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Record W4392136299 · doi:10.1002/jwmg.22565

American woodcock migration phenology in eastern North America: implications for hunting season timing

2024· article· en· W4392136299 on OpenAlexaffabout
Alexander C. Fish, Amber M. Roth, Greg Balkcom, Liam A. Berigan, Kylie Brunette, Sarah Clements, Gary Costanzo, Clayton L. Graham, William F. Harvey, Michael Hook, Douglas L. Howell, Seth Maddox, Scott R. McWilliams, Shawn W. Meyer, Theodore C. Nichols, J. Bruce Pollard, Christian Roy, Josh Stiller, Dawn Washington, Lisa Williams, Erik J. Blomberg

Bibliographic record

VenueJournal of Wildlife Management · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicAvian ecology and behavior
Canadian institutionsEnvironment and Climate Change Canada
FundersNational Institute of Food and Agriculture
KeywordsWoodcockPhenologyGeographySeasonal breederEcologyBiology

Abstract

fetched live from OpenAlex

Abstract Understanding the phenology of migration is fundamental to management of migratory gamebirds, in part because of the role migratory timing plays in setting harvest regulations. Migratory timing is particularly important for determining appropriate dates for hunting seasons, which may be selected to coincide with major periods of migration, according to local management objectives. We used global positioning system (GPS)‐transmitters to track American woodcock (Scolopax minor), characterize the timing of woodcock migration, and identify sources of variation in timing relative to current hunting season structures in eastern North America. We captured 304 woodcock in 3 Canadian provinces and 12 states from 2017 to 2020, primarily within the Eastern Woodcock Management Region. Using locations collected every 1.7 days on average, we assessed whether initiation, termination, or stopover timing of woodcock migration during fall and early spring varied geographically, differed among age and sex classes, or was influenced by individual body condition. During fall, woodcock migrating from summer use areas farther north and west (e.g., Ontario, Quebec, Canada) initiated and terminated migration earlier than woodcock migrating from areas farther south and east (e.g., Rhode Island, USA). Adult woodcock made multiday stopovers that were 3 days longer on average than juveniles and females made more stopovers on average (8.0 stopovers) compared to males (6.1 stopovers). During the onset of spring migration, woodcock that wintered farther west initiated migration before birds that spent the winter farther east, and males initiated migration on average 6 days earlier than females. Under the current 45‐day harvest regulatory framework in the United States, hunting seasons in northern breeding and southern wintering areas are generally consistent with migration phenology. At more intermediate latitudes, however, periods of migration are generally longer than 45 days, resulting in many circumstances where migrating woodcock are present during periods when hunting seasons are closed. Managers in mid‐latitude states could consider opening hunting seasons later, allowing hunters to harvest more migrant woodcock.

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)0.001
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 score0.594
Threshold uncertainty score0.817

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
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 head0.021
GPT teacher head0.278
Teacher spread0.258 · 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

Citations4
Published2024
Admission routes2
Has abstractyes

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