Bloom or bust: Search for phytoplankton community drivers using long-term time-series observations and field measurements in a model Great Lakes estuary
Notice bibliographique
Résumé
As sentinels of climate change and other anthropogenic effects, freshwater lakes are experiencing ecosystem disruptions at every level of the food web, beginning with the phytoplankton. One of the major threats to waterbodies around the world are cyanobacterial harmful algal blooms (HABs) resulting from anthropogenic eutrophication and exacerbated by climate change. Muskegon Lake, a drowned river mouth Great Lakes estuary on the east coast of Lake Michigan, is no exception and was declared an Area of Concern by the EPA in 1987 with nuisance algal blooms cited as a beneficial use impairment. Using long-term data and additional 2019 sampling, we performed multivariate and univariate analyses on environmental and phytoplankton data in order to visualize variation over the study period. The objective of this thesis was two-fold: 1) we aimed to quantify changes in HAB prevalence and community composition in Muskegon Lake over 16 years (2003-2019) and explore the environmental factors potentially driving the change, and 2) recognizing 2019 as a year of anomalous weather patterns, we explored the effects of heavy precipitation and cool temperatures on the phytoplankton community and cyanobacterial HABs in particular. For our first objective, we used two long-term data sets: the Muskegon Lake Observatory (MLO; 2011-2019) buoy data and data from the Muskegon Lake monitoring program (MLMP; 2003-2019) in addition to 2019 sampling. Principal component analysis (PCA) was used to visualize variation and patterns in environmental variables over time, non-metric multidimensional scaling (NMDS) was used to assess associations between HAB community composition and environmental variables, and a univariate comparison (paired T-test and Wilcoxon Rank Sum test) was made on environmental variables between a group of severebloom years and a group of mild-bloom years. Analyses revealed that, despite generally rising water temperatures, a reduction in nutrient concentrations likely led to decreases in HAB abundance over time. Additionally, HAB community composition appears to be driven by nutrient form and concentration and temperature, with Microcystis often being dominant. These results indicate that, while increasing temperatures in the future may enhance HABs and alter their community composition, it may be possible to manage their severity through sustained nutrient reductions in the watershed. For our second objective, we used biweekly sampling in 2019 at three locations on the lake to disentangle the connections between environmental conditions and phytoplankton community composition using multivariate analyses. Additionally, the long-term datasets from the MLO and MLMP allowed us to compare 2019 to previous years to capture how the aberrant weather of 2019 affected the phytoplankton community of Muskegon Lake. With the prevailing uncertainty regarding how future climate scenarios will impact HABs, knowledge of phytoplankton composition in years that experience anomalous weather patterns may be valuable. In 2019, the Muskegon Lake watershed experienced record-breaking amounts of precipitation and a relatively cool temperature regime. The cool spring and late onset of stratification delayed phytoplankton growth overall. Unexpectedly, diatoms were the dominant division throughout the entire 2019 study period, and the cyanobacteria community was diverse but negligible compared to previous years, likely as a result of frequent rain events that reduced residence time and cool temperatures that inhibited their growth. These results may provide insight into how phytoplankton communities, diatoms and HABs in particular, in temperate freshwater lakes may respond to a future climate change scenario in which precipitation is frequent and intense, water levels are highly variable, and some regions experience unexpected cooling.
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 enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».