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Record W3190346947

Spatiotemporal Shifts in Cyanobacterial Communities in a Northern Temperate Watershed – Applications of Next-Generation Sequencing and Implications for Monitoring and Climate Change Adaptation

2021· dissertation· en· W3190346947 on OpenAlexfundno aff
Ellen S. Cameron

Bibliographic record

VenueUWSpace (University of Waterloo) · 2021
Typedissertation
Languageen
FieldEnvironmental Science
TopicMicrobial Community Ecology and Physiology
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaNatural Resources CanadaEnvironment and Climate Change Canada
KeywordsTemperate climateWatershedAdaptation (eye)Climate changeGeographyClimate change adaptationEcologyEnvironmental scienceEnvironmental resource managementBiologyComputer science
DOInot available

Abstract

fetched live from OpenAlex

Cyanobacteria, a group of photosynthetic bacteria, threaten water quality and drinking water resources globally through the production of potent toxins and the formation of dense surface blooms. These bloom events are increasing in intensity, frequency, and duration due to warming climates and anthropogenic land use and require monitoring programs for water quality management. However, cyanobacteria vary both spatially and temporally and if sampling efforts do not reflect this variation, potentially toxic organisms may be undetected or underestimated. This thesis explores the spatiotemporal trends of cyanobacterial communities in a series of interconnected, oligotrophic lakes in a northern temperate watershed (Turkey Lakes Watershed; North Part, ON) using next-generation sequencing (NGS). \nNext-generation sequencing of marker genes allows for rapid characterization of environmental communities and has become increasingly accessible, allowing for interdisciplinary applications. Optimal approaches in data handling and analysis are debated due to key challenges arising due to the data structure. Amplicon sequencing samples will vary in library sizes—the total number of reads—but this variation is not biologically meaningful and library sizes must be normalized to account for these differences. Rarefying, the process of subsampling to a normalized size, is frequently used to account for this variation but has been highly criticized due to the omission of valid data. To address the concerns of data omission, repeated iterations of rarefying were evaluated as a normalization technique in diversity analyses (Chapter 2). Repeatedly rarefying was demonstrated to characterize variation introduced through subsampling for applications in diversity analyses. This technique was implemented in the subsequent analysis of cyanobacterial communities in this thesis. \nCyanobacterial communities are dynamic exhibiting heterogeneity in their spatial and temporal distribution in lakes. This spatiotemporal variation is driven by environmental conditions and physical characteristics (e.g., cell size, cell density) of taxa and can subsequently create challenges in monitoring. The spatiotemporal variation of cyanobacterial communities was characterized on both a diurnal scale (Chapter 3) and seasonal scale (Chapter 4) through amplicon sequencing of the V4 region of the 16S rRNA gene. Although the lakes in this study did not have visible bloom biomass, cyanobacterial sequences comprised up to 56% of the bacterial community and were frequently dominated by sequences classified as picocyanobacterial genera, which range from 0.2 – 2.0 µm in diameter. This dominance exemplifies the inability to rely on visual detection as a monitoring technique. In both studies, trends in the spatiotemporal variation varied between the lake sites due to differences in morphometry, thermal stratification and surrounding landscape processes demonstrating the impact of system specific characteristics on cyanobacterial dynamics. In combination with warming climates in temperate zones, cyanobacterial growth habits may change and appear as significant components of the bacterial community as early as May in oligotrophic lakes contrasting the previous perception of peak occurrence in the late summer requiring monitoring protocols to re-evaluate appropriate sampling time frames in temperate systems. \nThe research conducted in this thesis identifies key areas for developing ecologically relevant sampling guidelines for cyanobacterial monitoring in lakes. Monitoring protocols are frequently developed from characteristics of common bloom forming taxa resulting in reliance on visual observation of biomass at the surface of the water and focusing sampling efforts to the summer months when blooms typically occur. This research demonstrated the flaws in these assumptions and provides a discussion on appropriate recommendations. Specifically, cyanobacterial community dynamics were demonstrated to be impacted by system specific characteristics and sampling protocols must be tailored to reflect the (i) physicochemical characteristics of the system, and (ii) ecological community structure. The research presented herein demonstrates the need for re-evaluation of current guidelines due to shifts in cyanobacterial growth habits in response to warming climates, and the reported dominance of picocyanobacteria which may impose toxicity risks despite the absence of visible biomass.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.029
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.050
GPT teacher head0.234
Teacher spread0.184 · 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 source (direct Gemma or distilled Codex), 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
Published2021
Admission routes1
Has abstractyes

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