Abstract
Fisheries surveys over broad spatial areas are crucial in defining and delineating appropriate fisheries management areas. Yet accurate mapping and tracking of fishing activities remain largely restricted to developed countries with sufficient resources to use automated identification systems and vessel monitoring systems. For many countries, the spatial extent and boundaries of fishing grounds are not completely known. We used satellite images at night to detect fishing grounds in the Philippines for fishing gears that use powerful lights to attract coastal pelagic fishes. We used nightly boat detection data, extracted by U.S. NOAA from the Visible Infrared Imaging Radiometer Suite (VIIRS), for the Philippines from 2012 to 2016, covering 1713 nights, to examine spatio-temporal patterns of fishing activities in the country. Using density-based clustering, we identified 134 core fishing areas (CFAs) ranging in size from 6 to 23,215 km2 within the Philippines’ contiguous maritime zone. The CFAs had different seasonal patterns and range of intensities in total light output, possibly reflecting differences in multi-gear and multi-species signatures of fishing activities in each fishing ground. Using maximum entropy modeling, we identified bathymetry and chlorophyll as the main environmental predictors of spatial occurrence of these CFAs when analyzed together, highlighting the multi-gear nature of the CFAs. Applications of the model to specific CFAs identified different environmental drivers of fishing distribution, coinciding with known oceanographic associations for a CFA’s dominant target species. This case study highlights nighttime satellite images as a useful source of spatial fishing effort information for fisheries, especially in Southeast Asia.
Highlights
Monitoring and mapping of fishing activities are critical components of planning and management for marine fisheries [1]
We identified 134 Core Fishing Areas (CFAs) for light-assisted fishing in the Philippines based on HDBSCAN results using a ‘minimum cluster size’ of 4 and ‘minimum samples’ value of 25 (Figure 3; Supplementary Table S1)
While we have provided here a way to identify frequently visited fishing grounds and predict habitat suitability of non-fishing grounds based on nighttime lights activity and environmental attributes, delineation of meaningful fisheries management unit as part ecosystem approaches to fisheries management (EAFM) should be done in consultation with relevant stakeholders [2]
Summary
Monitoring and mapping of fishing activities are critical components of planning and management for marine fisheries [1]. Spatial information on fishing activities remains sparse in many countries, especially in developing ones, due to the large number of fishing vessels and the high costs associated with collecting these data. Southeast Asian countries, for example, have some of the highest fishing effort densities (in boat-meters per km2) in the world [10], yet the majority of fishing vessels in this region do not have automated identification system (AIS) transceivers. A global mapping of fishing activities using AIS highlighted a clear and striking gap in fishing boat detections within the greater Southeast Asia [11]. With many developing countries having tens of thousands of fishing vessels, other complementary sources of spatial data for fisheries need to be explored
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