Characteristics of Environmental Data Layers for Use in Species Distribution Modelling in the Eastern Canadian Arctic and Sub-Arctic Regions

Species distribution modelling (SDM) is a tool that utilizes the relationship between a species and its environment in known (sampled) locations to predict the species’ distribution in unsampled areas. Environmental data are typically collected at different spatial and temporal scales and often require spatial interpolation between data points to provide a continuous surface required by the modelling application. Here we provide detailed information on 111 environmental data layers collected over different spatial scales and temporal resolutions and interpolated using a geospatial method to provide continuous data surfaces for the Eastern Canadian Arctic and Sub-Arctic. Variables were obtained from a broad range of physical and biological data sources and spatially interpolated using ordinary kriging. For each environmental variable we show the distributional properties of the raw data prior to spatial interpolation, model performance indicators and assessment of model performance, and finally, maps of the prediction standard error and interpolation prediction surfaces. These layers have been archived in a common (raster) format at the Bedford Institute of Oceanography to facilitate future use. A subset of these variables has already been used in a conservation management application to identify deep-water coral and sponge Significant Benthic Areas in the Eastern Canadian Arctic.

物种分布模型（Species Distribution Modelling，简称SDM）是一种工具，它通过利用物种与其已知（采样）地点的环境之间的关系，来预测物种在未采样区域的分布。环境数据的收集通常在不同空间和时间尺度上进行，并且往往需要在数据点之间进行空间插值，以提供建模应用所需的连续表面。在本研究中，我们提供了关于111个环境数据层的详细信息，这些数据层是在不同的空间尺度和时间分辨率下收集的，并使用地理空间方法进行插值，以提供加拿大东部北极和亚北极地区的连续数据表面。变量来源于广泛的物理和生物数据源，并通过普通克里金法进行空间插值。对于每个环境变量，我们展示了空间插值前的原始数据的分布特性、模型性能指标以及对模型性能的评估，最后还提供了预测标准误差图和插值预测表面图。这些层已在Bedford海洋研究所以通用（栅格）格式存档，以便利未来的使用。这些变量中的一部分已被用于加拿大东部北极的深水珊瑚和海绵显著底栖区域的保护管理应用中。