Beyond body condition: Experimental evidence that plasma metabolites improve nutritional state measurements in a free-living seabird
Bibliographic record
Abstract
The ability to efficiently measure the health and nutritional status of wild populations in situ is a valuable tool, as many methods of evaluating animal physiology do not occur in real-time, limiting the possibilities for direct intervention. This study investigates the use of blood plasma metabolite concentrations, measured via point-of-care devices or a simple plate reader assay, as indicators of nutritional state in free-living seabirds. We experimentally manipulated the energy expenditure of wild black-legged kittiwakes on Middleton Island, Alaska, and measured the plasma concentrations of glucose, cholesterol, B-hydroxybutyrate, and triglycerides throughout the breeding season, along with measures of body condition (size-corrected mass [SCM] and muscle depth). Supplemental feeding improved the nutritional state of kittiwakes by increasing feeding rate (higher glucose and triglycerides, lower cholesterol), and flight-handicapping caused a slight nutritional decline (lower glucose and triglycerides, higher cholesterol and B-hydroxybutyrate). Glucose and triglycerides were the best indicators of nutritional state when used alongside SCM, and improved upon commonly used metrics for measuring individual condition (i.e. SCM or mass alone). Metabolite concentrations varied across the breeding period, suggesting that the pre-laying stage, when feeding rates tend to be lower, was the most nutritionally challenging period for kittiwakes (low glucose, high cholesterol). Muscle depth also varied by treatment and breeding stage, but differed from other nutritional indices, suggesting that muscle depth is an indicator of exercise and activity level rather than nutrition. Here we demonstrate potential for the use of blood plasma metabolites measured via point-of-care devices as proxies for evaluating individual health, population health, and environmental food availability.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".