Sequential rather than interactive effects of multiple stressors as drivers of phytoplankton community change in a large lake
Bibliographic record
Abstract
Abstract Predicting how freshwater biota respond to multiple stressors is currently a major challenge facing aquatic ecologists and environmental managers. Lake Simcoe is an ideal location to test the impact of multiple stressors on aquatic biota over time, as changes in climate warming, species invasions, nutrient loading, and human population growth have occurred throughout the last three decades. In this study, we used a suite of multivariate analyses to quantify the unique and interactive effects of physico‐climatic, water quality, and biological variables in explaining phytoplankton community composition from 1986 to 2012. Changes in water quality, zooplankton abundance and community composition (following the invasion of Bythotrephes longimanus ), euphotic zone temperature and thermal stability, and the invasion of zebra mussels ( Dreissena polymorpha ) acting at different times throughout the period of record, were associated with major shifts in the phytoplankton community of Lake Simcoe: dominance of eutrophic diatoms and cyanobacteria from 1986 to 1997 shifted to chlorophytes after 1998, then again to a diverse mix of taxa represented by all major categories of algae after 2004. Contrary to expectation, we found little evidence that interactions between multiple stressors are taking place in Lake Simcoe. Instead, our results demonstrated the sequential nature of multiple stressors in influencing interannual variation in phytoplankton community structure. By occurring consecutively over time, we propose a “sequence of stressors” has influenced the ecological health of the lake over the preceding three decades. Considered by category, biological stressors, followed by water quality, then temperature and climate, explained the largest amount of total variance in the phytoplankton data. The prominence of zooplankton community changes as significant predictors of phytoplankton community change throughout the period of record suggests the top‐down effect of these herbivores are also very important. Our results highlight the importance of extensive ecosystem monitoring in determining ecosystem health. Our approach may assist in the management of other aquatic systems, where identifying when multiple stressors are not operating may simplify the management process, allowing remediation efforts to focus on the most impactful stressors as primary targets for ecosystem restoration.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".