Code and data for: Are novel or locally adapted pathogens more devastating and why? : resolving opposing hypotheses

The naive host syndrome hypothesis suggests that pathogens are able to easily invade and become deadly to novel hosts because of a lack of co-evolutionary history, whereas the local adaptation hypothesis suggests that pathogens are better able to invade local hosts because of their co-evolutionary history, but rarely do studies on these two hypotheses cite one another or acknowledge their ostensibly mixed messages. By combining a continental-scale, factorial, common garden experiment with a global-scale meta-analysis, each on the amphibian-chytrid fungus host-pathogen system, we show that local host-pathogen interactions typically resulted in higher host mortality, greater infection success, and higher pathogen loads, but that there was substantial variation in novel host-pathogen outcomes and thus moving pathogens around the planet increases the likelihood of exposure to particularly virulent pathogen strains and particularly deadly host-pathogen combination. Therefore, we provide supp..., Data from the 5x6 experiment: \"5x6 experimental data.csv\"
Data used the meta analysis, previously published in Sauer et al. 2020: \"Sauer et al. 2020 data subset.csv\"
See metadata file for details
Code for all analyses: \"Code for Sauer et al Pathogen novelty.R\", , # Code and data for:Are novel or locally adapted pathogens more devastating and why?: resolving opposing hypotheses

[https://doi.org/10.5061/dryad.jwstqjqhp](https://doi.org/10.5061/dryad.jwstqjqhp)

\"5x6 experimental data.csv\" contains all the data from the 5x6 experiment that is used in all analyses of that experiment described in the main text and supplement. Detailed information about the dataset (units, variation definitions, etc) can be found in \"Sauer et al novelty metadata.csv\"

\"Sauer et al. 2020 data subset.csv\" contains the data used in the meta analysis described in the main text. This data is a subset of the previously published database from Sauer et al. 2020: [https://doi.org/10.1002/ecy.2979](https://doi.org/10.1002/ecy.2979)

Code for all analyses described in the main text and supplement can be found in the following R script file: \"Code for Sauer et al Pathogen novelty.R\" Library load lines are included in the script for all the packages needed to run the code. Both ...