Reflections from the 2025 EcoHack: AI & LLM Hackathon for Applications in Evidence-based Ecological Research & Practice

Jennifer D'Souza, Tarek Al Mustafa, Daphne Frederike Auer, Sarah T. Bachinger, Marc Brinner, Caren Daniel, Nayanika Das, Alexander Espig, Nadeen Fathallah, Edward Gow-Smith, Lorenz Gunreben, Nico Heider, Hrishikesh Jadhav, Basma Jalloul, Vamsi Krishna Kommineni, Samira Korani, Andrii Krutsylo, Zijian Ling, Vaishnavi Mendu, Shuhan Miao, Bartolome Ortiz Viso, Anne Peter, Moritz Plenz, Javad Razavian, Moiz Khan Sherwani, Mir Nafis Sharear Shopnil, Will Woof, Birgitta Koenig-Ries, Tina Heger

This paper presents outcomes from the inaugural "EcoHack: AI & LLM Hackathon for Applications in Evidence-based Ecological Research & Practice," which convened participants from across Europe and beyond, culminating in 11 team submissions. These submissions highlighted six broad application areas of AI for ecology: (1) AI-enhanced decision support and automation, (2) scientific search and communication, (3) knowledge extraction and reasoning, (4) AI for ecological modeling, forecasting, and simulation, (5) causal inference and ecological reasoning, and (6) AI for biodiversity monitoring and conservation. Each team’s project is summarized in a consolidated table—complete with links to source code—and described in brief papers in the appendix.

Beyond summarizing technical results, this paper offers insights into the hackathon’s hybrid structure, featuring an in-person gathering in Bielefeld, Germany, alongside a global online hub that facilitated both local and virtual engagement. Throughout the event, participants showcased how large language models (LLMs) can serve as both robust tools for diverse machine learning tasks and flexible platforms for rapidly prototyping novel research applications. These efforts underscore the importance of stronger technological bridges among stakeholders in ecology, including practitioners, local farmers, and policymakers. Overall, the EcoHack outcomes highlight the transformative potential of AI in driving scientific discovery and fostering interdisciplinary collaboration in ecology.