Collaborative Research: Effects of Changing Temperature on the Gulf of Alaska Ecosystem

这项研究有可能改变我们对气候如何影响海洋生态系统的理解，并改善基于生态系统的渔业管理。调查人员将分析现有数据，以确定气候特性随时间的变化如何影响阿拉斯加湾（果阿）的重要商业鱼类。 几十年来，海洋表面温度与果阿中一大类海洋生物丰度之间的重要关系为将环境数据纳入渔业管理战略提供了一些希望。然而，许多这些统计联系在20世纪80年代后期突然恶化，而在同一时间之间的相互作用果阿SST和气候条件的其他方面也发生了变化。这项研究将检验这样一种假设，即1980年代大规模气候变异的转变导致果阿大气和海洋特性之间关系的重组，这反过来又导致温度和海洋生物之间的联系发生变化。这些类型的“非模拟气候状态”在古生态学中得到了很好的认可，并被预期为气候变化的潜在结果，但很少有研究生态响应这种开关。该项目将支持一名博士后科学家，以及研究生和本科生研究人员。它将促进三所公立大学、一个非营利研究实验室和一个联邦管理机构的科学家之间的合作，以便联合收割机结合开展这项研究所需的一系列专业知识。这项研究有可能改变我们对气候如何影响海洋生态系统的理解。它是基于初步分析表明，SST和社区状态之间的统计关系，在阿拉斯加湾似乎是不稳定的，与驱动响应关系，1988/89年之前和之后有显着不同。 初步分析还表明，1988/89年前后，SST与其他一些果阿气候参数的相关关系有显著差异。 此外，1950年至今北太平洋海温异常的主要模态在1988/89年前后改变了它们的相对重要性，在过去的30年中，第二模态（北太平洋环流振荡，或NPGO）比第一模态（太平洋年代际振荡，或PDO）解释了更多的变化。这种变化与向非相似气候状态的转变是一致的，其特征是流域尺度气候过程之间的变异模式明显不同。将结合统计生态模型和数值海洋模型来检验所提出的假设。统计方法（阈值广义相加模型）将使用果阿的17种鲑鱼、底栖鱼和甲壳类种群的时间序列数据（1965年至今），在群落和种群一级对SST变异与生态特征之间的非平稳关系进行正式检验。此外，还将用海洋数据同化和后报模型产生的时间序列检验流域和区域尺度上大气和水文过程之间的非平稳关系。 最后，这些非平稳的关系，确定将被用来参数化统计模型的生物变异性，占非类似的系统中的状态。

This research has the potential to transform our understanding of how climate affects marine ecosystems and improve efforts toward ecosystem-based fisheries management. Investigators will analyze existing data to determine how shifts in climate properties over time may have affected commercially important fishes in the Gulf of Alaska (GOA). For several decades, significant relationships between sea surface temperature (SST) and abundance of a broad group of marine organisms in the GOA offered some promise for incorporating environmental data into fisheries management strategies. However, many of these statistical connections deteriorated abruptly in the late 1980s, while at the same time interactions between GOA SST and other aspects of climate conditions changed as well. This study will test the hypothesis that a switch in large-scale climate variability in the 1980s led to reorganization of relationships among GOA atmospheric and oceanographic properties, which in turn, produced a change in connection between temperature and ocean biology. These types of "no-analogue climate states" are well recognized in paleoecology and have been anticipated as a potential outcome of climate change, but few studies of ecological response to such switches are available. The project will support a postdoctoral scientist, as well as graduate and undergraduate student researchers. It will facilitate cooperation among scientists at three public universities, a non-profit research lab, and a federal management agency in order to combine the range of expertise that will be required to carry out this research.This research has the potential to transform our understanding of how climate affects marine ecosystems. It is based on preliminary analyses showing that the statistical relationships between SST and community state in the Gulf of Alaska appear to be nonstationary, with driver-response relationships that differ markedly before and after 1988/89. Preliminary analyses also show that correlations between SST and a number of other GOA climate parameters are significantly different before and after 1988/89. Additionally, leading modes in North Pacific SST anomalies for 1950-present changed their relative importance before and after 1988/89, with the second mode (North Pacific Gyre Oscillation, or NPGO) explaining more variability than the first mode (Pacific Decadal Oscillation, or PDO) during the past three decades. This change is consistent with a switch to a no-analogue climate state, characterized by markedly different patterns of variability among basin-scale climate processes. The proposed hypothesis will be tested with a combination of statistical ecological models and numerical ocean models. The statistical approach (threshold generalized additive models) will provide formal tests for nonstationary relationships between SST variability and ecological characteristics at the community and population level using time series data (1965-present) for 17 salmon, groundfish and crustacean populations from the GOA. In addition, nonstationary relationships among atmospheric and hydrographic processes at basin and regional scales will be tested with time series generated by ocean data-assimilation and hindcast models. Finally, those nonstationary relationships identified will be used to parameterize statistical models of biological variability that account for non-analogous states in the system.