Can zooplankton on the North American Great Plains “keep up” with climate‐driven salinity change?
Bibliographic record
Abstract
Abstract The Great Plains of North America are projected to become more arid over the next century. Paleolimnological studies show that lake salinity levels in this region are tightly linked with climate, and that lakes will become more saline as the climate becomes drier. One group of organisms that might be affected by increased salinity levels are the zooplankton. Although recent studies suggest that zooplankton can evolve to tolerate small increases in salinity over short time periods, few studies have examined how they respond when experiencing large increases over longer time frames. For this study, we used resurrection ecology to examine how a common zooplankter,Ceriodaphnia dubia, has responded to long‐term salinity change in Moon Lake, North Dakota over the last 150 yrs. We ran experiments to determine the salinity levels that inducedC. dubiaeggs to hatch and we ran toxicity experiments to determine the salinity tolerance of adults. These experiments showed thatC. dubiaeggs hatched in saltier water during periods of drought and in fresher water during wet periods. Similarly, our toxicity experiments showed that EC50 values forC. dubiawere higher during episodes of drought. The presence ofC. dubiaeggs throughout the sediment core during the last 150 yrs combined with their ability to adapt to changing salinity levels, suggests that they will likely be able to persist through coming droughts. Further studies will be needed to determine if other common zooplankton species in Great Plains' lakes are similarly adaptable.
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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 | 0.000 | 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 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".