An untegrated test of Natural-Enemy mediated tree BEta-Diversity across South American rain forests.


ANR 2013-2017


Christopher Baraloto


Tropical plant diversity is extraordinarily high both at local and regional scales, including a significant component of beta-diversity, or the turnover in species composition across habitats and regions. Yet we still know little about the factors underlying species distributions, with more than half of all tropical plant species having been collected only once. In particular, the relative roles of biogeography, abiotic factors, and biotic factors in limiting plant species distributions remain a subject of debate. Natural enemies (both fungi and insect herbivores) have recently been shown to exert strong forces on plant community composition, and it has been hypothesized that such biotic interactions are far more important in driving plant species turnover than other environmental drivers. Alternatively, natural enemies may be important at small scales, (local diversity) but may not influence turnover at the beta scale.

The NEBEDIV project represents a comprehensive evaluation of tropical forest beta-diversity across broad geographic and environmental gradients. We will integrate not only plot level analyses of more than 100 tree communities across Amazonia but also the first characterizations of soil fungi and insect herbivore communities at this scale. Critically, we will not only examine community-level correlations between these communities across spatio-environmental scales but also characterize host specialization of fungi and herbivorous insects, building on the extensive molecular phylogenetics work and collaborations we have established in previous international projects. We believe our teams are in a unique position to make this vital contribution given the extensive infrastructure we have established in recent years.

The databases and modeling approaches we develop in NEBEDIV will contribute to an increased understanding of the factors that influence species turnover in the most diverse forests on earth. All three groups on which we focus are critically understudied in the tropics, and no attempt to date has been made to study these groups simultaneously for the same sites, especially with a well-replicated experimental design. The datasets we generate will therefore be essential for regional estimates of biodiversity, to assist policy makers to choose protected areas across the region, and to improve models of biodiversity dynamics in response to climate and land use change scenarios.

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