DOI: 10.25338/b8kp8p
title: Numerical responses of omnivorous terrestrial arthropods to plant alternative resources suppress prey populations: a meta-analysis

description: Omnivory is ubiquitous in ecological communities. Yet, we lack a consensus
 of how plant alternative resources impact the ability of omnivores to
 suppress prey populations. Previous work suggests that plant alternative
 resources can increase, decrease, or have no effect on the magnitude of
 omnivore-prey interactions. This discrepancy may arise from 1) the ability
 of omnivores to numerically respond to plant alternative resources and 2)
 identity-specific effects of plant alternative resources. We used a
 meta-analysis to examine how omnivore numerical responses and the identity
 of plant alternative resources affect 1) predation rate by omnivores and
 2) omnivore impacts on prey density. Plant alternative resources reduced
 omnivore predation rate regardless of identity. The suppression of
 predation rate by flowers and flowering plants was magnified when pollen
 alone was tested as the alternative resource. Surprisingly, plant
 alternative resource availability reduced prey density, suggesting that
 omnivore predation increased with plant alternative resources. This
 discrepancy (plant alternative resources decreased omnivore predation
 rates but also decreased prey density) resulted from experimental
 differences in the ability of omnivores to respond numerically to plant
 alternative resources. In the presence of plant alternative resources,
 allowing omnivore numerical responses decreased prey density, while not
 allowing numerical responses increased prey density. Because omnivores
 commonly suppress prey density in the presence of plant alternative
 resources when numerical responses of omnivores are allowed, the
 effectiveness of biological control may depend upon the availability of
 such resources and the facilitation of numerical responses.
description: <em>Literature
 survey</em> We surveyed the literature using
 Google Scholar and the following search terms, (“omnivor*") AND
 ("consumptive effects" OR "herbivore interactions" OR
 “alternative resources” OR “plant provided foods” OR “non-prey foods” OR
 “pollen” OR “flowers”). The search was conducted on 24 October 2019. We
 used the preferred reporting practices outlined by PRISMA to structure our
 overall literature search (Moher et al. 2009). Our search identified 2,375
 potential manuscripts (after duplicate removal). For each potential
 manuscript, we read the abstract and determined if the study tested the
 interactions between plant alternative resources and omnivorous
 terrestrial arthropods. Our initial goal was to include omnivores of all
 taxa; however, we obtained few (&lt;5) manuscripts using
 non-terrestrial arthropod omnivores (e.g., gastropods) and thus chose to
 focus only on terrestrial arthropods for this review. This screening
 yielded 426 papers that we read in full to determine if they were eligible
 for inclusion in our meta-analysis (see Fig. 1). Studies were deemed
 eligible if they included measurements of omnivore top-down effects in the
 presence and absence of plant alternative resources. We targeted
 manuscripts that measured the effects of plant alternative resources on:
 (i) omnivore prey consumption and (ii) prey density (in the presence of
 omnivorous predators). Hereafter, these datasets will be referred to as
 the “animal prey consumption” and “prey density” dataset, respectively.
 Using these criteria, we identified 267 individual studies from 37 papers
 to include in our analysis (Fig. 1). Most of the excluded studies tested
 the effects of plant alternative resources on omnivore performance (e.g.,
 survival, fecundity, and body condition; see for example Eubanks and Denno
 1999, Rinehart and Long 2018). <em>Data
 collection</em> From each paper, we collected
 data on omnivore prey consumption and prey density in the presence and
 absence of plant alternative resources (Appendix S1: Table S1). We
 extracted data from tables, text, and figures (using Web Plot Digitizer to
 extract data from figures; Rohatgi 2015). For each relevant study, we
 extracted the sample size, mean, and variance (standard error or standard
 deviation). Because the mean was not reported for one manuscript (Robinson
 et al. 2008), we extracted the sample size, minimum, first quartile,
 median, third quartile, and maximum values of prey consumption for this
 study. We used this information to estimate the means and standard
 deviations for this manuscript’s studies (n = 3, sensu Wan et al.
 2014). If
 manuscripts contained multiple relevant independent studies, we extracted
 each individual study. Several of the manuscripts that measured omnivore
 impacts on prey density recorded it across multiple, non-independent
 timepoints (e.g., repeated measures or timeseries data). For these
 studies, we extracted the final timepoint of the dataset for each relevant
 study. We chose to use the final timepoint, rather than using the average
 across timepoints for three reasons. First, the final timepoint was the
 most comparable timepoint across all manuscripts because it was the only
 timepoint provided in 65% of manuscripts and 85% of the individual studies
 included in our dataset. Second, almost every study in the animal prey
 consumption dataset provided only the final timepoint (accept Choate and
 Lundgren 2013). Third, we found no effect of timepoint (final versus
 time-averaged) on our interpretation of plant alternative resource effects
 on prey density (see Appendix S2). This suggests that despite temporal
 variation in these data, the final timepoint is representative of the
 overall effect of plant alternative resources on prey density.
 For each extracted study,
 we also recorded the 1) plant alternative resource identity (pollen,
 flowers, flowering plants, or seeds and pods), 2) ability of omnivores to
 display numerical responses, 3) temporal scale (i.e., days run), 4)
 experimental spatial scale [i.e., replicate size (m2 or m3)], and 5)
 omnivore taxon. Omnivores were able to display numerical responses if the
 experiment 1) allowed omnivorous predators born outside of the
 experimental area to freely immigrate into the experimental area (i.e., no
 barriers to omnivore dispersal, such as cages) and 2) contained &gt;1
 individual of the omnivore species of mixed/ undetermined sex or
 introduced gravid females and allowed offspring to develop to predatory
 stages— where they can actively consume animal prey (i.e., the study did
 not remove eggs or larvae and ran long enough for development to occur,
 see Appendix S1: Table S1) <em>Metanalyses for effects
 of plant alternative resources on prey consumption and
 density.</em> We conducted our meta-analysis
 using OPEN MEE software (Build date: 26 July 2016; Wallace et al. 2017).
 We used both the Hedges’ d and the log response ratio (hereafter, d and
 LRR; respectively) to compare the effects of plant alternative resources
 (present/absent) on omnivore prey consumption and prey density (Hedges
 1981). We used these two measures of effect size to increase the
 robustness of our analysis because d is sensitive to differences in sample
 standard deviation and LRR can be biased by studies with small samples
 sizes (Osenberg et al. 1997, Lajeunesse 2003). For both effect sizes, a
 positive effect size indicates that plant alternative resources increased
 the response variable; while a negative effect size indicates that plant
 alternative resources decreased the response variable. In the absence of
 numerical responses, the effect sizes of animal prey consumption and prey
 density should be inversely correlated— with negative effects on animal
 prey consumption manifesting as positive effects on prey
 density. We used
 separate meta-analyses (random-effect models with a Der Simonian-Laird
 approach) to determine the overall effect of plant alternative resources
 on omnivore prey consumption and prey density. To minimize the effects of
 small sample sizes, we excluded covariates (e.g., plant alternative
 resource identities) supported by fewer than three separate papers (sensu
 Rinehart and Hawlena 2020). A synthesis of ecological meta-analyses
 suggested that three papers is the minimum number of separate papers that
 should be included (Koricheva and Gurevitch 2014). <em>Meta-regressions for
 the consequences of experimental methodology on the effect of plant
 alternative resources on prey consumption and density</em>
 We used meta-regressions
 (random-effect models with a restricted maximum likelihood approach) to
 understand the influence of our extracted covariates (e.g., plant
 alternative resource identity, ability of omnivores to display numerical
 responses, experimental length, experimental spatial scale [i.e.,
 experimental area (m2) and volume (m3)], and omnivore taxonomy) on the
 effect of plant alternative resources on animal prey consumption and prey
 density. We considered extracted covariates eligible for meta-regressions
 if each subgroup in the analysis (e.g., pollen vs. flowering plants for
 plant alternative resource identity) was supported by at least three
 separate papers and five studies (sensu Rinehart and Hawlena
 2020). <em>Dataset variability and
 publication bias.</em> For all meta-analyses and
 meta-regressions, we tested the heterogeneity of our dataset by
 calculating both Q (total heterogeneity) and I2 (heterogeneity due to
 between-study variance). We tested for potential publication bias by
 calculating Kendall’s Rank Correlations (Tb,) between effect size and
 pooled variance within each dataset (Begg and Mazumdar 1994). If potential
 bias was detected (Tb with p &lt; 0.05), we used funnel plots to
 visually identify potential outliers (Begg and Mazumdar 1994, Palmer
 1999). Additionally, we calculated the Rosenthal’s fail-safe number, Nfs,
 for all significant tests (Rosenthal 1979, Rosenberg 2005). Rosenthal’s
 fail-safe number predicts the number of additional studies with neutral
 effect sizes (effect size = 0) that would need to be added to the dataset
 to lose significance. We classified fail-safe analyses as robust if they
 were greater than 5n+10, where n is the number of studies for a given
 response variable (Rosenberg 2005). <em>Data extraction
 method</em> To understand if the timepoint
 used in our analysis affected our interpretation of plant alternative
 resources effects on omnivore prey consumption and prey density, we
 extracted all timepoints from studies using a repeated measures or
 timeseries design (i.e., multiple, non-independent measurements) and
 time-averaged the extracted data for each study. This was necessary
 because several studies in our dataset collected non-independent data by
 tracking the same prey populations for several days to months.
 Specifically, one study in the prey consumption dataset used multiple
 timepoints (Choate and Lundgren 2013), while 40 studies (56%) in the
 animal prey density dataset measured prey population density over multiple
 time points. Since only a single study in the prey consumption dataset
 used this study design, we excluded it from further time-averaging
 analyses and focused only on the prey density dataset. To generate our
 time-averaged prey density dataset, we extracted the mean prey density at
 each timepoint presented in the study. We then calculated an overall mean
 and standard deviation for the study using all extracted timepoint means
 for a given study. We compared the outcomes of our time-averaged
 (described here) and final timepoint (described in main text) using a
 meta-regression (random-effect models with a restricted maximum likelihood
 approach) which found that data extraction method had no effect on our
 interpretation of plant alternative resource effects on prey density—
 suggesting that our use of final timepoint does not bias the findings of
 our meta-analysis. Additionally, we ran meta-analyses (random-effect
 models with a Der Simonian-Laird approach) to compare the effect of
 omnivores on prey density. Here, we found further evidence that extraction
 timepoint had no effect on our overall conclusions.
description: For all datasets, Study_ID is the individual study code, while
 MS_ID is the code linked to each manuscript (containing multiple Studies).
 N_Present, Mean_Present, and SD_Present represent data extracted when
 plant alternative resources were availible in the system; while N_Absent,
 Mean_Absent, and SD_Absent represent data extracted when plant alternative
 resources were not availible to omnivores in the system. Num_Resp denotes
 if the study allowed for omnivores to numerically respond (i.e., aggregate
 or reproduce). Study_Area and Study_Volume denote the size of experimental
 replicates used in each study-- blanks in these columns represent missing
 data (data missing due to lack of reporting in individual manuscripts).
 Study_Length is the length (in days) that each study was run. d is the
 Hedges d effect size calculated for each study, Var (d) is the variation
 of the Hedges d calculation. In.Resp.R is the log response ratio
 calculated for each study, Var (In Resp.R) is the variation of the log
 response ratio calculation. All effect size calculations were preformed
 using OpenMEE software (see methods). Any cells filled with na represent
 data that was missing from the respective manuscript/study. 
 In the ExtractionTimpointComparison file, we compared the impact
 of the timepoint in the study (final vs. a time-averaged apporach) on our
 interpretation of plant alternative resource effects on prey supression.
 Thus, the column entitled "Dataset" denotes which approach
 was taken-- either final timepoint of the study or a calculated
 time-average across multiple timepoints in the study. 
 The Data_Summary file outlines the number of studies extracted
 from each manuscript included in the meta-analysis, as well as the
 Omnivore species, Prey species, Alternative resource type, and
 presence/absence of Numerical responses.