Under-Ice Ecology of Aquatic Insects in North-Temperate Lentic Ecosystems
Bibliographic record
Abstract
Winter in north-temperate lentic ecosystems is characterized by cold temperatures, limited light availability, and critically in aquatic ecosystems, periods of ice cover and hypoxia. Despite these challenging conditions, many aquatic invertebrates remain active throughout winter. Climate change is decreasing ice-coverage duration, with ice forming later and melting earlier. Current research on winter aquatic ecology is lacking, particularly on aquatic macroinvertebrates, a diverse group important to ecosystem function. My thesis examines how winter conditions shape aquatic invertebrate communities and the potential consequences of milder winters. My first set of experiments investigated why Sympetrum vicinum dragonflies exhibit clutch-splitting behaviour, ovipositing eggs in both terrestrial and aquatic environments where the eggs then spend the winter. I concluded that the dragonflies engage in risk-spreading in response to unpredictable levels of danger present in each environment; in the aquatic environments egg-depredation by winter-active detritivores such as caddisflies, and in the terrestrial environment the risk that eggs will not be inundated by spring flooding and fail to hatch. Next I examined patterns of overwinter decline in dragonfly community samples from Ontario and Michigan, then tested hypoxia as a possible driver of decline. I concluded that a large-bodied top-invertebrate-predator species, Anax junius, is likely experiencing hypoxia-driven winterkill in some of its overwintering habitats. My third set of experiments examined the effects of a prolonged warm pre-winter period (late onset winter) on overwinter survival as well as spring dispersal ability and reproductive success in Notonecta undulata. I found that late-onset winter conditions increase male survivorship over winter, as well as advanced the timing of egg laying and dispersal in females, although total egg production and dispersal attempts were not affected. Finally, I used open and closed greenhouses to manipulate ice coverage duration on in-ground pond mesocosms. I found that closed greenhouses successfully advanced the date of ice-off as well as decreased ice thickness in comparison to the open greenhouses. My thesis provides new insight into how winter affects ecological processes such as behaviour, dispersal, reproduction, survival, and community structure, as well as predictions of how these processes might change in response to milder winters.
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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 | 0.001 | 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) | 0.000 | 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".