Abstract
The spatial distributions of species and populations are both influenced by local variables and by characteristics of surrounding landscapes. Understanding how a landscape spatially structures the frequency of a trait in a population, the abundance of a species or the species’ richness is difficult since it requires estimating the intensity and the spatial scale effects of the landscape variables.
Here, we present ‘SILand’, an R package for analysing georeferenced point observations associated with landscape characteristics described in a Geographic Information System shapefile format. By modelling the effect of landscape variables using spatial influence functions, ‘SILand” simultaneously estimates the intensities and spatial scales of landscape variable effects. Different types of observations (continuous, discrete, proportion) are considered. Local, fixed and random effects are added.
‘SILand’ allows for testing the significance of local and landscape variables effects and for estimating the significant influence area of a landscape variable to create maps of their effects and to compare models by computing the AIC criteria.
We illustrate the main steps of a landscape analysis with a case study about codling moth density in a landscape composed of organic and conventional orchards and vineyards to demonstrate the functionality of SILand.
