DOI: 10.5061/dryad.dz08kps4r
title: Data from: Clay larvae do not accurately measure biogeographic patterns in predation

description: Aim: Spatial variation in predation can shape geographic patterns in
 ecology and evolution, but testing how predation varies across ecosystems
 is challenging as differing species compositions and defensive adaptations
 can mask underlying patterns.  Recently, biogeography has
 borrowed a tool from ecology: clay prey models. But clay models have not
 been adequately tested for geographic comparisons, and a well-known
 problem –that clay prey only appeal to a subset of potential
 predators– could bias detected geographic patterns whenever the
 relative importance of predator guilds varies among sites.  Here,
 we test whether clay larvae accurately capture geographic differences in
 predation on real larvae. Location: 90° of latitude and &gt;2000 m
 elevation across the Americas. Taxon: vertebrate and invertebrate
 predation on ‘superworms’ (Zophobas larvae). Methods: Across six sites
 that vary dramatically in latitude, elevation, and biome, we quantified
 predation on live, dead, and clay larvae. We physically excluded
 vertebrate predators from some larvae to distinguish total predation and
 invertebrate-only predation. Results: Predation on live superworms almost
 doubled from our high-elevation high-latitude site to our low-elevation
 tropical site. Geographic patterns were consistent among live and dead
 larvae, but clay larvae missed extremely high predation at some sites and
 therefore mis-measured true geographic patterns. Clay larvae did a
 particularly bad job at capturing geographic patterns in predation by
 invertebrates, although sample sizes for invertebrate predation were
 small. Main conclusions: Clay larvae are inappropriate for comparing
 predation rates across sites. They should be abandoned for biogeographic
 studies and reserved for comparisons within, rather than across, predator
 communities.
description: # Clay larvae do not accurately measure biogeographic patterns in
 predation
 [https://doi.org/10.5061/dryad.dz08kps4r](https://doi.org/10.5061/dryad.dz08kps4r) We tested whether clay models of larvae accurately detect geographic differences in predation experienced by real larvae, by pairing real and alive, real and dead, and clay model larvae. Attack rates were recorded after 24 hours. Data were collected from 6 sites in Canada, Panama, Colombia, and Argentina. Live larvae were not used in Argentina. ## Description of the data and file structure Data are compiled in a single csv file, which contains raw data from all experimental runs at all sites. Missing values (i.e. we could not find larvae) are already removed. Data are analyzed in a single R script. Variables are described in the R code and below: * Geographic variables: country, region, lat = latitude (decimal degrees), elev = elevation (m above sea level), siteID = short hand for each of the 6 sites in our study - Date variables refer to start of the 24 h experiment: year, month (numeric), date = year.month.day Experiment set up variables:  * plot = 1 to 30 (1 larvae per larvae-type per plot);  - cage.treat = cage treatment, either caged (CG) to exclude vertebrates and measure predation by invertebrates only, or uncaged control (CT) to measure predation by vertebrates and invertebrates (CT) * prey.type = type of superworm (Zophobas) larvae: alive, dead, or clay model - Predator marks: we attempted to assign predator marks to predator guild as commonly done in clay bait studies, but ultimately were not convinced that this was reliable. These columns are not used in analyses other than to calculate whether the larvae was attacked or not. 1 = yes (marks present) 0 = no marks * Predation variables: damaged = was the larvae damaged? 0 = not damaged, 1 = damaged (ie attacked). lost = '1' means larvae was gone but the plot and larvae marker were refound (ie larvae eaten) ## Code/Software The R script is formatted to be read in R Studio with the outline option visible (this provides a structured table of contents to help navigate). The R script includes all analyses and code to make the data panels for all figures, though final figures were compiled in PowerPoint. Libraries needed are loaded at the beginning of the script. Statistical results are from lme4 (for binomial mixed models, v 1.1.27.1), car (for likelihood ratio tests to compare models, v 3.0.12) and emmeans (for post-hoc comparisons within significant main effects, v 1.5.1). Visual inspection of data and results uses lattice and visreg.