Transmission and phylogenetic trees
We constructed transmission trees between pumas in each region using the R package TransPhylo (Didelot et al. 2017). TransPhylo uses a time-stamped phylogeny to estimate a transmission tree to gain inference into “who infected whom” and when. Briefly, this approach computes the probability of an observed transmission tree given a phylogeny using a stochastic branching process epidemiological model; the space of possible transmission trees is sampled using reversible jump Markov chain Monte Carlo (MCMC) (Didelot et al. 2017). This approach is particularly useful for pathogens where the outbreak is ongoing, and not all cases are sampled (Didelot et al. 2017), as is the case here. We leveraged our FIVpcoBayesian phylogenetic reconstructions from previous work and focused on the two clades of FIVpco that predominantly occurred in each region (see Fountain-Jones et al. 2019). Whilst the TransPhylo approach makes few assumptions, a generation time distribution (the time from primary infection to onward transmission) is required to calibrate the epidemiological model (Didelot et al. 2017). We assumed that generation time could be drawn from a Gamma distribution (k = 2, θ = 1.5) estimating onward transmission on average 3 years post-infection (95% interval: 0.3 - 8 years, based on average puma age estimates (Logan & Sweanor 2001)). Based on previous work (Lewis et al. 2015), we were confident that the proportion of cases (π) sampled was high, therefore we set the starting estimate of π to be 0.6 (60% of cases tested in each region), and allowed it to be estimated by the model. We ran multiple MCMC analyses of 400,000 iterations and assessed convergence by checking that the parameter effective sample size (ESS) was > 200. We computed the posterior distributions of R0, π, and the realized generation time from the MCMC output. We also estimated likely infection time distributions for each individual and compared these estimates to approximate puma birth dates to ensure that these infection time distributions were biologically plausible. We then computed a consensus transmission tree for each region to visualize the transmission probabilities between individuals through time. Lastly, we reformatted the tree into a network object (nodes as individual puma and edges representing transmission probabilities) and plotted it using the igraph package (Csárdi & Nepusz 2006) and overlaid puma sex as a trait. Overall weighted degree and weighted degree for each sex, including edges representing homophily (e.g., male-male) and heterophily (e.g., male-female), were also calculated using igraph .