Serology and statistical analyses
Blood samples were pre-diluted 1:30 and were analyzed for the presence of antibodies against EBLV-1 using a modified Rapid Fluorescent Focus Inhibition Test (RFFIT), in which EBLV-1b was used as challenge virus (Leopardi et al., 2018; Serra-Cobo, Amengual, Carlos Abellán, & Bourhy, 2002). Samples were analyzed on a three-fold dilution basis and titers were calculated through the Reed-Muench method and expressed as LogD50/ml. Samples were considered positive when able to inhibit viral growth at a minimum dilution of LogD50/ml ≥1.95.
We then tested how individual and environmental parameters influence both the likelihood of being seropositive and the serological titer of neutralizing antibodies against LYSVs in the target species M. myotis. In particular, the variables tested included geographic (altitude and coordinates), seasonal (before and after the birth pulse), demographic (age and sex) and genetic parameters (relative presence of individuals showing haplotypes clustering to distinct clades, using as a reference mitochondrial haplogroups defined by Ruedi et al, 2008). Full statistical analyses are reported as supplemental material (Supplementary Methods). Briefly, we applied a population averaged models using Generalized Estimating Equations (GEE) and a linear mixed model (LMM) to investigate the effects of all variables, respectively on the likelihood of showing neutralizing antibodies and on their titer (Dohoo, Wayne, & Stryhn, 2009; Liang & Zeger, 1986; West, Welch, & Galecki, 2007). For both types of models, we treated individuals sampled within the same colony as a cluster, thus assuming that all observations obtained within the same colony are correlated. Accordingly, we performed a robust estimation of the variances of the regression coefficients for the qualitative analysis and included a random-intercept for colony in the LMM (Rao et al., 2014; Ying & Liu, 2006). To assess the goodness of proposed models, we used the Quasi-likelihood under the independence model Criterion (QIC) and the Area under the ROC Curve (AUC) for GEE models, or the Akaike Information Criterion (AIC) and residual analysis for LMM models (Dohoo et al., 2009; Littell, Milliken, Stroup, Wolfinger, & Oliver, 2006; Samur, Coskunfirat, & Saka, 2014) considering a p-value <0.05 as significant.