JpGU-AGU Joint Meeting 2017

講演情報

[JJ] ポスター発表

セッション記号 A (大気水圏科学) » A-OS 海洋科学・海洋環境

[A-OS26] [JJ] 海洋生物資源保全のための海洋生物多様性変動研究

2017年5月21日(日) 10:45 〜 12:15 ポスター会場 (国際展示場 7ホール)

コンビーナ:小池 勲夫(琉球大学)、中田 薫(国立研究開発法人水産研究・教育機構)、藤倉 克則(海洋研究開発機構海洋生物多様性研究分野)、杉崎 宏哉(国立研究開発法人水産研究・教育機構 中央水産研究所)

[AOS26-P08] Predicting potential fish distributions in the western North Pacific: an attempt to construct species distribution models using commercial fisheries data

*松葉 史紗子1五十嵐 弘道1山北 剛久1石川 洋一1田中 裕介1屋良 由美子1藤倉 克則1 (1.国立研究開発法人海洋研究開発機構)

キーワード:ecological niche modelling, habitat suitability, sampling bias, fisheries data, mapping

Understanding the effects of ocean conditions on distributions of commercial fish is critical for elucidating potential distributions of fish and forecasting where they will be in the future. Species distribution modellings (SDMs) enable estimation of habitat suitability for each species at a site as a function of environment factors. Traditionally modelling of species distribution has been applied to species data surveyed through standardized methods that could collect both presence and absence records, but was incapable for using presence-only data, such as those collected from fisheries or citizen monitoring schemes. Maximum entropy (MaxEnt) model provides high predictability using a presence and pseudo-absence data, which relatively fewer studies applied it to marine areas than terrestrial fields. We developed MaxEnt model to relate the occurrence records from fisheries data obtained in the western North Pacific with environment condition such as annual sea temperature and salinity from Four-dimensional Variational Ocean ReAnalysis for the Western North Pacific (FORA-WNP30), and topology. Our model indicated Sebastolobus macrochir, for instance, was influenced by both ocean conditions and topology, and would potentially distribute in the area where was no catch record. MaxEnt models will contribute to infer the probability of species using data of which detection was imperfect.