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Record W2299838484 · doi:10.1002/ecs2.1211

Detritivore diversity promotes a relative contribution rate of detritus to the diet of predators in ponds

2016· article· en· W2299838484 on OpenAlexaff
Kayoko Fukumori, Erina Yoshizaki, Noriko Takamura, Taku Kadoya

Bibliographic record

VenueEcosphere · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicIsotope Analysis in Ecology
Canadian institutionsUniversity of Guelph
FundersJapan Society for the Promotion of Science
KeywordsDetritivoreDetritusTrophic levelFood webEcologyPredationBiologyEnergy flowEcosystem

Abstract

fetched live from OpenAlex

Abstract Theory suggests that bottom‐up effects of resource diversity to upper trophic levels increases ecosystem functioning. In particular, energy flux from the detritus to other organisms in an ecosystem affects food web dynamics. To our knowledge, no empirical studies have examined how detritivore diversity alters the energy flux to upper trophic levels in a food web. Here we test the hypothesis that the greater diversity of litter‐consuming detritivores promotes energy flow between the resource and top predators in agricultural ponds using stable isotope analysis. We found that the diversity and abundance of detritivores had a significant positive effect on the relative contribution rate of detritus to the diet of predators, even after confounding effects were controlled for. In addition, the number of functional feeding groups of detritivores was significantly correlated with the contribution of detritus to the diet of predators, and particularly, high functional diversity of detritivores increased the energy flow from the detritus to the detritivores compared to the flow from the detritivores to the predators. It is likely that high functional diversity within species induces complementarity effects on decomposition and this may be a potential mechanism leading to diversity effects on the energy flow in detritus‐based food web. Our study is the first to demonstrate bottom‐up effects of detritivore diversity on energy flow in food webs.

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)0.001
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 score0.017
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0020.000

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 head0.006
GPT teacher head0.207
Teacher spread0.201 · 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

Citations13
Published2016
Admission routes1
Has abstractyes

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