Heatwaves and cold snaps alter host-parasite population dynamics in the <i>Daphnia magna-Ordospora colligata</i> system
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».