Blanchard Ecological, North Pole, AK, 99705, USA.
Phistat Research and Consulting, North Vancouver, BC, Canada.
Environ Monit Assess. 2022 Oct 18;194(Suppl 1):741. doi: 10.1007/s10661-022-10018-7.
Energy densities of six dominant benthic groups (Actinopterygii, Amphipoda, Bivalvia, Cumacea, Isopoda, and Polychaeta) and total prey energy were modeled for the nearshore western gray whale feeding area, Sakhalin Island, Russia, as part of a multi-disciplinary research program in the summer of 2015. Energy was modeled using generalized additive mixed models (GAMM) with accommodations for zero-inflation (logistic regression and hurdle models) and regression predictions combined with kriging to interpolate energy densities across the nearshore feeding area. Amphipoda energy density was the highest nearshore and in the south whereas Bivalvia energy density was the highest offshore and in the northern portion of the study area. Total energy was the highest in mid-range distances from shore and in the north. Amphipoda energy density was higher than minimum energy estimates defining gray whale feeding habitats (312-442 kJ/m) in 13% of the nearshore feeding area whereas total prey energy density was higher than the minimum energy requirement in 49% of the habitat. Inverse distance-weighted interpolations of Amphipoda energy provided a broader scale representation of the data whereas kriging estimates were spatially limited but more representative of higher density in the southern portion of the study area. Both methods represented the general trend of higher Amphipoda energy density nearshore but with significant differences that highlight the value of using multiple methods to model patterns in highly complex environments.
作为 2015 年夏季多学科研究项目的一部分,对俄罗斯萨哈林岛近岸西部灰鲸觅食区的六个主要底栖类群(硬骨鱼、端足目、双壳类、等足目、等足目和多毛类)和总猎物能量进行了建模。使用广义加性混合模型(GAMM)对能量进行建模,同时考虑了零膨胀(逻辑回归和障碍模型)和回归预测,并结合克里金插值法对近岸觅食区的能量密度进行插值。端足目能量密度在近岸和南部最高,而双壳类能量密度在近海和研究区北部最高。总能量在离海岸中等距离和北部最高。在近岸觅食区的 13%的区域中,端足目能量密度高于界定灰鲸觅食栖息地的最低能量估计值(312-442kJ/m),而总猎物能量密度在 49%的栖息地中高于最低能量需求。端足目能量的倒数距离加权插值提供了数据的更广泛尺度表示,而克里金估计值的空间限制较大,但更能代表研究区南部的高密度。这两种方法都代表了近岸端足目能量密度较高的一般趋势,但存在显著差异,这突出了在高度复杂的环境中使用多种方法来模拟模式的价值。