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.
|Citation:||Rocchini, D.; Balkenhol, N.; Delucchi, L.; Ghisla, A.; Jordan, F.; Nagendra, H.; Vernesi, C.; Wegmann, M.; Neteler, M. (2011). Open Source spatial algorithms applied to landscape genetics. In: 7th ECEM (European Conference on Ecological Modelling), Riva del Garda, 30 May-1 June 2011. handle: http://hdl.handle.net/10449/21128|
|Organization unit:||Biodiversity and Molecular Ecology Department # CRI_2011-JAN2016|
|Authors:||Rocchini, D.; Balkenhol, N.; Delucchi, L.; Ghisla, A.; Jordan, F.; Nagendra, H.; Vernesi, C.; Wegmann, M.; Neteler, M.|
|Title:||Open Source spatial algorithms applied to landscape genetics|
|Appears in Collections:||03 - Conference object|