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Record W4405944732 · doi:10.1111/ele.14512

The Demographic Basis of Population Growth: A 32‐Year Transient Life Table Response Experiment

2024· article· en· W4405944732 on OpenAlexfundno aff
F. Stephen Dobson, David N. Koons, Claire Saraux, Anouch Tamian, Madan K. Oli, Vincent A. Viblanc

Bibliographic record

VenueEcology Letters · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicDemographic Trends and Gender Preferences
Canadian institutionsnot available
FundersInstitut écologie et environnementNatural Sciences and Engineering Research Council of CanadaDirectorate for Biological SciencesCentre National de la Recherche ScientifiqueUniversité de StrasbourgAlberta ParksFondation FyssenNational Science Foundation
KeywordsEcologyTable (database)PopulationPopulation growthBiologyEnvironmental scienceGeographyDemographyStatisticsMathematicsComputer science

Abstract

fetched live from OpenAlex

It has recently been recognised that populations are rarely in demographic equilibrium, but rather in a 'transient' state. To examine how transient dynamics influence our empirical understanding of the links between changes in demographic rates and population growth, we conducted a 32-year study of Columbian ground squirrels. The population increased rapidly for 10 years, followed by a 2-year crash, and a gradual 19-year recovery. Transient life table response experiment (LTRE) analysis showed that demographic stochasticity accounted for approximately one-fourth of the variation in population growth, leaving the majority to be explained by environmental influences. These relatively small rodents appeared to have a slow pace of life. But unlike the general pattern for large mammals with slow life histories, ground squirrel survival did not exhibit low variation associated with environmental 'buffering'; instead, survival varied substantially over time and contributed substantially (78%) to changes in abundance over the long-term study, with minor contributions from reproduction and unstable stage structure.

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)0.001
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 score0.362
Threshold uncertainty score0.373

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.019
GPT teacher head0.279
Teacher spread0.261 · 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

Citations4
Published2024
Admission routes1
Has abstractyes

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