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Record W1987485174 · doi:10.1890/10-0850.1

Community composition and consumer identity determine the effect of resource species diversity on rates of consumption

2010· article· en· W1987485174 on OpenAlexaff
Anita Narwani, Asit Mazumder

Bibliographic record

VenueEcology · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicIsotope Analysis in Ecology
Canadian institutionsUniversity of Victoria
FundersInnovative Research Group Project of the National Natural Science Foundation of China
KeywordsGeneralist and specialist speciesTrophic levelEcologyResource (disambiguation)Consumption (sociology)Species richnessEcosystemSpecies diversityPalatabilityResource consumptionDiversity (politics)BiologyHabitat

Abstract

fetched live from OpenAlex

The effect of species diversity on ecosystem function is commonly studied within a single trophic level, but less is known about how resource diversity affects species interactions between trophic levels. We conducted a grazing experiment to determine how resource species diversity affects rates of consumption by three species of freshwater zooplankton consumers. We measured the effect of resource diversity on rates of consumption for several resource community compositions. These compositions varied in terms of palatability for the consumers. The effect of resource diversity on consumption rates depended on the diet breadth of the consumer species (from specialist to generalist) and the community composition of resources. Overall, high resource diversity commonly caused a decline in consumption rates of consumers. The most selective grazer showed reduced consumption for nearly all community compositions, whereas the most generalist grazer showed accelerated consumption when all resource species were palatable. Our results demonstrate that resource species diversity can modulate rates of consumption through the action of multiple different mechanisms.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.014
GPT teacher head0.252
Teacher spread0.238 · 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 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

Citations29
Published2010
Admission routes1
Has abstractyes

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