Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/95110
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Type: Journal article
Title: Generalizing the use of geographical weights in biodiversity modelling
Author: Mellin, C.
Mengersen, K.
Bradshaw, C.
Caley, M.
Citation: Global Ecology and Biogeography, 2014; 23(11):1314-1323
Publisher: Wiley
Issue Date: 2014
ISSN: 1466-822X
1466-8238
Statement of
Responsibility: 
C. Mellin, K. Mengersen, C. J. A. Bradshaw, and M. J. Caley
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.
Keywords: Biodiversity; macroecological modelling; method; non-stationarity; prediction; spatial scale; species distribution modelling.
Rights: © 2014 John Wiley & Sons Ltd
RMID: 0030015684
DOI: 10.1111/geb.12203
Appears in Collections:Geography, Environment and Population publications

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