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Record W2788377312 · doi:10.1111/ele.12944

Plant controls on Late Quaternary whole ecosystem structure and function

2018· article· en· W2788377312 on OpenAlexfundno aff
Elizabeth S. Jeffers, Nicki J. Whitehouse, Adrian M. Lister, Gill Plunkett, Phil Barratt, Emma Smyth, P.R. Lamb, Michael Dee, Stephen J. Brooks, Katherine J. Willis, Cynthia A. Froyd, Jenny E. Watson, Michael B. Bonsall

Bibliographic record

VenueEcology Letters · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsnot available
FundersQueen's UniversityNatural Environment Research CouncilQueen's University BelfastSight Research UK
KeywordsEcosystemHerbivoreEcologyAbiotic componentPlant communityTerrestrial ecosystemBiomass (ecology)ShrubBiologyNutrient cycleTerrestrial plantEnvironmental scienceEcological succession

Abstract

fetched live from OpenAlex

Plants and animals influence biomass production and nutrient cycling in terrestrial ecosystems; however, their relative importance remains unclear. We assessed the extent to which mega-herbivore species controlled plant community composition and nutrient cycling, relative to other factors during and after the Late Quaternary extinction event in Britain and Ireland, when two-thirds of the region's mega-herbivore species went extinct. Warmer temperatures, plant-soil and plant-plant interactions, and reduced burning contributed to the expansion of woody plants and declining nitrogen availability in our five study ecosystems. Shrub biomass was consistently one of the strongest predictors of ecosystem change, equalling or exceeding the effects of other biotic and abiotic factors. In contrast, there was relatively little evidence for mega-herbivore control on plant community composition and nitrogen availability. The ability of plants to determine the fate of terrestrial ecosystems during periods of global environmental change may therefore be greater than previously thought.

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 categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
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)0.0020.001

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 head0.009
GPT teacher head0.203
Teacher spread0.194 · 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; both teacher heads agree on what is shown here.

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

Citations18
Published2018
Admission routes1
Has abstractyes

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