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
• Other Authors: Erikstad, Kjell Einar
• Format: Article in Journal/Newspaper
• Language: English
• Published: 2021
• Subjects: MaxENT; Uria lomvia; Uria aalge; Fratercula arctica; Alca torda; SST model; Norway; Barents Sea; fratercula; uria
• Online Access: https://zenodo.org/record/6379791; https://doi.org/10.5281/zenodo.6379791

The conservation of seabirds is increasingly important for their role as indicator species of ocean ecosystems, which are predicted to experience increasing levels of exploitation this century. To safeguard these ecosystems 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, interannual, basin-wide variation has the potential to invalidate these models, which depend on seasonal mesoscale variability. In this study, we present a novel solution to predict presence from spatially and temporally variable environmental predictors, while reducing the influence of irrelevant basin-wide variation. We model the Maximum Entropy (MaxENT) Model derived relationship between Standardised Monthly SST (StdSST) and Habitat Suitability using Gaussian curve models, and then convert independent StdSST data to produce heatmaps of predicted seabird presence. In this study we demonstrate StdSST to be a functional environmental predictor of seabird presence, within a Gaussian curve 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 species of Auk; Common Guillemots, Razorbills, Puffins and Brunnich’s Guillemots. Synthesis and Applications: We believe that the methodology we have developed and tested in this study can be used to guide ecosystem management practices by converting coupled-climate model predictions into predictions of future presence based on Habitat Suitability for the species, allowing us to consider the possible effects of climate change and yearly variation of SST on foraging Seabird hotspots in the Barents Sea.