Marine bird density and distribution on Canada's Pacific coast, 2005-2008

Original provider: Caroline Fox, Dalhousie University and Raincoast Conservation Foundation Dataset credits: Caroline Fox, Dalhousie University and Raincoast Conservation Foundation Abstract: Associated publication abstract: Increasingly disrupted and altered, the world’s oceans are subject to immense and intensifying anthropogenic pressures. Of the biota inhabiting these ecosystems, marine birds are among the most threatened. For conservation efforts targeting marine birds to be effective, quantitative information relating to their at-sea density and distribution is typically a crucial knowledge component. In this study, we generated predictive machine learning ensemble models for 13 marine bird species and 7 groups (representing 24 additional species) in Canada’s Pacific coast waters, including several species listed under Canada’s Species at Risk Act. Predictive models were based on systematic marine bird line transect survey information collected in spring, summer, and fall on Canada’s Pacific coast (2005−2008). Multiple Covariate Distance Sampling (MCDS) was used to estimate marine bird density along transect segments. Spatial and temporal environmental predictors, including remote sensing information, were used in model ensembles, which were constructed using 4 machine learning algorithms in Salford Systems Predictive Modeler v7.0 (SPM7): Random Forests, TreeNet, Multivariate Adaptive Regression Splines, and Classification and Regression Trees. Predictive models were subsequently combined to generate seasonal and overall predictions of areas important to marine birds based on normalized marine bird species or group richness and densities. Our results employ open access data sharing and are intended to better inform marine bird conservation efforts and management planning on Canada’s Pacific coast and for broader-scale geographic initiatives across North America and elsewhere.原始提供方：卡罗琳·福克斯、达尔豪西大学（Dalhousie University）以及雨岸保护基金会（Raincoast Conservation Foundation） 数据集署名：卡罗琳·福克斯、达尔豪西大学以及雨岸保护基金会 关联出版物摘要：全球海洋正遭受日益加剧的人为干扰与改造，承受着规模庞大且持续升级的人为压力。在栖息于此类海洋生态系统的生物类群中，海鸟属于受威胁程度最高的类群之一。若要使针对海鸟的保护行动取得实效，获取其在海面的种群密度与分布格局的定量数据，通常是不可或缺的核心知识支撑。 本研究针对加拿大太平洋沿海水域中的13种海鸟以及7个类群（涵盖额外24个物种）构建了预测性机器学习集成模型，其中包含若干被列入《加拿大濒危物种法案》（Species at Risk Act）的物种。预测模型的构建基于2005至2008年于加拿大太平洋沿岸春、夏、秋三季开展的系统性海鸟线路样带调查数据。本研究采用多协变量距离抽样（Multiple Covariate Distance Sampling，MCDS）估算样带区段内的海鸟种群密度。模型集成纳入了包括遥感信息在内的时空环境预测因子，建模过程借助索福德系统公司预测建模软件v7.0（Salford Systems Predictive Modeler v7.0，SPM7）中的4种机器学习算法完成，分别为随机森林（Random Forests）、TreeNet、多元自适应回归样条（Multivariate Adaptive Regression Splines）以及分类与回归树（Classification and Regression Trees）。随后，研究团队将各预测模型进行整合，基于标准化后的海鸟物种/类群丰富度与种群密度，生成了海鸟重要栖息区域的季节尺度与整体尺度预测结果。本研究成果采用开放获取数据共享模式，旨在为加拿大太平洋沿岸的海鸟保护工作与管理规划，以及北美乃至全球其他地区的更大尺度地理区域保护行动提供更科学的决策参考。