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Midpoint attractors and species richness: Modelling the interaction between environmental drivers and geometric constraints

  • Author(s): Colwell, RK
  • Gotelli, NJ
  • Ashton, LA
  • Beck, J
  • Brehm, G
  • Fayle, TM
  • Fiedler, K
  • Forister, ML
  • Kessler, M
  • Kitching, RL
  • Klimes, P
  • Kluge, J
  • Longino, JT
  • Maunsell, SC
  • McCain, CM
  • Moses, J
  • Noben, S
  • Sam, K
  • Sam, L
  • Shapiro, AM
  • Wang, X
  • Novotny, V
  • et al.

Published Web Location

https://doi.org/10.1111/ele.12640
Abstract

© 2016 John Wiley & Sons Ltd/CNRS. We introduce a novel framework for conceptualising, quantifying and unifying discordant patterns of species richness along geographical gradients. While not itself explicitly mechanistic, this approach offers a path towards understanding mechanisms. In this study, we focused on the diverse patterns of species richness on mountainsides. We conjectured that elevational range midpoints of species may be drawn towards a single midpoint attractor - a unimodal gradient of environmental favourability. The midpoint attractor interacts with geometric constraints imposed by sea level and the mountaintop to produce taxon-specific patterns of species richness. We developed a Bayesian simulation model to estimate the location and strength of the midpoint attractor from species occurrence data sampled along mountainsides. We also constructed midpoint predictor models to test whether environmental variables could directly account for the observed patterns of species range midpoints. We challenged these models with 16 elevational data sets, comprising 4500 species of insects, vertebrates and plants. The midpoint predictor models generally failed to predict the pattern of species midpoints. In contrast, the midpoint attractor model closely reproduced empirical spatial patterns of species richness and range midpoints. Gradients of environmental favourability, subject to geometric constraints, may parsimoniously account for elevational and other patterns of species richness.

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