Abstract
Decision strategies in fisheries management are often directed by the geographic distribution and habitat preferences of target species. This study used remote sensing data to identify the optimal feeding habitat of albacore tuna in the Southern Atlantic Ocean (SAO) using an empirical habitat suitability model applying longline fisheries data during 2009–2015. An arithmetic mean model with sea surface temperature (SST) and sea surface chlorophyll-a concentration (SSC) was determined to be suitable for defining the albacore habitat in the SAO. The optimal ranges of SST and SSC for the habitat were approximately 16.5 °C–19.5 °C and 0.11–0.33 mg/m3, respectively. The study revealed a considerable positive trend between the suitable habitat area and standardized catch per unit effort (r = 0.97; p < 0.05); due to the west-to-east and northward development of the suitable habitat, albacore schools moved to the northeast of the SAO, thus increasing catch probability in April to August in that region. Overall, the frontal structure of SST and SSC plays an essential role in the formation of potential albacore habitats in the SAO. Our findings could contribute to the establishment of regional ecosystem-based fisheries management in the SAO.
Highlights
Since 1978, multisatellite remote sensing data have been used for obtaining images of ocean sea surface temperature (SST), thermal and chlorophyll-a fronts [1,2,3], and phytoplankton pigment concentration, all of which have been useful in fisheries management, fisheries oceanography, and operational fisheries oceanography [4,5,6]
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Summary
Since 1978, multisatellite remote sensing data have been used for obtaining images of ocean sea surface temperature (SST), thermal and chlorophyll-a fronts [1,2,3], and phytoplankton pigment concentration, all of which have been useful in fisheries management, fisheries oceanography, and operational fisheries oceanography [4,5,6]. With its fast and large-scale collection of data, offers enormous potential for the support of fishery exploitation and management of pelagic species [7,8,9,10]. It can increase our understanding of tuna habitats and the influencing factors [4,11,12,13]. Remote sensing tools have provided new insights for the promotion of ecosystem-based fisheries management (EBFM), for example, in tuna regional fisheries management organizations (RFMOs) [15]. Remote sensing of the environmental variables that potentially shape habitat preferences can be used to assess the catchability of target species, and can be useful for both regional and global EBFM
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