Local adaptation (LA) is expected to arise when populations evolve higher fitness in their local environment than migrants, particularly in heterogeneous environments and coevolving host–parasite systems. Such adaptation can influence species interactions, including competition and parasitism, and is often predicted to help populations cope with environmental change. However, how LA to environmental conditions affects competition between resident and migrant individuals of the same species—and how parasitism modifies these interactions—remains poorly understood. Here, I conducted a reciprocal transplant experiment across 12 artificial pond populations in a naturally coevolving Daphnia–microparasite mesocosm system. Hosts from each population were placed in field cages in their home or away environments and either alone or mixed with individuals from another population. Treatments were exposed to an ancestral parasite or maintained parasite-free. Host fitness was measured as reproductive output. Contrary to theoretical expectations, residents showed no evidence of LA across populations. However, mixing residents with migrants reduced host fitness relative to unmixed treatments during the third week of the experiment, and this effect was significant only under parasite exposure. This pattern suggests that competition between genotypes became detectable when infection acted as a general stressor. Overall, our results show no detectable LA despite environmental heterogeneity in naturally coevolving host–parasite populations. The ability of non-locally adapted residents to compete with migrant genotypes under parasite exposure may facilitate gene flow among populations and potentially reduce future disease risk by increasing host genetic diversity and lowering transmission.

Human, animal and plant populations are at risk of large outbreaks of infectious diseases called ‘epidemics’. Despite the potential for strong antagonistic co-evolution between host and parasite infection traits, epidemiological research has often focussed on studying any given epidemic in isolation. Therefore, I propose a simple ‘Disease Cycle’ model that seeks to link the size of past and future epidemics of disease within the context of ongoing environmental perturbation. Specifically, I review some of the contemporary literature about evolutionary topics that are relevant to the Disease Cycle. I focus on three key axes of (co)evo-epidemiology, including (i) the link between epidemic size and the strength of selection, (ii) changes in host-parasite diversity as a result of the (co)evolution of host resistance or parasite infectivity and (iii) how either host or parasite population genetic diversity could impact on future epidemic size. I identify missing links in the Disease Cycle that could be filled by future work, but overall, I find compelling evidence supporting this model. Future work should focus on understanding how host (and parasite) population genetic diversity could impact on the variability of disease and how the strength of host or parasite-mediated selection might be linked to epidemic size. This Disease Cycle model can encourage the scientific community to conceptualise disease as part of an ongoing battle between the evolution of hosts and parasites that allows us to view epidemiology through a coevolutionary lens.

A key challenge for disease ecology is predicting the size of epidemics. Most models forecast disease in a single population using long-term historical data from that population. However, long-term data is not always available and a possible alternative is to borrow data from multiple similar populations to forecast disease for a population of interest. One step further is to weight the contribution of epidemics to the forecast based on their similarity to the focal population. In this study, we use data from twenty populations of the freshwater crustacean Daphnia magna and its sterilizing bacterial parasite Pasteuria ramosa tracked over four epidemic seasons (a total of 80 epidemics) to predict future epidemics. We evaluate single population, multiple average population and multiple weighted average population approaches for training three suites of forecast model: seasonal naïve, auto-regressive integrated moving average and time series regression models. We found that forecast accuracy depended on both the type of training data and the choice of forecast model, but models trained on data from multiple populations consistently outperformed those trained on single population data. Our study demonstrates the benefit of using a collection of similar populations to forecast disease for a focal population which has limited data.

All animals and plants respond to changes in the environment during their life cycle. This flexibility is known as phenotypic plasticity and allows organisms to cope with variable environments. A common source of environmental variation is predation risk, which describes the likelihood of being attacked and killed by a predator. Some species can respond to the level of predation risk by producing morphological defences against predation. A classic example is the production of pedestals and head spikes in the water flea, Daphnia pulex, which defend against predation from Chaoborus midge larvae. Previous studies of these defences have focussed on changes in pedestal size and the number of spikes along a gradient of predation risk. Although these studies have provided a model for continuous plasticity, they do not capture the whole-organism shape response to predation risk. In contrast, studies in fish and amphibians focus on shape as a complex, multi-faceted trait made up of different variables. In this study, we analyse how multiple aspects of shape change in D. pulex along a gradient of predation risk from C. flavicans. These changes are dominated by the inducible morphological defence, but there are also changes in the size and shape of the head and the body. We detected change in specific modules of the body plan and a level of integration among modules. These results are indicative of a complex, multi-faceted response to predation and provide insight into how predation risk drives variation in shape and size at the level of the whole organism.