Data from: Influence of device accuracy and choice of algorithm for species distribution modelling of seabirds: a case study using black-browed albatrosses

Species distribution models (SDM) based on tracking data from different devices are used increasingly to explain and predict seabird distributions. However, different tracking methods provide different data resolutions, ranging from < 10m to >100km. To better understand the implications of this variation, we modeled the potential distribution of black-browed albatrosses Thalassarche melanophris from South Georgia that were simultaneously equipped with a Platform Terminal Transmitter (PTT) (high resolution) and a Global Location Sensor (GLS) logger (coarse resolution), and measured the overlap of the respective potential distribution for a total of nine different SDM algorithms. We found slightly better model fits for the PTT than for GLS data (AUC values 0.958±0.048 vs. 0.95±0.05) across all algorithms. The overlaps of the predicted distributions were higher between device types for the same algorithm, than among algorithms for either device type. Uncertainty arising from coarse-resolution location data is therefore lower than that associated with the modeling technique. Consequently, the choice of an appropriate algorithm appears to be more important than device type when applying SDMs to seabird tracking data. Despite their low accuracy, GLS data appear to be effective for analyzing the habitat preferences and distribution patterns of pelagic species.

基于不同设备追踪数据的物种分布模型（Species Distribution Model, SDM）正日益被用于解释和预测海鸟的分布格局。然而，不同的追踪方法所提供的数据分辨率差异显著，区间可从不足10米延伸至超过100千米。为深入理解该分辨率变异带来的影响，我们针对来自南乔治亚岛的黑眉信天翁（Thalassarche melanophris）构建了潜在分布模型——这些个体同时搭载了平台终端发射器（Platform Terminal Transmitter, PTT，高分辨率）与全球位置传感器（Global Location Sensor, GLS）记录器（粗分辨率），并针对共计9种不同的SDM算法，计算了各自潜在分布的重叠度。研究发现，在所有测试算法中，基于PTT数据的模型拟合效果略优于GLS数据，二者的受试者工作特征曲线下面积（Area Under Curve, AUC）分别为0.958±0.048与0.95±0.05。相同算法下，不同设备类型得到的预测分布重叠度，高于同一设备类型下不同算法得到的重叠度。由此可见，由粗分辨率定位数据带来的不确定性，低于建模技术本身带来的不确定性。因此，在将SDM应用于海鸟追踪数据时，选择合适的建模算法似乎比设备类型更为重要。尽管GLS数据的定位精度较低，但其仍可有效用于分析远洋物种的栖息地偏好与分布模式。