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Record W2760373773 · doi:10.1111/1365-2435.12988

Biomarker of burden: Feather corticosterone reflects energetic expenditure and allostatic overload in captive waterfowl

2017· article· en· W2760373773 on OpenAlexafffund
David W. Johns, Tracy A. Marchant, Graham D. Fairhurst, John R. Speakman, Robert G. Clark

Bibliographic record

VenueFunctional Ecology · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsEnvironment and Climate Change CanadaUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of CanadaInstitute for Wetland and Waterfowl Research, Ducks Unlimited Canada
KeywordsAllostatic loadAllostasisBiologyCorticosteroneFeatherGlucocorticoidBasal metabolic rateContext (archaeology)LongevityEndocrinologyInternal medicineHormoneEcologyPhysiologyMedicine

Abstract

fetched live from OpenAlex

Abstract Allostatic load describes the interplay between energetic demand and availability and is highly context dependent, varying between seasons and life‐history stages. When energy demands exceed physiological set points modulated by glucocorticoid hormones, individuals may experience allostatic overload and transition between stages in sub‐optimal physiological states. Corticosterone, the major glucocorticoid hormone regulating energy expenditure in birds, is incorporated into growing feathers (CORT f ), and it has been suggested that CORT f reflects long‐term records of allostatic load during feather growth. However, relationships between allostatic load and CORT f have not been adequately evaluated. If such relationships exist, the use of CORT f to investigate cross‐seasonal effects could provide novel insights into impacts of past allostatic load and/or overload events. We tested whether experimental increases in daily workload during two adjacent life‐history stages would be reflected in CORT f levels, and examined if CORT f levels reflected either current energetic demand or allostatic load prior to feather growth. Daily workloads in female mallard Anas platyrhynchos ducklings were increased over a 6‐week period using physical obstacles and/or carrying back‐mounted weights. We measured daily energy expenditure, growth, body mass, and CORT f in growing ducklings. Then, we induced feather moult and reapplied combinations of workload treatments for an additional 6 weeks to investigate whether effects of past energetic demands would be detected in future CORT f levels. Ducklings confronted with higher workloads during development had reduced body mass, growth rates and consequently higher daily energy expenditure and CORT f values compared to controls. When ducklings were fully developed, CORT f patterns in birds re‐exposed to workload treatments reflected only current, rather than past, energetic demands. However, under allostatic overload conditions, past levels of CORT f were positively associated with CORT f in the subsequent moult. Our study confirms the previously untested assumption that CORT f reflects energetic demand during the period of feather growth in a precocial bird. We show that allostatic overload conditions early in life, which temporarily suppress growth, can be detected using CORT f , an event potentially missed in studies which rely solely on measures of body condition alone. We suggest that CORT f can provide a valuable biomarker of allostatic load and overload conditions during the period of feather growth, but highlight how context should be considered for studies using CORT f to investigate influences of carryover effects. Our study contributes to building a physiological foundation to inform interpretations of ecological patterns using CORT f . A plain language summary is available for this article.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.837
Threshold uncertainty score0.747

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.038
GPT teacher head0.254
Teacher spread0.217 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations32
Published2017
Admission routes2
Has abstractyes

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