Open Source spatial algorithms applied to landscape genetics

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Open Source spatial algorithms applied to landscape genetics

Duccio Rocchini1_{, Niko Balkhenol}2_{, Luca Delucchi}1_{, Anne Ghisla}1_{, Ferenc Jordan}3_{, Harini}

Nagendra4,5_{, Cristiano Vernesi}1_{, Martin Wegmann}6_{, Markus Neteler}1

1_{: Fondazione Edmund Mach, Research and Innovation Centre, Department of Biodiversity}

and Molecular Ecology, Via E. Mach 1, 38010 S. Michele all’Adige (TN), Italy

2_{: Leibniz-Institute for Zoo and Wildlife Research (IZW), Evolutionary Genetics, Postfach}

601103, 10252 Berlin, Germany

3_{: The Microsoft Research - University of Trento, Centre for Computational and Systems}

Biology, Piazza Manci 17, 38123, Trento (Povo), Italy

4_{: Ashoka Trust for Research in Ecology and the Environment, 659 5th A Main Hebbal,}

Bangalore 560024, India

5_{: Center for the Study of Institutions, Population, and Environmental Change, Indiana}

University, 408 N. Indiana Avenue, Bloomington, IN 47408, USA

6_{: University of Würzburg, Institute of Geography, Department of Remote Sensing, Remote}

Sensing and Biodiversity Research, Am Hubland, 97074 Würzburg, Germany

Recent seminal papers have introduced landscape genetics as a new discipline incorporating landscape ecology and genetic diversity. Linking landscape and genetic data has been

acknowledged as a key aspect when seeking to develop a spatial theory of population genetics (Balkenhol et al., 2009).

Recent advances have been made to implement Free and Open Source Software (FOSS) approaches for studying genetic diversity (Guillot et al., 2008). Nonetheless, explicit approaches devoted to a spatial treatment of genetic structure (the spatial distribution of genetic variation) in an Open Source space are still lacking.

In this study, we will present a number of new spatial algorithms running in GRASS

(Geographical Resources Analysis Support System, Neteler and Mitasova, 2008), related to i) network theory, ii) fuzzy set theory, iii) landscape metrics, iv) population dynamics and connectivity, v) detection of spatial barriers and resistance of the landscape to gene flow. The proposed spatial modelling techniques may help understanding genetic structure over space leading to the development of effective strategies for the conservation of genetic diversity.

References

Balkenhol, N., Gugerli, F., Cushman, S., Waits, L., Coulon, A.l., Arntzen, J., Holderegger, R., Wagner, H. and Participants of the Landscape Genetics Research Agenda Workshop 2009. Identifying future research needs in landscape genetics: where to from here? Landscape Ecol. 24:455-463.

Guillot, G., Santos, F. and Estopu, A. 2008. Analysing georeferenced population genetics data with Geneland: a new algorithm to deal with null alleles and a friendly graphical user

interface. - Bioinformatics 24:1406-1407.

Neteler, M. and Mitasova, H., 2008. Open Source GIS: A GRASS GIS Approach. Third Edition. The International Series in Engineering and Computer Science. Springer, New York. Session chosen B9 (B STANDARD PRESENTATION, 9 Methods: spatial models)