Modelling spatial point patterns of Asian Giant Hornets occurrences

Abstract

This study explores statistical methods for modelling the spatial patterns of Asian giant hornets using sighting records in the Republic of Korea from 2008 to 2013. The focus is on simulating the spatial distribution of hornets, with a key aspect being the statistical inference of their intensity. Gaussian kernel-smoothing estimation is utilized to model the hornets intensity based on sighting records, which also transforms the occurrences of honeybees, a primary prey of the hornets, into a covariate. Results show a significant dependency of hornets intensity on honeybees intensity, with a calculated probability that a hornet sighting location has higher honeybee intensity than a random location. By this finding, the parametric modelling of the hornets spatial intensity is applied with the covariate of honeybees in each year, with the basic inhomogeneous Poisson point process along with the log-linear model. The model is refined for each year using backward stepwise selection based on the Akaike Information Criteria. Model validation confirms the Poisson process assumption and shows promising results with raw residuals against honeybee intensity. The analysis demonstrates that the spatial pattern modelling method employed is both sensible and valid.