Pos Edwin, Guevara Andino Juan Ernesto, Sabatier Daniel, Molino Jean-François, Pitman Nigel, Mogollón Hugo, Neill David, Cerón Carlos, Rivas-Torres Gonzalo, Di Fiore Anthony, Thomas Raquel, Tirado Milton, Young Kenneth R, Wang Ophelia, Sierra Rodrigo, García-Villacorta Roosevelt, Zagt Roderick, Palacios Cuenca Walter, Aulestia Milton, Ter Steege Hans
Ecology and Biodiversity Group Utrecht University Utrecht The Netherlands.
Group of Dynamic Biodiversity Naturalis Biodiversity Center Leiden The Netherlands.
Ecol Evol. 2017 May 5;7(12):4254-4265. doi: 10.1002/ece3.2930. eCollection 2017 Jun.
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 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.
由于有许多复杂的方法可用于估计迁移，生态学家面临着为其特定工作选择方法的艰难决定。在此，我们测试并比较了几种方法，进行了合理性和稳健性测试，将其应用于大规模数据，并讨论了结果及解释。我们选择了五种方法，基于来自南美洲（圭亚那、苏里南、法属圭亚那和厄瓜多尔）的三个大规模实地数据集，比较它们从空间隐含和半显式模拟中估计迁移的能力。通过用于本地补充、从相邻地块或从集合群落迁移的离散概率质量函数，半显式地纳入了空间因素。大多数方法能够准确地从空间隐含模拟中估计迁移。对于空间半显式模拟，估计结果显示为从相邻地块和集合群落迁移的累加效应。只有当从集合群落的迁移超过相邻地块的迁移时，估计才是准确的，然而，区分是不可能的。我们表明，迁移应更多地被视为群落与区域物种库总和之间相似性的一种近似。将迁移估计应用于模拟实地数据集确实显示出相当好的拟合，并表明与早期研究相比，各数据集之间存在一致的差异。我们得出结论，使用这些方法估计的迁移更多地是随着时间推移本地群落之间同质化的一种近似，而不是对迁移的直接测量，因此与β多样性有直接关系。由于β多样性是许多（非）中性过程的结果，我们不得不承认，在空间明确的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总和。中性模型的参数 then 似乎更多地是中性理论揭示的一种新兴属性，而不是一个有效的机制参数，并且空间隐含模型作为森林动态的近似应该被拒绝。
