As mentioned before, the standard neutral model is widely used in testing certain hypotheses, e.g. that positive selection has acted on a particular genomic region. In the following I will describe the most important neutrality tests, as they are used throughout this thesis. These tests can roughly be grouped in those using intraspecific data only (i.e. polymorphism data obtained from DNA sequences) and tests making use of an outgroup, that is they include interspecific comparisons. Among the tests belonging to the first group is T AJIMA ' S (1989) D. Under the neutral-equilibrium model, the expected nucleotide variation for a diploid is given by θ = 4N e µ, where N e is the effective population size and θ is the mutation rate (K IMURA and C ROW 1964). The D statistic compares two parameter estimates of the population mutation rate, i.e. π (T AJIMA 1983), an estimator based on the average number of pairwise nucleotide differences, and θ W (W ATTERSON 1975), based on the number of segregating sites in a sample. Under neutrality, the difference between π and θ is expected to be zero. However, if a selective sweep completely removes nucleotide variation in the vicinity of a beneficial mutation, then mutations that arise subsequently to the sweep will initially be at very low frequency. In fact, after recent hitchhiking most SNPs will be singletons, producing a star-like phylogeny. Therefore, Tajima’s D is likely to be negative. In contrast, in the case of two approx. equally selected SNP variants (i.e. balancing selection) D will probably be positive since two major haplotypes can be observed in a given sample, thus resulting in a high number of pairwise differences. However, results of the D statistic have to be interpreted carefully. The reason is that demographic processes will lead to deviations from neutral-equilibrium conditions. For example, a population size expansion typically results in a negative D statistic as well, since many segregating sites will be at low frequency (S LATKIN and H UDSON 1991). Likewise, a population size bottleneck or hidden populationstructure can give a positive D.
Introgression of non autochthonous genes from introduced plants of different origin into local genepools is one of the major issues in conservation biology. Here we assess the populationstructure of the cornelian cherry Cornus mas L. (Cornaceae) at two natural stands in Thuringia and compare the genetic diversity with an artifical po- pulation based on samples of garden and town origin from Jena. The primary question to which no information for Cornus exists is if there is gene flow between populations and to what extent intro- gression from planted cornelian cherries, commonly used in planting vegetations of public parks or streets, can be detected. There is no information available if natural populations show any intrapopula- tional and/or interpopulational differentiation pointing towards restricted gene flow with possible necessity for a population based conservation strategy rather than a taxon based strategy. Natural populations of the mainly south-east european and Asia minor dis- tributed species (H EGI , 1927) Cornus mas are rare in Germany, the thuringian populations are of special interest because they might be outposts at the warm and dry limestone slopes of the Saale valleys and have been explicetly mentioned in the post-renaissance herbals e.g. by Z WINGER (1744) and since then (H EGI , 1927). As an early pasture for bees and a traditional used edible fruit Cornus mas is an important element of the native flora (B ARTELS , 1993; F RIEDRICH and P ETZOLD , 1993). Around Jena, cultural history reflects heavily on Cornus mas, for a long time hiking poles and batons for student leagues, the so called „Ziegenhainer“ were made in the small village Ziegenhain next to Jena and distributed through Germany (T RAEGER , 1993). The populations we studied were an artificial one from plants
Chapter 3 30 methodological limitations. So far, the most common method for identification of clones in the D. longispina complex has been allozyme electrophoresis (e.g. Ender et al., 1996; Spaak, 1996; Seda et al., 2007b; Rother et al., 2010), although RAPD markers were also used occasionally (e.g. Ender et al., 1996). However, allozyme studies are limited by the few polymorphic loci they provide; in most cases, it is likely that the multilocus genotypes defined by allozymes represented clonal groups (Thielsch et al., 2009). This substantially limits the power to trace the frequencies of single clones and to study clonal structure in general. RAPDs, although more variable, have often poor reproducibility (Devos and Gale, 1992) and, being dominant markers which cannot separate homozygotes from heterozygotes (Suvanto and Latva-Karjanmaa, 2005), have limited use in the analyses of populationstructure. Recently, microsatellite markers have been developed for the D. longispina complex (Brede et al., 2006). However, the subsequent studies employing these markers have focused so far on either a description of population state at a single time point (Thielsch et al., 2009; Dlouhá et al., 2010) or on exploring temporal changes at the taxon level only (Brede et al., 2009; Yin et al., 2010; Rellstab et al., 2011). In other systems, microsatellites have already been proven to be very powerful in tracing clonal lineages; for example, in the cyclically parthenogenetic aphid (Halkett et al., 2005) or in bacterial populations (Imhof and Schlotterer, 2001).
We assess the impact of populationstructure on economic growth. Following recent research, we focus on the generational turnover as a key driver of consumption growth. We characterize the impact of the average birth and death rates on the generational turnover, depending on the age-prole of consumption and on the extent of annuity market imperfection. Using recent data from the National Transfer Accounts on con- sumption proles for a number of countries, we assess in a comparative way the sign and magnitude of generational turnover and its impact on consumption growth. We nd considerable cross-country variation and trace it back to dierences in demographic rates and in the consumption structure.
The connectivity among marine populations is determined by the dispersal capabilities of adults as well as their eggs and larvae. Dispersal distances and directions have a profound effect on gene flow and genetic differentiation within species. Genetic homogeneity over large areas is a common feature of coral reef fishes and can reflect high dispersal capability resulting in high levels of gene flow. If fish larvae return to their parental reef, gene flow would be restricted and genetic differentiation could occur. Larabicus quadrilineatus (Labridae) is considered as an endemic fish species of the Red Sea and Gulf of Aden. The juveniles of this species are cleaner fish that feed on ectoparasites of other fishes. Here, we investigated the genetic populationstructure and gene flow in L. quadrilineatus among five locations in the Red Sea to infer connectivity among them. To estimate genetic diversity, we analysed 369 bp of 237 mitochondrial DNA control region sequences. Haplotype and nucleotide diversities were higher in the southern than in the northern Red Sea. Analysis of molecular variance (AMOVA) detected the highest significant genetic variation between northern and central/southern populations ( Ф ct = 0.01; p < 0.001). Migration analysis revealed
Comparing the results achieved in this study using cattle with those presented in human (Paschou et al. 2007), we needed slightly more PCAIMs to guarantee a true assignment of the animals. The underlying populationstructure of these two species as well as applied data sets is fairly different and different numbers are to be expected. However, the reason that obviously more markers are needed to investigate the populationstructure of cattle breeds may reflect the relative high relationships among investigated cattle breeds compared to human samples and likely differences in effective population size (Ne). The relationships between livestock samples are generally expected to be higher than in humans, where artificial insemination and cross-breeding is widespread. As our results demonstrate, especially closely related populations namely the subpopulations GLW-W, GLW-WBP and GLW-W, which could be detected using all autosomal SNPs (46,147), were not detectable with even hundreds of PCAIMs. Considering only the main populations we showed that using 200 PCA-correlated SNPs, we were able to assign the studied animals with 100% accuracy to their population of origin. However, a final advice on an optimal number of PCAIMs can not be provided at this stage. As our results show, the identification of the optimal number of PCAIMs is highly variable and strongly relies on the structure of the investigated data (e.g. Lewis et al. 2010 suggested an optimal number of 2,000 PCAIMs analysing 13 different cattle breeds as presented in the BovineHapMap project).
MLSA is a widely applied genotyping tool in studies of the evolution and populationstructure of microbial organism and also represents a novel standard in microbial molecular systematics. Population genetic analysis of Sphingomonadaceae by MLSA revealed a distinct population substructure among individual 16S rRNA phylotypes, providing insights into the diversity within bacterial species. A 'species' is the main taxonomic unit in the systematics of prokaryotes, but the subject of the species concept of prokaryotes has always been controversial. Until now there is no prokaryotic species concept that is accepted by all scientists. But for practical reasons, bacterial strains are affiliated to different species on the basis of DNA-DNA reassociation and diagnostic phenotypes. As DNA-DNA hybridization is difficult to be compared between laboratories and time consuming, MLSA becomes a valuable alternative to it. The population genetic structure revealed by MLSA is strongly associated with the results from DNA-DNA relatedness values. When sufficient numbers of suitable loci are selected, the concatenated sequence similarity values can in principle be used for species delineation.
This paper analyses migration processes and their influence on the transformation of multiethnic populationstructure in the Kali- ningrad region. The author uses official stati- stics (current statistics and census data), as well as interviews with the representatives of ethnic cultural associations as information sources. Special attention is paid to the mi- gration features associated with different ethnic groups. The author identifies major reasons behind the incoming and outgoing movement of population. In the post-Soviet period the Kaliningrad region has experien- ced positive net migration. This active migra- tion into the region has contributed to the de- velopment of “migration networks” and es- tablished a new basis for further population increase through migration. The article de- scribes changes in the regional multiethnic populationstructure and identifies key fac- tors behind them. It is concluded that migra- tion has played the decisive role in the pro- cess of multiethnic populationstructure transformation in the Kaliningrad region in the post-Soviet period. The author views mi- gration as a serious test for both the migrants and the receiving society. On the one hand, migrants have to adapt to a different natio- nal, cultural, and linguistic environment and look for the ways of successful integration into the receiving society. On the other hand, the receiving society also faces a serious transformation as a result of the changing population size and structure, the emergence of new elements in culture, rules of beha- viour, and the development of new attitudes.
While the ΔK method (Evanno et al., 2005 (see below)) revealed a hierarchical populationstructure with two clusters (K = 2) of subpopulations as the uppermost level of populationstructure, STRUCTURE analyses using the likelihood approach and clustering analyses with BAPS using a high upper limit for K are likely to have overestimated K. This is known to occur in STRUCTURE analyses (Falush et al., 2003; Evanno et al., 2005), especially when isolation by distance influences population differentiation (Frantz et al., 2009). Similarly, BAPS is known to overestimate weak differences in allele frequencies (and therefore overestimate K) when clustering individuals without spatial priors (Corander et al., 2007; Rowe & Beebee, 2007). This fits the observation that using a spatial prior reduced the BAPS estimate for the number of clusters. Furthermore, BAPS might have overestimated K because subpopulations were well differentiated (Latch et al., 2006). Additionally, the use of a correlated allele frequency model (as implemented in BAPS, STRUCTURE and optional in GENELAND) tends to underestimate admixture while overestimating K (Falush et al., 2003). To account for the problem of systematic overestimation of K, Evanno et al. (2005) developed the ad hoc statistic ∆K, based on the rate of change in the log probability of data between successive K values, to identify the uppermost or “true” level of populationstructure. The calculated ∆K value clearly supports a partition with two large clusters (K=2). The notable local ∆K maximum for K=3 and the optimal partition size according to clustering with GENELAND, however, suggest that the data might also support a partition of three clusters, possibly as the next-highest level of populationstructure.
In many cases, conservation strategies still focus mainly on species and ecological communities, and not so much the genetic diversity within a species (Coates et al., 2018). Of course, saving intact habitats is of major importance, as this allows the survival of multiple species at once. However, genetic diversity conservation is often neglected and only comes into mind when a species is already classified as “Critically Endangered”, with only a few individuals left in the wild. Then, breeding programs and translocations are considered to save as much genetic diversity within the remaining population. Yet, at this point, the majority of genetic variation which once occurred in a particular species or population may already be lost, reducing the chance of a successful conservation program due to the reduced ability to adapt to a changing environment and the increased risk of reduced fitness by inbreeding depression.
Generally, the pollen and/or seed dispersal potential of plants should affect their ability to maintain local genetic diversity. Hamrick and Godt (1996) showed that outcrossing species with limited pollen and/or seed dispersal tend to have greater genetic differentiation among populations than species with more potential for gene movement. In addition, it is most likely that the tall stature and comparatively low population densities of trees should result in larger dispersal distances for seeds and pollen than would be expected to occur in herbaceous species (e.g. wild tomatoes), whose individuals are generally shorter and found in more dense stands. Hence, pollen and/or seed dispersal under equilibrium conditions may not be sufficient to explain the patterns of population differentiation in our samples, especially the low differen- tiation between the northernmost and southernmost populations in S. peruvianum, despite the greatest geographic distance and the large differences in habitats between them. An alternative explanation for patterns of differentiation in our samples rests on the likely presence of soil seed banks, and historical association of tomato popula- tions mediated by climatic cycles (as alternative to equilibrium gene flow). Soil seed banks probably play an important role in maintaining the large genetic diversity in wild tomatoes (Roselius et al., 2005). The presence of seed banks can have a ma- jor impact on effective population size and consequently the maintenance of genetic diversity in plants (Levin, 1990; Nunney, 2002).
Traditionally, two mutation models have been considered for microsatellites, the infinite allele model (IAM: Kimura and Crow 1964) and the stepwise mutation model (SMM: Kimura and Ohta 1978). Under the IAM, a mutation involves any number of tandem repeats and always results in an allelic state not previously encountered in the population (Estoup and Angers 1998). The SMM predicts that mutation will result in an allele that is one repeat larger or smaller, with decreases and increases being equally likely. Therefore, alleles can mutate towards allele states already present in the population. More recently a model has been developed that more accurately explains the variation observed at dinucleotide repeat microsatellites (Di Rienzo et al. 1994). This model, the two phase model (TPM), incorporates the mutational process of the SMM, but also allows for mutation steps of several repeats. Most studies indicate that the TPM is the most realistic mutation model among the three models described above, and that microsatellites indeed mutate in a stepwise fashion at a relatively high rate (reviewed in Estoup and Angers 1998 and in Schlötterer 2000). Nevertheless, mutation rate and model can differ among the different types of microsatellites (see above), mutation rates can differ for different alleles at a locus, and microsatellites do not mutate into infinite lengths (Estoup and Angers 1998). Therefore, the mutational processes are still not fully understood (Jarne and Lagoda 1996; Goldstein and Pollock 1997). Deviations from the models have consequences for the selection of appropriate statistics that make specific assumptions about mutation models (reviewed by Goldstein and Pollock 1997). Since the early to mid-1990s, microsatellites have gradually replaced allozymes and other molecular markers as the tool of choice for population genetic studies, due to several reasons (reviewed in Hansen 2003):
Knowledge of the genetic structure of populations is important for the understanding of their ecology and evolution. The ability of a population to adapt to unique local conditions is not solely determined by the strength of natural selection, but countering effects of genetic drift and gene flow (Slatkin 1973; May et al.1975; Endler 1977). Gene flow among populations is a fundamental evolutionary force that can determine the geographical spread of novel adaptations, and therefore the potential for local adaptation and speciation (Fisher 1930; Mayr 1942; Mayr 1963; Ehrlich and Raven 1969). Elucidating the factors that influence the spatial extent of gene flow and the genetic structure of populations is fundamental in understanding species persistence. Various environmental factors such as geographical distance (Wright 1943; Kimura and Weiss 1964; Peterson and Denno 1998), habitat persistence (Roderick 1996; Peterson and Denno 1998), habitat patchiness (King 1987; Roderick 1996; Peterson and Denno 1998), physical barriers (Hartl 1980; Gerlach and Musolf 2000; Keller and Largiadèr 2003), and the frequency of extinction/colonization events (Whitlock and McCauley 1990; Hastings and Harrison 1994; Harrison and Hastings 1996) can promote gene flow and hence the relative isolation of populations. In addition, some intrinsic life history or ecological traits such as dispersal ability (Peterson and Denno 1997) and phenological asynchrony (Wood and Guttman 1982; Runyeon and Prentice 1996) are expected to have significant effects on gene flow and the genetic structure of populations.
60 the estimated wage effect of the youth share from models that exclude or include detailed information about an individual’s industrial and occupational affiliation.
In our model the effect that the regional youth share has on the wages of young workers is identified solely through the within-variation of this variable. However, as the relative size of the youth population within a labour market is potentially endogenous due to migration into high-wage areas, an instrumental variables (IV) identification strategy is employed: within a given region the instrument is defined as the share of individuals that are fifteen years younger and that are observed fifteen years earlier than the age group of the endogenous regressor. We find that the youth share has a statistically significant negative effect on the wages of young workers. Specifically, an increase by one percentage point is predicted to decrease wages by 3% in our baseline model. When using a district-based measure of the youth-share variable, the estimated coefficients are smaller by between 13% and 48%. Finally, we find that controlling for an individual’s industry and, particularly, occupation reduces the estimated wage decrease from 3% to 2%, which suggests that a substantial part of the negative effect of age-group size is the result of individuals in larger age groups being more likely to be employed in lower-paying occupations. According to these results, future generations of young workers can expect to benefit from demographic developments. Specifically, a decrease in the youth share by 2.5 percentage points, as projected to occur between 2010 and 2025, would be predicted to lead to an increase in young workers’ wages of about 5%, ceteris paribus.
Using detailed data from the National Transfer Accounts (Lee and Mason, 2011, 2014a,b) con- taining age-specific consumption and earnings profiles of important industrialized countries, we find considerable cross-country variation of the turnover, which we can trace back to the underlying de- mography and the consumption structure. In particular, economies with young populations and a comparatively flat age profile of consumption such as Chile and Taiwan feature a large positive gen- erational turnover effect, whereas most of the aging European economies with an older population and a steeper age profile of consumption exhibit a strong negative turnover.
Unfortunately, we were not able to satisfactorily resolve populationstructure in our study area because we were only able to obtain a moderate number of genotypes from the Bayburt province. None of the analyses could reject the hypothesis of a single population in our study area (e.g., Fig. 2 ), and we did detect one bear in both provinces, indicating that (current) landscape features do not hinder the movement of brown bears between these two areas. However, due to the uneven sampling between areas, which can be problematic for some analyses of populationstructure (e.g., Puechmaille, 2016 ), we prefer to refrain from definitively stating that there is no population subdivision. Consequently, we cannot claim that the bears in the GKM can be managed as a single conservation unit. Further work is clearly needed to address this, particularly with respect to additional sampling; ideally, also increasing the geographic scope of the current study.
Cyclic parthenogenesis occurs in over 15 000 animal species, spread over seven taxonomic groups (Monogononta, Cladocera, Digenea, Homoptera, Hymenoptera, Diptera, Coleoptera; Hebert 1987). As this mode of reproduction combines the advantages of sexuality with the high demographic potential of asexuality, it has been intensely studied by evolutionary biologists and ecologists (e.g., de Meeûs et al. 2007; Decaestecker et al. 2007; Gómez & Carvalho 2000; Sunnucks et al. 1997; Taylor et al. 1999). However, the impact of cyclic parthenogenesis on population genetic structure depends on many factors that determine the relative importance of sexual and parthenogenetic phases (De Meester et al. 2006; Hughes 1989). In addition, especially in aquatic taxa, sexual reproduction is associated with the production of dormant stages. This may at the same time alter local rates of micro- evolutionary processes and facilitate dispersal among populations (Figuerola et al. 2005; Hairston & De Stasio 1988). For example, dormant eggs of cladocerans and monogonont rotifers are usually produced in high numbers and, as they are produced by sexual recombination in most species, they represent a source of new recombinant genotypes. Thus, hatching from egg banks has a strong impact on the level of clonal diversity within populations as well as significant effects on the populationstructure and potential for local adaptation (Boersma et al. 1999; Brendonck & De Meester 2003; Cousyn et al. 2001; De Meester 1996; Declerck et al. 2001). On the other hand, the parthenogenetic part of the life cycle may result in the persistence of clonal lineages over long time periods and a reduction of local diversity through clonal erosion (De Meester et al. 2006).
composition of local populations and to monitor diversity and anti-discrimination programmes.
Ethnicity is a variable for which the use of Structure Preserving Estimators (SPREE) (Purcell and Kish, 1980) seems natural. Most SAE methods combine existing survey data for the variable of interest with relevant covariate information obtained from censuses or administrative sources, to obtain better estimates than those from the survey alone. For Labour Force status for instance, covariates such as sex, age or level of education can provide some explanatory power, see Molina et al. (2007) and Scealy (2010). In the case of ethnicity, on the other hand, it is difficult to identify such a set of covariates. Instead, for post-censal updates of the LA by ethnicity distribution, the corresponding aggregated census table can always be treated as a proxy for the table of interest.