Predicting Seabird Foraging Habitat for Conservation Planning in Atlantic Canada: Integrating Telemetry and Survey Data Across Thousands of Colonies

• Published in: Frontiers in Marine Science
• Main Authors: Robert A. Ronconi, David J. Lieske, Laura A. McFarlane Tranquilla, Sue Abbott, Karel A. Allard, Brad Allen, Amie L. Black, François Bolduc, Gail K. Davoren, Antony W. Diamond, David A. Fifield, Stefan Garthe, Carina Gjerdrum, April Hedd, Mark L. Mallory, Robert A. Mauck, Julie McKnight, William A. Montevecchi, Ingrid L. Pollet, Isabeau Pratte, Jean-François Rail, Paul M. Regular, Gregory J. Robertson, Jennifer C. Rock, Lucas Savoy, Katherine R. Shlepr, Dave Shutler, Stephanie C. Symons, Philip D. Taylor, Sabina I. Wilhelm
• Format: Article in Journal/Newspaper
• Language: English
• Published: Frontiers Media S.A. 2022
• Subjects: tracking; seabirds; machine-learning; conservation planning; North Atlantic; Science; Q; General. Including nature conservation; geographical distribution; QH1-199.5; Canada
• Online Access: https://doi.org/10.3389/fmars.2022.816794; https://doaj.org/article/b877e490f86d4586be41dcbec49a4617

Conservation of mobile organisms is difficult in the absence of detailed information about movement and habitat use. While the miniaturization of tracking devices has eased the collection of such information, it remains logistically and financially difficult to track a wide range of species across a large geographic scale. Predictive distribution models can be used to fill this gap by integrating both telemetry and census data to construct distribution maps and inform conservation goals and planning. We used tracking data from 520 individuals of 14 seabird species in Atlantic Canada to first compare foraging range and distance to shorelines among species across colonies, and then developed tree-based machine-learning models to predict foraging distributions for more than 5000 breeding sites distributed along more than 5000 km of shoreline. Despite large variability in foraging ranges among species, tracking data revealed clusters of species using similar foraging habitats (e.g., nearshore vs. offshore foragers), and within species, foraging range was highly colony-specific. Even with this variability, distance from the nesting colony was an important predictor of distribution for nearly all species, while distance from coastlines and bathymetry (slope and ruggedness) were additional important predictors for some species. Overall, we demonstrated the utility of tree-based machine-learning approach when modeling tracking data to predict distributions at un-sampled colonies. Although tracking and colony data have some shortcomings (e.g., fewer data for some species), where results need to be interpreted with care in some cases, applying methods for modeling breeding season distributions of seabirds allows for broader-scale conservation assessment. The modeled distributions can be used in decisions about planning for offshore recreation and commercial activities and to inform conservation planning at regional scales.