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Record W2590854460 · doi:10.1093/plankt/fbx002

Association between clonal diversity and species diversity in subarctic zooplankton communities

2017· article· en· W2590854460 on OpenAlexafffundabout
Kaven Dionne, Caroline José, Alain Caron, France Dufresne

Bibliographic record

VenueJournal of Plankton Research · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicAquatic Ecosystems and Phytoplankton Dynamics
Canadian institutionsUniversité de MonctonUniversité du Québec à Rimouski
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSubarctic climateDaphnia pulexZooplanktonDaphniaEcologyBiologyBiodiversityArcticSpecies diversityGenetic diversityPopulation

Abstract

fetched live from OpenAlex

Genetic diversity in a single species and species diversity (SD) in a whole community are often interrelated because they are frequently influenced by a common factor or interact directly with each other. This study examined the effect of environmental and spatial factors on the distribution of clonal diversity in Daphnia pulex and of SD in zooplankton communities in subarctic ponds, and tested the association of these diversity levels when the effect of these factors was partitioned out. Forty-one ponds in the Kuujjuarapik area, Nunavik, Canada, were characterized according to their physico-chemical properties, Daphnia clonal composition and zooplankton assemblage. Variation in clonal distribution in Daphnia was mostly explained by water conductivity, pH and depth. Conductivity, altitude, temperature, phaeopigments, surface of the pond and spatial factors accounted for 12% of the variation in the zooplankton community. There was a positive association between diversity levels suggesting that clonal diversity and SD affected each other directly, probably via the effect of clonal diversity in Daphnia on the rest of the zooplankton community. Our results suggest that a better understanding of the interaction between genetic and SD in subarctic and arctic environments will be important to predict biodiversity changes resulting from climate change.

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)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0000.001
Open science0.0010.004
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.118
GPT teacher head0.323
Teacher spread0.205 · 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

Citations2
Published2017
Admission routes3
Has abstractyes

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