Projecting future changes in distributions of small-scale pelagic fisheries of the southern Colombian Pacific Ocean

Abstract

Small-scale fisheries (SSF) contribute to nearly half of global landings and provide multiple socioeconomic benefits to coastal communities. The Pacific coast SSF represents 37% of the total fisheries landings in Colombia. Scientific literature continually shows that tropical marine habitats are most vulnerable to oceanic changes associated with climate change. This study prioritized three pelagic species (Euthynnus lineatus, Scomberomorus sierra, and Cynoscion albus) based on their landing statistics to develop potential current and future species distributions using five ensembled machine learning models including Artificial Neural Network (ANN), Maximum Entropy (MaxEnt), Boosted Regression Tree (BRT), Random Forest (RF), and Classification Tree (CT). Future distributions of these species in the medium-term (2050s) and long-term (2080s) were modeled using the Representative Concentration Pathways (RCP) 2.6 and 8.5 emission scenarios for four ensembled General Circulation Models (GCMs) obtained from the Coupled Model Intercomparison Project Phase 5 (CMIP5). In addition, change detections were calculated to identify contraction and expansion of areas, and the distributional core shift was determined to estimate the spatial movements. Results indicate that E. lineatus and S. sierra will potentially move to deeper waters away from the coastline. Alternatively, C. albus could be a species to potentially gain more importance for the fishing sector due to potential variations in climate. These results constitute a critical scientific basis for evaluating the climate change vulnerability of the fishing sector and the decision-making process in the future of small-scale fishery management in the southern Colombian Pacific Ocean.

Keywords

• Climate change
• Pelagic species
• Species distribution modelling
• Change detection
• Small-scale fisheries
• Colombian Pacific