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Record W2163155090 · doi:10.1098/rspb.2010.0812

Interactions between rate processes with different timescales explain counterintuitive foraging patterns of arctic wintering eiders

2010· article· en· W2163155090 on OpenAlexaff
Joel P. Heath, H. Grant Gilchrist, Ronald C. Ydenberg

Bibliographic record

VenueProceedings of the Royal Society B Biological Sciences · 2010
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsCarleton UniversitySimon Fraser University
Fundersnot available
KeywordsForagingCounterintuitiveOptimal foraging theoryEcologyArcticTrade-offScale (ratio)BiologyGeography

Abstract

fetched live from OpenAlex

To maximize fitness, animals must respond to a variety of processes that operate at different rates or timescales. Appropriate decisions could therefore involve complex interactions among these processes. For example, eiders wintering in the arctic sea ice must consider locomotion and physiology of diving for benthic invertebrates, digestive processing rate and a nonlinear decrease in profitability of diving as currents increase over the tidal cycle. Using a multi-scale dynamic modelling approach and continuous field observations of individuals, we demonstrate that the strategy that maximizes long-term energy gain involves resting during the most profitable foraging period (slack currents). These counterintuitive foraging patterns are an adaptive trade-off between multiple overlapping rate processes and cannot be explained by classical rate-maximizing optimization theory, which only considers a single timescale and predicts a constant rate of foraging. By reducing foraging and instead digesting during slack currents, eiders structure their activity in order to maximize long-term energetic gain over an entire tide cycle. This study reveals how counterintuitive patterns and a complex functional response can result from a simple trade-off among several overlapping rate processes, emphasizing the necessity of a multi-scale approach for understanding adaptive routines in the wild and evaluating mechanisms in ecological time series.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.196
Threshold uncertainty score0.304

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.028
GPT teacher head0.243
Teacher spread0.214 · 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

Citations30
Published2010
Admission routes1
Has abstractyes

Explore more

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