Species distribution models have become a paramount tool to biodiversity assessments. And this tool,in combination with remote sensing products and the current global datasets of species ground observations, results in a powerful approach to monitor biodiversity at multiple spatial and temporal scales. Even though, great advancement in the development of these tools has been reach, the strength of this combination - modelling methods, remote sensing products and species observations - relies in its informed, and careful, selection and application using integrative modelling approaches. The goal of this milestone is to provide the first species distribution models using throughput methods, relying on open source algorithms. To accomplish this goal we divided the milestone in four main areas: 1. Data sources: Species distribution models require reliable information regarding the environmental conditions at the study area, as well as ground observations of the focal species. There are multiple sources of this information, and they vary in their characteristics and quality, and therefore must be selected depending on the system and aim of the study. In this section of the milestone, there is a description of the sources of environment and species data that includes an assessment of the strengths and weaknesses for species distribution modelling. 2. Biotic interactions: Species distribute depending on their physical requirements (i.e. environmental variables) and the distribution of other species (e.g. predators, prey, hosts, parasites, etc.), thus quality of the distribution models also depend on the information regarding the characteristics and biotic interactions of the focal species. In this section, biotic interactions are described in the light of species distribution models, in order to provide a background for their inclusion in the integrative modelling approaches that are being developed as part of EU BON. 3. Species distribution models: In this section the first integrative species distribution modelling approaches that have been developed by the partners in task 4.1 are described, providing information on the methods, implementation and the outcomes. Additionally, there is an analysis of sampling bias due to data sources (environment and species) and site characteristics (e.g. small islands with high mountains) on species distribution models. 4. Representation of uncertainty: Uncertainty in the outcomes of the species distribution models is due to the variability in the characteristics and quality of the data sources and the modelling approaches. Such uncertainty must be explicitly presented in order to ensure transparency of the methods, and increase the applicability of the resulting models to end-users. This section includes the identification of uncertainty in the modelling approaches applied with a set of strategies to quantify and present it. Finally, the links to other work packages and the future directions on the work of task 4.1 are presented.