生物多様性ビッグデータに基づいたネイチャーの可視化：その現状と展望 (計量生物学) Biodiversity big data and prospects of nature visualization
久保田 康裕, 楠本 聞太郎, 塩野 貴之, 五十里 翔吾, 深谷 肇一, 高科 直, 吉川 友也, 重藤 優太郎, 新保 仁, 竹内 彰一, 三枝 祐輔, 小森 理, 生物多様性ビッグデータに基づいたネイチャーの可視化：その現状と展望, 計量生物学, 2022-2023, 43 巻, 2 号, p. 145-188, 公開日 2023/06/28, Online ISSN 2185-6494, Print ISSN 0918-4430, https://doi.org/10.5691/jjb.43.145,
Biodiversity big data plays an essential role in better understanding of biodiversity pattern in space and time and its underpinning macroecological mechanisms. Biodiversity as a concept is inductively quantified by the measurable multivariate data relative to taxonomic, functional and phylogenetic/genetic aspects. Therefore, conservation is also argued by using particular biodiversity metrics, context dependently, e.g., spatial conservation prioritization, design of protected areas network.Individual descriptive information accumulated in biogeography, ecology, physiology, molecular biology, taxonomy, and paleontology are aggregated through the spatial coordinates of biological distributions. Such biodiversity big data enables to visualize geography of 1) the richness of nature, 2) the value of nature, and 3) the uncertainty of nature, based on statistical models including maximum likelihood, machine learning, deep learning techniques. This special issue focuses on statistical and mathematical methods in terms of the quantitative visualization of biodiversity concepts. We hope that this special issue serves as an opportunity to involve researchers from different fields interested in biodiversity information and to develop into new research projects related to Nature Positive by 2030 that aims at halting and reversing the loss of biodiversity and ecosystem service.