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Laura J. Pollock

Membre académique associé
Professeure adjointe, McGill University, Département de biologie
Sujets de recherche
Biologie computationnelle
Modèles probabilistes

Biographie

J’occupe le poste de professeure adjointe en conservation, écologie, évolution et comportement au Département de biologie de l'Université McGill. Je suis une écologiste quantitative intéressée par les modèles à grande échelle de la biodiversité aux niveaux régional, continental et mondial. Mes recherches portent sur les effets du changement climatique sur la biodiversité, combinant de nombreuses données sur la biodiversité avec des modèles prédictifs. Mes travaux se concentrent également sur l'optimisation dans le but de cibler les zones clés de biodiversité et de proposer des solutions de conservation efficaces.

Publications

Graph embedding and transfer learning can help predict potential species interaction networks despite data limitations
Tanya Strydom
Salomé Bouskila
Francis Banville
Ceres Barros
Dominique Caron
Maxwell J. Farrell
Marie‐Josée Fortin
Benjamin Mercier
Laura J. Pollock
Rogini Runghen
Giulio V. Dalla Riva
Timothée Poisot
Metawebs (networks of potential interactions within a species pool) are a powerful abstraction to understand how large‐scale species inter… (voir plus)action networks are structured. Because metawebs are typically expressed at large spatial and taxonomic scales, assembling them is a tedious and costly process; predictive methods can help circumvent the limitations in data deficiencies, by providing a first approximation of metawebs. One way to improve our ability to predict metawebs is to maximize available information by using graph embeddings, as opposed to an exhaustive list of species interactions. Graph embedding is an emerging field in machine learning that holds great potential for ecological problems. Here, we outline how the challenges associated with inferring metawebs line‐up with the advantages of graph embeddings; followed by a discussion as to how the choice of the species pool has consequences on the reconstructed network, specifically as to the role of human‐made (or arbitrarily assigned) boundaries and how these may influence ecological hypotheses.
2023-10-19
Methods in Ecology and Evolution (publié)
doi.org
Conserving avian evolutionary history can effectively safeguard future benefits for people
Rikki Gumbs
Claudia L. Gray
Michael Hoffmann
Rafael Molina-Venegas
Nisha Owen
Laura J. Pollock
Phylogenetic diversity (PD)—the evolutionary history of a set of species—is conceptually linked to the maintenance of yet-to-be-discover… (voir plus)ed benefits from biodiversity or “option value.” We used global phylogenetic and utilization data for birds to test the PD option value link, under the assumption that the performance of sets of PD-maximizing species at capturing known benefits is analogous to selecting the same species at a point in human history before these benefits were realized. PD performed better than random at capturing utilized bird species across 60% of tests, with performance linked to the phylogenetic dispersion and prevalence of each utilization category. Prioritizing threatened species for conservation by the PD they encapsulate performs comparably to prioritizing by their functional distinctiveness. However, species selected by each metric show low overlap, indicating that we should conserve both components of biodiversity to effectively conserve a variety of uses. Our findings provide empirical support for the link between evolutionary history and benefits for future generations.
2023-09-20
Science Advances (publié)
doi.org
Addressing uncertainty when projecting marine species' distributions under climate change
Sarah C. Davies
Patrick L. Thompson
Catalina Gómez
Jessica Nephin
Anders Knudby
Ashley E. Park
Sarah K. Friesen
Laura J. Pollock
Emily M. Rubidge
Sean C. Anderson
Josephine C. Iacarella
Devin A. Lyons
Andrew MacDonald
Andrew McMillan
Eric J. Ward
Amber M. Holdsworth
Neil Swart
Jeff Price
Karen L. Hunter
2023-09-13
Ecography (publié)
doi.org
Trophic interaction models predict interactions across space, not food webs.
Dominique Caron
Ulrich Brose
Miguel Lurgi
F. Guillaume Blanchet
Dominique Gravel
Laura J. Pollock
Aim: Trophic interactions are central to our understanding of essential ecosystem functions as well as their stability. Predicting these int… (voir plus)eractions has become increasingly common due to the lack of empirical data on trophic interactions for most taxa in most ecosystems. We aim to determine how far and accurately trophic interaction models extrapolate to new communities both in terms of pairwise predator-prey interactions and higher level food web attributes (i.e., species position, food web-level properties).