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首页> 美国卫生研究院文献>Ecology and Evolution >Univariate Community Assembly Analysis (UniCAA): Combining hierarchical models with null models to test the influence of spatially restricted dispersal environmental filtering and stochasticity on community assembly
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Univariate Community Assembly Analysis (UniCAA): Combining hierarchical models with null models to test the influence of spatially restricted dispersal environmental filtering and stochasticity on community assembly

机译：单变量社区大会分析（UniCAA）：将分层模型与无效模型组合以测试空间受限的分散环境过滤和随机性对社区大会的影响

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摘要

Identifying the influence of stochastic processes and of deterministic processes, such as dispersal of individuals of different species and trait‐based environmental filtering, has long been a challenge in studies of community assembly. Here, we present the Univariate Community Assembly Analysis (UniCAA) and test its ability to address three hypotheses: species occurrences within communities are (a) limited by spatially restricted dispersal; (b) environmentally filtered; or (c) the outcome of stochasticity—so that as community size decreases—species that are common outside a local community have a disproportionately higher probability of occurrence than rare species. The comparison with a null model allows assessing if the influence of each of the three processes differs from what one would expect under a purely stochastic distribution of species. We tested the framework by simulating “empirical” metacommunities under 15 scenarios that differed with respect to the strengths of spatially restricted dispersal (restricted vs. not restricted); habitat isolation (low, intermediate, and high immigration rates); and environmental filtering (strong, intermediate, and no filtering). Through these tests, we found that UniCAA rarely produced false positives for the influence of the three processes, yielding a type‐I error rate ≤5%. The type‐II error rate, that is, production of false negatives, was also acceptable and within the typical cutoff (20%). We demonstrate that the UniCAA provides a flexible framework for retrieving the processes behind community assembly and propose avenues for future developments of the framework.
机译：长期以来，识别随机过程和确定性过程（例如不同物种的个体散布和基于特征的环境过滤）的影响一直是社区组装研究中的挑战。在这里，我们介绍了单变量社区组装分析（UniCAA）并测试了其解决三个假设的能力：社区内的物种出现（a）受空间限制的扩散限制； （b）经过环境过滤；或（c）随机性的结果-随着社区规模的减小-在本地社区以外常见的物种的发生概率比稀有物种高得多。与零模型的比较允许评估三个过程中的每个过程的影响是否不同于物种纯随机分布下的预期。我们通过模拟15种场景下的“经验”元社区对框架进行了测试，这些场景在空间受限的分散强度（受限与不受限）方面有所不同。栖息地隔离（低，中和高移民率）；和环境过滤（强，中，无过滤）。通过这些测试，我们发现UniCAA很少会因这三个过程的影响而产生假阳性，I型错误率≤5％。 II型错误率，即假阴性的产生率也是可以接受的，并且在典型的临界值（20％）之内。我们证明了UniCAA提供了一个灵活的框架来检索社区大会背后的流程，并为该框架的未来发展提供了途径。

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