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.

物种分布模型（SDM）用于各种科学和管理应用程序。对于与金枪鱼等漂流鱼类聚集装置（DFAD）相关的物种，空间模型可以帮助金枪鱼区域渔业管理组织（t-RFMO）了解其栖息地特征和动态。DFAD由卫星链接的回声浮标进行监视和跟踪，这些浮标可远程为渔民提供其下方鱼的粗略估计。尽管最近在科学研究中使用了这种类型的不依赖捕获的数据，但是从未将使用这些数据的SDM与使用依赖捕获的数据（即标称捕获数据）的模型进行比较。本研究调查了使用贝叶斯时空时空模型使用这两种数据源获得的结果，可以分析它们的优缺点，并比较预测的分布。尽管两个模型的输出总体上显示了在DFAD下金枪鱼的存在区域相似，但是从两个不同数据源得出的模型之间进行比较的最显着结果是，在预测图中确定了热点的精度。用声学数据获得的地图允许以较高的精度识别DFAD下金枪鱼存在的高概率区域，而从渔获数据得出的地图则不允许以更精细的比例观察任何变化。