openModeller Desktop provides a flexible, cross-platform environment where a fundamental niche model experiment is run.

openModeller Desktop includes facilities for reading species occurrence and environmental data, selection of environmental layers on that the model should be based, creating a fundamental niche model and projecting the model into an environmental scenario.

The openModeller Desktop project aims to provide a flexible, user friendly, cross-platform environment where the entire process of conducting a fundamental niche modeling experiment is carried out.

A number of fundamental niche modeling algorithms are provided as plug-ins, including Climate Space Model, Bioclimatic Envelopes, GARP and others. Additional algorithms are planned for the future. The submission of alternative algorithms are always welcome.

What's new in this release

• New algorithms - openModeller Desktop now ships with two recent algorithms (Support Vector Machines and Bioclim Score).
• GBIF Rest Data Fetcher (work sponsored by GBIF with our thanks!) - it is now possible to retrieve occurrence data using the recent GIF Rest query service. The data fetcher has as well had various cosmetic and useability improvements, and now functions as a wizard.
• Absence modelling is now supported - previous versions of openModellerDesktop did not handle absence data properly so absence modelling was not available.
• Web Service modelling improvements - improvement have been made to allow better job progress reporting when running as a web sevice.
• Experiments run in their own thread.- this makes for a more responsive user interface and you can look at completed models whilst the experiment is still running.
• Improved mapping capabilities - you can now specify occurrence symbol size and colour, and overlay a context layer (such as country boundaries) on your output map.

· Thresholding utility - you can now compute 'hotspot' models showing the predicted species count per cell by aggregating many model outputs into a single raster file. The threshold utility can as well be used for 'concensus' modelling where many algorithms are used for a single species and all the resulting probability distribution maps are combined to provide a map indicating where most algorithms are in agreement that a species will ocurr at a given cell. ...