Characteristics of Environmental Data Layers for Use in Species Distribution Modelling in the Newfoundland and Labrador Region

Species distribution models require spatially linked response (e.g., species, habitat type) and environmental (predictor) point data. Often, only limited environmental data types can be collected at the time of sampling, and it may be desirable to capture information both from different data sources and/or longer term data series in order to predict the distribution of a response variable. In order to link response and predictor variables that are not sampled at the same location or time, geospatial interpolation techniques are applied. Here, we provide a review of 104 environmental variables from each of 8 water column properties: Temperature, Salinity, Current Speed, Maximum Seasonal Mixed Layer Depth, Bottom Shear, Sea Surface Chlorophyll a, Primary Production and Dissolved Inorganic Nutrients for the ‘Newfoundland and Labrador Region’ (a combined spatial extent of DFO’s Placentia Bay-Grand Bank and Newfoundland and Labrador Shelves Large Ocean Management Areas). All of these variables have potential biological relevance to benthic invertebrate species. Original data sources were the Global Ocean Reanalyses and Simulations (GLORYS), the Sea-viewing Wide Field-of-view Sensor (SeaWIFS), and the World Ocean Database 2013 (WOD13). For each variable, the original data characteristics and diagnostics produced from spatial interpolation using ordinary kriging are detailed. Standard error and prediction maps are shown for each variable. Based on these diagnostics, a subset of these variables was subsequently used in species distribution models of corals, sponges, crinoids, ascidians and bryozoans in the Newfoundland and Labrador Region.物种分布模型需要空间关联的响应变量（如物种、栖息地类型）与环境（预测因子）点数据。在采样阶段通常仅能收集到有限类型的环境数据，因此为了预测响应变量的分布，往往需要整合不同数据源乃至长期时序数据中的信息。针对采样位置与时间均不匹配的响应变量与预测因子，需采用地理空间插值技术进行关联。本文梳理了纽芬兰与拉布拉多地区（覆盖DFO的普拉森舍湾-大滩海域与纽芬兰与拉布拉多陆架大型海洋管理区的合并空间范围）的8项水体属性下共104个环境变量，分别为温度、盐度、流速、季节性最大混合层深度、底切应力、海面叶绿素a浓度、初级生产力与溶解无机营养盐。上述所有变量均与底栖无脊椎动物类群具有潜在生物学关联。原始数据源包括全球海洋再分析与模拟系统（Global Ocean Reanalyses and Simulations, GLORYS）、宽视场海洋水色扫描仪（Sea-viewing Wide Field-of-view Sensor, SeaWIFS）以及2013年世界海洋数据库（World Ocean Database 2013, WOD13）。针对每个变量，本文详细阐述了其原始数据特征，以及通过普通克里金法（ordinary kriging）进行空间插值得到的诊断结果。所有变量均配有标准误差图与预测分布图。基于上述诊断结果，后续选取其中部分变量用于纽芬兰与拉布拉多地区珊瑚、海绵、海百合、被囊动物与苔藓虫的物种分布模型构建。