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Fluctuations in circumpolar seabird populations linked to climate oscillations

2008· article· en· W2023116139 on OpenAlexaff
David B. Irons, Tycho Anker‐Nilssen, Anthony J. Gaston, G. Vernon Byrd, Knud Falk, Grant Gilchrist, Martti Hario, Måns Hjernquist, Yuri Krasnov, Anders Mosbech, Bergur Olsen, Aevar Petersen, James B. Reid, Gregory J. Robertson, Hallvard Strøm, Kenton D. Wohl

Bibliographic record

VenueGlobal Change Biology · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsCarleton University
Fundersnot available
KeywordsSeabirdArcticTemperate climateClimate changeEcologyUria aalgeRegime shiftPopulationEcosystemMarine ecosystemEnvironmental scienceCircumpolar starOceanographyBiologyPredationGeology

Abstract

fetched live from OpenAlex

Abstract We found that synchronous fluctuations of two congeneric seabird species across the entire Arctic and sub‐Arctic regions were associated with changes in sea surface temperatures (SST) that were linked to two climate shifts, in 1977 and again in 1989. As the SST changes linked to climate shifts were congruent at the scale of ocean basins, fluctuations of these species occurred similarly at continental or basin scale. Changes in colony sizes were examined for a decade following climate shifts. The magnitude of the SST shift was more important than its direction in determining the subsequent rate of population change. Seabirds declined when the SST shift was large and increased when the shift was small, although the effect differed between the Arctic‐breeding species and the more temperate‐breeding congener. The Arctic species, Thick‐billed Murre ( Uria lomvia ) increased most rapidly when SST warmed slightly, while the temperate species, Common Murre ( Uria aalge ) showed most rapid increase with moderate cooling. Both showed negative trends with large temperature shifts in either direction. This pattern was replicated during both climate oscillations. Negative population trends in seabirds presumably indicate the alteration of underlying food webs. Hence, similar widespread fluctuations in response to climate shifts are likely for other ecosystem components (marine mammals, fish, and invertebrates).

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 categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.116
GPT teacher head0.323
Teacher spread0.207 · 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 teacher head, not a consensus.

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

Citations117
Published2008
Admission routes1
Has abstractyes

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