Skip to main content

Research Repository

Advanced Search

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

Rocchini, Duccio; Tordoni, Enrico; Marchetto, Elisa; Marcantonio, Matteo; Barbosa, A. Márcia; Bazzichetto, Manuele; Beierkuhnlein, Carl; Castelnuovo, Elisa; Gatti, Roberto Cazzolla; Chiarucci, Alessandro; Chieffallo, Ludovico; Da Re, Daniele; Di Musciano, Michele; Foody, Giles M.; Gabor, Lukas; Garzon-Lopez, Carol X.; Guisan, Antoine; Hattab, Tarek; Hortal, Joaquin; Kunin, William E.; Jordán, Ferenc; Lenoir, Jonathan; Mirri, Silvia; Moudrý, Vítězslav; Naimi, Babak; Nowosad, Jakub; Sabatini, Francesco Maria; Schweiger, Andreas H.; Šímová, Petra; Tessarolo, Geiziane; Zannini, Piero; Malavasi, Marco

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


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

Professor of Geographical Information

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


Ecological 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.

Journal Article Type Article
Acceptance Date Mar 23, 2023
Online Publication Date May 3, 2023
Publication Date 2023
Deposit Date May 4, 2023
Publicly Available Date May 4, 2023
Journal npj Biodiversity
Publisher Springer Science and Business Media LLC
Peer Reviewed Peer Reviewed
Volume 2
Issue 1
Article Number 10
Public URL
Publisher URL


You might also like

Downloadable Citations