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Interactive effects of insects and ungulates on root growth in a native grassland

2012· article· en· W2114393151 on OpenAlexafffund
Michael R. Clark, Malcolm D. Coupe, Edward W. Bork, James F. Cahill

Bibliographic record

VenueOikos · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsUngulateHerbivoreBiologyGrasslandBiomass (ecology)EcologyGrazingHabitat

Abstract

fetched live from OpenAlex

Insects and ungulates co‐occur in grasslands, often feeding on the same plants at the same time and potentially having interactive effects on plant growth. Further, ungulate–insect interactions may differ between native ungulate guilds and domesticated cattle. Despite the prevalence of insects and ungulates in native grasslands, experiments simultaneously manipulating the densities of both these groups are rare. Using large, replicated paddocks, as well as insecticide application, we restricted access to vegetation by each group of herbivores. We also manipulated the species identity of the ungulate assemblage, allowing us to determine whether there are differential effects between native ungulate guilds (bison, elk and deer) and cattle on plant biomass. We found interactive effects of insect and ungulate herbivores on root growth. When insects were suppressed, both native ungulates and cattle caused an approximate doubling of root biomass. However, this stimulatory effect of ungulate grazing was eliminated when insects were also present. In contrast, neither insects nor ungulates had significant effects on shoot biomass at these densities. As a result, the dominant effects of above‐ground herbivory was on belowground plant growth. We suggest the effects of insect and ungulate assemblages on root biomass appear important in regulating primary production in this grassland and may account for some of the contradictory plant responses to ungulate herbivory in the literature.

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 score0.009
Threshold uncertainty score0.176

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)0.0000.000

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 head0.004
GPT teacher head0.224
Teacher spread0.220 · 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

Citations11
Published2012
Admission routes2
Has abstractyes

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