Abstract: One of the most studied macro-ecological pattern is the Relative Species Abundance (RSA), describing the distribution of the population among species. Various natural ecosystems share a common shape of the empirical RSA and through years ecologists have used several recipes - some of them are rule of thumb prescriptions - to determine global RSA features from local information. Recently our group has developed a rigorous statistical framework able to predict global scale biodiversity from scattered local plots ([2], [3]). Such a framework is derived from a simple mathematical model informed by basic biological assumptions. In this talk we illustrate how to adapt/generalize such an approach to human activities. In particular we consider four datasets of man-made systems and/or network mediated human activities: e-mail communication, Twitter posts, Wikipedia articles and Gutenberg books. Once we set the proper correspondence to what we consider species and individuals of a species, our approach reveals RSA is scale-free in each mentioned dataset with a power law form maintained - with roughly the same exponent - through the different human activities considered. In turn, RSA scale-invariance allows us to use activity information on local scale to predict hidden features of the human dynamics at the global scale. E-mail communication is particular relevant to illustrate our method as well as our results and possible applications. We consider the senders’ activity network where each node is a sender and a directed link from node A to node B represents an email issued from user A to user B. We set the identity of a sender to identify the species and the number of sent emails to be the individuals pertaining to a species. Thus, for instance, if user A has sent n emails we say that the species A has n individuals. Now, suppose an observer has access to a small sample of sent emails, or equivalently to partial information on links and nodes of the email communication network. Our method is capable to infer the number of nodes (i.e. the number of users) and the degree distribution (i.e. the topology) of the whole network, thus revealing features of the dynamics unknown to the observer. Moreover, our framework predicts how the number of users grows with the number of links recorded, which represents another well known pattern in ecology called the Species-Accumulation Curve (SAC). These findings may have applications in resource management, optimal network design and information diffusion on graphs. This is a work in collaboration with M. Formentin, A. Tovo and S. Favaro [1].