Might macronutrient requirements influence grizzly bear–human conflict? Insights from nutritional geometry
Bibliographic record
Abstract
Abstract Knowledge of carnivore nutritional requirements offers a potentially powerful aid for conservation and management strategies, yet has received little attention. We discuss how nutritional ecology, nutritional geometry, and the concept of macronutrient (protein, lipid, and carbohydrate) balance can be used to further our understanding of behavioral regulatory mechanisms that may influence food‐related human–wildlife conflict, focusing on North American grizzly bears (Ursus arctos). We propose that the macronutrient preferences of omnivorous grizzly bears are a strong driver of their conflict with humans due to nutrient‐specific foraging behavior, which we predict will be particularly noticeable during periods in which “key” natural foods high in lipid or carbohydrate are limiting. We demonstrate how nutritional geometry can be used to investigate the concept of nutrient balance by integrating recent research on the macronutrient selection of the grizzly bear with nutritional estimates of potentially consumed anthropogenic foods. Our geometric analysis utilizing right‐angled mixture triangles suggested that anthropogenic foods offer grizzly bears nonprotein energy sources that may allow them to optimize macronutrient intake. This macronutrient‐focused approach gives rise to fundamentally different predictions (and potentially management strategies) than the conventional food and energy‐focused approaches. This article also provides insight into food‐related conflict among other bear and carnivore species, and human–carnivore conflict more generally, by outlining a nutritionally explicit predictive framework for understanding the potentially volatile interface between anthropogenic environments and the behavior of wild animals.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".