A social-ecological study of a Costa Rica fishery through fisher local ecological knowledge and satellite tracking

Local ecological knowledge (LEK) is increasingly being recognized as a valuable component of ecological modeling, including in the creation of species distribution models (SDMs). SDMs are used to make inferences about the distribution of suitable habitat for species of interest and can provide valuable information about the occurrence of these species. However, data and monitoring limitations can be especially pronounced in remote areas, creating a need for affordable, reliable, and timely information. In fisheries studies, incorporating LEK from fishers may be a valuable way to fill data gaps in predicting fish species occurrence over spatiotemporal scales and can be useful for marine spatial planning. In this study, we investigated the spatiotemporal and biophysical characteristics of a billfish fishery in Costa Rica through a mixed methods approach combining satellite tracking data of sailfish (Istiophorus platypterus) and blue marlin (Makaira nigricans) and sport fisher LEK elicited through semi-structured interviews and participatory mapping. To compare these two methods for understanding billfish distribution, we created SDMs using either satellite tag data or LEK participatory maps for billfish occurrences and examined the environmental profiles within the satellite tracking and participatory map locations. Overall, we found that although participatory mapping of fishing grounds is confined by distance to shore, the fisher-mapped sailfish and blue marlin niches provide valuable fine-scale near-shore data and overlap with several spatial and environmental features revealed by satellite tracking data. From this case study, we suggest important considerations when incorporating LEK and other ecological monitoring methods for ecological modeling and marine management. Our findings provide a social-ecological perspective of the Costa Rican billfish fishery while demonstrating the potential of fisher knowledge to help address data gaps and complement satellite tracking data in spatiotemporal modeling of economically and ecologically important billfish, with implications for data-poor fisheries worldwide.