Spatially clustered count data provide more efficient search strategies in invasion biology and disease control. Issue 5 (4th May 2021)

• Record Type: Journal Article
• Authors: Stevens, Michael C. A.; Faulkner, Sally C.; Wilke, André B. B.; Beier, John C.; Vasquez, Chalmers; Petrie, William D.; Fry, Hannah; Nichols, Richard A.; Verity, Robert; Le Comber, Steven C.
• Is Part Of: Ecological applications. Volume 31:Issue 5(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-05-04
• Subjects: Bayesian parameter estimation -- Dirichlet process -- epidemiology -- finite mixture model -- mapping -- mosquito; Ecology -- Periodicals; Environmental protection -- Periodicals; Biology, Economic -- Periodicals; 577.05
• DOI: 10.1002/eap.2329
• Languages: English
• ISSNs: 1051-0761

Abstract

Abstract: Geographic profiling, a mathematical model originally developed in criminology, is increasingly being used in ecology and epidemiology. Geographic profiling boasts a wide range of applications, such as finding source populations of invasive species or breeding sites of vectors of infectious disease. The model provides a cost‐effective approach for prioritizing search strategies for source locations and does so via simple data in the form of the positions of each observation, such as individual sightings of invasive species or cases of a disease. In doing so, however, classic geographic profiling approaches fail to make the distinction between those areas containing observed absences and those areas where no data were recorded. Absence data are generated via spatial sampling protocols but are often discarded during the inference process. Here we construct a geographic profiling model that resolves these issues by making inferences via count data, analyzing a set of discrete sentinel locations at which the number of encounters has been recorded. Crucially, in our model this number can be zero. We verify the ability of this new model to estimate source locations and other parameters of practical interest via a Bayesian power analysis. We also measure model performance via real‐world data in which the model infers breeding locations of mosquitoes in bromeliads in Miami‐Dade County, Florida, USA. In both cases, our novel model produces more efficient search strategies by shifting focus from those areas containing observed absences to those with no data, an improvement over existing models that treat these areas equally. Our model makes important improvements upon classic geographic profiling methods, which will significantly enhance real‐world efforts to develop conservation management plans and targeted interventions.