Automated Surface Feature Selection Using SALSA2D: Assessing Distribution of Elephant Carcasses in Etosha National Park

This paper describes the development of an automated knot selection method (selecting number and location of knots) for bivariate splines in a pure regression framework (SALSA2D). To demonstrate this approach we use carcass location data from Etosha National Park (ENP), Namibia to assess the spatial distribution of elephant deaths. Elephant mortality is an important component of understanding population dynamics, the overall increase or decline in populations and for disease monitoring. The presence only carcass location data were modelled using a downweighted Poisson regression (equivalent to a point-process model) and using developed method, SALSA2D, for knot selection. The result was a more realistic local/clustered intensity surface compared with an existing model averaging approach. Using the new algorithm, the carcass location data were modelled using additional environmental covariates (annual rainfall, distance to water and roads). The results showed high carcass intensity close to water holes (<3km) and roads (<2km) and in areas of the park with average rainfall (∼450mm annually). Some high risk areas were identified particularly in the north east of the park and the risk of death does not always coincide with elephant distribution across the park. These findings are an important component in understanding population dynamics and drivers for population and park management. Particularly for controlling elephant numbers and/or mitigation of anthrax or other disease outbreaks.