Abstract

Predicting species distribution in the ocean has become a crucial task to assess marine ecosystem responses to ongoing climate change. In the Humboldt Current System (HCS), the endemic copepod Calanus chilensis is one of the key species bioindicator of productivity and water masses. Here we modeled the geographic distribution of Calanus chilensis for two bathymetric ranges, 0–200 and 200–400 m. For the 0–200 m layer, we used the Bayesian Additive Regression Trees (BART) method, whereas, for the 200–400 m layer, we used the Ensembles of Small Models (ESMs) method and then projected the models into two future scenarios to assess changes in geographic distribution patterns. The models were evaluated using the multi-metric approach. We identifed that chlorophyll-a (0.34), Mixed Layer Depth (0.302) and salinity (0.36) explained the distribution of C. chilensis. The geographic prediction of the BART model revealed a continuous distribution from Ecuador to the southernmost area of South America for the 0–200 m depth range, whereas the ESM model indicated a discontinuous distribution with greater suitability for the coast of Chile for the 200–400 m depth range. A reduction of the distribution range of C. chilensis is projected in the future. Our study suggests that the distribution of C. chilensis is conditioned by productivity and mesoscale processes, with both processes closely related to upwelling intensity. These models serve as a tool for proposing indicators of changes in the ocean. We further propose that the species C. chilensis is a high productivity and low salinity indicator at the HCS. We recommend further examining multiple spatial and temporal scales for stronger inference.