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Record W2111915933 · doi:10.1111/ele.12146

Nutrient loading associated with agriculture land use dampens the importance of consumer‐mediated niche construction

2013· article· en· W2111915933 on OpenAlexafffund
Daniel E. Spooner, Paul C. Frost, Helmut Hillebrand, Michael T. Arts, Olivia Puckrin, Marguerite A. Xenopoulos

Bibliographic record

VenueEcology Letters · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicAquatic Invertebrate Ecology and Behavior
Canadian institutionsEnvironment and Climate Change CanadaTrent University
FundersOntario Ministry of Economic Development and Innovation
KeywordsEcologyBenthic zoneEcosystemNutrientColonisationBiologyBiodiversityMusselPrimary producersNicheEnvironmental sciencePhytoplanktonColonization

Abstract

fetched live from OpenAlex

The linkages between biological communities and ecosystem function remain poorly understood along gradients of human-induced stressors. We examined how resource provisioning (nutrient recycling), mediated by native freshwater mussels, influences the structure and function of benthic communities by combining observational data and a field experiment. We compared the following: (1) elemental and community composition (algal pigments and macroinvertebates) on live mussel shells and on nearby rocks across a gradient of catchment agriculture and (2) experimental colonisation of benthic communities on live vs. sham shells controlling for initial community composition and colonisation duration. We show that in near pristine systems, nutrient heterogeneity mediated by mussels relates to greater biodiversity of communities, which supports the notion that resource heterogeneity can foster biological diversity. However, with increased nutrients from the catchment, the relevance of mussel-provisioned nutrients was nearly eliminated. While species can persist in disturbed systems, their functional relevance may be diminished or lost.

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 categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score0.996

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.001

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 head0.008
GPT teacher head0.183
Teacher spread0.175 · 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

Citations62
Published2013
Admission routes2
Has abstractyes

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