Global warming is increasing average temperatures and causing extreme temperature fluctuations and heatwaves. These changes may affect when, where, and how often infectious disease outbreaks occur. This could have profound impacts on agriculture, human health, and wildlife.

Studying how extreme temperatures or temperature fluctuations alter infections in laboratory animals may help scientists to better understand the impact of climate change on disease. A small aquatic invertebrate, such as a water flea, is one good candidate for such studies. These tiny creatures can be grown in small glass jars in temperature-controlled aquariums.

Kunze, Luijckx et al. show that temperature fluctuations and heat waves have complex effects on parasitic infections in water fleas. In the experiments, water fleas housed with a parasite that infects them were exposed to constant temperatures, fluctuating temperatures, or three-day heatwaves, while being kept at a broad range of mean water temperatures. Then, Kunze, Luijckx et al. measured how these conditions affected the water fleas’ longevity, reproduction, and parasite infections.

This revealed that temperature variations had a unique effect on the life span, and reproduction and infection rates of the water fleas, depending on the average water temperature the animals were kept at. Heatwaves drastically increased the number of parasites in the water fleas at an average water temperature of 16 °C but had no effect at all or decreased the number of parasites at 19 °C and 22 °C, respectively. Similarly, at high average water temperatures (>24 °C), temperature fluctuations reduced the number of water fleas infected with parasites and the number of parasites in each infected flea. Moreover, the maximum temperature at which parasites were able to cause infections was 5 °C lower under fluctuating temperatures than under constant temperatures.

Kunze and Luijckx et al. show that consistent high temperatures, temperature changes, extreme weather events, and mean water temperature affect disease outcomes in water fleas. More studies are needed to assess how temperature variations change the course of diseases in other organisms and to understand the underlying mechanisms. Learning more about disease-temperature interactions will help scientists predict climate change-driven disease outbreaks.

One of the major challenges of the 21st century is understanding how infectious diseases, which have profound ecological and epidemiological impacts on human (Hotez et al., 2014), agricultural (Chakraborty and Newton, 2011), and wildlife (Harvell et al., 2019) populations, will be affected by climate change. It is now well-established that the interaction between hosts and their pathogens is sensitive to temperature (Kirk et al., 2020; Rohr et al., 2013). For example, disease transmission (Ben-Horin et al., 2013), host immunity (Dittmar et al., 2014; Rohr and Raffel, 2010), and pathogen growth (Gehman et al., 2018; Kirk et al., 2018) can increase with temperature, while other host-pathogen life-history traits such as lifespan and fecundity can decrease (Altizer et al., 2013). The interaction between temperature and multiple host and pathogen life-history traits highlights the inherent complexity of temperature effects on infectious diseases. Indeed, each host or pathogen trait may have a unique dependency on temperature and it is their combined effect (i.e., R₀, disease outbreak, virulence) that is often of interest. However, while a growing body of theoretical (Kirk et al., 2020; Rohr et al., 2013) and empirical (Ben-Horin et al., 2013; Dallas and Drake, 2016; Gehman et al., 2018; Kirk et al., 2020; Zhang et al., 2019) studies have quantified the effect of rising mean temperatures on host and pathogen traits (such as, e.g., within-host growth [Kirk et al., 2018], pathogen transmission [Kirk et al., 2019], and epidemiology [Gehman et al., 2018; Shocket et al., 2018]), the influence of variable temperature regimes such as heatwaves and temperature fluctuations remains unresolved (Claar and Wood, 2020; Rohr et al., 2013).

Climate change is predicted to increase not only mean temperatures, but also temperature fluctuations and the frequency and intensity of extreme weather events (Schär et al., 2004; Vasseur et al., 2014). Such changes in temperature variance have the potential to modify host-pathogen dynamics (Franke et al., 2019; Rohr et al., 2013). For instance, diurnal temperature fluctuations have been shown to increase malaria transmission at the lower end of the thermal range (Paaijmans et al., 2010), while short-term temperature fluctuations led to reduced transmission success due to lower filtration rates in a Daphnia-pathogen system (Dallas and Drake, 2016). The effect of extreme heat events on host and pathogen traits is also highly variable and may depend on the magnitude, duration, and intensity of the applied heatwave (Landis et al., 2012; Schreven et al., 2017; Zhang et al., 2019). In a parasitoid-insect interaction, a heatwave of 5°C resulted in greater parasitoid development while a 10°C increase reduced parasitoid growth (Schreven et al., 2017). These apparent contrasting results in response to variation in temperature (here used to refer both to fluctuating temperature regimes and extreme heat events) imply that alternate temperature regimes or exposure to temperature shifts of different magnitudes will have distinct impacts on host-pathogen interactions. Indeed, whether all temperature variation acts in the same way or leads to different disease outcomes has been identified as a key open question in the field (Rohr et al., 2013).

Here, we examine the effect of different types of temperature variation on host-pathogen interactions across a broad range of mean temperatures. Specifically, we used the Daphnia magna—Odospora colligata host-pathogen system to test experimentally how temperature variation alters the thermal performance of both the host and the pathogen across their natural temperature range. Daphnia are a well-established ecological model system (Miner et al., 2012) used frequently in climate change studies (e.g., Dallas and Drake, 2016; Hector et al., 2019; Kirk et al., 2020), while Ordospora transmission is representative of a classical environmentally transmitted pathogen (i.e., it mimics diseases such as SARS-CoV-2 and Vibrio cholerae) and meets the assumptions of conventional epidemiological models (e.g., infection following mass action [Kirk et al., 2019], continuous shedding of infectious particles [Ebert, 2005] and little or no spatial structure within host populations). Our microcosm experiment comprised three distinct temperature regimes: constant temperatures and two variable temperature regimes with diurnal fluctuations of ±3°C and 3-day heatwaves of 6°C above ambient, all replicated over the natural temperature range of the model system (i.e., 10–28°C, Figure 1). These variable temperature regimes were selected to mimic naturally occurring temperature events in habitats our study organisms encounter naturally (i.e., small ponds and rock pools) (Jacobs et al., 2008; Kuha et al., 2018).

Figure 1

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During the experiment, we measured host longevity, fecundity, infection status, and the number of O. colligate spores within the host gut (see Materials and methods for details). All measurements were conducted on individually kept Daphnia with up to 18 replicates per measurement. To compare the three different temperature regimes (i.e., constant, diurnal fluctuations, and heatwave; Figure 1), we fitted a Beta Function using a Bayesian framework. While there are numerous non-linear functions that can be used to fit thermal performance curves (e.g., Briére, Ratkowsky equation), the advantage of using the Beta Function is that it provides realistic predictions when extrapolating beyond the measured thermal range and that each of its parameters has a clear a biological meaning (see Shi et al., 2016, for a comparison of thermal performance equations), where F_m is the fitness at optimal performance for the fitted host or parasite trait, T_opt is the temperature at optimal performance, and T_min and T_max are, respectively, the critical minimum and maximum temperatures over which fitness of the trait becomes unviable.

Diurnal temperature fluctuations narrowed the thermal performance curve for infectivity compared with constant temperatures (Figure 2A). The estimated (using the Beta Function) maximum temperature at which spores were able to cause infections was 5°C lower under fluctuating temperatures than under constant temperatures (Figure 2A; T_max=25°C for fluctuating vs. 30°C for constant; confidence intervals for T_max do not overlap). The thermal performance curve for infectivity under the heatwave, where temperatures were raised by 6°C for 3 days and then returned to constant temperature (Figure 1), was almost identical to that under constant temperature (all confidence intervals overlap, Figure 2 and Supplementary file 2). However, unlike the constant temperature regime, the heatwave did not differ from the fluctuating regime, as estimates for the maximum temperature had broad confidence intervals, likely caused by lack of data at the higher temperatures. Remaining parameter estimates of the Beta Function were similar for the three temperature regimes, with the highest rate of infection at 19°C, a maximum infection rate of ~95% infection and no infections under 10°C (Figure 2A and Supplementary file 2; confidence intervals overlap for T_opt, F_m, and T_min). Thus, while diurnal fluctuations led to less infection at higher temperatures, a heatwave did not alter infection rates.

Figure 2

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Spore burden of the two variable temperature regimes deviated from both the constant temperature regime and from each other (Figure 2B). Consistent with infection rates, daily temperature fluctuation led to a lower maximum temperature (by ~3°C) for parasite growth within the host, resulting in a narrowed thermal performance curve for burden compared with the other temperature regimes (Figure 2B, Supplementary file 3, non-overlapping confidence intervals for T_max). This is supported further by the consistently lower spore burden for the fluctuating regime when compared with the constant temperature regime except near the optimum temperature of 19°C, where spore burdens of both temperature regimes were similar (confidence intervals for T_opt and F_m overlap). While infection rates and burden showed a similar thermal performance for diurnal fluctuations (both narrowing), the response to the heatwave differed between infection and burden (Figure 2). Compared to the constant temperature regime, spore burden in the heatwave showed a shift in the optimum temperature (from 19.4°C to 15.7°C), and an increase in the number of spore clusters (Figure 2B, confidence intervals for T_opt and F_m do not overlap). However, while spore burden was different at ~16°C, spore burden at ~19°C was nearly identical for all three temperature regimes. Moreover, due to the opposite effects at 16°C for both variable temperature regimes (i.e., a narrowing of performance under fluctuating temperatures, exacerbation under heatwave), spore burden at this temperature differed by almost an order of magnitude (i.e., 86 vs. 737 spore clusters).

Host fitness was generally reduced when exposed to Ordospora spores or when experiencing variable temperature regimes. Daphnia exposed to the parasite had lower reproductive success near the optimum temperature (~20°C) compared to control animals that were not exposed to Ordospora (non-overlapping confidence intervals for F_m) and lost between 8% (constant) and 24% (diurnal fluctuation) of reproductive output (Figure 3). Comparing host performance among the different temperature regimes shows that animals exposed to Ordospora in variable temperatures had lower reproductive success (Figure 2C, Supplementary file 4, non-overlapping 95% confidence intervals for F_m), with a small shift (1.1°C) in their thermal optimum under the heatwave regime (Figure 2C, non-overlapping 95% confidence intervals for T_opt). Control animals that were not exposed to Ordospora also had lower fitness after the heatwave (Figure 3, non-overlapping 95% confidence intervals for F_m) and, while reproduction at the optimal temperature of the control animals experiencing diurnal fluctuations was lower, confidence intervals overlapped with the constant temperature regime (Figure 3). The host response to the variable temperature regimes differed from that of the pathogen (compare thermal performance curves for the heatwave and diurnal fluctuating regimes between Figure 2A and B & C). While host performance was reduced (lower F_m) under both variable temperature regimes, parasite traits showed either a narrowing of the performance curve (for diurnal fluctuations) or no effect and greatly increased performance (for infection and burden under the heatwave).

Figure 3

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We show that, not only does temperature variation alter the thermal performance of host and pathogen life-history traits in a unique way—driving a shift in performance up to order-of-magnitude over and above the effect of mean temperature—but that the type of variation and the mean temperature at which it occurs are also critical. Indeed, each of the life-history traits we measured was affected differently by thermal variation. With global warming altering the mean and variance of temperature around the world, how this affects diseases and their dynamics is a critical outstanding question (Claar and Wood, 2020; Rohr et al., 2013). Our results demonstrate that the combined effect of changing temperature mean and variance can be highly complex, and may alter the vulnerability of host populations (Harvell et al., 2019), affect the evolution of host and parasites (Buckley and Huey, 2016), and, therefore, impede our ability to accurately predict future disease outbreaks.

Infection rates were reduced at higher temperatures when animals experienced diurnal fluctuations but not after experiencing a heatwave. Our estimates of maximum temperature for the heatwave, however, have broad confidence intervals, likely due to the lack of data at high temperatures, and extrapolations of our results beyond our highest heatwave temperature (which was 22°C and reached 28°C during the 3-day heatwave which was close to the estimated maximum thermal tolerance of the parasite) should be interpreted with caution. In Daphnia, filtration rates determine the contact rate between host and pathogen, and a reduction in filtration can thus lead to reduced levels of infection (Hall et al., 2010). As filtration rates of D. magna decline at higher temperatures (Kirk et al., 2019), average infection rates under diurnal temperature fluctuations would also be expected to be lower due to the non-linear nature of the thermal performance curve (i.e., Jensen’s inequality; Dowd et al., 2015). In addition, infection probability in our study system decreases sharply when temperatures surpass 22°C (Kirk et al., 2019), reducing infection rates under fluctuating temperatures that exceed this temperature (again, due to Jensen’s inequality). In systems where immune function depends on temperature (e.g., insects, mosquitos, and ectotherms in general; Paaijmans et al., 2013), heatwaves may interact with the immune system in complex ways (Murdock et al., 2012), particularly when the heatwave occurs early in the infection process. However, given that our heatwave occurred 20 days post-infection and that Daphnia are not known to recover from infection (Ebert, 2005), the effect of the heatwave on established infections may have been limited. Absence of an effect of a heatwave on infection rates has also been found for a pipefish-trematode host-parasite system (Landis et al., 2012). However, though the heatwave did not affect infection rates in our experiment, it did affect parasite burden.

Our results show that different types of temperature variation can alter parasite burden and thus affect pathogen growth within the host. While diurnal temperature fluctuations and heatwaves brought about an almost order of magnitude difference in spore burden at a mean temperature of 16°C, no differences were observed at ~19°C. Generally, similar to infection rates, the thermal performance curve for spore burden narrowed under fluctuating temperatures, as predicted by averaging over the non-linear thermal performance curve (Denny, 2017; Dowd et al., 2015). The impact of diurnal temperature fluctuations on parasite fitness has been studied previously, with multiple studies suggesting a shift in the thermal performance of parasite fitness under fluctuating temperatures (Dallas and Drake, 2016; Duncan et al., 2011; Greenspan et al., 2017a; Paaijmans et al., 2010). Indeed, our findings that Ordospora has a narrower thermal performance for spore burden and infectivity under fluctuating temperatures adds to a growing body of evidence (Dallas and Drake, 2016; Greenspan et al., 2017b; Hector et al., 2019; Roth et al., 2010) suggesting that estimates and predictions that ignore temperature variation may over- or underestimate disease burden and prevalence (Greenspan et al., 2017a; Raffel et al., 2012; Rohr et al., 2013). Moreover, with almost an order-of-magnitude difference between both our two variable temperature regimes at some, though not all, temperatures, our results highlight that both the context and type of temperature variance need to be considered when trying to understand how pathogen performance may be affected by climate change.

Spore burden increased following heatwaves, but the effect depended on the mean temperature to which the heatwave was applied. Indeed, the heatwave had either higher, similar or lower spore burden compared to the equivalent constant temperature regime. It was shown recently in a fish-tapeworm host-parasite system that parasite growth, egg production, and the number of first-stage larvae increased after a 1-week exposure to higher temperatures (increase up to 7.5°C) (Franke et al., 2019). Our findings corroborate that heatwaves associated with climate change may, under some conditions, increase disease burden. Indeed, we found a considerable increase in spore burden and a shift in the optimum temperature following a 3-day increase in temperature of 6°C at 16°C. Although some studies have reported increased disease susceptibility following heatwaves (Dittmar et al., 2014; Roth et al., 2010), others found no effect on immune function (Stahlschmidt et al., 2017) or reduced disease performance after exposure to high temperatures (Fayer et al., 1998). Our results may explain these conflicting findings—we found that the effects of a heatwave on spore burden are contingent on the mean temperature to which the heatwave is applied. That is, our results show that the heatwave has either lower or higher burden than equivalent constant temperatures. This context-dependency of heatwaves is supported further by studies in both plant-endoparasite (Schreven et al., 2017) and herbivore-parasitoid (Zhang et al., 2019) systems, which showed that the effect of a heatwave on parasite traits depended on the amplitude of the extreme event. As highlighted by a recent review (Claar and Wood, 2020), effects of warming events on disease traits remain difficult to generalize, and more studies and insight into underlying principles and mechanisms are needed to forecast the effect of extreme heat events on disease dynamics. Indeed, while it is clear from our experiment that a short, 3-day increase in temperature can drastically alter the thermal performance curve for parasite burden, the exact mechanism(s) underlying this change remains unidentified.

Differences in acclimatization speeds between hosts and pathogens may explain the observed increase in burden of Ordospora at 16°C following a heatwave. According to the temperature variability hypothesis (Raffel et al., 2012; Rohr et al., 2013), parasites, which have faster metabolic rates due to their smaller size, should acclimatise more rapidly to changing temperatures than their larger hosts. In unpredictable variable environments, such as our heatwave regime, parasites should thus have an advantage over their hosts. Moreover, host resistance may also decrease as a result of the trade-off between the energy demand for acclimatization and immunity (Nelson and Demas, 1996). That varying temperature can lead to higher infection prevalence has been established in Cuban tree frogs, red-spotted newts, and abalone (Ben-Horin et al., 2013; Raffel et al., 2012). While this hypothesis may explain our observation of high burden for the heatwave near 16°C, it does not, however, explain why the response depends on the mean (i.e., lower performance at higher temperatures). Though Ordospora should have an overall advantage under the temperature variability hypothesis, the realized advantage may be smaller as its thermal range is more restricted than its host (Kirk et al., 2018). The heatwave may thus cause proportionally more stress in the parasite than the host at high temperatures, consistent with the thermal stress hypothesis, which suggests that a shift in temperature may reduce the performance of either host or parasite (Paull et al., 2015). Indeed, that thermal stress can affect host and pathogen performance has been well supported (Gehman et al., 2018; Kirk et al., 2019; Schreven et al., 2017; Zhang et al., 2019). Alternatively, the observed increase in parasite burden due to heatwaves may be system-specific and not explained by differences in acclimatization speed. Estimates show that growth rates of Ordospora increase by a factor of 5 between 20°C and 24°C before declining again (Kirk et al., 2018). While the optimal performance of Ordospora occurs around 19°C, due to the balance of thermal performance curves of other host and pathogen traits (e.g., mortality, infectivity, etc.), a temporary increase to 22°C, as occurred under our heatwave at 16°C, may thus have exacerbated pathogen growth, particularly if different traits react differently to a temperature disturbance, which may have disrupted the balance between host and parasite.

Changes in host fecundity in response to temperature variation differed to the response of both parasite traits (i.e., infectivity and spore burden) we measured. While infectivity and burden had either a narrower thermal performance curve or showed a heightened and shifted peak, temperature variation lowered the reproductive output of the host near the thermal optimum. A reduction in reproductive output of the host under variable temperatures is consistent with previous work both on Daphnia (Schwartz et al., 2016) and in other systems (Craig and Kipling, 1983; Uvarov et al., 2011). Similarly, a reduction in host fecundity due to parasitism is well established (Ebert, 2005). Infection may also reduce the thermal tolerance of the host (Hector et al., 2019), which would explain the small shift of the thermal optimum for host reproduction under the heatwave regime. While host responses are thus consistent with expectations, the distinct responses to the different temperature regimes of the different life-history traits we measured (i.e., host fecundity, parasite infectively, and parasite burden) highlight that the effects of temperature variation on host-pathogen systems are complex. When trying to model disease dynamics and outbreaks, we often include a multitude of host and pathogen traits, each with their own thermal dependencies. Recent studies have made advances in predicting disease growth and spread under rising mean temperatures, integrating approaches, and identifying mechanisms that can capture and predict the thermal performance of host and pathogen traits within epidemiological models (e.g., metabolic theory) (Kirk et al., 2020). It remains to be seen, however, whether such modeling frameworks can be extended to incorporate temperature variation, especially considering the distinct responses for the life-history traits we measured to each of our variable temperature regimes.

Our study shows that temperature variation alters the outcome of host-pathogen interactions in complex ways. Not only does temperature variation affect different host and pathogen life-history traits in a distinct way, but the type of variation and the mean temperature to which it is applied also matters, with up to an order of magnitude change between diurnal fluctuations in temperature and extreme heat events. With global warming altering both the mean and variance of temperature around the world, we can thus expect to see unanticipated changes in disease dynamics of host-pathogen systems. Indeed, extreme temperature events such as El Niño have been linked to disease-driven collapses of keystone predators (Harvell et al., 2019), increases in diseases such as dengue and cholera (Anyamba et al., 2019), and shifts in the geographic distribution of pathogens (Claar and Wood, 2020). While temperature variation can thus affect disease dynamics in human, wildlife, and livestock populations—with potentially devastating economic and health consequences (Altizer et al., 2013)—the complexity of the effects of temperature and its variation currently limits our ability to move beyond system-specific predictions, in particular for extreme temperature events (Claar and Wood, 2020). We conclude that improving our mechanistic understanding of the role of temperature variation on disease dynamics, and exploring the generality of its effects and how it affects thermal performance curves of both hosts and parasites (Claar and Wood, 2020), are critical to predicting disease dynamics in a warming world.

Data and code are available on GitHub https://github.com/charlyknz/HostParasite.git (copy archived at swh:1:rev:5f2604fe866f547dd80d5a77f99ef8887b9f10e1).

1. 1. Kunze C
   2. Luijckx P
   3. Jackson A
   4. Donohui I

   (2021) Dryad Digital Repository

   Alternate patterns of temperature variation bring about very different disease outcomes at different mean temperatures.

   https://doi.org/10.5061/dryad.w0vt4b8s3

1. 1. Altizer S
   2. Ostfeld RS
   3. Johnson PTJ
   4. Kutz S
   5. Harvell CD

   (2013) Climate change and infectious diseases: from evidence to a predictive framework

   Science 341:514–519.

   https://doi.org/10.1126/science.1239401

   • PubMed
   • Google Scholar
2. 1. Anyamba A
   2. Chretien JP
   3. Britch SC
   4. Soebiyanto RP
   5. Small JL
   6. Jepsen R
   7. Forshey BM
   8. Sanchez JL
   9. Smith RD
   10. Harris R
   11. Tucker CJ
   12. Karesh WB
   13. Linthicum KJ

   (2019) Global disease outbreaks associated with the 2015-2016 El niño event

   Scientific Reports 9:1–14.

   https://doi.org/10.1038/s41598-018-38034-z

   • PubMed
   • Google Scholar
3. 1. Ben-Horin T
   2. Lenihan HS
   3. Lafferty KD

   (2013) Variable intertidal temperature explains why disease endangers black abalone

   Ecology 94:161–168.

   https://doi.org/10.1890/11-2257.1

   • PubMed
   • Google Scholar
4. 1. Buckley LB
   2. Huey RB

   (2016) How extreme temperatures impact organisms and the evolution of their thermal tolerance

   Integrative and Comparative Biology 56:98–109.

   https://doi.org/10.1093/icb/icw004

   • PubMed
   • Google Scholar
5. 1. Chakraborty S
   2. Newton AC

   (2011) Climate change, plant diseases and food security: an overview

   Plant Pathology 60:2–14.

   https://doi.org/10.1111/j.1365-3059.2010.02411.x

   • Google Scholar
6. 1. Claar DC
   2. Wood CL

   (2020) Pulse heat stress and parasitism in a warming world

   Trends in Ecology & Evolution 35:704–715.

   https://doi.org/10.1016/j.tree.2020.04.002

   • PubMed
   • Google Scholar
7. 1. Craig JF
   2. Kipling C

   (1983) Reproduction effort versus the environment; case histories of Windermere perch, Perca fluviatilis L., and pike, Esox lucius L

   Journal of Fish Biology 22:713–727.

   https://doi.org/10.1111/j.1095-8649.1983.tb04231.x

   • Google Scholar
8. 1. Dallas T
   2. Drake JM

   (2016) Fluctuating temperatures alter environmental pathogen transmission in a Daphnia-pathogen system

   Ecology and Evolution 6:7931–7938.

   https://doi.org/10.1002/ece3.2539

   • PubMed
   • Google Scholar
9. 1. Denny M

   (2017) The fallacy of the average: on the ubiquity, utility and continuing novelty of Jensen’s inequality

   The Journal of Experimental Biology 220:139–146.

   https://doi.org/10.1242/jeb.140368

   • PubMed
   • Google Scholar
10. 1. Dittmar J
    2. Janssen H
    3. Kuske A
    4. Kurtz J
    5. Scharsack JP

    (2014) Heat and immunity: an experimental heat wave alters immune functions in three-spined sticklebacks (Gasterosteus aculeatus)

    The Journal of Animal Ecology 83:744–757.

    https://doi.org/10.1111/1365-2656.12175

    • PubMed
    • Google Scholar
11. 1. Dowd WW
    2. King FA
    3. Denny MW

    (2015) Thermal variation, thermal extremes and the physiological performance of individuals

    The Journal of Experimental Biology 218:1956–1967.

    https://doi.org/10.1242/jeb.114926

    • PubMed
    • Google Scholar
12. 1. Duncan AB
    2. Fellous S
    3. Kaltz O

    (2011) Temporal variation in temperature determines disease spread and maintenance in Paramecium microcosm populations

    Proceedings. Biological Sciences 278:3412–3420.

    https://doi.org/10.1098/rspb.2011.0287

    • PubMed
    • Google Scholar
13. Book

    1. Ebert D

    (2005)

    Ecology; Epidemiology; and Evolution of Parasitism in Daphnia

    Bethesda (MD): National Library of Medicine (US); National Center for Biotechnology Information.

    • Google Scholar
14. 1. Elser JJ
    2. Fagan WF
    3. Denno RF
    4. Dobberfuhl DR
    5. Folarin A
    6. Huberty A
    7. Interlandi S
    8. Kilham SS
    9. McCauley E
    10. Schulz KL
    11. Siemann EH
    12. Sterner RW

    (2000) Nutritional constraints in terrestrial and freshwater food webs

    Nature 408:578–580.

    https://doi.org/10.1038/35046058

    • PubMed
    • Google Scholar
15. 1. Fayer R
    2. Trout JM
    3. Jenkins MC

    (1998)

    Infectivity of microsporidia spores stored in water at environmental temperatures

    The Journal of Parasitology 84:1165–1169.

    • PubMed
    • Google Scholar
16. 1. Franke F
    2. Raifarth N
    3. Kurtz J
    4. Scharsack JP

    (2019) Consequences of divergent temperature optima in a host–parasite system

    Oikos 128:869–880.

    https://doi.org/10.1111/oik.05864

    • Google Scholar
17. 1. Gehman ALM
    2. Hall RJ
    3. Byers JE

    (2018) Host and parasite thermal ecology jointly determine the effect of climate warming on epidemic dynamics

    PNAS 115:744–749.

    https://doi.org/10.1073/pnas.1705067115

    • PubMed
    • Google Scholar
18. 1. Greenspan SE
    2. Bower DS
    3. Roznik EA
    4. Pike DA
    5. Marantelli G
    6. Alford RA
    7. Schwarzkopf L
    8. Scheffers BR

    (2017a) Infection increases vulnerability to climate change via effects on host thermal tolerance

    Scientific Reports 7:1–10.

    https://doi.org/10.1038/s41598-017-09950-3

    • PubMed
    • Google Scholar
19. 1. Greenspan SE
    2. Bower DS
    3. Webb RJ
    4. Roznik EA
    5. Stevenson LA
    6. Berger L
    7. Marantelli G
    8. Pike DA
    9. Schwarzkopf L
    10. Alford RA
    11. Wilson R

    (2017b) Realistic heat pulses protect frogs from disease under simulated rainforest frog thermal regimes

    Functional Ecology 31:2274–2286.

    https://doi.org/10.1111/1365-2435.12944

    • Google Scholar
20. 1. Hall SR
    2. Becker CR
    3. Duffy MA
    4. Cáceres CE

    (2010) Variation in resource acquisition and use among host clones creates key epidemiological trade‐offs

    The American Naturalist 176:557–565.

    https://doi.org/10.1086/656523

    • PubMed
    • Google Scholar
21. 1. Harvell CD
    2. Montecino-Latorre D
    3. Caldwell JM
    4. Burt JM
    5. Bosley K
    6. Keller A
    7. Heron SF
    8. Salomon AK
    9. Lee L
    10. Pontier O
    11. Pattengill-Semmens C
    12. Gaydos JK

    (2019) Disease epidemic and a marine heat wave are associated with the continental-scale collapse of a pivotal predator (Pycnopodia helianthoides)

    Science Advances 5:eaau7042.

    https://doi.org/10.1126/sciadv.aau7042

    • PubMed
    • Google Scholar
22. 1. Hector TE
    2. Sgrò CM
    3. Hall MD

    (2019) Pathogen exposure disrupts an organism’s ability to cope with thermal stress

    Global Change Biology 25:3893–3905.

    https://doi.org/10.1111/gcb.14713

    • PubMed
    • Google Scholar
23. 1. Hotez PJ
    2. Alvarado M
    3. Basáñez MG
    4. Bolliger I
    5. Bourne R
    6. Boussinesq M
    7. Brooker SJ
    8. Brown AS
    9. Buckle G
    10. Budke CM
    11. Carabin H
    12. Coffeng LE
    13. Fèvre EM
    14. Fürst T
    15. Halasa YA
    16. Jasrasaria R
    17. Johns NE
    18. Keiser J
    19. King CH
    20. Lozano R
    21. Murdoch ME
    22. O’Hanlon S
    23. Pion SDS
    24. Pullan RL
    25. Ramaiah KD
    26. Roberts T
    27. Shepard DS
    28. Smith JL
    29. Stolk WA
    30. Undurraga EA
    31. Utzinger J
    32. Wang M
    33. Murray CJL
    34. Naghavi M

    (2014) The global burden of disease study 2010: interpretation and implications for the neglected tropical diseases

    PLOS Neglected Tropical Diseases 8:e2865.

    https://doi.org/10.1371/journal.pntd.0002865

    • PubMed
    • Google Scholar
24. 1. Jacobs AFG
    2. Heusinkveld BG
    3. Kraai A
    4. Paaijmans KP

    (2008) Diurnal temperature fluctuations in an artificial small shallow water body

    International Journal of Biometeorology 52:271–280.

    https://doi.org/10.1007/s00484-007-0121-8

    • PubMed
    • Google Scholar
25. 1. Kilham SS
    2. Kreeger DA
    3. Lynn SG
    4. Goulden CE
    5. Herrera L

    (1998) COMBO: A defined freshwater culture medium for algae and zooplankton

    Hydrobiologia 377:147–159.

    https://doi.org/10.1023/a:1003231628456

    • Google Scholar
26. 1. Kirk D
    2. Jones N
    3. Peacock S
    4. Phillips J
    5. Molnár PK
    6. Krkošek M
    7. Luijckx P

    (2018) Empirical evidence that metabolic theory describes the temperature dependency of within-host parasite dynamics

    PLOS Biology 16:e2004608.

    https://doi.org/10.1371/journal.pbio.2004608

    • PubMed
    • Google Scholar
27. 1. Kirk D
    2. Luijckx P
    3. Stanic A
    4. Krkošek M

    (2019) Predicting the thermal and allometric dependencies of disease transmission via the metabolic theory of ecology

    The American Naturalist 193:661–676.

    https://doi.org/10.1086/702846

    • PubMed
    • Google Scholar
28. 1. Kirk D
    2. Luijckx P
    3. Jones N
    4. Krichel L
    5. Pencer C
    6. Molnár PK
    7. Krkošek M

    (2020) Experimental evidence of warming-induced disease emergence and its prediction by a trait-based mechanistic model

    Proceedings. Biological Sciences 287:20201526.

    https://doi.org/10.1098/rspb.2020.1526

    • PubMed
    • Google Scholar
29. 1. Klüttgen B
    2. Dülmer U
    3. Engels M
    4. Ratte HT

    (1994) ADaM, an artificial freshwater for the culture of zooplankton

    Water Research 28:743–746.

    https://doi.org/10.1016/0043-1354(94)90157-0

    • Google Scholar
30. 1. Kuha J
    2. Arvola L
    3. Hanson PC
    4. Huotari J
    5. Huttula T
    6. Juntunen J
    7. Järvinen M
    8. Kallio K
    9. Ketola M
    10. Kuoppamäki K
    11. Lepistö A
    12. Lohila A
    13. Paavola R
    14. Vuorenmaa J
    15. Winslow L
    16. Karjalainen J

    (2018) Response of boreal lakes to episodic weather-induced events

    Inland Waters 6:523–534.

    https://doi.org/10.1080/IW-6.4.886

    • Google Scholar
31. Software

    1. Kunze C

    (2022) HostParasite, version swh:1:rev:5f2604fe866f547dd80d5a77f99ef8887b9f10e1

    Software Heritage.

    https://archive.softwareheritage.org/swh:1:dir:67964f43c1c8e968797fe3c1f002890b22d57a41;origin=https://github.com/charlyknz/HostParasite;visit=swh:1:snp:a9a3352f637b9aef2cc5e527489c51b5b538296f;anchor=swh:1:rev:5f2604fe866f547dd80d5a77f99ef8887b9f10e1
32. 1. Landis SH
    2. Kalbe M
    3. Reusch TBH
    4. Roth O

    (2012) Consistent pattern of local adaptation during an experimental heat wave in a pipefish-trematode host-parasite system

    PLOS ONE 7:e30658.

    https://doi.org/10.1371/journal.pone.0030658

    • PubMed
    • Google Scholar
33. 1. Larsson JIR
    2. Ebert D
    3. Vávra J

    (1997) Ultrastructural study and description of Ordospora colligata gen. et sp. nov. (microspora, ordosporidae fam. nov.), a new microsporidian parasite of Daphnia magna (Crustacea, Cladocera)

    European Journal of Protistology 33:432–443.

    https://doi.org/10.1016/S0932-4739(97)80055-7

    • Google Scholar
34. 1. Lunn D
    2. Spiegelhalter D
    3. Thomas A
    4. Best N

    (2009) The BUGS project: Evolution, critique and future directions

    Statistics in Medicine 28:3049–3067.

    https://doi.org/10.1002/sim.3680

    • PubMed
    • Google Scholar
35. 1. Miner BE
    2. De Meester L
    3. Pfrender ME
    4. Lampert W
    5. Hairston NG

    (2012) Linking genes to communities and ecosystems: Daphnia as an ecogenomic model

    Proceedings. Biological Sciences 279:1873–1882.

    https://doi.org/10.1098/rspb.2011.2404

    • PubMed
    • Google Scholar
36. 1. Murdock CC
    2. Paaijmans KP
    3. Bell AS
    4. King JG
    5. Hillyer JF
    6. Read AF
    7. Thomas MB

    (2012) Complex effects of temperature on mosquito immune function

    Proceedings. Biological Sciences 279:3357–3366.

    https://doi.org/10.1098/rspb.2012.0638

    • PubMed
    • Google Scholar
37. 1. Nelson RJ
    2. Demas GE

    (1996) Seasonal changes in immune function

    The Quarterly Review of Biology 71:511–548.

    https://doi.org/10.1086/419555

    • PubMed
    • Google Scholar
38. 1. Niehaus AC
    2. Angilletta MJ
    3. Sears MW
    4. Franklin CE
    5. Wilson RS

    (2012) Predicting the physiological performance of ectotherms in fluctuating thermal environments

    The Journal of Experimental Biology 215:694–701.

    https://doi.org/10.1242/jeb.058032

    • PubMed
    • Google Scholar
39. 1. Paaijmans KP
    2. Blanford S
    3. Bell AS
    4. Blanford JI
    5. Read AF
    6. Thomas MB

    (2010) Influence of climate on malaria transmission depends on daily temperature variation

    PNAS 107:15135–15139.

    https://doi.org/10.1073/pnas.1006422107

    • PubMed
    • Google Scholar
40. 1. Paaijmans KP
    2. Heinig RL
    3. Seliga RA
    4. Blanford JI
    5. Blanford S
    6. Murdock CC
    7. Thomas MB

    (2013) Temperature variation makes ectotherms more sensitive to climate change

    Global Change Biology 19:2373–2380.

    https://doi.org/10.1111/gcb.12240

    • PubMed
    • Google Scholar
41. 1. Paull SH
    2. Raffel TR
    3. LaFonte BE
    4. Johnson PTJ
    5. Hawley D

    (2015) How temperature shifts affect parasite production: testing the roles of thermal stress and acclimation

    Functional Ecology 29:941–950.

    https://doi.org/10.1111/1365-2435.12401

    • Google Scholar
42. 1. Plummer M
    2. Best N
    3. Cowles K
    4. Vines K

    (2006)

    CODA: convergence diagnosis and output analysis for MCMC

    Open Research Online. R News 6:7–11.

    • Google Scholar
43. Software

    1. R Development Core Team

    (2018) R: A Language and Environment for Statistical Computing

    R Foundation for Statistical Computing, Vienna, Austria.

    http://www.r-project.org
44. 1. Raffel TR
    2. Romansic JM
    3. Halstead NT
    4. McMahon TA
    5. Venesky MD
    6. Rohr JR

    (2012) Disease and thermal acclimation in a more variable and unpredictable climate

    Nature Climate Change 3:146–151.

    https://doi.org/10.1038/nclimate1659

    • Google Scholar
45. 1. Régnière J
    2. Powell J
    3. Bentz B
    4. Nealis V

    (2012) Effects of temperature on development, survival and reproduction of insects: Experimental design, data analysis and modeling

    Journal of Insect Physiology 58:634–647.

    https://doi.org/10.1016/j.jinsphys.2012.01.010

    • Google Scholar
46. 1. Rohr JR
    2. Raffel TR

    (2010) Linking global climate and temperature variability to widespread amphibian declines putatively caused by disease

    PNAS 107:8269–8274.

    https://doi.org/10.1073/pnas.0912883107

    • PubMed
    • Google Scholar
47. 1. Rohr JR
    2. Raffel TR
    3. Blaustein AR
    4. Johnson PTJ
    5. Paull SH
    6. Young S

    (2013) Using physiology to understand climate-driven changes in disease and their implications for conservation

    Conservation Physiology 1:cot022.

    https://doi.org/10.1093/conphys/cot022

    • PubMed
    • Google Scholar
48. 1. Roth O
    2. Kurtz J
    3. Reusch TBH

    (2010) A summer heat wave decreases the immunocompetence of the mesograzer, Idotea baltica

    Marine Biology 157:1605–1611.

    https://doi.org/10.1007/s00227-010-1433-5

    • Google Scholar
49. 1. Schär C
    2. Vidale PL
    3. Lüthi D
    4. Frei C
    5. Häberli C
    6. Liniger MA
    7. Appenzeller C

    (2004) The role of increasing temperature variability in European summer heatwaves

    Nature 427:332–336.

    https://doi.org/10.1038/nature02300

    • PubMed
    • Google Scholar
50. 1. Schreven SJJ
    2. Frago E
    3. Stens A
    4. de Jong PW
    5. van Loon JJA

    (2017) Contrasting effects of heat pulses on different trophic levels, an experiment with a herbivore-parasitoid model system

    PLOS ONE 12:e0176704.

    https://doi.org/10.1371/journal.pone.0176704

    • PubMed
    • Google Scholar
51. 1. Schwartz TS
    2. Pearson P
    3. Dawson J
    4. Allison DB
    5. Gohlke JM

    (2016) Effects of fluctuating temperature and food availability on reproduction and lifespan

    Experimental Gerontology 86:62–72.

    https://doi.org/10.1016/j.exger.2016.06.010

    • PubMed
    • Google Scholar
52. 1. Shi PJ
    2. Reddy GVP
    3. Chen L
    4. Ge F

    (2016) Comparison of thermal performance equations in describing temperature-dependent developmental rates of insects: (I) empirical models

    Annals of the Entomological Society of America 109:211–215.

    https://doi.org/10.1093/aesa/sav121

    • Google Scholar
53. 1. Shocket MS
    2. Strauss AT
    3. Hite JL
    4. Šljivar M
    5. Civitello DJ
    6. Duffy MA
    7. Cáceres CE
    8. Hall SR

    (2018) Temperature drives epidemics in a zooplankton-fungus disease system: A trait-driven approach points to transmission via host foraging

    The American Naturalist 191:435–451.

    https://doi.org/10.1086/696096

    • PubMed
    • Google Scholar
54. 1. Stahlschmidt ZR
    2. French SS
    3. Ahn A
    4. Webb A
    5. Butler MW

    (2017) A simulated heat wave has diverse effects on immune function and oxidative physiology in the corn snake (Pantherophis guttatus)

    Physiological and Biochemical Zoology 90:434–444.

    https://doi.org/10.1086/691315

    • PubMed
    • Google Scholar
55. 1. Uvarov AV
    2. Tiunov AV
    3. Scheu S

    (2011) Effects of seasonal and diurnal temperature fluctuations on population dynamics of two epigeic earthworm species in forest soil

    Soil Biology and Biochemistry 43:559–570.

    https://doi.org/10.1016/j.soilbio.2010.11.023

    • Google Scholar
56. 1. Vasseur DA
    2. DeLong JP
    3. Gilbert B
    4. Greig HS
    5. Harley CDG
    6. McCann KS
    7. Savage V
    8. Tunney TD
    9. O’Connor MI

    (2014) Increased temperature variation poses a greater risk to species than climate warming

    Proceedings. Biological Sciences 281:20132612.

    https://doi.org/10.1098/rspb.2013.2612

    • PubMed
    • Google Scholar
57. 1. Zhang YB
    2. Yang AP
    3. Zhang GF
    4. Liu WX
    5. Wan FH

    (2019) Effects of simulated heat waves on life history traits of a host feeding parasitoid

    Insects 10:1–13.

    https://doi.org/10.3390/insects10120419

    • PubMed
    • Google Scholar

Author details

1. Charlotte Kunze

   1. Institute for Chemistry and Biology of the Marine Environment [ICBM], Carl von Ossietzky University of Oldenburg, Oldenburg, Germany
   2. Department of Zoology, School of Natural Sciences, Trinity College Dublin, Dublin, Ireland

   Contribution

   Conceptualization, Formal analysis, Investigation, Methodology, Writing – original draft, Writing – review and editing

   Contributed equally with

   Pepijn Luijckx

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-1130-7417

2. Pepijn Luijckx

   Department of Zoology, School of Natural Sciences, Trinity College Dublin, Dublin, Ireland

   Contribution

   Conceptualization, Funding acquisition, Investigation, Methodology, Resources, Supervision, Writing – original draft, Writing – review and editing

   Contributed equally with

   Charlotte Kunze

   For correspondence

   luijckxp@tcd.ie

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-8173-6727

3. Andrew L Jackson

   Department of Zoology, School of Natural Sciences, Trinity College Dublin, Dublin, Ireland

   Contribution

   Formal analysis, Writing – review and editing

   Competing interests

   No competing interests declared

4. Ian Donohue

   Department of Zoology, School of Natural Sciences, Trinity College Dublin, Dublin, Ireland

   Contribution

   Investigation, Supervision, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-4698-6448

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