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Record W2913052863 · doi:10.1002/jwmg.21639

Age class dynamics of Canada geese in the Central Flyway

2019· article· en· W2913052863 on OpenAlexaboutno aff
Joshua L. Dooley, Michael L. Szymanski, Rocco J. Murano, Mark P. Vrtiska, Tom F. Bidrowski, Josh L. Richardson, Gary C. White

Bibliographic record

VenueJournal of Wildlife Management · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicAvian ecology and behavior
Canadian institutionsnot available
FundersU.S. Geological Survey
KeywordsFlywayJuvenileDemographyGeographyBiologyAbundance (ecology)EcologyHabitat

Abstract

fetched live from OpenAlex

ABSTRACT Abundance of temperate‐nesting Canada geese ( Branta canadensis ) in Central Flyway east‐tier states (ND, SD, NE, KS, OK, USA) increased since the 1970s. Hunting regulations were liberalized since the mid‐1990s in these states to increase harvest and reduce abundance of local populations. Because 2 age classes, juvenile and adult, are typically classified when banding, most dead‐recovery band analyses of Canada geese have only considered 2 age classes to estimate survival and recovery probabilities, despite a delayed breeding life history. We evaluated recovery distributions and survival and recovery probabilities of Canada goose age classes (i.e., juvenile [first year], subadult [second and third year], and adult [≥fourth year]) among Central Flyway east‐tier states relative to liberalized hunting regulations during 1990–2015. We also conducted simulations and evaluated bias in parameter estimates from 2‐age‐class dead‐recovery models when a subadult age class was not modeled. Models including 3 age classes were more supported than models including only 2 age classes. Mean juvenile survival estimates among states from the top 2‐age‐class models were 9–50% greater than an equivalent 3‐age‐class model, whereas differences were less or negligible for adult survival (−4% to −1%), adult recovery (1–12%), and juvenile recovery (−3–6%). Geese were primarily recovered in the state they were banded (range among states = 59–86%), and 91% of all recoveries occurred in the Central Flyway east‐tier states. Recovery distributions of subadults were broader and more northward than adults and juveniles. Recovery estimates (Brownie parameterization) of subadults among states ( = 0.091 ± 0.039 [SE] to 0.116 ± 0.029) were also generally greater than adults (0.061 ± 0.030 to 0.104 ± 0.033) and juveniles (0.049 ± 0.026 to 0.132 ± 0.041). Survival estimates of adults (0.713 ± 0.103 to 0.748 ± 0.119) and subadults (0.621 ± 0.197 to 0.801 ± 0.154) exhibited some decrease through time concurrent with liberalized harvest regulations, but survival estimate of juveniles (0.492 ± 0.093 to 0.686 ± 0.164) increased or were stable. Of the 5 Central Flyway east‐tier states, management actions to reduce local Canada goose populations were the most intensive in North Dakota and South Dakota, and these states had the greatest decrease in adult and subadult survival estimates. Our results provide some limited evidence that harvest regulations targeted at locally breeding Canada geese can affect their survival, have greatest effects when first implemented, and affect subadults from a broader spatial scale than adults and juveniles. More information is needed on how localized harvest regulations affect temperate‐nesting Canada geese from other areas, particularly subadults and molt migrants, and, conversely, how such geese affect the ability to achieve management objectives at varying spatial scales. Lastly, to minimize bias when analyzing temperate‐nesting Canada goose data, or other species with similar marking constraints and age‐class structure, consideration should be given to evaluating ≥3 age classes and using recapture data and joint live‐dead models when possible. © 2019 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)0.001
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 score0.720
Threshold uncertainty score0.564

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
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 head0.196
Teacher spread0.191 · 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".

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Citations12
Published2019
Admission routes1
Has abstractyes

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