Why bears consume mixed diets during fruit abundance
Bibliographic record
Abstract
In many ecosystems, grizzly bears (Ursus arctos) and black bears (Ursus americanus) feed heavily on berries and fruits in the fall. While a few birds and mammals are exclusively frugivorous, bears and most other animals consume fruit as part of a mixed diet. The mixed-diet strategy avoids potential calcium, protein, amino acid, or other nutrient deficiencies that can occur on fruit diets. However, we hypothesized that the high carbohydrate - low protein content of fruit would increase energy metabolism and force bears to use dietary mixing to meet protein requirements and, thereby, reduce energy metabolism. We examined the effects of six plant-based diets containing from 2.3 to 35% crude protein on intake, maintenance costs, and efficiency of gain of captive grizzly and black bears. In addition, the food habits of six populations of wild grizzly and black bears were analyzed, to determine the crude protein and digestible dry matter content of their diets. Efficiency of gain (0.53 ± 0.02 (±SD) g gain/g digestible dry matter intake) did not differ across diets. However, maintenance costs differed, ranging from 24 g·(kg0.75·day)-1 (120 kcal (1 cal = 4.1868 J) digestible energy (DE)·(kg0.75·day)-1) on the 35% protein pelleted diet to 80 g·(kg0.75·day)-1 (340 kcal DE·(kg0.75·day)-1) on fruit diets containing 2.3-5.6% protein (P = 0.0001). Supplementation of the fruit diet with additional protein increased mass gain but did not completely reverse the growth-depressing effect of the fruit-only diet. Protein limitations or other characteristics of fruit diets that increase energy metabolism and intake may be strategies that also directly benefit plants, by increasing either seed dispersal or propagation.
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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 | 0.001 |
| 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 | 0.001 | 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".