ABC All-Hands 2025 Recap

ABC Global Center convenes all-hands at Pennsylvania field station, charts next phase of ‘AI for Nature’

As Henry David Thoreau wrote, “In wildness is the preservation of the world.” For the ABC Global Center, that wildness is also a living laboratory where field notes, birdsong and camera traps become data, and data becomes decisions.

The all-hands meeting at the Pymatuning Lab of Ecology began with a dawn bird hike before shifting to big-picture sessions on multimodal sensing (camera traps, bioacoustics, drones, GPS and satellite imagery), the discovery of new species, the use of AI across scales in ecology, and more. Technical breakout sessions focused in detail on bioacoustics, benchmark data sets for model testing, detecting species interactions, statistical challenges in using species classifiers, and more.

“Our goal is both simple and hard: to build cross-disciplinary collaborations that will help us turn complex, messy, imperfect field data into fundamental breakthroughs across both ecology and computer science,” said Justin Kitzes, whose lab at the University of Pittsburgh hosted the meeting. “We’re building methods and tools that others can test, apply, and improve - for their own research and for making decisions on the ground.”

Participants reviewed progress on digital-twin pilots (including work at The Wilds in Ohio), discussed the use of image and audio datasets, and planned for capacity building to further increase ecology and computer science collaborations. Plain-language communication remained a priority, with teams workshopping quick definitions for “camera trap,” “bioacoustics” and “digital twin” to reach funders and policymakers.

The gathering coincided with national momentum around research rigor and the launch of Year Two of the NSF HDR Machine Learning Challenge, inviting computer scientists and ecologists to tackle out-of-distribution modeling on vetted, containerized workflows.

Next steps include expanding field deployments, publishing merged datasets with clear documentation, and inviting collaborators to co-develop benchmarks and decision-support tools. The through-line: AI for Nature—explainable, open, and actionable science that connects data to conservation outcomes.