Identifying the critical climatic time window that affects trait expression

Identifying the critical time window during which climatic drivers affect the expression of phenological, behavioral, and demographic traits is crucial for predicting the impact of climate change on trait and population dynamics. Two widely used associative methods exist to identify critical climatic periods: sliding-window models and recursive operators in which the memory of past weather fades over time. Both approaches have different strong points, which we combine here into a single method. Our method uses flexible functions to differentially weight past weather, which can reflect competing hypotheses about time lags and the relative importance of recent and past weather for trait expression. Using a 22-year data set, we illustrate that the climatic window identified by our new method explains more of the phenological variation in a sexually selected trait than existing approaches. Our new method thus helps to better identify the critical time window and the causes of trait respons...

明确气候驱动因子影响物候（phenological）、行为及种群统计（demographic）性状表达的关键时间窗口，对于预测气候变化对性状与种群动态的影响至关重要。目前用于识别关键气候时期的两类常用关联分析方法为：滑动窗口模型（sliding-window models），以及过往气象信息记忆随时间衰减的递归算子（recursive operators）。这两类方法各有优势，本研究将二者整合为一种统一的新方法。该方法采用灵活的函数对过往气象数据进行差异化加权，能够反映关于时间滞后效应、以及近期与过往气象对性状表达相对重要性的各类竞争性假说。本研究依托一套时长22年的数据集开展验证，结果表明：相较于现有方法，新方法所识别的气候窗口能够解释更多性选择（sexually selected）性状的物候变异。因此，该新方法有助于更精准地识别关键时间窗口，以及性状响应的成因……