Duccio Rocchini
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
Authors
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 FOODY giles.foody@nottingham.ac.uk
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
Abstract
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.
Citation
Rocchini, D., Tordoni, E., Marchetto, E., Marcantonio, M., Barbosa, A. M., Bazzichetto, M., …Malavasi, M. (2023). A quixotic view of spatial bias in modelling the distribution of species and their diversity. npj Biodiversity, 2(1), Article 10. https://doi.org/10.1038/s44185-023-00014-6
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 Nature |
Peer Reviewed | Peer Reviewed |
Volume | 2 |
Issue | 1 |
Article Number | 10 |
DOI | https://doi.org/10.1038/s44185-023-00014-6 |
Public URL | https://nottingham-repository.worktribe.com/output/20282724 |
Publisher URL | https://www.nature.com/articles/s44185-023-00014-6 |
Files
s44185-023-00014-6
(2.4 Mb)
PDF
Publisher Licence URL
https://creativecommons.org/licenses/by/4.0/
You might also like
Good practices for estimating area and assessing accuracy of land change
(2014)
Journal Article
Usability of VGI for validation of land cover maps
(2015)
Journal Article
Downloadable Citations
About Repository@Nottingham
Administrator e-mail: discovery-access-systems@nottingham.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2024
Advanced Search