Comparing the distribution of tropical tuna associated with drifting fish aggregating devices (DFADs) resulting from catch dependent and independent data

Abstract

Species distribution models (SDMs) are used for a variety of scientific and management applications. For species associated with drifting fish aggregating devices (DFADs), such as tuna, spatial models can help tuna Regional Fisheries Management Organizations (t-RFMOs) understand their habitat characteristics and dynamics. DFADs are monitored and tracked with satellite linked echo-sounder buoys, which remotely provide fishers rough estimates of the abundance of fish underneath them. Although this type of catch-independent data has been recently used in scientific studies, SDMs using these data have never been compared with models using catch-dependent data (i.e. nominal catch data). This study investigates the results obtained with both data sources using Bayesian Hierarchical spatio-temporal models, allowing to analyze their advantages and disadvantages, as well as compare the predicted distributions. Although the two model outputs show, in general, similar areas of tuna presence under the DFADs, the most remarkable result of the comparison between the models derived from the two different data sources is the precision of the hotspots identified in the prediction maps. The maps obtained with acoustic data allow identifying areas of high probability of tuna presence under the DFADs with greater precision, whereas the maps derived from catch data do not allow observing any variation on a finer scale. The application of spatio-temporal models of tuna associated with DFADs using acoustic data provided by fishers' echo-sounder buoys appears promising to identify the distribution dynamics of the species in a cost-effective way and may help designing integrated spatial programs for more efficient fishery management.