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 sort of information is amongst the most fundamental of all ecological knowledge and is of great societal importance. Distribution data can feed into almost all other biodiversity models, and can inform adaptive

Filzbach is a flexible, fast, robust, parameter estimation engine that allows you to parameterize arbitrary, non-linear models, of the kind that are necessary in biological sciences, against multiple, heterogeneous data sets. Filzbach allows for Bayesian parameter estimation, maximum likelihood analysis, priors, latents, hierarchies, error propagation and model selection, often with just a few lines of code.

Visualize your data over the web: add complex dynamic graphs and maps to your web application.

Retrieve global environmental data with the click of a button or a few lines of code

Climate change is the greatest global challenge of the 21st century. Models that reliably forecast future climates associated with different policy scenarios are urgently needed. This project has developed a new model for the key source of uncertainty in earth system models: the terrestrial carbon climate feedback, using a methodology to account for all known sources of uncertainty and enable robust estimates of the confidence that can be placed in predictions and objective model refinement.

Software for reading/writing and sharing multidimensional arrays of data

Forests contain two thirds of terrestrial biodiversity and store as much carbon and is currently in the atmosphere. We are combining new abstractions of forests, with various sources of data, via Bayesian statistics, to produce useful, predictive models of forest dynamics.

Data mining tools to explore exponentially growing archives of environmental sciences.