Gomez-Sal 1994)
even though they determined the effects of diversity in similar ways. Some of the controversy can be
traced to the variety of definitions for “stability”
(Pimm 1984), but the lack of empirical studies that
have included the direct experimental manipulation of diversity as an independent factor is probably more problematical in this regard. In many
studies, variation in S (species richness) has been
correlated with variations in other biological or
physical factors, the effects of which could have
been inadvertently ascribed to S (Givnish 1994;
Huston 1997). As a consequence, the impact of
disturbance on the functioning of systems that vary
in species richness is still controversial.
Herein we report the effects of simulated climate
extremes (heat waves) on model ecosystems of different S. Using a method unlike that of earlier investigators, we created species mixtures by controlled assembly so that S could be distinguished
from species composition (Naeem and others 1996;
Tilman 1997). It is possible that diverse communities are more resistant or resilient because they are
more likely to include a drought-resistant species or
a species with a high capacity for regeneration
(sampling effect) (Huston 1997). To avoid chamber
effects from enclosing the stands, we generated climatic perturbation in the field using a free air temperature increase technique (FATI) (Nijs and others 1996). Although both the number of
functional groups and the number of species
within functional groups can have significant effects on ecosystem functioning (Naeem and Li
1997), only temperate grasses were used to determine whether closely related species differ
along an axis of sensitivity to disturbance (compare Bond 1997). The following three hypotheses
were tested: Are species-rich ecosystems more
resistant to heat extremes (promoting survival of
the plants)? Does S affect the size, density, or size
distribution of the gaps that result from plant
mortality? And do diverse mixtures produce new
leaf area faster after pulsed stress (promoting regeneration of the surviving plants)?
MATERIALS
AND
METHODS
Plant Material
Eight cultivars of cool-temperate perennial Gramineae common to Western European grasslands
were used to create model ecosystems of different S:
Lolium perenne L. cv. Paddock (A), Festuca arundinacea L. cv. Barcel (B), Poa pratensis L. cv. Julia (C),
Festuca rubra L. cv. Ensylva (D), Bromus catharticus L.
cv. Banco (E), Dactylis glomerata L. cv. Athos (F),
Phleum pratense L. cv. Erecta (G), and Lolium multiflorum L. cv. Meryl (H). These species are not rhizomatous or clonal, and no stolons were observed.
The plants were sown in small pots between 15 and
29 April 1997. After standardization (small range of
tillers), they were transplanted between 11 and 20
May to plastic containers (26.0 ⫻ 15.5 and 14.2 cm
deep). The containers were filled with steam-sterilized and fertilized sandy loam and covered by a
metal grid of 40 square cells (3.5 ⫻ 3.5 cm each) to
form a matrix of eight rows by five columns with
one plant per cell. To minimize edge effects, only
the 18 core plants of each container were measured. The stands were cut three times (before
stress, after stress, and after the regeneration period) for biomass readings. Fertilizer was supplied
after every cut (7.88 g N m⫺2 in NH4NO3, 2.49 g K
m⫺2 in K2O, and 8.29 g P m⫺2 in P2O5).
Aboveground biomass (more than 3.5 cm) was collected separately for each species per container and
oven-dried for 48 hrs at 80°C. The soil was kept
close to field capacity by daily irrigation until a
drought stress period was begun, coinciding with a
heat wave.
Control of Species Richness
Species composition of the communities was based
on a controlled selection of species from a total
pool. To avoid the confounding of S and species
identity, the selection ensured that (a) all species
occurred in equal proportions (whole experiment,
every S level, within each community), (b) all combinations of different neighbor species occurred in
the same proportion (whole experiment, every S
level, within each community), and (c) species assemblages at a given S level differed maximally
(minimal number of species in common). This system guaranteed good representation of all possible
species combinations. For criterion (b) we considered only nearest neighbors and excluded intraspecific contacts to avoid clumping. Random drawing
(see, for example, Naeem and others 1996) was not
used because, with a limited series of species assemblages, equal representation of species at every S is
Species Richness and Heat Extremes
not guaranteed (by chance, species A could be
drawn more frequently than species B, for example).
Following these assemblage rules, we composed
24 different species mixtures to create four levels of
S. One set of 24 mixtures consisted of eight monocultures (A, B, C, D, E, F, G, H), eight mixtures of
two species (AB, CD, EF, GH, AD, CF, EH, BG), four
mixtures of four species (ABCD, EFGH, ABGH,
CDEF), and the mixture of eight species (ABCDEFGH) replicated four times with a different internal arrangement. Within one diversity level, the
sources of variation are (a) species composition, (b)
internal arrangement, and (c) intraspecific variability. For diversity level 8, however, (a) is missing
because the species pool contains as many species as
the highest level of S. Three replicate sets of the 24
containers were exposed to an extreme climatic
event (heat wave). Container positions were regularly rotated within plots.
Microclimate
To create realistic temperature extremes, we analyzed daily maximum temperature (Tmax) between
1968 and 1995 from the weather station at Ukkel,
45 km from our study site in Antwerp. Frequencies
of 20-day periods (anticipated duration of drought
cycle) with average Tmax above given thresholds
were calculated. Based on this information, we
aimed for a temperature increment of 8°C, which
increases the current long-term average Tmax in August of 21°C to 29°C. The latter was close to the
most extreme average Tmax of all 20-day warm periods in the record (31°C).
A series of hot days was simulated by irradiating
the plots with additional infrared radiation (IR)
(0.8 –3 ␮m), using the FATI system (Nijs and others
1996). In our second-generation prototype of this
device, three FATI modules were used to individually irradiate each of the three sets of 24 communities that were exposed. Canopy temperature (Tc)
of a fourth set of 24 communities, which was not
subjected to the heat wave, was used as a baseline
for heating (this set was not a control treatment; its
only purpose was to quantify the temperature extreme). Each FATI module consisted of a frame
with six 1500-W IR lamps, 1.2 m above the ground,
that homogeneously irradiated an area of 1.2 ⫻
1.2 m. On the unheated plot, a dummy construction was placed, with lamp enclosures but no IR
lamps. During the entire experiment, type T thermocouples (Stork Intermes, Antwerp, Belgium)
measured abaxial leaf temperature (Tl at 5 cm
height on five different locations), air temperature
(center position only, shielded from direct sun-
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light), and soil temperature (center position at 5 cm
depth). Canopy temperature (Tc) of the whole set of
24 containers was measured with noncontact IR
semiconductors at 60 cm height (view angle 90°)
(Stork Intermes). A DL3000/SA data logger (Delta-T, Burwell, UK) sampled all readings every 10
min, with all temperatures measured in the same
two plots (the unheated and one of the heated
plots).
During the stress period, average daily maximum
Tl in the warm plot was 5.0 ⫾ SD 0.83°C (n ⫽ 14)
above the unheated plot, while average daily maximum Tc was increased 3.3 ⫾ SD 0.94°C (n ⫽ 14).
Average instantaneous warming was 3.7 ⫾ SD
0.15°C and 3.3 ⫾ SD 0.21°C during the day (Tl and
Tc, respectively, n ⫽ 756) versus 5.5 ⫾ SD 0.10°C
and 6.0 ⫾ SD 0.15°C during the night (n ⫽ 1260).
Higher nighttime increments arise from stomatal
closure, which reduces latent heat loss from transpiration. Because a natural heat wave occurred
during the stress period, ambient average maximum Tc in August exceeded 21°C. Therefore, an
increment of approximately 5°C instead of 8°C was
sufficient to increase maximum Tc to 29°C. When
ambient maximum Tc exceeded 29°C, heating was
switched off to avoid unrealistic stress levels in the
heated plots; these days were excluded in the values above. After the heat wave, average daily maximum Tl of the (previously) warm plot was 21.7 ⫾
SD 0.73°C, while average daily maximum Tc was
19.3 ⫾ SD 0.84°C (n ⫽ 33).
Experimental Design
The experiment consisted of the stress period to
simulate the heat wave (5 August–18 August 1997,
starting 10 days after cutting on 25 July) and a
subsequent regeneration period. During the heating, irrigation was stopped, and a shelter above the
plots eliminated precipitation without obstructing
direct solar radiation. The heat wave was ended at
about 50% mortality, which was estimated from a
preliminary experiment. Changes in gravimetric
soil water content (W) between the beginning and
end of the stress were determined from the difference in container weight, expressed as the mass %
of total soil water in the containers at saturation.
Regeneration at ambient temperature with daily
irrigation lasted until 21 September 1997.
Resistance
To estimate the resistance of the stands (deviation
of the status prior to disturbance) (Pimm 1984), we
determined the survival of the individual plants by
counting the number of living and dead tillers. Be-
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cause of time limitations, one set of 24 communities
was assembled from the three replicate sets by
choosing, for each species mixture, the community
closest to the average survival of its three replicates.
In most cases, the selection was obvious and could
be performed visually; otherwise, living and dead
tillers of the three replicates were counted. The
probability (ri) that a randomly chosen plant i disappeared due to the stress was estimated as one
minus the proportion of surviving plants (calculated for each species in each mixture). These probabilities were used in the further analysis of gap
formation.
Regrowth of the Survivors
A second element of stability is resilience, which
assumes a return to conditions prior to disturbance
(Pimm 1991). Because the system may not return
to preperturbation conditions in such a short-term
experiment, an alternative measure was used—the
ability of the surviving plants to regrow after severe
stress. For each species in every mixture, three living plants were chosen that had the average number of living and dead tillers of all the plants of that
species (with a maximum deviation of one). In each
of these selected plants, the average tiller was chosen based on leaf length, which was labeled to
follow leaf area evolution (length ⫻ width) during
regrowth (measurements on 27 August, 2 and 3
September, and 8 and 9 September). Width was
estimated as the average of top, middle, and bottom
positions. For each species in each mixture, regrowth was broken down into the following two
factors:
Average leaf area per surviving plant
Computer Simulation of Gap Formation
The pattern of gaps that emerged in the community
after the stress was not analyzed directly because
many gaps made contact with the borders, so their
dimensions could not be determined. For this reason, we reconstructed the fragmented stands mathematically, using (a) the assembly rules used for the
experimental containers, and (b) the measured survival probabilities of each species in each container.
First, a computer simulation of the position of the
species in each mixture was made, using a FORTRAN-77 program. Per container, 100 matrices
(64 ⫻ 64), each representing a total community,
were generated by adding individuals one by one
next to the existing individuals (the starting point
was the upper left cell). Using the Manly random
number generator (Manly 1991), each added individual was selected from the species list following
the assembly rules in the section on “Control of
Species Richness.” The communities in our containers were thus mathematically extended up to a size
that allowed us to discard the border zone.
The second part of the reconstruction concerned
the formation of gaps. For every cell of the matrix,
the Manly algorithm was used to generate a random number between 0 and 1. This number was
compared with the ri value that was experimentally
observed for that species in that particular mixture.
When the random number was lower or equal to ri,
the cell was defined as empty, which recreated the
gaps in the communities. Also, for every vegetation,
gap recognition (number of gaps) and area calculation were executed using the geographical information system GRASS 4.1 (Geographical Resource
Analysis Support System) (USA-CERL 1993). Evenness of gap size was determined by the Gini index
(G⬘) (Nijssen and others 1998).
⫽ number of living tillers per plant
⫻ leaf area per tiller
(1)
Recovery
Leaf area recovery per community (which includes
both resistance and regrowth of surviving plants)
was calculated as follows:
Leaf area index
⫽ (proportion of surviving plants
⫻ initial number of plants
⫻ (number of living tillers per plant
⫻ leaf area per tiller))/container area
(2)
RESULTS
Resistance of the mixtures, measured as proportion
of surviving plants, significantly declined with S
(nonlinear regression, F3, 42 ⫽ 14.3, P ⬍ 0.05, r 2 ⫽
0.11) from an average 81% at S ⫽ 1 to 63% at S ⫽
8 (Figure 1A). Reduced survival corresponded with
higher water consumption at the end of the stress
period (nonlinear regression, F2, 22 ⫽ 882.4, P ⬍
0.05, r 2 ⫽ 0.20). Average W was 62%, 67%, 72%,
and 70% at S ⫽ 1, 2, 4, and 8, respectively. The
plants that survived at high S tended to have more
living tillers per plant (Figure 1B), which may explain the trend toward greater water use. At S ⫽ 8,
the tiller number was 25.7% higher than at S ⫽ 1,
but the effect was not significant (two-level nested
Species Richness and Heat Extremes
801
Figure 2. Leaf area per living plant (cm2) at three different times during regeneration (6, 13, and 20 days) as a
function of species richness (S). Each symbol represents
the average of a series of different species mixtures.
Figure 1. (A) Proportion of surviving plants in model ecosystems at the end of a controlled heat wave, as a function
of species richness (S). Closed symbols represent the species
survival in a particular species mixture; open symbols represent survival per container. The curve was fitted with the
function Y ⫽ a ⫹ b e⫺cS, with a, b, and c parameters. (B)
Number of living tillers per plant at the start of regeneration
after the heat wave. Each symbol represents a labeled plant
(three per species in each mixture). (C) Leaf area per tiller
(cm2) after 6 days of regrowth. Each symbol represents a
labeled tiller (three per species in each mixture). Squared
symbols in B and C are averages per S level.
ANOVA with “species” subordinate to “communities”, F3, 57 ⫽ 0.5, P ⬎ 0.05). The larger tiller number at high S was already present at the onset of the
climate extreme. The second component of regrowth, leaf area per tiller, did not depend on S,
neither after 6 days (Figure 1C) nor after 20 days of
regrowth (not shown) (two-level nested ANOVA,
F3, 55 ⫽ 0.8, P ⬎ 0.05). Leaf area recovery per living
plant (the product of tiller number and leaf area per
tiller) was significantly enhanced by S after 20 days
of regrowth (linear regression, F3, 55 ⫽ 10.7, P ⬍
0.05) (Figure 2), whereas leaf area index significantly declined with S (nonlinear regression, F3. 26
⫽ 27.9, P ⬍ 0.05, r 2 ⫽ 0.20) (Figure 3). After 6 and
13 days, the effect was not yet expressed in both
cases. For all components of leaf area recovery, we
also assessed the influence of S on individual species, rather than on communities, by treating the
data of every species by separate single classification
ANOVA of items. Few significant differences (P ⬍
0.05) were found among S levels, and there was no
pattern to them (not shown). In contrast to leaf
area, dry matter production per plant after regeneration did not significantly vary with S (two-level
nested ANOVA, F3, 68 ⫽ 0.4, P ⬎ 0.05), although it
showed an upward trend, with a 23.7% increase
from S ⫽ 1 (0.111 g) to S ⫽ 8 (0.138 g).
Gap characteristics were derived from the mathematically reconstructed stands. The resulting patterns are shown in Figure 4. Species richness significantly increased mean gap size (exponential
regression, F3, 18 ⫽ 5.3, P ⬍ 0.05, r 2 ⫽ 0.33)
(Figure 5) and gap density at low S (Figure 6). At
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Figure 3. Leaf area index (LAI) at three different times
during regeneration (6, 13, and 20 days) as a function of
species richness (S). Each symbol represents the average
of a series of different species mixtures.
higher S, gap density decreased because the gaps
became connected, yielding a maximum at S ⫽ 2.
Cumulated gap area (cm2) increased strongly at low
S but saturated rapidly as a consequence of the
mortality pattern (Figure 7A).
We also calculated the cumulated area of all gaps
with given minimum sizes (Figure 7B), which is
relevant for invasion thresholds. As minima, we
used the mean values per S level of Figure 3 (two,
four, six, and 27 cells for S ⫽ 1, 2, 4, and 8, respectively, or 24.5, 49.0, 73.5, and 330.7 cm2). There
was no trend in size distribution of the gaps, with an
average evenness of 0.17, 0.08, 0.15, and 0.11 in
S ⫽ 1, 2, 4, and 8, respectively.
DISCUSSION
The question of whether species diversity contributes to stability has ignited a longstanding debate
among ecologists. Early observations and experiments (Mac Arthur 1955; Elton 1958; Margalef
1968) and several of the more recent empirical
studies have lent support to this thesis, but other
studies have produced compelling evidence to the
contrary. Analysis of population dynamics, for example, suggests that it is easier for more diverse
communities to fall below critical threshold population sizes (Witkowski 1973). Conversely, Lepš
and others (1982) found that diversity correlated
positively with resistance but negatively with resilience. However, these relationships may not be
causal, because both diversity and the different elements of stability were determined by external
drivers (for example, soil fertility) and by the life-
Figure 4. Pattern of gap formation in plant communities,
arising from plant mortality after a heat wave. Open cells
represent survivors; filled cells represent dead plants. (A)
and (B) are representative examples of plant communities with one and eight grass species, respectively, on 25
August 1997. The vegetation patterns were reconstructed
mathematically, based on measured survival rates in assembled model communities.
history strategies (Grime 1979) of dominant species. Short life cycles and high relative growth rates
of R strategists or ruderals, for example, appears to
yield low community resistance and high resilience,
whereas the inverse is achieved with stress-tolerators. Tilman (1996) observed that diversity had positive effects on resistance (to drought) but an equivocal effect on rate of recovery, a finding that could
be ascribed to a confounding of species richness and
drought.
Unlike these empirical studies, we manipulated
diversity directly in the current experiment by separating it from species composition via controlled
Species Richness and Heat Extremes
Figure 5. Mean gap size in model ecosystems fragmented
by mortality after a controlled heat wave as a function of
initial species richness (S). Each symbol represents a different species mixture. The curve was fitted with the
function Y ⫽ a ⫹ b ecS, with a, b, and c parameters. Values
calculated by mathematical reconstruction of the containers.
assembly in model ecosystems. We observed reduced resistance (lower survival) at higher S, and
the associated lower W suggests that increased water use in diverse communities produced more negative soil water potentials at the end of the heat
wave. In other words, the intensity of the stress was
modulated by the (transpiration) response of the
stands.
Higher transpiration at high diversity has also
been observed in experiments with functional
groups (Hooper and Vitousek 1998) and could originate from increased leaf area, less intense stomatal
closure, or a combination of both. The increased
tiller number per plant that we observed, while leaf
area per tiller remained constant, suggests that an
enhanced leaf area index is most likely responsible
here. This idea is compatible with the improved
light interception often found in more diverse
grasslands (see, for example, Tilman and others
1996). However, competitive interactions also
seemed to play a role in the influence of S on
survival. In general, the lowest survival rate can be
expected in suppressed species, which are less buffered against drought due to their low biomass. Because competitive suppression occurs in mixtures of
species, such mixtures will be characterized by high
mortality in some species (the weak competitors)
and low mortality in others (the dominant ones).
This scenario would result in more heterogeneous
extinction probabilities in mixtures than in monocultures and lower survival, both of which are
supported by Figure 1A. On a longer time scale,
803
Figure 6. Gap density (no. gaps m⫺2) expressed as a
function of species richness (S). Circles represent individual containers; squares represent averages per S level.
Values calculated by mathematical reconstruction of the
containers.
minor species would confer considerable resilience
to the community if they were functionally analogous to dominant ones. In particular, by screening
graminoids for similarity, Walker and others (1999)
concluded that when environmental conditions become unfavorable for dominants, replacing them
with previously subdominant species could guarantee the persistence of ecosystem function.
In contrast to survival, leaf area per living plant
was enhanced by S, because leaf area per tiller was
not affected by species diversity while the number
of living tillers per plant increased with S. The increased tiller number per plant had already been
observed prior to the onset of the heat wave. In
concert with the few species interactions in the
mixtures due to gap formation, it is likely that the
linear increase of leaf area per living plant resulted
from this pre– heat wave diversity effect. Based on
different theoretical mechanisms, Tilman and others (1997), Loreau (1998), and Nijs and Roy (2000)
predicted that productivity should also increase
with S; such an increase was observed in several
previous data sets, but it was only manifested as a
trend here. Longer recovery periods may be needed
for its expression. Total recovery per container, expressed as the leaf area index, was significantly
lower at high S as a consequence of lower survival
in the mixtures.
While the empirical evidence showing how resistance and regrowth vary with S is scanty, the effects
of S on the fragmentation of vegetation following
an extreme event are virtually unknown. By mathematically extending the dimensions of our communities (as in Figure 4), it was possible to quantify
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L. Van Peer and others
Figure 7. (A) Cumulated gap area (all dimensions combined) as a function of species richness (S). Circles represent individual containers; squares represent averages per
S level. (B) Cumulated area of gaps with a given minimum size (24.5, 49.0, 73.5, and 330.7 cm2, respectively,
which are the observed average sizes of the four diversity
levels). Values calculated by mathematical reconstruction
of the containers.
the “gappiness” or “hole-iness” (Kaye 1989)—that
is, the degree to which the communities became
lacunar as a result of the extreme climate change. It
should be noted that these simulations merely extend the experimental communities to a size that
allows more precise calculation of gap characteristics. The simulations do not provide more insight
into the effects of diversity at higher spatial scales—
for example, diversity of habitats or landscapes.
Another notable aspect of the simulations is that,
for each species mixture, the gap pattern was reconstructed from the mortality probabilities of the
composite species. In other words, we used one
probability per species, derived from its observed
survival in that mixture. It might be argued, how-
ever, that in a mixture with S ⫽ 4 or 8, not all of the
individuals of a species have the same neighbors
and that neighbor identity might affect mortality.
For example, an individual surrounded by fastgrowing neighbors that consume more water might
experience more stress. So a question arises as to
whether or not separate mortality probabilities
should be used for every species–neighbor combination. It can be shown, however, that this condition does not generate a different gap distribution
pattern.
This can be explained as follows: assume that
every species–neighbor combination would indeed
be characterized by its particular mortality value
rather than by the average mortality value of all
species–neighbor combinations in that mixture. Because both the species and their neighbors are
spread randomly over the simulated matrix (respecting equal species abundance and equal neigborhood frequency), the combinations of species
and neighbors that yield high mortality probabilities—which create the gaps—are also randomly distributed. In the alternative case, if we apply the
average mortality probability for a given species in a
given mixture, the resulting gap pattern is also random. The reason for this is twofold: (a) as in the first
case, the species themselves are randomly distributed, and (b) on the maps, individuals are deleted
randomly until the average mortality value is obtained. In other words, irrespective of whether a
plant’s mortality is affected by its neighbors or not,
the distribution of gaps across the simulation map is
fully randomized (as in the experimental communities).
A simple example can help to clarify this. Consider two different species–neighbor combinations,
P1 and P2, spread randomly over the matrix. Assume a mortality rate of 100% for the central species in P1 and a rate of 0% for the central species in
P2. If the specific mortalities of the two species–
neighbor combinations are used, gaps will be randomly distributed according to the random positions of P1. On the other hand, if an average
mortality rate of 50% is used and randomly assigned, gaps will also be randomly distributed because every combination of P1 or P2 has a 50%
chance of disappearing. We conclude that even
when mortality depends in a complex way on
neighbor identity, the gap patterns associated with
species diversity can be realistically simulated and
analyzed.
We found that higher mortality rates in diverse
systems led to more gaps, which, by interconnecting, caused the mean gap size to increase exponentially with S. Total gap area calculated from the
Species Richness and Heat Extremes
805
reconstructions also increased with S but saturated
rapidly (Figure 7A). This simulated relationship is
theoretically the inverse of the measured one in
Figure 1A because the same survival rates were
used. These results could have repercussions for the
invasion of fragmented resident communities. Because invasibility probably depends more on gap
size than on total gap area (Bolger and others
1991), more diverse systems, containing the largest
gaps, would be more prone to successful invasion.
Only a few studies so far have investigated the
effects of gap formation on invasive success and its
subsequent repercussions for native species (Suarez
and others 1998). According to Fox and Fox (1986),
Crawley (1987), and Rejmanek (1989), disturbance
often changes communities in ways that increase
the success of invaders. The drastic changes in plant
composition that occur after invasion (Tilman
1997) could be a forewarning of how much ecosystem functions could change in the long term. The
current study demonstrates that initial species richness is crucial to this chain of events.
Frank DA, McNaughton SJ. 1991. Stability increases with diversity in plant communities: empirical evidence from the 1988
Yellowstone drought. Oikos 62:360 –2.
ACKNOWLEDGMENTS
Mac Arthur RH. 1955. Fluctuations of animal populations and a
measure of community stability. Ecology 36:533– 6.
This research was supported by the Federal Office
for Scientific, Technical, and Cultural Affairs (Prime
Minister’s Office, Brussels, Belgium), under the
Sustainable Development program. J.B. is indebted
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