MOLECULAR ECOLOGY

The global distribution, abundance, and diversity of microscopic freshwater algae demonstrate an ability to overcome significant barriers such as dry land and oceans by exploiting a range of biotic and abiotic colonization vectors. If these vectors are considered unlimited and colonization occurs in proportion to population size, then globally ubiquitous distributions are predicted to arise. This model contrasts with observations that many freshwater microalgal taxa possess true biogeographies. Here, using a concatenated multigene data set, we study the phylogeography of the freshwater heterokont alga Synura petersenii sensu lato. Our results suggest that this Synura morphotaxon contains both cosmopolitan and regionally endemic cryptic species, co-occurring in some cases, and masked by a common ultrastructural morphology. Phylogenies based on both proteins (seven protein-coding plastid and mitochondrial genes) and DNA (nine genes including ITS and 18S rDNA) reveal pronounced biogeographic delineations within phylotypes of this cryptic species complex while retaining one clade that is globally distributed. Relaxed molecular clock calculations, constrained by fossil records, suggest that the genus Synura is considerably older than currently proposed. The availability of tectonically relevant geological time (107–108 years) has enabled the development of the observed, complex biogeographic patterns. Our comprehensive analysis of freshwater algal biogeography suggests that neither ubiquity nor endemism wholly explains global patterns of microbial eukaryote distribution and that processes of dispersal remain poorly understood.

The extent to which the distribution of soil bacteria is controlled by local environment vs. spatial factors (e.g. dispersal, colonization limitation, evolutionary events) is poorly understood and widely debated. Our understanding of biogeographic controls in microbial communities is likely hampered by the enormous environmental variability encountered across spatial scales and the broad diversity of microbial life histories. Here, we constrained environmental factors (soil chemistry, climate, above-ground plant community) to investigate the specific influence of space, by fitting all other variables first, on bacterial communities in soils over distances from m to 102 km. We found strong evidence for a spatial component to bacterial community structure that varies with scale and organism life history (dispersal and survival ability). Geographic distance had no influence over community structure for organisms known to have survival stages, but the converse was true for organisms thought to be less hardy. Community function (substrate utilization) was also shown to be highly correlated with community structure, but not to abiotic factors, suggesting nonstochastic determinants of community structure are important Our results support the view that bacterial soil communities are constrained by both edaphic factors and geographic distance and further show that the relative importance of such constraints depends critically on the taxonomic resolution used to evaluate spatio-temporal patterns of microbial diversity, as well as life history of the groups being investigated, much as is the case for macro-organisms.

We investigated genetic diversity and structure of urban white-footed mouse, Peromyscus leucopus, populations in New York City (NYC) using variation at 18 microsatellite loci. White-footed mice are ‘urban adapters’ that occur at higher population densities as habitat fragments are reduced in area but have a limited ability to disperse through urbanized areas. We hypothesized that this combination of traits has produced substantial genetic structure but minimal loss of genetic variation over the last century in NYC. Allelic diversity and heterozygosity in 14 NYC populations were high, and nearly all of our NYC study sites contained genetically distinct populations of white-footed mice as measured by pairwise FST, assignment tests, and Bayesian clustering analyses performed by Structure and baps. Analysis of molecular variance revealed that genetic differences between populations separated by a few kilometres are more significant than differences between prehistorically isolated landmasses (i.e. Bronx, Queens, and Manhattan). Allele size permutation tests and lack of isolation by distance indicated that mutation and migration are less important than drift as explanations for structure in urban, fragmented P. leucopus populations. Peromyscus often exhibit little genetic structure over even regional scales, prompting us to conclude that urbanization is a particularly potent driver of genetic differentiation compared to natural fragmentation.

Hybrid zones have yielded considerable insight into many evolutionary processes, including speciation and the maintenance of species boundaries. Presented here are analyses from a hybrid zone that occurs among three salamanders –Plethodon jordani, Plethodon metcalfi and Plethodon teyahalee– from the southern Appalachian Mountains. Using a novel statistical approach for analysis of non-clinal, multispecies hybrid zones, we examined spatial patterns of variation at four markers: single-nucleotide polymorphisms (SNPs) located in the mtDNA ND2 gene and the nuclear DNA ILF3 gene, and the morphological markers of red cheek pigmentation and white flecks. Concordance of the ILF3 marker and both morphological markers across four transects is observed. In three of the four transects, however, the pattern of mtDNA is discordant from all other markers, with a higher representation of P. metcalfi mtDNA in the northern and lower elevation localities than is expected given the ILF3 marker and morphology. To explore whether climate plays a role in the position of the hybrid zone, we created ecological niche models for P. jordani and P. metcalfi. Modelling results suggest that hybrid zone position is not determined by steep gradients in climatic suitability for either species. Instead, the hybrid zone lies in a climatically homogenous region that is broadly suitable for both P. jordani and P. metcalfi. We discuss various selective (natural selection associated with climate) and behavioural processes (sex-biased dispersal, asymmetric reproductive isolation) that might explain the discordance in the extent to which mtDNA and nuclear DNA and colour-pattern traits have moved across this hybrid zone.

The general phylogeographical paradigm for eastern North America (ENA) is that many plant and animal species retreated into southern refugia during the last glacial period, then expanded northward after the last glacial maximum (LGM). However, some taxa of the Gulf and Atlantic Coastal Plain (GACP) demonstrate complex yet recurrent distributional patterns that cannot be explained by this model. For example, eight co-occurring endemic plant taxa with ranges from New York to South Carolina exhibit a large disjunction separating northern and southern populations by >300 km. Pyxidanthera (Diapensiaceae), a plant genus that exhibits this pattern, consists of two taxa recognized as either species or varieties. We investigated the taxonomy and phylogeography of Pyxidanthera using morphological data, cpDNA sequences, and amplified fragment length polymorphism markers. Morphological characters thought to be important in distinguishing Pyxidanthera barbulata and P. brevifolia demonstrate substantial overlap with no clear discontinuities. Genetic differentiation is minimal and diversity estimates for northern and southern populations of Pxyidanthera are similar, with no decrease in rare alleles in northern populations. In addition, the northern populations harbour several unique cpDNA haplotypes. Pyxidanthera appears to consist of one morphologically variable species that persisted in or near its present range at least through the latter Pleistocene, while the vicariance of the northern and southern populations may be comparatively recent. This work demonstrates that the refugial paradigm is not always appropriate and GACP endemic plants, in particular, may exhibit phylogeographical patterns qualitatively different from those of other ENA plant species.

Quorum sensing is the phenomenon, whereby bacteria use signal molecules to communicate with each other. For example, to establish a successful infection, pathogenic bacteria become virulent only when they reach a certain local concentration in their host. Bassler and others have highlighted the surprising observation that quorum sensing seems to repress Vibrio cholerae virulence factor expression (e.g. cholera toxin), in contrast to what has been observed for virulence gene expression in other bacteria. Here, I present a novel insight that may clarify the way V. cholerae quorum-sensing signals regulate its genes. Chironomids (Diptera; Chironomidae), which occur worldwide and are frequently the insect found most abundantly in fresh water bodies, are natural reservoirs of V. cholerae. Quorum-sensing signals in V. cholerae up-regulate the production of an extracellular enzyme, haemagglutinin protease (HAP), which degrades chironomid egg masses and prevents the eggs from hatching. HAP, therefore, is a virulence factor against chironomids. Indeed, in a survey carried out over the course of a year, V. cholerae and chironomids showed a pattern that mirrored the dynamics of predator-prey populations. Globally, the numbers of chironomids are much larger than those of humans, so quorum-sensing signals of V. cholerae and HAP gene regulation should be understood with regard to their role in chironomids rather than humans. Further research is needed to understand the role of cholera toxin in the environmental existence of V. cholerae.

Understanding how spatial genetic patterns respond to landscape change is crucial for advancing the emerging field of landscape genetics. We quantified the number of generations for new landscape barrier signatures to become detectable and for old signatures to disappear after barrier removal. We used spatially explicit, individual-based simulations to examine the ability of an individual-based statistic [Mantel’s r using the proportion of shared alleles’ statistic (Dps)] and population-based statistic (FST) to detect barriers. We simulated a range of movement strategies including nearest neighbour dispersal, long-distance dispersal and panmixia. The lag time for the signal of a new barrier to become established is short using Mantel’s r (1–15 generations). FST required approximately 200 generations to reach 50% of its equilibrium maximum, although G’ST performed much like Mantel’s r. In strong contrast, FST and Mantel’s r perform similarly following the removal of a barrier formerly dividing a population. Also, given neighbour mating and very short-distance dispersal strategies, historical discontinuities from more than 100 generations ago might still be detectable with either method. This suggests that historical events and landscapes could have long-term effects that confound inferences about the impacts of current landscape features on gene flow for species with very little long-distance dispersal. Nonetheless, populations of organisms with relatively large dispersal distances will lose the signal of a former barrier within less than 15 generations, suggesting that individual-based landscape genetic approaches can improve our ability to measure effects of existing landscape features on genetic structure and connectivity.

Pleistocene ice-ages greatly influenced the historical abundances of Pacific cod, Gadus macrocephalus, in the North Pacific and its marginal seas. We surveyed genetic variation at 11 microsatellite loci and mitochondrial (mt) DNA in samples from twelve locations from the Sea of Japan to Washington State. Both microsatellite (mean H = 0.868) and mtDNA haplotype (mean h = 0.958) diversities were large and did not show any geographical trends. Genetic differentiation between samples was significantly correlated with geographical distance between samples for both microsatellites (FST = 0.028, r2 = 0.33) and mtDNA (FST = 0.027, r2 = 0.18). Both marker classes showed a strong genetic discontinuity between northwestern and northeastern Pacific populations that likely represents groups previously isolated during glaciations that are now in secondary contact. Significant differences appeared between samples from the Sea of Japan and Okhotsk Sea that may reflect ice-age isolations in the northwest Pacific. In the northeast Pacific, a microsatellite and mtDNA partition was detected between coastal and Georgia Basin populations. The presence of two major coastal mtDNA lineages on either side of the Pacific Ocean basin implies at least two ice-age refugia and separate postglacial population expansions facilitated by different glacial histories. Northward expansions into the Gulf of Alaska were possible 14–15 kyr ago, but deglaciation and colonization of the Georgia Basin probably occurred somewhat later. Population expansions were evident in mtDNA mismatch distributions and in Bayesian skyline plots of the three major lineages, but the start of expansions appeared to pre-date the last glacial maximum.

Genetic variation was assessed in Atlantic wolffish, Anarhichas lupus, across the North Atlantic Ocean using microsatellite and amplified fragment length polymorphism (AFLP) markers. Despite unusual life history attributes such as large benthic eggs, large larvae, a limited pelagic stage and relatively sedentary adults, which suggest potential for strong population structure, range-wide FST values were comparable to other marine fishes (≤0.035). Nevertheless, both significant genetic differentiation among regions and isolation by distance were observed, suggesting limited dispersal in this species. AFLP loci, evaluated on a subset of samples, revealed slightly higher FST values, but similar patterns of differentiation and isolation-by-distance estimates, compared to microsatellites. The genetic structure of Atlantic wolffish has likely been shaped by its post-glacial history of recolonization, North Atlantic current patterns and continuity of habitat on continental shelves.

Hybridization is common among cyclical parthenogens, especially in zooplankton species assemblages of the genus Daphnia. To explore hybridization dynamics and the extent of clonal diversity in the Daphnia longispina complex, we analysed population structure in eight permanent lakes. Based on 15 microsatellite loci, three major taxonomic units emerged: two species, D. galeata and D. longispina and their F1 hybrids, supported by factorial correspondence analysis and two Bayesian methods. At the same time, the detection of backcross classes differed between methods. Mean clonal diversity was lowest in the F1 hybrids, as expected from the high rate of asexual reproduction. Within taxa, replicated genotypes were of clonal origin, but clonal lineages persisted in subsequent years in only one of three resampled lakes. In another lake, the taxon composition changed from being dominated by hybrids to complete dominance by one parental taxon. Such a year-to-year taxon replacement has not been reported for the D. longispina complex before. Our data on this hybrid complex illustrate that high-resolution genotyping is essential for the understanding of ecological and evolutionary outcomes of hybridization in partially clonal taxa.

Coupled biological and physical oceanographic models are powerful tools for studying connectivity among marine populations because they simulate the movement of larvae based on ocean currents and larval characteristics. However, while the models themselves have been parameterized and verified with physical empirical data, the simulated patterns of connectivity have rarely been compared to field observations. We demonstrate a framework for testing biological-physical oceanographic models by using them to generate simulated spatial genetic patterns through a simple population genetic model, and then testing these predictions with empirical genetic data. Both agreement and mismatches between predicted and observed genetic patterns can provide insights into mechanisms influencing larval connectivity in the coastal ocean. We use a high-resolution ROMS-CoSINE biological-physical model for Monterey Bay, California specifically modified to simulate dispersal of the acorn barnacle, Balanus glandula. Predicted spatial genetic patterns generated from both seasonal and annual connectivity matrices did not match an observed genetic cline in this species at either a mitochondrial or nuclear gene. However, information from this mismatch generated hypotheses testable with our modelling framework that including natural selection, larval input from a southern direction and/or increased nearshore larval retention might provide a better fit between predicted and observed patterns. Indeed, moderate selection and a range of combined larval retention and southern input values dramatically improve the fit between simulated and observed spatial genetic patterns. Our results suggest that integrating population genetic models with coupled biological-physical oceanographic models can provide new insights and a new means of verifying model predictions.

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.

Landscape genetics is a burgeoning field of interest that focuses on how site-specific factors influence the distribution of genetic variation and the genetic connectivity of individuals and populations. In this manuscript, we focus on two methodological extensions for landscape genetic analyses: the use of conditional genetic distance (cGD) derived from population networks and the utility of extracting potentially confounding effects caused by correlations between phylogeographic history and contemporary ecological factors. Individual-based simulations show that when describing the spatial distribution of genetic variation, cGD consistently outperforms the traditional genetic distance measure of linearized FST under both 1- and 2-dimensional stepping stone models and Cavalli-Sforza and Edward’s chord distance Dc in 1-dimensional landscapes. To show how to identify and extract the effects of phylogeographic history prior to embarking on landscape genetic analyses, we use nuclear genotypic data from the Sonoran desert succulent Euphorbia lomelii (Euphrobiaceae), for which a detailed phylogeographic history has previously been determined. For E. lomelii, removing the effect of phylogeographic history significantly influences our ability to infer both the identity and the relative importance of spatial and bio-climatic variables in subsequent landscape genetic analyses. We close by discussing the utility of cGD in landscape genetic analyses.

Spatial analytical methods have been used by biologists for decades, but with new modelling approaches and data availability their application is accelerating. While early approaches were purely spatial in nature, it is now possible to explore the underlying causes of spatial heterogeneity of biological variation using a wealth of environmental data, especially from satellite remote sensing. Recent methods can not only make inferences regarding spatial relationships and the causes of spatial heterogeneity, but also create predictive maps of patterns of biological variation under changing environmental conditions. Here, we review the methods involved in making continuous spatial predictions from biological variation using spatial and environmental predictor variables, provide examples of their use and critically evaluate the advantages and limitations. In the final section, we discuss some of the key challenges and opportunities for future work.

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.

Rapid climate change jeopardizes tree populations by shifting current climate zones. To avoid extinction, tree populations must tolerate, adapt, or migrate. Here we investigate geographic patterns of genetic variation in valley oak, Quercus lobata Née, to assess how underlying genetic structure of populations might influence this species’ ability to survive climate change. First, to understand how genetic lineages shape spatial genetic patterns, we examine historical patterns of colonization. Second, we examine the correlation between multivariate nuclear genetic variation and climatic variation. Third, to illustrate how geographic genetic variation could interact with regional patterns of 21st Century climate change, we produce region-specific bioclimatic distributions of valley oak using Maximum Entropy (MAXENT) models based on downscaled historical (1971–2000) and future (2070–2100) climate grids. Future climatologies are based on a moderate-high (A2) carbon emission scenario and two different global climate models. Chloroplast markers indicate historical range-wide connectivity via colonization, especially in the north. Multivariate nuclear genotypes show a strong association with climate variation that provides opportunity for local adaptation to the conditions within their climatic envelope. Comparison of regional current and projected patterns of climate suitability indicates that valley oaks grow in distinctly different climate conditions in different parts of their range. Our models predict widely different regional outcomes from local displacement of a few kilometres to hundreds of kilometres. We conclude that the relative importance of migration, adaptation, and tolerance are likely to vary widely for populations among regions, and that late 21st Century conditions could lead to regional extinctions.

Explaining functional connectivity among occupied habitats is crucial for understanding metapopulation dynamics and species ecology. Landscape genetics has primarily focused on elucidating how ecological features between observations influence gene flow. Functional connectivity, however, may be the result of both these between-site (landscape resistance) landscape characteristics and at-site (patch quality) landscape processes that can be captured using network based models. We test hypotheses of functional connectivity that include both between-site and at-site landscape processes in metapopulations of Columbia spotted frogs (Rana luteiventris) by employing a novel justification of gravity models for landscape genetics (eight microsatellite loci, 37 sites, n = 441). Primarily used in transportation and economic geography, gravity models are a unique approach as flow (e.g. gene flow) is explained as a function of three basic components: distance between sites, production/attraction (e.g. at-site landscape process) and resistance (e.g. between-site landscape process). The study system contains a network of nutrient poor high mountain lakes where we hypothesized a short growing season and complex topography between sites limit R. luteiventris gene flow. In addition, we hypothesized production of offspring is limited by breeding site characteristics such as the introduction of predatory fish and inherent site productivity. We found that R. luteiventris connectivity was negatively correlated with distance between sites, presence of predatory fish (at-site) and topographic complexity (between-site). Conversely, site productivity (as measured by heat load index, at-site) and growing season (as measured by frost-free period between-sites) were positively correlated with gene flow. The negative effect of predation and positive effect of site productivity, in concert with bottleneck tests, support the presence of source–sink dynamics. In conclusion, gravity models provide a powerful new modelling approach for examining a wide range of both basic and applied questions in landscape genetics.

Functional connectivity between spatially disjoint habitat patches is a key factor for the persistence of species in fragmented landscapes. Modelling landscape connectivity to identify potential dispersal corridors requires information about those landscape features affecting dispersal. Here we present a new approach using spatial and genetic data of a highly fragmented population of capercaillie (Tetrao urogallus) in the Black Forest, Germany, to investigate effects of landscape structure on gene flow and to parameterize a spatially explicit corridor model for conservation purposes. Mantel tests and multiple regressions on distance matrices were employed to detect and quantify the effect of different landscape features on relatedness among individuals, while controlling for the effect of geographic distance. We extrapolated the results to an area-wide landscape permeability map and developed a new corridor model that incorporates stochasticity in simulating animal movement. The model was evaluated using both a partition of the data previously set apart and independent observation data of dispersing birds. Most land cover variables (such as coniferous forest, forest edges, agricultural land, roads, settlements) and one topographic variable (topographic exposure) were significantly correlated with gene flow. Although inter-individual relatedness inherently varies greatly and the variance explained by geographic distance and landscape structure was low, the permeability map and the corridor model significantly explained relatedness in the validation data and the spatial distribution of dispersing birds. Thus, landscape structure measurably affected within-population gene flow in the study area. By converting these effects into spatially explicit information our model enables localizing priority areas for the preservation or restoration of metapopulation connectivity.

Landscape genetics has seen rapid growth in number of publications since the term was coined in 2003. An extensive literature search from 1998 to 2008 using keywords associated with landscape genetics yielded 655 articles encompassing a vast array of study organisms, study designs and methodology. These publications were screened to identify 174 studies that explicitly incorporated at least one landscape variable with genetic data. We systematically reviewed this set of papers to assess taxonomic and temporal trends in: (i) geographic regions studied; (ii) types of questions addressed; (iii) molecular markers used; (iv) statistical analyses used; and (v) types and nature of spatial data used. Overall, studies have occurred in geographic regions proximal to developed countries and more commonly in terrestrial vs. aquatic habitats. Questions most often focused on effects of barriers and/or landscape variables on gene flow. The most commonly used molecular markers were microsatellites and amplified fragment length polymorphism (AFLPs), with AFLPs used more frequently in plants than animals. Analysis methods were dominated by Mantel and assignment tests. We also assessed differences among journals to evaluate the uniformity of reporting and publication standards. Few studies presented an explicit study design or explicit descriptions of spatial extent. While some landscape variables such as topographic relief affected most species studied, effects were not universal, and some species appeared unaffected by the landscape. Effects of habitat fragmentation were mixed, with some species altering movement paths and others unaffected. Taken together, although some generalities emerged regarding effects of specific landscape variables, results varied, thereby reinforcing the need for species-specific work. We conclude by: highlighting gaps in knowledge and methodology, providing guidelines to authors and reviewers of landscape genetics studies, and suggesting promising future directions of inquiry.