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Record W7043547631

Spatially explicit assignment of harvested waterfowl using stable isotopes

2024· article· en· W7043547631 on OpenAlexaboutno aff

Bibliographic record

VenueScholarship@Western (Western University) · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicIsotope Analysis in Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsWaterfowlStable isotope ratioFeatherRange (aeronautics)Current (fluid)
DOInot available

Abstract

fetched live from OpenAlex

Naturally occurring stable isotopes within animal tissues can provide intrinsic markers for predictable assignment to origin of migratory animals, without additional tracking devices. The use of feather stable-hydrogen isotopes (δ2Hf) in waterfowl research has been limited until recently and the opportunity to use stable isotopes in general to inform waterfowl management, particularly when assessing source origins and connectivity, is unrealized. Many of the current waterfowl monitoring programs (e.g., preseason banding) are spatially limited due to accessibility, but intrinsic markers provide a complementary method to estimate harvest source areas and evaluate biases. In my Ph.D. dissertation, across four data chapters, I used δ2Hf measurements to inform direct connections between harvest and source areas for harvested waterfowl in eastern North America, assessing and improving on the current methods. First, I used δ2Hf and stable-carbon isotope (δ13C) measurements to evaluate differences in the origin of harvested American Black Duck (Anas rubripes) across its range (Chapter 2). I found evidence supporting the flyover hypothesis, where American Black Duck harvested in Atlantic Canada showed predominantly southern (local) origins, while those harvested elsewhere originated farther north in the boreal. Second, I critically evaluated the current methods used to predict origins based on stable isotopes in waterfowl feathers (Chapter 3). Here I aggregated known-origin calibration data (δ2Hf vs. δ2Hp) and informed the best practices for assignment methods moving forward. Lastly, focusing specifically on leg-band returns and how they can be directly integrated into likelihood-based assignment, I explored spatiotemporal patterns in the natal source areas of waterfowl in eastern North America (Chapter 4) and critically evaluated the use of band returns as a prior probability of origin, directly comparing source areas derived from band returns and δ2Hf measurements (Chapter 5). I found evidence of flyway-specific natal sources with northward shifts later in the harvest period. When used as a prior probability of origin, band returns greatly refined derived source areas, despite their spatial bias. Together, these contributions address key conservation questions for species of conservation concern, inform best practices when using stable isotopes, and demonstrate the value of stable isotopes as a tool for waterfowl management.

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)0.003
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 score0.022
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.073
GPT teacher head0.298
Teacher spread0.225 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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