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Phil. Trans. R. Soc. B (2011) 366, 1425–1437
doi:10.1098/rstb.2010.0241
Research
Genetic variation, predator–prey
interactions and food web structure
Jordi Moya-Laraño1,2, *
1
Cantabrian Institute of Biodiversity (ICAB), Universidad de Oviedo—Principado de Asturias,
33006 Oviedo, Asturias, Spain
2
Functional and Evolutionary Ecology, Estación Experimental de Zonas Áridas, CSIC, Carretera
de Sacramento s/n, 04120-La Cañada De San Urbano, Almerı́a, Spain
Food webs are networks of species that feed on each other. The role that within-population phenotypic and genetic variation plays in food web structure is largely unknown. Here, I show via
simulation how variation in two key traits, growth rates and phenology, by influencing the variability
of body sizes present through time, can potentially affect several structural parameters in the direction of enhancing food web persistence: increased connectance, decreased interaction strengths,
increased variation among interaction strengths and increased degree of omnivory. I discuss other
relevant traits whose variation could affect the structure of food webs, such as morphological and
additional life-history traits, as well as animal personalities. Furthermore, trait variation could
also contribute to the stability of food web modules through metacommunity dynamics. I propose
future research to help establish a link between within-population variation and food web structure.
If appropriately established, such a link could have important consequences for biological conservation, as it would imply that preserving (functional) genetic variation within populations could
ensure the preservation of entire communities.
Keywords: ecological networks; food web robustness and stability; genetic variation and diversity;
growth rate and phenology; individual variation; multivariate phenotypes
1. INTRODUCTION
Community genetics addresses the question of how, and
to what extent, within-population genetic variation may
affect ecological processes, including the outcome of
interactions among species. Usually, when community
genetics studies extend beyond one trophic level, these
focus on how genetic variation among herbivores or
their host plants affects herbivore performance [1,2]
or on the bottom-up effects of plant genetic variation
on herbivore and predatory arthropods ([3–5]; reviewed
in [6,7]). One exceptional study, however, focused on
genetic variation in a ladybird predator [8], and how an
indirect effect of plants mediated by the ladybird’s prey
(aphids) could affect the phenotype and performance of
different ladybird genotypes, demonstrating genetic variation in the response of ladybird traits to an indirect
ecological (bottom-up) effect. However, research on
how, and whether, genetic variation within predator
populations affects ecological processes is greatly needed.
Food webs are networks of interacting species that
feed on each other. Predator–prey interactions are
central in understanding the mechanisms behind food
web dynamics, and genetic variation in both predators
and prey could largely explain these dynamics. For
instance, genetic variation and rapid adaptive evolution
can dampen population fluctuations in predator–prey
systems [9,10]. However, beyond rapid evolution, there
is almost no information on how genetic ariation in
predator traits affects food web dynamics at ecological
time scales. Trophic polymorphisms are widespread, for
instance, among fish [11–13] and this polymorphic variation may be genetically driven [14]. However, the
ecological consequences of these polymorphisms have
rarely been studied (but see [15,16]). In particular, how
within-population genetic variation in traits that affect
predator–prey interactions could determine food web
structure in ways that could enhance its robustness and
stability, is largely unknown. In this paper, I propose a
series of traits whose variation has the potential to affect
food web structure, and provide a series of ideas for
how these structural changes could affect food web
dynamics, as well as the robustness and the stability of
these ecological networks. I use simple computer simulations to document the potential effect of trait variation
on food web structure and provide a framework for
future research. I argue that explicitly considering
genetic variation of traits within populations can serve
to link the preservation of variation in populations to
the preservation of entire ecological networks and
metacommunities.
* [email protected]
(a) Food web structure, robustness and stability
The relatively wide diet of generalist predators can
greatly shape the structure of food webs and the
One contribution of 13 to a Theme Issue ‘Community genetics: at
the crossroads of ecology and evolutionary genetics’.
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Genetic variation and food webs
subsequent dynamics of these widespread ecological
networks [17 – 21]. One important feature of generalist
predators that may strongly shape food web structure
is intraguild predation (IGP; [22,23]), by which
potential competitors that share prey kill and consume
each other. As conspecifics share the same resource
requirements (i.e. they belong to the same guild), cannibalism, or intraspecific predation, is similar to IGP
(e.g. [24]). This type of predation confers upon food
webs a high degree of omnivory—i.e. when predators
feed on more than one trophic level—and may even
lead to feeding loops in food webs—the shortest loop
is reciprocal IGP between pairs of species [25]. Widespread omnivory of generalist predators can in turn
modify another two key food web structural properties: the distribution of interaction strengths and
connectance. Interaction strength may be roughly
defined as the effect of one population on another
(see [26] for a review), while a measure of connectance
would be the proportion of all possible links that are
realized [27]. Understanding the structure of food
webs in terms of connectance, the degree of omnivory,
the strength of interactions and its variation across
species, as well as the length and density of loops, is
crucial to understanding what drives food web
dynamics, robustness and stability (e.g. [20,28 –34];
but see [35– 40]).
(b) The traits: growth rates and phenology
versus body size
One of the most important traits that explains ‘who
eats whom’ in many food webs is body size [21]. Successful predation may largely depend on the body size
ratio between the predator and the potential prey, predation being more likely to occur at larger ratios [41].
This causes diets to be highly nested within food webs,
with the diet of smaller generalist predators nested
within the diet of larger ones [21]. In addition, since
in natural communities, larger animals are usually
less abundant [42], larger top predators are much
less abundant than intermediate or basal species
[21,43]. These differences in abundances must probably affect the strength of interactions [26]. Also,
predator – prey body size ratios are positively associated
to interaction strengths [44,45] and can thus be linked to food web stability in turn; with larger ratios
leading to more stable food webs [46]. However, as
species embedded in food webs have ontogenies and
growth, body size is a highly dynamic trait that will
change through the ontogeny of every single individual. Thus, when one aims to investigate the role of
trait genetic variation on food web dynamics, (adult)
body size is not an appropriate trait, as it will only
include a snapshot of the interactions experienced by
each species in the food web.
Here, I argue that to study the role of genetic variation in food web structure, we should focus on two
key traits whose genetic variation may largely affect
food web structure and dynamics, growth rates and
phenology (e.g. emergence date). Genetic variation
for intrinsic growth rates has been widely documented
(reviewed in [47]), even for generalist predators
[48,49]. As body size changes with ontogeny, genetic
Phil. Trans. R. Soc. B (2011)
variation in growth rates leads to variation in body
size ratios between predators and prey, and may thus
determine ‘who eats whom’ as the season progresses.
Similarly, traits that are genetically variable and are
related to phenology independently of growth rate
(such as hatching time—e.g. [50]) will determine the
distribution of body sizes through time and thus influence feeding interactions. Hence, higher genetic
variation in growth rates and phenology can expand
the diet breadth of populations and thus modify the
entire structure of food webs, potentially affecting
the robustness and the stability of these ecological
networks.
(c) Can genetic variation in growth rates and
phenology contribute to food web structure?
The amount of genetic variation among interacting
populations can increase the chances of coexistence
[7,51,52]. Similarly, increased genetic variation in
growth rates and phenology can hypothetically
change the structure of the food web, i.e. increasing
connectance and changing the distribution of interaction strengths, by continuously changing the ratio
of body sizes between predators and prey through
the season (figure 1). Additionally, higher genetic variation in growth rates expands the window for
intraspecific predation (cannibalism), as the ratio of
body sizes between the fastest and the slowest growing
genotypes will be sufficiently high through a longer
period during the growing season (figure 1).
Below, I use computer simulations to investigate
whether realistic amounts of variation in growth rates
and phenology within an intermediate consumer can
potentially affect (i) the number of trophic links that
this consumer holds with other species in the web;
(ii) the magnitude and variability of interaction
strengths for these links; and also (iii) the degree of
cannibalism (omnivory).
(d) Genetic variation in growth rates
(i) Method
For simplicity, I assume that all variation in growth
rates has a (quantitative) genetic basis. However,
some of the predictions may apply equally well to
environmentally driven phenotypic variation. Nevertheless, this is just a preliminary investigation of how
genetic variation within a food web can contribute to
determine its structure. What I investigate in this
paper is merely how variation in growth rates and
phenology (see below) may contribute to change the
predator– prey ratios through the season, and how
this may widen or narrow the window for predation.
With this information, and based on predator– prey
body size ratios, I then estimate the potential of a
population to establish links with other populations
in the food web, as well as how strong and variable
these links could potentially be.
I simulated a population (hereafter, the target population) of generalist predators with high genetic
variation in growth rates (20 genotypes differing
in growth rates and/or phenology, see below) and
another with low genetic variation in growth rates
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Genetic variation and food webs
160
L
P
140
and
body size
120
OP ¼
J. Moya-Laraño
1427
OPi
i¼1
L
;
ð2:3Þ
100
*
80
60
40
20
0
50
100
time
150
200
Figure 1. Growth rates of predator populations embedded in
a food web. Solid lines represent the growth rate range for a
population with high variation of growth rates. Dashed lines
represent a population with lower genetic variation. The
asterisk shows the average size of another predator species
in the community (the growth curve has been omitted for
simplicity). Following equation (2.1) and taking R ¼ 2, the
population with high genetic variation in growth rates will
have sufficiently high predator–prey ratios (solid, doubleheaded arrows), for both predating upon the asterisk species
and being predated by it, while the population with low genetic variation (dashed, double-headed arrows) will not be
able to trophically interact with the asterisk species. Furthermore, when high genetic variation in growth rates is
present, predator–prey ratios will also involve a higher
opportunity for intraspecific predation (cannibalism),
which is represented here by the distance between the two
grey solid vertical lines. The space between the two grey
dotted vertical lines represents the lower opportunity for
intraspecific predation when low genetic variation in
growth rates is present. A growth rate, mortality trade-off
is also apparent by the shorter lifespans of individuals with
genotypes determining faster growth rates. Including
genetic variation in phenology involved establishing high or
low variation in the timing of the season in which minimum
body sizes were present (i.e. around the x-axis).
(20 genotypes differing less among each other). Each
target population was simulated to be embedded in a
complex food web, interacting with other generalist
predators (20 species, see below). For each predator – genotype pair, I then estimated what I call the
opportunity for predation (opij). For i ¼ 1 to L, j ¼ 1 to
G and k ¼ 1 to D, where L is the number of species
with which the target population can potentially be
linked (i.e. those species for which OPi in equation
(2.2) below is higher than zero), G is the number of
genotypes in the target species (20 in these simulations) and D is the season length (in days); and
taking rijk as the body size ratio between species i
and genotype j at day k,
opij ¼
D
X
1;
ð2:1Þ
opij
ð2:2Þ
k¼1
r ijk .R
OPi ¼
G
X
j¼1
Phil. Trans. R. Soc. B (2011)
where R is the minimum predator – prey body size ratio
beyond which predation will occur (figure 1). I ran
simulations with four values of R (1, 2, 3 or 4),
which lay well within the natural range of predatory – prey interactions for invertebrates [41]. Two
values were obtained, one for the target species
acting as predator (an estimate of the room for generality—number of prey species consumed—using the
genotype/species ratio rjik instead) and another for
the target species acting as prey (an estimate of the
room for vulnerability—number of consuming predator species—using r ijk). Since more days in the season
having the opportunity to feed on each other increases
the chances of predation, this opportunity for predation can be thought of as an estimate of the potential
interaction strength between two members of the
food web. However, as interaction strength will also
depend on the relative abundances of each predator
and prey (e.g. [26,53,54]), to better approach true
potential interaction strengths, I also calculated the
relative opportunity for predation (ropij ), for which I
multiplied each opportunity for predation (opij ) by
the abundance predator/prey ratio (ai /aj ) and then
calculated ROPi and ROP as OPi and OP above
(equations (2.2) and (2.3), respectively). For simplicity, I assumed that all genotypes in the target
population were equally abundant. Furthermore, as
variation in interaction strengths can also be important for determining food web stability, with a few
strong and many weak interactions determining
stability [29], I also calculated the across-species
coefficient of variation for both the opportunity for
predation (CV-OP) and the relative opportunity
for predation (CV-ROP).
For simulations, I use abundance and body size data
obtained from the community of arthropod generalist
predators of a deciduous forest-floor food web in Central
Kentucky, USA [55]. For simplicity, I include only the
20 more abundant taxonomic groups (.0.2 m22).
Most of these predators (18) are spiders, a group of
generalist predators for which the foraging biology is
quite well known (e.g. [24,56,57]) and for which genetic
variation in growth rates has been previously documented [49]. The other two predators are centipedes
(Lithobiomorpha or Scolopendromorpha—further
details can be found in the study of [55]). Only a relatively small set of interactions has been experimentally
solved for this food web (e.g. [55,58,59]). However, as
spiders are known to be intraguild predators that readily
feed on each other if size differences are large enough,
here I assume as a first approximation, that the body
size ratio is the only predatory constraint. Thus, all
generalist predators in this web should be able to feed
on each other if size differences are large enough.
I simulated variation in growth rates for a predator
of intermediate body size at maturation (Xysticus,
the spider ranked 10—over 20—for adult body size).
For simplicity, I assumed that all predators were univoltine. This assumption does not probably hold, as
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J. Moya-Laraño
Genetic variation and food webs
some groups show overlapping generations and a
single peak of adult emergence ( J. Moya-Laraño
2000 – 2002, personal observations), thus strongly
suggesting that some species in this forest-floor food
web take more than 1 year to mature. However,
assuming growth to maturation in a single year, probably included roughly the same body size stages as
including real growth trajectories and the different
overlapping generations. The only thing that probably
changed was the timing at which each instar was present in every stage of the season. Unfortunately, we
lack the necessary information to include real phenologies and growth rates. However, to test the robustness
of the results and to add a second (orthogonal) axis of
genetic variation, I also simulated the effect of
variation in phenology (see below).
Since, to my knowledge, there are no published
accounts of growth trajectories in Xysticus, I here use
the published curves for the wandering spider Pardosa
prativaga from Northern Europe [60] and fitted the
following three-parameter logistic curve to the two
most extreme growth curves (the slowest and the
fastest):
s¼
smax
1 þ ðt=xo Þb
ð2:4Þ
where s is size, smax is the maximum asymptotic body
mass, t is age in days, and b and x0 are the logistic
parameters fitted. I chose to fit this equation because
a close to perfect fit (i.e. r 2 . 0.97) be could easily
achieved and only three parameters needed to be
used to simulate variation (instead of the fourparameter Von Bertalanffy equation [61], which in
addition is not appropriate for arthropods [62]). In
any case, whatever the fitted equation, what matters
for the present simulations is the shape of the curves,
which are based on real growth trajectories.
I simulated high genetic variation in 20 genotypes
by taking the average parameters of the curves (b and
x0 in eqn 1 from [60], see above) and calculating 20
random growth curves with parameters b +
s.d. ¼ 22.8 + 0.05 and x0 + s.d. ¼ 53.3+5, where b
and x0 follow normal distributions. To approximate
real phenologies, I matched average body sizes
around 15 July (the approximate time of the year
when Moya-Laraño & Wise’s [55] data were collected). The assymptotic body size parameter smax
was set to the maximum body size recorded in the
field for Xysticus (smax + s.d. ¼ 12.4 + 5 for high genetic variation). Body size was approximated to body
mass by inputting body length in published equations
for spiders and centipedes [63]. I introduced a tradeoff between lifespan and growth rate [64,65], by first
obtaining the longevity (l ) of each of the 20 genotypes
(l + s.d. ¼ 180 + 15) and then subtracting twice the
body size at maturation from that value.
The population with low genetic variation had half
the variation in all parameters: b + s.d. ¼ 22.8 +
0.025, x0 + s.d. ¼ 53.3+2.5, smax + s.d. ¼ 12.4 +
2.5 and l + s.d. ¼ 180 + 7.5, including longevity,
which was necessary in order to include similar
growth rate/longevity trade-offs in both populations.
For simplicity, I assume that all genotypes are born
Phil. Trans. R. Soc. B (2011)
on the same day of the season and then tested the
robustness of this assumption with additional simulations including variation in the phenology of
emergence (see below). The adult body ranges for
the populations with high and low genetic variations
were (in milligrams): high, 3.6 – 20.6; low, 5.3 – 15.9.
These ranges lay well within the intraspecific natural
range of Xysticus species [66,67].
The growth curves for the community of 20 species
of generalist predators were generated with identical b
and x0 average parameters as above, while smax followed
the maximum body size observed for each species
(group) in Moya-Laraño and Wise’s dataset. However,
no genetic variation for these species was assumed and
thus only one single curve was constructed for each
species. All other procedures matched those of Xysticus,
with the exception that since larger species live longer, I
set across-species lifespans according to the equation:
l ¼ 3M 0.2, where M is body mass [68]. The final
range of adult body sizes was 0.3–98 mg.
Finally, to obtain opij and ropij, I contrasted the
20 curves of the high genetic variation population
against the curves for the 20 species and did the
same for the population with low genetic variation.
Within each Xysticus population (high and low genetic
variations), I also calculated the opportunity for intraspecific predation by contrasting the 20 genotype
growth curves against each other.
(ii) Results
When a population of the spider Xysticus is embedded in
a complex food web, the amount of genetic variation in
growth rates can positively affect the number of potential links that the population establishes within the
food web. However, this occurs only when Xysticus
acts as predator (generality), not as prey (vulnerability,
table 1). Furthermore, again only when Xysticus acts
as predator, as the threshold of the predator – prey
body size ratio for predation (R) increases, interactions
have the potential to be much weaker (ROP in table 1)
and much more variable (CV-ROP) in populations
with high genetic variation. Actually, when R is as
high as 3 or 4, these effects are very strong regardless
of whether species abundances are taken into account
or not (compare OP to ROP, and CV-OP to CVROP). In addition, the higher the amount of genetic
variation of growth rates, the higher the potential
for cannibalism (table 1). This last trend is also much
stronger as R increases.
Other results were less robust and thus not as relevant. When R is 2, interactions (Xysticus as predator)
seem weaker (ROP) and more variable (CV-ROP) in
populations with low genetic variation in growth
rates relative to populations with high genetic variation
(table 1). However, this effect is probably the result of
a synergistic effect of genetic variation in growth rates
and the particular distribution of abundances of the
species used in the simulations. Looking at OP
and CV-OP, the picture changes substantially, even
suggesting stronger interactions when genetic variation
is low. Similarly, when Xysticus acts as prey and R is 3,
interactions are potentially more variable (CV-OP) in
the population with low genetic variation (table 1).
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Genetic variation and food webs
J. Moya-Laraño
1429
Table 1. Results of a simulation emulating genetic variation in growth rates for Xysticus spiders embedded in a complex
leaf-litter forest-floor food web.
Ra
population
predator– preyb
OPc
CV-OPd
ROPe
CV-ROPf
links
cannibalismg
1
high genetic variation
predator
prey
predator
prey
predator
prey
predator
prey
predator
prey
predator
prey
predator
prey
predator
prey
407.9 (5.6)
1458.3 (20)
416.3 (5.7)
1479.2 (20.3)
247.8 (3.4)
1358.8 (18.6)
277.5 (3.8)
1137.5 (15.6)
192.7 (2.6)
937.6 (12.8)
455.7 (6.2)
870.4 (11.9)
285.5 (3.9)
799.8 (11)
603.5 (8.3)
743.2 (10.2)
56.6
60.7
62.9
61.8
118.1
75.0
130.2
82.2
156.5
32.4
82.2
95.0
110.5
94.8
13.7
108.0
2994 (4.3)
443.3 (20.4)
3062.7 (4.4)
451.5 (20.7)
1176.1 (1.7)
332.7 (15.3)
803.4 (1.1)
336.7 (15.5)
530.7 (0.8)
260.3 (12)
1159.9 (1.7)
229.4 (10.5)
746.6 (1.1)
211.7 (9.7)
1543.6 (2.2)
188.6 (8.7)
109.1
126.5
127.9
127.2
64.7
131.0
106.2
143.0
141.5
129.2
82.6
127.5
106.5
126.5
13.6
126.9
13
20
12
20
8
20
6
20
7
20
3
20
4
20
2
20
30 575 (20.9)
low genetic variation
2
high genetic variation
low genetic variation
3
high genetic variation
low genetic variation
4
high genetic variation
low genetic variation
31 646 (21.7)
10 836 (7.4)
2635 (1.8)
4282 (2.9)
145 (0.1)
1946 (1.3)
0 (0)
a
Predator– prey body size ratio beyond which predation can occur.
Whether Xysticus acts as potential predator (potential generality) or as potential prey (potential vulnerability).
c
Opportunity for predation averaged across species pairs (see text). Shown between parentheses are percentage OP relative to the
maximum possible (i.e. OPmax ¼ days in the season number of genotypes ¼ 365 20 ¼ 7300).
d
Coefficient of variation among OPis (see text).
e
Relative opportunity for predation after accounting for species abundances (see text). Shown between parentheses is the percentage
ROP relative to the maximum
PS possible, the latter differing depending on whether the target population acts as predator or as prey (i.e.
as predator, ROP
PSmax ¼ l¼1 365ðxyst=al Þ, where xyst is Xysticus abundance and al is the abundance of the lth predator; as
prey, ROPmax ¼ l¼1 365ðal =xystÞ. These figures were 69 873 and 21 873, respectively.
f
Coefficient of variation among ROPis (see text).
g
Intraspecific opportunity for predation. Again, between parentheses is the percentage relative to the maximum (i.e. 365 days 20 genoypes 20 genotypes ¼ 146 000).
b
However, after weighting for species abundances
(CV-ROP), this difference disappeared.
(e) Genetic diversity. Adding a second axis
of genetic variation: phenology
Below, I expand the simulations to a second trait,
phenology, understood as the date in which the offspring emerges into the population. First, I simulate
the potential effect of genetic variation in food web
structure when phenology (emergence date) is tightly
genetically and negatively correlated to growth rates.
This implies that a faster growing genotype, that
matures and reproduces early, will have early-hatching
offspring. Second, I simulate variation in phenology as
a completely independent (orthogonal) axis of variation relative to growth rates. Note that higher
genetic variation in two orthogonal axes (growth
rates and phenology) will translate into higher genetic
diversity (e.g. genetic variation across orthogonal axes
of variation—[69]; see also [70]) relative to the scenario of tight correlations between growth rate and
phenology. Third, to contrast the effect of this trait
with that of growth rate, I also simulate phenology as
the only trait showing genetic variation.
(i) Method
The Xysticus population with high genetic variation
included a hatching time window of 50 days, while
the population with low genetic variation included a
hatching time window of 27 days. These lay well
within the window in hatching time observed for at
least the spider Lycosa tarantula in nature, which can
Phil. Trans. R. Soc. B (2011)
be as high as 98 days [71]. I ran these simulations
only for R ¼ 3 when Xysticus acts as predator and calculated L, ROP and CV-ROP, as this set of parameters
was the most affected by genetic variation in growth
rates. For the first scenario, tight genetic correlation
between growth rates and phenology, I estimated
hatching time (H ) as: H ¼ 22l/3 for the population
with high genetic correlation and as H ¼ 2l, for the
population with low genetic correlation (where l is
longevity). To simulate orthogonal axes of variation
(second scenario), I randomly permutated the hatching times among the 20 genotypes from the first
scenario. For the third scenario (effect of phenology
alone), I used the same H as in scenario 1, but
set all genotypes to have identical (average) growth
curves.
(ii) Results
Adding genetic variation in phenology has a dramatic
effect on the potential number of links that Xysticus can
establish as a predator (table 2), increasing from five
(low genetic variation) to almost maximum of 17–19
(depending on the scenario) for the population with
high genetic variation. When phenology is tightly correlated with growth rate, interactions have the potential to
be 2 as strong with low genetic variation and 1.7 as
variable with high genetic variation. When growth rates
and hatching date are allowed to vary independently,
this figure changes to 3.5 and 2.3, respectively,
suggesting additive effects of orthogonal axes of
variation. When only variation in phenology was considered, the figure was intermediate (2.7 and 1.9,
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J. Moya-Laraño
Genetic variation and food webs
Table 2. Results of a simulation adding genetic variation in phenology (i.e. hatching or emergence date) to the genetic
variation in growth rates of Xysticus (table 1). All simulations include R ¼ 3 and are just for Xysticus acting as predator
(potential generality). See table 1 for explanation of columns.
type of between-trait covariation
population
ROP
CV-ROP
links
cannibalism
phenology correlated with growth rates
high genetic variation
low genetic variation
high genetic variation
low genetic variation
high genetic variation
low genetic variation
583.2 (0.8)
1144.6 (1.6)
307.5 (0.4)
1066.6 (1.5)
374.3 (0.5)
1002.6 (1.4)
128.4
76.2
196.0
84.6
179.2
93.6
19
5
18
5
17
5
7921 (5.4)
4114 (2.8)
5629 (3.9)
3149 (2.2)
3776 (2.6)
2791 (1.9)
phenology uncorrelated with growth rates
phenology alone
respectively). Interestingly, adding phenological variation opened up the window for cannibalism in the
population of low genetic variation (compared with
table 1) and differences between populations of high
and low genetic variation were not as high as for
growth rates. Thus, the smallest variation in phenology
may have profound effects on the opportunity for
cannibalism.
(f) Genetic variation can potentially contribute
to food web structure
These simulations show how a generalist predator
population embedded in a complex food web has the
potential to prey on a greater number of predator
species if genetic variation in growth rates and phenology within the population is relatively high.
Furthermore, more genetic variation can decrease
the average strength of predator– prey interactions
and also increase the variation in interaction strengths
across species pairs. These effects become much stronger as the threshold predator – prey body size ratio for
predation to occur increases. Moreover, higher genetic
variation in growth rates leads to an increase in
the potential for cannibalism, particularly when the
threshold body size ratio for predation increases. Variation in phenology can largely determine the number
of established links in the food web and has the potential to act additively with growth rate to determine
weaker and more variable (few strong, many weak)
interactions. In addition, the slightest increase in phenological variation has a great impact on the potential
for cannibalism. This is in agreement with recent findings that show how both family variation in growth
rates and high variability in emergence date, can largely explain juvenile – juvenile body size ratios and
juvenile – juvenile cannibalistic rates in a natural population [71]. However, neither variation in growth rates
nor in phenology seems to affect the potential for
vulnerability or its variability (see §5).
(g) Consequences for food web robustness
and stability
The above results show how an increase in genetic
variation within populations embedded in food webs
can potentially increase the connectance of the web
(number of links), and can decrease the average interaction strengths as well as increase the variability in
interaction strengths (i.e. there are many weak and
few strong interactions). Also, considering that
Phil. Trans. R. Soc. B (2011)
cannibalistic individuals are more likely to feed on
more than one trophic level, high genetic variation in
growth rates can increase the degree of omnivory.
Moreover, as the number of predator species that
feed on Xysticus would remain maximum and unaltered with genetic variation (table 1), an increase in
the number of predator species incorporated in the
Xysticus diet as prey and a decrease in the average
interaction strengths with increasing genetic variation,
suggests that the number of weak and long feeding
loops in the web can also increase with genetic variation. All these structural properties have been
documented to increase the robustness [20,30,31,
36,72] and the stability [28,29,32,38,39,72] of food
webs. Interestingly, the effect of higher genetic variation can be more pronounced as predator– prey
body size ratios increase. Thus, as higher ratios also
lead to more stable food webs [46], all of the above
effects could act synergistically to determine food
web stability. It seems that an increase in genetic
variation of growth rates and phenology within populations embedded in food webs has a strong potential
to contribute to the robustness and the stability of
naturally assembled food webs.
(h) Consequences for trophic cascades
Trophic level omnivory can dampen indirect ecological effects, such as trophic cascades, i.e. top predator
indirect positive effects cascading down through
the food web by the suppression or the frightening
of intermediate predators [73]. The fact that the
intraspecific opportunity for predation increases
substantially with increasing genetic variation in both
growth rates and emergence date, strongly suggests
that high genetic variation may lead to sufficiently
high levels of trophic level omnivory for dampening
trophic cascades. Notice that stronger cannibalistic
interactions with high genetic variation suggest that
this could also be the case for intraguild predators
belonging to different species when both have high
genetic variation, and as long as species pairs are
sufficiently similar in size. Thus, high genetic variation can potentially increase the interaction strength
among similar-sized species—increasing the potential to dampen trophic cascades—while decreasing
interaction strengths and increasing among-strength
variability across trophic levels. The dynamics and balance of all these interactions will depend, among other
things, on encounter rates, which will depend in turn
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Genetic variation and food webs
on the spatial distribution and the relative abundances
of each species.
2. OTHER POTENTIALLY RELEVANT TRAITS:
MORPHOLOGY, LIFE HISTORY AND ANIMAL
PERSONALITIES
Other traits that have not been simulated here could
also potentially affect food web structure. For instance,
some morphological traits may grow allometrically
with body size, and may independently affect the outcome of predator – prey interactions, such as jaw and
head dimensions for catching prey (e.g. [74]) and leg
length to run faster (e.g. [75]), the latter allowing
both escaping predators and capturing prey more efficiently. Similarly, genetic variation in diet breadth
(independent of body size) should be included in
future studies. Diet breadth has been documented to
have a genetic basis [14]; it may be associated to morphological differences and it may be highly variable
among individuals (e.g. [12]). Higher genetic variation
in diet breadth could actually help top predators to
stabilize different energy channels in food webs, which
has been shown to increase food web stability [76].
Variation in life-history traits other than growth rate
could affect food web structure. For instance, variation
in propagule size could affect food web interactions. In
simulations (figure 1), I have implicitly assumed that
all species and genotypes have identical propagule
(egg) sizes. However, substantial variation in propagule size has been documented both within and
across taxa (e.g. [77]), and a positive correlation
between female body size and propagule size has
been well established across spider species [78].
Whether genetically correlated with female growth
and body size or not, variation in propagule size
could also contribute to the number (and strength)
of links established by populations embedded in food
webs, as affecting the body size ratio between the offspring and other members of the food web will
indirectly affect offspring variation in vulnerability
and generality.
Furthermore, growth rates may be linked to animal
personalities [79,80], in the sense that faster growth
rates may be genetically correlated to boldness (e.g.
the degree of predation risk taken while foraging)
and aggression (e.g. the degree to which encountered
prey are attacked). Thus, animal personalities have
the potential to play a central role in determining
‘who eats whom’ in food webs and thus in food web
structure. First, genetically driven aggression can
determine to what extent a predator will attack an
encountered prey and, importantly, will positively
explain growth rates. In addition, the more aggressive
individuals in the population will attack prey at relatively lower predator – prey body size ratios, or will
even attack potentially dangerous prey. Hence, populations with high genetic variation in aggression levels
will include a wider range of individual types: those
that attack prey more readily, and those that are less
prone to attack relatively large or dangerous prey,
thus potentially contributing to increase the number
of links in the web by allowing intraspecific resource
partitioning. Notice that despite the (genetic)
Phil. Trans. R. Soc. B (2011)
J. Moya-Laraño
1431
correlation between growth rates and aggression,
there is an aggression effect that would be independent
of the effect of genetic variation in growth rates. Growing at different rates from differences in aggression can
indirectly increase the connectance of the web by
increasing the differences in body size between predators and prey. Thus, aggression may have two effects,
one indirect by virtue of its genetic correlation with
growth rates—and the subsequent increase in the
range of body sizes within the population—and
another, more direct, effect that confers variation in
attack rates regardless of the predator– prey body size
ratios. Another personality trait, boldness (as opposed
to shyness) may also be positively correlated to growth
rates [79], and genetic variation in boldness could also
independently affect food web structure. Bold individuals will be more active at prey searching, and as a
consequence they will be both more exposed to other
predators and will find more prey, thus having the
chance to establish more links with both predators
and prey. Shy individuals, on the other hand, will
search for prey less often and will therefore be at
lower predation risk, having a lower diversity or predators that attack them (lower number of links).
However, shy individuals will also encounter relatively
fewer potential preys during their lifetimes and will
have a lower chance to be choosy with what they eat.
The net food web effect of having higher or lower
genetic variation for boldness in populations could thus
be a higher number of links established by resource
partitioning (generality) between bold (picky by encountering a higher diversity of prey) and shy (tend to eat all
they find). Similarly, in intermediate consumers, having
high variation in boldness could promote resourcepartitioning among their predator species, with some
species feeding more likely on bold (mobile) individuals
and other species feeding on the shy (sedentary) individuals, thus also contributing to increase the number of
links (increase vulnerability).
Moreover, behavioural syndromes [81,82] show
how aggression and boldness may be positively correlated, and how these two behavioural traits may even
be correlated across contexts (e.g. generalist predators
behaving in the same way when encountering a predator as when encountering a prey). Relative to tame and
shy individuals, aggressive and bold individuals will be
able to encounter a greater variety of prey and to attack
more dangerous prey. Thus, both personality traits
have the potential to act synergistically on intraspecific
resource-partitioning.
However, although behavioural syndromes may
constrain behavioural plasticity [81,82], fine-tuning
context-dependent behaviour is also widespread (e.g.
[83 – 87]), and thus the constraint imposed by the correlation between personality traits may be frequently
overcome. Behavioural plasticity could even potentially allow individuals to escape from the correlation
between personality traits and growth rates (see a few
examples in table 1 of [79]). For instance, individuals
could potentially behave boldly and aggressively when
searching and encountering potential prey, and hide
and behave shyly when they assess the risk of predation
from the presence of predators in their home ranges.
Moreover, rather than constraining behavioural
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J. Moya-Laraño
Genetic variation and food webs
plasticity, behavioural correlations may be the consequence of adaptive phenotypic plasticity [88]. Thus,
the degree to which behaviour may be flexible or constitutive may have non-trivial consequences to food
web structure. A few recent models have actually
been very successful at predicting food web structure,
robustness and stability by including adaptive flexible
behaviour (both foraging and antipredatory) in food
webs [89 – 93]. However, population dynamics in
large food webs may be less dependent on behavioural
flexibility than in small food webs [94]. Nevertheless,
behavioural flexibility is actually at the core of behaviourally (or trait) mediated indirect interactions in
food webs [95,96], by which the (indirect) effect of
one species on a third species may depend on the behaviour of the second (intermediate) species. Thus,
the genetic architecture of growth rates and
personalities can affect both food web structure as well
as indirect ecological effects in food webs (i.e. food
web dynamics). Interestingly, recent findings show
how past predatory pressure can actually determine the
amount of genetic variation in the expression of personality behaviours [97], which implies that, in the context
of predator–prey interactions, flexible behaviour and
animal personalities are also heritable [98]. Clearly,
future research should investigate the role that genetic
variation in animal personalities has on food web
structure and dynamics.
In summary, there are a number of traits (life history, morphological, behavioural) whose genetic
variation has the potential to greatly affect food web
structure and dynamics. Different traits may affect
food web structure either synergistically, by virtue of
strong between-trait genetic and phenotypic correlations, or additively (i.e. independently of each
other), as suggested by the above results for growth
rates and phenology. Thus, both genetic variation
and genetic diversity (across axes of variation in multivariate phenotypes) should be considered. Whether
flexible or constitutive, the role that multivariate phenotypic variation plays in food web structure should
be investigated both theoretically and empirically.
3. CONSEQUENCES OF INDIRECT GENETIC
EFFECTS IN FOOD WEBS
Genetic variation can also act indirectly, by which
the genetics of an individual can potentially change
the expression of genes in another individual with
which it is interacting. These indirect genetic effects
[99,100] can potentially have important effects in
food webs. In particular, non-lethal predator – prey
interactions have important fitness consequences on
prey [101] and can thus be affected by interspecific
indirect genetic effects (IIGEs). For instance, the persistent presence of the most genetically aggressive and
bold individuals of a predator population may change
the behaviour of the frightened consumers by either
forcing a decrease in their activity and metabolic
rates, or forcing their emigration to safer habitat
patches. This change of genetic expression can then
have important consequences on basal resources,
increasing owing to the lack of consumption by the
frightened consumers (i.e. a trait-mediated indirect
Phil. Trans. R. Soc. B (2011)
interaction—see above). Thus, indirect genetic effects
could potentially trigger indirect ecological effects and
in turn modify the structure of the food web.
4. CONSEQUENCES OF GENETIC VARIATION
FOR METACOMMUNITY DYNAMICS OF FOOD
WEBS
A metacommunity is a set of communities linked by
dispersal of the multiple species that integrate them
[102], and the process of extinction – colonization in/
of patches by each species governs the dynamics of
metacommunities [103]. The spatial dynamics of
food webs has been typically studied using food web
modules; e.g. a three-trophic food chain embedded
in a more complex food web. In general, the spatial
dynamics of food web modules depend on the complexity of the module and on the mobility of the
different trophic levels [104]. For instance, a top predator in a food web module including a basal species
and an intermediate consumer, will persist if [105]:
h1 .
3
X
ek
k¼1
ck
;
ð4:1Þ
where h1 is the proportion of suitable patches in the
landscape for the basal species, and ek and ck are the
extinction and colonization rates of the basal species
(k ¼ 1), the intermediate consumer (k ¼ 2) and the
top predator (k ¼ 3). If the three species are predators
with nested diets (as is likely the case in modules of the
food web of the above simulations), populations of
basal species with higher genetic variation for different
traits could be able to establish in a wider number of
patches, thus increasing h1 and the chances that the
top predator persists in the metacommunity. Furthermore, with increasing genetic variation, any species in
the food web module could increase its chances to
colonize new patches because they would hold the
potential to establish food web links with a higher
number of other prey species (generalization) outside
the module. Low genetic variation, on the other
hand, would increase the chances of local extinction
by a lack of generality (e.g. lack of including individuals with genotypes that allow feeding on alternative
prey outside the module). In general, populations
holding higher amounts of genetic variation in traits
that are central to food web interactions will be more
able to persist in metacommunities by the higher
chance of holding genotypes which will successfully
establish new food web links upon arrival to novel
patches. Additionally, with increasing genetic variation, an increase of the probability for arriving
species to establish more links should feed back to
the entire network, increasing the chances of the interconnected species to persist locally (i.e. increased
robustness). This should be more relevant when metacommunity dynamics is driven by mass effects [106],
and relatively large numbers of individuals arrive to
novel patches, thus allowing the environment (i.e.
the established food web) to filter the arriving genotypes. Even if these genotypes sink in the arrival
patch (i.e. do not reproduce), they could still establish
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Genetic variation and food webs
temporary links that could momentarily help the entire
network.
5. FUTURE DIRECTIONS
My preliminary results suggest that reasonable amounts
of variation in growth rates and phenology within populations can shape food web structure. However, several
of the assumptions made may not hold for naturally
assembled food webs and some of them may even
have important consequences for the results obtained.
For instance, phenotypic variation in growth rates and
phenology is not only genetic, but has a strong environmental component as well (e.g. food availability and/or
quality—[49,60,71]). However, as environmental
effects on genetic expression can also be heritable (e.g.
[97,98,107]), and as phenotypic variation may have
such profound effects on food web structure and
dynamics, investigating the effect of phenotypic and
individual variation in food webs seems promising.
Actually, the biotic environment can have strong effects
on phenotypic plasticity, particularly in predator– prey
interactions [108,109].
Furthermore, growth rates and phenologies are
probably more variable among species than has been
simulated here, as some of the species in the food
web are not univoltine but instead take more than
1 year to mature. For instance, the parameters x0
and b in equation (2.4), which were assumed to be
the same for all predator species, likely change substantially from species to species. Information on real
growth rates and phenologies, and how they vary
within species, is necessary to understand better the
effect of genetic variation in food webs.
As some of the above assumptions could be unrealistic, to test for the generality of these findings I
simulated two different amounts of genetic variation
in three other species/groups in the food web above
(the largest, Agelenopsis; the second smallest, Erigoninae
and another intermediate predator, the centipede
Lithobius) and contrasted them with the other species
in the food web, assuming, as with Xysticus, no genetic
variation in the other species. The results were qualitatively consistent with the above simulations (not
shown). However, understandably, the results were
sensitive to the threshold ratio (R) at which the same
qualitative effects as with Xysticus were found. Interestingly, the room for vulnerability (or its variability) did
not covary with genetic variation in any of the species.
Further work with other food webs is needed to test for
the generality of this last finding.
Recent food web models have been successful in
predicting the stability and the robustness of food
webs [20,28 – 32,36,38,39,72,110] and some even
include body size as a central trait (e.g. [46,89,93]).
Most recently, two models have been successful in
using foraging theory and individual behaviour subject
to metabolic constraints to predict food web complexity [89,93]. One of these papers [93] calls for the need
to add new traits to food web models. Explicitly
including growth rates, phenology and the other
traits mentioned above, as well as individual variation
around them, could improve the predictive capabilities
of food web models.
Phil. Trans. R. Soc. B (2011)
J. Moya-Laraño
1433
The assumption made here that body size and
abundance are the only constraints to determine
‘who eats whom’ in the food web is probably too simplistic. Although it served as a first approximation to
uncover how the amount of genetic variation could
affect the potential generality and vulnerability of an
intermediate predator embedded in a poorly solved
food web, more realistic approximations are needed.
For instance, allometric metabolic constraints [111]
and other body allometric constraints such as energy
content of potential prey, predator attack rate and
handling time, will largely determine the efficiency of
individuals depending on their size [93]. Faster growing individuals could either be metabolically less
efficient, expending more energy per unit of growth,
particularly if growth rate is correlated with boldness
and aggression, or could attain faster growth rates by
increased assimilation efficiency (i.e. the proportion
of ingested mass that is successfully converted into
own mass). Defences others than size and the spatial
distribution of the different species, will also affect
‘who eats whom’. Therefore, all these parameters
should be considered in future studies addressing the
question of how genetic variation may affect food
web structure and dynamics.
Future analytical and simulation models should consider how phenotypic and genetic variation and diversity
(e.g. multivariate phenotypes including a few traits) in
one or in several of the species embedded in food webs
may affect the structure, dynamics, robustness and stability of the network. These models should include
metabolic constraints, demographic parameters and
basal resources. Furthermore, models should be developed to test some of the ideas outlined above, such as
(i) the effect of increased genetic variation on trophic
level omnivory and the subsequent dampening of
trophic cascades, (ii) how genetic variation may affect
metacommunity dynamics, and (iii) what is the role of
indirect genetic effects in food web dynamics. Spatially
explicit individual (or agent)-based models [112] are a
promising tool to tackle this complexity. However, an
important previous and non-trivial task will be to
gather the necessary data for model parametrization.
Albeit tedious, the role of genetic variation in natural
(or artificial) food webs can also be nicely addressed
experimentally. Focusing on a target species (e.g. an
intermediate predator), one can use quantitative genetic
designs (e.g. half-sib designs—[107]) to create replicated experimental food webs (e.g. in mesocosms) or
food web modules that include different amounts of
genetic variation for some of the traits described above.
Ideally, the heritability of these traits should be
measured prior to (or during) experimentation, for
which the same quantitative genetic design as above
(and even individuals of the same families) could be
used. One should then test whether food web structure
changes and how this determines its dynamics, not
only at the level of direct interactions on the target
species, but also at the level of indirect interactions, as
it is very likely that the effect propagates through the
web (e.g. increased genetic variation dampening trophic
cascades). Solving food webs quantitatively is not an
easy task [113–115]. However, as modern molecular
techniques [116] are being developed, this endeavour
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1434
J. Moya-Laraño
Genetic variation and food webs
is becoming increasingly more feasible. Finally, and
ideally, variations of the above experiments, complemented with computer simulations, could also be used
to assess the robustness and stability of experimental
webs holding different amounts of genetic variation
within their interacting species.
6. CONCLUSION
Genetic variation in growth rates and phenology
within intermediate predators embedded in food
webs can potentially affect crucial parameters that
are key to ensuring the robustness and stability of the
food web, namely connectance, the distribution of
interaction strengths, omnivory and looping. Future
studies should incorporate a larger number of traits
(e.g. multivariate phenotypes), and use more realistic
food web data in individual-based models that seek
to investigate the dynamics of different food webs.
Analytical models and experimental tests are also
greatly needed.
Importantly, if a link between genetic variation and
food web robustness and stability is established, this
would involve a novel way to link genetic variation
and diversity to the maintenance of species in communities (see [7,52] for reviews). Hence, maintaining
functional genetic diversity within populations may
have important consequences not only for the maintenance of the target species, but also for the
maintenance of the entire tangled web in which this
species is embedded. This has relevant implications
in conservation biology, as it would add an important
new reason to preserve genetic diversity within species.
I thank A. Rossberg, J. Bascompte, R. Vaclav, D. Vinković and
two anonymous reviewers for helpful comments on previous
drafts of the manuscript. I discussed some of the ideas in
this article with M. Montserrat and J. M. Montoya. This
work has been partially funded by grant FEDER 020/2008
from the Spanish Ministry for the Environment and by
NSF grant DEB-9815842 to D. H. Wise.
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REFERENCES
1 Ferrari, J., Scarborough, C. L. & Godfray, H. C. J. 2007
Genetic variation in the effect of a facultative symbiont
on host-plant use by pea aphids. Oecologia 153, 323 –
329. (doi:10.1007/s00442-007-0730-2)
2 Tétard-Jones, C., Kertesz, M. A., Gallois, P. & Preziosi,
R. F. 2007 Genotype-by-genotype interactions modified
by a third species in a plant–insect system. Am. Nat.
170, 492– 499. (doi:10.1086/520115)
3 Bailey, J. K., Wooley, S. C., Lindroth, R. L. & Whitham,
T. G. 2006 Importance of species interactions to community heritability: a genetic basis to trophic-level
interactions. Ecol. Lett. 9, 78–85. (doi:10./j.14610248.2005.00844.x)
4 Crutsinger, G. M., Cadotte, M. W. & Sanders, N. J.
2009 Plant genetics shapes inquiline community structure across spatial scales. Ecol. Lett. 12, 285 –292.
(doi:10.1111/j.1461-0248.2009.01288.x)
5 Johnson, M. T. J. 2008 Bottom-up effects of plant
genotype on aphids, ants, and predators. Ecology 89,
145– 154. (doi:10.1890/07-0395.1)
6 Haloin, J. R. & Strauss, S. Y. 2008 Interplay between
ecological communities and evolution review of feedbacks from microevolutionary to macroevolutionary
Phil. Trans. R. Soc. B (2011)
18
19
20
21
22
23
scales. Ann. NY Acad. Sci. 1133, 87–125. (doi:10.
1196/annals.1438.003)
Hughes, A. R., Inouye, B. D., Johnson, M. T. J.,
Underwood, N. & Vellend, M. 2008 Ecological consequences of genetic diversity. Ecol. Lett. 11, 609–623.
(doi:10.1111/j.1461-0248.2008.01179.x)
Astles, P. A., Moore, A. J. & Preziosi, R. F. 2005 Genetic variation in response to an indirect ecological
effect. Proc. R. Soc. B 272, 2577–2581. (doi:10.1098/
rspb.2005.3174)
Yoshida, T., Ellner, S. P., Jones, L. E., Bohannan,
B. J. M., Lenski, R. E. & Hairston, N. G. 2007 Cryptic
population dynamics: rapid evolution masks trophic
interactions. PLoS Biol. 5, 1868–1879. (doi:10.1371/
journal.pbio.0050235)
Yoshida, T., Jones, L. E., Ellner, S. P., Fussmann, G. F. &
Hairston, N. G. 2003 Rapid evolution drives ecological dynamics in a predator–prey system. Nature 424,
303–306. (doi:10.1038/nature01767)
Langerhans, R. B., Layman, C. A., Langerhans, A. K. &
Dewitt, T. J. 2003 Habitat-associated morphological
divergence in two Neotropical fish species. Biol. J. Linn.
Soc. 80, 689–698. (doi:10.1111/j.1095-8312.2003.
00266.x)
Quevedo, M., Svanbäck, R. & Eklöv, P. 2009 Intrapopulation niche partitioning in a generalist predator
limits food web connectivity. Ecology 90, 2263– 2274.
(doi:10.1890/07-1580.1)
Svanbäck, R., Eklöv, P., Fransson, R. & Holmgren, K.
2008 Intraspecific competition drives multiple species
resource polymorphism in fish communities. Oikos 117,
114–124. (doi:10.1111/j.2007.0030-1299.16267.x)
Svanbäck, R. & Eklöv, P. 2006 Genetic variation and phenotypic plasticity: causes of morphological and dietary
variation in Eurasian perch. Evol. Ecol. Res. 8, 37–49.
Harmon, L. J., Matthews, B., Des Roches, S., Chase,
J. M., Shurin, J. B. & Schluter, D. 2009 Evolutionary
diversification in stickleback affects ecosystem functioning. Nature 458, 1167–1170. (doi:10.1038/nature07974)
Post, D. M., Palkovacs, E. P., Schielke, E. G. &
Dodson, S. I. 2008 Intraspecific variation in a predator
affects community structure and cascading trophic
interactions. Ecology 89, 2019– 2032. (doi:10.1890/071216.1)
Bascompte, J., Melian, C. J. & Sala, E. 2005 Interaction
strength combinations and the overfishing of a marine
food web. Proc. Natl Acad. Sci. USA 102, 5443–5447.
(doi:10.1073/pnas.0501562102)
Closs, G. P., Balcombe, S. R. & Shirley, M. J. 1999
Generalist predators, interaction strength and foodweb stability. Adv. Ecol. Res. 28, 93–126. (doi:10.
1016/S0065-2504(08)60030-6)
Ings, T. C. et al. 2009 Ecological networks: beyond food
webs. J. Anim. Ecol. 78, 253–269. (doi:10.1111/j.13652656.2008.01460.x)
Montoya, J. M., Pimm, S. L. & Sole, R. V. 2006
Ecological networks and their fragility. Nature 442,
259 –264. (doi:10.1038/nature04927)
Woodward, G., Ebenman, B., Emmerson, M.,
Montoya, J. M., Olesen, J. M., Valido, A. & Warren,
P. H. 2005 Body size in ecological networks. Trends
Ecol. Evol. 20, 402 –409. (doi:10.1016/j.tree.2005.04.
005)
Polis, G. A. & Holt, R. D. 1992 Intraguild predation:
the dynamics of complex trophic interactions. Trends
Ecol.
Evol.
7,
151 –154.
(doi:10.1016/01695347(92)90208-S)
Polis, G. A., Myers, C. A. & Holt, R. D. 1989 The ecology and evolution of intraguild predation: potential
competitors that eat each other. Ann. Rev. Ecol. Syst.
Downloaded from http://rstb.royalsocietypublishing.org/ on May 11, 2017
Genetic variation and food webs
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
20, 297 –330. (doi:10.1146/annurev.es.20.110189.
001501)
Wise, D. H. 2006 Cannibalism, food limitation, intraspecific competition and the regulation of spider
populations. Ann. Rev. Entomol. 51, 441 –465. (doi:10.
1146/annurev.ento.51.110104.150947)
Polis, G. A. 1991 Complex trophic interactions in
deserts: an empirical critique of food-web theory. Am.
Nat. 138, 123 –155. (doi:10.1086/285208)
Berlow, E. L. et al. 2004 Interaction strengths in food
webs: issues and opportunities. J. Anim. Ecol. 73,
585 –598. (doi:10.1111/j.0021-8790.2004.00833.x)
Gardner, M. R. & Ashby, W. R. 1970 Connectance of
large dynamic (cybernetic) systems: critical values for
stability. Nature 228, 784. (doi:10.1038/228784a0)
McCann, K. & Hastings, A. 1997 Re-evaluating
the omnivory –stability relationship in food webs. Proc.
R. Soc. Lond. B 264, 1249–1254. (doi:10.1098/rspb.
1997.0172)
McCann, K., Hastings, A. & Huxel, G. R. 1998 Weak
trophic interactions and the balance of nature. Nature
395, 794 –798. (doi:10.1038/27427)
Sole, R. V. & Montoya, J. M. 2001 Complexity and
fragility in ecological networks. Proc. R. Soc. Lond. B
268, 2039 –2045. (doi:10.1098/rspb.2001.1767)
Dunne, J. A., Williams, R. J. & Martinez, N. D. 2002
Network structure and biodiversity loss in food webs:
robustness increases with connectance. Ecol. Lett. 5,
558 –567. (doi:10.1046/j.1461-0248.2002.00354.x)
Vandermeer, J. 2006 Omnivory and the stability of
food webs. J. Theor. Biol. 238, 497 –504. (doi:10.
1016/j.jtbi.2005.06.006)
May, R. M. 1973 Stability and complexity in model ecosystems. Princeton, NJ: Princeton University Press.
Montoya, J. M. & Sole, R. V. 2002 Small world patterns
in food webs. J. Theor. Biol. 214, 405 –412. (doi:10.
1006/jtbi.2001.2460)
Tanabe, K. & Namba, T. 2005 Omnivory creates chaos
in simple food web models. Ecology 86, 3411–3414.
(doi:10.1890/05-0720)
Dunne, J. A. & Williams, R. J. 2009 Cascading extinctions
and community collapse in model food webs. Phil. Trans.
R. Soc. B 364, 1711–1723. (doi:10.1098/rstb.2008.0219)
Ingram, T., Harmon, L. J. & Shurin, J. B. 2009 Niche
evolution, trophic structure, and species turnover in
model food webs. Am. Nat. 174, 56–67. (doi:10.
1086/599301)
Neutel, A. M., Heesterbeek, J. A. P., van de Koppel, J.,
Hoenderboom, G., Vos, A., Kaldeway, C., Berendse,
F. & de Ruiter, P. C. 2007 Reconciling complexity
with stability in naturally assembling food webs.
Nature 449, 599–602. (doi:10.1038/nature06154)
O’Gorman, E. J. & Emmerson, M. C. 2009 Perturbations to trophic interactions and the stability
of complex food webs. Proc. Natl Acad. Sci. USA 106,
13 393 –13 398. (doi:10.1073/pnas.0903682106)
Jonsson, T., Karlsson, P. & Jonsson, A. 2006 Food web
structure affects the extinction risk of species in ecological communities. Ecol. Model. 199, 93–106. (doi:10.
1016/j.ecolmodel.2006.06.012)
Brose, U. et al. 2006 Consumer-resource bodysize relationships in natural food webs. Ecology 87,
2411 –2417. (doi:10.1890/0012-9658(2006)87[2411:
CBRINF]2.0.CO;2)
Gaston, K. J., Blackburn, T. M. & Lawton, J. H. 1997
Interspecific abundance range size relationships: an
appraisal of mechanisms. J. Anim. Ecol. 66, 579–601.
(doi:10.2307/5951)
Cohen, J. E., Jonsson, T. & Carpenter, S. R. 2003 Ecological community description using the food web,
Phil. Trans. R. Soc. B (2011)
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
J. Moya-Laraño
1435
species abundance, and body size. Proc. Natl Acad.
Sci. USA 100, 1781–1786. (doi:10.1073/pnas.
232715699)
Emmerson, M. C., Montoya, J. M. & Woodward, G.
2005 Body size, interaction strength, and food web
dynamics. In Dynamic food webs (eds P. C. De Ruiter,
V. Wolters & J. C. Moore), pp. 167 –178. San Diego,
CA: Academic Press.
Emmerson, M. C. & Raffaelli, D. 2004 Predator–prey
body size, interaction strength and the stability of a
real food web. J. Anim. Ecol. 73, 399 –409. (doi:10.
1111/j.0021-8790.2004.00818.x)
Brose, U., Williams, R. J. & Martinez, N. D. 2006 Allometric scaling enhances stability in complex food webs.
Ecol. Lett. 9, 1228–1236. (doi:10.1111/j.1461-0248.
2006.00978.x)
Arendt, J. D. 1997 Adaptive intrinsic growth rates: an
integration across taxa. Q. Rev. Biol. 72, 149 –177.
(doi:10.1086/419764)
Heath, D. D., Bernier, N. J., Heath, J. W. & Iwama,
G. K. 1993 Genetic, environmental, and interaction
effects on growth and stress-response of chinook
salmon (Oncorhynchus tshawytscha) fry. Can. J. Fish.
Aquat. Sci. 50, 435 –442. (doi:10.1139/f93-049)
Uhl, G., Schmitt, S., Schafer, M. A. & Blanckenhorn,
W. 2004 Food and sex-specific growth strategies in a
spider. Evol. Ecol. Res. 6, 523 –540.
Akimoto, S. I. 1998 Heterogeneous selective pressures
on egg-hatching time and the maintenance of its genetic
variance in a Tetraneura gall-forming aphid. Ecol. Entomol. 23, 229 –237. (doi:10.1046/j.1365-2311.1998.
00127.x)
Vellend, M. 2006 The consequences of genetic diversity
in competitive communities. Ecology 87, 304 –311.
(doi:10.1890/05-0173)
Vellend, M. & Geber, M. A. 2005 Connections between
species diversity and genetic diversity. Ecol. Lett. 8,
767–781. (doi:10.1111/j.1461-0248.2005.00775.x)
Berlow, E. L., Dunne, J. A., Martinez, N. D., Stark,
P. B., Williams, R. J. & Brose, U. 2009 Simple prediction of interaction strengths in complex food webs.
Proc. Natl Acad. Sci. USA 106, 187– 191. (doi:10.
1073/pnas.0806823106)
Ruesink, J. L. 1998 Variation in per capita interaction
strength: thresholds due to nonlinear dynamics and
nonequilibrium conditions. Proc. Natl Acad. Sci. USA
95, 6843–6847. (doi:10.1073/pnas.95.12.6843)
Moya-Laraño, J. & Wise, D. H. 2007 Direct and
indirect effects of ants on a forest-floor food web.
Ecology 88, 1454 –1465. (doi:10.1890/05-1474)
Foelix, R. F. 1996 Biology of spiders. Oxford, UK:
Oxford University Press.
Wise, D. H. 1993 Spiders in ecological webs. Cambridge,
UK: Cambridge University Press.
Lensing, J. R. & Wise, D. H. 2004 A test of the hypothesis that a pathway of intraguild predation limits
densities of a wolf spider. Ecol. Entomol. 29, 294– 299.
(doi:10.1111/j.0307-6946.2004.00601.x)
Wagner, J. D. & Wise, D. H. 1996 Cannibalism regulates densities of young wolf spiders: evidence from
field and laboratory experiments. Ecology 77, 639–
652. (doi:10.2307/2265637)
Mayntz, D. & Toft, S. 2001 Nutrient composition of
the prey’s diet affects growth and survivorship of a generalist predator. Oecologia 127, 207 –213. (doi:10.1007/
s004420000591)
Von Bertalanffy, L. 1960 Principles and theory of growth.
In Fundamental aspects of normal and malignant growth
(ed. W. W. Nowinski), pp. 137–259. Amsterdam, The
Netherlands: Elsevier.
Downloaded from http://rstb.royalsocietypublishing.org/ on May 11, 2017
1436
J. Moya-Laraño
Genetic variation and food webs
62 Zotin, A. A. 2006 Equations describing changes in
weight and mass-specific rate of oxygen consumption
in animals during postembryonic development. Biol.
Bull. 33, 323 –331. (doi:10.1134/S1062359006040029)
63 Hódar, J. A. 1996 The use of regression equations for
estimation of arthropod biomass in ecological studies.
Acta Oecol. 17, 421 –433.
64 Biro, P. A., Abrahams, M. V., Post, J. R. & Parkinson,
E. A. 2006 Behavioural trade-offs between growth and
mortality explain evolution of submaximal growth
rates. J. Anim. Ecol. 75, 1165–1171. (doi:10.1111/j.
1365-2656.2006.01137.x)
65 Mangel, M. & Stamps, J. 2001 Trade-offs between
growth and mortality and the maintenance of individual
variation in growth. Evol. Ecol. Res. 3, 583–593.
66 Kaston, B. J. 1981 Spiders of Connecticut. Hartford,
CT: Department of Environmental Protection,
State Geological and Natural History Survey of
Connecticut.
67 Morse, D. H. 1983 Foraging patterns and time budgets
of the crab spiders Xysticus emertoni Keyserling and
Misumena vatia (Clerck) (Araneae, Thomisidae) on
flowers. J. Arachnol. 11, 87–94.
68 Speakman, J. R. 2005 Body size, energy metabolism
and lifespan. J. Exp. Biol. 208, 1717–1730. (doi:10.
1242/jeb.01556)
69 Schluter, D. 2000 The ecology of adaptive radiation.
Oxford, UK. Oxford University Press.
70 Moya-Laraño, J. 2010 Can temperature and water availability contribute to the maintenance of latitudinal
diversity by increasing the rate of biotic interactions?
Open Ecol. J. 3, 1 –13. (doi:10.2174/187421300
1003010001)
71 Rabaneda-Bueno, R., Rodrı́guez-Gironés, M. A.,
Aguado-De-La-Paz, S., Fernández-Montraveta, C.,
De Mas, E., Wise, D. H. & Moya-Laraño, J. 2008
Sexual cannibalism: high incidence in a natural population with benefits to females. PLoS ONE 3, e3484.
(doi:10.1371/journal.pone.0003484)
72 Jonsson, T., Karlsson, P. & Jonsson, A. 2006 Food web
structure affects the extinction risk of species in ecological communities. Ecol. Modell. 199, 93–106. (doi:10.
1016/j.ecolmodel.2006.06.012)
73 Finke, D. L. & Denno, R. F. 2005 Predator diversity
and the functioning of ecosystems: the role of intraguild
predation in dampening trophic cascades. Ecol. Lett. 8,
1299–1306. (doi:10.1111/j.1461-0248.2005.00832.x)
74 Wyckmans, M., Van Wassenbergh, S., Adriaens, D.,
Van Damme, R. & Herrel, A. 2007 Size-related changes
in cranial morphology affect diet in the catfish
Clariallabes longicauda. Biol. J. Linn. Soc. 92, 323 –
334. (doi:10.1111/j.1095-8312.2007.00846.x)
75 Bauwens, D., Garland, T., Castilla, A. M. &
Vandamme, R. 1995 Evolution of sprint speed in
lacertid lizards—morphological, physiological, and
behavioral covariation. Evolution 49, 848 –863.
(doi:10.2307/2410408)
76 Rooney, N., McCann, K., Gellner, G. & Moore, J. C.
2006 Structural asymmetry and the stability of diverse
food webs. Nature 442, 265 –269. (doi:10.1038/
nature04887)
77 Fox, C. W. & Czesak, M. E. 2000 Evolutionary ecology
of progeny size in arthropods. Ann. Rev. Entomol. 45,
341 –369. (doi:10.1146/annurev.ento.45.1.341)
78 Marshall, S. D. & Gittleman, J. L. 1994 Clutch size in
spiders: is more better? Funct. Ecol. 8, 118 –124.
(doi:10.2307/2390120)
79 Biro, P. A. & Stamps, J. A. 2008 Are animal personality
traits linked to life-history productivity? Trends Ecol.
Evol. 23, 361 –368. (doi:10.1016/j.tree.2008.04.003)
Phil. Trans. R. Soc. B (2011)
80 Stamps, J. A. 2007 Growth-mortality tradeoffs and
‘personality traits’ in animals. Ecol. Lett. 10, 355 –363.
(doi:10.1111/j.1461-0248.2007.01034.x)
81 Sih, A., Bell, A. & Johnson, J. C. 2004 Behavioral syndromes: an ecological and evolutionary overview. Trends
Ecol. Evol. 19, 372–378. (doi:10.1016/j.tree.2004.04.009)
82 Sih, A., Bell, A. M., Johnson, J. C. & Ziemba, R. E.
2004 Behavioral syndromes: an integrative overview.
Q. Rev. Biol. 79, 241–277. (doi:10.1086/422893)
83 Kontiainen, P., Pietiainen, H., Huttunen, K., Karell, P.,
Kolunen, H. & Brommer, J. E. 2009 Aggressive Ural
owl mothers recruit more offspring. Behav. Ecol. 20,
789 –796. (doi:10.1093/beheco/arp062)
84 Neff, B. D. & Sherman, P. W. 2004 Behavioral syndromes versus darwinian algorithms. Trends Ecol. Evol.
19, 621–622. (doi:10.1016/j.tree.2004.09.017)
85 Sinn, D. L., Gosling, S. D. & Moltschaniwskyj, N. A.
2008 Development of shy/bold behaviour in squid:
context-specific phenotypes associated with developmental plasticity. Anim. Behav. 75, 433 –442. (doi:10.
1016/j.anbehav.2007.05.008)
86 Sol, D., Timmermans, S. & Lefebvre, L. 2002 Behavioural flexibility and invasion success in birds. Anim.
Behav. 63, 495 –502. (doi:10.1006/anbe.2001.1953)
87 West-Eberhard, M. J. 2003 Developmental plasticity and
evolution. Oxford, UK: Oxford University Press.
88 Bell, A. M. & Sih, A. 2007 Exposure to predation
generates personality in threespined sticklebacks
(Gasterosteus aculeatus). Ecol. Lett. 10, 828–834.
(doi:10.1111/j.1461-0248.2007.01081.x)
89 Beckerman, A. P., Petchey, O. L. & Warren, P. H. 2006
Foraging biology predicts food web complexity. Proc.
Natl Acad. Sci. USA 103, 13 745 –13 749. (doi:10.
1073/pnas.0603039103)
90 Kondoh, M. 2003 Foraging adaptation and the relationship between food-web complexity and stability. Science
299, 1388–1391. (doi:10.1126/science.1079154)
91 Kondoh, M. 2007 Anti-predator defence and the
complexity–stability relationship of food webs. Proc. R.
Soc. B 274, 1617–1624. (doi:10.1098/rspb.2007.0335)
92 Loeuille, N. 2010 Consequences of adaptive foraging in
diverse communities. Funct. Ecol. 24, 18–27. (doi:10.
1111/j.1365-2435.2009.01617.x)
93 Petchey, O. L., Beckerman, A. P., Riede, J. O. &
Warren, P. H. 2008 Size, foraging, and food web structure. Proc. Natl Acad. Sci. USA 105, 4191–4196.
(doi:10.1073/pnas.0710672105)
94 Abrams, P. A. 2010 Implications of flexible foraging for
interspecific interactions: lessons from simple models.
Funct. Ecol. 24, 7– 17. (doi:10.1111/j.1365-2435.2009.
01621.x)
95 Schmitz, O. J., Krivan, V. & Ovadia, O. 2004 Trophic
cascades: the primacy of trait-mediated indirect
interactions. Ecol. Lett. 7, 153–163. (doi:10.1111/j.
1461-0248.2003.00560.x)
96 Werner, E. E. & Peacor, S. D. 2003 A review of
trait-mediated indirect interactions in ecological
communities. Ecology 84, 1083– 1100. (doi:10.1890/
0012-9658(2003)084[1083:AROTII]2.0.CO;2)
97 Dingemanse, N. J., Kazem, A. J. N., Reale, D. &
Wright, J. 2010 Behavioural reaction norms: animal
personality meets individual plasticity. Trends Ecol.
Evol. 25, 81–89. (doi:10.1016/j.tree.2009.07.013)
98 Dingemanse, N. J., Van der Plas, F., Wright, J., Reale,
D., Schrama, M., Roff, D. A., Van der Zee, E. &
Barber, I. 2009 Individual experience and evolutionary
history of predation affect expression of heritable
variation in fish personality and morphology.
Proc. R. Soc. B 276, 1285–1293. (doi:10.1098/rspb.
2008.1555)
Downloaded from http://rstb.royalsocietypublishing.org/ on May 11, 2017
Genetic variation and food webs
99 Shuster, S. M., Lonsdorf, E. V., Wimp, G. M., Bailey,
J. K. & Whitham, T. G. 2006 Community heritability
measures the evolutionary consequences of indirect
genetic effects on community structure. Evolution 60,
991 –1003.
100 Wolf, J. B., Brodie, E. D., Cheverud, J. M., Moore, A. J. &
Wade, M. J. 1998 Evolutionary consequences of indirect
genetic effects. Trends Ecol. Evol. 13, 64–69. (doi:10.
1016/S0169-5347(97)01233-0)
101 Preisser, E. L. & Bolnick, D. I. 2008 The many faces of
fear: comparing the pathways and impacts of nonconsumptive predator effects on prey populations. PLoS
ONE 3, e2465. (doi:10.1371/journal.pone.0002465)
102 Wilson, D. S. 1992 Complex interactions in metacommunities, with implications for biodiversity and higher
levels of selection. Ecology 73, 1984–2000. (doi:10.
2307/1941449)
103 Holyoak, M., Leibold, M. A. & Holt, R. D. (eds) 2005
Metacommunities: spatial dynamics and ecological communities. Chicago, IL: Chicago University Press.
104 Amarasekare, P. 2008 Spatial dynamics of foodwebs.
Ann. Rev. Ecol. Evol. Syst. 39, 479 –500. (doi:10.1146/
annurev.ecolsys.39.110707.173434)
105 Holt, R. D. & Hoopes, M. F. 2005 Food webs in a
metacommunity context: modules and beyond. In
Metacommunities: spatial dynamics and ecological communities (eds M. Holyoak, M. A. Leibold & R. D. Holt),
pp. 68–93. Chicago, IL: Chicago University Press.
106 Shmida, A. & Wilson, M. V. 1985 Biological determinants of species-diversity. J. Biogeogr. 12, 1 –20.
(doi:10.2307/2845026)
107 Roff, D. A. 1997 Evolutionary quantitative genetics.
New York, NY: Chapman and Hall.
Phil. Trans. R. Soc. B (2011)
J. Moya-Laraño
1437
108 Agrawal, A. A. 2001 Ecology—phenotypic plasticity in
the interactions and evolution of species. Science 294,
321 –326. (doi:10.1126/science.1060701)
109 Relyea, R. A. 2001 Morphological and behavioral
plasticity of larval anurans in response to different
predators. Ecology 82, 523 –540. (doi:10.1890/00129658(2001)082[0523:MABPOL]2.0.CO;2)
110 Williams, R. J. & Martinez, N. D. 2000 Simple rules
yield complex food webs. Nature 404, 180– 183.
(doi:10.1038/35006555)
111 Peters, R. H. 1983 The ecological implications of body size.
Cambridge, UK: Cambridge University Press.
112 DeAngelis, D. L. & Mooij, W. M. 2005 Individual-based
modeling of ecological and evolutionary processes. Ann.
Rev. Ecol. Evol. Syst. 36, 147–168. (doi:10.1146/
annurev.ecolsys.36.102003.152644)
113 Banasek-Richter, C., Cattin, M. F. & Bersier, L. F.
2004 Sampling effects and the robustness of
quantitative and qualitative food-web descriptors.
J. Theor. Biol. 226, 23–32. (doi:10.1016/S00225193(03)00305-9)
114 Bersier, L. F., Banasek-Richter, C. & Cattin, M. F.
2002 Quantitative descriptors of food-web matrices.
Ecology 83, 2394 –2407. (doi:10.1890/0012-9658
(2002)083[2394:QDOFWM]2.0.CO;2)
115 Woodward, G., Speirs, D. C. & Hildrew, A. G. 2005
Quantification and resolution of a complex, sizestructured food web. Adv. Ecol. Res. 36, 85–135.
(doi:10.1016/S0065-2504(05)36002-8)
116 Sheppard, S. K. & Harwood, J. D. 2005 Advances
in molecular ecology: tracking trophic links through
predator–prey food-webs. Funct. Ecol. 19, 751– 762.
(doi:10.1111/j.1365-2435.2005.01041.x)