Protected areas and biological stations around the world have been collecting data on biological communities and ecosystem functioning for decades. Museums and herbariums hold hundreds of millions of biodiversity records usually backed up by specimens that can go back centuries.

Conservation societies, volunteer naturalists and more recently citizen scientists have expanded exponentially the amount of behavioral observations, distribution records and morphological measurements. Also Earth Observation Agencies have made available petabytes of remotely sensed high-frequency long-term data from earth-orbiting instruments at very high spatial resolutions.

More data should be easily translated into better monitoring, more informed policy-making and more accurate forecasting. Nevertheless, global and regional data integration initiatives find that local data is often inaccessible from outside countries. Differences in methods, protocols, standards and even languages from different sources make datasets irreconcilable.

Also the great majority of species data, hosted in museums, has not been georeferenced and digitized. Moreover, despite the availability of data from dozens of multi- and hyper-spectral remote sensors circling the planet continuously, the biodiversity community has not yet been able to exploit the full potential of these data to detect and monitor pressure, state and response of biodiversity with some promising exceptions.


The grand challenge is to put all these pieces of information together into a global framework that can help us understand the biosphere as a system and how and why it is changing. As a response to this need, GEO BON is proposing a conceptual framework that should facilitate the harmonization of existing biodiversity monitoring initiatives and guide the implementation of new monitoring schemas.

This framework is a component of a larger GEO BON effort to improve our understanding of the biotic response to global change, by integrating previously disconnected dimensions of biodiversity and also by connecting local trends to regional and global trends, offering tests of the predictive capacity of models in response to global change, a critical step in making ecological forecasting more rigorous.