Data from: Salmonid species diversity predicts salmon consumption by terrestrial wildlife
Bibliographic record
Abstract
1. Resource waves - spatial variation in resource phenology that extends feeding opportunities for mobile consumers - can affect the behaviour and productivity of recipient populations. Interspecific diversity among Pacific salmon species (Oncorhynchus spp.) creates staggered spawning events across space and time, thereby prolonging availability to terrestrial wildlife. 2. We aim to understand how such variation might influence consumption by terrestrial predators compared with resource abundance and intra- and inter- specific competition. 3. Using stable isotope analysis, we investigated how the proportion of salmon in the annual diet of male black bears (Ursus americanus; n = 405) varies with species diversity and density of spawning salmon biomass, while also accounting for competition with conspecific black and grizzly bears (U. arctos horribilis), in coastal British Columbia, Canada. 4. We found that the proportion of salmon in the annual diet of black bears increased by ≈40% in the absence of grizzly bears, but detected little effect of relative black bear density and salmon biomass density. Rather, salmon diversity had the largest positive effect on consumption. On average, increasing diversity from one salmon species to ~four (with equal biomass contributions) approximately triples the proportion of salmon in diet. 5. Given the importance of salmon to bear reproduction, this work provides early empirical support for how resource waves may increase the productivity of consumers at population and landscape scales. Accordingly, terrestrial wildlife management might consider maintaining not only salmon abundance but also diversity.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.037 | 0.017 |
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".