Prioritizing species of concern monitoring using GIS-based fuzzy models

Abstract

The costs of monitoring species of concern in data-limited regions can hinder effective management. However, careful biological survey design can improve monitoring of critical areas, and help develop ecosystem-based approaches, including spatial management frameworks. The current study aims to reduce the cost of environmental monitoring of sea turtles, river dolphins and manatees within the Soure Marine Extractive Reserve (MER), a multiuse MPA adjacent to Marajo Island, Brazil by creating a data-driven monitoring prioritization framework. Here we present a novel and adaptable approach for designing surveys and prioritizing monitoring areas within the coastal and marine habitats of MER. We mapped all the anthropogenic activities occurring in the area and used the fuzzy logic framework to identify high priority locations. Fuzzy logic models go beyond binary categorization, allowing elements to belong to one or more categories. This framework also incorporates spatial uncertainty of reporting data. Our results indicate that approximately 30% of the Soure MER core area has a high monitoring priority, with some spillover into the buffer zone. The model defined the southeast portion of the core area as the largest single patch available for monitoring a species of concern, due to the higher concentration of fixed fishing gear operations. For the future, this model could be adapted to inform habitat suitability and test the effectiveness of the different use zones delimitated in the Soure MER management plan.