MétaCan
Menu
Back to cohort

Herbivore metabolism and stoichiometry each constrain herbivory at different organizational scales across ecosystems

2009· review· en· W2133311386 on OpenAlexaff
Helmut Hillebrand, Elizabeth T. Borer, Matthew E. S. Bracken, Bradley J. Cardinale, Just Cebrián, Elsa E. Cleland, James J. Elser, Daniel S. Gruner, W. Stanley Harpole, Jacqueline T. Ngai, Stuart A. Sandin, Eric W. Seabloom, Jonathan B. Shurin, Jennifer E. Smith, Melinda D. Smith

Bibliographic record

VenueEcology Letters · 2009
Typereview
Languageen
FieldEnvironmental Science
TopicPhysiological and biochemical adaptations
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsHerbivoreTrophic levelEcologyEcosystemBiologyPopulationEcological stoichiometryPer capitaAbundance (ecology)

Abstract

fetched live from OpenAlex

Plant-herbivore interactions mediate the trophic structure of ecosystems. We use a comprehensive data set extracted from the literature to test the relative explanatory power of two contrasting bodies of ecological theory, the metabolic theory of ecology (MTE) and ecological stoichiometry (ES), for per-capita and population-level rates of herbivory across ecosystems. We found that ambient temperature and herbivore body size (MTE) as well as stoichiometric mismatch (ES) both constrained herbivory, but at different scales of biological organization. Herbivore body size, which varied over 11 orders of magnitude, was the primary factor explaining variation in per-capita rates of herbivory. Stoichiometric mismatch explained more variation in population-level herbivory rates and also in per-capita rates when we examined data from within functionally similar trophic groups (e.g. zooplankton). Thus, predictions from metabolic and stoichiometric theories offer complementary explanations for patterns of herbivory that operate at different scales of biological organization.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.865
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.257
Teacher spread0.240 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations170
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueEcology LettersSame topicPhysiological and biochemical adaptationsFrench-language works237,207