TESTING INFERENCES ABOUT MICRO-EVOLUTIONARY PROCESSES BY MEANS OF SPATIAL AUTOCORRELATION ANALYSIS

We generated numerous simulated gene-frequency surfaces subjected to 200 generations of isolation by distance with, in some cases, added migration or selection. From these surfaces we assembled six data sets comprising from 12 to 15 independent allele-frequency surfaces, to simulate biologically plausible population samples. The purpose of the study was to investigate whether spatial autocorrelation analysis will correctly infer the microevolutionary processes involved in each data set. The correspondence between the simulated processes and the inferences made concerning them is close for five of the six data sets. Errors in inference occurred when the effect of migration was weak, due to low gene frequency differential or low migration strength; when selection was weak and against a background with a complex pattern; and when a random process—isolation by distance—was the only one acting. Spatial correlograms proved more sensitive to detecting trends than inspection of gene-frequency surfaces by the human eye. Joint interpretation of the correlograms and their clusters proved most reliable in leading to the correct inference. The inspection and clustering of surfaces were useful for determining directional components. Because this method relies on common patterns across loci, as many gene frequencies as feasible should be used. We recommend spatial autocorrelation analysis for the detection of microevolutionary processes in natural populations.     

Spatial Autocorrelation Analysis of Migration and Selection

We test various assumptions necessary for the interpretation of spatial autocorrelation analysis of gene frequency surfaces, using simulations of Wright's isolation-by-distance model with migration or selection superimposed. Increasing neighborhood size enhances spatial autocorrelation, which is reduced again for the largest neighborhood sizes. Spatial correlograms are independent of the mean gene frequency of the surface. Migration affects surfaces and correlograms when immigrant gene frequency differentials are substantial. Multiple directions of migration are reflected in the correlograms. Selection gradients yield clinal correlograms; other selection patterns are less clearly reflected in their correlograms. Sequential migration from different directions and at different gene frequencies can be disaggregated into component migration vectors by means of principal components analysis. This encourages analysis by such methods of gene frequency surfaces in nature. The empirical results of these findings lend support to the inference structure developed earlier for spatial autocorrelation analysis.     

Cranial variation in European populations: a spatial autocorrelation study at three time periods

This study reports on spatial variation of 10 cranial variables in European populations at 3 time periods. Means for these variables, based on 137, 108, and 183 samples from the Early Medieval, Late Medieval, and Recent periods, were subjected to one-dimensional and directional spatial autocorrelation analyses. Significant spatial structure was found for most variables. It becomes more pronounced as time progresses. The spatial patterns are not strongly clinal. Correlograms based on distances computed from all variables are monotonic only to 900, 1,650, and 1,350 km for the three periods. Regional patterns are seen for most variables and become more structured and significant with time. There is little similarity among the correlograms of the variables at any one period and virtually none among periods. Inferences about spatial structure of these populations, based on spatial autocorrelation analysis, suggest a pattern dominated by migration, followed by expansion and admixture rather than selection or chance fluctuations. The patterns of morphometric change seem to reflect the patterns of linguistic change in these areas.     

Spatial patterns of human gene frequencies in Europe

The aims of this study of spatial patterns of human gene frequencies in Europe are twofold. One is to present new methodology developed for the analysis of such data. The other is to report on the diversity of spatial patterns observed in Europe and their interpretation as evidence of population processes. Spatial variation in 59 allele and haplotype frequencies (26 genetic systems) for polymorphisms in blood antigens, enzymes, and proteins is analyzed for an aggregate of 3,384 localities, using homogeneity tests, one-dimensional and directional spatial correlograms, and SYMAP interpolated surfaces. The data matrices are reduced to reveal the principal patterns by clustering techniques. The findings of this study can be summarized as follows: 1) There is significant heterogeneity in allele frequencies among the localities for all but one genetic system. 2) There are significant spatial patterns for most allele frequencies. 3) There is a substantial minority of clinal patterns in these populations. Clinal trends are found more frequently in HLA alleles than for other variables. North-south and northwest-southwest gradients predominate. 4) There is a strong decline in overall genetic similarity with geographic distance for most variables. 5) There are few, if any, appreciable correlations in pairs of allele frequencies over the continent, and there is little interesting correlation structure in the resulting correlation matrix. 6) Few spatial correlograms are markedly similar to each other, yet they form well-defined clusters. Spatial variation patterns, therefore, differ among allele frequencies. Patterns of human gene frequencies in modern Europe are diverse and complex. No single model suffices for interpretation of the observed genetic structure. Some clinal patterns reported here support the Neolithic demic-expansion hypothesis, others suggest latitudinal selection. Most of the clinal patterns are in HLA alleles, but there is also evidence from ABO for east-west migration diffusion. The majority of patterns are patchy, consistent with hypotheses of isolation by distance or of settlement of genetically differing, subsequently expanding ethnic groups. While undoubtedly there has been an ongoing stochastic process of differentiation consistent with the isolation-by-distance model, this has not obscured the directional patterns caused by migration (demic diffusion), and has perhaps only reinforced the contribution from settlement of ethnic units to patterns of genetic variation. However, the impact of the latter is most difficult to discern and requires further methodological developments.     

A Test of Spatial Autocorrelation Analysis Using an Isolation-by-Distance Model

Using the isolation-by-distance model as an example, we have examined several assumptions of spatial autocorrelation analysis applied to gene frequency surfaces. Gene frequency surfaces generated by a simulation of Wright's isolation-by-distance model were shown to exhibit spatial autocorrelation, except in the panmictic case. Identical stochastic generating processes result in surfaces with characteristics that are functions of the process parameters, such as parental vagility and neighborhood size. Differences in these parameters are detectable as differences in spatial autocorrelations after only a few generations of the simulations. Separate realizations of processes with identical parameters yield similar spatial correlograms. We have examined the inferences about population structure that could have been made from these observations if they had been real, rather than simulated, populations. From such inferences, we could have drawn conclusions about the presence of selection, migration and drift in given natural systems.     

Genome-wide population genetic analysis identifies evolutionary forces establishing continuous population divergence

Elucidating the mechanism shaping the spatial variations of traits has long been a central concern of evolutionary biologists. Geographic clines of allele/morph frequencies along environmental gradients are suggested to be established and maintained by the balancing of two opposing evolutionary forces, namely selection that generates spatial differentiation in morph frequencies, and selection and/or stochastic factors that lead to the coexistence of multiple morphs within a population. Thus, testing for both selection and stochastic factors is necessary for a comprehensive understanding of the mechanism underlying clinal variation in morph/allele frequency in natural populations. Here, I identified the evolutionary forces responsible for clinal variation of color morph frequency in Ischnura senegalensis by comparing the population divergence of putatively neutral loci generated by high-throughput next-generation sequencing (F _STn) with that of the putative color locus (F _STc). No strong correlation was observed between F _STn and F _STc, suggesting that stochastic factors contribute less to color-locus population divergence. F _STc was less than F _STn between populations exposed to similar environmental conditions, but greater than F _STn between populations exposed to different environmental conditions, suggesting that both balancing selection and divergent selection act on the color locus. Therefore, two antagonistic selection factors rather than stochastic and historical factors contribute to establishing the clinal variation of morph frequency in I. senegalensis.     

Spatial and temporal dynamics in a sexual selection mosaic

Selective regimes and phenotypic optima could either change smoothly and in a clinal fashion or be spatially organized in a more unpredictable mosaic pattern over the geographic landscape. When natural or sexual selection is driven by intra- or interspecific biotic interactions, fine-grained spatial variation in selective regimes could result in selection mosaics rather than clinal variation in selection. We investigated temporal variation and spatial organization in sexual selection on male body size along an ecological coastal-inland gradient of a polymorphic damselfly Ischnura elegans. Body size increased in a clinal fashion along this gradient: animals were smaller in size at the coast, but became larger in the inland areas. In contrast, the sexual selection regimes on male body size showed evidence of more fine-grained spatial organization with no evidence for a clinal pattern and low spatial autocorrelations between populations. These spatially fine-grained sexual selection regimes varied in sign and magnitude and were driven by a combination of the densities of heritable female color morphs and local female body sizes. We suggest that the spatial organization of the selective regimes can be interpreted as a sexual selection mosaic that is influenced by highly localized density- and frequency-dependent social interactions.     

Disentangling complex genomic signals to understand population structure of an exploited,estuarine‐dependent flatfish

Interpreting contemporary patterns of population structure requires an understanding of the interactions among microevolutionary forces and past demographic events. Here, 4,122 SNP‐containing loci were used to assess structure in southern flounder (Paralichthys lethostigma) sampled across its range in the US Atlantic Ocean (Atlantic) and Gulf of Mexico (Gulf) and relationships among components of genomic variation and spatial and environmental variables were assessed across estuarine population samples in the Gulf. While hierarchical amova revealed significant heterogeneity within and between the Atlantic and Gulf, pairwise comparisons between samples within ocean basins demonstrated that all significant heterogeneity occurred within the Gulf. The distribution of Tajima''s D estimated at a genome‐wide scale differed significantly from equilibrium in all estuaries, with more negative values occurring in the Gulf. Components of genomic variation were significantly associated with environmental variables describing individual estuaries, and environment explained a larger component of variation than spatial proximity. Overall, results suggest that there is genetic spatial autocorrelation caused by shared larval sources for proximal nurseries (migration/drift), but that it is modified by environmentally driven differentiation (selection). This leads to conflicting signals in different parts of the genome and creates patterns of divergence that do not correspond to paradigms of strong local directional selection.     

Pattern‐oriented modelling of population genetic structure

Although several statistical approaches can be used to describe patterns of genetic variation and infer stochastic differentiation, selective responses, or interruptions of gene flow due to physical or environmental barriers, it is worthwhile to note that similar processes, controlled by several parameters in theoretical models, frequently give rise to similar patterns. Here, we develop a Pattern‐Oriented Modelling (POM) approach that allows us to determine how a complex set of parameters potentially driving empirical genetic differentiation among populations generate alternative scenarios that can be fitted to observed data. We generated 10 000 random combinations of parameters related to population size, gene flow and response to gradients (both driven by dispersal and selection) in a spatially explicit model, and analysed simulated patterns with F_ST statistics and mean correlograms using Moran's I spatial autocorrelation coefficients. These statistics were compared with observed patterns for a tree species endemic to the Brazilian Cerrado. For a best match with observed F_ST (equal to 0.182), the important parameters driving simulated scenario are mainly related to population structure, including low population size with closed populations (low N_m), strong distance decay of gene flow, in addition to a strong effect of the initial variance of allele frequencies. These scenarios present a low autocorrelation of allele frequencies. Best matching of correlograms, on the other hand, appears in simulations with a large population size, high N_m and low population differentiation and F_ST (as well as more gene flow). Thus, targeting the two statistics (correlograms and F_ST) shows that best matches with empirical data with two distinct sets of parameters in the simulations, because observed patterns involve both a relatively high F_ST and significant autocorrelation. This conflict can be resolved by assuming that initial variance in allele frequencies can be interpreted as reflecting deep‐time historical variation and evolutionary dynamics of allele frequencies, creating a relatively high level of population differentiation, whereas current patterns in gene flow creates spatial autocorrelation. This make sense in terms of the previous knowledge on population differentiation in D. alata, especially if patterns are explained by a combination of isolation‐by‐distance and allelic surfing due to range expansion after the last glacial maximum. This reveals the potential for more complex applications of POM in population genetics. © 2014 The Linnean Society of London, Biological Journal of the Linnean Society, 2014, 113 , 1152–1161.     

Microevolution in time and space: SNP analysis of historical DNA reveals dynamic signatures of selection in Atlantic cod

Little is known about how quickly natural populations adapt to changes in their environment and how temporal and spatial variation in selection pressures interact to shape patterns of genetic diversity. We here address these issues with a series of genome scans in four overfished populations of Atlantic cod (Gadus morhua) studied over an 80‐year period. Screening of >1000 gene‐associated single‐nucleotide polymorphisms (SNPs) identified 77 loci that showed highly elevated levels of differentiation, likely as an effect of directional selection, in either time, space or both. Exploratory analysis suggested that temporal allele frequency shifts at certain loci may correlate with local temperature variation and with life history changes suggested to be fisheries induced. Interestingly, however, largely nonoverlapping sets of loci were temporal outliers in the different populations and outliers from the 1928 to 1960 period showed almost complete stability during later decades. The contrasting microevolutionary trajectories among populations resulted in sequential shifts in spatial outliers, with no locus maintaining elevated spatial differentiation throughout the study period. Simulations of migration coupled with observations of temporally stable spatial structure at neutral loci suggest that population replacement or gene flow alone could not explain all the observed allele frequency variation. Thus, the genetic changes are likely to at least partly be driven by highly dynamic temporally and spatially varying selection. These findings have important implications for our understanding of local adaptation and evolutionary potential in high gene flow organisms and underscore the need to carefully consider all dimensions of biocomplexity for evolutionarily sustainable management.     

Spatio-temporal variation in the strength and mode of selection acting on major histocompatibility complex diversity in water vole (Arvicola terrestris) metapopulations

Patterns of spatio-temporal genetic variation at a class II major histocompatibility complex (MHC) locus and multiple microsatellite loci were analysed within and between three water vole metapopulations in Scotland, UK. Comparisons of MHC and microsatellite spatial genetic differentiation, based on standardised tests between two demographically asynchronous zones within a metapopulation, suggested that spatial MHC variation was affected by balancing selection, directional selection and random genetic drift, but that the relative effects of these microevolutionary forces vary temporally. At the metapopulation level, between-year differentiation for MHC loci was significantly correlated with that of microsatellites, signifying that neutral factors such as migration and drift were primarily responsible for overall temporal genetic change at the metapopulation scale. Between metapopulations, patterns of genetic differentiation implied that, at large spatial scales, MHC variation was primarily affected by directional selection and drift. Levels of MHC heterozygosity in excess of Hardy–Weinberg expectations were consistent with overdominant balancing selection operating on MHC variation within metapopulations. However, this effect was not constant among all samples, indicating temporal variation in the strength of selection relative to other factors. The results highlight the benefit of contrasting variation at MHC with neutral markers to separate the effects of stochastic and deterministic microevolutionary forces, and add to a growing body of evidence showing that the mode and relative strength of selection acting on MHC diversity varies both spatially and temporally.     

Population structure in the barn swallow,Hirundo rustica: a comparison between neutral DNA markers and quantitative traits

Local adaptation to variable environments can generate clinal variation in morphological traits. Alternatively, similar patterns of clinal variation may be generated simply as a result of genetic drift/migration balance. Teasing apart these different processes is a continuing focus in evolutionary ecology. We compare genetic differentiation at molecular loci and quantitative traits to analyse the effect of these different processes in a morphological latitudinal cline of the barn swallow, Hirundo rustica, breeding across Europe. The results obtained show no structuring at neutral microsatellite loci, which contrasts with positive structuring at five quantitative morphometric traits. This supports the hypothesis that the observed morphometric cline in barn swallows is the result of selection acting in a spatially heterogeneous environment. © 2010 The Linnean Society of London, Biological Journal of the Linnean Society, 2010, 99 , 306–314.     

On the relative roles of selection and genetic drift in shaping MHC variation

Genes of the major histocompatibility complex (MHC) have provided some of the clearest examples of how natural selection generates discordances between adaptive and neutral variation in natural populations. The type and intensity of selection as well as the strength of genetic drift are believed to be important in shaping the resulting pattern of MHC diversity. However, evaluating the relative contribution of multiple microevolutionary forces is challenging, and empirical studies have reported contrasting results. For instance, balancing selection has been invoked to explain high levels of MHC diversity and low population differentiation in comparison with other nuclear markers. Other studies have shown that genetic drift can sometimes overcome selection and then patterns of genetic variation at adaptive loci cannot be discerned from those occurring at neutral markers. Both empirical and simulated data also indicate that loss of genetic diversity at adaptive loci can occur faster than at neutral loci when selection and population bottlenecks act simultaneously. Diversifying selection, on the other hand, explains accelerated MHC divergence as the result of spatial variation in pathogen‐mediated selective regimes. Because of all these possible scenarios and outcomes, collecting information from as many study systems as possible, is crucial to enhance our understanding about the evolutionary forces driving MHC polymorphism. In this issue, Miller and co‐workers present an illuminating contribution by combining neutral markers (microsatellites) and adaptive MHC class I loci during the investigation of genetic differentiation across island populations of tuatara Sphenodon punctatus. Their study of geographical variation reveals a major role of genetic drift in shaping MHC variation, yet they also discuss some support for diversifying selection.     

Spatial detection of outlier loci with Moran eigenvector maps

The spatial signature of microevolutionary processes structuring genetic variation may play an important role in the detection of loci under selection. However, the spatial location of samples has not yet been used to quantify this. Here, we present a new two‐step method of spatial outlier detection at the individual and deme levels using the power spectrum of Moran eigenvector maps (MEM). The MEM power spectrum quantifies how the variation in a variable, such as the frequency of an allele at a SNP locus, is distributed across a range of spatial scales defined by MEM spatial eigenvectors. The first step (Moran spectral outlier detection: MSOD) uses genetic and spatial information to identify outlier loci by their unusual power spectrum. The second step uses Moran spectral randomization (MSR) to test the association between outlier loci and environmental predictors, accounting for spatial autocorrelation. Using simulated data from two published papers, we tested this two‐step method in different scenarios of landscape configuration, selection strength, dispersal capacity and sampling design. Under scenarios that included spatial structure, MSOD alone was sufficient to detect outlier loci at the individual and deme levels without the need for incorporating environmental predictors. Follow‐up with MSR generally reduced (already low) false‐positive rates, though in some cases led to a reduction in power. The results were surprisingly robust to differences in sample size and sampling design. Our method represents a new tool for detecting potential loci under selection with individual‐based and population‐based sampling by leveraging spatial information that has hitherto been neglected.     

INTEGRATING LANDSCAPE GENOMICS AND SPATIALLY EXPLICIT APPROACHES TO DETECT LOCI UNDER SELECTION IN CLINAL POPULATIONS

Uncovering the genetic basis of adaptation hinges on the ability to detect loci under selection. However, population genomics outlier approaches to detect selected loci may be inappropriate for clinal populations or those with unclear population structure because they require that individuals be clustered into populations. An alternate approach, landscape genomics, uses individual‐based approaches to detect loci under selection and reveal potential environmental drivers of selection. We tested four landscape genomics methods on a simulated clinal population to determine their effectiveness at identifying a locus under varying selection strengths along an environmental gradient. We found all methods produced very low type I error rates across all selection strengths, but elevated type II error rates under “weak” selection. We then applied these methods to an AFLP genome scan of an alpine plant, Campanula barbata, and identified five highly supported candidate loci associated with precipitation variables. These loci also showed spatial autocorrelation and cline patterns indicative of selection along a precipitation gradient. Our results suggest that landscape genomics in combination with other spatial analyses provides a powerful approach for identifying loci potentially under selection and explaining spatially complex interactions between species and their environment.     

Genetic diversity and differentiation in a wide ranging anadromous fish,American shad (Alosa sapidissima), is correlated with latitude

Studies that span entire species ranges can provide insight into the relative roles of historical contingency and contemporary factors that influence population structure and can reveal patterns of genetic variation that might otherwise go undetected. American shad is a wide ranging anadromous clupeid fish that exhibits variation in demographic histories and reproductive strategies (both semelparity and iteroparity) and provides a unique perspective on the evolutionary processes that govern the genetic architecture of anadromous fishes. Using 13 microsatellite loci, we examined the magnitude and spatial distribution of genetic variation among 33 populations across the species' range to (i) determine whether signals of historical demography persist among contemporary populations and (ii) assess the effect of different reproductive strategies on population structure. Patterns of genetic diversity and differentiation among populations varied widely and reflect the differential influences of historical demography, microevolutionary processes and anthropogenic factors across the species' range. Sequential reductions of diversity with latitude among formerly glaciated rivers are consistent with stepwise postglacial colonization and successive population founder events. Weak differentiation among U.S. iteroparous populations may be a consequence of human‐mediated gene flow, while weak differentiation among semelparous populations probably reflects natural gene flow. Evidence for an effect of reproductive strategy on population structure suggests an important role for environmental variation and suggests that the factors that are responsible for shaping American shad life history patterns may also influence population genetic structure.     

Mantel test in population genetics

The comparison of genetic divergence or genetic distances, estimated by pairwise F_ST 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.     

Comparative population genomics of latitudinal variation in Drosophila simulans and Drosophila melanogaster

Examples of clinal variation in phenotypes and genotypes across latitudinal transects have served as important models for understanding how spatially varying selection and demographic forces shape variation within species. Here, we examine the selective and demographic contributions to latitudinal variation through the largest comparative genomic study to date of Drosophila simulans and Drosophila melanogaster, with genomic sequence data from 382 individual fruit flies, collected across a spatial transect of 19 degrees latitude and at multiple time points over 2 years. Consistent with phenotypic studies, we find less clinal variation in D. simulans than D. melanogaster, particularly for the autosomes. Moreover, we find that clinally varying loci in D. simulans are less stable over multiple years than comparable clines in D. melanogaster. D. simulans shows a significantly weaker pattern of isolation by distance than D. melanogaster and we find evidence for a stronger contribution of migration to D. simulans population genetic structure. While population bottlenecks and migration can plausibly explain the differences in stability of clinal variation between the two species, we also observe a significant enrichment of shared clinal genes, suggesting that the selective forces associated with climate are acting on the same genes and phenotypes in D. simulans and D. melanogaster.     

Spatial autocorrelation in biology 2. Some biological implications and four applications of evolutionary and ecological interest

Spatial autocorrelation analysis tests whether the observed value of a variable at one locality is significantly dependent on values of the variable at neighbouring localities. The method was extended by us in an earlier paper to include the computation of correlograms for spatial autocorrelation. These show the autocorrelation coefficient as a function of distance between pairs of localities, and summarize the patterns of geographic variation exhibited by the response surface of any given variable. Identical variation patterns lead to identical correlograms, but different patterns may or may not yield different correlograms. Similarity in the correlograms of different variation patterns suggests similarity in the generating mechanism of the pattern.
The inferences that can be drawn from correlograms are discussed and illustrated. Examination and analysis of variation patterns of several characters or gene frequencies for one population, or of several populations in different places or at different times, permit some conclusions about the nature of the populational processes generating the observed patterns.
Autocorrelation analysis is applied to four biological situations differing in the nature of the data (interval or nominal), in the type of grid connecting the localities (regular or irregular), and the field of application (evolution or ecology). The examples comprise genotypes of individual mice, blood group frequencies in humans, gene frequency variation in a perennial herb, and the distribution of species of trees. The implications of our findings are discussed.     

Testing for microevolution in body size in three blue tit populations

Quantifying the genetic variation and selection acting on phenotypes is a prerequisite for understanding microevolutionary processes. Surprisingly, long-term comparisons across conspecific populations exposed to different environments are still lacking, hampering evolutionary studies of population differentiation in natural conditions. Here, we present analyses of additive genetic variation and selection using two body-size traits in three blue tit (Parus caeruleus) populations from distinct habitats. Chick tarsus length and body mass at fledging showed substantial levels of genetic variation in the three populations. Estimated heritabilities of body mass increased with habitat quality. The poorer habitats showed weak positive selection on tarsus length, and strong positive selection on body mass, but there was no significant selection on either trait in the good habitat. However, there was no evidence of any microevolutionary response to selection in any population during the study periods. Potential explanations for this absence of a response to selection are discussed, including the effects of spatial heterogeneity associated with gene flow between habitats.