Department of Ecology and Evolutionary Biology, Cornell University, Ithaca, New York, 14853, USA.
Institute for Culture and Environment, Alaska Pacific University, Anchorage, Alaska, 99508, USA.
Ecology. 2021 Nov;102(11):e03503. doi: 10.1002/ecy.3503. Epub 2021 Aug 20.
Frameworks exclusively considering functional diversity are gaining popularity, as they complement and extend the information provided by taxonomic diversity metrics, particularly in response to disturbance. Taxonomic diversity should be included in functional diversity frameworks to uncover the functional mechanisms causing species loss following disturbance events. We present and test a predictive framework that considers temporal functional and taxonomic diversity responses along disturbance gradients. Our proposed framework allows us to test different multidimensional metrics of taxonomic diversity that can be directly compared to calculated multidimensional functional diversity metrics. It builds on existing functional diversity-disturbance frameworks both by using a gradient approach and by jointly considering taxonomic and functional diversity. We used previously unpublished stream insect community data collected prior to, and for the two years following, an extreme flood event that occurred in 2013. Using 14 northern Colorado mountain streams, we tested our framework and determined that taxonomic diversity metrics calculated using multidimensional methods resulted in concordance between taxonomic and functional diversity responses. By considering functional and taxonomic diversity together and using a gradient approach, we were able to identify some of the mechanisms driving species losses following this extreme disturbance event.
专门考虑功能多样性的框架越来越受欢迎,因为它们补充和扩展了分类多样性指标提供的信息,特别是在应对干扰时。在功能多样性框架中应包括分类多样性,以揭示在干扰事件发生后导致物种丧失的功能机制。我们提出并测试了一个预测框架,该框架考虑了沿干扰梯度的时间功能和分类多样性响应。我们提出的框架允许我们测试可以直接与计算出的多维功能多样性指标进行比较的不同分类多样性多维指标。它既通过使用梯度方法,又通过共同考虑分类和功能多样性,构建在现有的功能多样性-干扰框架之上。我们使用了先前未发表的溪流昆虫群落数据,这些数据是在 2013 年发生的极端洪水事件之前和之后两年收集的。使用 14 条科罗拉多州北部山区溪流,我们测试了我们的框架,并确定使用多维方法计算的分类多样性指标导致分类和功能多样性响应之间的一致性。通过同时考虑功能和分类多样性并使用梯度方法,我们能够确定一些在这种极端干扰事件后导致物种丧失的机制。