Technical and Engineering Support Alliance, National Oceanic and Atmospheric Administration, National Marine Fisheries Service, Southeast Fisheries Science Center, Panama City, Florida, United States of America.
School of Forest, Fisheries, and Geomatics Sciences, University of Florida, Gainesville, Florida, United States of America.
PLoS One. 2024 Feb 26;19(2):e0298755. doi: 10.1371/journal.pone.0298755. eCollection 2024.
Queen snapper (Etelis oculatus) is of interest from an ecological and management perspective as it is the second most landed finfish species (by total pounds) as determined by Puerto Rico commercial landings (2010-2019). As fishing activities progressively expand into deeper waters, it is critical to gather data on deep-sea fish populations to identify essential fish habitats (EFH). In the U.S. Caribbean, the critically data-deficient nature of this species has made this challenging. We investigated the use of ensemble species distribution modeling (ESDM) to predict queen snapper distribution along the coast of Puerto Rico. Using occurrence data and terrain attributes derived from bathymetric datasets at different resolutions, we developed species distribution models unique to each sampling region (west, northeast, and southeast Puerto Rico) using seven different algorithms. Then, we developed ESDM models to analyze fish distribution using the highest-performing algorithms for each region. Model performance was evaluated for each ensemble model, with all depicting 'excellent' predictive capability (AUC > 0.8). Additionally, all ensemble models depicted 'substantial agreement' (Kappa > 0.7). We then used the models in combination with existing knowledge of the species' range to produce binary maps of potential queen snapper distributions. Variable importance differed across spatial resolutions of 30 m (west region) and 8 m (northeast and southeast region); however, bathymetry was consistently one of the best predictors of queen snapper suitable habitat. Positive detections showed strong regional patterns localized around large bathymetric features, such as seamounts and ridges. Despite the data-deficient condition of queen snapper population dynamics, these models will help facilitate the analysis of their spatial distribution and habitat preferences at different spatial scales. Our results therefore provide a first step in designing long-term monitoring programs targeting queen snapper, and determining EFH and the general distribution of this species in Puerto Rico.
皇后石斑鱼（Etelis oculatus）因其生态和管理意义而受到关注，它是波多黎各商业捕捞量（2010-2019 年）中第二大上岸的鱼类物种（按总磅数计算）。随着捕捞活动逐渐向深海扩展，收集深海鱼类种群的数据以确定关键鱼类栖息地（EFH）至关重要。在美国加勒比地区，由于该物种数据严重缺乏，这一工作极具挑战性。我们调查了使用集合物种分布模型（ESDM）来预测波多黎各沿海皇后石斑鱼的分布。使用来自不同分辨率的水深数据集的出现数据和地形属性，我们为每个采样区域（波多黎各西部、东北部和东南部）使用七种不同的算法开发了独特的物种分布模型。然后，我们使用每个区域表现最好的算法开发了 ESDM 模型来分析鱼类分布。对每个集合模型的性能进行了评估，所有模型的预测能力都非常出色（AUC>0.8）。此外，所有集合模型都描绘了“实质性一致”（Kappa>0.7）。然后，我们将这些模型与该物种分布范围的现有知识相结合，生成潜在皇后石斑鱼分布的二进制地图。在 30 米（西部区域）和 8 米（东北和东南区域）的不同空间分辨率下，变量重要性有所不同；然而，水深始终是皇后石斑鱼适宜栖息地的最佳预测因子之一。阳性检测显示出强烈的区域性模式，集中在大型地形特征（如海山和山脊）周围。尽管皇后石斑鱼种群动态的数据缺乏，但这些模型将有助于分析其在不同空间尺度下的空间分布和栖息地偏好。因此，我们的研究结果为设计针对皇后石斑鱼的长期监测计划以及确定波多黎各的 EFH 和该物种的一般分布提供了第一步。
