New developments in spatial analysis of biodiversity and the increasing availability of georeferenced environmental data (Box 1), have led to their rapid integration into the field of molecular ecology. For example, recent extensions of classic phylogeographic analyses have integrated geographic information system (GIS) analyses of environmental data and ecological niche modelling techniques into phylogeographic inference on both contemporary and historical timescales (Kidd & Ritchie 2006; Carstens & Richards 2007; Knowles et al. 2007; Kozak et al. 2008; Swenson 2008). Indeed, the unification of environmental and genetic data has given rise to an entire subdiscipline within the field of molecular ecology, landscape genetics, which uses these landscape-level data in spatially explicit studies to better understand the historical and contemporary factors that influence the distribution of intra-specific diversity (Manel et al. 2003).
Much of the work in landscape genetics and phylogeography seeks to identify the historical and contemporary landscape features that have shaped current patterns of biological diversity. Recent advances in GIS technologies and spatial statistics have increased the predictive power of spatial analyses by refining approaches that identify and quantify associations between environmental variables and intra-specific genetic or phenotypic diversity (e.g. Foll & Gaggiotti 2006; Joost et al. 2007) and use this information to project the response variable patterns in space (the landscape) and time. These statistical associations can be used to project patterns of diversity across unsampled areas of the landscape, resulting in continuous predictions of (genetic or phenotypic) variation. When projected onto historical or estimated future environmental conditions, these predictions can be used to understand the spatio-temporal dynamics of biological variation (i.e. any type of variation within or between species or communities, such as alpha or beta diversity) under various scenarios of changing environmental conditions. Detailed maps of genetic, phenotypic and demographic variation have recently been used to address a broad array of topics, such as the nature of microevolutionary processes that promote diversification (Carnaval et al. 2009; Pease et al. 2009; Freedman et al. 2010), the prioritization of areas for conservation (Vandergast et al. 2008; Thomassen et al. 2010) and the identification of current and future hotspots of disease outbreaks (Gilbert et al. 2008).
An understanding of the utility, predictive power and limitations of these analyses is an important prerequisite for their application in molecular ecology. As such, our aim here is to review the techniques that generate spatially explicit predictions of biological variation (Table 1). In doing so, we specifically focus on techniques that explicitly integrate environmental information (such as climate or vegetation characteristics; see Box 1), as opposed to methods that perform purely spatial analysis (and thus focus on the relative position of divergent populations, without taking into account habitat properties). We exclude from our review those analyses that do not visualize the patterns of intra-specific variation and instead refer the reader to a number of excellent reviews of these topics (Manel et al. 2003; Kidd & Ritchie 2006; Carstens & Richards 2007; Knowles et al. 2007; Storfer et al. 2007; Holderegger & Wagner 2008; Kozak et al. 2008; Balkenhol et al. 2009a,b; Manel & Segelbacher 2009). Similarly, in a recent review of spatial statistical methods, Guillot et al. (2009) focused on isolation-by-distance and clustering methods, but did not discuss approaches that incorporate environmental data. The aim here is to fill this gap by focusing on the intergration of spatial environmental information with the analysis of intra-specific variation in order to project and visualize patterns of variation across landscapes. While we will focus on the analysis of intra-specific genetic and phenotypic variation, it is important to note that these methods are often suitable for the analysis of species-level data, such as community composition, or alpha or beta diversity. For each of the methods discussed we: (i) provide a technical description; (ii) present an illustrative example; and (iii) critically assess each method’s advantages and limitations. Finally, in an effort to stimulate thought and spur further development of these powerful analytical approaches, we identify specific theoretical and practical challenges that confront researchers interested in projecting biological variation on heterogeneous landscapes.
|Method||Utility||Data type||Examples||Software and web address|
|Canonical trend surface analysis||Modelling of biological variation across landscape||Spatial coordinates, environmental variables||Wartenberg (1985); Grivet et al. (2008); Sork et al. (2010)||Implemented in SAS; trend surface analysis implemented in SAM (http://www.ecoevol.ufg.br/sam/)|
|Principal coordinates of neighbour matrices||Modelling of biological variation across landscape; purely spatial modelling step of which the results are used in subsequent (regression) analyses||Spatial co-ordinates||Borcard & Legendre (2002); Dray et al. (2006); Legendre et al. (2009); Ruggiero et al. (2009)||Implemented in SAM (http://www.ecoevol.ufg.br/sam/)|
|Tree regression, random forest||Modelling of biological variation across landscape; relating environmental heterogeneity to biotic differences||Any type of location-specific measurement||Breiman et al. (1984); Breiman (2001a,b); Prasad et al. (2006); Cutler et al. (2007)||R-packages tree and randomForest available from the R site; http://cran.r-project.org/web/packages/tree/index.html, http://cran.r-project.org/web/packages/randomForest/index.html|
|GDM||Modelling of biological variation across landscape; relating environmental heterogeneity to biotic differences||Any type of dissimilarity matrix and spatial coordinates||Ferrier et al. (2007); Thomassen et al. (2010); Freedman et al. (2010)||ArcView 3.2 in conjunction with SPlus (no official release of the software); R-package GDM_R_ Distribution_Pack_V1.1, but not full utility; authors are working on a version with full utility; http://www.biomaps.net.au/gdm/|