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Record W1998626862 · doi:10.1093/plankt/fbu094

Environmental filtering of crustacean zooplankton communities in fishless boreal lakes: expectations and exceptions

2014· article· en· W1998626862 on OpenAlexafffundabout
M.U. Mohamed Anas, Kenneth A. Scott, Björn Wissel

Bibliographic record

VenueJournal of Plankton Research · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicAquatic Ecosystems and Phytoplankton Dynamics
Canadian institutionsSaskatchewan Ministry of AgricultureUniversity of Regina
FundersMinistry of EnvironmentMinistry of Environment - SaskatchewanUniversity of Regina
KeywordsZooplanktonEcologyAbiotic componentDaphnia pulexInvertebrateDominance (genetics)Biotic componentDaphniaBiologyPredationEnvironmental science

Abstract

fetched live from OpenAlex

To test the paradigm that large-bodied zooplankton species dominate in fishless lakes, we evaluated crustacean zooplankton composition of 53 fishless boreal lakes across a broad geographic scale (north-west Saskatchewan, Canada) in relation to contemporary environmental factors. Lake productivity, acid–base status and intensity of invertebrate predation were the three major environmental filters of crustacean zooplankton composition of the survey lakes, while lake morphometry and drainage basin characteristics likely had indirect effects by influencing the variations in above environmental factors. The traditionally expected dominance of large-bodied zooplankton species in fishless conditions only occurred in well-buffered shallow lakes with higher productivity and intense invertebrate predation. Daphnia pulex was dominant in more eutrophic lakes, while higher abundances of Holopedium gibberum were favored by relatively mesotrophic conditions. In exception to the traditional view, small-bodied species dominated in 57% of survey lakes as influenced by interactions of biotic and abiotic factors. Bosmina longirostris and cyclopoid copepods dominated less productive, deeper lakes with low invertebrate predation. Predominance of Leptodiaptomus minutus was supported under unfavorable environmental conditions for other species, i.e. poor buffering capacity and/or low calcium concentration. The present study demonstrates that exceptions to the generally expected species composition in fishless lakes can be caused by interactions among abiotic and biotic factors.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.066
Threshold uncertainty score0.574

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 studies0.0000.000
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 head0.030
GPT teacher head0.293
Teacher spread0.263 · 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

Citations11
Published2014
Admission routes3
Has abstractyes

Explore more

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