Abstract
The ability to model biodiversity patterns is of prime importance in this era of severe environmental crisis. Species assemblage along environmental gradients is subject to the interplay of biotic interactions in complement to abiotic filtering and stochastic forces. Accounting for complex biotic interactions for a wide array of species remains so far challenging. Here, we propose using food web models that can infer the potential interaction links between species as a constraint in species distribution models. Using a plant–herbivore (butterfly) interaction dataset, we demonstrate that this combined approach is able to improve species distribution and community forecasts. The trophic interaction network between butterfly larvae and host plant was phylogenetically structured and driven by host plant nitrogen content allowing forecasting the food web model to unknown interactions links. This combined approach is very useful in rendering models of more generalist species that have multiple potential interaction links, where gap in the literature may occur. Our combined approach points toward a promising direction for modeling the spatial variation in entire species interaction networks.
Highlights
Sound predictions of the composition and function of future ecosystems are needed to inform decision-making in the face of global change but remain one of the greatest challenges facing ecological scientists (Mokany and Ferrier 2011; Nogues-Bravo and Rahbek 2011)
We tested whether the ability of the food web model to predict butterfly larvae association along with host plants differed among the main butterfly families using a Kruskal–Wallis test
We modeled the species distributions using the presence and absence for each species with four different techniques: generalized linear model (GLM), generalized additive model (GAM), gradient boosted model (GBM), and a random forest model (RF)
Summary
Sound predictions of the composition and function of future ecosystems are needed to inform decision-making in the face of global change but remain one of the greatest challenges facing ecological scientists (Mokany and Ferrier 2011; Nogues-Bravo and Rahbek 2011). Species distribution models are spatially explicit models that are used to fill the gaps in our knowledge of spatial distributions of biodiversity, and recent advances in these are used to generate community-level forecasts These models have only recently begun to incorporate the effect of biotic interactions, but so far, only account for these effects indirectly (e.g., based on correlations in occurrence patterns, Kissling et al 2012) and cannot divulge information about the way species may or may not interact (Guisan and Thuiller 2005).
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