Ne) determines whether selection or genetic drift is the predominant force shaping their genetic structure and evolution. Despite their high mutation rate and rapid evolution, this parameter is poorly documented experimentally in viruses, particularly plant viruses. All available studies, however, have demonstrated the existence of huge within-host demographic fluctuations, drastically reducing Ne upon systemic invasion of different organs and tissues. Notably, extreme bottlenecks have been detected at the stage of systemic leaf colonization in all plant viral species investigated so far, sustaining the general idea that some unknown obstacle(s) imposes a barrier on the development of all plant viruses. This idea has important implications, as it appoints genetic drift as a constant major force in plant virus evolution. By co-inoculating several genetic variants of Cauliflower mosaic virus into a large number of replicate host plants, and by monitoring their relative frequency within the viral population over the course of the host systemic infection, only minute stochastic variations were detected. This allowed the estimation of the CaMV Ne during colonization of successive leaves at several hundreds of viral genomes, a value about 100-fold higher than that reported for any other plant virus investigated so far, and indicated the very limited role played by genetic drift during plant systemic infection by this virus. These results suggest that the barriers that generate bottlenecks in some plant virus species might well not exist, or can be surmounted by other viruses, implying that severe bottlenecks during host colonization do not necessarily apply to all plant-infecting viruses.The effective size of populations ( Cauliflower mosaic virus, we mitigate this general idea by showing that at least one virus species can overcome such putative limiting barriers and massively invade leaves with hundreds to thousands of founding genome units.Whether selection or stochastic genetic drift is the major force driving the evolution of a virus depends largely on the size of the viral population, with the former being predominant in large populations and the latter taking over when population sizes are transiently or durably reduced. This question has been intensively debated in both plant and animal viruses, as demographic fluctuations throughout viral life cycles are poorly understood. In plant viruses, an extremely small population size—down to a few founder genome units colonizing each leaf—has been formally estimated in two instances, and all other virus species investigated so far have consistently been shown to undergo extreme demographic bottlenecks during systemic invasion of their host. This situation conveys the general idea that all viruses are confronted with “universal barriers” in plants, imposing repeated transient decreases in their population size, thus making genetic drift a major constant driver of their evolution. Here, using the example of  Ne) Ne significantly informs whether the evolution of a given population might be better described by a deterministic or a stochastic evolution model Ne is large, competition between genetic variants is fully acting, with no or little interference of random processes, and selection shapes the genetic composition of corresponding populations. Conversely, in populations with a small Ne, various processes resulting in stochastic “sampling” of genetic variants that will engender the next generation are prevalent and counter the effect of selection.The main forces driving changes of the frequencies of alleles within populations are selection (deterministic) and genetic drift (stochastic). While both forces obviously act concomitantly in most cases, the balance of their respective action is governed by a number of factors, one of the most important being the effective population size (Ne) is associated with relaxed selection and strong drift, dramatically reducing the mean fitness in the population census population size, i.e. the number of viral genomes accumulating in single hosts, it does not indicate what fraction thereof is actually actively replicating and propagating, or is efficiently progressing into new organs and tissues, yielding subsequent generations upon host colonization. Estimating viral Ne during systemic infection of a host has proved experimentally difficult, as illustrated by the impressive number of studies published, and still debated, on HIV Ne has been formally evaluated during invasion of various organs and tissues of the host remain extremely few in both plants and animals. The question of whether deterministic or stochastic models better explain the evolution of viral populations is thus pending further investigation of Ne.Experimental evolution using viruses as biological models has developed enormously during the past two decades. A series of converging reports on viruses of bacteria, animals, and plants confirmed that protocols maintaining constant large population sizes lead to selection of the fittest variants, thus augmenting the mean fitness of viral populations In plants, several specific barriers could impose severe bottlenecks on populations of invading viruses. The level at which the existence of such bottlenecks has been most documented is the colonization of new leaves, not only because of the obvious practical ease of comparing the genetic content of different leaves from the same host, but also because this level results from long-distance movement of the virus population, which is loaded into the vascular system from source leaves and unloaded into sink leaves  co-inoculated into tobacco plants, Li and Roossinck Plum pox virus (PPV) population, maintained in a perennial host tree for over 13 years While not directly estimating Wheat streak mosaic virus (WSMV) invading a wheat tiller Tobacco mosaic virus (TMV) invading a tobacco leaf The effective size of virus populations during systemic infection of host plants has been evaluated more formally in two instances. Populations of Ne. In contrast, a single counter example would demonstrate that, in some virus-plant associations, such barriers might not exist or might be surmounted by the virus. Further research on more diverse virus species, with different replication strategies and different life cycles, is still needed in order to assume extremely small Ne as a general rule during host plant colonization.All the studies cited above converged to the conclusion that the population size of plant viruses fluctuates dramatically and can be temporarily remarkably small, i.e. of the order of one to a few genome units founding the population that subsequently develops to billions of genomes in each systemically infected leaf. Such a demographic regime suggests that genetic drift is a major force in plant virus evolution, as proposed and discussed by several authors Cauliflower mosaic virus (CaMV), a DNA virus whose biological properties differ largely from those of the RNA viruses investigated in the studies cited above. Monitoring the frequency of several engineered allelic variants within leaves of single host plants revealed remarkably small stochastic fluctuations in the genetic structure of numerous CaMV populations, even over considerable periods of time. Beyond demonstrating that CaMV populations are not subject to intense genetic drift, and hence do not undergo severe demographic bottlenecks, the slight stochastic fluctuations detected were exploited to infer the effective size of CaMV populations upon systemic leaf colonization. Our estimates consistently indicate that several hundreds of genome units founded the viral populations in all leaves analyzed (originating from different plants). This value, which is ∼100-fold higher than values previously reported for other plant viruses, demonstrates that extremely small viral Ne during host plant colonization is not a general rule and opens up the possibility of trade-offs on viral traits that directly or indirectly depend on Ne.In this report, we assessed the importance of bottlenecks during systemic host colonization in populations of The six plasmids (pCa-VIT1 to pCa-VIT6) used in this study to generate the six CaMV allelic variants have been described in detail and characterized previously All are infectious full-length clones of the CaMV Cabb-S isolate Brassica rapa var. “Just Right”) were maintained in an insect-proof greenhouse under controlled conditions: 25/19°C day/night with a photoperiod of 16/8 h day/night.All turnip plants (Plants infected for 21 days with one of the CaMV-VIT1 to -VIT6 clones (one plant per clone) were used to prepare virus particle-enriched fractions as previously described At 13 dpi, i.e. very soon after systemic infection had developed, the inoculated leaves were discarded and all large expanded leaves were collected. In all cases, two to three young expanding or emerging leaves in the centre of the rosette were left unsevered in order to allow continuous growth of the plants for further sampling as described below. The viral population extracted from each plant at this early stage was considered as the initial population.th and the 12th newly formed leaves, was collected. The viral population extracted from each single leaf at this late stage was considered as the final population. All leaf samples were stored at −20°C until further viral DNA purification.Thirty-two days later (at 45 dpi) the same plants had grown continuously and produced 10 to 15 newly expanded leaves. At this stage, one single leaf from each plant, randomly chosen between the 5The rationale of this sampling protocol is further discussed below.Purification of viral DNA from the harvested leaf samples, as well as the analysis of the genetic structure of the corresponding viral genome populations were performed exactly as described previously, using the QSS (Quantitative Single-letter Sequencing) method Changes in the relative frequencies of the six alleles (-VIT1 to -VIT6) between the initial and final populations, sampled as described above, were precisely monitored in 50 replicate test plants.In order to calculate the number of founder genomes initiating each final population in a single leaf (N), all parameters accounting for the transition from p  to p′  must be evaluated. For any given plant the difference between p and p′ (Δp) may be potentially attributed to genetic drift and to selection:a priori reason to expect directional selection in favour of any of them. For the moment, we will thus assume that selection is negligible, and the markers effectively neutral, and will provide further arguments supporting this assumption at the end of this section.There is no a priori reason to assume that any selection affecting the frequency of the markers is heterogeneous across plants (e.g. favouring a marker in a plant and selecting against it in another plant). Moreover, given the way our markers were constructed, there is no We used two methods to estimate N, both based in the change of genetic variance between the two sampling events.p is the frequency of a given marker in initial populations. From Equations 2 and 3 we obtain:T is gene diversity assuming all populations form a single large population and HS is the average gene diversity within each population. In our case, each plant represents a population. Gene diversities express the probability to randomly draw two different alleles and are thus equal to ip is the frequency of each allele at the subdivision level under consideration. In our case HT represents the gene diversity obtained after calculating the average frequency of each marker across all plants, while HS represents the average across all plants of within plants gene diversities.The first method directly tracks changes in variance. The variance of the estimates of p and p′ between plants can be written as:V and NF respectively.Using standard population genetics theory e.g.  it can bV. We also calculated HT and HS, and from that estimated NF. We repeated this procedure 1,000 times and constructed a distribution of NV and NF. The 95% confidence intervals correspond to the 2.5 and 97.5 percentiles of these distributions. In several bootstraps, the final variance was smaller than the initial variance, or the final FST was smaller than the initial FST, yielding negative values of NV or NF. In such cases, our estimation method does not work. Such cases would correspond to conditions leading to convergence of marker frequencies across plants, i.e. conditions where drift is negligible, and thus N can be regarded as infinite. We conservatively discarded such cases from the calculation of the upper limit of the confidence interval.To provide a confidence interval for these estimates we used a resampling technique. We bootstrapped over plants: for each bootstrap we randomly drew a sample of 50 plants with replacement, calculated Var(p′), Var(p) and p, and from that estimated NN because, while directional selection would lead to a situation where marker frequencies would be more similar across plants at the second sampling event than at the first, yielding smaller final variance and FST, our data show the opposite, strongly contradicting the directional selection hypothesis. Another a posteriori justification is that while the neutrality tests indicate that one marker is slightly selected for, another is slightly selected against and another is effectively neutral, the NV estimates obtained form all three are of the same order of magnitude. These arguments suggest that selection, if present, can be neglected relative to drift in our experiment.The average changes in frequency of the markers proved very small see . NeutralFollowing systemic spread of infection in plants inoculated with a mixture of 6 CaMV genetic variants (Mix6VIT), populations of viral genomes in each plant were purified from all fully expanded and systemically infected leaves, excluding inoculated leaves. DNA samples from each of 50 replicate plants were considered as the initial populations and were submitted to QSS analysis to detect markers in CaMV genomes and quantify their relative frequency as described in The six CaMV-VIT genotypes were found in widely unequal ratios in all 50 cases, with mean relative frequencies being close to 0 for CaMV-VIT5 and -VIT6, and up to around 40% for CaMV-VIT1 and -VIT3. However, a striking observation was that each marker was found with a very similar frequency in all 50 replicate CaMV populations , as demoth and the 12th new expanded leaves, was harvested. The CaMV populations extracted from these sampled leaves are considered as “final populations,” and their genetic structure , demonstrating that very little change had occurred over this considerable time period, whatever the position or age of the analyzed leaf (see E(Δp) values , and comNe) of CaMV populations during host systemic colonization is likely to be surprisingly large (compared to data previously published on other plant viruses) as specifically evaluated in the next section.Taken together, these results argue for the absence of large stochastic variations in the genetic structure of a CaMV population upon progression of the infection into newly formed leaves. This suggests that the effective size  soon after systemic spread of infection was considered as the starting point of the experiment, putative subsequent changes thus occurring only under the influence of within-plant processes. At this initial stage, the virus population uploaded into, and circulating within, the vascular system is most likely best represented by the overall content of the systemically infected leaves, as they have either received viruses from the vasculature, exported viruses into it, or both. Expanded infected leaves were therefore harvested for initial analysis, carefully preserving two to three young newly expanding leaves on the still-growing plant. The infected plants were then left to grow for a period of 32 days, during which the virus population successively colonized 10 to 15 emerging and expanding new leaves. Regardless of where the virus population originates from during this process , analysis of single leaf  contents collected 32 days later should reveal the existence of any putative bottlenecks at any stage of the systemic infection. Indeed, final CaMV populations result largely from the successive leaf-to-leaf passages that occur sequentially when young sink leaves are infected, become sources and subsequently export virus into new sinks.All genetic variants (CaMV-VIT1 to CaMV-VIT6) were co-inoculated at similar locations and at the same time point onto the two first true leaves of turnip plantlets. The reason why the relative proportions of the six variants are highly unequal later in systemically infected plants was mostly that unequal proportions were already present in the initial inoculum, Mix6VIT . The remarkable observation that only relatively small variations in p′ are recorded in leaves from different plants , due to competition for host “territories” between variants of the same population. Accordingly, under this hypothesis, the absence of severe bottlenecks found for CaMV would suggest a higher MOI that is totally consistent with the remarkably high within-host recombination rate described for this virus The situation described here for CaMV re-opens the question as to what phenomena actually generate bottlenecks during virus infection. A reasonable hypothesis explaining the presence or absence of severe demographic bottlenecks would be the regulation of multiple infection of cells by several genomes of the viral population, in other words, regulation of the multiplicity of infection (MOI) of host cells. Indeed, it was proposed recently that bottlenecks seen in a PPV population infecting a Table S1Whole dataset from the 50 replicate infected plants. p represents the marker relative frequency in initial populations. p′ represents the marker relative frequency in final populations. All values are indicated as percentage of the viral genome population. -, marker not detected.(127 KB DOC)Click here for additional data file.Table S2aThe mixture Mix6VIT was prepared and analysed by QSS as described in bp0 = mean relative frequency determined from five independent repeats of QSS measurement. cStandard deviation calculated from the five repeated QSS analysis. All values are expressed as percent of the viral genome population. The distribution of the relative frequencies of all VIT1-6 variants in the initial Mix6VIT inoculum was globally similar to that in the 50 inoculated plants (VIT1≈VIT3>VIT4>VIT2>VIT5≈VIT6). Nevertheless, apart from VIT1 and VIT3 which increased by about 10% in frequency from inoculum to infected plants, all other variants equally decreased by approximately 5% Click here for additional data file.


Physalaemus pustulosus). We measure gene flow and migration within and between population clusters and compare genetic diversity between population clusters. By applying ecological niche modeling we determine whether the two most divergent genetic groups of the túngara frog (1) inhabit different habitats, and (2) are separated geographically by unsuitable habitat across a gap in the distribution.Uncovering how populations of a species differ genetically and ecologically is important for understanding evolutionary processes. Here we combine population genetic methods (microsatellites) with phylogenetic information (mtDNA) to define genetic population clusters of the wide-spread Neotropical túngara frog  is genetically homogenous while the Southern lineage (SW Costa Rica and Panama) is sub-divided into three population clusters by both microsatellite and mtDNA analyses. Gene flow is higher within the Northern lineage than within the Southern lineage, perhaps due to increased landscape heterogeneity in the South. Niche modeling reveals differences in suitable habitat between the Northern and Southern lineages: the Northern lineage inhabits dry/pine-oak forests, while the Southern lineage is confined to tropical moist forests. Both lineages seem to have had little movement across the distribution gap, which persisted during the last glacial maximum. The lack of movement was more pronounced for the Southern lineage than for the Northern lineage.This study confirms the finding of previous studies that túngara frogs diverged into two allopatric genetic lineages north and south of the gap in the distribution in central Costa Rica several million years ago. The allopatric distribution is attributed to unsuitable habitat and probably other unknown ecological factors present across the distribution gap. Niche conservatism possibly contributes to preventing movements across the gap and gene flow between both groups. Genetic and ecological data indicate that there is the potential for ecological divergence in allopatry between lineages. In this context we discuss whether the Northern and Southern lineages should be recognized as separate species, and we conclude that further studies of pre- and post-zygotic isolation are needed for a final assessment. Identified population clusters should motivate future behavioral and ecological research regarding within-species biodiversity and speciation mechanisms. A central goal of evolutionary ecology is to understand processes that determine the distribution of genetic diversity and population connectivity through gene flow. The current population genetic structure of a taxon is the result of many factors such as the dispersal capacity of the organism, the degree to which individuals of different populations recognize each other as potential mates, the connectivity of suitable habitats, historical events that impose geographical isolation (e.g. glaciations or elevation of mountains) and interactions with ecologically similar species or predators -4.Bufo marinus, .Population structure: A fundamental requirement of any inference about the genetic structure of populations is the definition of populations themselves. Pritchard et al. (2000)  developetructure software point out that it is not always possible to know the true value of K; they recommend that one may aim for the smallest value of K that captures the major genetic structure in the data. This can be achieved by selecting the lowest K when several values of K give similar estimates of Pr (X|K). For example in case that Pr (X|K) plateaus for higher values of K one would choose the K instead of K+1 when the difference in Pr (X|K) to Pr (X|K + 1) is small.The authors of the Structure[tructure analysis we calculated FST [ST values [The problem to detect the true number of clusters was addressed by Evanno et al. (2005) . They fotructure. We appltructure for situated FST  and RST T values  with theT values .tructure.Migration and assignment of individuals to populations: We used the Program GENECLASS2 to assign individuals to one or more sample localities based on their allele frequencies. The method we used to assign individuals probabilistically to sample localities was the partial Bayesian method . As a reΛ = L_home/L_max, which is the ratio of the likelihood computed from the population where the individual was sampled (L_home) over the highest likelihood value among all population samples including the population where the individual was sampled : We used the test statistic est; see ) to deteest; see . We calcest; see . The numST and RST values. The number of migrants per generation is estimated as follows: Nm = (1-FST)/(4 FST). Although some assumptions of population structure (infinite island model of population structure and gene flow) might be violated in most real situations this estimate is still considered a useful instrument for comparative purposes   for indies (e.g. ). The gees  natural logarithm of geographic distance versus RST values; and (3) natural logarithm of geographic distance versus RST/(1-RST). The last two approaches have been proposed [ST statistic to linearize the relationship between FST and distance; . The first approach, however, yielded the best results (highest correlation coefficient and coefficient of determination) in our study and only these data are presented here. We used RST instead of FST because FST tends to underestimate genetic differentiation when applied to microsatellite data [Isolation by distance: To investigate the effect of gene flow over distance we used Mantel tests  implemenproposed  for the ite data  and thisite data .Cytochrome B (Cyt B) sequences. We estimated time of divergence between genetic clusters by uncorrected p-distance divided by the pairwise evolutionary rate/MYR.To estimate genetic differentiation based on nucleotide differences we used MEGA 4.0  to calcuCyt B to determine the divergence time between the Northern and Southern lineage and the population clusters of túngara frogs, because this gene has been proven useful for intraspecific and genus level relationships in several amphibian species [Bufo bufo species group [P. pustulosus.We used  species ,75-77. E species ,79. Ratees group ), 0.64% es group ). Taken es group ,81). Simes group ,75,82. Tes group  found a Cyt B sequences. The program calculates the frequencies of each haplotype and the number of mutational steps between two haplotypes associated with the 95% limit for the probability of parsimony ('parsimony' criterion [Cyt B sequences applying DnaSP vs. 4.50.3 [We used TCS vs. 1.21  to constriterion ). Finall. 4.50.3  for the -5, maximum iterations = 500, regularization multiplier = 1.0. The logistic output from Maxent is a raster map with grid cell values ranging from 0 to 1, which can be interpreted as the probability of presence of suitable habitat for the species [We employed environmental niche modeling to: (1) predict the current and past distribution of suitable habitat for túngara frogs, (2) to compare the connectivity across the distribution gap in Costa Rica between present and glacial conditions, and (3) to assess the level of ecological divergence between the Northern and Southern lineage. Niche models are based on environmental values at localities of known occurrence of the target taxa, which then are used to identify geographic regions that have similar combinations of values. The input for model building consists of (1) a set of localities of known occurrence of the target species, and (2) environmental data from digital maps  for the target region. Niche models were obtained with Maxent, a maximum entropy algorithm that generates a probability distribution of habitat suitability across the target region . We chos species .Túngara frog localities were obtained from ,35, fielThe environmental data for niche modeling consisted of 12 raster maps  obtained from WorldClim ). The biTo estimate the past distribution of suitable habitat for túngara frogs, we projected the environmental niche model to climate conditions for the last glacial maximum . The LGM climate raster maps are based on the palaeoclimate ECHAM3 model and were assembled as described by Hijmans and Graham (2006)  and RuegNiche models were built independently for the Northern and the Southern lineages. Models were based on all available localities  except in tests of model performance. To tests model performance, the southern localities were partitioned into two halves with random assignment: a training and a testing set. The training set was used for model building and the testing set for model evaluation. By setting an arbitrary threshold, the logistic output of the model was transformed into a binary map (suitable vs. unsuitable conditions). Then, we applied a binomial test to compare the proportion of localities from the testing set correctly predicted within the suitable habitat vs. the expected proportion for a random model with the same amount of suitable habitat . This procedure was repeated 10 times, each for a random partition of localities. In each replicate, the binomial test was applied to 10 commonly used logistic thresholds. Because the northern group was built with only 9 localities, we used as testing set the localities of known presence of túngara frogs outside the study region (Nicaragua to Mexico). Model performance was also evaluated with the receiver operating characteristic (ROC) analysis, which consists of a sensitivity analysis of the presence and absence points predicted by the model .t-test, the LVs for the present model vs. the LVs for the glacial model contrasting overlapping pixels in the gap (hence the paired test).Connectivity and suitability across the distribution gap in Costa Rica: The two most divergent genetic lineages (North-South) are separated by a ~200 km distribution gap in Costa Rica. This suggests that genetic divergence is a result of geographic isolation. Because distribution ranges change with changing environmental conditions, we do not know whether both ranges were also unconnected during glacial times. To explore this question, we developed a new measure of landscape connectivity based on ecological niche modeling. Landscape connectivity is the degree to which the landscape allows movement across the distribution gap. We assumed that the resistance to movement across habitat is an inverse function of habitat suitability, as defined by the niche models. We obtained a raster map of habitat resistance by subtracting one minus the niche suitability values from the logistic output (ranging from 0 to 1). Based on this raster, we estimated the least-cost distance path  between HP designed the study, collected samples, carried out the molecular studies, performed statistical analyses, and drafted the manuscript. SRR performed niche-modeling analysis, interpreted data and helped writing the manuscript. MJR participated in the design of the study, interpreted data and helped draft the manuscript. All authors read and approved the manuscript.ST values among Middle American populations of túngara frogs Pairwise R. Matrix of pairwise RST values based on microsatellites between all sampled populations of túngara frogs.Click here for fileAssignment of individuals to sample localities. All individuals were genetically assigned to sample localities by the program GENECLASS2. The figures present the % of individuals in every sampled (= "home") population for those the most likely "source" population is the "home" population (black bars), the most likely "source" population is another population from the same population cluster (grey bars), the most likely "source" population is from another population cluster (light grey bars); A) Population cluster North, B) Population cluster South_1, C) Population cluster South_2, D) Population cluster South_3.Click here for fileFirst generation migrants. List of first generation migrants detected by GENECLASS2: the table contains all individual frogs which probably migrated from a "source" population to their "home" population .Click here for fileGenetic diversity in population clusters. Genetic diversity as measured by allelic richness with FSTAT in all populations organized by population clusters.Click here for fileCyt B sequencesGenBank accession numbers for . Sampling localities, genetic clusters and GenBank accession numbers of all Cyt B sequences from túngara frogs analyzed in this study.Click here for file


Gerbillus henleyi, we explored its population genetic structure alongside that of the locally common Gerbillus andersoni allenbyi. We trapped gerbils in several locations in Israel's western and inner Negev sand dunes. We then extracted DNA from ear samples, and amplified two mitochondrial sequences: the control region (CR) and the cytochrome oxidase 2 gene (CO2).One of the major challenges in evolutionary biology is identifying rare species and devising management plans to protect them while also sustaining their genetic diversity. However, in attempting a broad understanding of rarity, single-species studies provide limited insights because they do not reveal whether the factors that affect rare species differ from those that affect more common species. To illustrate this important concept and to arrive at a better understanding of the form of rarity characterizing the rare G. a. allenbyi, which showed no diversity. We could not detect any significant population genetic structure in G. henleyi. In contrast, G. a. allenbyi's CO2 sequence showed significant population genetic structure. Pairwise PhiPT comparisons showed low values for G. henleyi but high values for G. a. allenbyi. Analysis of the species' demographic history indicated that G. henleyi's population size has not changed recently, and is under the influence of an ongoing bottleneck. The same analysis for G. a. allenbyi showed that this species has undergone a recent population expansion.Nucleotide diversity was low for all sequences, especially for the CR of G. a. allenbyi are more isolated from each other, likely due to the high habitat specificity characterizing this species. The bottleneck pattern found in G. henleyi may be the result of competition with larger gerbil species. This result, together with the broad habitat use and high turnover rate characterizing G. henleyi, may explain the low level of differentiation among its populations. The evidence for a recent population expansion of G. a. allenbyi fits well with known geomorphological data about the formation of the Negev sand dunes and paleontological data about this species' expansion throughout the Levant. In conclusion, we suggest that adopting a comparative approach as presented here can markedly improve our understanding of the causes and effects of rarity, which in turn can allow us to better protect biodiversity patterns.Comparing the two species, the populations of  Studying rare species and devising management plans to protect them constitute major themes in ecology [e.g. ]. Doing Identifying the causes of rarity often leads to the following question: Are rare species rare because they are only capable of exploiting a narrow range of environmental conditions, or because the spatial extent of the conditions they can exploit is highly restricted, or both? All species are limited to some extent by environmental variables (i.e. abiotic and biotic), but the question is whether there is a tendency for the abundances and range sizes of rare species to be predominantly constrained by particular factors or combinations of factors . For insRecently, there has been a growing awareness among conservation biologists that molecular techniques can be better exploited to interpret the history, present status, and future prognosis of threatened species . MoreoveAttempts to devise a broad understanding of rarity will accrue only limited insight from single-species studies , which dGerbillus henleyi) and the locally common congener Anderson's Gerbil , while contrasting their demographic histories. Doing so enabled us to form a better mechanistic understanding of the type of rarity that characterizes the Pygmy Gerbil, i.e. rarity signified by widespread global distribution, low habitat specificity, and small population size. This form of rarity is especially difficult to explain, since one would expect such small populations to die out and that the species subsequently persist in fewer but larger populations.To illustrate the importance of this comparative approach, we explored the population genetic structure of the rare Pygmy Gerbil  and larger population size  will increase the connectivity between populations, especially those within the western Negev sand dunes. However, its high specificity to sand .We estimated genetic diversity at the mtDNA level by calculating the number of polymorphic sites, the number of haplotypes, and the nucleotide diversity, Pi  were constructed to examine the evolutionary relationships between mtDNA haplotypes, and thus identify geographically localized clades. The phylogenetic relationships between the mtDNA haplotypes were determined using the program NETWORK 4.5.1.6 . These relatively low values indicate no significant association between different mutations in the examined sequences.The proportion of polymorphic sites exhibiting a significant, non-random association was 117/406 (28.82%) for G. henleyi , but there were significant differences  in body mass between populations. No significant differences were found between regions, i.e. western vs. inner sands . The interaction between population and sex was not significant .No significant differences in body size were detected between male and female G. a. allenbyi individuals were caught in Nahal Secher  differences between the sexes. Sex had a significant effect  on tail length  but not on foot length  or body mass . There were significant differences in body size between populations . Individuals from the western Negev were found to be larger . No significant interaction was found between sex and population-of-origin .Because only female r Figure , its samG. henleyi, we compared its population genetic structure with that of G. a. allenbyi. Specifically, we compared the genetic structures and demographic histories of common and rare species in their continuous ranges and in isolated populations, while investigating a possible association between rarity and increased genetic differentiation.The purpose of this research was to demonstrate the importance of adopting a comparative approach in population and conservation genetic studies. To elucidate the links between the genetic make-up, phenotypic variation, and type of rarity characterizing G. henleyi is considered rare wherever it is found . HoweveG. a. allenbyi, differences among populations accounted for 13% of the molecular variance, while 87% of the variance was accounted for by differences within populations . PhiPT values for this species were much higher than for G. henleyi, and the highest values were obtained from comparisons of inner sands populations  with those of the western Negev, and also from the comparison of Mamshit and Mishor Yamin, both of which are located in the inner sands. The western Negev populations show relatively little to moderate levels of isolation from each other. However, the comparison of the Mifrasit and Mashabim populations gives a high PhiPT value of 0.2, indicating the potential for a high degree of isolation between populations even within the western sands. Other noteworthy moderate PhiPT values are those for Mishor Yamin and Mamshit vs. Agur South. Mishor Yamin and Mamshit are both part of the inner sands (thus rendering the relatively high degree of isolation between them surprising), and no sandy connection exists between these sands and the dunes of the western Negev more than 40 km west. Therefore, comparisons of the Mamshit and western Negev populations produce high PhiPT values. Why comparisons of Agur South with inner sands populations should be different in this respect remains unclear. One possibility is that individual G. a. allenbyi were transported by humans between Agur South and the inner sands at some point in the last few decades, a scenario that is supported by a study from 1957 [G. a. allenbyi in the inner sands, and by the first announcement of their presence in Mishor Rotem only three decades later [In the CO2 region of es later . If thisThree theoretical scenarios offer explanations for exceedingly low levels of genetic diversity and differentiation: a) selective sweep - a new mutation spreads out through positive selection, other genes hitchhike; should lead to strong linkage between polymorphic sites; b) background selection - purifying selection on a certain gene weeds out genetic variants, a process that is recognizable by the low accumulation of neutral mutations and low linkage disequilibrium ; and c) founder effect, or a rapid population expansion after a bottleneck, is characterized by low linkage disequilibrium. All three of these scenarios may help explain low genetic differentiation even without gene flow between the populations. Only the first and second scenarios are mutually exclusive.G. henleyi, two of the CR neutrality indices were positive, indicating an excess of older mutations, i.e. the entire population developed from a limited number of ancestors, but that population is still in a bottleneck . All the indices were negative for G. a. allenbyi, suggesting that this species underwent a recent population expansion. The low values detected by linkage disequilibrium tests for both species rule out the possibility of a selective sweep in either case. Both the ragged pattern produced by the pairwise-distribution analysis of G. henleyi and the Bayesian skyline plot indicate that its small population has not grown in many years. Similarly, the analysis of segregating sites indicated the existence of numerous old mutations that imply the presence of a limited number of common ancestors, which together attest to a more genetically variable past and suggest that the population experienced a bottleneck at some point. The experimental pattern of G. a. allenbyi resembled the wave-like pattern expected for a population that has undergone a historic change in population size. The segregating sites analysis revealed multiple new mutations-a pattern also indicative of a recent population expansion . Furthermore, the G. a. allenbyi Bayesian skyline plot showed an increase of ~500% in population size between 10,000-30,000 BP, further strengthening the conclusion that this species has undergone a recent population expansion. Aeolian sand became geologically significant in the western Negev around 25,000-30,000 BP. Sand dunes in considerable amounts are dated to 18,000-10,000 BP [G. a. allenbyi coincided with the incursion of sands from the Nile Delta during the upper quaternary. Our expansion calculation should be regarded as preliminary due to the small size of the analyzed sequence and rough estimation of the gene's mutation rate.In 0,000 BP ,52 and t0,000 BP . We inteG. henleyi and G. a. allenbyi. Existing in small populations characterized by low levels of genetic diversity and differentiation, G. henleyi appears to be in the midst of a population bottleneck. Although the reasons for this bottleneck are not clear, a possible explanation may rest on this species' inferior ability to compete with more common and stronger gerbil species such as G. pyramidum and G. a. allenbyi [G. henleyi may affect its overall genetic diversity, but it does not seem to be affecting the amount of genetic differentiation between its populations. G. a. allenbyi is a larger, stronger, more common species, and the reasons for its low genetic diversity, especially in the control region, remain unclear. Populations of G. a. allenbyi were shown to be more isolated than those of G. henleyi, the most feasible explanation of which could be G. a. allenbyi's higher habitat specificity.Our analyses revealed some unequivocal differences between allenbyi . The rarOur genetic analyses are based on mtDNA data, which is haploid and maternally inherited. This is a widely used marker, although it may suffer from bias due to sex-specific dispersal patterns. Clearly, further research should be done in order to explore the extent to which patterns which arise from the mtDNA analysis conform to those arising from other genetic markers screening the nuclear genome as well, such as microsatellites or AFLP.G. henleyi, but body mass varied significantly among populations. Regarding G. a. allenbyi, males were larger and had longer tails than females. This agrees with previous work on this species [G. a. allenbyi populations, and in a comparison between regions, individuals from the western sands were found to be larger than individuals from the inner sands. In a previous study [To test if different gerbil populations exhibit phenotypic divergence, we conducted morphometric analyses. Exploring geographically structured traits can reveal the extent to which local populations are non-interchangeable due to their distinct adaptations. No sexual size dimorphism was found in  species ,54 whichus study  showed nG. henleyi's type of rarity may be explained by two factors that are not mutually exclusive-high mobility rates, which decrease the level of divergence between populations, and an ongoing bottleneck, which lowers both the overall and within-population genetic diversity. It is important to note that the high mobility rates of G. henleyi are inferred from its high level of individual turnover [G. a. allenbyi and G. henleyi surprisingly revealed that the more common species exhibited higher levels of divergence between populations than the rare species. This may indicate that habitat specificity  plays a more important role than the degree of rarity in determining a species' genetic variation. We suggest that adopting a comparative approach as presented here could largely improve our understanding of the causes and effects of rarity. Such an understanding should enable us to better protect ecosystem integrity and prevent biodiversity loss.Our findings indicate that turnover ; we do nRR carried out the field work and the molecular genetic studies, and drafted the manuscript. ZA instructed RR in carrying out the field work and participated in the study's design. OO participated in the study's design, instructed RR in carrying out the entire research and performing the genetic and statistical analyses, and contributed towards drafting the manuscript. All authors read and approved the final manuscript.G. henleyi's CR & CO2 sequencesPairwise PhiPT values for . The low values indicate low levels of differentiation between populations. Probability values based on 999 permutations are above the diagonal and PhiPT values are below the diagonal. Note that Mamshit  is the only location from the inner sands.Click here for fileG. a. allenbyi CO2, G. henleyi CO2, CR and concatenated sequence)Haplotype networks of the four sequences  and CO2 Pairwise differences and allelic frequency graphs for . The ragged pattern in the pairwise differences graphs for both sequences in G. henleyi indicates that this species has maintained a stable population size over a long period of time. The allelic frequency graphs indicate that older mutations  predominate in G. henleyi, suggesting that this species has experienced a bottleneck.Click here for file


Aphidius transcaspicus Telenga (Hymenoptera: Braconidae) is a parasitoid of Hyalopterus aphids across a wide geographic range. This species shows a remarkable degree of genetic structure among western, central, and eastern Mediterranean population clusters. In this paper we attempt to better characterize this genetic structure.The integration of multiple complementary approaches is a powerful way to understand the processes of diversification and speciation. The parasitoid wasp Isolation with Migration model using mitochondrial and microsatellite markers together with climate-based ecological niche models to better understand the genetic structure of A. transcaspicus in the Mediterranean. The coalescent analysis revealed low levels of migration among western and eastern Mediterranean populations (Nm<1) that were not statistically distinguishable from zero. Niche models showed that localities within population clusters each occupy areas of continuously high environmental suitability, but are separated from each other by large regions of completely unsuitable habitat that could limit dispersal. Overall, environmental characteristics were similar among the population clusters, though significant differences did emerge.We use a Bayesian coalescent analysis of gene flow under the A. transcaspicus, which together with previous analyses indicating partial behaviorally mediated reproductive isolation, suggest that the early stages of cryptic speciation may be in progress.These results support contemporary allopatric isolation of Mediterranean populations of  Working to understand the processes that contribute to reproductive isolation and speciation is among the most active and challenging areas in evolutionary biology. Over the last several decades the use of genetic markers to identify evolutionarily distinct populations has become common practice One powerful way to improve genetic inferences about gene flow and recent speciation is through the incorporation of environmental data Geographic information systems based ecological niche modeling is a promising tool for predicting species distributions and identifying factors that have contributed to their divergence FST) may allow identification of evolutionary significant units, these approaches can be problematic for quantifying gene flow under recent divergence because they do not adequately separate the historical effects of shared ancestry from contemporary gene flow Once populations are shown to be structured both genetically and environmentally, a rigorous assessment of gene flow levels is needed to determine their evolutionary status and potential for speciation Aphidius transcaspicus Telenga (Hymenoptera: Braconidae) A. transcaspicus is a specialist parasitoid of aphids in the genus Hyalopterus Koch and is distributed throughout much of the Mediterranean and parts of central Asia. Bayesian cluster analyses of microsatellite markers have shown that individuals in the Mediterranean region are grouped into three main population clusters  FST = 0.68) and a unique set of haplotypes is restricted to parasitoids from K3 localities in the eastern Mediterranean, supporting the distinctness of this population K1 and K3, respectively) suggest that this genetic differentiation may be correlated with limited reproductive compatibility between parasitoids from the different regions A. transcaspicus populations in the Mediterranean may be in the early stages of speciation.We undertook this study in an attempt to explain the remarkably high levels of genetic differentiation previously observed among population clusters of the wasp s K1−K3;  that shoA. transcaspicus is most abundant in coastal Mediterranean areas, and becomes rare or absent above 42–43°N latitude in Europe and away from the coast in northern Africa. In contrast, its Hyalopterus hosts appear to have a much broader distribution, especially in Europe, where Hyalopterus pruni occurs as far north as England, Germany, Finland, and Sweden A. transcaspicus in the Mediterranean region and thus contributing to genetic differentiation at the region-wide scale through environmental barriers to gene flow. At the same time, the broad geographic area inhabited by A. transcaspicus may provide opportunities for ecological specialization in subdivided populations, and we assess this possibility by testing for differences in climatic variables experienced by parasitoids from each genetic cluster. To supplement this analysis, we reanalyze mitochondrial sequence and microsatellite data from Mediterranean populations Isolation with Migration (IM) model Sampling data and field observations suggest that A. transcaspicus in the Mediterranean. Briefly, these data consist of a 432 bp segment of the mitochondrial cytochrome oxidase I gene (COI) and nine microsatellite loci A. transcaspicus was observed , a cluster from Italy (K2), and an eastern cluster comprising parasitoids from Greece, Cyprus, Egypt, Turkey, and Israel (K3). The COI data provided less resolution, but one set of haplotypes was found only in localities within the K3 cluster, highlighting the uniqueness of parasitoids in the eastern Mediterranean.We used genetic data from a previous study observed  and founIsolation with Migration (IM) coalescent model as implemented in the IMa software A that, at time t in the past, diverged into two daughter populations of sizes N1 and N2 that have undergone regular gene flow at a rate of m1 and m2 since divergence. IMa utilizes a Bayesian Markov Chain Monte Carlo method to estimate posterior distributions of the six demographic parameters in the IM model, scaled by the mutation rate μ, including the population size parameters , the divergence time parameter (τ = tμ), and the post-divergence migration parameters . Upper bounds for the prior parameters were set at θ1,2 = 4, θA = 100, τ = 4, m1 = 10, and m2 = 10. These bounds were selected following preliminary runs with very large upper bounds followed by modification in subsequent trial runs depending on the resulting posterior distributions, as suggested in the IMa manual. For the final analysis, the program was run using 50 chains for a total of 5×107 steps (sampling every 100) following a burn-in period of 500,000 steps. Following multiple trial runs, geometric heating parameters were chosen to achieve sufficient mixing  such that no obvious trends were apparent in posterior distribution scatter-plots. The IMa analysis is computationally intensive, and can thus only analyze a two population model. Therefore, we attempted to perform three analyses, one for each pairwise comparison between K1, K2, and K3. We used the full set of mtDNA sequences from each cluster  under an infinite sites evolution model, and a subsample of 100 randomly selected individuals each from K1 and K3  from each of five loci  that did not obviously deviate from the stepwise mutation model . Unfortunately, convergence could not be achieved for comparisons involving K2 despite multiple attempts and so we focus here on the distinctiveness of the larger K1 and K3 clusters. The analysis was repeated for three short runs using different random seed values and visually assessed for consistency before performing the final run described above.Our present analyses focus on quantifying the degree of differentiation among these three clusters by fitting our data to the two-population A. transcaspicus . Because IMa is designed to return estimates given a value of θ equal to twice the effective number of nuclear gene copies (in this case 1.5×Ne), multiplied by μ, the nuclear microsatellite loci were given an inheritance scalar of 1.0, while the maternally inherited haploid mtDNA locus was given an inheritance scalar of 0.333 . We report estimates for all parameters scaled by μ  to statistically test the hypothesis of zero gene flow for both m-parameters as compared to the full model containing all parameters using log-likelihood ratio tests. Because in our alternative hypothesis m is bounded by zero, to test the significance of no gene flow we used the following approach, recommended by J. Hey . First we tested whether the hypothesis of equal gene flow could be rejected  where the −2LLR statistic was tested by comparing maximum posterior probabilities for each model using a χ2 distribution with 1 d.f. The second test compared the −2LLR of H0: m1 = m2 = 0 versus HA: m1 = m2, using a mixed distribution of m1 = m2 = 0 cannot be rejected, providing no support for gene flow levels greater than zero.We used 90% highest probability marginal-density (HPD) to examine the variance of parameter estimates. We also used the nested models approach implemented in the “L-mode” of IMa  and collecting any parasitized aphids found. In some countries samples were collected in more than one year during this period, for others only a single year of sampling was conducted. In total, A. transcaspicus was collected or observed at 70 locations throughout these regions . The four localities from Iran and Pakistan were used as training data for the ENM but are not discussed further. Finally, we used three localities in Greece obtained from a recent review of European Aphidius species Hyatlopterus occurs outside of our ‘presence localities’ have been searched on several occasions for A. transcaspicus , but no evidence of the parasitoid was found, despite an abundance of their aphid hosts  of a species, which may be reduced in size by biotic interactions from the area representing the true fundamental niche (β-niche) defined by climatic tolerance, this is of less concern for A. transcaspicus. As noted above, the distribution of A. transcaspicus is not limited by the distribution of its food resources  between each pair of variables for 1,000 randomly selected points from throughout the geographical extent selected for bioclimatic modeling. Climate data for each point were extracted using DIVA-GIS v5.4 (http://www.diva-gis.org). Variables with r>0.80 were considered as highly correlated and we selectively removed one variable from each of these pairs. Whenever possible, we retained variables that represent climatic seasonality or extremes rather than annual averages of precipitation and temperature, as they seem more likely to influence the range limits of a seasonal species like A. transcaspicus. The final model included 10 variables: BIO2 – mean diurnal temperature range [(mean of monthly (maximum – minimum temperature)]; BIO3 – isothermality [×100]; BIO4 - temperature seasonality (SD×100); BIO5 – maximum temperature of the warmest month; BIO6 – minimum temperature of the coldest month; BIO8 – mean temperature of the wettest quarter; BIO9 – mean temperature of the driest quarter; BIO13 – precipitation of the wettest month; BIO14 – precipitation of the driest month; and BIO15 – precipitation seasonality (coefficient of variation). In comparison to the ENM from the complete set of variables (not shown) this reduced set did not alter the results to a great extent, although it generated an ENM with a slightly broader distribution. Thus the model presented here is likely to have a lower rate of falsely omitted cells and should be a more conservative distribution against which to evaluate barriers to gene flow in A. transcaspicus based on predictions of climatic unsuitability.We first attempted to minimize model over-fitting by calculating Pearson's correlation coefficient (A. transcaspicus in the Mediterranean.We used logistic output from Maxent which generates a probability of presence (range 0–1) to each grid cell For visualization of ENM predictions, unsuitable habitat was defined as grid cells with a logistic probability less than the lowest value for any sampled locality . The remaining grid cells are presented continuously from light to dark shades representing the lowest to highest probability of occurrence, respectively. Finally, the importance of each variable to the final model was analyzed using the jackknife procedure implemented in Maxent, where separate models are analyzed that include each variable alone; the resulting effect on training gain can be viewed as a measure of the information contained by the particular variable.K1 or from K3 following the procedures above. K2 was not considered due to the small number of observations.To further examine whether different genetic clusters occupied distinct ecological niche space, in addition to the ‘full ENM,’ we created two ‘reduced ENMs’ using only localities from either A. transcaspicus differed among the three genetic clusters. We extracted values for the 10 bioclimatic variables used in the full niche model for each unique coordinate belonging to K1, K2, and K3 . We performed a principal components analysis (PCA) on correlations between variables using JMP (SAS Institute) followed by analysis of variance (ANOVA) among clusters on each of the first three principal components . We also tested the significance of differences between K1 and K3 localities separately for each climate variable using Students t-tests (once again K2 was excluded because of sample size). Bonferroni correction was applied so that α = 0.005 for 10 comparisons.We also investigated how the climate experienced by K2, so only the K1−K3 comparison is presented. Multiple runs of IMa converged on very similar posterior probability distributions over multiple short analyses (not shown), and below we present a final analysis consisting of 5×105 sampled genealogies . The highest posterior probabilities for migration were 0.305 and 0.585 for mK1 and mK3, respectively. The effective rate at which individuals enter a population per generation  was found by multiplying mK1 and mK2 by the highest posterior probability estimates of θK1 and θK3, respectively and then dividing the resulting values by three. This results in mean estimates of 0.036 effective migrants into K1 and 0.078 into K3 per generation. The likelihood ratio tests showed that the full IM model was not significantly better than the equal migration model, which in turn was not significantly better than the zero migration model  or over islands of high predicted suitability in the Aegean Sea. Furthermore, a distinct break in the ENM prediction is apparent in northern Africa (Libya), which could help explain the genetic discontinuity between K1 and K3 in this part of the A. transcaspicus range.The most striking feature of the full ENM, however, is the substantial area of unsuitable habitat predicted to occur between the K1  or K3  alone also performed well in predicting the range-wide distribution of A. transcaspicus , the three Italian localities (K2), and the southernmost Tunisian and all eastern Spanish localities (K1) classified as unsuitable. Comparison of logistic probabilities of occurrence among clusters and models shows clear declines for localities not included in reduced models relative to the full model , indicating some differences in the bioclimatic niche space occupied by parasitoids in each region, as suggested by locality omissions in the reduced ENMs. When compared individually between K1 (n = 30) and K3 (n = 28) localities, five of the 10 bioclimatic variables showed significant differences ; minimum temperature of the coldest month ; mean temperature of the driest quarter ; precipitation of the wettest month ; and precipitation seasonality . As in the ENMs, the Turkish locality was a noticeable outlier in most plots, again suggesting that this point falls outside the bioclimatic zone typical for A. transcaspicus in the Mediterranean. However, repeating the PCA and t tests on bioclimatic variables after exclusion of this locality did not change any patterns of significance (not shown).The first three axes of the PCA on bioclimatic variables extracted from sampling localities  show that the three Mediterranean he space . Howeverferences : mean moA. transcaspicus is subdivided into three major population clusters that encompass the Iberian Peninsula and the Maghreb region of northern Africa (K1), Italy (K2), and the eastern Mediterranean  and also aiding practical taxonomic decisions nean K3;  [19]. GiIM model. Modal values of the posterior probability distributions for the two migration parameters were consistent with very low levels of ongoing post-divergence migration between western (K1) and eastern (K3) Mediterranean populations, well below that often considered necessary for ecological or evolutionary trajectories to remain correlated IM models including symmetrical or asymmetrical migration parameters did not have significantly greater likelihood than the nested model excluding gene flow, indicating that contemporary migration is not necessary to explain the observed patterns of genetic variation  than between K1 and K3 (FST = 0.657), suggesting that there may be some slightly higher level of gene flow between western Mediterranean and Italian A. transcaspicus populations. Although this level of divergence is still very high for microsatellite data. The FST observed for K2 and K3 (FST = 0.601), however, is nearly as high as for K1 and K3, suggesting that genetic isolation for Italian and eastern Mediterranean populations is likely to be as strong as that observed here for western and eastern Mediterranean populations. Furthermore, any indirect gene flow between the eastern and western Mediterranean populations through Italy, or some other unsampled population, should still have been inferred as direct post-divergence migration in the IM model A. transcaspicus populations is unlikely to be affected by the absence of results for K2. We anticipate that additional sampling of parasitoids from Italy will reveal that K2 does not act as a major stepping-stone population in the Mediterranean, although the degree to which this population is connected by gene flow to either K1 or K3 remains to be assessed.It is unfortunate that models comparing location , might aK1 genetic cluster are broadly distributed in Spain and the Maghreb region of Africa (represented here by Morocco and Tunisia). These regions were well connected in the ENM, with only the Straits of Gibraltar as a potential barrier to dispersal. While previous studies have identified the Straits of Gibraltar as a strong phylogeographic barrier between Iberia and Africa for some organisms A. transcaspicus. At the northern border of the K1 cluster in Europe the full and K1-only ENMs predicted that habitat suitability declines between northern Spain into France, with only a small strip of coastal habitat predicted as marginally suitable for A. transcaspicus, corroborating our failure to collect A. transcaspicus in northern Spain and southern France. The models also suggest that parasitoids in the K2 cluster are likely to be restricted largely to southern and eastern coastal Italy. Again, this is consistent with absences during field observations (A. transcaspicus in the eastern Mediterranean (K3). Within the eastern Mediterranean, connectivity of predicted suitable habitat was high, both along the mainland coast as well as for islands groups. While we only had samples for the islands of Crete and Cyprus, our results suggest that A. transcaspicus is also likely to be present on many other islands in the region.Parasitoids belonging to the rvations . FinallyA. transcaspicus is likely to be restricted largely to the coastal Mediterranean, with climatic suitability generally declining inland. This supports the prediction that A. transcaspicus prefers “Mediterranean” environments to other habitat types found in the region A. transcaspicus, even more so than short stretches of water, and may explain the patterns of genetic structure present in the Mediterranean.In summary, the ENMs show that K2–K3 gap in the eastern Balkans, for which we have no observations of A. transcaspicus. Although a recent review of aphid parasitoids of southeastern Europe reported no evidence of A. transcaspicus from these countries One potential problem with identifying breaks in species distributions using ENMs is the difficulty in determining if such breaks are due to true environmental unsuitability or false omissions due to insufficient sampling effort. This is most problematic for the A. transcaspicus specimens (supplied by P. Starý), this locality was georeferenced to the city of Kahramanmaras, but we suspect that this may not be an accurate representation of the site of collection. The Turkish province Kahramanmaras extends well to the south of the city into an area predicted to be highly suitable for A. transcaspicus in our ENMs, and may represent the true source of these samples. This single locality did not affect our results, but does highlight a potential concern for generating ENMs if accurate georeferencing is not available for many data points Another anomaly that suggests model under-prediction is the omission of the Turkish locality of Kahramanmaras from all three ENMs. However, because this locality was an obvious outlier for most environmental variables, we favor an alternative explanation. Based on the label data included with the Turkish A. transcaspicus may actually over-predict the true geographic range of this species. This is a general concern when evaluating ENM predictions; these methods generate potential distributions based only on the environmental variables provided by the user, and will not accurately predict aspects of a distribution that are influenced by biotic or abiotic factors not included in the model. For example, ENMs will over-predict a species' range if climatic conditions in an area are suitable, but there is some external factor that prevents colonization, such as the presence of a competitor, absence of prey, or physical barrier such as a mountain range. Beyond the climatic conditions included in our ENMs, the distribution of A. transcaspicus will be affected by the distribution and abundance of Hyalopterus aphids, which in turn will be affected by the distribution of suitable primary and secondary host plants. Such factors might explain the high suitability predicted for areas along the Atlantic coast of Morocco despite our failure to detect A. transcaspicus in this region in 2006, as Hyalopterus aphids were also rare. However, in other areas, such as southern France, Hyalopterus are abundant, and we have never observed A. transcaspicus in this region. Hyalopterus species are, in fact, broadly distributed in Europe and Africa A. transcaspicus. Indeed, localities where Hyalopterus was collected but A. transcaspicus was absent generally had low probability of occurrence in the ENM .The results of our ENM analyses suggest that the niche space occupied by parasitoids belonging to different populations is similar, but certainly not identical. The ENMs generated using only a subset of localities from either the A. transcaspicus populations merits further research. Biological control practitioners have long recognized the need to consider climate when selecting natural enemy strains for pest management because parasitoid species often appear to exhibit adaptations to local climate in different geographic regions A.transcaspicus in the laboratory, it would be relatively straightforward to conduct experiments to test the effects of different ambient temperatures and moisture levels on colonies collected from different geographic regions. Studies on the climatic limitations for A. transcaspicus would also act as useful validation for the ENM presented here.The potential importance of these climatic differences for niche evolution in IM analysis and ENMs, we can begin to speculate on the recent evolutionary history of A. transcaspicus in the Mediterranean. We were clearly able to identify a lack of contemporary gene flow between environmentally isolated populations in the eastern and western Mediterranean, although our ENM only applies to a typical 20th century climate, and it is more difficult to make inferences regarding demographic processes that occurred prior to this period or how changes in climate may affect future patterns of dispersal and gene flow. And while our genetic results suggest that populations have been isolated since they diverged, estimating the divergence time itself is problematic with the present data. Problems in conversion of coalescent parameters to demographic values can arise, for example, from unpredictable scaling associated with intra-population substructure . The use of this rate leads to a divergence time estimate of ∼5,000 generations, or ∼500  years. However, microsatellite loci are known to have much slower rates of evolution for some insects, which would make true divergence times much older. For instance the use of a lower microsatellite mutation rate such as that observed in Drosophila melanogaster (5×10−6 per generation) Given results from the A. transcaspicus, although resolution among these will require further study. One possibility is that the present distribution of isolated populations originated during the last glacial maximum in Europe. Climatic oscillations have led to repeated expansion and contraction of Mediterranean species, with allopatric isolation in Pleistocene refugia, in particular, having been implicated in shaping the present genetic structure of much of the European biota A. transcaspicus today differs little from that which might be expected for Pleistocene refugia of many European species A. transcaspicus not have expanded further northward into Europe as suitable habitat became available? It is possible that Pleistocene populations were distributed as they are today, but remained trapped in their refugia, perhaps due in part to an inability to cross the east-west oriented mountain ranges dominant in Mediterranean refuges. This hypothesis would suggest that A. transcaspicus would have had to evolve rapidly in response to changing conditions, and could account for the somewhat different climatic niches occupied by the different genetic population clusters. Alternatively, A. transcaspicus may have been isolated in much smaller refugia with appropriate climatic conditions, perhaps at the southern limits of European peninsulas or in northern-most Africa, and subsequently expanded into the present distribution as favorable conditions spread. This latter hypothesis makes more sense in light of the low variation observed at COI  and microsatellite loci , as well as the absence of isolation by distance within population clusters Given such uncertainty in the divergence time, there are a number of possible hypotheses for the origins of genetic structure in −4 per generation) microsatellite mutation rate. For example, it is plausible that A. transcaspicus spent the last glaciation completely outside of the Mediterranean  Prunus and their associated Hyalopterus aphids A can be inflated in an IM model by the presence of ‘unsampled’ contemporary populations, such as K2 and Middle Eastern populations not considered here). This hypothesis is particularly intriguing in light of partial reductions in mating success observed in reciprocal crosses between A. transcaspicus collected in Spain and Israel A. transcaspicus could have been historically distributed given its current climatic tolerances A. transcaspicus today may not match those experienced by historical populations. However, a better understanding of how Hyalopterus and their Prunus host plants were distributed during the Pleistocene may provide important clues as to where A. transcaspicus could have survived this period.A second scenario could involve even more recent processes consistent with dates associated with the faster Click here for additional data file.


The Falkland Islands and Patagonia are traditionally assigned to the Magellan Biogeographic Province. Most marine species in Falkland waters are also reported from southern Patagonia. It remains unclear if relatively immobile, marine benthic, shallow-water species maintain gene flow, and by what mechanism. Recurrent fluctuations in sea level during glacial cycles are regarded as a possible mechanism that might have allowed genetic exchange between the regions. However, the realized genetic exchange between the Falkland Islands and Patagonia has never been estimated.Serolis paradoxa  from the Falkland Islands and southern Patagonia  applying seven nuclear microsatellites and a fragment of the mitochondrial 16S rRNA gene. Both marker systems report highest genetic diversity for the population from the central Strait of Magellan and lowest for the Falkland Islands. The estimated effective population sizes were large for all populations studied. Significant differentiation was observed among all three populations. The magnitude of differentiation between Patagonia and the Falkland Islands  was an order of magnitude higher than between populations from within Patagonia. This indicates that there is currently no effective gene flow for nominal S. paradoxa between these two regions and it has been absent for time exceeding the last glacial maximum. We argue that specimens from the Strait of Magellan and the Falkland Islands very likely represent two distinct species that separated in the mid-Pleistocene (about 1 MY BP).This study analyses the genetic structure of three populations of the marine shallow-water isopod Serolis paradoxa. The patterns of genetic diversity suggest that the only recently inundated Strait of Magellan was colonized by different source populations, most likely from Atlantic and Pacific coastal waters. Our results demonstrate that more systematic testing of shared faunal inventory and realized genetic exchange between Patagonia and the Falkland Islands is needed before a consensus concerning the position of the Falkland Islands relative to the Magellan zoogeographic province can be reached.The results of this study indicate limited gene flow between distant populations of the brooding isopod  Present-day distribution of a species is the result of a complex interplay between 1) extrinsic factors such as isolation of landmasses, climatic conditions and availability of niches and 2) intrinsic factors such as dispersal capability and physiological tolerance. Extrinsic factors typically influence the distribution of many species in the same way because they act on an ecosystem scale. Over time, this leads to a characteristic assemblage of species with similar distribution patterns within larger geographical areas, so called biogeographic provinces, and distinct gaps between them  were assessed using an FST and AMOVA framework as implemented in ARLEQUIN. Assuming neutrality, evidence of a population expansion was tested applying Tajima's D , corresponding to a high value of ΦCT = 0.905 .We sequenced a 490 bp fragment of the 16S rRNA gene for a subset of 27 specimens from population PA, 22 from AO and 22 from FI . Eleven6S rRNA, ,26-28,31D and Fu's Fs were negative and significantly different from zero for FI, and for Fu's Fs only for population AO  and for the Falkland Islands lineage 0.643 MY . Thus, from both inferences, evidence for a splitting event in the mid-Pleistocene is supported.Dating of the time to the most recent common ancestor (tMRCA) between both Serolis paradoxa turned out to have extensive spatial structure. The differences in mutation rates and coalescent dynamics of the two marker systems help describe present-day population structure and reconstruct historical demographic processes.The genetic variability within the nominal species ST, RST differentiation values and strong support from Bayesian cluster analyses  and the Falkland Island population is that populations form two shallow subnetworks, corresponding to the two geographic regions. The nuclear microsatellite markers support the geographic partitioning of variation with high and significant Fs Figure .The geographic positions of our sampling locations along an East-West axis might suggest testing for isolation by distance effects (IBD). However, in the context of this study, the IBD is an inappropriate method and it is unlikely that this would become more meaningful even if more intermediate sampling locations were available. This is because the central Strait of Magellan became available for (re)colonization only very recently, approximately 9–14 KY BP ,48. ThisAbsence of effective gene flow between the Falkland Islands and Patagonia is strongly suggested by nearly fixed population specific differences in fast evolving microsatellites and the perfect congruence of haplotype identity and geography for the 16S rDNA data. The long branch connecting the two groups of haplotypes Figure  and theiS. paradoxa was able to survive by following the rising and falling sea levels. In Patagonia, however, major parts of today's distribution of nominal S. paradoxa became unavailable due to ice coverage and/or low sea levels. Western Patagonia was covered by a contiguous ice shield similar to the Antarctic Peninsula today and the central Strait of Magellan was inundated only after the LGM, approximately 14-9 KY BP [S. paradoxa was presumably continuously present over evolutionary times, this species was forced to immigrate into the Strait of Magellan only recently after the retreat of the glaciers. Surprisingly, genetic diversity estimates for population PA from central Magellan Strait  with a rate of 0.37% per MY for transitions and transversions, the time of divergence was estimated to have occurred several hundreds of thousands of years before present. Thus the initial separation of lineages predates the last glaciations and took place in mid-Pleistocene (average estimates 0.643 – 0.948 BP). In view of the strong genomic signatures of differentiation between Patagonia and the Falkland Islands we must therefore reject the hypothesis that low sea levels during glacial periods led to significantly elevated levels of gene flow between populations of S. paradoxa due to greater proximity of shallow-water habitats. A similar argument applies to potential migration between Patagonia and the Falkland Islands via passive rafting on drifting substrates. Although there are major directional ocean currents that frequently transport substrates suitable for transportation of even rather immobile species [S. paradoxa. This species exclusively inhabits soft-bottom shallow waters and is frequently half-buried in the sediment (Held pers. observ.). Its capability to colonize new island habitats and maintain genetic continuity across barriers to dispersal and over evolutionary times is therefore small. Further sampling effort should focus on sampling specimens from the West Falkland Islands. It cannot be excluded that members of both lineages live in sympatry today.In summary, our data are in agreement with the following scenario: Populations of an ancestral species were separated geographically and evolved in allopatry . Applying a rate for the accumulation of substitutions in 16S rDNA estimated by Held  for the  species -103 thisST estimate for totally isolated populations based on microsatellites can reach the maximum value of FST = 1 only theoretically. Due to the high mutation rate of microsatellites [ST, differentiation between PA and FI is 0.86, between AO and FI 0.91 and among the Magellan Strait populations PA and AO 0.063 and thus about three times larger than without this correction. These values underpin that populations from Patagonia and the Falkland Islands are almost fixed for different alleles at the seven loci investigated. The results point out the importance of the recently introduced standardization approach [ST estimates between PA and AO revealed much higher differentiation than inferred using microsatellite data. The most plausible explanation is that the fourfold smaller effective population size of mitochondrial DNA [The equilibrium Ftellites ,104,105 tellites , but seetellites ), the inapproach ,68 in orrial DNA  lead to S. paradoxa, separate the samples and no major disturbances in the regions were reported for the time in-between the samplings. Thus, we regard this a negligible issue.In principle, differentiation estimates can also be biased due to comparing samples obtained in different years . However, as only few years, corresponding to even fewer generations of S. paradoxa. The time estimates are based on 16S rRNA substitution rates commonly used for other Crustacea [Concerning the dating of the split between the two lineages, it must be stated that genetic distances between two lineages increase much faster than predicted by molecular clocks if populations experience population bottlenecks . Thus, trustacea .Serolis paradoxa  consists of two reproductively isolated species one of which occurs in Patagonia while the other is presumably confined to shallow waters around the Falkland Islands. As the type was originally described by Fabricius as Oniscus paradoxum Fabricius, 1775 from the Falkland Island the species from Patagonia is in need of formal description and a scientific name.The genetic data strongly suggest that nominal Serolis paradoxa imply that both are highly abundant and neither needs to be considered endangered.The occurrence of cryptic species has important implications for the conservation of biodiversity in general . If a crIn summary, our data prove low but significant differentiation among populations within the Strait of Magellan and the absence of effective gene flow among populations from the Strait of Magellan and the Falkland Islands. In fact, specimens from both regions belong to two cryptic lineages that probably diverged in the mid-Pleistocene and may already represent reproductively isolated species. The 16S rDNA data supports a genetically rich central Strait of Magellan population, an intermediate population near the Atlantic opening of the Strait of Magellan and a genetically depauperate Falkland Island population. The results are in line with the expectations of colonization of the central Strait of Magellan from both sides of Patagonia after the last glacial maximum approximately 9-14 KY after deglaciation of the habitat and rise of sea levels.While the fauna of the Falkland Islands has often been accepted to share most of their faunal inventory with Patagonia our results indicate that shallow water species with low mobility may in fact be strongly differentiated populations of one species or even reproductively isolated species.The authors declare that they have no competing interests.CH and FL designed the study and sampled the specimens. FL and AK carried out the molecular genetic analyses. FL performed the computational analyses, was responsible for the interpretation of population genetic data and drafted the manuscript. CH served as PI for the project and made substantial contributions to the interpretation of the data and together with FL wrote the final manuscript. JWW participated in the coordination of the study and contributed to revising the manuscript. AK helped draft the manuscript. All authors read and approved the final version of the manuscript.Terrestrial (black) and marine habitats  available at last glacial maximum  and at the present interglacial (right panel). During last glacial maximum the sea level was considerably lower thus bringing shallow water habitats of the Patagonian Coast and the Falkland Islands in close proximity. Adapted from [ted from .Click here for fileSerolis paradoxa populations Allele frequencies at seven microsatellite loci for three . Private alleles are shown in bold.Click here for fileSerolis paradoxa  assuming three different mutation models Statistical tests for significant heterozygosity (H) excess or deficiency in three populations of . P-values of the Sign Test and Standardized Differences Test and one-tailed probability for heterozygosity deficiency are based on a 1000 permutations. Significant P-values are printed in bold. The TPM was adjusted to allow for 80% mutations according to a SMM and 20% to an IAM model.Click here for fileMismatch analysis based on the 16S rDNA data sets. Confidence intervals (CI give the 5% and 95% values, respectively for the parameters estimated by 50,000 bootstrap replicates.Click here for file