Heatwaves and cold snaps alter host-parasite population dynamics in the <i>Daphnia magna-Ordospora colligata</i> system
Bibliographic record
Abstract
Abstract Climate change is driving more frequent and severe temperature extremes, including heatwaves and cold snaps, with growing implications for ecology and disease. Yet, our understanding of how heatwaves and cold snaps influence disease dynamics remains underexplored. Using the host Daphnia magna infected with its microsporidian microparasite Ordospora colligata, pathogen fitness and host population size were measured in experimental populations using a factorial design at four baseline temperatures (14, 17, 20 and 23°C). A heatwave or cold snap treatment with an amplitude of ±6°C was administered four weeks after measurements began and lasted for ten days. The effect of heatwaves is dependent on baseline temperature but can induce long-lasting increases in burden (>4 weeks). The impact of cold snaps were also temperature-dependent, leading to short-term increases in parasite fitness at higher temperatures. Host population size also varied in response to temperature and treatment. Importantly, burden and host density were interdependent, jointly shaping infection patterns. At lower temperatures, parasite burden and host population size were positively correlated, whereas at higher temperatures, increased host population size corresponded with reduced burden. These patterns were consistent at both individual and population levels, underscoring how individual physiological responses can scale up to impact disease dynamics across populations. Thus, extreme temperature variation can have complex, context-specific outcomes on disease dynamics. As climate extremes become more frequent, understanding these nuanced responses is critical for predicting and managing disease risk in natural populations. Author Summary We are experiencing more extreme weather events around the world, including heatwaves and cold snaps, but we don’t fully understand how these temperature extremes will affect wildlife diseases. In our study, we tested how heatwaves and cold snaps influence both parasite success and host population size using a small aquatic animal, the water flea, and its naturally occurring gut parasite. We ran experiments at four average temperatures and simulated a heatwave or cold snap by raising or lowering the temperature by 6°C for ten days. We found that heatwaves often led to long-lasting increases in parasite burden, while cold snaps caused short-term spikes in parasite fitness, especially at warmer average temperatures. These effects also depended on the baseline temperature and were linked to changes in the host population. At cooler temperatures, parasite levels increased as host populations grew, but at warmer temperatures, the opposite happened, resulting in negative density-dependence. This suggests that the impact of extreme weather on disease isn’t straightforward; it depends on when and where the event occurs. As extreme temperatures become more common with climate change, understanding these complex interactions is important for predicting disease outbreaks in the wild.
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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".