Callosobruchus maculatus, which is known to exhibit post-copulatory female choice. From reciprocal crosses of inbred lines, we assayed egg production, egg-to-adult survival, and lifetime offspring production of the outbred F1 daughters (F1 productivity).Quantifying the amount of standing genetic variation in fitness represents an empirical challenge. Unfortunately, the shortage of detailed studies of the genetic architecture of fitness has hampered progress in several domains of evolutionary biology. One such area is the study of sexual selection. In particular, the evolution of adaptive female choice by indirect genetic benefits relies on the presence of genetic variation for fitness. Female choice by genetic benefits fall broadly into good genes (additive) models and compatibility (non-additive) models where the strength of selection is dictated by the genetic architecture of fitness. To characterize the genetic architecture of fitness, we employed a quantitative genetic design  in a population of the seed beetle 1 productivity, but lower genetic variance in egg-to-adult survival, which was strongly influenced by maternal and paternal effects.We used the bio model to estimate six components of genetic and environmental variance in fitness. We found sizeable additive and non-additive genetic variance in F1 productivity (CVA = 14%) is sufficient to generate indirect selection on female choice. However, our results also show that the major determinant of offspring fitness is the genetic interaction between parental genomes, as indicated by large amounts of non-additive genetic variance (dominance and/or epistasis) for F1 productivity. We discuss the processes that may maintain additive and non-additive genetic variance for fitness and how these relate to indirect selection for female choice.Our results show that, in order to gain a relevant understanding of the genetic architecture of fitness, measures of offspring fitness should be inclusive and should include quantifications of offspring reproductive success. We note that our estimate of additive genetic variance in F Female choice is defined as when a trait in females  biases reproductive success among males towards certain male phenotypes over others . The evoFemale choice may result from indirect selection, by a process known as the "good genes" process . This reGenetic compatibility scenarios are based on females deriving indirect genetic benefits from pairing with genetically compatible males ,9,10,15.h2) of a trait represents the ratio of additive genetic variance (VA) to the total phenotypic variance (VP) and the heritability of traits largely affected by non-additive effects or environmental factors will thus by definition be low   2] :The "observational" variance components (denoted by sigma's) have the following interpretations in terms of "causal" components , σ2n = 1/2 FVA + 1/4 F2VAA, where VA and VAA are the additive genetic and additive by additive epistatic variances, respectively, in the base population, and higher order epistasis is ignored for simplicity  plicity  σ2m: maternal effect variance Vm, including both maternal genotype and common-environment effects, as well as possible interactions or co-variances between maternal nuclear and maternal extra-nuclear effects.3) σ2p: paternal effect variance Vp, including both paternal genotype and common-environment effects, as well as possible interactions or co-variances between paternal nuclear and paternal extra-nuclear effects.4) σ2k: interaction variance of paternal and maternal effects, and of nuclear and extra-nuclear effects (e.g. cyto-nuclear), Vk.5) σ2rep : variance among replicate crosses within line combinations.6) σ2w: (for F1 productivity only) variance among females within replicate crosses.7) σ2rep or σ2w (or their sum) can be equated with the environmental variance VE, because unless the parental lines are fully inbred (F = 1), both σ2rep and σ2w will have genetic as well as environmental contributions. To estimate VE, we subtracted the other causal components of variance from the total phenotypic variance (Vtot):VE = Vtot - VA - VD - VM - VP - VK. Vtot was estimated as the sum of all the observational components of variance, taking σ2n twice (because N appears twice in the model equation).Note that neither Coefficients of genetic variation (CV) for each parameter estimate were calculated using the additive genetic coefficient of variation, =100VA/X¯ where X¯σ2m and σ2p, representing line-specific maternal and paternal effects, respectively. The strong maternal effect is not surprising, because this term includes the effect of the female's own (inbred) genotype on her egg production. The maternal effect term could also include a contribution of common environment effects shared by different females from the same line. The highly significant paternal effect on egg production, which is of the same magnitude as the maternal effect, is more surprising. This indicates that the genotype of the inbred line males, or the environment shared by different males from the same line, strongly affected egg production by their mates. In contrast, there was no evidence for nuclear additive effects (σ2n) on egg production, and nuclear non-additive effects (σ2t), while significant, were smaller than the paternal and maternal effects. These results are not surprising, because we would not expect that the genotype of a female's eggs would have much if any direct influence on the number of eggs she produces. Finally, there was no evidence for non-reciprocal interaction effects (σ2k) on egg production. Variance components and significance levels for lifetime offspring production were similar to those for lifetime egg production .The proportion surviving in the initial crosses showed no evidence for nuclear additive or non-reciprocal interaction effects, marginally significant nuclear dominance and maternal effects, and a significant paternal effect Table . The lat1 females from each initial cross were mated to randomly selected outbred males, and their productivity measured. We treat productivity of these females as a trait of the females themselves, rather than of the cross, as this allows us to investigate the co-expression of female dam and sire genotypes. This approach is justified by the fact that male genotype explained only a small fraction of the productivity variation (see Methods). We found highly significant nuclear additive variance (σ2n) for F1 productivity, and marginally significant nuclear non-additive variance (σ2t). While σ2m also contributed significantly to F1 productivity (possibly reflecting cytoplasmic effects), σ2p did not. In contrast to the results from the first cross, the non-reciprocal compatibility variance σ2k also contributed significantly to the variance in F1 productivity. Finally, there was a highly significant effect of initial replicate cross (σ2rep), meaning that full-sibs resembled each other in productivity more than non-full sibs from the same cross combination . This likely largely reflects common environment effects, although because the lines were not completely inbred, some genetic divergence between replicate full-sibships may have contributed as well.Up to six F1 productivity was affected by sizeable components of both additive and non-additive genetic variation as well as by sex-specific interactions between the maternal and paternal genomes. The generally strong interaction effects are illustrated in Figure Estimates for causal variance components and coefficients of genetic variation are listed in Table 1 productivity. We note that the latter effect, where full co-expression is expected, was stronger than the former  in C. maculatus, illustrating that the diallel cross offers a useful empirical route to provide insights into rather complex aspects of the genetic architecture of fitness. In addition, we found large paternal and maternal effects indicative of direct effects on egg production. We also document striking differences in the genetic architecture of fitness traits measured in different life history stages: while egg-to adult survival was significantly affected by paternal effects, we found considerable additive and non-additive genetic variation for F1 productivity. This stresses the importance of obtaining comprehensive measures of fitness when assessing the potential for indirect genetic benefits. Below we discuss these findings in more detail, and use our results to discuss potential processes that may contribute to genetic variance in fitness. Finally, we ask what the implications are of our results in terms of the evolution of female choice by indirect selection.Our analyses revealed sizeable additive and non-additive components of genetic variation for our most comprehensive fitness variable  [In general, we found fairly high levels of both additive and non-additive genetic variance in Ffemales) ,60,61. Tfemales)   . Thus, sfemales) , our stuPrevious studies employing reciprocal crosses/the bio model have dealt with life history traits of the parental lines (e.g. fecundity) or of the offspring, typically in the juvenile stage -44,64,65σ2t suggesting that reciprocal epistatic interactions between nuclear genes and/or dominance significantly affect fitness in our population. There was also a sizeable effect of more complex and sexually non-reciprocal genetic interactions (σ2k) in F1 productivity. Sexually non-reciprocal epistasis may reflect cyto-nuclear genetic interactions, which has been shown to affect fitness related traits in both C. maculatus [Wolbachia infections [Wolbachia infections has been sought for but never found in C. maculatus [We found significant non-additive variance, aculatus ,70 and oaculatus ,72. Sexufections ,74, but aculatus .Both variance in parental environmental effects and nuclear genetic variation among parents will contribute to parental effects. In our case, however, we suggest that parental effects are largely due to genetic variation, because rearing conditions were highly standardized and thus uniform across lines. Parental effects were large for lifetime egg and offspring production but lower for egg-to-adult survival and F1 productivity (Table C. maculatus [Pieris napi is heritable, and male genotype influence female fecundity and longevity [Sizeable maternal effects, both genetic and environmental, are well documented in aculatus -78. Furtaculatus , and ouraculatus  and femaaculatus ,81,82. Taculatus ,79,83,84ongevity . This suThe presence of additive genetic variance provides the raw material for indirect selection by the good genes process ,3,14,17.C. maculatus, suggesting a role for indirect selection under at least some conditions. Second, indirect selection on female choice arises as a result of linkage disequilibrium between female choice/preference genes and genes with additive effects that confer high fitness in offspring [In general, the presence of genetic variation for fitness has been interpreted as supporting a role for indirect genetic benefits in the evolution of female choice . Howeverffspring . In ordeffspring . This prffspring  and someffspring ,95.C. maculatus.Third, perhaps the most fundamental problem in assessing the potential for indirect genetic benefits in sexual selection lies in estimating net selection on female choice . In ordeC. maculatus, in sufficient amounts for females to potentially derive indirect genetic benefits of mate choice. The genetic architecture of fitness traits varied dramatically with the life history stage in which fitness traits were measured. Parental effects, in particular paternal effects, were significant in the juvenile stage, while sizeable additive and non-additive effects were seen in the lifetime reproductive success of F1 daughters. Our results support the notion that comprehensive fitness measures should include offspring breeding value. Although our results show that there is a potential for females to derive indirect genetic benefits from mate choice, we note that several facts may nevertheless make selection for indirect genetic benefits ineffectual. These include direct selection, lack of accuracy in female choice and sexually antagonistic genetic variation.In conclusion, we found significant standing genetic variance for fitness related traits in our study population of TB participated in the design of the study, collected data, performed statistical analyses and wrote the paper. UF and AM participated in the design of the study and collected data. JF participated in the statistical analyses and writing of the paper. GA participated in the design of the study and the writing of the paper. All authors read and approved the final manuscript.


Brachionus plicatilis s. s. (sensu stricto) (Rotifera: Monogononta) on a global scale. We examine the phylogenetic relationships of geographic isolates from four continents using a 603 bp fragment of the mitochondrial COI gene to investigate patterns of phylogeographic subdivision in this species. In addition we investigate the relationship between genetic and geographic distances on a global scale to try and reconcile the paradox between the high vagility of this species and the previously reported patterns of restricted gene flow, even over local spatial scales.Small vagile eukaryotic organisms, which comprise a large proportion of the Earth's biodiversity, have traditionally been thought to lack the extent of population structuring and geographic speciation observed in larger taxa. Here we investigate the patterns of genetic diversity, amongst populations of the salt lake microscopic metazoan B. plicatilis s. s. reveals the presence of four allopatric genetic lineages: North American-Far East Asian, Western Mediterranean, Australian, and an Eastern Mediterranean lineage represented by a single isolate. Geographically orientated substructure is also apparent within the three best sampled lineages. Surprisingly, given this strong phylogeographic structure, B. plicatilis s. s. shows a significant correlation between geographic and genetic distance on a global scale ('isolation by distance' – IBD).Analysis of global sequence diversity of B. plicatilis s. s. is strongly structured at a global scale. IBD patterns have traditionally been interpreted to indicate migration-drift equilibrium, although in this system equilibrium conditions are incompatible with the observed genetic structure. Instead, we suggest the pattern may have arisen through persistent founder effects, acting in a similar fashion to geographic barriers for larger organisms. Our data indicates that geographic speciation, contrary to historical views, is likely to be very important in microorganisms. By presenting compelling evidence for geographic speciation in a small eukaryote we add to the growing body of evidence that is forcing us to rethink our views of global biodiversity.Despite its cosmopolitan distribution and potential for high gene flow,  Microscopic organisms have long been thought to occur globally wherever suitable ecological conditions exist . Such coEverything is everywhere, but, the environment selects such as small size, low specific gravity, protective coating, a dormant stage, and so forth. Species that are optimal for all these factors may have world-wide ranges, such as certain tardigrades, rotifers, and fresh-water crustaceans. A successful cosmopolitan with an essentially panmictic species population is evidently barred from geographic speciation ." This pIn striking contrast to these views, a paradigm shift seems to be occurring, as illustrated by a number of recent phylogeographic and population structure analyses. These studies uncover high levels of genetic structuring at a regional scale amongst zooplankton populations, and indicate very low levels of gene flow despite the high potential for these organisms to disperse -17.Brachionus plicatilis s. s. (sensu stricto) (Rotifera: Monogononta) on a global scale using a fragment of the cytochrome c oxidase subunit I gene (COI or cox1) gene. We try to reconcile the seemingly contradictory high vagility of this species with the observed patterns of restricted gene flow, even over local spatial scales. Our analysis of new and published data on mitochondrial DNA sequence variation underscores the strong phylogeographic structure of B. plicatilis s. s. both between and within continents, indicating the operation of incipient allopatric speciation. In addition, and surprisingly, given the non-equilibrium conditions and strong genetic drift operating in the system, a strong correlation between genetic and geographic distance is found. This paradoxical situation suggests that the colonization process by itself can create such patterns. Our results indicate that the evolutionary processes operating in small metazoans are similar to those known to be occurring in macro-organisms. Our observations challenge the intuitive dogma that high vagility leads to decreased biodiversity. As small eukaryotes constitute a remarkably diverse group, being amongst the most numerous organisms on the planet  and Amami; [GenBank: AY785174], , Laguna de las Eras [GenBank: AF266895], Torreblanca Marsh Poza Norte [GenBank: AF266858], Forgotten Lake , all Western Australian sequences [GenBank: EF524543-EF524555] and Grosbeak Lake .In total, the alignment contained 120 polymorphic sites, 89 of which were parsimony informative. The entire data set displayed a high level of synonymous substitution with only six polymorphic sites resulting in amino acid substitutions in individuals from Hay Camp [GenBank:The model of nucleotide substitution that best fits the data is the transition model with unequal base frequencies and rate variations among sites following a gamma distribution (TIM + G shape parameter 0.18). Raw sequence divergence (p-distance) ranged from 0.17% to 8.29% .B. plicatilis s. s. haplotypes is shown in Figure AY785189]) differed by a unique single base substitution from sequences found in several lakes on the Iberian Peninsula , while the second exception from the East coast in the USA  differed by four synonymous substitutions from a Spanish haplotype , two of these substitutions were unique to the Sapelo sequences. In both cases individuals closely related to European populations are found in Atlantic American locations. A third sequence  falls within this group although it was sampled from the coast of the Red Sea. A pattern of geographic substructure is apparent within some of these lineages. The Australian clade contains Eastern and Western groups and a strong substructure in the Western Mediterranean lineage is well illustrated within the Iberian Peninsula.The inferred phylogenetic relationship of r2 = 0.73, P = < 0.01, y = 1.11 × 10-3 + 5.25 × 10-6x).Figure r2 = 0.91, P = < 0.01, y = 1.13 × 10-2 + 4.93 × 10-6x).Consideration of the regression residuals revealed that the largest deviation from the regression model was represented by pairwise comparisons involving two laboratory kept clones  ) and especially Grand Cayman Island  that appear to have originated from Spain or North Africa. Conversely, populations having positive residuals, including mainly pairwise comparisons involving Spanish populations, but also ATL in the Eastern Mediterranean, exhibit greater than expected genetic distances for their geographic distance. These samples may represent a situation whereby regional populations have started to undergo geographic and reproductive separation. Alternatively, historical habitat fragmentation with recent recolonisation from refugia may account for these outliers, potentially located near the contact zones between divergent lineages. This inference is supported by Gómez et al.  and had their contents released by crushing them against the side of the tube. The samples were then heated to 56°C for 20 min, 100°C for 10 min, and then cooled at 4°C for 30 min. All DNA extractions were stored at 4°C until needed.COI gene was amplified by PCR with LCO1490 and HCO2198 primers .PCR products were sequenced on a CEQ 8000 Genetic Analysis System (Beckman Coulter Laboratories) using CEQ DTCS (Dye Terminator Cycle Sequencing) Quick Start Kits (Beckman Coulter Laboratories) and CEQ LPA-1 (linear polyacrylamide) denaturing gel, following ethanol precipitation and cleanup. Complementary strands of DNA were sequenced and all inconsistencies and polymorphisms were manually checked against original chromatograms in CodonCode Aligner v 1.3.4 (CodonCode Corporation). All B. plicatilis s. s. COI sequences from previous studies .In addition  studies ,19-21 weWe carried out a maximum likelihood (ML) phylogenetic reconstruction implemented in PHYML , using tCOI p-distances were calculated in PAUP* [Raw in PAUP* , while pin PAUP* . For lakDistances between field sites were estimated using the great arc formulae for calculating distances on a spheroid, with a value of 6371.01 km as the mean radius of the Earth . For allWhere lat1 and lat2 are the latitudes of two given sites, 1 and 2, and long1 and long2 are the longitudes of these two sites. In order for the equation to function correctly, southern latitudes and eastern longitudes were entered as negative decimal values while positive decimal values were assigned to northern latitudes and western longitudes. The error of great circle calculations is <1% of the given estimate for the entire globe across all scales.COI distances, TIM + G as selected by ModelGenerator [Reduced major axis (RMA) linear regression between genetic and geographic distances was performed on corrected enerator , for theenerator .SM carried out the molecular genetic studies, contacted researchers to profile geographic data and carried out the phylogenetic analysis. SM, DHL and AG conceived and designed the study and jointly wrote the manuscript. All authors read and approved the final manuscript.


Understanding the nature, timing and geographic context of historical events and population processes that shaped the spatial distribution of genetic diversity is critical for addressing questions relating to speciation, selection, and applied conservation management. Cladistic analysis of gene trees has been central to phylogeography, but when coupled with approaches that make use of different components of the information carried by DNA sequences and their frequencies, the strength and resolution of these inferences can be improved. However, assessing concordance of inferences drawn using different analytical methods or genetic datasets, and integrating their outcomes, can be challenging. Here we overview the strengths and limitations of different types of genetic data, analysis methods, and approaches to historical inference. We then turn our attention to the potentially synergistic interactions among widely-used and emerging phylogeographic analyses, and discuss some of the ways that spatial and temporal concordance among inferences can be assessed. We close this review with a brief summary and outlook on future research directions. Althoi.e., topology plus branch lengths) of gene trees reconstructed using molecular phylogenetic methods was used as the basis for inferring organismal history , the population frequencies of DNA sequence haplotypes, and/or the spatial locations from which they were sampled ,23,24. Pcariance , gene flcariance , populatcariance ,27, effecariance ). Howevecariance –32, and cariance –40. The via simulations under a particular divergence scenario or demographic model, and in this way the expected level of gene-to-gene variance is quantified )3.2.a posteriori, model-driven methods assess the support for well-defined a priori scenarios, and attempt to discriminate statistically among them [a priori scenario, and then empirical data are fitted to it. In this case, estimates of the model’s parameter values and their confidence intervals are used to distinguish among historical scenarios that make contrasting predictions about these values. In addition to statistical discrimination among alternatives, an advantage of model-driven methods is that they explicitly account for some of the inherent noise in real genetic datasets that is a consequence of coalescent stochasticity . Moong them ,54,102. ong them ,53), andasticity . However4.Some general approaches to historical inference rely more heavily on a single analysis method than do others, but most studies integrate different methods to some extent, with the aim of generating more robust inferences. In this Section, we overview widely-used approaches to phylogeographic inference and provide examples of empirical studies that have implemented them. One emerging approach—approximate Bayesian computation—is not covered here because its utility has largely been limited to tests of co-vicariance ,103–106,4.1.Ne can be assessed from a sample of DNA sequences via the frequency distribution of haplotypes , the frequency distribution of segregating sites , or pair-wise nucleotide differences , theTajima’s  D; Ramos Rozas’s  R2), or ss index ,27). In analysis  or Bayesanalysis ) can detlidation . This prlidation –113. Simlidation ,114,115.i.e., past vicariance, range expansion, restricted gene flow and dispersal) is unique to NCPA [Ne estimated over ecological vs. long-term evolutionary timescales [A practical limitation of the consensus vote approach is that an inferred historical event or process (or combination thereof) can be considered ‘strongly supported’ only when two or more alternative analytical methods that serve similar purposes are available. For example, the temporal assembly of simple components of evolutionary history . Finallmescales .4.2.via phylogenetic analysis of DNA sequences, and if geographically localized clades are identified from the estimated gene tree, these are subsequently used as population units in analyses that focus on demography over intermediate timescales . These same population units can then be re-examined using genotypic or allele frequency-based measures of population structure that are potentially informative about contemporary landscape-level barriers to gene flow .Analytical methods that focus on different timescales can be used in combination with one another to permit inferences over a broad temporal spectrum, thereby making full use of the historical signal carried by molecular data ,113,118.ariation ) or recuThe sequential approach to phylogeography is partly based on the principle of re-assessment and refinement of the current working hypothesis, as advocated by Buckley . Each ad4.3.The availability of coalescent methods for estimating historical demographic parameters, and in some cases, the timing of past events such as population divergence or size changes, has increased dramatically over recent years ,32. Briefluctuate—This software, now incorporated into the lamarc package [Neμ for diploid autosomal genes, where μ is the per-site per-generation neutral mutation rate), and the exponential growth rate parameter, g . Simulations have shown that although values of Θ and g tend to be upwardly biased , fluctuate performs modestly well when the true Θ-value is relatively large and DNA sequences from multiple unlinked loci are analyzed together [ package , distingtogether .et al. [fluctuate to investigate the causes of low genetic variation within populations of boreal North American mammals compared to high levels of variation seen in tropical Amazonian mammal species. Strong evidence for demographic growth based on mitochondrial DNA (mtDNA) sequence datasets was found only in the North American taxa, pointing towards a scenario of rapid northward range expansions following the retreat of Quaternary ice sheets in that region. In another study, this time centered in the Wet Tropics of north-eastern Australia, Hugall et al. [fluctuate analysis of mtDNA from a low-mobility forest-restricted land snail to assess whether genetic data were consistent with predictions from paleoclimatic modeling of the species’ Quaternary habitat distributions. Forest patches that persisted as long-term stable refugia throughout the Last Glacial Maximum and then subsequently expanded during the Holocene were found to harbor snail populations that showed evidence for marked demographic growth, confirming the predictions of paleoclimatic projections. In the Lessa et al. [et al. [g, fluctuate could produce ambiguous results when expansion events were less pronounced. Carstens et al. [i.e., estimated Ne values, number of sampled individuals and DNA characters). These datasets were then analyzed using fluctuate to determine the null distribution of g-values relevant to the empirical salamander dataset. Ultimately, population size constancy was rejected, consistent with an a priori hypothesis of range expansion out of a mesic forest refuge in the Pacific Northwest of North America.In a comparative phylogeographic study, Lessa et al.  used flul et al.  used flua et al.  and Huga [et al.  studies,s et al.  accommodmigrate—Unlike traditional estimates of migration rates drawn from molecular data, such as those derived from Weir and Cockerham’s [FST, migrate can distinguish between gene flow into a recipient population, versus out of a source population [n populations that have been exchanging genes for an indefinitely long period of time , and scaled migration rates M1→2 and M2→1 . migrate is particularly flexible in that it allows a user-specified migration matrix in which some M-values can be fixed at zero to produce 1- or 2-dimensional stepping stone models (c.f. the full island model). Also, some or all M- and Θ-values can be set as equal among population pairs . Likelihood-ratio tests can then assess whether a simplified matrix has a significantly worse likelihood score than the unconstrained (more complex) migration matrix, permitting statistical discrimination among phylogeographic scenarios that make different predictions about the parameter values. Using simulated datasets, Beerli [M-values were relatively robust. However, an important limitation of migrate is the assumption that populations have been exchanging migrants for an indefinitely long period of time, at a constant rate. Potential sources of error included recently-diverged populations that have not yet reached equilibrium, episodic busts of migration, or human-mediated long-distance dispersal.kerham’s  FST, migpulation . The sof, Beerli  examinedet al. [migrate analyses provided evidence for peripheral isolation of island populations on the basis of asymmetrical migration: island-to-mainland rates were consistently lower than in the reverse direction. Conversely, gene flow among mainland populations was relatively high, and based on the estimated M-values, this was sufficient to counteract the effects of drift among continental (but not island) populations. In another empirical application of migrate, Pfenninger and Posada [migrate to distinguish among a set of three phylogeographic scenarios. The analysis favored a source-sink island model, and the directionality of gene flow inferred by migrate was consistent with a scenario of northward expansion, as seen in other population-level studies in the region. In a comparative phylogeographic study, Garrick et al. [migrate to examine congruence between two syntopic, rotting-log-dependent Collembola species  from Tallaganda in southeastern Australia. Likelihood-ratio tests of constrained versus unconstrained migration matrices were used to assess the relative importance of two forest refuges as sources of recolonization of neighboring areas. Together with other analyses that focused on different timescales, migrate revealed that although the two species show similar spatial patterns of genetic variation, their demographic histories were idiosyncratic with respect to the locations of major refuges.In a phylogeographic study of marbled murrelets from continental British Columbia and Alaska, and the western Aleutian Islands, Congdon et al.  estimated Posada  investigk et al.  used migim—Distinguishing between incomplete lineage sorting and gene flow has been a persistent challenge in phylogeography and related fields [mdiv, which has now been superseded by im [ima [i.e., gene trees are not reciprocally monophyletic), and which have not exchanged migrants with any other population except their immediate ancestor , whereas the latter permits likelihood-ratio tests of nested models. Also of note, the genealogy sampler used in ima explores the parameter space more efficiently [migrate , im and ima provide point-estimates and confidence intervals for the ancestral population mutation rate parameter (ΘA) and the time since divergence (Tdiv). Unlike migrate, the isolation-with-migration model does not assume that populations are at mutation-drift equilibrium. The recent release of ima2 alleviates one of the major limitations of the method because it permits the analysis of more than two populations at a time. However, two simulation studies that assessed how robust parameter estimates generated by im are to violations of underlying assumptions reached rather disparate conclusions [d fields ,126,127.d fields  developeed by im  and ima  im [ima . The latancestor . From a iciently , which iclusions ,131, sugim to investigate population divergence history of the red-backed fairy wren across a multi-taxon biogeographic break in northern Australia (the Carpentarian barrier). In addition to ongoing gene flow between populations, the authors found evidence for an unexpectedly recent initial splitting, which is at odds with divergence dates estimated for a co-distributed grass finch. Lee and Edwards [im model parameter values to compare demographic histories of rainforest skinks in the Australian Wet Tropics. In this landscape system, three mostly allopatric mtDNA lineages are separated by two well-characterized biogeographic breaks (Black Mountain Corridor and the Burdekin Gap). Two of these lineages are morphologically indistinguishable but show evidence for partial reproductive isolation at a narrow contact zone, whereas the third lineage is diagnosable on the basis of male throat coloration. Surprisingly, the two morphologically distinct lineages were found to have diverged most recently . Furthermore, im indicated little or no apparent difference in Ne across all lineages, which suggests that the relative contribution of drift is likely to have been equal across all lineages. As a result, this work highlighted the potential for different evolutionary outcomes to arise from the contrasting effects of extrinsic selection driving phenotypic stasis as opposed to geographically divergent intrinsic  selection. Muster et al. [im in a phylogeographic investigation of a cold-adapted montane wolf spider. In this species, population connectivity was probably highest during periods of maximum ice sheet extent, and so this study represents a nice counter-point to the numerous studies that have uncovered evidence for divergence among populations isolated during Pleistocene glaciations. The principal question was whether relatively recent  or contemporary gene flow, versus longstanding genetic isolation coupled with incomplete lineage sorting, best explain the observed spatial-genetic patterns. Extensive simulations determined to what extent im parameter estimates could realistically discriminate among these and other competing scenarios, and the authors used this information to guide interpretation of results from im analysis of empirical data. Ultimately, scenarios that differed in the tempo of migration  and time since population splitting were not readily distinguishable.Estimation of isolation-with-migration model parameters from empirical DNA sequence datasets now plays a central role in phylogeographic inference. Lee and Edwards  used im  Edwards  also sho Edwards  used im r et al.  used im i.e., fluctuate, migrate and im), we have illustrated some diverse applications of the parameter estimation approach to phylogeographic inference, but their limitations warrant consideration. First, in most coalescent methods the basic underlying population model is forced on the data, yet it may nonetheless fit quite poorly [Ne-values), and to extend coalescence times [Using three exemplar methods  DNA sequence characters are evolved along the braches of the coalescent trees, (3) the simulated DNA sequence datasets are used to generate null distributions for the value of a chosen summary statistic under each scenario, and (4) the same summary statistic is calculated from the empirical DNA sequence dataset and then compared to the null distributions. Because each alternative scenario is treated as the null hypothesis when comparing observed  and expected (simulated) summary statistic values, the outcome of a test is to either ‘reject’ or ‘fail to reject’ the scenario at hand. The advantages of using simulations within population trees are that stochasticity associated with coalescent and DNA substitution processes are explicitly accommodated, divergence models can be tailored to reflect relevant components of the particular biogeographic landscape and species under study, and the underlying cause of spatial-genetic structure is directly linked to the hypothesis tests themselves.Hypotheses about population divergence history usually consist of information relating to the number, geographic locations, and timing of splitting events. Tree-like representations capture most of the key parameters needed to characterize a model of past vicariance, thereby providing a framework for phylogeographic inference using coalescent simulations ,101,102.Ne . Tree tos—a summary statistic that measures the degree of discord between a population tree and a gene tree, assuming zero post-divergence migration. Despite considerable incomplete lineage sorting, the empirical mtDNA sequence data were consistent with a scenario of multiple refugia. DeChaine and Martin [i.e., a frog, salamander, vole, and willow tree species), and used the same analytical framework for inferring population divergence history. The authors were able to demonstrate that, in some taxa, similar spatial-genetic patterns can emerge in the face of idiosyncratic ancestral distributions coupled with different demographic histories. This finding has far-reaching implications because it shows that if ‘biogeographic consensus’ is inferred for a given geographic region based only on spatial-genetic patterns, these conclusions may be erroneous. Similar findings have recently been reported in other taxa and landscape systems [In one of the first phylogeographic applications of coalescent simulations within population trees, Knowles  investigd Martin  used a sd Martin  also use systems ,141.Ne, or locus-specific per-generation mutation rates, are often not known [Despite the appeal of using coalescent simulations to test the fit of empirical data to alternative landscape-specific divergence scenarios, implementation of the approach can be quite challenging. The identification of simple yet biologically meaningful models that can be distinguished with the molecular data at hand is critical . This reot known . Moreoveot known . Accordi4.5.a priori hypothesis included in a set of candidate models [versus those that receive very little support from the empirical data [C) can be used as a measure of fit whereby complex models are penalized in proportion to the number of estimated parameters they contain. Through identifying the simplest model that best explains the data, the relative ranking approach can be used to understand the degree to which each of several historical events or processes  contribute to explaining observed spatial-genetic patterns.In contrast to the simulations within population trees approach that is based on testing a series of null hypotheses one at a time, information-theoretic methods focus on evaluating the strength of empirical evidence in favor of each alternative e models . Rather cal data . Model scal data ,144,145.et al. [ima’s isolation-with-migration model. Given an empirical multi-locus DNA sequence dataset from a mesic forest salamander, 16 different hypotheses  were ranked relative to the best-fit model via AIC, and then the overall probability of each one was quantified. Interestingly, rather than identifying a single best model, the authors found roughly equivalent support for two very different models—one that included non-zero post-divergence migration, and one that did not. Despite this ambiguity, all of the models that had a single value for the Θ parameter  fit the data very poorly, whereas all credible models allowed for low values of ancestral Θ compared to that of descendant populations (ΘA < Θ1 and Θ2). This result was consistent with previous investigations of the species’ evolutionary history, which provided evidence for range expansion out of isolated refugia [Carstens et al.  examined refugia ,54,110.et al. [a priori hypotheses, and so outcomes are heavily dependent on the quality of the candidate models that are included in the set [The coalescent simulations within population trees approach assumes that the particular scenario at hand is true and then calculates the probability of the data under this model, whereas the relative ranking approach calculates that probability of alternative models given the observed data . One benet al. , nested  the set . Indeed, the set ,144. In  the set .5.i.e., where data exploration precedes model-driven hypothesis-testing), should help to design an overall analytical framework focused on biologically-relevant scenarios [Using a variety of genetic data types and analytical methods that exploit phylogeographic signal across broad temporal and spatial spectra generates opportunities for integrative approaches to historical inference . For exacenarios . Below w5.1.fluctuate), to be at mutation-drift equilibrium and to have been exchanging migrants for an indefinitely long period of time , or to have exchanged genes only with a sister population and the immediate common ancestor . Other important assumptions can include constant population size over time, or that approximate Ne-values are known for all extant and ancestral populations . Employing these model-driven coalescent methods necessarily incurs a cost—oversimplification of biological reality. However, the use of complementary analyses that are not subject to the same limitations can provide important insights into which underlying assumption(s) might be violated, and to what extent. The subset of model-driven analyses introduced earlier (Sections 4.3 and 4.4) are representative of some common assumptions that are built into coalescent models of historical demography and population divergence. For example, the operational taxonomic units of these analyses are usually panmictic, unstructured populations or natural genetic clusters. These groups may be assumed to exchange no migrants with each other . This necessitates performing at least some analyses that are capable of making unexpected discoveries. The examples outlined above illustrate the concept of complementarity: the strengths and weaknesses of NCPA have been debated elsewhere. When coupled with a basic understanding of species’ biology and landscape history, both the ‘consensus vote’ and ‘sequential’ approaches to phylogeographic inference should facilitate designing an analytical framework that identifies key historical events and processes that impacted genetic structuring of a species, or whole ecological communities (but see Section 7).6.i.e., genotypic, population allele frequencies and gene genealogies); or between co-distributed species that have evolved in the same landscape setting. Regardless of the level of biological organization, basic measures of concordance include the nature of the primary inference, degree of spatial co-occurrence, and temporal synchrony [Concordance of phylogeographic inferences can be considered at several levels of biological organization: among independent DNA sequence loci; across different types of genetic data  will usually be inadequate. Indeed, the error associated with phylogenetic estimation alone may account for any apparent incongruence. Parametric bootstrapping tests of monophyly [vs. gene tree estimation). Temporal congruence of vicariance events inferred by two or more DNA sequence markers is amenable to testing via increasingly sophisticated molecular dating techniques—even when a strict clock-like substitution rate does not hold [Fragmentation of an ancestral population can result in two or more daughter lineages that now have allopatric distributions, separated by geographic areas in which the species is absent  are used for the analysis of different DNA sequence loci and/or genetic data types. In the case of co-distributed species, however, the number of natural genetic clusters and their locations may show only partial or weak correspondence across taxa. To facilitate comparison in these cases, it may be necessary to sacrifice spatial resolution and assign each natural genetic cluster to a higher level geographic region based on proximity, after tests for population size changes have been performed. However, it will be important to ensure that these broad scale regions are still be biologically meaningful for the species under study . Most of the methods for detecting the signal of growth from DNA sequences listed in Two different  components of a range expansion event are amenable to testing: evidence for population growth, and evidence for spatial expansion. There are several analytical methods available for addressing the former component using DNA sequences drawn from a single panmictic population  is likely to be particularly important. For example, although the expected mismatch distribution for a sample of DNA sequences can be derived under a continent-island model of spatial expansion [versus those simulated under alternative landscape-specific range expansion scenarios could provide a framework for assessing congruence across loci [In contrast to growth, few methods explicitly assess evidence for spatial expansion but see ,148,166),166148,1xpansion ,125,134.oss loci . Even sioss loci ,73.Despite the potential utility of molecular dating for examining temporal concordance among inferences of population growth or vicariance, which is a particularly promising endeavor when several external calibration points are available ,159,168,7.i.e., genotypic, population allele frequency, and gene genealogies; A central theme of this review has been to highlight the complementarity of three hierarchical levels of genetic information (a priori null hypotheses have been implicitly (or explicitly) considered more valuable than exploratory methods. It is interesting to contrast this with the related field of molecular phylogenetics. Here, a de novo hypothesis about evolutionary relationships among species or higher taxa is generated using what is essentially a data exploration procedure . The estimated phylogenetic tree is then used as a framework for making a posteriori inferences about biological phenomena as diverse as phenotypic trait evolution [We have advocated a duality between what we loosely refer to as exploratory and model-driven analyses . Over revolution , the modvolution , or the volution . These ea priori scenarios under consideration do not capture the true history, the limited search of the phylogeographic scenario space needs to be conditioned on external information. Furthermore, given the potential for idiosyncratic process or events to exert strong impacts on genetic structuring of extant species, the basic phylogeographic model estimated using exploratory analyses of genetic data should at least be included in the set of alternative scenarios to be tested. This raises the issue of potential circularity, because data that generated a hypothesis are subsequently re-used to test it. However, this issue could be avoided by borrowing approaches from ecological modeling. For example, species-habitat relationship models are usually constructed by first identifying the basic parameters  with a training dataset, and these models are then re-evaluated using a test dataset [Model-driven hypothesis-testing approaches are essential for discriminating statistically among alternative explanations for observed spatial-genetic patterns. However, to avoid a situation where the small set of alternative  dataset . An impo dataset . Traininfluctuate [im [im [migrate [versus historical processes in shaping spatial-genetic structure [In addition to the potential for exploratory and model-driven methods to be more fully integrated, simulated datasets can be used to determine false positive rates or discriminatory power—given the particular genetic dataset and scenario set at hand—for any analytical method. Several studies have demonstrated that simulations can be very effective in guiding the interpretation of results obtained from empirical DNA sequence datasets . Si [im [im ,131, mig[migrate , species[migrate ). Contin[migrate . Several[migrate  open thetructure , and in tructure . Similartructure ,188). Acet al. [In closing, it is worthwhile to briefly reflect on the diverse applications for which these insights into organismal evolutionary history have been used, and to speculate on the impact that next-generation sequencing might have on the field in coming years. First, applications of phylogeography in conservation biology have included the identification of ‘evolutionarily significant units’ and ‘management units’ ,17–19, det al. , and thede novo assembly and alignment of contigs. Ultimately, the traditional separation between marker development and population screening may become obsolete if read lengths and coverage are significantly improved over coming years, to the extent that high throughput genotyping of SNPs, microsatellites and gene genealogy-yielding DNA sequence makers can be conducted without first enriching for these genomic regions using PCR, gene capture on microarray chips, or other time consuming pre-sequencing steps. While the next-generation sequencing technology is bound to continue to improve rapidly and running costs should also decrease, its application to phylogeography will require flexible bioinformatics software tailored towards evolutionary biologists who work with non-model organisms, as well as new phylogeographic analyses that can accommodate unprecedented amounts of data. The field is developing very rapidly, and it has the potential to integrate the power of cutting-edge genomics technology with long-standing Earth science disciplines.With the emerging promise of next-generation sequencing, perhaps the most immediate benefits to phylogeography will be the rapid development of nuclear markers for non-model species ,195. Imp


A total of 129 adults and nestlings, representing 5–9 families annually from 2004–2007, were genotyped at up to ten microsatellite loci. The results of genetic assignment tests confirmed that monogamous parentage characterized the majority  of broods, and that most  nestlings were the offspring of their social parents. Two of 80 nestlings (2.5%) in two of 26 broods (7.7%) were sired by extrapair males and quasi-parasitism occurred in 3.8% (3/80) of nestlings and 7.7% (2/26) of broods. Hence, the levels of extrapair parentage were low, possibly because both genetic polygyny and polyandry are constrained by the high paternal effort required for parental care. The co-occurrence of low levels of extrapair paternity and quasi-parasitism are discussed in light of ecological and behavioural factors characterizing the species biology.Molecular techniques have revealed that avian mating systems are more diverse and complex than previously thought. We used microsatellite markers to determine genetic parentage, the prevalence of extrapair paternity and quasi-parasitism ( Extra-pair fertilizations, which can result from females engaging in copulations with extra-pair males , or from males copulating with extra-pair females that lay eggs in the male's nest  Picoides tridactylus) which is typically socially monogamous Genetic parentage studies have been conducted only in four out of more than 200 woodpecker species see . One of The main purpose of the present study was to estimate the prevalence of EPP and QP in the three-toed woodpecker. Since the males of this species allocate significantly more time to territory defence, cavity excavation and feeding of the young than females The methods were approved by the institution that coordinates ringing activity in Finland , based on the regulation by the Ministry of the Environment (No. 17/5713/2002).Picoides tridactylus) is a cavity nesting habitat specialist inhabiting coniferous taiga forests in the north, and high elevation alpine coniferous forest at the southern edge of the boreal zone 2 in an intensive studied area of 150 km2 in southern Finland The three-toed woodpecker  in southern Finland in an area of 150 km2. The study area has been described in more detail in The study was conducted in the Evo area  following the manufacturer's instructions. The sex of all samples was identified following a simple and universal method for molecular sexing of non-ratite birds using PCR amplification of the CHD1 gene as detailed in FIS; Deviations from Hardy-Weinberg equilibrium (HWE) for each locus and from linkage equilibrium between all pairs of loci were tested with Fisher's exact tests based on the approach of P>0.05; data not shown for the results of tests for linkage disequilibrium). The cumulative exclusion probabilities for the set of loci used in the parentage analysis were high: 0.9935 for the first parent and 0.9998 for the second parent . Thus, a sampling rate of 25% was used for the 2004 cohort and 40% for the 2005–2007cohorts. The proportion of successfully genotyped loci was on average 98.5% as estimated from the genetic data  For complete families in which both putative parents were sampled (N = 46 nestlings in 14 broods), we first assigned maternity with unknown paternity using the program CERVUS. Once a female was assigned, we then attempted to assign paternity to either the putative father or a potential breeding male from the population with known maternity; otherwise, paternity assignments were implemented with unknown maternity. We included the putative parents when possible and all potential females/males in the population as possible candidates for maternity/paternity. (ii) For families which there was sample available for only the putative father (N = 34 nestlings in 12 broods) we again attempted to assign paternity using CERVUS with unknown maternity. There were no cases where a DNA sample was available for just the putative mother.We assigned parentage under two scenarios of steps. (i.e. assigned to putative parents) assigned on the basis of matching eight or more loci are all positive and the majority of them are greater than three  in the 14 complete families. Moreover, the putative mothers were confirmed as the genetic mothers of offspring for the vast majority of cases  with >80% confidence  of offspring to the putative mother with a high degree of confidence  of genetic data. The results provide the first genetic evidence for the co-occurrence of polyandry and polygamy in the three-toed woodpecker. To this end, they add to our understanding of breeding behaviour of non-passerine birds, and to an increasing number of studies reporting occurrence of extra-pair fertilizations in natural bird populations see .This is the first genetic study showing that EPP and QP occur within a single woodpecker species. Overall, however, this species is predominantly genetically monogamous. Since mate switching within a breeding season has never been visually observed in this species see , EPPs anca. 11% in passerines, but more frequent than in some other genetically monogamous species such as the New Zealand saddlebacks Philesturnus carunculatus and robins Petroica australis where no EPP has ever been detected ca. 11% for e.g. passerines The frequency of extrapair paternity varies markedly within and between species see . Our poiPasser domesticus, Agelaius phoeniceus, P = 0.37) higher rate of EPP (3.6−5.5%) in a German population of three-toed woodpeckers n = 55 chicks, 95% CI: 41.79−63.21), the lower-resolution molecular tools (multi-locus DNA fingerprinting) and statistical methods  employed  frequency of extrapair maternity resulting from quasi-parasitism. This has rarely been reported in the related woodpeckers such as the lesser spotted woodpecker Actitis hypoleucos, 5.7%, Riparia riparia, 2.4%, Interestingly, the rate of QP for the females in three-toed woodpeckers (3.75%) is at the lower range of estimates reported for many shorebirds (e.g. Common sandpiper. P = 0.41) or proportion of broods containing one or more extrapair young .We detected no case of conspecific brood parasitism (CBP) due to egg dumping in this study while a single case of CBP, as a result of egg-dumping or QP, has been reported from a German population of three-toed woodpeckers In conclusion, our results of genetic analysis found the co-occurrence of low levels of EPP and QP in the three-toed woodpeckers. Although alternative explanations may exist for the observations in our study species see , our dat


Scomberomorus sierra) is a pelagic fish species distributed throughout the tropical and subtropical waters of the eastern Pacific. Seasonal spawning in different areas across the species range, as well as a limited dispersal, may result in a population genetic structure. Identification of genetically discrete units is important in the proper conservation of the fishery.Testing connectivity among populations of exploited marine fish is a main concern for the development of conservation strategies. Even though marine species are often considered to display low levels of population structure, barriers to dispersal found in the marine realm may restrict gene flow and cause genetic divergence of populations. The Pacific Sierra mackerel  and central areas (Oaxaca and Chiapas) of the species range, exhibiting slight genetic differences with respect to the samples collected in the southern region (Peru), together with a "chaotic genetic patchiness" pattern of differentiation and no evidence of isolation by distance. Historical demographic parameters supported the occurrence of past population expansions, whereas the divergence times between populations coincided with the occurrence of glacial maxima some 220 000 years ago.The population genetic structure detected for the Pacific Sierra mackerel is associated with a limited dispersal between the main abundance areas that are usually linked to the spawning sites of the species. Population expansions have coincided with glacial-interglacial episodes in the Pleistocene, but they may also be related to the increase in the SST and with upwelling areas in the EEP since the early Pleistocene. The definition of factors that determine genetic divergence in populations from marine ecosystems has become a subject of interest for evolutionary biology and conservation. The subdivision of populations in an apparently homogeneous environment has been found to be complex . EvolutiScomberomorus sierra) is an epipelagic fish species distributed in tropical and subtropical waters along the North, Central and South American Pacific coast. Mackerels are a diversified group consisting of 18 species distributed across the continental margins of every ocean [S. concolor confined to the Gulf of California in the Pacific, and S. sierra that is widely distributed along the eastern Pacific coast [Scomberomorus species have been found to have a short larval period (8.3-9.1 days) [The Pacific Sierra mackerel (ry ocean . Reporteic coast . Althougic coast  reported.1 days) . DispersPopulation genetic studies on pelagic species in the eastern Pacific have reported some extent of population subdivision. Examples include yellowfin tuna , relatedThe Pacific Sierra mackerel is a major fishery in Mexico with catch statistics estimated at 10,000 metric tons in 2006, and with an increasing trend in fishing effort . No manaIn order to test the hypothesis on the genetic homogeneity of Pacific mackerel, the genetic diversity of samples across the range of species was analysed using a 648 bp segment of the hypervariable mtDNA- control region. Sequence analyses consisted in applying a coalescence approach to estimate historical demographic parameters and to determine the occurrence of significant population fluctuations in the past. Pairwise migration rates and divergence times among populations were estimated through a coalescent-based approach, in order to distinguish between recent isolation and low levels of contemporary migration. Genetic differences among samples from the three different regions were recorded, in addition to the occurrence of population expansions related to the climate in the past and/or to ecological changes in the eastern equatorial Pacific.S. sierra revealed very high levels of variation. Of 246 individuals studied, 153 variable sites consisted of 59 transitions and 10 transversions that produced 239 haplotypes, 59 of which consisted of singleton sites. Ninety-four parsimonious informative sites were identified, with nucleotide differences averaging 12.695. Overall, haplotype (h) and nucleotide diversity (π) for all sequences were 0.997 (± 0.0004) and 0.01959 (± 0.00857) respectively  (Table ST -estimates.The pairwise-sample estimates of Φ3) Table . HoweverST revealed highly significant genetic differences mainly for the comparisons involving the Sinaloa samples (SIN and SIN04), the Oaxaca sample, and the Peru sample , SIN04-OAX  and SIN04-PE  remained significant after correcting for multiple testing (α = 0.05/15 = 0.003). Therefore, a clear divergence pattern was identified for the comparison between the locations that were most separated (SIN04 and PE), but especially between the northernmost  and intermediate locations . In contrast, no differences were detected between the southernmost sample (PE) and the intermediate samples, after correcting for multiple testing. As a result, when the AMOVA was applied to the samples grouped in the northern (SIN and MICH), central (OAX and CH) and southern (PE) regions, based on the differences found from pairwise sample ΦST-estimates and proximity of sample locations, the analysis indicated there were no significant differences  among these groups. However, the differences among populations within groups were confirmed .Estimates of pairwise sample Φle Table , althougST = 0.094; P < 0.001) and CH  samples, remained significant after correcting for multiple testing.Additional genetic differences between some comparisons involving the SIN, OAX and PE locations, and the MICH and CH samples, were also observed. However, only the differences resulting from the comparison between the highly divergent sample SIN04, and the MICH . No significant correlations were found between the distance and the Nm estimates  when the SIN04 sample was omitted. Neither was there any correlation when the SIN sample was omitted, and the correlation was similar . This pattern was clearly influenced by the slight differences observed between the SIN and the Peru samples, based on the pairwise ΦST estimates.Considering the observed differences between the SIN and SIN04 samples, the isolation by distance model was verified with the Mantel test, omitting one of the Sinaloa samples and using the number of migrants by generation (SSD) between the observed and the expected mismatch distributions were observed besides very high and significant Fu estimations  and after (N1) the expansion. In contrast, the expansion was slow for the SIN, OAX and CH populations, and may have occurred from a relatively high number of females (N0 = 18 000-55 000).The distribution of mismatches was unimodal for all samples Figure . Similars Figure . Consides Figure . The lowN = 16 500-69 400) with a very low rate of exchange with the original population (Table N = 7.5) with a very high migration rate  obtained with the MDIV, may be used to distinguish short divergence times with low gene flow from long divergence times with moderate gene flow, as an explanation for population divergence [T for all pairwise-sample comparisons (Table The extent of the difference between the estimates of the time expected since the most common ancestor (TMRCA) and the divergence time , central (OAX) and southern (Peru) groups of samples (Table T and TMRCA (41 700 and 11 300 years respectively) in the MICH - OAX comparison, suggesting a re-establishment of gene flow after isolation. In general, populations from the northern group  and the southernmost sample (PE) diverged early from the central populations (OAX and CH).Larger differences were observed between estimates of es Table . HoweverThe coincidence in the divergence time and the time since the occurrence of the population expansion must be highlighted. The separation of populations, except for SIN04 which expanded more recently, took place after the demographic or spatial expansion event, suggesting a possible relation between divergence and the drastic reduction caused by glacial events that occurred during the Pleistocene.One may also obtain estimates of the ancestral population size (θ) and migration (M) for all pairwise-sample comparisons from the MDIV. Estimates of θ were high (37.7-131.4), indicating that populations originated from relatively large ancestral population sizes. For the various θ estimates between population pairs, those involving the Peru sample presented the largest values, indicating that this was the ancestral population Table .Migration estimates between populations from the northern group  were low in relation to the central samples (1.7-9.9) and the southern sample (2.1-7.7), and were consistent with a model of divergence that resulted from restrained gene flow. Migration estimates between the central and southern populations were higher (7.5-14.8), and this, together with the similarity in divergence times and the modest levels of differences among both groups observed, suggests there was a secondary contact between populations after divergence.S. sierra could be explained by the very high estimates of ancestral effective population size in conjunction with recent expansion events.The analyses of a 648 bp fragment of mtDNA-Dloop in the present study revealed high levels of genetic diversity in the analyzed populations. The highly significant genetic diversity in terms of haplotypes for The differences observed among some Sinaloa samples of different years, and the lack of temporal sampling in Peru, made it impossible to be fully conclusive about the temporal genetic stability of the Pacific mackerel population, regardless of the homogeneity between the temporal collections of the Oaxaca and Chiapas populations, as well as for the 2003 and 2008 Sinaloa samples. The 2004 Sinaloa samples exhibited marked differences with respect to the 2003 and the 2008 Sinaloa samples, and to the rest of the samples. They presented notoriously different molecular diversity indices Table  with a lDespite the evidence recorded for the age differences, the evidence observed for the "chaotic genetic patchiness" that resulted from the non-random sampling of cohorts of Pacific Sierra mackerel, require further examination and should be considered for future studies.Despite the "chaotic genetic patchiness observed", the pairwise-sample divergence estimates indicated the existence of clear differences between the samples from the northern (SIN), central (OAX) and southern (PE) areas of the eastern Pacific, and especially between two of the areas of greatest abundance  that are also regarded as spawning areas in the Mexican Pacific ,9. RegarConsidering that Pacific mackerel exhibit migratory behaviour, the observed population structure could be related to specific spatial or temporal spawning regimes; spawning off Mexico takes place in July, August and September, and in lower latitudes it may occur from December through April .Adult mackerel have a limited capacity to migrate as a result of their body density, short pectoral fins and body length, in comparison with the large pelagic scombrids . BesidesScomberomorus species is strongly dependent on the availability of food [Several important upwelling areas in the Eastern Pacific include regions off Baja California, Sinaloa, Oaxaca, Panama and Peru. Given that larval survival of  of food , upwelliThe northern and central populations of our study showed well differentiated units. However, there were differences recorded between the central and the Peru populations that were not consistent after the Bonferroni correction. These populations ought to be re-evaluated since they seem to reflect independent evolutionary histories that should not be underestimated. Similarly, as gene flow estimates from MDIV were substantially lower between the northern and central populations than between the central and southern populations (Peru), gene flow could be reestablished as a result of a secondary contact favored by an increase in the number of high productivity areas in the eastern equatorial Pacific, and by the intense southern currents during the last glacial maxima  that migSSD) were observed. The estimations of the divergence time and the time of occurrence of population fluctuations are controversial since these are based on the accuracy of the molecular clock used. Generalized mutational rates estimated for other similar species were used, in the absence of available data for calibration. Thus, these estimates may be inaccurate when determining the exact time of occurrence of events, and the divergence time and/or time since population expansions occurred should be considered rough approximations. However, the pattern for the occurrence of these events will remain, even though departures from the mutational rate that was used to estimate divergence times might result in either deeper or shallower estimations.The mismatch analyses of the molecular variation were consistent in supporting the occurrence of expansions of Pacific Sierra mackerel populations after drastic reductions since unimodal mismatch distributions, as well as non significant deviations for the sum of square deviations  in the equatorial Pacific . The timHowever, because the time for the occurrence of expansions is a rough estimation, the coincidence of an expansion and an interglacial event is also approximate. Alternative explanations for population expansions of species in the eastern equatorial Pacific could be suggested. Evidence based on paleoceanographic studies has documented a trend for an increase in the SST and upwelling areas in the EEP since the early Pleistocene ,32. ThusS. sierra has been established in the Scomberomorus regalis group that formed in the eastern Atlantic and dispersed into the eastern Pacific through the Trans-isthmian channel before the closure of the Panamian isthmus [S. sierra population could then be located in Central America, and the northern and southern populations probably derived from this ancestral population as the result of a dispersal process following the colonisation of new areas. Our evidence reveals that population expansions associated with historical events, whether climatic  and/or ecological during interglacial periods or glaciations, have had a prominent role in the evolutionary history and in the determination of the present molecular architecture of the Pacific Sierra mackerel populations.The mismatch analyses were also consistent with the continent island model of spatial expansion, which coincides with the development of favourable conditions in the EEP. Small peripheral isolates of populations might possibly have broken away from the ancestral population to establish divergent populations in new areas. Ancestral populations are characterised by their larger sizes . The pai isthmus   fishing boats operating on a local scale in the eastern Pacific , 50 mM KCl, 1.5 mM MgCl2, 0.2 mM of each dNTP, 0.1 mM of each primer and 2.5 units of platinum Taq DNA polymerase . PCR amplifications consisted of 35 cycles of 1 min at 95°C for denaturalisation, 1 min at 59°C for annealing, and a final extension at 65°C for 3 min. PCR products were purified with either a QIAquick purification kit (QIAgen No. Cat. 28104) or the ChargeSwitch PCR Clean-Up kit . Purified products were sequenced in the forward direction on an ABI 3730xl automated sequencer applying the dye-termination method (Applied Biosystems). Pairwise alignment was made between each individual sequence and the most frequent haplotypes for every location in order to solve ambiguities and/or confirm nucleotide assignments.The complete sequence of the mitochondrial region control of mackerel was amplified (900 bp) by using the primers Pro5-M13 (5'-TAATCCTGCCGCAATTATCC-3') and Teleost (5'-AGGCCTTCCAGGTTAGGTGT-3') reported in Carlsson et al. . SequencS. concolor sequences, and a bootstrap resampling [FST's expressed in terms of the coalescence times [N diverged τ generations ago from a population of identical size without exchanging any migrants. A UPGMA tree was generated using the coalescence times among locations using Mega 4.0.Multiple alignment was performed with ClustalX ver. 1.8  as well sampling  based once times  were estF-statistics ΦST , for hierarchical FST [ST analyses (20100 permutations) on mtDNA sequence data. The ΦST analyses were performed using a matrix of Tamura-Nei distances, with a gamma shape parameter of 0.4.The Arlequin software package was used to obtain the unbiased estimator of Wrights ical FST , and pai0, and θ1 were obtained from nucleotide mismatch distributions with the Arlequin software in order to examine the impact of demographic fluctuations related to past glaciations on the molecular architecture of Pacific mackerel populations. Considering the significant ΦST estimates within populations, samples were analysed separately. As regards demographic expansion, the parameter Tau (τ) is an estimator of the mutational time since the occurrence of population expansion, that can be used to obtain estimates of the actual time (T): this is true as population expansion by T = 2μ/τ, where μ is the mutation rate estimated at 3.6%/Ma, for the mtDNA control region of "germinate species" of snook of the genus Centropomus [N0, assumed to grow to size N1 under Roger's sudden expansion model [0 = 2μN and θ1 = 2μN where μ is the mutational rate considering a generation time of 2 years for Pacific mackerel [0 = θ1, assuming that N = N0 and M = 2Nm where m is the rate at which the sampled deme would exchange migrants with a single population of infinite size; we used the same methods to estimate the demographic expansion parameters as implemented in Arlequin. The estimate of the effective number of females needed to explain the actual mismatch distribution under the spatial expansion model is given by N = θ/2μ. Finally, Arlequin was also used to obtain the sum of square deviations (SSD) between the observed and the expected mismatch as a statistical test for the fit to a unimodal mismatch distribution and to calculate estimates of Fu's neutrality test (FS) and explore whether mutations should be considered neutral or influenced by selection where significantly negative values are also indicative of population expansion [The demographic parameters τ, θtropomus . Estimaton model , were obmackerel . Mismatcmackerel ,16. Therxpansion .ST/2FST (assuming that M = 2 Nm for haploid data). This was done to evaluate the correlation of the distance between localities measured as a geographic separation in nautical miles (nm), and the M values obtained by means of the Mantel test [The Isolation by Distance model (IBD)  was testtel test  as impleNefμ) where Nef is the effective population size and μ is the mutation rate, M (M = 2Nef m) where m is the migration rate, and T (T = t/Nef) where t is the divergence time. These parameters were obtained using a finite sites model (HKY). The length of the Markov chains consisted of 5,000,000 cycles with a burn-in time of 500,000. Likelihood values for θ, M and T were those displaying the highest posterior probabilities.The migration rate and the divergence time from pairwise-sample comparisons were obtained with the Markov chain Monte Carlo approach as implemented in the MDIV software to test the "isolation with migration" model , in ordeMDL participated in sample collection, carried out the laboratory work, designed primers, worked in the sequence optimisation and drafted the manuscript. MUA and PDJ designed the study, supervised the genetic studies, analysed data and improved the manuscript. All authors read and approved the final manuscript.


Local adaptation is of fundamental importance in evolutionary, population, conservation, and global-change biology. The generality of local adaptation in plants and whether and how it is influenced by specific species, population and habitat characteristics have, however, not been quantitatively reviewed. Therefore, we examined published data on the outcomes of reciprocal transplant experiments using two approaches. We conducted a meta-analysis to compare the performance of local and foreign plants at all transplant sites. In addition, we analysed frequencies of pairs of plant origin to examine whether local plants perform better than foreign plants at both compared transplant sites. In both approaches, we also examined the effects of population size, and of the habitat and species characteristics that are predicted to affect local adaptation. We show that, overall, local plants performed significantly better than foreign plants at their site of origin: this was found to be the case in 71.0% of the studied sites. However, local plants performed better than foreign plants at both sites of a pair-wise comparison  only in 45.3% of the 1032 compared population pairs. Furthermore, we found local adaptation much more common for large plant populations  than for small populations  for which local adaptation was very rare. The degree of local adaptation was independent of plant life history, spatial or temporal habitat heterogeneity, and geographic scale. Our results suggest that local adaptation is less common in plant populations than generally assumed. Moreover, our findings reinforce the fundamental importance of population size for evolutionary theory. The clear role of population size for the ability to evolve local adaptation raises considerable doubt on the ability of small plant populations to cope with changing environments. Local adaptation is of fundamental importance in evolutionary, population, conservation, and global-change biology. However, while it is commonly assumed that most plant populations are locally adapted the generality of local adaptation in plants and whether and how it is influenced by specific species, population and habitat characteristics is not clear. Currently, many plant populations are small and isolated and at the same time often facing rapidly changing environments to which they need to adapt to. The ability to adapt may, however, be compromised in small populations because of reduced genetic diversity In addition to reduced genetic variation and genetic drift, local adaptation can also be constrained by variation in natural selection and gene flow Plant traits such as mating system, longevity, and clonality have been suggested to affect the evolution of local adaptation mainly due to their effects on the level and distribution of genetic variation. Short-lived and self-compatible species tend to be more strongly differentiated at a smaller scale than long-lived and outcrossing species We conducted the first quantitative review on local adaptation in plants using 35 published studies on 32 plant species reporting 1032 pairwise comparisons of the performance of plants from local and foreign populations See . We usedHowever, in the strict sense . Moreover, among all individual comparisons of local and foreign plant origins at one site local plants performed better than foreign plants in 71.0% of the cases.Our meta-analysis revealed that, overall, local plants clearly outperformed foreign plants . Finally, maladaptation, indicated as better performance of foreign plants at both compared sites (“NEG-NEG”), occurred in only 3.3% of the cases .The reaction norms for fitness crossed . In addition, the frequencies of crossing and non-crossing reaction norms for fitness indicate that in large populations divergent selection and local adaptation sensu Kawecki and Ebert 2χ = 25. 5, N = 212, P = 0.0001). These results suggest that small populations lack the potential to adapt to local environments.Local plants performed better than foreign plants in a given environment only in large plant populations as indicated by a significant positive overall effect size, whereas in small populations there was no significant difference in plant performance between plant origins , mating system , or clonality  , on whether the sites had been selected randomly or because of specific habitat differences , or on whether the habitats were considered spatially heterogeneous or homogeneous by the authors  . Also, none of these descriptors of life-history or habitat characteristics was related to how frequently the reaction norms crossed.Local adaptation was independent of the plant or habitat characteristics considered in our study . In our = 0.491) . Moreove= 0.545) . We founN = 429, Qb = 2.241, df = 1, P = 0.134, N = 26, Qb = 0.133, df = 1, P = 0.715). However, variation in the strength of adaptation was greater at smaller than at larger geographic scale . This suggests that, although smaller-scale environmental variation was on average large enough to lead to local adaptation, this was less consistently so than at larger scales.The strength or direction of the effect size were not significantly associated with geographic distance between the compared sites of plant origin  database using combinations of the key word “plant” with “local adaptation”, “reciprocal transplant*”, “adaptation” and “adaptive evolution”. The search resulted in a list of 211 articles. Moreover, we screened the reference lists of these articles to identify further potentially relevant articles. The criterion for including published studies in our meta-analysis was that they reported mean values, variance and sample sizes of performance of local plants and foreign plants at one or several sites. Because early studies on local adaptation dThe reported fitness-relevant measures included measures of plant reproductive success , plant size , survival rates, and germination rates. We extracted altogether 1032 pairwise comparisons of the performance of local plants and foreign plants at a given site. For each performance measure we calculated the effect size, Hedge's To test for the importance of different sources of variation we classified our data according to characteristics of populations, habitats, studies, and plant life-history. This data was to a large extent obtained directly from the authors. As population characteristic we tested the effect of population size. Information on population size had been provided only in very few articles. Thus, we inquired this information directly from the authors. Since population size had not been considered explicitly in most of the studies, the authors could not provide count data on population sizes but were able to state with certainty whether populations in their experiments were smaller or larger than 1000 flowering individuals. Although a finer classification would have been desirable, we consider this coarse classification nevertheless appropriate, because genetic problems in terms of reduced genetic variation and increased inbreeding of small population have been predicted for population sizes lower than 100–1000 We avoided non-independent pair-wise comparisons, which would correspond to pseudo-replication, by pooling data by species and by measure of plant performance  when testing for effects of population size, life-history traits, type of study and habitat heterogeneity and homogeneity on the strength of the effect. Data was pooled by pair-wise site comparison to test for the effect of geographic distance on the strength of the effect (see below). When effect sizes for several measures of plant performance were obtained per study we pooled the data by calculating mean effect sizes and their pooled variances. Of course, it would be best to analyse whole life-cycle estimates of fitness rather than the single or few components provided by the published studies. At least, to some degree the pooling of data by study and species takes aspects of total fitness into account, because fitness components with opposing trends cancel out each other in pooling.bQ. To account for the problem caused by multiple statistical tests we used the Bonferroni adjustment to modify the significance criterion  To test whether the effect sizes differed depending on the different plant, habitat or study characteristics we examined between-group heterogeneity using the chi-square test statistic, To examine the association of geographic distance and effect size we used random-effects continuous-model meta-analysis We used Meta Win 2.0 We analysed the frequencies of cases where the measures of plant performance were higher for local plants at both sites Click here for additional data file.


Identifying causal relationships in phylogeographic and landscape genetic investigations is notoriously difficult, but can be facilitated by use of multispecies comparisons.Strix occidentalis caurina; red tree vole: Arborimus longicaudus; southern torrent salamander: Rhyacotriton variegatus; and western white pine: Pinus monticola). Visualizations suggested that, despite occupying the same geographical region and habitats, species responded differently to prevailing historical processes. S. o. caurina and P. monticola demonstrated directional patterns of spatial genetic structure where genetic distances and diversity were greater in southern versus northern locales. A. longicaudus and R. variegatus displayed opposite patterns where genetic distances were greater in northern versus southern regions. Statistical analyses of directional patterns subsequently confirmed observations from visualizations. Based upon regional climatological history, we hypothesized that observed latitudinal patterns may have been produced by range expansions. Subsequent computer simulations confirmed that directional patterns can be produced by expansion events.We used data visualizations to identify common spatial patterns within single lineages of four taxa inhabiting Pacific Northwest forests (northern spotted owl: We discuss phylogeographic hypotheses regarding historical processes that may have produced observed patterns. Inferential methods used here may become increasingly powerful as detailed simulations of organisms and historical scenarios become plausible. We further suggest that inter-specific comparisons of historical patterns take place prior to drawing conclusions regarding effects of current anthropogenic change within landscapes. Landscape genetic and phylogeographic investigations generally rely on a three-step process: data generation, pattern identification, and pattern explanation. Though data generation is generally a straightforward process, the latter two stages can be difficult in many scenarios. Most genetic studies of natural populations produce large, complex data sets . Challenges subsequently ensue when attempting to identify inherent quantitative signals  that provide insights regarding key historical and recent factors that have shaped contemporary genetic structure. Once identified, the set of putative causal mechanisms that may explain said patterns must likewise be discerned. Identification of causal mechanisms becomes notoriously difficult in most cases due to the non-experimental (non-manipulative) nature of investigations and the fact that only a single realization of the evolutionary process can be observed for a given species in a specific geographical region.Despite the challenges associated with identification of causal factors, researchers can nonetheless choose among a wide array of computer programs and analytical procedures that facilitate the inferential process When analyses identify patterns within data sets, causal relationships can nonetheless still be difficult to identify due to the fact that different processes can plausibly produce similar qualitative and quantitative signals In this study, we used a four-stage process to help identify and explain shared patterns of spatial genetic structure in four taxa occupying the same habitat in space and time. First, we used a recently-described visualization technique , a tree vole (Arborimus longicaudus  =  Phenacomys longicaudus), a salamander (Rhyacotriton variegatus), and a tree (Pinus monticola).The goal of this investigation was to explore patterns of spatial genetic structure within single phylogenetic lineages of four different species. Four published data sets were used that met three important criteria: 1) they included reasonable levels of sampling within our region of interest, 2) longitude/latitude or zone 10 UTM coordinates of each sampling location were available, and 3) distinct, single phylogenetic-lineage data sets could be defined for each taxon within the region. Studies meeting these criteria included analyses of a bird . All data for “northern” haplotypes were excluded in our new analyses.Analyses of ng areas . For conp) between individuals for S. o. caurina, A. longicaudus, and R. variegates. For P. monticola, Genetic distances (D) listed in Table 5 of p/(1 – p) or D/(1 – D) and the natural logarithm of geographic distance  in analyses of S. o. caurina, A. longicaudus, and R. variegatus. Nei's genetic distances (D) listed in Table 5 of P. monticola data set. Given that there was variation in the geographical distances between sampling locations (p/(1 – p) or D/(1 – D)] versus the natural logarithm of geographical distances in analyses. Theoretically, this approach accounted for correlations between genetic and geographical distances that may be present and ensured that large interpolation peaks were not resolved solely due to the fact that one or a few sampling areas were geographically isolated Data were subsequently analyzed using the “Genetic Landscape Shape” procedure ocations , we usedOur Genetic Landscape Shape visualizations suggested distinct directional patterns of spatial genetic structure along a North/South axis see . To form−4; δ = 10.0, µ = 10−4; δ = 1.5, µ = 2.5*10−5; δ = 10.0, µ = 2.5*10−5. Note that through manipulation of the above expressions, our dispersal parameters nominally correspond to values of Wright's neighborhood sizes 2) of 18 and 800 for δ  = 1.5 and δ = 10, respectively. Twenty-five simulation replicates of each parameter combination were performed. In the initial phase, simulations were allowed to run for 25,000 generations (µ = 10−4) or 85,000 generations (µ = 2.5*10−5) to permit quasi-equilibrium patterns of spatial genetic structure and diversity to evolve. In the case of a population of 10,000 individuals and µ = 10−4, population genetic theory predicts an expected number of alleles (a) and heterozygosity (H) to be 34.59 and 0.8, respectively, at equilibrium . We followed our normal simulation procedure as described for phase 1, with the exception that empty landscape cells became inhabited during range expansion only when both randomly chosen parental landscape cells were already occupied. For simplicity, we assumed that a newly-occupied cell was always inhabited in all remaining generations. Consequently, over the course of successive simulation generations, the remainder of the landscape was filled at a rate associated with values of δ specified during simulation runs. At two time points during the range expansion event, data sets comprised of a single haplotype from 200 randomly selected individuals were exported for analysis. Ultimately, we examined time points where 1) the lower two-thirds of the landscape (a 100×200 region) was inhabited, and 2) the landscape was completely inhabited. On average, simulations using δ  = 1.5 required 424.21 generations (S.D. = 6.81) and 824.53 generations (S.D. = 12.53) to reach the two-thirds full and completely inhabited states, respectively. Likewise, simulations using δ  = 10 required 62.63 generations (S.D. = 1.07) and 110.78 generations (S.D. = 1.44) to reach the two-thirds full and completely inhabited states, respectively.Once data were generated, AIS was used to analyze simulated data sets using the Genetic Landscape Shape procedure. Resulting surface plots revealed directional patterns of spatial genetic structure where inter-individual genetic distances were smaller in the expansion zone relative to the southern refugial region see . ConsequA. longicaudus and R. variegatus . Likewise, spatial autocorrelations illustrated that pairwise genetic distances were significantly smaller than average over shorter distances and were significantly larger than random expectations as geographic distances increased. In contrast, both analyses suggested that no  spatial genetic structure was present for S. o. caurina and P. monticola  were present in the south relative to the north. In contrast, A. longicaudus and R. variegatus indicated opposite trends. Our post-hoc analyses illustrated significant associations between UTM northing and genetic distances in all four species . In contrast, correlations for A. longicaudus and R. variegatus were positive .Genetic Landscape Shape visualizations suggested two primary patterns of directional spatial genetic structure and diversity . In S. o species . Consist−4) relative to low (µ = 2.5*10−5). Finally, in three of the four parameter combinations, more significant directional patterns were observed after the landscape became completely inhabited relative to the two-thirds inhabited case.Simulated range expansions revealed directional patterns whereby inter-individual genetic distances were reduced in the direction of the expansion event , Table 1Most population genetic and phylogeographic investigations produce large, complex data sets. For example, the (n*(n-1))/2 genetic distances that can be calculated from all pairwise combinations of n samples or sampling locations may generate extremely unwieldy matrices that are difficult to interpret, especially when there is an interest in identifying patterns based on joint analyses of spatial and genetic information. Historically, such data sets have been analyzed using Mantel tests A. longicaudus and R. variegatus showed strong spatial genetic structure, whereas P. monticola and S. o. caurina revealed no (or little) evidence of such patterns. When visualizations were implemented, an alternate view of spatial genetic structure within these species emerged. Interestingly, the overt patterns observed were of a type not generally considered in most investigations: shared directional patterns of genetic structure existed along a South to North transect . In a regional context, this pattern reflected greater genetic diversity in southern relative to northern locales. In contrast, genetic distances were larger in the North relative to the South for A. longicaudus and R. variegatus. Although visualizations are qualitative descriptions of spatial genetic structure patterns, additional post-hoc statistical analyses confirmed the graphical depictions generated  likely avoided cold temperature regimes during glacial periods by actively moving to more suitable southern refugia. As suggested for other avian species S. o. caurina likely expanded its range northward following glacial retreat once environmental conditions became favorable. Similarly, we suggest that P. monticola, while clearly not capable of avoiding unfavorable climatic conditions, likely was instead extirpated from northern regions of its range during glacial periods. Again, following glacial retreat, the species may have been reestablished in northern areas through wind and animal-mediated seed dispersal. P. monticola seeds generally disperse <120 m from parental trees, but have been reported to travel upwards of 800 m in some cases Pinus species, mammal and bird species actively feed upon, cache, and disperse P. monticola seeds S. o. caurina and P. monticola, we suggest that this process may have disrupted a previously-existing isolation-by-distance process and lead to the inability of these two procedures to detect underlying patterns.Though our empirical data reflected the same general geographical area, our analyses indicated that different species responded differently to the same historical events and processes that occurred.  results , as bothR. variegatus and A. longicaudus displayed patterns opposite to those identified in S. o. caurina, P. monticola, and the strict northward range expansion scenario encapsulated in simulations  is protected under the U.S. Endangered Species Act as a threatened subspecies A. longicaudus is currently the only mammal protected by the “Survey and Manage” portion of the Northwest Forest Plan due to its importance as a food source for S. o. occidentalisR. variegatus was previously petitioned, but deemed not warranted, for listing under the U.S. Endangered Species Act P. monticola and A. longicaudus, respectively (estimated from r2 values obtained from squaring correlation coefficients; Three of the four species investigated are of conservation importance in the Pacific Northwest United States. The northern spotted owl (Debates over the most appropriate tools for genetic studies of natural populations are not likely to end in the near future, particularly when undertaking the challenge of comparing multiple species. We therefore agree with points raised by others regarding use of different integrative analytical tools in a heuristic manner when making inferences