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Enregistrement W7055146522

Characterization of Micronutrient Dependent Growth by Several Temperate Freshwater Phytoplankton

2021· article· en· W7055146522 sur OpenAlexaboutno aff

Notice bibliographique

RevueScholars Commons (Wilfrid Laurier University) · 2021
Typearticle
Langueen
DomaineEngineering
ThématiqueParticle accelerators and beam dynamics
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésPhytoplanktonAlgal bloomCyanobacteriaMicronutrientNutrientDominance (genetics)Primary producersBloom
DOInon disponible

Résumé

récupéré en direct d'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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: Expérimental (laboratoire)
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,050
Score d'incertitude au seuil0,821

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,001
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,006
Tête enseignante GPT0,173
Écart entre enseignants0,167 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2021
Routes d'admission1
Résumé présentoui

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