Modelling the spatial shifts of functional groups in the Barents Sea using a climate-driven spatial food web model

• Published in: Ecological Modelling
• Main Authors: Nascimento, Marcela Conceicao, Husson, Berengere, Guillet, Lilia, Pedersen, Torstein
• Format: Article in Journal/Newspaper
• Language: English
• Published: 2023
• Subjects: Barents Sea
• Online Access: https://hdl.handle.net/11250/3065531; https://doi.org/10.1016/j.ecolmodel.2023.110358

We built a dynamic, spatial food web model for the Barents Sea, developed with Ecospace by including species’ habitat requirements and ecological interactions. The model was used to test the spatial shifts of different functional groups due to warming. We compared model-predicted and field-surveyed biomass of functional groups (FGs) spatial distributions in relatively cold and warm years. The Ecospace model included habitat foraging capacities for environmental parameters such as water temperature and bottom depth for 74 FGs out of a total of 108 FGs. We created two plausible scenarios, one representing a relatively cold year (2004) and another representing a warm year (2013) with differences of ca. 0.3 °C in bottom temperature, 0.6 °C in surface temperature, and 7% less ice coverage between them. Comparison of centre of gravity, inertia, and spatial overlap of the modelled and surveyed spatial distributions in warm and cold years showed that the model represented the past distributions of the functional groups satisfactorily. We observed poleward shifts of 41 and 68 km for the modelled and observed distributions, respectively, in the average centre of gravity position for the 35 FGs with lowest sampling uncertainty. The model predicted that the whole community had shifted distribution towards the northeast at an average rate of 4.4 km year−1 and 67 km °C-1 between 2004 and 2013. We conclude that our Ecospace model represents past observed species distributions in the Barents Sea satisfactorily, and may predict the direction and magnitude of temperature-driven changes in spatial distributions. This ability may be useful for predicting the impact of climate changes on species and FG distributions in future scenarios. publishedVersion