| 要旨トップ | ESJ73 シンポジウム 一覧 | | 日本生態学会第73回全国大会 (2026年3月、京都) 講演要旨 ESJ73 Abstract

シンポジウム S17 3月13日 9:00-12:00 Room D: 京大4号11

#### Data Science Frontiers in Ecology【E】

Hideyuki DOI（Kyoto University）, Kenta SUZUKI（RIKEN BRC）

Recent advances in data science are revolutionizing how ecologists explore and interpret complex ecosystem dynamics. This symposium provides an overview of the evolution of data-driven approaches in ecology—from traditional statistical and mechanistic modeling to modern artificial intelligence (AI) techniques. Special focus will be placed on time-series analysis methods, which offer new possibilities for integrating heterogeneous ecological and environmental data to enhance forecasting and system-level understanding. Beyond analytical methods, we will emphasize the growing importance of global ecological datasets—including biodiversity monitoring networks, animal-borne recording, remote sensing, and open-access repositories—that enable large-scale synthesis and model training. By linking global data infrastructures with advanced modeling frameworks, this talk will illustrate how the convergence of data-centric and model-centric paradigms is transforming ecological prediction, knowledge, and applications for ecosystem management.
Commentators: Momoko Ichinokawa (Fish Res & Educ Agency) and Shin-Ichiro Matsuzaki (NIES)

[S17-1]
When the parts express the whole: misunderstanding binary links in ecological networks *George SUGIHARA（UCSD, SIO/HDSI）

[S17-2]
Harnessing reservoir computing for ecology: prediction, understanding and beyond *Kenta SUZUKI（RIKEN BRC）

[S17-3]
Strategies for extrapolating with biodiversity models *Jamie M KASS（Tohoku U）

[S17-4]
What Mongolian gazelles see: Quantitative evaluation of environments using animal-borne camera data with machine learning approaches *Rei NAGAYA（Kyoto University）, Lina A. KOYAMA（Kyoto University）, Takehiko Y. ITO（Hokkaido Res. Org., Azabu University）, Munkhbat UUGANBAYAR（WWF Mongolia）, Buyanaa CHIMEDDORJ（WWF Mongolia）

[S17-5]
Global ecological data and foundation models: A new era of data science in ecology *Hideyuki DOI（Kyoto Univ.）

[S17-6]
Unified data collection, sharing and utilization for ecosystem monitoring and conservation *Masae ISHIHARA（Kyoto Univ.）

日本生態学会