Bos taurus primigenius). Nowadays in Romania, less than 100 Grey animals are being reared and included in the national gene reserve. We examined the genetic diversity among Romanian Grey, Brown, Spotted and Black and White cattle breeds, with a particular focus on Romanian Grey through the use of (i) 11 bovine specific microsatellite markers on 83 animals and (ii) 638 bp length of mitochondrial DNA (mtDNA) D-loop region sequence data from a total of 81 animals. Both microsatellite and mtDNA analysis revealed a high level of genetic variation in the studied breeds. In Romanian Grey a total of 100 alleles were found, the mean number of observed alleles per locus was 9.091; the average observed heterozygosity was 0.940; the Wright’s fixation index (FIS) was negative (-0.189) and indicates that there is no inbreeding and no selection pressure. MtDNA analysis revealed 52 haplotypes with 67 variable sites among the Romanian cattle breeds without any insertion or deletion. Haplotype diversity was 0.980 ± 0.007 and ranged from 0.883 ± 0.056 (Brown) to 0.990 ± 0.028 (Spotted and Black and White). The highest genetic variability of the mtDNA was recorded in the Grey breed, where 18 haplotypes were identified. The most frequent mtDNA D-loop region belonged to T3 haplogroup (80.247%), which was found across all studied breeds, while T2 haplotypes (16.049%) was only found in Grey, Spotted and Black and White genotypes. The T1 haplotypes (3.704%) were found in the Grey and Spotted. The current results contribute to the general knowledge on genetic diversity found in Eastern European cattle breeds and could prove a valuable tool for the conservation efforts of animal genetic resources (FAnGR).The Eastern European Grey cattle are regarded as the direct descendants of the aurochs ( Cattle are an important livestock species that have played a special role in the human history and culture, and had a considerable impact on human society. The worldwide population of cattle is estimated to 1.4 billion animals, of which 159 million (11%) are found in Europe and Central Asia . RomaniaBos taurus mtDNA were carried out and revealed three predominant and geographically structured taurine haplogroups: T1 found in Africa; T2 originates in the Near East and Western Asia; and T3 found in Europe [Currently, in the European countries a total of 464 cattle breeds are classified as either local or regional according to the list of Domestic Animal Diversity ,6. Howevn Europe ,8 and orn Europe . Strong n Europe .th lactation, with a content of 4.71% fat and 3.71% protein [Autochthonous cattle breeds, important in agriculture, are considered historical heritage and valuable genetic resources due to their adaptation to local conditions and the unique genetic material, aspects that are important for breeding and conservation programmes. The Romanian Grey is a valuable reservoir of genes with high importance for the Romanian agriculture. Studies conducted on the current population of Romanian Grey from Moldavia (Dancu—Iasi) outlined the low milk production potential of the breed, which ranged between 1589 kg in the first lactation and 2535 kg in the 5 protein . Despite protein .in situ conservation [Bos taurus primigenius and is included in the South-Estern European Podolian breed group . There is a small population of local Grey cattle in Estonia as well, which appear, however, to be a mix of old native cattle stock and modern breeds [Bos taurus primigenius descent [Transylvanian, Moldavian, Bucsan, Ialomițean and Dobrogean [The Romanian representatives for Food and Agriculture Organization (FAO) classified the Romanian Grey as endangered and in urgent need for ervation . The Romn breeds . The orin breeds , while tn breeds . Geneticn breeds –19. Unti descent , namely  descent . The breobrogean ,20,21. Tobrogean ,20,22, aThe aim of the current study was to evaluate the genetic diversity among Romanian Grey, Brown, Spotted and Black and White cattle breeds with a particular focus on the endangered Romanian Grey breed, in order to provide information for future breeding programmes and conservation management strategy of the breed. We investigated the above mentioned cattle breeds using the analysis of 11 microsatellite loci and the D-loop region of mtDNA. In addition, a comparative analysis between the current investigated cattle breeds and that of 525 mtDNA from GenBank was performed in order to reveal genetic relationships of the studied breeds with other breeds.Altogether 93 individuals belonging to different breeds were included in the study: Romanian Grey , Romanian Brown , Romanian Spotted , Romanian Black and White  and German Spotted . The animals were unrelated and were randomly selected from different herds and geographical regions in order to avoid genetic similarities . The colvena cava) of the animals in vacutainers containing K3EDTA as anticoagulant . Authorized veterinarians performed collection of blood samples. During the blood collection the animals were kept confined as to prevent injuries. Genomic DNA was isolated from 300 μl blood samples using a commercial DNA extraction kit , according to manufacturers’ instructions.Total DNAs were extracted from blood and semen. Fresh blood (2 ml) was collected from the tail vein , 50 μl DTT 500 mM and 10 μl protease K (20 mg/ml), protein precipitation with a 6M NaCl solution and extraction of the total genomic DNA by precipitation with 70% ethanol and isopropanol solution, followed by rehydration of the DNA in TE (TRIS 10 mM pH 7.4 and EDTA 1 mM). DNA concentration was evaluated spectrophotometrically, with NanoDrop-2000 , and visually by standard agarose gel electrophoresis (1% agarose (w/v) in TBE).The research activities were performed in accordance with European Union’s Directive for animal experimentation (Directive 2010/63/UE). The experimental design, sampling collection protocols and procedures were approved by the Ethics Committee of the Research and Development Station for Bovine Arad, throughout the Decision no.14/06.12.2012.® Analysis software (Applied Biosystems) was used to determine the PCR fragments sizes by comparison with internal size standards Rox.All animals were genotyped with a panel of eleven microsatellite loci: TGLA227, BM2113, TGLA53, ETH10, SPS115, TGLA126, TGLA122, INRA23, ETH3, ETH225 and BM1824, respectively. These loci are recommended by the International Society for Animal Genetics (ISAG) / Food and Agriculture Organization of the United Nations (FAO) Advisory Committee for genetic diversity studies [O) and expected heterozygosity (HE) according to Levene [ST values to estimate phylogenetic relationships among breeds. In addition, the Factorial Correspondence Analysis (FCA) was performed to visualize the relationships, possible admixtures among the individuals from different breeds using GENETIX v. 4.05.2 [ARLEQUIN v.3.1. , GENEPOPo Levene . Breed go Levene . Genetico Levene . Deviatio Levene , using to Levene . Parameto Levene  was usedo Levene  was usedo Levene  and Brooo Levene . Polymoro Levene . Sign, so Levene  in ordero Levene  was used. 4.05.2 .Bos taurus mitochondrial D-loop DNA reference sequence [5’-CAGAATTTGCACCCTAACCAA-3’ and reverse: 5’-GGGGCCTGCGTTTATATATTG-3’. Polymerase chain reaction (PCR) was performed in a 10 μl reaction mixture containing 60–80 ng of genomic DNA, 0.1 mM dNTP, 2 mM MgCl2, 0.7 U of GoTaq DNA Polymerase, 5X Colorless GoTaq Reaction Buffer  and 10 pmol of each primer. PCR conditions were as follows: 10 min at 95°C, 35 cycles of 30s at 95°C, 45 s at 60°C, 30s at 72°C and a final step at 72°C for 10 min. Sequencing reactions were performed by Macrogen Inc. (The Netherlands)Amplification was carried out by using primers designed to amplify an 825 bp fragment length from the D-loop of mitochondrial DNA (mtDNA) between positions 103 and 927 (accession number U92243), based on the published sequence . The twoSequences were aligned using CLUSTALW , and manIn addition, networks were also calculated using a median-joining approach with default settings using NETWORK v. 4.111 . NetworkE value was 0.794, with a minimum value of 0.574 (SPS115) and a maximum value of 0.890 (TGLA122). The observed heteorozygosity (HO) values were ranging from 0.552 (SPPS11) to 1.000 .Eleven bovine microsatellite markers were successfully amplified in 83 animals from the five breeds: Romanian Grey (N = 29), Romanian Brown (N = 16), Romanian Spotted (N = 13), Romanian Black and White (N = 13) and German Spotted (N = 12). A total of 124 alleles were found at the eleven loci in the 83 animals belonging to five breeds. In this study we focused on Romanian Grey, therefore diversity indexes for the Grey breed are presented in IS. The global FIS was estimated to -0.197 and was not significantly different from zero (P < 0.001).The mean expected heterozygosity ranged from 0.755 (Romanian Spotted) to 0.794 (Romanian Grey), while the observed heterozygosity ranged from 0.895 (Romanian Spotted) to 0.966 (Romanian Brown), . A heterThe estimated mean Ne for the Romanian Grey population was 37.8 (95% CIs = 25.0–67.2), 31.7 (95% CIs = 23.7–45.4) and 46.3 (95% CIs = 34.2–68.3) under different excluded allele frequencies: <5%, <2% and <1%, respectively. LDNe estimated finite estimates only in that breed.ST values indicate that around 4.5% of the total genetic variation could be explained by differences between breeds and the remaining 95.6% may correspond to differences between individuals.The calculated FP = 0.001) model. The Wilcoxon test, which gives high statistical accuracy and can be used for a low number of polymorphic loci and any number of individuals, also indicated an excess in heterozygosity and bottleneck in one model with probability values of 0.002 (IAM). Mode-shift indicator test as a second test for potential bottleneck was used. The allelic class and proportion of alleles showed a normal „L” shaped distribution for all studied breeds . The highest genetic differentiation was found between Romanian Brown and the Romanian Spotted, with pairwise FST: 0.381 and Romanian Brown and Romanian Black and White (0.257). Romanian Grey had the least genetic differentiation from the Romanian Spotted (0.031).Pairwise estimates of F Spotted . This brBased on the result of an assignment test, 30.77% (Romanian Spotted) to 81.25% (Romanian Brown) of individuals were correctly assigned to their breeds .ST genetic distance values (A phylogenetic tree was constructed using the neighbour—joining (NJ) method based on Fe values  and revee values .Similar clustering results were obtained for phylogenetic relationship of the individuals from the studied breeds by factorial correspondence analysis . The firAfter aligning and trimming the sequences of 32 Romanian Grey, 19 Romanian Brown and 15–15 Romanian Spotted and Romanian Black and White with the fragment size of 638 bp were further analysed. The haplotypes, haplogroups and variable nucleotide positions observed in Romanian cattle breeds are presented in relation to the reference sequence V00654  from GenAmong the 81 samples of the Romanian breeds, 52 haplotypes (GenBank accession numbers: KR608070-KR608121) were detected by 67 polymorphic sites . EighteeAs expected for the European cattle breeds, the T3 haplogroup had the highest frequency (80.247%). A total of 65 animals were assigned to the T3, thirteen to the T2 (16.049%) and three to T1 (3.704%) . The T3 Haplotype diversity was 0.980 ± 0.007, ranged from 0.883 ± 0.056 (Romanian Brown) to 0.990 ± 0.028 (Romanian Spotted and Romanian Black and White). Nucleotide diversity values were also high in all studied Romanian breeds.Median-joining network of 52 haplotypes was constructed  and showFs test, which is based on the distribution of haplotypes, was negative and significantly different from zero for all breeds, except Romanian Brown. Nonsignificant Harpending's raggedness index and sum of square deviations (SSD) indicates a good fit and support the expansion. SSD values were not significant and Harpending’s raggedness indexes were also positive and non-significant, indicating a good support of past expansion  and Portuguese and Spanish breeds .Our sequences were aligned with 525 mtDNA cattle sequences with fully overlapping standard 379 bp in length obtained from the GenBank database . The RomCurrent results show that there is a large genetic diversity among and within the Romanian local cattle breeds. However, some breeds are at risk because of the human activities such as: pollution, agriculture intensification, overexploitation of natural resources etc. Romania has a unique geographical localization since it is part of the Carpathian basin and Balkanic region, and has been affected by the different neighbouring biogeographical regions, while maintains a significant genetic reservoir for several domestic species: Mangalica and Bazna pigs, Carpatina and Banat’s White goats, Transylvanian naked-necked chicken, Hucul horse, Turcana and Racka Zackel sheep, the diverse Tsigai sheep group, Transylvanian and Palas Merino, Romanian buffalo etc. and wild animal species: brown bear, wolf, European hare, wild boar, etc. ,50–57.Genetic diversity of cattle that is being lost or at risk is of great value to the agricultural, economic, social and cultural sectors. From that point of view every country must take action to conserve the local cattle breeds which are better adapted to their environment and are important for their unique genetic resource and to prepare conservation and breeding strategies for all endangered breeds and populations. However, these actions are impossible without the knowledge of the genetic diversity of the existing breeds and populations. There is a few number of genetic studies about Podolian cattle ,19,58–62For the investigated breeds a number of 124 alleles were detected at the 11 studied loci. The highest number of alleles for all investigated breeds was founded at TGLA122 locus (20). At this locus an emphasized genetic difference among the studied breeds was found. Thereby, 13 alleles were detected in Italian Podolian cattle, 26 in Turkish cattle, 11 in Slavonian Syrmian Podolian cattle and 23 in Croatian Podolian cattle –64. For The average number of alleles per locus in Romanian Grey cattle was of 9.091, higher than reported in other Podolic cattle, such as in Italian Podolica (8.5), Bulgarian Grey (7.6) ,62 and lThe Romanian Grey breed showed a high level of mean expected (0.794 ± 0.083) and observed (0.940 ± 0.127) heterozygosity values. At the TGLA122 locus the observed heterozygosity was, 0.740 in Italian Podolian cattle, 0.570 in Slavonian Syrmian Podolian cattle and 0.644 in Austrian and Hungarian cattle breeds ,61,66. TAnother parameter analyzed in Romanian Grey breed was the polymorphism information content (PIC). None of the loci showed PIC values lower than 0.5. PIC of all 11 loci in Romanian Grey breed was quite high with the average of 0.752. Analyzing the individual loci, the highest value for this parameter was observed among TGLA122 (0.863), TGLA53 (0.842) and INRA23 (0.810) loci. In a previous study on Simmental breed conducted on the same 11 loci, the PIC value ranged from 0.58 (ETH10) to 0.88 (TGLA53) with the average of 0.75 .E) was also observed in present study [IS) for each breed was negative. The negative values of FIS, ranging from -0.235 (Romanian Brown) to -0.189 (Romanian Grey) represents an excess of heterozygotes and indicates that there is no inbreeding in all investigated Romanian breeds. The studied breeds point out a high level of genetic variation visible from a high number of alleles per locus. The most genetically variable breed was Romanian Grey.Overall Ho values were higher than He for all investigated breeds. The mean expected  and observed  heterozygosity for Romanian Brown, Romanian Spotted and Romanian Black and White breeds was found to be higher than those reported for Brown Swiss , Holstein Friesian , Simmental , and Piemontese  . Contrarnt study . The inbBottleneck analysis revealed that the breeds have not undergone any recent bottlenecks, neither any recent reduction in the effective population size, they are in mutation drift equilibrium.The assignment tests , neighboOur study included mtDNA data of Romanian cattle breeds from geographical regions not characterised previously, the Romanian Grey breed considered to be endangered. In spite of few remaining Romanian Grey animals, all three haplogroups T3, T2 and T1 were identified. Analysis of variability of the mtDNA D-loop sequences revealed a high number of haplotypes (52) and polymorphic sites (67) among the 81 animals without any insertion or deletion. In case of Romanian Grey cattle, among 32 animals, 18 haplotypes were identified, indicating a high genetic variability (0.927) of the maternal component of the breed. The detected number of haplotypes in our study was larger than detected in other Podolian-like breeds. In Istrian cattle 50 animals were analysed with 13 haplotypes, in 47 Slavonian Syrmian Podolian cattle 5 haplotypes , and in The mismatch analysis and the two statistically significant neutrality tests and non-significant raggedness and SDD indices applied to the mtDNA D-loop sequences support a demographic expansion of all studied breeds. However the absence of the star-like pattern with a highly dominant haplotypes among the detected haplotypes in the MJ network  does notThe current study on molecular characterization of local breeds can contribute to the conservation of the Romanian Grey breed. The results obtained within the small population of endangered Romanian Grey cattle proves the availability of high genetic variability and highlights the relevance of the breed in forming of the overall genetic cattle biodiversity. The high genetic variability found might be the outcome of low selective pressure for production traits. Therefore, the genetic improvement of those productive performances could be a serious threat in the process of preservation the genetic diversity of Romanian Grey breed and must be avoided. In order to conserve the few remaining animals some major measures must be applied. We recommend that immediate actions to be taken and proper allocation of financial resources to be made available in order to conserve the valuable genetic variation of the Romanian Grey cattle breed, which still represents a reservoir of genetic diversity. Further, other priority measures like semen storage and artificial insemination could be applied in order to preserve the autochthonous Romanian Grey breed. Saving the Romanian Grey breed from extinction will be in benefits of both the Romanian agriculture and the international genetic heritage.A surprisingly high level of genetic diversity was found in the endangered Romanian Grey cattle population. All of our results confirmed that the breed’s genetic diversity is preserved correctly through the application of the current conservation program, however the number of the Romanian Grey individuals is extremely low and needs to be urgently increased. Results of the present study can doubtlessly help in improving the present conservation program in order to preserve the genetic diversity and protect the Romanian Grey breed from genetic loss. Furthermore, the results contribute to the general knowledge on genetic diversity found in Eastern European cattle breeds and could prove a valuable tool for the conservation efforts of animal genetic resources.In order to design proper and effective conservation strategies, further attention and precise knowledge about all existing animals have to be taken into account. Therefore, urgent further studies, with other part of the mitochondrial genome  and Y chromosome , higher number of microsatellites, ancient DNA analysis or to involve all existent animals from the Romanian Grey breed, are recommended that ensure a better understanding of the actual genetic structure and past history of these studied breed.S1 Table(DOC)Click here for additional data file.S2 Table(PDF)Click here for additional data file.S3 TableSequences were downloaded from GenBank.(XLSX)Click here for additional data file.


Tc) of tetragonal Fe1+δSe was enhanced from 8.5 K to 44 K by chemical structure modification. While insertion of large alkaline cations like K or solvated lithium and iron cations in the interlayer space, the [Fe2Se2] interlayer separation increases significantly from 5.5 Å in native Fe1+δSe to >7 Å in KxFey1−Se and to >9 Å in Lix1−Fex(OH)Fey1−Se, we report on an electrochemical route to modify the superconducting properties of Fe1+δSe. In contrast to conventional chemical (solution) techniques, the electrochemical approach allows to insert non-solvated Li+ into the Fe1+δSe structure which preserves the native arrangement of [Fe2Se2] layers and their small separation. The amount of intercalated lithium is extremely small (about 0.07 Li+ per f.u.), however, its incorporation results in the enhancement of Tc up to ∼44 K. The quantum-mechanical calculations show that Li occupies the octahedrally coordinated position, while the [Fe2Se2] layers remain basically unmodified. The obtained enhancement of the electronic density of states at the Fermi level clearly exceeds the effect expected on basis of rigid band behavior.The superconducting transition temperature ( The Li1x1−Tex results in the formation of LiyFeSex1−Tex for 0.25 ≤ x ≤ 1  are not consistent with the claimed formation of the Li-intercalated phases. Moreover, the structural characterization is insufficient. Carbon-coated FeSe-nanoparticles were investigated as an anode material for lithium-ion batteries with a sustainable reversible capacity of 340 mAh·g−1  Å and c = 5.5231(4) Å obtained from powder X-ray diffraction data . In the 1st cycle the reduction peak is at ca. 1.3 V, the oxidation peak is at ca. 1.85 V. Upon cycling, the potential of the reduction peak shifts towards more positive potential values (1.4–1.5 V), while the position of the corresponding oxidation peak does not change considerably, although a significant broadening of the peak occurs. Ex-situ PXRD data for the electrode material charged or discharged at different potential values show a drastic decrease of the reflections intensities of Fe1+δSe without noticeable lattice parameters variation. No evidence for new crystalline phases is observed (Cyclic voltammetry (CVA) studies of polycrystalline Fe. Li/Li+  revealedobserved . These fst cycle according to reaction The observed capacity value suggests irreversible decomposition of iron selenide in the 1st reduction curve including additional peaks at ca. 2.1, 1.8 and 1.6 V vs Li/Li+ (ca. 1.5 V), similarly to previous observationsca. 2.1 V to the electrolyte reduction at the carbon surface (ca. 11 mAh·g−1 (0.055 Li+ per f.u.). Further application of CA at 1.7 V for a few days did not result in significant growth of the current. This voltage is between the peaks of the assumed Li+ ion intercalation and the main conversion process in the differential capacity plot  all samples exhibit the same behavior: after turning-off the electrochemical cell, the potential increases rather rapidly from 1.7 V to approx. 2−2.5 V vs. Li/Li+ as in case of initial Fe1+δSe. We regard this effect as the result of the spontaneous decomposition of the intercalated phase LixFe1+δSe. Indeed, magnetic measurements of the electrode treated at 1.7 V vs. Li/Li+ and kept inside the glovebox at room temperature for several days showed the presence of the initial Fe1+δSe phase only.The reduction process was studied in more detail by means of slow scan-rate linear voltammetry (LVS) and potentiostatic intermittent-titration technique (PITT) combined with chronoamperometry (CA). This study reveals the complex character of the 1s Li/Li+  precedin surface . The chaity plot . We supp1+δSe electrode treated with LVS and CA at 1.7 V vs. Li/Li+ unveiled drastic changes in comparison with native Fe1+δSe (1+δSe15M(H) dependence measured at 2 K is decorated with a weak diamagnetic response seen in low fields due to the presence of the superconducting phase. Thus, the redox process between 1.8 V and 1.6 V vs Li/Li+ is characterized by a very small capacity value and accompanied by the conservation of the Fe1+δSe structure. The observed capacity value corresponds to a small amount of Li reacted with Fe1+δSe and being intercalated as Li+. The spontaneous potential increase after cell turning-off together with the magnetic measurements data can be understood taking into account the instability of the product of the electrochemical redox process if the potential is not applied.Magnetic measurements of the polycrystalline Fe Fe1+δSe . The tem1+δSe in the applied electrochemical potentials, in-situ PXRD studies in galvanostatic mode (GCPL) were performed. In the potential region of 2−1.5 V vs. Li/Li+ (corresponding to approximately 0−10 mAh·g−1 in the E−C curve), the original Fe1+δSe structure exhibits no measurable alterations , but the 001 reflection broadens and its center of gravity shifts to slightly lower angles  results in the rapid decomposition of Fe1+δSe monitored by the drastic decrease in the intensities of both the 001 and 101 reflections .To clarify the behavior of Feerations . The intr angles . Concordlections . No new 1+δSe electrode. In agreement with earlier results1+δSe to metallic Fe. Electrochemical data together with in-situ PXRD measurements unveil that at higher potentials (ca. 1.7 V vs. Li/Li+) a subtle redox process takes place. This process conserves the initial structure topology and leads to the formation of a ternary phase LixFe1+δSe with slightly larger lattice parameter c compared with the one of Fe1+δSe. The potential increase which is observed after turning-off the electrochemical cell can be interpreted as spontaneous decomposition of the ternary phase due to its instability (cf. below). The small lattice parameters variation together with the small charge transfer indicates the insertion of a tiny amount of Li+ which leads to the formation of the phase LixFe1+δSe with preserved structure topology. However, studies of bulk LixFe1+δSe are hampered by its extreme instability and the overlap of intercalation and decomposition processes in the narrow potential region.The results above can be summarized in a scenario for the electrochemical processes of a polycrystalline Fe1+δSe crystal aggregates instead of a polycrystalline electrode can kinetically inhibit the decomposition process. Polycrystalline electrodes consist of a large amount of relatively small crystallites (1–10 μm in size), crystal aggregates contain several single crystals of much larger size (100–1000 μm). Surface effects  in the latter case should be much less pronounced. Furthermore, the application of crystals allows avoid the use of carbon black and polyvinylidenfluorid in the measurement cell that makes the corresponding electrochemical side reactions negligible. The electrochemical behavior of Fe1+δSe crystal agglomerates differs only slightly from that of the polycrystalline electrodes (ca. 1.8 V vs. Li/Li+. It is followed by the major peak (attributed to the conversion reaction) with an onset at ca. 1.7 V. This major peak is distorted in the CVA curve because the drastic volume change causes the destruction of the crystal. I–t curves obtained by long-time potentiostatic experiment at 1.7 V are close to those of the polycrystalline material. The current (in module) increases in the course of the 30 hours experiment. The amount of charge passed through the cell before the current increases corresponds to ca. 0.06 Li+ per f.u.We assumed that the use of Feectrodes . The dif1+δSe crystal aggregates after treatment at 1.7 V indicate the presence of inhomogeneous superconducting phases. The temperature dependence of the magnetic susceptibility (T) curve shows three kinks at T1 ~ 9 K, T2 ~ 30 K and T3 ~ 44 K. It becomes positive in the range T2 < T < T3. The T1 anomaly can be attributed to the superconducting phase transition in binary  Fe1+δSe which predominates in the studied sample. The magnetization curve measured at 2 K  can be estimated as 0.06 Li per f.u. Fe1+δSe. The result is in perfect agreement with the electrochemical data but this agreement merely reflects the tendencies for the different redox processes since capacity values are strongly sensitive to experimental conditions like the distribution of Fe1+δSe particles in the electrode active layer and its thickness.The enhancement of ectrodes . The higIn-situ PXRD data together with the magnetic measurements indicate bulk (i.e. not surface) formation of the superconducting phase which is structurally similar to tetragonal Fe1+δSe. In case of tetragonal Fe1+δSe, such a Tc increase is mainly achieved by electron doping4561+δSe structure can be considered as one possible way of electron doping. Due to the close-packed features of the Fe1+δSe structure (defect anti-fluorite blocks), there are tetrahedral and distorted octahedral voids (ca. 2.28 Å) are significantly shorter than those in the Li2Se structure (2.6 Å)ca. 2.57–2.95 Å) are comparable to the ones in Li2Se, while the Li−Fe distances (ca. 2.39 Å) are too short. However, shift of the Li in [001] direction results in a square-pyramidal coordination of Li with longer Li−Fe contacts.al voids . For tet+ coordination. Here, Li cations stabilize the whole structure because of Fe3+/Fe2+ reduction. It should be noted that the size of the anti-fluorite [Fe2As2] layer in LiFeAs is very close to that of the [Fe2Se2] layer in Fe1+δSe. Obviously, complete Li insertion into Fe1+δSe would correspond to the complete reduction Fe2+/Fe1+, thereby limiting the amount of inserted Li.A similar effect is observed in LiFeAs (PbClF type)x was calculated to investigate the stability of LixFeSe with Li atoms occupying different sites. From chemical experience, it is expected that Li occupies tetrahedral or octahedral voids between the Se atoms (P1) in a Li1/18FeSe super cell  with respect to its decomposition into Li2Se, Fe and FeSe. Li1/18FeSe is not stable and thus should, under equilibrium conditions, decompose towards FeSe, Li2Se and Fe with an energy gain between 0.20 eV (for non-magnetic Fe clusters) and 0.42 eV (for magnetic Fe clusters) per Li  per Li . This isxFe1+δSe, the change of the electronic density of states near the Fermi level EF upon Li insertion is a crucial parameter. Comparison of the calculated DOS (1/18FeSe and the parent compound FeSe yields three main results: (i) Surprisingly, the insertion of Li leads to a sizeable re-structuring of the DOS which goes beyond the changes expected on basis of a rigid band picture and results in an increase of the DOS at EF. (ii) The total band width is basically unchanged. (iii) The main increase of the DOS at EF originates from more distant Se and Fe neighbors and not from the closer Li environment. This demonstrates that the additional electron donated by the Li ion is rather delocalized. However, a possible formation of polarons  might modify this picture.For a better understanding of the superconducting properties of Liated DOS  for Li1/1+δSe. The appearance of a DOS spike at the Fermi level is in line with the observed higher critical temperature of the superconductivity.The observed changes in the electronic structure evidence the formation of a ternary compound which is chemically different from Fe1+δSe structure pattern has a strong tendency to preserve the electron count of the [Fe2Se2] layers. Thus, the homogeneity range of these structure units with respect to heterovalent substitution or the insertion of electropositive metals is rather limited. Even in case of structure alterations which result in substantial increases of the Tc values, the charge of the anti-fluorite layer changes only marginally7Tc-enhancement of Fe1+δSe can be achieved by a merely minute reduction of the Fe cations without increasing the [Fe2Se2]-interlayer separation. Therefore, it appears promising to investigate if the carrier concentration  influences the Tc-value of layered iron-based selenides to a much larger extent than the interlayer separation.In summary, our results are in line with earlier findings that the Fe1+δSe was prepared under argon atmosphere using iron pieces  and selenium shots . Glassy carbon crucibles with a lid were filled with Fe/Se mixture with molar ratio close to 1 : 1 (Fe : Se = z : 1 and 0.98 ≤ z ≤ 1.02), placed into vacuumed quartz ampoule and subjected to high-temperature annealing  scanning microscope. Phase identification and lattice parameters determination  were performed using room-temperature X-ray powder diffraction data obtained by image plate Guinier camera Huber G670, . Crystalline Ge  in reflection mode. The electrodes previously were covered by air-protective one side sticky tape.Microstructure investigation of polycrystalline samples and crystals was performed with JEOL JSM 5510  was used. The glass fiber was applied as separator. Working electrode  was prepared by screen printing the slurry containing the active material, carbon black and PVDF (mass ratio 85 : 5 : 10) in N-methylpyrrolidone onto Al-substrate. Load of active material was approx. 1 mg·cm−2. In a case of Fe1+δSe crystals, one crystal was placed by its flat face between current collector and separator directly. Electrochemical cells were assembled and disassembled in Ar atmosphere. Potentiostat/galvanostat Biologic VMP-3 was used for data collecting. Several electrochemical techniques were applied: linear and cyclic voltammetry (LVS and CVA) between 1.0 V and 2.5 V vs. Li/Li+ with scan rate of 0.02 mV·s−1 and 0.05 mV·s−1; potentiostatic intermittent titration technique (PITT) with 0.01 V step in combination with chronoamperometry (CA) at selected potentials; and galvanostatic charge with potential limitation (GCPL) at current density of approx. 20 mA·g−1.Electrochemical treatments were performed in two-electrode setup with metallic Li as counter and reference electrodes. The commercial electrolyte  in reflection mode. During the experiment, a constant current of −15 μA (approx. 20 mA·g−1) was applied at the working electrode; diffraction patterns were collected every 30 min until the potential decreased to 0.7 V vs. Li/Li+ and the electrochemical cell was switched off.The experiment was carried out on polycrystalline electrodes. The two-electrode electrochemical cell was analogous to the mentioned above but with Be window at the anode side and porous polypropylene separator  instead of glass fiber. The diffraction data were collected using Bruker D8-Advance diffractometer  applying fields of 10–1000 Oe. The specimens  were put inside plastic holders and then placed into the magnetometer in Ar atmosphere.The lithium/iron ratio in electrochemically treated electrodes was analyzed by ICP-MS by means of ICP-MS spectrometer ELAN DRC II.k points depending on the cell volume. For all calculations, the respective experimental lattice parameters have been used.Relativistic density functional (DFT) electronic structure calculations were performed using the full-potential local-orbital FPLO code22How to cite this article: Alekseeva, A. M. et al. New superconductor LixFe1+δSe  by an electrochemical route. Sci. Rep.6, 25624; doi: 10.1038/srep25624 (2016).


Many applications in ecological genetics involve sampling individuals from a mixture of multiple biological populations and subsequently associating those individuals with the populations from which they arose. Analytical methods that assign individuals to their putative population of origin have utility in both basic and applied research, providing information about population-specific life history and habitat use, ecotoxins, pathogen and parasite loads, and many other non-genetic ecological, or phenotypic traits. Although the question is initially directed at the origin of individuals, in most cases the ultimate desire is to investigate the distribution of some trait among populations. Current practice is to assign individuals to a population of origin and study properties of the trait among individuals within population strata as if they constituted independent samples. It seemed that approach might bias population-specific trait inference. In this study we made trait inferences directly through modeling, bypassing individual assignment. We extended a Bayesian model for population mixture analysis to incorporate parameters for the phenotypic trait and compared its performance to that of individual assignment with a minimum probability threshold for assignment. The Bayesian mixture model outperformed individual assignment under some trait inference conditions. However, by discarding individuals whose origins are most uncertain, the individual assignment method provided a less complex analytical technique whose performance may be adequate for some common trait inference problems. Our results provide specific guidance for method selection under various genetic relationships among populations with different trait distributions. The widespread implementation of highly polymorphic genetic markers has opened important new avenues in ecological genetic studies. For example, it is now possible using genetic stock identification (GSI) to accurately allocate a mixture of unknown individuals to probable source populations, either as individuals or as modeled proportions by using a baseline data set of genetic information from individuals of known origin Oncorhynchus spp.). These studies are possible because of the availability of powerful, range-wide, genetic baseline data. Numerous genetic baselines have been developed to support mixed fishery applications for harvest management. For example, a large interagency collaboration, the Genetic Analysis of Pacific Salmonids (GAPS) consortium, recently produced a shared, standardized, microsatellite baseline, data set for Chinook salmon (O. tshawytscha) that nearly covers the species range There is much current interest in assignment of individuals to population of origin Orcinus orca) trophic studies of predation on specific Chinook salmon populations The availability of these genetic baseline data sets has sparked considerable interest among ecologists and oceanographers who recognize that genetic information from mixed fishery samples can be used to infer population-specific ecological traits among sources. Most applications of individual assignment (IA) relate to study of genetic structure, connectivity and estimation of population genetic metrics and parameters The current general approach of trait inference studies is to sample individual animals in mixed-population aggregates, use genetic information to assign individuals to source populations , and then treat their ecological traits as a representative sample of those traits in in the reporting groups contributing to the mixture, e.g., Despite the significant power provided by many contemporary genetic baseline data sets,  is first made at the level of populations, and then those assignment probabilities are combined to the higher-level reporting groups . Bias will be minimized when the reporting groups closely reflect the patterns of genetic relatedness among populations. However, research hypotheses or management questions may require genetically similar stocks to be placed in different reporting groups. Reporting groups that are not clearly distinguished genetically result in lower probabilities of assignment to true reporting group of origin because assignment probability may be split between genetically similar reporting groups. Those split probabilities can result in omission of that individual fish if no single assignment value exceeds the minimum probability threshold, e.g., 0.8. Even if the reporting groups were genetically distinct, the distribution of assignment probabilities will differ among stock groups, and omission of individuals on that basis could bias stock composition estimates. This bias may or may not extend to estimation of population-specific phenotypic and ecological traits depending on reporting group structure and trait distribution. Researchers generally assume the trait of interest varies independently from assignment probability, minimizing any bias in trait estimation, even in the presence of mixture composition bias. The current study explores potential violations of that independence when using different analytical models and under different distributions of the trait value and genetic similarity.Early applications of genetic markers used allozyme loci, whose low diversity largely precluded accurate IA. For that reason, research focused on the development of models that allowed direct estimation of the parameters of primary interest at the time, which was mixture composition in fisheries that included multiple source populations and regions In this study, we seek the same statistical robustness for ecological and phenotypic trait inference that has been demonstrated for mixture modeling as compared to IA. We extend the Bayesian genetic mixture model to incorporate parameters associated with phenotypic traits. The conditional maximum likelihood (CML) model of Bromaghin et al. To achieve the overarching goals above, we consider two diverse example applications, reanalyzing recently published data in light of simulation results. Finally, we make recommendations for specific methods based on their performance under various distributions of the ecological/phenotypic trait and genetic distance among populations. Although we draw heavily on Pacific salmon research, we emphasize the general applicability of our analyses and especially the diversity of genetic and non-genetic traits that can be examined. Our analysis is relevant to all marker classes . Applications are limited only by available baseline data for known-origin individuals. We expect increasing interest in our methods as those data become available for more taxa.Oncorhynchus tshawytscha) We explored two methods of using genetic data to estimate descriptive parameters of phenotypic traits among populations inferred from a genetic mixture. One method, IA, assigned individuals to putative population of origin, using a MAP rule with various probability threshold values to exclude individuals whose origins were less certain. Parameters of interest were then estimated by conditioning on the individual assignments and treating each partitioned subset as an independent sample from the contributing population. The second method utilized a Bayesian mixture model (BMM) to estimate directly the distribution of a phenotypic trait, irrespective of IA. Our study had two elements, first was a test of concept, an analysis of simulated data to inform and direct the re-analysis of two published data sets in the second element of our study. The reanalyzed data sets included fecundity in Yukon River adult Chinook salmon  were assigned dissimilar infection rates . The rates for the other four population groups were selected to maximize the correlation between the average FST and the difference between reporting group BKD infection rates . The opon rates .BKD infection (presence or absence) for a mixture individual was randomly determined by sampling from a Bernoulli distribution with mean infection rate established under either the positive or negative case. We simulated mixtures of 300 individuals with equal proportions (∼16.7%) from each of the six reporting groups, each population within a group contributing equally. We generated 25 replicate datasets for each of the negative and positive cases and analyzed each dataset with IA s and the estimated proportion of population s in the mixture sample c is the number of populations. ONCOR Method 1 was implemented by genetic mixture analysis software ONCOR The distributions of the phenotypic character among reporting groups were estimated from the mixture individuals assigned to the reporting groups and their measurements of the trait. A mixture individual was assigned to the reporting group for which its probability of assignment was maximal, so long as that probability exceeded a given threshold. Assignments to population of origin were considered uncertain when the probability of assignment fell below the threshold, and those individuals were excluded from further analysis. Origins of assigned fish were assumed without error, and their measurements of the phenotypic character were treated as a representative sample from each reporting group.To investigate the tradeoff between MAP threshold and potential assignment error, we examined the effect of using values for MAP-rule probability threshold ranging from 0.5 to 0.9. For the analysis of empirical data (see below), we utilized a probability threshold of 0.80. The expectation was that a higher probability threshold would result in more accurate individual assignments but at the cost of reduced sample size because a larger number of fish would have assignment probabilities below the threshold and would be excluded from analysis.The BMM was implemented by modifying the BAYES software program Bromaghin et al. Bromaghin et al. m (mF) with mid-eye to tail fork length (mL) from population group j was modeled as a normal variate with mean equal to Following The likelihood model, with notation slightly modified from M  =  number of mixture individuals,j,k of reporting group j,k of reporting group j given baseline allele frequencies p and Q in Lj are the lengths for population group j. Following p and Q were drawn from Dirichlet posterior distributions. Specifying the uniform prior distribution on In addition to the Dirichlet priors for Rhodes et al. m was modeled as a Bernoulli random variable (md) with probability j. The density is defined asmd = 1 if a fish was infected and 0 otherwise. We replaced the normal model of the fecundity-length relationship in Equation 5, with the Bernoulli model above (Equation 6). We used uniform  prior distributions for the infection rates, resulting in Beta posterior densities for The BKD infection of fish In the positive case, where genetically different Groups 1 and 6 had different BKD prevalence, and genetically similar Groups 4 and 5 had similar BKD rates, the BMM performed similarly to IA. IA estimates for Group 1, with a low BKD value, showed a slight negative bias at all probability thresholds, whereas the BMM slightly over estimated BKD prevalence . The oppDifferences were greatest between methods for the negative case, where genetically similar groups 4 and 5 had quite different BKD infection prevalence . Both methods over estimated the low BKD prevalence in Group 4 and under estimated the high BKD prevalence in Group 5 . Bias waOf 403 mixture individuals, 355 were assigned to one of three reporting groups  using a MAP probability threshold of 0.80 or greater, whereas 48 individuals (12%) fell below that probability threshold and were excluded from further analysis. Fecundity estimates were based on the remaining 41, 125, and 189 fish assigned to the Lower, Middle, and Upper Yukon River reporting groups, respectively. For reporting, we fit linear regression models Eight chains of 20,000 MCMC samples thinned by 10 were generated using the modified BAYES program. Convergence of the second halves of chains to the posterior distribution was confirmed, and simulated draws from the posterior distributions of the fecundity regression parameters were summarized with a mean and standard deviation for each reporting group . Note thOf the 373 mixture individuals with BKD data, 73 (20%) did not meet the 0.8 MAP probability threshold and were removed from further analysis. The BKD infection prevalence estimates were based on the remaining 23 fish assigned to the Fraser and Thompson group, 27 to the Nooksack, 142 to Whidbey Basin, 92 to the South Puget Sound fall-run and Hood Canal group, and 16 to South Puget Sound Spring. Infection prevalence rates were calculated as the fraction of fish with BKD assigned to each group , Fig. 5.In both example applications that re-analyzed published Chinook salmon data, we obtained similar estimates for the group-specific phenotypic traits from both IA and BMM. Our estimates were also comparable to those previously reported Our analysis of simulated data suggested that for simple mixture problems, such as a large mixture sample including only a few genetically distinct source populations, both IA and BMM perform similarly. This was especially true if the trait was positively correlated with genetic similarity among reporting groups. BMM provided slightly better accuracy and precision when the trait was negatively correlated with genetic similarity. In general, however, IA with a 0.8 MAP threshold performed better than expected. We suspect the favorable performance of IA observed here is attributable to a combination of the 0.8 MAP threshold maintaining adequate sample sizes and accuracy of IA based on the GAPS-Chinook microsatellite baseline.IA for trait inference does not necessarily suffer the same bias as IA for population mixture composition estimates Although bias was not necessarily expected in comparing IA and BMM (at least not where the trait is positively correlated with genetics), our results serve to underscore a classical problem in biological sampling – the need to balance accuracy of IA with sample size and therefore statistical power. On one hand, IA with a high MAP-rule threshold will better assure that individuals included in the analysis are accurately classified. On the other hand, if mixture samples are small to begin with, as they often are in ecological studies, then the number of individuals with assignment probabilities above the MAP threshold can be quite small. If one lowers the threshold to include more individuals, assignment error and therefore trait estimation bias can increase. This is not such a problem if the trait distribution is correlated with the genetic structure of reporting groups , but if genetically similar reporting groups have very different trait values, then clearly misassignments will bias estimates of population-specific traits. We saw signs of such bias in our simulation studies where we created trait distributions having both positive and negative correlation with genetic distance. The genetic data we used were based on a regional subset of the GAPS-Chinook microsatellite baseline. The 13 GAPS loci are highly variable, with nearly 500 alleles coast wide and highly significant allele frequency differences among populations and among regions Potential applications of our method are extremely broad, as has been noted previously In practical application of mixture analysis, decisions must be made regarding the structure of the reporting groups. Mixture allocations are made to individual populations and those allocations  with their associated trait values are combined to estimate the trait values for the reporting groups. Two considerations are important to this application, 1) different levels of genetic similarity among populations within reporting groups, and 2) potential variation in the trait of interest among populations within reporting groups. These considerations reflect a fundamental challenge of natural resource management and especially of fisheries. Rarely do management units fully align with population genetic structure. Genetically similar units sometimes have different trait values and must be managed separately. BMM tends to be more robust to these complications than IA We compare the performance of two available methods, individual assignment and Bayesian mixture models, for investigating the distribution of phenotypic traits among populations by using a single mixture sample and population-specific genetic baseline data. The most attractive feature of IA is its ease of implementation using available software . Moreover, despite theoretical shortcomings, it performs well under a range of conditions in which the number of individuals assigned to each group is adequate and assignment errors are relatively rare between reporting groups. As a precaution, we recommend careful assessment of the power of a baseline to accurately assign individuals to reporting groups that are of research or management interest. While the performance of BMM equals or exceeds that of IA, the former is more difficult to implement, and software is not available that is capable of handling the diverse array of traits that may be of interest. However, if a preliminary IA analysis indicates that the trait of interest appears negatively correlated with genetic similarity among populations, then the BMM approach is probably essential to provide minimally biased group-specific trait inference. If these situations are common, then additional effort may be warranted to develop custom software solutions.


Pinus halepensis Miller, on Mt. Carmel, Israel. We tested the existence of spatial genetic structure, which is expected due to the special spatial demographic structure of the post-fire seedling and sapling populations of this species. Explicitly, we asked whether or not seedlings that germinated under large, burned, dead pine trees are also their offspring. The results revealed that the post-fire seedling population is polymorphic, diverse, and reflects the pre-fire random mating system. In contrast to our prediction, we found no division of the post-fire seedling population to distinct sub-populations. Furthermore, as a result of post-fire seed dispersal to longer range than the average pre-fire inter-tree distance, seedlings found under individual burned trees were not necessarily their sole offspring. Although the population as a whole showed a Hardy-Weinberg equilibrium, significant excess of heterozygotes was found within each tallest seedlings group growing under single, large, burned pine trees. Our finding indicates the possible existence of intense natural selection for the most vigorous heterozygous genotypes that are best adapted to the special post-fire regeneration niche, which is the thick ash bed under large, dead, pine trees.To study the effects of wildfire on population genetics of a wind pollinated and wind dispersed tree, we have analyzed the genetic structure of a post-fire, naturally regenerating seedling population of  The preP. pinaster  is the nonrandom spatial distribution of genotypes and alleles, which commonly results from fine-scale aggregation of siblings within a population , reported a limited pre-fire pollen dispersal and consequently, significant genetic structure and high kinship. Hence, the post-fire open landscape permits pollen gene flow over greater distances.Shohami and Nathan , studyinP. halepensis, as a model for trees with wind dispersed seeds and pollen. Specifically we: (1) examined the genetic structure and variability of the first regenerating post-fire pine seedling population and compared it to near non-burned adult pine stands; (2) checked whether there was a spatial genetic structure in this regenerating seedling population, and (3) determined whether the tallest seedlings growing under large burned trees are also their descendant siblings. We hypothesized that: (1) The genetic structure and variability of the first post-fire seedling population is similar to that of unburned populations in the same geographical region . (2) There is a spatial genetic structure in the regenerating post-fire seedlings population; namely, the variation within the seedlings groups growing under a dead tree will be smaller than that among the seedling groups growing under different dead trees. (3) The seedlings growing under a burned tree are also its descendant siblings.Disentangling the effects of fire on spatial genetic structure of post-fire regenerating seedling populations is an important challenge that has not yet been addressed. In this research we aimed to study the effects of fire on the genetic composition, variability and spatial genetic structure of the first naturally regenerating post-fire seedling population of P. halepensis forest area in Lubim on Mt. Carmel, Israel (E35°00′ N32°44′). This site was an abandoned field that has been colonized by native pines about 60 years ago. The climate is typical east Mediterranean one with mean annual rainfall of 700 mm that falls mainly from December to February, and a long, hot and dry summer from May to October. The forest was completely burned  in autumn 2006 and was left for natural regeneration. Prior to the fire in 2006, the multi-ages stand (ca 140 ha), was comprised of about 150 large trees; in a distance of more than 1Km from any other pine population. The site has not been under fire prior to 2006.The study site was located within a native After the 2006 fire, within the 140 ha site, we have selected, numbered and mapped 20 large dead trees with canopy diameter of 5 to 10 m. The canopies of the chosen trees were at least 6 m apart  were crushed to powder using the TissueLyser device (QIAGEN). Lysis buffer AP1/RTL (QIAGEN) was added to the samples. DNA was extracted from the lysate using Biosprint 15 . Nine known nuclear microsatellite loci were amplified by PCR Table , 2012a. Amplification products were separated using ABI 3130xl Florescence-Reader (Applied Biosystems). Manual scoring of PCR product size with reference to a 500-Liz standard marker (Applied Biosystems) was made using peak scanner version 1.0 (Applied Biosystems) and GeneMapper software version 4 (Applied Biosystems).1A2 in a locus, all her offspring should comprise of either A1 or A2). If a group of seedlings presented more than such two mutual alleles, they should share more than one founder. Additional alleles can be contributed by various fathers. This test cannot confirm our “each group one mother” hypothesis, but it can negate it, if many seedling groups show a minimum of more than one mother that had contributed to their genetic structure.Unfortunately, we failed to amplify DNA from the burned trees, and thus could not genetically type them. Therefore, to estimate the minimal possible numbers of mothers for each seedling group, we compared the genetic structure of seedlings within each group, using the locus ITPH4516, which displayed 15 different alleles within the studied population. If a seedling group shared a mutual heterozygote mother tree, each seedling within the group should show either one of the two possible alleles in the ITPH4516 locus  from the full data set (N = 181). We used GenAlEx 6.41  of the three sets and the whole data . The four data sets did not differ from each other . Therefore, we considered our data set as representing the whole Lubim population. To substantiate that the Lubim population truly represents the Carmel native pine populations, we compared gene diversity parameters of Lubim to three additional populations , Hardy-Weinberg equilibrium (HWE), and genetic distance (D), for the whole Lubim seedling population and separately for each seedling group. Data were analyzed by Tools for Population Genetic Analyses (TFPGA) V 1.3  and calculated the level of unbiased expected heterozygosity (He), inbreeding coefficient . Considering the controversial use of Bonferroni correction . The inference of the probable number of clusters was extracted by the log likelihood for each putative number of populations , Ln P(D) = L(K), and by the delta K method  that divided the samples into possible homogenous groups (sub populations) according to their degree of similarity  for supplementary cluster analysis (UPGMA). The UPGMA cluster analysis assumed equal evolutionary rate along all branches which considered suitable for comparing regenerating seedlings groups within a population. Each of the 20 seedling groups had at least one individual in this analysis, and 17 groups had at least two seedlings.FIS) were generally negative ; genotypic frequency within individuals relative to the population (FIT) was positive and close to 0 . Heterozygote excess was observed for all microsatellites within each group  of 2.5 ± 0.1 alleles per locus per seedling group , of which the most frequent were of 158 bp (29.6%) and 140 bp (26.2%). Eighteen of the 20 seedling groups presented four or more alleles in this locus, allowing assessment of maternal contribution. At least 11 seedlings groups, each growing under different burned pine tree, were comprised of seedlings that originated from two or more different maternal trees Table .FST = 0.144 ± 0.032, and the number of migrants between group, Nm = 1.786 ± 0.243; Table rxy = 0.075; p = 0.32; R2 = 0.0056; y = 0.0001x + 0.0625). Molecular analysis of variance (AMOVA) indicated that most of the variance (95%) was found within groups and only 5% originated from differences among groups , also showed no indication of spatial genetic structure, or a tendency of individuals from the same group to be clustered together  that was not significantly different from zero, as was also found for other unburned populations in the Carmel   of Seed dispersal distance after fire should not be shorter than that in the absence of fire. The destruction of dense living pine canopies by fire creates two factors that have a positive effect on post-fire dispersal distances in pine samaras: wind speed in the burned forest at canopy height is higher than in the unburned forest, and the absence of the physical obstruction of a dense canopy increases the distance of seed dispersal  were found in most of the post-fire seedling groups, implying a general genomic phenomenon. Selective pressure that favors heterozygote individuals at the germination or establishment stages , thus highlighting the general phenomenon of the entire genome of the post-fire seedling group. The strict criteria for outlining balancing selection or heterozygote advantage selection put forward by Hedrick  cannot bTo conclude, the effects of fire on population genetic structure are complex. Our results indicate that the seedling groups growing in their favored regeneration niche under large burned trees are not entirely their sole offspring. The effective seed dispersal distances are probably larger than expected in the post-fire environment, overruling the expected spatial genetic structure. It is likely that intense natural selection pressure in the post-fire regeneration niche causes excess of heterozygous and more vigorous individuals. The mode of the selection is not yet clear, and further studies are needed for revealing its significance. Additional research is required also to further analyze the spatial genetic structure of the developing seedling generation.http://dx.doi.org/10.5061/dryad.6r725Data available from the Dryad Digital Repository: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Brachionus calyciflorus as a predator and the preySynura petersenii, a colony-forming alga that exhibits genetic variation in its propensity to form colonies and colony size (larger colonies are a defense against predators). Prey populations of either low initial genetic diversity and low adaptive capacity or high initial genetic diversity and high adaptive capacity were crossed with predator presence and absence. Dynamics measured over the last 127 days of the 167-day experiment revealed no effects of initial prey genetic diversity on the average abundance or temporal variability of predator populations. However, genetic diversity and predator presence/absence interactively affected prey population abundance and stability; diversity of prey had no effects in the absence of predators but stabilized dynamics and increased total prey abundance in the presence of predators. The size structure of the genetically diverse prey populations diverged from single strain populations in the presence of predators, showing increases in colony size and in the relative abundance of cells found in colonies. Our work sheds light on the adaptive value of colony formation and supports the general view that genetic diversity and intraspecific trait variation of prey can play a vital role in the short-term dynamics and stability of planktonic predator-prey systems.Heterogeneity among prey in their susceptibility to predation is a potentially important stabilizer of predator-prey interactions, reducing the magnitude of population oscillations and enhancing total prey population abundance. When microevolutionary responses of prey populations occur at time scales comparable to population dynamics, adaptive responses in prey defense can, in theory, stabilize predator-prey dynamics and reduce top-down effects on prey abundance. While experiments have tested these predictions, less explored are the consequences of the evolution of prey phenotypes that can persist in both vulnerable and invulnerable classes. We tested this experimentally using a laboratory aquatic system composed of the rotifer Prey heterogeneity can mediate trophic-level responses to enrichment and weaken top-down limitation of prey communities by facilitating numerical dominance by defended prey12. Such shifts in dominance can have significant dynamic consequences, potentially reducing the propensity and severity of predator-prey oscillations and stabilizing community dynamics9. While empirical research has largely focused on the consequences of interspecific variation in prey edibility, a growing body of experiments has begun to highlight the dynamic consequences of intraspecific trait variation. When prey populations exhibit phenotypic variation in their susceptibility to predators and show rapid adaptive responses to predation pressure, they can fundamentally alter the strength and dynamic consequences of their interactions with their consumers18.A large body of theoretical and empirical work has shown that the presence of variation among prey in their susceptibility to predation can have profound impacts on the structure and dynamics of predator-prey communities22. Such oscillatory dynamics become more probable and increase in amplitude with increasing prey carrying capacity21. Inclusion of prey heterogeneity in the form of species that are defended from predation can in theory stabilize predator-prey cycles. This readily occurs when prey species trade off their ability to compete for shared resources with their capacity to resist predation. In such instances, predators can facilitate the invasion and persistence of defended prey which, in turn, siphon resources from more edible species, reducing their carrying capacity. This can, in some cases, decrease the amplitude of predator-edible prey cycles or move systems from periodic to point attractors23. Predator-mediated increases in the relative abundance of defended prey may also weaken top-down limitation of trophic-level abundance causing an increase in total prey abundance relative to prey community’s lacking trait heterogeneity12.The effects of prey heterogeneity on predator-prey dynamics has been extensively explored in the context of endogenously driven population cycles. Cycles are a common feature of simple, two-species predator-prey models that incorporate nonlinear functional responses24. Several models have also explored how adaptive responses in prey defense may impact predator-prey dynamics and stability26. These demonstrate the capacity for prey evolution to dramatically alter predator-prey dynamics but stabilization is not a generalizable prediction. While adaptive responses in prey defense may stabilize predator or prey populations under certain conditions24, evolution can also give rise to destabilization of predator-prey cycles and enhanced extinction probability26.While the abovementioned models were developed with the intent of understanding the dynamic consequences of heterogeneity among prey species, their general predictions may, under certain conditions, apply to heterogeneity that occurs within species. For instance, models of trophic structure that incorporate prey evolutionary dynamics show that heritable variation in defense against predators may allow prey populations to adaptively respond to predation pressure, shifting regulation of prey populations from top-down control to stronger bottom-up control27. Moreover, previous experiments have only considered phenotypes or species with fixed traits. For many organisms, ecological strategies may involve transitions between dynamic classes that vary in their susceptibility to predators, with variation among phenotypes consisting of variation in state transition rates or degree of invulnerability. This could apply to organisms that change behavioral/physiological states or who move in and out of spatial refuges. It could also occur with colony-forming organisms in which colonies of increasing size are more resistant to predators. Prior work has shown that such prey strategies can stabilize predator-prey cycles1. However, this has not been examined within an evolutionary context.Only a handful of studies have attempted to address the dynamic consequences of intraspecific variability in prey edibility using direct manipulations in which prey evolution was either suppressed xor promotedBrachionus calyciflorus and the algal-preySynura petersenii, in which the potential for prey evolution was either enhanced or reduced through direct manipulations of initial prey genetic diversity. Our study differed from prior work in its use of an algal-prey species that can transition between a vulnerable and predator-resistant class.Synura petersenii is a common freshwater flagellate that may transition between two states: either free-living cells (which are more susceptible to zooplankton predators) or as swimming colonies (which are less susceptible to zooplankton feeding due to their larger size). Reproduction can occur in either the free-living state or in the colony state through binary fission. The strains ofS. petersenii used in our study exhibit heritable variation in their propensity to form colonies and their degree of vulnerability when in the colony state due to variation in colony size . Five strains ofSynura were used in the experiment, four of which  were obtained from UTEX  and one (CBS) from Carolina Biological Supply .Synura stock cultures were initiated with a single cell isolated from serial dilutions in sterile medium to ensure that all stocks were initially isogenic. All fiveSynura strains produced populations composed of a mix of single cells and colonies when under semi-continuous culture conditions. However, short-term trait assays revealed significant genetic variation among the strains in population densities, colony size (number of cells per colony), and the relative abundance of total cells found in colony form . The treatments withBrachionus present were replicated four times; treatments withoutBrachionus were replicated three times. At the initiation of the experiment, flasks were first inoculated with their respectiveSynura strains from stock cultures. The high genetic diversity treatment received 20 cells/mL of each of the five strains while the low diversity treatment received a single strain at 100 cells/mL. Thus, totalSynura density added was kept constant at 100 cells/mL across treatments. While the five strains were added at lower initial densities in the high genetic diversity treatment, each flask received a total of 8000 cells of each strain. Hence, the probability of losing a strain through demographic stochasticity and genetic drift at the initiation of the experiment was low.Synura populations were allowed to grow in the absence of predators for seven days at which time 10Brachionus adults were added to each plus predator treatment. We refer to this as day 0 of the experiment.Brachionus populations were allowed to grow for 12 days (with periodic medium replacement) at which time sampling was initiated.All experimental materials were autoclave-sterilized prior to use. Experimental containers consisted of 500mL flasks filled with 400mL of COMBO mediumBrachionus was enumerated using a stereomicroscope whileSynura was enumerated using a CASY particle counter . Maximum cell diameter ofSynura in our cultures ranged between 6–10µm (mean=7.9µm); we found no significant differences among our strains in mean maximum diameter. Consequently, we programmed the CASY to perform total counts and counts of particles below 10.5µm mean diameter as an estimate of the density of free living cells. Colony densities were then calculated by subtracting single cell counts from totals. To measure the density of cells in colonies, we multiplied the colony counts by estimates of the mean number of cells per colony for each treatment replicate. Estimates of cells per colony were performed at four time points during the experiment: at the initiation of the experiment, on day 30, day 97 and at the end of the experiment. To obtain estimates, subsamples of phytoplankton from each replicate were preserved in acid Lugols solution and counts performed using a compound microscope. We counted cells/colony for up to 25 haphazardly chosen colonies per sample and averaged the values. Because we did not have mean colony size estimates for all dates, we time-averaged the estimates of cells/colony over days 30 to 167 to create conversion constants for each replicate prior to multiplying by the colony counts. This should have provided conservative estimates of treatments effects since treatments had not completely diverged by the day 30 sample. Total cell density was calculated by summing free-living cell densities with estimates of the density of cells found in colonies.Sampling occurred every 2–3 days up to day 167, the final day of the experiment. To sample the experiment, flasks were first gently swirled to homogenize their contents and 12.5% of the volume (50mL) was poured into a sample bottle, which served as both a zooplankton and phytoplankton sample. Removed medium was replaced with sterile COMBO medium. Thus, the experiment was maintained under semi-continuous culture conditions.Brachionus populations in the low diversity treatment rapidly went extinct in all replicates of the LB239, LB2403, LB2405, and LB2406 strains. We consequently discontinued sampling of these monocultures and focused on the CBS strain for the low genetic diversity treatment.Brachionus populations exhibited an initial exponential growth phase followed by a population decline between days 0 and 40 before settling into more regular population oscillations. To remove the influence of initial transitory dynamics we analyzed data over days 51 to 167. Significant linear trends over time inSynura andBrachionus densities were evident in several replicates (see Results). To remove the influence of temporal trends on measures of temporal variability, we first performed linear regressions of log transformed densities versus time for each replicate for both species. Mean absolute values of the regression residuals were then used as measures of temporal variability (an inverse measure of stability). Analyses of meanBrachionus andSynura densities were performed on log10 transformed values averaged over days 51 to 167. To analyze changes in the degree of colony formation, we examined colony densities and the relative abundance of cells found in colonies . Response variables were analyzed using ANOVA. Because replication was not equal among replicates, we used type III sums of squares and max t-tests for post hoc comparisons, which are robust to unbalanced designs and non-normal data32. All response variables met assumptions of normality using Lilliefor’s test. Statistics were performed inR version 2.1533. Code for running max t-tests can be found in32.Brachionus populations persisted in all high genetic diversity treatments. However, populations of the zooplankton failed to establish in all low diversity replicates that were composed of the LB239, LB2403, LB2405, and LB2406 genotypes (sampling of these monocultures was discontinued).Brachionus also went extinct mid-experiment in one low genetic diversity replicate containing the CBS strain; we have removed this replicate from all analyses.Brachionus dynamics for all replicates in the presence of either low initial genetic diversity (Brachionus densities over time in one low genetic diversity replicate (p=0.02). Three of four high genetic diversity replicates exhibited significant negative trends in logBrachionus densities over time . Examining regression residuals,Synura genetic diversity had no effect on detrended temporal variability ofBrachionus . Effects of genetic diversity onSynura stability were only strongly evident in the presence ofBrachionus, significantly decreasing temporal variability in the high genetic diversity treatment relative to low genetic diversity . Genetic diversity andBrachionus presence/absence interactively affected temporal variability of colony cell abundances . Genetic diversity reduced temporal variability in the presence  and absence ofBrachionus , but effects were stronger whenBrachionus was present . WhileBrachionus presence/absence had no effects on mean cell densities in the high genetic diversity treatment  but no main effect of genetic diversity or interaction were detected . While the number of colonies was unaffected by initial genetic diversity, colony size (number of cells per colony) varied greatly between diversity treatments . Colony size in the high genetic diversity treatment and in the presence ofBrachionus was greater compared to all other treatment combinations . Colony size in the high genetic diversity treatment withoutBrachionus was also significantly greater than both low genetic diversity treatments . In the low genetic diversity treatment, no effect ofBrachionus presence/absence was detected . Effects on colony size were also apparent when examining the relative number of cells in colonies, which increased greatly in the presence ofBrachionus and high genetic diversity . There was no difference between low and high genetic diversity treatments whenBrachionus was not present  but a strong positive effect ofSynura genetic diversity in the presence ofBrachionus  examining phenotypic variation among the five genotypes of Synura used in the experimentClick here for additional data file.Time series of mean number of cells per Synura colony for all treatments and replicatesClick here for additional data file.23 – a prediction that has garnered some empirical support from studies that have manipulated prey species composition35 and clonal composition15. Our work further demonstrates that intraspecific variation can stabilize dynamics when prey persist in different dynamic classes that vary in predator resistance. The mechanisms that can give rise to vulnerable-invulnerable class structures within prey populations are diverse and include refuge use, inter-individual variation in behavioral or physiological states, and spatial variation in predation pressure1. Thus, our general findings may have broader applicability beyond planktonic systems of colony-forming prey.We found clear evidence that the presence of intraspecific trait heterogeneity can positively impact the stability and abundance of prey populations when in the presence of predators. These effects were linked to increases in the degree of aggregation of prey individuals into colonies; diverse prey populations produced larger colonies and a greater relative abundance of cells found in colony form when predators were present. Our results bear many similarities to prior studies that have examined the dynamic consequences of interspecific variation in prey defense against predators. As outlined in our Introduction, several models have shown that the presence of weakly interacting prey can stabilize dynamics by shunting resources away from more edible forms and subsequently reducing the amplitude of predator-edible prey oscillations25. For example, Hairston, Ellner and colleagues have examined the dynamic consequences of evolutionary responses among prey using models and an experimental system composed ofBrachionus calyciflorus and unicellular green algae as prey17. They have shown both theoretically and experimentally that rapid prey adaption in prey edibility can have significant effects on predator-prey dynamics, though effects are not always stabilizing and dependent on the degree of prey phenotypic variation present and the strength of trade-offs among phenotypes in anti-predator strategies and competitive ability for shared resources. When predator-resistance is effective but costly to defended phenotypes, rapid prey adaptation can induce large predator-prey oscillations and destabilization18. In contrast, when defense against predators is effective but costs in competitive ability are low, prey adaptation can result in cryptic prey cycles in which predators continue to oscillate and total prey abundance is stabilized as edible and predator-resistant phenotypes oscillate out of phase of each other18. The production of cryptic cycles via rapid prey adaptation shows a passing similarity to our results, in which high genetic diversity had no effects on predator stability but led to stabilization of totalSynura abundance in the presence ofBrachionus. Because our sampling intervals were uneven, spanning 2 or 3 days, we could not examine covariation between free-living cells and colony cell abundances using traditional cross-correlation analysis. However, visual examination of detrended abundances of free-livingSynura cells and colony cells over time revealed no strong support for asynchronous oscillations between the two groups  for the low diversity treatment. This is an approach that has been used in prior experiments in which dynamics in the presence of a highly edible prey phenotype are contrasted with dynamics in which the edible prey has been supplemented with additional phenotypes that vary in their susceptibility to predatorsstrains . The onlstrains , which l40. A hurdle in achieving this initial transition is the fitness costs associated with colony formation such as reductions in competitive ability, reproductive rates or buoyancy. Our work supports the general idea that colony formation and large colony size provide a selective advantage in the presence of predators and a disadvantage when predators are absent and resource competition is strong. This finding complements previous studies that have shown similar effects of predators on colony formation in unicellular algae41 and supports the general hypothesis that a driver in the early evolution of multicellularity was the emergence of heterotrophic life forms and phagotrophy – a transition point where the disadvantages of cellular aggregation began to be outweighed by the advantages of increasing size and predator escape29. Our study also adds to a growing body of experiments demonstrating the effects of rapid evolutionary change on the dynamics of consumer-resource interactions42 and the strength of top-down control of prey abundance37. How such effects translate to natural communities remains largely unresolved; the majority of prior experiments, including ours, have utilized highly simplified laboratory settings with a minimal number of interacting species. Moreover, the consequences of coevolutionary responses between predator and prey populations on ecological dynamics have not been deeply explored. Natural prey populations are of course exposed to multiple species of predators and embedded in prey communities with a large degree of interspecific variation in competitive ability and defense against predators. Understanding the dynamic consequences of evolution and coevolution within the context of complex communities remains a daunting challenge in ecology but an exciting avenue for future exploration.Cellular aggregation is viewed as an evolutionary stepping stone between unicellular and multicellular life forms This article shows how increasing genetic variation in prey can stabilize the dynamics, especially since predator can barely establish in communities without variability in prey. This last finding is by far the strongest and I think that it adds substantially to a current important topic. A second relevant finding is that, despite having only a single strain with low variability, the high genetic variability replicates had also more stable dynamics than the one single strain.The finding that colony formation was linked to predator presence and that there was a trend in which colony formation increased through time also provides a hint on how multicellularity evolved.However, unfortunately I do not think that this article provides convincing evidence of an evolutionary response of defense through colony formation as the authors claim. First, in their preliminary assays the authors did not combine the different strains to see how they form colonies when different strains are present in the same habitat. We do not know if they are able to combine and to cooperate with other strains (at least with the information provided) and if there are emergent patterns of colony formation when more than one strain is combined.The findings that defense (aggregative behaviour) increase when predators are present could be just the result of complex patterns of (plastic) cooperation among strains which change through time, for instance as a response to persistent predation risk. Assays at the end of the experiment to test if after removing predators, colonies were still maintained at substantially high rates (e.g. as in strain LB2405) would have been a convincing evidence for adaptive evolution ocurring during the dynamics.I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. per se, as opposed to the identity effects of this individual genotype. I agree with the authors that it is a major limitation in interpreting this experiment.The authors of this study performed a laboratory mesocosm experiment that sought to address the role of intraspecific variation in predator-prey dynamics. While the underlying questions presented an interesting and relevant avenue of research, four of the five clonal lines went extinct in their single-genotype treatment.  As a result, their contrasts between low and high prey genetic diversity were performed with a single genotype (CBS strain). Consequently, it is unclear to me whether conclusions could be drawn about the consequences of genetic diversityI have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. This paper demonstrates that prey heterogeneity stabilizes prey population dynamics in an experimental predator-prey system. It should, however, be noted that if this happens via the better-defended prey shunting away resources from the edible prey, then the density of the predator should be lower in the high-prey-diversity treatment. But, according to Fig 3B, this is not the case.I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.


Chorthippus parallelus. We sampled a total of 610 individuals at 22 heterogeneous grassland sites in the Hainich-Dün region of Central Germany. For each of these grassland sites we assessed habitat size, LUI , and the proportion of grassland adjoining the sampling site and the landscape heterogeneity . We found only marginal genetic differentiation among all local populations and no correlation between geographical and genetic distance. Habitat size, LUI and landscape characteristics had only weak effects on most of the parameters of genetic diversity of C. parallelus; only expected heterozygosity and the grasshopper abundances were affected by interacting effects of LUI, habitat size and landscape characteristics. The lack of any strong relationships between LUI, abundance and the genetic structure might be due to large local populations of the species in the landscape, counteracting local differentiation and potential genetic drift effects.Land-use intensity (LUI) is assumed to impact the genetic structure of organisms. While effects of landscape structure on the genetics of local populations have frequently been analysed, potential effects of variation in LUI on the genetic diversity of local populations have mostly been neglected. In this study, we used six polymorphic microsatellites to analyse the genetic effects of variation in land use in the highly abundant grasshopper  Biota living in the remaining habitat islands often suffers from increasing fragmentation and thus a rise of geographical isolation accompanied by a decrease in habitat size and potential negative edge effects . NegativChorthippus parallelus showed that individual fitness strongly depends on dietary mixing and thus on the composition of the vegetation. Survival as well as fecundity of C. parallelus increased with the number of foodplants in experimental studies . D. DC. parrocedure ,18.3.5icro-Checker v. 2.0  with the natural logarithm (ln) of the geographical distance, using the Isolation by Distance Web Service v. 3.23 (http://ibdws.sdsu.edu/)  and the natural logarithm of the geographical distances over our 40 km×40 km study range showed no significant relationship . Genetic and geographical pairwise distances are given in the electronic supplementary material, S1 and S2; all molecular data generated for this study are provided as genepop inputfile in the electronic supplementary material, S3.The genetic differentiation across all 22 local populations was very low . LUI varied from very low (0.59) to very high (2.73) (with a mean of 1.67±0.67 s.d.). The percentage of grassland in the adjoining environment varied from 0 to 88.0% (with a mean of 39.4±32.2%). The heterogeneity of the surrounding environment measured as Shannon diversity index ranged from 0.00 to 1.12 (0.62±0.29). Values for each site are given in C. parallelus individuals between 3 and 124 per site (mean 24±2.97 s.d.). The abundance of the grasshopper species was significantly correlated with the size of patches, habitat heterogeneity of the surrounding environment and LUI  [The abundance of f mowing ,30. Humb [et al. ,30 and G [et al.  suggestenalysis) . Generalnalysis) , so thatC. parallelus was significantly affected by habitat size and the heterogeneity of the surrounding environment, yet the direction and significance of the respective relationship depended on LUI. Chortippus parallelus seems to benefit from increasing habitat size but suffers from higher environmental heterogeneity under high LUIs. Greater patch sizes and lower landscape diversity in the surrounding patches probably result in larger areas of suitable habitat and subsequent larger population sizes and increasing connectivity.In our study, the abundance of C. parallelus across our study area might additionally be the result of differences in abiotic conditions such as microclimate , or different plant species compositions [C. parallelus is positively affected by the temperature and moisture of the grasslands [The strong variation in the abundances of ositions . Previouasslands , and thaasslands ,33. Thus2 are swept, and that only 25% of all individuals become sampled [Taking into account that with 10 double sweeps about 8 m sampled , our res sampled .C. parallelus and by habitat size; this correlation was strongly positive but only found in plots with high LUI. Furthermore, this goes in line with higher abundances of C. parallelus found in these plots. This relationship between population sizes and genetic diversity (here expressed by expected heterozygosity) can frequently be observed in wild populations [in situ diversity found in individuals) based on allele frequencies and the Hardy–Weinberg equilibrium assumptions, which makes the significance of this correlation between LUI and genetic diversity arguable [Genetic parameters showed no significant correlation with any of the habitat characteristics . Only the expected heterozygosity was significantly affected by the abundance of ulations . Howeverarguable .C. parallelus may lead to high abundances, even in landscapes with intensive land-use regimes. This allows the species to exist in large population sizes counteracting potential genetic drift effects. It needs to be added that the total area of grassland in the study area is still quite large, so possibly stronger fragmentation and decreased total habitat area may lead to a decrease in genetic diversity and higher differentiation. Second, even if habitats and populations are small (as observed in some sites for some years), high abundances can easily counteract potential drift effects by gene flow from adjoining populations, and thus prevent the loss of genetic diversity. These two explanations might play a pivotal role in buffering potential effects of genetic drift, and finally in producing a population genetic structure characterized by high diversity and low differentiation.In conclusion, the lack of effects from different LUIs can be explained by two scenarios. First, the unspecific habitat requirements and high ecological tolerance of