1 Ecology and Evolutionary Biology Department, Yale University, 165 Prospect Street, New Haven, CT 06520, USA; Center for Biodiversity and Global Change, Yale University, New Haven, CT, USA. Electronic address: shubhi.sharma@yale.edu.
2 Ecology and Evolutionary Biology Department, Yale University, 165 Prospect Street, New Haven, CT 06520, USA; Center for Biodiversity and Global Change, Yale University, New Haven, CT, USA.
3 Department of Biology, McGill University, 1205 Docteur Penfield, Montreal, Quebec, H3A 1B1, Canada.
4 Resource Ecology and Fisheries Management Division, Alaska Fisheries Science Center, National Marine Fisheries Service, Seattle, WA 98115, USA.
5 Ecology and Evolutionary Biology Department, Yale University, 165 Prospect Street, New Haven, CT 06520, USA; Center for Biodiversity and Global Change, Yale University, New Haven, CT, USA; Research Centre for Ecological Change, Research Programme of Organismal and Evolutionary Biology, Faculty of Biological and Environmental Sciences, University of Helsinki, Helsinki, 00014, Finland.
6 Eversource Energy Center and Ecology and Evolutionary Biology, University of Connecticut, Storrs, CT 06269, USA.
7 Department of Biology, Concordia University, Montreal, Quebec, H3A 1B1, Canada; Department of Biology, Memorial University of Newfoundland and Labrador, Saint John's, NL A1C 5S7, Canada.
8 Faculty of AI and Decision Making, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
9 EcoVision Lab, Photogrammetry and Remote Sensing, ETH Zürich, Zürich, 8093, Switzerland.

We lack the data needed to detect and understand biodiversity change for most species, despite some species having millions of observations. This unequal data coverage impedes conservation planning and our understanding of biodiversity patterns. The 'borrowing strength' approach leverages data-rich species to improve predictions for data-deficient species. We review multi- and joint-species distribution models that incorporate traits and phylogenies (termed 'ancillary information') and highlight how they could improve data-deficient spatial predictions. When ancillary information is informative of niche similarity, it has immense potential to improve estimates for data-deficient species distributions and address the Wallacean shortfall. While no statistical method can replace data-collection efforts, approaches discussed in this review offer an important contribution toward closing existing data gaps.