Biotic and abiotic variables show little redundancy in explaining tree species distributions

Authors

  • Eliane S. Meier,

  • Felix Kienast,

  • Peter B. Pearman,

  • Jens-Christian Svenning,

  • Wilfried Thuiller,

  • Miguel B. Araújo,

  • Antoine Guisan,

  • Niklaus E. Zimmermann


E. S. Meier (eliane.meier@wsl.ch), F. Kienast, P. B. Pearman and N. E. Zimmermann, Land Use Dynamics, Swiss Federal Research Inst. WSL, Zurcherstrasse 111, CH-8903 Birmensdorf, Switzerland. – J.-C. Svenning, Ecoinformatics and Biodiversity Group, Dept of Biological Sciences, Aarhus Univ., Ny Munkegade 114, DK-8000 Aarhus C., Denmark. – W. Thuiller, Laboratoire d'Ecologie Alpine, CNRS UMR 5553, Univ., Joseph Fourier, BP 53, FR-38041 Grenoble Cedex 9, France. – M. B. Araújo, Depto de Biodiversidad y Biologia Evolutiva, Museo Nacional de Ciencias Naturales, CSIC, /José Gutiérrez Abascal, 2, ES-28006 Madrid, Spain, and ‘Rui Nabeiro’ Biodiversity Chair, CIBIO, Univ. de Évora, Largo dos Colegiais, PT-7000 Évora, Portugal. – A. Guisan, Dept of Ecology and Evolution, Inst. of Geology and Paleontology, Univ. of Lausanne, Biophore Building, CH-1015 Lausanne, Switzerland.

Abstract

Abiotic factors such as climate and soil determine the species fundamental niche, which is further constrained by biotic interactions such as interspecific competition. To parameterize this realized niche, species distribution models (SDMs) most often relate species occurrence data to abiotic variables, but few SDM studies include biotic predictors to help explain species distributions. Therefore, most predictions of species distributions under future climates assume implicitly that biotic interactions remain constant or exert only minor influence on large-scale spatial distributions, which is also largely expected for species with high competitive ability. We examined the extent to which variance explained by SDMs can be attributed to abiotic or biotic predictors and how this depends on species traits. We fit generalized linear models for 11 common tree species in Switzerland using three different sets of predictor variables: biotic, abiotic, and the combination of both sets. We used variance partitioning to estimate the proportion of the variance explained by biotic and abiotic predictors, jointly and independently. Inclusion of biotic predictors improved the SDMs substantially. The joint contribution of biotic and abiotic predictors to explained deviance was relatively small (~9%) compared to the contribution of each predictor set individually (~20% each), indicating that the additional information on the realized niche brought by adding other species as predictors was largely independent of the abiotic (topo-climatic) predictors. The influence of biotic predictors was relatively high for species preferably growing under low disturbance and low abiotic stress, species with long seed dispersal distances, species with high shade tolerance as juveniles and adults, and species that occur frequently and are dominant across the landscape. The influence of biotic variables on SDM performance indicates that community composition and other local biotic factors or abiotic processes not included in the abiotic predictors strongly influence prediction of species distributions. Improved prediction of species' potential distributions in future climates and communities may assist strategies for sustainable forest management.

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