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Meta-analysis of cyanobacterial effects on zooplankton population growth rate: species-specific responses

2008· article· en· W2047286451 on OpenAlexaff
Angeline R. Tillmanns, Alan E. Wilson, Frances R. Pick, Orlando Sarnelle

Bibliographic record

VenueFundamental and Applied Limnology / Archiv für Hydrobiologie · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicAquatic Ecosystems and Phytoplankton Dynamics
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsZooplanktonBiologyEcologyCyanobacteriaPopulationPopulation growthGrowth rateEnvironmental scienceFisheryDemographyMathematicsBacteria

Abstract

fetched live from OpenAlex

We analyzed a large data set of laboratory experiments to examine the effects of cyanobacteria containing or lacking intracellular toxic metabolites and of different morphology on zooplankton population growth rates across multiple genera and species of cladocerans, rotifers and cyanobacteria. Twenty-one of the 29 zooplankton species maintained positive growth rates when fed a diet containing cyanobacteria even though cyanobacteria were a poor food source for half of the zooplankton species tested relative to a diet containing only green algae and/or flagellates. Differences among zooplankton species could not be explained by grazer species body lengths, even when experiments were restricted to those that used only filamentous cyanobacteria. Single-celled cyanobacteria were more detrimental to a larger number of zooplankton species compared to filamentous or chroococcoid colonial cyanobacteria. We also found no clear effect of putative cyanobacterial toxins on the growth of seven zooplankton species but we did detect a negative effect for the largest cladoceran species, Daphnia magna. Among the cyanobacterial genera, Microcystis had the largest negative effect on zooplankton population growth and there was no consistent difference between M. aeruginosa strains that produced microcystins and those that did not. Our results highlight the large variation in species-specific responses of zooplankton to cyanobacteria. Although cyanobacterial toxicity and mechanical interference may be important drivers in particular cyanobacteria-zooplankton interactions, we did not find general support for these mechanisms through the use of this meta-analysis.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.506
Threshold uncertainty score0.953

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0010.000

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 head0.048
GPT teacher head0.242
Teacher spread0.194 · 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.

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

Citations160
Published2008
Admission routes1
Has abstractyes

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