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Record W2238201475 · doi:10.1002/jwmg.1035

American woodcock migratory connectivity as indicated by hydrogen isotopes

2016· article· en· W2238201475 on OpenAlexaffabout
Daniel S. Sullins, Warren C. Conway, David A. Haukos, Keith A. Hobson, Leonard I. Wassenaar, Christopher E. Comer, I‐Kuai Hung

Bibliographic record

VenueJournal of Wildlife Management · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicAvian ecology and behavior
Canadian institutionsUniversity of SaskatchewanEnvironment and Climate Change Canada
FundersU.S. Forest Service
KeywordsWoodcockGeographyPopulationRange (aeronautics)FeatherJuvenileEcologyPhysical geographyBiologyDemography

Abstract

fetched live from OpenAlex

ABSTRACT To identify factors contributing to the long‐term decline of American woodcock, a holistic understanding of range‐wide population connectivity throughout the annual cycle is needed. We used band recovery data and isotopic composition of primary (P1) and secondary (S13) feathers to estimate population sources and connectivity among natal, early fall, and winter ranges of hunter‐harvested juvenile American woodcock. We used P1 feathers from known‐origin pre‐fledged woodcock (n = 43) to create a hydrogen δ2Hf isoscape by regressing δ2Hf against expected growing‐season precipitation (δ2Hp). Modeled δ2Hp values explained 79% of the variance in P1 δ2Hf values, indicating good model fit for estimating woodcock natal origins. However, a poor relationship (r2 = 0.23) between known‐origin, S13 δ2Hf values, and expected δ2Hp values precluded assignment of early fall origins. We applied the δ2Hf isoscape to assign natal origins using P1 feathers from 494 hunter‐harvested juvenile woodcock in the United States and Canada during 2010–2011 and 2011–2012 hunting seasons. Overall, 64% of all woodcock origins were assigned to the northernmost (>44°N) portion of both the Central and Eastern Management Regions. In the Eastern Region, assignments were more uniformly distributed along the Atlantic coast, whereas in the Central Region, most woodcock were assigned to origins within and north of the Great Lakes region. We compared our origin assignments to spatial coverage of the annual American woodcock Singing Ground Survey (SGS) and evaluated whether the survey effectively encompasses the entire breeding range. When we removed the inadequately surveyed Softwood shield Bird Conservation Region (BCR) from the northern portion of the SGS area, only 48% of juvenile woodcock originated in areas currently surveyed by the SGS. Of the individuals assigned to the northernmost portions of the breeding range, several were harvested in the southern extent of the wintering range. Based upon this latitudinal winter stratification, we examined whether woodcock employed a leapfrog migration strategy. Using δ2Hf values and band‐recovery data, we found some support for this migration strategy hypothesis but not as a singular explanation. The large harvest derivation of individuals from the northernmost portions of the breeding range, and the difference in breeding distributions within each Management Region should be considered in future range‐wide conservation and harvest management planning for American woodcock. © 2016 The Wildlife Society.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.034
Threshold uncertainty score0.067

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.005
GPT teacher head0.222
Teacher spread0.216 · 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 source (direct Gemma or distilled Codex), 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

Citations18
Published2016
Admission routes2
Has abstractyes

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