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A Benford’s law-based framework to determine the threshold of occurrence sites for species distribution modelling from ecological monitoring databases

The use of data-based species distribution models (SDMs) has increased significantly in recent years. However, studies of determining the minimum requirements of occurrence sites from ecological monitoring datasets used in species distribution modelling remain insufficient. Therefore, this study pro...

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Autores principales: Shim, Taeyong, Kim, Zhonghyun, Jung, Jinho
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10556063/
https://www.ncbi.nlm.nih.gov/pubmed/37798344
http://dx.doi.org/10.1038/s41598-023-44010-z
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author Shim, Taeyong
Kim, Zhonghyun
Jung, Jinho
author_facet Shim, Taeyong
Kim, Zhonghyun
Jung, Jinho
author_sort Shim, Taeyong
collection PubMed
description The use of data-based species distribution models (SDMs) has increased significantly in recent years. However, studies of determining the minimum requirements of occurrence sites from ecological monitoring datasets used in species distribution modelling remain insufficient. Therefore, this study proposed a framework to determine the threshold of minimum occurrence sites for SDMs by assessing compliance with Benford’s law. The compliance test verified that the national-scale freshwater fish monitoring dataset was natural and reliable. Results derived from true skill statistics (TSS) determined the minimum number of occurrence sites for reliable species distribution modelling was 20 with a TSS value of 0.793 and an overall accuracy of 0.804. The Benford compliance test has shown to be a useful tool for swift and efficient evaluation of the reliability of species occurrence datasets, or the determination of the threshold of occurrence sites before species distribution modelling. Further studies regarding the evaluation of this method’s transferability to other species and validation using SDM performance are required. Overall, the framework proposed in this study demonstrates that Benford compliance test applied to species monitoring datasets can be used to derive a universal and model-independent minimum occurrence threshold for SDMs.
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spelling pubmed-105560632023-10-07 A Benford’s law-based framework to determine the threshold of occurrence sites for species distribution modelling from ecological monitoring databases Shim, Taeyong Kim, Zhonghyun Jung, Jinho Sci Rep Article The use of data-based species distribution models (SDMs) has increased significantly in recent years. However, studies of determining the minimum requirements of occurrence sites from ecological monitoring datasets used in species distribution modelling remain insufficient. Therefore, this study proposed a framework to determine the threshold of minimum occurrence sites for SDMs by assessing compliance with Benford’s law. The compliance test verified that the national-scale freshwater fish monitoring dataset was natural and reliable. Results derived from true skill statistics (TSS) determined the minimum number of occurrence sites for reliable species distribution modelling was 20 with a TSS value of 0.793 and an overall accuracy of 0.804. The Benford compliance test has shown to be a useful tool for swift and efficient evaluation of the reliability of species occurrence datasets, or the determination of the threshold of occurrence sites before species distribution modelling. Further studies regarding the evaluation of this method’s transferability to other species and validation using SDM performance are required. Overall, the framework proposed in this study demonstrates that Benford compliance test applied to species monitoring datasets can be used to derive a universal and model-independent minimum occurrence threshold for SDMs. Nature Publishing Group UK 2023-10-05 /pmc/articles/PMC10556063/ /pubmed/37798344 http://dx.doi.org/10.1038/s41598-023-44010-z Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Shim, Taeyong
Kim, Zhonghyun
Jung, Jinho
A Benford’s law-based framework to determine the threshold of occurrence sites for species distribution modelling from ecological monitoring databases
title A Benford’s law-based framework to determine the threshold of occurrence sites for species distribution modelling from ecological monitoring databases
title_full A Benford’s law-based framework to determine the threshold of occurrence sites for species distribution modelling from ecological monitoring databases
title_fullStr A Benford’s law-based framework to determine the threshold of occurrence sites for species distribution modelling from ecological monitoring databases
title_full_unstemmed A Benford’s law-based framework to determine the threshold of occurrence sites for species distribution modelling from ecological monitoring databases
title_short A Benford’s law-based framework to determine the threshold of occurrence sites for species distribution modelling from ecological monitoring databases
title_sort benford’s law-based framework to determine the threshold of occurrence sites for species distribution modelling from ecological monitoring databases
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10556063/
https://www.ncbi.nlm.nih.gov/pubmed/37798344
http://dx.doi.org/10.1038/s41598-023-44010-z
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