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Marble Algorithm: a solution to estimating ecological niches from presence-only records
We describe an algorithm that helps to predict potential distributional areas for species using presence-only records. The Marble Algorithm is a density-based clustering program based on Hutchinson’s concept of ecological niches as multidimensional hypervolumes in environmental space. The algorithm...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4585690/ https://www.ncbi.nlm.nih.gov/pubmed/26387771 http://dx.doi.org/10.1038/srep14232 |
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author | Qiao, Huijie Lin, Congtian Jiang, Zhigang Ji, Liqiang |
author_facet | Qiao, Huijie Lin, Congtian Jiang, Zhigang Ji, Liqiang |
author_sort | Qiao, Huijie |
collection | PubMed |
description | We describe an algorithm that helps to predict potential distributional areas for species using presence-only records. The Marble Algorithm is a density-based clustering program based on Hutchinson’s concept of ecological niches as multidimensional hypervolumes in environmental space. The algorithm characterizes this niche space using the density-based spatial clustering of applications with noise (DBSCAN) algorithm. When MA is provided with a set of occurrence points in environmental space, the algorithm determines two parameters that allow the points to be grouped into several clusters. These clusters are used as reference sets describing the ecological niche, which can then be mapped onto geographic space and used as the potential distribution of the species. We used both virtual species and ten empirical datasets to compare MA with other distribution-modeling tools, including Bioclimate Analysis and Prediction System, Environmental Niche Factor Analysis, the Genetic Algorithm for Rule-set Production, Maximum Entropy Modeling, Artificial Neural Networks, Climate Space Models, Classification Tree Analysis, Generalised Additive Models, Generalised Boosted Models, Generalised Linear Models, Multivariate Adaptive Regression Splines and Random Forests. Results indicate that MA predicts potential distributional areas with high accuracy, moderate robustness, and above-average transferability on all datasets, particularly when dealing with small numbers of occurrences. |
format | Online Article Text |
id | pubmed-4585690 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Nature Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-45856902015-09-29 Marble Algorithm: a solution to estimating ecological niches from presence-only records Qiao, Huijie Lin, Congtian Jiang, Zhigang Ji, Liqiang Sci Rep Article We describe an algorithm that helps to predict potential distributional areas for species using presence-only records. The Marble Algorithm is a density-based clustering program based on Hutchinson’s concept of ecological niches as multidimensional hypervolumes in environmental space. The algorithm characterizes this niche space using the density-based spatial clustering of applications with noise (DBSCAN) algorithm. When MA is provided with a set of occurrence points in environmental space, the algorithm determines two parameters that allow the points to be grouped into several clusters. These clusters are used as reference sets describing the ecological niche, which can then be mapped onto geographic space and used as the potential distribution of the species. We used both virtual species and ten empirical datasets to compare MA with other distribution-modeling tools, including Bioclimate Analysis and Prediction System, Environmental Niche Factor Analysis, the Genetic Algorithm for Rule-set Production, Maximum Entropy Modeling, Artificial Neural Networks, Climate Space Models, Classification Tree Analysis, Generalised Additive Models, Generalised Boosted Models, Generalised Linear Models, Multivariate Adaptive Regression Splines and Random Forests. Results indicate that MA predicts potential distributional areas with high accuracy, moderate robustness, and above-average transferability on all datasets, particularly when dealing with small numbers of occurrences. Nature Publishing Group 2015-09-21 /pmc/articles/PMC4585690/ /pubmed/26387771 http://dx.doi.org/10.1038/srep14232 Text en Copyright © 2015, Macmillan Publishers Limited http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ |
spellingShingle | Article Qiao, Huijie Lin, Congtian Jiang, Zhigang Ji, Liqiang Marble Algorithm: a solution to estimating ecological niches from presence-only records |
title | Marble Algorithm: a solution to estimating ecological niches from presence-only records |
title_full | Marble Algorithm: a solution to estimating ecological niches from presence-only records |
title_fullStr | Marble Algorithm: a solution to estimating ecological niches from presence-only records |
title_full_unstemmed | Marble Algorithm: a solution to estimating ecological niches from presence-only records |
title_short | Marble Algorithm: a solution to estimating ecological niches from presence-only records |
title_sort | marble algorithm: a solution to estimating ecological niches from presence-only records |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4585690/ https://www.ncbi.nlm.nih.gov/pubmed/26387771 http://dx.doi.org/10.1038/srep14232 |
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