Data from: Characterizing driver-response relationships in marine pelagic ecosystems for improved ocean management

Scientists and resources managers often use methods and tools that assume ecosystem components respond linearly to environmental drivers and human stressor. However, a growing body of literature demonstrates that many relationships are non-linear, where small changes in a driver prompt a disproportionately large ecological response. Here we aim to provide a comprehensive assessment of the relationships between drivers and ecosystem components to identify where and when non-linearities are likely to occur. We focus our analyses on one of the best-studied marine systems, pelagic ecosystems, which allowed us to apply robust statistical techniques on a large pool of previously published studies. In this synthesis, we (1) conduct a wide literature review on single driver-response relationships in pelagic systems, (2) use statistical models to identify the degree of non-linearity in these relationships, and (3) assess whether general patterns exist in the strengths and shapes of non-linear relationships across drivers. Overall we found that non-linearities are common in pelagic ecosystems, comprising at least 52% of all driver-response relationships. This is likely an underestimate, as papers with higher quality data and analytical approaches reported non-linear relationships at a higher frequency - on average 11% more. Consequently, in the absence of evidence for a linear relationship, it is safer to assume a relationship is non-linear. Strong non-linearities can lead to greater ecological and socio-economic consequences if they are unknown (and/or unanticipated), but if known they may provide clear thresholds to inform management targets. In pelagic systems, strongly non-linear relationships are often driven by climate and trophodynamic variables, but are also associated with local stressors such as overfishing and pollution that can be more easily controlled by managers. Even when marine resource managers cannot influence ecosystem change, they can use information about threshold responses to guide how other stressors are managed and to adapt to new ocean conditions. As methods to detect and reduce uncertainty around threshold values improve, managers will be able to better understand and account for ubiquitous non-linear relationships.科学家与资源管理者常采用预设生态系统组分对环境驱动因子与人为胁迫因子呈线性响应的方法与工具。然而，越来越多的研究文献表明，诸多关联实为非线性关系——即驱动因子的微小变化会引发不成比例的巨大生态响应。本研究旨在全面评估驱动因子与生态系统组分间的关联，以明确非线性关系易出现的场景与时机。我们将分析聚焦于研究最为深入的海洋系统之一：中上层水域生态系统（pelagic ecosystems），借此可对大量已发表的研究成果运用稳健统计方法开展分析。在本次综合研究中，我们（1）针对中上层水域生态系统中单驱动因子-响应关系开展系统性文献综述；（2）借助统计模型识别此类关系中的非线性程度；（3）评估不同驱动因子下的非线性关系强度与形态是否存在通用模式。总体而言，我们发现非线性关系在中上层水域生态系统中十分普遍，占所有驱动因子-响应关系的比例至少达52%。这一数值大概率被低估：因为采用更高质量数据与分析方法的文献，报告非线性关系的频率平均高出11个百分点。因此，在无线性关系证据的情况下，更稳妥的假设是关联为非线性。若未被知晓（或未被预见），强非线性关系可能引发更严重的生态与社会经济后果；但一旦被探明，则可为管理目标制定提供明确的阈值参考。在中上层水域生态系统中，强非线性关系常由气候与营养动力学变量驱动，同时也与过度捕捞、污染等管理者更易管控的局地胁迫因子相关。即便海洋资源管理者无法直接影响生态系统变化，仍可借助阈值响应相关信息，指导其他胁迫因子的管理工作，并适应新的海洋环境条件。随着阈值检测方法与降低阈值相关不确定性的技术不断完善，管理者将能更好地理解并应对无处不在的非线性关系。