An international workshop in Davis, California, brought together researchers, practitioners, and industry leaders from six countries to map how artificial intelligence and remote sensing can improve detection and management of invasive species, which cost the global economy an estimated $423 billion annually. The June 14-16 event was organized by the United Nations University Institute for Water, Environment and Health (UNU-INWEH) at UC Davis, with funding from the U.S.-Israel Binational Agricultural Research and Development Fund (BARD).
Biological invasions are among the leading drivers of global biodiversity loss. As globalization accelerates the spread of invasive species, scientists are applying AI for Science & Research methods to strengthen detection, monitoring, and response. The workshop convened an interdisciplinary group from 13 institutions to explore how these tools can be integrated into invasion science workflows.
What the workshop covered
Day one focused on identifying current challenges, open questions, and knowledge gaps in invasion science and technology. Day two shifted toward developing opportunities and conceptualizing practical solutions, with the aim of translating emerging AI tools into actionable approaches for research, monitoring, and management.
Mornings featured presentations on current work and emerging methods. Afternoons were dedicated to working groups where participants discussed priorities, tested ideas collaboratively, and developed shared directions. Sessions covered species distribution modeling, early detection systems, risk assessment frameworks, remote sensing applications, and the governance of open biodiversity data.
Key themes from the sessions
Several cross-cutting themes emerged. On foundation models and AI in ecology, participants examined how large-scale models pre-trained on ecological and remote sensing data could accelerate species identification, range forecasting, and threat prioritization at global scales.
On data integration and open science, the group emphasized harmonizing biodiversity platforms such as GBIF and iNaturalist with new sensor and IoT data streams to build near-real-time monitoring pipelines. Working groups also examined gaps in weed risk assessment frameworks and how machine learning could improve the speed and consistency of regulatory decisions across jurisdictions.
Advances in biogeographic distribution modeling highlighted opportunities to better quantify propagule pressure, trade pathway dynamics, and climate-driven range shifts. Participants also stressed the need to bridge technical innovation and on-the-ground management so new tools are accessible and actionable for practitioners.
Planned next steps
A key output of the workshop is a collaborative research agenda and a set of priority recommendations to inform future funding proposals, manuscript development, and international policy engagement. Organizers plan to develop a collaborative manuscript synthesizing the findings, engage with international bodies on data governance frameworks, and continue the partnership among UNU-INWEH, UC Davis, and the broader invasion science community.
These efforts will inform future Research funding proposals and policy engagement. For more information about the workshop, including the full agenda and participant list, visit the workshop site.
Why this matters for science and research professionals
For researchers at the intersection of ecology and computation, the workshop signals a shift toward AI-driven tools as standard practice in invasion biology. The collaborative agenda and recommendations coming out of the sessions will shape future funding priorities and research directions. Professionals in this space should watch for the planned manuscript and policy engagement, which will define how these technologies move from the lab into regulatory and management practice.
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