A novel integrative method for measuring body condition in ecological studies based on physiological dysregulation
Bibliographic record
Abstract
Summary The body condition of free‐ranging animals affects their response to stress, decisions, ability to fulfil vital needs and, ultimately, fitness. However, this key attribute in ecology remains difficult to assess, and there is a clear need for more integrative measures than the common univariate proxies. We propose a systems biology approach that positions individuals along a gradient from a ‘normal/optimal’ to ‘abnormal/suboptimal’ physiological state based on Mahalanobis distance computed from physiological biomarkers. We previously demonstrated the validity of this approach for studying ageing in humans; here, we illustrate its broad potential for ecological studies. As an example, we used biomarker data on shorebirds and found that birds with an abnormal condition had a lower maximal thermogenic capacity and higher scores of inflammation, with important implications for their ecology and health. Moreover, Mahalanobis distance captured a signal of condition not detected by the individual biomarkers. Overall, our results on birds and humans show that individuals with abnormal physiologies are indeed in worse condition. Moreover, our approach appears not to be particularly sensitive to which set of biomarkers is used to assess condition. Consequently, it could be applied easily to existing ecological data sets. Our approach provides a general, powerful way to measure condition that helps resolve confusion as to how to deal with complex interactions and interdependence among multiple physiological and condition measures. It can be applied directly to topics such as the effect of environmental quality on body condition, risks of health outcomes, mechanisms of adaptive phenotypic plasticity, and mechanisms behind long‐term processes such as senescence.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".