Predicting the emergence, spread and evolution of parasites within and among host populations requires insight to both the spatial and temporal scales of adaptation, including an understanding of within‐host up through community‐level dynamics. Although there are very few pathosystems for which such extensive data exist, there has been a recent push to integrate studies performed over multiple scales or to simultaneously test for dynamics occurring across scales. Drawing on examples from the literature, with primary emphasis on three diverse host–parasite case studies, we first examine current understanding of the spatial structure of host and parasite populations, including patterns of local adaptation and spatial variation in host resistance and parasite infectivity. We then explore the ways to measure temporal variation and dynamics in host–parasite interactions and discuss the need to examine change over both ecological and evolutionary timescales. Finally, we highlight new approaches and syntheses that allow for simultaneous analysis of dynamics across scales. We argue that there is great value in examining interplay among scales in studies of host–parasite interactions.

The occurrence and magnitude of disease outbreaks can strongly influence host evolution. In particular, when hosts face a resistance-fecundity trade-off, they might evolve increased resistance to infection during larger epidemics but increased susceptibility during smaller ones. We tested this theoretical prediction by using a zooplankton-yeast host-parasite system in which ecological factors determine epidemic size. Lakes with high productivity and low predation pressure had large yeast epidemics; during these outbreaks, hosts became more resistant to infection. However, with low productivity and high predation, epidemics remained small and hosts evolved increased susceptibility. Thus, by modulating disease outbreaks, ecological context (productivity and predation) shaped host evolution during epidemics. Consequently, anthropogenic alteration of productivity and predation might strongly influence both ecological and evolutionary outcomes of disease.

Research in global change ecology relies heavily on global climatic grids derived from estimates of air temperature in open areas at around 2 m above the ground. These climatic grids thus fail to reflect conditions below vegetation canopies and near the ground surface, where critical ecosystem functions are controlled and most terrestrial species reside. Here we provide global maps of soil temperature and bioclimatic variables at a 1-km² resolution for 0-5 and 5-15 cm depth. These maps were created by calculating the difference (i.e., offset) between in-situ soil temperature measurements, based on time series from over 1200 1-km² pixels (summarized from 8500 unique temperature sensors) across all of the world's major terrestrial biomes, and coarse-grained air temperature estimates from ERA5-Land (an atmospheric reanalysis by the European Centre for Medium-Range Weather Forecasts). We show that mean annual soil temperature differs markedly from the corresponding 2 m gridded air temperature, by up to 10°C (mean = 3.0 ± 2.1°C), with substantial variation across biomes and seasons. Over the year, soils in cold and/or dry biomes are substantially warmer (3.6 ± 2.3°C warmer than gridded air temperature), whereas soils in warm and humid environments are on average slightly cooler (0.7 ± 2.3°C cooler). The observed substantial and biome-specific offsets underpin that the projected impacts of climate and climate change on biodiversity and ecosystem functioning are inaccurately assessed when air rather than soil temperature is used, especially in cold environments. The global soil-related bioclimatic variables provided here are an important step forward for any application in ecology and related disciplines. Nevertheless, we highlight the need to fill remaining global gaps by collecting more in-situ measurements of microclimate conditions to further enhance the spatiotemporal resolution of global soil temperature products for ecological applications.

Summary1. Predators could reduce disease prevalence in prey populations by culling infected hosts and reducing host density. However, recently observed positive correlations between predator density and disease burdens in prey ⁄ hosts suggest that predators do not always 'keep the herds healthy'. Several possible mechanisms could explain this 'unhealthy herds' effect, including a predator-induced change in prey ⁄ host traits which enhances susceptibility or alters other epidemiologically important traits. 2. Here, we use an invertebrate predator, zooplankton host, yeast parasite system to demonstrate such trait-mediated indirect effects. We exposed ten genotypes of the prey ⁄ host Daphnia dentifera to infochemicals ('kairomones') produced by the invertebrate predator Chaoborus and to a yeast parasite. 3. We found that kairomone exposure induced larger and more susceptible D. dentifera. Clones that showed substantial increases in body length also yielded more spores upon death. However, exposure to kairomones did not alter reproduction from uninfected hosts. All of these results were captured with a dynamic energy budget model of parasitism. 4. Overall, our empirical and theoretical results show that predators can have strong indirect effects on host-parasite interactions that could produce positive correlations between predation intensity and disease burden.

Organisms that can resist parasitic infection often have lower fitness in the absence of parasites. These costs of resistance can mediate host evolution during parasite epidemics. For example, large epidemics will select for increased host resistance. In contrast, small epidemics (or no disease) can select for increased host susceptibility when costly resistance allows more susceptible hosts to outcompete their resistant counterparts. Despite their importance for evolution in host populations, costs of resistance (which are also known as resistance trade-offs) have mainly been examined in laboratory-based host-parasite systems. Very few examples come from fieldcollected hosts. Furthermore, little is known about how resistance trade-offs vary across natural populations. We addressed these gaps using the freshwater crustacean Daphnia dentifera and its natural yeast parasite, Metschnikowia bicuspidata. We found a cost of resistance in two of the five populations we studied -those with the most genetic variation in resistance and the smallest epidemics in the previous year. However, yeast epidemics in the current year did not alter slopes of these trade-offs before and after epidemics. In contrast, the no-cost populations showed little variation in resistance, possibly because large yeast epidemics eroded that variation in the previous year. Consequently, our results demonstrate variation in costs of resistance in wild host populations. This variation has important implications for host evolution during epidemics in nature.