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Record W7055146522

Characterization of Micronutrient Dependent Growth by Several Temperate Freshwater Phytoplankton

2021· article· en· W7055146522 on OpenAlexaboutno aff

Bibliographic record

VenueScholars Commons (Wilfrid Laurier University) · 2021
Typearticle
Languageen
FieldEngineering
TopicParticle accelerators and beam dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsPhytoplanktonAlgal bloomCyanobacteriaMicronutrientNutrientDominance (genetics)Primary producersBloom
DOInot available

Abstract

fetched live from OpenAlex

Harmful algal blooms (HABs) are a growing problem in many freshwater waterbodies in Canada and around the world. HABs have wide-ranging impacts to ecosystems and economies. Since the 1970s, the primary focus of policies with respect to curbing HABs has been to lowering the inputs of P into waters. However, reports of HABs continue to increase even with all these P-removing policies in place. Some jurisdictions have chosen to focus on N-removal instead or in conjunction with P-removal to help with HABs. However, because some cyanobacteria have the ability to fix atmospheric N2,N-removal might only lead to the dominance of N-fixing HABs. Recent research has suggested that micronutrients, such as Fe and Co, are key in algal bloom development and biochemistry.\nThe goals of this thesis are to assess their dependence on micronutrients and investigate how micronutrients impact algal bloom growth, to assess possible mitigation strategies and to explore new techniques to help in answering these questions. Chapters 2 and 6 explore how Fe and Co may play a role in phytoplankton bloom development and growth of N-fixing cyanobacteria. Chapters 4 and 5 explore how Fe(II) removal might impact cyanobacterial growth and affect HABs. Chapters 3 and 7 use modified and new techniques to study algal growth by using stable isotopes to characterize growth of phytoplankton in culture and using a smartphone app to quantify algal biomass.\nWe found the nutrient threshold concentrations (below which growth ceases) and relative affinities for Fe at low concentrations for seven different phytoplankton. We determined that N-fixing cyanobacteria in N-replete conditions have the lowest iron threshold at 76±2pM, green algae have a mean iron threshold of 245±5pM, the non-fixer Microcystis aeruginosa has a threshold of 663±17pM, and N-fixing cyanobacteria that are grown without nitrate have a mean iron threshold of 736±17pM. At low Fe concentrations, Microcystis aeruginosa had the highest affinity, followed by N-fixing cyanobacteria grown in N-replete conditions, then N-fixing cyanobacteria who had to fix N2 and green algae had the lowest affinity at low Fe concentrations. These findings reinforce the importance of Fe in HABs growth and development and provide insight into how certain species and species types could dominate at low concentrations of Fe.\nBy using a simple mixing-model, we were able tease apart the isotopic composition of newly accumulated biomass from the measured bulk samples, we found that estimated isotopic composition of new biomass differed up to 15‰ from the measured bulk sample. We characterized the growth of cultures of ten different phytoplankton species using the new estimates of isotopic composition and found that fractionation factors between dissolved nutrient and biomass vary largely with time and in diazotrophic species, the fractionation factor is much higher than previously reported. We used these newly estimated isotopic compositions and fractionation factors and correlated them to instantaneous growth rates and found moderate correlations among growth rates and carbon isotopic fractionation factors.\nFe is an important nutrient from algal bloom development and recent research has pointed specifically to Fe(II). We assessed the impacts of Fe(II) removal on cyanobacterial growth using a colourometric Fe(II) chelator, ferrozine (FZ). We found that adding FZ in a variety of FZ:Fe molar ratios does not impact the growth of phytoplankton in the conditions we used. Therefore, we must continue to explore the efficacy and impacts of Fe(II) removal on HABs in the environment.\nThis work let to further exploration of the properties of FZ while other chelators, like citrate and EDTA are present. We also studied the impacts of other metal chelators on the formation of the Fe-FZ complex. While citrate did not inhibit the formation of theFe-FZ complex, EDTA might play an Fe oxidizing role in cell medium.\nLike Fe, increases in Co can increase growth and N-fixation in cyanobacteria. How-ever, unlike Fe, the mechanism of how this occurs is largely unknown. We investigated the impacts of increasing Co concentrations on the growth and heterocyst abundance of filamentous N-fixing cyanobacteria. Our results show that increasing Co significantly increases the percentage of heterocysts found in cultures of N-fixing filamentous cyanobacteria. We also combined our culture studies with field and literature data and found similar results. This may point to a possible role of Co in heterocyst synthesis and thus N-fixation and how that might impact algal bloom growth and development, especially in environments with low N.\nAll of this work relied on the optical measurement of algal biomass using a spectrophotometer. However, this method is not feasible when it comes to field measurements or engaging citizens to participate in monitoring efforts. We devised an unique and simple strategy to accurately measure algal biomass using a smartphone app, using the RGB colour model. A good linear relationship between algal absorbance at 750nm using a spectrophotometer and (R + B + G)/G was found. We also correlated this relationship to cell numbers in culture at a species level. This method offers a promising detection method for algal biomass determination with simple operation, fast response and low cost.\nThis work highlights the importance of Fe and Co to the growth and proliferation of HABs and attempts to use this micronutrient dependence to control the problem. We also show the use of new and innovative models and methods to make the characterization of this complex problem more efficient and accurate.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.050
Threshold uncertainty score0.821

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 communication0.0000.001
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 head0.173
Teacher spread0.167 · 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 designBench or experimental
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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