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Record W3081319147 · doi:10.1111/conl.12745

Squeezed by a habitat split: Warm ocean conditions and old‐forest loss interact to reduce long‐term occupancy of a threatened seabird

2020· article· en· W3081319147 on OpenAlexaff
Matthew G. Betts, Joseph M. Northrup, Jennifer A. Bailey Guerrero, Lindsay J. Adrean, S. Kim Nelson, Jennifer L. Fisher, Brian D. Gerber, Marie-Sophie García-Heras, Zhiqiang Yang, Daniel D. Roby, James W. Rivers

Bibliographic record

VenueConservation Letters · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicAvian ecology and behavior
Canadian institutionsTrent UniversityMinistry of Natural Resources and Forestry
Fundersnot available
KeywordsOccupancyThreatened speciesHabitatSeabirdExtinction (optical mineralogy)Environmental scienceEcologyRange (aeronautics)PredationFisheryGeographyBiology

Abstract

fetched live from OpenAlex

Abstract Theory predicts that species requiring multiple habitat types simultaneously should have heightened sensitivity to anthropogenic pressures, yet tests of this prediction are especially rare. We tested whether breeding site occupancy of the threatened marbled murrelet ( Brachyramphus marmoratus ) was driven by the synergistic effects of nesting habitat loss in forests, and changing ocean conditions. We paired 70,700 murrelet surveys at 19,837 sites across 20 years from the Oregon Coast Range with annual data on the extent of old forest and biophysical ocean conditions. Dynamic occupancy models indicated that local murrelet colonization rates were strongly reduced during warm ocean conditions with low prey availability. Landscapes that contained more old forest and were closer to the ocean showed reduced rates of local extinction. Given predictions of accelerated ocean warming and increased global timber demand, our results suggest murrelets may continue to be imperiled by deterioration of the two habitats upon which they depend.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.099
Threshold uncertainty score0.550

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 studies0.0000.000
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 head0.018
GPT teacher head0.258
Teacher spread0.240 · 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.

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

Citations23
Published2020
Admission routes1
Has abstractyes

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