Despite being a fundamental aspect of biodiversity, little is known about what controls species range sizes. This is especially the case for hyperdiverse organisms such as plants. We use the largest botanical data set assembled to date to quantify geographical variation in range size for ∼ 85 000 plant species across the New World. We assess prominent hypothesised range-size controls, finding that plant range sizes are codetermined by habitat area and long- and short-term climate stability. Strong short- and long-term climate instability in large parts of North America, including past glaciations, are associated with broad-ranged species. In contrast, small habitat areas and a stable climate characterise areas with high concentrations of small-ranged species in the Andes, Central America and the Brazilian Atlantic Rainforest region. The joint roles of area and climate stability strengthen concerns over the potential effects of future climate change and habitat loss on biodiversity.
Early View (EV): 1-EV by broad-scale models, then stacking independent species distribution models to predict species assemblages (sensu Rahbek 2011, Calabrese et al. 2014) will provide misleading predictions of fine-scale community assembly. Thus, a better understanding of species associations across scales could improve predictions of the dynamics of local community composition in changing environments.The goal of this paper is to improve the tools needed to detect interspecific associations from co-occurrence data. We first briefly describe the development of co-occurrence methods and then draw from different lines of research to present a more complete and flexible general framework for inferring species associations that overcomes multiple challenges faced by previous approaches.
From experiments to co-occurrence methodsEfforts to infer species associations and their role in structuring communities have a long history. Traditionally, associations have been derived from small-scale field observations Positive and negative associations between species are a key outcome of community assembly from regional species pools. These associations are difficult to detect and can be caused by a range of processes such as species interactions, local environmental constraints and dispersal. We integrate new ideas around species distribution modeling, covariance matrix estimation, and network analysis to provide an approach to inferring non-random species associations from local-and regional-scale occurrence data. Specifically, we provide a novel framework for identifying species associations that overcomes three challenges: 1) correcting for indirect effects from other species, 2) avoiding spurious associations driven by regional-scale distributions, and 3) describing these associations in a multi-species context. We highlight a range of research questions and analyses that this framework is able to address. We show that the approach is statistically robust using simulated data. In addition, we present an empirical analysis of 1000 North American tree communities that gives evidence for weak positive associations among small groups of species. Finally, we discuss several possible extensions for identifying drivers of associations, predicting community assembly, and better linking biogeography and community ecology.NM-H and BB contributed equally to this project.
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