Optimising occurrence data in species distribution models: sample size, positional uncertainty, and sampling bias matter

IF 5.4 1区环境科学与生态学 Q1 BIODIVERSITY CONSERVATION

Ecography Pub Date : 2024-08-02 DOI:10.1111/ecog.07294

Vítězslav Moudrý, Manuele Bazzichetto, Ruben Remelgado, Rodolphe Devillers, Jonathan Lenoir, Rubén G. Mateo, Jonas J. Lembrechts, Neftalí Sillero, Vincent Lecours, Anna F. Cord, Vojtěch Barták, Petr Balej, Duccio Rocchini, Michele Torresani, Salvador Arenas-Castro, Matěj Man, Dominika Prajzlerová, Kateřina Gdulová, Jiří Prošek, Elisa Marchetto, Alejandra Zarzo-Arias, Lukáš Gábor, François Leroy, Matilde Martini, Marco Malavasi, Roberto Cazzolla Gatti, Jan Wild, Petra Šímová

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引用次数: 0

Abstract

Species distribution models (SDMs) have proven valuable in filling gaps in our knowledge of species occurrences. However, despite their broad applicability, SDMs exhibit critical shortcomings due to limitations in species occurrence data. These limitations include, in particular, issues related to sample size, positional uncertainty, and sampling bias. In addition, it is widely recognised that the quality of SDMs as well as the approaches used to mitigate the impact of the aforementioned data limitations depend on species ecology. While numerous studies have evaluated the effects of these data limitations on SDM performance, a synthesis of their results is lacking. However, without a comprehensive understanding of their individual and combined effects, our ability to predict the influence of these issues on the quality of modelled species–environment associations remains largely uncertain, limiting the value of model outputs. In this paper, we review studies that have evaluated the effects of sample size, positional uncertainty, sampling bias, and species ecology on SDMs outputs. We build upon their findings to provide recommendations for the critical assessment of species data intended for use in SDMs.

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优化物种分布模型中的出现数据：样本大小、位置不确定性和取样偏差问题

物种分布模型（SDMs）已被证明在填补我们对物种出现的知识空白方面具有重要价值。然而，尽管 SDMs 具有广泛的适用性，但由于物种出现数据的局限性，SDMs 仍然存在严重的缺陷。这些局限性尤其包括与样本大小、位置不确定性和取样偏差有关的问题。此外，人们普遍认为，SDM 的质量以及用于减轻上述数据限制影响的方法取决于物种生态学。虽然已有大量研究评估了这些数据限制对 SDM 性能的影响，但缺乏对这些研究结果的综合分析。然而，如果不全面了解这些数据限制的单独和综合影响，我们预测这些问题对模型物种-环境关联质量的影响的能力在很大程度上仍不确定，从而限制了模型输出的价值。在本文中，我们回顾了评估样本大小、位置不确定性、取样偏差和物种生态学对 SDMs 输出影响的研究。在这些研究结果的基础上，我们提出了对拟用于 SDMs 的物种数据进行关键评估的建议。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Ecography 环境科学-生态学

CiteScore

11.60

自引率

3.40%

发文量

122

审稿时长

8-16 weeks

期刊介绍： ECOGRAPHY publishes exciting, novel, and important articles that significantly advance understanding of ecological or biodiversity patterns in space or time. Papers focusing on conservation or restoration are welcomed, provided they are anchored in ecological theory and convey a general message that goes beyond a single case study. We encourage papers that seek advancing the field through the development and testing of theory or methodology, or by proposing new tools for analysis or interpretation of ecological phenomena. Manuscripts are expected to address general principles in ecology, though they may do so using a specific model system if they adequately frame the problem relative to a generalized ecological question or problem. Purely descriptive papers are considered only if breaking new ground and/or describing patterns seldom explored. Studies focused on a single species or single location are generally discouraged unless they make a significant contribution to advancing general theory or understanding of biodiversity patterns and processes. Manuscripts merely confirming or marginally extending results of previous work are unlikely to be considered in Ecography. Papers are judged by virtue of their originality, appeal to general interest, and their contribution to new developments in studies of spatial and temporal ecological patterns. There are no biases with regard to taxon, biome, or biogeographical area.