ABSTRACT: Bottom-contact fisheries present risks to vulnerable marine ecosystems (VMEs) such as deep-water coral and sponge communities. Managing these risks requires better knowledge about VME spatial distribution within fishing areas. In this paper, we develop predictive species distribution models for alcyonacean (Order Alcyonacea) corals at SGaan Kinghlas-Bowie Seamount (SK-B) in British Columbia, Canada, based on direct presence/absence observations obtained from deep-water cameras attached to commercial fishing gear. We obtained in situ presence/absence observations of deep-water corals (Order Alcyonacea, Order Antipatharia, Order Pennatulacea, Family Stylasteridae) and sponges (Class Hexactinellida, Class Demospongiae) at 124 locations during commercial fishing trips at the SK-B marine protected area. We developed species distribution models for alcyonacean corals at SK-B and compared the performance of models using 4 different estimators of trap landing position (surface drop position and 3 Bayesian estimators) to account for spatial uncertainty in observation locations. We found that the different estimators for landing position affected variable selection, model performance, and model predictions. The best-fitting models using the 4 different landing position estimators had mean AUC values ranging from 0.71 to 0.78 and maximum kappa values ranging from 0.36 to 0.47. This study demonstrates how collaborative research surveys with commercial fisheries can provide fine-scale spatial data for coral and sponge habitat mapping using an approach that is scalable for benthic habitat risk assessment for large, possibly remote, areas where fisheries operate.

中文翻译：

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深水珊瑚栖息地的物种分布模型，说明了陷阱相机渔业数据中的空间不确定性

摘要：与海底接触的渔业对脆弱的海洋生态系统（VME）（如深水珊瑚和海绵群落）构成威胁。管理这些风险需要更好地了解捕捞区内VME的空间分布。在本文中，我们基于从深水获得的直接存在/不存在观测值，开发了加拿大不列颠哥伦比亚省S G aan K inghlas- B owie海山（SK-B）的拟南藻（Order Alcyonacea）珊瑚的预测物种分布模型。摄像机连接到商业捕鱼工具。我们就地获得在SK-B海洋保护区进行商业捕鱼时，在124个地点有无深水珊瑚（Alcyonacea目，Antipatharia目，Pennatulacea目，Stylasteridae目）和海绵（Hexactinellida目，Demospongiae目）的存在/不存在观察。我们在SK-B处开发了阿尔卡尼斯珊瑚的物种分布模型，并使用陷阱落地位置的4个不同估计量（表面下降位置和3个贝叶斯估计量）比较了模型的性能，以说明观测位置的空间不确定性。我们发现着陆位置的不同估算器会影响变量选择，模型性能和模型预测。使用4种不同着陆位置估算器的最佳拟合模型的平均AUC值范围为0.71至0.78，最大kappa值范围为0.36至0.47。