As a group, parasites are extraordinarily diverse. Even closely related parasites may behave very differently, infecting different host species, causing different pathologies, or infecting different tissues. For example, Escherichia coli bacteria, a typically harmless inhabitant of the human gut, can, in different forms, cause diarrhea, intestinal bleeding, urinary tract infections, kidney bleeding, meningitis, and other diseases [1]. Underlying this diversity is evolution. It is widely appreciated that parasites are prone to rapid evolution, and because of their often short generation times and large population sizes, parasites may evolve far more rapidly than their hosts. Attempts to understand parasite evolution, and the relevance of that evolution to disease, go back at least half a century to the first observations of drug resistance evolution in bacteria [2]. However, the application of evolutionary theory to parasites remains fertile ground for original research [3]. Indeed, evolutionary biology and parasitology have undergone such rapid advances in recent years that it has been difficult to keep abreast of both. Some recent papers, including the study of Babayan et al. in this issue of PloS Biology [4], apply results from one branch of evolutionary theory—life history theory—to the characteristics of pathogens of medical interest such as parasitic roundworms (nematodes) and malaria [5]. Babayan et al. propose that the life history of parasitic microfilarial worms shows evidence of adaptive “plasticity.” Specifically, they propose that worm development inside a mammalian host changes in response to the host's immunity, and that the parasite's response matches predictions from life history theory. Most basically, life history theory addresses the birth and death schedule of an organism in the context of its environment: how is natural selection expected to shape an organism's age of first reproduction, its fecundity, and survival? (See [6]–[9] for reviews.) Body size and other phenotypic traits are also often considered in the theory. As a typical example, a population that loses half its individuals each year to predation is expected to evolve to begin reproducing at a younger age than a population losing only 10% of its individuals to predation annually. This occurs even though early reproduction has costs that would reduce lifetime fecundity if predation is low. This early maturity increases the chance that that an individual survives to maturity, a feature that is increasingly important with an increasing mortality rate. As might be expected intuitively, increases in juvenile mortality select for earlier maturation, while increases in adult mortality do not have this effect [10],[11]. The specific life history that will evolve by natural selection depends on details such as the different mortality rates and fecundity schedules that accrue to individuals with different ages of maturation. In order to avoid telling just-so stories about the evolution of life history traits, it is important to make predictions and then test them. It is hard to make predictions for a single species, in a single environment, at one point in time. The difficulty is that we do not know what life history options are available to the organism, and unless those options are known, prediction is hard. To escape this dilemma, life history theory applications have developed almost completely in a comparative context, predicting how birth and death schedules should vary across populations of the same species in different environments. If population P1 inhabits environment E1 and population P2 inhabits environment E2, the theory leads to straightforward relative predictions based on the differences between E1 and E2. These predictions are easy because the birth–death constraints of the organism will be the same for population P1 as for P2, so even though we cannot predict the exact birth–death schedules favored in E1 and E2, we can predict the direction of the differences (whether the population in E1 should mature earlier or produce more offspring early in life, for example). The situation encountered in the study of Babayan et al. is slightly more complex. Their problem does not involve two separate populations of the same parasite in different environments, but instead involves one parasite population responding to different environments. It is of course ubiquitous for organisms to live in variable environments: the seeds of a single plant may encounter a range of soil moistures and sunlight availabilities to germinate; a parasite may encounter hosts having different levels of immunity. One possible response of the organism is to evolve a compromise life history, one that produces the best average response to all conditions. Another possibility is that the organism evolves a plastic response: in each environment the organism displays a different life history suited to that environment. Whether a fixed or plastic response is optimal depends on the costs of sensing and regulation versus the benefits of plasticity. An approach taken to determine if an organism displays adaptive phenotypic plasticity is to compare the responses in the two environments and see if the differences observed are consistent with what might be expected if the organism had optimized to the two environments.