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Record W4412929002 · doi:10.1101/2025.08.01.668077

Heatwaves and cold snaps alter host-parasite population dynamics in the <i>Daphnia magna-Ordospora colligata</i> system

2025· preprint· en· W4412929002 on OpenAlexaff
Niamh McCartan, Louise Bezborodko, Floriane O’Keeffe, Pepijn Luijckx

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2025
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEvolution and Genetic Dynamics
Canadian institutionsTrinity College
Fundersnot available
KeywordsHost (biology)Daphnia magnaParasite hostingBiologyDaphniaPopulationDynamics (music)EcologyZoologyDemographyPhysicsChemistryComputer scienceSociology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.005
GPT teacher head0.215
Teacher spread0.210 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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