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Evaluation of genetic variation among endangered sea brown trout young fish for restocking to Caspian sea.

Implications

The brown trout (Salmo trutta Caspius) are economically important and displays interesting biological characteristics for the study of genetic. In restocking program of Sea trout in Iran the base population for artificial propagation is from the sea. Stock identification is a crucial problem in fisheries management. We present a study of molecular investigation that allows identifying and running after the stocks of Caspian brown trout in subsequent generation. This method relies on specific information on the multilocus genotypes of the parents producing subsequent generations. Because this method relies on knowledge of the parental genotypes, it was useful in situations of our cases that the pool of potential parents were sampled successfully.

Introduction

In the last decade, geneticists have helped aquaculture managers by implementing breeding programs and genetic variability to obtain higher productivity and sustainability in fish hatcheries. The use of molecular markers has significantly helped this goal. In particular, the use of microsatellites has allowed the determine of effective breeding numbers (Ne) through parentage assignments in several species, including Atlantic salmon (Salmo salar) [20]. Microsatellites are also useful for the characterization of genetic stocks [5]. This method provides significant information in genetic of fish. Investigation of genetic diversity by microsatellite have been described within genes of a variety of fish species, including Caspian Sea Cyprinus carpio [29], Catla catla [1], Esox lucius [12], Oncorhynchus masou [11] and Caspian Sea Salmo trutta [27].

The brown trout, Salmo trutta L., is today widely distributed in many continents. It is a polytypic species with a wide variety of adaptations to different environments. On the southern Caspian (Iranian costal), Sea trout, Salmo truta Caspius, have access to approximately 5 streams for spawning, but mainly in Tonkabon river.

The spawning migration normally extends from October to January. The timing of the spawning period is highly dependent on water flow but normally most of the sea trout spawning takes place in November and December. The eggs hatch during April-May and sea trout generally remain 2 years in fresh water before smoltification and migration to the sea. These streams run with clear water.

Caspian salmon (Salmo trutta caspius) is an anadermus fish species and it is an important food source for the people of South Caspian Sea. Recently the populations of Caspian sea trout are reduced in numbers due to human activities such as deforestation, drainage, and dam constructions. Many streams have become less suitable for spawning trout and their progeny. The depletion of wild fish stocks in the south Caspian Sea [27] led to artificial propagation of this fish. In hatchery, cultivated stocks have been reared for enhancement and protection of wild populations and maintenance of this species To study the genetic structure of Salmo trutta caspius improve management plan that aim to conserve diversity of this species.

In our previous work we studied the Stock Identification and Genetic variation at Microsatellite Loci of Caspian Sea Salmon (Salmo trutta caspius) to clarify the status of genetic structure of one of the mast valuable fish of Caspian sea, which by Gazazestan scientist it is denoted as endanger fish. In present study we analyzed the same microsatellite locus to identify genetic variation among the young fishes are released to Caspian Sea to evaluate the results of artificial propagation in gene conservation of Salmo trutta Caspius.

Material and methods

Sample of specimen:

From a total of 200 Caspian Brown trout adult individuals that were available in December of 2006 in a hatchery in Iran (Caspian Sea Trout restocking center of Mazandran), 27 females and 27 males average weight of 2800 [+ or -] 200g.

The male were grouped to using a combinatorial optimization approach that each group of 3 male has crossed with one female. Theoretically, they produced 27 half-sib groups that male shared their genome more comparing in random 1 x1 crossing.

The breeders were spawned and after fertilization, eggs coming from a single female incubated under identical conditions until hatching. All larvae were reared in isolated trough and then in large semen tank with freshwater running system (20-[m.sup.3] capacity). The water temperature was maintained at 10 [+ or -] 2 [degrees]C and oxygen contained 9 [+ or -] 1 mg/l. At an intermediate stage of grow the 20 gram a small piece of the dorsal fin was preserved in 96% ethanol for genetic analysis.

Microsatellite amplification and scoring:

Genomic DNA was purified from a small piece of the dorsal fin. The fin tissue crushed and then 300 [micro]l STE, 50 ml SDS (20%), and 3 [micro]L proteinase-k were added to the crushed tissue and incubated at 55-60 [degrees]C overnight. Then, 500 [micro]L phenols were added to fin extract, centrifuged at 8500 for 5min. Total genomic DNA extracted by phenol-chloroform according to Moritz and Hillis [10]. The quality and quantity of DNA were assessed by 1% agarose gel electrophoresis and spectrophotometer (model Cecil DE2040), and then stored at -20 [degrees]C until use. DNA was quantified and diluted to 100 ng/[micro]L for polymerase chain reaction (PCR) amplification. Samples were assayed for allelic variation at eight dinuucleotide. microsatelitte loci known to be polymorphic in Salmo trutta caspius. Eight microsatelitte loci were analyzed: Strutta12, Strutta 58, Otsg474, Otsg83, Otsg409, OmyF, Otsg108 and Otsg100 (Nelson and Beacham, 1999). PCRs were performed in 20 1volumes containing 0.15 U Taq DNA polymerase, 1xPCR buffer, 0.2 mM dNTP mix, 1-2 mM MgCl2, 1 M of each primer set and about 100 ng template DNA. The PCR conditions for all loci were 3 min at 94[degrees]C followed by 30 cycles of 1 min at 94[degrees]C, 0.5min at the optimal annealing temperature, 51-62[degrees]C with a final extension of 5 min at 72 [degrees]C. Amplification products were resolved via 6% denaturing polyacrylamide gel and visualized by silver-staining. A DNA ladder (Invitrogen) was used as a reference marker for allele size determination.

Microsatellite analysis. Microsatellite alleles were sized by using UVI DOC Version V.99.04 software (Table1). In order to calculate allelic and genotype frequencies, observed (Ho) and expected(He) heterozygosity, deviations from Hardy-Weinberg expectations (HWE), [F.sub.ST] value, AMOVA (Analysis of Molecular Variance) were conducted by using the GenAlex 6.2 software (Peakall and Smouse, 2006). The Genetic distance and the genetic identity between the populations was estimated from Nei standard genetic distance and genetic similarity index (Nei, 1972), UPGMA computed in TFPGA version 1.3 and the presence of null alleles was checked by using Microcheker (Version 2.2.3).

Results and Discussion

The molecular and allele frequencies for the eight microsatellite loci of young Caspian salmon results from a cross of 27 female to 3 from 27 group of male brood fish are shown in table 1. Seventy two alleles were found in the sample, ranging from 7 for locus Ostg478 to 11 for three locus. The eight microsatellite loci were all polymorphic in the population.

The expected and observed hetrozygosity are given in table 2. A large variation in observed hetrozygocity, averaged over all samples was observed among loci and ranged from 0.370 in Otsg478 to 0.852 in strutta12 with a mean of 0.73. The mean expected hetrozygocity was ranged from 0.81 to 0.89. The (He) were higher than Ho in almost all loci except in Strutta12. Compared to the He, the Ho were significantly reduced in this groups (P<0.04). Another test for determining the polymorphism, is the score of effective allele in each microsatellite loci. The number of effective allele are determined and presented in table 2. In average effective allele in male was 7.1 [+ or -] 1.3.

Test for departure from Hardy-Weinberg (H.W) expectations yielded a number of significant across all loci sampled. According to the table 3, not deviation from Hardly-Weinberg equilibrium observed for the loci Otsg483 and Omyf.

Discussion:

The brown trout (Salmo trutta Caspius) displays some interesting biological characteristics for the study of genetic. The difference in hatchery and wild of brown trout salmon have been searched by different methods of molecular genetics e.g. mitochondrial DNA analysed to assess the amount and distribution of genetic variation among different populations [14]. Between-population haplotype differences found in their work support the hypothesis of a recent divergence of all the Spanish brown trout from a common ancestral genotype [15]. In the other hand, microsatellite revealed a high level of genetic variability in terms of the number of alleles and gene diversity. Thus, comparing with isozyme and mitochondrial DNA data for a similar broodstock [29] a high genetic variability was observed in the population.

In restocking program of Sea trout in Iran the base population is from the sea catch at the time of migration and not based on hatchery program. Hatchery programs for anadromous salmonids can supplement natural production but may have negative ecological consequences for wild populations [23]. In particular, juvenile hatchery salmonids may have competitive advantages over wild conspecifics [4,25] that could contribute to the displacement of wild fish [16]. Therefore it seems artificial propagation from the wild population that are practices have several advantageous compare to hatchery population propagation are done in some hatchery in Europe and USA. Swain and Riddell [25] also demonstrated that newly emerged coho salmon (Oncorhynchus kisutch) fry from populations that had been cultured for several generations (i.e., domesticated) were more aggressive than fry from geographically Proximate wild populations.

Stock identification is a crucial problem in fisheries management. We present a study of molecular investigation that allows identifying and running after the stocks of Caspian brown trout in subsequent generation. In this method the basic stock unit was all the family propagated at spawning times. This method relies on specific information on the multilocus genotypes of the parents producing subsequent generations. Because this method relies on knowledge of the parental genotypes, it was useful in situations of our cases that the pool of potential parents were sampled successfully.

We used high number of brood stocks as reference for assessing the genetic of next generation to be released to rivers. Analysis of Caspian Sea brown trout is necessary to evaluate the genetic diversity of this fish; this shows whether the propagated samples analyzed can be considered as representative of those used for stocking or if there is too much variability among propagated and wild genetic structure. There are argument between scientist that whether artificial propagation of Caspian Sea brown trout conserve gene bank of this species and prevent genetic drift or not. We tried to verify this assumption using microsatellites analysis and prove with a certain number of breeders and mating of one female with several male to obtained Hardy Weinberg equilibrium. However to conserve Caspian brown trout is difficult to be achieved, because there is still impact on the genetic fitness of the wild population, and less success to minimize the impact while still maintaining a fishery of rare fish are observed.

In any stocking programme it may be to use artificial propagation to mitigate, restore, enhance and conserve populations within the scope of natural production or it may be to increase the population beyond that which could be supported by the natural carrying capacity of the habitat, in order to increase the harvest. The objective of the former is to facilitate the long-term self-sustaining capacity of the population being managed. The second type of intervention is one for which the limitations of natural production are exceeded in order to expand production and as a consequence to possibly replace a situation of self-sustainability with one which must be artificially maintained. The biological considerations of these two types of intervention are different and for the most part these objectives are mutually exclusive [3]. In artificial propagation of Caspian Sea trout both objective are in aim of government and scientists as well.

Genetic changes occur in hatcheries due to human- controlled for selecting of fitness of reproductive traits or using lower effective population size and increasing inbreeding due to family mating. These will decrease genetic variation in hatchery stocks. it is essential that a proper brood stock management plan to be put into practice in order to ensure successful restocking the Salmo trutta in Caspian Sea. Although currently the supply Caspian Sea trout in Iran is mainly dependent on the natural stocks but the process of propagation is dependent on the operations of hatcheries. Therefore, the information on genetic diversity of these hatchery propagated fry and wild stocks is urgently required in order to sustain the quality of restocking method.

As mentioned before, the present study aimed at investigating levels of genetic variation of the fingerlings of the current wild stocks of Salmo trutta Caspius using microsatellite DNA markers. Genetic variation of Salmo truta caspius in Iran was characterized by moderate allele diversity (A=10 [+ or -] 1.5, yousefian, 2010) the same as freshwater fish (A=9.1 [+ or -] 6.1) averaged across 13 species, DeWoody and Avise, 2000 and low of marine fish (A=19.9 [+ or -] 6.6) averaged across 12 species, DeWoody and Avise, 2000.

The allele frequency of progeny was (A=8.8 [+ or -] 1.2) a bite lower than their parents. This is due to the fact that probably not all the breeders contribute in the zygote production or due to variation between the sperm and eggs of identical male and female and some allele diminish in offspring. The identification of some alleles, especially those of large length, is sometimes rather difficult; therefore the number of alleles per locus to the next generation of this locus should be carefully determined.

The reduction of genetic variability of hatchery stocks of some marine species compared to their breeding stocks or to the local natural populations, has been recorded with isozymes as genetic markers [6,26]. The findings of the present study are in contrast with these observations but in agreement with [22] in their study in red sea bream used microsatellite DNA markers and their statement that even though the number of alleles per locus was reduced, the heterozygosity was maintained from the broodstock to the progeny. The observed heterozygosity was moderate (Ho= 0.72 [+ or -] 0.15), lower than the average of parents (Ho= 0.74 [+ or -] 0.2), reported by Yousefian, 2010. This results also are lower than marine fish (Ho =0.77 [+ or -] 0.2) reported by De Woody and Avise, [8] studied on 12 fish species.

The number of alleles observed in this set of loci can be considered low or at least moderate if they are compared with data noted by McConnell et al., 1995, who report of 52 alleles per locus on Atlantic salmon (Salmo salar ). In general, a small number of alleles is a signature of bottleneck (Norris et al, 1999) which, in the case of a wild population, may occur due to population isolation or dramatic reduction of effective population size. We used crosses of 27 female with 3 different batch of 27 males. In this method no significant differences observed in allelic frequency and observed heterozygosity between breeders and their progenies, to keep the level of heterozigocity may is due to the method of crossing. The moderate level of genetic variation detected in the present study was similar to that found in Atlantic salmon, Salmo salar (Slettan et al., 1995 ), Brown trout Salmo trutta L. (Estoup et al., 1993) and brook charr (Angers et al., 1995), rainbow trout, Oncorhynchus mykiss (Morris et al., 1996), that similar numbers of alleles per locus were reported. No significant difference in the mean allelic richness or expected heterozygosity was observed between progeny and the wild parent's population in this study.

In Iran, hatchery of Brown trout use approximately 2-5 hundreds of males and females from Tonkabon river in each spawning season every year. The standard breeding practices of the Kelardasht hatchery appear to have minimized the loss of genetic variability levels but when we used low number of population for restocking significant departures from HWE in the direction of heterozygote deficiency were observed in most of progeny alleles and in parent populations (Yousefian, 2010). Dahle et al. 2006) in study of costal cod (Gadus morpha), stated that several factor such as increasing homozigosit, genetic drift, selection, low sample size and null alleles is possibly cause of the Hardy-Weinberg disequilibrium. The mating of male and female was based on a systematic breeding method, therefore a deviation from Hardy-Weinberg Equilibrium may result from non-random mating, null alleles, frequently found in microsatellite loci as well as low sample size.

Population differentiation detected by allele frequency distribution (Table 4) was highest between female parents (obtained in previous work; Yousefian, 2010) and their progeny and the lowest between the parents. These [F.sub.ST] also gave similar results.

Conclusion:

We use a systematic mating with wild stock population and higher number of male participates in mating. Very few reduced allelic diversity occur in this method of crossing by participating more male. By this way, we may keep the level of heterozigocity and avoiding allele reduction which is very important in stock assessment.

Acknowledgments

We are grateful of Trout restocking center of Mazandran for their help in collecting Salmo trutta caspius samples. This work was supported by funding of the Caspian Sea Ecological Center. We thank F. Laloei and M.J. Tagavi for their technical advising and assistance with microsatellite analysis.

Reference

[1.] Alam S. and S. Islam, 2005. Population genetic structure of Catla catla (Hamilton) revealed by microsatellite DNA markers. Aquaculture, 246: 151-160.

[2.] Angers B.L., L. Bernatchez, A. Angers and L. Desgroseillers, 1995. Specific microsatellite loci for brook charr (Salvelinus fontinalis Mitchill) reveal strong population subdivision on a microgeographic scale. Journal of Fish Biology, 47: 177-185.

[3.] Aprahamian M.W., K. Martin Smith, P. McGinnity, S. McKelvey and J. Taylor, 2003. Restocking of salmonids--opportunities and limitations. Fisheries Research, 62: 211-227

[4.] Chandler G.L. and T.C. Bjornn, 1988. Abundance, growth, and interaction of juvenile steelhead relative to time of emergence. Transactions of the American Fisheries Society, 117: 432-443.

[5.] Chistiakov D.A., B. Hellemans and F.A.M. Volckaert, 2006. Microsatellites and their genomic distribution, evolution, function and applications: A review with special reference to fish genetics. Aquaculture, 255: 1-29.

[6.] Cross T.F. and J. King, 1983. Genetic effects of hatchery rearing in Atlantic salmon. Aquaculture, 33: 33-40.

[7.] Dahle G., K.E. Jorstad, H.E. Rusaas and H. Ottera, 2006. Genetic characteristics of broodstock collected from four Norwegian coastal cod (Gadus morpha) populations. ICES Journal of Marine Science, 63: 209-215.

[8.] DeWoody J.A. and J.C. Avise, 2000. Microsatellite variation in marine, freshwater and anadromous fishes compared with other animals. Journal of Fish Biology, 56: 461-473.

[9.] Estoup A., P. Presa, F. Krieg, D. Vaiman and R. Guyomard, 1993. (CT)n and (GT)n microsatellites: a new class of genetic markers for Salmo trutta L. (brown trout). Heredity, 71: 488- 496.

[10.] Moritz C. and D.M. Hillis, 1990. Molecular systematics: context and controversies. In: Hillis DM, Moritz C, Mable BK, editors. Molecular systematics. 2nd ed. Sunderland, USA: Sinauer Associates; 1997. pp: 1-13.

[11.] Kitanishi S., T. Yamamoto and S. Higashi, 2009. Microsatellite variation reveals fine-scale genetic structure of masu salmon, Oncorhynchus masou, within the Atsuta River. Ecology of Freshwater Fish, 18: 65-71.

[12.] Lucentini L., A. Palomba, H. Lancioni, L. Gigliarelli, M. Natali and F. Panara, 2006. Microsatellite polymorphism in Italian population of northern pike (Esox lucius L.), Fisheries Research, 80: 251-262.

[13.] McConnell S.K., P. O'Reilly, L. Hamilton, J. M.Wright and P. Bentzen, 1995. Polymorphic micro satellite loci from Atlantic salmon (Salmo salar): genetic differentiation of North American and European populations. Canadian Journal of Fisheries and Aquatic Sciences, 52: 1863-1872

[14.] Moran P., A.M Pendas and E. Garcia-Vazquez, 1996. Mitochondrial DNA variation in wild and hatchery brown trout (Salmo trutta L.) populations from Spain. Aquaculture, 141: 59-65.

[15.] Morris D.B., K.R. Richard and J.M. Wright, 1996. Microsatellites from rainbow trout (Oncorhynchus mykiss) and their use for genetic study of salmonids. Canadian Journal of Fisheries and Aquatic Sciences, 53: 120-126.

[16.] Nickelson T.E., M.F. Solazzi and S.L. Johnson, 1986. Use of hatchery coho salmon (Oncorhynchus kisutch) presmolts to rebuild wild populations in Oregon coastal streams. Canadian Journal of Fisheries and Aquatic Sciences, 43: 2443-2449.

[17.] Nei M., 1972. Genetic distance between populations. Published by the University of Chicago press for the American Society of Naturalists. The American Naturalist., 106: 283-292.

[18.] Norris A.T., D.G. Bradley and E.P. Cunningham, 1999. Microsatellite genetic variation between and within farmed and wild Atlantic salmon (Salmo salar) populations. Aquaculture, 180: 247-264.

[19.] Nelson R.J. and T.D. Beacham, 1999. Isolation and cross species amplification of microsatellite loci useful for study of pacific salmon. Animal Genetic, 30: 228-229

[20.] Norris J.P., G.F. Marani and J.B. Bonnell, 2000. Connection between energy-dependent lags and peak luminosity in gamma-ray bursts. The Astrophysical Journal, 534: 248-257.

[21.] Peakall R. and P.E. Smouse, 2006. GenAlex 6: genetic analysis in Excel. Population genetic software for teaching and research. Molecular Ecology Resources, 6: 288-295.

[22.] Perez-Enriquez R., M. Takagi and N. Taniguchi, 1999. Genetic variability and pedigree tracing of a hatchery-reared stock of red sea bream (Pagrus major) used for stock enhancement, based on microsatellite DNA markers. Aquaculture, 173: 413-423

[23.] Steward C.R. and T.C. Bjornn 1990. Supplementation of salmon and steelhead stocks with hatchery fish: a synthesis of published literature. Tech. Rep. No. 90-1. Bonneville Power Administration, Portland, Oreg.

[24.] Slettan A., I. Olsaker and O. Lie, 1995. Atlantic Salmon, Salmo salar, microsatellites at the Ssosl25, Ssosl85, Ssosl311, Ssosl417 loci. Animal Genetic, 26: 281-282.

[25.] Swain D.P. and B.E. Riddell, 1990. Variation in agonistic behavior between newly emerged juveniles from hatchery and wild populations of coho salmon, Oncorhynchus kisutch. Canadian Journal of Fisheries and Aquatic Sciences, 47: 566-571.

[26.] Taniguchi N., K. Sumantadinata and S. Iyama, 1983. Genetic change in the first and second generations of hatchery stock of black seabream. Aquaculture, 35: 309-320

[27.] Yousefian M., 2010. Stock Identification and Genetic Variation at Microsatellite Loci of Caspian Sea Salmon (Salmo trutta caspius.) World Journal of Fish and Marine Sciences, 2: 508-512

[28.] Yousefian M., A. Motalebi, M. Rezvani, H. Golshahi, F. Laloei, M.J. Tagavi and C. Amirinia, 2011. Genetic parameters of growth in Salmo trutta Caspius as a function of body weight and Length. In Press (2011). Iranian J. Fisheries Sci., (IJFS).

[29.] Yousefian M. and F. Laloei, 2011. Genetic Variations and Structure of Common Carp, (Cyprinus carpio) Populations by Use of Biochemical, Mitochondrial and Microsatellite Markers. Middle-East Journal of Scientific Research, 7: 339-345.

Mehdi Yousefian, Department of Fishery, Qaemshahr branch, Islamic Azad University, Qaemshahr, Iran.

Corresponding Author

Mehdi Yousefian, Department of Fishery, Qaemshahr branch, Islamic Azad University, Qaemshahr, Iran.

E-mail: yousefianeco@yahoo.com
Table 1: Allele molecular weight and frequencies of eight
microsatellite loci of fingerlings Salmo trutta Caspius

Allele No   Omyf         Otsg 100     Otsg 409     Otsg 483

1           188   0.18   112   0.11   100   0.08   140   0.02
2           200   0.18   120   0.02   108   0.17   172   0.06
3           208   0.18   128   0.13   116   0.20   146   0.07
4           212   0.04   156   0.17   120   0.13   184   0.20
5           216   0.13   172   0.06   124   0.10   192   0.08
6           228   0.13   176   0.15   128   0.06   200   0.13
7           232   0.04   220   0.04   132   0.10   204   0.10
8           240   0.10   224   0.06   140   0.04   208   0.11
9           252   0.02   232   0.04   144   0.08
10                       236   0.10
11                       260   0.15

Allele No   Otsg 478     Otsg 108     Strutta 58   Strutta 12

1           140   0.06   112   0.17   148   0.10   144   0.24
2           144   0.28   156   0.13   164   0.10   148   0.11
3           148   0.22   216   0.02   172   0.11   156   0.11
4           152   0.11   236   0.11   180   0.10   160   0.11
5           252   0.20   252   0.17   192   0.11   164   0.20
6           156   0.06   264   0.10   236   0.6    176   0.06
7           164   0.07   280   0.04   240   0.10   180   0.11
8                        292   0.28   260   0.06   184   0.11
9                                     280   0.15   196   0.04
10                                    284   0.15   224   0.02
11

Table 2: The observed (Ho) and expected (He) heterozygosity
of eight microsatellite loci of Salmo trutta Caspius

locus        He     Ho     Na   Ne

Strutta 12   0.85   0.85   9    6.5
Strutta 58   0.89   0.81   10   9.2
Otsg 108     0.83   0.63   8    5.8
Otsg 474     0.81   0.37   7    5.2
Otsg 483     0.88   0.81   10   7.8
Otsg 429     0.86   0.77   9    7.2
Otsg 100     0.88   0.74   11   8.4
Omyf         0.85   0.81   9    6.7

Table 3: The results of test of Hardy-Weinberg
at different loci in Salmo trutta Caspius

Locus        DF   Chi-square   Prob.   Sig

Strutta 12   36   66.3         0.002   **
Strutta 58   45   122.9        0       ***
Otsg 108     28   101.4        0       ***
Otsg 474     21   79.6         0       ***
Otsg 483     45   58.3         0.088   Ns
Otsg 429     36   54.9         0.022   *
Otsg 100     55   104          0       ***
Omyf         36   49.9         0.062   Ns

Table 4: The estimate of [F.sub.ST] and allele frequency
distribution (Nm) of a wild stock and their progenies (No:27).

Groups             [F.sub.ST]   Nm      Prob

Male     Female    0.026        9.37    0.01
Male     Progeny   0.021        11.90   0.01
Female   Progeny   0.005        51.96   0.13
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Title Annotation:Original Article
Author:Yousefian, Mehdi
Publication:Advances in Environmental Biology
Article Type:Report
Geographic Code:7IRAN
Date:Aug 1, 2011
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