Linking Land Use to Atlantic Salmon Production to Guide Recovery Planning
Bibliographic record
Abstract
Abstract Quantifying functional relationships between abundance and land use is critical during recovery planning for endangered freshwater and diadromous fishes. However, there is little practical guidance on how much abundance might be expected to change from specific types or magnitudes of land use, making it difficult to identify specific sites for restoration or to prioritize among remediation actions. To address these needs for endangered Atlantic Salmon Salmo salar inhabiting the Southern Upland region of Nova Scotia, Canada, we developed a suite of hierarchical models to evaluate the functional form and magnitude of response of populations to land use at two spatial scales. Juvenile distribution patterns showed a strong longitudinal gradient throughout the stream network, with higher densities occurring in headwaters, which suggests that maintaining connectivity as well as unmodified landscapes in the upper reaches of rivers should be prioritized to aid recovery. Relative to threats, there was no single type of land use that was primarily associated with changes in juvenile abundance. Instead, responses to the combined suite of land-use types were nonlinear and did not appear dependent on spatial scale. When the proportion of natural forest cover was high, populations appeared to benefit from low levels of anthropogenic land use, declining with increasing human activity only once the average proportion of natural forest cover was low. To use threat relationships in recovery planning, we propose a simple quantitative index based on the extent of development in the vicinity of rivers to identify sites for remediation or protection. To aid future monitoring, we demonstrate why site-specific electrofishing catchability must be estimated when evaluating population responses to landscape change.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".