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

Title data

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 ; Šímová, Petra ; Tessarolo, Geiziane ; Zannini, Piero ; Malavasi, Marco:
A quixotic view of spatial bias in modelling the distribution of species and their diversity.
In: npj Biodiversity. Vol. 2 (2023) Issue 1 . - 10.
ISSN 2731-4243
DOI: https://doi.org/10.1038/s44185-023-00014-6

Abstract in another language

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.

Further data

Item Type: Article in a journal
Refereed: Yes
Institutions of the University: Faculties
Faculties > Faculty of Biology, Chemistry and Earth Sciences
Faculties > Faculty of Biology, Chemistry and Earth Sciences > Department of Earth Sciences
Faculties > Faculty of Biology, Chemistry and Earth Sciences > Department of Earth Sciences > Chair Biogeography
Faculties > Faculty of Biology, Chemistry and Earth Sciences > Department of Earth Sciences > Chair Biogeography > Chair Biogeography - Univ.-Prof. Dr. Carl Beierkuhnlein
Research Institutions
Research Institutions > Research Centres
Research Institutions > Research Centres > Bayreuth Center of Ecology and Environmental Research- BayCEER
Graduate Schools
Graduate Schools > Elite Network Bavaria
Graduate Schools > Elite Network Bavaria > Global Change Ecology
Result of work at the UBT: Yes
DDC Subjects: 500 Science > 550 Earth sciences, geology
500 Science > 580 Plants (Botany)
500 Science > 590 Animals (Zoology)
Date Deposited: 05 May 2023 05:51
Last Modified: 05 May 2023 05:51
URI: https://eref.uni-bayreuth.de/id/eprint/76202