Climate change, diapause termination and zooplankton population dynamics: an experimental and modelling approach
Bibliographic record
Abstract
Summary 1. Earlier spring warming as predicted for climate change will alter combinations of water temperature and photoperiod that act as emergence cues for zooplankton resting stages. As a result, water temperature cue thresholds will be experienced at shorter photoperiods, a variable independent of weather variations. Also, light intensity, another potentially important cue for zooplankton emergence, could decrease in many lakes if symptoms of climate change resemble those of eutrophication. 2. We designed a laboratory experiment to test the effects of three factors, temperature (6, 9 and 12 °C), photoperiod (13L : 11D and 16L : 8D) and light intensity (20 and 35 μE m−2 s−1) on hatchling abundance and timing of hatching of daphniids (Daphnia ambigua) and rotifers (Keratella spp. and Synchaeta pectinata) from resting eggs. Further, we investigated the implications of potential changes in hatching dynamics, following variations in hatching cues, on zooplankton spring population development using predator–prey simulation models. 3. For hatchling abundance and timing of hatching, photoperiod had a significant effect for D. ambigua but not rotifers. Daphnia ambigua hatchling abundance decreased by 50% when incubated at conditions mimicking early spring (12 °C + 13‐h photoperiod) compared to a later spring (12 °C + 16‐h photoperiod). Light intensity has a significant effect only for S. pectinata, producing greater hatchling abundance at lower light intensity. 4. Simulation models suggest that in contrast to a later spring, an early warming produces a shift in spring zooplankton community composition, from daphniid to rotifer dominance. These patterns are primarily driven by differential zooplankton emergence response with variations in temperature–photoperiod cues. 5. Overall, our laboratory experiments and simulation models suggest that lakes with strong dependence on the ‘resting egg‐bank’, characteristic of many shallow north‐temperate lakes or in years with low winter survivorship of adult zooplankton, may be most susceptible to climate change. Further, fewer large grazers such as daphniids with an earlier spring may result in less control of cyanobacterial blooms in eutrophic lakes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".