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Evaluating Bayesian spatial methods for modelling species distributions with clumped and restricted occurrence data.

David W Redding1, Tim C D Lucas1,2, Tim M Blackburn1,3

  • 1Centre for Biodiversity and Environment Research, Department of Genetics, Evolution and Environment, University College London, London, United Kingdom.

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|December 1, 2017
PubMed
Summary
This summary is machine-generated.

Spatial Bayesian Species Distribution Models (SDMs) offer superior accuracy for predicting species ranges, especially with clumped or restricted data. These models effectively account for spatial autocorrelation, improving predictions compared to non-spatial methods.

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Area of Science:

  • Ecology
  • Computational Biology
  • Statistical Modeling

Background:

  • Species Distribution Models (SDMs) commonly use occurrence data, which is often spatially clumped and geographically restricted.
  • Existing SDM methods may not fully address these data limitations, potentially impacting predictive accuracy.
  • The effectiveness of spatially-explicit approaches for SDMs, particularly concerning spatial autocorrelation, requires further investigation.

Purpose of the Study:

  • To compare the predictive performance of a spatial Bayesian SDM method against non-spatial methods (MAXENT, BRT).
  • To evaluate model performance across various data sampling scenarios, including different levels of clumping and geographic restriction.
  • To assess the impact of recommended settings for accounting for spatial autocorrelation on SDM inference.

Main Methods:

  • Simulated 1000 species' ranges to generate occurrence data with varying spatial characteristics.
  • Compared Maximum Entropy Modelling (MAXENT), boosted regression trees (BRT), and a spatial Bayesian SDM (fitted using R-INLA).
  • Assessed model accuracy using AUC scores under different data sampling conditions (random, clumped, restricted).

Main Results:

  • The spatial Bayesian SDM was consistently the most accurate method, ranking in the top 2 for 7 out of 8 scenarios.
  • All methods performed similarly with high-coverage datasets.
  • When data were clumped, the spatial Bayesian SDM showed 4-8% better AUC than other methods; random data showed minor improvements for BRT (1-3%).
  • Restricted data significantly reduced accuracy (10-12%) across all methods, with higher variability.

Conclusions:

  • Spatially-explicit Bayesian SDMs, utilizing methods like R-INLA, effectively account for spatial autocorrelation and improve species distribution predictions.
  • These models can better elucidate the role of covariates in predicting species occurrence by incorporating random effects.
  • Given uncertainties in empirical data's spatial properties, Bayesian SDMs are recommended for modelling species distributions.