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.

Despite recent advances in the identification of genetic population structure through molecular‐marker technology, the definition of intraspecific units for conservation remains problematic, particularly when genetic or phenotypic variation is continuously distributed in geographic space. We show that spatial autocorrelation analysis, applied to phenotypic or molecular data, can be used to describe the geographic structure and therefore can help define optimum strategies for conserving genetic variability within species. We propose that the intercept of a spatial correlogram can be an indication of the minimum distance between samples that can conserve and assess genetic diversity with maximum efficiency at lower costs. This parameter can be used both to define units and to establish sampling strategies for conservation programs. We illustrate the utility of this approach by autocorrelation analyses applied to three data sets: isozyme variability among Eugenia dysenterica populations in Brazilian Cerrado and within populations of Adenophora glandiflora in Korea, and microsatellite variation among Ursus arctos populations in North America. Our results suggest that the intercept of spatial correlograms is a useful parameter for establishing operational units for intraspecific conservation in continuous populations, based on overall genetic or phenotypic variability, by defining the minimum geographic distance at which samples are independent.

Venom gland transcriptomes and proteomes of six Micrurus taxa (M. corallinus, M. lemniscatus carvalhoi, M. lemniscatus lemniscatus, M. paraensis, M. spixii spixii, and M. surinamensis) were investigated, providing the most comprehensive, quantitative data on Micrurus venom composition to date, and more than tripling the number of Micrurus venom protein sequences previously available. The six venomes differ dramatically. All are dominated by 2–6 toxin classes that account for 91–99% of the toxin transcripts. The M. s. spixii venome is compositionally the simplest. In it, three-finger toxins (3FTxs) and phospholipases A2 (PLA2s) comprise >99% of the toxin transcripts, which include only four additional toxin families at levels ≥0.1%. Micrurus l. lemniscatus venom is the most complex, with at least 17 toxin families. However, in each venome, multiple structural subclasses of 3FTXs and PLA2s are present. These almost certainly differ in pharmacology as well. All venoms also contain phospholipase B and vascular endothelial growth factors. Minor components (0.1–2.0%) are found in all venoms except that of M. s. spixii. Other toxin families are present in all six venoms at trace levels (<0.005%). Minor and trace venom components differ in each venom. Numerous novel toxin chemistries include 3FTxs with previously unknown 8- and 10-cysteine arrangements, resulting in new 3D structures and target specificities. 9-cysteine toxins raise the possibility of covalent, homodimeric 3FTxs or heterodimeric toxins with unknown pharmacologies. Probable muscarinic sequences may be reptile-specific homologs that promote hypotension via vascular mAChRs. The first complete sequences are presented for 3FTxs putatively responsible for liberating glutamate from rat brain synaptosomes. Micrurus C-type lectin-like proteins may have 6–9 cysteine residues and may be monomers, or homo- or heterodimers of unknown pharmacology. Novel KSPIs, 3× longer than any seen previously, appear to have arisen in three species by gene duplication and fusion. Four species have transcripts homologous to the nociceptive toxin, (MitTx) α-subunit, but all six species had homologs to the β-subunit. The first non-neurotoxic, non-catalytic elapid phospholipase A2s are reported. All are probably myonecrotic. Phylogenetic analysis indicates that the six taxa diverged 15–35 million years ago and that they split from their last common ancestor with Old World elapines nearly 55 million years ago. Given their early diversification, many cryptic micrurine taxa are anticipated.

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.