Parallelism, the evolution of similar traits in populations diversifying in similar conditions, provides strong evidence of adaptation by natural selection. Many studies of parallelism focus on comparisons of different ecotypes or contrasting environments, defined a priori , which could upwardly bias the apparent prevalence of parallelism. Here, we estimated genomic parallelism associated with components of environmental and phenotypic variation at an intercontinental scale across four freshwater adaptive radiations (Alaska, British Columbia, Iceland, Scotland) of the three-spined stickleback ( Gasterosteus aculeatus ). We combined large-scale biological sampling and phenotyping with RAD-sequencing data from 73 freshwater lake populations and four marine ones (1,380 fish) to associate genome-wide allele frequencies with continuous distributions of environmental and phenotypic variation. Our three main findings demonstrate: 1) quantitative variation in phenotypes and environments can predict genomic parallelism; 2) genomic parallelism at the early stages of adaptive radiations, even at large geographic scales, is founded on standing variation; and 3) similar environments are a better predictor of genome-wide parallelism than similar phenotypes. Overall, this study validates the importance and predictive power of major phenotypic and environmental factors likely to influence the emergence of common patterns of genomic divergence, providing a clearer picture than analyses of dichotomous phenotypes and environments.
Parallelism, the evolution of similar traits in populations diversifying in similar conditions, provides good evidence of adaptation by natural selection. Many studies of parallelism have focused on comparisons of strongly different ecotypes or sharply contrasting environments, defined a priori, which could upwardly bias the apparent prevalence of parallelism. Here, we estimated genomic parallelism associated with individual components of environmental and phenotypic variation at an intercontinental scale across four adaptive radiations of the three-spined stickleback (Gasterosteus aculeatus), by associating genome-wide allele frequencies with continuous distributions of environmental and phenotypic variation. We found that genomic parallelism was well predicted by parallelism of phenotype-environment associations, suggesting that a quantitative characterization of phenotypes and environments can provide a good prediction of expected genomic parallelism. Further, we examined the explanatory power of genetic, phenotypic, and environmental similarity in predicting parallelism. We found that parallelism tended to be greater for geographically proximate, genetically similar radiations, highlighting the significant contingency of standing variation in the early stages of adaptive radiations, before new mutations accumulate. However, we also demonstrate that distance within multivariate environmental space predicts parallelism, after correction for genetic distance. This study thus demonstrates the relative influences of environment, phenotype and genetic contingency on repeatable signatures of adaptation in the genome.
Parasite virulence varies greatly. Theory predicts that this arises from parasites optimising a trade‐off between the mortality they inflict on current hosts, and their transmission to future hosts. The effect of the environment on this co‐evolution is rarely considered. Geographic mosaics are fertile systems for studying co‐evolution, but again, the diversity of outcomes is often assumed to result from co‐evolutionary dynamism, rather than being moulded by the environment. Here, we quantify variation in virulence among lakes in a geographic mosaic of co‐evolution between a trematode ectoparasite (Gyrodactylus arcuatus) and its three‐spined stickleback (Gasterosteus aculeatus) host. Virulence varies greatly in this system, and parasites are generally locally adapted to their hosts. Parasites are also locally adapted to the water in their own lake, and virulence is strongly related to lake pH, the dominant axis of abiotic environmental variation in this system. These results suggest that the evolution of virulence can be substantially affected by the abiotic environment, which has important implications for understanding co‐evolution. There are also implications for the evolutionary management of disease, e.g. ectoparasites in aquaculture, the impacts of which might be expected to reduce given ongoing acidification of aquatic ecosystems. A http://onlinelibrary.wiley.com/doi/10.1111/1365-2435.12921/suppinfo is available for this article.
Seasonal disease and parasitic infection are common across organisms, including humans, and there is increasing evidence for intrinsic seasonal variation in immune systems. Changes are orchestrated through organisms' physiological clocks using cues such as day length. Ample research in diverse taxa has demonstrated multiple immune responses are modulated by photoperiod, but to date, there have been few experimental demonstrations that photoperiod cues alter susceptibility to infection. We investigated the interactions among photoperiod history, immunity and susceptibility in laboratory-bred three-spined stickleback (a long-day breeding fish) and its external, directly reproducing monogenean parasite Gyrodactylus gasterostei . We demonstrate that previous exposure to long-day photoperiods (PLD) increases susceptibility to infection relative to previous exposure to short days (PSD), and modifies the response to infection for the mucin gene muc2 and Treg cytokine foxp3a in skin tissues in an intermediate 12 L : 12 D photoperiod experimental trial. Expression of skin muc2 is reduced in PLD fish, and negatively associated with parasite abundance. We also observe inflammatory gene expression variation associated with natural inter-population variation in resistance, but find that photoperiod modulation of susceptibility is consistent across host populations. Thus, photoperiod modulation of the response to infection is important for host susceptibility, highlighting new mechanisms affecting seasonality of host–parasite interactions.
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