Data from: 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 response to environmental variability.明确气候驱动因子影响物候、行为与种群统计性状表达的关键时间窗口，对于预测气候变化对性状及种群动态的影响至关重要。目前主流的关键气候期识别关联分析方法分为两类：滑动窗口模型（sliding-window models），以及过往天气影响记忆随时间衰减的递归算子（recursive operators）。两类方法各有长处，本研究将二者整合为一种统一的分析方法。本方法通过灵活函数对过往天气数据进行差异化加权，能够适配关于时间滞后效应、以及近期与过往天气对性状表达相对重要性的各类竞争性假说。依托一套22年的数据集，本研究证实：相较于现有方法，新方法识别出的气候窗口可解释更多某一性选择性状（sexually selected trait）的物候变异（phenological variation）。因此，本研究提出的新方法能够更精准地识别关键时间窗口，以及性状对环境变异产生响应的内在成因。