Tracking and forecasting community responses to climate perturbations in the California Current Ecosystem PLOS Climate

Ocean ecosystems are vulnerable to climate-driven perturbations, which are increasing in frequency and can have profound effects on marine social-ecological systems. Thus, there is an urgency to develop tools that can detect the response of ecosystem components to these perturbations as early as possible. We used Bayesian Dynamic Factor Analysis (DFA) to develop a community state indicator for the California Current Ecosystem (CCE) to track the system’s response to climate perturbations, and to forecast future changes in community state. Our key objectives were to (1) summarize environmental and biological variability in the southern and central regions of the CCE during a recent and unprecedented marine heatwave in the northeast Pacific Ocean (2014–2016) and compare these patterns to past variability, (2) examine whether there is evidence of a shift in the community to a new state in response to the heatwave, (3) identify relationships between community variability and climate variables; and (4) test our ability to create one-year ahead forecasts of individual species responses and the broader community response based on ocean conditions. Our analysis detected a clear community response to the marine heatwave, although it did not exceed normal variability over the past six decades (1951–2017), and we did not find evidence of a shift to a new community state. We found that nitrate flux through the base of the mixed layer exhibited the strongest relationship with species and community-level responses. Furthermore, we demonstrated skill in creating forecasts of species responses and community state based on estimates of nitrate flux. Our indicator and forecasts of community state show promise as tools for informing ecosystem-based and climate-ready fisheries management in the CCE. Our modeling framework is also widely applicable to other ecosystems where scientists and managers are faced with the challenge of managing and protecting living marine resources in a rapidly changing climate.

海洋生态系统极易受气候驱动的扰动影响，此类扰动的发生频率正不断攀升，并会对海洋社会-生态系统造成深远影响。因此，亟需开发可尽早探测生态系统组分对这类扰动的响应的工具。本研究采用贝叶斯动态因子分析（Bayesian Dynamic Factor Analysis, DFA），为加利福尼亚洋流生态系统（California Current Ecosystem, CCE）构建了群落状态指标，用于追踪该系统对气候扰动的响应，并预测群落状态的未来变化。本研究的核心目标如下：（1）梳理2014-2016年东北太平洋前所未有的海洋热浪期间，加利福尼亚洋流生态系统南部与中部区域的环境与生物变异性，并将该时段的变化模式与历史变异性进行对比；（2）探究此次海洋热浪是否引发群落向新状态跃迁的相关证据；（3）明确群落变异性与气候变量之间的关联；（4）验证基于海洋环境条件，开展单个物种响应及整体群落响应的一年期超前预测的能力。分析结果显示，群落对此次海洋热浪存在明确响应，尽管该响应未超出1951-2017年这六十年来的正常变异性范围，且未发现群落跃迁至新状态的相关证据。研究发现，流经混合层底部的硝酸盐通量与物种及群落尺度的响应存在最为显著的关联。此外，本研究证实，基于硝酸盐通量的估算值，可有效实现物种响应及群落状态的预测。本研究构建的群落状态指标及预测结果，有望作为加利福尼亚洋流生态系统中基于生态系统管理与气候适配型渔业管理的决策支撑工具。本研究的建模框架同样可广泛应用于其他生态系统——在这些生态系统中，科研人员与管理者正面临着在快速变化的气候背景下管理与保护海洋生物资源的挑战。