Abbie Gail Jones

McGill University
Ph.D. candidate

Supervisor: Brian Leung
Laura Pollock
Start: 2019-09-03


Integrative species distribution models for extensive biodiversity datasets: insights and patterns from applications at large scales
Describing the spatial distribution of species is vital for successful conservation policies; however, geographic species occurrence data are rarely complete and are vulnerable to systematic biases. Species distribution models (SDMs) are commonly used to estimate species ranges by joining occurrence data of a species of interest to related predictors (e.g., environmental, socio-economic) with the goal of accurately predicting species presences and absences in a particular habitat. While a useful numerical tool, SDMs have weaknesses; for example, they can also be sensitive to prevailing biases (i.e., spatial or taxonomical) present in the occurrence databases on which they are based. These biases often translate into imperfect detections in the form of significant omission or commission errors with the potential of harming conservation efforts. The S2BaK model, a novel integrative SDM, derives an ameliorated estimation approach by combining opportunistic species data with systematic species surveys to fit models and build bias-adjustment kernels. This has previously resulted in improved predictive power and reduced systematic underestimations in comparison to traditional models in a Panamanian setting. Throughout my doctorate degree, I will expand the use of S2BaK to both new large-scale landscapes to create and interpret a complete vegetation biodiversity layer. In my proposed research, I first seek to integrative SDMs and bias-adjustment kernels for the European flora using available plant species distribution using available plant species data. Using the models I generated, I will then aim to identify key areas in Europe where functional (e.g., distribution of different plant trait characteristics) and phylogenetic diversity (i.e., distribution of phylogenetic difference) overlap with each other, with human-interest ecosystem services (e.g., plant benefits to mankind), and with the current network of protected areas across the continent. Finally, I will utilize S2BaK to generate a Canadian flora biodiversity distribution layer that adjusts for the systematic data inequalities present in the subarctic and arctic and incorporates indigenous traditional knowledge from collaborators at CICADA and the Wemindji Cree Nation. My project will provide the most complete vegetation biodiversity baseline for Europe and Canada to date, with biodiversity estimates corrected for regional or taxonomical biases, thus increasing predictive ability. Such a baseline will be important for future research addressing fundamental or applied ecological topics concerning plant distributions on varying geographic scales.


invasive species, SDMs, biodiversity, Data bias, Europe, Canada, Arctic, Species Traits, Model Transferability