The Influence of Drought and Re-Acidification on Zooplankton Emergence from Resting Stages
Bibliographic record
Abstract
The recovery of biota in lakes damaged by cultural acidification may be influenced by a number of factors, including El Nino-related droughts. Long-term zooplankton records from Swan Lake, Sudbury, Canada, indicated that crustacean species richness temporarily increased from an annual mean of 10 species to 18 species in the year following a two-year drought and subsequent re-acidification of the lake. We hypothesized that this unexpected increase in richness was the result of an emergence of zooplankton from resting stages that were historically deposited in the lake sediments. The drought and re-acidification event resulted in several changes in the lake that may have triggered the emergence of zooplankton, including desiccation of littoral sediments (and the zooplankton resting stages residing there), increased water clarity, increased profundal temperature, and increased oxygen concentration in the bottom waters. Using in situ emergence traps, we investigated the influence of desiccation, light, temperature, and oxygen concentration on the emergence of crustacean zooplankton from resting stages. Responses were species-specific; four taxa had higher emergence when sediments were dried over the winter, the emergence of six taxa was influenced by temperature, and the emergence of three taxa was influenced by light. This suggests that climatic events, such as droughts, that alter the physical and chemical properties of lakes may alter zooplankton communities by triggering the emergence of resting stages residing in the lake sediments. This is particularly significant if, as in Swan Lake, emerging zooplankton are faced with inhospitable water quality. Under this scenario, emergence may act to deplete the egg bank, reducing the number of potential colonists available to repopulate the lake when environmental conditions improve.
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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) | 0.001 | 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 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".