Nonparametric Estimation of Natural Selection on a Quantitative Trait using Mark-Recapture Data

Authors

Olivier Gimenez, University of Kent at Canterbury - U.K.Follow
Rita Covas, University of Cape TownFollow
Charles R. Brown, University of TulsaFollow
Mark D. Anderson, Department of Tourism, Environment and ConservationFollow
Mary Bomberger Brown, University of TulsaFollow
Thomas Lenormand, Centre d’Ecologie Evolutive et FonctionnelleFollow

Document Type

Article

Date of this Version

2006

Citation

Evolution, 60(3), 2006, pp. 460–466

Comments

Open Access licensed.

Abstract

Assessing natural selection on a phenotypic trait in wild populations is of primary importance for evolutionary ecologists. To cope with the imperfect detection of individuals inherent to monitoring in the wild, we develop a nonparametric method for evaluating the form of natural selection on a quantitative trait using mark-recapture data. Our approach uses penalized splines to achieve flexibility in exploring the form of natural selection by avoiding the need to specify an a priori parametric function. If needed, it can help in suggesting a new parametric model. We employ Markov chain Monte Carlo sampling in a Bayesian framework to estimate model parameters. We illustrate our approach using data for a wild population of sociable weavers (Philetairus socius) to investigate survival in relation to body mass. In agreement with previous parametric analyses, we found that lighter individuals showed a reduction in survival. However, the survival function was not symmetric, indicating that body mass might not be under stabilizing selection as suggested previously.