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Statistical learning for species distribution models in ecological studies

  • Original Paper
  • Modern Methods and Applications for Biodiversity Studies
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Abstract

We discuss species distribution models (SDM) for biodiversity studies in ecology. SDM plays an important role to estimate abundance of a species based on environmental variables that are closely related with the habitat of the species. The resultant habitat map indicates areas where the species is likely to live, hence it is essential for conservation planning and reserve selection. We especially focus on a Poisson point process and clarify relations with other statistical methods. Then we discuss a Poisson point process from a view point of information divergence, showing the Kullback–Leibler divergence of density functions reduces to the extended Kullback–Leibler divergence of intensity functions. This property enables us to extend the Poisson point process to that derived from other divergence such as \(\beta \) and \(\gamma \) divergences. Finally, we discuss integrated SDM and evaluate the estimating performance based on the Fisher information matrices.

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Acknowledgements

We would like to thank two referees for careful reading and useful suggestions, which much improve quality of our manuscript. Part of this work is supported by JSPS KAKENHI No. JP18H03211 and No. JP22K11938.

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Authors and Affiliations

  1. Department of Computer and Information Science, Seikei University, 3-3-1 Kichijoji-kitamachi, Musashino, Tokyo, 180-8633, Japan

    Osamu Komori

  2. Department of Biostatistics, School of Medicine, Yokohama City University, 3-9 Fukuura, Kanazawa, Yokohama, Kanagawa, 236-0004, Japan

    Yusuke Saigusa

  3. Research Center for Medical and Health Data Science, The Institute of Statistical Mathematics, 10-3 Midori-cho, Tachikawa, Tokyo, 190-8562, Japan

    Shinto Eguchi

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  1. Osamu Komori
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  2. Yusuke Saigusa
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Correspondence to Osamu Komori.

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Komori, O., Saigusa, Y. & Eguchi, S. Statistical learning for species distribution models in ecological studies. Jpn J Stat Data Sci 6, 803–826 (2023). https://doi.org/10.1007/s42081-023-00206-1

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