Climate change is impacting both the distribution and abundance of vegetation, especially in far northern latitudes. The effects of climate change are different for every plant assemblage and vary heterogeneously in both space and time. Small changes in climate could result in large vegetation responses in sensitive assemblages but weak responses in robust assemblages. But, patterns and mechanisms of sensitivity and robustness are not yet well understood, largely due to a lack of long-term measurements of climate and vegetation. Fortunately, observations are sometimes available across a broad spatial extent. We develop a novel statistical model for a multivariate response based on unknown cluster-specific effects and covariances, where cluster labels correspond to sensitivity and robustness. Our approach utilizes a prototype model for cluster membership that offers flexibility while enforcing smoothness in cluster probabilities across sites with similar characteristics. We demonstrate our approach with an application to vegetation abundance in Alaska, USA, in which we leverage the broad spatial extent of the study area as a proxy for unrecorded historical observations. In the context of the application, our approach yields interpretable site-level cluster labels associated with assemblage-level sensitivity and robustness without requiring strong a priori assumptions about the drivers of climate sensitivity.

中文翻译：

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使用协变量信息成分的多元贝叶斯聚类及其对北方植被敏感性的应用

气候变化正在影响植被的分布和丰度，尤其是在遥远的北纬地区。气候变化对每种植物组合的影响各不相同，并且在空间和时间上都存在差异。气候的微小变化可能会导致敏感组合中的大植被响应，但会导致稳健组合中的微弱响应。但是，敏感性和稳健性的模式和机制尚未得到很好的理解，这主要是由于缺乏对气候和植被的长期测量。幸运的是，有时可以在广阔的空间范围内进行观察。我们基于未知的集群特定效应和协方差为多元响应开发了一种新的统计模型，其中集群标签对应于灵敏度和稳健性。我们的方法利用集群成员的原型模型，该模型提供灵活性，同时在具有相似特征的站点之间强制执行集群概率的平滑性。我们通过对美国阿拉斯加植被丰度的应用来展示我们的方法，其中我们利用研究区域的广泛空间范围作为未记录历史观察的代理。在应用程序的上下文中，我们的方法产生与组合级敏感性和稳健性相关的可解释的站点级集群标签，而无需对气候敏感性驱动因素进行强有力的先验假设。其中我们利用研究区域的广泛空间范围作为未记录的历史观察的代理。在应用程序的上下文中，我们的方法产生了与组合级别的敏感性和稳健性相关的可解释的站点级集群标签，而无需对气候敏感性驱动因素进行强有力的先验假设。其中我们利用研究区域的广泛空间范围作为未记录的历史观察的代理。在应用程序的上下文中，我们的方法产生了与组合级别的敏感性和稳健性相关的可解释的站点级集群标签，而无需对气候敏感性驱动因素进行强有力的先验假设。