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

Type Article
Date 2023-05-03
Language English
Author(s) Rocchini Duccio1, 2, Tordoni Enrico3, Marchetto Elisa1, Marcantonio Matteo4, Barbosa A. Márcia5, Bazzichetto Manuele2, Beierkuhnlein Carl6, Castelnuovo Elisa1, Gatti Roberto Cazzolla1, Chiarucci Alessandro1, Chieffallo Ludovico1, Da Re Daniele7, Di Musciano Michele1, 8, Foody Giles M.9, Gabor Lukas10, 11, Garzon-Lopez Carol X.12, Guisan Antoine13, 14, Hattab Tarek15, Hortal Joaquin16, Kunin William E.17, Jordán Ferenc18, Lenoir Jonathan19, Mirri Silvia20, Moudrý Vítězslav2, Naimi Babak21, Nowosad Jakub22, Sabatini Francesco Maria1, 23, Schweiger Andreas H.24, Šímová Petra2, Tessarolo Geiziane25, Zannini Piero1, Malavasi Marco26
Affiliation(s) 1 : BIOME Lab, Department of Biological, Geological and Environmental Sciences, Alma Mater Studiorum University of Bologna, via Irnerio 42, 40126, Bologna, Italy
2 : Czech University of Life Sciences Prague, Faculty of Environmental Sciences, Department of Spatial Sciences, Kamýcka 129, Praha - Suchdol, 16500, Czech Republic
3 : Department of Botany, Institute of Ecology and Earth Science, University of Tartu, J. Liivi 2, 50409, Tartu, Estonia
4 : Evolutionary Ecology and Genetics Group, Earth and Life Institute, UCLouvain, 1348, Louvain-la-Neuve, Belgium
5 : CICGE (Centro de Investigação em Ciências Geo-Espaciais), Universidade do Porto, Porto, Portugal
6 : Biogeography, BayCEER, University of Bayreuth, Universitaetsstraße 30, 95440, Bayreuth, Germany
7 : Georges Lemaître Center for Earth and Climate Research, Earth and Life Institute, UCLouvain, Louvain-la-Neuve, Belgium
8 : Department of Life, Health and Environmental Sciences, University of L’Aquila, Piazzale Salvatore Tommasi 1, 67100, L’Aquila, Italy
9 : School of Geography, University of Nottingham, Nottingham, UK
10 : Dept of Ecology and Evolutionary Biology, Yale University, New Haven, CT, USA
11 : Center for Biodiversity and Global Change, Yale University, New Haven, CT, USA
12 : Knowledge Infrastructures, Campus Fryslan University of Groningen, Leeuwarden, The Netherlands
13 : Department of Ecology and Evolution, University of Lausanne, 1015, Lausanne, Switzerland
14 : Institute of Earth Surface Dynamics, University of Lausanne, 1015, Lausanne, Switzerland
15 : MARBEC, Univ Montpellier, CNRS, Ifremer, IRD, Sète, France
16 : Department of Biogeography and Global Change, Museo Nacional de Ciencias Naturales (MNCN-CSIC), Madrid, Spain
17 : University of Leeds, Leeds, UK
18 : University of Parma, Parma, Italy
19 : UMR CNRS 7058 “Ecologie et Dynamique des Systèmes Anthropisés” (EDYSAN), Université de Picardie Jules Verne, 1 Rue des Louvels, 80000, Amiens, France
20 : Department of Computer Science and Engineering, Alma Mater Studiorum University of Bologna, via Irnerio 42, 40126, Bologna, Italy
21 : Rui Nabeiro Biodiversity Chair, MED Institute, University of Évora, Évora, Portugal
22 : Institute of Geoecology and Geoinformation, Adam Mickiewicz University, Krygowskiego 10, 61-680, Poznan, Poland
23 : Faculty of Forestry and Wood Sciences, Czech University of Life Sciences Prague, Prague - Suchdol, Czech Republic
24 : Department of Plant Ecology, Institute of Landscape and Plant Ecology, University of Hohenheim, Stuttgart, Germany
25 : Federal University of Goiás, Campus Central, Anápolis, Brazil
26 : University of Sassari, Department of Chemistry, Physics, Mathematics and Natural Sciences, Sassari, Italy
Source npj Biodiversity (2731-4243) (Springer Science and Business Media LLC), 2023-05-03 , Vol. 2 , N. 1 , P. 10 (11p.)
DOI 10.1038/s44185-023-00014-6
Keyword(s) Biodiversity, Biogeography, Community ecology, Ecological modelling

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.

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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 (2023). A quixotic view of spatial bias in modelling the distribution of species and their diversity. npj Biodiversity, 2(1), 10 (11p.). Publisher's official version : https://doi.org/10.1038/s44185-023-00014-6 , Open Access version : https://archimer.ifremer.fr/doc/00835/94711/