Tropical tree‐species richness is positively correlated with annual precipitation, but the mechanisms remain unclear. Phytopathogens promote tree‐species coexistence by disproportionately afflicting seedlings of locally abundant species, generating a rare species advantage. We consider whether increased plant–pathogen interactions in humid conditions favourable for phytopathogens could drive the precipitation‐richness relationship by accentuating the rare species advantage. Support for this mechanism requires that increases in disease under humid conditions disproportionately affect locally abundant species without spreading to rarer species. This criterion would be augmented by either increased phytopathogen host‐specificity under humid conditions, or increased asynchronicity in germination of different tree species. Research suggests that precipitation increases the rare species advantage. Increased precipitation enhances phytopathogen transmission, making escape from specialist pathogens more difficult. Additionally, drought stress predisposes plants to disease, especially by opportunistic pathogens. As seasonality in wet forests decreases, scope for asynchronous germination among species increases, potentially concentrating disease transmission within species. Synthesis. The pathways we identify could drive the precipitation‐richness relationship, but finding direct evidence for them remains a priority. Researching these pathways is especially important because decreasing precipitation due to climate change could disrupt key species coexistence mechanisms and erode tree‐species richness.
The analysis of spatial point patterns has greatly advanced our understanding of ecological processes. However, the methods currently available for analyzing replicated spatial point patterns (RSPPs) are rarely used by ecologists. One barrier to the use of RSPP analyses is a lack of software to implement the approaches that have been developed in the statistical literature. Here, we provide a practical guide to RSPP analysis and introduce the RSPPlme4 R package that implements the approaches we discuss. The methods we outline use a linear modeling framework to link variation in the spatial structure of point patterns to discrete and continuous explanatory covariates. We describe methods for linear models and mixed-effects models of RSPPs, including approaches to estimating confidence intervals via semi-parametric bootstrapping. The syntax for model fitting is similar to that used in linear and linear mixed-effects modeling packages in R. The RSPPlme4 package also allows users to easily plot the results of model fits. We hope that this tutorial will make methods for RSPP analysis accessible to a wide range of ecologists and open new avenues for gaining insight into ecological processes from spatial data.
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