Complementary food resources of carnivory and frugivory affect local abundance of an omnivorous carnivore
Bibliographic record
Abstract
A major unresolved question for omnivorous carnivores, like most species of bears, is to what degree are populations influenced by bottom–up (food supply) or top–down (human‐caused mortality) processes. Most previous work on bear populations has focused on factors that limit survival (top–down) assuming little effect of food resource supply. When food resources are considered, most often they consider only the availability/supply of a single resource, particularly marine‐subsidized or terrestrial sources of protein (carnivory) or alternately hard or soft mast (frugivory). Little has been done to compare the importance of each of these factors for omnivorous bears or test whether complementary resources better explain individual animal and population measures such as density, vital rates, and body size. We compared landscape patterns of digestible energy (kcal) for buffaloberry (a key source of carbohydrate) and ungulate matter (a key source of protein and lipid) to local measures in grizzly bear Ursus arctos abundance at DNA hair snag sites in west‐central Alberta, Canada. We tested support for bottom–up hypotheses in either single (carnivory [meat] versus frugivory [fruit]) or complementary (additive or multiplicative) food resources, while accounting for a well‐known top–down limiting factor affecting bear survival (road density). We found support for both top–down and bottom–up factors with complementary resources (co‐limitation) supported over single resource supplies of either meat or fruit. Our study suggests that the availability of food resources that provide complementary nutrients is more important in predicting local bear abundance than single foods or nutrients (e.g. protein) or simply energy per se. This suggests a nutritionally multidimensional bottom–up limitation for a low density interior population of grizzly bears.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".