Abstract
Assessing the relative importance of different processes that determine the spatial distribution of species and the dynamics in highly diverse plant communities remains a challenging question in ecology. Previous modelling approaches often focused on single aggregated forest diversity patterns that convey limited information on the underlying dynamic processes. Here, we use recent advances in inference for stochastic simulation models to evaluate the ability of a spatially explicit and spatially continuous neutral model to quantitatively predict six spatial and non-spatial patterns observed at the 50 ha tropical forest plot on Barro Colorado Island, Panama. The patterns capture different aspects of forest dynamics and biodiversity structure, such as annual mortality rate, species richness, species abundance distribution, beta-diversity and the species–area relationship (SAR). The model correctly predicted each pattern independently and up to five patterns simultaneously. However, the model was unable to match the SAR and beta-diversity simultaneously. Our study moves previous theory towards a dynamic spatial theory of biodiversity and demonstrates the value of spatial data to identify ecological processes. This opens up new avenues to evaluate the consequences of additional process for community assembly and dynamics.
Highlights
A fundamental goal of ecology is to understand the mechanisms that determine the spatial distribution of species and the dynamics of communities [1,2]
Neutral theory initiated a controversial debate in community ecology, and it has been ardently discussed if neutral models provide a better fit to observed species abundance distribution (SAD) than previous models [8,10,11]
Our study shows that the parametrization of spatial models of community dynamics requires the simultaneous use of different observed biodiversity patterns
Summary
A fundamental goal of ecology is to understand the mechanisms that determine the spatial distribution of species and the dynamics of communities [1,2]. We show that the neutral model is able to fit each pattern individually with a precision similar to the observed variation during the 20-year census period; that the model correctly reproduces several non-spatial and spatial patterns at the same time; but that it is unable to predict the SAR and betadiversity simultaneously This suggests that these two patterns convey important information on the underlying dynamic processes. To answer our first question (i.e. whether neutral dynamics provide realistic predictions for each pattern independently), we applied the rejection sampling approach for each summary statistic independently and set f 1⁄4 1, which means the model predictions had to match observations at the uncertainty level of the data. To exclude parameter sets with poor match, we always limit f  ei , 0.2, we expect in any case that simulated and observed summary statistics deviate on maximum by 20%
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