Temperature modulates the interaction between fungicide pollution and disease: evidence from a Daphnia-microparasitic yeast model

Abstract

Temperature is expected to modulate the responses of organisms to stress. Here, we aimed to assess the influence of temperature on the interaction between parasitism and fungicide contamination. Specifically, using the cladoceran Daphnia as a model system, we explored the isolated and interactive effects of parasite challenge (yeast Metschnikowia bicuspidata) and exposure to fungicides (copper sulphate and tebuconazole) at two temperatures (17 and 20 °C), in a fully factorial design. Confirming a previous study, M. bicuspidata infection and copper exposure caused independent effects on Daphnia life history, whereas infection was permanently suppressed with tebuconazole exposure. Here, we show that higher temperature generally increased the virulence of the parasite, with the hosts developing signs of infection earlier, reproducing less and dying at an earlier age. These effects were consistent across copper concentrations, whereas the joint effects of temperature (which enhanced the difference between non-infected and infected hosts) and the anti-parasitic action of tebuconazole resulted in a more pronounced parasite × tebuconazole interaction at the higher temperature. Thus, besides independently influencing parasite and contaminant effects, the temperature can act as a modulator of interactions between pollution and disease.

Temperature and host diet jointly influence the outcome of infection in a Daphnia‐fungal parasite system

“…The number of offspring and timing of each clutch were recorded for all individuals. Per capita intrinsic rates of increase ( r ) were computed for each combination of food quality, temperature, and host genotype (12 treatments), following Euler–Lotka's equation:with r as the rate of population increase (/day), x the age class in days, l x the probability of surviving to age x , and m x the fecundity at age x (Cuco et al, ; McCallum, ). Pseudovalues were generated by jackknifing and reassigned as individual values for each replicate in a given treatment (Meyer, Ingersoll, McDonald, & Boyce, ).…”

“…This strain was later propagated on a laboratory-reared Daphnia magna clone (Hesse, Engelbrecht, Laforsch, & Wolinska, 2012). Due to its low host specificity, the parasite can be raised on D. magna-a larger host species which conveniently provides high spore output upon death-and later used to infect other Daphnia species (Cuco et al, 2018;Hesse et al, 2012).…”

“…with r as the rate of population increase (/day), x the age class in days, l x the probability of surviving to age x, and m x the fecundity at age x (Cuco et al, 2018;McCallum, 2000). Pseudovalues were generated by jackknifing and reassigned as individual values for each replicate in a given treatment (Meyer, Ingersoll, McDonald, & Boyce, 1986).…”

“…Such medicinal properties have been suggested for the common cyanobacterium Microcystis aeruginosa against two parasites of Daphnia: the viral agent of white fat cell disease (Coopman, Muylaert, Lange, Reyserhove, & Decaestecker, 2014) and the yeast Metschnikowia bicuspidata (Sánchez, Huntley, Duffy, & Hunter, 2019). Overall, despite the substantial effort to relate the fitness of Daphnia parasites to single factors, such as food quality (Hall, Knight, et al, 2009a;Sánchez et al, 2019), nutrient availability (Frost, Ebert, & Smith, 2008;Narr, Ebert, Bastille-Rousseau, & Frost, 2019) and water temperature (Cuco, Castro, Gonçalves, Wolinska, & Abrantes, 2018;Vale et al, 2008), the combined effects of these stressors remain relatively unexplored in this system (but see Garbutt, Scholefield, Vale, & Little, 2014). As cyanobacteria blooms and heat waves are concurrent phenomena in nature (Joehnk et al, 2008), a comprehensive approach is required to make better epidemiological predictions in freshwater ecosystems.…”

Variation in chronic radiation exposure does not drive life history divergence among Daphnia populations across the Chernobyl Exclusion Zone

“…Where x represents the age of each organism in days, l x is the proportion of surviving females at each age classification, and m x is the number of offspring produced at each corresponding age (Birch, 1948;Cuco, Castro, Gonçalves, Wolinska, & Abrantes, 2017;Grant & Grant, 1992). We tested for variation in r across lake populations and by dose rate using generalized least squares models (GLS models using the nlme package) (Pinheiro, Bates, DebRoy, & Sarkar, 2018), where the intercept was allowed to vary by lake population.…”