Temporal scope influences ecosystem driver-response relationships: A case study of Lake Erie with implications for ecosystem-based management

Michael E Fraker; James S Sinclair; Kenneth T Frank; James M Hood; Stuart A Ludsin

doi:10.1016/j.scitotenv.2021.152473

Temporal scope influences ecosystem driver-response relationships: A case study of Lake Erie with implications for ecosystem-based management

Sci Total Environ. 2022 Mar 20:813:152473. doi: 10.1016/j.scitotenv.2021.152473. Epub 2021 Dec 29.

Authors

Michael E Fraker¹, James S Sinclair², Kenneth T Frank³, James M Hood⁴, Stuart A Ludsin²

Affiliations

¹ 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. Electronic address: mfraker@umich.edu.
² Aquatic Ecology Laboratory, Department of Evolution, Ecology, and Organismal Biology, The Ohio State University, Columbus, OH 43212, USA.
³ Bedford Institute of Oceanography, Department of Fisheries and Oceans, Dartmouth, NS B2Y 4A2, Canada.
⁴ Aquatic Ecology Laboratory, Department of Evolution, Ecology, and Organismal Biology, The Ohio State University, Columbus, OH 43212, USA; Translational Data Analytics Institute, The Ohio State University, Columbus, OH 43210, USA.

PMID: 34973328
DOI: 10.1016/j.scitotenv.2021.152473

Abstract

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.

Keywords: Anthropogenic; Ecosystem-based management; Great Lakes; Integrated ecosystem assessment; Temporal ecology.

MeSH terms

Agriculture
Animals
Ecosystem*
Fishes
Lakes*
Nutrients