Laura Pollock examines the ‘Wallacean gap’ and how to close it

Quantitative ecologist Laura Pollock discussed how to confront the “Wallacean gap,” the persistent lack of species-occurrence data across space, during the International Biogeography Society’s Funk Biogeography Seminar on Nov. 8. 

Pollock, an associate professor of biology at McGill University and a member of Mila – Quebec AI Institute, outlined practical strategies to make biogeographic data more complete, useful and actionable for science and conservation.

Pollock framed the Wallacean gap as a solvable, engineering-meets-ecology problem: researchers must prioritize where new sampling will most reduce uncertainty, integrate heterogeneous data sources and publish interoperable datasets with clear provenance. She emphasized triaging investments—filling geographic blind spots, mobilizing museum and community science records, and designing monitoring that captures environmental gradients relevant to species’ ranges.

Her team’s work sits at the intersection of biodiversity modeling and conservation planning. Pollock co-leads the Modeling Axis of the Québec Center for Biodiversity Science and serves as a co-principal investigator of the AI and Biodiversity Change (ABC) Global Center, which connects biologists and computer scientists to build AI-ready data and predictive tools. She also contributes to open-science initiatives, including GEO BON and Open Nature, that push common standards so data flow more easily between disciplines and applications.

Pollock argued that better data standards and targeted sampling will improve multi-species, multi-scale models used for conservation decisions, from tracking range shifts to supporting 30×30 planning. She highlighted the importance of transparent model evaluation—publishing code, uncertainty estimates and fit-for-purpose metrics—so managers can judge when models are ready for use.

Next steps include coordinating international sampling campaigns to close priority gaps, expanding partnerships with community scientists and museums to digitize records, and aligning datasets with FAIR principles so AI methods can accelerate discovery. For the ABC Global Center community, Pollock’s talk reinforced a shared agenda: invest in data that are standardized, discoverable and spatially explicit, then pair them with open models that help decision-makers act faster with greater confidence.