New computer program predicts future extinctions
A new computer program for predicting future species extinctions has been developed as part of a PhD thesis at the University of Gothenburg.
Tobias Andermann, PhD student at the Department of Biological and Environmental Sciences is part of a research team that works with software development. The team has made it their mission to produce computer programs that enable modern biologists to work with a broad range of new technologies and with large datasets.
Based on public data
Tobias Andermann now presents some of the latest programs in his doctoral dissertation. One of these programs is designed to predict future species extinctions. The program is based on publicly available data.
“It is amazing how much freely available data is stored in online databases and we are only beginning to fully utilize the potential of these data. This can for example be data about where species were sighted and information about how threatened with extinction a species is. All of this information can be used in our evolutionary models to predict the most likely future for a given species,” says Tobias Andermann.
It is important to predict future extinctions
Predicting future extinctions and extinction risks is something that is of the utmost importance, Tobias Andermann believes.
“We are in the middle of an unfolding mass extinction and in order to counteract this trend, it is crucial for us to be able to predict when, where, and which species are likely to go extinct.”
“Our program can also be used to simulate the effects of specific conservation strategies and the results give us hope. We can make a huge positive impact if we act now.”
Another program for large DNA datasets
A program that helps evolutionary biologists to analyze the enormous amounts of data produced with new DNA sequencing technologies is also presented in Tobias Andermann’s PhD thesis.
“The amount of data you receive from modern DNA sequencing machines is simply overwhelming. We are talking about billions of DNA nucleotides that these machines can produce for a single sample, all scattered across millions of short sequences. Researchers then need to put this gigantic puzzle back together to be able to work with these data. That’s exactly where our new program comes in,” says Tobias Andermann.
The program has been downloaded more than 19,000 times within a short time frame by researchers and students from around the world.
Contact: Tobias Andermann, Department of Biological and Environmental Sciences, phone: +46 (0) 760 - 90 11 06, email: email@example.com
Dissertation title: Advancing Evolutionary Biology: Genomics, Bayesian Statistics, and Machine Learning
Digital thesis publication: http://hdl.handle.net/2077/66848
Supervisors: Alexandre Antonelli and Daniele Silvestro