Population structure and landscape genetics in the endangered subterranean rodent Ctenomys porteousi

In order to devise adequate conservation and management strategies for endangered species, it is important to incorporate a reliable understanding of its spatial population structure, detecting the existence of demographic partitions throughout its geographical range and characterizing the distribution of its genetic diversity. Moreover, in species that occupy fragmented habitats it is essential to know how landscape characteristics may affect the genetic connectivity among populations. In this study we use eight microsatellite markers to analyze population structure and gene flow patterns in the complete geographic range of the endangered rodent Ctenomys porteousi. Also, we use landscape genetics approaches to evaluate the effects of landscape configuration on the genetic connectivity among populations. In spite of geographical proximity of the sampling sites (8–27 km between the nearest sites) and the absence of marked barriers to individual movement, strong population structure and low values of gene flow were observed. Genetic differentiation among sampling sites was consistent with a simple model of isolation by distance, where peripheral areas showed higher population differentiation than those sites located in the central area of the species’ distribution. Landscape genetics analysis suggested that habitat fragmentation at regional level has affected the distribution of genetic variation among populations. The distance of sampling sites to areas of the landscape having higher habitat connectivity was the environmental factor most strongly related to population genetic structure. In general, our results indicate strong genetic structure in C. porteousi, even at a small spatial scale, and suggest that habitat fragmentation could increase the population differentiation.     

Uncovering the genetic basis of adaptive change: on the intersection of landscape genomics and theoretical population genetics

A workshop recently held at the École Polytechnique Fédérale de Lausanne (EPFL, Switzerland) was dedicated to understanding the genetic basis of adaptive change, taking stock of the different approaches developed in theoretical population genetics and landscape genomics and bringing together knowledge accumulated in both research fields. Indeed, an important challenge in theoretical population genetics is to incorporate effects of demographic history and population structure. But important design problems (e.g. focus on populations as units, focus on hard selective sweeps, no hypothesis‐based framework in the design of the statistical tests) reduce their capability of detecting adaptive genetic variation. In parallel, landscape genomics offers a solution to several of these problems and provides a number of advantages (e.g. fast computation, landscape heterogeneity integration). But the approach makes several implicit assumptions that should be carefully considered (e.g. selection has had enough time to create a functional relationship between the allele distribution and the environmental variable, or this functional relationship is assumed to be constant). To address the respective strengths and weaknesses mentioned above, the workshop brought together a panel of experts from both disciplines to present their work and discuss the relevance of combining these approaches, possibly resulting in a joint software solution in the future.     

An under-appreciated difficulty: sampling of plant populations for analysis using molecular markers

Results of studies using molecular markers for determining demographic and genetical population parameters especially in plants or sessile animals under field conditions are strongly dependent on the sampling strategy adopted. There are two critical decisions to make when determining this strategy: (i) what is the unit to be sampled?, (ii) how should units to be sampled in the field be selected? For the first decision, there are two conceptually different approaches: sampling ramets of clonal plants as units (to get information about within-genet parameters, such as genet sizes or numbers) and sampling genets of clonal or non-clonal plants as units (to get information of the genetic structure of the population). For the second decision, it is critically important to make the goal of the study explicit. We argue that in this case fully random sampling is needed only when an estimate of the true value of the population parameter is needed; if a comparison between populations is the goal, however, other sampling schemes may be adopted. The efficiency of different types of sampling strategies to recover relative values in a spatially extended population is studied by means of a spatially explicit simulation model. The results show that a regular pattern of sampling is best for obtaining information on genet sizes or inbreeding coefficients; in contrast, random or hierarchical sampling strategies are better for obtaining information on parameters that are based on comparison of pairs of individuals, such as distribution of genet sizes or autocorrelation in genetic structure. A set of recommendations is provided for designing a good sampling strategy.     

Sample size for collecting seeds in germplasm conservation: the case of the Lima bean (Phaseolus lunatus L.)

 The design of optimum sampling strategies integrating criteria of efficiency relevant to multilocus models and many target populations has been investigated with respect to the number of plants and the number of seeds per plant to be sampled for a Lima bean (Phaseolus lunatus L.) gene pool. This study, using five populations and six polymorphic enzyme loci, shows that the number of plants rather than the number of seeds collected per plant primarily determines the success of seed sampling, suggesting that plant number plays an essential part in maintaining the allelic multiplicity of predominantly selfing species like Lima bean. According to the results, it appears that among Lima bean populations an efficient sampling procedure is achieved by collecting 1–4 seeds from 200 to 300 plants. These sample sizes will retain 8–10 alleles, regardless of their frequencies. When we consider polymorphism at the 5% level, it is expected that sampling 10–80 plants will collect combinations of 4–8 alleles. Based on data from genetic and demographic studies, we suggest an efficient sampling scheme for Lima bean germplasm at both population and geographical levels. Received: 10 March 1998 / Accepted: 1 April 1998     

LANDSCAPE GENOMICS IN ATLANTIC SALMON (SALMO SALAR): SEARCHING FOR GENE–ENVIRONMENT INTERACTIONS DRIVING LOCAL ADAPTATION

A growing number of studies are examining the factors driving historical and contemporary evolution in wild populations. By combining surveys of genomic variation with a comprehensive assessment of environmental parameters, such studies can increase our understanding of the genomic and geographical extent of local adaptation in wild populations. We used a large‐scale landscape genomics approach to examine adaptive and neutral differentiation across 54 North American populations of Atlantic salmon representing seven previously defined genetically distinct regional groups. Over 5500 genome‐wide single nucleotide polymorphisms were genotyped in 641 individuals and 28 bulk assays of 25 pooled individuals each. Genome scans, linkage map, and 49 environmental variables were combined to conduct an innovative landscape genomic analysis. Our results provide valuable insight into the links between environmental variation and both neutral and potentially adaptive genetic divergence. In particular, we identified markers potentially under divergent selection, as well as associated selective environmental factors and biological functions with the observed adaptive divergence. Multivariate landscape genetic analysis revealed strong associations of both genetic and environmental structures. We found an enrichment of growth‐related functions among outlier markers. Climate (temperature–precipitation) and geological characteristics were significantly associated with both potentially adaptive and neutral genetic divergence and should be considered as candidate loci involved in adaptation at the regional scale in Atlantic salmon. Hence, this study significantly contributes to the improvement of tools used in modern conservation and management schemes of Atlantic salmon wild populations.     

Pop‐Inference: An educational application to evaluate statistical differences among populations

Pop‐Inference is an educational tool designed to help teaching of hypothesis testing using populations. The application allows for the statistical comparison of demographic parameters among populations. Input demographic data are projection matrices or raw demographic data. Randomization tests are used to compare populations. The tests evaluate the hypothesis that demographic parameters differ among groups of individuals more that should be expected from random allocation of individuals to populations. Confidence intervals for demographic parameters are obtained using the bootstrap. Tests may be global or pairwise. In addition to tests on differences, one‐way life table response experiments (LTRE) are available for random and fixed factors. Planned (a priori) comparisons are possible. Power of comparison tests is evaluated by constructing the distribution of the test statistic when the null hypothesis is true and when it is false. The relationship between power and sample size is explored by evaluating differences among populations at increasing population sizes, while keeping vital rates constant.     

Optimising long‐term monitoring projects for species distribution modelling: how atlas data may help

Long‐term biodiversity monitoring data are mainly used to estimate changes in species occupancy or abundance over time, but they may also be incorporated into predictive models to document species distributions in space. Although changes in occupancy or abundance may be estimated from a relatively limited number of sampling units, small sample size may lead to inaccurate spatial models and maps of predicted species distributions. We provide a methodological approach to estimate the minimum sample size needed in monitoring projects to produce accurate species distribution models and maps. The method assumes that monitoring data are not yet available when sampling strategies are to be designed and is based on external distribution data from atlas projects. Atlas data are typically collected in a large number of sampling units during a restricted timeframe and are often similar in nature to the information gathered from long‐term monitoring projects. The large number of sampling units in atlas projects makes it possible to simulate a broad gradient of sample sizes in monitoring data and to examine how the number of sampling units influences the accuracy of the models. We apply the method to several bird species using data from a regional breeding bird atlas. We explore the effect of prevalence, range size and habitat specialization of the species on the sample size needed to generate accurate models. Model accuracy is sensitive to particularly small sample sizes and levels off beyond a sufficiently large number of sampling units that varies among species depending mainly on their prevalence. The integration of spatial modelling techniques into monitoring projects is a cost‐effective approach as it offers the possibility to estimate the dynamics of species distributions in space and over time. We believe our innovative method will help in the sampling design of future monitoring projects aiming to achieve such integration.     

Sample design effects in landscape genetics

An important research gap in landscape genetics is the impact of different field sampling designs on the ability to detect the effects of landscape pattern on gene flow. We evaluated how five different sampling regimes (random, linear, systematic, cluster, and single study site) affected the probability of correctly identifying the generating landscape process of population structure. Sampling regimes were chosen to represent a suite of designs common in field studies. We used genetic data generated from a spatially-explicit, individual-based program and simulated gene flow in a continuous population across a landscape with gradual spatial changes in resistance to movement. Additionally, we evaluated the sampling regimes using realistic and obtainable number of loci (10 and 20), number of alleles per locus (5 and 10), number of individuals sampled (10–300), and generational time after the landscape was introduced (20 and 400). For a simulated continuously distributed species, we found that random, linear, and systematic sampling regimes performed well with high sample sizes (>200), levels of polymorphism (10 alleles per locus), and number of molecular markers (20). The cluster and single study site sampling regimes were not able to correctly identify the generating process under any conditions and thus, are not advisable strategies for scenarios similar to our simulations. Our research emphasizes the importance of sampling data at ecologically appropriate spatial and temporal scales and suggests careful consideration for sampling near landscape components that are likely to most influence the genetic structure of the species. In addition, simulating sampling designs a priori could help guide filed data collection efforts     

Adaptive Cluster Sampling in the Context of Restoration

Abundance is an important population state variable for monitoring restoration progress. Efficient sampling often proves difficult, however, when populations are sparse and patchily distributed, such as early after restoration planting. Adaptive cluster sampling (ACS) can help by concentrating search effort in high density areas, improving the encounter rate and the ability to detect a population change over time. To illustrate the problem, I determined conventional design sample sizes for estimating abundance of 12 natural populations and 24 recently planted populations (divided among two preserves) of Lupinus perennis L. (wild blue lupine). I then determined the variance efficiency of ACS relative to simple random sampling at fixed effort and cost for 10 additional planted populations in two habitats (field vs. shrubland). Conventional design sample sizes to estimate lupine stem density with 10% or 20% margins of error were many times greater than initial sample size and would require sampling at least 90% of the study area. Differences in effort requirements were negligible for the two preserves and natural versus planted populations. At fixed sample size, ACS equaled or outperformed simple random sampling in 40% of populations; this shifted to 50% after correcting for travel time among sample units. ACS appeared to be a better strategy for inter‐seeded shrubland habitat than for planted field habitat. Restoration monitoring programs should consider adaptive sampling designs, especially when reliable abundance estimation under conventional designs proves elusive.     

A Monte Carlo Permutation Test for Random Mating Using Genome Sequences

Testing for random mating of a population is important in population genetics, because deviations from randomness of mating may indicate inbreeding, population stratification, natural selection, or sampling bias. However, current methods use only observed numbers of genotypes and alleles, and do not take advantage of the fact that the advent of sequencing technology provides an opportunity to investigate this topic in unprecedented detail. To address this opportunity, a novel statistical test for random mating is required in population genomics studies for which large sequencing datasets are generally available. Here, we propose a Monte-Carlo-based-permutation test (MCP) as an approach to detect random mating. Computer simulations used to evaluate the performance of the permutation test indicate that its type I error is well controlled and that its statistical power is greater than that of the commonly used chi-square test (CHI). Our simulation study shows the power of our test is greater for datasets characterized by lower levels of migration between subpopulations. In addition, test power increases with increasing recombination rate, sample size, and divergence time of subpopulations. For populations exhibiting limited migration and having average levels of population divergence, the statistical power approaches 1 for sequences longer than 1Mbp and for samples of 400 individuals or more. Taken together, our results suggest that our permutation test is a valuable tool to detect random mating of populations, especially in population genomics studies.     

A practical sampling design for acoustic surveys of bats

Acoustic surveys are widely used for describing bat occurrence and activity patterns and are increasingly important for addressing concerns for habitat management, wind energy, and disease on bat populations. Designing these surveys presents unique challenges, particularly when a probabilistic sample is required for drawing inference to unsampled areas. Sampling frame errors and other logistical constraints often require survey sites to be dropped from the sample and new sites added. Maintaining spatial balance and representativeness of the sample when these changes are made can be problematic. Spatially balanced sampling designs recently developed to support aquatic surveys along rivers provide solutions to a number of practical challenges faced by bat researchers and allow for sample site additions and deletions, support unequal-probability selection of sites, and provide an approximately unbiased local neighborhood-weighted variance estimator that is efficient for spatially structured populations such as is typical for bats. We implemented a spatially balanced design to survey canyon bat (Parastrellus hesperus) activity along a stream network. The spatially balanced design accommodated typical logistical challenges and yielded a 25% smaller estimated standard error for the mean activity level than the usual simple random sampling estimator. Spatially balanced designs have broad application to bat research and monitoring programs and will improve studies relying on model-based inference (e.g., occupancy models) by providing flexibility and protection against violations of the independence assumption, even if design-based estimators are not used. Our approach is scalable and can be used for pre- and post-construction surveys along wind turbine arrays and for regional monitoring programs. © 2011 The Wildlife Society.     

Quantifying the importance of geographic replication and representativeness when estimating demographic rates,using a coastal species as a case study

Demographic rates are rarely estimated over an entire species range, limiting empirical tests of ecological patterns and theories, and raising questions about the representativeness of studies that use data from a small part of a range. The uncertainty that results from using demographic rates from just a few sites is especially pervasive in population projections, which are critical for a wide range of questions in ecology and conservation. We developed a simple simulation to quantify how this lack of geographic representativeness can affect inferences about the global mean and variance of growth rates, which has implications for the robust design of a wide range of population studies. Using a coastal songbird, saltmarsh sparrow Ammodramus caudacutus, as a case study, we first estimated survival, fecundity, and population growth rates at 21 sites distributed across much of their breeding range. We then subsampled this large, representative dataset according to five sampling scenarios in order to simulate a variety of geographic biases in study design. We found spatial variation in demographic rates, but no large systematic patterns. Estimating the global mean and variance of growth rates using subsets of the data suggested that at least 10–15 sites were required for reasonably unbiased estimates, highlighting how relying on demographic data from just a few sites can lead to biased results when extrapolating across a species range. Sampling at the full 21 sites, however, offered diminishing returns, raising the possibility that for some species accepting some geographical bias in sampling can still allow for robust range‐wide inferences. The subsampling approach presented here, while conceptually simple, could be used with both new and existing data to encourage efficiency in the design of long‐term or large‐scale ecological studies.     

Effect of field sampling design on variation partitioning in a dendritic stream network

Variation partitioning is one of the most frequently used method to infer the importance of environmental (niche based) and spatial (dispersal) processes in metacommunity structuring. However, the reliability of the method in predicting the role of the major structuring forces is less known. We studied the effect of field sampling design on the result of variation partitioning of fish assemblages in a stream network. Along with four different sample sizes, a simple random sampling from a total of 115 stream segments (sampling objects) was applied in 400 iterations, and community variation of each random sample was partitioned into four fractions: pure environmentally (landscape variables) explained, pure spatially (MEM eigenvectors) explained, jointly explained by environment and space, and unexplained variance. Results were highly sensitive to sample size. Even at a given sample size, estimated variance fractions had remarkable random fluctuation, which can lead to inconsistent results on the relative importance of environmental and spatial variables on the structuring of metacommunities. Interestingly, all the four variance fractions correlated better with the number of the selected spatial variables than with any design properties. Sampling interval proved to be a fundamentally influential sampling design property because it affected the number of the selected spatial variables. Our findings suggest that the effect of sampling design on variation partitioning is related to the ability of the eigenvectors to model complex spatial patterns. Hence, properties of the sampling design should be more intensively considered in metacommunity studies.     

Effect of sampling scale on the assessment of epiphytic bacterial populations

Bacterial populations on above-ground plant surfaces were estimated at three different biological scales, including leaflet disks, entire leaflets, and whole plants. The influence of sample scale on the estimation of mean bacterial population size per unit and per gram and on the variability among sampling units was quantified at each scale. Populations were highly variable among sampling units at every scale examined, suggesting that there is no optimal scale at which sample variance is reduced. The distribution of population sizes among sample units was sometimes, but not consistently, described by the lognormal. Regardless of the sampling scale, expression of population sizes on a per gram basis may not reduce variance, because population size was not generally a function of sample unit weight within any single sampling scale. In addition, the data show that scaling populations on a per gram basis does not provide a useful means of comparing population estimates from samples taken at different scales. The implications of these results for designing sampling strategies to address specific issues in microbial ecology are discussed. Correspondence to: L.L. Kinkel     

The Paternal Landscape along the Bight of Benin – Testing Regional Representativeness of West-African Population Samples Using Y-Chromosomal Markers

Patterns of genetic variation in human populations across the African continent are still not well studied in comparison with Eurasia and America, despite the high genetic and cultural diversity among African populations. In population and forensic genetic studies a single sample is often used to represent a complete African region. In such a scenario, inappropriate sampling strategies and/or the use of local, isolated populations may bias interpretations and pose questions of representativeness at a macrogeographic-scale. The non-recombining region of the Y-chromosome (NRY) has great potential to reveal the regional representation of a sample due to its powerful phylogeographic information content. An area poorly characterized for Y-chromosomal data is the West-African region along the Bight of Benin, despite its important history in the trans-Atlantic slave trade and its large number of ethnic groups, languages and lifestyles. In this study, Y-chromosomal haplotypes from four Beninese populations were determined and a global meta-analysis with available Y-SNP and Y-STR data from populations along the Bight of Benin and surrounding areas was performed. A thorough methodology was developed allowing comparison of population samples using Y-chromosomal lineage data based on different Y-SNP panels and phylogenies. Geographic proximity turned out to be the best predictor of genetic affinity between populations along the Bight of Benin. Nevertheless, based on Y-chromosomal data from the literature two population samples differed strongly from others from the same or neighbouring areas and are not regionally representative within large-scale studies. Furthermore, the analysis of the HapMap sample YRI of a Yoruban population from South-western Nigeria based on Y-SNPs and Y-STR data showed for the first time its regional representativeness, a result which is important for standard population and forensic genetic applications using the YRI sample. Therefore, the uniquely and powerful geographical information carried by the Y-chromosome makes it an important locus to test the representativeness of a certain sample even in the genomic era, especially in poorly investigated areas like Africa.     

Optimizing sampling design and sequencing strategy for the genomic analysis of quantitative traits in natural populations

Mapping the genes underlying ecologically relevant traits in natural populations is fundamental to develop a molecular understanding of species adaptation. Current sequencing technologies enable the characterization of a species’ genetic diversity across the landscape or even over its whole range. The relevant capture of the genetic diversity across the landscape is critical for a successful genetic mapping of traits and there are no clear guidelines on how to achieve an optimal sampling and which sequencing strategy to implement. Here we determine, through simulation, the sampling scheme that maximizes the power to map the genetic basis of a complex trait in an outbreeding species across an idealized landscape and draw genomic predictions for the trait, comparing individual and pool sequencing strategies. Our results show that quantitative trait locus detection power and prediction accuracy are higher when more populations over the landscape are sampled and this is more cost-effectively done with pool sequencing than with individual sequencing. Additionally, we recommend sampling populations from areas of high genetic diversity. As progress in sequencing enables the integration of trait-based functional ecology into landscape genomics studies, these findings will guide study designs allowing direct measures of genetic effects in natural populations across the environment.     

Regional environmental pressure influences population differentiation in turbot (Scophthalmus maximus)

Unravelling the factors shaping the genetic structure of mobile marine species is challenging due to the high potential for gene flow. However, genetic inference can be greatly enhanced by increasing the genomic, geographical or environmental resolution of population genetic studies. Here, we investigated the population structure of turbot (Scophthalmus maximus) by screening 17 random and gene‐linked markers in 999 individuals at 290 geographical locations throughout the northeast Atlantic Ocean. A seascape genetics approach with the inclusion of high‐resolution oceanographical data was used to quantify the association of genetic variation with spatial, temporal and environmental parameters. Neutral loci identified three subgroups: an Atlantic group, a Baltic Sea group and one on the Irish Shelf. The inclusion of loci putatively under selection suggested an additional break in the North Sea, subdividing southern from northern Atlantic individuals. Environmental and spatial seascape variables correlated marginally with neutral genetic variation, but explained significant proportions (respectively, 8.7% and 10.3%) of adaptive genetic variation. Environmental variables associated with outlier allele frequencies included salinity, temperature, bottom shear stress, dissolved oxygen concentration and depth of the pycnocline. Furthermore, levels of explained adaptive genetic variation differed markedly between basins (3% vs. 12% in the North and Baltic Sea, respectively). We suggest that stable environmental selection pressure contributes to relatively strong local adaptation in the Baltic Sea. Our seascape genetic approach using a large number of sampling locations and associated oceanographical data proved useful for the identification of population units as the basis of management decisions.     

Intraspecific comparative analysis of the species Salinibacter ruber

Salinibacter ruber is the first extremely halophilic member of the Bacteria domain of proven environmental relevance in hypersaline brines at or approaching NaCl saturation, that has been brought to pure culture. A collection of 17 strains isolated from five different geographical locations (Mallorca, Alicante, Ebro Delta, Canary Islands, and Peruvian Andes) were studied following the currently accepted taxonomic approach. Additionally, random amplification of genomic DNA led to the phenetic analysis of the intraspecific diversity. Altogether the taxonomic study indicated that S. ruber remained highly homogeneous beyond any geographical barrier. However, genomic fingerprints indicated that populations from different isolation sites could still be discriminated.     

A comparative study of four dredges used for sampling benthic macroinvertebrates in rivers

SUMMARY. After considering the large number of dredges described in the literature, four light-weight dredges were chosen for manual operation from a small boat or the bank: Irish triangular dredge, small Fast dredge, medium-sized and large Naturalist's dredges. The dredges were tested in a series of trials at three sites in two rivers. A stratified random sample (number of sampling units, n = 5) was taken at each site and the modal particle sizes at sites 1–3 were 1–2 mm (fine gravel), 64–128 mm (larger stones) and 128–256 mm, respectively. The dredges usually took a similar range of stone sizes at each site but the design of the Fast dredge excluded larger stones (>16 mm). The Irish dredge sometimes failed to operate correctly. Variations in the volume of substrata taken with each dredge were large, both between sampling units in the same sample and between samples. The latter differences were partially due to the increase in the modal size of the stones, especially between sites 1 and 2, the different sampling areas of the dredges and the depth of penetration into the substratum. Penetration depth was probably greatest for the two Naturalist's dredges, smaller for the Fast dredge and smallest for the Irish dredge. In field trials, the relative abundances of major taxa were similar for most dredges at each site; major exceptions were the Fast dredge at site 2 and the Irish dredge at site 3. There was a high variability between sampling units in the same sample and therefore a lack of precision in the estimates of the mean number of invertebrates per sample. Therefore, the dredges cannot be used as quantitative samplers for the estimation of population density. Their adequacy as qualitative samplers for the estimation of total number of taxa per sample varied considerably and maximum estimates of their efficiencies for a small sample (n= 5) were <40% for the Irish and Fast dredges, >57% for the medium-sized Naturalist's dredge and >76% for the large Naturalist's dredge. There was a clear relationship between the number of taxa and the number of invertebrates taken at each site and this relationship was well described by a power law with an exponent within the range 0.18–0.53. The number of sampling units in the sample had no significant effect on the power-law equations for each site. The power-law equation was very similar for most of the dredges at each site, the only major exception being the Fast dredge at site 1. The implications of this relationship are discussed.