In ecology, spatial data is often represented using polygons. These polygons can represent a variety of spatial entities, such as ecological patches, animal home ranges, or gaps in the forest canopy. Researchers often need to determine if two spatial processes, represented by these polygons, are independent of each other. For instance, they might want to test if the home range of a particular animal species is influenced by the presence of a certain type of vegetation. To address this, Godoy et al. (2022) (<doi:10.1016/j.spasta.2022.100695>) developed conditional Monte Carlo tests. These tests are designed to assess spatial independence while taking into account the shape and size of the polygons.