Predicting the response of fish populations to changes in river connectivity using individual-based models
Bibliographic record
Abstract
Barrier removal restores physical stream processes and improves accessibility of critical habitats to migratory fishes. Although increasing connectivity benefits stream systems and migratory fishes, barrier removals may also lead to increased production of undesirable or invasive migratory species, as well as myriad other concerns (e.g., reduced recreational opportunities). Few studies have predicted how migratory fish populations will respond to enhanced fish passage, despite being a critical step in the decision-making process. We developed an individual-based model framework to forecast the response of migratory fishes to changes in connectivity and applied the framework to six species under multiple fish passage scenarios for the FishPass project on the Boardman River, MI, which outlets into Lake Michigan. Population response to barrier removal was species-specific and varied based on initial population size and distribution within the watershed, number of fish passed upstream, and species life history traits. Species restricted to below the barrier prior to removal benefitted most; non-native species were found to have greater production potential under full passage scenarios than native Great Lakes species. With increasing passage of non-native Pacific salmonids, steelhead Oncorhynchus mykiss surpassed brook trout Salvelinus fontinalis as the system’s dominant species. Our results will inform decision-makers on management alternatives for fish passage on the Boardman River and our model framework can be modified, updated, and applied to additional river systems as more barrier removal projects are conducted in the future.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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) | 0.002 | 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".