Potato virus Y (PVY) is an important plant pathogen causing considerable economic loss to potato production. Knowledge of the population genetic structure and evolutionary biology of the pathogen, particularly at a transnational scale, is limited but vital in developing sustainable management schemes. In this study, the population genetic structure and molecular evolution of PVY were studied using 127 first protein (P1) and 137 coat protein (CP) sequences generated from isolates collected from potato in China and Japan. High genetic differentiation was found between the populations from the two countries, with higher nucleotide diversity in Japan than China in both genes and a KST value of .216 in the concatenated sequences of the two genes. Sequences from the two countries clustered together according to their geographic origin. Further analyses showed that spatial genetic structure in the PVY populations was likely caused by demographic dynamics of the pathogen and natural selection generated by habitat heterogeneity. Purifying selection was detected at the majority of polymorphic sites although some clade‐specific codons were under positive selection. In past decades, PVY has undergone a population expansion in China, whereas in Japan, the population size of the pathogen has remained relatively constant.

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Chilli veinal mottle virus (ChiVMV) is an important plant pathogen with a wide host range. The genetic structure of ChiVMV was investigated by analyzing the coat protein (CP) genes of 87 ChiVMV isolates from seven Asian regions. Pairwise F ST values between ChiVMV populations ranged from 0.108 to 0.681, indicating a significant spatial structure for this pathogen. In phylogeny-trait association analysis, the viral isolates from the same region tended to group together, showing a distinct geographic feature. These results suggest that geographic driven adaptation may be an important determinant of the genetic diversity of ChiVMV.

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Shifts in the marginal distribution of covariates from training to the test phase, named covariate-shifts, often lead to unstable prediction performance across agnostic testing data, especially under model misspecification. Recent literature on invariant learning attempts to learn an invariant predictor from heterogeneous environments. However, the performance of the learned predictor depends heavily on the availability and quality of provided environments. In this paper, we propose a simple and effective non-parametric method for generating heterogeneous environments via Random Sample Weighting (RSW). Given the training dataset from a single source environment, we randomly generate a set of covariate-determining sample weights and use each weighted training distribution to simulate an environment. We theoretically show that under appropriate conditions, such random sample weighting can produce sufficient heterogeneity to be exploited by common invariance constraints to find the invariant variables for stable prediction under covariate shifts. Extensive experiments on both simulated and real-world datasets clearly validate the effectiveness of our method.

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