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Record W3197516392 · doi:10.1093/plankt/fbab050

Taxonomic and functional differences between winter and summer crustacean zooplankton communities in lakes across a trophic gradient

2021· article· en· W3197516392 on OpenAlexaff
Kirill Shchapov, Paul Wilburn, Andrew J. Bramburger, Greg M. Silsbe, Leif G. Olmanson, Christopher J. Crawford, Elena Litchman, Ted Ozersky

Bibliographic record

VenueJournal of Plankton Research · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicIsotope Analysis in Ecology
Canadian institutionsEnvironment and Climate Change Canada
FundersNational Science Foundation of Sri Lanka
KeywordsZooplanktonTrophic levelEcologyAbundance (ecology)OverwinteringBiologyEutrophicationCommunity structurePredationPhytoplanktonEnvironmental scienceNutrient

Abstract

fetched live from OpenAlex

Abstract Despite increasing interest in winter limnology, few studies have examined under-ice zooplankton communities and the factors shaping them in different types of temperate lakes. To better understand drivers of zooplankton community structure in winter and summer, we sampled 13 lakes across a large trophic status gradient for crustacean zooplankton abundance, taxonomic and functional community composition and C/N stable isotopes. Average winter zooplankton densities were one-third of summer densities across the study lakes. Proportionally, cladocerans were more abundant in summer than winter, with the opposite pattern for calanoids and cyclopoids. In green (eutrophic) lakes, zooplankton densities were higher under the ice than in brown (dystrophic) and blue (oligotrophic) lakes, suggesting better conditions for zooplankton in productive lakes during winter. Overall, zooplankton communities were more similar across lakes under the ice than during the open water season. Feeding group classification showed a decrease in herbivore abundance and an increase in predators from summer to winter. C/N stable isotope results suggested higher lipid content in overwintering zooplankton and potentially increased reliance on the microbial loop by winter zooplankton. Our results show substantial variation in the seasonality of zooplankton communities in different lake types and identify some of the factors responsible for this variation.

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)0.002
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 score0.008
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 integrity0.0000.001
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 head0.093
GPT teacher head0.333
Teacher spread0.240 · 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

Citations19
Published2021
Admission routes1
Has abstractyes

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