Predicting the Foraging Patterns of Wintering Auks Using a Sea Surface Temperature Model for the Barents Sea

• Main Authors: Hodges, Samuel, Erikstad, Kjell-Einar, Reiertsen, Tone Kirsten
• Format: Text
• Language: English
• Published: Zenodo 2021
• Subjects: MaxENT; Uria lomvia; Uria aalge; Fratercula arctica; Alca torda; SST model; Norway; Barents Sea; common guillemot; fratercula; uria
• Online Access: https://dx.doi.org/10.5281/zenodo.4445075; https://zenodo.org/record/4445075

Background: The conservation of seabirds is becoming increasingly important due to their status as indicator species of ocean ecosystems, which are predicted to experience increasing levels of exploitation and human conflict this century. To safeguard these ecosystems against these threats will require predictive, spatial studies of seabird foraging hotspots. Current research on seabird foraging hotspots has established a significant relationship between probability of presence and several environmental variables, including Sea Surface Temperature (SST). However, all of these studies have been directed at understanding present or historical distributions of seabird populations. Methods: In this study, we present a novel solution to predict presence from spatially and temporally variable environmental predictors. We model the Maximum Entropy (MaxENT) Model derived relationship between Standard SST and Habitat Suitability using Gaussian models, and then convert independent Standard SST data to produce heatmaps of predicted seabird presence. Results: In this study we demonstrate Standard SST to be a functional environmental predictor of seabird presence, within a Gaussian model framework. We demonstrate accurate predictions of the model’s training data and of independent seabird presence data to a high degree of accuracy (Area under the ROC Curve > 0.65) for four different species of Auk; Puffins, Razorbills, Common Guillemot, and Brunnich’s Guillemot. Conclusions: We believe that the methodology we have developed and tested in this study can be used to guide conservation policy and ecosystem management practices by converting Ocean Climate model predictions into predictions of future Habitat Suitability for the species, allowing us to consider the possible effects of climate change and yearly variation of SST on foraging Seabird hotspots.