Inferring condition in wild mammals: body condition indices confer no benefit over measuring body mass across ecological contexts
Bibliographic record
Abstract
Abstract Many studies assume that it is beneficial for individuals of a species to be heavier, or have a higher body condition index (BCI), without accounting for the physiological relevance of variation in the composition of different body tissues. We hypothesized that the relationship between BCI and masses of physiologically important tissues (fat and lean) would be conditional on annual patterns of energy acquisition and expenditure. We studied three species with contrasting ecologies in their respective natural ranges: an obligate hibernator (Columbian ground squirrel, Urocitellus columbianus ), a facultative hibernator (black-tailed prairie dog, Cynomys ludovicianus ), and a food-caching non-hibernator (North American red squirrel, Tamiasciurus hudsonicus ). We measured fat and lean mass in adults of both sexes using quantitative magnetic resonance (QMR). We measured body mass and two measures of skeletal structure (zygomatic width and right hind foot length) to develop sex- and species-specific BCIs, and tested the utility of BCI to predict body composition in each species. Body condition indices were more consistently, and more strongly correlated, with lean mass than fat mass. The indices were most positively correlated with fat when fat was expected to be very high (pre-hibernation prairie dogs). In all cases, however, BCI was never better than body mass alone in predicting fat or lean mass. While the accuracy of BCI in estimating fat varied across the natural histories and annual energetic patterns of the species considered, measuring body mass alone was as effective, or superior in capturing sufficient variation in fat and lean in most cases.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".