Intraspecific variation in silver birch leaf spectral reflectance and surface secondary metabolites

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uef.fi

PUBLICATIONS OF

THE UNIVERSITY OF EASTERN FINLAND Dissertations in Forestry and Natural Sciences

ISBN 978-952-61-3682-0

Dissertations in Forestry and Natural Sciences

DISSERTATIONS | MAYA DEEPAK | INTRASPECIFIC VARIATION IN SILVER BIRCH LEAF SPECTRAL REFLECTANCE AND... | No 415

MAYA DEEPAK

Intraspecific variation in silver birch leaf spectral reflectance and surface secondary metabolites

PUBLICATIONS OF

THE UNIVERSITY OF EASTERN FINLAND

Silver birch has a wide geographical distribution and high genetic diversity. This thesis provides an analysis of silver birch leaf

spectral reflectance within individual tree crowns and variation in spectral reflectance

and leaf surface secondary metabolites among genotypes and provenances grown in a common garden trial. The results demonstrate

the significance of crown position in leaf spectral reflectance and the existence of a strong genetic component in leaf optical properties and surface secondary metabolite

profiles of silver birch genotypes and provenances.

MAYA DEEPAK

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INTRASPECIFIC VARIATION IN SILVER BIRCH LEAF SPECTRAL

REFLECTANCE AND SURFACE

SECONDARY METABOLITES

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Maya Deepak

INTRASPECIFIC VARIATION IN SILVER BIRCH LEAF SPECTRAL

REFLECTANCE AND SURFACE SECONDARY METABOLITES

Publications of the University of Eastern Finland Dissertations in Forestry and Natural Sciences

No 415

University of Eastern Finland Joensuu

2020

Academic dissertation

To be presented by permission of the Faculty of Science and Forestry for public examination in the Auditorium M100 in the Metria Building at the University of Eastern Finland, Joensuu, on December, 15, 2020, at

12 o’clock noon

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Maya Deepak

INTRASPECIFIC VARIATION IN SILVER BIRCH LEAF SPECTRAL

REFLECTANCE AND SURFACE SECONDARY METABOLITES

Publications of the University of Eastern Finland Dissertations in Forestry and Natural Sciences

No 415

University of Eastern Finland Joensuu

2020

Academic dissertation

To be presented by permission of the Faculty of Science and Forestry for public examination in the Auditorium M100 in the Metria Building at the University of Eastern Finland, Joensuu, on December, 15, 2020, at

12 o’clock noon

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Grano Oy Jyväskylä, 2020 Editor: Raine Kortet

Distribution: University of Eastern Finland / Sales of publications www.uef.fi/kirjasto

ISBN: 978-952-61-3682-0 (nid.) ISBN: 978-952-61-3683-7 (PDF)

ISSNL: 1798-5668 ISSN: 1798-5668 ISSN: 1798-5676 (PDF)

Author’s address: Maya Deepak

University of Eastern Finland

Depart. of Environmental and Biological Sciences P.O. Box 111

80101 JOENSUU, FINLAND email: maya.deepak@uef.fi

Supervisors: Professor Elina Oksanen, Ph.D.

80101 JOENSUU, FINLAND email: elina.oksanen@uef.fi

Dr. Markku Keinänen, Ph.D. University of Eastern Finland

80101 JOENSUU, FINLAND email: markku.keinanen@uef.fi

Dr. Sarita Keski-Saari, Ph.D. University of Eastern Finland

80101 JOENSUU, FINLAND email: sarita.keski-saari@uef.fi

Reviewers: Professor Jaana Bäck, Ph.D University of Helsinki

Department of Forest Sciences P.O. Box 27

00014, HELSINKI, FINLAND email: jaana.back@helsinki.fi Docent Matti Mõttus, Ph.D

VTT Technical Research Centre of Finland P.O. Box 1000

02044, ESPOO, FINLAND email: matti.mottus@vtt.fi

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Grano Oy Jyväskylä, 2020 Editor: Raine Kortet

Distribution: University of Eastern Finland / Sales of publications www.uef.fi/kirjasto

ISBN: 978-952-61-3682-0 (nid.) ISBN: 978-952-61-3683-7 (PDF)

ISSNL: 1798-5668 ISSN: 1798-5668 ISSN: 1798-5676 (PDF)

Author’s address: Maya Deepak

80101 JOENSUU, FINLAND email: maya.deepak@uef.fi

Supervisors: Professor Elina Oksanen, Ph.D.

80101 JOENSUU, FINLAND email: elina.oksanen@uef.fi

Dr. Markku Keinänen, Ph.D.

80101 JOENSUU, FINLAND email: markku.keinanen@uef.fi

Dr. Sarita Keski-Saari, Ph.D.

80101 JOENSUU, FINLAND email: sarita.keski-saari@uef.fi

Reviewers: Professor Jaana Bäck, Ph.D University of Helsinki

Department of Forest Sciences P.O. Box 27

00014, HELSINKI, FINLAND email: jaana.back@helsinki.fi Docent Matti Mõttus, Ph.D

VTT Technical Research Centre of Finland P.O. Box 1000

02044, ESPOO, FINLAND email: matti.mottus@vtt.fi

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Opponent: Docent Matthew Robson, Ph.D University of Helsinki

Faculty of Biological and Environmental Sciences P.O. Box 65

00014 HELSINKI, FINLAND email: matthew.robson@helsinki.fi

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Opponent: Docent Matthew Robson, Ph.D University of Helsinki

Faculty of Biological and Environmental Sciences P.O. Box 65

00014 HELSINKI, FINLAND email: matthew.robson@helsinki.fi

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Deepak, Maya

Intraspecific variation in silver birch leaf spectral reflectance and surface secondary metabolites

Joensuu: University of Eastern Finland, 2020 Publications of the University of Eastern Finland Dissertations in Forestry and Natural Sciences 2020; 415 ISBN: 978-952-61-3682-0 (print)

ISSNL: 1798-5668 ISSN: 1798-5668

ISBN: 978-952-61-3683-7 (PDF) ISSN: 1798-5676 (PDF)

ABSTRACT

Due to its wide range of distribution, silver birch (Betula pendula Roth) is expected to have high genetic diversity and variation based on its provenance of origin. This thesis investigates the optical (spectral reflectance) and chemical (surface secondary metabolites) properties of silver birch leaves. Common garden field trials were used to unrav- el the intra- and inter-tree variations of the same genotype and the genotypic and provenance-related variations in silver birch leaves. The genome sequencing of the silver birch genotypes studied provided an understanding of how related the genotypes were, and it was also relevant to understanding the association between ecophysiological traits and spectral reflectance, as the optical properties are defined by these traits.

There was clear difference between the upper and the lower canopy leaves. The high chlorophyll content in the upper canopy is likely due to the high light conditions in the upper canopy. The cardinal direction studied showed no clear pattern of variation in leaf reflectance. Though there was variation among the individual trees of the same genotype, the canopy layer variation was more prominent than the inter-tree variation. Red edge position, which is related to the leaf chlorophyll content, displayed the largest variation. A spectral shift in the red edge inflection point (REIP) towards a longer wavelength is reported in the upper canopy leaves. Chlorophyll was the most important leaf trait affecting the spectral reflectance, as measured in the chlorophyll content index (chlorophyll meter) and the reflectance indices for both the intra-tree variation and the intraspecific variation. Even though a clear genotypic background was detected for the spectral reflectance, the most prominent difference was detected from the provenance of origin, which was consistent throughout both years.

The variation was more prominent in the genotype than in the provenance of origin for the secondary metabolites. Secondary metabolites showed both qualitative and quantitative differences. The presence of a triterpenoid aglycone in only one genotype suggest a strong genotypic background in the secondary metabolites synthesis.

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Deepak, Maya

Intraspecific variation in silver birch leaf spectral reflectance and surface secondary metabolites

Joensuu: University of Eastern Finland, 2020 Publications of the University of Eastern Finland Dissertations in Forestry and Natural Sciences 2020; 415 ISBN: 978-952-61-3682-0 (print)

ISSNL: 1798-5668 ISSN: 1798-5668

ISBN: 978-952-61-3683-7 (PDF) ISSN: 1798-5676 (PDF)

ABSTRACT

Due to its wide range of distribution, silver birch (Betula pendula Roth) is expected to have high genetic diversity and variation based on its provenance of origin. This thesis investigates the optical (spectral reflectance) and chemical (surface secondary metabolites) properties of silver birch leaves. Common garden field trials were used to unrav- el the intra- and inter-tree variations of the same genotype and the genotypic and provenance-related variations in silver birch leaves. The genome sequencing of the silver birch genotypes studied provided an understanding of how related the genotypes were, and it was also relevant to understanding the association between ecophysiological traits and spectral reflectance, as the optical properties are defined by these traits.

There was clear difference between the upper and the lower canopy leaves. The high chlorophyll content in the upper canopy is likely due to the high light conditions in the upper canopy. The cardinal direction studied showed no clear pattern of variation in leaf reflectance. Though there was variation among the individual trees of the same genotype, the canopy layer variation was more prominent than the inter-tree variation. Red edge position, which is related to the leaf chlorophyll content, displayed the largest variation. A spectral shift in the red edge inflection point (REIP) towards a longer wavelength is reported in the upper canopy leaves. Chlorophyll was the most important leaf trait affecting the spectral reflectance, as measured in the chlorophyll content index (chlorophyll meter) and the reflectance indices for both the intra-tree variation and the intraspecific variation. Even though a clear genotypic background was detected for the spectral reflectance, the most prominent difference was detected from the provenance of origin, which was consistent throughout both years.

The variation was more prominent in the genotype than in the provenance of origin for the secondary metabolites. Secondary metabolites showed both qualitative and quantitative differences. The presence of a triterpenoid aglycone in only one genotype suggest a strong genotypic background in the secondary metabolites synthesis.

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Sampling procedure is an important part of research, and this study confirms the need to understand and include the lower canopy layer in the canopy reflectance study, as the lower canopy layer also contribute to the canopy reflectance even though the upper canopy is considered the most influential in canopy reflectance. This study also improved the compatibility of spectral reflectance in laboratory conditions to achieve repeatability of the experimental procedures. Furthermore, the provenance- related variations of the genotypes differed in their response to the latitude of origin, their adaptation to the climatic conditions of the provenance of origin or their acclimation to the prevailing environment in the common garden field. The potential role of secondary metabolites in herbivore resistance requires further analysis of the plant- herbivore interaction.

Universal Decimal Classification: 547.9, 535.312, 581.13, 581.15, 581.45, 582.622.2 CAB Thesaurus: Betula pendula; leaves; genotypes; provenance; latitude; optical properties;

reflectance; spectroscopy; canopy; variation; secondary metabolites; flavonoids; triterpenoids;

metabolomics; HPLC; mass spectrometry; chlorophyll; normalized difference vegetation index;

climate change; genome analysis; genetic diversity

Yleinen suomalainen ontologia: koivut; rauduskoivu; lehdet (kasvinosat); genotyyppi; pro- venienssi; alkuperä; optiset ominaisuudet; reflektanssi; spektroskopia; muuntelu (biologia);

vaihtelu; aineenvaihduntatuotteet; flavonoidit; triterpeenit; suuren erotuskyvyn nestekromato- grafia; massaspektrometria; klorofylli; ilmastonmuutokset; perimä; monimuotoisuus

ACKNOWLEDGEMENTS

I am happy to express my immeasurable appreciation and gratitude to those who have in one way or another contributed to making this thesis possible.

I must give my most respectful gratitude to my main supervisor, Prof. Elina Oksanen, for having confidence in me and for offering me an opportunity to carry out this project. Working with you has motivated me to mature as a researcher, and you inspired me with your integrity and professionalism. My sincere thanks and gratitude to my supervisor Dr Markku Keinänen for sharing his immense knowledge on the subject and for his continuous support during my doctoral studies. Thanks for inspiring the confidence to know that the sky is not falling during difficult times and to always have plans from A–Z. I wish to express my deepest gratitude to my supervisor Dr Sarita Keski-Saari for her support, guidance and supervision. Thanks for all the wonderful lunch hour discussions, jovial conference travels, motivational talks and warm hugs for rejuvenation. I wish to thank Sari Kontunen-Soppela for her support and encouragement and for her contribution to the experiment. Thank you for your help in the practical matters and for sharing your ideas and expertise with me.

I wish to record my gratitude to my former colleague, officemate and badminton part- ner Laure Fauch for all those funny moments at work, our serious discussions about work and the valuable comments on the article. Many thanks to my wonderful colleague and my first officemate Lars Granlund for his help in all small and big things during lab hours, invaluable comments on article and for his assistance with my driv- er’s license, with buying tools as a gift for someone special and many more.

I am thankful to Jenna Lihavainen for her contribution in chemical analyses and writing the third article, hours of lab work and countless warm moments. I am extremely grateful to Jarkko Salojärvi for his contribution in genome sequencing. I wish to thank Kaisa Heimonen and Antti Tenkanen for their contribution to the third article. My sincere thanks to my former colleagues Jennifer and Ilkka for their support during the experiment and our wonderful outdoor activities. I wish to thank Segun, Jussi, Flobert, Raja and the many others who helped me during the sampling and lab experiments. I would like to thank all those people who have directly or indirectly helped me throughout my doctoral studies inside and outside the university campus. I extend my gratitude to all my teachers and friends in Finland and India.

I was privileged enough to work with the TEKES-Olympus project during the first half of my doctoral studies. Thanks to all the local and international teams of workers who were part of the project. Special thanks to Dr Markku Hauta-Kasari for his support and

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Sampling procedure is an important part of research, and this study confirms the need to understand and include the lower canopy layer in the canopy reflectance study, as the lower canopy layer also contribute to the canopy reflectance even though the upper canopy is considered the most influential in canopy reflectance. This study also improved the compatibility of spectral reflectance in laboratory conditions to achieve repeatability of the experimental procedures. Furthermore, the provenance- related variations of the genotypes differed in their response to the latitude of origin, their adaptation to the climatic conditions of the provenance of origin or their acclimation to the prevailing environment in the common garden field. The potential role of secondary metabolites in herbivore resistance requires further analysis of the plant- herbivore interaction.

Universal Decimal Classification: 547.9, 535.312, 581.13, 581.15, 581.45, 582.622.2 CAB Thesaurus: Betula pendula; leaves; genotypes; provenance; latitude; optical properties;

reflectance; spectroscopy; canopy; variation; secondary metabolites; flavonoids; triterpenoids;

metabolomics; HPLC; mass spectrometry; chlorophyll; normalized difference vegetation index;

climate change; genome analysis; genetic diversity

Yleinen suomalainen ontologia: koivut; rauduskoivu; lehdet (kasvinosat); genotyyppi; pro- venienssi; alkuperä; optiset ominaisuudet; reflektanssi; spektroskopia; muuntelu (biologia);

vaihtelu; aineenvaihduntatuotteet; flavonoidit; triterpeenit; suuren erotuskyvyn nestekromato- grafia; massaspektrometria; klorofylli; ilmastonmuutokset; perimä; monimuotoisuus

ACKNOWLEDGEMENTS

I am happy to express my immeasurable appreciation and gratitude to those who have in one way or another contributed to making this thesis possible.

I must give my most respectful gratitude to my main supervisor, Prof. Elina Oksanen, for having confidence in me and for offering me an opportunity to carry out this project. Working with you has motivated me to mature as a researcher, and you inspired me with your integrity and professionalism. My sincere thanks and gratitude to my supervisor Dr Markku Keinänen for sharing his immense knowledge on the subject and for his continuous support during my doctoral studies. Thanks for inspiring the confidence to know that the sky is not falling during difficult times and to always have plans from A–Z. I wish to express my deepest gratitude to my supervisor Dr Sarita Keski-Saari for her support, guidance and supervision. Thanks for all the wonderful lunch hour discussions, jovial conference travels, motivational talks and warm hugs for rejuvenation. I wish to thank Sari Kontunen-Soppela for her support and encouragement and for her contribution to the experiment. Thank you for your help in the practical matters and for sharing your ideas and expertise with me.

I wish to record my gratitude to my former colleague, officemate and badminton part- ner Laure Fauch for all those funny moments at work, our serious discussions about work and the valuable comments on the article. Many thanks to my wonderful colleague and my first officemate Lars Granlund for his help in all small and big things during lab hours, invaluable comments on article and for his assistance with my driv- er’s license, with buying tools as a gift for someone special and many more.

I am thankful to Jenna Lihavainen for her contribution in chemical analyses and writing the third article, hours of lab work and countless warm moments. I am extremely grateful to Jarkko Salojärvi for his contribution in genome sequencing. I wish to thank Kaisa Heimonen and Antti Tenkanen for their contribution to the third article. My sincere thanks to my former colleagues Jennifer and Ilkka for their support during the experiment and our wonderful outdoor activities. I wish to thank Segun, Jussi, Flobert, Raja and the many others who helped me during the sampling and lab experiments. I would like to thank all those people who have directly or indirectly helped me throughout my doctoral studies inside and outside the university campus. I extend my gratitude to all my teachers and friends in Finland and India.

I was privileged enough to work with the TEKES-Olympus project during the first half of my doctoral studies. Thanks to all the local and international teams of workers who were part of the project. Special thanks to Dr Markku Hauta-Kasari for his support and

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collaboration during the early stages of my doctoral studies. I would like to express my appreciation to Arctic research programme (BETUMICS Project) of Academy of Fin- land, Juho and Lempi Pitkänen Foundation, Niemi Foundation and University of East- ern Finland for their financial support and facilities provided for the fulfilment of the thesis.

I am grateful to Professor Jaana Bäck and Docent Matti Mõttus for reviewing my thesis, which helped to improve it. I would like to express my special thanks to Docent Matthew Robson for accepting his role as an opponent.

I would like to express my eternal appreciation towards my family. Thanks to my father, Sivaraman Nair for boosting my confidence, teaching me to live bravely and for being the most wonderful mentor. I appreciate you more than words could ever say.

Thanks also go to my mother, Janaki Sivaraman for being someone with whom I can discuss anything. Thanks to my cute little brother, Shyam who is not little anymore, for all the loving and happy moments. I would also like to give my sincere gratitude to my father-in-law, Premachandran Nair for his support and encouragement for pursuing doctoral studies.

Deepak, my love, I am not thanking you because there are no words that can express my appreciation for you. I appreciate your full spectrum of love. You have been so understanding and supportive, tolerating all the mood swings and frustration throughout this period. I would like to thank my little one, Rayan, who joined us during the early stages of the doctoral study, showering us with unlimited happiness and pleasure. Thank you for teaching me to find happiness in those small things and for being my father at times when I needed advice.

Finally, I am extremely happy to appreciate my best friend, Krishna, who has continu- ously supported me throughout my life and has always stayed with me and helped me realise what love and care are, who I am and what my dreams are.

Joensuu, 15 December 2020 Maya Deepak

LIST OF ABBREVIATIONS

APCI atmospheric-pressure chemical ionization CAD charged aerosol detector

DAPC discriminant analysis of principal components GATK Genome Analysis Toolkit

HPLC high-performance liquid chromatography IBD identity by descent

LD linkage disequilibrium LDA linear discriminant analysis MS mass spectrometer

NDVI normalized difference vegetation index PCA principal component analysis

PLS-DA partial least squares discriminant analysis PRI photochemical reflectance index

RGB red-green-blue SLA specific leaf area

SNP single nucleotide polymorphism SWIR short wave infrared

VNIR visible and near-infrared

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collaboration during the early stages of my doctoral studies. I would like to express my appreciation to Arctic research programme (BETUMICS Project) of Academy of Fin- land, Juho and Lempi Pitkänen Foundation, Niemi Foundation and University of East- ern Finland for their financial support and facilities provided for the fulfilment of the thesis.

I am grateful to Professor Jaana Bäck and Docent Matti Mõttus for reviewing my thesis, which helped to improve it. I would like to express my special thanks to Docent Matthew Robson for accepting his role as an opponent.

I would like to express my eternal appreciation towards my family. Thanks to my father, Sivaraman Nair for boosting my confidence, teaching me to live bravely and for being the most wonderful mentor. I appreciate you more than words could ever say.

Thanks also go to my mother, Janaki Sivaraman for being someone with whom I can discuss anything. Thanks to my cute little brother, Shyam who is not little anymore, for all the loving and happy moments. I would also like to give my sincere gratitude to my father-in-law, Premachandran Nair for his support and encouragement for pursuing doctoral studies.

Deepak, my love, I am not thanking you because there are no words that can express my appreciation for you. I appreciate your full spectrum of love. You have been so understanding and supportive, tolerating all the mood swings and frustration throughout this period. I would like to thank my little one, Rayan, who joined us during the early stages of the doctoral study, showering us with unlimited happiness and pleasure. Thank you for teaching me to find happiness in those small things and for being my father at times when I needed advice.

Finally, I am extremely happy to appreciate my best friend, Krishna, who has continu- ously supported me throughout my life and has always stayed with me and helped me realise what love and care are, who I am and what my dreams are.

Joensuu, 15 December 2020 Maya Deepak

LIST OF ABBREVIATIONS

APCI atmospheric-pressure chemical ionization CAD charged aerosol detector

DAPC discriminant analysis of principal components GATK Genome Analysis Toolkit

HPLC high-performance liquid chromatography IBD identity by descent

LD linkage disequilibrium LDA linear discriminant analysis MS mass spectrometer

NDVI normalized difference vegetation index PCA principal component analysis

PLS-DA partial least squares discriminant analysis PRI photochemical reflectance index

RGB red-green-blue SLA specific leaf area

SNP single nucleotide polymorphism SWIR short wave infrared

VNIR visible and near-infrared

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LIST OF ORIGINAL PUBLICATIONS

This thesis is based on data presented in the following articles, referred to by the Roman Numerals I- III.

I Deepak M, Keski-Saari S, Fauch L, Granlund L, Oksanen E, Keinänen M. (2019).

Leaf Canopy Layers Affect Spectral Reflectance in Silver Birch. Remote Sensing, 11:2884.

II Deepak M, Keski-Saari S, Fauch L, Granlund L, Oksanen E, Keinänen M. (2020).

Spectral Reflectance in Silver Birch Genotypes from Three Provenances in Finland. Remote Sensing, 12:2677.

III Deepak M, Lihavainen J, Keski-Saari S, Kontunen-Soppela S, Salojärvi J, Tenkanen A, Heimonen K, Oksanen E, Keinänen M. (2018). Genotype- and provenance-related variation in the leaf surface secondary metabolites of silver birch. Canadian Journal of Forest Research, 48:494-505.

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LIST OF ORIGINAL PUBLICATIONS

This thesis is based on data presented in the following articles, referred to by the Roman Numerals I- III.

I Deepak M, Keski-Saari S, Fauch L, Granlund L, Oksanen E, Keinänen M. (2019).

Leaf Canopy Layers Affect Spectral Reflectance in Silver Birch. Remote Sensing, 11:2884.

II Deepak M, Keski-Saari S, Fauch L, Granlund L, Oksanen E, Keinänen M. (2020).

Spectral Reflectance in Silver Birch Genotypes from Three Provenances in Finland. Remote Sensing, 12:2677.

III Deepak M, Lihavainen J, Keski-Saari S, Kontunen-Soppela S, Salojärvi J, Tenkanen A, Heimonen K, Oksanen E, Keinänen M. (2018). Genotype- and provenance-related variation in the leaf surface secondary metabolites of silver birch. Canadian Journal of Forest Research, 48:494-505.

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AUTHOR’S CONTRIBUTION

I) The author planned the experiments together with Elina Oksanen, Markku Keinänen, Sarita Keski-Saari, Laure Fauch and Lars Granlund. The author was responsible for sampling the leaves, performing hyperspectral measurements and other leaf trait measurements, data processing and analyses and writing the article. Laure Fauch contributed to the methodology of the experiment and to editing the final draft. Markku Keinänen and Sarita Keski-Saari contributed to reviewing the final draft of the article.

II) The author contributed to the conceptualisation along with all the other authors.

Markku Keinänen, Laure Fauch and Lars Granlund contributed to the methodology of the experiment. The author was responsible for the sampling, data cu- ration, pre-processing, analysis and writing of the article. Markku Keinänen and Sarita Keski-saari contributed to reviewing the final draft of the article.

III) The author was responsible for conducting the experiment along with Sarita Keski-Saari and Jenna Lihavainen. Jenna Lihavainen, together with Sarita Keski- Saari and Markku Keinänen, developed the UHPLC/CAD-MS method for analysis. The author was responsible for wax extraction with Jenna Lihavainen. The author performed chemical analysis, data processing and statistics. The author wrote the article with Jenna Lihavainen. Jarkko Salojärvi performed the genome sequencing and analysis, and all the authors contributed to the final draft of the article.

1.1 Intraspecific variation ... 17

1.2 Why silver birch is a good model for this study ... 18

1.3 Provenance trials ... 18

1.4 Reflectance Spectroscopy: A tool for monitoring leaf traits ... 19

1.5 Metabolomics ... 20

1.6 Leaf spectral reflectance ... 20

1.6.1 Leaf position in the canopy affects spectral reflectance …….21

1.6.2 Between-tree variation affects leaf spectral reflectance………...22

1.6.3 Spectral reflectance of silver birch leaves among the genotypes and provenances of origin ... 22

1.7 The leaf surface secondary metabolites ... 22

1.7.1

Intraspecific variation in the leaf surface secondary metabolites of silver birch ... 23

1.8 Study overview ... 23

1.9 Aims and objectives

...

24

2 MATERIALS AND METHODS ... 27

2.1 Sampling ... 27

2.2 Spectral reflectance ... 28

2.2.1 Spectral correction and preprocesing ... 29

2.2.2 Reflectance indices used in this study ... 29

2.3 Metabolomics analysis ... 30

2.3.1 Wax extraction and anlaysis ... 30

2.3.2 Surface secondary metabolite determination ... 30

2.4 Herbivore damage ... 31

2.5 Relatedness of the genotypes ... 31

2.6 Statistical anlaysis ... 32

3 RESULTS AND DISCUSSION ... 35

3.1 Factors affecting the spectral reflectance ... 35

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AUTHOR’S CONTRIBUTION

I) The author planned the experiments together with Elina Oksanen, Markku Keinänen, Sarita Keski-Saari, Laure Fauch and Lars Granlund. The author was responsible for sampling the leaves, performing hyperspectral measurements and other leaf trait measurements, data processing and analyses and writing the article. Laure Fauch contributed to the methodology of the experiment and to editing the final draft. Markku Keinänen and Sarita Keski-Saari contributed to reviewing the final draft of the article.

II) The author contributed to the conceptualisation along with all the other authors.

Markku Keinänen, Laure Fauch and Lars Granlund contributed to the methodology of the experiment. The author was responsible for the sampling, data cu- ration, pre-processing, analysis and writing of the article. Markku Keinänen and Sarita Keski-saari contributed to reviewing the final draft of the article.

III) The author was responsible for conducting the experiment along with Sarita Keski-Saari and Jenna Lihavainen. Jenna Lihavainen, together with Sarita Keski- Saari and Markku Keinänen, developed the UHPLC/CAD-MS method for analysis. The author was responsible for wax extraction with Jenna Lihavainen. The author performed chemical analysis, data processing and statistics. The author wrote the article with Jenna Lihavainen. Jarkko Salojärvi performed the genome sequencing and analysis, and all the authors contributed to the final draft of the article.

1.1 Intraspecific variation ... 17

1.2 Why silver birch is a good model for this study ... 18

1.3 Provenance trials ... 18

1.4 Reflectance Spectroscopy: A tool for monitoring leaf traits ... 19

1.5 Metabolomics ... 20

1.6 Leaf spectral reflectance ... 20

1.6.1 Leaf position in the canopy affects spectral reflectance …….21

1.6.2 Between-tree variation affects leaf spectral reflectance………...22

1.6.3 Spectral reflectance of silver birch leaves among the genotypes and provenances of origin ... 22

1.7 The leaf surface secondary metabolites ... 22

1.7.1

Intraspecific variation in the leaf surface secondary metabolites of silver birch ... 23

1.8 Study overview ... 23

1.9 Aims and objectives

...

24

2 MATERIALS AND METHODS ... 27

2.1 Sampling ... 27

2.2 Spectral reflectance ... 28

2.2.1 Spectral correction and preprocesing ... 29

2.2.2 Reflectance indices used in this study ... 29

2.3 Metabolomics analysis ... 30

2.3.1 Wax extraction and anlaysis ... 30

2.3.2 Surface secondary metabolite determination ... 30

2.4 Herbivore damage ... 31

2.5 Relatedness of the genotypes ... 31

2.6 Statistical anlaysis ... 32

3 RESULTS AND DISCUSSION ... 35

3.1 Factors affecting the spectral reflectance ... 35

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3.1.1 Variation within the individual trees ... ...35 3.1.2 Variation between individual trees ... 36 3.1.3 Variation among the genotypes and provenances of origin…36 3.2 Factors affecting leaf surface secondary metabolites ... 38 3.2.1 Variation in leaf surface chemistry among the genotypes ... 38 3.2.2 Variation in leaf surface chemistry among the

provenances ... 39 3.2.3 Relatedness of the genotype ... 39 3.2.4 Relationship between secondary metabolites, chlorophyll

content and herbivore resistance ... 40

4 CONCLUDING REMARKS AND FUTURE PERSPECTIVES ... 43 5 BIBLIOGRAPHY ... 45

1 INTRODUCTION

1.1 INTRASPECIFIC VARIATION

Researchers have long acknowledged variations among species due to the influences of the surrounding ecosystems; however, only recently has evidence accumulated for the ecological importance of variation within species. In a recent meta-analysis, the effects of intraspecific variation on ecological responses were found to often be compa- rable to, and sometimes stronger than, the effects of variation among species (Des Roches et al., 2018). Variation at multiple levels of biodiversity, within the individuals, between the individuals, within the populations and between the populations, reflects differences in the microclimate, genetics and the phenotypic plasticity in response to environmental conditions (Albert et al., 2011; Zagajewski et al., 2017). However, there still remains a knowledge gap in understanding the significance of intraspecific variation at multiple levels of ecosystem dynamics.

Intraspecific natural variation or genetic variation, i.e. genetic differences within the species, is an inherent property maintained by the evolutionary process (Whitham et al., 2008). This variation allows flexibility and adaptation of a species to changing environmental circumstances (Andrew et al., 2010). Species with a large geographical distribution range are likely to have a variable genetic makeup (Eckert et al., 2008). For example, plants of the same species in a northern latitude may be genetically different from those in southern latitudes. Different genotypes in a population (provenance) may respond differently to different environmental conditions (Andrew et al., 2010).

The natural variation within a species is manifested in various physiological, morphological and chemical attributes (Laitinen et al., 2000; Possen et al., 2014).

Apart from the variation among the provenance and genotypes, there exist foliar variation between and within individual trees. However, the arrangement of leaves across the tree is complex, varying within an individual tree (Li et al., 2013). Plant leaves are key players in maintaining the structure and functioning of terrestrial ecosystems, particularly in carbon, nutrient and water cycling. Plants strategise the distribution of critical metabolic processes across the canopy position (Coble et al., 2016).

For instance, plants have high nutrient content in the upper canopy, where they re- ceive high irradiance (Hirose et al., 1987). Plants, including the temperate deciduous and tropical forest trees, maintain metabolic balance by translocating nutrients to the upper canopy (Hikosaka et al., 2005). This affects the leaf’s morphological, chemical and ecophysiological traits, which in turn affect the leaf’s spectral reflectance.

Studies on forest trees have been conducted to analyse variations in different traits related to genotype or provenance of origin through provenance trials (Savolainen et al., 2007; Vitasse et al., 2009). Those studies have provided information regarding trees acclimation to different environmental conditions. Intraspecific genetic diversity has

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3.1.1 Variation within the individual trees ... ...35 3.1.2 Variation between individual trees ... 36 3.1.3 Variation among the genotypes and provenances of origin…36 3.2 Factors affecting leaf surface secondary metabolites ... 38 3.2.1 Variation in leaf surface chemistry among the genotypes ... 38 3.2.2 Variation in leaf surface chemistry among the

provenances ... 39 3.2.3 Relatedness of the genotype ... 39 3.2.4 Relationship between secondary metabolites, chlorophyll

content and herbivore resistance ... 40

4 CONCLUDING REMARKS AND FUTURE PERSPECTIVES ... 43 5 BIBLIOGRAPHY ... 45

1 INTRODUCTION

1.1 INTRASPECIFIC VARIATION

Researchers have long acknowledged variations among species due to the influences of the surrounding ecosystems; however, only recently has evidence accumulated for the ecological importance of variation within species. In a recent meta-analysis, the effects of intraspecific variation on ecological responses were found to often be compa- rable to, and sometimes stronger than, the effects of variation among species (Des Roches et al., 2018). Variation at multiple levels of biodiversity, within the individuals, between the individuals, within the populations and between the populations, reflects differences in the microclimate, genetics and the phenotypic plasticity in response to environmental conditions (Albert et al., 2011; Zagajewski et al., 2017). However, there still remains a knowledge gap in understanding the significance of intraspecific variation at multiple levels of ecosystem dynamics.

Intraspecific natural variation or genetic variation, i.e. genetic differences within the species, is an inherent property maintained by the evolutionary process (Whitham et al., 2008). This variation allows flexibility and adaptation of a species to changing environmental circumstances (Andrew et al., 2010). Species with a large geographical distribution range are likely to have a variable genetic makeup (Eckert et al., 2008). For example, plants of the same species in a northern latitude may be genetically different from those in southern latitudes. Different genotypes in a population (provenance) may respond differently to different environmental conditions (Andrew et al., 2010).

The natural variation within a species is manifested in various physiological, morphological and chemical attributes (Laitinen et al., 2000; Possen et al., 2014).

Apart from the variation among the provenance and genotypes, there exist foliar variation between and within individual trees. However, the arrangement of leaves across the tree is complex, varying within an individual tree (Li et al., 2013). Plant leaves are key players in maintaining the structure and functioning of terrestrial ecosystems, particularly in carbon, nutrient and water cycling. Plants strategise the distribution of critical metabolic processes across the canopy position (Coble et al., 2016).

For instance, plants have high nutrient content in the upper canopy, where they re- ceive high irradiance (Hirose et al., 1987). Plants, including the temperate deciduous and tropical forest trees, maintain metabolic balance by translocating nutrients to the upper canopy (Hikosaka et al., 2005). This affects the leaf’s morphological, chemical and ecophysiological traits, which in turn affect the leaf’s spectral reflectance.

Studies on forest trees have been conducted to analyse variations in different traits related to genotype or provenance of origin through provenance trials (Savolainen et al., 2007; Vitasse et al., 2009). Those studies have provided information regarding trees acclimation to different environmental conditions. Intraspecific genetic diversity has

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been reported in the leaf morphological and physiological traits of silver birch with their relationship to growth (Possen et al., 2014; Tenkanen et al., 2020).

In addition to optical properties, variations in the chemical properties of silver birch leaves have been extensively studied (Keinänen & Julkunen-Tiitto, 1998; Lihavainen et al., 2017; Makhnev et al., 2012). Secondary metabolites (defensive compounds), which are present on the waxes covering the surface of birch leaves, provide a highly complex defense mechanism against herbivores, pathogens (Samuels et al., 2008), excessive light and UV radiation (Falcone Ferreyra et al., 2012). Secondary metabolites have been shown to exhibit genotypic variation (Barchet et al., 2014), and change in the environment has a direct effect on metabolite concentrations (Makhnev et al., 2012).

1.2 WHY SILVER BIRCH IS A GOOD MODEL FOR THIS STUDY?

While species diversity in boreal forests is limited, great diversity is displayed in the structure and dynamics of the forest; thus, species-specific studies contribute to monitoring ecosystem change (Kuuluvainen & Aakala, 2011; Lähde et al., 1999). Silver birch is one of the pioneer species to flourish after the Ice Age in boreal forests, which com- prise 30% of the global forests (Brandt et al., 2013). The climate change scenarios cause substantial natural variation in boreal forests (Aitken et al., 2008). The adaptability of tree species to changing environmental conditions depends on their genetic diversity (Aitken et al., 2008). Silver birch (Betula pendula) is an excellent model system for eluci- dating the adaptation and acclimation capacity of forest trees to rapidly changing climate due to its wide latitudinal and longitudinal distribution. Recent advances were made in the genomics and the evolutionary history of these species (Salojärvi et al., 2017), where the whole genome sequencing of silver birch provided a better understanding of how the genome works and its response to environmental factors. Silver birch has been rigorously studied for carbon (C) and nitrogen (N) economy, photosyn- thetic efficiency, metabolism, chemistry, herbivore resistance and phenology (Heimo- nen et al., 2017; Lihavainen et al., 2017; Possen et al., 2014; Silfver et al., 2015; Tenkanen et al., 2020). Because of fast growth and good stem form, silver birch is commercially an important source of biomass production, particularly for plywood and the furniture industry (Hynynen, 2010).

1.3 PROVENANCE TRIALS

Provenance trials (common garden studies) are trials or experiments to test the effect of environments by transferring trees out of their natural environment to a common environment where the climatic conditions are similar for all the plants of different origin (Aitken & Bemmels, 2015; Raulo, 1976). Numerous provenance trial (common garden fields) studies have been done where many genotypes are planted in a com-

mon site to study the genetic variation (e.g. Hiura & Nakamura, 2013; Pratt et al., 2014;

Slimestad, 1998). Whether spectral and chemical variation is under genetic control or related to environmental factors, such as different light conditions, photoperiod, tem- perature and other climatic factors in a north-to-south latitudinal gradient, can also be studied with provenance trials. The common garden experiment is a valuable approach for assessing ecologically important traits in silver birch provenances and genetic variation within a provenance, as found for most temperate and boreal forest trees (Alberto et al., 2013; Sork et al., 2013). Information from provenance trials can also be utilised for managing forest trees in a changing climate and to find genetic re- sources for resistance breeding.

1.4 REFLECTANCE SPECTROSCOPY: A TOOL FOR MONITORING LEAF TRAITS

Spectroscopy explores how light behaves in a particular sample, and it identifies the sample based on its specific spectral signature (Gristey et al., 2019). Spectroscopy is a general methodological approach that employs radiation to obtain data on the structure and properties of a sample. The fundamental principle of any light energy results in a spectrum, and the response of a sample to the beam of electromagnetic radiation is shared in spectroscopy, which is recorded as a function of radiation wavelength and plotted as a spectrum (Beć et al., 2020). Imaging spectroscopy is a spatially resolved technique that uses spectroscopic technology to record electromagnetic radiation in different wavelength ranges from visible to infrared (Caporaso et al., 2018). Imaging spectroscopy collects spectral information together with spatial information, where a large number of continuous wavelengths with high spectral resolutions are measured, and it is a well-accepted technology with a wide variety of applications due to its non- invasiveness, accuracy and reliability (Park, 2016). Imaging spectroscopy can be used to detect variations that are not visually apparent to human vision, which captures light in only three spectral bands. Unlike RGB (red-green-blue) images, spectral cam- eras capture hundreds of bands as a set of images in the form of data cubes. This extra information provides higher discriminative power for extracting desired patterns from the images.

The foliar reflectance spectrum provides detailed information about a leaf’s optical properties (Lenk et al., 2007; Li et al., 2013), and it has the capacity to identify and map a sample’s chemical, physical and biological properties. A specific spectral signature unique to a sample can provide ample information to identify the sample and monitor various biochemical alterations in leaves. The reflectance-based detection of spectral bands indicates an understanding of plants’ responses to their surrounding environment (Knipling, 1970; Zhang, 2003). This understanding of the plants’ responses to biotic and abiotic stress is based on studies that show stress interferes with a plant’s ecophysiological and biochemical traits, which alters the leaf’s reflectance spectra (Prabhakar et al., 2012). Remote sensing explores the variation from a distance that

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been reported in the leaf morphological and physiological traits of silver birch with their relationship to growth (Possen et al., 2014; Tenkanen et al., 2020).

In addition to optical properties, variations in the chemical properties of silver birch leaves have been extensively studied (Keinänen & Julkunen-Tiitto, 1998; Lihavainen et al., 2017; Makhnev et al., 2012). Secondary metabolites (defensive compounds), which are present on the waxes covering the surface of birch leaves, provide a highly complex defense mechanism against herbivores, pathogens (Samuels et al., 2008), excessive light and UV radiation (Falcone Ferreyra et al., 2012). Secondary metabolites have been shown to exhibit genotypic variation (Barchet et al., 2014), and change in the environment has a direct effect on metabolite concentrations (Makhnev et al., 2012).

1.2 WHY SILVER BIRCH IS A GOOD MODEL FOR THIS STUDY?

While species diversity in boreal forests is limited, great diversity is displayed in the structure and dynamics of the forest; thus, species-specific studies contribute to monitoring ecosystem change (Kuuluvainen & Aakala, 2011; Lähde et al., 1999). Silver birch is one of the pioneer species to flourish after the Ice Age in boreal forests, which com- prise 30% of the global forests (Brandt et al., 2013). The climate change scenarios cause substantial natural variation in boreal forests (Aitken et al., 2008). The adaptability of tree species to changing environmental conditions depends on their genetic diversity (Aitken et al., 2008). Silver birch (Betula pendula) is an excellent model system for eluci- dating the adaptation and acclimation capacity of forest trees to rapidly changing climate due to its wide latitudinal and longitudinal distribution. Recent advances were made in the genomics and the evolutionary history of these species (Salojärvi et al., 2017), where the whole genome sequencing of silver birch provided a better understanding of how the genome works and its response to environmental factors. Silver birch has been rigorously studied for carbon (C) and nitrogen (N) economy, photosyn- thetic efficiency, metabolism, chemistry, herbivore resistance and phenology (Heimo- nen et al., 2017; Lihavainen et al., 2017; Possen et al., 2014; Silfver et al., 2015; Tenkanen et al., 2020). Because of fast growth and good stem form, silver birch is commercially an important source of biomass production, particularly for plywood and the furniture industry (Hynynen, 2010).

1.3 PROVENANCE TRIALS

Provenance trials (common garden studies) are trials or experiments to test the effect of environments by transferring trees out of their natural environment to a common environment where the climatic conditions are similar for all the plants of different origin (Aitken & Bemmels, 2015; Raulo, 1976). Numerous provenance trial (common garden fields) studies have been done where many genotypes are planted in a com-

mon site to study the genetic variation (e.g. Hiura & Nakamura, 2013; Pratt et al., 2014;

Slimestad, 1998). Whether spectral and chemical variation is under genetic control or related to environmental factors, such as different light conditions, photoperiod, tem- perature and other climatic factors in a north-to-south latitudinal gradient, can also be studied with provenance trials. The common garden experiment is a valuable approach for assessing ecologically important traits in silver birch provenances and genetic variation within a provenance, as found for most temperate and boreal forest trees (Alberto et al., 2013; Sork et al., 2013). Information from provenance trials can also be utilised for managing forest trees in a changing climate and to find genetic re- sources for resistance breeding.

1.4 REFLECTANCE SPECTROSCOPY: A TOOL FOR MONITORING LEAF TRAITS

Spectroscopy explores how light behaves in a particular sample, and it identifies the sample based on its specific spectral signature (Gristey et al., 2019). Spectroscopy is a general methodological approach that employs radiation to obtain data on the structure and properties of a sample. The fundamental principle of any light energy results in a spectrum, and the response of a sample to the beam of electromagnetic radiation is shared in spectroscopy, which is recorded as a function of radiation wavelength and plotted as a spectrum (Beć et al., 2020). Imaging spectroscopy is a spatially resolved technique that uses spectroscopic technology to record electromagnetic radiation in different wavelength ranges from visible to infrared (Caporaso et al., 2018). Imaging spectroscopy collects spectral information together with spatial information, where a large number of continuous wavelengths with high spectral resolutions are measured, and it is a well-accepted technology with a wide variety of applications due to its non- invasiveness, accuracy and reliability (Park, 2016). Imaging spectroscopy can be used to detect variations that are not visually apparent to human vision, which captures light in only three spectral bands. Unlike RGB (red-green-blue) images, spectral cam- eras capture hundreds of bands as a set of images in the form of data cubes. This extra information provides higher discriminative power for extracting desired patterns from the images.

The foliar reflectance spectrum provides detailed information about a leaf’s optical properties (Lenk et al., 2007; Li et al., 2013), and it has the capacity to identify and map a sample’s chemical, physical and biological properties. A specific spectral signature unique to a sample can provide ample information to identify the sample and monitor various biochemical alterations in leaves. The reflectance-based detection of spectral bands indicates an understanding of plants’ responses to their surrounding environment (Knipling, 1970; Zhang, 2003). This understanding of the plants’ responses to biotic and abiotic stress is based on studies that show stress interferes with a plant’s ecophysiological and biochemical traits, which alters the leaf’s reflectance spectra (Prabhakar et al., 2012). Remote sensing explores the variation from a distance that

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enables mapping the forest canopy, providing intraspecific and interspecific variations from satellites or airborne systems.

1.5 METABOLOMICS

Metabolomics is a set of analytical and bioinformatics methods for the comprehensive and unbiased identification and quantification of low molecular weight compounds (metabolome) present in a specific biological sample grown under defined conditions (Fiehn, 2002). Non-targeted metabolomics use the relative quantification of metabolites, where the analytical detector’s (such as a mass spectrometer) signal intensities are normalised by the use of internal or external standard compounds. The principle objec- tive of metabolomics is to define the biochemical changes within the plant. The concen- tration of metabolites is inherently dynamic, both within and between the biological systems and in response to environmental changes. The high variability in a field study compared to a study in a controlled environment presents great challenges in plant growth related to various biotic and abiotic factors. Understanding the variation in plant metabolites is crucial for grasping how the metabolites respond to various stress factors (Morrison et al., 2007). Thus, metabolomics offers enormous potential for understanding plant plasticity, environmental adaptation and genetic modification.

1.6 LEAF SPECTRAL REFLECTANCE

Leaves are the primary harvester of incident radiation. When light enters the leaf, a part of the light is absorbed, a part is reflected and a part is transmitted throughout the leaf. The measures of the energy absorbed, transmitted and reflected at specific wavelengths are called absorbance, transmittance and reflectance, respectively (Peñuelas &

Filella 1998). Transmittance (T) and reflectance (R) are similar for specific vegetation, wherein generally transmittance and reflectance are low in the visible spectrum and high in the infrared spectrum for green vegetation. The chlorophyll meter (Dualex) measures chlorophyll content from leaf transmittance in two wavelengths: red, where light is absorbed by chlorophyll, and infrared, where light is transmitted. The leaf spectral reflectance factor is defined by the leaf surface and internal properties and their interaction with the surrounding environment (Peñuelas & Filella 1998). The reflectance in the visible (400–700 nm) spectrum is mainly affected by the absorption properties of leaf pigments, such as chlorophyll, carotenoids and anthocyanins (Gitel- son et al., 2001; Sims & Gamon 2002). Furthermore, leaf surface reflectance is influenced by the leaf’s morphological features, such as the trichomes and the density of epicuticular waxes (Buschmann et al., 2012), which could filter light penetration into the leaves in the visible and near-infrared (700–1000 nm) spectrums (Buschmann et al., 2012; Qui et al., 2019). Reflectance in the shortwave infrared spectrum (1000–2500 nm)

is strongly influenced by water content (Carter 1991). In addition to water, biochemical compounds, such as cellulose, lignin, protein and nitrogen, also affect this region.

These chemical constituents of chlorophyll and nitrogen content have been reported to correlate with structural features, such as specific leaf area (SLA) (Atherton et al., 2017). Variation in leaf spectral reflectance can be used to investigate biodiversity across different plant ecosystems (Jacquemond & Ustin, 2019). Genetic diversity is an important factor for forest sustainability. Thus, understanding genotypic variation is critical for showing the influence of a plant’s surrounding environment. In this thesis, the reflectance spectrum of leaves is utilised for detecting intraspecific variance. The high accuracy of proximal sensing compared to airborne and spaceborne sensors makes it suitable for a detailed study in a laboratory benchtop or in situ portable sensors (Gamon et al., 2020). Proximal sensing results can be readily related to leaf traits;

thus, this method is an appropriate tool to validate the optical measurements against other traits (Gamon et al., 2019).

1.6.1 Leaf position in the canopy affects spectral reflectance

Due to the distribution and availability of different environmental factors, such as light exposure and nutrient availability, various leaf properties are likely to vary depending on their position within the tree (Rijkers et al., 2000). The physiological status of the leaf could be detected from the leaf reflectance. Silver birch responds to the light environment and the leaves exposed to high light generally have high chlorophyll content (low reflectance in the visible spectrum) (Atherton et al., 2017). Thus, variation could be detected in the reflectance spectra of leaves positioned in different parts of the tree.

Upper canopy leaves have been shown to have less reflectance in the visible region than the leaves in the lower canopy due to the high chlorophyll content in the upper leaves (Lichtenthaler et al., 2007). Such studies can estimate the extent of variation within a tree, which can then be considered when planning sampling procedures.

There is a general assumption that the upper canopy dominates canopy reflectance (Thomas et al., 2008). However, the implications of field sample protocol and the importance of the lower canopy in canopy reflectance, particularly in short canopies, has been recently explored (Gara et al., 2019). This implies that the extent of within a tree variation has relevance to the choice of comparison material, particularly in a short canopy field where collecting leaves from upper, middle and lower canopies is crucial for examining the contribution of each canopy layer.

While the vertical heterogeneity of spectral reflectance has been clearly demonstrat- ed (Gara et al., 2018; Hovi et al., 2017; Lukeš et al., 2013), little information has been presented regarding the horizontal heterogeneity (or the cardinal direction) of the tree crown. In a study of the sun-exposed needles of Norway spruce (Picea abies), no differ- ence was reported in the biochemical and structural parameters studied from the branches of different cardinal directions (Lhotáková et al., 2007).

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