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Record W3161101355 · doi:10.1111/ele.13762

Montane species track rising temperatures better in the tropics than in the temperate zone

2021· article· en· W3161101355 on OpenAlexafffund
Benjamin G. Freeman, Yiluan Song, Kenneth J. Feeley, Kai Zhu

Bibliographic record

VenueEcology Letters · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicSpecies Distribution and Climate Change
Canadian institutionsUniversity of British Columbia
FundersBanting Research FoundationNational Science Foundation
KeywordsTemperate climateLatitudeTropicsEnvironmental scienceEquatorRange (aeronautics)Global warmingClimate changeGlobal changeAtmospheric sciencesEcologyTemperate rainforestTropical climateTemperate forestGeographyEcosystemBiologyGeology

Abstract

fetched live from OpenAlex

Many species are responding to global warming by shifting their distributions upslope to higher elevations, but the observed rates of shifts vary considerably among studies. Here, we test the hypothesis that this variation is in part explained by latitude, with tropical species being particularly responsive to warming temperatures. We analyze two independent empirical datasets-shifts in species' elevational ranges, and changes in composition of forest inventory tree plots. Tropical species are tracking rising temperatures 2.1-2.4 times (range shift dataset) and 10 times (tree plot dataset) better than their temperate counterparts. Models predict that for a 100 m upslope shift in temperature isotherm, species at the equator have shifted their elevational ranges 93-96 m upslope, while species at 45° latitude have shifted only 37-42 m upslope. For tree plots, models predict that a 1°C increase in temperature leads to an increase in community temperature index (CTI), a metric of the average temperature optima of tree species within a plot, of 0.56°C at the equator but no change in CTI at 45° latitude (-0.033°C). This latitudinal gradient in temperature tracking suggests that tropical montane communities may be on an "escalator to extinction" as global temperatures continue to rise.

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 score0.083
Threshold uncertainty score0.999

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)0.0080.000

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 head0.017
GPT teacher head0.226
Teacher spread0.209 · 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

Citations125
Published2021
Admission routes2
Has abstractyes

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