See GLOBIO in use
Madingley Model website
Watch a short film
Both terrestrial and marine ecosystems are under pressure from growing human populations, which cause habitat loss and degradation, overexploitation and climate change. Making sense of how ecosystems are likely to respond under different scenarios is important for planning future biodiversity conservation strategies and for assisting decision makers facing questions that have an impact on ecosystems. It is also an essential part of shaping the agenda for future scientific research.
The current generation of biodiversity-focused computer models are generally based on statistical modelling of data, rather than of underlying ecological mechanisms, and tend to focus on only a few types of species. This can lead to a narrow view that does not take into account the dynamics of the broader ecological communities or how individual species contribute to the functioning of ecosystems. Furthermore, with the advent of rapid anthropogenic climate change, the past is becoming less reliable as a guide to the future. This requires novel models that are based on understanding the underlying mechanisms of ecosystem structure and response to disturbance.
UNEP-WCMC is developing three computer models for predicting future changes in biodiversity. Although they take different approaches, each model incorporates aspects of the broader view of ecosystems and their components; this allows more accurate future predictions.
PREDICTS (Projecting Responses of Ecological Diversity In Changing Terrestrial Systems) is a statistical model that represents many more species than previous models. It makes the most comprehensive predictions to date about past and future structure and diversity of ecological communities. The project:
GLOBIO is a statistical model used to assess past, present and future human impact on biodiversity. It focuses on a single measure of the intactness of ecological communities: the average abundance of all species. As a policy tool, it is regularly applied in global, regional and national assessments. Like the PREDICTS model, GLOBIO is based on data about cause–effect relationships derived from the literature, and currently focuses on the terrestrial environment. A module for the freshwater aquatic environment is under development (GLOBIO-aquatic).
The Madingley Model is the first ever attempt to model the biological and ecological interactions of the world’s ecosystems – both on land and in the ocean – for all organisms. It does this by simulating the processes that drive the distribution and abundance of individual organisms. This pioneering initiative aims to prove that the concept of a General Ecosystem Model – similar to the General Circulation Models used by climate change scientists – is viable.
The Madingley Model team has:
The Madingley Model is beginning to inform new avenues of scientific research and will enable investigation of policy questions, but the goal to date has been to demonstrate that building a working General Ecosystem Model is possible. The hope is that this will trigger a shift in biodiversity modelling which will help governments, companies and citizens to better manage the environment.
The UNEP-WCMC scientists involved in PREDICTS, GLOBIO and the Madingley Model are all experienced ecosystem modellers who have backgrounds in a broad range of relevant disciplines. Ecosystem scientist Mike Harfoot and marine biodiversity specialist Derek Tittensor work on the Madingley Model with terrestrial ecologist Tim Newbold, who also works on PREDICTS.
PREDICTS Partners: The Natural History Museum in London, Imperial College London, Microsoft Research, University College London and University of Sussex. It is funded by the UK Natural Environment Research Council (NERC), Microsoft Research, Arcadia and The Royal Society.
GLOBIO Partners are the Netherlands Environmental Assessment Agency (PBL), GRID-Arendal, University of British Colombia (UBC). It is funded by UNEP-WCMC.
The Madingley Model is being developed in partnership with Microsoft Research, and is funded by Microsoft Research and UNEP-WCMC