AbstractEcological processes are often spatially and temporally structured, potentially leading to autocorrelation either in environmental variables or species distribution data. Because of that, spatially-biased in-situ samples or predictors might affect the outcomes of ecological models used to infer the geographic distribution of species and diversity. There is a vast heterogeneity of methods and approaches to assess and measure spatial bias; this paper aims at addressing the spatial component of data-driven biases in species distribution modelling, and to propose potential solutions to explicitly test and account for them. Our major goal is not to propose methods to remove spatial bias from the modelling procedure, which would be impossible without proper knowledge of all the processes generating it, but rather to propose alternatives to explore and handle it. In particular, we propose and describe three main strategies that may provide a fair account of spatial bias, namely: (i) how to represent spatial bias; (ii) how to simulate null models based on virtual species for testing biogeographical and species distribution hypotheses; and (iii) how to make use of spatial bias - in particular related to sampling effort - as a leverage instead of a hindrance in species distribution modelling. We link these strategies with good practice in accounting for spatial bias in species distribution modelling.

A quixotic view of spatial bias in modelling the distribution of species and their diversity

Duccio Rocchini;Elisa Marchetto;Roberto Cazzolla Gatti;Alessandro Chiarucci;Ludovico Chieffallo;Silvia Mirri;Francesco Maria Sabatini;Piero Zannini;
2023

Abstract

AbstractEcological processes are often spatially and temporally structured, potentially leading to autocorrelation either in environmental variables or species distribution data. Because of that, spatially-biased in-situ samples or predictors might affect the outcomes of ecological models used to infer the geographic distribution of species and diversity. There is a vast heterogeneity of methods and approaches to assess and measure spatial bias; this paper aims at addressing the spatial component of data-driven biases in species distribution modelling, and to propose potential solutions to explicitly test and account for them. Our major goal is not to propose methods to remove spatial bias from the modelling procedure, which would be impossible without proper knowledge of all the processes generating it, but rather to propose alternatives to explore and handle it. In particular, we propose and describe three main strategies that may provide a fair account of spatial bias, namely: (i) how to represent spatial bias; (ii) how to simulate null models based on virtual species for testing biogeographical and species distribution hypotheses; and (iii) how to make use of spatial bias - in particular related to sampling effort - as a leverage instead of a hindrance in species distribution modelling. We link these strategies with good practice in accounting for spatial bias in species distribution modelling.
2023
Duccio Rocchini; Enrico Tordoni; Elisa Marchetto; Matteo Marcantonio; A. Márcia Barbosa; Manuele Bazzichetto; Carl Beierkuhnlein; Elisa Castelnuovo; Roberto Cazzolla Gatti; Alessandro Chiarucci; Ludovico Chieffallo; Daniele Da Re; Michele Di Musciano; Giles M. Foody; Lukas Gabor; Carol X. Garzon-Lopez; Antoine Guisan; Tarek Hattab; Joaquin Hortal; William E. Kunin; Ferenc Jordán; Jonathan Lenoir; Silvia Mirri; Vítězslav Moudrý; Babak Naimi; Jakub Nowosad; Francesco Maria Sabatini; Andreas H. Schweiger; Petra Šímová; Geiziane Tessarolo; Piero Zannini; Marco Malavasi
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/954500
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