| Affiliation: | 1. Odum School of Ecology, University of Georgia, Athens, GA, USA;2. Department of Ecology and Evolutionary Biology, Brown University, Providence, RI, USA;3. Department of Environmental Science and Policy, George Mason University, Fairfax, VA, USA;4. Department of Biology, University of New Mexico, Albuquerque, NM, USA;5. Department of Biology, McGill University, Montreal, Quebec, Canada;6. Cary Institute of Ecosystem Studies, Millbrook, New York, USA;7. Senckenberg Biodiversity and Climate Research Centre (BiK‐F), Senckenberg Gesellschaft für Naturforschung, Frankfurt, Germany;8. School of Electrical Engineering and Computer Science, Oregon State University, Corvallis, OR, USA;9. Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, CO, USA;10. Biological Sciences, Duke University, Durham, NC, USA;11. ITD Data Analysis and Modelling, DuPont Agricultural Biotechnology, Wilmington, DE, USA;12. Department of Environmental Sciences, Emory University, Atlanta, GA, USA;13. Department of Zoology, University of Otago, Dunedin, New Zealand |
| Abstract: | Identifying drivers of infectious disease patterns and impacts at the broadest scales of organisation is one of the most crucial challenges for modern science, yet answers to many fundamental questions remain elusive. These include what factors commonly facilitate transmission of pathogens to novel host species, what drives variation in immune investment among host species, and more generally what drives global patterns of parasite diversity and distribution? Here we consider how the perspectives and tools of macroecology, a field that investigates patterns and processes at broad spatial, temporal and taxonomic scales, are expanding scientific understanding of global infectious disease ecology. In particular, emerging approaches are providing new insights about scaling properties across all living taxa, and new strategies for mapping pathogen biodiversity and infection risk. Ultimately, macroecology is establishing a framework to more accurately predict global patterns of infectious disease distribution and emergence. |
