Current and future habitat suitability of Northern fur seals and overlap with the commercial walleye pollock fishery in the Eastern Bering Sea

Understanding the abiotic and biotic drivers of species distribution is critical for climate-informed ecosystem management. We aimed to understand habitat selection of Northern fur sealsin the Eastern Bering Sea, a declining population that is also a key predator of walleye pollock, the target species for the largest U.S. commercial fishery. We developed species distribution models using random forest models by combining satellite telemetry data from lactating female fur seals tagged at different rookery complexes on the Pribilof Islands in the Eastern Bering Sea with regional ocean model simulations. We exploredhow data aggregation at two spatial scales (Pribilof-wide and complex-specific) impacted modelperformance and predicted distributions. Spatial predictions under hindcasted (1992 - 2018) and projected (2050 - 2059) physical and biological conditions were used to identify areas of core habitat, overlap with commercial fishery catches, and potential changes in future habitat suitab..., Lactating adult female Northern fur seals from St. Paul (57.19º N, 170.25º W) and St. George Islands (56.60º N, 169.55º W) Alaska, USA were instrumented with satellite tags between 1992 and 2018. Tags remained on females for 1 - 14 foraging trips. Satellite telemetry data were analysed using a continuous-time correlated random walk model to generate hourly predicted locations for each trip. Pseudo-absences were generated using a first-order vector-autoregressive model that resulted in 100 simulated trips that mimicked the speed and duration of each original trip. Environmental data from a regional ocean simulation model for the Bering Sea were extracted at each presence and pseudo-absence, including bathymetry and dynamic physical (e.g., bottom temperature) and biological (e.g., phytoplankton biomass) variables. Absences and pseudo-absences were classified to a habitat type based on bathymetry (continental shelf or basin) and datasets were subsequently split by habitat type. Hourly loca..., , # Current and future habitat suitability of Northern fur seals and overlap with the commercial walleye pollock fishery in the Eastern Bering Sea [https://doi.org/10.5061/dryad.d51c5b0cd](https://doi.org/10.5061/dryad.d51c5b0cd) ## Description of the data and file structure Northern fur seal data were generated from satellite tagging efforts of adult females between 1992 and 2018. Environmental variables were derived from regional ocean model simulations ([https://github.com/beringnpz/roms-bering-sea).](https://urldefense.com/v3/__https:/github.com/beringnpz/roms-bering-sea__;!!K-Hz7m0Vt54!g5NeitFAV_3QDabHdExrYtaYhlevqpopPTOov174OwYIPH-cJpZJ9_HmICr43-YwHDcG6cXcynw8SZbFmUOAtNGF$)Data are provided in a zip file that contains data in both an excel and R data file, where each row corresponds to a single location (either a presence or an absence) with associated values of environmental variables at that location. ### Files and variables #### File: McHuron et al\_Northern\_fur\_seal\_habi...,明晰物种分布的非生物与生物驱动因子，对于开展气候适配性生态系统管理至关重要。本研究旨在明晰白令海东部海域北海狗（Northern fur seal）的栖息地选择机制。该种群数量正持续下降，同时也是黄眼狭鳕（walleye pollock）的关键捕食者——而黄眼狭鳕正是美国规模最大的商业捕捞目标鱼种。我们结合了在白令海东部普里比洛夫群岛不同繁殖群复合体佩戴卫星追踪标签的泌乳雌性北海狗的卫星遥测（satellite telemetry）数据，与区域海洋模式模拟结果，构建了物种分布模型（species distribution model），模型采用随机森林（random forest）算法。我们探究了两种空间尺度（普里比洛夫群岛全域尺度与繁殖群专属尺度）下的数据聚合方式对模型性能与预测分布结果的影响。我们利用后报（1992-2018年）与未来预估（2050-2059年）的物理与生物环境条件开展空间预测，以此识别核心栖息地范围、与商业捕捞渔获物的重叠区域，以及未来栖息地适宜性的潜在变化…… 1992年至2018年间，研究人员为来自美国阿拉斯加州圣保罗岛（57.19°N，170.25°W）与圣乔治岛（56.60°N，169.55°W）的成年泌乳雌性北海狗佩戴卫星追踪标签。每只标记个体的标签会在其1至14次觅食巡弋期间持续留存。研究采用连续时间相关随机游走模型对卫星遥测数据进行分析，为每一次觅食巡弋生成每小时的预测定位点。我们利用一阶向量自回归模型生成伪缺失位点（pseudo-absence），共得到100条模拟巡弋轨迹，其运动速度与持续时长均与原始轨迹一致。研究从白令海区域海洋模拟模型中提取每个存在位点与伪缺失位点的环境数据，涵盖水深地形（bathymetry）、动态物理变量（如底层水温）与生物变量（如浮游植物生物量）。研究人员依据水深地形将缺失位点与伪缺失位点划分为两类栖息地类型（大陆架（continental shelf）或海盆（basin）），随后按栖息地类型对数据集进行拆分。每小时的定位…… # 白令海东部海域北海狗当前与未来栖息地适宜性及其与商业黄眼狭鳕捕捞作业的重叠情况 [https://doi.org/10.5061/dryad.d51c5b0cd](https://doi.org/10.5061/dryad.d51c5b0cd) ## 数据与文件结构说明 北海狗数据来源于1992年至2018年间针对成年雌性个体的卫星标记工作。环境变量则源自区域海洋模式模拟结果（[https://github.com/beringnpz/roms-bering-sea](https://github.com/beringnpz/roms-bering-sea)）。数据集以压缩包形式提供，内含Excel与R数据两种格式的文件，每一行对应一个单一位点（存在位点或缺失位点），并附带该位点的相关环境变量数值。 ### 文件与变量说明 #### 文件：McHuron等人_北海狗栖息地_……