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

The dynamics of host-parasite interactions are highly temperature-dependent and may be modified by increasing frequency and intensity of climate-driven heat events. Here, we show that altered patterns of temperature variance lead to an almost order-of-magnitude shift in thermal performance of host and pathogen life history traits over and above the effects of mean temperature and, moreover, that different temperature regimes affect these traits differently. We found that diurnal fluctuations of ±3°C lowered infection rates and reduced spore burden compared to constant temperatures in our focal host Daphnia magna exposed to the microsporidium parasite Ordospora colligata. In contrast, a three-day heatwave (+6°C) did not affect infection rates, but increased spore burden (relative to constant temperatures with the same mean) at 16°C, while reducing burden at higher temperatures. We conclude that changing patterns of climate variation, superimposed on shifts in mean temperatures due to global warming, may have profound and unanticipated effects on disease dynamics.

Methods
Date of collection: 04/04/2019 to 08/05/2019

These datasets contain observations on host fecundity, infection status and severity of infection collected during an experiment on Daphnia magna and its parasite Odospora colligata at School of Natural Sciences at the Trinity College Dublin in spring 2019. In this study we compared two patterns of temperature variation, a diurnal temperature fluctuation (of ±3°C in a 12:12 cycle) and a heat wave (of +6°C with 3 days duration 20 days after exposure to the parasite), with a constant treatment, which had the same mean temperature over the whole experimental period. Data were analyzed using R version 3.6.1(R Core Team, 2018) interfacing with JAGS. A Beta Function was fitted to each of our different fitness estimates (that is, host fecundity, parasite infectivity and burden) for each of the three temperature regimes using a baysian framework.