Data from: Habitat-based species distribution modelling of the Hawaiian deepwater snapper-grouper complex

Deepwater snappers and groupers are valuable components of many subtropical and tropical fisheries globally and understanding the habitat associations of these species is important for spatial fisheries management. Habitat-based species distribution models were developed for the deepwater snapper-grouper complex in the main Hawaiian Islands (MHI). Six eteline snappers (Pristipomoides spp., Aphareus rutilans, and Etelis spp.) and one endemic grouper (Hyporthodus quernus) comprise the species complex known as the Hawaiian Deep Seven Bottomfishes. Species occurrence was recorded using baited remote underwater video stations deployed between 30 and 365 m (n = 2381) and was modeled with 12 geomorphological covariates using GLMs, GAMs, and BRTs. Depth was the most important predictor across species, along with ridge-like features, rugosity, and slope. In particular, ridge-like features were important habitat predictors for E. coruscans and P. filamentosus. Bottom hardness was an important predictor especially for the two Etelis species. Along with depth, rugosity and slope were the most important habitat predictors for A. rutilans and P. zonatus, respectively. Models built using GAMs and BRTs generally had the highest predictive performance. Finally, using the BRT model output, we created species-specific distribution maps and demonstrated that areas with high predicted probabilities of occurrence were positively related to fishery catch rates.

深水笛鲷与石斑鱼是全球众多亚热带、热带渔业的核心组成部分，明晰该类物种的栖息地关联特征对空间渔业管理具有重要意义。本研究针对夏威夷主群岛（Main Hawaiian Islands, MHI）的深水笛鲷-石斑鱼复合类群，构建了基于栖息地的物种分布模型。该复合类群被称为“夏威夷深水七种底栖鱼类”，包含6种滨鲷亚科笛鲷（紫鱼属Pristipomoides spp.、红鸢笛鲷Aphareus rutilans以及Etelis spp.）与1种特有石斑鱼（Hyporthodus quernus）。研究采用布设深度介于30至365米的诱饵式远程水下视频站（样本量n=2381）记录物种出现情况，并选取12种地貌协变量，通过广义线性模型（Generalized Linear Models, GLMs）、广义加性模型（Generalized Additive Models, GAMs）以及提升回归树（Boosted Regression Trees, BRTs）开展建模分析。整体而言，水深是所有物种最重要的预测因子，此外还包括脊状地形特征、地形粗糙度与坡度。具体而言，脊状地形特征对闪光笛鲷（E. coruscans）与丝鳍紫鱼（P. filamentosus）的栖息地预测具有关键作用。底质硬度则是重要的预测因子，尤其针对两种Etelis属物种。与水深协同，地形粗糙度与坡度分别为红鸢笛鲷（A. rutilans）与带纹紫鱼（P. zonatus）最重要的栖息地预测因子。采用广义加性模型与提升回归树构建的模型通常具备最优的预测性能。最后，本研究基于提升回归树模型的输出结果生成了物种专属的分布地图，并证实物种出现预测概率较高的区域与渔业捕捞产量呈显著正相关关系。