Download can intraspecific competition drive disruptive selection?

Evolution, 58(3), 2004, pp. 608–618
CAN INTRASPECIFIC COMPETITION DRIVE DISRUPTIVE SELECTION?
AN EXPERIMENTAL TEST IN NATURAL POPULATIONS OF STICKLEBACKS
DANIEL I. BOLNICK
Section of Evolution and Ecology, Center for Population Biology, Storer Hall, University of California, Davis, California 95616
E-mail: [email protected]
Abstract. Theory suggests that frequency-dependent resource competition will disproportionately impact the most
common phenotypes in a population. The resulting disruptive selection forms the driving force behind evolutionary
models of niche diversification, character release, ecological sexual dimorphism, resource polymorphism, and sympatric
speciation. However, there is little empirical support for the idea that intraspecific competition generates disruptive
selection. This paper presents a test of this theory, using natural populations of the three-spine stickleback, Gasterosteus
aculeatus. Sticklebacks exhibit substantial individual specialization associated with phenotypic variation and so are
likely to experience frequency-dependent competition and hence disruptive selection. Using body size and relative
gonad mass as indirect measures of potential fecundity and hence fitness, I show that an important aspect of trophic
morphology, gill raker length, is subject to disruptive selection in one of two natural lake populations. To test whether
this apparent disruptive selection could have been caused by competition, I manipulated population densities in pairs
of large enclosures in each of five lakes. In each lake I removed fish from one enclosure and added them to the other
to create paired low- and high-population-density treatments with natural phenotype distributions. Again using indirect
measures of fitness, disruptive selection was consistently stronger in high-density than low-density enclosures. These
results support long-standing theoretical arguments that intraspecific competition drives disruptive selection and thus
may be an important causal agent in the evolution of ecological variation.
Key words.
minima.
Competitive speciation, fitness function, Gasterosteus aculeatus, individual specialization, stable fitness
Received June 3, 2003.
Accepted October 8, 2003.
Classical ecological theory claims that population density
will grow to the point where intraspecific competition reduces
individuals’ survivorship and/or fecundity. The population
density reaches an equilibrium when, on average, each individual can only garner enough resources to produce one
surviving offspring. While the population size is at an equilibrium, its genetic make-up may not be, as natural selection
will favor genotypes that exceed the average reproductive
rate. Such genotypes may capture a disproportionately large
share of the limiting resources; use their existing resource
income more efficiently to produce offspring; or use novel
and exclusive resources, thereby escaping intraspecific competition. This third effect formed the core of Darwin’s explanation of why a population might evolve to use a broader
array of resources (Darwin 1859, p. 113). In the latter half
of the 20th century, theoreticians formalized this idea that
intraspecific competition favors increased ecological and genetic variation (Ludwig 1950; Levene 1953; Dempster 1955;
Lewontin 1955; Li 1955; Levins 1962, 1963; Maynard Smith
1962). More recent theory has emphasized the importance of
stable underdominance (Wilson and Turelli 1986) or disruptive selection (Dieckmann and Doebeli 1999) in driving this
process.
Competitive disruptive selection is an example of a more
general and somewhat counterintuitive theory of stable fitness
minima (Abrams et al. 1993). One way to understand this
effect is to visualize the fitness landscape for a population.
The classical view of a fitness landscape is of an unchanging
topography in which every phenotype has a specific fitness,
indicated by the height of the landscape at the point corresponding to that phenotype (Wright 1932). For example, a
unimodal resource distribution would produce a single peak
in the fitness landscape: the phenotype that can most effi-
ciently use the most abundant type of resource has the highest
fitness, and the population should evolve toward this optimal
phenotype.
In contrast to this static fitness landscape, density and frequency dependence results in a dynamic landscape: a fitness
‘‘sphagnum bog’’ (Rosenzweig 1978). Sphagnum bogs consist of a floating mat of vegetation, often of varying thickness,
which can be depressed below the water surface by any
weight set on it. In this metaphor, the thickness of the mat
at any point indicates the carrying capacity of the corresponding phenotype, while the weight resting on and depressing this surface reflects the population density of that
phenotype. A population’s mean phenotype will still evolve
uphill toward the highest carrying capacity (Rosenzweig
1978; Abrams et al. 1993), but the increasing weight of numbers depresses this fitness peak as competition intensifies.
If all phenotypes compete equally with each other (pure
density dependence), the entire fitness surface is depressed
evenly in proportion to total population density, while the
location and relative height of the fitness peak remains the
same. However, if competition is stronger between similar
phenotypes (density and frequency dependence), then the
weight of numbers only depresses the fitness surface locally.
Such competition can disproportionately affect the most
abundant (mean) phenotype even though it is adapted to the
most abundant resource. Rarer consumer phenotypes may
have fewer resources available, but also have fewer competitors with which to share those resources, so their overall
fitness is relatively high. The population is thus subject to
disruptive selection; that is, its mean phenotype is at or near
a minimum on the fitness landscape (Dieckmann and Doebeli
1999).
In the classical static model, a fitness minimum represents
an unstable equilibrium. Although a population centered on
608
q 2004 The Society for the Study of Evolution. All rights reserved.
609
DISRUPTIVE SELECTION DUE TO COMPETITION
this minimum will not show a directional phenotypic response, any perturbation to the phenotype distribution will
lead to net directional selection away from the minimum.
Disruptive selection should therefore be a rare and transient
phenomenon. By contrast, disruptive selection can be stable
when fitness is negatively frequency dependent. Theory suggests that populations will actually converge toward an optimum phenotype (determined by resource availability) that
then becomes a fitness minimum (Dieckmann and Doebeli
1999). The population mean remains stuck at this minimum
because perturbations to the phenotype distribution alter the
shape of the fitness function driving the population back to
the minimum, imparting stability (Abrams et al. 1993). This
stability implies that disruptive selection could in fact be
fairly common (Kingsolver et al. 2001).
Stable fitness minima form the basis for a number of models of evolutionary diversification, including niche expansion
(Roughgarden 1972), character displacement (Slatkin 1980),
ecological sexual dimorphisms (Slatkin 1984), and sympatric
speciation (Rosenzweig 1978; Dieckmann and Doebeli 1999;
Doebeli and Dieckmann 2000). Despite its significant theoretical utility, there is little empirical support for the idea
that intraspecific competition generates disruptive selection.
Indeed one major proponent has recently written that ‘‘at
present, this mechanism for fitness-minimizing equilibria is
only a theoretical possibility, but a wide variety of ecological
circumstances should lead to fitness functions with the necessary type of frequency-dependence. . . [including] a number of models of competition’’ (Abrams 2001, p. 278).
One testable prediction of this body of theory is that populations experiencing higher competition should show faster
niche expansion or stronger diversifying selection. Laboratory experiments using bacterial cultures (Helling et al. 1987;
Rainey and Travisano 1998; Travisano and Rainey 2000;
Buckling and Rainey 2002) and populations of fruit flies,
Drosophila melanogaster (Bolnick 2001) have confirmed that
competition can favor niche expansion. This paper adds to
this literature by experimentally testing whether intraspecific
competition can generate disruptive selection in natural populations of the three-spine stickleback, Gasterosteus aculeatus. Previous experimental studies of sticklebacks have
shown that elevated competition strengthens directional selection for character displacement (Schluter 1994, 2003), and
that phenotypically intermediate fish may experience reduced
fitness due to resource competition (Hatfield and Schluter
1999; Vamosi et al. 2000). However, these studies used laboratory-reared populations with nonnatural phenotype distributions, generated by hybridizing already-diverged benthic
and limnetic species pairs, and they did not directly test for
disruptive selection.
In this paper I describe an observational survey showing
that, in natural populations, phenotypically intermediate
sticklebacks are, on average, smaller and have lower relative
gonad mass than phenotypically extreme individuals. Given
that size and relative gonad mass are good predictors of fitness, these results suggest that natural populations are subject
to disruptive selection on trophic morphology. By experimentally manipulating population density, I show that this
disruptive selection depends on the level of competition.
MATERIALS
AND
METHODS
Study System
Three-spine sticklebacks, G. aculeatus, are a widely distributed north temperate fish, whose marine populations repeatedly invaded freshwater habitats (Wootton 1984) and
sometimes diversified into phenotypes using distinct microhabitats and eating different prey (Lavin and McPhail 1985).
This diversification is most pronounced in a handful of lakes
in British Columbia, where multiple invasions established
benthic and limnetic species pairs (Taylor and McPhail
2000). However, the majority of populations in coastal British Columbia are composed of only a single species per lake.
These solitary populations offer an ideal setting to test the
theory of competitive disruptive selection because they fulfill
two important assumptions underlying the theory.
The first assumption is that competition is frequency dependent. Although stickleback populations have very catholic
diets (Allen and Wootton 1984), individuals tend to specialize
on subsets of the resources used by the population as a whole
(Reimchen 1982; Reimchen and Nosil 2001a, b). This withinpopulation niche partitioning can occur between sexes
(Reimchen and Nosil 2001b), or different phenotypes (Cresko
and Baker 1996; Robinson 2000) and parallels the phenotypehabitat associations in two-species lakes. Individuals caught
in open water tend to have longer gill rakers and smaller gape
widths than individuals caught in the littoral zone (Schluter
and McPhail 1992), reflecting strong functional trade-offs
between using benthic and limnetic resources (Schluter 1995;
Robinson 2000). This morphological variation has repeatedly
been shown to have a significant heritable component (Day
et al. 1994; Hatfield 1997). Because phenotypically divergent
individuals are known to use different resources, the level of
competition experienced by any given individual depends on
the frequencies of similar and dissimilar phenotypes in its
population (Schluter 2003).
A second assumption is that competition is strong enough
to drive natural selection. In the laboratory, sticklebacks show
20-fold variation in egg production depending on food supply
(Wootton 1985), while male breeding activity and color depend on energy reserves (Stanley and Wootton 1986; Frischknecht 1993). Field experiments have shown that both growth
and survival rates are depressed by competition, particularly
when competition occurs between very similar phenotypes
(Schluter 1994, 1995, 2003; Hatfield and Schluter 1999; Rundle et al. 2000; Vamosi et al. 2000; Pritchard and Schluter
2001). Finally, demographic studies of sticklebacks provide
time-series data showing cyclical population fluctuations
consistent with density-dependent population regulation
(Wootton and Smith 2000).
Given that sticklebacks fulfill these two prerequisites for
competitive disruptive selection, I first predicted that trophic
morphology may naturally be subject to disruptive selection
in solitary populations. I tested this hypothesis in two lakes
in the Amor de Cosmos River watershed in northern Vancouver Island, British Columbia (Table 1). Next, I experimentally manipulated population density in five lakes in this
watershed to test whether the observed disruptive selection
was driven by competition (Fig. 1).
610
DANIEL I. BOLNICK
TABLE 1. Information on the six lakes used in this study, including longitude and latitude, the year in which observational and
experimental studies were conducted in a given lake, the number of fish transferred out of low-density enclosures (LD) and into highdensity enclosures (HD), and sample sizes of fish captured from each enclosure at the end of the five-week experiments. All lakes are
part of the Amor de Cosmos watershed of Vancouver Island, British Columbia.
Lake
Coordinates
Cecil Lake
Cedar Lake
Dugout Lake1
Farewell Lake
Little Mud Lake
Muskeg Lake
508149200N,
508129340N,
508109510N,
508119580N,
508129240N,
508119580N,
1258329310W
125834930W
1258319310W
125835990W
125832900W
1258349380W
Test for
disruptive
performance
1999
2001
Density
experiment
No. fish
removed
from LD
No. fish
added to HD
20022
56
76
126
74
240
223
409
401
337
267
420
469
2002
2002
20023
2001
20023
No. fish recaptured
LD
HD
142
97
182
47
49
50
202
180
119
65
150
50
1
No official name available.
2 Experiment not feasible due to dense submerged logs and branches.
3 Experiment failed due to insufficient recapture rates.
Testing for Disruptive Selection
To test for disruptive selection, one must measure the morphology and fitness of a large sample of individuals and then
determine whether fitness is a quadratic function of morphology. Unfortunately, sticklebacks are small fish that exist
at high population densities, precluding techniques such as
mark-recapture that are necessary to directly estimate individual survival or fecundity. Also, key aspects of trophic
morphology can only be measured invasively. Consequently,
I did not directly estimate fitness, but instead measured two
performance variables.
Both body size and relative gonad mass are known to be
correlated with energy income and potential fecundity, and
so they are useful proxies for fitness (Wootton 1976, 1994;
Bell and Foster 1994). Total fecundity in females can be
divided into several components: clutch size, spawning frequency, and egg quality (mass, nutrient content). Prebreeding
food supplies determine an individual’s growth rate and
hence size at initial spawning, the primary determinant of
clutch size (Wootton 1973a, b; Fletcher and Wootton 1995).
Spawning frequency is also an important component of fecundity, as females can range from zero to 10 spawnings per
season in the field depending on their energy income during
breeding (r2 5 0.829, Ali and Wootton 1999; see also Wootton 1977). In turn, high spawning frequency is reflected in
higher average gonad mass through time. In males, energy
income is also used for growth (favored by sexual selection)
and to maintain spermatogenesis (Huntingford et al. 2001).
Although overall male gonad mass is quite low, body energy
reserves are significantly depleted to build and maintain testes
mass, and energy-limited males have reduced testes mass and
function (Huntingford et al. 2001). Hence, both body size
and relative gonad mass are known to respond to foraging
success and competition and to predict reproductive potential.
I captured sticklebacks from marsh and open-water areas
of Cedar Lake (1999, N 5 171) and Muskeg Lake (2001, N
5 150), using dipnets and minnow traps. All fish were euthanized in MS-222, following University of California at
Davis Animal Use and Care protocol no. 9334. Specimens
were immediately placed in liquid nitrogen for transport back
to Davis, where they were stored at 2808C until needed for
analysis. I sexed and weighed each fish and measured (to
0.01 mm) the standard length, head length, snout length, eye
width, interorbital width, interopercular width, head depth,
body depth, upper jaw length, and (open) gape width on each
individual. All measurements followed Lavin and McPhail
(1985), except for gape width (which they measured on the
closed mouth) and interopercular width, the maximum width
between the right and left opercula when the mouth is closed.
I then counted the number of gill rakers on the first right
branchial arch. I used an ocular micrometer to measure the
lengths of the three longest gill rakers. Gonads were weighed
to 0.0001 g immediately (,5 sec) after removal. This precision is reliable within the first 8–10 sec following removal,
FIG. 1. Three hypotheses as to how disruptive fitness functions might respond to experimental manipulation of population density. (A)
If fitness is density independent, both high- and low-density populations (HD and LD) will have similar fitness functions. (B) If fitness
is density dependent, HD should result in lower fitness, but all phenotypes are affected equally so the slope will not change. (C) If fitness
is density and frequency dependent, disruptive selection will be reduced in LD and exaggerated in HD.
DISRUPTIVE SELECTION DUE TO COMPETITION
after which desiccation introduces significant measurement
error (D. I. Bolnick, unpubl. data).
I tested for disruptive performance functions (a proxy for
disruptive selection) by relating body size or relative gonad
mass to size-independent axes of trophic morphology. To
isolate orthogonal axes of morphological variation for each
lake, I ran a principal components analysis (PCA) on the
correlation matrix of the log-transformed morphometric data
and retained individuals’ factor scores for the first three principal component axes (PC1, PC2, and PC3). Body size was
measured as the first principal component axis (PC1). To
quantify relative gonad mass, I used individuals’ residuals
from the regression of log-transformed gonad mass on log
body mass, sex, and a mass 3 sex interaction. Fish infected
with the cestode Schistocephalus solidus (which fill the body
cavity and can weigh over 25% of the fish’s body mass) have
severely atrophied gonads (Heins and Baker 2003) and were
excluded from this analysis. All statistical analyses were performed using Systat 9.0 (Systat Inc., Evanston, IL).
Quadratic regression of each performance variable on morphology indicates disruptive performance when the slope of
the quadratic term (g) is significantly positive (Lande and
Arnold 1983). Using body size as the fitness proxy, PC1 was
regressed on PC2, PC22, PC3, and PC32. Gonad mass residuals were regressed on PC1, PC12, PC2, PC22, PC3, and
PC32. The PC1 terms were included as independent variables
in the gonad mass regression to check whether gonad residuals were independent of body size (confirmed in all tests).
I used a partial F-test to determine whether any significant
quadratic term significantly improved the fit of the model.
The data fit the assumptions of the GLM (normally distributed
variables and homoscedastic residuals).
Quadratic regression has two important weaknesses. First,
even monotonic functions can yield significant quadratic
terms if they are sufficiently concave or convex, even if there
is no minimum or maximum within the range of the data
(Mitchell-Olds and Shaw 1987). Second, quadratic regression
is very sensitive to outliers and can yield false positives.
Nonparametric cubic spline circumvents both flaws and allows estimation of a fitness function without an a priori assumption about its shape (Schluter 1988). I used cubic spline
to visualize the fitness functions to look for a minimum within
the range of the data, with 1000 bootstraps of the data to
estimate 95% confidence intervals around this curve (program
from D. Schluter).
Experimental Test of the Role of Competition
I experimentally manipulated population density to test
whether disruptive selection was stronger in populations experiencing higher competition (Fig. 1). The experiment was
carried out in Muskeg Lake in 2001 and in five lakes in 2002
(Table 1). In each lake, I built a pair of large enclosures by
placing two semicircular barriers against the lakeshore, thereby enclosing both open water, marsh, and shoreline habitat.
The barriers were made of panels of 1/16-in seine net (in
2001) or 4 mm SuperDura greenhouse plastic (in 2002; AT
Plastics Inc, Brompton, Ontario, Canada), 33 m long and 2–
5 m wide. Panels were secured to the lake bottom by lead
line, rocks, stakes, and metal chain, while the top of the panel
611
was held above the surface of the water by closely spaced
fishing net floats (2001) or styrofoam tubes (2002). The ends
of the panels were attached to 1-m high 1/16-in plastic fencing buried 10 cm into the substrate, which extended the enclosures through shallow water, marsh, and up onto dry land.
Each enclosure contained at least 150 m2 and as much as 250
m2 of lake surface, including marsh, channels, and open water
up to 2 m (Muskeg, Dugout Lakes) or 4 m (Cecil, Farewell,
Mud Lakes) deep. Scuba divers checked to ensure that the
bottoms of all enclosures were sealed to the substrate.
Within each lake, I used minnow traps and dipnets to remove fish from one enclosure to generate a low-density treatment (LD) and added those fish to the second enclosure to
yield a high-density treatment (HD). To further elevate density in HD, I added additional fish caught from the unenclosed
lake. My intent was to reduce the LD enclosure to approximately half its normal density and to double the density in
HD. The upper target was chosen based on densities used in
the many experimental pond studies of competition by Schluter and colleagues (e.g., Schluter 1994, 2003). The advantage
of this approach was that it allowed me to use a natural
phenotype distribution drawn from the resident population
within each lake. Natural populations are more likely to have
converged to a stable fitness minimum than fish introduced
from a laboratory breeding program. The mean phenotype in
natural populations appears to track the relative availability
of benthic and limnetic prey items (Lavin and McPhail 1986).
The disadvantage of using the native stickleback population was that density manipulations were necessarily inexact
because the population densities in each enclosure could not
be known precisely. I estimated population densities in the
LD enclosure and in an unenclosed area of each lake, using
Zippin’s maximum-likelihood removal method (Zippin 1956,
1958; Southwood and Henderson 2000). Capture rates decline over time as individuals are removed from an area.
Assuming a constant capture effort and constant capture probability per individual, it is possible to estimate the original
density based on the time series of how capture effort declines. For example, in Muskeg Lake in 2001 I estimated
that the LD enclosure contained 480 fish. Consequently, I
removed 240 fish from LD, and added 420 to HD (the 240
from LD plus as close to 240 as I could capture in the available time from outside the enclosures). Because capture probability is not likely to be constant, I treated these estimates
only as a rough guide.
In Mud and Dugout Lakes, fish densities in LD were unusually low even before trapping, presumably due to disturbance while building the enclosures. Equivalent habitat just
outside the enclosures yielded five times higher capture rates.
Consequently, I removed few fish from LD in those lakes,
but still added a high number of fish to HD from outside the
enclosures to ensure high density (Table 1).
In early June, five weeks after density manipulation, I captured all surviving fish (but not more than 200) from LD and
HD (Table 1), using dipnets, minnow traps, and a seine net.
Mud and Muskeg Lakes had very low capture rates (Table
1) and were excluded from subsequent analyses because sample sizes were too low to offer any reasonable power to accurately estimate quadratic slopes. The decision to omit these
lakes was made prior to data collection or analysis. In Fare-
612
0.04
0.04
0.03
0.03
0.05
0.06
0.09
0.03
0.06
0.02
0.03
0.96
20.04
20.04
20.05
7.22
PC3
PC2
0.20
0.21
0.14
0.08
0.18
0.89
0.10
0.19
0.18
0.12
0.18
20.16
20.73
20.75
20.79
15.37
0.97
0.96
0.98
0.97
0.92
0.92
0.94
0.96
0.96
0.97
0.88
20.23
0.67
0.65
0.60
72.75
0.08
0.09
0.08
0.08
0.08
0.07
0.09
0.07
0.07
0.06
0.07
0.76
20.27
20.27
20.28
5.81
0.13
0.12
0.08
0.08
0.11
0.06
0.13
0.12
0.13
0.06
0.08
20.62
20.45
20.48
20.47
7.86
0.98
0.98
0.99
0.98
0.97
0.97
0.98
0.99
0.98
0.98
0.98
20.18
0.84
0.83
0.81
84.35
0.04
0.03
0.02
20.02
0.05
0.01
0.04
0.02
0.03
0.00
0.02
20.95
20.14
20.15
20.18
4.37
0.14
0.13
0.10
0.07
0.11
0.08
0.12
0.10
0.12
0.07
0.11
0.23
20.36
20.40
20.44
6.54
0.98
0.99
0.99
0.98
0.97
0.94
0.98
0.99
0.98
0.98
0.98
20.23
0.91
0.90
0.87
86.51
0.00
0.03
0.02
0.03
0.02
0.00
0.02
0.02
0.00
0.02
0.04
0.99
20.08
20.07
20.06
6.60
0.17
0.17
0.13
0.12
0.17
0.08
0.16
0.14
0.15
0.08
0.12
20.17
20.63
20.66
20.70
10.36
0.98
0.97
0.99
0.98
0.95
0.94
0.98
0.98
0.97
0.97
0.98
20.04
0.76
0.74
0.69
79.70
0.01
0.01
0.02
0.04
0.01
20.07
0.01
0.01
0.00
0.05
0.06
20.99
0.08
20.05
20.05
5.93
0.15
0.15
0.10
0.04
0.10
0.02
0.24
0.17
0.16
0.08
0.11
0.12
20.75
20.76
20.77
11.75
0.98
0.98
0.98
0.96
0.91
0.88
0.95
0.96
0.96
0.95
0.92
20.02
0.65
0.64
0.62
74.67
Body mass
Standard length
Head length
Snout length
Eye width
Interorbital width
Interopercular width
Head depth
Body depth
Upper jaw length
Gape width
Gill raker number
Gill raker length (1)
Gill raker length (2)
Gill raker length (3)
Percentage variance
Muskeg Lake
PC1
PC3
PC2
Farewell Lake
PC1
PC3
PC2
Dugout Lake
PC1
PC3
PC2
Cecil Lake
PC1
PC3
PC2
Principal component analysis identified three major axes
of morphological variation in each lake (axes that explain .
5% of the variation; Table 2). PC1 can be interpreted as a
measure of body size, because linear morphometric measurements show strong even loadings on this axis. PC2 and
PC3 load very heavily on gill raker length and gill raker
number respectively (Table 2).
Fitness proxy estimates of the fitness function suggest that
sticklebacks in Cedar Lake were subject to disruptive selection on size-adjusted gill raker length. Both body size (PC1)
and relative gonad mass had significant positive quadratic
regression terms with respect to gill raker length (PC2; Table
3). In both cases, partial F-tests confirmed that including
PC22 in the multiple regression significantly improved the fit
Cedar Lake
Disruptive Performance Functions
PC1
RESULTS
Trait
well Lake, more fish were recovered from LD than from HD.
This could reflect high mortality in HD, unnoticed damage
to the HD enclosure that let fish escape, or damage to the
LD enclosure that let fish enter. In the following analyses,
the Farewell HD enclosure was nevertheless treated as the
high-competition population. This judgment is supported by
zooplankton sampling showing lower zooplankton densities
in HD than LD (D. I. Bolnick, unpubl. data), and because
HD fish were significantly smaller than LD fish at the end
of the experiment (see Results).
As described above, specimens were euthanized in MS222, frozen in liquid nitrogen, returned to Davis, California,
then weighed, measured, and sexed. I again used PCA to
estimate individuals’ body size (PC1) and two size-independent measures of trophic morphology (PC2, PC3), and regression residuals to calculate relative gonad mass. Both PCA
and the gonad mass regression were carried out, pooling all
individuals (HD and LD) caught in a given lake. To test for
disruptive selection within each experimental enclosure, I ran
quadratic regressions of body size (PC1) and relative gonad
mass as functions of gill raker length (PC2) and the square
of gill raker length (PC22). The estimated coefficient of the
quadratic term, g, indicates the strength and direction of the
quadratic selection for each treatment in each lake. Statistically significant quadratic terms were double-checked by
cubic spline analysis.
To test whether disruptive selection is stronger in HD than
LD within a given lake, I used a general linear model with
each performance variable dependent on treatment, PC2,
PC22, PC2 3 density, and PC22 3 density. A significant
quadratic 3 treatment interaction term indicates that quadratic selection is different between competition treatments
within the lake, and the direction of this difference is found
by comparing the estimates of the quadratic slopes, g. Note
that the initial hypothesis is directional (HD . LD), so onetailed tests are appropriate and the P-values for the PC22 3
density interaction terms are adjusted accordingly. To analyze the results across all replicate lakes together, I ran a
paired t-test comparing the g estimates from HD and LD,
paired by lake. This tests the one-tailed hypothesis that disruptive selection was consistently larger in HD than LD (Fig.
1C).
TABLE 2. Loadings of morphological variables on the first three principal component axes (PC1, PC2, and PC3), and the percent variance explained by each axis, divided
up by lake. Mud Lake and the 2002 experiment in Muskeg Lake are not included because too few fish were recaptured to warrant measurement and analysis.
DANIEL I. BOLNICK
613
DISRUPTIVE SELECTION DUE TO COMPETITION
TABLE 3. Tests for disruptive performance functions in two natural populations. Two performance variables serve as proxies for fitness:
body size variation (PC1) and relative gonad mass. Regression terms are listed for each fitness proxy within each lake, together with
the estimated coefficient for each term, its standard error (SE), t-statistic, and probability of the null hypothesis that the estimated
coefficient is zero. Positive coefficients represent disruptive selection, negative coefficients are stabilizing. Bold rows highlight the
statistically significant regression terms.
Lake, year
Cedar Lake, 1999
Performance
variable
body size (PC1)
relative gonad mass
Muskeg Lake, 2001
body size (PC1)
relative gonad mass
Regression
term
Coefficient
SE
t
P
PC2
PC22
PC3
PC32
PC1
PC12
PC2
PC22
PC3
PC32
PC2
PC22
PC3
PC32
PC1
PC12
PC2
PC22
PC3
PC32
20.061
0.284
0.023
20.086
0.019
20.019
20.115
0.139
0.047
20.071
20.032
0.031
0.002
0.125
0.008
20.0176
0.004
20.078
20.083
0.027
0.073
0.067
0.072
0.061
0.079
0.058
0.058
0.06
0.09
0.071
0.095
0.046
0.085
0.062
0.066
0.046
0.072
0.035
0.067
0.047
20.834
4.246
0.32
21.396
0.238
20.332
21.923
2.382
0.522
21.001
20.339
0.659
0.022
2.03
0.127
20.381
0.055
22.238
21.236
0.566
0.405
,0.001
0.749
0.164
0.813
0.741
0.058
0.02
0.603
0.32
0.735
0.511
0.983
0.044
0.899
0.704
0.956
0.027
0.219
0.572
of the model (PC1: F1,168 5 18.0, P , 0.001; relative gonad
mass: F1,168 5 4.53, P 5 0.036). Cubic spline analysis confirmed that there was a performance minimum. Individuals
with intermediate gill raker lengths (PC2) had lower mean
body size (Fig. 2A) and relative gonad mass (Fig. 2B) than
individuals with long or short gill rakers.
In contrast, body size variation in Muskeg Lake showed
no relationship with PC2 (gill raker length). Gill raker number (PC3) did have a significant quadratic term (Table 3),
but did not significantly improve the fit of the regression
model (partial F-test: F1,133 5 3.62, P 5 0.059). In contrast,
relative gonad mass showed a weakly stabilizing performance
function with respect to gill raker length (Table 3), supported
by cubic spline analysis and a partial F-test (F1,133 5 5.08,
P 5 0.026).
Effect of Competition
Experimental manipulation of population density had a
weak but significant effect on the quadratic slope of the performance function, matching the prediction derived from the
theory of competitive disruptive selection (Fig. 1C). This
effect is seen most clearly in the 2001 Muskeg Lake experiment (Fig. 3). Gill raker length (PC2) is subject to disruptive
selection in HD but not in LD (Table 4), using either PC1
or relative gonad mass as a fitness proxy.
A general linear model analyzing Muskeg Lake’s HD and
LD together supports two conclusions. First, fish from the
HD enclosure were significantly smaller (density effect:
F1,193 5 11.264, P 5 0.001) and had less gonad mass given
their size (density effect: F1,193 5 14.066, P , 0.001) than
LD fish. This suggests that density manipulations had the
expected effect on performance variables, underscoring their
utility as measures of the detrimental effects competition.
Quantitative zooplankton samples with a Wisconsin zoo-
plankton net independently confirmed that the HD enclosure
had significantly lower zooplankton density (D. I. Bolnick,
unpubl. data). Second, there was a significant quadratic 3
experimental treatment interaction term, indicating that HD
fish experienced significantly stronger disruptive performance than LD fish (PC1: F1,193 5 3.355, P 5 0.035; relative
gonad mass: F1,193 5 5.184, P 5 0.012; Fig. 3).
Replicating this experiment across five lakes in 2002, the
results within individual lakes are less compelling than the
2001 experiment. As noted above, Mud and Muskeg Lakes
were not analyzed because of low recapture rates. Analyzing
the three remaining lakes (Cecil, Dugout, and Farewell) yielded variable results. Density manipulations significantly reduced body size in HD relative to LD in two of the three
lakes (Cecil and Farewell; Table 5), indicating that competition had effectively reduced growth rates. Using body size
as the fitness proxy, gill raker length was subject to significant
disruptive selection in HD enclosures in two of the three lakes
(Dugout and Farewell; Table 5). There was a significant treatment 3 PC22 interaction effect only within Dugout Lake, in
which disruptive performance was stronger in HD than LD
(F1,249 5 4.7, P 5 0.031). However, relative gonad mass
showed no significant effect of competition, no significant
disruptive selection in HD, and no treatment 3 PC22 interaction in any of the three lakes (Table 5). The nonsignificant
results must be treated with caution, as the sample sizes from
any given enclosure are low enough to limit statistical power
to detect quadratic regression terms. It is generally recommended that tests for disruptive selection use sample sizes
of 500–1000 individuals (Kingsolver et al. 2001). Unfortunately, the sample sizes in this study were limited by enclosure size and survivorship within each enclosure.
Although the 2002 experiments only revealed an interaction between competition treatment and a quadratic perfor-
614
DANIEL I. BOLNICK
FIG. 2. Cubic spline estimates of fitness functions for gill raker
length (PCA-2), in the Cedar Lake population. Two performance
variables serve as proxies for fitness: (A) body size variation (PCA1) and (B) relative gonad mass. Cubic splines (thicker center line)
are bracketed by their 95% confidence intervals estimated from
1000 bootstrap replicates.
mance slope within one lake, analyzing all four experimental
lakes together (including Muskeg Lake from 2001) yields a
more consistent picture. Pairing quadratic slopes, g, from the
two enclosures within each lake, a one-tailed paired t-test
confirmed that g was consistently larger in HD than LD using
either performance variable (body size: t3 5 2.383, P 5
0.048; Fig. 4A; relative gonad mass: t3 5 2.49, P 5 0.044;
Fig. 4B). Hence, while disruptive performance was not always statistically significant within HD enclosures, and only
half the lakes showed a significant interaction between competition and the quadratic term, the direction of the effect is
consistent across lakes. This supports the original hypothesis
that disruptive selection is stronger in environments with
higher competition.
This conclusion is further illuminated by considering the
among-lake variation in the effect of competition on g. While
this variation may well be a result of idiosyncratic variation
FIG. 3. Quadratic regression estimates of gill raker length fitness
functions in high- and low-density treatments (HD and LD) in Muskeg Lake, 2001. The quadratic regression term is significant in HD
(V) but not LD (3) for both fitness proxies. Fitness proxies are;
(A) body size (PC1) and (B) relative gonad mass.
among the lakes, it may also reflect differential success in
manipulating population density. To test this possibility, I
used the ratio of the number of recaptured fish in HD and
LD (Table 1) as a very rough measure of the efficacy of the
density treatment. The contrast between HD and LD quadratic
slopes (gHD 2 gLD) is positively correlated with this density
ratio (body size: r 5 0.794, P 5 0.206; relative gonad mass;
r 5 0.798, P 5 0.202). While these correlations are not
statistically significant (perhaps due to very low power), they
are at least worth mentioning because the correlation coefficients are large and in a direction consistent with competitive disruptive selection.
DISCUSSION
Intraspecific competition has long been thought to drive
niche expansion and favor increased trophic variation within
a population (Darwin 1859; Mayr 1942, 1963; Ludwig 1950).
615
DISRUPTIVE SELECTION DUE TO COMPETITION
TABLE 4. Tests of disruptive selection on gill raker length (PC2) within each density treatment (HD or LD) in each lake. Body size
(PC1) and relative gonad mass provide independent estimates of fitness. The quadratic regression coefficient (g), its standard error (SE),
and its statistical significance (P) are provided for every fitness function. Within each lake a general linear model (GLM) tested for an
effect of density manipulation (density), and for a density by quadratic interaction term (density 3 PC22). Bold terms indicate statistically
significant effects. Note that because the experiment was testing a one-tailed hypothesis (disruptive selection is stronger in high density),
the P-vales for the density 3 PC22 interaction terms are adjusted to reflect the one-tailed test.
GLM P-values
Quadratic regression term
Fitness measure
Relative body size
Lake
Cecil
Dugout
Farewell
Muskeg1
Relative gonad mass
Cecil
Dugout
Farewell
Muskeg1
1
Treatment
g
SE
P
HD
LD
HD
LD
HD
LD
HD
LD
HD
LD
HD
LD
HD
LD
HD
LD
0.052
0.019
0.177
0.018
0.11
0.094
0.203
0.06
20.002
20.009
0.03
20.07
0.04
20.017
0.124
20.037
0.042
0.073
0.057
0.069
0.047
0.06
0.053
0.057
0.04
0.054
0.052
0.063
0.049
0.049
0.045
0.056
0.232
0.766
0.002
0.796
0.021
0.119
0.0001
0.3
0.96
0.963
0.95
0.276
0.425
0.73
0.01
0.67
Density
Density 3
PC22
0.034
0.337
0.65
0.031
0.005
0.44
0.001
0.035
0.96
0.46
0.351
0.19
0.833
0.249
,.001
0.012
A 2001 experiment.
Ludwig (1950) referred to this competitive niche diversification as ‘‘annidation.’’ While this particular term never
gained wide usage, the basic process has continued to play
an important role in a number of evolutionary-ecology models (Schluter 2000). For example, verbal (Rosenzweig 1978;
Gibbons 1979), analytical (Slatkin 1984; Doebeli 1996; Drossel and McKane 2000), and simulation models (Dieckmann
and Doebeli 1999; Bolnick and Doebeli 2003) have all suggested that competition can generate disruptive selection. In
turn, this selection may serve as the driving force for niche
expansion (Roughgarden 1972), sexual dimorphism (Slatkin
1984), or speciation (Doebeli 1996; Dieckmann and Doebeli
1999). Despite the substantial theoretical efforts built on this
foundation, there have been few if any direct empirical
tests—observational or experimental—of whether intraspecific competition causes disruptive selection.
This paper describes one such empirical test, using phenotypically intermediate populations of the three-spine stickleback as a study system. Sticklebacks show substantial within-population niche variation (Schluter and McPhail 1992;
Schluter 1993, 1995; Cresko and Baker 1996; Robinson 2000;
Vamosi et al. 2000; Reimchen and Nosil 2001a, b; D. I.
Bolnick, unpubl. data), and hence are likely to experience
frequency-dependent competition, which can in turn produce
stable fitness minima (disruptive selection). In a survey of
two natural populations, I found evidence that trophic morphology (gill raker length) was under disruptive selection in
one lake. Two independent proxies for fitness both indicated
that phenotypically intermediate fish fared worse than fish
with large or small gill rakers.
In contrast, trophic morphology did not appear to be subject to disruptive selection in Muskeg Lake. The fact that
Muskeg Lake gave the strongest experimental result indicates
that there is nothing intrinsic to the lake or its populations
that would prevent natural disruptive selection. This dis-
crepancy may reflect year-to-year variation in the level of
competition, either due to fluctuations in resource density or
stickleback population density. Natural stickleback populations are known to fluctuate cyclically, suggesting that the
intensity of intraspecific competition experienced by any population may be quite variable through time (Wootton and
Smith 2000). Population densities in Muskeg Lake appeared
to be experiencing a short-term decline, as capture rates (in
different parts of the lake) fell between my first visit in 2000
and the 2001 survey reported here and were even lower in
2002 (pers. obs.).
While the results from Cedar Lake were consistent with
predictions derived from theory, such observational approaches cannot directly identify the ecological mechanism
causing the selection. For example, Robinson and Wilson
(1996) showed that phenotypically intermediate pumpkinseed sunfish (Lepomis gibbosus) were smaller and had lower
stored lipid levels, but did not establish the causal mechanism
behind this pattern. They argued that their results were evidence for functional trade-offs, in which intermediate fish
were ‘‘jacks of all trades but masters of none’’ (Fig. 1A, B),
but did not test the alternative hypothesis of competitive disruptive selection (Fig. 1C).
The experimental manipulation of population densities reported in this paper allows me to reject both the densityindependent and purely density-dependent explanations (Fig.
1 A, B) of the quadratic function observed in the unmanipulated population in Cedar Lake. The quadratic slope of both
fitness proxies were consistently if only slightly steeper in
high-density than in low-density populations. This strongly
implies that intraspecific competition can generate disruptive
selection and hence could be responsible for the inferred
disruptive selection in the unmanipulated Cedar Lake population. It is worth noting that the slopes of experimentally
616
DANIEL I. BOLNICK
FIG. 4. Effect of competition (high- and low-density treatments,
HD and LD) on the quadratic slope of the gill raker length fitness
function (g). Two performance variables serve as proxies for fitness:
(A) relative body size (PC-1) and (B) relative gonad mass. Lines
connect the quadratic slopes from HD and LD enclosures paired
within a given lake (M, Muskeg Lake 2001; D, Dugout Lake; F,
Farewell Lake; C, Cecil Lake). Points above the thin horizontal line
have a positive (disruptive) slope. Asterisks indicate the statistical
significance (*P , 0.05; **P , 0.01; ***P , 0.001) of quadratic
slopes. Asterisks next to the lines connecting enclosures indicate
the significance of the density 3 PC22 interaction within the lake.
induced disruptive selection in high-density treatments were
similar to the quadratic slope observed in Cedar Lake.
Although quadratic slopes were statistically significant,
there was substantial scatter around the regression lines, and
the function slopes were shallow. The estimated quadratic
slopes (g) ranged from 0.05 to 0.2 (body size) and 20.002
to 0.124 (relative gonad mass) in the high-density treatments.
This raises the obvious concern that the observed disruptive
selection may be statistically but not biologically significant,
too weak to drive any evolutionary response. Several observations suggest that this concern is unwarranted. First, the
magnitude of disruptive selection documented here is similar
to that observed in other studies of disruptive selection. The
median slope of quadratic fitness functions in natural populations is only 0.10 (Kingsolver et al. 2001), although it is
worth noting that their review did not distinguish between
curved monotonic slopes and true disruptive selection that
has a minimum. Second, even very weak selection gradients
can have substantial evolutionary consequences over the long
term. Population genetic models generally assume selection
strengths that are orders of magnitude weaker than what we
can observe empirically, yet still find that selection can be
effective in changing gene frequencies and phenotype distributions (in particular, changing the variances, because selection is quadratic here). Theoretical models of sympatric
speciation suggest that the condition for evolutionary branching is the presence or absence of disruptive selection and not
its strength (Dieckmann and Doebeli 1999).
Although the results documented in this paper demonstrate
that frequency-dependent competition can generate disruptive selection, whether it commonly does so in undisturbed
populations is an open question. The answer will depend on
both the selective importance of intraspecific competition in
natural populations and on the degree of individual specialization needed to generate frequency dependence. In testing
the theory of competitive disruptive selection, I consciously
selected a study system that satisfied both of these conditions.
A survey of gut contents in Cedar Lake sticklebacks found
that over 60% of the resource breadth in that population could
be attributed to between-individual variation: using Roughgarden’s Shannon-Weaver index of intrapopulation variation
(Roughgarden 1974, 1979; Bolnick et al. 2002), total niche
width 5 1.63; within-individual component 5 0.61; betweenindividual component 5 1.02, so WIC/TNW 5 0.37 (D. I.
Bolnick, unpubl. data). If this variability is highly unusual,
then the dynamics described in this paper may be quite rare.
Currently, it is difficult to address this question, as very few
studies have quantified the degree of individual specialization
in a comparable manner. However, there are many published
examples of individual-level niche variation, found across a
broad array of taxa (Bolnick et al. 2003). Furthermore, quadratic fitness and performance function slopes found in other
studies (Robinson and Wilson 1996; McLaughlin et al. 1999;
Kingsolver et al. 2001) suggest that disruptive selection may
be more common than generally appreciated.
Even if competitive disruptive selection proves to be common, its evolutionary consequences are not straightforward.
Random mating, phenotypic plasticity, balancing effects of
other selective forces such as predation or sexual selection,
or fluctuations in the level of competition could all mitigate
the long-term effect of this selection. It is unclear how these
factors affect the likelihood of evolutionary processes such
as adaptive speciation that are thought to rely on competitive
disruptive selection (Rosenzweig 1978; Udovic 1980; Dieckmann and Doebeli 1999; Kondrashov and Kondrashov 1999;
Drossel and McKane 2000). Furthermore, there are multiple
alternative evolutionary responses to disruptive selection. For
example, theory suggests that both speciation and sexual dimorphisms are possible responses to disruptive selection
(Bolnick and Doebeli 2003). Which one occurs will depend
on the genetic covariance between male and female traits and
the genetic potential for assortative mating (Bolnick and Doebeli 2003). Polymorphisms or simply increased phenotypic
variance are also reasonable outcomes. Consequently, we
cannot yet make any statements about the generality of competitive disruptive selection or its evolutionary consequences.
What is clear is that competition does have the potential to
drive evolutionary diversification in natural populations.
ACKNOWLEDGMENTS
I thank P. Abrams, R. Calsbeek, P. Nosil, T. Schoener, T.
Smith, and P. Wainwright for their comments on this man-
DISRUPTIVE SELECTION DUE TO COMPETITION
uscript. J. Boughman, D. McPhail, H. Rundle, D. Schluter,
S. Vamosi, and P. Wainwright provided helpful advice and
criticism. D. A. Bolnick, D. J. Bolnick, C. Buck, B. Spitzer,
and K. van der Laan helped set up the field experiments. L.
Carswell at the Ministry of Environment, Lands and Parks
arranged permits for field work. This research was supported
by a National Science Foundation (NSF) Graduate Research
Fellowship, an NSF Dissertation Improvement Grant (DEB0105147) to DIB and P. Wainwright, a Northern California
Phi Beta Kappa Fellowship, Sigma Xi Research Grant, and
support from the Daphne and Ted Pengelly Fund, JastroShields Fellowship, Shirley Ashton Fellowship, Humanities
Research Grant, and Center for Population Biology at the
University of California, Davis.
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Corresponding Editor: T. Smith