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A unifying framework for analyzing temporal changes in functional and taxonomic diversity along disturbance gradients
Ecology ( IF 4.4 ) Pub Date : 2021-07-27 , DOI: 10.1002/ecy.3503
Erin I Larson 1, 2 , N LeRoy Poff 3, 4 , W Chris Funk 3 , Rachel A Harrington 5 , Boris C Kondratieff 6 , Scott G Morton 3 , Alexander S Flecker 1
Affiliation  

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 We used previously unpublished stream insect community data collected prior to, and for the two years following, an extreme flood event that occurred in 2013.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.

中文翻译:

用于分析功能和分类多样性沿扰动梯度随时间变化的统一框架

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。分类多样性应包含在功能多样性框架中,以揭示干扰事件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,该框架考虑了沿扰动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分类多样性的不同多维指标,这些指标可以直接与计算出的多维功能多样性指标进行比较。它通过使用梯度方法并通过联合考虑分类和功能多样性,建立在现有的功能多样性-干扰框架之上。我们使用了在 2013 年发生的极端洪水事件之前和之后的两年内收集的先前未发表的河流昆虫群落数据。我们使用 14 条科罗拉多州北部的山间溪流测试了我们的框架,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,我们能够确定在这种极端干扰事件后导致物种损失的一些机制。在接下来的两年里,发生在 2013 年的极端洪水事件。我们使用 14 条科罗拉多州北部的山间溪流测试了我们的框架,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,我们能够确定在这种极端干扰事件后导致物种损失的一些机制。在接下来的两年里,发生在 2013 年的极端洪水事件。我们使用 14 条科罗拉多州北部的山间溪流测试了我们的框架,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,我们能够确定在这种极端干扰事件后导致物种损失的一些机制。
更新日期:2021-07-27
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