Hi Loreta, I recommend the R package adehabitatHR (Calenge 2006). This package allows you to estimate home ranges using GPS point locations as those you have. The package implements several home range estimation methods from the traditional minimum convex polygon to more sophisticated functions like the classical Kernel method, which gives you a probability to relocate an individual in a certain area (the utilization distribution, UD). Moreover, you could display isopleth or contour lines that allow you to see the areas or patches were the animal spent more time on average. This package also implements several methods (e.g. Brownian bridge kernel) that take into account not only the position of relocations but also the path traveled by the animal between successive relocations. I only have used the MCP and the classical Kernel. For Kernel, it is key to have at least a minimum of 50 relocations per territorial pair or focal individual to make acceptable estimates of home ranges. If your point samples per invididual / pair are less than 50, Kernel could overestimate the home range. In this case, it is good to perform a bootstrap analysis of randomly selected points (see Anich et al., 2009, Auk 126: 626-634).

Gracias Jorge, muy completa la información!