Developing habitat suitability criteria for water management: A case study
Bibliographic record
Abstract
A method for assessing the relative quality of a wetland as habitat for fish based on hydrological factors is described. Its three major steps include: (a) simulating the hydrologic behavior based on some water management policy; (b) computing habitat suitability indexes time series derived from the values of the simulated hydrologic variables; and (c) assessing system performance based on those suitability indexes values. The method was applied to a coastal wetland of Lake Ontario in the US. Sequences of daily water levels and water temperature in the wetland were generated considering alternative watershed land use practices and Lake Ontario water level management policies. These variables were converted to habitat suitability time series for a bioindicator, the Northern Pike (Esox lucius), focusing on its early life cycles that take place in the wetland. The overall habitat suitability performance was analyzed in terms of various reliability, resilience, and vulnerability performance indices derived from those habitat suitability time series. Results show that at least for the early life cycles of the chosen bioindicator a less variable water level regime that comes mainly from Lake Ontario regulation is more beneficial than from one that results from natural variation. A more variable regime, however, seems to favour the marsh or the wetland plant habitat upon which the fish are dependent, based on an analysis not presented in this paper. In this case study the larger bordering lake had a much greater influence on wetland water levels and temperatures than did the upstream watershed. This particular study is just part of an overall evaluation of lake level management policies based on economic and social as well as ecological criteria currently underway by a joint Canadian‐United States commission. Analyses of this type based on multiple species indicators and on a broader set of environmental factors would provide a way to judge the relative ecological impacts associated with diverse lake level regulation policies.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".