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Numerical responses of omnivorous terrestrial arthropods to plant alternative resources suppress prey populations: a meta-analysis

Citation

Rinehart, Shelby; Long, Jeremy (2021), Numerical responses of omnivorous terrestrial arthropods to plant alternative resources suppress prey populations: a meta-analysis, Dryad, Dataset, https://doi.org/10.25338/B8KP8P

Abstract

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.

Methods

Literature survey

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 (<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).

Data collection

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 >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)

Metanalyses for effects of plant alternative resources on prey consumption and density.

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).

Meta-regressions for the consequences of experimental methodology on the effect of plant alternative resources on prey consumption and density

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).

Dataset variability and publication bias.

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 < 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).

Data extraction method

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.

Usage Notes

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. 

Funding

National Science Foundation Graduate Research Fellowship, Award: DGE-1321850

Zuckerman STEM Leadership Program

Lady Davis Trust

Minerva Center for Movement Ecology

University of Alabama