Predicting phytoplankton community dynamics: understanding water quality responses to global change
Bibliographic record
Abstract
A fundamental focus in ecology is understanding interactions between environmental heterogeneity and ecological community structure, both of which are currently undergoing unprecedented alterations due to global change. In particular, many freshwater phytoplankton communities are experiencing multiple global change stressors, altering phytoplankton community composition, biomass, and spatial distribution. I used multiple approaches to characterize the interactions between spatial distribution and community structure of phytoplankton and quantify uncertainty in predictions of phytoplankton temporal dynamics. First, I analyzed data from 51 lakes to determine the environmental drivers of phytoplankton vertical distributions across the water column for different phytoplankton groups. I show that the relative importance of environmental drivers varies according to the functional traits of each phytoplankton group. Second, I conducted whole-ecosystem experiments in a reservoir to assess phytoplankton responses to surface water mixing events, which may become more prevalent as storms increase under global change. My results demonstrate that aggregated phytoplankton biomass has inconsistent responses to mixing over the short term, but responses of morphology-based functional groups of phytoplankton to mixing are more predictable. Third, I conducted a long-term whole-ecosystem experiment to assess phytoplankton responses to changes in water column thermal gradients which are predicted to increasingly occur under global change. I found that phytoplankton depth distributions responded similarly to thermal gradient disturbance over multiple years, and changes in depth distributions were related to changes in community composition. Fourth, I produced weekly hindcasts of phytoplankton density in a lake for two years to determine the dominant sources of uncertainty in phytoplankton density predictions. I found that better estimation of current phytoplankton density improved representation of error in phytoplankton models, and incorporation of additional life history stages to model structure may improve phytoplankton predictions. Overall, my dissertation chapters demonstrate that the vertical distribution and community structure of phytoplankton are linked, and that the interaction of phytoplankton community structure with environmental heterogeneity is more predictable over longer-term (e.g., months to years) than shorter-term (e.g., days to weeks) scales. My research emphasizes that consideration of phytoplankton community dynamics and the uncertainty associated with phytoplankton predictions are needed for freshwater management under global change.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 source (direct Gemma or distilled Codex), 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".