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（sea otter）为顶级捕食者（apex predator），可对其栖息的近岸群落产生显著调控作用。自20世纪初濒临灭绝以来，海獭在阿拉斯加东南部正实现显著恢复，尤其是在全球最大的保护性潮汐冰川峡湾——冰川湾（Glacier Bay）内。海獭在冰川湾的种群扩张，既为监测与管护工作带来了挑战，也为研究一种全新顶级捕食者在多样且高生产力生态系统中的下行调控效应提供了前所未有的契机。本研究的目标是整合不同营养级、空间与时间尺度的监测数据，以量化并绘制出海獭与黄油蛤（butter clams，Saxidomus gigantea）之间的捕食者-猎物相互作用关系；黄油蛤是冰川湾的优势大型双壳类之一，同时也是海獭偏爱的猎物。为此，我们开发了一套建模框架，以同时考虑影响黄油蛤丰度与动态的上行调控与下行调控因子。对于上行调控因子，我们采用了由Drew等人（2013）开发的冰川湾潮汐环流模型所预测的均方根流速（单位：m/s）。对于海獭种群动态的下行调控因子，我们采用了Lu等人（2019）得到的海獭丰度后验均值估计值。本数据集仓库包含了Drew等人（2013）生成的均方根流速栅格（raster）数据（分辨率为100m×100m），以及Lu等人（2019）生成的、用于生成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个时层，用于估算海獭丰度的后验均值。这些数据图层已被用于Leach等人（2023）的研究，以建模冰川湾内各采样点的黄油蛤种群动态。