Deep-sea corals (DSCs) are important living marine resources, forming both oases of biodiversity and three-dimensional habitat structure for fishes and invertebrates. However, because of logistical difficulties and expense of deep-sea exploration, much less is known about the distribution of DSCs than is known for their shallow-water counterparts. Predictive modeling, therefore, is essential for estimating the extent of DSC habitat in areas that are unexplored in order to support conservation efforts, to provide information for effective management of offshore activities affecting the seafloor, and for future exploration and research. In support of research and management efforts in the U.S. Northeast (Cape Hatteras, NC north to the Canadian border), we developed a comprehensive set of habitat suitability models covering this entire geographic region for nine taxonomic groups of DSCs (Alcyonacea, gorgonian corals, non-gorgonian corals, Scleractinia, Caryophylliidae, Flabellidae, Pennatulacea, Sessiliflorae, and Subselliflorae). Maximum entropy (MaxEnt) models were fit to DSC presence records and spatially-explicit environmental predictors depicting depth and seafloor topography, surficial sediment characteristics, and oceanography. A stepwise model selection procedure was then implemented to identify the set of predictor variables that maximized predictive performance for each taxonomic group. To allow for comparisons across taxonomic groups, the standard MaxEnt logistic predictions were converted into calibrated classes of habitat suitability. Overall, model performance was high for all taxonomic groups. Model fit was best for Caryophylliidae, Sessiliflorae, and Flabellidae, whereas model stability was greatest for the three taxonomic groups of Alcyonacea. Model results reported here corroborate known distributions of corals in the region. For example, large structure-forming taxa are predicted to occur mainly in canyon environments, particularly in areas of steep slope (>30°); sea pens in softer sediments of the continental shelf and slope. Additionally, the models successfully predicted DSC locations during field testing. Despite the limitations of presence-only data, several novel extensions to the traditional MaxEnt analysis workflow improved model selection, accuracy assessment, and comparability of results across taxonomic groups. This approach, when integrated with management processes, could be a powerful tool for science-based conservation, management, and spatial planning for these marine resources.

深海珊瑚（DSC）是重要的海洋生物资源，既构成生物多样性的绿洲，又构成鱼类和无脊椎动物的三维栖息地结构。但是，由于后勤方面的困难和深海勘探的费用，对DSC的分布知之甚于其浅水对应物。因此，预测模型对于估算未勘探区域的DSC栖息地范围至关重要，以支持保护工作，为有效管理影响海底的近海活动以及未来的勘探和研究提供信息。为了支持美国东北部（北卡罗来纳州哈特拉斯角，加拿大边界以北）的研究和管理工作，我们针对九种分类标准的DSC（Alcyonacea，gorgonian珊瑚，nongorgonian珊瑚，Scleractinia，Caryophylliidae，flabellidae，Pennatulacea，Sessiliflorae和Subselliflorae）开发了一套涵盖整个地理区域的综合栖息地适应性模型。最大熵（MaxEnt）模型适合于DSC的存在记录以及描述深度和海底地形，表面沉积物特征以及海洋学的空间显式环境预测因子。然后执行逐步模型选择过程，以识别可将每个分类组的预测性能最大化的一组预测变量。为了允许在分类组之间进行比较，标准的MaxEnt后勤预测已转换为栖息地适宜性的校准类别。总体，所有分类组的模型性能都很高。模型拟合最适合于石竹科，Sessiliflorae和Flabellidae，而模型稳定性对Alcyonacea的三个分类组最大。此处报告的模型结果证实了该地区珊瑚的已知分布。例如，预计形成大型结构的分类单元主要发生在峡谷环境中，尤其是在陡坡（> 30°）区域中；海笔在大陆架和斜坡的较软沉积物中。此外，模型在现场测试期间成功预测了DSC位置。尽管仅存在数据的局限性，对传统MaxEnt分析工作流程的一些新颖扩展改进了模型选择，准确性评估以及分类组中结果的可比性。这种方法，