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Modelling the influence of biotic factors on species distribution patterns

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  • Leach, Katie
  • Montgomery, W. Ian
  • Reid, Neil

Abstract

Biotic interactions can have large effects on species distributions yet their role in shaping species ranges is seldom explored due to historical difficulties in incorporating biotic factors into models without a priori knowledge on interspecific interactions. Improved SDMs, which account for biotic factors and do not require a priori knowledge on species interactions, are needed to fully understand species distributions. Here, we model the influence of abiotic and biotic factors on species distribution patterns and explore the robustness of distributions under future climate change. We fit hierarchical spatial models using Integrated Nested Laplace Approximation (INLA) for lagomorph species throughout Europe and test the predictive ability of models containing only abiotic factors against models containing abiotic and biotic factors. We account for residual spatial autocorrelation using a conditional autoregressive (CAR) model. Model outputs are used to estimate areas in which abiotic and biotic factors determine species’ ranges. INLA models containing both abiotic and biotic factors had substantially better predictive ability than models containing abiotic factors only, for all but one of the four species. In models containing abiotic and biotic factors, both appeared equally important as determinants of lagomorph ranges, but the influences were spatially heterogeneous. Parts of widespread lagomorph ranges highly influenced by biotic factors will be less robust to future changes in climate, whereas parts of more localised species ranges highly influenced by the environment may be less robust to future climate. SDMs that do not explicitly include biotic factors are potentially misleading and omit a very important source of variation. For the field of species distribution modelling to advance, biotic factors must be taken into account in order to improve the reliability of predicting species distribution patterns both presently and under future climate change.

Suggested Citation

  • Leach, Katie & Montgomery, W. Ian & Reid, Neil, 2016. "Modelling the influence of biotic factors on species distribution patterns," Ecological Modelling, Elsevier, vol. 337(C), pages 96-106.
  • Handle: RePEc:eee:ecomod:v:337:y:2016:i:c:p:96-106
    DOI: 10.1016/j.ecolmodel.2016.06.008
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    1. Takuya Iwamura & Kerrie A Wilson & Oscar Venter & Hugh P Possingham, 2010. "A Climatic Stability Approach to Prioritizing Global Conservation Investments," PLOS ONE, Public Library of Science, vol. 5(11), pages 1-9, November.
    2. Håvard Rue & Sara Martino & Nicolas Chopin, 2009. "Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(2), pages 319-392, April.
    3. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Van Der Linde, 2002. "Bayesian measures of model complexity and fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639, October.
    4. James E. M. Watson & Takuya Iwamura & Nathalie Butt, 2013. "Mapping vulnerability and conservation adaptation strategies under climate change," Nature Climate Change, Nature, vol. 3(11), pages 989-994, November.
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    Cited by:

    1. Humphreys, John M. & Elsner, James B. & Jagger, Thomas H. & Pau, Stephanie, 2017. "A Bayesian geostatistical approach to modeling global distributions of Lygodium microphyllum under projected climate warming," Ecological Modelling, Elsevier, vol. 363(C), pages 192-206.

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