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Record W7052221070

Relationship between toxic cyanobacterial blooms, physico-chemical factors and multiple source excreta contamination in affected watershed

2013· other· en· W7052221070 on OpenAlexvenueno aff

Bibliographic record

VenueLibrary and Archives Canada (Government of Canada) · 2013
Typeother
Languageen
FieldEngineering
TopicElectromagnetic Compatibility and Measurements
Canadian institutionsnot available
Fundersnot available
KeywordsEutrophicationMicrocystisNutrientBayPopulationMicrocystis aeruginosaWatershedFreshwater ecosystemWater quality
DOInot available

Abstract

fetched live from OpenAlex

Missisquoi Bay (MB) is a temperate eutrophic freshwater ecosystem located in an agricultural watershed and it frequently experiences toxic Microcystis-dominated cyanobacterial blooms. Cyanobacterial population dynamics are influenced by a plethora of factors that may differ from system to system, requiring a site-specific assessment of bloom-promoting factors to design more effective bloom prevention or remediation strategies. This is the first biomonitoring study that combined data from high-throughput 16S rRNA gene amplicon sequencing, qPCR and environmental parameters from temporal and spatial samples to identify the main bloom-promoting factors. In addition, high-throughput amplicon sequencing of mitochondrial DNA genes was performed to qualitatively identify potential external sources of nutrients originating from animal excreta. Particular emphasis was placed on 1) determining whether there was a link between nutrients from external sources and cyanobacterial blooms and 2) analyzing in situ the effect of environmental factors (particularly nitrogen (N) and phosphorus (P) concentrations) on the dynamics of cyanobacterial community composition, abundance and toxicity.The concentrations of total P (TP) and total N (TN) in MB in 2009 correlated significantly with the abundance of total cyanobacterial cells, the Microcystis 16S rRNA and mcyD genes and intracellular microcystin. The results suggest that external sources of nutrients, such as surface runoff and animal excreta, played a significant role in the load of nutrients into the bay and thus in the proliferation of toxic cyanobacterial blooms. This was indicated by the detection of non-aquatic mitochondrial DNA hosts in the bay and the relationships between the pattern of surface runoff, nutrient concentrations, E. coli counts and total cyanobacterial abundance. Potential sources of nutrients from non-aquatic animal excreta in the system comprised rodents, birds, cattle and humans, indicating that efforts are required to control pollution from animal excreta in MB. During the growing season, the major cyanobacterial taxa were members of the orders Chroococcales and Nostocales. The genus Microcystis was identified as the main mcyD-carrier and main microcystin producer, hence the most problematic taxon in the cyanobacterial bloom. The correlations observed with environmental parameters suggest that increasing nutrient concentrations and TN:TP (mass) ratios approaching 11:1, coupled with an increase in temperature, promoted Microcystis-dominated toxic cyanobacterial blooms. Although the importance of nutrient ratios and absolute concentrations on cyanobacterial and Microcystis dynamics has been documented, this is the first time that an optimum TN:TP ratio for Microcystis dominance has been observed in the field. This observation provides further support to the theory that nutrient supply ratios are an important determinant of species composition in natural phytoplankton assemblages. Although the validity and prediction potential of this optimum ratio for Microcystis dominance has yet to be verified through longer-term studies, it may provide practical guidelines for nutrient management strategies to avoid the proliferation of this toxin producing cyanobacterial genus in MB.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.006
GPT teacher head0.138
Teacher spread0.132 · 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

Citations0
Published2013
Admission routes1
Has abstractyes

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