Crowding as a primary source of stress in an endangered fragment‐dwelling strepsirrhine primate
Bibliographic record
Abstract
Abstract Nutritional and social challenges arising from habitat fragmentation can be significant sources of stress for animals. If prolonged, such stressors can pose a threat to the longevity of a species within a fragmented landscape. While each may elicit a physiological response, the coupled and often additive nature of these stressors can make it difficult to determine their relative impact on an individual or population. We measured fecal glucocorticoids (fGC) in two populations of Lemur catta, an endangered strepsirrhine primate, inhabiting forest fragments that vary markedly in resource structure and population density. We also examined the relative importance of behavioral variables indicative of feeding environment, intergroup territoriality, and intragroup social interactions in predicting fGC levels in these populations. Lemur catta living with ample food resources but at high population density exhibited higher fGC concentrations throughout the study period, independent of sex or reproductive state. At both sites, fGC levels reflected consistent seasonal variation, with lowest levels occurring during the resource‐rich pre‐mating period. Foraging effort was positively associated with fGC levels at each site, yet the population exhibiting the highest foraging effort had consistently lower levels of fGC. Intergroup territoriality was a positive predictor and intragroup agonism a negative predictor of fGC levels; however, trends in these variables were inconsistent when examining the two sites separately. Within‐site group differences highlighted the additive nature of nutritional and social stressors in predicting fGC levels. Our results suggest that the intense or unpredictable impact of crowding and, correspondingly, heightened intergroup resource defense may be an important consideration when addressing long‐term conservation initiatives for fragment‐dwelling L. catta.
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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 | 0.000 |
| 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 | 0.000 | 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".