Habitat connectivity, complexity, and quality: predicting adult coho salmon occupancy and abundance
Bibliographic record
Abstract
The distribution, quality, and connectivity of instream habitat can influence adult salmon occupancy and abundance patterns and alter population dynamics. In this study, we evaluated the relationships between adult coho salmon (Oncorhynchus kisutch) occupancy and abundance with instream habitat conditions, including measures of spawning gravel, habitat complexity, and juvenile rearing habitat. We used corresponding adult salmon spawning and instream habitat data collected within coastal Oregon watersheds as part of a long-term monitoring program. We modeled two processes as a function of habitat characteristics: the number of coho salmon when they were present and the occupancy probabilities of coho salmon. The results from both submodels were then combined into an estimate of total abundance at each site. Adult coho salmon occupancy was best predicted by the capacity of the habitat to support parr during the winter, complex pools, percent bedrock, and site distance to the ocean. Although lacking the predictive capacity of the occupancy model, increases in adult coho counts at sites were also influenced by the site distance to the ocean, and there is evidence that both percent gravel and complex pools may also be valuable predictors. By taking advantage of long-term datasets with broad spatial range, using an integrative approach across coho salmon life stages, and utilizing innovative Bayesian modeling techniques, this study is a unique approach to understanding a complicated ecological narrative. Combined, our results indicate the spatial distribution and proximity of spawning and rearing habitats may maximize productivity for coho salmon in coastal Oregon watersheds.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".