Hereditary factors which lead to population change

One of the most influential framework on the distribution of genetic variation across species' ranges is the Abundant-Centre Hypothesis ACH [ 7 ]. It states that individuals of a species should become most abundant in areas where the conditions for reproduction and thus population growth are most favourable. In contrast, the number of populations and population density should decline towards areas with less advantageous environments until survival becomes impossible [ 4 ].

Approaching the niche limits, populations should therefore become rarer; less populated and be subject to increased turn-over [ 2 , 8 — 10 ]. Consequently, geographically marginal populations are expected to harbour less genetic variation and to be more strongly isolated from one another [ 11 ], because the population size and its recurrent fluctuations determine the loss rate of genetic variation due to genetic drift.

Asymmetrical gene-flow from larger sized, more abundant central populations to the range margins can counteract the previously described setting. Such gene-flow may prevent local adaptation by constantly supplying 'maladapted' alleles from the core range into marginal populations [ 6 ]. Under this scenario, the genetic variation in marginal populations should not differ much from the core area and population differentiation should be low.

A recent exhaustive review across different taxa showed that in about two out of three empirical studies genetic variability indeed decreased and population differentiation increased towards range margins, as expected under the ACH [ 12 ]. However, most of these studies were based on rather small parts of the species range or a rather restricted number of populations. Moreover, not only the geographic marginality of a population or its connectivity can influence the genetic variation present.

Only few studies so far tested possible alternative factors responsible for the observed patterns and none incorporated a historical perspective. We outline below other factors potentially influencing the distribution of genetic variability across species ranges. Populations may not only be marginal with respect to their geographic position, but also with respect to their environmental habitat quality [ 5 ]. Populations inhabiting low quality sites may be subject to increased population turn-over due to challenging environmental conditions and their variability, which may also negatively influence their genetic variability by increased drift [ 12 ].

Genetic variability across species ranges may also be influenced by local biotic interactions, in particular by competition with closely related, ecologically similar species or hybridisation with them in parapatric settings [ 12 ]. While the former process should result rather in a decrease of genetic variation due to increased population turnover, the latter is predicted to increase genetic diversity due to introgression of alleles in the hybrid zone [ 13 ].

Also contingent historic events like presence of geological dispersal barriers, population fragmentations and range expansions e. Here, the expectations on the distribution depend on the actual population history and may include decrease in genetic variation due to founder effects and population bottlenecks or an increase e.

Table 1 summarises the factors expected to influence genetic variability across species ranges, their predicted influence on genetic variation and the population processes by which they act. While most factors act on genetic variability in a one-way direction, the mating system both influences genetic variability and its prevalence can be driven by at least some of the above described factors.

On the one hand, a mixed mating system decreases the effective population size. Populations with a mixed mating system or purely selfing populations are therefore expected to experience increased drift [ 19 ]. Local differences in the proportion of selfing versus outcrossing individuals can thus determine the distribution of genetic variability [ 20 ].

On the other hand, habitat stability [ 21 ], population density and range expansions [ 22 ] can influence the preference for selfing or outcrossing via mating system evolution or phenotypic plasticity. The factors described above provide alternative, but not necessarily mutually exclusive explanations for the distribution of genetic variation.

Thus testing only a single factor at a time may lead to erroneous conclusions on the factors and processes governing the distribution of genetic variability over species' ranges [ 12 ]. Empirical studies explicitly addressing these hypotheses comprehensively are therefore needed to understand these factors and processes more fully [ 12 ].

In the present study, we tackled this issue using a pulmonate freshwater snail Radix balthica as model organism. This species is one of several species in the morphologically cryptic species complex Radix Montfort [ 22 ].

It is distributed throughout North-Western Europe from Northern Sweden to the South of France over a wide range of environmental conditions. As in many other pulmonate species [ 23 , 24 ], R. Without demanding a particular substrate or water quality, the species occurs in rather lentic water bodies like the shore zone of lowland lakes and ponds, oxbows, irrigation channels and fountains, but also in slow flowing rivers and streams [ 25 , 26 ].

Like in most other non-flying freshwater organisms, active dispersal depends crucially on continuous habitat; however, water-fowl mediated passive transport is probably the major mechanism for dispersal among unconnected habitats [ 27 , 28 ].

With the mentioned characteristics, the species is typical in most regards for many freshwater molluscs and other freshwater invertebrates lacking active long range dispersal capacities. Since more than a single factor may contribute to the distribution of genetic variability, we analysed the population structure, mating system and simultaneously tested the influence of the various factors outlined above by assessing the geographic distribution of supposedly neutral nuclear and mitochondrial genetic variability across the species range of R.

We identified individuals sampled from 64 sites as R. Together with previously identified R. In total, more than sites with Radix specimen were barcoded. For their spatial distribution and the distribution of other Radix taxa, see Additional File 1.

Sampling site distribution and their grouping to predictor variables. Circles represent sampling points. The colour gradient from light grey extreme climate to black average climate represents environmental marginality marg regarding climate variation as inferred from PCA analysis see Additional File 3. The convex polygon around all sampling points indicates the species range limits considered.

Populations grouped to different predictors are indicated by differentially hatched lines. The Holocene expansion area hol comprises populations neither situated in the refugial nor in the recent range expansion area. Including samples from previous studies, we genotyped individuals from 81 sampling sites with eight microsatellite markers.

For seven sites used for microsatellite analysis, less than ten individuals could be typed, leading to an unbalanced sampling. However, since omitting these sites from subsequent analyses did not change the results, we did not exclude them from the study. COI sequence data of more than bp length was analysed from individuals sampled at 66 sites GenBank accession numbers of new sequences HQHQ, GUGU, other sequences used were from [ 22 ] and [ 29 ].

The average overall F ST estimate was 0. The colour coded cluster memberships of each individual are depicted in Figure 2. There was no obvious geographical pattern; many sampling sites harboured individuals with a single majority cluster membership, but there were also sites with highly admixed individuals. Also the distribution of the clusters followed no obvious pattern; sites with different clusters were found in close proximity while the same clusters were found hundreds of kilometres apart Figure 2.

The minimum population spanning tree revealed, that the most similar populations were, with few exceptions, arranged in Southwest-Northeast direction, however, regardless of geographic distance between them Figure 3. This was also reflected in the plot of population pairwise F ST s against the geographical distance Figure 4. It was thus not necessary to correct the following analyses for geographical distance among populations [ 30 ]. Population structure analysis inferred from Bayesian clustering.

The more colours appear in a bar, the more admixed is the individual. The bars from a sampling site are arranged in blocks, connected with a line to the respective sampling site. Populations with similar genetic composition have therefore blocks with similar colour patterns. Plot of minimum spanning tree on distribution map.

Based on their nuclear differentiation most similar populations are connected by a blue line. Clearly, populations along a Southwest-Northeast axis are clustered together. Plot of pair-wise geographic population distances against the population pairwise linearised F ST estimated from microsatellite data. The average expected heterozygosity over all loci H E was 0. The average number of alleles per locus A was The observed minimum value was 1.

The average population selfing rate was 0. A total of mitochondrial haplotypes was identified over the species range. After rarefaction, 4. All measures of diversity per sampling site and a graphical representation of their spatial distribution can be found in Additional File 2. It was possible to test the 34 non-selfing populations on signs of recent population bottlenecks. The populations with recent bottlenecks were widely distributed over the species range, but not in the recent expansion area see Additional File 2 Figure A4.

Expected heterozygosity H E was above the overall average in the sampling sites grouped by the predictor variables dispersal barrier bar , biotic interaction bio , LGM refugia ref , Holocene expansion hol and distance to range limit lim. By contrast, it was reduced relative to the mean in the expansion sampling sites exp and environmentally marginal sites marg , Figure 5A This pattern was identical for the rarefied average number of alleles per locus Figure 5B.

A expected heterozygosity H E , B number of rarefied alleles A , C population selfing estimate s and D number of rarefied haplotypes H mt. For the dichotomous variable size , the mean for the smaller habitats are presented. The population selfing estimate s was on average lower than the overall average in sites grouped by the predictor variables bar , bio , ref , hol and size , while it was higher in exp , marg and lim.

However, the variance was very high in each group Figure 5C. The number of mitochondrial haplotypes H mt was increased at sites with presumed biotic interaction bio and to a lesser extent in the Holocene expansion sites hol.

In all other groupings, the haplotype diversity was decreased with the strongest effect observed in the recent expansion sites exp , Figure 5D. As the difference in all diversity measures from the refugial area and the Holocene expansion sites were not significantly different from zero, these categories were merged and contrasted against the effect of the recent expansion area in subsequent analyses.

The distribution of expected heterozygosity H E was explained by the additive effect of four models with two or three variables. It was best supported by the additive effect of dispersal barriers bar and expansion area exp Akaike weight 0.

The rarefied number of alleles A was best explained by the additive effect of the factors biotic interaction bio and exp Akaike weight 0. Variance in population selfing estimates was best explained by the additive effects of the four variables marg , exp , lim and size Akaike weight 0. The haplotype variability was best explained by the additive effect of the model with three variables bar , exp and range limits lim Akaike weight 0. Except for recent expansion exp , none of the other predictors yielded a significantly stronger or weaker structured grouping.

In the recent expansion area, the average population pair-wise F ST was 0. This difference proved to be significantly different from zero with an error probability of less than 0. Test on heterogeneity in population differentiation among central vs. The comparison between hol and ref was not significantly different. Like in most flightless freshwater taxa, dispersal of R. In particular lentic habitats are ephemeral on an intermediate time-scale, thus selecting on populations with good dispersal capacities [ 32 ].

The minimum spanning tree Figure 3 adds credibility to this assumption, as it clusters the respectively most similar populations mainly along the major bird migration route of the East Atlantic flyway in Southwest-Northeast direction. The suggested connection pattern of the minimum spanning tree beard a striking resemblance to the inferred initial postglacial recolonisation dispersal pattern, where also bird migration routes were implicated [ 29 ]. Other chapters in Help Me Understand Genetics.

Genetics Home Reference has merged with MedlinePlus. Learn more. The information on this site should not be used as a substitute for professional medical care or advice. Contact a health care provider if you have questions about your health. How are gene variants involved in evolution?

From Genetics Home Reference. Topics in the Variants and Health chapter What is a gene variant and how do variants occur? How can gene variants affect health and development? The cost of inbreeding in natural populations has been recently reviewed Crnokrak and Roff ; Keller and Waller ; Leimu et al. For threatened species of trees, this cost has been little investigated, despite the urgent need for such studies, given the potential role of genetic effects in population extinction Leimu et al.

Trees might not display similar responses to those observed in other life forms with respect to inbreeding and population size. Outcrossing and gene flow are usually higher in trees, as is the mutational load, owing to a greater individual longevity and an expected higher number of mitotic events per generation Petit and Hampe Pinus chiapensis Mart.

Andresen P. This pioneer species colonizes degraded areas, such as those resulting from the abandonment of maize fields managed under the slash-and-burn system of cultivation. The forests dominated by P. Therefore, P. Furthermore, this species is a valuable timber tree del Castillo and Acosta The capacity for invading open areas, a critical factor for survival in fragmented habitats Primack and Miao , varies widely among P. By contrast, in some populations, forest fragmentation has opened new areas for colonization favoring population expansion.

Regeneration in these large populations is abundant in open areas, even in the presence of human activities Table 1. These differences in establishment among large and small populations suggest the involvement of genetic effects associated with population size, in which case such differences should persist in a common environment and be associated with the size of the source population.

Clearly, there is a need to evaluate the genetic consequences of small population size to formulate strategies for effective conservation and sustainable management of genetic resources for species inhabiting degraded landscapes Newton et al.

If ignored, genetic effects associated with population size in P. Details of the populations of Pinus chiapensis used in this study, including population name, state Mexico or department Guatemala , geographic coordinates, altitude, approximate population size, mean diameter at breast height DBH , density of seedlings, and the number of sampled maternal trees. This study documents the relationships between population size, genetic variation, and fitness estimators in P.

To disentangle the effects of habitat quality from those of population size, we included small populations from both disturbed and conserved habitats and conducted our fitness estimations in a common environment.

We also took into account sample size effects in the analyses. Forests dominated by P. The habitat of P. This species is restricted to the windward side of tropical humid mountain areas, usually above the tropical rain forest and below frost-exposed areas in disturbed places see Newton et al. Dispersal and gene flow in P. At least three key factors apparently threaten the populations: i the continuous reduction, isolation, and degradation of many populations by land use change and timber overexploitation; ii an unusual low genetic diversity compared with other species of pine; and iii substantial inbreeding depression detected at the seed germination stage Newton et al.

We sampled 15 populations of contrasting size throughout the range of P. A population was defined as a group of individuals of P.

Extensive walks, vehicle ridings, and the inspection of aerial photographs and satellite imagery, when available, helped to discern populations. Population size was estimated in two ways. In the four smallest populations studied, all adult individuals were tallied. For the other populations, estimations were based on field surveys assessing the number of reproductive individuals in 0.

Map of Mexico and Guatemala showing the location of the Pinus chiapensis populations investigated in this study. Population numbers as in Table 1. The smallest populations were forest remnants in a matrix of agricultural fields and pasturelands. Adult mortality was unusually high, probably because of aging and self-thinning; in this population, The rest of the populations were probably younger, judging by the tree sizes DBH, Table 1 and some age estimates, and consisted of fragments of secondary forest that have established on previous maize fields in tropical montane cloud forest or tropical rain forest Escolapa areas.

None of them had evidence of massive adult mortality. Most forests in tropical areas in Mexico remained well preserved before Challenger Taking such information and assuming a generation time of 20 years, which is approximately the lowest age for reproduction in P. In each locality, cones were collected by climbing trees separated usually at least 10—20 m from each other. Analysis of allozyme variation was conducted on seed radicles ground in 0.

Table 2 shows the enzymes analyzed, buffer systems, and staining protocols. A single seed per maternal tree was used in the analyses.

Limited number of viable seeds precluded the survey all the enzyme systems in some populations. We used a variable number of seeds and maternal plants reflecting between-population variation in number of maternal trees available for sampling and seed viability.

Enzymes analyzed, enzyme commission number, buffer system, and staining protocols employed in the electrophoretic analyses. Seed germinability and seedling performance growth and survival were used as fitness estimators.

Seed germinability was defined as the fraction of seeds with emergent radicle. Ungerminated seeds were judged to be unviable because of their lack of enzymatic activity and their usually necrotic embryo. Therefore, seed germinability obtained in this way can be taken as the upper limit to the probability of seed germination and an estimate of seed viability.

Seedling survival and growth were used to evaluate seedling performance. Most seeds did not germinate or the resulting seedlings died before the cotyledons protruded. A total of live well-developed seedlings, that is, with protruded cotyledons, from 39 progenies and eight populations survived to conduct the experiment, which followed a complete randomized design.

In each bag, a single seedling was left when more than one seedling emerged in the bags by carefully removing the extra seedlings. The first height measurement h1 was taken Approximately 5 months after the beginning of the experiment The first three attributes are standard statistics developed to measure genetic variability in samples Charlesworth and Charlesworth However, they do not control for sample size differences, which also affect the estimates see Weir The allelic richness estimator standardizes the samples from populations based on different sample size to a common sample size, usually the smallest of all the samples, using the rarefaction method El Mousadik and Petit Therefore, this estimator is good at comparing genetic variation between samples of different sizes, but sacrifices information from populations with large sampling size.

Finally, the observed heterozygosity is expected to be related to fitness if inbreeding and drift influence population fitness. To account for sample size effects in the genetic diversity estimators, we included sample size as partial correlate in all analyses exploring the relationship between genetic diversity and population size. We performed a regression analysis with a stepwise selection procedure for each of the studied population genetics attributes Draper and Smith This procedure adds, one at a time, to a model with no variables, the predictor variable that produces the highest F statistics, and is statistically significant.

Prior to statistical analyses, we transformed population size into a log 10 scale to normalize residual variation. However, they were based on very small sample sizes owing to small population sizes and scarcity of viable seed Table 1. To check the robustness of the conclusions reached, we conducted another set of stepwise regression analyses as described earlier, but excluding these three populations, and calculated the allelic richness estimations based on a sample of 8 genes, in which the three smallest populations could not be included, and on a sample of two genes to include data of all studied populations.

To explore the relationship between population size and fitness, we regressed seed germinability and the plant performance index, to population size. The fitness estimates data could not be normalized nor the variances homogenized by standard variable transformations.

Therefore, prior to statistical analyses, the data were replaced by their ranks, because this transformation is less likely to be distorted by non-normality and unusual observations Montgomery To explore the relationship between fitness estimates and heterozygosity, the mean of the germination data and the plant performance indices classified by population origin and heterozygosity class 1, 0—0.

All of these estimates were significantly and positively correlated with population size when sample size was included as a covariate Table 3. This variable was not significant during variable selection of the stepwise regression procedure for any estimator of genetic variability.

At the end of this procedure, population size remained significant for all studied estimators of genetic variability. Allelic richness based on a sample of eight genes in which the three populations with the smallest sample size were left out of the model increased more rapidly with population size than when based on a two gene sample Table 4 , Fig. In general, the same trends were observed when the smallest populations were excluded from the analyses, but the final r 2 values from the stepwise elimination procedure were always higher than final r 2 including all populations for each of the response variables.

Also, slightly larger estimates of the parameters relating population size to the population genetic variables analyzed were obtained in this subsample compared to the analyses including the entire data set. However, for all genetic parameters, the subsample estimates were located within one standard error interval of the estimates obtained from the entire sample Table 4.

Summary of the results of the correlation analyses correlation coefficient and one tail significance probability in parentheses relating allelic richness based on a random sample of 2 and 8 genes per population, the fraction of polymorphic loci, the mean number of alleles per locus, average expected heterozygosity genetic diversity , and average observed heterozygosity per population with population size log10 transformed.

Sample size was included as correlate in the analyses of population size. See methods for details. Final results of the regression analyses using the stepwise elimination procedure for: allelic richness based on a random sample of 2 and 8 genes per population, the fraction of polymorphic loci, the mean number of alleles per locus, average expected heterozygosity genetic diversity , and average observed heterozygosity related with population size log 10 transformed, N.

Positive associations between population size and A the fraction of polymorphic loci; B the mean number of alleles per locus uncorrected for sample size ; C the mean expected number of alleles allelic richness in a random sample of 2n and 8n genes per population; D expected heterozygosity; and E observed heterozygosity in Pinus chiapensis.

The least square fitted lines are also shown. Seeds from maternal trees and 15 populations were analyzed for germinability. Our data suggest that a decrease in one order of magnitude in population size is associated with 6. A total of plants from eight populations were analyzed in a common garden experiment Table 1. Thus, plants from larger populations probably will have higher probabilities of surviving the earlier stages of the life cycle than those of small populations other things being equal.

The relation of average population germinability and plant performance to population size. The least square fitted line is also shown. For both attributes, the lowest performance was observed at the lowest heterozygosity levels. Sample sizes number of populations per class are shown above the error bars.

Our results show a strong and consistent relationship between population size and genetic variation in P. Sampling size did not affect the strength of the associations between population size and genetic variation, in agreement with Leimu et al. All standard estimators of genetic variation used, including or excluding the three smallest populations, and the allelic richness estimators, which directly correct for sample size differences, increased in an approximately linear fashion with the logarithm of population size, and explained a highly significant proportion of the total variance in their explored relationship with population size.

Only two individuals in the second generation reproduce and, by chance, these individuals are homozygous dominant for brown coat color. As a result, in the third generation the recessive b allele is lost. Small populations are more susceptible to the forces of genetic drift. Large populations, on the other hand, are buffered against the effects of chance.

Genetic drift can also be magnified by natural events, such as a natural disaster that kills a large portion of the population at random. The bottleneck effect occurs when only a few individuals survive and reduces variation in the gene pool of a population. The genetic structure of the survivors becomes the genetic structure of the entire population, which may be very different from the pre-disaster population.

Effect of a bottleneck on a population : A chance event or catastrophe can reduce the genetic variability within a population. Another scenario in which populations might experience a strong influence of genetic drift is if some portion of the population leaves to start a new population in a new location or if a population gets divided by a physical barrier of some kind. In this situation, it is improbable that those individuals are representative of the entire population, which results in the founder effect.

The Founder Effect : The founder effect occurs when a portion of the population i. The founder effect is believed to have been a key factor in the genetic history of the Afrikaner population of Dutch settlers in South Africa, as evidenced by mutations that are common in Afrikaners, but rare in most other populations. This was probably due to the fact that a higher-than-normal proportion of the founding colonists carried these mutations.

The Hardy—Weinberg principle states that within sufficiently large populations, the allele frequencies remain constant from one generation to the next unless the equilibrium is disturbed by migration, genetic mutation, or selection.

Because the random sampling can remove, but not replace, an allele, and because random declines or increases in allele frequency influence expected allele distributions for the next generation, genetic drift drives a population towards genetic uniformity over time.

Once an allele becomes fixed, genetic drift for that allele comes to a halt, and the allele frequency cannot change unless a new allele is introduced in the population via mutation or gene flow. Thus even while genetic drift is a random, directionless process, it acts to eliminate genetic variation over time. Genetic drift over time : Ten simulations of random genetic drift of a single given allele with an initial frequency distribution 0. In these simulations, alleles drift to loss or fixation frequency of 0.

An important evolutionary force is gene flow: the flow of alleles in and out of a population due to the migration of individuals or gametes. While some populations are fairly stable, others experience more movement and fluctuation. Many plants, for example, send their pollen by wind, insects, or birds to pollinate other populations of the same species some distance away.

Even a population that may initially appear to be stable, such as a pride of lions, can receive new genetic variation as developing males leave their mothers to form new prides with genetically-unrelated females. This variable flow of individuals in and out of the group not only changes the gene structure of the population, but can also introduce new genetic variation to populations in different geological locations and habitats. Gene flow : Gene flow can occur when an individual travels from one geographic location to another.

Maintained gene flow between two populations can also lead to a combination of the two gene pools, reducing the genetic variation between the two groups. Gene flow strongly acts against speciation, by recombining the gene pools of the groups, and thus, repairing the developing differences in genetic variation that would have led to full speciation and creation of daughter species.

For example, if a species of grass grows on both sides of a highway, pollen is likely to be transported from one side to the other and vice versa. If this pollen is able to fertilize the plant where it ends up and produce viable offspring, then the alleles in the pollen have effectively linked the population on one side of the highway with the other. Species evolve because of the accumulation of mutations that occur over time. The appearance of new mutations is the most common way to introduce novel genotypic and phenotypic variance.

Some mutations are unfavorable or harmful and are quickly eliminated from the population by natural selection. Others are beneficial and will spread through the population. Whether or not a mutation is beneficial or harmful is determined by whether it helps an organism survive to sexual maturity and reproduce.