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.