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Inferring distance-based species turnover patterns from plot-based data
  • Justin Kitzes
Justin Kitzes
University of Pittsburgh
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Abstract

Spatial scaling patterns, which describe how species presence or abundance changes with plot area, have been widely studied from both theoretical and empirical perspectives. It has long been questioned whether such measures of spatial scaling can be used to predict species turnover, distance-based patterns that describe species presence or abundance at two locations separated by a known distance. Here I show that a common plot-based scaling pattern, the multi-scale quadrat count distribution, contains sufficient information to infer the shape of a widely used distance-based turnover metric, the pair correlation function. I demonstrate that this method makes exact predictions for simulated species distributions that following a Thomas process, and that it can accurately predict the shape of the pair correlation function for 758 species occurring in six forest plots.