An Ecosystem Model for Exploring Lake Restoration Effects on Fish Communities and Fisheries in Florida
Bibliographic record
Abstract
Developing quantitative ecosystem–scale expectations of habitat restoration projects and examining trade‐offs associated with alternative approaches has been a challenge for restoration ecology. Many of the largest freshwater lake restoration projects have occurred in Florida to remediate degradation to vegetated littoral habitats resulting from stabilized water levels, but effects across lake food‐webs have not been assessed. We developed an ecosystem model using Ecopath with Ecosim and Ecospace for a generalized large, eutrophic Florida Lake to explore how simulated restoration activities could influence fish communities with emphasis on sport fish abundance. We modeled three habitat restoration scenarios: (1) “no control,” (2) a “10‐year control” that restored littoral habitat every 10 years, and (3) a “combined control” scenario that restored littoral habitat every 10 years with maintenance controls between 10‐year periods. Our “combined control” scenario provided the largest long‐term habitat restoration benefits for sport fish abundance and the fisheries they support. In Ecospace, we simulated a littoral habitat restoration project that reduced lake‐wide tussock coverage from 30 to 15%. Ecospace predicted positive benefits to sport fish and fisheries following the restoration simulation and highlighted the importance of habitat edge effects, spatial design of habitat restoration projects, and sampling designs for evaluating restoration projects.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".