The geometry of resource constraint: An empirical study of the golden snub‐nosed monkey
Bibliographic record
Abstract
Apposite conceptualization and measurement of resource variation is critical for understanding many issues in ecology, including ecological niches, persistence and distribution of populations, the structure of communities and population resilience to perturbations. We apply the nutritional geometry framework to conceptualize and quantify the responses of a temperate-living primate, the golden snub-nosed monkey Rhinopithecus roxellana to variation in resource quality and quantity and in nutrient requirements associated with seasonal environments. We present a geometric model distinguishing qualitative constraint, quantitative constraint and 'pseudo-constraint' whereby nutrient intakes resemble response to qualitative resource constraint but are in fact driven by variation in nutrient requirements. The model is applied to analyse nutrient intakes recorded in 164 full-day observations of monkeys from two populations, one wild and the other captive, across seasons. Additionally, we recorded the diet of a single animal over 32 consecutive days in the wild. Despite considerable differences in available resources, the captive and wild populations showed marked similarities in nutrient intakes, including indistinguishable amounts and ratios of ingested macronutrients during summer and autumn and strong year-round maintenance of protein compared to seasonally variable fat and carbohydrate intakes. These similarities suggest homeostatically regulated nutritional targets and provide reference points to identify factors driving population differences in macronutrient intake in winter and spring. Our framework enabled us to distinguish examples of quantitative, qualitative and 'pseudo-constraint'. We suggest that this approach can increase the resolution at which resource constraint is conceptualized and measured in ecological studies.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| 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".