Aaron Isaà Plex Sulá, BS
PhD Student
Plant Pathology Department, Global Food Systems Institute, 3Emerging Pathogens Institute, University of Florida, Gainesville, FL, USA
Gainesville, FL, USA
Krishna Keshav
MSc
Plant Pathology Department, University of Florida
Gainesville, Florida, United States
Ashish Adhikari, PhD (he/him/his)
Plant Pathology Department, University of Florida
Gainesville, Florida, United States
Romaric Armel Mouafo-Tchinda, PhD
PhD
Plant Pathology Department, Global Food Systems Institute, 3Emerging Pathogens Institute, University of Florida, Gainesville, FL, USA
GAINESVILLE, Florida, United States
Jacobo Robledo
Plant Pathology Department, University of Florida
Gainesville, Florida, United States
Stavan Shah
Plant Pathology Department, University of Florida
Gainesville, Florida, United States
Karen A. Garrett, PhD
Preeminent Professor
Plant Pathology Department, Global Food Systems Institute, 3Emerging Pathogens Institute, University of Florida, Gainesville, FL, USA
Gainesville, Florida, United States
Mapping host availability and environmental conduciveness is a common approach to determine which locations are particularly important in epidemics. Mapping pathogen habitat connectivity takes geographic risk analysis a step further, evaluating the potential roles of locations in epidemics. Locations with abundant habitats may play a minor role in pathogen spread if they are isolated. Yet, a location with limited habitat may play a major role in epidemics if it acts as a bridge between regions that would otherwise be separated. Here we introduce the geohabnet R package, which maps the importance of locations for the likely spread of pathogens through habitat landscapes. Unlike most software analyzing landscape connectivity, geohabnet incorporates key factors such as dispersal likelihoods and habitat availability, which are needed to understand habitat connectivity for host-dependent species like pathogens and insect vectors. geohabnet uses publicly available or user-provided datasets of host distribution, user-specified parameters for short- and long-distance dispersal, six network metrics, and a user-selected geographic scale (global, national, or smaller). We provide examples of the use of geohabnet in Africa and the Americas. We illustrate how users can apply geohabnet for their pathogen of interest, to generate maps of the likely importance of geographic locations in epidemics. geohabnet provides a quick and open-source approach to identifying candidate priority locations for managing transboundary diseases, as a baseline before more detailed studies can be performed.