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

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.

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.

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. ...