Data from: Conservation implications of a lack of relationship between baseline glucocorticoids and fitness in a wild passerine
Bibliographic record
Abstract
The application of physiological measures to conservation monitoring has been gaining momentum and, while a suite of physiological traits are available to ascertain disturbance and condition in wildlife populations, glucocorticoids (i.e., GCs: cortisol and corticosterone) are the most heavily employed. The interpretation of GC levels as sensitive indicators of population change necessitates that GCs and metrics of population persistence are linked. However, the relationship between GCs and fitness may be highly context-dependent, changing direction, or significance, depending on the GC measure, fitness metric, life history stage, or other intrinsic and extrinsic contexts considered. We examined the relationship between baseline plasma corticosterone (CORT) levels measured at two periods of the breeding season and three metrics of fitness (offspring quality, reproductive output, and adult survival) in female tree swallows (Tachycineta bicolor). Specifically, we investigated whether: i) a relationship between baseline CORT metrics and fitness exists in our population; ii) whether the inclusion of energetic contexts such as food availability, reproductive investment, or body mass could alter or improve the strength of the relationship between CORT and fitness; iii) whether energetic contexts could better predict fitness compared to CORT metrics. Importantly, we investigated these relationships in both natural conditions and under an experimental manipulation of foraging profitability (feather clipping) to determine the influence of an environmental constraint on GC-fitness relationships. We found a lack of relationship between baseline CORT and both short- and long-term metrics of fitness in control and clipped birds. In contrast, loss in body mass over reproduction positively predicted reproductive output (number of chicks leaving the nest) in control birds; however, the relationship was characterized by a low R2 (5%), limiting the predictive capacity, and therefore the application potential, of such a measure in a conservation setting. Our results stress the importance of ground-truthing GC-fitness relationships and indicate that baseline GCs will likely not be easily employed as conservation biomarkers across many species and life history stages. Given the accumulating evidence of temporally-dynamic, inconsistent, and context-dependent GC-fitness relationships, placing effort towards directly measuring fitness traits, rather than plasma GC levels, will likely be more worthwhile for many conservation endeavours.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".