Download interspecific competition alters nonlinear selection on offspring size

O R I G I NA L A RT I C L E
doi:10.1111/j.1558-5646.2012.01749.x
INTERSPECIFIC COMPETITION ALTERS
NONLINEAR SELECTION ON OFFSPRING SIZE
IN THE FIELD
Dustin J. Marshall1,2,3 and Keyne Monro1,2
1
School of Biological Sciences, The University of Queensland, Brisbane 4072, Australia
2
School of Biological Sciences, Monash University, Clayton 3800, Australia
3
E-mail: [email protected]
Received August 18, 2011
Accepted July 16, 2012
Data Archived: Dryad doi:10.5061/dryad.d6v18
Offspring size is one of the most important life-history traits with consequences for both the ecology and evolution of most
organisms. Surprisingly, formal estimates of selection on offspring size are rare, and the degree to which selection (particularly
nonlinear selection) varies among environments remains poorly explored. We estimate linear and nonlinear selection on offspring
size, module size, and senescence rate for a sessile marine invertebrate in the field under three different intensities of interspecific
competition. The intensity of competition strongly modified the strength and form of selection acting on offspring size. We found
evidence for differences in nonlinear selection across the three environments. Our results suggest that the fitness returns of a
given offspring size depend simultaneously on their environmental context, and on the context of other offspring traits. Offspring
size effects can be more pervasive with regards to their influence on the fitness returns of other traits than previously recognized,
and we suggest that the evolution of offspring size cannot be understood in isolation from other traits. Overall, variability in the
form and strength of selection on offspring size in nature may reduce the efficacy of selection on offspring size and maintain
variation in this trait.
KEY WORDS:
Egg size, life-history evolution, maternal effect.
Offspring size is a life-history trait of fundamental importance that
affects the ecology and evolution of many organisms. Estimating
the relationship between offspring size and fitness has been a
major goal of evolutionary ecology, and over the past 70 years,
we have accumulated a wealth of estimates of the offspring size–
fitness relationship (Lack 1947; Bagenal 1969; Stanton 1984;
Williams 1994; Bernardo 1996; Marshall and Keough 2008a).
Despite this intense research effort, our understanding of the selective forces acting on offspring size remains surprisingly incomplete. For example, while early theory predicted a single, optimal
offspring size for any one species (Smith and Fretwell 1974), in
nature, offspring size typically varies among populations, mothers, broods, and offspring (Fox and Czesak 2000; Marshall and
Keough 2008b; Krist 2011). The maintenance of offspring size
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variation at all levels of biological organization is thought to be
due to environmentally driven variation in selection on offspring
size (Einum and Fleming 2002), but this variation in selection
(formally defined) has received relatively little attention.
The effect of offspring size on offspring performance appears
to be highly context dependent. A range of abiotic factors (e.g.,
temperature) and biotic factors (e.g., competition, predation) can
alter the fitness benefits of a particular offspring size (Bernardo
1996; Marshall and Keough 2008a). Yet, despite the increasing
number of studies that examine the influence of the local environment on offspring size effects, there are very few formal estimates
of selection on offspring size (Einum and Fleming 2000; Dibattista
et al. 2007; Johnson et al. 2010), and even fewer that estimate how
selection changes across environments. Meanwhile, much of the
C 2012 The Society for the Study of Evolution.
2012 The Author(s). Evolution !
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NONLINEAR SELECTION ON OFFSPRING SIZE
observed variation in offspring size is assumed to be adaptive.
For example, population-level variation in offspring size is often described as an adaptive response to large-scale environmental variation (Johnston and Leggett 2002). Variation in offspring
size among clutches is viewed as an adaptive response driven by
mothers encountering different environments (Fox et al. 1997;
Allen et al. 2008), and variation in offspring size within clutches
is assumed to be an adaptive strategy to cope with environmental undertainty (i.e., bet-hedging Einum and Fleming 2002;
Crean and Marshall 2009). For patterns in offspring size variation at any scale to reflect adaptation, variation in selection must
exist.
Importantly, environmental variation cannot only cause
changes in directional (linear) selection in offspring size, it can
also cause changes in nonlinear selection. The impact of environmental variation on nonlinear selection on offspring size remains
completely unknown. Indeed, while the fitness returns of different offspring sizes are often modeled theoretically as a nonlinear
function (Smith and Fretwell 1974), formal estimates of nonlinear selection are rare (Einum and Fleming 2000; Dibattista et al.
2007). Nonlinear selection on offspring size is important because
it includes quadratic selection and correlational selection, both of
which are likely to be of key importance for an understanding of
offspring size evolution.
Quadratic selection occurs when either extreme trait values are favored (known as disruptive selection) or intermediate
trait values are favored (known as stabilizing selection). In his
influential review, Bernardo (1996) predicted that selection on
offspring size was likely to be stabilizing, but lamented that tests
were lacking. Recent theoretical considerations of maternal bethedging strategies have re-emphasized the importance of estimating quadratic selection on offspring size (Crean and Marshall
2009). Under some circumstances, diversified bet-hedging with
regards to offspring size is predicted to be favored, but only when
offspring size is subject to stabilizing selection, otherwise, conservative bet-hedging strategies should be favored (Einum and
Fleming 2004; Marshall et al. 2008). Those studies that have
looked for evidence of stabilizing selection on offspring size have
found it (Einum and Fleming 2000; Dibattista et al. 2007), but
few studies have examined how environmental variation affects
quadratic selection on offspring size.
Correlational selection occurs when selection acts on the covariance between traits in combination rather than on individual
traits alone (Blows and Brooks 2003). Estimating correlational selection on offspring size and other traits is particularly important
because the benefits of offspring size are unlikely to be independent of the context of other traits. In other words, the fitness
consequences of increased offspring size are likely to depend on
other traits that the offspring expresses. Ignoring the potential
for correlational selection on combinations of offspring size and
other offspring traits could lead to erroneous conclusions about
the benefits of increased offspring size, either overestimating or
underestimating these benefits (depending on the distribution of
values of other traits in the population).
Overall, there is a clear need to examine whether selection
(both linear and nonlinear) on offspring size varies among environments. Here, we address this important knowledge gap by
applying a well-known multiple regression approach to estimate
both directional and nonlinear selection on offspring size, and two
other traits: a morphological trait (zooid size) and a life-history
trait (rate of colony senescence) in a colonial marine invertebrate (see Materials and Methods for a description of these traits).
These two traits were selected because they are important determinants of performance in colonial organisms. In marine benthic
communities, interspecific competition can be intense—space is
a limiting resource and overgrowth by other species can result in
mortality and reduced fecundity (Hart and Marshall 2009). We
therefore manipulated the level of interspecific competition that
our focal colonies experienced with three treatments: the absence
of interspecific competition, the presence of moderate interspecific competition, and in the presence of intense interspecific
competition.
Materials and Methods
STUDY SPECIES AND SITE
Watersipora subtorquata is an encrusting bryozoan and an abundant member of the fouling community on man-made structures
along the coast of Australia. Watersipora filters planktonic food
from the water column, and as a colony, it grows outwards with
the leading edge of zooids (the feeding units that make up the
colony) growing, with a middle band of reproductive zooids, and
an inner region of senescing and senesced zooids. It broods its
larvae for approximately 1 week, whereupon the nonfeeding larvae are released and spend only minutes to hours in the plankton before settling and metamorphosing (Marshall and Keough
2004). Previous studies on this species have shown that offspring
size can strongly affect postmetamorphic performance, but the effects of offspring size are highly variable over large spatial scales
(Marshall and Keough 2004, 2008b, 2009). Our experiment was
done at Redcliffe Marina, Brisbane, Australia in Austral Spring–
Summer, 2008.
Reproductively mature Watersipora colonies were collected
from Redcliffe, transported to the laboratory, and induced to
spawn after a being kept in constant darkness in lightproof aquaria
for 72 h. We collected larvae from at least 45 colonies and
took care to collect larvae of a range of sizes from each colony
though we did not track which larva came from which colony. We
collected the spawned larvae, settled them on pieces of acetate
sheeting in a drop of seawater, and then allowed the settlers to
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metamorphose for 48 h in a constant temperature cabinet at 23◦ C.
The metamorphosed settlers, known as ancestrulae, were then
measured using standard techniques under a dissecting microscope (Marshall and Keough 2008b), and the acetate sheets (with
the settlers attached) were then glued to PVC settlement plates
(100 × 100 × 8 mm). The range of offspring sizes used in this
study reflected the natural variation in offspring size displayed by
this species in the field (Marshall and Keough 2008b).
TRAIT AND FITNESS MEASUREMENTS
We selected two traits along with offspring size that are
thought to be important determinants of performance in modular organisms—senescence rate and module size. We chose these
traits in particular as we hypothesized that each could influence
selection on offspring size.
Offspring size
We used size of settlers approximately 12 h after settlement as our
measure of offspring size. Settler size is highly correlated with
larval size in Watersipora, and the correlation is independent of
the length of the larval period (Marshall and Keough 2004), such
that offspring size can be reliably inferred from measurements of
newly settled juveniles.
Zooid size
Zooid size is an important morphological trait in bryozoans as it
can affect a range of life-history parameters in modular organisms
(McKinney and Jackson 1991). For example, larger modules may
acquire more resources but take more resources to produce in the
first place. After 4 weeks in the field, we returned the colonies to
the laboratory and measured the size of zooids within the colony
by photographing each colony under a dissecting microscope. We
measured zooid size at one time only because collecting these
data is extremely time consuming (every zooid was measured to
generate a mean zooid size for the colony), and we wanted to
minimize the time that the colonies spent in the laboratory. Pilot
studies suggested that zooid size remains relatively constant over
time in Watersipora.
Senescence
Senescence, where portions of the colony become inactive or undergo apoptosis, is common in many bryozoans, but the adaptive
significance of senescence in this group generally remains unclear
(McKinney and Jackson 1991; Orive 1995). In Watersipora, as
the colony ages, the central (i.e., oldest) portions of the colony
senesce, and the senescing region slowly spreads from the center
(Hart and Keough 2009). The rate at which senescence occurs
varies among colonies and may be related to how quickly resources are transferred from senescing zooids to reproduction,
given that the zooids immediately adjacent to the senescing re330
EVOLUTION FEBRUARY 2013
gion tend to be those that are reproductive at that time (Hart
and Keough 2009). Thus in Watersipora, senescence has been
described as an adaptive strategy by which the least productive
zooids in the colony (i.e., those in the center of the colony that
access water that has been depleted of planktonic food) senesce
and free up resources that are used for reproduction elsewhere
(Hart and Keough 2009). Zooids that have senesced are easily
distinguished from zooids that remain alive, with senesced regions clearly visible as black regions on the colony. We calculated
the proportion of the colony that had senesced at the end of the
experimental period as our measure of senescence.
Fitness
We estimated reproduction weekly and defined our measure of
individual fitness as the total number of offspring produced by
each colony over the entire experimental period. We recognize
that this estimate of fitness probably does not encompass the
entire reproductive output of the colonies but we are confident
that this estimate is a good and most practical predictor of true
fitness. Importantly, estimates of fecundity across the study period
were correlated positively (correlation coefficients among weeks
ranged between 0.484 and 0.74), suggesting that there was no
trade-off between early and late reproduction. Zooids that are
brooding larvae are also clearly distinguishable in Watersipora
and reproduction can be estimated based on digital photographs
of the colony. Watersipora colonies brood their offspring for less
than a week in Brisbane, and brooded larvae that are identifiable
in 1 week have been released from the colony by the subsequent
week.
COMPETITION TREATMENTS AND FIELD
DEPLOYMENT
New settlers were numbered and randomly assigned to one of
three treatments: competition-free, intermediate competition, and
high competition. Settlers assigned to the competition-free environment were attached to blank settlement plates that were
cleaned weekly throughout the experiment to remove any organisms that had naturally colonized the plates in the field. Settlers
assigned to the intermediate competition environment were attached to blank settlement plates that were allowed to accumulate
settlement from other organisms (including tubeworms, ascidians,
bryozoans, and sponges) throughout the experimental period. Settlers assigned to the high competition environment were attached
to settlement plates on which a fouling community had been allowed to develop by placing the plates into the field for 3 weeks
prior to the initiation of the experiment. Previous studies show that
these treatment conditions reflect the range of natural conditions
and are known to affect performance in this species (Marshall and
Keough 2009). In this study, our treatments resulted in very different growing conditions for our focal colonies: individuals in the
NONLINEAR SELECTION ON OFFSPRING SIZE
competition-free environment were the largest and most fecund at
the end of the experiment, and individuals in the high competition
environment were the smallest and the least fecund (see Results
and Figures S1 and S2). Note that because the treatments were
applied at the scale of settlement plates, we had 60 independent
replicates of each treatment.
Once the settlers were attached onto the settlement plates,
we assigned settlement plates haphazardly to PVC backing plates
(400 × 400 × 12 mm), taking care to ensure that there was an
equal number of plates from each treatment per backing plate.
We then deployed the backing plates into the field by suspending
the settlement plates facedown 1 m below the water surface (for
a detailed description of the field deployment, see Marshall and
Keough 2009). Each week, plates within each backing panel were
rearranged haphazardly so as to preclude artefactual covariance
between the values of our focal traits, local environmental conditions, and fitness (Rausher 1992). As our focal traits varied at the
scale of plate, this was the appropriate scale of rearrangement.
We deployed a total of 180 settlers (60 in each treatment)
across 30 backing plates. We monitored the survival, growth,
senescence rate, and reproduction of our focal colonies weekly
for 6 weeks in the field by photographing each colony with a
digital camera in the field. On a few occasions (n = 9), we were
unable to adequately discern colony size, reproductive status, or
senescence rate from our photographs and as such, some measures
for some colonies were omitted from the final analyses. None of
the traits was correlated with another (correlation coefficients varied between 0.02 and 0.09), precluding the possibility of indirect
selection being generated by one trait on another.
advantage of generating standardized estimates of selection that
can be later used in meta-analyses of selection to great effect
(Kingsolver et al. 2001). Canonical analyses essentially extend
the traditional multiple regression approach and they have the advantage of providing a more complete picture of selection on trait
combinations, and can be more sensitive than the standard multiple regression approach (Blows and Brooks 2003). The outputs
of canonical analyses are more difficult to interpret and are less
amenable to making comparisons across studies, however, and so
we present results from both the multiple regression analyses and
the canonical analyses.
We used partial F tests to test for differences in the strength
and form of selection experienced by individuals in the different
environments, following the model reduction approach described
in Chenoweth and Blows (2005).
ESTIMATING SELECTION GRADIENTS
Standardized linear (β) and nonlinear (γ) selection gradients were
estimated in each environment by regressing relative fitness (relative to the population mean in each environment) on standardized phenotypic traits. Importantly, we used separate models to
estimate linear and nonlinear gradients as appropriate and we
calculated the appropriate coefficient values for each type of selection (Stinchcombe et al. 2008). We then used randomization
tests of significance of all gradients as such tests avoid the problems of parametric tests on non-normal distributions (Reynolds
et al. 2010) when transformation of fitness is inadvisable. The
results of these analyses are presented in Figure S3.
NONLINEAR SELECTION
DATA ANALYSIS
We first determined whether there was an effect of the competitive
environment that colonies experienced on colony size and colony
reproductive output with repeated measures ANOVA. We then
explored the effect of including the random factor "Backing plate"
in our analyses, but there were no significant interactions between
the random factor and the fixed factor of interest and backing plate
explained little of the variation so Backing plate was excluded
from the final model. There was very little mortality (<10%)
across the experimental period, and the little mortality that did
occur was unrelated to our traits of interest, so we did not analyze
mortality formally. We found strong effects of our treatments on
the performance of our focal colonies; we also found that some
mean trait values were affected by our treatments (see Results and
Figures S1 and S2).
We estimated selection on our traits of interest with a multiple regression approach (Lande and Arnold 1983), along with an
extension of this approach as advocated by Phillips and Arnold
(1989). The multiple regression approach is probably more familiar and intuitively appealing to many readers, and it has the
It can be difficult to interpret the strength and significance of
nonlinear selection from examination of the γ matrix alone, as
nonlinear selection is frequently strongest on trait combinations
(Phillips and Arnold 1989; Blows and Brooks 2003). Therefore,
to assess multivariate selection on traits, we performed a canonical rotation of the γ matrix. Canonical analysis determines the
normalized eigenvectors (mi ), which contain the loadings of the
original traits on canonical axes, and their associated eigenvalues (λi ), which describe the form of nonlinear selection (Phillips
and Arnold 1989; Blows and Brooks 2003). Positive eigenvalues indicate concave selection along eigenvectors (which may
indicate disruptive selection), and negative eigenvalues indicate
convex selection (which may indicate stabilizing selection). The
significance of eigenvalues was tested using the double linear
regression method of Bisgaard and Ankenman (1996), with Pvalues calculated from permutation tests (Reynolds et al. 2010).
We used thin-plate splines (with a smoothing parameter that minimized the generalized cross validation score) to visualize the
major axes of the fitness surface identified by canonical analyses (Blows and Brooks 2003). As only significant eigenvectors
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were visualized, the fitness surface was plotted on a single axis
for the competition-free environment, and on two axes for the
intermediate- and high-competition environments.
It is important to note that the classic regression approach to
measuring selection assumes that all focal traits affect fitness directly. Thus, we also used path analyses of selection (see Scheiner
et al. 2000) to explore a different causal structure among our focal
traits—namely, that offspring size and senescence affect fitness
directly, but that offspring size and zooid size also affect fitness indirectly via their impacts on senescence. We restricted these analyses to linear selection only, given the complexity of modeling
indirect nonlinear effects within this framework. Because comparisons of model fit in each competitive environment found little
support for path models over regression models (based on differences in model AIC scores, converted to P-values to aid interpretation; Anderson 2008), we present these results in Figure S3 only.
Results
EFFECTS OF THE ENVIRONMENT ON PERFORMANCE
The environment that colonies experienced had a strong effect
on both the growth rates and the total reproductive output of the
colonies (Effects on growth: Treatment: F 2,160 = 90.2, P < 0.001;
Treatment × Time: F 12,960 = 78.15, P < 0.001; Figure S1; Effect on reproduction: F 2,160 = 59.67, P < 0.001; Figure S2A).
Colonies that experienced no competition grew and reproduced
the most while colonies that experienced competition from an established community grew and reproduced the least. There was a
significant difference in the amount of colony senescence among
the different treatments: colonies experiencing high competition
had a higher proportion of senesced zooids than colonies experiencing intermediate or no competition (F 2,131 = 3.73, P = 0.0265;
Figure S2B). There was no significance difference in either the
size of zooids or initial offspring size among the treatments (Zooid
size: F 2,131 = 0.3024, P = 0.739; Offspring size: F 2,131 = 1.02,
P = 0.361).
DIFFERENCES IN SELECTION AMONG
ENVIRONMENTS
There was marginal evidence for linear selection varying among
environments (F 3,157 = 2.25, P = 0.084). Explorations with ANCOVA suggested that linear selection on offspring size differed
among environments (F 2,155 = 3.23, P = 0.042), but no differences in selection on the other two traits across environments.
We found strong evidence that nonlinear selection varied among
environments (F 6,139 = 2.52, P = 0.023). Further analyses suggested that quadratic selection differed among environments and
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Reproductive output
PATH ANALYSES
1.5
1.0
0.5
0.0
-2
-1
0
1
2
3
4
M3
Less senescence
Larger zooid size
More senescence
Smaller zooid size
Figure 1. Fitness of Watersipora subtorquata colonies in a
competition-free environment showing multivariate selection on
phenotypic trait combinations. Individuals with intermediate values of offspring size, zooid size, and senescence rate had the highest fecundity. Line represents best quadratic approximations of
selection based on multivariate canonical analyses.
marginal evidence for differences in correlational selection among
environments (quadratic: F 3,145 = 3.06, P = 0.030; correlational:
F 3,145 = 2.35, P = 0.074). Exploration with ANCOVA suggested
that this difference in quadratic selection among environments
was driven entirely by differences in quadratic selection on offspring size among environments (F 2,145 = 3.93, P = 0.022).
Inspection of the data and the β and γ matrices suggested that
there was very little selection on our traits of interest in the
most benign environment, but there was strong selection in the
other two environments (Table 1). In the intermediate environment, there was strong stabilizing selection while in the harshest environment, positive directional selection acted on offspring
size.
Our canonical analyses revealed a complex picture of selection on our traits of interest. We detected significant convex
selection acting on one eigenvector (m3) of trait combinations in
the competition-free environment (Table 2). In this environment,
senescence loads positively, offspring size loads very little, and
zooid size loads negatively, and the visualization of the fitness
surface along this canonical axis suggests intermediate values of
zooid size and senescence yield the highest fitness (Figure 1).
In the intermediate-competition environment, strong convex
selection acted along two eigenvectors (Figure 2): on one
NONLINEAR SELECTION ON OFFSPRING SIZE
Table 1. Standardized gradients of directional selection (β) and nonlinear selection (γ) on offspring size, senescence rate, and zooid
size across competition environments in Watersipora subtorquata. Quadratic and correlational selection gradients are the diagonal and
off-diagonal elements of γ respectively, errors shown in parentheses below each estimate (P < 0.05 shown in bold).
γ
Competition free
Offspring size
Senescence rate
Zooid size
Intermediate competition
Offspring size
Senescence rate
Zooid size
High competition
Offspring size
Senescence rate
Zooid size
Table 2.
β
Offspring size
Senescence rate
Zooid size
0.093
(0.074)
−0.021
(0.079)
−0.036
(0.080)
0.001
(0.058)
−0.050
(0.093)
−0.090
(0.016)
−0.078
(0.065)
0.086
(0.0880)
−0.053
(0.069)
0.038
(0.133)
−0.003
(0.131)
0.061
(0.134)
−0.205
(0.093)
0.015
(0.148)
0.016
(0.177)
−0.118
(0.089)
−0.079
(0.142)
−0.129
(0.091)
0.422
(0.157)
−0.162
(0.151)
−0.150
(0.156)
0.140
(0.129)
−0.388
(0.209)
−0.111
(0.167)
0.0107
(0.101)
−0.180
(0.197)
−0.064
(0.113)
Matrix of eigenvectors (mi ) and their associated eigenvalues (λ) from the canonical analysis of γ for total reproductive output,
under different levels of competition (P < 0.05 shown in bold).
Selection
λ (Std. dev.)
Competition free
m1
m2
m3
Intermediate competition
m1
m2
m3
High competition
m1
m2
m3
Original traits
Offspring size
Senescence rate
Zooid size
0.042 (0.097)
−0.061 (0.123)
−0.111 (0.098)
0.782
0.617
0.084
−0.345
0.542
−0.765
−0.518
0.569
0.637
−0.084 (0.083)
−0.161 (0.085)
−0.208 (0.066)
0.002
0.234
0.972
0.753
0.639
−0.156
−0.658
0.732
−0.175
0.280 (0.147)
0.017 (0.146)
−0.211 (0.111)
0.806
−0.372
0.460
−0.591
−0.466
0.659
0.031
0.802
0.596
eigenvector (m2), offspring size loads lightly while both senescence and zooid size load heavily; on the other eigenvector, offspring size loads very heavily while other traits load very little
(Table 2; Figure 2).
In the high-competition environment, canonical analysis
showed both significant concave and convex selection acting
along two eigenvectors of trait combinations (m1 and m3 respectively, Table 2). On the first eigenvector, offspring size loads
positively and strongly while senescence rate loads negatively
and zooid size loads very little. The fitness surface suggests that
larger offspring sizes and low senescence rates yield the highest fitness returns (Figure 3). On the third eigenvector, all three
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Discussion
The strength and form of selection on offspring size varied among
the different environments in the field: nonlinear selection on
offspring size was strongest in the harsher environments and weak
to nonexistent in the most benign environment. Our results also
suggest that failing to consider nonlinear selection could lead to an
incomplete understanding of the relationship between offspring
size and fitness.
Larger offspring size
Less senescence
Smaller zooids
Larger zooids
More Senescence
Smaller offspring size
More senescence
Larger zooids
Smaller zooids
Less senescence
Figure 2. Fitness of Watersipora subtorquata colonies in an
intermediate-competition environments showing multivariate selection on phenotypic trait combinations. Individuals with in-
termediate values of offspring size, zooid size, and senescence
rate had the highest fecundity. Surface represents best quadratic
approximations of selection based on multivariate canonical
analyses.
Larger offspring size
More senescence
Larger zooids
Larger offspring size
Less senescence
Smaller offspring size
Less senescence
Smaller zooids
Smaller offspring size
More senescence
Figure 3. Fitness of Watersipora subtorquata colonies in a highcompetition environments showing multivariate selection on phenotypic trait combinations. Individuals with the greatest values
of offspring size, zooid size, and lowest values for senescence
rate had the highest fecundity. Surface represents best quadratic
approximations of selection based on multivariate canonical
analyses.
traits load positively and approximately equally and the fitness
surface suggests that an intermediate phenotype (with regards to
the linear combination of all three traits) is favored (Figure 3).
Overall, in the high-competition environment, the phenotype
with the highest fitness returns combined the highest values for
m1 and intermediate values of m2 (but with the latter of lesser
importance).
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CAUSES AND CONSEQUENCES OF ENVIRONMENTAL
VARIABILITY IN SELECTION ON OFFSPRING SIZE
We found no nonlinear selection on offspring size in the most
benign environment. Our study joins a growing list showing that
under benign conditions, selection on offspring size is weaker
than under harsher conditions (Mousseau and Fox 1998), but it
is important to note that this pattern is not universal (Allen et al.
2008). In nature, it is probably rare for Watersipora to encounter
entirely competition-free environments for sustained periods of
time, so we suspect that selection in this environment only plays
a minor part in shaping offspring size variation. Nevertheless, our
finding does illustrate that excluding the effects of interspecific
competition (as is typical of most laboratory-based studies of offspring size) can result in the dramatic underestimation of selection
on offspring size.
In the two harsher environments, nonlinear selection was
strong but context dependent. We found evidence for stabilizing
selection on offspring size in the intermediate environment and
evidence for nonlinear selection for increased offspring size in
the harshest environment. In the harshest environment used here,
competition for food is intense and so increased size (and presumably, energy reserves) may assist offspring in coping with
this competition. The detection of stabilizing selection on offspring size in the intermediate environment is harder to explain;
we expected similar benefits of increased offspring size in the
intermediate environment if food competition alone drove our
effects. It could be that the two surrounding communities (the
intermediate and the harshest environment) were composed of
different species and this difference led to a difference in selection on offspring size. Regardless, this effect warrants further
exploration as it was not anticipated.
The detection of stabilizing selection on offspring size in two
environments has some interesting implications for the way we
view offspring size effects. Traditionally, it has been tempting
to view the performance of larger offspring as invariably either
greater than, or at least equal to, the performance of smaller offspring (Marshall et al. 2006; Jorgensen et al. 2011). However,
our detection of stabilizing selection suggests that, in this species,
very large offspring will have lower performance than offspring
of intermediate size under some conditions. This finding is in
NONLINEAR SELECTION ON OFFSPRING SIZE
keeping with some studies that have similarly found that larger
offspring do not always perform better and sometimes even do
worse than smaller offspring (Kaplan 1992; Einum and Fleming
2000; Dibattista et al. 2007; Jacobs and Sherrard 2010). As noted
in the Introduction, the form of selection on offspring size will
have a critical impact on the type of bet-hedging that mothers
may employ to cope with unpredictable environments (Crean and
Marshall 2009). Our finding suggests that, in Watersipora, selection may favor mothers that employ a diversified bet-hedging
strategy when the offspring environment is unpredictable. Importantly, stabilizing selection on offspring size does not mean
that mothers should produce the particular offspring size that
selection most favors (Marshall and Uller 2007). Instead, the optimal offspring size that mothers should produce will probably
be product of both the offspring size–performance relationship
and the trade-off between offspring size and number (Smith and
Fretwell 1974; Wolf and Wade 2001; Johnson et al. 2010). To estimate selection on mothers, rather than offspring, our estimates
of selection on offspring size would have to be combined with
estimates of a size-number trade-off that mothers face as recently
done by Johnson et al. (2010), but such data are lacking for our
species.
Our finding that selection strongly favored larger offspring
in the harshest environment is in keeping with offspring provisioning patterns in this species. Previous work has shown that
Watersipora colonies grown in the presence of a competitive community produce larger offspring than colonies grown in the absence of competitors (Marshall and Keough 2009). Our results
here would suggest that this plasticity in offspring provisioning
represents an "anticipatory maternal effect" (sensu Marshall and
Uller 2007), whereby colonies respond to environmental cues
in their own habitat and provision their offspring accordingly.
It should be noted however that offspring from a single colony
probably encounter both habitat types in nature because of the
scale of dispersal; hence mothers face the daunting challenge of
provisioning their offspring on the chance that they will encounter
either environment.
A number of recent reviews have highlighted the importance
of variability in selection for determining the tempo and trajectory
of evolution (Orr 2009; Bell 2010; Calsbeek 2012). Our results
show that selection is highly variable over small spatial scales.
Furthermore, our environments span the range of successional
stages in the surrounding community that Watersipora will experience, sometimes within a single lifetime (recall that to create
our harshest environment, we allowed a community to develop for
only 3 weeks prior to starting the experiment and W. subtorquata
colonies can live for twice this time). As such, our results suggest
that environmentally driven variation in selection will reduce its
efficacy and may maintain variance in offspring size.
CORRELATION SELECTION ON OFFSPRING SIZE
Offspring size effects have long been recognized as highly context
dependent: the local environmental conditions strongly affect the
offspring size–performance relationship (Mousseau and Dingle
1991; Bernardo 1996). Importantly, our findings suggest that offspring size is also context dependent with regards to other trait
values—the fitness return of a particular offspring size depends on
values of other traits. For example, canonical analysis suggested
that the fitness benefits of increased offspring size are increased in
individuals with lower senescence rates when they experience intense competition. Such correlational selection should, over time,
result in increased negative covariance between offspring size and
senescence rates (Blows and Brooks 2003). More generally, there
is some evidence for covariance between offspring size and other
traits at both the population level (Johnston and Leggett 2002;
Bashey 2008; Walsh and Reznick 2010), and within populations
(Martin and Pfennig 2010). Coevolution between offspring size
and other traits has also been implicated in a range of other systems
(Krug and Zimmer 2000; Walsh et al. 2006; Benton et al. 2008;
Miles and Wayne 2009) suggesting offspring size often interacts
with other traits to affect fitness. It would therefore be interesting
to examine whether correlational selection operates on offspring
size and other traits in organisms where covariance between these
traits has already been detected. For example, Pfennig and Martin
(2009) did not formally analyze correlational selection on offspring size and "carnivory traits" in spadefooted toads (Pfennig
and Martin 2009), but we suspect that correlational selection is
acting in this species, given increases in offspring size alter selection on other carnivory traits. We suggest that the prevalence of
correlational selection on offspring size has been underestimated
more generally.
Biologists have long recognized that offspring size has pervasive longitudinal influences throughout the life history (Lack
1947). For example, some studies show that initial offspring size
can affect subsequent size throughout the life history (Dias and
Marshall 2010). The presence of correlational selection on offspring size in combination with other traits, however, suggests
that variation in maternal investment will also generate selection
on traits other than offspring size where there would otherwise
be none (Blows and Brooks 2003). Thus, the influence of initial
maternal investment on the phenotype of offspring may be more
pervasive than has been recognized previously—affecting selection on traits that are not directly correlated with offspring size.
For example, variation in initial offspring provisioning could constrain the range of values of other traits that yield a fitness benefit
for offspring. This maternal effect is more insinuating than those
typically discussed, and warrants further exploration. On the other
hand, the presence of correlational selection also raises the possibility that the range of offspring sizes that yield high fitness
EVOLUTION FEBRUARY 2013
335
D. J. M A R S H A L L A N D K . M O N RO
returns is more constrained than has been recognized. If the fitness benefits of increased offspring size are highly dependent on
the values of other traits (as they are when correlational selection
occurs), mothers may evolve to provision offspring according to
the values of other offspring traits.
We found evidence for correlational selection on senescence
in conjunction with other traits, but this selection depended on
the local environment. In environments with less competition,
colonies that senesced more had higher reproductive output than
colonies that senesced very little, whereas in the harshest environment, this pattern was reversed. While there has been much
debate regarding the evolution of senescence in clonal organisms
and marine invertebrates in particular (McKinney and Jackson
1991; Chadwickfurman and Weissman 1995; Orive 1995; Bayer
and Todd 1997; Gardner and Mangel 1997), there are few empirical studies of senescence, and as far as we are aware, no formal
estimates of the selective value of senescence in a marine invertebrate. It appears that some senescence can benefit colony fitness,
but that the benefits of senescence are highly context dependent—
both the context of other life-history traits (i.e., offspring size) and
local environmental conditions (intensity of competition) can alter the fitness returns of senescence. An interesting next step will
be to determine whether genetic trade-offs between senescence
and other components of fitness exist in this species.
Overall, we found a complex web of selection acting on offspring size that varied among environments. Our results suggest
that further examinations of variability in selection in the field
are required as our results suggest that selection can vary over
remarkably small spatial and temporal scales. We suspect that
nonlinear selection on offspring size and other offspring traits is
common but without more studies, it remains impossible to know
its prevalence.
ACKNOWLEDGMENTS
The authors wish to thank the K. Donohue, C. Fox, and two anonymous
reviewers for comments that greatly improved the manuscript. We thank
S. Chenoweth for statistical advice. We thank Tane Sinclair-Taylor for
assistance in the field and laboratory. DJM and KM were supported by
grants and fellowships from the Australian Research Council.
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Associate Editor: K. Donohue
Supporting Information
The following supporting information is available for this article:
APPENDIX
Figure S1. Effect of competition on colony size in Watersipora subtorquata in the field.
Figure S2. Effect of competition on (A) total reproductive output; and (B) proportion of colony that had senesced in Watersipora
subtorquata.
Figure S3. Comparisons of regression models versus path models of selection in different competitive environments.
Supporting Information may be found in the online version of this article.
Please note: Wiley-Blackwell is not responsible for the content or functionality of any supporting information supplied by the
authors. Any queries (other than missing material) should be directed to the corresponding author for the article.
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Colony"area"
(mm2)"
1500"
Low"
1000"
Intermediate"
500"
High"
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Week"
Figure"2)"Effect"of"compeGGon"on"colony"size"in"Watersipora*subtorquata*
in"the"field."
500"
Total"ReproducGon"
a)"
300"
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b)"
ProporGon"senesced"
0.15"
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Figure"2)"Effect"of"compeGGon"on"a)"total"reproducGve"output;"and""b)"
proporGon"of"colony"that"had"senesced"in"Watersipora*subtorquata"
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Appendix!2!Figure!1.!!Comparisons!of!regression!models!vs#path!models!of!selection!in!
different!competitive!environments.!In!regression!models,!paths!(single>arrow!lines)!show!
the!direct!effects!(black!lines!and!coefficients)!of!offspring!size,!colony!senescence!and!colony!
zooid!size!on!fitness,!given!trait!variances!(V)!and!covariances!(CV,!double>arrow!lines).!In!
path!models,!paths!show!the!direct!effects!of!offspring!size!and!senescence!on!fitness,!plus!the!
indirect!effects!(grey!lines!and!coefficients)!of!offspring!size!and!zooid!size!on!fitness,!acting!
via!their!direct!effects!on!senescence!(with!paths!now!replacing!covariances!between!these!
traits).!!Significant!coefficients!are!in!bold.!For!all!environments,!differences!in!the!fit!of!
alternative!models!(measured!by!changes!in!their!AIC!scores,!converted!to!P>values!to!aid!
interpretation)!are!minimal,!indicating!similar!support!for!each!model!type.!