Blue World Research, 2710 E. 20th Ave., Anchorage, AK, 99508, USA; Institute for Marine and Antarctic Studies, University of Tasmania, Private Bag 129, Hobart, TAS, 7001, Australia.
Glob Chang Biol. 2014 Jan;20(1):38-50. doi: 10.1111/gcb.12373. Epub 2013 Nov 17.
In areas of the North Pacific that are largely free of overfishing, climate regime shifts - abrupt changes in modes of low-frequency climate variability - are seen as the dominant drivers of decadal-scale ecological variability. We assessed the ability of leading modes of climate variability [Pacific Decadal Oscillation (PDO), North Pacific Gyre Oscillation (NPGO), Arctic Oscillation (AO), Pacific-North American Pattern (PNA), North Pacific Index (NPI), El Niño-Southern Oscillation (ENSO)] to explain decadal-scale (1965-2008) patterns of climatic and biological variability across two North Pacific ecosystems (Gulf of Alaska and Bering Sea). Our response variables were the first principle component (PC1) of four regional climate parameters [sea surface temperature (SST), sea level pressure (SLP), freshwater input, ice cover], and PCs 1-2 of 36 biological time series [production or abundance for populations of salmon (Oncorhynchus spp.), groundfish, herring (Clupea pallasii), shrimp, and jellyfish]. We found that the climate modes alone could not explain ecological variability in the study region. Both linear models (for climate PC1) and generalized additive models (for biology PC1-2) invoking only the climate modes produced residuals with significant temporal trends, indicating that the models failed to capture coherent patterns of ecological variability. However, when the residual climate trend and a time series of commercial fishery catches were used as additional candidate variables, resulting models of biology PC1-2 satisfied assumptions of independent residuals and out-performed models constructed from the climate modes alone in terms of predictive power. As measured by effect size and Akaike weights, the residual climate trend was the most important variable for explaining biology PC1 variability, and commercial catch the most important variable for biology PC2. Patterns of climate sensitivity and exploitation history for taxa strongly associated with biology PC1-2 suggest plausible mechanistic explanations for these modeling results. Our findings suggest that, even in the absence of overfishing and in areas strongly influenced by internal climate variability, climate regime shift effects can only be understood in the context of other ecosystem perturbations.
在北太平洋大部分没有过度捕捞的地区,气候制度的转变——低频气候变率模式的突然变化——被认为是导致数十年尺度生态变化的主要驱动因素。我们评估了主要气候变率模式(太平洋十年涛动(PDO)、北太平洋旋度振荡(NPGO)、北极涛动(AO)、太平洋-北美型(PNA)、北太平洋指数(NPI)、厄尔尼诺-南方涛动(ENSO))解释北太平洋两个生态系统(阿拉斯加湾和白令海)气候和生物变率的能力。我们的响应变量是四个区域气候参数[海面温度(SST)、海平面气压(SLP)、淡水输入、冰盖]的第一主成分(PC1)和 36 个生物时间序列[鲑鱼(Oncorhynchus spp.)、底栖鱼、鲱鱼(Clupea pallasii)、虾和水母]的 PC1-2。我们发现,气候模式本身无法解释研究区域的生态变化。仅使用气候模式的线性模型(用于气候 PC1)和广义加性模型(用于生物学 PC1-2)产生的残差具有显著的时间趋势,表明模型未能捕捉到生态变化的一致模式。然而,当剩余气候趋势和商业渔业捕捞时间序列被用作附加候选变量时,由此产生的生物学 PC1-2 模型满足独立残差的假设,并且在预测能力方面优于仅使用气候模式构建的模型。根据效应大小和 Akaike 权重,剩余气候趋势是解释生物学 PC1 变化的最重要变量,商业捕捞是解释生物学 PC2 变化的最重要变量。与生物学 PC1-2 密切相关的类群的气候敏感性和开发历史模式表明,这些建模结果存在合理的机制解释。我们的研究结果表明,即使在没有过度捕捞且受内部气候变率强烈影响的地区,也只能在其他生态系统干扰的背景下理解气候制度转变的影响。