Discussion
The principal result of this study is that the effect of predation risk
on the abundance of a given prey taxon varied strongly across
experiments conducted under similar conditions, objectives, measurables
and implementation; the effect of caged fish on the abundance of the
same taxa ranged among experiments from negative or no effect, positive
or no effect, and negative, positive or no effect. A multivariate
analysis similarly indicated an influence of caged fish on the
zooplankton community composition that varied among experiments. Our
approach is conservative in nature: variability in unmeasured factors
that influence risk effects is expected to be much larger in nature than
what we found in our experiment. Our study indicates that if a predator
is found to have a positive or negative risk effect on prey abundance in
a particular system, or at a given time, this alone may yield limited
information on the existence or magnitude of a risk effect on prey
abundance in another location or time.
Predation-risk effects result from a predator inducing changes in prey
phenotype (e.g., behavior, morphology), which can lead to changes in
prey fitness components (e.g., fecundity, survival), which in turn
influences prey population growth rate and abundance (Sheriff et al.
2020, Wirsing et al. 2020), and contingency could arise at any one of
these scales. Empirical studies have shown that the magnitude, and even
existence, of a trait response is contingent on the balance of costs and
benefits associated with the trait change, each of which is strongly
dependent on biotic and abiotic factors, such as resource level, the
presence of other predators, con- and heterospecific density, and
abiotic factors such as temperature and landscape features (Peacor et
al. 2013, Thaker et al. 2011, Tollrian et al. 2015, Donelan et. al.
2017, Wirsing et al. 2020). For example, prey are predicted to show a
lower response at lower resource levels, which can be affected by many
factors including competitors.
The effect of a trait response on fitness components (i.e., NCEs) has
also been shown to be strongly contingent on abiotic and biotic factors.
For example, the cost of a habitat shift (the trait response) will
depend on consequent experienced changes in temperature, resources, and
mortality risk from other causes (e.g. parasites or other predators).
Further, it has been shown that the level of trait responses of
intraspecific or interspecific competitors to predators can cause the
NCE on a focal prey’s fitness to be magnified, dampened and even
reversed (Relyea 2000, Peacor and Werner 2004).
Previous studies that intentionally manipulate aspects of the abiotic or
biotic environment thus clearly show contingency in risk effects on both
trait and fitness components, and altering either response could alter
predation-risk effects on prey abundance. Indeed, theoretical studies
show very clearly that altering responses “upstream” of prey abundance
can alter abundance responses (Abrams and Vos 2003; Bolker et al. 2003;
Peacor and Cressler 2012). However, this is the first empirical study to
show contingency in risk effects on prey abundance.
Moreover, there was high within-treatment replicate variability compared
to typical NCE experiments. For example, different replicate mesocosms
within an experiment often varied in characteristics across experiments
such as periphyton growth and zooplankton taxa density (density
coefficient of variation equal to 1.2 ± 0.62 [mean ± stdev] within
treatments for all taxa across the four experiments). The high levels of
variability observed within treatments, despite the relatively
simplicity of the environment, has two important implications. First,
high variation within treatments would make significant risk effects
less likely to be observed, as any effect of the caged fish on prey
density had to be large to be statistically detectable over the high
within-replicate variation. Second, the fact that we observed
contingency despite the high similarity among experiments relative to
differences among natural systems, and despite the high within treatment
variation relative to typical experiments of risk effects, suggests that
this study provides a conservative test of the contingency of risk
effects on prey abundance in natural settings.
Nearly all of the important species in our experimental communities have
been reported to be components of bluegill diet in lakes and ponds. In
particular, Daphnia , Ceriodaphnia , Bosmina ,Chydorus , copepods, ostracods, and Simocephalus can at
times be a dominant or substantial component of bluegill diets (e.g.,
Hall et al. 1970, Keast 1985, Rettig and Mittelbach 2002, Dewey 1997,
Bremigan and Stein 1994). Consequently, these species have an
evolutionary history of risk to bluegill predation, and indeed, many
have been shown to respond phenotypically to presence of fish, including
in some cases bluegill, and to other predators. For example, a large
number of studies have documented phenotypic response in Daphniato a wide array of predators including bluegill (see reviews in Lass and
Spaak (2003) and Diel et al. (2020)). Diel et al. (2020) also report
evidence of phenotypic responses to predators in Bosmina(including to fish), Diaphanosoma , Simocephalus (including
to fish), Ceriodaphnia (including to fish), copepods, andChydorus (to injured conspecifics, Pecor et al. 2016). In
experiments of the same venue used here, we have further observed fish
kairomones to affect the spatial position of Alona, Bosmina,
Chydorus, Diaphanosoma, Scapholeberis and ostracods (unpublished data).
Thus, many of the important components of the zooplankton communities in
the experiments have the potential to respond to the presence of
bluegill kairomones and initiate effects we see of bluegill presence on
community structure.
Perhaps surprisingly, there was no risk effect of caged fish on the
abundance of some vulnerable zooplankton prey in some or multiple
experiments. For example, using bluegill of the same size and origin, we
found that bluegill preference (as measured by Chesson α selectivity
index) was highest for D. pulex and Ceriodaphnia (Rafalski
et al. 2023) which is consistent with other studies that report
preferred prey of small bluegill (Bremigan and Stein 1994). Yet, there
was no NCE on D. pulex or Ceriodaphnia abundance in any of
the four experiments, (though there is a strong negative trend in Exp. 2
on Ceriodaphnia ). The lack of an effect cannot be explained by
the absence of a cue of predation risk in the experimental venue (e.g.,
kairomones can be affected by properties of the water (Peacor 2006,
Turner and Chislock 2010)) because other species were affected by caged
fish in Exp.1, 2 and 4, while in Exp. 3 multiple species responded
behaviorally to caged fish (Rafalski et al. unpublished). A power
analysis confirmed that the lack of any significant effects in Exp. 3
was not due to a lower sample size or higher variance in that
experiment, but rather to a true lack of effect of fish on the densities
of any species (Supporting Information). Our results therefore highlight
that predation risk may have little or no effect on the abundance of
highly vulnerable prey (Hoverman and Relyea 2012), and the lack of any
significant responses on any species in one of four experiments further
highlights the contingency of the risk effects found in this study.
It is beyond the scope of this study to identify the specific mechanisms
responsible for the observed contingency in NCEs in our study, but we
can speculate. Incidental differences in factors such as temperature and
initial relative abundances could lead to variation in community
assembly processes across experiments (Drake 1991, Chase 2003) that
could lead to the differences in species densities observed among
experiments, and those differences in species densities could influence
predation risk effects. For example, in Exp. 2 Ceriodaphniadensity was higher in the no-fish treatment than it was in the other
experiments, and it was the most abundant zooplankton taxon (Table 1,
Fig. 2). We have found that Ceriodaphnia responds behaviorally to
caged bluegill by moving horizontally away from the tank center
(unpublished data) that would likely be associated with reduced foraging
rates or freeing of resources in the center of the tanks. The indirect
benefit to other zooplankton taxa due to competitive release would
increase as a function of Ceriodaphnia density, and thus would be
most likely observed in Exp. 2 where indeed positive effects were
observed on a number of taxa. There was a strong non-significant trend
for predation risk to reduce Ceriodaphnia density in Exp. 2,
which could have further contributed to the observed positive effects on
other species densities. Additionally, the length of the experiments
(relative to generation times; even the species with the longest
generation time experienced at least three generations; Peacor et al
2012), allowed ample time for incidental differences among experiments
to generate ecological feedbacks, for example, generating indirect
effects on resources. Indirect positive effects on resources could also
vary among experiments due to differences in species densities among
experiments, or a predator-induced reduction in foraging on particular
phytoplankton species that facilitated growth of other phytoplankton
species favored by different zooplankton species.
The presence of Hydra in Exp. 1 could add an additional mechanism
by which caged fish influence zooplankton and hence cause contingency.
If caged fish induce a habitat shift in zooplankton to be closer or
further from surfaces where Hydra reside, this would make them
more or less vulnerable to Hydra predation, respectively. Ensuing
effects on density of one zooplankton species could then have indirect
effects on the abundance of other zooplankton species. Note that effects
of Hydra cannot be responsible for all of the contingencies
observed, as different effects of caged fish remain if Exp. 1 is omitted
(top box, Table 1).
It is also possible that some contingency is manifest due to species
composition differences in broader taxonomic groups in our study (e.g.,
cyclopods and calanoids) across experiments (and such species reacted
differently to the bluegills). This possibility, however, does not apply
to more circumscribed categories, for example there is only one species
of Chydorus in the region (Chydorus sphaericus) , and caged
fish had a positive, neutral, and negative effect on Chydorusdensity.
It is important to establish the degree to which risk effects on prey
abundance are contingent for at least two reasons. First, contingency
will make it challenging to translate findings of strong risk effects in
simple experimental settings to natural communities, or translate strong
trait-responses of prey seen in the field to a consistent prediction of
ensuing effects on prey fitness and abundance. Second, much of the
literature on risk effects has addressed whether they are strong
relative to consumptive effects (CEs). For example, empirical (Werner
and Peacor 2006) and meta-analysis studies (Preisser et al. 2005) have
compared the influence of risk effects and CEs, with an implicit
assumption that they contribute different relative fractions of the net
effect of a predator. But if risk effects on prey abundance are strongly
contingent, then it is more difficult to make inferences from single
reports of the magnitude of risk effects. If contingencies of risk
effects on prey abundance are important, this highlights the need to
understand the mechanisms underlying risk effects, and how environmental
factors will influence the consequences of risk effects. Resolving such
questions has clear implications for ecological theory and attempts to
include the effects of risk effects into conservation and management.
We have demonstrated that predation risk effects on abundance of prey
species can be highly contingent on conditions, varying even in sign,
using methodology that likely underestimates the influence of
contingency in natural systems. Many investigators state that risk
effects are known to be important, implicitly implying that risk effects
influence the abundance of species in natural systems (Sheriff et al.
2020). But few studies, in very few ecological systems, have actually
tested for such effects on abundance in natural systems (Peacor et al.
2022), and until such tests are performed, our results on the
contingency of risk effects suggest that results from experimental work
may not translate to natural systems. Rather, our results highlight the
necessity of examining the underlying mechanisms before predicting the
consequences of predation risk to natural systems or applying these
ideas to problems in conservation and management. Indeed, theoretical
studies have argued that risk effects have been exaggerated by omitting
crucial factors such as stage structure that can decrease the influence
of risk effects (Luttbeg et al. 2003; Persson and De Roos 2003), or by
methodological problems used in experimental studies (Abrams 2010).
Identifying the mechanisms underlying risk effects will allow us to
understand the contingencies that can arise in the cascading effects of
predation risk on prey traits, ensuing effects on prey fitness
components and indirect effects on resources, competitors and other
predators, which combine to influence prey abundance, and make informed
predictions of the influence of risk.