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
BIOME Lab, Department of Biological, Geological and Environmental Sciences, Alma Mater Studiorum University of Bologna, via Irnerio 42, 40126, Bologna, Italy.
Czech University of Life Sciences Prague, Faculty of Environmental Sciences, Department of Spatial Sciences, Kamýcka 129, Praha - Suchdol, 16500, Czech Republic.
NPJ Biodivers. 2023 May 3;2(1):10. doi: 10.1038/s44185-023-00014-6.
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.
生态过程通常在空间和时间上具有结构，这可能导致环境变量或物种分布数据中出现自相关。因此，空间偏向的原位样本或预测变量可能会影响用于推断物种地理分布和多样性的生态模型的结果。评估和测量空间偏差的方法和途径存在很大差异；本文旨在解决物种分布建模中数据驱动偏差的空间成分，并提出明确测试和考虑这些偏差的潜在解决方案。我们的主要目标不是提出从建模过程中消除空间偏差的方法，因为在不充分了解产生偏差的所有过程的情况下这是不可能的，而是提出探索和处理空间偏差的替代方法。特别是，我们提出并描述了三种可能合理考虑空间偏差的主要策略，即：（i）如何表示空间偏差；（ii）如何基于虚拟物种模拟零模型以检验生物地理和物种分布假设；（iii）如何将空间偏差——特别是与采样努力相关的偏差——作为一种助力而非物种分布建模中的障碍加以利用。我们将这些策略与物种分布建模中考虑空间偏差的良好实践联系起来。
