GIS and Remote Sensing Unit at FEM-CRI

Landscape genetics

Genetic diversity is important for the maintenance of the viability and the evolutionary and adaptive potential of populations and species.

Two previously separate research areas, genetics and landscape ecology have been integrated into the new discipline "landscape genetics" (see Holderegger and Wagner, Bioscience, 2008). This combined approach merges population genetics, landscape ecology and spatial statistics, typically performed in a GIS (Geographical Information System) environment. The combination of genetic markers with related spatio-environmental data is used to examine population demographics and evolutionary processes. For this purpose, genetic characteristics of a species are mapped across a landscape or differences between population are modeled by using landscape compositional and structural metrics. Neutral markers such as mitochondrial and Y chromosome haplotypes, microsatellite frequencies, single-nucleotide polymorphisms as well as genetic markers like the Major Histocompatibility Complex (MHC) allele frequencies are used.

Genetic diversity can be adaptive or neutral. Selectively neutral genetic variation is generally believed to not affect the fitness of the species. From patterns of neutral genetic diversity and differentiation demographic and evolutionary events like bottleneck, expansion, isolation, gene flow, divergence can be inferred. Adaptive markers, in contrast, are subject to selection constraints and are, therefore, better suited for studying the response to environmental changes. The opportunity to analyse neutral and adaptive variation in a GIS framework makes it possible to start evaluating i) the putative role of biotic and abiotic factors in shaping genetic diversity and ii) the differentiation in a determined area.

For instance, the genetic and landscape ecological data can be analysed for identifying barriers, gradients or transitions thus obtaining crucial information about connectivity among natural populations. By separating historic and recent gene flow, global and local changes may be identified which lead to changes, sometimes resulting in a loss of biodiversity. As an advantage, landscape genetics does not usually require to distinguish discrete populations in advance. Analyses are performed at population as well as individual levels.

We collect and integrate field data with GIS and climatic data, e.g. to find current and potential faunal corridors in Trentino. For key species, hypothesis will be discussed about population dimensions, measured as effective population size by means of molecular markers, with regards to expected temperature increase, precipitation decrease and increasing human impact on the territory. As outcome, predictive maps for the next 50 years will be created which display the expected population changes from today to future. As a result of our landscape genetics studies, the role of landscape variables in shaping genetic diversity and population structure will be better understood. The outcome is relevant for managing properly the genetic diversity of threatened and endangered populations. The study of genetic differences would permit to locate biodiversity hotspots or, in a time series, to investigate whether levels of biodiversity has changed. Finally, using Partial Mantel tests the effect of spatial distance between populations will be partialled out and the correlation between the genetic and environmental distance-matrices will be estimated (see e.g. Balkenhol et al., Ecography, 2009). This will allow us to detect not only the location of genetic diversity spots but even the ecological processes accounting for the pair-wise differentiation of populations.

References

• ACE-SAP project (Alpine Ecosystems in a Changing Environment: Biodiversity Sensitivity and Adaptive Potential)
• Rocchini, D., Balkenhol, N., Carter, G.A., Foody, G.M., Gillespie, T.W., He, K.S., Kark, S., Levin, N., Lucas, K., Luoto, M., Nagendra, H., Oldeland, J., Ricotta, C., Southworth, J., Neteler, M. (2010). Remotely sensed spectral heterogeneity as a proxy of species diversity: recent advances and open challenges. Ecological Informatics, 5: 318-329.
• Rocchini, D., Delucchi, L., Bacaro, G., Cavallini, P., Feilhauer, H., Foody, G.M., He, K.S., Nagendra, H., Porta, C., Ricotta, C., Schmidtlein, S., Spano, L.D., Wegmann, M., Neteler, M. (2011). Calculating generalized entropy as a measure of landscape diversity in an Open Source space. 54th Meeting of the International Association for Vegetation Science (IAVS), Lyon, France, 20-24 June 2011 (book of abstracts)
• 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. 7th ECEM (European Conference on Ecological Modelling), Riva del Garda, 30 May-1 June 2011 (book of abstracts)
• Rocchini et al.: Bayesian theory applied to landscape genetics: testing spatial autocorrelation of genetic similarity by a Markov Chain Monte Carlo test. Vegetation Databases and Climate Change. 9th international Meeting on Vegetation Databases. Hamburg, 24–26 February 2010