Leiopelma hochstetteri (Hochstetter’s frog) is a small, semi-aquatic frog with numerous, fragmented populations scattered across New Zealand’s North Island. We characterized a major histocompatibility complex (MHC) class II B gene (DAB) in L. hochstetteri from a spleen transcriptome, and then compared its diversity to neutral microsatellite markers to assess the adaptive genetic diversity of five populations .The New Zealand native frogs, family Leiopelmatidae, are among the most archaic in the world. L. hochstetteri possessed very high MHC diversity, with 74 DAB alleles characterized. Extremely high differentiation was observed at the DAB locus, with only two alleles shared between populations, a pattern that was not reflected in the microsatellites. Clustering analysis on putative peptide binding residues of the DAB alleles indicated four functional supertypes, all of which were represented in 4 of 5 populations, albeit at different frequencies. Otawa was an exception to these observations, with only two DAB alleles present.This study of MHC diversity highlights extreme population differentiation at this functional locus. Supertype differentiation was high among populations, suggesting spatial and/or temporal variation in selection pressures. Low DAB diversity in Otawa may limit this population’s adaptive potential to future pathogenic challenges.The online version of this article (doi:10.1186/s12862-015-0342-0) contains supplementary material, which is available to authorized users. Leiopelma [Leiopelma hochstetteri is a small, semi-aquatic species [L. hochstetteri was historically more widely distributed throughout the North Island and in the northern half of the South Island [The frog family Leiopelmatidae contains four species of the genus eiopelma . These feiopelma  and only species . It is t species ,5. Sub-fh Island . The inth Island .Figure 1L. hochstetteri is estimated at greater than 100,000 mature individuals, but a population decline of at least 10% in total population or area of occupancy over the next three generations has been predicted [, L. hochstetteri has been classified as “Vulnerable” on the IUCN Red List of Threatened Species [L. hochstetteri faces many threats, most significantly the alteration of rocky stream ecosystems by land management processes such as logging, farming or by feral stock, as well as predation by introduced mammals [Populations show significant genetic and cytogenetic distinctiveness -9, with redicted . As such Species  and as ‘ Species . In its  mammals -14.L. hochstetteri, combined with these threatening processes, has significant conservation implications [High population structuring in ications ,10. Prevications . The claications . MHC claications . These dications -20.In natural populations, diversity of functional genomic regions, such as the MHC, is affected by both selective and neutral evolutionary forces. By contrast, non-functional genetic regions will reflect primarily only neutral forces, such as genetic drift and gene flow. Comparing functional and neutral diversity allow researchers to infer the relative influence of selection on this adaptive gene region -25. For L. hochstetteri spleen transcriptome to identify a MHC class II B gene (DAB). We then characterized diversity in the β1 domain (exon 2) and compared the results to diversity from nine microsatellite markers across five ESUs. Our results showed high MHC polymorphism with extremely high differentiation between studied ESUs, a pattern that was not reflected in the microsatellites.In this study, we generated a L. hochstetteri individual . This study used samples from six sites, representing five of the 13 ESUs described by Fouquet et al. : the BryA total of 168 individuals . These data allowed us to sequence a 745-bp fragment of MHC IIB  incorporating partial exon 2 through to the 3’ UTR, as predicted from alignment with X. laevis MHC class II beta sequence . Full dX. laevis MHC class II beta sequence. We carried out two PCRs per individual and cloned these using the pGEM-T Easy Vector System II (Promega), following the manufacturer’s recommendations. PCRs were carried out at Landcare Research Auckland laboratories  prior to export of the synthetic DNA products to the Australian Wildlife Genomics Group laboratories at the University of Sydney  where all further protocols were carried out. Full PCR and cloning methods are provided in Additional file To assay population levels of diversity, PCR primers (LehoIIBUpper: 5΄− GCGAAGTCTCAGTGTT −3΄ and LehoIIBLower: 5΄− CTTGTCTACAGTGTAAGGTT −3΄) were designed using Oligo6 , targeting a 249–252 bp fragment within exon 2 of the MHC DAB gene (full length of exon 2 predicted to be 282 bp). These primers were designed to anneal to the most evolutionarily conserved regions, as predicted from multiple sequence alignments with anuran class II beta genes, while including the maximal number of putative peptide binding sites predicted from Cloned DAB sequences were checked for quality, trimmed, and aligned using ClustalW, all within BioEdit v7 . For eacHd; number of polymorphic sites, S; nucleotide diversity, π; average number of nucleotide differences, k) were calculated using DnaSP v4.10 [HO, and expected heterozygosity, HE). Arlequin v3.5 [DAB sequence polymorphism statistics . We used the HyPhy package [L. hochstetteri DAB alleles was detected using single breakpoint (SBP) analysis using small sample AIC (AICC) [A global test for positive selection on MHC was carried out in MEGA using a codon-based Z test for selection across the DAB alleles (test statistic:  package  implemen package  for mode package  was usedC (AICC) . RecombiC (AICC) , random-C (AICC) , and mixC (AICC) , which uk-means clustering algorithm based on Bayesian Information Criterion (BIC); we used a ∆BIC ≤2 to identify optimal numbers of clusters. DAPC was then performed on retained principal components to assign LehoDAB alleles to a supertype. Population differentiation based on supertypes was estimated using SMOGD and isolation by distance analyzed by Mantel testing, as described above.Amino acid sites under positive selection as identified above were used for cluster analysis to define DAB supertypes, following Doytchinova and Flower . All othTwo microsatellite markers (Lhoc10 and Lhoc26) showed variable amplification success across sampled ESUs and were therefore removed from the analysis. All individuals were successfully genotyped at the remaining nine microsatellite markers. A total of 34 alleles were observed . The microsatellite markers showed an average observed heterozygosity of 0.164 across the ESUs (ranging from 0.073-0.327). We observed eight occurrences where microsatellite allele frequencies deviated from Hardy-Weinberg Equilibrium (HWE) but these were not statistically significant after Holm-Bonferroni correction for multiple comparisons . DAB alleles were between 216–219 nucleotides, encoding a 72–73 amino acid product that varied in length on account of a single amino acid indel at position 69. These 74 alleles contained 76 polymorphic nucleotide sites with an average of 19.6 nucleotide differences (k), gene diversity (Hd) of 0.958, and nucleotide diversity (π) of 0.0906 (Table dN-dS) of 2.08 . Lower DAB diversity was observed in the Southern Coromandel and Otawa ESUs and lower microsatellite diversity was observed in Northern Coromandel, Southern Coromandel and Otawa .At the MHC class II-DAB locus, 74 sequence variants  were characterized from 121 P-values <0.001) was observed at the DAB locus for all ESUs, except Otawa . We could not rule out the presence of null alleles in these four ESUs. If null alleles are present, they may indicate a single common allele or cluster of alleles that were not amplified by our DAB primers, despite employing a low annealing temperature during PCR (53°C) and designing primers for conserved regions of the DAB exon 2. If the homozygote excess is due to null alleles, there may be more DAB alleles present in Brynderwyn, Northern Coromandel, Central Coromandel, and Southern Coromandel, in excess to those characterized here. The Otawa population appeared to be in Hardy-Weinberg equilibrium.Statistically significant homozygous excess  , while the four ESUs from the Coromandel Peninsula displayed the lowest differentiation from one-another (DEST 0.039-0.098). For MHC, ESUs were highly differentiated at the DAB locus, with only three alleles (4.1%) observed in more than one ESU . Correspondingly, population differentiation measures for the DAB locus were very high .Genetic differentiation estimated across microsatellite markers ranged from LehoDAB alleles present  and 0.012-0.397 (G’ST)  occurring at a frequency of 0.947, and the alternate allele (LehoDAB*73) only present in two heterozygotes . This could reflect strong directional selection acting in this ESU drawing the LehoDAB*74 allele close to fixation, or a depletion in DAB diversity due to population decline or bottleneck. A meta-analysis of the relative roles of genetic drift and balancing selection on MHC variation revealed that there is may be greater loss of MHC than neutral diversity during population bottlenecks [We observed a correlation between allelic richness of microsatellites and DAB, implying the contribution of genetic drift in shaping MHC variation in our studied ESUs Table . Notablytlenecks . Furthertlenecks .L. hochstetteri disease and pathogens are lacking, but have attracted high priority in the recently published native frog recovery plan [Leiopelma spp proposed that the Otawa L. hochstetteri population was conservation dependent [We suggest that the Otawa ESU, with its limited MHC variation, may be at a greater risk from disease outbreaks. Investigations into ery plan  and woulery plan , and furependent . Human iLeiopelma hochstetteri provides an important benchmark for future MHC studies in other Leiopelma species, which are substantially more vulnerable, with fewer numbers and fewer populations [Leiopelma archeyi occurs in two natural populations on the New Zealand North Island: on the Coromandel Peninsula and in the Whareorino forest [Leiopelma pakeka occurs in a stable, natural population in remnant forest on Maud Island, and in two introduced populations, in another habitat on Maud Island and on Motuara Island [Leiopelma hamiltoni is found in a single natural population in a small rock tumble on Stephens Island, with a population estimate of only 300 mature individuals, and an introduced population established in 2004 on Nukuwaiata Island [Leiopelma genus.ulations . Leiopelo forest ,66. Betwo forest  and is na Island ,69. Leioa Island ,70-72. TL. hochstetteri as a result of positive selection and extremely high population differentiation. Nearly every population possessed a unique DAB allele pool. DAB- supertype differentiation was high among ESUs suggesting that selection pressures vary spatially and/or temporally. Northern and Central Coromandel were exceptions to this, with lower differentiation of DAB supertype frequencies, which may imply similar selective pressures as a result of shared environmental characteristics. Very low DAB diversity in Otawa, with only two alleles present, may contribute to a greater extinction risk from disease outbreaks in this ESU.Our study found high MHC-DAB allelic diversity in Nucleotide sequences for partial MHC class II beta transcript and the 74 DAB alleles are available on Genbank (Accessions numbers: KP892996 - KP893070).


Gryllus texensis and G. rubens. This work has included analysis of morphology, behavior, and the mitochondrial DNA molecule. The molecular work in particular has dramatically re-shaped my interpretation of the speciational history of these taxa, suggesting that rather than ‘sister’ species we should consider these taxa as ‘mother-daughter’ species with G. rubens derived from within a subset of ancestral G. texensis.The last 25 years or so has seen a huge resurgence of interest in speciation research. This has coincided with the development and widespread use of new tools in molecular genetics, especially DNA sequencing, to inform ecological and evolutionary questions. Here I review about a decade of work on the sister species of field crickets  Evolution by natural selection  was generally considered to be such a slow process that intrinsically historical research agendas, such as speciation, were considered nearly intractable. Subsequent direct observation of natural selection in the wild  courtship interactions reduce the likelihood of mating, mostly mediated by female selectivity not male selectivity. These studies, while informative, obviously beg the question of eco-morphological divergence, and the degree of genetic isolation and divergence. That is, the observed levels of species behavioral divergence, the absence of readily identifiable hybrids in the field, and the results showing strong behavioral isolation all suggest that some degree of ecological divergence might facilitate species persistence and also suggests that genetic reproductive isolation should be quite strong.To summarize the results discussed so far: behavioral reproductive isolation between these taxa appears to be very strong. First, isolation by male calling song and female phonotaxis approaches 100%—male pulse rate distributions are nearly non-overlapping, and females show strong preferences for pulse rates of conspecifics. Second, in those few instances of phonotaxis to heterospecific song that might occur [or encounters in the absence of calling song  and G. pennsylvanicus show a similar pattern: both have recent shared evolutionary history, both are egg diapausing species, but G. firmus has a considerably longer ovipositor than the more inland G. pennsylvanicus [G. rubens and G. texensis. This would be especially true for G. rubens populations in the southeastern US coastal areas, including peninsular Florida.At the time that Bill Cade and I started our character displacement study, there were no known morphological means of distinguishing between these two species. That situation is unchanged for males, however during our work we observed that female r length . In partr length –73. G. rlvanicus ,74. Ovip4.Unfortunately, at the present time, the only data available to test genetic divergence and illuminate the species' deeper evolutionary history are mitochondrial sequence data; however those mitochondrial data show clear and interesting patterns that greatly inform our understanding of speciation in these taxa.G. texensis had much higher levels of genetic variation than did G. rubens, that the gene trees showed species level separation, but were not reciprocally monophyletic, and that G. rubens appeared closely related to only one subset of the G. texensis variation [G. rubens [Our first study was with the mitochondrial Cytochrome c Oxidase I (COI) and cytochrome b (CYT-B) genes. It showed that ariation . That st. rubens . AlthougG. rubens had far lower genetic variation than G. texensis, (2) G. texensis sequences could be divided into two mostly discrete groups, (3) G. rubens sequences were mostly nested within one subset of the G. texensis sequences, (4) that G. rubens, but not G. texensis, showed indications of both recent population expansion and isolation by distance, and (5) that recent hybridization/gene flow is an unlikely explanation for the observed lack of reciprocal gene tree monophyly.To really get insight into these species' evolutionary past, I wanted a much larger sample of crickets, both in terms of numbers of individuals and geographic coverage. Fortunately, I had kept, preserved in 100% ethanol, nearly all of the many hundreds of wild caught crickets described in the previous behavioral section of this review, and had even collected or acquired a number of additional samples from parts of the species' ranges not before sampled. Because our first study  showed hG. rubens arose from within a subgroup of G. texensis, i.e., that G. rubens is the daughter species of G. texensis, and that G. rubens arose in isolation, probably in sandy soil habitat within and/or near peninsular Florida, and has subsequently expanded demographically and geographically throughout its current range. Additionally, it seems likely that the significant song evolution that is the primary reproductive isolating barrier between these species took place in proto-rubens with perhaps little or no song change in G. texensis. Such a scenario also implies that G. rubens may have persisted at low population density prior to expansion. If true, this may have favored the reduced mating selectivity in G. rubens (compared to G. texensis) that we have observed in our courtship studies [Taken together these results suggest that  studies , analogo studies .-rubens lineage , showed that there is a strong genetic correlation between male PPT and female preference for PPT in the wild, but that that genetic correlation disappears after random mating in the laboratory [G. texensis. In contrast, an important recent study of pulse rate and pulse rate preference quantitative trait loci in the Hawaiian crickets Laupala kohalensis and L. paranigra demonstrated that the observed strong genetic correlation was due mostly to either close physical linkage or pleiotropy [G. texensis pulse rate/preference genetic correlation is unknown, and the corresponding genetic correlation has not been investigated in G. rubens. However, the phylogeographic data polarize the ancestral state of proto-rubens and suggest that that ancestor was, in effect, the G. texensis lineage that has led to the current G. texensis. As G. texensis of today show a strong pulse rate/preference genetic correlation, that shared ancestor likely did also, and so—in the absence of any supporting data whatsoever—it is reasonable to think that a similar genetic correlation may have been important in proto-rubens song/preference evolution.In short, the molecular phylogeographic work  altered  lineage . Does thboratory . This deeiotropy . The gen5.etc. should be used to confirm or refute the major conclusions of the mitochondrial data. Second, determination of a pulse rate song/preference genetic correlation and its mechanistic basis in G. rubens is essential. Third, were candidate genes available, comparative work on the genetics of species divergence would be super interesting given that: (1) three features of G. rubens are currently known to be strongly divergent from G. texensis: male calling song pulse rate, female preference for calling song pulse rate, and female ovipositor length; and (2) other features are less divergent, but clearly have diverged: male courtship song rate, female preference for courtship song rate, and cuticular hydrocarbons (by implication from the courtship data in Gray 2005). For example, a comparative study of selection at the molecular genetic level for ovipositor length and cuticular hydrocarbon genes would be very interesting; I would guess that G. rubens ovipositor length evolved by comparatively recent strong selection and hydrocarbons by weak selection or drift; I would also guess that the courtship song and preference divergence evolved as a correlated response to the calling song divergence, probably because of pleiotropy of calling song/courtship song genes and also pleiotropy of calling song preference/courtship song preference genes. All of this would make excellent further study, and clarify the mechanistic basis of speciation. I hope someone takes it up.The molecular genetic data to date have dramatically changed my interpretation of speciation between these taxa. Yet the genetic data presently available are miniscule compared to the numbers of unresolved questions that could be addressed with molecular genetics. First, and most obviously, nuclear DNA sequences, microsatellites, and/or AFLPs,


Many species contain evolutionarily distinct groups that are genetically highly differentiated but morphologically difficult to distinguish . The presence of cryptic species poses significant challenges for the accurate assessment of biodiversity and, if unrecognized, may lead to erroneous inferences in many fields of biological research and conservation.Baetis alpinus across several major European drainages in the central Alps. Bayesian clustering and multivariate analyses of nuclear microsatellite loci, combined with phylogenetic analyses of mitochondrial DNA, were used to assess population genetic structure and diversity. We identified two genetically highly differentiated lineages (A and B) that had no obvious differences in regional distribution patterns, and occurred in local sympatry. Furthermore, the two lineages differed in relative abundance, overall levels of genetic diversity as well as patterns of population structure: lineage A was abundant, widely distributed and had a higher level of genetic variation, whereas lineage B was less abundant, more prevalent in spring-fed tributaries than glacier-fed streams and restricted to high elevations. Subsequent morphological analyses revealed that traits previously acknowledged as intraspecific variation of B. alpinus in fact segregated these two lineages.We tested for cryptic genetic variation within the broadly distributed alpine mayfly Taken together, our findings indicate that even common and apparently ecologically well-studied species may consist of reproductively isolated units, with distinct evolutionary histories and likely different ecology and evolutionary potential. These findings emphasize the need to investigate hidden diversity even in well-known species to allow for appropriate assessment of biological diversity and conservation measures.The online version of this article (doi:10.1186/s12862-016-0643-y) contains supplementary material, which is available to authorized users. Historically, fixed morphological differences have been used to categorize the diversity of life and morphological traits remain important in taxonomy. However, phenotypic differentiation does not always correlate with genetic diversification. On the one hand, phenotypic plasticity can lead to pronounced phenotypic differentiation despite genetic similarity . On the Following the introduction of molecular techniques, it has become clear that a substantial proportion of biological diversity is morphologically hidden: many morphologically delimited species  consist of distinct evolutionary lineages that show varying levels of adaptive divergence and reproductive isolation . In the Identifying and characterizing cryptic species, and the failure to do so, have far-reaching implications for basic and applied research. Critically, due to their distinct evolutionary histories, cryptic species may possess unique adaptations and evolutionary potential, and hence must be considered as separate evolutionarily significant units . IgnorinThe recognition of cryptic species, as well as their evolutionary histories and ecological requirements, is also crucial for predicting how climate change affects biodiversity , 10. CliBaetis (Ephemeroptera) comprises particularly remarkable examples of hidden diversity. Thus far, population genetic and phylogenetic studies have reported cryptic species complexes within B. bicaudatus [B. rhodani [B. vernus [B. harrisoni [B. alpinus [Importantly in this context, cryptic species are commonly reported in freshwater invertebrates , 14–17, caudatus , B. rhod rhodani , B. vern. vernus  and B. harrisoni . The pre alpinus –25, a wi alpinus , 27).B. alpinus across the Swiss Alps using both nuclear microsatellite and mitochondrial DNA (mtDNA) markers. First, we used the combination of these two types of markers to reliably identify putative cryptic lineages testing the hypothesis of lineage separation. We followed the unified species concept [In this study, we investigated patterns of genetic diversity in  concept , conside concept . DNA bar concept , includi concept . However concept , introgr concept  or incom concept  stress t concept ), emphas concept –39) and  concept , 41.B. alpinus species group from multiple basins in the central Alps in Switzerland. We used both large scale  and within basin  sampling, combined with population genetic analyses, to test the following predictions. First, we tested for the presence of cryptic species within B. alpinus. These would be evident as strongly distinct lineages in both mtDNA and nuclear microsatellite markers. Our molecular data confirm the presence of two genetically highly distinct groups within B. alpinus  as well as some individuals from the closely related and superficially morphologically similar B. melanonyx. To gain insight into putative differences among these B. alpinus lineages, we investigated their relative abundances, habitat affinities and differences in population genetic structure, and tentatively re-analyzed morphological variation of lineages A and B. In particular, we tested whether the different lineages i) are associated with different environments  – indicating ecological differences arising from either differences in habitat preference or local adaptation, ii) differ in population genetic variation and population structure - reflecting potential differences in evolutionary history as well as population demography, and iii) exhibit morphological differences in traits used in taxonomic identification of species.We sampled Mayflies (Ephemeroptera) have aquatic egg to larval stages, strictly associated to freshwater habitats, followed by two short-lived winged stages: the pre-reproductive subimago and the sexually mature imago. Mayflies spend the longest part of their life cycle as larvae, the development of which is strongly dependent on water temperature , 43. TheBaetis was divided into 11 species groups [Baetis group alpinus encompasses 11 species [B. alpinus s.s. and B. melanonyx having restricted spatial distributions or being considered as endemic  s.s., B. melanonyx and B. nubecularis. B. alpinus s.s. is a widespread and abundant alpine mayfly of the Palearctic region, reported from the Iberian Peninsula to the Ukrainian Carpathians [B. melanonyx is also widely distributed in the West Palearctic from the Iberian Peninsula to Ukraine. B. nubecularis is restricted to a few localities in the Swiss and French Jura mountains [The genus s groups , with sos groups ). Among  species , all butic e.g. , 48). In. InBaetipathians , 49. B. B. alpinus larvae are eurythermal and found in swift-flowing stony streams between 200 and at least 2600 m above sea level (a.s.l.). After mating, similarly to other Baetis species, adult females usually fly upstream from the emergence site and oviposit underneath large, stable protruding rocks [B. alpinus is described as plastic with regard to emergence timing and voltinism  [B. melanonyx occurs in rhithral streams mainly between 600 and 1400 m a.s.l. In syntopic populations of B. alpinus, larval abundance was observed to decrease with elevation [B. melanonyx is distinguished from B. alpinus by a set of morphological traits [ng rocks , where eng rocks . The lifvoltine) , 51–54 avoltine) . B. melalevation , 56. B. l traits .B. alpinus at both regional and local scales. To examine regional patterns, 24 sites were sampled from multiple basins within each of four major European drainages in Switzerland , Rhone (Ro) and Danube (Da) drainages  were genotyped at 10 unlinked and polymorphic microsatellites, including both cDNA-  and genomic DNA-derived  markers. The markers were developed by Ecogenics  and are described in detail in the online supplement . Th. ThB. al2O to make up a total reaction volume of 10 μL. Primers were used at different concentrations to achieve similar signal intensities at the different loci  was performed in two separate multiplex reactions  was followed by 40 cycles of 15 s at 94 °C, 15 s at 48 °C, 15 s at 72 °C, and a final extension of 72 °C for 1 min following Kapa standard instructions. PCR products were purified using the Wizard SV96 standard protocol (Promega) prior to sequencing . When low concentration amplicons were obtained, the amplification was repeated twice, and both reactions were pooled in the purification step. Sequences were edited using CODONCODEALIGNER 5.1.5  and aligned using ClustalW in BIOEDIT 7.2.5 [In a subset of 44 individuals, we sequenced a part of the Cytochrome Oxidase subunit I (COI) to determine the degree of mtDNA divergence among the cryptic species identified based on microsatellite markers see , and to IT 7.2.5 .B. alpinus populations, and we found no consistent deviations from HWE or deviations from linkage equilibrium. In contrast, B. melanonyx showed high probabilities of null alleles for some site-locus combinations together with significant deviation from HW genotypic proportions. We therefore refrained from any further detailed population genetic analysis of this species. Detailed information on the number of alleles, observed and expected heterozygosities, and estimated null allele frequencies is provided in Additional file All microsatellite loci were tested for variability and departures from Hardy-Weinberg equilibrium (HWE) within samples expected to be panmictic . All HWE tests were performed with a minimum sample size of eight individuals using the randomisation procedure implemented in FSTAT 2.9.3.2 . LinkageFor the microsatellite markers, cryptic species were identified in accordance with the ‘genotypic cluster’ definition, within the unified species concept framework . Under t6 Markov chain Monte Carlo (MCMC) iterations following a burn-in period of 1x105 iterations. The likelihood of different numbers of genetic clusters (K) was assessed for K = 1–30 using 30 runs per K. The ad hoc statistic ΔK [K. Similarity coefficients between runs and the average matrices of individual membership proportions were estimated using CLUMPP 1.1.2 [K  to detePP 1.1.2 . As multadegenet for R.3.13 [On the same data, we also performed a Discriminant Analysis of Principal Components (DAPC), a model-free multivariate method to identify genetic clusters, genetic clines and hierarchical structures . The disr R.3.13 .B. alpinus and 3 B. melanonyx) produced in our study and 42 B. alpinus sequences from GenBank to gain insights into genetic variation across multiple mountain massifs within the range of B. alpinus  to gain insight into the extent of genetic divergence between B. alpinus cryptic lineages compared to taxonomically described species of the B. alpinus group. Using a fragment of 613 bp from all 89 sequences, 70 unique haplotypes were identified using the haplotype function in the ape package for R [phymltest function of the R package ape and PhyML 3.01 software [Baetis rhodani was used as an outgroup . A small subset of specimens (N = 112) identified as B. melanonyx were recovered as a monophyletic lineage. The relationship between the relative abundance of B. alpinus lineage A and B and spatio-ecological attributes  was tested using generalized linear models with logit link and binomial error. These analyses were conducted separately at the regional and local scales. First, at the regional scale, we tested for the effect of elevation and drainage (fixed effects) on the relative abundance of the two lineages. To reduce bias due to differences in sampling design among basins that included regional vs. local scale data, one site was randomly selected within the Ro, Ri and Da basins for regional analyses . Secondly, at the local scale , we tested for the effects of stream-type  and basin  by including these as fixed factors in the model. As overdispersion was detected for both the regional and local models, we fit quasi-binomial models and used F-tests for model comparison [stats for R 3.1.3 [We found two genetically strongly distinct mparison . Terms w R 3.1.3 .A and B, we used nuclear microsatellite markers and three complementary approaches: 1) Bayesian clustering (STRUCTURE), 2) multivariate ordination without considering the geographic origin of the samples (DAPC), and 3) multivariate ordination with spatial information . Each iK = 1–10 in the STRUCTURE analysis. The sPCA complements the first two methods by explicitly identifying spatial patterns of genetic structuring  across the landscape and accounting for spatial autocorrelation caused, for instance, by spatially limited mating or uneven distribution of sampling sites [adegenet package [STRUCTURE and DAPC were run as described above, but we assessed only ng sites , 82. We ng sites , we testng sites . All ana package  for R 3. package .F-statistics according to Weir and Cockerham [FST values were significantly different from 0 for each locus and over all loci . Further, we tested for an association between geographical and genetic distance between populations accounting for genetic substructure using a partial Mantel test based on three distance matrices: 1) Pairwise genetic distance (FST) between populations of at least eight individuals, 2) pairwise Euclidean distance, and 3) a 0/1 matrix indicating if two populations belong to the same or different subgroups within cryptic lineages. To create this last matrix, we assigned the population as a whole to the subgroup where, averaged across all individuals, it had the highest membership probability. The significance of r values was tested with a partial Mantel test  performed in the package ecodist for R [Population differentiation across all populations was assessed using classical ockerham . A permust for R .B. alpinus lineage was examined based on all populations with at least eight individuals by calculating the total and mean number of alleles , allelic richness (Ar) and gene diversity (HE) in FSTAT 2.9.3.2 [ArP) was computed following the rarefaction procedure (n = 8) in the ADZE software [Ar, ArP, and HE) were compared between B. alpinus lineages. Differences were tested using the Kruskal-Wallis Rank Sum test and the multiple comparison test implemented in the R package pgirmess [Genetic diversity within each  2.9.3.2 . Allelic 2.9.3.2  based onsoftware . Levels pgirmess .B. alpinus specimens was based on standard taxonomic criteria related to (in particular) mouth parts, legs and tergites of larvae [B. alpinus larvae . These specimens were selected according to their lineage and genetic subgroup identity, and collected from the same site (LM3 in the Ro basin) when possible. As some genetic subgroups were only present either in eastern or western Switzerland, additional specimens belonging to genetic subgroups not present in LM3 were selected in Da. Specimens, preserved in ethanol (96 %), were dissected using a stereo-microscope and mounted on slides in Canadian balsam after a short stay in Creosote solution. Morphological characters included: i) the number of spine-like setae at the apex of the maxillary palp, ii) the number of rows of long setae on the dorsal margin of the femora, iii) the presence and number of setae at the apex of the tarsus, iv) the triangular spines of the distal margins of tergum IV, v) the presence of micropores on tergum IV, vi) the presence and number of scales of tergum IV, and vii) the margins of the gills IV.The initial identification of the f larvae . Upon geB. alpinus and one to B. melanonyx . The strong separation of the genetic clusters was due to both private alleles at some loci and allele frequency differences at all loci. A total of 1543 out of 1591 genotypes (97.0 %) could be clearly assigned to one of the three clusters (membership probability ≥0.85) and only 48 genotypes (3.0 %) showed admixed membership proportions.Bayesian clustering of the microsatellite data revealed three distinct genetic groups, of which two corresponded to B. alpinus and 2 B. melanonyx) were identified in our set of 44 mtDNA sequences. Our mtDNA sequences, together with those from GenBank, yielded a total of 70 haplotypes. In our data, the clustering observed based on mtDNA sequences matched that based on microsatellite genotypes  while genetic divergence among clades ranged from 4.9 to 27.1 % . However, the B. alpinus lineages showed pronounced differences in abundance: 1001 individuals were assigned to lineage A and 478 to B, indicating that A is much more abundant than lineage B. Some 112 individuals belonged to B. melanonyx which occurred only at eight sites. Mitochondrial DNA sequences revealed high genetic similarity of Swiss haplotypes of each lineage with those from other mountain ranges . There was also an association of lineages with particular drainages  did not differ significantly between the two lineages.In accordance with differences in relative abundance of the lineages, genetic diversity, as measured by n B Fig. , while pB. alpinus lineages was evident. For lineage A, Bayesian clustering indicated two peaks of ΔK at K = 3 and K = 6. Visual inspection of the STRUCTURE barplots indicated that plots for K = 6 were still informative with respect to population substructure  tended to be similar even when they were in different major drainages  and Euclidean geographical distances once the effect of genetic substructure on population differentiation was taken into account .Within lineage population pairwise ely Fig. . In bothA and B. The number of setae at the apex of the maxillary palp was higher in A (12–18) than in B (5–10) as was the number of rows of setae on the dorsal margin of the femora  and mitochondrial COI sequence divergence (18.9 %) are in agreement with values reported between other congeneric species , suggesting at most very rare hybridization. Extremely low levels of gene flow among the lineages are further supported by the observation that individuals consistently clustered by genetic group independent of their geographic origin. For example, individuals from lineage A always clustered with other A individuals even from geographically distant sites rather than B individuals from the same site. Finally, a preliminary morphological analysis of the cryptic lineages, revealed subtle but consistent morphological differences in several traits. In most cases, the observed differences were previously already known but were considered as intraspecific variation [In this comprehensive study, we show that the nominal species  concept . Severalsee also –21, 31).ariation , 60.B. alpinus from our study region to genetic diversity found in other European studies. We found that B. alpinus haplotypes sharing high sequence similarity to those found in Switzerland (our study) have been reported from other European mountain ranges: central Apennines, Ligurian Apennines, Pyrenees and Sierra Nevada  and were brought into secondary contact upon range expansion. This scenario is further supported by the observation that individuals consistently clustered by genetic lineage rather than by geographic origin, suggesting a common evolutionary history of all populations within a species. Hence, the divergence observed here likely follows a pattern seen in many European plants and animals due to past changes in global climate and related range contractions (and divergence in isolation) and expansions (secondary contact) . Finally, post-zygotic intrinsic barriers due to genetic incompatibilities, as well as extrinsic barriers due to reduced viability or fertility of migrants and hybrids, may play an important role in impeding gene flow and recombination [While it seems likely  that the initial divergence occurred in allopatry, it is possible that barriers to gene flow between these incipient species have continued to accumulate after secondary contact . Today, bination . HoweverThe determinants of phenotypically similar but genetically distinct cryptic lineages are diverse, ranging from phenotypic plasticity to non-visual mating signals and morphological stasis . In our B. alpinus cryptic lineages can coexist, as also suggested by the patterns observed in other European mountain massifs  indicates that lineage A and B differ in larval cohort structure and hence in life-history strategies . Ongoing work is investigating to what extent these differences are driven by environmental variation and relate to temporal differences in adult emergence times.First, the wide geographic ranges and, in particular, frequent sympatric co-occurrence suggest that these Pyrenees ). StablePyrenees . Our datA was clearly more abundant than lineage B and showed higher levels of population genetic variation. B. alpinus lineage B appears to be more patchily distributed but relatively more abundant at high elevations, and exhibits lower genetic diversity , than lineage A. The differences in genetic diversity between lineage A and B may be explained by spatial distribution and demographic processes. A potentially lower effective and demographic population size, lower evolutionary potential and greater extinction risk may be of particular concern with respect to biodiversity loss expected as a result of ongoing global change [B that appears to have narrower habitat preferences, an upstream distributional shift to higher elevation sites is not possible once glaciers disappear.Second, we found that lineage l change , 7, 10. A there are additional genetic subgroups indicating some level of reproductive isolation or reduced gene flow. This possibility is supported by at least two lines of evidence. First, in several cases neighbouring lineage A populations showed pronounced differences in genetic composition. A particularly striking example is that the individuals from spring-fed tributaries in Loetschental appear to belong to a genetic cluster completely absent from other nearby sites . Mitochondrial COI sequences have been deposited at the ENA database (accessions LT546400 - LT546427). The supporting results and data are provided as Additional files


Oligomerization of transcription factors controls their translocation into the nucleus and DNA-binding activity. Here we present a fluorescence microscopy analysis termed pCOMB (pair correlation of molecular brightness) that tracks the mobility of different oligomeric species within live cell nuclear architecture. pCOMB amplifies the signal from the brightest species present and filters the dynamics of the extracted oligomeric population based on arrival time between two locations. We use this method to demonstrate a dependence of signal transducer and activator of transcription 3 (STAT3) mobility on oligomeric state. We find that on entering the nucleus STAT3 dimers must first bind DNA to form STAT3 tetramers, which are also DNA-bound but exhibit a different mobility signature. Examining the dimer-to-tetramer transition by a cross-pair correlation analysis (cpCOMB) reveals that chromatin accessibility modulates STAT3 tetramer formation. Thus, the pCOMB approach is suitable for mapping the impact oligomerization on transcription factor dynamics. Oligomerisation of transcription factors regulates their translocation into the nucleus, DNA binding affinity and sequence specificity. Here, Hinde et al. present a microscopy analysis, pair correlation of molecular brightness, that tracks the molecular mobility of different oligomeric species in the same cell. Transcription factors are DNA-binding proteins that affect the rate of transcriptional initiation and employ oligomerization to define discrete regulatory pathways23467Förster resonance energy transfer (FRET)9101213141516181924A limitation of moment analysis in general is that multiple species of different brightness cannot be resolved using only the first and second moments of a brightness distribution. Higher moments are needed and this demands acquisition of a much larger data set. For example, while PCH analysis can resolve multiple species in a single point by considering the entire distribution of photon counts collected, it is computationally too laborious to apply the PCH analysis to all pixels within an image4Spatiotemporal correlation spectroscopy can reveal the diffusive route of a population of molecules in real time and across cellular boundaries2127283031Here we call this method of analysis pair correlation of molecular brightness (pCOMB), and demonstrate the capacity of this approach to quantify the impact transcription factor oligomerization has on nuclear transport and DNA-binding dynamics. From the application of pCOMB to wild-type and mutant STAT3 fluorescent proteins in live HeLa cells we discovered that phosphorylated STAT3 dimers needed to bind DNA before forming tetramers, and from extension to a cross-pair correlation analysisThe current state of the literature allows for two scenarios: either STAT3 dimers in the nucleus first form tetramers that subsequently bind DNA (scenario I in δr) which tested molecular flow across the nuclear envelope and DNA-binding regions. This yielded a pCOMB carpet  on each brightness carpet  value of the heterogeneous population  of the tetrameric population  value of the monomeric population (green line in δr>0) is that the translocation path of a small population of higher-order oligomer (10% in G(0) value for monomeric molecular flow at a given δr is known. It should be noted that the number of subunits within an oligomeric complex determines the accuracy with which this method can resolve this complex's translocation path. Thus, while detection of even 1–10% tetramer in the presence of monomer was relatively easily resolved based on correlation amplitude  and GFP pentamers in the range of 0.001–0.025 (55 × 0.001=0.025). On the basis of this assignment, the pCOMB carpet B carpet  revealedB carpet , dimers B carpet . The difThus, we could experimentally extract and measure the diffusive route of GFP oligomers in the presence of GFP monomers, and concluded that the ‘rules' of pair correlation brightness carpets are (1) the amplitude is proportional to the weighted square of the molecular brightness of the fluorescent molecules and (2) the peak maximum is indicative of the translocation time. It should be noted that while the translocation path of the higher-order oligomer can definitively be assigned based on the correlation amplitude, a single-channel acquisition may have difficulties distinguishing smaller oligomers when their delay times are similar  in the same HeLa cell. The N-terminal deletion mutantNext we investigated whether the translocation dynamics of STAT3 depends on the oligomeric state of the transcription factor and thus tested whether scenario I or II  applied The brightness map derived for STAT3-NTD-YFP reconfirmed that deletion of the N-terminal domain inhibited cytoplasmic dimer formation and nuclear tetramer formation . CompariOur results so far suggest that DNA-bound dimers are required for tetramer formation, and STAT3 tetramers do not diffuse freely in oncostatin-stimulated cells. We next examined a DNA-binding mutant (STAT3-DB-GFP) that contained a double mutation within its DNA-binding domain 38Given that both STAT3 DNA binding of GAS elements and tetramer formation are reported to require tyrosine phosphorylation40A weakness of the pCOMB analysis is that monomers and dimers can be difficult to distinguish. Given the importance of dimerization for many transcription factors and other signalling proteins, we establish cpCOMB, using again two-channel acquisitions. When two subunits are fluorescently labelled with two different fluorophores, cpCOMB only tracks heterocomplexes. By expressing STAT3-GFP and STAT3-mCherry in the same cell, cpCOMB enables the translocation path of STAT3 dimers to be easily distinguished from that of STAT3 tetramers and monomers. This is because the cross-correlation function further amplifies the highest-order oligomer whilst removing the contribution of monomers . To expeWe next asked whether chromatin accessibility regulated the basal STAT3 dimer-to-tetramer transition given that DNA binding was found to be necessary for STAT dimer–dimer interaction. We first employed two drugs, trichostatin A and actinomycin D , that ar36424344To amplify how the STAT3 dimer-to-tetramer transition is regulated during transcriptional activation, we next conducted cpCOMB analysis in HeLa cells expressing STAT3-GFP and STAT3-mCherry, before and after prolonged stimulation with oncostatin M . STAT3-GN=6 cells). Then, on cytokine stimulation when STAT3 accumulation in the nucleus was observed, STAT3 dimer transport across the nuclear envelope remained bi-directional  and the intra-nuclear dimer–tetramer translocation dynamics detected in N=6 cells). After prolonged cytokine exposure when STAT3 puncta had formed, STAT3 dimers entered the nucleus on an even more delayed timescale of ∼700 ms, and the intra-nuclear dimer–tetramer transition was promoted in a similar manner to what was observed on loosening of chromatin, with trichostatin A (N=6 cells). Interestingly, a more detailed analysis of the cpCOMB carpet at this time point revealed that nuclear STAT3 tetramers and dimers adopted alternating localizations  but also distinguishes homo- from hetero-complexes and higher-order complexes from sub-complexes (here tetramers from dimers).Understanding how transcription factors employ oligomerization to maintain the many differentiation programs present in metazoans relies on a method that can probe protein mobility as a function of protein stoichiometry We applied the pCOMB and cpCOMB methods to STAT3 and examined the relationship of this transcription factor's oligomeric state with nuclear entry and DNA binding. In particular, we compared wild-type STAT3 with different mutants that inhibit oligomerization74849In summary, we report a new quantitative method that can unravel how oligomerization modulates transcription factor transport and DNA-binding dynamics. Further, because pCOMB and cpCOMB distinguish the translocation of homo- from hetero-complexes and higher-order complexes from sub-complexes, it can also be employed to investigate how complex formation affects intracellular transport, for example, through the nuclear pore complex. This means that one can investigate how protein complex formation modulates access and subsequent interaction with intracellular structures. For example, proteins that employ homo- and/or hetero-oligomerization as a control of their function can now be characterized in the context of the local intracellular architecture. This is particularly important for understanding transcription factor target search, the kinetics of which is greatly influenced by the exploration geometry imparted by chromatin organization5152532. Freshly split cells were plated onto 35-mm glass-bottom dishes coated with fibronectin and then, after 24 h, transiently transfected and co-transfected with the following plasmids: GFP, 2GFP and 5GFP (purchased from Euroscarf); STAT3-GFP, STAT3-DB-GFP and STAT3-YF-GFP (cloned by Ivan Ng in the laboratory of Marie Bogoyevitch and David Jans); STAT3-mCherry (cloned by Zhengmin Yang in the laboratory of Katharina Gaus); and STAT3-YFP and STAT3-NTD-YFP (kindly provided by the laboratory of Gerhard Muller-Newen). Transient transfection was carried out with Lipofectamine 3000 according to the manufacturer's protocol. Stimulation of wild-type and mutant STAT3 activity was carried out by treating the HeLa cells with oncostatin M  for 15 min. STAT3 activation was assessed by the degree of nuclear accumulation. Loosening of chromatin was carried out by treating the HeLa cells transiently transfected with STAT3-GFP and STAT3-mCherry, with trichostatin A  for 18 h. Compacting of chromatin was carried out by treating the HeLa cells transiently transfected with STAT3-GFP and STAT3-mCherry, with actinomycin D  for 30 min. The compaction status of chromatin was assessed by staining the DNA of HeLa cells with Hoechst 33342  15 min before imaging. In all line- and frame-scan experiments, cells exhibiting a low STAT3 fluorescent construct expression level were selected, as both the brightness and pCOMB methods are fluctuation-based analyses. Under this selection criteria, variations in STAT3-mCherry expression levels had negligible impact on STAT3 oligomerization  were grown in high-glucose medium from Invitrogen, supplemented with 10% fetal bovine serum, 5 ml of Pen-Strep and HEPES at 37 °C and in 5% COrization .2 s−1. In all, 100 particles were used in each simulation, and the fraction of tetramer was varied from 10 to 90% of this population. When the particles exited either boundary of the box, they were re-integrated within the box by the periodic boundary condition. The intensity carpet was produced by convolution of a moving Gaussian beam  across the central 64 pixels (3.2 μm). The pixel dwell time was set to 1.56 μs, and its intensity was a convolution of the beam with the particles positions integrated over this time period. At the end of the 64-pixel line, the beam was repositioned back to the first pixel, and the next line was scanned in a similar fashion. The total of 128,000 lines were generated in this way, producing the intensity carpet. To mimic different oligomerization states, a multiplicative factor of 1 and 4 was applied to the convolution step, for a monomer and tetramer, respectively. Analogous simulations were set-up for an increasing fraction of dimers in the presence of monomers and variation of the oligomeric population's diffusion coefficient. All of these simulations were generated in SimFCS from the Laboratory for Fluorescence Dynamics ( www.lfd.uci.edu).Two sets of simulations were performed to verify the capability of the pCOMB analysis to resolve the dynamics of different oligomers. In one simulation, monomer and tetramer populations were set to diffuse freely in a two-dimensional 4-μm square box, with the diffusion coefficients equal to 1 μm2. The probabilities for dimers of crossing to the right or to the left of the nuclear membrane were set to 0.7 and 0.3, respectively. This ensured that dimer was effectively partitioning and accumulated inside the nuclear (to the right of the barrier) region. The monomer particles were not allowed to diffuse into the nuclear region, while tetramer particles were not allowed to leave the nucleus (translocate to the left of the barrier). Tetramer particles were entering and leaving chromatin compartments with probabilities of 0.9 and 0.1, respectively. This ensured that tetramer was effectively accumulated inside the chromatin-like compartments. The particle densities were set such that their relative ratio was 50:40:10 for monomer: dimer: tetramer. Finally, the diffusion coefficients were set to 5, 3 and 1 μm2 s−1 for monomer, dimer and tetramer species, respectively. The periodic boundary conditions were applied along the y axis, but hard boundary (reflective) was used on left and right edges of the simulation box. This was imposed to avoid that monomers appear inside (right compartment) or tetramer outside (left compartment) of the nucleus. The intensity carpets were generated from the particles' positions using the same approach as described above, and the scanning line was selected to be at least 30 pixels away from either edge, to avoid the potential artefacts in the particles' motion due to the boundary conditions. All of these simulations were generated by a routine written in Matlab.For the nuclear translocation simulation, a semi-permeable barrier was placed at one-third of the simulation box mimicking the nuclear envelope. To the right of the barrier, a uniformly randomly semi-permeable discs were placed to simulate the chromatin compartments. The discs were 0.75 μm in radius at surface density of 0.5 for every μm2 for monomer, dimer and tetramer, respectively. Their diffusion rates were set to 6, 4 and 2 μm2 s−1, respectively. All of these simulations were generated by a routine written in Matlab.For the two-colour simulations monomers were assigned either a green or a red channel with 50:50 ratio. Dimers were split into green+green (GG), red+red (RR) and green+red (GR) with 25:25:50 ratios. Finally, tetramers were seeded with four greens (GGGG), four reds (RRRR), three greens+one red (GGGR), three reds+one green (RRRG) and two greens+two reds (GGRR) at 5:5:20:20:50 ratio. The density was set to 3, 1.5 and 0.5 particles per μm5 times in one or two channels at maximum speed (pixel dwell time 6.3 μs and line time 0.945 ms), with fluorescence being sampled every 200 nm (64 pixels to a line). Calibration of the monomeric brightness and pCOMB amplitude of each fluorescently labelled STAT3 construct tested was performed by the measurement of cells transfected with free GFP, YFP or mCherry under identical frame- and line-scan experimental conditions . STAT3 constructs fluorescently labelled with GFP, YFP or mCherry were excited with the 488 nm emission line of an Argon laser, 514 nm emission of the Argon laser and the 561 nm emission line of a diode pump solid state laser, respectively. For single  and sequnditions . FRET innditions .F(t)〉 (first moment) and variance Brightness analysis of frame- and line-scan acquisitions was performed using a moment analysis described in previously published papers2223ɛ) of the molecules are related to the measured apparent brightness (B) by B=ɛ+1, where 1 is the brightness contribution of the detector given that the photon-counting detector variance (σ2detector) should be equal to the average intensity of the detector noise S factor, which returns the background brightness to 1 so that the molecular brightness of the molecules can be extracted. The number of intensity time points used for each moment calculation needs to contain a sufficient number of fluctuations to ensure good statistics and span over a temporal window that allows the dynamics being probed to decay. Depending on the width of the temporal window and the sampling frequency of the acquisition, the analysis is tuned to detect different timescales of mobility. Here we use 100 points to derive each brightness value in the frame and line scans. This gives rise to a single brightness map for the image acquisition. In the case of the line scans (which contain 105 lines), we shift the 100 time points window by a single line, and for every window position calculate B to obtain a brightness carpet. It is represented by the x coordinate corresponding to the point along the line (pixels), the y coordinate corresponding to the time of acquisition and the pseudocolour indicates the local effective brightness value. All brightness calculations were made by a routine written in Matlab and can also be carried out from the scanning FCS page in SimFCS from the Laboratory for Fluorescence Dynamics ( www.lfd.uci.edu).In the case of a photon counting detector, the true molecular brightness . The average value of the amplitude maxima along the calibration line was set as the threshold for monomer versus oligomer translocation. The extrapolated amplitude maxima value for higher-order oligomer translocation was expected to range from Gmonomer to Gmonomer × oligomeric stateδr) was set to 6–10 pixels, as these conditions tested molecular flow across the cytoplasm, across the nuclear envelope and within the nucleus. All pair correlation calculations were made by a routine written in Matlab and can also be carried out from the scanning FCS page in SimFCS from the Laboratory for Fluorescence Dynamics ( www.lfd.uci.edu).The pair correlation brightness function is displayed in pseudocolours in an image in which the assigned . Pair coCross-pair correlation analysis of the derived brightness fluctuations along each line-scan acquisition was performed using the following function that is adapted from previously published papersx coordinate corresponds to the pixel position along the line and the y coordinate corresponds to the correlation time in a log scale. The amplitude of each cross-pair correlation function is proportional to the weighted square of the brightness of the molecules that translocate from that location in channel 1 with respect to channel 2. Thus, the correlation calibrations carried out for monomeric GFP and mCherry molecular flow can be used to predict the correlation amplitude for heterodimer translocation. All cross-pair correlation calculations were made by a routine written in Matlab and can also be carried out from the scanning FCS page in SimFCS from the Laboratory for Fluorescence Dynamics ( www.lfd.uci.edu).The cross-pair correlation brightness function is displayed in pseudocolours in an image in which the How to cite this article: Hinde, E. et al. Quantifying the dynamics of the oligomeric transcription factor STAT3 by pair correlation of molecular brightness. Nat. Commun. 7:11047 doi: 10.1038/ncomms11047 (2016).Supplementary Figures 1-10


Sprattus fuegensis, a small pelagic species that populates the inner waters of the continental shelf, channels and fjords of Chilean Patagonia and Argentina, as a model species to test whether environmental variables of fjords relate to population genetic structure. A total of 282 individuals were analyzed from Chilean Patagonia with eight microsatellite loci. Bayesian and non-Bayesian analyses were conducted to describe the genetic variability of S. fuegensis and whether it shows spatial genetic structure. Results showed two well-differentiated genetic clusters along the Chilean Patagonia distribution , but no spatial isolation by distance (IBD) pattern was found with a Mantel test analysis. Temperature and nitrate were correlated to the expected heterozygosities and explained the allelic frequency variation of data in the redundancy analyses. These results suggest that the singular genetic differences found in S. fuegensis from inside TicToc Bay (East of the Corcovado Gulf) are the result of larvae retention bya combination of oceanographic mesoscale processes , and the local geographical configuration . We propose that these features generated an isolated area in the Patagonian fjords that promoted genetic differentiation by drift and a singular biodiversity, adding support to the existence of the largest marine protected area (MPA) of continental Chile, which is the Tic-Toc MPA.Marine environmental variables can play an important role in promoting population genetic differentiation in marine organisms. Although fjord ecosystems have attracted much attention due to the great oscillation of environmental variables that produce heterogeneous habitats, species inhabiting this kind of ecosystem have received less attention. In this study, we used  Marine environmental landscape parameters play an important role in promoting population genetic differentiation in marine organisms . ConsequFjords are deep, high-latitude estuaries at have been excavated or modified by glaciers –12. Thes2 [Sprattus fuegensis is a small pelagic marine fish of economic importance that inhabits from latitude 41°S, specifically in inner waters and fjords in the south of Chile to latitude 40°S in Argentina, including the Falkland Islands [S. fuegensis are mainly abundant in the inner waters of Chiloé Island, channels and fjords in Chile [S. fuegensis as a model to investigate how environment can shape the genetics structure of populations because: (1) it inhabits fjords and channels which have been shown to have high environmental oscillations and in general are habitats with low levels of pollution [The Chilean Patagonian fjords constitute one of the largest fjord regions in the world, extending from latitude 41.5°S (Reloncaví Fjord) to latitude 55.9°S (Cape Horn) and covering a total of 240,000 km2 . The geo2 . In addi2  and circ2 . The Pat Islands –38. It l Islands  and it i Islands . Female  Islands ,43,44. Tin Chile ,43,44, ain Chile ,45. We uollution ,14,15; (ollution –38 whereollution –48); (3)ollution –38, alloollution .S. fuegensis has a large population genetic differentiation promoted by local fjord conditions. Given the geography of the area, we expected to find at least two genetic clusters: one group from the north of Chilean Patagonia (i.e. inner water of Chiloé (~42°-43°S)and fjords close to Aysén (~45°S), and another group in the most distant locality of the Strait of Magellan (~53° S). To test this hypothesis, we genotyped 282 adult S. fuegensis that were collected in 10 locations using eight species-specific microsatellites. We described the genetic diversity and population structure of S. fuegensis along the Chilean fjords and we evaluated the effect of marine environmental variables  that are related to the causal mechanisms  of a population structure. Based on non-Bayesian and Bayesian approaches we found a strong genetic structure in this species, which is correlated with temperature ranges and nitrate concentration, two factors that could be affecting local productivity, growth rates and therefore population dynamics. Finally, we discuss how the oceanographic landscape can promote this divergence and how our results support the existence of the Marine Protected Area (MPA) located in this area (i.e. Tic-Toc MPA).Based on the characteristics of the Chilean fjord, we propose that the small pelagic fish S. fuegensis has been recorded. Locations were selected based on early studies from scientific cruises [S. fuegensis in order to focus the sampling efforts in this area). In addition, samples collected in this southern location (i.e. 53°S) are important to the study questions given that the southern tip of South America is significantly different in temperature, phosphate and nitrate from the habitats inhabited by the northern populations (e.g. 41°S) [-1), phosphate-nitrate (i.e. 7 μM), and silicate (i.e. 9 μM), which support the differences among physical and chemical characteristics of the water column from Chilean Patagonia [S. fuegensis spawning season (September and December) because this season represents the most robust period for delineating population genetic structure [A total of 282 individuals were collected from ten locations in the Chilean Patagonian fjords , includi cruises ,50 betweg. 41°S) –48,51–56atagonia ,58. We structure . Samplintructure ,60). In tructure ,61. Muschttp://www.shoa.cl/n_cendhoc/productos/reporte_datos.php), whose goal was to compile oceanographic and biological information from the inner sea of Chiloé, channels, estuaries and fjords from the Chilean Patagonia using the same cruise routes in different years but in similar seasons (i.e. spring—summer). The oceanographic environmental measures from the CIMAR-FIORDOS program used in this study were temperature, salinity, pH, oxygen, phosphate, and nitrate. We obtained data from the CIMAR-Fiords program for each variable, from nine different depth of the water column  in each of the 10 sampling locations. Then, we estimated the maximum, minimum, average and range in each location for the above-mentioned six marine environmental variables, in order to capture the range of conditions experienced by this species. We show the detailed information on cruises, seasons, year and references in The currently available global marine environmental databases  have large gaps in information related to the inner sea of Chiloé and the fjords. We therefore compiled an environmental marine database based on the published literature and oceanographic research cruises. These records were obtained from the CIMAR-FIORDOS oceanographic research program conducted between 1995–2006  and carried out according to the manufacturers' recommendations. The quality and quantity of DNA purification were measured in an Eppendorf biophotometer  and the template DNA was diluted to 20 ng/μL for the polymerase chain reaction (PCR) amplifications. We used eight tetranucleotide microsatellites with loci described for s et al.   in HP-RARE [Because large samples are expected to have more alleles than small samples, and the number of individuals per locality was not homogeneous, we conducted a rarefaction analysis to estimate how many individuals we would need in order to detect all alleles present in a population , we A), expected (HE) and observed (HO) heterozygosity were estimated to determine the genetic variability of the samples; these parameters were calculated for each locus and locality using GENALEX v6.5 software [ST and RST comparisons between sampling locations were obtained from ARLEQUIN where the p-value was obtained after 10,100 permutations. In addition, two standardized measures of genetic differentiation were included in order to infer demographic processes such as genetic drift and migration on genetic population structure, as suggested by Meirmans and Hedrick [The total number of alleles  in our sample set, we used two Bayesian approaches based on the clustering method which differed in that they: a) incorporated or not a null allele model, and b) useda non-spatial or spatial algorithm. We selected this approach because Bayesian models capture genetic population structure by describing the genetic variation in each population using a separate joint posterior probability distribution over loci, therefore they incorporate uncertainty into the analyses. We used STRUCTURE v.2.3.3 ,86, whicST/(1-FST) and RST/(1-RST)), and geographic distance (i.e. logarithms of the linear distance between locations). Pearson correlation coefficients (i.e. r) were calculated in the VEGAN package of R functions [A, HO, HE) that show correlations with average environmental variables , we conducted correlation analyses in the VEGAN package of R functions.To identify patterns of population genetic variation that derive from spatially-limited gene flow , we conducted a Mantel test using a transformed genetic matrix . We ran ten pilot runs (burn-in period) to have priors of mean and variance in the distribution of alpha parameters . After these pilot runs, we ran 10,000 MCMC iterations with a thinning interval of 100 and all those runs were replicated five times each. In all, combinations of marine environmental variables were considered and evaluated using estimates of posterior probability, and the degree of uncertainty of the estimations was measured by the 95% highest probability density interval (HPDI) [ordistep function) with the Akaike information criterion in VEGAN. P-values were estimate based on 10,000 permutations. The Pearson coefficient correlation (r) was estimated for only the environmental variables that better explain the data variability, in order to fulfill the non-correlation for multivariate analysis [ordistep function.Environmental factors that promote changes at the microevolutionary level (i.e. population genetic structure) were estimated using hierarchical Bayesian models. We conducted analyses in GESTE v2 , in ordel (HPDI) . In ordeanalysis . FinallyS. fuegensis samples, where the NA per locus ranged from 7 to 26, HE ranged from 0.636 to 0.955, and HO ranged from 0.148 to 0.957 (A (12.8) and HE (0.803)  , and the (0.803) , the hig (0.803) . Pairwisocations . The staocations .P> 0.05).Significant deviations from the Hardy-Weinberg equilibrium were found at loci for some locations of samples due to homozygote excess as indicated by MICROCHECKER outcomes . No pairBayesian approaches based on the clustering method were congruent among them, despite the fact that STRUCTURE does not include a model that incorporates locus with possible null allele as in GENELAND. In addition, analyses that were run by each locus at a time showed a convergence in outcomes and found the same two clusters in 5 out of 8 loci . The smaST/(1-FST) and RST/(1-RST)) and the natural log of geographic distance did not reveal any association of genetic distances with geography in the Mantel tests: low, non-significant negative correlations between distance matrices  and RST ) were inferred, indicating the absence of isolation by distance, even when performing a posterior analysis and excluding the divergent zone D  and RST ). The coefficient of determination (R2) between genetic diversity indices showed values rangingfrom 0.03 to 0.48. The relationship between HO and the average temperature was the highest correlation with a Pearson correlation coefficient (r) of 0.69.Transformed pairwise genetic distances between locations  and the other extending through the rest of the Chilean Patagonia (LC). We propose that the small cluster that is located at Tic-Toc MPA is the result of the singular oceanographic characteristics of this enclosed embayment. We find that temperature and nitrate correlate with the allelic frequency of the small cluster. We propose that larval retention and the effect of temperature and nitrate could be generating population genetic differentiation in this area. In addition, the spatial location of SC coincides with the location of the recently established marine protected area in Tic-Toc Bay, reinforcing the idea that this area has unique biogeographic characteristics.Our results show two genetic clusters in S. fuegensis is remarkably similar to the variability obtainedusing microsatellite in marine, anadromous and freshwater fishes by DeWoody and Avise [E = 0.80–0.90) in this study was similar to that of the congeneric S. sprattus  and it .7–0.95, ) and C. 85–0.84, ).The SC showed less allele number and less private alleles than LC . AlthougS. fuegensis along its Chilean Patagonia distribution. The FST and RST indices were consistent with the Bayesian analyses, which means that our results are not dependent on the approach used. The result found in this study is in contrast with the genetic homogeneity found in other marine species in the same geographic area . Howevesprattus ,99, Clupharengus ,100–102  pallasi ,103,104. pallasi  found sihthyidae , Galaxiaaculatus ). In ourS. fuegensis have not been identified in its Chilean Patagonia distribution. Nonetheless, mature adults have been identified in the inner sea of Chiloé [S. fuegensis individuals, Galleguillos et al. [S. fuegensis along the Argentinean Patagonian coast with a juvenile production of 1.3x109 individuals. Similarly, a probable explanation of the non-genetic differences found in Genypterus blacodes along inner waters, channels and fjords in Chilean Patagonia, was the close proximity of spawning grounds in the same study area [S. fuegensis has not been recorded to date, however, indirect evidence (i.e. microchemistry of otoliths and parasites tags) has pointed out that an active dispersal of adults must exist between the inner sea of Chiloé and the fjord close to Aysén [Engraulis ringens [Strangomera bentinki [The largest cluster (LC) found in our study extends from ~41° to ~53° LS . We hypof Chiloé . Moreovef Chiloé ,107. In f Chiloé ,42,44. Us et al.  showed tudy area . Adult mto Aysén . The sambentinki , two smabentinki ,107, we S. fuegensis can be recorded, they described a broad Subantarctic body of water that goes into Boca del Guafo and then divides northward intot he inner sea of Chiloé and southward to the fjord and channels close to Aysén [The environmental characteristics can also explain the low genetic differentiation of LC. Water bodies and circulation patterns could be causing migration via passive dispersal . Sieversto Aysén . TherefoThe smallest cluster (SC) has a restricted geographic distribution inside Tic-Toc Bay. The SC showed highly significant differences, giving strong support for its existence. Based on GESTE analyses, none of the tested environmental variables, physical (i.e. temperature) or chemical  of the datasets incorporated in this study were better than the null model . The RDAS. fuegensis from outside the bay. Nonetheless, we found individuals from LC within SC and vice versa, and we propose that they are the result of adult migration between these areas, preferentially from LC to SC.The SC was an unexpected outcome considering that, based on previous environmental information whereby we expected to find genetic differences between the more isolated areas. The SC is localized within the Chiloense Marine Ecoregion , an ecorThis SC is concordant with the marine protected area (MPA) created in 2014 by the Chilean government, which was based on the high biodiversity and unique biotic and abiotic features of the zone. The Tic-Toc MPA has a surface area of 97,929 ha surrounding the Corcovado Gulf  and accoIn conclusion, our data show that the singular genetic differences found inside the Tic-Toc MPA are the result of genetic drift, probably due to larval retention throughout a combination of oceanographic mesoscale processes, geographical configuration, and the local effect of the environmental variables on genetic variation. These features have generated the isolated and restricted area that promoted genetic differentiation. Further analyses should be carried out to confirm this spatial genetic pattern, test whether this pattern is stable in the long term and also whether environmental features not explicitly tested in the present study (i.e. currents of water bodies) are able to better explain the population genetic structure of this species.S1 TableAve: average, Rang: Range, Max: Maximum, Min: Minimum.(DOCX)Click here for additional data file.S2 TableAdUn: admixture and uncorrelated model, AdCo: admixture and correlated model, NAdUn: no admixture and uncorrelated model, NAdCo: no admixture and correlated model.(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.S5 TableGESTE analyses included all 6 factors. Bold value indicates the two highest factor scores.(DOCX)Click here for additional data file.S6 TablePhos: Phosphate, Lat: Latitude, Long: Longitude, Nit: Nitrate, Oxy: Oxygen, Sal: Salinity, Tem: Temperature. Ave: average, Rang: Range, Max: Maximum, Min: Minimum. Bold values show significant p-values. Variable kept means environmental variables that explain variation in allele frequencies among locations.(DOCX)Click here for additional data file.S7 TableBold values show the correlation coefficient between environmental variables kept in the RDA analysis.(DOCX)Click here for additional data file.S1 FigA) Evanno et al.  plot for(EPS)Click here for additional data file.S2 FigA) surface layer (0–~30m); B) intermediate layer ~30–~150m); and C) deep layer (~150 m to bottom of the sea). Image modified from Sievers and Silva [0m; and C(EPS)Click here for additional data file.S1 Dataset(XLSX)Click here for additional data file.


To estimate heritable variance of ES, the coheritability of ES was derived using selection index theory. The BW records from 43,040 rainbow trout performing either in freshwater or seawater were analysed using a reaction norm model. High additive genetic variance for ES (9584) was observed, inferring that genetic changes in ES can be expected. The coheritability for ES was either -0.06 (intercept at PE) or -0.08 (intercept at BE), suggesting that BW observation in either PE or BE results in low accuracy of selection for ES. Yet, the rg was negative (-0.41 to -0.33) indicating that selection for BW in one environment is expected to result in more sensitive fish. To avoid an increase of ES while selecting for BW, it is possible to have equal genetic gain in BW in both environments so that ES is maintained stable.Rainbow trout is farmed globally under diverse uncontrollable environments. Fish with low macroenvironmental sensitivity (ES) of growth is important to thrive and grow under these uncontrollable environments. The ES may evolve as a correlated response to selection for growth in one environment when the genetic correlation between ES and growth is nonzero. The aims of this study were to quantify additive genetic variance for ES of body weight (BW), defined as the slope of reaction norm across breeding environment (BE) and production environment (PE), and to estimate the genetic correlation ( Daphnia (water fleas) which develops as a protective response to the chemical cues of a predatory Chaoborus present in the water , where A is the additive genetic relationship matrix, GRN is genetic covariance matrix from the reaction norm model, and MVN is multivariate normal distribution. The cn is the random full-sib tank effect (unique numbers in different year classes), explaining an effect common to full-sibs other than additive genetics (tank effect due to the separate rearing of the families prior to tagging and non-additive genetic effect), 0, I⊗CRN], where CRN is common environmental covariance matrix and I is the identity matrix. The e~ N for slope obtained from model (1) were used to illustrate the range of additive genetic values of slope available for selection.The reaction norm model was:The magnitude and the sign of a genetic correlation between the slope and intercept, and genetic variance for the intercept, can change depending on which environment the intercept is defined. Hence, the model was run twice, either with PE (X). The X [X in this study are 0 and 1. The n is the number of animals with a record for an animal trait, The strict sense of heritability for ES is the ratio between additive genetic variance of a slope to phenotypic variance of the slope. Due to the lack of phenotypic variance of the slope, it is not possible to calculate the heritability for ES. Three alternative parameters were used here to describe genetic characteristics of ES. Following Scheiner , heritabnce of X , i.e., σ (ANOVA) . Becausearameter . FurtherP) of an individual that includes reaction norm parameters can be defined as (1). We assume no covariances among a, c, and e, because there is no relationship among a, c, and e. The phenotypic variance  of the breeding value of slope on phenotype is:Estimated additive genetic effect of slope (b in Eq (X). Hence, the magnitude and sign of coheritability is dependent on the value of X. A negative coheritability is possible if the absolute of −σa,int, sl is greater than X is negative and absolute σa,int, sl. The sign of the coheritability explains the change in correlated response of ES when mass selection for higher phenotypic values is performed. When intercept is placed to the environment, in which selection is practised on P, X becomes zero, leading to:h. This value is equal to The b in Eq  is “coherIH) of predicting breeding value for ES when a selection criterion is BW in one of the environments is equal to:Finally, to understand the potential genetic response in ES, the accuracy . The rg different from unity indicates a present of genotype re-ranking. The E1 and E2, respectively.Coheritability of ES changes depending on a degree and forms of GxE. To demonstrate the relationship between coheritability and GxE in both forms, i.e. genotype re-ranking and heterogeneity of variances, Eq  is rearrAssume that there is no heterogeneity of additive genetic variances  in either E1 or E2 does not influence the magnitude of the coheritability , Eq  is equalh2int changes, depending on the intercept (X = 0) which is placed in either E1 or E2 as shown in In contrast to Eq , Eq 9)  9) showsX = 0. For the slope of reaction norms, the X = 0) as it reflects actual situation of selective breeding in aquaculture. The coheritability was calculated twice, either having BE or PE as the selection environment. The genetic correlation between intercept and slope ) was calculated as: For the intercept of reaction norms (body weight at the intercept environment), heritability  were calculated. The choice of GxE papers in aquaculture species was based on the following: growth traits as the studied trait, at least 30 full-sib families, and providing all the parameters needed for the calculations. In total, 17 studies were found, the species covering Arctic charr  [Gadus morhua) [Cyprinus carpio) [Coregonus lavaretus) [Dicentrarchus labrax) [Oreochromis niloticus) [Litopenaeus vannamei) [Oreochromis shiranus) [There are no previous studies using reaction norm model to study environmental sensitivity in aquaculture. Hence, to compare the reaction norm parameters of the present study to the previous GxE studies, (co)variance components of the previous studies calculated using multi-trait model were used to calculate the (co)variance components for reaction norm parameters (see  morhua) , Common  carpio) , Europeavaretus) , Europea labrax) , Nile tiloticus) ,36, Paciannamei) , Rainbowannamei) ,16–21,38hiranus) .rg of BW between BE and PE was 0.73, and heterogeneity of genetic variances  was considerable and the The magnitude of heritability for ES of log-transformed BW was similar to the heritability for ES of observed BW. The additive genetic variance of slope of log-transformed BW was 69% in PE and 65% in BE of the additive genetic variance of intercept, relatively slightly higher than on the observed scale (57% in PE and 53% in BE). This indicates that simple scale effects did not generate genetic variation for macroenvironmental sensitivity.When PE was the intercept, the slope EBVs for sires (-234.5 to 228.8) and animals (-210.6 to 199.8) ranged from strongly negative to positive . A positrg (SE) between BW in a given environment and ES ranged from -0.33 (0.10) to -0.41 (0.10), depending on the environment used as the intercept environment   between growth traits and ES varied from -1.00 to 0.94 (median = -0.386) as shown in In the previous aquaculture studies on GxE in growth, the  at E2) . The rgSubstantial additive genetic variance of macroenvironmental sensitivity (ES) for both observed and log-transformed body weight (BW) indicates potential for genetic response to selection on ES. After the log-transformation of BW, the variance components of ES were reduced but The Scheiner  is a desresponse , the cohresponse .rg = -0.27) and the presence of heterogeneity of genetic variance  –21,30–39= 0.001) . This clTo gain more understanding of potential for a change in sensitivity, we calculate accuracy of selection by using Komoldin and Bijma’s equation  . For ins,slσ^Ph) –44. WhenThe genetic correlation between slope and intercept is negative when the intercept is placed either in BE (-0.41) or PE (-0.33). In other words, in our data, placing the intercept in one or the other environments has no large effect on the magnitude of genetic correlation between intercept and slope. Hence, selection for fast growth in one environment will lead to increased sensitivity, i.e., to steeper negative slopes. The genetic correlation is low enough that both traits can be improved simultaneously. However, this would compromise the genetic gain in the selected environment. When looking at the sires with the most extreme EBVs for slope, the sires with flat slope EBVs have low overall growth in both environments . It is uAlthough, the genetic correlations in this study are negative, the sign of genetic correlation will be the opposite (from negative to positive) when setting the environmental variable for example to -1 and 0. The pros and cons of the reaction norm model are detailed by Sae-Lim et al. . Consideantagonistic selection, i.e., selection for high value of phenotype in an environment that has a lower phenotype comparing to another environment, reduces ES, while synergistic selection, i.e., selection for high value of phenotype in an environment that has a higher phenotype comparing to another environment, increases ES. Falconer's review [It is possible to gain additional understanding of ES in rainbow trout by considering Jinks-Connolly’s rule ,46 when s review ,47 proveKG is the ratio of the additive genetic variances of a trait in high-performing environment to low-performing environment. In the presence data, BE is the high-performing environment. Hence, the KG can be calculated as 16754.9/18040 = 0.93. Substituting KG (0.93) and rg (0.73) into the equation results in the genetic correlation between mean performance and sensitivity of -0.11. Hence, selection for increased mean performance of BW or the current selection index may increase sensitivity in Finnish rainbow trout population across the two environments, a conclusion similar to if selection is practiced only in one of the environments.In some breeding programmes, BW is selected in two environments to improve mean performance of BW over the two environments. However, there may be a trade-off between selection for increased mean BW and decreased ES . To undeDaphnia galeata at three temperature levels, 10, 15 and 20°C [It may be interesting to limit such trade-off by implementing restricted selection on environmental sensitivity, i.e., zero change in environmental sensitivity while selecting for high phenotype. In case of 2 environments, it is possible to maintain genetic gains of BW in two environments and thus ES is maintained stable. Alternatively, if the selection index includes mean BW across environments and sensitivity , selectiand 20°C . This teand 20°C . InteresThe future research may continue to identify responsible environmental variables explaining GxE between environments . SubsequIn animal breeding, reduced environmental sensitivity is considered to be beneficial in many aspects. First, it improves stability of animal performance across environments. Farmed fish being well adapted to multiple environments and variable environmental conditions may increase overall survival and animal welfare. Secondly, this simultaneously increases aquaculture industry profit because stability in fish performance may lead to on average higher production. However, the opposite is arguable. First, farmed animal performances are expected to respond positively when an environmental variable is improved. For example, fish are expected to grow faster when quality of feed is improved. Second, if a mean performance of a fish stock or a genotype is low and the genotype does not respond to any change across environments, it may not be desirable for a fish breeder because fish will perform poorly across environments . PositivGenetic changes in macroenvironmental sensitivity of body weight in rainbow trout can be expected due to high additive genetic variance. Macroenvironmental sensitivity is increased when selecting for high body weight in either environment. To avoid an increase of macroenvironmental sensitivity while selecting for body weight, it is possible to maintain fixed genetic gains of body weight in two environments and thus macroenvironmental sensitivity is maintained stable. Alternatively, if the selection index includes mean body weight across environment and macroenvironmental sensitivity, selection index weights producing desired genetic gains should be implemented.S1 Appendix(DOCX)Click here for additional data file.S2 Appendix(DOCX)Click here for additional data file.