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Record W2475861909 · doi:10.1111/evo.13007

Wind patterns as a potential driver in the evolution and maintenance of a North American migratory suture zone

2016· article· en· W2475861909 on OpenAlexaboutno aff
Jennifer D. McCabe, Brian J. Olsen, David E. Hiebeler

Bibliographic record

VenueEvolution · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAeolian processes and effects
Canadian institutionsnot available
FundersUniversité du MaineU.S. Department of Agriculture
KeywordsBiologyFibrous jointEvolutionary biologyAnatomy

Abstract

fetched live from OpenAlex

Suture zones are areas where range contact zones and hybrid zones of multiple taxa are clustered. Migratory divides, contact zones between divergent populations that breed adjacent to one another but use different migratory routes, are a particular case of suture zones. Although multiple hypotheses for both the formation and maintenance of migratory divides have been suggested, quantitative tests are scarce. Here, we tested whether a novel factor, prevailing winds, was sufficient to explain both the evolution and maintenance of the Cordilleran migratory divide using individual-based models. Empirical observations of eastern birds suggest a circuitous migratory route across Canada before heading south. Western breeders, however, travel south along the Pacific coast to their wintering grounds. We modeled the effect of wind on bird migratory flights by allowing them to float at elevation using spatially explicit modeled wind data. Modeled eastern birds had easterly mean trajectories, whereas western breeders showed significantly more southern trajectories. We also determined that a mean airspeed of 18.5 m s(-1) would be necessary to eliminate this difference in trajectory, a speed that is achieved by waterfowl and shorebirds, but is faster than songbird flight speeds. These results lend support for the potential importance of wind in shaping the phylogeographic history of North American songbirds.

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 score0.032
Threshold uncertainty score0.997

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)0.0000.000

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 head0.003
GPT teacher head0.174
Teacher spread0.171 · 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

Citations2
Published2016
Admission routes1
Has abstractyes

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