A review of techniques for spatial modeling in geographical, conservation and landscape genetics

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.

Population structure of Eupemphix nattereri (Amphibia, Anura, Leiuperidae) from Central Brazil

This study reports on 156 specimens of the amphibian Eupemphix nattereri, a widely distributed leiuperid, obtained from 11 municipalities of central Brazil. The extent of genetic variation was quantified by determining the mean number of alleles per locus and the proportion of polymorphic loci. An analysis of molecular variance (AMOVA) was performed on the random amplified polymorphic DNA (RAPD) haplotypes. The genetic distances obtained by calculating pairwise phist among local samples were used to determine population relationships using the unweighted pair-group method (UPGMA) and non-metric multidimensional scaling (NMDS). The cophenetic correlation was calculated to confirm agreement between the genetic matrix and the unweighted pair group method with averages (UPGMA) dendrogram. To determine if genetic distances were correlated to geographical distances we constructed pairwise genetic distance and geographical distance matrices and compared them using the Mantel test. The AMOVA results indicated significant genetic differences (p < 0.001) between E. nattereri populations, representing 69.5 percent of the within population genetic diversity. The Mantel test showed no significant correlation (r = 0.03; p = 0.45) between the genetic and geographical distance matrices. Our findings indicate that the genetic variation of E. nattereri populations was randomly distributed in geographic space and that gene flow for this species is probably structured at spatial scales smaller than those between our samples.

Optimization procedures for establishing reserve networks for biodiversity conservation taking into account population genetic structure

Conservation genetics has been focused on the ecological and evolutionary persistence of targets (species or other intraspecific units), especially when dealing with narrow-ranged species, and no generalized solution regarding the problem of where to concentrate conservation efforts for multiple genetic targets has yet been achieved. Broadly distributed and abundant species allow the identification of evolutionary significant units, management units, phylogeographical units or other spatial patterns in genetic variability, including those generated by effects of habitat fragmentation caused by human activities. However, these genetic units are rarely considered as priority conservation targets in regional conservation planning procedures. In this paper, we discuss a theoretical framework in which target persistence and genetic representation of targets defined using multiple genetic criteria can be explicitly incorporated into the broad-scale reserve network models used to optimize biodiversity conservation based on multiple species data. When genetic variation can be considered discrete in geographical space, the solution is straightforward, and each spatial unit must be considered as a distinct target. But methods for dealing with continuous genetic variation in space are not trivial and optimization procedures must still be developed. We present a simple heuristic and sequential algorithm to deal with this problem by combining multiple networks of local populations of multiple species in which minimum separation distance between conserved populations is a function of spatial autocorrelation patterns of genetic variability within each species.

Null expectation of spatial correlograms under a stochastic process of genetic divergence with small sample sizes

Nesse artigo, um processo estocástico Ornstein-Uhlenbeck foi utilizado para simular a relaçäo exponencial entre divergência genética e distância geográfica, conforme é esperado em modelos de isolamento-por-distância, alpondras ou coalescência. As simulaçöes foram realizadas a partir de um dendrograma UPGMA estimado a partir das distâncias geográficas entre 13 populaçöes locais. As superfícies espaciais de freqüências alélicas simuladas foram analisadas através de autocorrelaçäo espacial e construçäo de distâncias genéticas de Nei, com base em diferentes números de alelos. A divergência entre populaçöes locais produziu padröes espaciais significativos, tanto em nível univariado (correlogramas espaciais) quanto em nível multivariado (teste de Mantel entre distâncias de Nei e distâncias geográficas). Entretanto, se as análises säo baseadas em um pequeno número de populaçöes locais, os perfis dos correlogramas variam consideravelmente e as distâncias Manhattan calculadas entre eles podem ser maiores do que as previamente estabelecidas em outros estudos de simulaçäo. O método proposto permite assim estabelecer uma amplitude de perfis que podem ser obtidos pelo mesmo processo estocástico de divergência genética. A comparaçäo de correlogramas observados com esses perfis permite assim evitar o uso de outros mecanismos microevolutivos para explicar essa divergência genética.

Spatial pattern and genetic diversity estimates are linked in stochastic models of population differentiation

Nessa comunicaçäo, nós utilizamos análises de dados simulados e reais para demonstrar que, sob processos estocásticos de diferenciaçäo entre populaçöes, os conceitos de heterogeneidade espacial e padräo espacial säo equivalentes. Nesses processos, a proporçäo de variaçäo entre populaçöes locais, estimada com base nas estatísticas FST, GST ou èP, está correlacionada com o coeficiente angular do teste de Mantel relacionando distâncias genéticas de Nei e distâncias geográficas. O intercepto dessa regressäo matricial indica o valor da divergência genética quando a distância geográfica é zero, estando assim correlacionado com o valor de 1-GST. Além do interesse conceitual, a avaliaçäo da relaçäo entre medidas de heterogeneidade e padräo espacial pode ser utilizada para testar desvios de processos estocásticos de divergência genética, comparando diferentes loci ou grupos de espécies.