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LINEAGES THAT CHEAT DEATH: SURVIVING THE SQUEEZE ON RANGE SIZE

2010· article· en· W1510811126 on OpenAlexaff
Anthony Waldron

Bibliographic record

VenueEvolution · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsExtinction (optical mineralogy)BiologyDiversification (marketing strategy)Range (aeronautics)Resistance (ecology)EcologyExtinction eventEvolutionary biologyGenetic algorithmClimate changeDemographyPopulationBiological dispersal

Abstract

fetched live from OpenAlex

Evolutionary lineages differ greatly in their net diversification rates, implying differences in rates of extinction and speciation. Lineages with a large average range size are commonly thought to have reduced extinction risk (although linking low extinction to high diversification has proved elusive). However, climate change cycles can dramatically reduce the geographic range size of even widespread species, and so most species may be periodically reduced to a few populations in small, isolated remnants of their range. This implies a high and synchronous extinction risk for the remaining populations, and so for the species as a whole. Species will only survive through these periods if their individual populations are "threat tolerant," somehow able to persist in spite of the high extinction risk. Threat tolerance is conceptually different from classic extinction resistance, and could theoretically have a stronger relationship with diversification rates than classic resistance. I demonstrate that relationship using primates as a model. I also show that narrowly distributed species have higher threat tolerance than widespread ones, confirming that tolerance is an unusual form of resistance. Extinction resistance may therefore operate by different rules during periods of adverse global environmental change than in more benign periods.

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 score0.023
Threshold uncertainty score0.733

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)0.0010.001

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 head0.011
GPT teacher head0.229
Teacher spread0.218 · 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

Citations51
Published2010
Admission routes1
Has abstractyes

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