Quantifying Nutritional Trade-Offs across Multidimensional Performance Landscapes

Juliano Morimoto (1) , Mathieu Lihoreau (2)

1 Macquarie University [Sydney]
2 CRCA - Centre de Recherches sur la Cognition Animale - UMR5169

Online enhancements: R code, supplemental material. Dryad data: https://dx.doi.org/10.5061/dryad.tp7519s. abstract: Animals make feeding decisions to simultaneously maximize fitness traits that often require different nutrients. Recent quantitative methods have been developed to characterize these nutritional trade-offs from performance landscapes on which traits are mapped on a nutrient space defined by two nutrients. This limitation constrains the broad applications of previous methods to more complex data, and a generalized framework is needed. Here, we build on previous methods and introduce a generalized vector-based approach-the vector of position approach-to study nutritional trade-offs in complex multi-dimensional spaces. The vector of position approach allows the estimate of performance variations across entire landscapes (peaks and valleys) and comparison of these variations between animals. Using landmark published data sets on life span and reproduction landscapes, we illustrate how our approach gives accurate quantifications of nutritional trade-offs in two-and three-dimensional spaces and can bring new insights into the underlying nutritional differences in trait expression between species. The vector of position approach provides a generalized framework for investigating nutritional differences in life-history trait expression within and between species, an essential step for the development of comparative research on the evolution of animal nutritional strategies.

Mots clés: nutritional trade-off; nutritional geometry; fitness; performance landscapes

Domaines: Biodiversité et Ecologie; Neurosciences

Juliano Morimoto, Mathieu Lihoreau. Quantifying Nutritional Trade-Offs across Multidimensional Performance Landscapes. The American Naturalist, 2019, 193 (6), ⟨10.1086/701898⟩. ⟨hal-02105095⟩