Environmental change occurs over a broad range of timescales. Aquatic ecosystems can change rapidly from disturbances that drastically affect structure and function. Other changes progress more slowly, due to processes such as climate change, eutrophication, changes in watershed land use and flow regime, biodiversity loss, and biological invasions. These long‐term drivers tend to cause directional, slow change or the abrupt crossing of thresholds, leading to temporal trends or regime shifts. However, other processes operating at long timescales drive variability to ecosystem structure and function without necessarily resulting in directional change.

Multiannual dynamics, or wavy, periodic, and quasiperiodic oscillations operating over timescales from 2 years to over a decade, are often a substantial source of variability that can be independent of long‐term trends. Although multiannual dynamics are often treated as “operating in the background,” drivers of oscillations and trends operate at all timescales, in some cases individually et al synergistically, to regulate the structure and function of aquatic ecosystems. These multiannual dynamics have been shown to be important in long‐term studies such as the eutrophication and recovery of Lake Washington (Hampton et al. 2006), the effect of climate oscillations on calanoid copepods (Fromentin and Planque 1996), overexploitation as in the northwestern Atlantic cod collapse (Hutchings and Myers 1994), and species invasion as in the effects zebra mussels on the Hudson River (Strayer et al. 2014).

In some ecosystems, there may be complete absence of trends in a variable of interest over multiannual timescales, but not an absence of pattern. For example, monthly mean nitrate concentrations for the past 40 years in the Des Moines River have no discernable trend despite being quite variable, ranging from below detection to greater than 18 mg L⁻¹ (Fig. 1A). The lack of a trend is somewhat surprising given the history of land use change and agricultural intensification in the region during this time period (Yu and Lu 2018). However, while there are no long‐term trends in nitrate concentrations in the river, there are strong oscillatory patterns in the time series. A wavelet analysis of the multiannual dynamics of the nitrate time series reveals that there are repeating oscillations at seasonal, annual, 3–5 year, and 10–14 year timescales (Fig. 1B) (see Supporting Information Appendix S1 for method details). This example illustrates the rich information that can be gleaned from multiannual pattern analysis of long‐term data.

Interest in characterizing multiannual dynamics is not new, per se. Oceanographers have long appreciated multiyear dynamics in currents, ocean–atmosphere connections, and impacts of this variation on distributions and populations of marine fauna (Di Lorenzo et al. 2013; Tommasi et al. 2017). Lotic ecologists have analyzed variations in stream and river discharge to assess directional, extreme, and periodic changes on long timescales (Palmer and Ruhi 2019). Similarly, lake researchers have conducted long‐term studies of ecological variation in the context of the interactions of external drivers with internal processes (Hampton et al. 2006; Keitt and Fischer 2006). Yet, we argue that as research in limnology and oceanography is increasingly marked by long‐term, intensive ecosystem monitoring, cross‐disciplinary research, big data, and open science, the discipline is well positioned to begin routinely incorporating multiannual dynamics into our analysis of changes in aquatic ecosystems.

Additionally, the increasing ubiquity of environmental change due to multiple anthropogenic stresses playing out over varying spatial and temporal scales necessitates disentangling multiannual trends and oscillations in order to effectively manage and conserve aquatic ecosystems long term. To that end, in this essay, we discuss the drivers that lead to multiannual variability, the consequences of multiannual variability on ecosystem functioning, and suggest strategies for characterizing and incorporating multiannual variability into aquatic ecosystem research, conservation, and management.

环境变化发生的时间范围很广。水生生态系统可以从严重影响结构和功能的干扰中快速变化。由于气候变化，富营养化，流域土地利用和流域变化，生物多样性丧失和生物入侵等过程，其他变化的进展更为缓慢。这些长期驱动因素往往会导致方向性，缓慢的变化或阈值的突然越过，从而导致时间趋势或政权转移。但是，其他长时间运行的过程会导致生态系统结构和功能的变化，而不必导致方向变化。

在两年到十年以上的时间范围内运行的多年动态或波状，周期性和准周期振荡通常是可变性的重要来源，可以独立于长期趋势。尽管通常将多年动态视为“在后台运行”，但波动和趋势的驱动因素在所有时间尺度上都起作用，在某些情况下，个别地等协同作用来调节水生生态系统的结构和功能。这些多年期动力学已被证明在长期研究中很重要，例如华盛顿湖的富营养化和恢复（Hampton等人，2006年），气候振荡对类an足足足类动物的影响（Fromentin和Planque 1996年）。），西北大西洋鳕鱼塌陷导致过度开发（Hutchings和Myers 1994），以及哈德逊河上斑马贻贝的影响导致物种入侵（Strayer等人2014）。

在某些生态系统中，在多年的时间尺度上可能完全没有兴趣变量的趋势，但没有格局。例如，得梅因河过去40年的月平均硝酸盐浓度尽管变化很大，但没有明显的趋势，范围从低于检测到大于18 mg L ^-1（图1A）。鉴于这一时期该地区土地利用变化和农业集约化的历史，缺乏这种趋势令人感到意外（Yu and Lu 2018）。但是，尽管河流中的硝酸盐浓度没有长期趋势，但时间序列中存在强烈的振荡模式。对硝酸盐时间序列的多年动态的小波分析表明，在季节，年度，3-5年和10-14年的时间尺度上存在重复的振荡（图1B）（有关方法的详细信息，请参见支持信息附录S1）。该示例说明了可以从长期数据的多年模式分析中收集的丰富信息。

表征多年动态的兴趣本身并不新鲜。长期以来，海洋学家一直欣赏洋流，海洋与大气之间的多年动态关系，以及这种变化对海洋动物分布和种群的影响（Di Lorenzo等，2013； Tommasi等，2017）。长期的生态学家分析了河流和河流流量的变化，以评估长期尺度上的方向，极端和周期性变化（Palmer and Ruhi 2019）。同样，湖泊研究人员在外部驱动因素与内部过程相互作用的背景下进行了长期的生态变化研究（Hampton等，2006； Keitt和Fischer，2006）。）。但是，我们认为，随着长期，密集的生态系统监控，跨学科研究，大数据和开放式科学越来越多地成为对森林学和海洋学的研究，该学科已经做好了将常规研究纳入多年动态分析的良好条件。水生生态系统的变化。

此外，由于在不同的时空尺度上出现的多种人为压力导致环境变化的普遍性，必须解开多年趋势和振荡，才能长期有效地管理和保护水生生态系统。为此，在本文中，我们讨论了导致多年生变化的驱动因素，多年生变化对生态系统功能的影响，并提出了表征多年生变化并将其纳入水生生态系统研究，保护和管理的策略。