Bloom or bust: Search for phytoplankton community drivers using long-term time-series observations and field measurements in a model Great Lakes estuary
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".