Cooperative Institute for Great Lakes Research and Michigan Sea Grant, School for Environment and Sustainability, University of Michigan, 4840 S. State, Ann Arbor, MI 48108, USA.
Aquatic Ecology Laboratory, Department of Evolution, Ecology, and Organismal Biology, The Ohio State University, Columbus, OH 43212, USA.
Sci Total Environ. 2022 Mar 20;813:152473. doi: 10.1016/j.scitotenv.2021.152473. Epub 2021 Dec 29.
Understanding environmental driver-response relationships is critical to the implementation of effective ecosystem-based management. Ecosystems are often influenced by multiple drivers that operate on different timescales and may be nonstationary. In turn, contrasting views of ecosystem state and structure could arise depending on the temporal perspective of analysis. Further, assessment of multiple ecosystem components (e.g., biological indicators) may serve to identify different key drivers and connections. To explore how the timescale of analysis and data richness can influence the identification of driver-response relationships within a large, dynamic ecosystem, this study analyzed long-term (1969-2018) data from Lake Erie (USA-Canada). Data were compiled on multiple biological, physical, chemical, and socioeconomic components of the ecosystem to quantify trends and identify potential key drivers during multiple time intervals (20 to 50 years duration), using zooplankton, bird, and fish community metrics as indicators of ecosystem change. Concurrent temporal shifts of many variables occurred during the 1980s, but asynchronous dynamics were evident among indicator taxa. The strengths and rank orders of predictive drivers shifted among intervals and were sometimes taxon-specific. Drivers related to nutrient loading and lake trophic status were consistently strong predictors of temporal patterns for all indicators; however, within the longer intervals, measures of agricultural land use were the strongest predictors, whereas within shorter intervals, the stronger predictors were measures of tributary or in-lake nutrient concentrations. Physical drivers also tended to increase in predictive ability within shorter intervals. The results highlight how the time interval examined can filter influences of lower-frequency, slower drivers and higher-frequency, faster drivers. Understanding ecosystem change in support of ecosystem-based management requires consideration of both the temporal perspective of analysis and the chosen indicators, as both can influence which drivers are identified as most predictive of ecosystem trends at that timescale.
理解环境驱动-响应关系对于实施有效的基于生态系统的管理至关重要。生态系统通常受到多个作用于不同时间尺度且可能是非平稳的驱动因素的影响。反过来，根据分析的时间视角，可能会出现对生态系统状态和结构的不同观点。此外，对多个生态系统组成部分（例如生物指标）的评估可以有助于确定不同的关键驱动因素和联系。为了探讨分析的时间尺度和数据丰富度如何影响大型动态生态系统中驱动-响应关系的识别，本研究分析了伊利湖（美国-加拿大）的长期（1969-2018 年）数据。为了量化趋势并在多个时间间隔（20 至 50 年）内识别潜在的关键驱动因素，本研究汇总了生态系统的多个生物、物理、化学和社会经济组成部分的数据，使用浮游动物、鸟类和鱼类群落指标作为生态系统变化的指标。在 20 世纪 80 年代，许多变量同时发生了同步变化，但指示物种种群之间存在异步动态。在间隔之间，预测性驱动因素的强度和排名顺序发生了变化，有时是特定于分类群的。与营养负荷和湖泊营养状况相关的驱动因素一直是所有指标时间模式的强有力预测因素；然而，在较长的间隔内，农业土地利用的度量是最强的预测因素，而在较短的间隔内，较强的预测因素是支流或湖内营养浓度的度量。物理驱动因素在较短的间隔内也往往具有更高的预测能力。研究结果强调了所研究的时间间隔如何过滤低频、较慢的驱动因素和高频、较快的驱动因素的影响。为了支持基于生态系统的管理，了解生态系统变化需要考虑分析的时间视角和所选指标，因为这两者都会影响在该时间尺度下确定哪些驱动因素对生态系统趋势最具预测性。
