Primary Convener:
Yanghui Kang, Virginia Tech

Convener:
Maoya Bassiouni, University of California, Berkeley
Trevor Keenan, University of California, Berkeley
Qing Zhu, Lawrence Berkeley National Laboratory

Early Career Convener:
Doaa Aboelyazeed, Pennsylvania State University Main Campus

Chair:
Yanghui Kang, University of Wisconsin Madison
Maoya Bassiouni, SLU Swedish University of Agricultural Sciences Uppsala
Doaa Aboelyazeed, Pennsylvania State University Main Campus

Rapid advances in machine learning are transforming ecosystem sciences, generating new insights into terrestrial, aquatic, and marine systems. Growing data availability, from ground-based measurements to remote sensing, increasingly enables data-driven discoveries. Integrating data-driven and process-based approaches, including physical laws and causal relationships, is driving fundamental and applied innovations to address land and resource management challenges posed by climate change.This session invites contributions that leverage cutting-edge machine learning to advance our understanding and management of ecosystems. Relevant areas include physics-informed machine learning, differentiable modeling, foundation models, generative AI, large language models, digital twins, causal inference, trustworthy AI, transfer learning, information theory, and uncertainty-aware modeling. We welcome applications spanning biogeochemistry, ecosystem services, agroecosystem modeling, biodiversity, conservation biology, nature-based climate solutions etc. This session aims to foster dialogues about the opportunities and considerations associated with AI applications to improve the understanding and prediction of ecosystem dynamics.

Index Terms
0426 Biosphere|atmosphere interactions
0439 Ecosystems, structure and dynamics
1942 Machine learning
1968 Scientific reasoning|inference

Cross-Listed:
IN - Informatics
GC - Global Environmental Change

Neighborhoods:
3. Earth Covering