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 pre...深水笛鲷与石斑鱼是全球诸多亚热带、热带渔业的高经济价值类群，明晰其栖息地关联对于空间渔业管理至关重要。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。该类群俗称“夏威夷深海七种底栖鱼类”，包含6种滨鲷亚科笛鲷（锯鳞笛鲷属（Pristipomoides spp.）、红齿笛鲷（Aphareus rutilans）及笛鲷属（Etelis spp.）物种）与1种特有石斑鱼（奎氏低纹鮨（Hyporthodus quernus））。研究通过部署于30~365米水深的带饵远程水下视频站（样本量n=2381）记录物种出现数据，并采用12种地貌协变量，借助广义线性模型（Generalized Linear Models, GLMs）、广义可加模型（Generalized Additive Models, GAMs）及提升回归树（Boosted Regression Trees, BRTs）开展建模分析。对于所有受试物种而言，水深均为最重要的预测因子，类似海脊的地形特征、地形粗糙度与坡度同样具有较高重要性。具体而言，类似海脊的地形特征对于黄眼笛鲷（E. coruscans）与丝鳍锯鳞笛鲷（P. filamentosus）的栖息地预测具有关键作用。底质硬度亦是一项重要的预测因子[原文未完整收尾]。