The comparison of genetic divergence or genetic distances, estimated by pairwise FST and related statistics, with geographical distances by Mantel test is one of the most popular approaches to evaluate spatial processes driving population structure. There have been, however, recent criticisms and discussions on the statistical performance of the Mantel test. Simultaneously, alternative frameworks for data analyses are being proposed. Here, we review the Mantel test and its variations, including Mantel correlograms and partial correlations and regressions. For illustrative purposes, we studied spatial genetic divergence among 25 populations of Dipteryx alata (“Baru”), a tree species endemic to the Cerrado, the Brazilian savannas, based on 8 microsatellite loci. We also applied alternative methods to analyze spatial patterns in this dataset, especially a multivariate generalization of Spatial Eigenfunction Analysis based on redundancy analysis. The different approaches resulted in similar estimates of the magnitude of spatial structure in the genetic data. Furthermore, the results were expected based on previous knowledge of the ecological and evolutionary processes underlying genetic variation in this species. Our review shows that a careful application and interpretation of Mantel tests, especially Mantel correlograms, can overcome some potential statistical problems and provide a simple and useful tool for multivariate analysis of spatial patterns of genetic divergence.

Most evolutionary processes occur in a spatial context and several spatial analysis techniques have been employed in an exploratory context. However, the existence of autocorrelation can also perturb significance tests when data is analyzed using standard correlation and regression techniques on modeling genetic data as a function of explanatory variables. In this case, more complex models incorporating the effects of autocorrelation must be used. Here we review those models and compared their relative performances in a simple simulation, in which spatial patterns in allele frequencies were generated by a balance between random variation within populations and spatially-structured gene flow. Notwithstanding the somewhat idiosyncratic behavior of the techniques evaluated, it is clear that spatial autocorrelation affects Type I errors and that standard linear regression does not provide minimum variance estimators. Due to its flexibility, we stress that principal coordinate of neighbor matrices (PCNM) and related eigenvector mapping techniques seem to be the best approaches to spatial regression. In general, we hope that our review of commonly used spatial regression techniques in biology and ecology may aid population geneticists towards providing better explanations for population structures dealing with more complex regression problems throughout geographic space.

Systematic Conservation Planning (SCP) involves a series of steps that should be accomplished to determine the most cost-effective way to invest in conservation action. Although SCP has been usually applied at the species level (or hierarchically higher), it is possible to use alleles from molecular analyses at the population level as basic units for analyses. Here we demonstrate how SCP procedures can be used to establish optimum strategies for in situ and ex situ conservation of a single species, using Dipteryx alata (a Fabaceae tree species widely distributed and endemics to Brazilian Cerrado) as a case study. Data for the analyses consisted in 52 alleles from eight microsatellite loci coded for a total of 644 individual trees sampled in 25 local populations throughout species' geographic range. We found optimal solutions in which seven local populations are the smallest set of local populations of D. alata that should be conserved to represent the known genetic diversity. Combining these several solutions allowed estimating the relative importance of the local populations for conserving all known alleles, taking into account the current land-use patterns in the region. A germplasm collection for this species already exists, so we also used SCP approach to identify the smallest number of populations that should be further collected in the field to complement the existing collection, showing that only four local populations should be sampled for optimizing the species ex situ representation. The initial application of the SCP methods to genetic data showed here can be a useful starting point for methodological and conceptual improvements and may be a first important step towards a comprehensive and balanced quantitative definition of conservation goals, shedding light to new possibilities for in situ and ex situ designs within species.

Complex and integrative approaches may be necessary to understand the abundant-centre model and the patterns in genetic diversity that may be explained by this model. Here we developed an integrated framework to study spatial patterns in genetic diversity within local populations, coupling genetic data, niche modelling and landscape genetics, and applied this framework to evaluate population structure of Caryocar brasiliense, an endemic tree from the Brazilian Cerrado. We showed different geographical patterns for genetic diversity, allelic richness and inbreeding levels, estimated using microsatellite data for ten local populations. Ecological suitability was estimating by combining five niche modelling techniques. Genetic diversity tend to follow a central-periphery model and is associated with ecological variables. On the other hand, inbreeding levels may be alternatively explained by isolation processes and habitat fragmentation more related to intense recent human occupation in the southern border of the biome, or by deeper historical patterns in the origin of the populations. Although still suffering from some of the problems of central-periphery analysis (small number of local populations), our analyses show how these patterns can be better investigated and offering a better understanding of the processes structuring genetic diversity within species' geographic ranges.

Since evolutionary processes, such as dispersal, adaptation and drift, occur in a geographical context, at multiple hierarchical levels, biogeography provides a central and important unifying framework for understanding the patterns of distribution of life on Earth. However, the advent of molecular markers has allowed a clearer evaluation of the relationships between microevolutionary processes and patterns of genetic divergence among populations in geographical space, triggering the rapid development of many research programmes. Here we provide an overview of the interpretation of patterns of genetic diversity in geographical and ecological space, using both implicit and explicit spatial approaches. We discuss the actual or potential interaction of phylogeography, molecular ecology, ecological genetics, geographical genetics, landscape genetics and conservation genetics with biogeography, identifying their respective roles and their ability to deal with ecological and evolutionary processes at different levels of the biological hierarchy. We also discuss how each of these research programmes can improve strategies for biodiversity conservation. A unification of these research programmes is needed to better achieve their goals, and to do this it is important to develop cross‐disciplinary communication and collaborations among geneticists, ecologists, biogeographers and spatial statisticians.