Detection of individual ploidy levels with genotyping‐by‐sequencing (GBS) analysis

Ploidy levels sometimes vary among individuals or populations, particularly in plants. When such variation exists, accurate determination of cytotype can inform studies of ecology or trait variation and is required for population genetic analyses. Here, we propose and evaluate a statistical approach for distinguishing low‐level ploidy variants (e.g. diploids, triploids and tetraploids) based on genotyping‐by‐sequencing (GBS) data. The method infers cytotypes based on observed heterozygosity and the ratio of DNA sequences containing different alleles at thousands of heterozygous SNPs (i.e. allelic ratios). Whereas the method does not require prior information on ploidy, a reference set of samples with known ploidy can be included in the analysis if it is available. We explore the power and limitations of this method using simulated data sets and GBS data from natural populations of aspen (Populus tremuloides) known to include both diploid and triploid individuals. The proposed method was able to reliably discriminate among diploids, triploids and tetraploids in simulated data sets, and this was true for different levels of genetic diversity, inbreeding and population structure. Power and accuracy were minimally affected by low coverage (i.e. 2×), but did sometimes suffer when simulated mixtures of diploids, autotetraploids and allotetraploids were analysed. Cytotype assignments based on the proposed method closely matched those from previous microsatellite and flow cytometry data when applied to GBS data from aspen. An R package (gbs2ploidy) implementing the proposed method is available from CRAN.     

QST–FST comparisons with unbalanced half‐sib designs

Q_ST, a measure of quantitative genetic differentiation among populations, is an index that can suggest local adaptation if Q_ST for a trait is sufficiently larger than the mean F_ST of neutral genetic markers. A previous method by Whitlock and Guillaume derived a simulation resampling approach to statistically test for a difference between Q_ST and F_ST, but that method is limited to balanced data sets with offspring related as half‐sibs through shared fathers. We extend this approach (i) to allow for a model more suitable for some plant populations or breeding designs in which offspring are related through mothers (assuming independent fathers for each offspring; half‐sibs by dam); and (ii) by explicitly allowing for unbalanced data sets. The resulting approach is made available through the R package QstFstComp.     

Global patterns of population genetic differentiation in seed plants

Evaluating the factors that drive patterns of population differentiation in plants is critical for understanding several biological processes such as local adaptation and incipient speciation. Previous studies have given conflicting results regarding the significance of pollination mode, seed dispersal mode, mating system, growth form and latitudinal region in shaping patterns of genetic structure, as estimated by F_ST values, and no study to date has tested their relative importance together across a broad scale. Here, we assembled a 337‐species data set for seed plants from publications with data on F_ST from nuclear markers and species traits, including variables pertaining to the sampling scheme of each study. We used species traits, while accounting for sampling variables, to perform phylogenetic multiple regressions. Results demonstrated that F_ST values were higher for tropical, mixed‐mating, non‐woody species pollinated by small insects, indicating greater population differentiation, and lower for temperate, outcrossing trees pollinated by wind. Among the factors we tested, latitudinal region explained the largest portion of variance, followed by pollination mode, mating system and growth form, while seed dispersal mode did not significantly relate to F_ST. Our analyses provide the most robust and comprehensive evaluation to date of the main ecological factors predicted to drive population differentiation in seed plants, with important implications for understanding the basis of their genetic divergence. Our study supports previous findings showing greater population differentiation in tropical regions and is the first that we are aware of to robustly demonstrate greater population differentiation in species pollinated by small insects.     

Little to no inbreeding depression in a tapeworm with mixed mating

Meta‐studies on hermaphrodites have found a negative relationship between primary selfing rates and levels of inbreeding depression (ID) and, thus, generally support purging in inbred systems. However, in plants, high among‐taxa variance in ID results in no difference in the mean ID between outcrossing and mixed‐mating taxa. Selective interference likely explains high ID among mixed‐mating taxa, whereas low levels of ID among mixed‐mating taxa are not as stressed. Among animal hermaphrodites, primarily molluscs, there are little data on mixed‐mating systems. To fill a taxonomic and mating system gap, we tested for ID in a mixed‐mating tapeworm, Oochoristica javaensis. We provide a direct estimate of ID across infection of an intermediate host by comparing selfing rates at two life history stages. We found little to no evidence for ID, and the level of ID falls in line with what is reported for highly selfing species even though O. javaensis has mixed mating. We discuss this result within the context of kin mating in O. javaensis. Our results emphasize that primary selfing rates alone may be insufficient to classify the inbreeding history in all species when testing for a relationship to ID. Mixed‐mating taxa, and possibly some outcrossing taxa, may exhibit low levels of ID if biparental inbreeding is also driving purging. We advocate that ID studies report estimates of inbreeding history (e.g. F_IS or identity disequilibrium) from nature‐derived adult samples to provide context rather than relying on primary selfing rates alone.     

Molecular genetics of coat colour variations in White Galloway and White Park cattle

White Galloway cattle exhibit three different white coat colour phenotypes, that is, well marked, strongly marked and mismarked. However, mating of individuals with the preferred well or strongly marked phenotype also results in offspring with the undesired mismarked and/or even fully black coat colour. To elucidate the genetic background of the coat colour variations in White Galloway cattle, we analysed four coat colour relevant genes: mast/stem cell growth factor receptor (KIT), KIT ligand (KITLG), melanocortin 1 receptor (MC1R) and tyrosinase (TYR). Here, we show that the coat colour variations in White Galloway cattle and White Park cattle are caused by a KIT gene (chromosome 6) duplication and aberrant insertion on chromosome 29 (Cs₂₉) as recently described for colour‐sided Belgian Blue. Homozygous (Cs₂₉/Cs₂₉) White Galloway cattle and White Park cattle exhibit the mismarked phenotype, whereas heterozygous (Cs₂₉/wt₂₉) individuals are either well or strongly marked. In contrast, fully black individuals are characterised by the wild‐type chromosome 29. As known for other cattle breeds, mutations in the MC1R gene determine the red colouring. Our data suggest that the white coat colour variations in White Galloway cattle and White Park cattle are caused by a dose‐dependent effect based on the ploidy of aberrant insertions and inheritance of the KIT gene on chromosome 29.     

Does GST underestimate genetic differentiation from marker data?

The widely applied genetic differentiation statistics F_ST and G_ST have recently been criticized for underestimating differentiation when applied to highly polymorphic markers such as microsatellites. New statistics claimed to be unaffected by marker polymorphisms have been proposed and advocated to replace the traditional F_ST and G_ST. This study shows that G_ST gives accurate estimates and underestimates of differentiation when demographic factors are more and less important than mutations, respectively. In the former case, all markers, regardless of diversity (H_S), have the same G_ST value in expectation and thus give replicated estimates of differentiation. In the latter case, markers of higher H_S have lower G_ST values, resulting in a negative, roughly linear correlation between G_ST and H_S across loci. I propose that the correlation coefficient between G_ST and H_S across loci, r_GH, can be used to distinguish the two cases and to detect mutational effects on G_ST. A highly negative and significant r_GH, when coupled with highly variable G_ST values among loci, would reveal that marker G_ST values are affected substantially by mutations and marker diversity, underestimate population differentiation, and are not comparable among studies, species and markers. Simulated and empirical data sets are used to check the power and statistical behaviour, and to demonstrate the usefulness of the correlation analysis.     

A landscape genetics approach reveals ecological‐based differentiation in populations of holm oak (Quercus ilex L.) at the northern limit of its range

The holm oak plays a relevant role in the functioning of Mediterranean forests. In the area north of Garda Lake, Italian Prealps, holm oak populations are at the northernmost edge of their distribution. Being peripheral, these populations are of particular interest for ecological, evolutionary and conservation studies. Through an explicit individual‐based landscape genetics approach, we addressed the following questions: (1) are levels of genetic variation reduced in these marginal populations compared with central populations?; (2) despite the narrow geographical scale, do individual‐based analyses have some power to detect genetic differentiation?; (3) do environmental and/or climatic factors exert a role in shaping patterns of genetic variation and differentiation? Through a Bayesian method, we identified three clusters whose genetic variability can be considered to be of the same order as that recorded in central Quercus ilex populations. Although being geographically very close (< 20 km), the differentiation was statistically significant (P < 0.05) with global F _st and Φ _Pt values of 0.019 and 0.038, respectively. Geography and phylogeography could not be invoked to explain this differentiation. A redundancy discriminant analysis revealed that relevant eco‐pedological and climatic features, such as soil depth, aspect, elevation and humidity, were correlated with the observed pattern of differentiation. Toblino was ecologically separated from the other clusters, as it lies on deep soil with subhumid conditions. The differentiation of the Brione–Ranzo–Val Busa cluster appeared to be related to superficial soils and drier conditions, whereas the Nanzone–Padaro cluster was differentiated mainly according to its mid‐elevation. Coupling spatial and genetic information on a local scale proved to be effective to investigate the evolutionary and demographic history of peripheral populations. © 2012 The Linnean Society of London, Biological Journal of the Linnean Society, 2012, ?? , ??–??.     

Estimation of vegetation photosynthetic capacity from space‐based measurements of chlorophyll fluorescence for terrestrial biosphere models

Photosynthesis simulations by terrestrial biosphere models are usually based on the Farquhar's model, in which the maximum rate of carboxylation (V_cmax) is a key control parameter of photosynthetic capacity. Even though V_cmax is known to vary substantially in space and time in response to environmental controls, it is typically parameterized in models with tabulated values associated to plant functional types. Remote sensing can be used to produce a spatially continuous and temporally resolved view on photosynthetic efficiency, but traditional vegetation observations based on spectral reflectance lack a direct link to plant photochemical processes. Alternatively, recent space‐borne measurements of sun‐induced chlorophyll fluorescence (SIF) can offer an observational constraint on photosynthesis simulations. Here, we show that top‐of‐canopy SIF measurements from space are sensitive to V_cmax at the ecosystem level, and present an approach to invert V_cmax from SIF data. We use the Soil‐Canopy Observation of Photosynthesis and Energy (SCOPE) balance model to derive empirical relationships between seasonal V_cmax and SIF which are used to solve the inverse problem. We evaluate our V_cmax estimation method at six agricultural flux tower sites in the midwestern US using spaced‐based SIF retrievals. Our V_cmax estimates agree well with literature values for corn and soybean plants (average values of 37 and 101 μmol m^?2 s^?1, respectively) and show plausible seasonal patterns. The effect of the updated seasonally varying V_cmax parameterization on simulated gross primary productivity (GPP) is tested by comparing to simulations with fixed V_cmax values. Validation against flux tower observations demonstrate that simulations of GPP and light use efficiency improve significantly when our time‐resolved V_cmax estimates from SIF are used, with R² for GPP comparisons increasing from 0.85 to 0.93, and for light use efficiency from 0.44 to 0.83. Our results support the use of space‐based SIF data as a proxy for photosynthetic capacity and suggest the potential for global, time‐resolved estimates of V_cmax.     

Effect of Yangtze River on population genetic structure of the relict plant Parrotia subaequalis in eastern China

Parrotia subaequalis (Hamamelidaceae) is a Tertiary relic species endemic in eastern China. We used inter‐simple sequence repeat (ISSR) markers to access genetic diversity and population genetic structure in natural five populations of P. subaequalis. The levels of genetic diversity were higher at species level (H = 0.2031) but lower at population level (H = 0.1096). The higher genetic diversity at species levels might be attributed to the accumulation of distinctive genotypes which adapted to the different habitats after Quaternary glaciations. Meanwhile, founder effects on the early stage, and subsequent bottleneck of population regeneration due to its biological characteristics, environmental features, and human activities, seemed to explain the low population levels of genetic diversity. The hierarchical AMOVA revealed high levels (42.60%) of among‐population genetic differentiation, which was in congruence with the high levels of Nei's genetic differentiation index (G_ST = 0.4629) and limited gene flow (N_m = 0.5801) among the studied populations. Mantel test showed a significant isolation‐by‐distance, indicating that geographic isolation has a significant effect on genetic structure in this species. Unweighted pair‐group method with arithmetic average clustering, PCoA, and Bayesian analyses uniformly recovered groups that matched the geographical distribution of this species. In particular, our results suggest that Yangtze River has served as a natural barrier to gene flow between populations occurred on both riversides. Concerning the management of P. subaequalis, the high genetic differentiation among populations indicates that preserving all five natural populations in situ and collecting enough individuals from these populations for ex situ conservation are necessary.     

Using RAD‐seq to develop sex‐linked markers in a haplodiplontic alga

For many taxa, including isomorphic haplodiplontic macroalgae, determining sex and ploidy is challenging, thereby limiting the scope of some population demographic and genetic studies. Here, we used double‐digest restriction site‐associated DNA sequencing (ddRAD‐seq) to identify sex‐linked molecular markers in the widespread red alga Agarophyton vermiculophyllum. In the ddRAD‐seq library, we included 10 female gametophytes, 10 male gametophytes, and 16 tetrasporophytes from one native and one non‐native site (N = 40 gametophytes and N = 32 tetrasporophytes total). We identified seven putatively female‐linked and 19 putatively male‐linked sequences. Four female‐ and eight male‐linked markers amplified in all three life cycle stages. Using one female‐ and one male‐linked marker that were sex‐specific, we developed a duplex PCR and tested the efficacy of this assay on a subset of thalli sampled at two sites in the non‐native range. We confirmed ploidy based on the visual observation of reproductive structures and previous microsatellite genotyping at 10 polymorphic loci. For 32 vegetative thalli, we were able to assign sex and confirm ploidy in these previously genotyped thalli. These markers will be integral to ongoing studies of A. vermiculophyllum invasion. We discuss the utility of RAD‐seq over other approaches previously used, such as RAPDs (random amplified polymorphic DNA), for future work designing sex‐linked markers in other haplodiplontic macroalgae for which genomes are lacking.     

A trait‐based approach to predict population genetic structure in bees

Understanding population genetic structure is key to developing predictions about species susceptibility to environmental change, such as habitat fragmentation and climate change. It has been theorized that life‐history traits may constrain some species in their dispersal and lead to greater signatures of population genetic structure. In this study, we use a quantitative comparative approach to assess if patterns of population genetic structure in bees are driven by three key species‐level life‐history traits: body size, sociality, and diet breadth. Specifically, we reviewed the current literature on bee population genetic structure, as measured by the differentiation indices Nei's G_ST, Hedrick's G′_ST, and Jost's D. We then used phylogenetic generalised linear models to estimate the correlation between the evolution of these traits and patterns of genetic differentiation. Our analyses revealed a negative and significant effect of body size on genetic structure, regardless of differentiation index utilized. For Hedrick's G′_ST and Jost's D, we also found a significant impact of sociality, where social species exhibited lower levels of differentiation than solitary species. We did not find an effect of diet specialization on population genetic structure. Overall, our results suggest that physical dispersal or other functions related to body size are among the most critical for mediating population structure for bees. We further highlight the importance of standardizing population genetic measures to more easily compare studies and to identify the most susceptible species to landscape and climatic changes.     

wisepair: a computer program for individual matching in genetic tracking studies

Individual‐based data sets tracking organisms over space and time are fundamental to answering broad questions in ecology and evolution. A ‘permanent’ genetic tag circumvents a need to invasively mark or tag animals, especially if there are little phenotypic differences among individuals. However, genetic tracking of individuals does not come without its limits; correctly matching genotypes and error rates associated with laboratory work can make it difficult to parse out matched individuals. In addition, defining a sampling design that effectively matches individuals in the wild can be a challenge for researchers. Here, we combine the two objectives of defining sampling design and reducing genotyping error through an efficient Python‐based computer‐modelling program, wisepair . We describe the methods used to develop the computer program and assess its effectiveness through three empirical data sets, with and without reference genotypes. Our results show that wisepair outperformed similar genotype matching programs using previously published from reference genotype data of diurnal poison frogs (Allobates femoralis) and without‐reference (faecal) genotype sample data sets of harbour seals (Phoca vitulina) and Eurasian otters (Lutra lutra). In addition, due to limited sampling effort in the harbour seal data, we present optimal sampling designs for future projects. wisepair allows for minimal sacrifice in the available methods as it incorporates sample rerun error data, allelic pairwise comparisons and probabilistic simulations to determine matching thresholds. Our program is the lone tool available to researchers to define parameters a priori for genetic tracking studies.     

Vegetation productivity patterns at high northern latitudes: a multi‐sensor satellite data assessment

Satellite‐derived indices of photosynthetic activity are the primary data source used to study changes in global vegetation productivity over recent decades. Creating coherent, long‐term records of vegetation activity from legacy satellite data sets requires addressing many factors that introduce uncertainties into vegetation index time series. We compared long‐term changes in vegetation productivity at high northern latitudes (>50°N), estimated as trends in growing season NDVI derived from the most widely used global NDVI data sets. The comparison included the AVHRR‐based GIMMS‐NDVI version G (GIMMS_g) series, and its recent successor version 3g (GIMMS_3g), as well as the shorter NDVI records generated from the more modern sensors, SeaWiFS, SPOT‐VGT, and MODIS. The data sets from the latter two sensors were provided in a form that reduces the effects of surface reflectance associated with solar and view angles. Our analysis revealed large geographic areas, totaling 40% of the study area, where all data sets indicated similar changes in vegetation productivity over their common temporal record, as well as areas where data sets showed conflicting patterns. The newer, GIMMS_3g data set showed statistically significant (α = 0.05) increases in vegetation productivity (greening) in over 15% of the study area, not seen in its predecessor (GIMMS_g), whereas the reverse was rare (<3%). The latter has implications for earlier reports on changes in vegetation activity based on GIMMS_g, particularly in Eurasia where greening is especially pronounced in the GIMMS_3g data. Our findings highlight both critical uncertainties and areas of confidence in the assessment of ecosystem‐response to climate change using satellite‐derived indices of photosynthetic activity. Broader efforts are required to evaluate NDVI time series against field measurements of vegetation growth, primary productivity, recruitment, mortality, and other biological processes in order to better understand ecosystem responses to environmental change over large areas.     

High degree of genetic differentiation in marine three‐spined sticklebacks (Gasterosteus aculeatus)

Populations of widespread marine organisms are typically characterized by a low degree of genetic differentiation in neutral genetic markers, but much less is known about differentiation in genes whose functional roles are associated with specific selection regimes. To uncover possible adaptive population divergence and heterogeneous genomic differentiation in marine three‐spined sticklebacks (Gasterosteus aculeatus), we used a candidate gene‐based genome‐scan approach to analyse variability in 138 microsatellite loci located within/close to (<6 kb) functionally important genes in samples collected from ten geographic locations. The degree of genetic differentiation in markers classified as neutral or under balancing selection—as determined with several outlier detection methods—was low (F_ST = 0.033 or 0.011, respectively), whereas average F_ST for directionally selected markers was significantly higher (F_ST = 0.097). Clustering analyses provided support for genomic and geographic heterogeneity in selection: six genetic clusters were identified based on allele frequency differences in the directionally selected loci, whereas four were identified with the neutral loci. Allelic variation in several loci exhibited significant associations with environmental variables, supporting the conjecture that temperature and salinity, but not optic conditions, are important drivers of adaptive divergence among populations. In general, these results suggest that in spite of the high degree of physical connectivity and gene flow as inferred from neutral marker genes, marine stickleback populations are strongly genetically structured in loci associated with functionally relevant genes.     

Response to competition of bulbous geophyte Allium oleraceum differing in ploidy level

Experimental studies that explore the possible causes of ploidy distributions and niche differentiation are rare. Increased competitive ability may be advantageous for survival in dense vegetation and may strongly affect local and regional abundances of cytotypes and potentially contribute to invasion success. We compared survival, growth and reproduction of plants originating from bulbils of three cytotypes (2n = 4x, 5x, 6x) of Allium oleraceum growing with and without a competitor (Arrhenatherum elatius). There was a strong negative effect of competition but no effect of ploidy or ploidy × competition on survivorship, height and total dry mass of A. oleraceum, i.e. no support for different competitive abilities of the ploidy levels. However, slightly different responses of populations to competition treatments within all cytotypes suggest differentiation within cytotypes. Under competition, plant survivorship was low, surviving plants were small, had low dry mass and produced neither sexual nor asexual propagules. Without competition, plant survivorship was high, and cytotypes differed in three traits after 2 year's growth: dry mass of flowers, number of flowers and ratio of the dry mass of sexual to asexual propagules all decreased with increasing ploidy level. We additionally tested tetra‐ and pentaploids as to whether plants originating from different types of propagule (bulbils, seeds) differ in survivorship, growth and reproduction when growing with and without a competitor. Plants originating from bulbils had higher survivorship, were more robust, flowered earlier and produced more propagules when compared to plants originating from seeds and grown without competition. Under competition, differences in performance between plants originating from seeds and bulbils mostly disappeared, with higher survivorship only for plants originating from bulbils.     

The effects of locus number,genetic divergence,and genotyping error on the utility of dominant markers for hybrid identification

In surveys of hybrid zones, dominant genetic markers are often used to identify individuals of hybrid origin and assign these individuals to one of several potential hybrid classes. Quantitative analyses that address the statistical power of dominant markers in such inference are scarce. In this study, dominant genotype data were simulated to evaluate the effects of, first, the number of loci analyzed, second, the magnitude of differentiation between the markers scored in the groups that are hybridizing, and third, the level of genotyping error associated with the data when assigning individuals to various parental and hybrid categories. The overall performance of the assignment methods was relatively modest at the lowest level of divergence examined (F_st ~ 0.4), but improved substantially at higher levels of differentiation (F_st ~ 0.67 or 0.8). The effect of genotyping error was dependent on the level of divergence between parental taxa, with larger divergences tempering the effects of genotyping error. These results highlight the importance of considering the effects of each of the variables when assigning individuals to various parental and hybrid categories, and can help guide decisions regarding the number of loci employed in future hybridization studies to achieve the power and level of resolution desired.     

mmod: an R library for the calculation of population differentiation statistics

mmod is a library for the R programming language that allows the calculation of the population differentiation measures D_est, G″_ST and φ′_ST. R provides a powerful environment in which to conduct and record population genetic analyses but, at present, no R libraries provide functions for the calculation of these statistics from standard population genetic files. In addition to the calculation of differentiation measures, mmod can produce parametric bootstrap and jackknife samples of data sets for further analysis. By integrating with and complimenting the existing libraries adegenet and pegas , mmod extends the power of R as a population genetic platform.