Characterization of the abiotic drivers of abundance of nearshore Arctic fishes

Fish are critical ecologically and socioeconomically for subsistence economies in the Arctic, an ecosystem undergoing unprecedented environmental change. Our understanding of the responses of nearshore Arctic fishes to environmental change is inadequate because of limited research on the physicochemical drivers of abundance occurring at a fine scale. Here, high‐frequency in situ measurements of pH, temperature, salinity, and dissolved oxygen were paired with daily fish catches in nearshore Alaskan waters of the Beaufort Sea. Due to the threat that climate change poses to high‐latitude marine ecosystems, our main objective was to characterize the abiotic drivers of abundance and elucidate how nearshore fish communities may change in the future. We used generalized additive models (GAMs) to describe responses to the nearshore environment for 18 fish species. Relationships between abundance and the physicochemical environment were variable between species and reflected life history. Each abiotic covariate was significant in at least one GAM, exhibiting both nonlinear and linear associations with abundance. Temperature was the most important predictor of abundance and was significant in GAMs for 11 species. Notably, pH was a significant predictor of abundance for six species: Arctic cod (Boreogadus saida), broad whitefish (Coregonus nasus), Dolly Varden (Salvelinus malma), ninespine stickleback (Pungitius pungitius), saffron cod (Eleginus gracilis), and whitespotted greenling (Hexagrammos stelleri). Broad whitefish and whitespotted greenling abundance was positively associated with pH, while Arctic cod and saffron cod abundance was negatively associated with pH. These results may be a bellwether for future nearshore Arctic fish community change by providing a foundational characterization of the relationships between abundance and the abiotic environment, particularly in regard to pH, and demonstrate the importance of including a wider range of physicochemical habitat covariates in future research.