Using Long‐Term Ecological Datasets to Unravel the Impacts of Short‐Term Meteorological Disturbances on Phytoplankton Communities
Bibliographic record
Abstract
ABSTRACT Extreme meteorological events such as storms are increasing in frequency and intensity, but our knowledge of their impacts on aquatic ecosystems and emergent system properties is limited. Understanding the ecological impacts of storms on the dynamics of primary producers remains a challenge that needs to be addressed to assess the vulnerability of freshwater ecosystems to extreme weather conditions and climate change. One promising approach to gain insights into storm impacts on phytoplankton community dynamics is to analyse long‐term monitoring datasets. However, such an approach requires disentangling the impacts of short‐term meteorological disturbances from the effects of the seasonal trajectories of meteorological conditions. To this end, we applied boosted regression tree models to phytoplankton time series from eight relatively large lakes on four continents, coupled with a procedure adapted to detect and quantify rare events. Overall, the patterns and potential drivers we identified provide important insights into the responses of lakes to short‐term meteorological events and highlight differences in the response of phytoplankton communities according to lake morphological characteristics. Our results indicated that deepened thermoclines and lake‐specific combinations of drivers describing altered thermal structures caused deviations from the typical trajectories of seasonal phytoplankton succession. For shallow polymictic lakes, shifts in phytoplankton succession also depended on changes in light availability. Overall, our study highlights the value of long‐term monitoring to improve our understanding of phytoplankton sensitivity to short‐term meteorological disturbances.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".