Species Distribution Modelling (SDM) aims to explain why species occur where they do, and why they do not occur anywhere else. For instance, why does an oak tree not occur further south in hotter and dryer regions, and why it may not occur further north in colder and wetter regions? This information is of great societal importance, and amongst the most fundamental of all ecological understanding. Species distribution information underpins almost all other biodiversity models, and can inform adaptive management in biodiversity conservation, carbon management all the way to climate change and agricultural policy.

Why would a species occur in these places and nowhere else? SDM seeks to answer these questions so we can predict what will happen in the future.

Our work is developing novel methods for SDM – Species Distribution Modelling – that are based on Bayesian methods. This change in the computational technology allows us to build new kinds of models that account for important uncertainties that traditional methods cannot. For instance, there is relatively little data, and that data is often low resolution being recorded in coarse maps. This means we have to do a lot with very little! For instance, our observations may be more representative of where a species can get to through movement or how species compete for space, rather than being representative of their true tolerance to temperature; our methods have to account for taht by including ecological processes. 

New Methods and Software: Traditional ‘correlative’ modelling methods can't necessarily account for these "ecological errors", making SDM one of the key sources of uncertainty raised in the IPCC report in 2007. We are taking on the challenge to build better models, and then to build that into new and freely available software for SDM. Most of this work has been funded by Microsoft Research Connections (http://research.microsoft.com/en-us/collaboration/), which has also supported research into users software requirements and work on novel visualisation strategies. We are trying to build a framework that can explicitly account for major uncertainties whilst being user friendly and scientifically robust.

This figure shows how competition between species may stop us observing the full range of a species' underlying response to climate. The pink species could actually live in all the regions shown in the grey-scale heat map (a). However, the other species stop the species living there through competition. We need to account for this in our data analysis, because we only see where a species actually occurs (b), not where it could occur.

Some of our new methods have addressed the difference between the true and measured values of environmental variables. We use multi-species models to better estimate what the real temperature is, helping better estimates of all species tolerances to climate. Without this solution we may over estimate species' tolerances.Some of methods have addressed the difference between the true and measured values of environmental variables. We use multi-species models to better estimate what the real temperature is, helping better estimates of all species tolerances to climate. Without this solution we may over estimate species' tolerances.