An intricate interplay between fauna and flora is required to maintain and enhance the essential ecological attributes such as structure, biodiversity, and functionality. However, nowadays, the availability of natural resources to sustain the fauna has dropped considerably due to the extent of land use and occupation by human activities, which are often associated with habitat degradation. Furthermore, many invasive species have become major threats to native fauna, mainly due to competition for resources and disease transmission. Understanding trends and fluctuations in populations of wild and invasive species is essential for developing biodiversity conservation policies. Most monitoring programs rely on counts obtained by camera traps, but estimating density when individuals can’t be individually identified is challenging. Methods are often based on fragile assumptions such as spatial homogeneity, representative sampling across habitats, and independent movement patterns for the animals—assumptions that don’t hold for fragmented landscapes. In this study, an agent-based simulation platform was developed in R, where users can import realistic landscapes, set up habitat weights for landscape classes, simulate trajectories across the landscape, and collect data from virtual camera traps. The program exports detection tables, density estimates from the Random Encounter Model (REM), and descriptive statistics for the simulated paths. As a case study, we simulated the movement dynamics of the invasive species Sus scrofa (wild boar) in the Southwest of São Paulo, Brazil, one of the worst invasive species in the world, associated with habitat destruction, strong competitive pressure on native pigs, crop destruction, and the transmission of several diseases. The study aimed to evaluate the performance of REM estimates on a heterogeneous fragmented landscape.