Eutrophication, water quality, and fisheries: a wicked management problem with insights from a century of change in Lake Erie
Bibliographic record
Abstract
Human-driven nutrient inputs into aquatic ecosystems must be managed to preserve biodiversity and to ensure that valued fishery and water quality services are not compromised by hypoxia and harmful algal blooms. Aiming for nutrient inputs that achieve an intermediate level of ecosystem productivity is expected to provide both high fish yield and good water quality. However, we argue that such an intermediate “optimum” may not exist for many aquatic ecosystems that support multiple fisheries with differing tolerances to eutrophication and that must provide multiple water quality services. We further support this argument with an empirical case study of nearly a century (1915–2011) of change in the productivity of Lake Erie and its lake whitefish (Coregonus clupeaformis), walleye (Sander vitreus), and yellow perch (Perca flavescens) fisheries. We discuss and show how the harvest of each fishery has been historically maximized at different levels of ecosystem productivity. Additionally, we examine how anticipated management efforts to improve water quality by reducing nutrient inputs (i.e., oligotrophication) may favor certain fisheries over others, resulting in no single optimal range of nutrient inputs that achieves all valued fishery and water quality objectives. Our synthesis and case study illustrate how the need to balance multiple services in aquatic ecosystems can create a wicked management problem with inevitable trade-offs. To navigate these trade-offs, we recommend the use of ecosystem-based management approaches, which can help decision makers identify and resolve complex trade-offs by facilitating cooperative research and communication among water quality regulators, fisheries managers, and end users.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".