Recovery pathways for lake ecosystems

在过去的100年里，全球对水的需求增长了600%，预计在未来几十年里还会大幅增长。在当前和未来的情景下，水生生态系统受到多重压力的威胁，最明显的是人口增长及其对水、粮食和能源安全的需求往往相互冲突，导致淡水数量和质量下降。全球30%以上的生物多样性丧失是由于水资源和水生生态系统的污染。在这里，我们关注的是这些挑战对淡水湖的影响，在淡水湖中，营养物质的富集被认为是主要的压力。在世界各地，越来越多的湖泊被污染。例如，2005年，英国所有14000个湖泊都进行了生态状况风险评估，其中一半以上没有达到“良好”的阈值，被认为有风险，可能需要一些补救措施。最近，在2020年，环境署的新数据显示，英格兰只有14%的湖泊被列为生态健康状况良好。与此同时，在273个中国湖泊的研究中，在受人类影响的湖泊中，只有22%的湖泊表现出相对稳定和有弹性的状态。显然，就全球淡水利用和运作而言，这种轨迹是不可持续的。许多国家已经认识到这个问题的紧迫性，并正在制定地方和全球政策（如欧盟、联合国）来恢复淡水湖。尽管投入了大量资金，但目前的恢复项目往往基于短期假设，因此由于缺乏对系统崩溃后中长期恢复途径的了解，可能会失败。我们的项目解决了这一挑战，在我们对湖泊生态系统恢复途径的理解上迈出了一步。将湖泊生态系统恢复到“良好”（有弹性）的清澈状态的大部分困难是由于滞后性（系统状态对其历史的依赖），通过循环利用营养物质而保持不需要的浑浊（污染）状态占主导地位，而没有任何进一步的补充。因此，简单地去除污染源（当可能或需要时）并不一定会导致湖泊生态系统的恢复。我们的项目将通过量化影响淡水湖生态系统从不良状态恢复的因素的平衡来应对这一挑战。虽然对营养物（弥漫性和/或点源）的控制通常是至关重要的，但除了气候变化等其他多维压力源之外，再循环率和内部反馈循环也可能是需要考虑的重要因素。在我们的项目中，我们将审查当前的全球湖泊数据集，并对已经完全或部分恢复的湖泊子集进行详细的实证研究。在这两个数据集（即全球湖泊，以及我们在所有营养水平上进行新的实证分析的子集）中，我们将使用新开发的生态结构指标来评估湖泊可能采取的恢复途径（即线性、阶跃变化或滞后），以及这种恢复可能需要多长时间。我们将利用这些知识来建立基于系统的模型，这些模型可以模拟湖泊退化、生态系统崩溃和一系列淡水湖系统恢复的多维原因。这些模型将能够模拟不同的未来湖泊状态，这取决于输入和相关的管理决策。我们将与广泛的利益相关者合作，运行和测试这些模型，以便最终确定以最小滞后进行恢复的最佳行动方案。总而言之，我们项目的阶梯式变化结果将是对不同湖泊恢复路径、不同驱动因素组合的相对作用、应答生态系统营养水平和整体恢复能力进行改进预测的能力。我们的项目将有助于解决人类面临的主要挑战之一：地球淡水资源的可持续管理。

Global water demand has increased by 600% over the past 100 years and is predicted to grow significantly over the next few decades. Under current and future scenarios, aquatic ecosystems are under threat from multiple pressures, most notably human population growth with its often-conflicting demands for water, food and energy security, leading to declining freshwater quantity and quality. More than 30% of global biodiversity loss is due to pollution of water resources and aquatic ecosystems. Here, we focus on the contribution these challenges are having on freshwater lakes, where nutrient enrichment has been recognised as the key pressure. Across the world, increasing numbers of lakes are collapsing into polluted states. For example, in 2005, all 14,000 UK lakes >1 ha in size were risk-assessed for ecological status, of which over half failed the 'good' threshold, being judged at risk and likely to require some remediation. Most recently, in 2020, new data from the Environment Agency revealed that only 14% of lakes were classed as in good ecological health in England. Concomitantly, in a study of 273 Chinese lakes, amongst those with human impacts, only 22% showed relatively stable and resilient states. It is clear that such trajectories are unsustainable in terms of global freshwater use and functioning. Many countries have recognised the urgency of the problem, and are enacting local and global policies (e.g. EU, UN) for freshwater lake restoration. Despite substantial investment, current restoration projects are often based on short-term assumptions, and hence can fail due to the lack of knowledge of mid- to long-term recovery pathways after system collapse.Our project addresses this challenge, to deliver a step change in our understanding of pathways to lake ecosystem recovery. Much of the difficulty in restoring lake ecosystems to 'good' (resilient) clear water states is due to hysteresis (the dependence of the state of a system on its history), keeping the unwanted turbid (polluted) state dominant by recycling of nutrients without any further additions. Consequently, simply removing the pollutant source (when possible or desirable) does not necessarily lead to lake ecosystem recovery. Our project will tackle this challenge by quantifying the balance of factors that influence the recovery of freshwater lake ecosystems from undesirable states. While controls on nutrients (diffuse and/or point-source) are often critical, the recycling rates, and internal feedback loops, in addition to other multidimensional stressors such as climate change, can also be important factors to consider. In our project, we will review current global lake datasets, and undertake detailed empirical work on a subset of lakes that have undergone full or partial recovery. In both datasets (i.e. global lakes, and our subset with new empirical analyses across all trophic levels), we will use newly developed indicators of ecological structure to assess which recovery pathway lakes can take (i.e. linear, step change, or hysteretic), and how long such recovery may take. We will use this knowledge to build systems-based models that can simulate the multidimensional causes of lake degradation, ecosystem collapse, and recoveries in a range of freshwater lake systems. The models will be able to simulate different future lake states, depending on inputs and associated management decisions. We will work with a wide range of stakeholders to run and test the models, so that ultimately, we can determine the best course of action for recovery with minimal hysteresis.In summary, the step-change outcome of our project will be an ability to model an improved prediction of different lake recovery pathways, the relative roles of differing driver combinations, and respondent ecosystem trophic level and overall resilience. Our project will contribute to meeting one of the major challenges facing humanity: the sustainable management of Earth's freshwater resources.