Mapping predicted dung beetle diversity in the lowland tropical forests of Borneo by utilizing indices derived from a satellite image

In tropical regions, assessing biodiversity across expansive areas through direct data collection presents significant challenges. The advancement of remote sensing techniques for mapping predicted biodiversity offers a viable solution. To map predicted dung beetle diversity and its associated ecological functions using satellite images, we selected optimal multivariate regression models to predict overall species richness and abundance, forest species richness and abundance, biomass (logarithmic dry weight) associating ecological functions of the beetle, Simpson’s diversity index (1/D), and forest community integrity (measured as the score on axis 1 of nonmetric multidimensional scaling ordination analysis), utilizing the Akaike information criterion (AIC). The explanatory variables were extracted from a SPOT-5 satellite image utilizing varying grid cell configurations up to 290 m². The objective variables consisted of dung beetle collection data from intact, burnt, or artificially degraded natural forests; plantation forests; and grasslands within a 20 × 20 km area near Balikpapan, Indonesia. When the predicted and observed values were compared, the coefficients of determination of the linear model exhibited marked significance, with all relations having an <i>R</i>_<i>2</i> exceeding 0.25. Mapping these predicted values revealed that the areas with high values generally coincided with the large intact natural forests and the remnants surrounding burnt areas, except for overall and forest species abundances, which displayed higher values along the burnt ridges surrounding the intact natural forests. These findings demonstrate that the mapping of predicted dung beetle diversity and its associated ecological functions within tropical regions can be effectively achieved through the use of satellite images.