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Null models provide a critical baseline for the evaluation of predictive disease models. Many studies consider only the grand mean null model (i.e. R2) when evaluating the predictive ability of a model, which is often misleading in conveying the predictive power of a model. We evaluated ten null models for human cases of West Nile virus (WNV), a zoonotic mosquito-borne disease introduced to the United States in 1999. The Negative Binomial, Historical (i.e. using previous cases to predict future cases) and Always Absent null models were the strongest overall, and the majority of null models significantly outperformed the grand mean. Somewhat surprisingly, the length of the training timeseries did not strongly affect the performance of most null models. We argue that a combination of null models is needed to evaluate the forecasting performance of predictive models and the grand mean is the lowest bar. Competing Interest Statement The authors have declared no competing interest. Footnotes * counties with frequent (cases in >50% of the time series) and infrequent (cases in <= 50% of the time series) were analyzed separately to avoid zero-inflation and to check for different patterns for different data structures.

Understanding host persistence with emerging pathogens is essential for conserving populations. Hosts may initially survive pathogen invasions through pre-adaptive mechanisms. However, whether pre-adaptive traits are directionally selected to increase in frequency depends on the heritability and environmental dependence of the trait and the costs of trait maintenance. Body condition is likely an important pre-adaptive mechanism aiding in host survival, although can be seasonally variable in wildlife hosts. We used data collected over seven years on bat body mass, infection, and survival to determine the role of host body condition during the invasion and establishment of the emerging disease, white-nose syndrome. We found that when the pathogen first invaded, bats with higher body mass were more likely to survive, but this effect dissipated following the initial epizootic. We also found that heavier bats lost more weight overwinter, but fat budgeting depended on infection severity. Lastly, we found little support that bat mass increased in the population after pathogen arrival, and there was high annual plasticity in individual bat masses. Overall, our results suggest that factors that contribute to host survival during pathogen invasion may diminish over time, and are potentially replaced by other host adaptations.Competing Interest StatementThe authors have declared no competing interest.