Unraveling dynamic omnivory and community interactions between primary producers and an apex predator
Bibliographic record
Abstract
The effects of climate and plant phenological changes on herbivorous species are widely recognized, yet less research has focused on omnivorous and carnivorous species, even though they also have vegetative components to their diet. The historical focus on predators regarding simple interactions between obligate carnivores and their prey over-simplifies many species’ roles within ecological communities by casting them purely as predators and minimizes other, equally important roles within the community. We used a long-term, individual-based data set on a polyphagous mammal, the brown bear (Ursus arctos), to estimate diet over 25 years identify long-term patterns and factors contributing to annual variation in diet. We used carbon and nitrogen stable isotope values measured in hair and Bayesian mixing models to determine annual diet among three demographic bear classes, and then used linear regression models to relate diet to indices of food availability. We found that while diet varied among years and demographic classes, variation in both carbon and nitrogen values were explained by bilberry (Vaccinium myrtillus) productivity. Additionally, proportions of animal-derived foods decreased through time, while proportions of bilberry increased, even as the moose population in Sweden increased over this same period. While meat and animal-derived foods are considered higher quality foods for bears, bear diet did not respond to changing moose availability. Our results highlight that even though vegetative diet components in predators are typically considered less important to predator ecology, brown bears in Sweden responded to changes in primary production, regardless of prey availability. It will be crucial to put more emphasis on the vegetative parts of diets as we predict how species and ecological communities respond to climate change because predators serve many more functions within their community besides predation alone.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".