title: Nonparametric estimation of natural selection on a quantitative trait using mark-recapture data.
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.
authors.affiliation: Institute of Mathematics, Statistics and Actuarial Sciences, University of Kent, Canterbury, England. olivier.gimenez@cefe.cnrs.fr
authors.forename: Olivier
authors.initials: O
authors.lastname: Gimenez
authors.forename: Rita
authors.initials: R
authors.lastname: Covas
authors.forename: Charles R
authors.initials: CR
authors.lastname: Brown
authors.forename: Mark D
authors.initials: MD
authors.lastname: Anderson
authors.forename: Mary Bomberger
authors.initials: MB
authors.lastname: Brown
authors.forename: Thomas
authors.initials: T
authors.lastname: Lenormand
mesh_terms: D000818:Animals; D001499:Bayes Theorem; D008390:Markov Chains; D008954:Models, Biological; D009010:Monte Carlo Method; D012641:Selection, Genetic; D020308:Songbirds / Q000033:anatomy & histology*; D015996:Survival Rate