The spatial distribution of genetic variation has long been recognized as an important feature of population genetics. Our understanding of the basic spatial-temporal dynamics of genetic variation in populations continues to improve through theoretical and experimental studies. Dating back to the original work of Wright (1943) and Malecot (1948), theoretical work has indicated that spatial distributions of genetic variation should often differ strongly from random or uniform distributions. Nonrandomness, or spatial structuring, can strongly influence, and be strongly influenced by, many other important aspects of population genetics, including mating system, individual fitness, inbreeding depression, and the action of various other forms of natural selection, including environmental selection (e.g., Sokal, 1979; Epperson, 1990a). A large body of experimental studies of spatial structure of genetic variation confirms the theoretical predictions. Extensive reviews include those by Endler (1977), Bradshaw (1984), Nagylaki (1986), and Slatkin (1985, 1987). The importance of spatial structure can be separated into three components. First, spatial structure in some cases is inextricably linked to evo-

We apply the Space-Time AutoRegressive Moving Average (STARMA) modeling methods in an investigation of the spreading dynamics of a West Nile virus (WNV) epidemic in crows in the Detroit Metro area in 2002. The data fit very closely those expected from a purely STAR (Space-Time AutoRegressive) process having low spatial and temporal orders. The model can be used to characterize the past and possibly even predict the future dynamics of spreading behavior and, most importantly, to provide information about the factors which govern the spreading behavior. Use of the STARMA model allows estimation of the rate of spread of WNV at different spatial scales and thus characterization of the spatial and temporal scales expected. Determination of spatial-temporal autoregressive parameters using STARMA holds considerable promise for characterizing emerging infectious diseases.

Population genetics theory is primarily based on mathematical models in which spatial complexity and temporal variability are largely ignored. In contrast, the field of landscape genetics expressly focuses on how population genetic processes are affected by complex spatial and temporal environmental heterogeneity. It is spatially explicit and relates patterns to processes by combining complex and realistic life histories, behaviours, landscape features and genetic data. Central to landscape genetics is the connection of spatial patterns of genetic variation to the usually highly stochastic space-time processes that create them over both historical and contemporary time periods. The field should benefit from a shift to computer simulation approaches, which enable incorporation of demographic and environmental stochasticity. A key role of simulations is to show how demographic processes such as dispersal or reproduction interact with landscape features to affect probability of site occupancy, population size, and gene flow, which in turn determine spatial genetic structure. Simulations could also be used to compare various statistical methods and determine which have correct type I error or the highest statistical power to correctly identify spatio-temporal and environmental effects. Simulations may also help in evaluating how specific spatial metrics may be used to project future genetic trends. This article summarizes some of the fundamental aspects of spatial-temporal population genetic processes. It discusses the potential use of simulations to determine how various spatial metrics can be rigorously employed to identify features of interest, including contrasting locus-specific spatial patterns due to micro-scale environmental selection.

Landscape genetics plays an increasingly important role in the management and conservation of species. Here, we highlight some of the opportunities and challenges in using landscape genetic approaches in conservation biology. We first discuss challenges related to sampling design and introduce several recent methodological developments in landscape genetics (analyses based on pairwise relatedness, the application of Bayesian methods, inference from landscape resistance and a shift from population-based to individual-based analyses). We then show how simulations can foster the field of landscape genetics and, finally, elaborate on technical developments in sequencing techniques that will dramatically improve our ability to study genetic variation in wild species, opening up new and unprecedented avenues for genetic analysis in conservation biology.

Landscape features exist at multiple spatial and temporal scales, and these naturally affect spatial genetic structure and our ability to make inferences about gene flow. This article discusses how decisions about sampling of genotypes (including choices about analytical methods and genetic markers) should be driven by the scale of spatial genetic structure, the time frame that landscape features have existed in their current state, and all aspects of a species' life history. Researchers should use caution when making inferences about gene flow, especially when the spatial extent of the study area is limited. The scale of sampling of the landscape introduces different features that may affect gene flow. Sampling grain should be smaller than the average home-range size or dispersal distance of the study organism and, for raster data, existing research suggests that simplifying the thematic resolution into discrete classes may result in low power to detect effects on gene flow. Therefore, the methods used to characterize the landscape between sampling sites may be a primary determinant for the spatial scale at which analytical results are applicable, and the use of only one sampling scale for a particular statistical method may lead researchers to overlook important factors affecting gene flow. The particular analytical technique used to correlate landscape data and genetic data may also influence results; common landscape-genetic methods may not be suitable for all study systems, particularly when the rate of landscape change is faster than can be resolved by common molecular markers.

Although many properties of spatial autocorrelation statistics are well characterized, virtually nothing is known about possible correlations among values at different spatial scales, which ultimately would influence how inferences about spatial genetics are made at multiple spatial scales. This article reports the results of stochastic space-time simulations of isolation by distance processes, having a very wide range of amounts of dispersal for plants or animals, and analyses of the correlations among Moran's I-statistics for different mutually exclusive distance classes. In general, the stochastic correlations are extremely large (>0.90); however, the correlations bear a complex relationship with level of dispersal, spatial scale and spatial lag between distance classes. The correlations are so large that any existing or conceived statistical method that employs more than one distance class (or spatial scale) should not ignore them. This result also suggests that gains in statistical power via increasing sample size are limited, and that increasing numbers of assayed loci generally should be preferred. To the extent that sampling error for real data sets can be treated as white noise, it should be possible to account for stochastic correlations in formulating more precise statistical methods. Further, while the current results are for isolation by distance processes, they provide some guidance for some more complex stochastic space-time processes of landscape genetics. Moreover, the results hold for several popular measures other than Moran's I. In addition, in the results, the signal to noise ratios strongly decreased with distance, which also has several implications for optimal statistical methods using correlations at multiple spatial scales.

Understanding the genetic basis of species adaptation in the context of global change poses one of the greatest challenges of this century. Although we have begun to understand the molecular basis of adaptation in those species for which whole genome sequences are available, the molecular basis of adaptation is still poorly understood for most non‐model species. In this paper, we outline major challenges and future research directions for correlating environmental factors with molecular markers to identify adaptive genetic variation, and point to research gaps in the application of landscape genetics to real‐world problems arising from global change, such as the ability of organisms to adapt over rapid time scales. High throughput sequencing generates vast quantities of molecular data to address the challenge of studying adaptive genetic variation in non‐model species. Here, we suggest that improvements in the sampling design should consider spatial dependence among sampled individuals. Then, we describe available statistical approaches for integrating spatial dependence into landscape analyses of adaptive genetic variation.

The Ponderosae subsection of the genus Pinus contains numerous taxa in disjunct mountain ranges of southern Arizona and New Mexico, differing for several leaf and cone traits, key among which is the number of leaf needles per fascicle. Trees with three needles are often found together with trees having five needles and mixed numbers. One taxonomic hypothesis is that there are swarms of hybrids between P. ponderosa and P. arizonica. A second hypothesis is that there are spatial mixtures of two separate taxa, five-needle P. arizonica and a "taxon X" containing three needle and mixed needle trees. We genotyped chloroplasts in one putative hybrid swarm on Mt. Lemmon using microsatellite markers and show that cpDNA is almost completely differentiated between two separate morphotypes corresponding to P. arizonica and "taxon X." Little if any introgression has occurred on Mt. Lemmon, and the simplest explanation is that little or no effective hybridization has occurred. Further results indicate that not only is taxon X not of hybrid origin, it is more closely related to nonregional Ponderosae other than P. ponderosa and P. arizonica. The results further suggest that other putative hybrid swarms in the region are also spatial mixtures of distinct taxa.

We characterized the population genetics of two Neotropical dioecious palm species of Chamaedorea with contrasting life strategies from the region that is both the northernmost extent and most species rich of the genus. Chamaedorea tepejilote is a common, wind-pollinated arboreal understory palm. Although most adult plants reproduce each year, only a few individuals produce the majority of flowers and seeds. Chamaedorea elatior , conversely, is an uncommon climbing subcanopy palm with entomophilous flowers (insect-pollinated characteristics). Most of the mature palms do not reproduce in consecutive years and fruiting is episodic. Isozymes with a total of 107 alleles for 27 loci of 17 enzymes from six populations were assessed. For both species, co-occurrence of high levels of genetic variation and homozygosity was observed ( C. tepejilote : H e: 0.385–0.442, f : 0.431–0.486; C. elatior : H e: 0.278–0.342, f : 0.466–0.535). Genetic differentiation of C. elatior was much lower ( θ =0.0315) than that for C. tepejilote ( θ =0.152). The contrast in differentiation may be influenced by differences in the spatial scale of the genetic neighborhoods of the two species. The simultaneous maintenance of inbreeding and of a large number of alleles within the populations is attributable to the low and variable number of mating pairs. Demographic studies indicate that this pattern could be explained by low reproductive frequency among individuals and over years in C. elatior and by reproductive dominance in C. tepejilote .

Amplified fragment length polymorphism (AFLP) analysis is a rapid and efficient method for producing DNA fingerprints and molecular characterization. Our objectives were to: estimate genetic similarities (GS), marker indices, and polymorphic information contents (PICs) for AFLP markers in almond cultivars; assess the genetic diversity of almond cultivars and wild species, using GS estimated from AFLP fingerprints and molecular characterization; and facilitate the use of markers in inter-specific introgression and cultivar improvement. The genetic diversity of 45 almond cultivars from Iran, Europe, and America, were studied assaying 19 primer combinations. In addition, several agronomic traits were evaluated, including flowering and maturity times, self-incompatibility, and kernel and fruit properties. Out of the 813 polymerase chain reaction fragments that were scored, 781 (96.23%) were polymorphic. GS ranged from 0.5 to 0.96, marker indices ranged from 51.37 to 78.79, and PICs ranged from 0.56 to 0.86. Results allowed the unique molecular identification of all assayed genotypes. However, the correlation between genetic similarity clustering as based on AFLP and clustering for agronomic traits was low. Cluster analysis based on AFLP data clearly differentiated the genotypes and wild species according to their origin and pedigree, whereas, cluster analysis based on agronomic data differentiated according the pomological characterization. Our results showed the great genetic diversity of the almond cultivars and their interest for almond breeding.

A theoretical relationship between isolation by distance or spatial genetic structure (SGS) and seed and pollen dispersal is tested using extensive spatial-temporal simulations. Although for animals Wright's neighbourhood size N(e) = 4pisigma(2)(t) has been ascertained also, where sigma(2)(t) is the axial variance of distances between parents and offspring, and it was recently confirmed that N(e) = 4pi(sigma(2)(f) + sigma(2)(m))/2 when dispersal of females and males differ, the situation for plants had not been established. This article shows that for a very wide range of conditions, neighbourhood size defined by Crawford's formula N(e) = 4pi(sigma(2)(s) + sigma(2)(p)/2) fully determines SGS, even when dispersal variances of seed (sigma(2)(s)) and pollen sigma(2)(p)) differ strongly. Further, self-fertilization with rate s acts as zero-distance pollen dispersal, and N(e) = 4pi[sigma(2)(s) + sigma(2)(p)(1 - s)/2] fully determines SGS, for most cases where there are both likely parameter values and substantial SGS. Moreover, for most cases, there is a loglinear relationship, I(1) = 0.587 - 0.117 ln(N(e)), between SGS, as measured by I(1), Moran's coefficient for adjacent individuals, and N(e). However, there are several biologically significant exceptions, namely for very low or large N(e), SGS exceeds the loglinear values. There are also important exceptions to Crawford's formula. First, plants with low seed dispersal, high outcross pollen dispersal and high selfing rate show larger SGS than predicted. Second, in plants with very low (near zero) seed dispersal, selfing decreases SGS, opposite expectations. Finally, in some cases seed dispersal is more critical than pollen dispersal, in a manner inconsistent with Crawford's formula.

Resource sustainability requires a thorough understanding of the influence of forest management programs on the conservation of genetic diversity in tree populations. To observe how differences in forest structure affect the genetic structure of eastern white pine ( Pinus strobus L.), we evaluated six eastern white pine sites across the 234 000 acre (1 acre = 0.4046856 ha) Menominee Indian Reservation in northeastern Wisconsin (45°00′N, 88°45′W). The six sites sampled for nuclear and chloroplast DNA microsatellite markers were of contrasting densities and managed by different management systems: shelterwood, pine release, plantation, and old growth. Three of the sites had natural regeneration, which was also sampled. Mean values of spatial genetic autocorrelation were positive in all mature populations and variable; the strongest spatial structuring of genes occurred in the least disturbed old-growth site (I – E(I) = 0.031). Genetic structuring at the historical old-growth site fit the isolation-by-distance model for a neighborhood size of 130 individuals. Significant inbreeding occurred in five populations, but the seedling or sapling populations as a group (f = 0.088) are significantly less inbred than the local mature populations (f = 0.197). The increase in heterozygosity between generations was attributed to harvesting having reduced the spatial genetic structure of the mature trees.

Human activities can change the spatial distribution of individuals within wildlife populations that in turn alters population allele frequencies and spatial genetic structure at fine scales. Artificial feeding is one such activity whose impact on wildlife physical condition, population dynamics, and transmission of disease has been well documented. To evaluate the impact of artificial feeding on the spatial distribution and social organization of white-tailed deer (Odocoileus virginianus) we estimated allele frequencies at 3 microsatellite loci for 2,177 hunter-harvested deer and characterized microgeographic genetic structure in 2 regions of the northeast lower peninsula of Michigan, USA, during and following cessation of artificial feeding. While artificial feeding was ongoing we observed no evidence of spatial genetic structure across either region. Spatial homogeneity of allele frequencies over such a large area was surprising given numerous studies that have documented spatial genetic structure in other deer populations, and it was likely a function of the aggregation of multiple kin-structured social groups (i.e., matrilines) at artificial feeding sites. Subsequently, when artificial feeding was banned, we found significant genetic differentiation among groups of deer in both regions. Detection of microgeographic genetic structure consistent with a pattern of isolation-by-distance following the ban on artificial feeding was likely the result of increased spatial segregation of social groups of related deer. Our results illustrate how analyses of the degree to which natural populations are spatially genetically structured can be used to infer the effects of human actions on wildlife movement patterns, breeding behaviors, and disease transmission that are difficult to determine using traditional methods.

The question of whether or not the high rates (mu) of mutation that occur for some hypervariable markers can affect commonly used empirical measures of spatial structure of genetic variation within populations is addressed. The results show that values of these measures are approximately halved when mu is 10(-2). Finest spatial-scale correlations, measured by either Moran's I-statistics or conditional kinship, are reduced by 30%-50%. When the mutation rate is 10 times lower, much smaller reductions result, e.g. averaging 7% for the finest scale correlations. Still smaller orders of magnitude of mu cause negligible changes in spatial structure, where any effects normally would not be detectable. The reductions are caused by forward mutations, and when the reductions are measured as percentages, they are nearly independent of the amount of structure produced sans mutation, except when dispersal is nearly minimal. The percent reductions are also nearly independent of the number of alleles and of back mutations, hence of the nature of the mutation process (e.g. stepwise or not). The results demonstrate that some hypervariable loci should have reduced spatial structuring, and that marker choice may affect the values observed in experimental surveys. Moreover, if fine-scale correlations are used to indirectly estimate dispersal distances, then mutation at high rates could inflate estimates, easily up to two- to three-fold.

A series of theoretical studies has formed a strong connection between spatial statistics observed in populations and summary measures of the amount of dispersal. Synthesized, these developments allow dispersal to be indirectly estimated from standing spatial patterns of genetic variation under a range of conditions broad enough to be likely met in most populations of either plants or animals. The spatial correlations at the shortest distances are particularly robust to range of conditions and have disproportionately high statistical power. This review integrates theoretical results in a way that maximizes robustness and flexibility in the use of short distance autocorrelation to estimate Wright's neighborhood size, or the total variance in dispersal distances. Empirical guidelines are developed that are meant to be as practical and broad as possible. The guidelines focus on Moran's I-statistics for diploid genotypes converted to allele frequencies, but are also extended to or compared with several other approaches.