Comparison of two methods for identifying alien genotypes in clonal seed orchards and consequences of misidentification

Schlüsselbegriffe: Züchtung, Pinus sylvestris, Samenplantage, molekulare Marker, genetische Verbesserung

Download as PDF

Abstract

Genetic gains in forestry are often implemented by producing improved forest seeds in seed orchards. However, unwanted alien genotypes are often accidentally introduced into seed orchards, or genotypes are planted in incorrect locations, both of which can reduce genetic gains. Such errors can be detected using markers, mainly isoenzymatic proteins and microsatellite DNA. These markers differ in their sensitivity, meaning that they can yield different assessments of seed orchard genetic material even when plant material is identical. The main objective of this paper was to compare these two verification methods and their consequences for genetic improvement. Two uneven-aged Scots pine clonal seed orchards were analysed using sets of isoenzymatic and microsatellite loci identified in other studies. The statistical analysis allowed comparison of the actual architecture of seed orchards to the planned layout. The number of clones was also compared to the effective number of clones. The results of microsatellite DNA analysis indicate that misplaced ramets are present from 12.29% to 30.89% of the time. Errors had an impact on breeding efficiency by reducing the relative effective number of clones. Isoenzyme and microsatellite methods had different discriminatory powers, which affected the results of the study. This study indicates that seed orchards can contain large numbers of incorrectly identified individuals. Microsatellite DNA analysis is recommended over isoenzyme analysis for detecting such errors, as the former is a more sensitive analytical method.

Zusammenfassung

Der Züchtungsfortschritt im Forstwesen wird meistens durch die Verwendung von verbessertem Material aus Samenplantagen erreicht. Züchtungsprogramme der Forstbäume führen in der Praxis oft zum Auftreten fremder Genotypen in Samenplantagen, welche den Erfolg des Züchtungsfortschritts reduzieren. Molekulare Marker wie Isoenzyme und DNA-Mikrostelliten eignen sich, um solche Fehler festzustellen. Diese Methoden unterscheiden sich hinsichtlich ihrer Genauigkeit des Ergebnisses. Daher ist es möglich, für identisches Pflanzenmaterial zu unterschiedlichen Ergebnissen zu kommen. Der Vergleich dieser Überprüfungsmethoden und die Konsequenzen der getroffenen Entscheidung ist das Ziel dieser Arbeit. Die Analysen wurden auf zwei nicht-gleichaltrigen Klonsamenplantagen unter Anwendung von Isoenzym- sowie Mikrosatellitenmarkersets durchgeführt. Eine statistische Analyse ist erfolgt. Sie ermöglichte sowohl den Vergleich zwischen dem Plan und der tatsächlichen Anordnung der Klone als auch eine Schätzung der effektiven Anzahl der Klone. Die Ergebnisse basierend auf Mikrosatellitenmarkern beweisen das Auftreten von fremden Genotypen in einer Häufigkeit zwischen 12.29 % und 30.89 %. Die Fehler beeinflussten die Züchtungseffizienz negativ dadurch, dass die effektive Anzahl der Klone reduziert war. Unterschiede bei der Aussagekraft der beiden Ansätze hatten einen Einfluss auf die Ergebnisse. Die vorliegende Studie unterstreicht den Nutzen von Klonidentifikationsanalysen in Samenplantagen. Es empfiehlt sich, auf Mikrosatelliten-DNS als die sensitivere analytische Methode zurückzugreifen.

Introduction

Modern forest management can benefit from advances in scientific knowledge, everywhere from seed production to felling technology. In many countries, seed production is based on forest tree breeding. In practice, this means that seeds obtained from clonal seed orchards account for an increased share of the seeds used in forest regeneration (Przybylski et. al. 2015). Gains from forest tree breeding are based on phenotypic selection of forest trees displaying desirable traits. Clones of selected individuals are then reproduced within seed orchards (Chałupka et al. 2011). For maximum genetic gains, it is necessary to avoid errors when establishing orchards, because mistakes cause undesirable genotypes to be present in an orchard’s genetic pool (Kaya & Isik 2009). Genotypes not from a plus tree ramet may be accidentally introduced into clonal seed orchards, displacing a plus tree from the gene pool of the orchard. This phenomenon was mentioned by Paule (1991), Burczyk et al. (2000), Gömöry et al. (2003) and Kaya & Isik (2009). Two categories of errors in Polish seed orchard establishment were described by Odrzykoski (2007). Category I errors are caused by mistakes in the spatial architecture of the seed orchard, due to planting ramets in the wrong place but using clones that are a part of the seed orchard design. Category II errors arise from misidentification, which place individuals that are not part of the breeding population into the orchard (Odrzykoski 2007).

Detection of alien genotypes in seed orchards can be done using molecular markers, which help identify planting errors. Markers can be divided into three groups: morphological markers, biochemical markers and DNA markers (Dzialuk and Burczyk, 2001). This paper focuses on the use of biochemical and DNA markers.

Until recently, the analysis of morphological features was the primary method of describing tree population genetic variability. However, plant phenotype is of limited value because morphological variability among genotypes can be low and the number of useful phenotypic features is limited. Therefore, the probability of committing an error using morphological markers is high. In addition, determining the inheritance of traits and environmental effects on overall morphological variability is not possible, and therefore morphological features are of limited value beyond the initial stages of clone identification (Dzialuk and Burczyk, 2001).

The application of biochemical marker analyses was a breakthrough in population genetics. These approaches were used by a number of researchers investigating graft errors in seed orchards, such as Breitenbach-Dorfer et al. (1992), Burczyk et al. (2000), Gömöry et al. (2003), and Przybylski et al. (2019). One of the newer techniques for identifying individuals is microsatellite DNA markers (Simple Sequence Repeats; SSR), used in criminal trials for alleged theft of wood (White et al. 2000). They are widely used in affinity relatedness and to obtain unique genetic profiles to identify individuals (Burczyk 2006; Dzialuk, 2009). Unfortunately, SSR markers have some disadvantages, such as the presence of null alleles and high costs of testing.

Comparative tests using molecular markers involve developing a set of markers that will provide the maximum chance to detect differences between genotypes, with minimal financial outlay. In the human population, the discriminatory power of an 11 SSR marker set reaches 0.949156 (94.91%), which is accepted for identification of individuals in cases of criminology and contested paternity (Konarzewska et al. 2006). The highest discriminatory power of DNA markers obtained in tests reach 0.99999 (99.99%) (Wysocka et al. 2008). Recent studies of plant populations have achieved high discriminatory power using SSR markers (de Barba et al. 2016).

A review of literature showed that two biochemical testing tools, isoenzymatic markers and microsatellite DNA, may be used to verify seed orchard identities. However, these methods differ in discriminatory power. The main objective of the present study was, therefore, to evaluate the discriminatory power of systems based on isoenzymatic proteins and DNA and to evaluate the consequences of applying these methods on estimates of breeding efficiency.

Methods

Plant material

The study consisted of the molecular verification of individuals in two scots pine clonal seed orchards. The Susz District seed orchard (N53°45΄21˝, E19°12΄16˝) was established in 1977-1978 and Pniewy District seed orchard (N52°30΄32˝, E16°15΄24˝) was established in 1993 (Figure 1). These are first generation seed orchards, and as a result the genetic value (breeding value) of cloned plus trees planted in these orchards is unknown. In the Susz seed orchard, 9 838 ramets were planted from 50 plus tree clones, and in Pniewy, 1 733 ramets were planted from clones of 53 plus trees. Material from 138 ramets at Susz and 131 ramets at Pniewy was collected for molecular analyses. Samples were collected from two blocks within each analysed seed orchard from all trees that remained after the most recent systematic thinning.

The reference samples for the Susz seed orchards came from the original parental plus trees. When it was not possible to collect reference material in this way, because plus trees were dead, a repeated set of 4 ramets from the tested seed orchard constituted the reference sample. For the Pniewy orchard, the reference samples were obtained from a clonal archive outside of the seed orchard.

Genotype analysis

Isoenzymatic proteins (IZO)

Variation was examined in eight enzymatic proteins (Table 1) extracted separately from winter buds. Protein extraction was conducted using a 150-µl extraction buffer (Odrzykoski, 2002). After filtering through Miracloth filter paper, the resulting solutions were used to moisten pieces of Whatman 3ET filter paper, which were subsequently placed onto electrophoresis gel. Using two buffer systems (Odrzykoski, 2007), the electrophoresis was conducted in 13% starch gel (Starch-Art). Following electrophoresis, gels were cut into 1.5-mm slices, which were individually used to visualise the phenotype of the analysed proteins. Location of individual bands was determined using the methods of Conkle et al. (1982), with a modification comprising the use of an overlay procedure with 2% agar, excluding EstB and Got. Identification of the loci and interpretation of the obtained zymograms were carried out using the approach described by Odrzykoski (2002).

Microsatellite DNA (SSR)

DNA was isolated with a commercially available set of microsatellite markers (Marchel-Nagel – Nucleo Spin Plant II) after extraction from 3-4 needles from each graft. Needles were ground in a mortar using liquid nitrogen, until obtaining about 20 mg of dry powdered mass. The yield of DNA extracted for each sample was evaluated by measuring absorbancy at wavelengths of 260 and 280 nm. (Quawell Q500). An average absorbancy for wavelengths of 260/280 nm was 1.80, at which the samples produced 80 ng DNA/µl, which was diluted to 20 ng DNA/µl. Four microsatellite loci were used in testing: Spac11.6, Spag7.14 (Soranzo et al. 1998), and PtTX3107, PtTX4001 (Elsik and Williams 2001), which differ in the type of repeating motif and the length of alleles. A multiplex PCR was used for loci Spac11.6, PtTX3107 and PtTX4001. A separate reaction was used for locus Spag7.14. In all cases, reactions were performed with 25µl of a solution composed of ultrapure water, Taq DNA polymerase (0.1 U/µl), MgCl2 (4mM), dNTPs (0.5mM), primers and matrix DNA. The optimised thermal profile of the reaction is provided in Table 2. For locus Spac11.6, the touchdown PCR method was used (Balletti et al. 2012). The mixture of all PCR products was separated in a Beckman Coulter sequencer, in the polymer LPA I, using the Frag I length standard (as recommended by the manufacturer). The results obtained were analysed with CEQTM800 software, which provided chromatograms that then were used to determine the lengths of individual alleles.  

Deficiency of heterozygotes may prove the presence of null alleles, which may not be detected in the sample directly. Therefore, an analysis was carried out to determine their presence and frequency, using the methods of Chakraboty et al. (1992), Brookfield (1996) and van Oosterhout et al. (2004). Analyses were performed by Micro Checker v2.2.3. (van Oosterhout et al. 2004).

Statistical analyses

To determine the discrimination power of the clonal verification system, the cumulative probability of obtaining identical genotypes (P_ID) for two different clones was calculated as:

where p_i = the mean allele frequency (i^thalleles) in the sample of grafts from the two tested seed orchards combined (Taberlet & Luikart, 1999). Calculations were performed using GeneAlEx 6.5 software (Peakall & Smouse, 2006). The genotypes obtained were used to estimate the rates of category l and ll errors in seed orchards. In order to assess the impact of errors on genetic variability, the actual composition of genotypes and their spatial arrangement (W1) was compared with the originally designed seed orchard population (W0). For W0, it is assumed that no errors were made and all ramets were planted as specified in the planting plan. For both W0 and W1, the effective numbers of clones (Nc) was calculated based on Kang & Lindgren (1999) and Kang et al. (2001):

where: Ng = corrected census number of genotypes after molecular verification and CV% = coefficient of variation for the number of ramets. The relative effective number of clones was calculated as Nr = Nc/N (Kang et al. 2001).

Results

Null alleles were not relevant for the interpretation of the results. Based on the data obtained, null alleles were proven to be present for the locus Spac11.6 in Susz and for loci PtTX3107 and Spag7.14 in Pniewy. The null allele frequency ranged from 0.09 to 0.14 (Table 3).

The discriminatory power of markers varied. Using four microsatellite DNA loci, discriminatory power was 4x10^-6, while the use of thirteen isoenzymatic loci resulted in a sensitivity of 8x10^-5, which is one order of magnitude less (Table 4). It should be noted that any of the proposed marker systems can be used to verify identity in clonal seed orchards.

The presence of category I and category II errors was proven in both seed orchards. The error rates differed depending on verification method. A category II error was found using SSR, while the IZO verification system for the same ramets either did not detect an error or indicated a category I error. The SSR and IZO methods gave different results for 11 ramets from the Susz seed orchard and for 3 ramets from the Pniewy seed orchard. In addition, for SSR analyses, due to the poor quality of extracted DNA, genotype was not determined for 15 ramets from Susz and 9 ramets from Pniewy. Due to differences between analytical methods, the error rate in seed orchards is interpreted as a percentage ratio between false ramets and all ramets analysed in a seed orchard. A detailed summary of false ramets is given in Table 5. On the basis of IZO, the category I error rate ranged from 10.69% in the Pniewy orchard to 13.04% in Susz orchard. For SSR analyses, category l error rate was 5.69% in Susz and 8.20% in Pniewy. The number of category II errors for IZO ranged from 9.16% in the Pniewy orchard to 24.64% in Susz and for SSR it ranged from 12.29% in Pniewy to 30.89% in Susz.

Error rates affected seed orchard parameters. The results showed that the planned number of genotypes was lower than the actual number of genotypes in orchards, where the latter increased from 10 to 13 genotypes per orchard for SSR analyses (Table 6). This indicates an average increase in the number of orchard genotypes by 0.22. The SSR method allowed reporting of the increased number of additional genotypes, with 1 to 3 additional genotypes, depending on orchard (Table 6). Additional genotypes affected the effective and relative effective number of clones. In summary, in each case, alien genotypes decreased the relative effective number of clones, from 0.14 for the Susz seed orchard to 0.04 for Pniewy using SSR analyses (Table 6).

Discussion

In most clonal seed orchards that have been verified, errors involving incorrect graft assignment were identified (Burczyk et al. 2000, Dzialuk 2009, Gömöry et al. 2003). Alien genotypes are introduced when errors occur during the complex procedures of establishment and maintenance of seed orchards. Errors have various causes, but since they are all due to human mistakes, with care they can be eliminated. The main causes of errors include: misidentification when collecting plant material; errors made during work in a forest nursery; poor legibility of labels or label removal during ramet transport; errors when manually copying seed orchard designs; and cases where the rootstock outgrows the scion. It appears that errors during the establishment of orchards are not large (Suchowera and Chełmiński 2009). Regardless of the cause of an error, the impact of each type of error is different. Category I errors do not alter the gene pool of a seed orchard. However, they can increase the risk of self-pollination. In contrast, Category II errors modify the gene pool by introducing alien pollen, which reduces breeding efficiency (Kaya et al. 2006). Alien genotypes may also modify the basic genetic characteristics of seed orchards, influencing heterozygosity and introducing unwanted alleles into the gene pool of seed orchard progeny (Stoehr et al. 2004).

The discriminatory power of a microsatellite DNA marker set can be evaluated by calculating the probability that two different individuals in a population have identical genotypes (P_ID) for the marker set. Dzialuk and Burczyk (2005) used P_ID to evaluate the discriminatory power of a set of 7 microsatellite loci (5 chloroplast loci and 2 nuclear loci), which can be used to identify stolen trees. A probability of P_ID from 10–3 to 10–4 is usually accepted as sufficient to identify individuals (Waits et al., 2001). Similar values could be adopted to identify Scots pine plus trees in Poland. The calculation of P_ID requires data on allele frequency in a reference population. Where ramets are identified in seed orchards, a reference population should consist of all Scots pine plus trees. Unfortunately, no such data exist for loci used in the present study. For the current analysis, material from 245 Scots pine plus trees was available. Polish populations of Scots pine are distinctive, with low interpopulation diversity, as determined using microsatellite loci, with F_ST = 0.033 (Nowakowska, 2007). This feature is common in various Pinus species (e.g. Marquardt and Epperson, 2004). Considering this, it can be assumed that allele frequencies in the 245 plus trees in this study provides a reliable estimate of allele frequency in the entire population of Scots pine plus trees in Poland.

An important result obtained here is that the discriminatory power of microsatellite markers is higher than that of isoenzymatic loci. This is consistent with other studies (Cieślewicz 2009). In the present study, a higher value of P_ID obtained for microsatellite DNA markers allowed category II errors to be identified, which has not been observed in isoenzymatic analyses. On the other hand, although isoenzymatic analyses are an older analytical technique than molecular biology, their effectiveness in verifying seed orchard identities remains high. The usability of isoenzymatic techniques is confirmed by the values of P_ID obtained using it. Przybylski et al. (2019) found P_ID of 0.0001, while Dzialuk & Burczyk (2005) report a P_ID of 0.004. Thus, given there are around 3,650 plus trees in Poland (unpublished data from the Forestry Research Institute), it is possible to assign an individual genotype to each ortet in seed orchards throughout Poland. It should be added that isoenzymatic tests are cheaper than microsatellite DNA analysis, the issue being that many steps in microsatellite cannot yet be automated.

Markers selected in this study were used previously for verification of ramets in seed orchards. Breitenbach-Dorfer et al. (1992), in studying a polymorphism of four enzymatic loci in Abies alba, demonstrated the usefulness of these loci for identifying individual clones. Burczyk et al. (2000), in analysing the variability of 14 isoenzymatic loci, detected errors in the distribution of plus tree grafts in one plot of the Gniewkowo seed orchard. Gömöry et al. (2003) used 12 isoenzymatic loci to verify the identity of clones in three seed orchards, detecting errors of from 27% to 49% in graft assignment. In turn, Lewandowski (2006) describes using eleven isoenzymatic loci to identify grafts in a seed orchard. The analysis of 10 microsatellite loci from chloroplast DNA enabled the identification of erroneously distributed grafts in one plot of a seed orchard in the Gniewkowo forest district (Burczyk et al., 2000). Slavov et al. (2004), using three microsatellite loci, verified 152 ramets representing genotypes of 59 trees in a Douglas spruce orchard.

The presence of alien genotypes should be considered in terms of their effects on genetic gain in clonal seed orchards. Genetic gain refers to the capacity to pass genetic information on to the next generation (Trojankiewicz and Burczyk 2005). One important factor determining the high genetic gain of clonal seed orchards is the uniform distribution of unrelated clones in an orchard, which determines the maximum distance between ramets of the same clone. The lack of quantitative balance and the non-synchronised phenology of flowering between individual clones can lead to mating of related individuals (Burczyk 1998). This study has shown the negative effects of unwanted genotypes on the relative effective number of clones, which was reduced by 0.14. Moreover, errors in identification increased the number of genotypes by 0.22, thus reducing the efficiency of spatial arrangements in seed orchards.

Production seed orchards are created by planting a limited number of individuals. The main purpose of production orchards is to produce seeds that transfer desirable genetic traits to the progeny. For this reason, special attention should be paid to establishing an orchard correctly, arranging it with optimal clone spacing and ensuring a balanced representation of clones. Since errors in establishment of clonal orchards are common, clones in existing orchards should be verified. The best tool for verifying seed orchard clonal identities is microsatellite DNA markers, which possess high discriminatory power. The lower accuracy of isoenzymatic markers, as with morphological markers, makes them more appropriate for earlier stages of verification.

Acknowledgments

This project was funded by the Ministry of Science and Higher Education within the statutory activity of the IBL (240233)

References

De Barba A., Miquel C., Lobreaux S., Quenette P.Y., Swenson J.E., Taberlet P. 2016. High‐throughput microsatellite genotyping in ecology: improved accuracy, efficiency, standardization and success with low‐quantity and degraded DNA. Mol. Ecol. Vol. 17, Issue 3, pp. 492-507. 10.1111/1755-0998.12594

Balletti P., Ferrazzini D., Piotti A., Monteleone I., Ducci F. 2012. Genetic variation and divergence in Scots pine (Pinus sylvestris L.) within natural range in Italy. Eur. J. Forest Res. 131:1127-1138.

Breitenbach-Dorfer M., Pinsker W., Hacker R., Müller F. 1992. Clone identification and clinal allozyme variation in populations of Abies alba from the eastern Alps (Austria). Plant System Evolution 181: 109-120.  

Brookfield J.F.Y. 1996. A simple new method for estimating null alleles frequency from heterozygote deficiency. Mol. Ecol. 5: 453-455. 

Burczyk J., Dzialuk A., Lewandowski A. 2000. Zmienność genetyczna sosny zwyczajnej (Pinus sylvestris L.) na klonowej plantacji nasiennej w Gniewkowie. Sylwan 2000(4): 65-47. 

Burczyk J. 1998. Mating system variation in a Scots pine clonal seed orchard. Silvae Genetica 47: 155-158.  

Burczyk J., Adams W.T., Birkes D.S., Chybicki I.J. 2006. Using genetic markers to directly estimate gene flow and reproductive success parameters in plants based on naturally regenerated seedlings. Genetics 173: 363-372.

Chakraborty R., De Andrade M., Daiger S.P., Budowle B. 1992. Apparent heterozygote deficiencies observed in DNA typing data and their implications in forensic applications. Annals of Human Genetics 56: 45-57.

Chałupka W., Matras J., Barzdajn W., Burczyk J., Tarasiuk S., Sabor S., Kawalczyk J., Fonder W., T. Grądzki, P. Kacprzak, Cz. Kozioł, Z. Rzońca, T. Pytko, Z. Szeląg, Z. Gryzo, S. Blonkowski 2010. Program zachowania leśnych zasobów genowych i hodowli selekcyjnej drzew leśnych w Polsce na lata 2010–2035. CILP, Warszawa, 142 p. ISBN: 978-83-61633-60-0 

Cieślewicz A. 2009. Charakterystyka wybranych loci mikrosatelitarnych u sosny zwyczajnej i ich wykorzystanie do identyfikacji szczepów drzew matecznych. UAM w Poznaniu, maszynopis rozprawy doktorskiej pp. 118. 

Conkle M.T., Hodgskiss P.D., Nunnaly L.B., Hunter S. C. 1982. Starch Gel Electrophoresis of Conifer Seeds: a laboratory manual. USDA Forest Service. Pacific Southwest Forest and Range Experimental Station. General Technical Report PSW-64: 18.

Dzialuk A., Burczyk J. 2005 Zmiany struktury genetycznej pomiędzy populacja rodzicielską a potomną w drzewostanie nasiennym sosny zwyczajnej (Pinus sylvestris L.). Sylwan vol. 10:30-38. 

Dzialuk A. 2009. Zmienność sosny zwyczajnej w: Opracowanie i wdrożenie do praktyki leśnej metod identyfikacji i wczesnej oceny leśnego materiału rozmnożeniowego w oparciu o markery molekularne. Maszynopis, sprawozdanie końcowe.

Dzialuk A., Burczyk J. 2001. Molekularne markery DNA jako narzędzie badawcze genetyki drzew leśnych. Sylwan 8: 67-83.

Gömory D., Bruchanik R., Longauer R. 2003. Fertility variation and flowering asynchrony in Pinus sylvestris: consequences for the genetic structure of progeny in seed orchards. Forest Ecology and Management 174:117-126. 

Kang K., Lindgren D. 1999. Fertility variation among clones of Korean pine (Pinus koraiensis S. et Z.) and its implications on seed orchards management. Forest Genetics 2: 183−192

Kang K.S., Harju A.M., Lindgren D., Nikkanen T., Amquist C., (2001): Variation in effective population number of clones in seed orchards. New Forests 21:17-33. 

Kaya N., Isik K, 2009. Genetic identification of clones and the genetic structure of seed crops in a Pinus brutia seed orchard. Turkish Journal of Agriculture and Forestry. 34: 127–134.

Kaya N., Isik K., Adams W. 2006. Mating system and pollen contamination in a Pinus brutia seed orchard. New Forest 31: 409–416. doi: 10.1007/s11056-005-0876-x.

Konarzewska M., Spólnicka M., Sołtyszewski I., Berent J. 2006. Polimorfizm locus HUMHUU w populacji polskiej – doniesienie wstępne. Arch. Med. Sąd. Krym. LVI: 99-102.

Lewandowski A. 2006. Wykorzystanie izoenzymów jako markerów genetycznych. W: Sabor J. (red.) Elementy genetyki i hodowli selekcyjnej drzew leśnych. CILP 2006: 77-81.

Marquardt P.E., Epperson B.K. 2004. Spatial and population genetic structure of microsatellites in white pine. Mol. Ecol. 13: 3305-3315.

Nowakowska J.A. 2007. Zmienność genetyczna polskich wybranych populacji sosny zwyczajnej (Pinus sylvestris L.) na podstawie analiz polimorfizmu DNA. Prace IBL, rozprawy i monografie, Sękocin Stary 2007.

Odrzykoski I. 2002. Badania nad zmiennością genetyczną kosodrzewiny (Pinus mugo) z wykorzystaniem markerów biochemicznych i molekularnych. Wyd. Nauk. UAM.

Odrzykoski I. 2007. Weryfikacja zgodności genetycznej szczepów sosny zwyczajnej na terenie plantacji nasiennej 08-14-3-11-95A-m-00 (PN-34 C1) w Nadleśnictwie Zdrojowa Góra (RDLP Piła). Niepublikowany raport dla Nadleśnictwa Zdrojowa Góra.

Van Oosterhout C, Hutchinson W.F., Wills D.P.M., Shipley P. 2004. MICRO-CHECKER – software for identifying and correcting genotyping errors in microsatellite data. Molecular Ecology Notes 4: 535-538.

Paule L 1991. Clone identity and contamination in a Scots pine seed orchard. In: Proceedings of the Meeting of the Nordic Group for Tree Breeding (Ed. D Lindgren). Swedish University of Agricultural Sciences, Department of Forest Genetics and Plant Physiology Umea 10: 22–32.

Peakall R., Smouse P. 2006. GENEALEX 6: Genetic Analysis in Excel. Population genetic software for teaching and research. Molecular Ecology Notes 6: 288-295.

Przybylski P. 2015. Czy na plantacjach nasiennych zawężamy zmienność genetyczną? Próba odpowiedzi na podstawie analiz mikrosatelitarnego DNA szczepów rosnących na plantacji nasiennej sosny zwyczajnej z Nadleśnictwa Susz. Leśne Prace Badawcze 76 (3): 240–249, doi: 10.1515-frp-2015-0023.

Przybylski P., Kowalczyk J., Odrzykoski I., Matras J. 2019. Identyfing alien genotypes and their conseqences for genetic variation in clonal seed orchards of Pinus sylvestris L. Dendrobiology vol 81, 40-46. 

Slavov G.T., Howe G.T., Yakovlev I., Edwards K.J., Krutovskii K.V., Tuskan G.A., Carlson J.E., Strauss S.H., Admas W.T. 2004. Highly variable SSR markers in Douglas-fir: Mendelian inheritance and map locations. Theor. Appl. Genet. 108: 873-880.

Soranzo N., Provan J., Powell W. (1998): Characterization of microsatellite loci in Pinus sylvestris L. Molecular Ecology 7: 1260-1261.

Suchowera K., Chełmicki M. 2009. Wyniki weryfikacji zgodności genetycznej szczepów sosny zwyczajnej (Pinus sylvestris L.) na terenie plantacji nasiennej w Nadleśnictwie Zdrojowa Góra. Uniwersytet Rolniczy im. H. Kołłątaja w Krakowie, Wydział Leśny. Praca dyplomowa.

Stoehr M, Webber J, & Woods W (2004) Protocol for rating seed orchard seedlots in British Columbia: quantifying genetic gain and diversity. Forestry 77(4):297-303. doi: 10.1093/forestry/77.4.297

Taberlet P., Luikart G. 1999. Non-invasive genetic sampling and individual identification. Biological Journal of the Linnean Society 68, 4155, doi:10.1111/j.1095-8312.1999.tb01157.x.

Trojankiewicz M., Burczyk J. 2005. Efektywna liczba klonów na plantacjach nasiennych sosny zwyczajnej (Pinus sylvestris L.) Sylwan 11: 50-58.

Waits L.P., Luikart G., Taberlet P. 2001. Estimating the probability of identity among genotypes in natural populations: cautions and guidelines. Molecular Ecology 10, pp. 249-256.

White E., Hunter J., Dubetz C., Brost R., Bratton A., Edes S., Sahota R. 2000. Microsatellite markers for individual tree genotyping: application in forest crime prosecutions. Journal of Chemical Technology & Biotechnology 75: 923-926.

Wysocka J., Kapińska E., Cybulska L., Rebała K., Szczerkowska Z. 2008. Identyfikacja osobnicza w oparciu o analizę 11 polimorficznych loci DNA typu STR. Badania populacji Polski północnej z wykorzystaniem zestawu AmpFISTR® SEfiler™ kit. Ann. Acad. Med. Gedan. 38: 91-96.