Altered climate, including weather extremes, can cause major shifts in vegetative recovery after disturbances. Predictive models that can identify the separate and combined temporal effects of disturbance and weather on plant communities and that are transferable among sites are needed to guide vulnerability assessments and management interventions. We asked how functional group abundance responded to time since fire and antecedent weather, if long‐term vegetation trajectories were better explained by initial post‐fire weather conditions or by general five‐year antecedent weather, and if weather effects helped predict post‐fire vegetation abundances at a new site. We parameterized models using a 30‐yr vegetation monitoring dataset from burned and unburned areas of the Orchard Training Area (OCTC) of southern Idaho, USA, and monthly PRISM data, and assessed model transferability on an independent dataset from the well‐sampled Soda wildfire area along the Idaho/Oregon border. Sagebrush density increased with lower mean air temperature of the coldest month and slightly increased with higher mean air temperature of the hottest month, and with higher maximum January–June precipitation. Perennial grass cover increased in relation to higher precipitation, measured annually in the first four years after fire and/or in September–November the year of fire. Annual grass increased in relation to higher March–May precipitation in the year after fire, but not with September–November precipitation in the year of fire. Initial post‐fire weather conditions explained 1% more variation in sagebrush density than recent antecedent 5‐yr weather did but did not explain additional variation in perennial or annual grass cover. Inclusion of weather variables increased transferability of models for predicting perennial and annual grass cover from the OCTC to the Soda wildfire regardless of the time period in which weather was considered. In contrast, inclusion of weather variables did not affect transferability of the forecasts of post‐fire sagebrush density from the OCTC to the Soda site. Although model transferability may be improved by including weather covariates when predicting post‐fire vegetation recovery, predictions may be surprisingly unaffected by the temporal windows in which coarse‐scale gridded weather data are considered.

中文翻译：

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天气影响鼠尾草-草原社区的火灾后恢复以及站点之间的模型可传递性

气候变化（包括极端天气）可能导致干扰后植物恢复的重大变化。需要预测模型来识别扰动和天气对植物群落的单独和组合的时间影响，并且可以在站点之间转移，以指导脆弱性评估和管理干预措施。我们询问了功能组的丰度对火灾和先行天气以来的时间有何反应，是否可以通过最初的火灾后天气条件或一般的五年前天气更好地解释长期植被的轨迹，以及天气影响是否有助于预测火灾后的植被在一个新站点上的数量很多。我们使用来自美国爱达荷州南部果园训练区（OCTC）烧毁和未烧毁地区的30年植被监测数据集对模型进行参数化，并提供PRISM每月数据，并在爱达荷州/俄勒冈州边界沿线采样良好的苏打野火地区的独立数据集上评估了模型的可移植性。鼠尾草的密度随最冷月份的平均气温降低而增加，随最热月份的平均气温升高而升高，并且一月至六月的最大降水量也略有增加。与较高的降水量相比，多年生草地的覆盖率增加，这是在火灾后的前四年和/或火灾年份的9-11月间每年进行测量的。一年生草与火灾后的3月至5月较高的降水量有关，但与火灾年9月至11月的降水量无关。最初的大火后天气状况解释了鼠尾草密度的变化比最近的5年前天气多1％，但没有解释多年生或年度草覆盖率的其他变化。包括天气变量，可以提高用于预测从OCTC到苏打野火的多年生和一年生草覆盖量的模型的可移植性，而与考虑天气的时间段无关。相比之下，纳入天气变量并不会影响从OCTC到苏打水地点的火后鼠尾草密度预测的可传递性。尽管在预测火后植被恢复时可以通过包括天气协变量来改善模型的可传递性，但是考虑到粗糙网格天气数据的时间窗，预测可能会出乎意料地受到影响。包括天气变量，可以提高用于预测从OCTC到苏打野火的多年生和一年生草覆盖量的模型的可移植性，而与考虑天气的时间段无关。相比之下，纳入天气变量并不会影响从OCTC到苏打水地点的火后鼠尾草密度预测的可传递性。尽管在预测火后植被恢复时可以通过包括天气协变量来改善模型的可传递性，但是考虑到粗糙网格天气数据的时间窗，预测可能会出乎意料地受到影响。包括天气变量，可以提高用于预测从OCTC到苏打野火的多年生和一年生草覆盖量的模型的可移植性，而与考虑天气的时间段无关。相比之下，纳入天气变量并不会影响从OCTC到苏打水地点的火后鼠尾草密度预测的可传递性。尽管在预测火后植被恢复时可以通过包括天气协变量来改善模型的可传递性，但是考虑到粗糙网格天气数据的时间窗，预测可能会出乎意料地受到影响。包含天气变量并不会影响从OCTC到苏打水场的后山艾树密度预测的可传递性。尽管在预测火后植被恢复时可以通过包括天气协变量来改善模型的可传递性，但是考虑到粗糙网格天气数据的时间窗，预测可能会出乎意料地受到影响。包含天气变量并不会影响从OCTC到苏打水场的后山艾树密度预测的可传递性。尽管在预测火后植被恢复时可以通过包括天气协变量来改善模型的可传递性，但是考虑到粗糙网格天气数据的时间窗，预测可能会出乎意料地受到影响。