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Record W2125798860 · doi:10.1525/cond.2013.120118

Agricultural Lands Subsidize Winter Diet of the Dunlin at Two Major Estuaries

2013· article· en· W2125798860 on OpenAlexafffund
Keith A. Hobson, Gary Slater, David B. Lank, Ruth L. Milner, Rachel Gardiner

Bibliographic record

VenueOrnithological Applications · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicIsotope Analysis in Ecology
Canadian institutionsSimon Fraser UniversityEnvironment and Climate Change Canada
FundersNature ConservancyU.S. Forest ServiceCalifornia Department of Fish and WildlifeWashington Department of Fish and WildlifeUniversity of Saskatchewan
KeywordsEstuaryCalidrisHabitatFisheryAgricultureMarshEcologyGeographyEnvironmental scienceδ15Nδ13CWetlandBiologyStable isotope ratio

Abstract

fetched live from OpenAlex

On the western coast of North America, several estuaries provide shorebirds with important winter and stopover habitat. These habitats include not only aquatic estuarine resources but also adjacent upland agricultural lands. The extent to which shorebirds use estuarine vs. upland habitats at these stopover sites is difficult to quantify but crucial to designing strategies for their conservation. We measured stable isotopes ( 13 C, 15 N) in whole blood of Dunlins (Calidris alpina) and their prey from two major estuaries in north Puget Sound, Washington, USA, to estimate their relative use of estuarine vs. upland agricultural zones. We identified four isotopically distinct dietary inputs (agriculture high in 15 N, other agriculture, marsh/marine, and freshwater plume). Isotopic sampling and modeling was informed by movements and habitat use derived from radiotelemetry. This isotopic structure allowed us to conclude that these Dunlins obtained about 62% of the protein in their diet from agricultural lands and 38% from the estuary. Our results underline the urgent need to combine management of estuaries and upland agricultural areas in strategies for shorebird conservation.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0100.004

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 head0.007
GPT teacher head0.219
Teacher spread0.212 · 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; both teacher heads agree on what is shown here.

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

Citations7
Published2013
Admission routes2
Has abstractyes

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