Vincent Careau1, Robbie S Wilson2. 1. Canada Research Chair in Functional Ecology, Department of Biology, University of Ottawa, Ottawa, ON, Canada. 2. School of Biological Sciences, The University of Queensland, QLD St Lucia, Australia.
Abstract
SYNOPSIS: All animals experience performance trade-offs as they complete tasks such as capturing prey, defending territories, acquiring mates, and escaping predators. Why then, is it so hard to detect performance trade-offs at the whole-organismal level? Why do we sometimes even obtain positive correlations between two performance traits that are predicted to be negatively associated? Here we explore two plausible explanations. First, most analyses are based on individual maximal values (i.e., personal best), which could introduce a bias in the correlation estimates. Second, phenotypic correlations alone may be poor indicators of a trade-off when contrasting processes occur at the among- versus within-individual levels. One such scenario is the "big houses big cars" model developed in life-history theory to explain the existence of "uberfleas" that are superior in all regards (because they acquire more resources than others). We highlight that the exact opposite scenario might occur for performance trade-offs, where among-individual trade-offs may be masked by within-individual changes in physical condition. One of the best ways to test among these alternative scenarios is to collect repeated pairs of performance traits and analyze them using multivariate mixed models (MMMs). MMMs allow straightforward and simultaneous examination of trait correlations at the among- and within-individual levels. We use a simple simulation tool (SQuID package in R) to create a population of Krakens, a mythical giant squid-like sea creature whose morphology generates a performance trade-off between swimming speed and strength or ability to sink ships. The simulations showed that using individual maximum values introduces a bias that is particularly severe when individuals differ in the number of repeated samples (ntrial). Finally, we show how MMMs can help detect performance (or any other type of) trade-offs and offer additional insights (e.g., help detect plasticity integration). We hope researchers will adopt MMMs when exploring trade-offs in whole-animal performances.
SYNOPSIS: All animals experience performance trade-offs as they complete tasks such as capturing prey, defending territories, acquiring mates, and escaping predators. Why then, is it so hard to detect performance trade-offs at the whole-organismal level? Why do we sometimes even obtain positive correlations between two performance traits that are predicted to be negatively associated? Here we explore two plausible explanations. First, most analyses are based on individual maximal values (i.e., personal best), which could introduce a bias in the correlation estimates. Second, phenotypic correlations alone may be poor indicators of a trade-off when contrasting processes occur at the among- versus within-individual levels. One such scenario is the "big houses big cars" model developed in life-history theory to explain the existence of "uberfleas" that are superior in all regards (because they acquire more resources than others). We highlight that the exact opposite scenario might occur for performance trade-offs, where among-individual trade-offs may be masked by within-individual changes in physical condition. One of the best ways to test among these alternative scenarios is to collect repeated pairs of performance traits and analyze them using multivariate mixed models (MMMs). MMMs allow straightforward and simultaneous examination of trait correlations at the among- and within-individual levels. We use a simple simulation tool (SQuID package in R) to create a population of Krakens, a mythical giant squid-like sea creature whose morphology generates a performance trade-off between swimming speed and strength or ability to sink ships. The simulations showed that using individual maximum values introduces a bias that is particularly severe when individuals differ in the number of repeated samples (ntrial). Finally, we show how MMMs can help detect performance (or any other type of) trade-offs and offer additional insights (e.g., help detect plasticity integration). We hope researchers will adopt MMMs when exploring trade-offs in whole-animal performances.
Authors: Frédéric Douhard; Andrea B Doeschl-Wilson; Alexander Corbishley; Adam D Hayward; Didier Marcon; Jean-Louis Weisbecker; Sophie Aguerre; Léa Bordes; Philippe Jacquiet; Tom N McNeilly; Guillaume Sallé; Carole Moreno-Romieux Journal: Evol Appl Date: 2022-07-12 Impact factor: 4.929
Authors: Simon P Lailvaux; Avdesh Mishra; Pooja Pun; Md Wasi Ul Kabir; Robbie S Wilson; Anthony Herrel; Md Tamjidul Hoque Journal: PLoS One Date: 2022-01-21 Impact factor: 3.240