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Data from: Wide variation in spatial genetic structure between natural populations of the European beech (Fagus sylvatica) and its implications for SGS comparability

Identification and quantification of spatial genetic structure (SGS) within populations remains a central element of understanding population structure at the local scale. Understanding such structure can inform on aspects of the species' biology, such as establishment patterns and gene dispersal distance, in addition to sampling design for genetic resource management and conservation. However, recent work has identified that variation in factors such as sampling methodology, population characteristics, and marker system can all lead to significant variation in SGS estimates. Consequently, the extent to which estimates of SGS can be relied upon to inform on the biology of a species or differentiate between experimental treatments is open to doubt. Following on from a recent report of unusually extensive SGS when assessed using amplified fragment length polymorphisms (AFLP) in the tree Fagus sylvatica, we explored whether this marker system led to similarly high estimates of SGS extent in other apparently similar populations of this species. In the three populations assessed, SGS extent was even stronger than this previously reported maximum, extending up to 360 m, an increase of up to 800% in comparison with the generally accepted maximum of 30 - 40 m based on the literature. Within this species, wide variation in SGS estimates exists, whether quantified as SGS intensity, extent, or the Sp parameter. Consequently, we argue that greater standardisation should be applied in sample design and SGS estimation and highlight five steps that can be taken to maximize the comparability between SGS estimates.

opencc-zeroDec 2017View details →

Data from: Landscape genetic analyses of Cervus elaphus and Sus scrofa: comparative study and analytical developments

Red deer and wild boar are two major game species whose populations are managed and live in areas impacted by human activities. Measuring and understanding the impact of landscape features on individual movements and spatial patterns of genetic variability in these species is thus of importance for managers. A large number of individuals sampled across Wallonia (Belgium) for both species have been genotyped using microsatellite markers (respectively >1700 and >1200 genotyped individuals) and some individuals have also been followed using a capture-mark-recapture (CMR) protocol. The combined data set represents an unprecedented opportunity to study and compare the environmental factors impacting the interconnectivity of these large mammals. The present study describes and uses a landscape genetic workflow to compare spatial patterns of genetic variability and the impact of environmental factors on genetic differentiation. For the latter analyses, we investigate the correlation between genetic and environmental distances (pairwise approach) and also between local genetic dissimilarity and environmental conditions (point approach). Preliminary analyses of CMR data confirm that motorways act as significant barriers to dispersal. However, analyses performed with the pairwise approach do not highlight any evidence of an impact of motorways on genetic differentiation, which is presumably due to their recent establishment. Complementary analyses performed with the point approach reveal that low altitude tends to be associated with higher genetic dissimilarity. From a methodological point of view, the present workflow illustrates the complementary application of both pairwise and point approaches as well as univariate and multivariate analyses.

Data from: Comparative population genetic analysis of bocaccio rockfish Sebastes paucispinis using anonymous and gene-associated simple sequence repeat loci

Comparative population genetic analyses of traditional and emergent molecular markers aid in determining appropriate use of new technologies. The bocaccio rockfish Sebastes paucispinis is a high-gene-flow marine species off the west coast of North America that experienced strong population decline over the past three decades. We used 18 anonymous and 13 gene associated simple sequence repeat loci (EST-SSRs) to characterize range-wide population structure with temporal replicates. No FST-outliers were detected using the LOSITAN program, suggesting that neither balancing nor divergent selection affected the loci surveyed. Consistent hierarchical structuring of populations by geography or year class was not detected regardless of marker class. The EST-SSRs were less variable than the anonymous SSRs, but no correlation between FST and variation or marker class was observed. General Linear Model analysis showed that low EST-SSR variation was attributable to low mean repeat number. Comparative genomic analysis with Gasterosteus aculeatus, Takifugu rubripes, and Oryzias latipes showed consistently lower repeat number in EST-SSRs than SSR loci that were not in ESTs. Purifying selection likely imposed functional constraints on EST-SSRs resulting in low repeat numbers that affected diversity estimates, but did not affect the observed pattern of population structure.

opencc-zeroDec 2014View details →

Data from: How a haemosporidian parasite of bats gets around: the genetic structure of a parasite, vector and host compared

Parasite population structure is often thought to be largely shaped by that of its host. In the case of a parasite with a complex life cycle, two host species, each with their own patterns of demography and migration, spread the parasite. However, the population structure of the parasite is predicted to resemble only that of the most vagile host species. In this study we tested this prediction in the context of a vector-transmitted parasite. We sampled the haemosporidian parasite Polychromophilus melanipherus across its European range, together with its bat fly vector Nycteribia schmidlii and its host, the bent-winged bat Miniopterus schreibersii. Based on microsatellite analyses, the wingless vector, and not the bat host, was identified as the least structured population and should therefore be considered the most vagile host. Genetic distance matrices were compared for all three species based on a mitochondrial DNA fragment. Both host and vector populations followed an isolation-by-distance pattern across the Mediterranean, but not the parasite. Mantel tests found no correlation between the parasite and either the host or vector populations. We therefore found no support for our hypothesis; the parasite population structure matched neither vector nor host. Instead, we propose a model where the parasite's gene flow is represented by the added effects of host and vector dispersal patterns.

Figure 2 in Comparative genetic differentiation study of three coexisting mangrove crabs in western Atlantic

Figure 2. Map showing the sampling sites for Leptuca thayeri along the western Atlantic, with collection localities (circles) inside each designated population (dashed areas – names indicated). Different colours represent different populations. States and/or countries of each locality are indicated. For each population, the number of sequences obtained for both mtDNA COI and 16S rDNA genes is indicated. Details of the localities sampled are reported in Table 1. Abbreviations of States: Mexico: CC: Campeche; Brazil: AP: Amapá; PA: Pará; MA: Maranhão; CE: Ceará; PE: Pernambuco; AL: Alagoas; BA: Bahia; ES: Espírito Santo; RJ: Rio de Janeiro; SP: São Paulo; PR: Paraná; SC: Santa Catarina.

Figure 1 in Comparative genetic differentiation study of three coexisting mangrove crabs in western Atlantic

Figure 1. Map showing the sampling sites for Aratus pisonii along the western Atlantic, with collection localities (circles) inside each designated population (dashed areas – names indicated). Different colours represent different populations. States and/or countries of each locality are indicated. For each population, the number of sequences obtained for both mtDNA COI and 16S rDNA genes is indicated. Details of the localities sampled are reported in Table 1. Abbreviations of States: Mexico: CC: Campeche; VZ: Veracruz; TC: Tabasco; TS: Tamaulipas; YN: Yucatán; Brazil: AP: Amapá; PA: Pará; MA: Maranhão; CE: Ceará; RN: Rio Grande do Norte; PE: Pernambuco; AL: Alagoas; BA: Bahia; ES: Espírito Santo; RJ: Rio de Janeiro; SP: São Paulo; PR: Paraná; SC: Santa Catarina.

Figure 5 in Comparative genetic differentiation study of three coexisting mangrove crabs in western Atlantic

Figure 5. Leptuca thayeri. Median-Joining haplotype network for mtDNA COI (a) and 16S rDNA (b), showing the distribution of the haplotypes (H); the circle size is proportional to the haplotype frequency; black circles indicate the median vector; different colours represent different populations; dashed circles indicate suggested isolated geographic regions. (c) Bayesian analysis for genetic differentiation for mtDNA COI among populations.

Figure 4 in Comparative genetic differentiation study of three coexisting mangrove crabs in western Atlantic

Figure 4. Aratus pisonii. Median-Joining haplotype network for mtDNA COI (a) and 16S rDNA (b), showing the distribution of the haplotypes (H); the circle size is proportional to the haplotype frequency; black circles indicate the median vector; different colours represent different populations; dashed circles indicate suggested isolated geographic regions. (c) Bayesian analysis for genetic differentiation for mtDNA COI among populations.

Figure 7 in Comparative genetic differentiation study of three coexisting mangrove crabs in western Atlantic

Figure 7. Mismatch distribution for mtDNA COI of respective clades I (a), II (b), and III (c) of Aratus pisonii; clades I (d), and II (e) of Leptuca thayeri; and for all samples of Goniopsis cruentata (f), showing the frequency of distribution of the number of pairwise nucleotide differences among all individuals, under the sudden expansion model. Columns show the observed frequency distribution and the lines represent the simulated values under the sudden expansion model. Statistics for SSD and HRI with probability values are indicated.

FIGURE 6 in Comparing genetic markers' efficiencies for discrimination between two commercially important holothuroids in the Mediterranean Sea, Holothuria polii and Holothuria sanctori

FIGURE 6. BI phylogenetic analysis for H. polii, H. sanctori, and different Mediterranean holothuroids is based on partial sequences of the 28S rDNA gene. Four Markov Chains Monte Carlo (MCMC) samples were analyzed for 10 million (ngen=10,000,000) generations. Bootstrap values are shown above the branches. Bold labels refer to sequences obtained in the current study for H. polii and H. sanctori.

FIGURE 3 in Comparing genetic markers' efficiencies for discrimination between two commercially important holothuroids in the Mediterranean Sea, Holothuria polii and Holothuria sanctori

FIGURE 3. Distribution of 28S rDNA-based genetic pairwise distances among different Mediterranean species obtained in the current study and available in the GenBank. (a): H. polii vs. Mediterranean holothuroids, (b) H. sanctori vs. the Mediterranean holothuroids.

FIGURE 5 in Comparing genetic markers' efficiencies for discrimination between two commercially important holothuroids in the Mediterranean Sea, Holothuria polii and Holothuria sanctori

FIGURE 5. BI phylogenetic analysis for H. polii, H. sanctori, and different Mediterranean holothuroids is based on partial sequences of the 16S rDNA gene. Four Markov Chains Monte Carlo (MCMC) samples were analyzed for 10 million (ngen=10,000,000) generations. Bootstrap values are shown above the branches. The abbreviation "Hap" denotes haplotype. Bold labels refer to sequences obtained in the current study for H. polii and H. sanctori.

FIGURE 2 in Comparing genetic markers' efficiencies for discrimination between two commercially important holothuroids in the Mediterranean Sea, Holothuria polii and Holothuria sanctori

FIGURE 2. Distribution of 16S rDNA-based genetic pairwise distances among different Mediterranean species obtained in the current study and available in the GenBank. (a): H. polii vs. Mediterranean holothuroids, (b) H. sanctori vs. the Mediterranean holothuroids. For the 28S rDNA, the genetic distance for H. polii was the lowest with H. forskali (0.01) but the highest with H. sanctori (1.08) (Fig. 2A). For H. sanctori, the lowest pairwise distance was with H. polii (1.08), while the highest was with P. longicauda (1.163) (Fig. 2B).

FIGURE 4 in Comparing genetic markers' efficiencies for discrimination between two commercially important holothuroids in the Mediterranean Sea, Holothuria polii and Holothuria sanctori

FIGURE 4. Distribution of H3-based genetic pairwise distances among different Mediterranean species obtained in the current study and available in the GenBank. (a): H. polii vs. Mediterranean holothuroids, (b) H. sanctori vs. the Mediterranean holothuroids.

FIGURE 7 in Comparing genetic markers' efficiencies for discrimination between two commercially important holothuroids in the Mediterranean Sea, Holothuria polii and Holothuria sanctori

FIGURE 7. BI phylogenetic analysis for H. polii, H. sanctori, and different Mediterranean holothuroids, based on partial sequences of H3 gene. Four Markov Chains Monte Carlo (MCMC) samples were analyzed for 10 million (ngen=10,000,000) generations. Bootstrap values are shown above the branches. Bold labels refer to sequences obtained in the current study for H. polii and H. sanctori.

opennotspecifiedMar 2022View details →

FIGURE 4 in Comparative study and genetic diversity of Salicornia persica (Chenopodiaceae) using SCOT molecular markers

FIGURE 4. PCA plot of Salicornia persica populations based on morphological characters. Numbers are according to Table 1.

opennotspecifiedMar 2022View details →

FIGURE 3 in Comparative study and genetic diversity of Salicornia persica (Chenopodiaceae) using SCOT molecular markers

FIGURE 3. PCoA plot of populations in Salicornia persica based on SCoT molecular markers, (Population numbers are according to Table 1.)

opennotspecifiedMar 2022View details →

FIGURE 2 in Comparative study and genetic diversity of Salicornia persica (Chenopodiaceae) using SCOT molecular markers

FIGURE 2. WARD tree of populations in Salicornia persica based on SCoT molecular markers, (Population numbers are according to Table 1.)

Data from: Using a reference population yardstick to calibrate and compare genetic diversity reported in different studies: an example from the brown bear.

In species with large geographic ranges, genetic diversity of different populations may be well studied, but differences in loci and sample sizes can make the results of different studies difficult to compare. Yet, such comparisons are important for assessing the status of populations of conservation concern. We propose a simple approach of using a single well-studied reference population as a "yardstick" to calibrate results of different studies to the same scale, enabling comparisons. We use a well-studied large carnivore, the brown bear (Ursus arctos), as a case study to demonstrate the approach. As a reference population, we genotyped 513 brown bears from Slovenia using 20 polymorphic microsatellite loci. We used this dataset to calibrate and compare heterozygosity and allelic richness for 30 brown bear populations from 10 different studies across the global distribution of the species. The simplicity of the reference population approach makes it useful for other species, enabling comparisons of genetic diversity estimates between previously incompatible studies and improving our understanding of how genetic diversity is distributed along a species range.

Data from: Comparative landscape genetic analyses show a Belgian motorway to be a gene flow barrier for red deer (Cervus elaphus), but not wild boars (Sus scrofa)

While motorways are often assumed to influence the movement behaviour of large mammals, there are surprisingly few studies that show an influence of these linear structures on the genetic make-up of wild ungulate populations. Here, we analyse the spatial genetic structure of red deer (Cervus elaphus) and wild boars (Sus scrofa) along a stretch of motorway in the Walloon part of Belgium. Altogether 876 red deer were genotyped at 13 microsatellite loci, and 325 wild boars at 14 loci. In the case of the red deer, different genetic clustering tools identified two genetic subpopulations whose borders matched the motorway well. Conversely, no genetic structure was identified in the case of the wild boar. Analysis of isolation-by-distance patterns of pairs of individuals on the same side and on different sides of the motorway also suggested that the road was a barrier to red deer, but not to wild boar movement. While telemetry studies seem to confirm that red deer are more affected by motorways then wild boar, the red deer sample size was also much larger than that of the wild boars. We therefore repeated the analysis of genetic structure in the red deer with randomly sub-sampled datasets of decreasing size. The power to detect the genetic structure using clustering methods decreased with decreasing sample size.