Spatial autocorrelation in fitness affects the estimation of natural selection in the wild

1. Natural selection is typically estimated in the wild using Lande and Arnold's multiple regression approach. Despite its utility for evolutionary ecologists, this method is subject to the classical assumptions of multiple regressions, which could result in potential analytical problems. In particular, spatial autocorrelation in fitness violates the assumption of residuals independence. Although widespread in the wild, the consequences of this effect have yet to be investigated in the context of Lande and Arnold's regression and resulting selection estimation. 2. Here we first described four spatially explicit models that allow to control for spatial autocorrelation in residuals of the Lande and Arnold's regression: a generalized least square (GLS) model with a distance-based exponential covariance function, two simultaneous autoregressive models (SAR, the lagged-response model (SAR-lag) and the spatial error model (SAR-err)) and a 5-step procedure using the principal coordinates of ne...

1. 在野外，自然选择的估计通常采用Lande和Arnold提出的多元回归方法。尽管该方法对进化生态学家具有实用价值，但它受限于多元回归的经典假设，这可能导致潜在的分析问题。特别地，适合度（fitness）中的空间自相关会违反残差独立性假设。尽管这种效应在野外普遍存在，但在Lande和Arnold回归及由此产生的选择估计语境下，其后果尚未得到研究。
2. 本文首先描述了四种空间显式模型，这些模型可用于控制Lande和Arnold回归残差中的空间自相关：一种基于距离的指数协方差函数的广义最小二乘（GLS）模型；两种同时自回归模型（SAR），即滞后响应模型（SAR-lag）和空间误差模型（SAR-err）；以及一种使用主坐标的五步法（ne...）。