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Estimating and interpreting migration of Amazonian forests using spatially implicit and semi‐explicit neutral models

机译:使用空间隐性和半显性中性模型估算和解释亚马逊森林的迁移

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摘要

With many sophisticated methods available for estimating migration, ecologists face the difficult decision of choosing for their specific line of work. Here we test and compare several methods, performing sanity and robustness tests, applying to large‐scale data and discussing the results and interpretation. Five methods were selected to compare for their ability to estimate migration from spatially implicit and semi‐explicit simulations based on three large‐scale field datasets from South America (Guyana, Suriname, French Guiana and Ecuador). Space was incorporated semi‐explicitly by a discrete probability mass function for local recruitment, migration from adjacent plots or from a metacommunity. Most methods were able to accurately estimate migration from spatially implicit simulations. For spatially semi‐explicit simulations, estimation was shown to be the additive effect of migration from adjacent plots and the metacommunity. It was only accurate when migration from the metacommunity outweighed that of adjacent plots, discrimination, however, proved to be impossible. We show that migration should be considered more an approximation of the resemblance between communities and the summed regional species pool. Application of migration estimates to simulate field datasets did show reasonably good fits and indicated consistent differences between sets in comparison with earlier studies. We conclude that estimates of migration using these methods are more an approximation of the homogenization among local communities over time rather than a direct measurement of migration and hence have a direct relationship with beta diversity. As betadiversity is the result of many (non)‐neutral processes, we have to admit that migration as estimated in a spatial explicit world encompasses not only direct migration but is an ecological aggregate of these processes. The parameter m of neutral models then appears more as an emerging property revealed by neutral theory instead of being an effective mechanistic parameter and spatially implicit models should be rejected as an approximation of forest dynamics.
机译:由于有许多复杂的方法可用于估计迁移,生态学家面临着选择特定工作线的艰难决定。在这里,我们测试和比较了几种方法,进行了健全性和鲁棒性测试,适用于大规模数据并讨论了结果和解释。根据来自南美的三个大型现场数据集(圭亚那,苏里南,法属圭亚那和厄瓜多尔),选择了五种方法进行比较,以从空间隐性和半显性模拟中评估迁移的能力。通过离散概率质量函数将空间半显式地合并,用于局部募集,从相邻地块或元社区的迁移。大多数方法都能够从空间隐式模拟中准确估计迁移。对于空间半显性模拟,估计显示为邻近地块和元社区迁移的叠加效应。仅当从元社区迁移的数量超过相邻地块的迁移量时才是准确的,但是事实证明,这种区分是不可能的。我们表明,应将移徙视为社区与总区域物种库之间的相似之处。应用迁移估计值模拟田野数据集确实显示出很好的拟合度,并且表明与早期研究相比,各组之间具有一致的差异。我们得出的结论是,使用这些方法进行的迁移估计更多是随着时间推移本地社区之间同质化的近似值,而不是迁移的直接度量,因此与beta多样性有着直接关系。由于β多样性是许多(非)中性过程的结果,我们必须承认,在空间显性世界中估计的迁移不仅包括直接迁移,而且是这些过程的生态集合。中性模型的参数m然后更多地显示为中性理论所揭示的新兴属性,而不是有效的机械参数,因此应拒绝使用空间隐式模型作为森林动力学的近似值。

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