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\begin{document}

\title{Similar conditions with opposite effects: Predation-risk effects on prey
abundance are highly contingent}



\author[1]{Scott Peacor}%
\author[2]{Clayton Cressler}%
\author[3]{Kevin Pangle}%
\author[1]{Alexandra Rafalski}%
\author[4]{Chao Song}%
\author[5]{Earl Werner}%
\affil[1]{Michigan State University}%
\affil[2]{University of Nebraska-Lincoln}%
\affil[3]{Central Michigan University}%
\affil[4]{Lanzhou University}%
\affil[5]{University of Michigan}%


\vspace{-1em}



  
  \date{June 12, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
Experiments have shown that predation risk effects on prey fitness can
be highly contingent on environmental conditions, suggesting a potential
difficulty in generalizing risk effects on prey abundance in natural
settings. We examine this problem with a novel approach. Rather than
study the influence of a particular controlled factor, we examined the
influence on risk effects of study conditions that would likely be
deemed inconsequential, i.e., to factors incidental to a study. If such
incidental factors influence can influence the magnitude and even
direction of risk effects, this suggests risk effects on prey population
density are profoundly influenced by context in natural communities. We
compared results of multiple experiments conducted under similar
conditions, objectives, measurables and implementation, and which
captured much of the complexity of natural systems (e.g., they were
performed with diverse prey assemblages ([?]11 taxa) over multiple prey
generations). There was consistently no risk effect of fish on some
zooplankton prey abundance, but great variability effect on other prey
including combinations of negative, absent or positive effects on the
same prey's abundance. We review mechanisms that could underlie these
results. Our findings highlight the need to understand the mechanisms
linking trait responses to fitness and, ultimately, to abundance of
prey, to understand risk effects across studies and systems.%
\end{abstract}%



\sloppy


Title: Similar conditions with opposite effects: Predation-risk effects
on prey abundance are highly contingent

Scott D. Peacor\textsuperscript{2}\textsubscript{,} Clayton E.
Cresler\textsuperscript{1,3}\textsubscript{,} Kevin L.
Pangle\textsuperscript{4}, Alexandra V. Rafalski\textsuperscript{2},

Chao Song\textsuperscript{2,5}, and Earl E. Werner\textsuperscript{6}

\textsuperscript{1} Corresponding Author: 402 Manter Hall, 1104 T St.,
Lincoln, NE 68588, (402) 890 7300, ccressler2@unl.edu,
https://orcid.org/0000-0002-6281-2798\textsuperscript{2} Department of
Fisheries and Wildlife, Michigan State University, East Lansing,
Michigan 48824.\textsuperscript{3} School of Biological Sciences,
University of Nebraska, Lincoln, Nebraska 68588\textsuperscript{4}
Department of Biology and Institute of Great Lakes Research, Central
Michigan University, Mount Pleasant, MI 48858

\textsuperscript{5}State Key Laboratory of Herbage Improvement and
Grassland Agro-Ecosystems, and College of Ecology, Lanzhou University,
Lanzhou, Gansu 730000, China.

\textsuperscript{6} Department of Ecology and Evolutionary Biology,
University of Michigan, Ann Arbor, MI 48109

\section*{Acknowledgements}

{\label{acknowledgements}}

We thank L. Schiesari and over 20 undergraduate research assistants for
field assistance and enumeration of zooplankton. We thank the Ecology
and Evolutionary Biology department of the University of Michigan for
use of the E. S. George Reserve. This work was supported by the National
Science Foundation DEB-0089809 and DEB-1145808. S. D. Peacor
acknowledges support from AgBioResearch of Michigan State University.

\textbf{Conflict of Interest}

None of the authors have any conflicts of interest to declare.

\textbf{Author Contributions}

SDP and EEW conceived the ideas. SDP, CEC, KLP, and EEW designed
methodology. SDP, CEC, KLP, and AVR collected the data. SDP, CEC, KLP,
and CS analyzed the data. SDP led the writing of the manuscript. All
authors contributed critically to the drafts and gave final approval for
publication. Our study was conducted at the field station of a public
university in the United States and provided valuable field experiences
and authentic experiential learning opportunities to undergraduate
students from the local area.

\textbf{Data Accessibility Statement}

Data and analysis scripts for this manuscript are archived on GitHub at
https://github.com/claycressler/ZooplanktonCTs.

\section*{Abstract.}

{\label{abstract.}}

Experiments have shown that predation risk effects on prey fitness can
be highly contingent on environmental conditions, suggesting a potential
difficulty in generalizing risk effects on prey abundance in natural
settings. We examine this problem with a novel approach. Rather than
study the influence of a particular controlled factor, we examined the
influence on risk effects of study conditions that would likely be
deemed inconsequential, i.e., to factors incidental to a study. If such
incidental factors influence can influence the magnitude and even
direction of risk effects, this suggests risk effects on prey population
density are profoundly influenced by context in natural communities. We
compared results of multiple experiments conducted under similar
conditions, objectives, measurables and implementation, and which
captured much of the complexity of natural systems (e.g., they were
performed with diverse prey assemblages ([?]11 taxa) over multiple prey
generations). There was consistently no risk effect of fish on some
zooplankton prey abundance, but great variability effect on other prey
including combinations of negative, absent or positive effects on the
same prey's abundance. We review mechanisms that could underlie these
results. Our findings highlight the need to understand the mechanisms
linking trait responses to fitness and, ultimately, to abundance of
prey, to understand risk effects across studies and systems.

\section*{Keywords}

{\label{keywords}}

Non-lethal effect, Trait-mediated, Ecology of fear, \emph{Lepomis} ,
Context dependent, Non-consumptive effect

\section*{Introduction}

{\label{introduction}}

Presence of predators often causes prey to modify traits, including
behavior, physiology, morphology, and life history, in order to reduce
predation risk (Lima and Dill 1990, Lima 1998a, Agrawal 2001). Such
risk-induced trait responses and their ensuing consequences, termed risk
effects (also nonlethal effects and fear effects; terminology reviewed
in Peacor et al. 2020), have received much attention. Whereas
risk-induced trait response in prey generally benefit prey by reducing
predation risk, they also may have associated costs to fitness
components in the prey. These consequences of the trait response on prey
are termed non-consumptive effects (NCEs), and include reduced growth or
condition, reduced fecundity, or increased mortality from other
predators, and consequent effects on prey population growth rate and
prey abundance (Abrams 1984, Brown 1988, Lima 1998b, Peckarsky et al.
2008, Ohgushi et al. 2012, Sheriff et al. 2020, Peacor et al. 2022). The
risk-induced trait-responses of the prey can also have indirect effects
(termed trait-mediated indirect effects, TMIEs; Peacor et al. 2020),
with other species including their resources, competitors, or other
predators (Sih et al. 1998, Werner and Peacor 2003). NCEs and TMIEs have
received much attention given their potential to profoundly influence
ecological communities and management of natural resources (reviewed in
Werner and Peacor 2003, Schmitz et al. 2004, Creel and Christianson
2008, Heithaus et al. 2008, Peckarsky et al. 2008, Ohgushi et al. 2012,
Say-Sallaz 2019).

Indeed, it is often claimed that risk effects are as important to the
net effect of predators on prey abundance as their direct consumptive
effects (Sheriff et al. 2020). However, that conclusion has been based
largely on experiments done in relatively simple systems with only a few
interacting species and little temporal or spatial environmental
heterogeneity (Nelson 2007, Sheriff et al. 2020), even when performed in
natural settings (Sheriff et al. 2020, Peacor et al. 2022). Thus, if we
are to incorporate risk effects into management plans, it is important
to know that the effects demonstrated in simplified systems are
generalizable to more complex systems incorporating many interacting
species and spatial and temporal heterogeneity.

Here we investigate risk effects of fish on the abundance of different
zooplankton prey species in complex experimental communities. Here we
investigate risk effects of fish on the abundance of different
zooplankton prey species in complex experimental communities. The
novelty of our approach is that we look at these effects across multiple
experiments, each with a very similar experimental conditions,
objectives, measurables and implementation. Thus, any observed
variability in the effect of predation risk across experiments arises
from differences in the experiments that are purely incidental (i.e.,
differences that were not of consequence to the study design or
implementation). These incidental differences in environmental
conditions are much smaller than the environmental variation that would
be expected in natural settings. Our approach thereby represents a
conservative examination of contingency in natural settings, which
allows us to ask whether risk effects on prey abundance are robust and
generalizable.

\section*{Methods}

{\label{methods}}

\subsection*{General approach: a conservative test of
contingency}

{\label{general-approach-a-conservative-test-of-contingency}}

Our overall approach is to compare the NCE of fish on the abundance of
different zooplankton taxa across four experiments performed in
different years, but with similar conditions, objectives, measurables
and implementation. We are unaware of any prior studies that have
examined context dependence of risk effects on prey abundance. The
experiments all shared treatments that allow us to examine NCEs of fish
on zooplankton abundance by comparing caged-fish and no-fish treatments.
The NCE on the abundance of given zooplankton taxa could thus be
compared across experiments to identify context dependence of risk
effects.

The venue was constructed to capture much of the complexity of natural
systems. Each experiment used the same large mesocosms set outdoors
(subject to natural environmental variation) and used a natural resource
base (phytoplankton) supported by nutrient addition, and a speciose
(\textgreater{}10 taxa) zooplankton prey assemblage. Each experiment ran
for 3-5 generations of the prey species with the longest generation
times (see Peacor et al. 2012), allowing the potential for ecological
feedbacks to affect abundance. Although the venue is obviously less
complex than a natural system (e.g., lacking in other predator taxa), it
incorporates much more of the complexity of a natural system than a
typical laboratory experiment that has few prey species, a simple and
controlled resource base, and controlled abiotic conditions.

Our approach is thus different than most studies that have identified
context dependency in the effect of ecological factors on fitness
components over short time scales. Typically, a particular biotic
factor, such as prey density, or abiotic factor, such as physical
complexity from plants, is systematically varied to investigate the
influence of that factor. We ask, rather, could factors that are
typically ignored or unmeasured (because they are deemed inconsequential
to the study of the risk effect) influence the results of a study of
risk effects on abundance in a natural system?

Differences that developed among the complex experimental communities
among years are likely caused by factors such as variation in starting
abundances that could lead to priority effects (Drake 1991, Chase et al.
2009), small differences in start dates across years, nutrient addition,
annual temperature variation, etc. These differences naturally caused
variation across experiments, such as the amount of periphyton in the
tanks and the absolute and relative abundance of a given taxa. It is
these incidental differences that we hypothesize could underlie NCE
contingencies. They are small relative to differences among natural
systems (e.g. different ponds and lakes), because natural systems have
e.g. higher spatial-temporal variation and multiple predators.
Subsequently, our test of contingency among experiments is a
conservative test for the contingencies relative to those expected in
natural systems. Note that by terming the differences ``incidental,'' we
do not mean they were solely due to stochastic environmental
differences. Rather, the differences were incidental to the experimental
test of NCEs on prey abundance; i.e. they would not be considered
important to the outcomes of a study of risk effects of fish on
zooplankton abundance.

We provide a description of the experimental methodology that was common
to all experiments. Where there were minor differences among
experiments, such as small differences in the average size of fish used,
a range is provided. Wherever a range is provided in the methods the
specific details are provided in a table in the Supporting Information.
Other differences among experiments and methodological differences are
summarized after the general description and described in more detail in
Supporting Information. The timing of manipulations and samplings are
given relative to the day experimental treatments were initiated (as
provided in Supporting Information), defined as day 0.

\subsection*{Methodology shared among
experiments}

{\label{methodology-shared-among-experiments}}

Outdoor mesocosm experiments were conducted at the E.S. George Reserve
(ESGR) of the University of Michigan near Pinckney, Michigan, USA
(42\selectlanguage{ngerman}°28'N, 84°00'W). Cylindrical cattle watering tanks were employed and
contained approximately 1100 L of well water. Tanks were 1.9 m in
diameter and 0.75 m tall, and were filled to a depth of 45 cm. Washed
sand was added to each mesocosm as a bottom substrate. Each mesocosm was
covered with a fiberglass window screen lid to deter colonization by
insects. On particularly sunny days, 60\% green shade cloth lids were
used on top of the window screen lids to reduce heating.

The design of each experiment included the presence/absence of fish
kairomone (i.e. chemicals) and additional treatments to explore
different questions in each experiment (Supporting Information). A
randomized block design was used for each experiment, and replicates
ranged from 6-16 depending on the treatment and experiment. The
non-consumptive effect of fish was created by maintaining one
zooplanktivorous bluegill sunfish, \emph{Lepomis macrochirus} (standard
lengths given in Supporting information) in each of two or three
floating cages within each kairomone treatment mesocosom. No-fish
treatments consisted of mesocosms with the same number of empty floating
cages. Large holes in the sides and bottom of the boxes were covered
with fine netting to allow for diffusion of fish kairomones without
permitting zooplankton to pass through. In Exps. 1 and 2, three large
snails (\emph{Planorbella cf. trivolvis} \textgreater{}11.2 mm in
diameter) were kept inside each cage to graze on periphyton that could
grow on the mesh windows. Adding snails was deemed extraneous and not
implemented in Exps. 3 and 4.

Fish originated from Patterson Lake, Livingston County, Michigan. In
order to ensure fish health and to equalize fish cue (e.g. in case fish
differed in cue production) we rotated the fish from the experimental
mesocosms to a culture tank once a week. Culture tanks consisted of
outdoor mesocosms of \textasciitilde{}50 fish of similar size fed
zooplankton three times per week. Fish in culture tanks were not fed for
24 hours before being rotated back into the experiment. While in the
experimental cages, each fish was fed twice a week, including the day
they were added to the cages. To feed fish, an average of
200\emph{Daphnia} were added per cage. The no-fish cages received an
equal amount of \emph{Daphnia} that were killed by microwaving to ensure
a population did not build in the cages.

Calculations of nutrient inputs indicate that nutrients from fish
excretion were inconsequential, as they were overwhelmed by other
sources, including external supply and internal recycling by zooplankton
(See Supporting Information in Peacor et al. 2012). Two experiments also
provide evidence that nutrient inputs from fish had no ecological
effects. In one, caged fish had no effect on phytoplankton growth in
chambers placed outside, but near, the cages and performed soon after
(8-10 days) treatments were initiated. In a second in-situ experiment,
fish excretion had no effect on phytoplankton growth in chambers placed
within the fish cages (Rafalski and Peacor, unpublished).

An initial pulse of nutrients (see below) and a phytoplankton inoculum
were added to each tank between 35 and 56 days before the start of the
experiment. The phytoplankton inoculum consisted of water collected from
ponds in the ESGR and filtered through 35 \selectlanguage{greek}µ\selectlanguage{english}m Nitex mesh. A community of
zooplankton was added to each tank between day -49 and -27 (Exp. 3
received two inoculums). This initial inoculum came from a single lake
(Exps. 2-4) or multiple lakes (Exp. 1, see Supporting Information for
details on source lakes). We collected zooplankton using a 64\selectlanguage{greek}µ\selectlanguage{english}m
zooplankton net, and undesirable animals such as insects
(e.g.\emph{Chaoborus} ) and \emph{Hydra} were removed prior to adding
the inoculum to the mesocosms. To decrease zooplankton heterogeneity
among mesocosms, on day -24 to -6 we collected samples of zooplankton
from each mesocosm using a 64 \selectlanguage{greek}µ\selectlanguage{english}m zooplankton net, mixed all samples, and
redelivered subsamples of this mixed community to all mesocosms. The
procedure was done twice for Exps. 3 and 4. Although this procedure
reduces variation in initial densities among tanks, there will clearly
be more variation at treatment initiation than in a more controlled
experiment that would start with constant densities.

Inorganic nutrients were supplied to the mesocosms to support
phytoplankton growth as a resource for zooplankton at an N:P ratio of
15:1 or 20:1 (Supporting Information). An initial pulse of
NH\textsubscript{4}NO\textsubscript{3} and
KH\textsubscript{2}PO\textsubscript{4} was added to each mesocosm when
phytoplankton were added, and thereafter we supplied maintenance doses
weekly. The rate and overall amount of nutrients added were similar
among experiments (Supporting Information), though there was some
variation in rates and the frequency at which nutrients were added,
varying from twice per week to continuous (Supporting Information). In
some cases, the weekly rate was reduced to reduce phytoplankton and
filamentous algal growth when deemed excessive (Supporting Information).
To reduce periphyton growth and to cycle nutrients back to the water
column, we collected visible filamentous algae by hand each week,
clumped it, allowed it to dry and returned it to the mesocosms. In Exps.
1 and 2, we also added \emph{Planorbella cf. trivolvis} snails to each
mesocosm to reduce periphyton growth.

Zooplankton were sampled on day 31-71. Samples were passed through a
53-64 \selectlanguage{greek}µ\selectlanguage{english}m mesh sieve and preserved. Zooplankton were identified and
enumerated. Zooplankton identification was resolved to species or genus
for Cladocera, adult cyclopoid and adult calanoid for Copepoda (referred
to as ``cyclopoid'' and ``calanoid'', exception described below), and
ostracods for Ostracoda.

There were a number of methodological differences among experiments due
to the specific questions addressed which we summarize here, and
describe in more detail in Supporting Information. (1) The zooplankton
collection method differed; in Exps. 1 and 2 we subsampled different
positions in the tank to determine both zooplankton position in the
tanks and overall density, while in Exps. 3 and 4 we measured overall
density from combined samples of the entire water column. This
difference should not affect our assessment of an NCE of fish on
zooplankton abundance. (2) The experiments examined different aspects of
risk effects with additional treatments. In three experiments, the
additional treatments were included in the no-fish or caged-fish
treatment, as they had no influence and their inclusion increased the
power to identify treatment effects (Supporting Information). (3) The
origin of the zooplankton was the same (a single lake) for Exps. 2-4,
but differed for Exp. 1 in which collection was from multiple lakes.
(4)\emph{Hydra} , which are predators of some zooplankton species, were
present in Exp. 1, but not Exps. 2-4. (5) Because the size and
ecological role of the juvenile stage of cyclopoids can be variable
(Burns and Gilbert 1993), we counted only adult cyclopoids. However, in
Exp. 4 the counting resources were more limited and thus the cyclopoid
counts include both juveniles and adults.

All procedures involving animals were approved by University of
Michigan's Committee on the Use and Care of Animals under approval
number 07765 (for experiments 1 and 2) and Michigan State University's
Institutional Animal Care and Use Committee approval no. 02/12-025-00
(for experiments 3 and 4).\selectlanguage{english}
\begin{longtable}[]{@{}lll@{}}
\toprule
\begin{minipage}[b]{0.30\columnwidth}\raggedright\strut
Scale of inference\strut
\end{minipage} & \begin{minipage}[b]{0.30\columnwidth}\raggedright\strut
Scale at which the factor of interest is applied\strut
\end{minipage} & \begin{minipage}[b]{0.30\columnwidth}\raggedright\strut
Number of replicates at the appropriate scale\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.32\columnwidth}\raggedright\strut
Species\strut
\end{minipage} & \begin{minipage}[t]{0.32\columnwidth}\raggedright\strut
Community\strut
\end{minipage} & \begin{minipage}[t]{0.32\columnwidth}\raggedright\strut
Exp. 1: 12 no fish treatments; 6 fish treatments Exp. 2: 16 no fish
treatments; 8 fish treatments Exp. 3: 18 no fish treatments; 18 fish
treatments Exp. 4: 14 no fish treatments; 14 fish treatments\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

\emph{Statistical analysis}

We compared the NCE of fish on zooplankton densities across the four
experiments for the subset of zooplankton taxa that were represented in
all experiments (see Results, Table 1). We used a multivariate approach
to evaluate the overall response of the zooplankton community to the
presence of fish kairomones, and our initial analysis simultaneously
tested the effect of experiment, treatment and the interaction between
experiment and treatment on the subset of taxa using a permutational
multivariate analysis of variance (PERMANOVA, McArdle and Anderson
2001). This analysis uses dissimilarity matrices, and we chose to use a
dissimilarity matrix based on Kulczynski distances, which are
particularly robust in identifying patterns in community data (Faith et
al. 1987). Probabilities from the analysis were calculated based on
9,999 permutations. These effects were also investigated graphically by
comparing experiments and treatments across different axes generated
through non-metric multidimensional scaling (NMDS). The NMDS was based
on Kulczynski distances and was constrained to three dimensions, which
was the minimum number of dimensions that resulted in a stress measure
less than 0.20 (Clarke 1993).

The effect of treatment on the density of individual taxa within an
experiment was analyzed using quasi-Poisson regressions.

\textbf{Results}

Zooplankton species richness was similar in all four experiments, with
the ten taxa that had the highest average density across experiments
found in all experiments (Table 1). The mean and standard deviation of
the density of taxa varied greatly among experiments (Fig. 1, Table 1).
The density ranks also varied among experiments; e.g., the most common
(rank 1) species in one experiment could be anything from the
2\textsuperscript{nd} to the 14\textsuperscript{th} most common taxa in
another experiment. Nevertheless, there were some strong similarities
among experiments. For example, the rank of some taxa (\emph{Chydorus} ,
cyclopoids and calanoids) did not vary by more than 4.
Further,\emph{Ceriodaphnia} was the first or second most common species
in Exps. 1-3 (but much less common in Exp. 4 in which \emph{Daphnia}
and\emph{Bosmina} were particularly common). We also report occupancy of
each taxon as the percentage of tanks in which it was found. Only in one
case was the occupancy of a common taxon (i.e. found in all four
experiments) less than 70\% (in Exp. 1, \emph{Bosmina} was found in 54\%
of the tanks). Variation in community composition of zooplankton across
experiments was much larger than the variation within an experiment, as
shown by the significant effects of experiment (p = 0.0001) in the
permutation multivariate analysis, and illustrated in Fig. 2, in which
the NMDS values differ among experiments.

Caged fish had an effect on zooplankton community composition that
varied across experiments, as evidenced by the significant experiment x
caged fish interaction (p = 0.0036), but no effect of caged fish when
averaged across the four experiments (p = 0.47). This varying effect of
caged fish on community composition is evident in Fig. 2, as the NMDS
values of the caged-fish treatment were observed to be higher, lower, or
not different than the values of the no-fish treatment in different
experiments (e.g. for NMDS dimension 3, the fish treatment is higher
than the no-fish treatment in Exp. 1, but lower than the no-fish
treatment in Exp. 4).

Caged fish had a significant effect on the abundance of some taxa, and
in some cases the direction of that effect varied among experiments
(bottom box, Table 1). For two taxa all three possible effects occurred:
the presence of caged fish increased, decreased, and had no effect on
the density of calanoids and \emph{Chydorus} . For four taxa two
possible effects occurred: fish increased or did not affect the density
cyclopoids, \emph{Bosmina} and \emph{Scapholeberis} ; fish decreased or
did not affect the density of ostracods. There were also several cases
where there was a significant positive or negative effect of fish on a
taxon in one experiment, and a nonsignificant trend in the opposite
direction in another experiment. For example, there is a strong negative
fish effect in Exp. 4 on ostracods, and the ``no effect'' result in Exp.
1 shows a trend in the positive direction with small enough
within-treatment variation that the influence is unlikely in the
negative direction. In all cases in which there was a positive or
negative effect of fish, the taxa were observed in all tanks in both
treatments. For four taxa fish did not have a significant effect in any
of the experiments (\emph{Alona} , \emph{Ceriodaphnia} , \emph{D. pulex}
, and \emph{Diaphanosoma} ).

\section*{Discussion}

{\label{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 \selectlanguage{ngerman}± 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, \emph{Daphnia} , \emph{Ceriodaphnia} , \emph{Bosmina}
,\emph{Chydorus} , copepods, ostracods, and \emph{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
\emph{Daphnia}to 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
\emph{Bosmina}(including to fish), \emph{Diaphanosoma} ,
\emph{Simocephalus} (including to fish), \emph{Ceriodaphnia} (including
to fish), copepods, and\emph{Chydorus} (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
\emph{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 \selectlanguage{greek}α \selectlanguage{english}selectivity
index) was highest for \emph{D. pulex} and \emph{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 \emph{D. pulex} or \emph{Ceriodaphnia} abundance in any of
the four experiments, (though there is a strong negative trend in Exp. 2
on \emph{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
\emph{Ceriodaphnia}density 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 \emph{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 \emph{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 \emph{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 \emph{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 \emph{Hydra} reside, this would make them
more or less vulnerable to \emph{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 \emph{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 \emph{Chydorus} in the region (\emph{Chydorus sphaericus)} , and
caged fish had a positive, neutral, and negative effect on
\emph{Chydorus}density.

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.

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\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

Table 1. Top box: Zooplankton mean density (/liter) and standard
deviation across four experiments, ordered by most to least common from
left to right. \emph{Daphnia retrocurva} and \emph{Macrothrix} are not
included as they were at very low densities in a small number of tanks
of Exp. 1 only. Middle box: Density rank of each taxa in each
experiment, and the percent of tanks the taxon was found in as indicated
by: plain text for 90-100\% occupied, italicized bold text for 70-90\%
occupied, underlined text for 50-70\% occupied, an asterisk for 20-50\%
occupied, and N for no tanks occupied. Bottom box: The caged-fish
treatment effect combinations of no effect, positive effect or negative
effect, seen among experiments (see Fig. 1) is given by a capital X for
each of the 10 taxa found in all four experiments. The lower case x
represents the combination trend if p \textless{} 0.1 is used rather
than p \textless{} 0.05.

\textbf{Figure Legends}

\textbf{Figure 1} . Whisker plots of the density (\# ind/l) of the 12
most common zooplankton taxa in the four experiments. Significant
effects are indicated with p-values (in solid outlined boxes). Where a
strong trend is evident in the figure, p values are also provided (in
dashed outlined boxes), and n.s. signifies p \textgreater{} 0.2. Filled
and empty boxes represent positive and negative effect of the caged
predator, respectively Taxa given in order (right to left, top to
bottom) of average density (Table 1). The dark line in the boxes is the
median, and the bottom and top of the box is the 25th (Q1) and 75th
percentile (Q3) of the data. The lower whisker is Q1 - 1.5*IQR (the
interquartile range, between the 25th and 75th percentile), and the
upper whisker is Q3 + 1.5*IQR. Points are outliers, and the following
density values were removed from the figure to better illustrate the
core distribution of densities: 2400 for \emph{Bosmina} in Experiment 2,
Fish Treatment; 238.0 for \emph{Chydorus} in Experiment 3, Control
Treatment; 43.8 for cyclopoids in Experiment 2, Fish Treatment; and
505.5 for \emph{Alona} in Experiment 3, Fish Treatment.

\textbf{Figure 2} . NMDS values for each of the four experiments in the
caged fish and no fish treatments. Whisker plot as in Figure 2.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

Figure 1.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

Figure 2.

\selectlanguage{english}
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