Outputs of current speed and sea otter abundance models in Glacier Bay, Alaska

Sea otters are apex predators that can exert considerable influence over the nearshore communities they occupy. Since facing near extinction in the early 1900s, sea otters are making a remarkable recovery in Southeast Alaska, particularly in Glacier Bay, the largest protected tidewater glacier fjord in the world. The expansion of sea otters across Glacier Bay offers both a challenge to monitoring and stewardship and an unprecedented opportunity to study the top-down effect of a novel apex predator across a diverse and productive ecosystem. Our goal was to integrate monitoring data across trophic levels, space, and time to quantify and map the predator-prey interaction between sea otters and butter clams (Saxidomus gigantea), one of the dominant large bivalves in Glacier Bay and a favored prey of sea otters. To do so, we developed a modeling framework to account for both bottom-up and top-down drivers of butter clam abundance and dynamics. For the bottom-up driver, we used the root-mean-square current speed (m/s) predicted by a tidal circulation model of Glacier Bay developed by Drew et al. (2013). For top-down sea otter dynamics, we used the posterior mean sea otter abundance estimates from Lu et al. (2019). This repository contains the current speed raster (100m x 100m resolution) produced by Drew et al. (2013) and the files and model output from Lu et al. (2019) necessary to generate a time series of rasters (400m x 400m resolution raster brick with 26 layers for the years 1993-2018) of estimated posterior mean sea otter abundance. These data layers are used in Leach et al. (2023) to model butter clam dynamics at sampling sites across Glacier Bay.

海獭是顶级捕食者（apex predators），能够对其栖息的近岸群落施加显著影响。自20世纪初濒临灭绝以来，海獭在阿拉斯加东南部实现了显著的种群恢复，尤其是在全球最大的受保护潮汐冰川峡湾——冰川湾（Glacier Bay）。海獭在冰川湾内的种群扩张，既为监测与管护工作带来了挑战，也为研究一种新兴顶级捕食者在多样且高生产力生态系统中的自上而下（top-down）调控效应提供了前所未有的契机。本研究旨在整合不同营养级、空间与时间尺度的监测数据，以量化并可视化海獭与黄油蛤（Saxidomus gigantea）之间的捕食者-猎物相互作用；黄油蛤是冰川湾优势大型双壳类之一，同时也是海獭偏好的猎物类群。为此，我们构建了一套建模框架，用以阐释调控黄油蛤种群丰度与动态的自下而上（bottom-up）与自上而下驱动因子。对于自下而上驱动因子，我们采用了Drew等人（2013）构建的冰川湾潮汐环流模型所预测的均方根流速（单位：米/秒）。针对海獭种群动态的自上而下调控，我们使用了Lu等人（2019）得到的海獭种群丰度后验均值估计值。本数据集仓库包含Drew等人（2013）生成的均方根流速栅格数据（分辨率为100m×100m），以及Lu等人（2019）提供的、用于生成海獭丰度后验均值估计值时间序列栅格数据所需的文件与模型输出；该时间序列栅格数据为分辨率400m×400m的栅格砖（raster brick），包含1993-2018年共26个时次的图层。上述数据图层已被用于Leach等人（2023）的研究，以模拟冰川湾内各采样点的黄油蛤种群动态。