Forest pathogens are important drivers of tree mortality across the globe but it is exceptionally challenging to gather and build unbiased quantitative models of their impacts, which has resulted in few estimates matching the scale of disease. Here we harness the rare dataset matching the spatial scale of pathogen invasion, host, and disease heterogeneity to estimate infection and mortality for the four most susceptible host species of Phytophthora ramorum, an invasive pathogen that drives the most important biological cause of tree mortality in a broad geographic region of coastal California and southwest Oregon. As of 2012, the most current field survey year, we estimate 17.5 (± 4.6 95% CI) million tanoak (Notholithocarpus densiflorus) stems were pathogen-killed with an additional 71 (± 21.5) million infected. We estimated 9.0 million (± 2.2) coast live oak (Quercus agrifolia) and 1.7 million (± 0.5) California black oak (Quercus kelloggii) stems are disease impacted (mortality and infection combined). Lastly, our estimates showed 95.23 million (± 8.6) million infected California bay laurel (Umbellularia californica), which does not suffer mortality from infection and represents a critical source of continued spread. Using an extensive dataset on mortality rates of infected individuals we estimate that cumulative mortality likely increased from 20.8 to 42.8 million individual stems between 2012 and 2019 for all species. While these impacts are substantial, the majority of host populations occur in a yet to be invaded region of northern California indicating that the disease will intensify in the coming decades.

Field data were collected with a standardized fixed plot survey using 1/20 ha plots and a survey of all stems greater than or equal to 1cm diameter at breast height. Counts of infected trees are those from plots with laboratory confirmation of infection (direct pathogen culturing). Host maps and environmental variables are derived data and details of the methods and sources are described in the accompanying meta data. Field data was processed following entry into a standardized Access database and packages into individual queries for species counts, mortality, and infection within species. Model derived data can be reproduced via the accompanying R code by using the stacked sets of rasterized environmental data and host maps.

The document "Cobb_et_al_Metadata.docx" explains how the data were used in processing. The R file titled "Cobb_et_al_SOD_impact_estimation.r" computes the models, generates the outputs, and does model validation and assessment.