Spatially structured food webs in a coloured environment
Sara Gudmundson, Frida Lögdberg and Uno Wennergren*
Department of Theoretical Biology, Linköping University, Sweden, *Correspondence: Email:
unwen@ifm.liu.se
Introduction
Species rich natural food webs contain complex interaction patterns evolved through
historical processes in dynamic environments (May 1973). Food webs are known to endure
unstable environments for several generations (Vasseur & Fox 2007). However, theoretical
studies predict that large complex food webs should be unstable and extinction-prone because
of high connectance, many modes of oscillation and positive feedback loops (May 1974;
Tilman 1999; Green & Sadedin 2005; Borrvall & Ebenman 2008). Models in ecology are not
able to explain the complexity of nature. To address this lack of knowledge, we have
investigated food web stability in a different way than what is most commonly done in
theoretical studies. We have also added coloured environmental variation and spatial
structure in order to decrease the gap between our model and conditions found in natural
ecosystems.
Food web stability is often measured as variability, which usually is calculated as the
coefficient of variation, standard deviation divided by the mean (McCann 2000). Decreased
variability implies decreased population variance which is likely to lower extinction risk
(Lande 1993; McCann 2000). Addition of stabilizing mechanisms in models has been shown
to facilitate food web complexity. For instance, network connectivity incorporating a mix of
weak and strong links of interactions can inhibit oscillatory subsystems limiting species
richness (Polis 1991; McCann et al. 1998). However, measurements of the coefficient of
variation, CV, may not be enough for determining food webs able to withstand stresses. An
increase in stability, measured as CV, can either imply an increase in mean density or a
decrease in variance. A population consisting of just a few individuals can misleadingly be
seen as robust to stresses as long as its variance is low in comparison to its mean. To address
the risk of misinterpreting results of stability, we have evaluated mean and variance one by
one in addition to variability measurements of food web biomass and species abundances.
The environmental variance, measured in impact and frequency of extreme weather events,
is increasing (Easterling et al. 2000). The change in climate is likely to cause increased
variability and extinction risk of ecological systems (Lande 1993; Halley & Dempster 1996;
Ripa & Lundberg 1996; Ripa & Lundberg 1996; Kaitala et al. 1997; Fontaine & Gonzalez
2005). When investigating the effect of environmental variation, it is important to consider
different magnitudes of variance. Another important property of environmental variation is its
correlation in time. Variation found in nature is considered to be the best represented by pink
1/f noise (Caswell & Cohen 1995; Halley 1996; Ripa & Lundberg 1996; Cuddington &
Yodzis 1999). It describes correlations in many different scales and does not priorities
between timescales of disturbances (Halley 1996). In order to investigate the effect of
environmental variation on food webs, we incorporate 1/f noise with different magnitudes of
variance and redness.
Landscapes are known to hold different biotic and abiotic conditions giving rise to spatially
separated subpopulations inhabiting patches. Dispersal between subpopulations enables reestablishment of extinct patches which can prolong the whole population’s time to extinction
(Engen et al. 2002; Liebhold et al. 2004; Greenman & Benton 2005). In order to investigate
stabilizing properties of dispersal between subpopulations, we position our food web in six
spatially separated patches. All subpopulations are connected with dispersal. When modelling
subpopulations in a variable environment, one also has to specify each subpopulations
environmental response. Do all subpopulations respond the same to the environmental
variation or do they have different responses? The answer will affect how subpopulation
densities fluctuate in relation to each other. This property is called synchrony and can also be
measured between species.
Synchrony between species has been shown to have a substantial effect on food web
stability and extinction risk. Asynchronous consumers coupled with uncorrelated
environmental fluctuations can improve food web stability (1/CV) by dampening oscillations
between resource and consumers (McCann et al. 1998; Vasseur & Fox 2007). High
synchrony between species implies a lower species extinction risk than during asynchronous
response (Borrvall & Ebenman 2008). We have also measured synchrony between species,
according to {{42 Vasseur,D.A. 2007}}. In addition, we have measured the correlation
between species and their environmental variation. By adding this measure we aim to
increase the understanding of how common environmental fluctuations can affect species
synchrony.
Density regulation, environmental autocorrelation and dispersal are known to affect local
population dynamics and should be included in investigations regarding population dynamics
and extinction risks (Engen et al. 2002). We have used a food web withholding a mix of
strong and weak links of interaction and investigated the effects of coloured environmental
variation and dispersal between spatially separated subpopulations. We aim to increase the
knowledge regarding what enables species diversity in food webs. Our results show that
coloured environmental variation can change relative abundance of species, increasing food
web robustness by increasing the density of the species with the smallest population size in a
constant environment. Stabilizing effects of dispersal between spatially separated
subpopulations show the importance of spatial and temporal variability for food webs
resistance.
Method
The diamond shaped food web contains four species. Two consumers share one resource and
have one common predator (Fig. 1). The dynamics are described by a continuous-time
differential equation system, modelled by Vasseur & Fox (2007) after McCann et al. (1998).
Resources grow logistically and consumers and predator have natural background mortality.
Consumption is limited by a type II nonlinear functional response (Yodzis & Innes 1992;
McCann et al. 1998; Vasseur & Fox 2007). The biologically plausible parameter values have
previously been used by Vasseur & Fox (2007) and McCann et al. (1998) (Table 1). The
values are estimated from studies on species’ body mass versus metabolic and ingestion rate
(Dickie et al. 1987; Yodzis & Innes 1992; McCann et al. 1998; Vasseur & Fox 2007).
Resource gain and predator preference are set higher for C1 than for C2. C1 is the strongest
resource competitor and preferred prey of P. The competition irregularity causes intrinsic
asynchronous fluctuations of consumers. Species densities fluctuate in stable limit cycles in
constant environment.
Figure 1 The diamond shaped food web with differential equation system (modeled after
McCann et al. 1998). P is the density of the top predator, C1 first consumer, C2 second
consumer, R the resource species and Ωi,j, represent the trophic interaction strength between
the species.
Table 1 Parameter explanation and their values.
The standard deviation, σenv, colour, γ, and cross-correlation, ρenv, of environmental variation
are independent parameters affecting the mortality rates of the consumers. Uncorrelated,
white, environmental variation was generated from a random normal distribution with zero
mean and σenv2 variance. Fourier transform was used to generate coloured 1/f noise. The
discrete Fourier transform of the coloured noise, P(ƒ), was scaled according to:
𝑃(𝑓) = |𝑋(𝑓)|2 𝑓 −𝛾
(1)
where ƒ is frequency, X(ƒ) is the discrete Fourier transform of the previously generated white
noise and the colour of P(ƒ) was determined by the value of the spectral exponent, γ, where γ
= 0 gives white and γ > 0 gives red noise. After colouring the time series, inverse Fourier
transform was used on P(ƒ) to generate the coloured environmental noise, envi(t). The food
web model was integrated across a range of σenv, 0 to 0.6 in steps of 0.05, and γ, 0 to 0.6 in
steps of 0.2.
Environmental variation affects the two consumers’ mortality rates through an exponential
filter ({{59 Gillooly,J.F. 2001; 42 Vasseur,D.A. 2007}}:
MCi (t) = MCi (0)eenvi (t)
(2)
where MCi(t) is the mortality rate at time t, MCi(0) is the medial mortality rate, envi(t) is the
environmental variation at time t for consumer i.
In order to determine the effect of dispersal between spatially separated subpopulations, all
measurements in our study were taken from one patch of 6 in the landscape. Patches,
containing the food web, were either isolated or interconnected with the other patches by
dispersal. Dispersal between subpopulations was governed by a mass-action mixing process
without distance dependence. Subpopulations with dispersal were interconnected through a
dispersal matrix (Caswell 2001 and Wennergren et al. 1995) (Table 2).
Table 2 Dispersal matrix with four patches. dij represents the proportion of the subpopulation
in patch i that migrates to patch j in one time step.
Migrating proportions, dij, were generated from a random normal distribution with mean
(patches)-1 and variance 0.2*(patches)-1. The distribution was truncated by 0 and
1.2*(patches)-1. The same dispersal matrix was used for all four species in this study.
Cross-correlation of environmental variation, ρenv, had values -1, 0 and 1. -1 represented
negative correlation between species and positive correlation between subpopulations within
species. Two perfectly negatively correlated time series of environmental variation was
generated for each species, all subpopulations within species was affected by the same time
series. ρenv = 0 represented independent response of all subpopulations. Independent time
series of environmental variation was generated for all subpopulations, independent of
species. Finally, ρenv = 1 represented positive correlation between all subpopulations. In this
case all subpopulations were affected by the same time series of environmental variation.
Simulations were made in Matlab 7.5.0 (R2007b, The Mathworks, Natick, MA, USA) with
100 replicates and 3000 time-steps. Initial subpopulation densities where chosen on the
uniform interval; 0.1 to 1.0. Extinction risk was calculated as the risk of populations
decreasing below the extinction boundary 10-6 and by how many replicates that had all
subpopulations staying above the extinction boundary until the end of the simulation. With
dispersal, populations were considered to decrease below the extinction boundary when the
sum of all subpopulations within species decreased below 10-6. Replicates with extinctions
were only analysed in respect to extinction risk.
The first quarter of the simulated time series was excluded from analysis to avoid initial
transients. Mean, variance and stability of patch density, species density and food web
biomass, consumer synchrony and extinction risk were calculated for each of the
combinations of varied parameters. Food web biomass was the sum of all subpopulations.
Stability was measured as density variability:
1
𝜇𝑖
=
𝐶𝑉
𝜎𝑖
(3)
where CV is the coefficient of variation, σi the standard deviation and μi the mean of
population i’s density time series {{42 Vasseur,D.A. 2007}}.
Consumer synchrony was calculated through:
𝜌𝐶 =
1
𝑁𝜎𝐶1 𝜎𝐶2
𝑁
∑(𝐶1 (𝑡) − 𝜇𝐶1 ) (𝐶2 (𝑡) − 𝜇𝐶2 ) (4)
𝑡=1
where N is time series length, σi standard deviation and μi mean of consumer species i’s time
series {{42 Vasseur,D.A. 2007}}. Consumer synchrony and environmental variation was
calculated as equation (4), when ρenv =1, in order to evaluate the impact of environmental
variation on each consumer.
Results
The magnitude of environmental variance was of great importance for food web stability and
extinction risk. Low-to-moderate variance lowered variability of biomass and all species
densities, except the resource, whereas higher variance destabilises the system (Fig. 2a, d, Fig.
3a). The standard deviation of environmental variation, σenv, generating maximum stability,
was species specific. C1 and P gained their maximum stability from higher σenv than C2 and R.
The same pattern was found for each value of cross-correlation of environmental variation,
ρenv. Reddening of the environmental variation decreased the stabilising effect of low-tomoderate σenv and enhanced the destabilising effect of higher σenv. In addition, it lowered the
σenv values generating maximum stability (Fig. 2d). Dispersal had minor affect during
correlated environmental variation (Fig. 3). However, during uncorrelated environmental
variation, the stabilising effect of low-to-moderate σenv was enhanced and the destabilising
effect of higher σenv was reduced with dispersal (Fig. 2d, Fig. 3). Studies on time series of
biomass and species abundances revealed that addition of dispersal between subpopulations
resulted in maintenance of intrinsic dynamics during moderate σenv. The stable limit cycles
where not as apparent in isolated patches during the same environmental variance (Fig. 4).
Mean food web biomass decreased and biomass variance increased with increasing σ env (Fig.
2e, f), regardless of ρenv. However, a constant environment did not give the lowest variance in
biomass. Low-to-moderate σenv actually resulted in a minor decrease in biomass variance.
Measurements on time series of species densities showed that the value of σenv affected the
relative abundance of species (Fig. 2b). Mean density of the species with the smallest
population in constant environment, C1, increased with increased σenv (Fig. 2c). In contrast to
C1, high σenv decreased mean density and resulted in a major increase in variance for the
largest species in constant environment, C2. Mean density of R increased where as the mean
of P decreased with increased σenv. Reddening of the environmental variation enhanced the
effects of increased σenv on biomass (Fig. 2e, f) and each species. The same change in relative
species abundance occurred, but for lower values of σenv. Dispersal coupled with uncorrelated
environmental variation reduced the effects of increasing σenv on food web biomass (Fig. 2e, f)
and species densities.
Figure 2 Stability, mean and variance for species population densities and food web biomass
with environmental fluctuation strength, σenv and uncorrelated environmental variation,
ρenv=0. Left column; measurements on species population density with white environmental
variation of γenv=0, without dispersal. P is predator, C1 first consumer, C2 second consumer
and R resource. Right column; measurements on food web biomass with coloured
environmental variation of γenv=0-0.6, without and with (crosshatch lines) dispersal.
Figure 3 Stability of food web biomass with standard deviation of environmental variation,
σenv and cross-correlation of environmental variation, ρenv. a) no dispersal b) with dispersal.
Figure 4 System responses to continual synchronous point perturbations with standard
deviation of environmental variation, σenv= 0.3 and uncorrelated environmental variation,
ρenv=0. Dispersal maintains intrinsic dynamics of the food web.
Subpopulation extinction risk increased with increased σenv, regardless of the value of ρenv.
ρenv = -1 gave the highest extinction risk whereas ρenv =1 gave the lowest. A similar pattern
was found for each species, where C2 showed the highest sensitivity to increased σenv.
Reddening of the environmental variation increased population extinction risk where as
dispersal coupled with uncorrelated environmental variation reduced the risk of extinction.
Both consumers become increasingly negatively correlated with their environmental
variation during low-to moderate σenv. However, results differed for σenv values above 0.3.
The negative correlation between C1 and the environmental variation continued to increase
while the negative correlation between C2 and environmental variation started to decrease for
higher σenv. Reddening of the environmental variation amplified the effect where as dispersal
coupled with uncorrelated environmental variation decreased the effect of increased σenv. The
pattern of differences in correlation was retained for all different scenarios tested.
Consumer synchrony increased with increased σenv, regardless of ρenv. Reddening of the
environmental variation enhanced the effect where as dispersal coupled with uncorrelated
environmental variation reduced the synchronising effect of increased σenv.
Discussion
Natural food webs include complex interactions between species in different trophic levels
and between each species and its environment. Low-to-moderate white environmental
variation can increase food web stability (1/CV), by interrupting initial consumer asynchrony
(Vasseur & Fox 2007). Our study confirms these results (Fig. 2a, d, Fig. 3). Consumer
synchronisation caused a shift in total resource predation pressure affecting resource density.
The shift in resource density caused another quick consumer response. Quick dynamic
responses from consumers and resource dampened predator fluctuations and decreased
biomass variance. For isolated patches, the lowest level of variability occurred during
positive correlation of environmental variation, ρenv (Fig. 3a). Similar results have been found
in a model where environmental variation affects the growth rates of all species in a food web
(Borrvall & Ebenman 2008). Higher values of σenv decreased food web stability by increasing
the variability of species densities. However, measuring food web stability by variability can
be misleading without additional independent studies on mean and variance. The positive
effect of low-to-moderate environmental variation can be questioned because of the resulting
decrease in mean food web biomass and increased extinction risk. A decreased mean has
negative effects on population persistence, such as increased effects of demographic
stochasticity and catastrophes (Lande 1993). However, our independent studies of species
mean densities reviled an environmental variation induced shift in relative abundance of
species. It contributed to an increase of the species with the smallest population in a constant
environment (Fig. 2b).
The shift in relative abundance of species was caused by the intrinsic dynamics of the food
web. It is important to have in mind that food web structure and choice of model parameter
will affect the degree of sensitivity to different kinds of environmental variation (Greenman
and Benton 2005). In our model, C2 has a lower ingestion rate than C1. This means that C1
has a better ability to take advantage of its resource than C2 during high environmental
variance. Despite the increase in available resources, C1 was still affected by a high predation
pressure from P limiting its density increase. Differences between the consumers were
apparent in the results of cross-correlation between each consumer and the environmental
variation. The cross-correlation between C1 and the environmental variation increased
continuously with increased σenv where as the cross-correlation between C2 and the
environmental variation started to decrease after reaching a threshold value of environmental
variance. P’s population decreased with increased σenv. Even though P prefers C1, it was
negatively affected by the drastic density decrease of the originally large C2 population. A
density increase of the smallest population, C1, will have positive effect on food web
persistence. Food webs withholding species with large populations will have a smaller total
risk of suffering from catastrophes and demographic stochasticity than food webs
withholding small populations.
In addition to measures of stability, we have investigated how different magnitudes of
environmental variance affect the extinction risks in our model food web. As expected from
earlier studies (Lande 1993, Engen et al. 2002), extinction risk for each species in the food
web increased with increased σenv. Lowered mean densities and increased variance increased
the risk of populations reaching extinction boundaries. C2:s poor resource tracking abilities
gave C2 the highest extinction risk at high σenv, despite being the largest population in a
constant environment. The result was caused by C2:s high density variance (Fig. 2c). Isolated
subpopulations with uncorrelated and negatively correlated environmental variation had
higher mean extinction risks than isolated subpopulations withholding a positively correlated
environmental variation. Results may be explained by decreased variance in species densities
with increased correlation (Borrvall & Ebenman 2008). Results of shifts in relative
abundance with increased σenv indicate that addition of stress factors, such as catastrophes and
demographic stochasticity, may affect the relationship between extinction risk and
environmental variance. Moderate environmental variation may decrease the risk of
extinction by increasing the density of the species with the lowest population in a constant
environment. Further studies including these mechanisms may further clarify the effect of
environmental variation and the importance of multiple measures when analysing food web
stability and extinction risk.
Environmental variation found in nature is considered to be positively correlated in time
(Caswell & Cohen 1995; Halley 1996; Ripa & Lundberg 1996; Cuddington & Yodzis 1999).
Red variation is dominated by low frequencies which result in bad/good conditions being
retained for several time steps. Red environmental variation gives populations more time to
respond to differences in their environment, increasing the probability of environmental
fluctuation tracking (Ripa & Lundberg 1996). The stabilising power of low-to-mediate
environmental variation was reduced and extinction risks where increased with increased
redness. Maximum food web stability was found for lower σenv than during white
environmental variation (Fig. 2d). Results are caused by reddened environmental variation
causing larger time series variance differences than white variation (Greenman and Benton
2005). Cuddington and Yodzis (1999) have shown that reddening of variation can decrease
mean persistence time in overcompensating single population models. However, our result
showed that reddening of environmental variation amplified the shift in relative abundances
of species, increasing the density of the species with the smallest population size. Reddening
also increased the cross-correlation of consumers which has been observed to reduce
extinction risks in food webs without spatial structure (Borrvall & Ebenman 2008). Redness
increasing the positive correlation between populations has also been shown in Greenman
and Benton (2005). Reduced stabilizing effects and increased extinction risks caused by
redness speak against the importance of environmental variation as an important stabilising
property of food webs. However, addition of dispersal between subdivided populations
clarifies the importance of spatial and temporal variability.
Dispersal had a strong stabilising effect during uncorrelated environmental variation (Fig.
2d, Fig. 3, Fig. 4). Individuals from patches with good conditions were able to migrate to
patches with poor conditions (Engen et al. 2002, Liebhold et al. 2004). The migration
undermined consumer synchronisation and evened out destabilising effect of environmental
variation. The equalising effect caused by dispersal had major implications for food web
stability and extinction risks. The food web with dispersal affected by dark pink
environmental variation was actually more stable than the food web in an isolated patch
affected by white variation. Extinction risks were close to zero, during the interval of
environmental variance and redness. However, higher σenv values generated similar
destabilising effects of redness as in the case with isolated subpopulations. Kaitala et al.
(1997) supports our results by showing that increased system complexity can reduce the
effect of redness. Engen et al. (2002) showed that increasing dispersal between patches,
withholding single species, results in longer time to extinction. Mass action mixing has no
distance dependence. This infers similar probabilities of dispersal between all patches. The
assumption can be far from dispersal found in nature. However, results from Petchey et al.
(1997) showed minor differences in population persistence when comparing landscapes with
global and local dispersal. Despite the lack of distance dependence, a minor increase in
stability was observed in some patches when adding dispersal during correlated
environmental variation. This effect can be explained by the variance in dispersal rate
between patches caused by our dispersal matrixes generation method. The small variance in
dispersal rates makes it possible for individuals in patches with larger dispersal rates to save
other patches with lower dispersal rates at the cost of their original subpopulation density.
Further studies on distance dependent dispersal withholding negative effects, such as
additional death rates on dispersers, would further clarify the importance of dispersal between
subdivided populations.
Food web stability and extinction risk were measured at subpopulation level in this study. It
is important to consider the differences between patch and landscape level when estimating
food web resistance. The choice will have large effects on estimated extinction risks! When
comparing species with and without dispersal during correlated environmental variation, the
stability of the food web in the landscape without dispersal was much higher than the one
with dispersal. Without dispersal, all subpopulations affected by weak variation, will
fluctuate in their own phase, depending on initial densities. This asynchrony minimises the
variance of the sum of all subpopulations which leads to larger landscape stability. With
dispersal, patches that originally fluctuate in their own phase will eventually be more
synchronised, preserving the variance in the sum of all subpopulations. Time lagged
dispersal, more close to dispersal found in nature would decrease this synchronising effect.
However, it is still important to think of these different scales both when investigating model
food webs and when measuring populations empirically.
Coloured environmental variation can have a stabilising effect on food webs by increasing
the coefficient of stability and changing the relative abundance of species. An important
implication of variation induced shifts in relative abundance is that present large population
sizes may not give insurance towards future increase in environmental variance. Crosscorrelation of environmental variation and dispersal between spatially separated patches had
major influences on food web stability and extinction risks. Weak and strong interaction
pathways, exemplified in our study, can repeat at different resolutions, making food web
stability scale invariant (McCann 2009). Our model can be seen as a building block for more
complex food webs indicating that weak and strong pathways coupled by variability in space
and time is essential for the resistance of food webs found in nature.