Orthetrum coerulescens (Odonata: Libellulidae), was monitored over a time period from 1989 to 2013. Study site was an artificial irrigation ditch in one of the last European stone steppes and “nature heritage”, the Crau in Southern France. This artificial riverine habitat has an unusual high diversity of odonate species, prominent indicators for evaluating freshwater habitats. A clearing of the canal and destruction of the bank vegetation in 1996 was assumed to have great negative impact on the odonate larval and adult populations. Two mitochondrial markers (CO1 & ND1) and a panel of nuclear microsatellite loci were used to assess the genetic diversity. Over time they revealed a dramatic decline in diversity parameters between the years 2004 and 2007, however not between 1996 and 1997. From 2007 onwards the population shows a stabilizing trend but has not reached the amount of genetic variation found at the beginning of this survey. This decline cannot be referred to the clearing of the canal or any other direct anthropogenic impact. Instead, it is most likely that the populations’ decay was due to by extreme weather conditions during the specific years. A severe drought was recorded for the summer months of these years, leading to reduced water levels in the canal causing also other water parameters to change, and therefore impacting temperature sensitive riverine habitat specialists like the O. coerulescens in a significant way. The data provide important insights into population genetic dynamics and metrics not always congruent with traditional monitoring data (e.g. abundance); a fact that should be regarded with caution when management plans for developed landscapes are designed.Modern conservationists call for long term genetic monitoring datasets to evaluate and understand the impact of human activities on natural ecosystems and species on a global but also local scale. However, long-term monitoring datasets are still rare but in high demand to correctly identify, evaluate and respond to environmental changes. In the presented study, a population of the riverine dragonfly,  Due to increasing anthropogenic impact on natural ecosystems the number of species that call for conservation management grows steadily . Long-teGenetic monitoring provides important insights into population genetic metrics and offers an over-time evaluation . Only loOrthetrum coerulescens in France was genetically monitored since 1989. Odonata (dragonflies & damselflies) with their complex life cycles—aquatic larvae and terrestrial imagos—are important model systems for evaluating freshwater ecosystems worldwide [In this study one of the largest populations of the libellulid dragonfly orldwide , 6. Theyorldwide .O. coerulescens has a high specificity to small running waters and fens e.g. [Riverine habitats are especially prone to anthropogenic influences. The main driver of loss of biodiversity in this case is land use, as modelling data show . Dredginens e.g. . It is rens e.g. , 11. OneThe Crau is a unique ecosystem due to its extreme ecological conditions. Here, the dry Mediterranean climate  is combOrthetrum coerulescens population, as an indicator species for this vulnerable freshwater habitat. Hereby the focus is on the influence of the dredging and the population’s potential to recover from its effect. A panel of microsatellite loci developed by Hadrys et al.[This study aims to detect possible genetic consequences that could be referred to direct anthropogenic impact, especially a clearing of the channel in 1996. We monitored the largest CdV dragonfly population, the ys et al., 21. WitCanal de Vergières (CdV) in southern France, located in the “nature heritage”, the Crau (43°36'17.7"N 4°48'34.4"E). Together with the adjacent Camargue, the Crau is one of Europe’s richest odonate biotopes [Study habitat is the biotopes . Decadesbiotopes  and has biotopes , 23–25. O. coerulescens population tissue samples of 285 adult individuals were collected over a period from 1989 up to 2013  following a modified protocol. The amplified PCR products were sequenced on an ABI PRISM® 310 Genetic Analyzer (Applied Biosystems) following the manufacter’s protocol using Sephadex Gel Filtration for purification .Genomic DNA was extracted from a single midleg of each individual following a standard phenol-chloroform protocol by Hadrys et al. . Two mit [et al.  for ND1 h) and nucleotide diversity (p) between generations of O. coerulescens from the CdV for all years investigated. Sequence divergence within a population as well as between all generations were calculated in MEGA 6 [Fst) [For data analyses, DNASP version 4.0  was empln MEGA 6  accordinn MEGA 6 . Genetic 6 [Fst)  was comp 6 [Fst) ). To vis 6 [Fst) ). Defaulet al. [™ Size Standard  on an ABI PRISM® 310 Genetic Analyzer (Applied Biosystems). For the allele size determination the GeneScan® Analysis Software (Applied Biosystems) was used.Based on the results of the mitochondrial sequence analyses, populations from specific years between 1989 and 2011  were selO. coeruelscens populations between the sampled years were assessed by comparing allele frequencies, expected heterozygosity (HE), observed heterozygosity (HO), and allelic richness (A) for each locus via the software FSTAT  were calculated with FIS, based on observed heterozygosities as well as FST values based on expected heterozygosities with the software ARLEQUIN , the probabilities for conformity with Hardy-Weinberg proportions and linkage disequilibrium were calculated using the Markov chain method implemented in GENEPOP , which were clustered by distinctive allele frequencies. In accordance with the number of sampled years, runs with K values from one to seven were repeated 20 times using the admixture model with correlated frequencies. Runs were performed with a burn-in period of 105 steps followed by 105 Markov chain Monte Carlo replicates. The online version of STRUCTURE HARVESTER [K out of the STRUCTURE run result files.Genetic diversity parameters of 2.9.3.2; ). Inbree3.5.1.2; ). FST va 4.0.10, ). STRUCT 4.0.10, ) was useARVESTER  was applO. coerulescens for the analyzed years. For ND1 eight haplotypes were detected in total (GenBank Accessions KY807703—KY807710), CO1 shows 15 haplotypes (GenBank Accessions KY807688—KY807702). The number of haplotypes per year varied between both markers. It ranges from one to four for ND1, with a maximum of three private haplotypes for the generation in the year 2013 , corrected for the smallest sample size, showed less variation ranging from 5.19 to 5.42 per population and locus for all studied years. The number of alleles scored within populations ranged from 33 in 2007 to 58 in 1998 with at least one private allele found in each year. The highest number of private alleles was scored in the year 1998 with a total number of eight. Observed heterozygosities across all loci ranged from 0.541 in 1989 to 0.770 in 2011 . Generations in the years 1989, 1998 and 2004 displayed three to six loci with highly significant deviance from the HWE indicating a heterozygosity deficit. Generations after 2004 experienced a (less) significant heterozygous excess. The average of the observed heterozygosity is lower than expected from 1989 to 2007 and from thereon higher than expected, reaching values up to 0.77 in 2011 see .Allele frequencies for each locus per year are visualized in the supplementary data. For almost all loci different patterns in allele composition before and after 2004 can be found. In detail, for locus H04 and J04 different alleles dominated in the years 1989, 1998 and 2004 compared to the period from 2007 onwards. Furthermore, the number of alleles decreased after 2004 and rare alleles were lost, as most notable for locus F04 .FIS and FST values were calculated, whereas the latter were assessed pairwise between all generations and years  very low FST values were scored, but negative FIS values  is scored in the year 1998 ; each cluster is characterized by a set of allele frequencies at each locus. Clusters were inferred without any predefined sets. Results revealed two main groups of individuals, containing populations from 1989, 1998 and 2004 in one group. Whereas populations from 2007 onwards were defined as the second genetic cluster . The maximum number of haplotypes per generation was four (ND1) and six (CO1), respectively. Especially for ND1 higher variations are known for libellulid dragonflies demonstrated for example for Trithemis arteriosa [Crocothemis erythrea [Trithemis and Crocothemis (see above).The mitochondrial markers ND1 and CO1 were successfully used in several odonate population genetic studies e.g. , 21. In rteriosa  and Crocerythrea  at compaerythrea , 44. In erythrea , 46. As erythrea  a highera priori low(er) genetic variation for O. coerulescens, classic population genetic reasons for an induced loss of genetic diversity have to be taken into account. These are for instance a small population size, subsequent inbreeding, and genetic drift after a severe population decline [Despite this assumption of an  decline –51. TheyFor the years 1989 and 1998 adult densities were observed along the CdV in 20m long transects in July/ August three times a day between 11.00 a.m. to 16.00 p.m. Before dredging (1989), the average density was 28.5 individuals per transect whereas after this event only 7.6 individuals were found in 1998. The larval densities dropped from 35–40 individuals per square meter to 9–15 Ind/qm. All the very few other potential habitats in the surrounding (in 25km radius) had low population densities as well . Despitelocus 5.5 from former 9.0 in 2004) and values remained low but stabilized in the following years up to 6.7 in 2011.Interestingly, the highest diversity regarding the number of alleles, private alleles and alleles per locus was scored in 1998, which was only two years after the dredging event, indicating that the dredging had not affected the population’s viability. This result is interestingly contradicting the population density data that indeed show a dramatic decline after the dredging. Both the larval and adult density dropped significantly. From 1989 to 1998 the population showed an increasing trend in terms of all analyzed genetic diversity parameters. After 1998 genetic diversity decreased, with a significant decline between 2004 and 2007. Individuals showed then the lowest allelic diversity  displayed more loci with highly significant deviances from HWE as any other generations, indicating a heterozygosity deficit. Whereas generations after 2004 revealed less significant values and assuming a heterozygote excess, which is the typical sign of the genetic bottleneck , 54 we eFST values supports the overall trend of a change in genetic diversity, possibly caused by a severe population size decline between the years 2004 and 2007 which could not be recovered from the surrounding satellite populations.The split into two clusters based on microsatellite analyses before and after 2004, as well as strong genetic differentiation found between these generations by O. coerulescens are particularly dependent on specific water chemistry patterns, temperature, oxygen amount, sediment quality and flow velocity [During this time period there was no anthropogenic impact on the canal, e.g. dredging. Instead a severe drought in 2003 was observed, followed by extreme hot and dry summers during 2004 to 2007. Although the CdV did not dry out, the water level was much lower than usual with no flow velocity . As the larvae of velocity  the unusvelocity , but thevelocity .O. coerulescens individuals from surrounding ditches  located in the Crau. Consequently, after a drastic local population depletion due to the dredging a recolonization by sink-individuals would not only slowly stabilize the population size, but also cause the unexpected gain of genetic diversity we observed between 1989 and 1998.A combination of both, extreme and unlikely weather conditions is most likely the cause for the population decline between 2004 and 2007. In contrast, the clearing of the canal did not lead to a comparable dramatic effect. We assume that this local event could be compensated by (i) the surviving individuals and (ii) migration of closely related  compare . This exO. coerulescens in the area are affected. The change in the genetic make-up of the CdV population we detected after 2004 can only be explained by genetic drift due to changing ecological conditions and immigration of less related individuals to the former population. Consequently, we observed an increase in heterozygosity and FIS values.A change in climatic parameters affects the ecosystem on a larger scale. Extreme weather conditions like the recorded drought periods influence all freshwater habitats in the Crau. Hence, local population declines or even extinctions cannot be compensated out of a genetic metapopulation because all habitats of Small freshwater ecosystems in general and riverine ecosystems in special are increasingly prone to environmental change, especially in arid as well as developed landscapes . The artO. coerulescens at the CdV is rather affected by natural factors, like extreme weather conditions than by direct anthropogenic impacts. The population decline between 2004 and 2007 was not noticed by conventional surveys on this species abundance. The low genetic diversity despite a large population size and subsequent decrease of the ability to deal with environmental changes cannot be determined by traditional monitoring efforts. This stresses the value of long-term data sets on a regional scale for population genetics and conservation biology in order to understand the causal mechanisms on small scales behind global changes.In sum, only the long-term genetic monitoring data could reveal that the population of Orthetrum coerulescens at the CdV in Southern France revealed an overall low genetic diversity and a population decline between the years 2004 and 2007. Afterwards the population shows a stabilizing trend but is not reaching the amount of genetic variation from 1989. Surprisingly, the decline cannot be referred to the clearing of the canal in 1996, an anthropogenic event assumed to have great negative impact to the larval population of the target species. Instead, it is most likely that the populations’ decay was due to extreme climate conditions in 2004 and the following two years. Based on this local monitoring scheme we now can conclude that for the viability of this highly habitat specific insect species it might be important to study the effects of climate change to its riverine ecosystems on a long term basis and in a global setting.Genetic analyses of data collected over a period of more than 20 years to the S1 FigDifferent alleles are represented by different colours.(TIF)Click here for additional data file.S2 FigDifferent alleles are represented by different colours.(TIF)Click here for additional data file.


A major function of the captive panda population is to preserve the genetic diversity of wild panda populations in their natural habitats. Understanding the genetic composition of the captive panda population in terms of genetic contributions from the wild panda populations provides necessary knowledge for breeding plans to preserve the genetic diversity of the wild panda populations.The genetic contributions from different wild populations to the captive panda population were highly unbalanced, with Qionglai accounting for 52.2 % of the captive panda gene pool, followed by Minshan with 21.5 %, Qinling with 10.6 %, Liangshan with 8.2 %, and Xiaoxiangling with 3.6 %, whereas Daxiangling, which had similar population size as Xiaoxiangling, had no genetic representation in the captive population. The current breeding recommendations may increase the contribution of some small wild populations at the expense of decreasing the contributions of other small wild populations, i.e., increasing the Xiaoxiangling contribution while decreasing the contribution of Liangshan, or sharply increasing the Qinling contribution while decreasing the contributions of Xiaoxiangling and Liangshan, which were two of the three smallest wild populations and were already severely under-represented in the captive population. We developed three habitat-controlled breeding plans that could increase the genetic contributions from the smallest wild populations to 6.7–11.2 % for Xiaoxiangling, 11.5–12.3 % for Liangshan and 12.9–20.0 % for Qinling among the offspring of one breeding season while reducing the risk of hidden inbreeding due to related founders from the same habitat undetectable by pedigree data.The three smallest wild panda populations of Daxiangling, Xiaoxiangling and Liangshan either had no representation or were severely unrepresented in the current captive panda population. By incorporating the breeding goal of increasing the genetic contributions from the smallest wild populations into breeding plans, the severely under-represented small wild populations in the current captive panda population could be increased steadily for the near future.The online version of this article (doi:10.1186/s12863-016-0441-y) contains supplementary material, which is available to authorized users. Ailuropoda melanoleuca) is an endangered species threatened by its own reproductive difficulties as well as habitat loss and fragmentation. As part of the effort to preserve this endangered species, ex situ conservation or captive breeding is important to increase the number of pandas outside their natural environment [ex situ conservation is to maintain genetic diversity that is representative of the wild population [The giant panda  were calculated using the 2014 pedigree of the captive panda population [th panda (cijk), the contribution of founder k to the captive population (Ck), the contribution of habitat j to the ith panda (Cij), and the contribution of habitat j to the captive population (Cj) were calculated as:Founder and habitat contributions were calculated through coancestry or kinship coefficients between descendants and founders in the captive panda population. Pedigree coancestry coefficients (pulation . The confijk = coancestry (kinship)coefficient [th descendant of founder k in habitat j, njk = number of descendants of founder k in habitat j, nj = number of founders in habitat j, and n = number of pandas in the captive population. Note that the sum of all founder contributions to an individual is Ci = 0.5, and 2 pandas each having Ci = 0.75.where fficient  of the ifjk values between individual j and its r relatives, i.e.,The mean kinship (coancestry) coefficient (MK) of individual j is defined as the average of all i) as a weighted number of mates, where the maximum number of mates allowed for a founder from each habitat is weighted by the habitat contribution from that habitat, i.e.,Three alternative plans of habitat-controlled breeding were developed to increase the genetic contributions from the smallest wild populations to the captive population while reducing the risk of hidden inbreeding due to related founders from the same habitat. Plan A sought to maximize the contributions of three under-represented habitats in the priority order of Xiaoxiangling, Liangshan, Qinling and Minshan while minimizing the contribution of Qionglai, with the restriction of ten mates per male panda. Plans B and C calculated the maximum number of mates for each male breeding candidate  with MSI scores of 1–3 for high priority mating pairs, 4–6 for mating pairs to be avoided, and 3 F and 4 F for mating pairs involving founders with high genetic values [The analysis of the genetic contributions showed that the Qionglai (QIO) wild founders had the largest genetic contribution 52.2 %) to the captive panda population, followed by Minshan (MIN) with 21.5 %, Qinling (QIN) with 10.6 %, Liangshan with 8.2 %, Xiaoxiangling (XXL) with 3.6 %, and Sichuan with 0.7 %. The Daxiangling (DXL) population with a similar population size as Xiaoxiangling had no contribution to the captive population  Existing genetic contribution from each wild population to a breeding candidate, 2) Expected genetic contribution from each wild population to the progeny of a mating pair, and 3) The limit number of mates a male breeding candidate may have according to the genetic contributions from different wild populations to this breeding candidate. Among the 397 pandas in the captive population, we determined that 140 females and 126 males were breeding candidates for the 2016 breeding season. From these 140 males and 126 females, 17,640 male–female pairs were possible  was a Qionglai founder, while the other two females were crossbreds: panda 425 was a crossbred between Xiaoxiangling (25 %), Minshan (50 %) and Qionglai (25 %), and panda 557 with the only surviving triplets was a crossbred between Minshan and Qionglai (Table 8 was a QThe genetic composition of the captive panda population had dominant genetic contributions from the two largest wild populations and a small fraction of the genetic contributions from the two smallest wild populations. The currently available breeding candidates would allow significant increases in the genetic contributions from the two smallest wild populations and reduce the risk of hidden inbreeding using habitat-controlled breeding plans that monitor and control the genetic contributions from different wild panda populations to the captive panda population.


Ralstonia solanacearum species complex (RSSC) is hampering the production of many sustainable and cash crops. To thoroughly analyze the genetic diversity of the RSSC in the SWIO, we performed a wide sampling survey  that yielded 1,704 isolates from 129 plots, mainly from solanaceous crops. Classification of the isolates to the four major RSSC phylogenetic groups, named phylotypes, showed that 87% were phylotype I, representing the most prevalent strain in each of the SWIO islands. Additionally, 9.7% were phylotype II, and 3.3% were phylotype III; however, these isolates were found only in Reunion. Phylotype IV (2 isolates), known to be restricted to Indonesia-Australia-Japan, was reported in Mauritius, representing the first report of this group in the SWIO. Partial endoglucanase (egl) sequencing, based on the selection of 145 isolates covering the geographic and host diversity in the SWIO , revealed 14 sequevars with Reunion and Mauritius displaying the highest sequevar diversity. Through a multilocus sequence analysis (MLSA) scheme based on the partial sequencing of 6 housekeeping genes  and 1 virulence-associated gene (egl), we inferred the phylogenetic relationships between these 145 SWIO isolates and 90 worldwide RSSC reference strains. Phylotype I was the most recombinogenic, although recombination events were detected among all phylotypes. A multilocus sequence typing (MLST) scheme identified 29 sequence types (STs) with variable geographic distributions in the SWIO. The outstanding epidemiologic feature was STI-13 (sequevar I-31), which was overrepresented in the SWIO and obviously reflected a lineage strongly adapted to the SWIO environment. A goeBURST analysis identified eight clonal complexes (CCs) including SWIO isolates, four CCs being geographically restricted to the SWIO, and four CCs being widespread beyond the SWIO. This work, which highlights notable genetic links between African and SWIO strains, provides a basis for the epidemiological surveillance of RSSC and will contribute to BW management in the SWIO.Epidemiological surveillance of plant pathogens based on genotyping methods is mandatory to improve disease management strategies. In the Southwest Indian Ocean (SWIO) islands, bacterial wilt (BW) caused by the  Neisseria meningitidis   and dicots  gene  islands , BW is regularly encountered and represents one of the most important factors that limits solanaceous crop production. The last devastating event caused by RSSC strains in the SWIO is still occurring in the potato production areas of Madagascar, seriously compromising food security , race 3/biovar 2 , and race 1/biovar 1 (phylotype III), with phylotype I as the most prevalent. Moreover, they reported that phylotype IIB-1 strains, responsible for potato brown rot, were exclusively present in the highlands , but the other strains were present in low- and mid-altitude areas. In Mauritius, race 1/biovar 3  strains were reported  the genetic diversity of RSSC isolates in the SWIO was assessed and (ii) compared with the genetic diversity of worldwide reference strains, and (iii) recombination events within the genes were revealed. Additionally, (iv) the structures of the RSSC populations in the SWIO area were evaluated and (v) compared with worldwide reference strains using MLSA/MLST, and (vi) we determined the effectiveness of the MLSA/MLST method.egl sequencing and MLSA/MLST revealed a very high prevalence of one genetic lineage  throughout the SWIO islands that appeared to be closely linked to continental African RSSC strains.This study revealed the presence of four phylotypes in the SWIO, with the first report of two phylotype IV isolates in Mauritius. The phylotypes' frequencies were highly variant among the islands, with Reunion displaying 3 phylotypes , Mauritius with 2 phylotypes (I and IV) and only phylotype I in Mayotte, Comoros, Seychelles, and Rodrigues. Surprisingly, phylogenetic and genotyping approaches involving partial Solanum lycopersicum, S. tuberosum, S. melongena, and Capsicum annuum) and from Pelargonium cv. rosé, a traditional crop in Reunion Island that is grown for its very valuable essential oil, which is mainly used in the perfume industry and is known to be susceptible to BW . When possible, we collected ~30 random plants that showed BW disease-like symptoms in each plot  isolated during this study  or from previous surveys . The third collection, C3, corresponded to C1+C2 . The SWIO collection (C1) was subdivided into seven sub-collections: 51 isolates from Reunion , 29 from Mauritius, , 25 from Madagascar , 18 from Comoros , 12 from Seychelles , 7 from Mayotte , and 3 from Rodrigues . The worldwide collection (C2) included RSSC strains from Africa (n = 23), America (n = 36), Asia (n = 28), Europe (n = 1), and Oceania (n = 2).Ten collections of RSSC isolates were defined for our genetic studies Table . The firegl gene. The Mmx-PCR was conducted as previously described  on the ITS region and a phylogenetic analysis based on the 2, 2.4 μM of each phylotype-specific primer , 7.2 μM of Nmult 23:AF, 1.6 μM of each species-specific primer, 0.625 U of GoTaq flexi DNA polymerase (Promega) and 2 μl of DNA suspension. The thermocycling conditions included an initial melting phase at 96°C for 5 min, followed by 30 cycles at 94°C for 15 s, 59°C for 30 s, and 72°C for 30 s and a final hold at 72°C for 10 min. Amplification was performed in a Verity technology thermocycler . The PCR products were analyzed by electrophoresis in 1.5% (110 V) agarose gels for 90 min and were revealed under UV light. A 100-bp DNA ladder and positive controls for each phylotype were added to each electrophoresis gel.A 1-μl loop of fresh colonies from a Petri dish was suspended in 200 μl of sterile HPLC water and was used as a template for PCR amplification. The Mmx-PCR used phylotype-specific primers to characterize all SWIO RSSC isolates. The identification of phylotypes I, II, III and IV was accomplished with four forward primers: Nmult 21:1F, Nmult 21:2F, Nmult 23:AF, and Nmult 22:InF, respectively, and Nmult 22:RR in the reverse direction. Each phylotype had an expected specific length of the amplified fragments . Mmx-PCR also included the RSSC-specific primer pair 759/760. The PCR conditions were as described by Fegan and Prior : 25-μl regl) gene. PCR amplification of a 750-bp region of the egl gene was performed using the Endo-F and Endo-R primers pair as previously described , 1 μl of dNTP mix (10 mM each), 1.25 μl of a primer pair mix (10 μM each), 0.25 μl of GoTaq® G2 Flexi DNA Polymerase (PROMEGA), and 31.25 μl of sterile HPLC water. The steps of the protocol were carried out as follows: initial denaturation at 96°C for 9 min; 30 cycles of denaturation at 95°C for 1 min, 70°C for 1 min, and elongation at 72°C for 90 s; and a final extension at 72°C for 10 min. Then, 5 μl of the amplified PCR products was separated on a 1.5% agarose gel to visualize the amplification quality. PCR products were sent to Genewiz  for DNA double-stranded sequencing (forward and reverse). Forward and reverse chromatograms were edited using the Geneious v8.1.8 software package  and one virulence-associated gene (egl) were selected for MLSA and MLST studies as previously described  were concatenated. For MLST, each unique sequence of a gene was assigned an allele number, and the combination of allele numbers for each isolate defined the sequence type (ST).Six housekeeping genes *, and Fu's F*) were performed to elicit information about the evolutionary forces operating on each gene. Under conditions of neutrality, the expected value of these estimates is “0”; for the diversifying selection of genes, the expected value is positive; and under conditions of purifying selection, the expected value is negative. The Nei and Gojobori  and of each phylotype were inferred by calculating the number of haplotypes (Hap), the number of polymorphic sites (S), the haplotype diversity (Hd), and the nucleotide diversity (Θπ) using DnaSP 5.0 . The allelic richness (A) estimates the genetic diversity in a population; it was calculated per locus and collection using the allelic.richness function in the “hierfstat” package  drawn from the collection. The discriminatory power of MLST was evaluated by calculating the Hunter-Gaston Discriminatory Index (HGDI) using the Discriminatory Power Calculator tool available at http://insilico.ehu.es/mini_tools/discriminatory_power/index.php .Based on the MLST data, the level of polymorphism was evaluated for each of the ten RSSC collections using the GenAlEx v6.5 software package , Reunion , Seychelles , Comoros , and Rodrigues , representing 70% of the collected isolates, were isolated throughout the island from sea level up to 1,120 m. Phylotype I isolates displayed a wider host range than phylotypes II and III and were collected more frequently from S. lycopersicum, S. melongena, and Pelargonium cv. rosé and more scarcely from S. tuberosum, Capsicum spp., Phaseolus vulgaris, and weeds  were isolated mainly from S. tuberosum and less frequently from S. lycopersicum  were collected only in the western part of the island, mainly from Pelargonium cv. rosé but also from S. tuberosum and S. lycopersicum , but on the other SWIO islands, no significant associations were observed between the phylotypes and the hosts.In Reunion, phylotypes I, II, and III were identified with variable distributions and frequencies. Phylotype I isolates  were collected than in Reunion and Mauritius. On these islands, the RSSC genetic diversity was narrow since only phylotype I isolates were isolated, mostly from S. lycopersicum in Comoros, S. melongena in Seychelles and S. lycopersicum and C. frutescens in Rodrigues , 145 SWIO isolates (C1) were positioned within the global RSSC phylogenetic diversity by partial sequencing of the e Figure . In all e Figure  in all tgdhA, gyrB, rplB, leuS, adk, mutS, and egl). The total length of the concatenated sequences was 4,245 bp, corresponding to 603 bp for gdhA, 391 bp for gyrB, 654 bp for rplB, 723 bp for leuS, 468 bp for adk, 693 bp for mutS, and 713 bp for egl  than C1 . When considering C1, nucleotide diversity (Θπ) was 1.6% and was similar across genes, except for egl, which was more variable (Θπ = 3.5%), but it was 3.2% for C2 . When we focused on the number of polymorphic sites, adk was the least diverse gene (S = 32 for C1 and C2), and egl was the most diverse gene (S = 121 for C1 and 193 for C2). Among the phylotypes  in the SWIO and the worldwide collections, phylotypes II and III appeared as the most diverse , and phylotype I appeared as the least diverse (Θπ = 0.2% for C1 and 0.6% for C2).Genetic diversity analyses were performed on the partial sequences of seven genes . From each gene separately, significant recombination was revealed in mutS for phylotype II and in egl for phylotypes II, III, and IV  Figure . Except IV Table .gdhA, was recombination-free. The six other genes showed one to four recombination events, egl being the most recombinogenic. These recombination events were identified in all phylotypes confirming that recombination is ubiquitous within the RSSC. Phylotype I appeared as the most recombinogenic since recombination affected 137, 2, 3, and 11 isolates belonging to phylotypes I–IV, respectively. Three recombination events  were detected in SWIO isolates, two from phylotype I, and one from phylotype IV. Recombinant sequences originated primarily from phylotypes I and IV.Using RDP4, 13 individual recombination events were detected in the concatenated gene sequences set , Tamura-Nei+I (for leuS), Hasegawa-Kishino-Yano+I (for adk), Hasegawa-Kishino-Yano+I+G (for gyrB and mutS) and TIM1+I+G (for concatenated sequences) were the best-fit substitution models (Table rplB and leuS trees were the most congruent (Icong = 7.441), and the concatenate and adk trees were the least congruent (Icong = 2.923). Overall, the concatenated tree was the least congruent with other genes (mean Icong = 3.284) than with isolates' genes .To reconstruct the phylogeny of the RSSC isolates, we first estimated using jModelTest2 for the best-fit substitution model for each gene and for concatenated sequences , a very high polymorphism was revealed for each of the 7 loci . MLST showed very high discriminatory power (HGDI = 0.999), identifying 88 haplotypes, including 87 singletons that were epidemiologically unrelated between countries. On the SWIO scale , a high level of polymorphism was also revealed for each of the 7 loci, but logically to a lesser extent than on the worldwide scale . The discriminatory power of MLST was high (HGDI = 0.94). The 145 isolates were resolved into 55 haplotypes with 30 singletons, suggesting the presence of some epidemiologically related strains between SWIO countries. On the island scale, contrasting levels of genetic diversity were observed. On the Comoros (Anjouan and Mohéli)  and Seychelles (Mahé and Praslin)  archipelagos, no genetic diversity was revealed. On the other islands, different levels of genetic diversity were shown, with high levels in Reunion, Mauritius, and Madagascar , a moderate level in Mayotte  and a low level in Rodrigues . The discriminatory power of MLST varied depending on the SWIO islands (from HGDI = 0.65 for Reunion to HGDI = 0.96 for Mauritius).Based on the MLST data, genetic diversity was measured on worldwide (C2), SWIO (C1), and island from C4 to C10) scales (Table  to C10 sn = 145), MLST identified a total of 29 STs , the genetic diversity of the RSSC isolates that are prevalent on the SWIO islands has been scarcely documented. In this study, we first conducted a broad sampling to cover crop production areas, mainly Solanaceae, in the SWIO islands . This sampling is the largest ever reported for BW epidemiosurveillance, yielding 1704 RSSC isolates from 129 plots. This large collection provided us with a solid foundation to apply MLSA/MLST to infer the phylogenetic relationships among RSSC strains, reveal the distribution and the level of RSSC genetic diversity, and conduct molecular epidemiological studies throughout the SWIO area. Interestingly, this approach confirmed the well-known epidemiological and genetic traits of the RSSC as well as revealing new ones.egl sequences , Seychelles (Mahé and Praslin), and Rodrigues, where only scarce isolates were assigned as phylotype I before this study , this phylotype was identified exclusively, which has also been reported in Mayotte  and in another island in Asia and in Taiwan  chosen by Ravelomanantsoa et al. (fliC and ppsA were not amplified from any tested strains. Compared to the worldwide collection of strains (C2), the level of genetic diversity in the SWIO isolates (C1) was lower, but overall, a similar trend of RSSC genetic diversity was observed. According to the genetic diversity estimators , the same genes presented the lowest and the highest diversities in both collections. Phylogenetic reconstructions on individual genes and on the concatenated sequences revealed incongruent topologies as it was already observed  previously on a worldwide RSSC collection  appeared homogenous since MLST did not differentiate these sequevars into several STs. This result was also observed for phylogenetically closely related lineages such as the sequevar IIB-4 strains, including strains that are pathogenic on bananas (causing Moko disease) and strains that are not pathogenic on bananas  and STII-29  formed a CC (CCII-3), which confirmed the clonal structure of sequevar IIB-1  throughout the SWIO islands. This genetic lineage was distributed in all SWIO islands, except for the Madagascar highlands, and was detected in 48% of the sampled plots. However, its prevalence varied depending upon the islands. In the Comoros and Seychelles archipelagos, it was a unique genetic lineage, but in Reunion and Mayotte, it was the most prevalent genetic lineage . In Mauritius, its prevalence was lower, representing only 19% of the tested collection. Whatever the level of prevalence, this overrepresentation obviously reflects a lineage that has strongly adapted to the SWIO environment. This adaptation is not recent since the oldest SWIO strain (RUN3510) of this lineage was isolated in 1957 in Mauritius.Pelargonium , which occurred in all SWIO islands (except the Madagascar highlands) and exhibited genetic links with strains from continental Africa. Further studies are now required to understand the ecology of this lineage and why it is so strongly adapted to the SWIO environment. Further population genetic studies on different scales are also necessary to reconstruct the evolutionary history of this lineage and to decipher the epidemiological relationships between the SWIO and African populations. Such studies could be conducted with more conclusive methods such as the multilocus variable number tandem repeat analysis (MLVA), which was developed to study RSSC populations from cropping areas to continents (N'guessan et al., NY: conceived and designed the experiments, performed the experiments, analyzed the data, wrote the paper, and prepared the figures and tables; J-JC: conceived and designed the experiments; SR: provided the unpublished Madagascar strains; AH, BP, RJ, YJ-F, JéF, and JaF: participated in the surveys; BH, FG, GC, PP, and SP: conceived and designed the experiments and revised the manuscript.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.


Mercurialis annua is a wind-pollinated annual plant that has long been used as a model for the study of ploidy and sexual-systems evolution. However, no molecular markers are yet available for genetic studies of its diploid populations. Here, we develop and characterize a set of eight polymorphic microsatellite markers for diploid dioecious M. annua.M. huetti and M. canariensis, with successful amplification for seven and six of the eight loci, respectively. The novel microsatellite markers proposed here will be crucial for a multitude of genetic studies of M. annua and further establish its importance as a model species for addressing ecological and population genetic questions.Following an SSR-enrichment protocol, 13 microsatellite markers were proposed, eight of which yielded successful amplification and polymorphism. We screened the eight microsatellite loci in 100 individuals. The number of alleles per marker ranged from 6 to 12, and observed heterozygosity ranged from 0.57 to 0.76. To estimate potential allele scoring errors, these individuals’ offspring were genotyped for the same loci, and error rates were estimated from parentage analyses. Error rates ranged from 0 to 6.8%. Cross-amplification tests were performed for congeneric  Mercurialis annua is a wind-pollinated annual species that can be found in central and western Europe and around the Mediterranean Basin [M. annua displays remarkable variation in both ploidy, ranging from diploid to 12-ploid, and sexual system, ranging from dioecy to monoecy through androdioecy [M. annua has been used in studies of population range-expansion [e.g. M. annua as a model system see [an Basin . Across rodioecy . In the ion e.g.  a topic stem see  and refeM. annua is currently limited by a lack of modern genetic markers. Indeed, the tools available are limited to isozyme loci [M. annua, these do not amplify in the diploid form [M. annua. Particularly, we are interested in studying selection gradients and kin selection in addition to metapopulation processes and population subdivision. Hence, we developed eight polymorphic microsatellite markers for diploid dioecious M. annua and examined cross-species amplification success in two other Mercurialis species.The scope for population-genetic and mating-system studies in diploid yme loci , internayme loci . Althougoid form . Due to oid form . Additioa) with different fluorescent tags and size ranges . Size-selected fragments from genomic DNA were enriched for simple sequence repeat (SSR) content using magnetic streptavidin beads and biotin-labelled CT and GT repeat oligonucleotides. The SSR-enriched library was analysed on an Illumina MiSeq platform using the Nano 2 × 250 v2 format. After assembly, 756 contigs/singlets contained a microsatellite insert with a tetra- or a trinucleotide of at least 6 repeat units or a dinucleotide of at least 10 repeat units. Of these, 465 contigs were suitable for primer design, pointing to the potential of 13 microsatellites markers with number of alleles ranging from 3 to 8. Individual tests for amplification and polymorphism were performed on 15 individuals, after which eight of the 13 potential markers were retained  following the manufacturer’s instructions, and eluted in 100 µl of distilled water. PCR amplification was carried out in a final volume of 10 µl, including 2× Multiplex PCR Master Mix , 1 µl of extracted DNA, and variable primer concentrations . These plants originated from a mix of European populations encompassing the natural distribution of diploid To estimate the accuracy of allele scoring, 7–8 seeds per female  were sown at the greenhouses of the Department of Ecology and Evolution of the University of Lausanne, Switzerland, and the resulting plants were genotyped for all markers. For each marker separately, we counted a mismatch when the offspring and its mother had no alleles in common. The error rate per marker was calculated as the proportion of mismatches across all offspring.The number of alleles per locus ranged from 6 to 12, the observed heterozygosity ranged from 0.57 to 0.76, and the expected heterozygosity ranged from 0.60 to 0.85 (Table M. annua: M. huetii (diploid) and M. canariensis (tetraploid). Five individuals of each sister species were genotyped for the three multiplexes in the same conditions as those described above, and similar genetic diversity calculations and tests for HW equilibrium were also performed. For the tetraploid M. canariensis, microsatellite scoring of alleles followed the gene dosage method, as described in [Cross-amplification was checked in two sister species of ribed in , and genribed in .M. canariensis . The seFor some studies, eight markers may not be sufficient, for example to detect weak levels of structure within and between populations.The use of individuals from our common garden experiment after a few generations of crosses within a relatively small population may have yielded lower levels of polymorphism per marker than a wild population would.


SGDCs) is critical for designing appropriate conservation and management strategies to protect biodiversity. Yet, few studies have explored the impact of changing land use patterns on SGDCs specifically in aquatic communities. This study examined patterns of genetic diversity in roach (Rutilus rutilus L.) together with fish species composition across 19 locations in a large river catchment, spanning a gradient in land use. Our findings show significant correlations between some, but not all, species and genetic diversity end points. For example, genetic and species differentiation showed a weak but significant linear relationship across the Thames catchment, but additional diversity measures such as allelic richness and fish population abundance did not. Further examination of patterns in species and genetic diversity indicated that land use intensification has a modest effect on fish diversity compared to the combined influence of geographical isolation and land use intensification. These results indicate that environmental changes in riparian habitats have the potential to amplify shifts in the composition of stream fish communities in poorly connected river stretches. Conservation and management strategies for fish populations should, therefore, focus on enhancing connectivity between river stretches and limit conversion of nearby land to arable or urban use to maintain current levels of biodiversity.Understanding the environmental factors driving species‐genetic diversity correlations ( To better evaluate the consequences of environmental threats, loss, and management of biodiversity, it is now possible to exploit the link between genetic diversity in populations of one species  and the species diversity of the associated assemblage. Positive correlations between species and genetic diversity have been demonstrated across a variety of environments and species  the extent to which the genetic and species structure of fish communities within the catchment were correlated; and (ii) the extent to which fluctuations in genetic and species diversity can be attributed to patterns of connectivity between localities, and, separately, to a gradient of watershed land use. Outcomes of this work will improve our understanding of the importance of environmental factors acting simultaneously to drive population success, thereby increasing our ability to improve fish population conservation and to mitigate the most influential factors impacting riverine communities.22.12, much of which is dominated by the capital city of London and associated urban conurbations. Consequently, the region is characterized by human‐modified landscapes, giving rise to an aquatic environment influenced by extensive urbanization and intensive agriculture practices. At the time of the last mapping exercise in 2004, 35% of the Thames Region was classified as arable, 19% grassland and 11% woodland, the rest being urban. The dominance of urban land is now likely to be significantly larger than in 2004 as the Thames catchment has undergone extensive human modification and management and, as a result, many contemporary riparian habitats within the catchment are expected to be influenced by both current and legacy effects of urbanization . A sing2.4n = 869) for genetic analysis. Fin clips were stored in 100% ethanol, and scales were kept dry in scale packets for subsequent DNA extraction. All animals used in this research were treated humanely and with regard to the alleviation of suffering; all procedures were subject to approval by the local ethical review process as required under the U.K. Animals .All 19 locations were sampled during one season (2010) using 3‐pass catch depletion sampling of the total fish population  diversity index  were calculated for every sample location. More complex indices of species diversity, including evenness (the relative abundance of rare and common species) and the Shannon (n = 869) were genotyped at 14 microsatellite loci and checked for null alleles and Hardy–Weinberg equilibrium, as described in Hamilton et al. (HO and AR) for each roach population were taken from Hamilton et al. (IR), a multilocus measure of relatedness and inbreeding. IR estimates the similarity between parental half‐genotypes within an individual and weights the importance of each allele according to its frequency in the population  and gray seal ; mean IR was then calculated for each site by averaging IR from all individuals. The Rhh package also creates outputs of homozygosity by locus ; we present only results for IR here, as IR, SL, and HL were highly correlated .Roach samples  landscape composition was assessed at each sample site from a digital land cover map (LCM2007). This map is derived from categorized parcels of satellite images, projected in ArcGIS 10 . The LCM2007 25 m raster dataset  provides digital cartography of the UK, defining broad habitats seen in the UK Biodiversity Action Plan (UK BAP). LCM2007 is the most up to date digital map overlay available, however, it is appropriate to be mindful of changes to land use that may have occurred in the interim, which could alter river habitats over more recent timescales.For the purpose of this work, an assessment of landscape characteristics within a circular buffer zone of 2 km surrounding each sampling site were recorded as the proportion of arable, grass, wood, and urban land Table . A 2 km The movement of fish between sites and the physical carrying capacity of each location can determine the number of individuals and species present. Accordingly, the waterway distance of each sample site from the main stem Thames (km) was included as an additional covariate when comparing species diversity and roach genetic diversity between sites. Measurement of pairwise waterway distance between sites (km) was calculated via ArcMap 10  using the Fastest Path routine in conjunction with Network Analyst. The same approach was used to calculate waterway distances from each sample location to the main river. Pairwise waterway distance and distance to the main stem Thames were used to examine the role of isolation from the main migration corridor and to define the influence of watershed location within the Thames on the basis of genetic and species assemblage similarity.2.7IR, AR, and HO), abundance and species diversity of the fish assemblage  were compared to assess the strength of pairwise association between indices. Partial Pearson correlations accounting for distance from the main stem Thames were also conducted to examine if species‐genetic diversity relationships changed when the influence of isolating distance was removed.Pearson correlation coefficients were used to examine relationships between roach abundance, the genetic diversity of roach populations and overall species diversity across all sites. More specifically, measures of mean population internal relatedness and genetic diversity (FST = FST/1‐FST) among localities and in relation to the number of physical barriers between sites.Pairwise Bray–Curtis dissimilarity in species composition of fish assemblages between sites was calculated using multidimensional scaling (MDS) as implemented in SAS 9.3 . Mantel tests (using GenAlEx 6.5) were used to compare pairwise estimates of geographic distance to estimates of species assemblage dissimilarity between all 19 sites, to evaluate the strength of biogeographical structuring. Mantel tests were also conducted to assess the strength of association between pairwise estimates of assemblage differentiation (Bray–Curtis dissimilarity) and genetic differentiation (linearized H), abundance and species richness; (ii) allelic richness (AR), genetic diversity (HO), and mean internal relatedness (IR) of roach populations. Geographic distance from the main stem Thames was also included in regression analyzes as a covariate alongside “disturbed” land use percentages. All test variables used were inspected prior to analysis for normality using the Kolmogorov–Smirnov (K–S) test and, if requirements of a normal distribution were not met, data were log‐transformed. α = 0.05 was chosen as the accepted significance level for all statistical tests. Unless otherwise specified, statistical analyses were carried out with SPSS, Ver. 20 .Differences in genetic and species diversity of fish populations were compared across categories of dominant land use using ANOVA. Linear regressions and correlations performed in SPSS were used to examine relationships between land use percentages and (i) species diversity . Only paired genetic diversity measures (IR vs HO) and species diversity  showed a significant linear increase in relation to each other across all correlation analyses conducted.In total, genetic data were obtained for 869 individual roach from 19 sites in the Thames catchment, encompassing a gradient of land use and covering a riverine distance of 176 km . Genotype data revealed high genetic diversity in all roach populations examined across the Thames catchment Table . When te3.2Catch data from routine surveys of 19 Thames catchment sites allowed examination of fish population composition in parallel with genetic diversity of roach populations. Sixteen freshwater fish species were commonly recorded throughout the Thames catchment in various proportions at each site, with roach being the most abundant species encountered Table ; the hig3.3FST , which was also evident when testing assemblage divergence of stream fishes across the Thames catchment . Our results indicated a strong relationship between population‐genetic divergence (linearized FST) and fish assemblage divergence , which was not evident when examining simple correlations between species and genetic diversity measures (Table FST) and fish assemblage divergence (Bray–Curtis distance). Statistical correlation between pairwise matrices was significant only when examining linearized FST  in relation to the number of physical barriers between sample sites within the Thames catchment. This suggests increasing genetic divergence between sites with a larger number of physical barriers between them – a relationship that was not replicated at the species (diversity) level.Accounting for connectivity between sample sites is of crucial importance when examining spatial patterns of species and genetic diversity. The role of distance in driving patterns of genetic and species differentiation was, therefore, examined to see if evidence of limited dispersal was present in the Thames and, overall, increasing isolation between sample sites appeared to promote both assemblage and genetic differentiation. Here, pairwise linearized Slatkin,  of 19 sies Table . In orde3.4H, HO, or IR  when compared to grassland populations, a finding which agrees with the results shown in Table H, HO, and IR when controlling for downstream distance to the main stem Thames  and increased inbreeding . However, this interaction between % disturbed land and distance from the main stem Thames was not a significant predictor of AR, roach abundance or total abundance of fish assemblage.Testing species and genetic diversity patterns in relation to combined urban and arable (disturbed) land use practices showed a negative influence of % disturbed land on aquatic biota . In our study, only genetic and species assemblage divergences appear to covary across the Thames catchment (FST vs Bray–Curtis p = .01) suggesting inconsistent patterns between the different measures of diversity used. A plausible explanation for the lack of symmetry in diversity responses is that the genetic diversity of roach was strongly driven by population size at the time of sampling, whereas Shannon diversity was less affected by changes in roach abundance, a similar result to that reported by Blum et al. (H), because smaller roach populations allow greater overall species diversity and richness. Genetic diversity of an individual species is likely to be more affected by fluctuations in abundance observed across sampling sites, altering genetic variation through the presence/absence of different alleles under different population size scenarios , but this change in abundance does not negatively impact species diversity indices. Genetic diversity of roach populations fell with declining population size,  due to the removal of few individuals from an abundant species, which appears to have only minimally affected assemblage structure. This is likely due to the high level of competition for resources in the Thames, such that when a species or a number of individuals are removed from a population, another species/individual flourishes and replaces them quickly.Positive relationships between species diversity and genetic diversity have been found in a range of other wild species and environments Vellend,  includinm et al. . Changes4.2River networks offer an opportunity to study the influence of landscape and proximal environmental attributes on diversity, as organisms are intrinsically tied to their geomorphological landscape. Many of the cases reported in the literature which document positive patterns between species and genetic diversity have been conducted in isolated situations where the number of species and genetic diversity will be naturally reduced at a small geographic scale  or more sensitive to environmental degradation  would perhaps offer further insight into the relative importance of connectivity or habitat heterogeneity as the most important deterministic features shaping other species in stream fish assemblages within the Thames. For example, multispecies studies – which collect species data and genetic data from multiple species in the same system  as a proxy of human activity – an approach that has previously been used elsewhere as a surrogate for degradation in stream ecosystems Allan, . ExploitIR: p = .001). Previous declines in genetic diversity of stream fishes have been linked to intensive land use patterns (Blum et al., Daphnia magna was found to be negatively impacted by agricultural land use intensity (Coors, Vanoverbeke, De Bie, & De Meester, Patterns of genetic diversity have previously been linked to habitat quality (Waits, Bagley, Blum, McCormick, & Lazorchak, 4.4IR in isolated/degraded river stretches reinforces their apparent tolerance to habitat disturbance in comparison with other freshwater fish species. By way of example, this study documents high levels of genetic diversity in many roach populations in the study area, whereas some sites within the Thames catchment exhibit low fish species richness and assemblage diversity. Thus, we recognize that further testing of our findings across different river catchments and exploration of patterns of genetic diversity within other focal species could prove highly insightful. Comparisons of genetic diversity in fish species that are less tolerant or less vagile than roach may be more indicative of the risk posed by habitat disturbance or fragmentation (Struebig et al., The sustained presence of roach and also a higher mean None declared.Microsatellite genotypes: see supplementary file in Hamilton et al., EN participated in the design of the study, collected field data, carried out the statistical analyzes, and drafted the manuscript. SJ and JS helped conceive and draft the manuscript. All authors gave final approval for publication. Click here for additional data file. Click here for additional data file.


The overall breeding objective for a nucleus swine selection program is to improve crossbred commercial performance. Most genetic improvement programs are based on an assumed high degree of positive relationship between purebred performance in a nucleus herd and their relatives’ crossbred performance in a commercial herd. The objective of this study was to examine the relationship between purebred and crossbred sow longevity performance. Sow longevity was defined as a binary trait with a success occurring if a sow remained in the herd for a certain number of parities and including the cumulative number born alive as a measure of reproductive success. Heritabilities, genetic correlations, and phenotypic correlations were estimated using THRGIBBS1F90.Results indicated little to no genetic correlations between crossbred and purebred reproductive traits. This indicates that selection for longevity or lifetime performance at the nucleus level may not result in improved longevity and lifetime performance at the crossbred level. Early parity performance was highly correlated with lifetime performance indicating that an indicator trait at an early parity could be used to predict lifetime performance. This would allow a sow to have her own record for the selection trait before she has been removed from the herd.Results from this study aid in quantifying the relationship between purebred and crossbred performance and provide information for genetic companies to consider when developing a selection program where the objective is to improve crossbred sow performance. Utilizing crossbred records in a selection program would be the best way to improve crossbred sow productivity. In a commercial swine breeding herd, sow longevity is a contributing factor to the operation’s overall success and profitability. A sow must remain in the herd between 3 and 4 parities to produce enough piglets before the investment in her reaches a positive net present value or “she pays for herself” . IncreasAdditionally, removing females in early parities does not allow a swine operation to benefit from the higher performance of sows compared to gilts and down-stream grow-finish performance from pigs produced from parity 1 sows versus performance from pigs produced by older sows (parity 2 and greater). Not only do sows tend to have larger litters compared to gilts , pigletsThe overall breeding objective for a swine nucleus selection program is to improve crossbred commercial performance. Most genetic improvement programs are based on an assumed high degree of positive relationship between purebred performance in a nucleus herd and their relatives’ crossbred performance in a commercial herd. In addition to the sows’ genetic makeup differences (i.e. purebred versus crossbred) in nucleus and commercial herds, there are differences in management practices between the two types of herds. Sow longevity is an economically important trait at the commercial level; however, nucleus sows typically produce fewer parities due to the desire to shorten the generation interval in order to increase the rate of genetic improvement. Removing sows at the nucleus level before they have fully expressed their lifetime potential does not allow for direct selection on longevity in the nucleus herd. Since nucleus animals do not have phenotypic records for lifetime measurements, an indicator trait expressed at an earlier parity would be necessary to select for longevity. Few sows remain in a nucleus herd after the 2nd parity due to culling for genetic potential. If 2nd parity reproductive performance is indicative of lifetime performance in commercial animals and if a genetic correlation exists between purebred and crossbred performance, selection on nucleus animals after only 2 parities could have a positive impact on improving sow longevity and lifetime production in commercial herds.The objective of this study was to examine the relationship between purebred and crossbred lifetime reproductive performance. The traits of interest were whether or not a sow remained in the herd up to a certain number of parities and the total number born alive for a sow across parities, or cumulative born alive. Defining the relationship between purebred and crossbred sow longevity will allow genetic suppliers to improve selection methods for increased longevity at the commercial level.Five nucleus herds and 2 commercial herds were used for this analysis. All 7 herds were owned by the same company and operated using the same management protocols. The commercial herds were part of the downstream production pyramid from the nucleus herds. Removal records for 11,506 purebred sows and 12,897 crossbred sows were evaluated for this study. Purebred sows were from a Landrace pure line, and the crossbred sows were F1 offspring from a cross between the Landrace pure line and a Large White pure line. All crossbred animals were produced using a Large White female and Landrace male. There were 1,039 and 213 sires for the purebred and crossbred sows, respectively. Of the 213 sires used at the crossbred level, 205 had dams with records at the purebred level, providing genetic ties between the purebred and crossbred herds. No sires were used at both the nucleus and commercial herds. Purebred records were from sows first farrowing in February 1993 through May 2011. Crossbred sows first farrowed in November 2004 to May 2011. Sows that were active or that were continuing to produce (not yet completed their lifetime record) in the herd were included in the analysis, but not treated as censored data. Previous work has shown that if the number of completed records overwhelms the number of censored records, there is little re-ranking among the breeding value estimations . PedigreSow longevity traits were defined based on the sow’s removal parity. At the nucleus level, sows were considered successful if they completed parity 2 (farrowed and weaned their second litter). This trait is called PL2. At the commercial level, four degrees of success  were analyzed as separate traits. These traits are called CL2, CL3, CL4, and CL5, respectively. Additionally, cumulative number born alive traits were defined for purebred and crossbred sows. For purebred sows, cumulative number born alive up to parity two (PNBA2) and for a lifetime (PNBALF) were defined. For crossbred sows, cumulative number born alive up to parity 2 (CNBA2), parity 3 (CNBA3), parity 4 (CNBA4), parity 5 (CNBA5) and for a lifetime (CNBALF) were defined. Descriptive statistics for the cumulative and lifetime born alive traits are shown in Table All traits were analyzed using THRGIBBS1F90  with 100y is the vector of observations, μ is the mean, f is the vector of contemporary group effects, u is the additive genetic animal effects, e is the vector of residual effects, and X, W, and Z are known incidence matrices. Herd, year, and month of last farrowing were used to define the contemporary groups. Contemporary group was fitted as a fixed effect for all traits analyzed. All models included a random animal effect. Bivariate analyses were used to estimate genetic and phenotypic correlations, and univariate analyses were used to estimate heritabilities.where Heritability was defined asσg2 is the additive genetic variance, and σe2 is the residual variance. The genetic correlation between traits x and y was calculated aswhere x and y, x, and y. The phenotypic correlation between traits x and y was calculated aswhere x and y, x, and y. The estimates were calculated for each sample.where Using the genetic variance estimate for PNBALF, the relative economic importance for lifetime pigs produced compared to other reproductive traits was determined. The genetic variance along with the heritability indicates how much genetic progress can be made in a trait. The economic value of a one unit improvement in the trait can be used to determine the relative economic performance of the potential genetic progress compared with other traits in the index. The other traits included in the analysis were number born alive (NBA), number weaned (NW), litter weaning weight (LW), litter birth weight (LBW), and wean to estrus interval (WTE). The genetic standard deviations for these traits were obtained from commercial seedstock suppliers. The economic weights were $27.93 for a pig born alive, $38.57 for weaned pig, $0.45 per pound of live weight, and $1.90 cost for a nonproductive day . The staThe removal parity distributions for the purebred and crossbred sows are in Table The heritability, genetic correlation, and phenotypic correlation estimates are presented in Table The genetic correlations between the purebred and crossbred cumulative and lifetime born alive traits were not significantly different from 0. However, the genetic correlations between all crossbred cumulative born alive traits were high (>0.70), suggesting that early parity performance is indicative of a sow’s lifetime performance in a commercial herd.The relative economic weights for reproductive traits are shown in Table Improving crossbred performance is the goal for most swine breeders. However, most swine genetic companies make selection decisions based on purebred performance records though genetic correlations between purebred and crossbred performance traits are not 1, indicating that the relationship is not perfect –16. WhilThe greater genetic correlation estimates between crossbred and purebred longevity found in previous studies –16 compaPreviously reported literature estimates have shown that heritabilities and genetic correlations between traits in a selection index vary between crossbred and purebred populations –19. The In this study, over 40 % of the purebred sows remained in the herd for greater than 2 parities. This number is inflated from what would be expected in some companies if a nucleus herd was trying to maximize genetic progress as quickly as possible. Sows may remain in a nucleus herd due to a reduction in available gilts to enter the herd or fewer lower parity sows. This decreased supply of gilts and low parity sows could be a result from a health challenge to the system. Keeping animals in the herd that would have otherwise been removed could impact the heritability associated with sow longevity. Typically, a female is retained in a nucleus herd for additional parities when the older female has an index value greater than the index value of potential replacement gilts. Changing the selection criteria would result in changing the longevity definition, which affects the genetic parameters associated with the trait. For example, sows that survive a health challenge may not have the same characteristics as sows that have high reproductive performance in a herd with a high health status.For this analysis, there were approximately equal numbers of purebred and crossbred sows due to the longer data collection period for purebred sows. Since this analysis presented evidence that there is little to no genetic correlation between purebred and crossbred longevity measures, genetic companies should make use of maternal records from their crossbred sow population through the development of a commercial test herd or develop relationships with customers willing to keep meticulous records. In either case, these records can be used to augment the purebred records for more lowly heritable traits to improve accuracy and speed genetic progress.Heritability estimates of 0.03 and 0.08 for lifetime born alive in crossbred sows, which are smaller than the estimate found in this study, have been reported previously . AdditioBecause of the large relative emphasis that should be placed on lifetime born alive, genetic companies must focus on adding sow lifetime production to a selection program. One way to do this would be to implement a commercial test herd. With the commercial test herd, genetic companies could collect data from crossbred animals to be used in the genetic evaluations. Increasing the amount of data collected on crossbred females would allow genetic companies to more accurately select to improve crossbred performance in commercial herds.


Scylla serrata provides an important source of income and food to coastal communities in East Africa. However, increasing demand and exploitation due to the growing coastal population, export trade, and tourism industry are threatening the sustainability of the wild stock of this species. Because effective management requires a clear understanding of the connectivity among populations, this study was conducted to assess the genetic diversity and connectivity in the East African mangrove crab S. serrata. A section of 535 base pairs of the cytochrome oxidase subunit I (COI) gene and eight microsatellite loci were analysed from 230 tissue samples of giant mud crabs collected from Kenya, Tanzania, Mozambique, Madagascar, and South Africa. Microsatellite genetic diversity (He) ranged between 0.56 and 0.6. The COI sequences showed 57 different haplotypes associated with low nucleotide diversity (current nucleotide diversity = 0.29%). In addition, the current nucleotide diversity was lower than the historical nucleotide diversity, indicating overexploitation or historical bottlenecks in the recent history of the studied population. Considering that the coastal population is growing rapidly, East African countries should promote sustainable fishing practices and sustainable use of mangrove resources to protect mud crabs and other marine fauna from the increasing pressure of exploitation. While microsatellite loci did not show significant genetic differentiation (p > 0.05), COI sequences revealed significant genetic divergence between sites on the East coast of Madagascar (ECM) and sites on the West coast of Madagascar, mainland East Africa, as well as the Seychelles. Since East African countries agreed to achieve the Convention on Biological Diversity (CBD) target to protect over 10% of their marine areas by 2020, the observed pattern of connectivity and the measured genetic diversity can serve to provide useful information for designing networks of marine protected areas.The giant mud crab  Scylla serrata) is widely distributed in the Indo-Pacific and it is the only Scylla species found at African shores . –36. 34–35´ TTGATTTTTTGGTCATCCAGAAGT 3´ [5´ TTAAGTCCTAGAAATGTTRGGGA 3´ [Thermus aquaticus DNA polymerase, 0.2 μM of each primer, 0.2 mM DNTP, 3 mM MgCl2, 1x Taq buffer, and 0.4 mg bovine serum albumin. The PCR profiles included an initial denaturation step of 5 min at 95°C, followed by 35 cycles of 30 s at 95°C, 30 s at 50°C, and 1 min at 72°C. A final extension step of 10 min at 72°C was added to ensure complete amplification. Agarose gel electrophoresis was performed to assess the quality of the PCR products. Sequencing of both strands of the fragment of COI gene was done with an ABI 3700 XL sequencer. For each sample, the obtained forward and reverse sequences were edited and aligned using the ClustalW algorithm as implemented in MEGA ver. 6.0 [A fragment (557 bp) of the COI gene was also amplified using the primers mtd10 GAAGT 3´  and C/N RGGGA 3´ . The PCRver. 6.0  to generver. 6.0 .IS (within sub-population inbreeding coefficient) and to test whether it is significantly different from zero. Prior to these analyses, samples with missing data at three or more loci were removed from the data set. The data set was also checked for null alleles, large allele drop out, and scoring errors due stuttering using the software Micro-Checker ver. 2.2 [Microsatellite based estimates of the observed and expected heterozygosity were determined with the software GenAlEx ver. 6.5 . The samver. 2.2 .π, based on pairwise differences), and historical nucleotide diversity  were calculated with the program DnaSP ver. 5.10 [A total of 230 COI sequences were obtained from the analysed tissues. Alignment of the edited sequences was performed with MEGA ver. 6.1  to generer. 5.10 .ST values and the within subpopulation genetic diversity (Hs) was performed using the computer program CoDiDi (Correlation between Diversity and Differentiation) ver. 1.0 [ST is an appropriate measure of genetic differentiation for the sampled populations. Generally, GST gives correct estimates of genetic differentiation if the effect of mutation is lower than other demographic forces. Mutation effects are lower than other demographic factors when the correlation between GST and Hs (within subpopulation expected heterozygosity) is not significant [ST was performed with GenAlEx ver. 6.5 [ST values was determined according to the Holm-Bonferroni sequential procedure [k) in the dataset and giving the corresponding probabilities. STRUCTURE HARVESTER ver. 0.6.94 was used to infer the optimal k through the Δ k statistic, which is based on the rate of change of log probability of the data between successive k–values [The analysis of molecular variance (AMOVA) of the microsatellite data was performed with the software Arlequin ver. 3.5.1.2 , in ordever. 1.0 . This wanificant . Pairwisver. 6.5 , in orderocedure . To testrocedure  was perfk–values .ST values, which were calculated from haplotype frequencies. The significance of pairwise FST values was calculated by 10,000 random permutations of haplotypes between populations. The FST p-values were adjusted using the Holm-Bonferroni sequential procedure [ST and ΦST) was determined with 10,000 permutations. A minimum spanning haplotype network was constructed with the software PopART ver. 1.7 [The 230 analysed COI sequences were combined with 125 previously published sequences ,33, to frocedure . Hierarcver. 1.7  to examiver. 1.7 . The prover. 1.7 .Fu’s Fs  and TajiIS) were not significantly different from zero ).The eight analysed microsatellite loci did not show significant evidence of large allele drop out or scoring errors due to stuttering. In addition, the analysed loci did not show significant evidence of null alleles in all sampled populations, except locus Scse43-1 at site 4. The loci Ssse96-1 and Scpa-INI-SSR showed significant deviation from the HWE at site 3. The locus Scse43-1 showed significant departure from the HWE at sites 4 and 7. Significant departure from the HWE was also shown by the loci SCY38 and SCY12 at sites 6 and 10, respectively. The locus Scse53-1 was monomorphic at all sites, except site 1 . The totπ < θw).A total of 230 COI sequences each with 535 base pairs were obtained. Diversity indices were calculated only for sites with at least 14 sequences. The analysed sequences showed 40 haplotypes. The highest haplotype diversity was observed at sites 2 and 8 . The lowA multiple alignment of the 230 sequences obtained during this study and the 125 previously published COI sequences was performed. The sequences were collapsed with the program FaBox DNA collapser ver. 1.41  to generST) and the within subpopulation expected heterozygosity (Hs) is not significant . The analysis of molecular variance of the microsatellite data showed that the variation among sites was not significant . STRUCTURE analysis did not detect meaningful genetic clusters . Pairwise comparison of FST-values showed variable connectivity among the sample sites. With the exception of site 8, populations from Seychelles, Kenya, Tanzania, Mozambique, South Africa, and the West Coast of Madagascar did not show significant genetic differentiation . The observed pattern of genetic differentiation was also revealed in the haplotype network  are low compared to the reported levels of genetic diversity in the mangrove crabs Uca hesperiae , Perisesarma guttatum , and Neosarmatium africanum  [Uca occidentalis   from the ± 0.04% ) from th the WIO ,58. The hina Sea . Since ghina Sea , these fS. serrata populations in the study area. Mismatch distribution of the pooled sample produced a unimodal distribution, supporting the null hypothesis of population expansion . When the hypothesis of neutral evolution was tested for each population, significant departure from the hypothesis were observed at all sampled populations, except the populations at sites 6, 7, 11, 13, 14, and 15 . This inxpansion . The ragxpansion . This suST) and the within subpopulation heterozygosity was not significant (p > 0.05). This shows that single loci GST-values are marker independent and that GST is the best estimate of genetic differentiation in the study area [ST = 0.00424, p > 0.05, 100172 permutations). In contrast, COI showed significant genetic differentiation among sites . The contrasting patterns of genetic differentiation between nuclear and mitochondrial DNA are reported in several other species [S. serrata is the only Scylla species occurring in the study area [S. serrata populations was revealed by the mismatch analysis . The fact that previous studies did not include samples from the ECM [S. serrata has a planktonic larval stage, the SEC is expected to transport and disperse larvae from the ECM to East Africa through the EACC, MC and AC. The observed genetic differentiation indicate that there is limited larval exchange between sites in the ECM and sites in mainland East African and the Seychelles. This is supported by the measured mutation scaled immigration rate, which showed lowest rate of immigration to mangroves at the ECM . This shows that the studied population experienced bottlenecks in its recent history. Considering that indications of mud crab overexploitation and mangrove degradation are reported in the study area [The study showed that the current genetic diversity is low compared to historical genetic diversity  strategic plan for biodiversity 2011–2020, which is targeting to achieve effective protection of 10% of the global marine ecoregions by 2020 . ProgresS1 Table(DOCX)Click here for additional data file.