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
{"title":"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","authors":"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","doi":"10.1111/ecog.07294","DOIUrl":null,"url":null,"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.","PeriodicalId":51026,"journal":{"name":"Ecography","volume":"78 1","pages":""},"PeriodicalIF":5.4000,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Optimising occurrence data in species distribution models: sample size, positional uncertainty, and sampling bias matter\",\"authors\":\"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\",\"doi\":\"10.1111/ecog.07294\",\"DOIUrl\":null,\"url\":null,\"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. 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Optimising occurrence data in species distribution models: sample size, positional uncertainty, and sampling bias matter
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.
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.