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Record W2156544188 · doi:10.1139/er-2015-0027

A review of the effects of <i>Bythotrephes longimanus</i> and calcium decline on zooplankton communities — can interactive effects be predicted?

2015· review· en· W2156544188 on OpenAlexaffvenueabout
Shakira Azan, Shelley E. Arnott, Norman D. Yan

Bibliographic record

VenueEnvironmental Reviews · 2015
Typereview
Languageen
FieldEnvironmental Science
TopicAquatic Ecosystems and Phytoplankton Dynamics
Canadian institutionsMinistry of the Environment, Conservation and ParksQueen's University
Fundersnot available
KeywordsZooplanktonEcologyDaphniaBiology

Abstract

fetched live from OpenAlex

Anthropogenic stressors including acid deposition, invasive species, and calcium (Ca) decline have produced widespread damage to Canadian Shield lakes, especially to their zooplankton communities. Here, we review current knowledge on the individual effects on zooplankton by the non-indigenous predator Bythotrephes longimanus and Ca decline; we identify knowledge gaps in this literature and examine the likely interactive impacts of Bythotrephes invasions and Ca decline on zooplankton. The negative impacts of Bythotrephes longimanus on zooplankton communities are well known, whereas current understanding of the effects of declining Ca on zooplankton is restricted to Daphnia spp.; hence, there is a large knowledge gap on how declining Ca may affect zooplankton communities in general. The co-occurring impacts of Bythotrephes and declining Ca have rarely been studied at the species level, and we expect daphniids, particularly Daphnia retrocurva and Daphnia pulicaria, to be the most sensitive to both stressors. We also expect a synergistic negative interaction on cladocerans in lakes with both stressors, leaving a community dominated by Holopedium glacialis and (or) copepods. Our predictions form testable hypotheses but since species and ecosystem response to multiple stressors are difficult to predict, we may actually see ecological surprises in Canadian Shield lakes as Bythotrephes continues to spread and Ca levels continue to fall.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.786
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.025
GPT teacher head0.287
Teacher spread0.262 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations30
Published2015
Admission routes3
Has abstractyes

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