Bioregionalization analyses with the bioregion R-package
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866909525960818688 |
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| author | Denelle, Pierre Leroy, Boris Lenormand, Maxime |
| author_facet | Denelle, Pierre Leroy, Boris Lenormand, Maxime |
| contents | Bioregionalization consists in the identification of spatial units with similar species composition and is a classical approach in the fields of biogeography and macroecology. The recent emergence of global databases, improvements in computational power, and the development of clustering algorithms coming from the network theory have led to several major updates of the bioregionalizations of many taxa. A typical bioregionalization workflow involves five different steps: formatting the input data, computing a (dis)similarity matrix, selecting a bioregionalization algorithm, evaluating the resulting bioregionalization, and mapping and interpreting the bioregions. For most of these steps, there are many options available in the methods and R packages. Here, we present bioregion, a package that includes all the steps of a bioregionalization workflow under a single architecture, with an exhaustive list of the bioregionalization algorithms used in biogeography and macroecology. These algorithms include (non-)hierarchical algorithms as well as community detection algorithms coming from the network theory. Some key methods from the literature, such as the network community detection algorithm Infomap or OSLOM (Order Statistics Local Optimization Method), that were not available in the R language are included in bioregion. By combining different methods coming from different fields to communicate easily, bioregion will allow a reproducible and complete comparison of the different bioregionalization methods, which is still missing in the literature. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2404_15300 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Bioregionalization analyses with the bioregion R-package Denelle, Pierre Leroy, Boris Lenormand, Maxime Quantitative Methods Bioregionalization consists in the identification of spatial units with similar species composition and is a classical approach in the fields of biogeography and macroecology. The recent emergence of global databases, improvements in computational power, and the development of clustering algorithms coming from the network theory have led to several major updates of the bioregionalizations of many taxa. A typical bioregionalization workflow involves five different steps: formatting the input data, computing a (dis)similarity matrix, selecting a bioregionalization algorithm, evaluating the resulting bioregionalization, and mapping and interpreting the bioregions. For most of these steps, there are many options available in the methods and R packages. Here, we present bioregion, a package that includes all the steps of a bioregionalization workflow under a single architecture, with an exhaustive list of the bioregionalization algorithms used in biogeography and macroecology. These algorithms include (non-)hierarchical algorithms as well as community detection algorithms coming from the network theory. Some key methods from the literature, such as the network community detection algorithm Infomap or OSLOM (Order Statistics Local Optimization Method), that were not available in the R language are included in bioregion. By combining different methods coming from different fields to communicate easily, bioregion will allow a reproducible and complete comparison of the different bioregionalization methods, which is still missing in the literature. |
| title | Bioregionalization analyses with the bioregion R-package |
| topic | Quantitative Methods |
| url | https://arxiv.org/abs/2404.15300 |