Disturbance-driven changes in ecosystem variability: a new tool for understanding ecological dynamics

生态系统变异性的干扰驱动变化：理解生态动态的新工具

• 批准号: RGPIN-2018-05199
• 负责人: Rusak, James
• 金额: $ 2.04万
• 依托单位: Queen's University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=682275
• 关键词: Disturbance; driven; changes; ecosystem; variability

Statistics teaches us that all variables can be initially characterized by two parameters - their central tendency and their variability. Both impart meaningful information about the distribution of a response variable and thus provide a foundation for inference, prediction, and theory. However, variance is seldom considered in ecological studies as a response of interest. As a result, little is known about the functional importance of, or the mechanistic basis for, changes in the variance of ecosystem dynamics. ******Disturbances have the potential to affect all aspects of ecosystems, from species diversity to structure and function, by generating significant spatial and temporal heterogeneity that can profoundly alter system dynamics. Defined by their frequency, severity, and extent, disturbances can directly or indirectly contribute to changes in variability. Examining how a disturbance alters variance can reveal ecological change that might otherwise be overlooked and may also suggest the underlying mechanisms that produce such dynamics. ******Building on previous work, I use freshwater lakes, with complex multi-dimensional habitat structure, well-delineated ecosystem boundaries and multiple trophic levels with rapid generation times as ideal candidates for modelling and experimentation. The current proposal will test disturbance-variance predictions across multiple temporal and spatial scales: 1) among lakes with large “system-wide” disturbances occurring annually over decades; 2) within lakes (and years) with different mixing frequencies (i.e., disturbance regimes) and stressor regimes; and 3) experimentally, using mesocosms to test predictions arising from different disturbance regimes. Increasing variance has been identified as an indicator of state change and I will also assess the utility of using variance as an early warning metric and a tool to trigger management and mitigation activities. In a world where multiple stressors are now the norm rather than the exception and increased variability further reduces our chances of detecting their effects, developing a predictive understanding of the response of ecosystem variability to disturbance is an imperative. Lakes are of critical importance to human well-being and many of the threats to freshwater ecosystems globally (e.g., climate warming, nutrients), are readily observed and documented in the long-term and high-frequency datasets captured by long-term monitoring program. These projects will explore the most important attributes of both disturbance and ecosystem variability and will ultimately improve our ability to manage our freshwater resources.******This initiative will draw on the best of both worlds to collaboratively train 2 Ph.D., 1 M.Sc. and 2 undergraduates with involvement from a top university-based limnological laboratory as well as a pre-eminent provincial government freshwater research centre.

统计学告诉我们，所有变量最初都可以用两个参数来表征——它们的集中趋势和变异性。两者都传递有关响应变量分布的有意义的信息，从而为推理、预测和理论提供基础。然而，在生态学研究中，方差很少被视为感兴趣的响应。因此，人们对生态系统动态变化的功能重要性或机制基础知之甚少。******扰动有可能影响生态系统的各个方面，从物种多样性到结构和功能，通过产生显著的时空异质性，可以深刻地改变系统动力学。根据其频率、严重程度和范围的定义，扰动可以直接或间接地促成变异的变化。研究干扰如何改变变异可以揭示可能被忽视的生态变化，也可能提示产生这种动态的潜在机制。******在先前工作的基础上，我使用淡水湖作为建模和实验的理想候选者，这些淡水湖具有复杂的多维栖息地结构，清晰的生态系统边界和快速生成时间的多种营养水平。目前的建议将在多个时间和空间尺度上测试扰动方差预测：1)在几十年内每年发生大型“全系统”扰动的湖泊中；2)不同混合频率（即干扰机制）和压力机制的湖泊（和年份）内；3)实验上，使用中观系统来测试由不同扰动机制引起的预测。方差的增加已被确定为状态变化的一个指标，我还将评估使用方差作为预警度量和触发管理和缓解活动的工具的效用。在一个多重压力源已成为常态而非例外的世界里，不断增加的可变性进一步降低了我们发现其影响的机会，因此，发展对生态系统可变性对干扰的反应的预测性理解是势在必行的。湖泊对人类福祉至关重要，对全球淡水生态系统的许多威胁（如气候变暖、营养物）很容易被观察到，并记录在长期监测计划捕获的长期和高频数据集中。这些项目将探索干扰和生态系统变化的最重要属性，并最终提高我们管理淡水资源的能力。******该计划将充分利用两国的优势，共同培养2名博士、1名硕士和2名本科生，他们分别来自一个顶尖的大学湖沼学实验室和一个杰出的省政府淡水研究中心。