Generalizing the use of geographical weights in biodiversity modelling

Authors

  • C. Mellin,

    Corresponding author
    1. Australian Institute of Marine Science, Townsville, Qld, Australia
    2. The Environment Institute and School of Earth and Environmental Sciences, The University of Adelaide, Adelaide, SA, Australia
    • Correspondence: C. Mellin, Australian Institute of Marine Science, PMB no. 3, Townsville MC, Townsville, Qld 4810, Australia.

      E-mail: camille.mellin@adelaide.edu.au

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  • K. Mengersen,

    1. Queensland University of Technology, School of Mathematical Sciences, Brisbane, Qld, Australia
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  • C. J. A. Bradshaw,

    1. The Environment Institute and School of Earth and Environmental Sciences, The University of Adelaide, Adelaide, SA, Australia
    2. South Australian Research and Development Institute, Henley Beach, SA, Australia
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  • M. J. Caley

    1. Australian Institute of Marine Science, Townsville, Qld, Australia
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  • Editor: José Alexandre Diniz-Filho

Abstract

Aim

Determining how ecological processes vary across space is a major focus in ecology. Current methods that investigate such effects remain constrained by important limiting assumptions. Here we provide an extension to geographically weighted regression in which local regression and spatial weighting are used in combination. This method can be used to investigate non-stationarity and spatial-scale effects using any regression technique that can accommodate uneven weighting of observations, including machine learning.

Innovation

We extend the use of spatial weights to generalized linear models and boosted regression trees by using simulated data for which the results are known, and compare these local approaches with existing alternatives such as geographically weighted regression (GWR). The spatial weighting procedure (1) explained up to 80% deviance in simulated species richness, (2) optimized the normal distribution of model residuals when applied to generalized linear models versus GWR, and (3) detected nonlinear relationships and interactions between response variables and their predictors when applied to boosted regression trees. Predictor ranking changed with spatial scale, highlighting the scales at which different species–environment relationships need to be considered.

Main conclusions

GWR is useful for investigating spatially varying species–environment relationships. However, the use of local weights implemented in alternative modelling techniques can help detect nonlinear relationships and high-order interactions that were previously unassessed. Therefore, this method not only informs us how location and scale influence our perception of patterns and processes, it also offers a way to deal with different ecological interpretations that can emerge as different areas of spatial influence are considered during model fitting.

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