Nonparametric estimation of natural selection on a quantitative trait using mark-recapture data.
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.
Gimenez
Olivier
O
Institute of Mathematics, Statistics and Actuarial Sciences, University of Kent, Canterbury, England. olivier.gimenez@cefe.cnrs.fr
Covas
Rita
R
Brown
Charles R
CR
Anderson
Mark D
MD
Brown
Mary Bomberger
MB
Lenormand
Thomas
T
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