An application of fuzzy logic to build ecological sympatry networks
| Autor: | Marisol Flores, Andrés Torres-Miranda, Miguel Raggi, Víctor Hugo Anaya-Muñoz, Luis Miguel García-Velázquez, Daniele Colosi |
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| Rok vydání: | 2019 |
| Předmět: |
0106 biological sciences
Source code Geographic information system Computer science Process (engineering) media_common.quotation_subject computer.software_genre 010603 evolutionary biology 01 natural sciences Fuzzy logic Environmental data Ecology Evolution Behavior and Systematics media_common Ecology business.industry End user 010604 marine biology & hydrobiology Applied Mathematics Ecological Modeling Locality Computer Science Applications Computational Theory and Mathematics Modeling and Simulation Data mining business computer Network analysis |
| Zdroj: | Ecological Informatics. 53:100978 |
| ISSN: | 1574-9541 |
| DOI: | 10.1016/j.ecoinf.2019.100978 |
| Popis: | In recent years sympatry networks have been proposed as a mean to perform biogeographic analysis, but their computation posed practical difficulties that limited their use. We propose a novel approach, bringing closer the application of well-established network analysis tools to the study of sympatry patterns using both geographic and environmental data associated with the occurrence of species. Our proposed algorithm, SG ra F u L o , combines the use of fuzzy logic and numerical methods to directly compute the network of interest from point locality records, without the need of specialized tools, such as geographic information systems, thereby simplifying the process for end users. By posing the problem in matrix terms, SG ra F u L o is able to achieve remarkable efficiency even for large datasets, taking advantage of well established scientific computing algorithms. We present sympatry networks constructed using real-world data collected in Mexico and Central America and highlight the potential of our approach in the analysis of overlapping niches of species that could have important applications even in evolutionary studies. We also present details on the design and implementation of the algorithm, as well as experiments that show its efficiency. The source code is freely released and datasets are also available to support the reproducibility of our results. |
| Databáze: | OpenAIRE |
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