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Record W2898792551 · doi:10.1073/pnas.1804224115

Climate change causes upslope shifts and mountaintop extirpations in a tropical bird community

2018· article· en· W2898792551 on OpenAlexafffund
Benjamin G. Freeman, Micah N. Scholer, Viviana Ruiz‐Gutiérrez, John W. Fitzpatrick

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicSpecies Distribution and Climate Change
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaNational Science Foundation
KeywordsElevation (ballistics)Extinction (optical mineralogy)Climate changeGlobal warmingTropicsRange (aeronautics)Abundance (ecology)EcologyGeographyBiology

Abstract

fetched live from OpenAlex

Montane species worldwide are shifting upslope in response to recent temperature increases. These upslope shifts are predicted to lead to mountaintop extinctions of species that live only near mountain summits, but empirical examples of populations that have disappeared are sparse. We show that recent warming constitutes an "escalator to extinction" for birds on a remote Peruvian mountain-high-elevation species have declined in both range size and abundance, and several previously common mountaintop residents have disappeared from the local community. Our findings support projections that warming will likely drive widespread extirpations and extinctions of high-elevation taxa in the tropical Andes. Such climate change-driven mountaintop extirpations may be more likely in the tropics, where temperature seems to exert a stronger control on species' range limits than in the temperate zone. In contrast, we show that lowland bird species at our study site are expanding in range size as they shift their upper limits upslope and may thus benefit from climate change.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.006
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.102
GPT teacher head0.331
Teacher spread0.229 · 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

Citations519
Published2018
Admission routes2
Has abstractyes

Explore more

Same venueProceedings of the National Academy of SciencesSame topicSpecies Distribution and Climate ChangeFrench-language works237,207