Centre d'Ecologie Evolutive et Fonctionnelle, UMR 5175, 1919 Route de Mende, 34293 Montpellier, Cedex 5, France.
Evolution. 2009 Dec;63(12):3097-105. doi: 10.1111/j.1558-5646.2009.00783.x. Epub 2009 Jul 28.
Understanding how selection operates on a set of phenotypic traits is central to evolutionary biology. Often, it requires estimating survival (or other fitness-related life-history traits) which can be difficult to obtain for natural populations because individuals cannot be exhaustively followed. To cope with this issue of imperfect detection, we advocate the use of mark-recapture data and we provide a general framework for both the estimation of linear and nonlinear selection gradients and the visualization of fitness surfaces. To quantify the strength of selection, the standard second-order polynomial regression method is integrated in mark-recapture models. To visualize the form of selection, we use splines to display selection acting on multivariate phenotypes in the most flexible way. We employ Markov chain Monte Carlo sampling in a Bayesian framework to estimate model parameters, assessing traits relevance and calculating the optimal amount of smoothing. We illustrate our approach using data from a wild population of Common blackbirds (Turdus merula) to investigate survival in relation to morphological traits, and provide evidence for correlational selection using the new methodology. Overall, the framework we propose will help in exploring the full potential of mark-recapture data to study natural selection.
理解选择如何作用于一组表型特征是进化生物学的核心。通常，这需要估计生存（或其他与适应度相关的生活史特征），但由于无法对个体进行详尽的跟踪，因此很难从自然种群中获得这些数据。为了解决这种不完全检测的问题，我们提倡使用标记重捕数据，并提供了一个通用框架，用于估计线性和非线性选择梯度，并可视化适应度曲面。为了量化选择的强度，标准的二阶多项式回归方法被整合到标记重捕模型中。为了可视化选择的形式，我们使用样条以最灵活的方式显示对多元表型的选择。我们在贝叶斯框架中使用马尔可夫链蒙特卡罗抽样来估计模型参数，评估性状的相关性，并计算最佳的平滑量。我们使用来自普通黑鹂（Turdus merula）野生种群的数据来说明我们的方法，以调查与形态特征有关的生存情况，并使用新方法提供相关选择的证据。总的来说，我们提出的框架将有助于充分利用标记重捕数据来研究自然选择。
