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Record W2994459327 · doi:10.1111/ele.13435

Mismatched outcomes for biodiversity and ecosystem services: testing the responses of crop pollinators and wild bee biodiversity to habitat enhancement

2019· letter· en· W2994459327 on OpenAlexaff
Charlie Nicholson, Kimiora L. Ward, Neal M. Williams, Rufus Isaacs, Keith S. Mason, Julianna K. Wilson, Julia Brokaw, Larry J. Gut, Nikki L. Rothwell, Thomas J. Wood, Sujaya Rao, George D. Hoffman, Jason Gibbs, Robbin W. Thorp, Taylor H. Ricketts

Bibliographic record

VenueEcology Letters · 2019
Typeletter
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsUniversity of Manitoba
FundersDivision of Graduate EducationNational Institute of Food and AgricultureNational Science Foundation
KeywordsBiodiversityEcosystem servicesPollinationAbundance (ecology)PollinatorHabitatAgricultural biodiversityAgroforestryEcosystemEcologySpecies richnessAgricultureBiologyGeographyPollen

Abstract

fetched live from OpenAlex

Supporting ecosystem services and conserving biodiversity may be compatible goals, but there is concern that service-focused interventions mostly benefit a few common species. We use a spatially replicated, multiyear experiment in four agricultural settings to test if enhancing habitat adjacent to crops increases wild bee diversity and abundance on and off crops. We found that enhanced field edges harbored more taxonomically and functionally abundant, diverse, and compositionally different bee communities compared to control edges. Enhancements did not increase the abundance or diversity of bees visiting crops, indicating that the supply of pollination services was unchanged following enhancement. We find that actions to promote crop pollination improve multiple dimensions of biodiversity, underscoring their conservation value, but these benefits may not be spilling over to crops. More work is needed to identify the conditions that promote effective co-management of biodiversity and ecosystem services.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0050.002
Insufficient payload (model declined to judge)0.0060.002

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 head0.042
GPT teacher head0.207
Teacher spread0.165 · 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 source (direct Gemma or distilled Codex), 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

Citations54
Published2019
Admission routes1
Has abstractyes

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