Doktoritoo Sandra Metslaid.Indd

SANDRA METSLAID

VIIS VIIMAST KAITSMIST

LEILA PAZOUKI EMISSION, GENE REGULATION AND FUNCTION OF TERPENOIDS IN TOMATO (SOLANUM LYCOPERSICUM) AND YARROW (ACHILLEA MILLEFOLIUM) TERPENOIDIDE EMISSOONI FÜSIOLOOGILISED JA MOLEKULAARSED KON- TROLLMEHHANISMID TOMATI (SOLANUM LYCOPERSICUM) JA H. RAUDROHU (ACHILLEA MILLEFOLIUM) NÄIDETEL Professor Ülo Niinemets ( ASSESSMENT OFCLIMATE EFFECTS ONSCOTS PINE

17. jaanuar 2017 Pinus sylvestris

MAILIIS TAMPERE IMPACT OF SLURRY FERTILIZATION ON NUTRIENT LEACHING AND ON THE ABUNDANCE OF ANTIBIOTIC RESISTANCE GENES IN AGRICULTURAL SOIL VEDELSÕNNIKUGA VÄETAMISE MÕJU TOITAINETE LEOSTUMISELE JA ANTI- BIOOTIKUMI RESISTENTSUSGEENIDE ARVUKUSELE PÕLLUMULLAS L.) GROWTH INESTONIA Vanemteadur Evelin Loit, teadur Henn Raave ASSESSMENT OF CLIMATE EFFECTS ON SCOTS PINE 2. veebruar 2017 (Pinus sylvestris L.) GROWTH IN ESTONIA

KADRI JUST BEGOMOVIRUS INFECTION IN TOMATO FRUIT BEGOMOVIIRUSTE INFEKTSIOON TOMATI VILJAS KLIIMA MÕJU HINDAMINE HARILIKU MÄNNI Professor Leif Anders Michael Kvarnheden (Rootsi Põllumajandusteaduste Ülikool), emeriitprofessor Anne Luik (Pinus sylvestris L.) KASVULE EESTIS 3. veebruar 2017

MEELIS TEDER THE ROLE OF INSTITUTIONAL INNOVATION IN THE DEVELOPMENT OF THE ESTONIAN FOREST SECTOR INSTITUTSIONAALSE INNOVATSIOONI ROLL EESTI METSASEKTORI ARENGUS Dr. Paavo Kaimre, professor Peeter Muiste SANDRA METSLAID 13. veebruar 2017

KALEV ADAMSON DISTRIBUTION AND POPULATION GENETIC ANALYSES OF THE AGENTS OF IN- VASIVE NEEDLE AND SHOOT DISEASES OF CONIFERS IN NORTHERN EUROPE A Thesis INVASIIVSETE OKKA- JA VÕRSEHAIGUSTE LEVIK JA NENDE TEKITAJATE POPU- for applying for the degree of Doctor of Philosophy in Forestry LATSIOONIDE VÕRDLEV ANALÜÜS OKASPUUDEL PÕHJA-EUROOPAS Dotsent Rein Drenkhan 28. märts 2017 Väitekiri ﬁ losooﬁ adoktori kraadi taotlemiseks metsanduse erialal ISSN 2382-7076 ISBN 978-9949-569-83-0 (trükis) ISBN 978-9949-569-84-7 (pdf)

Trükitud taastoodetud paberile looduslike trükivärvidega © Kuma Print Tartu 2017 Eesti Maaülikooli doktoritööd

Doctoral Th eses of the Estonian University of Life Sciences

ASSESSMENT OF CLIMATE EFFECTS ON SCOTS PINE (Pinus sylvestris L.) GROWTH IN ESTONIA

KLIIMA MÕJU HINDAMINE HARILIKU MÄNNI (Pinus sylvestris L.) KASVULE EESTIS

SANDRA METSLAID

A Th esis for applying for the degree of Doctor of Philosophy in Forestry

Väitekiri fi losoofi adoktori kraadi taotlemiseks metsanduse erialal

Tartu 2017 Institute of Forestry and Rural Engineering Estonian University of Life Sciences

According to verdict No 2, of March 8, 2017, the Defence Board of PhD Th eses in Forestry of the Estonian University of Life Sciences has accepted the thesis for the defence of the degree of Doctor of Philosophy in Forestry.

Opponent: Prof. Gediminas Brazaitis, PhD Faculty of Forest Sciences and Ecology Aleksandras Stulginskis University

Supervisors: Prof. Andres Kiviste, PhD Institute of Forestry and Rural Engineering Estonian University of Life Sciences

Assoc. Prof. Ahto Kangur, PhD Institute of Forestry and Rural Engineering Estonian University of Life Sciences

Defence of the thesis: Estonian University of Life Sciences, room 1B27, Kreutzwaldi 5, Tartu on June 9, 2017, at 10:00.

Th e English and the Estonian language of the thesis were revised by Mrs. Karit Jäärats.

ISSN 2382-7076 ISBN 978-9949-569-83-0 (trükis) ISBN 978-9949-569-84-7 (pdf) CONTENTS

LIST OF ORIGINAL PUBLICATIONS ...... 7 ABBREVIATIONS ...... 9 1. INTRODUCTION ...... 10 2. REVIEW OF LITERATURE ...... 14 2.1. Shift towards empirical single tree growth models ...... 14 2.2. Eff ect of weather fl uctuations on tree annual increment ...... 15 2.3. Tree-ring analysis to study annual climatic variability in tree growth ...... 16 2.4. Climate eff ects on site index ...... 18 2.5. Climate eff ects on radial growth ...... 19 2.6. Scots pine habitats and climatic conditions in Estonia ...... 20 2.7. Research needs ...... 22 3. AIMS OF THE STUDY ...... 24 4. MATERIALS AND METHODS ...... 25 4.1. Study sites ...... 25 4.2. Meteorological data ...... 28 4.3. Investigation of changes in height growth patterns (I) ...... 30 4.4. Dendroclimatic analysis (II, III, IV) ...... 31 4.4.1. Increment core sampling, preparation and measurements ...... 31 4.4.2. Standardization of tree-ring series ...... 32 4.4.3. Development of chronologies ...... 33 4.4.4. Assessment of chronology quality ...... 34 4.4.5. Analysis of growth-climate relationships ...... 35 4.4.6. Investigation of spatial and temporal response patterns (IV) ...... 36 4.4.7. Identifi cation of pointer years (II) ...... 37 4.5. Modelling basal area increment (III) ...... 38 5. RESULTS ...... 41 5.1. Changes in long-term forest productivity ...... 41 5.2. Scots pine radial growth characteristics and relationship to inter-annual weather variations ...... 43 5.2.1. Scots pine chronologies ...... 43 5.2.2. Radial growth-climate relationships ...... 48 5.2.3. Temporal patterns of Scots pine response to climate variation ...... 52 5.2.4. Evidence of infl uences of extreme climatic conditions on radial growth ...... 55 5.3. Radial growth patterns and the BAI model for Scots pine on reclaimed areas ...... 57 6. DISCUSSION ...... 60 6.1. Tree growth deviations from expected growth ...... 60 6.2. Infl uence of annual climate variability on Scots pine growth in Estonia ...... 62 6.3. Modelling the basal area increment of Scots pine ...... 65 7. CONCLUSIONS ...... 68 REFERENCES ...... 70 SUMMARY IN ESTONIAN ...... 87 ACKNOWLEDGEMENTS ...... 96 ORIGINAL PUBLICATIONS ...... 97 CURRICULUM VITAE...... 168 ELULOOKIRJELDUS ...... 172 LIST OF PUBLICATIONS ...... 175 LIST OF ORIGINAL PUBLICATIONS

Th e thesis is based on the following publications, referred to in the text by the corresponding Roman numerals. Th e papers are reproduced by the kind permission of the publishers.

I Metslaid, S., Sims A., Kangur, A., Hordo, M., Jõgiste, K., Kiviste, A., Hari, P. 2011. Growth patterns from diff erent forest generations of Scots pine in Estonia. Journal of Forest Research, 16 (3): 237243.

II Hordo, M., Metslaid, S., Kiviste, A. 2009. Response of Scots pine (Pinus sylvestris L.) radial growth to climate factors in Esto- nia. Baltic Forestry, 15 (2): 195205.

III Metslaid, S., Stanturf, J.A., Hordo, M., Korjus, H., Laarmann, D., Kiviste, A. 2016. Growth responses of Scots pine to climatic factors on reclaimed oil shale mined land. Environmental Sci- ence and Pollution Research, 23: 1363713652.

IV Metslaid, S., Hordo, M., Korjus, H., Kiviste, A., Kangur, A. 20xx. Spatio-temporal variability in Scots pine response to annual climate fl uctuations in hemiboreal forests of Estonia. Submit- ted to Agricultural and Forest Meteorology.

7 Th e contributions from the authors to the articles are as follows:

I II III IV Original idea AKa, SM MH AK, SM SM

Study design AK, AKa, AK, HK, MH, MH SM SM AK

Data collection AKa, MH, All All All SM

Data analysis AK, AS, MH AK, SM SM SM

Preparation of manuscript All All All All AK – Andres Kiviste; AS – Allan Sims, HK – Henn Korjus; AKa – Ahto Kangur; MH – Maris Hordo, SM – Sandra Metslaid; All – all authors of the article.

8 ABBREVIATIONS

AIC Akaike Information Criterion AC1 First-order autocorrelation ENFRP Estonian Network of Forest Research Plots EPS Expressed population signal ESTEA Estonian Environmental Agency BAI Basal area increment BCC Bootstrapped correlation coeffi cients HCA Hierarchical cluster analysis IC Mean interseries correlation ISL Islands JJp JuneJuly precipitation sum JJt JuneJuly temperature mean GAM Generalized additive model Glk Gleichläufi gkeit MEE Mean estimation error MS Mean sensitivity NAO North Atlantic Oscillation NE Northeast PCA Principal components analysis PC1 First principal component SD Standard deviation SE Southeast

SIx Site index at reference age x (SI40) SNR Signal to noise ratio SW Southwest RMSE Root mean square error TBP T-values with Baillie Pilcher standardization TRW Tree-ring width

9 1. INTRODUCTION

Forest growth and yield models are important tools in forest management planning (Hasenauer, 2006) for predicting short- and long-term growth, assessing management options and updating the information on available resources (Burkhart, 1990). As forest management decisions have a great impact on forest stand development, this considerably infl uences economic return, ecological conditions, and other ecosystem services (Duncker et al., 2012) of a managed area. Th erefore, model predictions should be accurate and reliable enough to avoid wrong decisions and economic loss.

Modellers and researchers continuously aim to build better models for forestry use (Eastaugh et al., 2013), to target questions that are more specifi c (Porté and Bartelink, 2002) and to improve the scope or scale of predictions. In addition, global climate change observed since the 1950s (Jones and Mann, 2004; IPCC, 2014) created the need for models capable of predicting growth dynamics with greater resolution and to take into account long-term changes in environmental conditions.

In Estonia the mean annual air temperature over the second half of the 20th century increased by 1.0–1.7°C throughout the country (Jaa- gus, 1998; Jaagus, 2006a; Tarand et al., 2013). Th e greatest warming is observed for the spring season. For example air temperature in March over 19512000 increased by 35°C, depending on the sub-region and weather conditions from cold and snowy, changed to mild and snow-free for that month (Jaagus, 2006a). Signifi cant warming also took place in winter, especially in January and February (Jaagus, 2006a). However, the magnitude of the warming tendency varies across diff erent parts of the country. Due to higher temperatures at the end of the cold season, the duration of the time period with snow cover has signifi cantly decreased (Jaagus, 2006a, 2006b; Tarand et al., 2013) and the start of the growing period has shifted to earlier dates, as observed by the advanced spring and summer phenology of some plants (Ahas and Aasa, 2006). Besides that, temperature seasonality, defi ned as the diff erence between summer and winter mean temperatures, is also decreasing in this part of Europe, suggesting a latitudinal shift in climatic conditions equal to 47° N towards the equator (Xu et al., 2013). In addition to the increasing mean temperatures and altered precipitation regimes, rising CO2 concentrations (Knorr, 2009; IPCC, 2014, BACC Author Team, 2015) and an increased deposition of nitrogen (Erisman and Vries, 2000) accompany 10 the climate change in the region. Th e warming tendency with an annual precipitation increase of 1020%, mainly during the cold season is expected to continue in Estonia through the 21st century (Jaagus and Mändla, 2014).

Th e ongoing warming and processes associated with it force trees to respond to the environmental changes through physiological mechanisms, such like photosynthesis, transpiration, nutrient and water uptake (Morison and Lawlor, 1999; Niu et al., 2008) and consequently may have an impact on forest growth, productivity, survival and tree species distribution patterns (Lindner et al., 2010; 2014). In forestry, the concerns mainly emanate from how climate change will infl uence the forest growth in a certain region. It still remains uncertain, whether the impact will be positive or negative on forest productivity and tree vitality, and what economic, ecologic and social consequences it may cause for the countries.

Uncertainties could be reduced and climate change eff ects on forest growth evaluated, based on future growth predictions, using models that incorporate tree response to climatic variations. Most of the forest growth models used in Estonia are stand-level growth models (Krigul, 1969; Tappo, 1982; Kiviste, 1999a, 1999b; Kasesalu, 2003; Nilson, 2005; Kiviste and Kiviste, 2009) developed based on an empirical approach. For a long time, these models were an appropriate instrument for defi ning stand growth over the time. However, due to shifts in environmental conditions and because of changing needs of forest managers such models become outdated. Th ey are not applicable for predicting forest stand growth under various management alternatives and cannot be directly applied in heterogeneous stands (Teuff el et al., 2006). Besides that, most of the models developed during the last century are based on the assumption that site conditions are steady (Assmann, 1970), which relate the models to the time when they were developed. Th us, such models cannot account for the eff ects of the ongoing environmental changes on forest growth, most likely resulting in inadequate long-term predictions. Considering this shortcoming, during the early attempts to assess the eff ects of environmental changes on forest growth in Estonia, Nilson and Kiviste (1984, 1986) and Nilson et al. (1999) used site index (SI) models (which predict long-term mean height development of the stand) and compared model predictions and actual forest growth to assess diff erences in forest growth, possibly caused by environmental 11 changes. Uncertainties remained in the results because forest response to climate variation was not directly assessed.

Th e increased need for more capable managerial tools forces development of more fl exible forest growth models (Hasenauer, 2006) which could consider changes in growth conditions. Climate infl uence on tree growth can be modelled and forest growth in changing climatic conditions assessed (Girardin et al., 2008) using either a process-based, empirical approach or hybrid modelling approach, which is a combination of both these methods. Process-based modelling is a procedure where the behaviour of a system is derived from a set of functional components and their interactions with each other and with the environment, through physical and mechanistic processes occurring over time (Bartelink, 2000; Mäkelä, 2003). Process-based models commonly simulate basic growth processes, like photosynthesis, water balance, nutrient cycling, etc. within a tree (Mäkelä, 2003), but require a large number of input parameters, which are not commonly available through regular forest inventories.

In Estonia, individual tree growth models based on empirical relationships could be used to predict forest growth in changing environmental conditions. Empirical forest growth and yield models for prediction of growth and yield use statistical techniques and are calibrated on comprehensive datasets and are widely used for decision support in diff erent forest management scenarios (Peng, 2000). Such models may operate at different organizational levels (e.g. tree-, or stand-level), however, tree-level models are more fl exible in the sense of tree species composition and for uneven-aged stands, compared to stand-level models. Jõgiste (2000) has developed an individual tree basal area increment model for Norway spruce (Picea abies (L.) Karst.), but climatic shifts cannot be predicted using this model since tree response to inter-annual climatic fl uctuations is not considered in the model.

Dendroclimatic analyses, based on tree-ring series could aid in quan- tifying climate infl uence on tree growth, by linking the annual growth rate and inter-annual weather variability over a longer time scale (II, III, IV). Th e most infl uential climate variables or variable sets are usually identifi ed and could be further used in empirical growth models to make models climate-explicit. Th e inclusion of climate-related information into growth models could improve growth predictions as demonstrated by Huang et al. (2013). 12 Scots pine is an economically and ecologically important coniferous species in Europe (Pretzsch et al., 2014), including Estonia (Kaimre, 2010). As a predominant tree species, Scots pine accounts for 32.5 % (738,200 ha) of the forested land and 37.3 % (180.1 million m3) of the growing stock (Raudsaar et al., 2016) in Estonia. Th erefore, this thesis focuses on Scots pine growth and examines the infl uence of long-term local weather variation on Scots pine growth on a temporal and spatial scale (II, III, IV) using dendrochronological methods. A possibility for simultaneously incorporating climate-related information and response to thinning into a growth model is tested (III) by compiling an individual tree radial growth model for Scots pine. Possible climate change impacts on the height growth and site index of Scots pine are investigated (I), as the site index commonly describes site productivity in forest growth and yield models.

In meteorology and climatology the terms “weather” and “climate” are commonly used to describe atmospheric conditions and are clearly distinguished. Th e weather describes short-term atmospheric variation, while climate is seen as an average pattern of weather fl uctuations over a longer period (∼30 years long) for a particular area (Monkhouse, 1978). In dendrochronology (Cook and Kairiūkštis, 1990) and in this thesis, these two terms are used as synonyms, because variables (temperature and precipitation) describing weather and climatic conditions are related to growth using short (monthly) and long-time scales (time window longer than 20 years) simultaneously.

13 2. REVIEW OF LITERATURE

2.1. Shift towards empirical single tree growth models

A forest growth model is a simplifi cation of forest growth dynamics (Weiskittel et al., 2011), which allows performing a simulation of certain characteristics (e.g. height, diameter, basal area, volume) of the tree or forest stand over time (Vanclay, 1994). Generally, forest growth models are mathematical and biometric expressions of relationships between growth processes and the environment (Pretzsch, 2009). Forest growth can be modelled either at a stand or at individual tree level. Growth dynamics at individual tree level are usually more fl exible because more details and interactions within the system surrounding the reference tree can be considered. Summing up or averaging the values of single trees produces stand level values.

Th e use of tree level models is continuously increasing in Europe (Pretzsch et al., 2008) because of their ability to predict growth in heterogeneous stands (Porté and Bartelink, 2002). Most of the developed individual tree growth models are diameter or basal area growth models (Monserud and Sterba, 1996; Jõgiste, 2000, Nyström and Kexi, 1997; Andreassen and Tomter, 2003), with several models designated to predict the height of individual trees (Ritchie and Hamann, 2008; Vaughn et al., 2010).

To describe the growth rate at a given time interval, input variables, such as age, stand density or basal area, commonly available through forest inventories are used in empirical models, making them widely applicable in forest management practice (Peng, 2000). However, empirical models have a drawback since they rely on descriptive relationships, implying that their applicability is limited to historical growth conditions, which are expected to remain relatively unchanged over time (Porté and Bartel- ink, 2002). Th is makes empirical models unreliable to be used in changing climatic conditions (Shugart et al., 1992). Several previous studies (Bravo-Oviedo et al., 2011; Nunes et al., 2011) showed that the productive capacity of the site and in turn stand dynamics (Pretzsch et al., 2014) are linked to climate and soil characteristics. Th erefore, shortcomings in static empirical models could be eliminated by defi ning the environmental variables that control the growth of a given area (Bravo-Oviedo et al., 2011) and by incorporating them into a model (Girard et al., 2014) to 14 make it dynamic (González-García et al,. 2015) and suitable for use in varying environmental conditions.

2.2. Eff ect of weather fl uctuations on tree annual increment

Th e growth of trees is determined by their genetic potential and is a result of interactions of physiological processes, controlled by environmental factors (Kramer and Kozlowski, 1979, Landsberg and Sands, 2011). Trees, like other terrestrial plants, need sunlight, water, and nutrients to conduct photosynthesis and expand in size. Equally important for tree growth is the thermal regime, usually defi ned by air temperature. On an intra-annual scale, temperature regulates all biological processes in the trees, including cambial activity (Mäkinen et al., 2003, Rossi et al., 2006), and photosynthetic carbon assimilation (Mäkelä et al., 2004). However, biological processes in the trees might be restricted or even terminated when water shortage appears (Kramer and Kozlowski, 1979; Landsberg and Sands, 2011). Water is the primary component of photosynthesis and transpiration and is crucial for nutrient uptake (Bergh et al., 1999), therefore water defi cit may reduce tree growth and vigour while extended periods of water defi cit may lead to tree starvation, nutrient defi ciency, wilting and even tree death. Nevertheless, diff erent environmental factors have a positive eff ect on tree growth only when certain species-specifi c requirements are satisfi ed and interaction among them is possible.

At northern latitudes, trees do not grow continuously all year round but follow the annual growth cycle, divided into four seasonal developmen- tal phases: growth reactivation, meristematic activity, growth senescence and winter dormancy (Sarvas, 1972), emerging due to gradual changes in the photoperiod length and thermal regime. Because of this interaction, most of the hemiboreal zone trees produce one apical (shoot) and radial increment (tree-ring) every year. In general, phenological development in trees, from growth onset through winter dormancy, is endogenously controlled (Larcher, 2003), whereas daily height (Kanninen et al., 1982) and radial growth (Seo et al., 2007) rates are governed by temperature (Antonova and Stasova, 1993; Vaganov et al., 2006).

Meristematic tissues, located between the xylem and phloem layers in 15 tree stems, branches and roots are responsible for shoot elongation (primary growth) and outward thickening, referred to as wood production or secondary growth (Plomion et al., 2001). Apical growth has priori- ty compared to radial growth. Besides that, tree growth in height and expansion in diameter on the intra-annual scale do not coincide. For instance, Seo et al. (2010) monitored phenophases of Scots pine in the northern boreal part of Finland and found that height growth started in the middle of May followed by radial growth a few weeks later, with some variation among the sites and years. In general, wood formation onset and progress to a large extent is controlled by air temperature (Va- ganov et al., 1999; Deslauriers and Morin, 2005; Vaganov et al., 2006; Gričar et al., 2006, 2007; Seo et al., 2007; Rossi et al., 2008). Th is process might be interrupted during the growing season, when suffi cient moisture is not available, which is a common case for dry sites, and on soils with low water holding capacity.

Plants, including trees, can use the received energy for growth, only if water is readily available, otherwise the received energy will only heat and stress the plant or vice versa, water is unused when energy supply is insuffi cient (Stephenson, 1990). A prolonged water defi ciency may lead to premature cessation of wood formation (Güney et al., 2015) and lower increments. Th erefore, climate factors, e.g. temperature and available moisture in the growing season, together with soil characteristics are important aspects that regulate tree growth and site productivity on the local scale.

2.3. Tree-ring analysis to study annual climatic variability in tree growth

A large number of tree-ring based studies have revealed that a considerable part of the variability in tree-ring widths, resulting from complex interactions between trees and the environment, can be explained by weather fl uctuations prior to or during the growing season (Fritts, 1976; Schweingruber, 1990). Wider tree-rings are produced when environmental conditions favour growth and narrower over adverse times. Th erefore, tree-rings have been suggested to be natural environmental archives (Vaganov et al., 2006) and can be used for establishing a relationship between tree growth and weather fl uctuations. Consequently, 16 most of the knowledge in reference to climate-growth relationships are being acquired from tree-ring analyses. Due to more laborious and ex- pensive sampling, the use of other annually resolved growth measures, like height or volume increments for growth-climate calibrations, has been limited but possible (Mäkinen, 1998; Salminen and Jalkanen, 2005; Bouriaud et al., 2005; Rais et al., 2014).

In the recent past, climate-radial growth relationships have been extensively used to reconstruct past climate (Fritts, 1976) and recently more often employed to forecast future growth (Williams et al., 2010; Bauwe et al., 2016) and to assess possible impacts of climate change on forest growth (Chhin et al., 2008). Knowledge on tree sensitivity acquired through dendroclimatological studies have been further used in growth and yield models (Girard et al., 2014), to make models climate-sensitive and improve their predictive capacities.

Tree-ring measurement series show long-term fl uctuations in tree growth and are advantageous to physiological observations because annually resolved growth data can be studied in a long-term context, and on a greater spatial scale (providing a greater variety of environmental conditions). Tree growth, however, is very complex, and annual (radial/height) increment is usually a result of complex interactions between intrinsic features of the studied individual and external factors (Fritts, 1976). De- spite that fact, annual increments, wood density, and other structures of annual growth have been successfully related to basic climatic variables like monthly or seasonal temperatures and precipitation as well as to not so commonly used variables, such as vapour pressure defi cit, cloudiness, radiation or derived indexes (e.g. drought, the Palmer Index and Stan- dardized Precipitation Evapotranspiration Index (SPEI)) which incorporate the eff ect of several above-mentioned variables.

For practical reasons or due to restrictions related to data availability, climatic variables used in dendroclimatic calibrations are resolved monthly, seasonally or annually. Th e longest meteorological series are usually available at monthly resolution. Aggregated meteorological data is used to test if longer time periods (seasons, several consecutive months, a year) can be related to a studied growth attribute.

Th e results of correlation or response function analysis provide a direct 17 or indirect association between tree growth and a studied climatic component, however, they are not able to explain the cause. Th e physiological basis is usually found, based on the results of ecophysiological research, which is commonly used to interpret signifi cant climate-growth relationships.

2.4. Climate eff ects on site index

Forest site productivity and site index depend on soil characteristics, grown tree species and long-term climatic conditions (Assmann, 1970), which have a direct infl uence on height growth and physiological processes. Th erefore, if any of these components is changing, site productivity is also aff ected.

Th e inherent productive capacity of the site or site quality (Skovsgaard and Vanclay, 2008) has a great practical importance in forestry and usually describes the productive potential of the site to grow timber (Clutter et al., 1983). Site index, or mean dominant height of the forest stand at indexed age, has been widely used as an indirect and low-cost measure of site productivity (Clutter et al., 1983; Skovsgaard and Vanclay, 2008; Bontemps and Bouriaud, 2013). Th erefore, it is an important input variable in stand growth simulators and yield tables (Pretzsch, 2009).

Historically, when most of the site index equations were developed, long-term changes in forest soils and mean climatic conditions were mi- nor, therefore it has been assumed that site index remains constant over time in a given area (Assmann, 1970). Variables, related to direct drivers of productivity such as nutrient availability or weather fl uctuations were not included unless SI models were intended for large areas characterised by diverse climatic conditions.

As tree height growth is climate-dependent (Mäkinen, 1998; Gamache and Payette, 2004, Kilpeläinen et al., 2006), the ongoing environmental changes (more favourable climatic conditions due to milder winters, extended growing periods; Menzel et al., 2003), including atmospheric fertilization (Kilpeläinen et al., 2006), may have an infl uence on forest productivity (Hari et al., 1984; Spiecker et al., 1996; Boisvenue and Running, 2006) in the long run. In Estonia, changes in forest productiv- 18 ity have been studied by Kiviste (1999a, 1999b), Nilson et al., (1999), and Paal et al. (2010). Site index increase and growth acceleration for Scots pine, birch and spruce stands were reported by Kiviste (1999a, 1999b) and Nilson et al. (1999). Whereas Paal et al. (2010) studied how uniform growing and soil conditions are for a single forest type in Esto- nia under diff erent environmental conditions and found that tree layer productivity, described by tree height, diameter and timber volume were considerably lower in Western Estonia compared to the Southern part.

2.5. Climate eff ects on radial growth

Tree growth response to weather variability is species-specifi c (Friedrichs et al., 2009), but may be mediated by soil properties (Lévesque et al., 2015), the hydrological regime (Dauškane and Elferts, 2011) and even tree size (Zang et al., 2012), tree age (Carrer and Urbinati, 2004), or tree social position within the stand (Lebourgeois et al., 2014). Topograph- ic features of the landscape may also infl uence tree response to climate (Oberhuber and Kofl er, 2000; Adams et al., 2014). Besides these factors, variability in local climate can contribute to contrasting tree responses (Sweingruber, 1988; Mazza et al., 2014). Tree response for example to negative drought eff ects can be modifi ed to some extent through changes in stand structure (Martín-Benito et al., 2010; Guillemot et al., 2015). Due to a long list of factors causing diff erentiated tree response to annual climatic variability, it is strongly suggested to verify the climatic infl u- ence on tree growth using comprehensive tree-ring chronology networks (Büntgen et al., 2007), considering local climate variability (Mazza et al., 2014).

Several researchers have investigated climate components in relation to the annual growth of Scots pine in the Eastern part of Europe. Most of the dendroclimatic studies on Scots pine rings in this region report its growth sensitivity to low winter temperatures (Bitvinskas, 1974; Špalte, 1978; Läänelaid, 1982; Cedro, 2001; Vitas, 2004). Growth response to precipitation is weaker but observed for several locations, including Southern Lithuania (Karpavičius et al., 1996; Vitas, 2004), Poland (Ce- dro, 2001), Southern Finland (Henttonen, 1984; Helama et al., 2005) and Northern Estonia (Hordo et al., 2011).

19 In Estonia, early dendroclimatological research on Scots pine has been focusing on developing tree-ring chronologies for wood dating purposes and for determining the climatic signal stored in tree-rings (Läänelaid, 1982; Läänelaid and Eckstein, 2003). Pärn (2003) studied Scots pine radial growth response to climate on dust-polluted sites in Northeast Estonia and Lõhmus (1992) investigated the climatic factors limiting the radial growth in Scots pine with respect to growing conditions. Recently, Scots pine radial growth variation in relation to weather fl uctuations (Hordo et al., 2011), climatic variations and available soil water (Hent- tonen et al., 2014) were studied along a latitudinal gradient extending across Finland and including the Northern part of Estonia.

In the recent past, high tree sensitivity to climate was one of the main prerequisite for dendroclimatic research (Fritts, 1976; Cook and Kairiūkštis, 1990), therefore, earlier studies targeted on sample trees growing in stressed environments. Under such site conditions, the radial tree growth would be limited preferably by a single climatic factor (Cook and Kairiūkštis, 1990). Due to this prerequisite, some of the regions and growing conditions could be underrepresented.

2.6. Scots pine habitats and climatic conditions in Estonia

Scots pine is a tree species adapted to grow under various climatic conditions (Eckenwalder, 2013). Hence, its distribution range extends across Eurasia (Navasaitis, 2004; Eckenwalder, 2013; Sibul, 2014), from North- ern Turkey to Northern Scandinavia, and continuing eastwards to Siberia and westwards reaching central Spain (Fig. 2.1). Th is indicates that Scots pine is a tree species with a wide ecological amplitude (Fritts, 1976).

As a pioneer tree species, it is able to grow on a variety of diff erent sites, including nutrient-poor dry soils. Th e optimal substrate conditions for Scots pine in North-Eastern Europe are suggested to be on sandy soils with some admixture of clay (sandy clay or clayey sand; Lõhmus, 2004). It is also common on waterlogged bog-type areas. In Estonia, Scots pine is present almost in all forest types, with the greatest share (40.3%) growing in mesotrophic forests. Scots pine in Estonia predominates in bog forests (89%), on the reclaimed areas of oil shale mining (74%) and is common in alvar forests (56 %) characterised by a thin topsoil layer. 20 Fig. 2.1. The natural distribution range (blue area) of Scots pine (EUFORGEN, 2009).

Concerning the climate, Scots pine growth is better in continental climate, but the species can survive both very low and very high temperatures (Steven and Carlisle, 1959). Because of its great tolerance to drought, Scots pine has recently gained much attention as a potential tree species suitable for cultivation in changing climate conditions. Esto- nia is located almost in the centre of this tree species’ natural distribution range (Fig. 2.1), suggesting that climatic conditions are optimal for this species to grow here.

Th e climate in Estonia is characterised by cold winters and warm summers and is strongly infl uenced by the North Atlantic Oscillation (NAO; Uvo, 2003). Despite the small size of the country, two climatic zones can be distinguished within the country. Maritime climate with milder winters prevails on the islands and in coastal areas and semi-continental climate is characteristic of the inland of the country. Climatic continen- tality in Estonia signifi cantly increases from the west towards the east, and especially towards the Southeast (Jaagus, 2006a).

Depending on the sub-region (reference period 19662010; Tarand et al., 2013), mean annual temperature ranges from 4.6 to 6.8 °C. Th e 21 coldest month is February, with mean temperature ranging from -6.5 to -2.5 °C and the warmest month is July, with mean monthly temperature ranging from 16.5 to 17.8 °C. Seasonal delay is characteristic of the coastal areas. Spring and summer start fi rst in Southeast Estonia and after a short delay proceed towards North and the coastal areas (Jaagus and Ahas, 2000). Due to the warming eff ect of the sea surface, air temperatures from the end of summer until the middle of winter are considerably higher on the islands and along the coast, than in the inland. Th ermal diff erences in air temperature between the coastal and inland areas increase towards the cold season, reaching the maximum in January (Tarand et al., 2013). Th e Northeast of Estonia is slightly cooler than the rest of the sub-regions of the country all year round, and the growing period is slightly shorter compared to southern Estonia and the coastal areas (Tarand et al., 2013). Deviations in mean temperature and precipitation patterns, as related to global climate warming are also smaller in the Northeastern part of Estonia, compared to the rest of the country.

Estonia is located in a zone of excessive moisture, where total precipitation is greater than evaporation (Tarand et al., 2013). Annual precipitation ranges from 570 to 740 mm and is highly varying throughout the year. However, dry periods are common in the summer time (Tammets et al., 2011; Tammets and Jaagus, 2013). Spatial distribution of precipitation across the country is mainly infl uenced by landscape topography (Jaagus, 1992, 1999). Based on long-term observations, the Southern and Southwestern part of Estonia receives more rainfall compared to the other sub-regions (Jaagus, 2003).

2.7. Research needs

Th e environmental conditions under which forests grew in the recent past have changed (Jaagus and Ahas, 2000; Jaagus 2006a, Tarand et al., 2013), while previously elaborated empirical forest growth models, developed for past growing conditions are still used in practice. However, there are indications (Nilson et al., 1999) that predictions may not follow recent growth dynamics. Th erefore, there is a need to re-evaluate the predictive power of existing forest growth models and to identify the infl uence of weather fl uctuations on native tree species in Estonia. Th ere is also a need to develop forest growth models that would include variables 22 related to climate variability, which would enable the models to be used under the conditions of a continuous environmental shift.

Several researchers (Läänelaid, 1982; Lõhmus, 1992; Pärn, 2003; 2008; Läänelaid and Eckstein, 2003; Hordo et al., 2011) have been studying climate infl uences on Scots pine growth in Estonia, but most of these studies are restricted to a certain location, or neglect variation in site conditions. Lõhmus (1992) investigated climatic variables that could limit Scots pine growth in respect of site conditions, but he did not consider diff erences between local climates. Th us, spatially and temporally explicit knowledge of climatic infl uences on Scots pine growth, with respect to the site and local climate conditions is still limited on the spatial scale in Estonia.

To predict Scots pine growth under changed climatic conditions sim- ple linear models based on climate-growth relationships could be used. However, such predictions would be robust, and due to methodological drawbacks the responses to changes in stand structure (e.g. thinning) are removed during standardization and averaging. Whereas thinning, as a growth manipulation measure, is proposed as one of the activities of adaptive forest management, and tree response to stand structural changes needs to be included into the modelling process. Th erefore, it would be reasonable to incorporate climate-related information further into growth models that already include the response to thinning.

Individual tree growth models, based on empirical relationships, could be an option for growth prediction because structural and composition- al diversity of forest stands is considered more accurately. Besides, tree response to management activities, particularly to thinning (Hynynen, 1995) and climate infl uence (Fritts, 1976), are predicted more properly at tree level.

23 3. AIMS OF THE STUDY

With regard to the ongoing climate change, the aim of this doctoral dissertation is to study long-term weather variability infl uences on Scots pine growth by establishing relationships between tree radial growth and climatic variables and integrating climatic information into tree growth modelling. In this way, the eff ects of climate shifts can be taken into account when predicting future growth. As the radial growth of trees is not only driven by local weather fl uctuations but it also responds to changes in stand structure, there is an aim to incorporate tree response to thinning and climatic information into the model.

Th e specifi c aims of the study were:

1) To analyse long-term changes of Scots pine growth in Estonia following the alterations in temperature and precipitation regimes (I). 2) To identify the main climatic variables driving annual variability in Scots pine radial growth across a range of site and climate conditions in Estonia, using dendrochronological methods (II, III, IV). 3) To analyse the annual radial increment of individual Scots pine trees, and to assess whether the inclusion of thinning and climate variables improve the model of basal area growth predictions (III). 4) To investigate the temporal stability of tree response to local weather fl uctuations (IV).

Th e following hypotheses were tested:

1. Scots pine stands established after 1950 show higher growth rates than stands established several decades ago, growing on similar sites (I).

2. Relationships between climate and radial growth of Scots pine vary across the sub-regions of Estonia (II, III, IV).

3. Basal area growth of Scots pine can be modelled as a function of tree size, site productivity and climatic variables, whereas changes in competition can be included as tree response to thinning (III).

4. Climate-growth relationships, established between monthly climatic factors are not stable in time due to shifts in environmental conditions (IV). 24 4. MATERIALS AND METHODS

4.1. Study sites

Th e studies presented in the thesis were carried out at 135 sites distributed across Estonia, the area between 59°50ʹN, 28°00ʹE and 58°00ʹN, 22°00ʹE, located in North-East Europe (Fig. 4.1). Radial increment core sampling was carried out, using a layout of the Estonian Network of Forest Research Plots (ENFRP; Kiviste et al., 2015) restricting sampling to plots (n=131) established in Scots pine dominated stands. Twelve of these plots are established in Scots pine plantations on reclaimed areas of oil shale (Narva quarry) open-surface excavation (III). Th e remaining 119 plots (II, IV) are located in Scots pine forests growing on forest land (Table 4.1).

Such a sampling strategy was subjectively chosen in anticipation of the results obtained during this thesis to be coupled with other information available in the ENFRP for further use in tree-growth modelling. Besides that, the ENFRP is distributed across Estonia, covering a large geographical area and since it was designed to provide forest growth information for modelling purposes (Kiviste et al., 2015), it includes stands of different age, densities and covers a range of diff erent growing conditions (Table 4.1).

Sampling and analysis of pine forests on forest land were restricted to the forests growing on mineral soils, classifi ed into mesotrophic and heath forest types and forest site types related to these forest types. Th e heath forest type is characterised by sparsely stocked pine stands with slow growth on dry and nutrient-poor soils. Heath forests include Scots pine forests growing on Cladonia and Calluna forest site types. Mesotrophic forests are found on moderately humid or temporarily moist sandy soils with a slightly better nutritional status (Lõhmus, 2004). Growing conditions in mesotrophic forests are described in more detail in article IV with Myrtillus and Rhodococcum forest site types.

25 ) -1 ha 2 (m Basal area area Basal (cm) Diameter Diameter (m) Height Height ) -1 (trees ha (trees Stand density Stand Range Range Mean SD Mean SD Mean SD (years) Stand age Stand trees* 34 24 31 28-1083 58-1156 218 500-3080 540-1960 47 69-240 621 16.62 15.6 40-858 6.2 25-94 4 3.5 290-2100 15 19.3 21.0 537 8.4 520-21609 17.2 7.4 49-130 40 680-3400 27.9 38-105 25.7 3.8 49 20.0 4.0 63 11.2 15.1 21.2 230-1010 4.8 12.9 33-75 2130 6.4 460-1020 51-65 20.9 28.7 15.5 20.7 6.5 0.5 5.7 24.2 9.9 12.1 540-1853 29.5 520-800 23.4 3.7 24.6 - 4.8 8.4 16.7 18.4 24.3 10.3 22.9 18.7 4.1 2.9 - 2.5 7.8 28.7 19.8 23.3 17.6 6.3 3.8 1.9 22.5 - 28.1 3.5 4.7 1411 102 9525 32-104 31-105 192 570-3110 51-185 450-2090 18.018 210-2370 20.8 4.8 3.6 131 20.8 15.1 24.2 6.5 32-125 2.5 4.2 26.9 16.7 34.4 7.9 4.9 270-1860 7.3 24.2 2.7 21.1 5.7 21.5 7.5 26.0 5.8 Summarised characteristics of the sampled Scots pine stands. SD - standard deviation. characteristics of the sampled Scots pine stands. SD - standard Summarised Cladonia MESOTROPHIC Myrtillus Rhodococcum 25NortheastHEATH Calluna 197 Cladonia MESOTROPHIC Myrtillus 54 Rhodococcum 31-105 14 28RECLAIMED 29Southeast 322 HEATH Calluna 109 213 Cladonia 12 203 450-3110MESOTROPHIC 25-240 Myrtillus 51-240 25-94 Rhodococcum 128 32-130 26 16Southwest 3 19.2MESOTROPHIC Myrtillus 210-3400 4.5 Rhodococcum 28-47 184 112 210-2370 19 520-3400 16Total 230-2130 22.3 15.7 32-125 5.8 33-75 112 39-130 15.4 17.2 830-2680 21.6 3.8 30.2 2.6 6.1 16.7 33-75 5.6 131 8.4 270-1860 520-1850 17.2 14.5 230-2130 17.8 5.5 22.1 6.0 901 6.4 2.1 24.5 7.4 22.0 21.0 520-1850 24.7 17.8 26.0 14.5 5.4 25.9 25-240 5.3 2.9 3.5 8.6 1.9 7.5 6.4 21.0 22.4 21.8 22.3 19.8 6.7 3.4 210-3400 3.3 14.4 6.7 26.8 18.3 21.8 25.7 6.0 18.3 3.3 5.6 5.0 25.7 5.0 5.0 19.8 6.4 26.1 6.7 FOREST TYPE site type Forest 32Islands HEATH Calluna 252 28-115 7 55 450-3110 28-115 18.6 4.6 500-3080 21.9 6.0 16.1 29.4 4.5 8.4 20.2 7.2 26.6 8.4 Region of sites No. of No. Table 4.1. Table used in the analysis. to the number of trees *Refers 26 NE

ISL Pärnu SW

Fig. 4.1. Map showing the geographical location of the sampling sites (dots) and meteorological stations (triangles) included in the study. The colour of the dots refers to different datasets as follows: blue dots indicate stem-analysis sites (I), red dots represent Scots pine plantations on reclaimed oil shale areas (III), and green dots refer to the rest of the ENFRP plots (II, IV). Dashed lines show site division into geographical sub-regions. Abbreviations refer to the following sub-regions: ISL – Islands, SW – Southwest, SE – Southeast, NE – Northeast.

To investigate the local climate infl uence on Scots pine growth, sampling sites were assigned to one of four geographical sub-regions (Northeast (NE), Southeast (SE), Islands (ISL) and Southwest (SW)), defi ned by the geographical site location. In addition to the ENFRP, four Scots pine stands (Table 1 in I) belonging to the NOLTFOX network, growing in South-eastern Estonia in an area managed by Järvselja Training and Experimental Forest Centre (58°25ʹN, 27°46ʹE) were chosen to study deviation in height growth patterns (I). To retain similarities among the stands and growing conditions as much as possible, even-aged Scots pine stands, growing in a close vicinity of each other but with diff erent germi- 27 nation years (1891, 1952 and 1969) were selected. All four stands grow on Oxalis-Rhodococcum site type and are managed for timber production (more details in I).

4.2. Meteorological data

Th e Estonian Weather Service (the Estonian Environmental Agency, ES- TEA) provided monthly temperature and precipitation data from fi ve meteorological stations, including Kunda (Northeast; II, IV), Ristna (Islands, Southwest II), Pärnu (IV) and Tõravere (Southeast; I, II, IV).

Table 4.2. General information about meteorological stations and mean climatic conditions over the period of 19552006 (except Jõhvi meteorological station) in the study sub-regions. Means, based on Jõhvi meteorological records, are calculated for the period of 19652007.

Northeast, Northeast, Region Islands Southeast Southwest coast inland Station name Ristna Tõravere Pärnu Kunda Jõhvi Latitude 59°16ʹ27ʺN 58°16ʹ09ʺN 58°23ʹ09ʺN 59°29ʹ54ʺN 59°21ʹ33ʺN Longitude 23°43ʹ55ʺE 26°27ʹ30ʺE 24°29ʹ49ʺE 26°31ʹ33ʺE 27°25ʹ15ʺE Record start year 1945 1866 1946 1919 1965 Mean annual temperature (°C) 6.4 5.3 5.9 5.2 4.6 Mean annual precipitation (mm) 621 606 673 558 695 Growing period 11.7 14.0 12.7 13.3 11.7 temperature (°C) Growing period 291 331 351 331 403 precipitation (mm) Mean July 16.5 17.1 17.6 16.6 16.6 temperature (°C) Mean February -2.9 -6.1 -5.1 -5.6 -6.6 temperature (°C)

28 Southeast (Tõravere) Southwest (Pärnu) 20 100 20 100 T= 5.3 °C P = 606 (mm) T= 5.9 °C P= 673 (mm) 15

15 ) 80 80 (°C

(mm) (°C) 10

10 (mm) 60 60 5 5 40 40 0 0 Temperature Precipitation Temperature Precipitation Ͳ5 20 Ͳ5 20 Ͳ10 0 Ͳ10 0 Jul Jan Jun Oct Apr Feb Sep Dec Aug Nov Mar Jul May Jan Jun Oct Apr Feb Sep Dec Aug Nov Mar May Northeast coast (Kunda) Northeast inland (Jõhvi) 20 100 20 100 T= 5.2 °C P = 558 (mm) T= 4.6 °C P= 695 (mm) 15 15 ) 80 )

(°C (°C

(mm) 10 10 (mm)

60 60 5 5 40 40 0 0 Temperature Temperature Precipitation Ͳ5 20 Ͳ5 20 Precipitation

Ͳ10 0 Ͳ10 0 Jul Jul Jan Jan Jun Jun Oct Oct Apr Apr Feb Sep Feb Sep Dec Dec Aug Aug Nov Nov Mar Mar May May

Islands (Ristna) 20 100 T= 6.4 °C P = 621 (mm) 15

) 80

(°C

10 (mm) 60 5 40 0 Temperature Precipitation Ͳ5 20 TEMP PREC Ͳ10 0 Jul Jan Jun Oct Apr Feb Sep Dec Aug Nov Mar May

Fig. 4.2. Climate diagrams for separate study sub-regions. Temperature (T) and pre- Fig.cipitation 4.2. (P) means for individual stations/sub-regions are calculated for the reference period of 19552006.

Th e length of climatic data records diff ered among the stations (Ta- ble 4.2). Daily meteorological data for the period of 19652014 was available from Jõhvi meteorological station (Northeast, reclaimed areas; III). Seasonal climatic variables were calculated based on the provided III monthly meteorological records, as an arithmetic mean of three consecutive months: December, January and February for winter, March, May and April for spring, June, July, August for summer and September, Oc- tober, November for autumn. Seasonal precipitation sums were calcu- 29 lated respectively. Mean annual temperatures and total rainfall were calculated for a calendar (JanuaryDecember) and for a hydrological year, starting from September of the previous growing season until August of the current year. Th e growing season was defi ned as a period from April to September unless specifi ed otherwise.

4.3. Investigation of changes in height growth patterns (I)

For studying deviations from long-term growth, the site index comparison approach (Untheim, 1996; Bontemps et al., 2010) was applied by comparing cumulative height growth (expressed as the site index (SI)) of three Scots pine generations growing in similar conditions but established with diff erent germination dates. Th e growth of the oldest pines (mean age 115 years) was used as a reference for the expected growth and trees from two consequently younger generations (mean age 56 and 40 years, respectively) were used for analysing signifi cant growth deviations. Since long-term observations on tree height growth were not available, we applied the stem analysis technique (Spiecker, 2002), which allows retrospective reconstruction of annual height increments for the whole tree lifespan, including early tree height development (Karlsson, 2000). Only dominant and co-dominant trees were selected to study changes in the height growth pattern because the height growth of trees growing above and within tree crown level are not strongly aff ected by thinning (Valinger et al., 2000).

Selected pine trees were felled and cut into sections of 2.5 m in length and annual height increments were reconstructed for the sectioned trees, with an accuracy of 0.1 cm by measuring distances between two consecutive whorls along the split trunk of the tree (I). Radial increments were measured on cross-sections taken at 1.30 m height above the ground and at the base of each section.

For time-dependent growth comparisons, height and diameter cumulative growth was calculated for each age group, based on measured height and radial increments (Fig. 3 in I). A three-parameter Richards (1959) growth function and a non-parametric generalized additive model (GAM; Wood, 2006) were fi t on the measured cumulative height growth curves of individual trees to approximate height development and to calculate 30 the SI at a reference age of 40 years (SI40). Diff erences in height growth rates were analysed with one-way ANOVA. In addition, the height development of three cohorts was compared to mean Scots pine height development on Rhodococcum site in Estonia, using the model for stand height prediction (Kiviste, 1997; Kiviste and Kiviste, 2009).

In addition, mean climatic conditions, over three 40-year-long periods when three studied tree generations grew, were investigated using pair-wise one-way ANOVA with Tukey’s post hoc test. Th e period of 18911930 refers to old tree growth, the period of 19521991 to middle-aged trees and 19622008 to the youngest tree growth period. Cli- matic conditions were characterised by mean annual temperature and total precipitation, mean seasonal temperatures and seasonal rainfall, mean temperature and total precipitation of the growing period, defi ned as a period from May to August as well as mean monthly temperatures and monthly precipitation sums. Meteorological data (monthly mean temperature and total precipitation) from Tõravere weather station was used for this analysis.

4.4. Dendroclimatic analysis (II, III, IV)

4.4.1. Increment core sampling, preparation and measurements

Tree-ring increment cores were extracted from the living Scots pine trees at 1.3 m above the ground level, using a Pressler increment borer. Dou- ble radii containing cores were taken only from dominant and co-dominant trees in the stand by selecting the trees at four locations, described by cardinal directions (North, East, South, West) outside the permanent sample plot (II, III, IV). Th e number of sampled trees per site ranged from 8 to 15. Th e trees were inspected for visual stem and top damage before coring and excluded from sampling if any damage was detected.

Th e cores were mounted into a holder and their surface was shaved with a razor blade to enlighten the borders of the rings (Stokes and Smiley, 1968). If a temporal holder was used (II), then the cores were soaked in water for 1015 minutes to regain full size before preparation. Chalk was applied on the cores to enhance the visibility of rings. Ring widths were measured with an accuracy of 0.01 mm using LINTABTM 5 measuring device and TSAPWinTM software (RINNTECH, Heidelberg, 31 Germany). Th e measured series were visually and statistically cross-dated to inspect for measuring mistakes and missing or false rings (Schwein- gruber, 1996). Th e measurements of two radii were averaged for each tree to reduce within tree growth variability. Th e resulting average tree- ring series were further used to control the cross-dating quality with the statistical software COFECHA (Lamont-Doherty Earth Observatory, Palisades, USA; Holmes, 1983; Grissino-Mayer, 2001), which estimates tree-ring series correlation with the master chronology (built from the rest increment series in the set) and identifi es poorly matching segments (Grissino-Mayer, 2001). Tree-ring series with synchronous variation within and between the sites are expected to contain a common signal, which is suggested to be caused by common environmental infl uence, e.g. climate (Fritts, 1976). Th e series poorly correlating with the master chronology (p<0.05) were discarded from the analysis, since they could have contained a non-climatic signal, associated with stand dynamics.

4.4.2. Standardization of tree-ring series

Raw tree-ring width is a result of complex interactions between a tree and physiological drivers, among which non-climatic factors, like age trend, are responsible for ring width decrease with increasing tree age (Fritts, 1976; Cook and Kairiūkštis, 1990). In a growing tree, the surface, where cells are being laid down increases, while wood volume remains considerably constant. Before conducting a climatic analysis, non-climatic age trend should be removed, otherwise, tree-ring values will refl ect the age eff ect rather than the climate signal (Fritts, 1976).

In the thesis, to emphasise the climatic signal, tree-ring series were detrended (standardized) individually by fi tting either a negative exponential curve (II) or spline (III, IV). Tree-ring indices were computed for each individual tree-ring series by dividing the observed ring width value by the value of the curve. Spline rigidity was determined to be 67% of the length of each individual ring width series (Cook and Peters, 1981). Standardization for studies III and IV was performed using the functions provided in the package dplR (Bunn, 2008) within the R software (R Core Team, 2016) while detrending of the tree-ring series in study II was carried out with the program ARSTAN (Lamont-Doherty Earth Observatory, Palisades, USA; Cook and Krusic, 2006).

32 Due to the particularity of physiological processes within the tree, radial growth increments tend to autocorrelate, which means that the current year’s growth depends to some level on the previous year’s growth. Such dependency is not desired in modelling and may cause biased estima- tions (Salminen et al., 2009). Th erefore, autoregressive modelling (Box and Jenkins, 1970) was applied to remove temporal autocorrelation and to enhance high-frequency variation, which is assumed to be caused by weather fl uctuations. Akaike (1974) information criteria were used to determine the order of the autoregressive process.

4.4.3. Development of chronologies

Detrended series were further used for construction of the chronologies. It is noted that trees growing in similar site conditions tend to synchronise the physiological processes, like cambium or other living cell reaction to a particular combination of factors, resulting in ring-width sequences with great similarity (Schweingruber, 1988; 1996). Based on that knowledge, Scots pine response to weather fl uctuations was analysed by grouping annual growth patterns and developing chronologies according to ecological site conditions. Growing conditions in pine forests on forest land, during current dendroclimatic studies, were characterised by forest type (II) and forest site type (IV) in pine forests on forest land. Th e grouping according to forest site type was done because it was thought that tree response by forest type can be too robust, since with averaging, some of the signal characteristic of a certain forest site type may become lost. Besides, forest site type is the main classifi cation unit in forest management (Lõhmus, 2004; Forest Act, 2006) and is often used as an indirect indicator of potential site productivity.

Scots pine response to climatic variability on reclaimed oil shale areas where growing conditions vary greatly with respect to soil characteristics (including continuous substrate formation) as well as stand structures, was studied at site level by combining index series by plot and by grouping growth patterns into three sites, characterized by similar age and stand structure. In all cases, composite chronologies were developed by averaging all the series in the set into mean value chronology. Th e averaging process aims to reduce the growth impulses inhered in a given stand, arising due to local disturbances (e.g. thinning). Th e averaging of 33 tree-ring indices was achieved and standard chronologies for each target group were produced by using Tukey’s biweight robust mean that mini- mises the infl uence of possible outliers (Mosteller and Tukey, 1977). Th e index values surpassing the estimated mean value by 6 or more standard deviations were excluded along the averaging procedure.

By using the residuals from the autoregressive modelling, residual chronologies were computed. Pre-whitened (residual) chronologies tend to contain a stronger climatic signal (Cook and Pederson, 2011). Chronologies for study II were developed in the program ARSTAN (Cook and Holmes, 1996), and in studies III and IV for chronology calculation, the package dplR (Bunn, 2008; Bunn et al., 2013) within the R software (R Core Team, 2016) was used.

4.4.4. Assessment of chronology quality

Before using chronologies for dendroclimatic analysis, chronology quality and its reliability for growth-climate analysis needs to be evaluated and the strength of a common signal assessed. For this purpose, a set of statistics commonly used in dendrochronology were calculated for the ring width index series. Coherence and common signal among the growth patterns were assessed by calculating the series inter-correlation (IC) and Gleichläufi gkeit (Glk). Th e IC is an average correlation of each index series to a master chronology, which is compiled from all other series. A higher IC indicates a stronger common signal in the studied population. Th e Glk is another measure of accordance among the series (Eckstein and Bauch, 1969), showing mean percentage of years in which ring widths in two series being compared synchronously increase or decrease (Fritts, 1976). Values above 60% indicate acceptable correspondence of growth patterns, whereas Glk values above 70% indicate a great level of common variance (Esper et al., 2008) and are characteristic of climate-sensitive trees (Frank and Esper, 2005). Average mean sensitivity (MS) shows mean relative change between two adjacent ring widths. It has been suggested that the MS in trees containing a stronger common signal tends to be greater compared to trees with complacent growth. Similarly to the MS, average standard deviation (SD) indicates the magnitude of annual growth fl uctuations. Greater standard deviations are expected in highly responsive trees (Fritts, 1976). 34 To assess the strength of the common signal, signal to noise ratio (SNR) as well as the expressed population signal (EPS) were calculated for each set of indices. Th e EPS is used to assess common variability among the ring width index series and to evaluate chronology reliability for dendroclimatic analysis (Briff a and Jones, 1990). Th e EPS indicates how close the developed chronology is to the hypothetical one. An EPS equal or higher than 0.85 was proposed by Wigley et al. (1984) as a threshold for chronology confi dence and is widely used among dendrochronologists. Th e SNR and EPS for each chronology were defi ned using the following equations:

ݐܾݎή݊ ܴܵܰ ൌ (ݐ (1ܾݎͳെ

ݐܾݎ ܧܲܵ ൌ (ݐ (2ܾݎͳെ ݐ ൅ܾݎ ݊ where rbt is the average correlation between tree-ring series in the set, calculated for the common period of all index records (Briff a and Jones, 1990) and n is the number of mean tree-ring width series in the set. First-order autocorrelation (AC1) was calculated to determine the current year’s growth dependency on the previous year’s infl uence, and to evaluate the need for autoregressive modelling. For the rest of the above-listed statistical indicators, higher values indicate greater tree sensitivity and common signal, which is assumed to be caused by large-scale external factors, like the climate (Fritts, 1976).

4.4.5. Analysis of growth-climate relationships 4.4.5. Analysis of growth-climate relationships Th e infl uence of annual climate variability on Scots pine growth was assessed based on the relationships established between tree-ring widths and climatic variables (temperature and precipitation). Residual chronologies were used in all climate-growth analyses by relating standardized ring-width data to monthly meteorological data (temperature and precipitation) from the closest meteorological station (Fig. 4.2) and calculating Pearson’s correlation coeffi cients and response function analysis also known as principal components regression. Response func- 35 tion is a kind of multiple regression where ring-width chronologies are used as dependent variables and principle components calculated on the weather data are used as growth predictors (Fritts, 1976; Briff a and Cook, 1990). Response function analysis enables identifying climatic variables that are not inter-correlated between each other, so multicol- linearity problems in multiple regressions can be avoided. Th e significance of the correlation and response function coeffi cients was tested with the bootstrapping procedure (Guiot et al., 1982; Guiot, 1990; Zang and Biondi, 2013) based on 1,000 iterations, using the 95th percentile rank. Th e length of the time window used for climate-growth calculations within each study (II, III, IV) varied depending on the available growth and meteorological data. Th e climate-growth calibration period was the shortest for Scots pine plantations, restricted to the period of 19932013 (21 years; III). Relationships between radial growth and climatic variables were investigated in interval between 19552006 (52 years), which was a common period for all forest site types in the four studied sub-regions (IV). Th e analysis was restricted to a common time interval of 19452006 (62 years) for relating radial growth to weather fl uctuations at forest type level (II). Th e climate-growth analysis was performed within the R software (R Core Team, 2016), using the functions bootRes (III; Zang and Biondi, 2013) and treeclim (IV; Zang and Biondi, 2015).

4.4.6. Investigation of spatial and temporal response patterns (IV)

To identify sites with similar growth patterns and better understand the tree responses to weather fl uctuations across the studied area, chronologies for each forest site type, based on the common interval, were compared using Pearson’s correlation and hierarchical cluster analysis (HCA). In HCA, chronologies were assigned into clusters according to dis/similarities, expressed as the Euclidean distance that was established with Ward’s minimum variance method, using an algorithm containing the initial Ward’s clustering criterion where dissimilarities are squared before cluster updating (Murtagh and Legendre, 2014).

Moreover, to identify groups of forest site types with similar response and sensitivity to climate at regional level, the principal components analysis (PCA) was performed on the BCC matrix (Weber et al., 2007; Méri- 36 an et al., 2011). Th e PCA was conducted based on variance-covariance, considering that the magnitude of descriptors was similar (Legendre and Legendre, 1998; Mérian et al., 2011).

To evaluate whether climate-growth relationships between tree-ring width indices and monthly climate variables remained stable over time moving correlations were calculated over 30-year-long intervals shifted by one year. Bootstrapping was used to estimate the 95% confi dence intervals for correlation intervals. Temporal stability of growth-climate relationships over 1955-2006 period (over 1976-2013 for pine plantations on reclaimed areas) were tested using Scots pine chronologies developed for separate forest site types at diff erent sub-regions of Estonia.

4.4.7. Identifi cation of pointer years (II)

Growth response of Scots pine to extreme climatic events was studied at forest type level (II) using pointer year analysis (Schweingruber et al., 1990; Neuwirth et al., 2007). Th e pointer year analysis together with the correlation or response function analysis facilitate more comprehensive understanding of growth limiting factors on the corresponding site (Neuwirth et al., 2007).

Pointer year analysis aims to identify and explain the appearance of markedly narrow or wide rings, which are formed due to extreme climatic conditions, commonly referred to as climatic anomalies. Two terms are used in pointer year analysis: event years, referring to years of extraor- dinary growth at the individual tree level, and pointer years when certain event years replicate in a larger number of individuals (e.g. in most of the trees at the site; Schweingruber et al., 1990; van der Maaten-Th eunissen et al., 2015). In this thesis, pointer years were indicated by applying the method proposed by Cropper (1979), according to which normalisation was applied in a moving 13-year window. Pointer years were calculated using the following equation:

ܴܹܶ௜ െܴܹܶ௠௘௔௡ሾ௪௜௡ௗ௢௪ሿ ܥ௜ ൌ (3) ܴܹܶ௦௧ௗ௩ሾ௪௜௡ௗ௢௪ሿ

4.5. Modelling basal area increment (III)

III where Ci is a Cropper value, TRWi is tree-ring width formed in the year i; TRWmean[window] is the arithmetic mean tree-ring width in the moving window, and TRWstdv[window] is the standard deviation of ring widths in the moving window. Pointer years were identifi ed on non-standardised tree-ring series considering the maximum data period, restricted to replication by at least 5 series. Years containing Ci values above 1 or below -1 were identifi ed as positive or negative pointer years, respectively (Neu- wirth et al., 2007; Pourtahmasi et al., 2007). Moreover, following Neu- wirth et al. (2007) pointer years were divided into three intensity groups, defi ned by Ci values as follows: Ci >1, weak pointer year; Ci >1.28, strong pointer year, C >1.645, extreme pointer year. 4.5.i Modelling basal area increment (III)

4.5. Modelling basal area increment (III)

Annually resolved growth data from 128 Scots pine trees growing on 12 sites of reclaimed areas was used to fi t individual tree radial growth model. Th e nonlinear modelling approach was applied to model basal III area growth of individual Scots pine trees growing on reclaimed areas of open surface-mining of oil shale (III). Tree-ring widths were converted into annual basal area increment (BAI) series to account the diff erences related to tree size (Biondi, 1999; Biondi and Qeadan, 2008), using the following equation: ଶ ଶ ܤܣܫ௧ ൌߨήሺܴ ௧ െܴ௧ିଵ ሻ (4) where R is tree radius and t is the year when the tree-ring was formed. Since the aim was to elaborate the model as parsimonious as possible, the constructed model had a multiplicative structure, containing three components: the basic growth function, tree response to thinning and climate variability, and was expressed as follows:

(௜ ൌ ݂ଵሺݐሻ ή ݂ଶሺݐ݄ሻ ή ݂ଷሺܽݒሻ ൅ߝ (5ܾܽ݅ where the dependent variable iba is the annual basal increment in the 2 -1 year i (cm year ), ݂ଵሺݐሻ is a growth function describing individual tree basal area growth assuming open-space conditions, ݂ଶሺݐ݄ሻ is a function -describing tree response to thinning, and ݂ଷሺܽݒሻ is a function describ ing annual growth fl uctuations as related to weather variation, and ߝ is 38 the error term. Each function was described by a sub-model. Consider- ing that basal area increment can be described by an asymptotic pattern (Pokharel and Dech, 2012), Weber growth function (cf. Kiviste et al., 2002) in relation to tree size and site quality was used to model the com- :ponent ݂ଵሺݐሻ

(ሻሻ (6ܣ ଵሺݐሻ ൌ݌௧௥௘௘ ή ሺͳ െ ሺെ݌௦௜௧௘ ή݂

where ptree is the parameter related to tree growth potential (current tree size/diameter) and defi ning the magnitude of the asymptote; psite is the parameter related to site productivity potential; A is the tree breast height age (years).

One of the components in the radial growth model is usually attributed to competition. Instead of that, a function depicting tree response to a reduced competition level was introduced in developed model. Indi- vidual tree response to thinning was modelled as an asymptotic growth increase, lasting for 13 years, as proposed by Hynynen (1995). Since information on thinning intensities are rarely available in practice and for this study, time elapsed since thinning was used to describe tree response to reduction in competition. Th erefore, the equation proposed by Hynynen (1995) was adjusted to the available variables as follows: ݐ݄ ௖ ݐ݄௖ (ሺݐ݄ሻ ൌ ͳ ൅ ݌ ൬ ൰ ή ቆെ ቇ (7 ݂ ଶ ௧௛ ܾ ܾ where th is the time elapsed since thinning (years); b and c are the estimates of parameters, respectively b=13.8 and c=1.58; and pth is the parameter related to thinning to be estimated.

Annual growth fl uctuations, as related to variations in weather condi- -tions, in the model were described by function ݂ଷሺܽݒሻ and were mod elled by including indicesIIIIII (III), since tree-ring width indices are usually calculated to refl ect climatic forcing on tree radial growth. It was additionally tested whether the use of a set of the most infl uential climatic variable would improve model importance. Th erefore, based on the dendroclimatic study resultsIIIIII (Fig.4. in III) a least-squares stepwise multiple regression model was compiled taking into account climatic variables explaining the most of annual variation and indices replaced by the following model: 39 III

III

ܫ௧ ൌ ͲǤ͹Ͷ͸͹ͻͳͺ ൅ ͲǤͲͲͲͺͷͲʹ ή ܲ௃௃ ൅ ͲǤͲʹͺ͸͸ͻͳ ή ܶ௦௣௥௜௡௚ (8) where It is the tree-ring index in the year t; PJJ is total June–July precipitation (mm), Tspring is the mean spring (March-April-May) temperature (°C). Th e stepwise procedure with a forward selection approach was used to include model climatic components and to estimate the parameters. General chronology developed as mean composite chronology for reclaimed areas was used as a dependent variable when compiling the regression analysis.

Model evaluation was carried out based on the graphical analysis of residual distribution and by evaluating model fi t statistics, including the root mean square error (RMSE), which indicated the precision of estimates, mean estimation error (MEE), which shows mean deviation of predicted values from the observed, and pseudo R2 estimating, the total explained variance. Th e fi t statistics were calculated using the following equations:

σሺܤܣܫ෢ െܤܣܫሻଶ ܴܯܵܧ ൌ ඨ (9) ݊

σሺܤܣܫ෢ െܤܣܫሻ ܯܧܧ ൌ (10) ݊

ܴܯܵܧ ଶ ܴଶ ൌͳെ൬ ൰ (11) ܵܦ

Where ܤܣܫ෢ is predicted basal area increment (cm2/year), ܤܣܫ is observed basal increment (cm2/year) and n is number of observations, SD is mean standard deviation of all BAI observations, used to build the model. Th e Akaike Information Criterion (AIC) was used in model evaluation (Burnham and Anderson, 2002).

40 5. RESULTS

5.1. Changes in long-term forest productivity

Both mean annual height and radial increment rates (Fig. 5.1a; Fig. 3 in I) were signifi cantly (p<0.0001) greater in two younger age groups, established between 1950-1970, compared to the oldest group, established before 1900, resulting in the diff erent development of cumulative growth curves (Fig. 5.1a; Fig. 3 in I). At the reference age of 40 years, mean height (SI40) for the young, middle-aged and old cohorts were 24.0, 21.8 and 19.5 m, respectively (Fig. 4 in I), or 12% greater in the middle-aged cohort and 23% greater in the youngest group compared to the growth of the oldest trees. Th e diff erence in SI40 between the youngest and middle-aged trees was 11%, indicating greater growth of the youngest generation, established around 1970. When diff erences between average Scots pine height in Rhodococcum site type and mean height growth of each group were calculated, diverse trends for age groups were observed (Fig. 2b in I).

Fig. 5.1. Mean cumulative height curves of the studied trees (a) and mean relationship trends between annual height increment and cumulative tree growth (b) for three studied cohorts. Grey continuous lines represent single tree growth, dark lines refer to cohort mean and dashed dark red lines are 95% conﬁ dence intervals.

41 Fig. 5.2. Signifi cant diff erences in climatic conditions in which three studied cohorts grew for the fi rst 40 years. Th e period of 18911939 refers to old (Old), 19521991 to middle-aged (Mid) and 19692008 to young (Young). Periods with signifi cantly diff erent (p<0.05) climatic conditions, described by mean annual temperature (T annual), mean spring temperature (T spring), mean temperature in April (T April), mean temperature in August (T August), total winter precipitation (P winter) and total precipitation in February (P February) are identifi ed with dashed lines and p-values.

Th e comparison of climatic conditions in which three studied cohorts grew for their fi rst 40 years, provided evidence that there were statistically signifi cant diff erences (Fig. 5.2). It was revealed that thermal growth conditions throughout the year were signifi cantly warmer for the youngest trees, established around 1970 (Tannual = 5.4±1.1 °C) than for middle-aged, established around 1950 (4.8±1.1°C) and the oldest pine trees, established a century ago (4.7±0.9°C). Mean temperature during the growing season, defi ned as the period from May to August, was also signifi cantly higher over the period, when the youngest pine trees grew 42 (Tveg = 14.9±0.9°C) compared to other two periods (middle-aged 14.4±0.9°C vs. old 14.3±1.1°C). Springtime (March-April-May) was warmer by 1.0°C and mean temperature in the summer season was greater by 0.5°C for the youngest pine generation compared to the earlier growth periods. On a monthly basis, signifi cant warming appeared in April and August.

No signifi cant diff erences in thermal conditions were detected between two earliest periods (18911939 (old) vs. 19521991 (middle-aged)). However, Tukey’s post hoc test displayed signifi cant diff erences in the precipitation regime for these two periods, suggesting that winter precipitation (total Pwinter = 115.3±39.7 mm vs. 97.9±26.6 mm) and particularly precipitation in February was greater when the oldest trees grew (total

PFeb = 34.7±17.7 mm), compared to the growth period of middle-aged pine stands (total PFeb = 25.9±13.3 mm). No signifi cant diff erences in winter precipitation were detected when two recent periods (19521991 vs. 19692008) were compared.

5.2. Scots pine radial growth characteristics and relationship to inter-annual weather variations

5.2.1. Scots pine chronologies

Dendroclimatic Scots pine studies (II, III, IV) conducted during compilation of this thesis supported the hypothesis (2) of climate-growth relationship variability across the sub-regions of Estonia and also with respect to site conditions.

For identifying the most infl uential climatic variables on Scots pine growth, tree-ring data from more than 900 living Scots pine trees (Table 5.1.) were used and a number of ring-width chronologies were developed (Fig. 5.2.; II, III, IV), considering similarities in ecological and climatic growth conditions, representing radial growth at the investigated site. Separate chronologies were built for Scots pine trees growing in mesotrophic and heath forests at four sub-regions of Estonia (Figures 4, 5 and 6, in II) and fourteen residual ring-width chronologies were constructed for fi ve forest site types at four regions (Fig. 5.3). Twelve plot-level (data not presented) and three site-level residual chronologies (Fig. 3 in III)

43 were developed to investigate in more detail the climate infl uence on the growth of Scots pine plantations established on reclaimed oil shale areas.

Fig. 5.3. Mean residual chronologies by forest site types within four sub-regions of Estonia. The chronologies’ abbreviations are explained in Table 5.1.

Chronologies developed for heath and mesotrophic Scots pine forests were the longest (83221 years; Table 1 in II) and well replicated, while chronologies for pine plantations on reclaimed areas were only 2138 years long, but replicated by a suffi cient number of samples (Table 5.1; Table 3 in III). Pine ring-width chronologies by forest site type spanned 52126 years with mean replication of 62 trees, (range from 14 to 173; Table 5.1).

44 Statistical parameters (Table 5.1) calculated for each Scots pine chronology by forest site type indicated that the chronologies are reliable for use in growth-climate calibrations.

Mean series inter-correlation characterising similarities among the individual tree growth patterns ranged from 0.463 to 0.650 (overall mean 0.530). Myrtillus forest site type chronologies tended to exhibit a lower correspondence among the index series, except on the Islands. Mean Glk, characterising the synchrony among the growth patterns was 61% with little variation between 57 to 66%. Th e mean sensitivity ranged between 0.17 to 0.23 (overall mean 0.19), with no clear pattern along the ecological gradient. On the other hand, year-to-year variation was slightly higher for all sites in the Northeastern sub-region. Th e fi rst-order autocorrelation, estimated on standardized series and indicating infl u- ence of the previous year’s growth on the current growth was moderate (0.360.55; mean 0.42 for all sites, except Scots pine plantations on the reclaimed areas (0.16)). Common signal strength, as measured by the EPS ranged from 0.85 to 0.98. For Calluna-NE chronology, the replication threshold of 5 trees was not suffi cient to achieve the EPS value of 0.85, therefore chronologies were truncated to the year when this threshold was fulfi lled. Common variance, accounted by the PC1, ranged between 24.5 to 53.9% (overall average 36.0%) and tended to be greater for Northeast sub-region sites, except for Myrtillus site. More than 50% of common variance summarised in PC1 was detected for Cladonia forest site type and reclaimed areas from the Northeast sub-region.

45 (%) IV lation 2.7 0.85 35.4 13.2 0.93 38.0 7.1 0.88 32.3 44.7 0.98 53.9 rst order autocorrelation, autocorrelation, rst order MS SD AC1 SNR EPS PC1 (%) 0.540 62.8 0.17 0.17 0.16 46.8 0.980.463 50.3 56.9 0.17 0.20 0.35 9.2 0.90 33.6 0.507 64.1 0.18 0.21 0.36 5.6 0.850.494 58.2 31.1 0.20 0.25 0.38 6.5 0.85 33.7 IC Glk rst principal component. Span (n>4 series) Fig. 5.4. 2013 37 2007 1162007 772008 110 0.4972007 59.3 0.19 52 2007 0.512 0.24 62.0 52 0.506 0.44 0.21 60.0 0.27 0.18 0.55 0.23 7.2 0.44 0.575 63.0 17.1 0.88 0.17 0.95 37.8 0.20 28.0 0.40 2006 93 2006 1012006 1052006 97 0.5082007 59.6 0.19 58 0.5362007 60.2 0.24 0.18 126 0.47 2007 0.518 0.22 60.6 74 0.40 0.182007 9.3 0.23 70 0.650 0.47 66.4 0.90 12.4 0.23 33.6 0.470 0.93 0.30 59.2 28.1 0.44 0.20 0.585 64.6 0.34 0.20 0.54 0.25 5.4 0.47 0.85 13.9 24.5 0.93 44.0               Range (n>4 series) gkeit, MS-mean sensitivity, SD-standard deviation, AC1-fi SD-standard gkeit, MS-mean sensitivity, Series length (years) min/mean/max No. No. of trees* 19 (38) 17/73/96 1913

II ReclNECladSE 128 (258) 15/28/39MyrtSE 14 (28)RhodSE 24 (48) 27/74/127 1976 MyrtSW 97 (194) 39/67/96RhodSW 1891 31 (62) 20/67/114 48 (96) 1930 20/40/54 1898 34/45/73 1955 1955 name CladISLMyrtISL 29 (58)RhodISL 90 (180) 41/85/134CaluNE 77 (154) 21/65/210 1905 20/74/135CladNE 21 (42) 1901 MyrtNE 173 (346) 38/89/222 1909 35/68/205RhodNE 40 (80) 1949 24 (48) 26/58/82 1881 52/65/73 1933 1937 Southeast Southeast Southeast Southwest Southwest Sub-region Chronology Islands Islands Islands Northeast Northeast Northeast Northeast Islands CaluNE Summary of the basic statistics of Scots pine forest site type chronologies in four sub-regions. Abbreviations: IC-inter corre Abbreviations: in four sub-regions. site type chronologies of the basic statistics Scots pine forest Summary Cladonia Myrtillus Rhodococcum Myrtillus Rhodococcum Cladonia Myrtillus Rhodococcum Calluna Cladonia Myrtillus Rhodococcum Reclaimed Northeast Forest site Forest type Calluna SNR-signal to noise ratio, EPS-expressed population signal, PC1-variance explained by the fi explained by population signal, PC1-variance SNR-signal to noise ratio, EPS-expressed between ring-width measurement series, Glk-Gleichläufi ring-width measurement between Table 5.1. Table used to compute the chronology. of trees *Number 46 Th e hierarchical cluster analysis applied on standardised Scots pine growth patterns grouped chronologies into three clusters (Fig. 5.4; Fig. 5 inIV IV), implying closest similarities among the growth patterns within the same sub-region, and suggesting that local climate is the main driver of a common growth.

Fig. 5.4. Fig. 5.4. Dendrogram of hierarchical cluster analysis applied on the 14 residual forest site type chronologies, sharing a common period of 19762006. Grey lines show clusters. Chronology abbreviations are explained in Table 5.1.

A separate cluster was produced for the Islands and Northeast sub-regions, while forest sites from the Southeast and Southwest sub-regions were aggregated into one cluster. Chronology for Scots pine plantations on reclaimed areas was aggregated into a cluster with other chronologies from the Northeast, however there was a considerable separation between Scots pine chronology of reclaimed areas and other chronologies, indicating that growth in the disturbed areas diff ers from the growth patterns of natural forest sites. Scots pine chronologies by forest type were compared to the general chronology for Scots pine (reference chronology) previously developed by Lõhmus (1992) (Figure 4, Table 1 inII II). Th e accordance between separate new chronologies and the reference chronology ranged between 56.1 to 80.7% based on the Glk and was greater for heath forests. T-values with Baillie-Pilcher standardization (TBP) were also relatively high (ranging between 4.57.9) except for two chronologies of mesotrophic forests (Northeast TBP=3.7 and Southwest TBP=3.4), indicating a lower similarity between these chronologies and the reference chronology.

47 5.2.2. Radial growth-climate relationships

Th e results of correlation and response function analysis indicated that climate infl uence on Scots pine growth was not homogenous across the studied area and varied both on the spatial scale and with respect to site conditions (II, III, IV).

Tree-ring width growth on both studied forest types (heath and mesotrophic) was positively correlated with mean winter temperature prior to the growing season and mean temperature of the growing period. Th e radial growth in the Northeast and on the Islands was negatively associated with mean August temperature of the prior growing season and positively related to the total rainfall in that month. Radial increment in the mesotrophic forest of Southwest Estonia was positively related to precipitation in February. Th e annual radial increment of Scots pine in the Southwest and on the Islands for both mesic sites was signifi cantly positively correlated to mean annual temperature.

Since the relationships established between Scots pine growth at forest type level is a robust summary of pine response at forest site type level, in addition Scots pine response to climate analysis was conducted to defi ne climate-growth relationships at forest site type level. Th erefore, tree-ring- width dataset from mesotrophic forests was downscaled and grouped into Myrtillus and Rhodococcum forest site types and heath forests into Calluna and Cladonia forest site types according to sub-region. Th erefore, the response patterns at forest site type level represent ecological growth conditions more precisely, and consequently obtained response patterns describe Scots pine response to climate variability better.

Th e greatest number of signifi cant positive correlations at forest site type level (Fig. 5.5) were established between radial growth and mean temperature in February (n=10), March (n=9) and April (n=7) and with the total precipitation of the previous August (n=8). Th e number of signifi - cant correlations per chronology ranged from one, very strong (r=0.49) for Scots pine plantations on reclaimed areas to nine moderately strong monthly climatic variables for Rhodococcum forest site on the Islands. A positive relationship to mean air temperatures from December/January to April was established for both mesic habitats (Myrtillus and Rhodococ- cum) from the Islands, the Southwest and Southeast (MyrtIsl, MyrtSW, 48 RhodIsl, RhodSW, RhodSE). For Myrtillus in the Southeast, the relationship was stronger only for mean temperature in March (r=0.32). Th e sites in the Southwest (MyrtSW, RhodSW) showed a strong correlation (r=0.450.53) with mean temperatures in March and April, whereas this relationship was weaker for the sites on the Islands. Sensitivity to mean monthly temperatures of the cold period and early spring was characteristic of a mesic sites and weaker for Cladonia and Calluna sites and all sites in the Northeast region.

Th e mean temperature of the previous August was signifi cantly negatively related to the growth in all sites on the Islands and mesic sites of the Northeast region (MyrtNE, RhodNE) and Rhodococcum site in the Southeast, whereas a positive response to the total rainfall for this month was displayed by eight chronologies from the Northeast and Is- lands. Sensitivity to the total rainfall in the previous August was stronger for the Scots pine trees growing on the Islands (r=0.33–0.43) than in the Northeast region (r=0.240.37), suggesting that the climatic water defi - cit in these sites might be infl uential for radial growth during the next growing season. Several Scots pine chronologies for mesic sites, mainly in the Southwest and Islands (RhodSW, RhodISL, MyrtISL, RhodSE) showed a signifi cantly negative relationship (r=-0.33-0.39) to the total rainfall of the previous September. A considerably weaker negative correlation between the total precipitation of the previous October was displayed for radial growth in Cladonia site in the Southeast.

Th e response function verifi ed the signifi cant relationships established by the correlation analysis, but the number of climatic variables and relationship strength was considerably lower. No signifi cant relationships were observed between radial growth on Calluna site on the Islands and climatic variables.

Fig. 5.5 Visualisation of Pearson’s correlation (bars) and response function (lines) coeffi cients between pre-whitened Scots pine chronologies by forest site type in four sub-regions and mean monthly climatic variables. Dark bars and fi lled dots indicate statistically signifi cant (p<0.05) relationships from the previous June (j) to the current August (A) of the current year of ring formation.

50 Th e PCA analysis applied on BCC suggested that Scots pine climatic sensitivity depends on geographical sub-region. Th e fi rst two principal components explained 70.9 % of total variance (Fig. 6 in IV). Th e fi rst principal component captured 48.3% of the total inertia and described Scots pine sensitivity to current growing season climatic conditions. Most of the chronologies from Southeast, Islands and Southwest were positively related to this component while Northeast chronologies were clearly separated. Th e second principal component captured 22.6% of the total variance and described Scots pine sensitivity to water availability at the end of previous year summer. It was the most limiting climatic factor for chronologies from Islands, but had opposite infl uence to pine populations in the Southwest.

Scots pine response to climate on reclaimed areas of oil shale was investigated at a fi ner scale, by grouping trees into three sites (more details in III). A strong positive relationship (r range of 0.390.66) between the radial growth of Scots pine plantations on reclaimed areas and rainfall in the period of JuneJuly for all age groups was established through Pearson’s moment correlation analysis. Warmer springs were positively related to the tree-ring widths of the two oldest sites (r=0.50 and r=0.69, site 1 and site 2, respectively). A moderately strong relationship between mean temperature in January and ring width was established for trees in the densest stands (site 2; r=0.39) and a much stronger relationship (r=0.56) for the youngest trees growing on more fertile soils (site 3). Th e radial growth of trees on this site was also negatively associated (r=-0.45) with mean temperatures in the summer.

Pearson’s correlation analysis between the BCC and variables describing the soil and stand (Table 1 and Table 2 in III) indicated that the sensitivity of Scots pine trees to temperature in reclaimed areas was defi ned by stand characteristics, while the sensitivity to precipitation was dependent on soil characteristics and got stronger with increasing tree dimensions (Table 4 in III). Denser stands, characterised by a higher stand volume, relative density and basal area were sensitive to spring temperatures (r=0.580.68). Radial growth sensitivity to mean temperatures in Janu- ary were negatively related to age and increased with site productivity, as defi ned by greater SI50 and content of clay in topsoil. Trees growing in stands where thicker layers of the organic matter were apparent tended

51 to be less sensitive to the negative infl uence of temperatures in August. A signifi cant negative relationship was established between sensitivity to rainfall in June and soil pH.

5.2.3. Temporal patterns of Scots pine response to climate variation

Moving correlation functions calculated for 30-year windows and shifted by one year revealed the growth response to inter-annual weather variation dynamics over the period of 19552006 (Fig. 5.6, Fig. A.2 in IV). Relationships between radial growth of Scots pine and winter and early spring temperatures were signifi cant and quite stable over the last fi fty years for mesic sites (Rhodococcum and Myrtillus) in the Southwest of Estonia and Islands, but associations with dry and nutrient poor sites of Cladonia and Calluna were weaker, and not always signifi cant. Relation- ships to winter and spring temperatures for these sites were strongest at the beginning of analysis, but towards the twentieth century end associations with winter temperature became weaker (e.g. MyrtISL, MyrtSW, MyrtSE). Relationships between Scots pine growth in all forest site types (except Myrtillus) in the Northeast were weakly linked to cold season temperatures already in the middle of 20th century.

Positive linkage between growth and August precipitation of the previous year over studied period got stronger for pine on Cladonia site in the Northeast and Islands. Climate infl uence on radial growth was the least stable for all sites in the Southeast sub-region.

Fig. 5.6. Moving correlation functions between residual Scots pine chronologies and mean monthly air temperatures and precipitation sums for the period of 19552006, except ReclNE. Th e moving correlations were calculated using a 30-year moving window shifted by one year. Climatic variables are arranged from the previous June to the current August. Capital letters refer to current year (T - temperature, P - precipitation) and small letters (p, t) indicate climatic variables of the previous year. Numbers defi ne calendar months (1 - January, 2 - February, etc.) Asterisk indicates signifi cant correlations (p<0.05).

Fig. 5.6. Continued

5.2.4. Evidence of infl uences of extreme climatic conditions on radial growth

A diff erent number of years with exceptionally small or large radial growth was detected depending on the studied site and region (Fig. 5.7; Table 2 in II). Th e strongest growth reduction when Cropper values were below -1.645 (extreme pointer years) was observed in most forest types and regions in 1940 and 1985, indicating large-scale extreme climatic conditions.

Fig. 5.7. Pointer years in Scots pine radial growth by forest type and sub-region. Grey

Fig.bars 5.7.show strong pointer years, Ci >1.28 and black bars refer to extreme pointer years,

Ci >1.645.

According to documented meteorological records (Tarand et al., 2013) summer of 1939 was warm and dry and followed by exceptionally cold winter of 19401941. Th e winter of 19841985 was also cold. Th e number of event and pointer years varied among the sites and regions due to diff erent chronology lengths, therefore a greater number was detected for heath forests than for mesotrophic forests, because dry-forest chronologies were longer (II). II

56 5.3. Radial growth patterns and the BAI model for Scots pine on reclaimed areas

Radial growth patterns, expressed as the BAI, for all three sites on reclaimed oil shale mining areas (Fig. 5 in III) showed an exponential increase. Sudden increases in BAI patterns following thinning were observed on the oldest and the youngest sites (site 1 and site 3, respectively), whereas growth curve fl attening appeared at middle-aged site (site 2) after the year 2003.

A sharp growth reduction in 20022003 was observed among the youngest trees, which was a consequence of the dry summer in 2002. BAI reduction was signifi cantly lower for that year in older trees. How- ever, pre-commercial thinning on the youngest site contributed to a fast radial growth recovery, as indicated by the sudden growth increase in 2004. As a response to thinning, the BAI of the remaining dominant trees on the oldest site increased by 58%, on average.

In total, sixteen alternative models were fi tted (Table 5.3 and Table 5 in III) on more than 3,600 annually resolved Scots pine increment data, expressed as the BAI, expanding over the period of 19762013 (37 years).

In addition, two models (M15M16, including hypothetical thinning and without it) were compiled to test the eff ects of growth index substitu- tion with climatic variables. Th e parameter estimates in all models were highly signifi cant (p<0.0001). Th e explained variance by diff erent combinations of independent variables ranged from 19% to 63%. Th e most important variables in our developed model were tree size, described by tree diameter, followed by the response to thinning and site factor. Tree diameter defi ned the asymptote better than SI50 and explained 19% of the variance, therefore it was kept in the model. Fine soil depth was used to describe site quality for radial growth because it was a slightly better predictor than SI50. In general, the diff erence between these two variables was marginal. Th e inclusion of the response to thinning improved model prediction power signifi cantly and increased the explained variance by 14%.

57 Table 5.3. BAI model fi t statistics and parameter estimates. ptree- tree size parameter,

Dmax -tree DBH (cm); pth-response to thinning parameter, described as years since thinning ; psite- site productivity parameter described by either SI50 – site index at reference age of 50 years or fi ne soil - fi ne soil thickness (cm); f1(A)-basic grow function, f2(th)-response to thinning function, f3(av)-function describing annual variation related to climate; f2(hth)-response to thinning function with included hypothetical thinning. RMSE - root mean square error; general chron – general Scots pine tree-ring width chronology for reclaimed areas; site chron – tree-ring width chronologies for three study sites (site1, site 2, site 3); plot chron – tree-ring width chronologies for twelve study plots, PJJ – total precipitation in June-July (mm), Tspring –mean spring season temperature(°C), AIC - Akaike Information Criterion; MEE - the mean error of estimate. Th e best models with and without hypothetical thinning is marked in bold.

Model Components RMSE AIC pseudo-R2 MEE

M1 f1(A),ptree 3.813 19916 0.186 -0.002

M2 f1(A),ptree, psite 3.811 19913 0.187 -0.029

M3 f1(A),ptree - Dmax, psite 3.306 18888 0.388 -0.150

M4 f1(A),ptree - SI50, psite 3.673 19647 0.245 -0.058

M5 f1(A),ptree - Dmax, psite - SI50 3.255 18775 0.407 -0.143

M6 f1(A),ptree - Dmax, psite - fi ne soil 3.250 18763 0.409 -0.136

f1(A),ptree - Dmax, psite - fi ne soil * M7 2.824 17749 0.553 -0.172 f2(th)

f1(A),ptree - Dmax, psite - fi ne soil* M8 2.746 17548 0.578 -0.174 f2(th)*f3(av) - general chron

f1(A),ptree - Dmax, psite - fi ne soil* M9 2.804 17698 0.559 -0.139 f2(th)*f3(av) - site chron

f1(A),ptree - Dmax, psite - fi ne soil* M10 2.723 17486 0.585 -0.168 f2(th)*f3(av) - plot chron

f1(A),ptree - Dmax, psite - fi ne soil* M11 2.746 17549 0.578 -0.175 f2(hth)

f1(A),ptree - Dmax, psite - fi ne soil* M12 2.663 17327 0.603 -0.173 f2(hth)*f3(av) - general chron

f1(A),ptree - Dmax, psite - fi ne soil* M13 2.664 17329 0.603 -0.154 f2(hth)*f3(av) - site chron

f1(A),ptree - Dmax, psite - fi ne soil* M14 2.586 17114 0.626 -0.184 f2(hth)*f3(av) - plot chron

f1(A),ptree - Dmax, psite - fi ne soil* M15 2.748 17554 0.577 -0.182 f2(th)*f3(av) - PJJ+Tspring

f1(A),ptree-Dmax, psite - fi ne soil* M16 2.700 17425 0.592 -0.172 f2(hth)*f3(av) PJJ+Tspring

58 Th e variable transformation was not applied during model compilation. Th e performance of alternative models was compared based on the Akaike Information Criterion (AIC), the root mean square error (RMSE), pseudo-R2, the mean error of estimate (MEE) and analysis of residuals in relation to the observed data (data not presented). Th e model M14 was able to explain the greatest variance (63%) and contained the smallest RMSE and AIC, therefore it was selected as the fi nal one. By using this model, the annual BAI of Scots pine trees can be predicted based on tree diameter, mean soil depth in the stand and time since thinning. Furthermore, climatic variability could be accounted in the modelling process by replacing the growth index through the regression equation with the total precipitation in JuneJuly and mean spring temperature.

Th e total explained variance was 5759% for the models (M15M16) including climatic variables.

A visual comparison of the observed and predicted BAI curves shows a good reproduction of growth dynamics by the model (Fig. 5a and 5b, in III). In the beginning, the lack of fi t was observed for the youngest sites, when the model was not able to predict a smaller peak, but the inclusion of hypothetical thinning improved the model performance signifi cantly (Fig. 5b, Table 5, in III). Th e replacement of growth indices with climatic variables weakened the accuracy of model predictions just marginally, with the total explained variance reaching 59% (compared to 63% based on growth indices).

59 6. DISCUSSION

6.1. Tree growth deviations from expected growth

Th e results obtained during compilation of this thesis provided evidence that height growth of Scots pine on Oxalis-Rhodococcum site in South- eastern Estonia is not following the trajectory of the reference growth (Figs. 2a, 3a, 3b and 4 in I) and supported the hypothesis (1) that pine stands with a later establishment date grow faster than those established several decades earlier. Because of the small sample size (only one forest site type was considered, in a single location), the fi ndings from this study are of indicative nature, and can be valid only for trees growing in similar conditions as our studied stands. Diff erences in height growth patterns may not be the same for sites with a poorer nutritional status (Pretzsch et al., 2014) and may depend on the local climatic conditions (Sharma et al., 2012). Despite the limitations of result applicability, our study results are in agreement with previously reported fi ndings where a representative data set was used and the SI increase for Scots pine and other tree species in Estonia was reported (Kiviste, 1999b; Nilson et al., 1999).

Observed tree growth deviation from the expected growth in Estonia is not a phenomenon on its own. An increased growth of forest tree species and also a decline (Spiecker, 1996) have been reported in Europe (Kahle et al., 2008) and other parts of the world (Bontemps et al., 2009). For instance, Elfving and Tegnhammar (1996) studied Scots pine and Norway spruce growth in Sweden and found that the annual height and basal area growth rate over the period of 19531992 increased about 0.50.8%. In other studies carried out in Sweden (Elfving and Nyström, 1996) and in Norway (Sharma et al., 2012), where growth trends in pine stands were explored by comparing the site index of plantations established in diff erent years, researchers discovered a site index increase of 2.4 cm per annum over 19001977, with the most signifi cant diff erences detected between stands established before and after 1940. Th e evaluation of height growth in our study was based on data from repeated measurements in the experimental plots, therefore height growth over time was approximated with two diff erent site index functions. Some researchers (Elfving and Nyström, 1996) indicated that the results were dependent on the function selection. Th e fi ndings from our study are based on actual annual height and radial growth increments, reconstructed from individual tree covering the full lifespan of the trees, therefore the results

60 are precise and do not include estimation bias related to the function choice (Elfving and Nyström, 1996; Subedi and Sharma, 2011). A similar increasing trend in the site index with a breakpoint in the 1930s in pine and spruce stands with diff erent germination dates was observed by Sharma et al. (2012), with a signifi cantly greater deviation in climatically warmer parts of Norway compared to the colder regions. Similarly to our study, Lebourgeois et al. (2000) compared the growth of young and old black pine stands in France, growing under the same climatic and soil conditions, and also concluded that younger stands grow faster. A study conducted by Pretzsch et al. (2014) used long-term experiment data and in parallel to evidence on increased forest stand growth, provided proof on accelerated forest growth dynamics in spruce and beech forests of Central Europe.

To defi ne the causes of the accelerated growth in the younger stands is a challenging task. Th e greater growth in the younger stands could be a result of changes in a site or long-term climatic conditions. By selecting pine stands growing on the same forest site type we attempted to exclude any diff erences in site conditions. However, considering that the assignment to a forest site type was based on the ground vegetation composition, some nutritional diff erences could still have persisted (Paal et al., 2010).

While most of the studies referred above, attribute the observed deviations in forest tree growth to environmental changes, caused by the ongoing climate warming, Elfving and Tegnhammar (1996) and Elfving and Nyström (1996) do not exclude the possible infl uences on growth due to changes in management and suggest that stand tending at an early age (Kuliešis et al., 2010) and initial spacing (Elfving, 1975) or spacing shape (Gil, 2014) might have had a positive eff ect on height growth. Th e stand establishment and management diff ered among our studied stands, therefore more favourable growing conditions could have been created for pine stands established later (I). On the other hand, it is known for a long time that the top height growth and yields of the forest stands growing on the sites of comparable quality but in climatically diff erent regions, are not the same, and diff erences are climatically conditioned.

Th e comparison of mean climatic conditions that prevailed over the fi rst 40 years for each cohort indicates warmer climatic conditions for middle-aged trees and signifi cantly warmer for the youngest trees. Hy-

61 drological conditions remained almost the same, except for lower precipitation in winter over the period of 19521991 when middle-aged trees grew (Fig. 5.2). Peltola et al. (2002) investigated the impacts of elevated temperature and CO2 concentration under controlled conditions on diameter growth in 20-year-old Scots pine trees and discovered that under warmer conditions, the cumulative diameter growth over a three-year-period increased by 26% while higher CO2 concentrations and greater temperatures enhanced pine radial growth up to 67% compared to the control group. In natural conditions, the assessment of climate change eff ects on tree growth is complicated, due to a large number of factors acting simultaneously. For elucidating the infl uence of climate, all the other factors should be kept constant, which is not straightfor- ward bearing in mind the longevity of trees. Stem analysis allows to reconstruct individual tree growth, however, it fails to provide information on the tree’s social status over its lifetime, therefore some uncertainty related to the tree’s dominance remains.

Site index deviations (I) from the expected trajectory indicate changes in productivity. It also shows that site index models are not able to predict the accelerating height growth in younger pine generations. Top-height growth models in the recent past have been developed assuming that the climate remains unchanged, therefore such models are unable to account for time dependent variability. Given that, tree stem volume is commonly not directly measured in the forest, but is rather calculated based on tree dimensions described by tree diameter and height. Accurate estima- tions of height development over time (SI) would enable more precise stem volume calculations (Zhang et al., 2004).

6.2. Infl uence of annual climate variability on Scots pine growth in Estonia

Scots pine radial growth response to annual climate fl uctuations varied at the site level (III), along the ecological gradient (forest site types and forest types) (II, IV), among the regions (II, IV), suggesting that Scots pine sensitivity in Estonian forests is site-specifi c and depends on the local climatic conditions.

Following the results of the studies in this thesis, late winter-early spring 62 temperatures and meteorological water availability during late summer prior to the ring formation season, are the main climatic factors driving Scots pine growth in Estonia (II−IV). In general, the infl uence of temperature on pine growth is stronger than the importance of precipitation and this is in agreement with previous fi ndings for the Baltic Sea region (Lõhmus, 1992; Läänelaid and Eckstein, 2003; Pärn, 2008; Hordo et al., 2011; Henttonen et al., 2014). Läänelaid et al. (2012) compared several Scots pine chronologies from the Baltic Sea region and confi rmed that the control of winter NAO from December to March on Scots pine growth is characteristic of the studied region. However, negative infl u- ences of dry conditions, arising as a result of high temperatures and low precipitation at the end of the previous growing season, have not been extensively reported (Hordo et al., 2011). Warm late summers with low precipitation in August have been reported to be negatively related with ring-width for this species in the dust polluted areas (Pärn, 2003), but also for pines growing in the South and East Finland (Lindholm et al,. 2000) of Northern Europe. A limited number of sampling locations in Estonia could be an explanation for the low number of studies reporting this relationship.

When investigating growth-climate relationships at a fi ner spatial scale (II, III, IV), it was also revealed that Scots pine response patterns to climate can be very diverse, especially at site level (Fig. 4 in III), even when the area studied is not very large. High diversity in responses was also obtained by Oberhuber et al. (1998) who studied Scots pine growth on sites exposed to dryness and Bauwe et al. (2013) who studied the climatic sensitivity of Scots pines growing on soils with varying water availability. In our case, Scots pine sensitivity to weather variability on reclaimed areas varied due to soil and stand structure conditions (III), pointing out that physical substrate characteristics and stand structure manipulations through management (thinning activities) are very important factors, defi ning the dependency on climatic conditions. Scots pine plantations, established on reclaimed areas of oil shale excavation in Estonia were analysed for the fi rst time by applying dendrochronological techniques (III). Radial growth patterns of pine plantations on reclaimed areas (Fig. 5.3 and Fig.5.4) as well as the response to climatic variables, were distinctive from the pine populations growing on the natural forest sites (Fig. 5.5).

Correlation analysis of the three Scots pine chronologies from reclaimed areas indicated that year-to-year variation in these areas can be quite dif- 63 ferent at the site level. In the HCA analysis (Fig. 5.4), a general residual chronology of reclaimed areas was segregated from other forest site type chronologies and could be assigned to a separate cluster, still bearing some resemblance to the chronologies from the same region.

In boreal forests, the climate is one of the major factors controlling forest tree growth (Briff a et al., 1998), however, the climatic signal is weaker where optimal growth conditions for trees are met (Fritts, 1976). It has been reported by a number of studies that several climatic drivers simultaneously infl uence radial growth in temperate forests and climatic sensitivity tends to be lower compared to the relationships established for trees growing at the borders of a distribution area or on a site where extreme growth conditions persist. Th e fi nal size and other features of tree rings is a result of environmental conditions that prevailed in the tree environment over the growing season and sometimes during the time before the start of the radial growth. According to Vaganov et al. (2006), the fi nal width of an annual tree ring depends mainly on the xylem cell production rate. Th e time interval when cell division takes place during the growing season is considerably short, i.e. less than a month (Vaganov et al., 1999). For that reason, dendroclimatic calibrations make use of monthly climatic variables, aiming to establish stronger relationships.

Climatic variables calculated on the basis of calendar months are one of the drawbacks of dendrochronological methods. In reality, phenological periods do not follow the calendar months and such use of resolution seems not to have biological meaning. Some researchers (Rathgeber et al., 2005) suggest calibrating radial growth to more bioclimatic variables, like temperature sums, indexes representing moisture availability. However, strong relationships may not always be guaranteed even if bioclimatic variables are used. Besides, some of the relationships established between monthly climate variables and radial growth during this thesis were not stable in time (IV), which makes them unsuitable for future growth predictions.

Extreme yearly growth reductions caused by extreme climatic events slightly varied among the sites, however, low radial growth was observed in 1940 and 1985 across the sites and sub-regions of Estonia. Karpaviči- us (2001) noted that trees of diff erent species tend to synchronise their growth after extreme climatic events. For instance due to the cold winter 64 in 1940−41 and 1979−80 in Lithuania, the radial growth of the common oak and Scots pine growing on soils with normal humidity decreased. He suggests that the growth dynamics and dependency on climatic conditions is preconditioned by soil characteristics and the depth of ground water.

According to the results of the HCA analysis (Fig.5.4; Fig.5 in IV), applied on index series and PCA analysis conducted on BCC (Fig. 6 in IV), Scots pine populations investigated in our study could be divided into two groups, following the geographical sub-regions of Estonia. Similar- ities in growth patterns and growth responses were observed between the Islands and the Northeast sub-region and as well as between South- east and Southwest sub-regions. Such groupings in dendroclimatological studies are common. For instance, in Lithuania Kairaitis and Karpaviči- us (1996) have proposed to distinguish between four types of growth-climate responses for oak.

Despite the extensive Scots pine tree-ring sampling network in Estonia, some parts of the country still remain underrepresented and need further investigations to verify Scots pine responses to climate. Th e studies presented in this thesis have a low representation in sampling on the island of Saaremaa and in central Estonia. Also, not all the sites have a comparable number of replications for chronology building in the investigated sub-regions, therefore additional dendroclimatological studies should be carried out to make the tree-ring network more equally representative. Since growth-climate relationships change in time, temporal stability in growth-climate relationships and its causes should be studied in more detail. Th erefore, the need for new dendroclimatic studies on Scots pine remains.

6.3. Modelling the basal area increment of Scots pine

Th e study also aimed to incorporate the eff ect of thinning and climatic variation simultaneously into a basal area increment model of individual trees (III). A model with a multiplicative structure (Eq. (3)) consisting of three sub-components was selected to achieve the goal. Models of a similar structure have been used earlier (Quicke et al., 1994), and because each individual subcomponent of the model is fi tted simultaneously to the data, overall model errors are minimised (Gadow and Hui, 1999). 65 Th e linear mixed eff ect model is the other commonly used approach for annual basal area increment modelling in individual trees (e.g upon the need to handle multilevel data).

Th e modelled time step in the BAI model developed during the study is one year, which is appropriate for evaluating climatic forcing on growth, considering that increment forms over the calendar year. Besides that, tree response to diff erent silvicultural measures can be easily simulated since the length of response may vary depending on site conditions, species composition and silvicultural treatments. In the current study, it was assumed that medium intensity thinning is applied to pine stands, therefore the thinning intensity was not diff erentiated but could be tested as proposed by Hynynen (1995) in subsequent studies.

In models with the multiplicative structure, the potential growth function (basic function) aims to describe the maximum tree growth, whereas modifi ers represent the restrictions commonly related to competition. In our case we targeted at describing the response to reduced competition. To avoid over-parameterisation, we maintained a low number of parameters in the model. For the same reason, the two-parameter Weber function was chosen to describe unrestricted tree growth. Th e function itself was able to explain 18% of total variation in the annual basal increment (M1, M2 in Table 5.3). Tree diameter was used to describe the asymptote because tree radial growth is size dependent (Wykoff , 1990) and it is able to explain a considerable amount of variation. According to Weiskittel et al. (2011) tree size already integrates competition to some extent. In addition to tree size, fi ne soil depth describing the potential site fertility was included in the model. Th e use of site-related variables enables using the model for mixed stands where site index estimation is complicated. However, if data about fi ne soil depth is not available, the site index could be reliably used in the model due to a marginal diff erence between the eff ect of using either one of these two variables.

Th e inclusion of the thinning eff ect signifi cantly improved predictions, and reduced prediction errors. However, climate related variables (index chronologies and climate variables) did not have the expected eff ect. Th is contradicted to earlier fi ndings where climate-related variable inclusion improved the performance of the model. In our model, climate-related variables explained around ~3% of BAI variability but enabled the model to mimic annual fl uctuations. Th e results of the present study 66 demonstrate the potential of using climate and management related information simultaneously for assessing the management alternatives in changing climatic conditions (Pukkala et al., 2009). However, the model still needs to be improved and verifi ed on an independent data set to make the predictions more accurate and applicable in practice.

67 7. CONCLUSIONS

1. Th e comparison of growth patterns of three generations, growing in diff erent time periods showed long-term changes in Scots pine growth (I). Th erefore, the fi rst hypothesis was proved. Height and radial growth rates of both studied Scots pine stands established after 1950 were greater compared to the tree growth that were established before 1900, resulting in diff erent growth development over long-term.

Consequently, SI40 on average was 2.2 m (or 12%) greater for younger stands. While, causes of the accelerated growth are not very clear, meteorological data show that climatic conditions after 1970 was signifi cantly warmer compared to the earlier periods, which could favour Scots pine growth. Th is means, that site index equations for Scots pine used nowadays might not be able to account for an accelerated tree growth and need to be re-developed.

2. Th e second hypothesis was proved. Scots pine response to climate in Estonia strongly depends on the local climatic conditions but also varies across the ecological site gradient (II, III, IV). Th e growth of Scots pine on mesic forest sites (Myrtillus, Rhodococcum), especially on the Islands and in the Southwest and Southeast are determined by winter and early spring (FebruaryApril) temperatures. Whereas radial growth of Scots pine stands on all sites in the Northeast and on dry sites on the Islands are negatively related to high temperatures of the previous August and positively to the total rainfall of the same month, suggesting that the climatic water defi cit during this month negatively infl uences next year’s radial growth.

3. Th e third hypothesis was partly proved. Scots pine radial growth (expressed as the BAI) on reclaimed areas can be predicted with the annual resolution, using the following input variables: tree DBH, fi ne soil depth, time since thinning, and climate depicting variables – precipitation sum of JuneJuly and mean spring temperature (III). Th e inclusion of the thinning eff ect to model behaviour was very important (R2 ∼15%) for radial growth, while the inclusion of climate-related variables did not have the expected eff ect and improve model predictions just marginally (R2 ∼3%). Our study shows the potential of using climate and management related information simultaneously, however model needs further improvements and verifi cation.

68 4. Scots pine response to inter-annual weather variation was not stable in time in the case of all studied forest site types (IV), therefore the forth hypothesis was only partly proved. Relationships between radial growth and winter season temperatures got progressively weaker over 1955- 2006 period in all sites in the Southwest and Southeast and dry sites on the Islands. While associations with summer precipitation are becoming stronger especially for pine populations growing in the Northeast of Es- tonia and on the Islands.

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86 SUMMARY IN ESTONIAN

KLIIMA MÕJU HINDAMINE HARILIKU MÄNNI (Pinus sylvestris L.) KASVULE EESTIS

Sissejuhatus

Metsanduslike tegevuste kavandamine tugineb tänapäeval olulisel määral puistute kasvumudelite kasutamisele (Hasenauer, 2006). Seetõttu on vajalik, et koostatud mudelid oleksid võimalikult täpsed ja usaldusväärsed ning aitaksid võtta vastu metsamajanduslikke otsuseid ka muutuvate kliimatingimuste korral. Sarnaselt paljudes teistes riikides kasutusel olevate empiiriliste kasvumudelitega (Assmann, 1970) on ka Eestis nii teaduslikus kui ka praktilises metsanduses seni olnud kasutusel puistu kasvufunktsioonid ja -mudelid (Krigul, 1969; Tappo, 1982; Kiviste 1999a, 1999b; Kasesalu, 2003; Nilson, 2005; Kiviste ja Kiviste, 2009), mis on välja töötatud valdavalt eelmise sajandi metsade kasvuandmetel põhinevalt. Praeguseks on aga jõutud olukorda, kus puude kasvukesk- kond, sealhulgas nii metsamullad kui ka kliima, on varasemaga võrreldes oluliselt muutunud, seetõttu ei ole puistute kasvu prognoosimine ole- masolevate mudelitega enam piisavalt usaldusväärne.

Globaalset kliima muutumist on mõõtmisandmetele põhinevalt kirjel- datud alates 1950ndatest aastatest üle kogu maailma (Jones ja Mann, 2004) ja sarnaste muutuste täheldamine on nii Läänemeremaades (BACC II Autor Team, 2008) kui ka Eestis (Jaagus, 1998, 1999; Jaagus, 2006a, 2006b; Tarand jt, 2013) toonud avalikkuse tähelepanu alla to- imuvad pikaajalised muutused keskkonnatingimustes. Alates 20. sajandi teisest poolest on Eestis kliima olnud tunduvalt soojem kui varasematel perioodidel. Eesti erinevates paikades on aasta keskmine temperatuur tõusnud 1,01,7 °C (Jaagus, 2006a; Tarand jt, 2013). Järk-järgult on tal- vised keskmised temperatuurid tõusnud ja lumekattega periood jäänud lühemaks, mille tulemusena on ka vegetatsiooniperioodi algus nihkunud varasemaks (Ahas ja Aasa, 2006). Sademerežiimi muutumisele on Eestis iseloomulik järjest kasvav ruumiline ja ajaline varieeruvus, seejuures sademete hulga suurenemise tendents on täheldatav eelkõige külmal aasta- ajal (oktoobermärts) ja suvel juunis. Piirkondliku kliimamudeli (Jaagus ja Mändla, 2014) prognoosid ennustavad kliima soojenemise jätkumist vähemalt käimasoleva sajandi lõpuni. Samuti on mõnedes Eesti piirkon- 87 dades sademete kasvu 1020% võrra talvisel perioodil ning varasemaga võrreldes vähesema sademete hulgaga perioodi suve keskpaigas.

Temperatuur ja sademed avaldavad vahetut mõju peamistele puude füsi- oloogilistele protsessidele nagu fotosüntees, hingamine, toitainete ja vee transport (Morison ja Lawlor, 1999; Niu jt, 2008) ning mõjutavad ka kasvuks olulist kambiumi aktiivsust ning ksüleemi rakkude jagunemi- skiirust ja dünaamikat. Viimati nimetatud protsess määrab omakorda puude aastasisese kasvurütmi ja kasvuperioodil toodetud puidu koguse. Pikaajalised soodsad või ebasoodsad (puudele stressi tekitavad) ilmasti- kutingimused mõjutavad vahetult üksikpuude aastasisest kasvu (positiiv- selt või negatiivselt), suunates sellega metsade kasvudünaamikat ja toot- likkust laiemalt (Beck jt, 2011).

Kliimamuutuse mõju metsa kasvule prognoositakse mudelitega, mis võimaldavad arvestada seoseid puudes toimuvate protsesside ja muutuvate kasvutingimuste vahel ning võimaldavad nende seoste rakendamist koos teiste kasvudünaamikat kirjeldavate tunnustega. Sellest tulenevalt on vajalik hinnata erinevate kliimatingimuste mõju puude kasvule ja tuvastada need ilmastikunäitajad, mida on võimalik kasutada puistu kasvumudelites. Mahukat puude aastarõngaste mõõtmisandmestikku ning varasemaid meteoroloogiliste mõõtmiste andmeid kasutav dendroklimaatiline analüüs võimaldab puistute kasvu kirjeldamiseks välja sõeluda olulise mõjuga ilmastikunäitajad ning kirjeldada nende tunnuste omavahelist mõju ja sesoonset ajastatust. Kliimatingimuste pikaajalisest muutumisest sõltuvalt on oluline kontrollida ka teadaolevate empiiriliste seoste ajalist püsivust ja ruumilist muutlikkust.

Käesoleva doktoritöö fookuses on hariliku männi (Pinus sylvestris L.) kasvuanalüüs, kuna mänd on Eesti metsades enim levinud ning ühtlasi ka majanduslikult ja ökoloogiliselt olulisem puuliik. Doktoritöö peamine eesmärk oli uurida ilmastiku pikaajalise varieerumise mõjusid hariliku männi kasvule, tehes seda läbi puude radiaaljuurdekasvu ja kliimatun- nuste vaheliste seoste kirjeldamise. Doktoritöös uuritakse ka kliimanäi- tajate kasutamise võimalusi puude kasvu täpsemaks modelleerimiseks ja puude kõrguskasvu pikaajalise dünaamika muutumist.

88 Doktoritöö detailsemad eesmärgid olid:

1. analüüsida pikaajalisi muutusi hariliku männi kasvus Eestis seo- tult muutustega kasvukeskkonna keskmises temperatuuris ja sademete režiimis (I); 2. dendrokronoloogilisi meetodeid kasutades selgitada välja peamised kliimanäitajad, mis mõjutavad hariliku männi radiaaljuurdekasvu aastast varieeruvust erinevate kliima- ja kasvukoha tingimuste korral Eestis (II, III, IV); 3. analüüsida hariliku männi üksikpuude aastast radiaaljuurdekasvu ja hinnata, kas harvendusraie ja ilmastikunäitajate lisamine parandab puistu rinnaspindala kasvu prognoosimudelit (III); 4. uurida lokaalsete kliimatingimuste muutumisest tulenevat puude kasvureageeringu stabiilsust ajas (IV).

Doktoritöös püstitati järgnevad hüpoteesid:

1. Sarnastes kasvukohtades kasvavad kuid kronoloogiliselt hilisema rajamisaastaga (20 saj. teisel poolel) hariliku männi puistud on kiirema kasvuga kui puistud, mis on rajatud aastakümneid varem (20 saj. esimesel poolel ja 19 saj. teisel poolel)(I). 2. Kliimanäitajate ja hariliku männi radiaaljuurdekasvude vahelised seosed varieeruvad Eesti erinevate piirkondade vahel (II, III, IV). 3. Hariliku männi rinnaspindala kasvu saab modelleerida puude suuruse, kasvukoha viljakuse ja kliimanäitajate funktsioonina ning puudevaheliste konkurentsitingimuste muutusi saab mudeli- tesse lisada sarnaselt kui puude reageeringut harvendusraiele (III). 4. Kliimanäitajate ja radiaaljuurdekasvu vahelised seosed ei ole ajas stabiilsed keskkonnatingimuste pideva muutumise tõttu (IV).

Materjal ja metoodika

Katsealad

Kagu-Eestis viidi läbi uurimus (I) männikute erinevate rajamisaegade- ga puistute kõrguskasvu erinevuste väljaselgitamiseks. Järvselja Õppe- ja Katsemetskonnas (58°25ʹN, 27°46ʹE) valiti tüveanalüüside jaoks puud 89 (n=28) neljast hariliku männi enamusega (Pinus sylvestris L.) üksteisele lähedal asuvast puistust.

Hariliku männi radiaaljuurdekasvude andmed (puursüdamikud) koguti kokku 135 erinevalt proovitükilt üle Eesti (joonis 4.1, tabel 4.1), mis pai- knesid Eesti metsa kasvukäigu püsiproovitükkide võrgustiku proovialadel (Kiviste jt, 2015). Proovide (puursüdamikud ja tüveanalüüsid) võtmiseks valiti hariliku männi enamusega proovitükid nii mineraalmuldadel kasvavatesse puistutes (n=119) (I, II, IV) kui ka Kirde-Eestis põlevkivi kaevandamise järgselt taastatud aladele rajatud metsakultuurides (n=12) (III). Hariliku männi kasvutingimuste varieeruvuse uurimiseks jaotati proovialad nende geograafi lise asukoha järgi nelja piirkonna vahel: Kirde-Eesti (NE), Kagu-Eesti (SE), Edela-Eesti (SW) ja saared (ISL).

Muutused kõrguskasvu pikaajalises dünaamikas

Uurimaks, kas puistute kõrguskasvus esineb kolme kasvu alguse poolest ajaliselt oluliselt erineva hariliku männi põlvkonna (keskmiste vanustega 115, 56 ja 40 aastat) vahel erinevusi, võeti 28 männipuude tüveanalüüsid, mille tulemusena rekonstrueeriti puude tegelikud kõrguse ja läbimõõdu juurdekasvud. Iga puu kohta arvutati kumulatiivsed kõrguskõverad ja võrreldi kõrgusindekseid kontrollvanusel 40 aastat. Võrreldi kolmepara- meetrilise Richardi kasvufunktsiooni (1959) ja mitteparameetrilise üldi- statud aditiivse mudeli (Wood, 2006) arvutatud lähendeid. Kõrguskasvu erinevusi analüüsiti ühefaktorilise dispersioonanalüüsiga. Kolme erineva puistupõlvkonna kõrguskasvude dünaamikat võrreldi ka hariliku männi üldise keskmise kõrguskasvuga pohla kasvukohatüübis.

Täiendavalt analüüsiti kasvuandmeid (kolme 40-aastase perioodi kohta 1891−1930, vana; 1952−1991, keskmise vanusega; 1962−2008, noor) vastavate perioodide Tõravere ilmajaama meteoroloogilistele andmetega. Erinevate perioodide kliimatunnuseid analüüsiti kasutades ühefaktorilist dispersioonanalüüsi ja Tukey post-hoc testi.

Dendrokronoloogilised meetodid

Kasvuaastate vahelist radiaalkasvu varieerumise uurimiseks kasutati rohkem kui 900 hariliku männi analüüsipuu mõõteandmeid ning saadud aastarõngaste kronoloogiad omakorda rühmitati vastavalt 90 metsa kasvukoha tüübirühmade (II) või siis metsakasvukohatüüpide (IV) (Lõhmus, 2004) lõikes. Põlevkivi kaevandamise järgselt taasmet- sastatud aladel analüüsiti hariliku männi kasvu oluliselt detailsemal tasemel, seda eelkõige tasandatud puiste suure varieeruvuse ja sellest tuleneva puistu takseertunnuste suure varieeruvuse tõttu. Kokku koostati kolm kasvukohatüübipõhist kronoloogiat ja kaksteist proo- vitükipõhist kronoloogiat kliimamõjude hindamiseks kultuurmänni- kutes. Doktoritöös koostatud kronoloogiaid võrreldi alale lähimast meteoroloogiajaamast pärinevate igakuiste kliimaandmetega (eelkõige temperatuur ja sademed). Seoste uurimiseks aastase juurdekasvu ja kliimanäitajate vahel kasutati korrelatsioon- (II, III, IV) ja vastavus- funktsiooni analüüsi, mis teostati puude kasvuandmete ja meteoroloogiliste andmeseeriate kattuva perioodi kohta. Kliimanäitajate ja puude kasvu vaheliste seoste ajalist stabiilsust uuriti libiseva kor- relatsioonanalüüsiga. Varasemate ekstreemsete kliimanähtuste mõju radiaalkasvule uurimiseks kasutati näitaasta analüüsi (Schweingruber jt, 1990; Neuwirth jt, 2007) võttes aluseks Cropperi (1979) analüü- simetoodika. Vastavalt Neuwirth jt (2007) kriteeriumitele jaotati näi- taastad signaali intensiivsuse alusel kolme erinevasse rühma.

Hariliku männi populatsioonide sarnaselt reageerivate gruppide tuvasta- miseks kasutati peakomponentanalüüsi, kus korrelatsioonikoefi tsiendid arvutati radiaalkasvu indeksite ja igakuiste kliimanäitajate vahel bootstrap-meetodiga. Täiendavalt hinnati puude radiaalkasvu dünaamika sarnasusi kasutades hierarhilist klasteranalüüsi.

Rinnaspindala juurdekasvu mudel

Kliimamuutuste ja majandamistegevuste pikaajaliste mõjude prognoosimiseks koostati üksikpuude kohta rinnaspindala juurdekasvu mudel, kasutades selleks 128 puu puursüdamike andmeid endistesse põlevkivikarjääridesse rajatud hariliku männi kultuuridest. Saadud multiplikatiivne mudel koosneb kolmest komponendist, mis (1) kirjeldab puude radiaalset juurdekasvu piiramata tingimuste korral, (2) esitab juurdekasvu muutumist vastavalt harvendusraie intensiivsusele ja (3) kirjeldab aastasisest ilmastikutingimuste varieeruvust. Puude rinnaspindala juurdekasvu assümptootilise dünaamika kirjeldamiseks kasutati Weberi kasvufunktsiooni ja selle seost puude suuruse ja kasvu- 91 koha headusega. Aastase juurdekasvu järjepidevat häiringuteta kasvu, mis mändidel kestis 13 aastat Hynyneni (1995) järgi, modelleeriti kir- jeldamaks puudevahelise konkurentsi mõju. Iga-aastaseid juurdekasvu muutumisi, mis tulenesid ilmastikutingimuste varieerumisest, modelleeriti kas kliimaindeksite või kliimanäitajate lisamisega mudelisse (nt. sademete hulk juunist juulini ja keskmine kevadine temperatuur). Mudeli hindamine põhines mudeli headust kirjeldavate statistikute ja mudeli jääkide jaotuste analüüsil.

Tulemused ja arutelu

Muutused metsade pikaajalises produktsioonis

Täheldatud erinevused hariliku männi kolme põlvkonna kõrguskasvu dünaamikas toetasid pikaajalist kasvu muutumise hüpoteesi (hüpotees 1), mille kohaselt kasvavad nooremad puistud (rajatud pärast 1950 a.) kiiremini võrreldes nendega, mis on rajatud mitukümmend aastat varem. Kuna uurimuses kasutatud mõlema noorema põlvkonna keskmine aastane radiaal- ja kõrgusjuurdekasv olid statistiliselt oluliselt (p<0.0001) suuremad, saadi tulemuseks erineva kujuga kumulatiivsed kasvukõverad (joonis 5.1a; joonis 3 artiklis I), kus noore ning keskmise vanusega puistus oli kõrgusindeks (SI40) vastavalt 12% ja 23% võrra suurem võrreldes kõige vanema puistuga (joonis 4 artiklis I). Kuigi seda antud uurimuses ei käsitletud, võib saadud trendi võimalike põhjustena välja tuua ka selgelt jälgitavad pikaajalised muutused kliimatingimustes (aasta keskmise temperatuuri kasv ja kasvuaasta pikenemine).

Meteoroloogiliste andmete täiendav analüüs näitas, et hilisema kasvualgusega puistute kasvuajal valitsesid oluliselt soojemad temperatuurid (joonis 5.2) võrreldes kahe varasema kasvuperioodiga. Kevaded, suved ja vegetatsiooniperioodid (maist augustini) olid keskmiselt samuti soojemad hilisema (pärast 1960 a.) kasvualgusega puistus võrreldes varem (enne 1950 a.) kasvama hakanud puistute vanuseliselt võrreldava kasvuperioodiga.

Hariliku männi ja hariliku kuuse aastase kõrguskasvu suurenemist on täheldatud samuti Skandinaaviamaades (Elfving ja Tegnhammar, 1996; Elfving ja Nyström, 1996; Subedi ja Sharma, 2010; Sharma jt, 2012) ja Kesk-Euroopas (Lebourgeois jt, 2000; Pretzsch jt, 2014). Seega ei ole 92 käesolevas doktoritöös leitud trend pikaajalises mändide kõrguskasvus iseenesest midagi erakordset. Eestis varasemalt saadud tulemused (Kiviste, 1999b; Nilson jt, 1999) koos käesoleva uurimuse tulemustega osutavad aga sellele, et konstantsete kasvutingimuste eeldusel põhinevad baaskõr- guse mudelid ei anna täna enam usaldusväärseid kõrguskasvu prognoose kiirenenud kasvuga nooremate männipõlvkondade puhul ning nende kasutamisel hindame kasvu olulisel määral alla.

Aastaste kliimamuutuste mõju hariliku männi aastarõngaste laiusele

Aastarõngaste laiuse dendroklimaatiline analüüs näitas, et hariliku män- ni kasv Eestis on positiivses seoses hilistalviste/varakevadiste temperatuuridega. Negatiivne seos eelmise aasta augusti keskmise temperatuuriga ja positiivne seos sama kuu sademete hulgaga näitas, et niiskusdefi t- siit vegetatsiooniperioodi lõpus omab järgmise aasta aastarõnga laiuse kujunemisele negatiivset mõju. See seos oli tugevam kuivades ja toit- ainevähestes kasvukohtades (sambliku ja kanarbiku kasvukohatüübid), eriti Kirde-Eestis ja saartel kasvavate harilike mändide puhul. Vilja- kamatel ja parasniisketel muldadel (mustika ja pohla kasvukohatüüp) kasvavatel mändidel olid tugevamad seosed talvise puhkeperioodi lõpu temperatuuridega. Kuivade ja toitainevaeste kasvukohtade mändidel oli nõrgem seos külma perioodi temperatuuridega. Hariliku männi kasv Edela-Eesti parasniisketes kasvukohtades ja saartel näitas tugevat korrelatsiooni külma perioodi temperatuuridega.

Endistesse põlevkivikarjääridesse rajatud hariliku männi kultuuride kasvu ja kliimanäitajate vahelisi seoseid uuriti kasvuperioodil 1993−2013. Puis- tut ja kasvukohta iseloomustavad näitajad mängisid olulist rolli hariliku männi reageerimisel ilmastikunäitajate muutumisele. Aastarõngaste laius oli positiivses seoses suurenenud sademete hulgaga sama aasta juulis ja kõrgemate temperatuuridega kevadel. Siiski oli täheldav negatiivne seos suurema kivisusega substraadil ja tihedamates puistutes kasvavate män- nipuude radiaaljuurdekasvu ning suve, iseäranis just kasvuaasta augusti, keskmiste temperatuuride vahel.

Hariliku männi metsakasvukohatüüpide indeksite kronoloogiad jagu- nesid hierarhilise klasteranalüüsi põhjal kolmeks klastriks. Kagu-Eesti ja Edela-Eesti kasvukohatüüpide kronoloogiad koondusid ühte klas- trisse ning kuigi endiste põlevkivikarjäärialade kronoloogia oli sarnasem 93 Kirde-Eesti puistutega, saab nende alusel moodustada eraldi klastri. Enim sarnasusi täheldati sama piirkonna kronoloogiate vahel, sama met- sakasvukohatüübi kronoloogiad erinevates piirkondades olid üksteisest suhteliselt erinevad. Hariliku männi kasvu Kirde-Eestis ja saartel piiras enim sademete vähesus kasvuperioodile eelnenud vegetatsiooniperioodi lõpus. Edela- ja Kagu-Eestis kasvavatele männipuistutele avaldasid tu- gevalt positiivset mõju talve ja v arakevade madalad temperatuurid.

Näitaastate analüüs tõi välja enamiku harilike mändide vähenenud radiaaljuurdekasvu aastatel 1940 ja 1985 kõigis uuritud piirkondades. Erakordselt väikest radiaaljuurdekasvu oli põhjustatud äärmuslikult külmadest talvedest ja neile järgnenud põuast kasvuperioodi ajal.

Järeldused

Kliimatingimusi on alati iseloomustanud suur ajalis-ruumiline varieeruvus. Ilmastik koos lokaalsete kasvukohta iseloomustavate tingimuste- ga on peamised tegurid, mis määravad puude võimaliku kasvu kindlas geograafi lises piirkonnas. Vaatamata kliimanäitajate olulisusele ei ole neid Eestis metsade empiirilistes kasvumudelites seni sisendina kasutatud. Doktoritöös saadud tulemused (I, II, III, IV) näitavad, et il- mastikutingimustel on otsene mõju puude kasvule konkreetsel ajal ja konkreetses kohas. Erinevused kõrguskasvu dünaamikas (I) näitavad, et puude aastast kõrguskasvu soodustavate kliimatingimuste mõju võib pikema aja jooksul kumuleeruda, andes tulemuseks puudele varasemaga võrreldes suuremad mõõtmed ja puistutele kõrgema tootlikkuse ning seda isegi suhteliselt lühikese aja jooksul.

Käesolevas doktoritöös seostati hariliku männi radiaaljuurdekasv kliimanäitajate varieeruvusega 20−50 aastaste perioodide ulatuses ning leiti, et kliimanäitajate mõju harilikule männile sõltub kohalikest ilmastikutingimustest ja kasvukoha omadustest (II, III, IV), kuid puude kasvu võivad mõjutada ka konkreetse puistu tunnused (puistu tihed- us, vanus jt.; IV). Uudseid teadmisi hariliku männi kasvu reageerim- isest kliimanäitajate muutumisele saab kasutada hariliku männi kasvu edasisel modelleerimisel. Ilmastikunäitajate lisamine doktoritöös kasutatud kasvumudelisse parandab mudeli prognoose vaid vähesel määral, mistõttu tuleb metsa kasvumudelit edasi arendada ning ka täiendavalt valideerida. Koostada oleks vaja ka uus mudel metsamaal kasvava hari- 94 liku männi radiaalkasvu prognoosimiseks. Doktoritöö tulemused näita- vad, et kliimamuutuste ajalist ja ruumilist mõju puude kasvule ei tohiks alahinnata, seda eriti erinevaid Eesti geograafi lisi piirkondi võrreldes või analüüsides puude kasvu varasematel perioodidel.

95 ACKNOWLEDGEMENTS

Firstly, I would like to thank my supervisors Professor Andres Kiviste and Associate Professor Ahto Kangur for guidance through the research studies, continuous support and help in my PhD studies. I appreciate your patience, encouragement and valuable comments received during the time of research and while preparing this thesis.

I would also like to thank Prof. Henn Korjus for the opportunity to be part of Forest Management Department team and for his advice. I thank all the co-authors of the research articles for cooperation and contributions while preparing the articles for publication. Special thanks to Dr. John A. Stanturf and Dr. Lee E. Frelich for their insightful comments and language improvements during research article preparation.

My sincere thanks go to Associate Professor Maris Hordo, who introduced me to tree rings and dendrochronology, and with whom I explored the forests of Estonia while conducting fi eldwork and collecting samples.

I also thank Dr. Regino Kask and Alar Padari and the students who assisted me during fi eldwork, Dr. Allan Sims for helping with data analysis. I am grateful to Dr. Mait Lang for valuable conversations and assistance, and the rest of my colleagues from the Forest Management Department of Forest Management who supported me during this thesis preparation.

I especially thank my family-husband Marek, who encouraged me during the hard times and helped me with practical issues, and my sons Lukas and Jonas who has been an inspiration in my life. I am grateful to my parents, relatives and friends for their continuous care, concern and encouragement during the years of this thesis preparation.

Th e studies of this thesis were fi nancially supported by the Estonian Science Foundation (Grant No. 8890), by the Estonian Ministry of Education and Research (SF0170014s08), and by the ESTEA (MUU 8-2/T8047MIM). Th e ESTEA fi nanced increment core collection (MUU 8-2/T7103MIMI) and provided meteorological data. Th e European Regional Development Fund represented by the Archimedes Foundation covered the costs related to participation in courses taken outside Estonia. 96 I

ORIGINAL PUBLICATIONS

97 Metslaid, S., Sims A., Kangur, A., Hordo, M., Jõgiste, K., Kiviste, A., Hari, P. 2011. Growth patterns from diff erent forest generations of Scots pine in Estonia. Journal of Forest Research, 16 (3): 237243. 98 J For Res (2011) 16:237–243 DOI 10.1007/s10310-011-0275-4

SPECIAL FEATURE: ORIGINAL ARTICLE Approaches for forest disturbances studies: natural variability and tree regeneration

Growth patterns from different forest generations of Scots pine in Estonia

Sandra Metslaid • Allan Sims • Ahto Kangur • Maris Hordo • Kalev Jo˜giste • Andres Kiviste • Pertti Hari

Received: 21 July 2010 / Accepted: 19 March 2011 / Published online: 15 May 2011 Ó The Japanese Forest Society and Springer 2011

Abstract There is strong evidence that climate change Introduction alters tree growth in boreal forests. In Estonia, the analysis of ring measurements has been a common method to study ‘‘Forest stands are open systems’’ (Pretzsch 2009, pp. 6–7) growth changes. In this study, annual height growth data and they tend to respond to changes in environmental from dominant or co-dominant Scots pine (Pinus sylvestris conditions, which may arise as a result of natural events or L.) trees were used to develop a growth model for three can be induced by human activities. Global climate change, forest generations. Stem analysis was applied and annual characterized by rising temperatures, altered precipitation height growth was measured as the distance between patterns and chemical composition of air, affects tree whorls, as detected by branch knots of whorls on the split phenology, evapotranspiration demands, and at the same stem surface. Retrospective analysis of height growth time influences soil moisture and nutrient supply. These produced comparative trends for three different age groups. changes may cause temporary or long-term alterations in Statistical analyses were used to estimate the impact of forest growth (e.g., Kahle et al. 2008). different factors on growth. Annual height growth was The number of forest research studies concerning considered the best indicator for detecting possible trends growth trends of forests has increased since the beginning in the growth potential of trees. Study results indicate that of the 1980s, but the results have been controversial with three generations (separated by time periods of high spatial variation (e.g., Spiecker et al. 1996). Growth 30–40 years) showed significant differences in growth increases have been found in boreal and hemi-boreal for- patterns caused by shifts in climatic factors and manage- ests (Hari et al. 1984; Nilson and Kiviste 1986; Mielikaïnen ment regimes (anthropogenic and natural disturbances). and Timonen 1996; Lopatin 2007) and in temperate forests of Europe (Eriksson and Johansson 1993; Wenk and Vogel Keywords Annual growth Forest growth change 1996; Lebourgeois et al. 2000; Mellert et al. 2004; Bon- Growth modelling Stem analysis temps et al. 2009), while no long-term growth changes have been detected in some studies (e.g., Tveite 1994, as cited in Elfving et al. 1996; Zetterberg et al. 1996; Miel- ikaïnen and Sennov 1996; Sinkevich and Lindholm 1996). Analysis of tree-ring chronologies from a circumpolar S. Metslaid (&) A. Sims A. Kangur M. Hordo network showed tree growth decline since the middle K. Jo˜giste A. Kiviste 1900s (Lloyd and Bunn 2007). Spatial and temporal growth Institute of Forestry and Rural Engineering, variability was observed for trees growing at the cold Estonian University of Life Sciences, Kreutzwaldi 5, 51014 Tartu, Estonia margins of the boreal forest in three regions in Alaska e-mail: [email protected] (Lloyd and Fastie 2002). The signs of changing climate have been observed in P. Hari Estonia. A warming trend in spring and delayed beginning Department of Forest Sciences, University of Helsinki, of winter resulting in a longer growing season have been P.O. Box 27, 00014 Helsinki, Finland reported by Ahas and Aasa (2006) for the period 123

99 238 J For Res (2011) 16:237–243

1951–1999. Additionally, more intense solar radiation has moderately cool and moist climate. Average annual tem- been recorded since the 1990s (Wild et al. 2005). The perature for the reference period of 1891–2008 was 4.1°C forests of Estonia belong to hemi-boreal ecosystems and average annual precipitation amounted to 600 mm (Kangur 2009), but changes in forest growth have not been (EMHI 2011). The vegetation growth period, described by studied extensively, nor covered by larger scale projects growing degree days (calculated as average of minimum over the last century in the Baltic countries. Several and maximum temperatures and compared to a base tem- attempts to detect changes in forest growth in Estonia have perature of 5°C) on average lasts for 170 days, starting in been done during recent decades. Kiviste (1999) and Nil- the beginning of April and ending in mid-October (Rau- son et al. (1999) used forest inventory data and observed tiainen et al. 2009). changes in site index. Pa¨rn (2008) investigated the changes in radial growth in two consecutive generations of Scots Stand selection and field sampling for sample trees pine stands, using the constant cambial age method. All selection these studies reported positive trends. However, a better understanding of how single-tree growth on different forest Stands from a contiguous area, with the same site condi- site-types is affected by changing climate conditions is tions and similar stand characteristics, but of different needed in Estonia for the development of growth models of germination year, were selected for the study. Altogether trees and stands on different sites for efficient (sustainable) four Scots pine-dominated stands, growing on the fertile forest management planning. Oxalis-Rhodococcum site type, were chosen. As a refer- The aim of this work was to study the effects of climate ence for past growth, two pine stands with average tree age change on height and radial growth in Estonian Scots pine of 115 years were selected. For growth comparison, one (Pinus sylvestris L.). It is a dominant and one of the most middle-age stand (average tree age 56 years) and one economically important tree species in Estonia, growing on young stand (average tree age 40 years) were sampled. The 35% of forest land, with a growing stock of 143.6 million oldest stands include 80 years of measurements on per- cu m (MMK 2008). Significant changes in growth of Scots manent sample plots, while the two younger stands were pine stands may have a substantial impact on timber mar- sampled here for the first time. kets due to altered availability and supply of industrial All selected stands grow on previously cultivated agri- round wood (Solberg et al. 2003). Dominant height is cultural land (oldest stands are first generation and younger assumed to be a more advantageous growth variable than stands second generation), where the regeneration was basal area growth, because it is relatively unaffected by accomplished by seeding. The oldest stands were estab- stand density and management (Ma¨kinen and Isoma¨ki lished with direct seeding on a ploughed field, the middle- 2004) and at the same time is a good proxy for site pro- age stand was sowed on scarified patches and the young ductivity (Clutter et al. 1983). stand was sowed on a deeply ploughed clear-cut area. Stand Achievement of the main goal of this study requires development history is known for the two oldest stands development of a novel method to detect growth changes in (Sims et al. 2009), as they belong to one of the oldest long- annual height increments of trees. To evaluate long-term term forest growth and yield experimental plot series still in changes in forest growth at the site level, we compared the existence, and their location together with measurement growth of tree groups with different germination years. We information is published in the Northern European Database hypothesize that stands with three different germination of Long-Term Forest Experiments (NOLTFOX) where they years (separated by a time period of 30–40 years) will can be found under experiment number M046. show significant differences in growth patterns, caused by A circular sample plot was established within each shifts in climate regime and due to evolution of site fertility selected stand. The radii of sample plots varied from 15 m during the last 100 years. in young stand to 20 m in middle-age and old stands. The radius of plots varied because of the fixed minimum size of sampled subpopulations: each plot was required to Materials and methods encompass at least 100 main canopy trees. All trees on the plots with DBH [4 cm were recorded and DBH and tree Study area location measured. In addition, tree height was measured for every fifth tree. Four sampling sites were located in even-aged Scots pine- To describe the social rank of trees within stands, the dominated forest stands, at the Ja¨rvselja Training and sampling methodology described by Havimo et al. (2008) Experimental Forest Centre (JTEFC) (58°250N, 27°460E), was used. The trees were assigned to one of five dominance situated in south-east Estonia (Fig. 1). The forests managed classes by DBH: class 1 (top 5% of the trees by DBH), by JTEFC belong to the hemi-boreal zone with a class 2 (top 6–20%), class 3 (top 21–50%), class 4 123

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Fig. 1 Study sites in Ja¨rvselja TEFC (black circle) and the location of To˜ravere meteorological station (black triangle)

Table 1 Main characteristics of the sampled trees Age group Number of trees Age range (years) Growth period DBH (cm) Height (m) Mean SD Mean SD

Old 13 113–116 1891–2008 44.0 5.7 32.3 2.4 Middle-age 9 53–57 1952–2008 31.6 3.2 27.8 1.0 Young 6 39–43 1966–2008 29.7 5.3 24.0 0.8

(51–80%), and class 5 (the remaining 20%). Depending on Stem analysis method for radial and apical increment the stand average age and tree dominance class, we selected 6–11 trees for sectioning per sample plot. This The stem analysis technique was applied for retrospective resulted in 34 sample trees: 10 trees in class 1, 10 trees in assessment of annual growth, to obtain long-term radial class 2, 8 trees in class 3, 3 trees in class 4, and 3 trees in and apical increment series. Selected trees were cut into class 5. Five trees were felled in 2006 and 29 trees in 2009. 2.50-m-long sections. Disks were cut from the bottom of For the analyses of current study, only dominant or co- every trunk section; number and widths of annual rings dominant trees were taken into account (e.g., trees from the were later recorded in the laboratory. Two additional disks ﬁrst three dominancy classes, 28 of the 34 trees felled). were obtained from every tree: at breast height and at the Detailed information about the selected trees is presented live crown base. The stem sections were split in half, in Table 1. north–south direction, and a board was cut from the central

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101 240 J For Res (2011) 16:237–243 part of the tree along each section of trunk. The disks and mean growth rate for that time period. For long-term height panels were labeled and delivered to the laboratory for development prediction with Kiviste’s model, the GAM measurements. model estimates of individual tree growth prediction were The surfaces of the disks were sanded and the number of used. rings as well as the annual ring-widths were measured to the nearest 0.01 mm in two perpendicular directions. A LINTAB tree-ring measuring table and the computer Results program TSAPWin Scientific Version 0.59 (Rinn 2003) were used. Measured series were cross-dated (Pilcher Analysis of height measurement data from 28 trees showed 1990) visually by comparing ring-width graphs. The different height development growth rates (estimated with quality of visual cross-dating was also checked statistically the GAM model) for the three age groups (Fig. 2a). Higher using COFECHA (Holmes 1983; Grissino-Mayer 2001). height growth rates were observed in both younger age Total tree age was estimated as the number of tree-rings groups. Deviation trends of differences between long-term from the basal disk. The annual tree-ring measurements height development prediction (with Kiviste’s model) and from breast height were used for analysis in this study. age group mean height development predictions (with the Annual height growth of each tree was determined by GAM model) for each age group are presented in Fig. 2b. measuring distances between consecutive whorls with The comparison of deviation trends showed growth rate accuracy of 0.1 cm; whorls were detected as bud scars on differences over the observation period. the split stem surface. The number of height increments The almost horizontal solid curve in Fig. 2b shows that was cross-checked with the number of tree rings, counted height growth of the middle-age group behaves similarly to on every cross-section (disc). Kiviste’s model. This is expected, because the data collected are from same time period as the data used in cali- Assessment of differences in forest growth brating Kiviste’s model. The old age group deviation trend shows that over a long period height growth rate has A number of growth functions can be used to model tree increased. height development (e.g., Zeide 1993; Kiviste et al. 2002). Comparisons of age group mean annual height incre- In this study, each tree height–age series was approximated ment predictions with cumulative height (Fig. 3a) and age with the three-parameter Richards’ (1959) growth function group mean annual radial increment with cumulative h ¼ a ðÞ1 expðÞb t c ð1Þ diameters (estimated with the GAM model) (Fig. 3a, b) show that younger age groups have higher height and radial where h is tree height, t is tree age, and a, b, and c are increment rates than the old group for the same cumulative parameters. Nonlinear regression analysis was used for height and diameter. The effect of the age group was highly parameter estimation. significant (p \ 0.0001) in case of both height and diam- To characterize height growth rate of each individual eter. The adjusted R2 of the GAM model for height was tree, height predictions with Eq. 1 at age of 40 years were R2 = 0.621 while adjusted R2 of the GAM model for calculated. We tested different reference ages for height diameter was R2 = 0.531. Therefore, the annual height predictions (data not presented); however, the most similar increment variation is better described than the annual results appeared at the age of 40 years, which was the oldest diameter increment variation for the whole observation measured age for the young age group. Considering that the period with the GAM model. three-parameter Richards’ function (Eq. 1) is not flexible Figure 4 presents tree height predictions at age 40 years, enough to describe single tree height development trends, both with the Richards function (Rch) and with the non- another smoothing method—the non-parametric general- parametric GAM model (GAM). One-way ANOVA ized additive model (GAM model) (Wood 2006)—was revealed significant (p \ 0.0001) difference of individual applied. Age group effect on individual tree growth pre- tree predictions at the age of 40 years among age groups diction at the age of 40 years was analyzed with one-way (see mean and 95% confidence intervals at the bottom of ANOVA. Statistical analysis was performed and graphs Fig. 4). The average predictions were 24.0, 21.8 and 19.5 m were drawn with R (R Development Core Team 2009). for the young, middle and old age groups, respectively. For a comparison of long-term growth trends, Kiviste’s mean stand height development model (Kiviste 1997; Kangur et al. 2007; Kiviste and Kiviste 2009) was used as a Discussion reference to describe typical Estonian forest growth on particular site. The model was created based on Estonian To detect changes in forest growth, data from long-term forest inventory data from 1984–1993 and represents a observations during stand development are needed, but 123

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Fig. 2 Height development (estimated with GAM model) of different age groups, in bold in (a); sampled trees data series are presented in the background (in gray) and deviation trends of difference between age group mean height and the long-term prediction in (b)

Fig. 3 The relationships between age group mean annual height increment and cumulative height (a), and between age group mean annual radial increment and cumulative diameter (b). The relationships are presented in bold together with the 95% conﬁdence intervals, sampled trees data series are presented in the background (in gray)

repeated measurements of tree diameter and height usually Curtis and Wang (1998), Ainsworth and Long (2005) and cover only several decades. To overcome these problems, Hari and Nikinmaa (2008) state that the positive growth retrospective techniques, such as stem analysis and tree- response of trees in recent years is due to elevated CO2 ring analysis, can be successfully applied (Spiecker 1999). concentration.

This method provides annual data on height, diameter and In addition to global climate variables (elevated CO2 volume growth for each individual tree. concentration and nitrogen deposition) effects on tree In previous studies where the cohort comparison growth, we consider that silvicultural methods (site prep- approach (e.g., Untheim 1996; Salminen et al. 2009; Bon- aration for regeneration, selective cuttings not recorded), temps et al. 2010) was applied, substantial differences site conversion from agricultural to forest land, and the between generations were detected, showing that growth of unknown origin of second generation trees must equally be young trees is accelerating. The results of our study support considered as causes of increases in site index. The single the growth acceleration findings in earlier studies by tree height prediction at the age of 40 years in our study is showing increased growth with consecutively younger age a good indicator of site-specific growth potential, and the groups. These changes are observable for both height and comparison between the different age groups indicates a diameter increment and for cumulative height and diameter. difference of approximately 2 m in height at the age of Studies by Spiecker et al. (1996); Karjalainen et al. 40 years. Our results coincide with the results observed by (1999) and Kahle et al. (2008) suggested that increased Nilson et al. (1999), where the average increase in the site growth of Scots pine (Pinus sylvestris L.) and Norway index (H50) of Estonian Scots pine stands for the period spruce (Picea abies L. Karst) stands in Central Europe is 1950–1999 was estimated at 2 m. Traditionally, site mainly due to higher supply of nitrogen, whereas temper- indexes were used to describe the growth potential of the ature and elevated concentrations of CO2 may have sig- forest site. The changes in site index imply improvement of nificant contributions in the future (Kahle et al. 2008). site productivity for woody vegetation.

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local climate change better than older stands that were already growing in the previous climate conditions.

Acknowledgments This study was supported by Ja¨rvselja Training and Experimental Forest Centre, Estonian Environmental Investment Centre, State Forest Management Centre, Metsa¨miesten Sa¨a¨tio¨ and The Ministry of Education and Research (project SF0170014s08 and grant no ETF8890). We are very grateful to Lee E. Frelich, University of Minnesota, for discussion and language correction.

References

Ahas R, Aasa A (2006) The effects of climate change on the phenology of selected Estonian plant, bird and fish populations. Int J Biometeorol 51:17–26 Ainsworth EA, Long SP (2005) What have we learned from 15 years of free-air CO2 enrichment (FACE)? A meta-analytic review of responses of photosynthesis, canopy properties and plant production to rising CO2. New Phytol 165:351–372 Bontemps JD, Herve JC, Dhote JF (2009) Long-term changes in forest productivity: a consistent assessment in even-aged stands. For Sci 55:549–564 Fig. 4 Sample tree height prediction at age 40 years with Richards Bontemps JD, Herve JC, Dhote JF (2010) Dominant radial and height (1959) function (Rch) versus the prediction with GAM model (GAM). growth reveal comparable historical variations for common In the lower part, mean prediction values (Rch) by age groups with beech in north-eastern France. For Ecol Manag 259:1455–1463 95% confidence intervals are presented Clutter JL, Fortson JC, Pienaar LV, Brister GH, Bailey RL (1983) Timber management: a quantitative approach. Wiley, New York Curtis PS, Wang XZ (1998) A meta-analysis of elevated CO2 effects For vegetation growth, the most relevant limiting factors on woody plant mass, form and physiology. Oecologia 113:299–313 are availability of light, nutrients, water, and temperature Danz NP, Reich PB, Frelich LE, Niemi GJ (2010) Vegetation controls (e.g., Hari et al. 2008). Ecological boundaries are affected vary across space and spatial scale in a historic grassland-forest by complex environmental factors and their interactions biome boundary. Ecography. doi:10.1111/j.1600-0587. (Danz et al. 2010; Frelich and Reich 2010). The response 2010.06561.x Elfving B, Tegnhammar L, Tveite B (1996) Studies on growth trends of growth to increasing temperature can be divergent of forests in Sweden and Norway. In: Spiecker H, Mielikaïnen within one generation of trees, especially for species K, Kohl M, Skovsgaard J (eds) Growth trends in European growing near the edge of the range. Temperature rise can forests: studies from 12 countries. Springer, Berlin, pp 61–70 create direct positive or negative effects, or negative effects EMHI (2011) Estonian climate. Estonian Meteorological and Hydro- logical Institute. http://www.emhi.ee/. Accessed 2 Feb 2010 mediated by water deficit (Lloyd and Bunn 2007). Eriksson H, Johansson U (1993) Yields of Norway spruce (Picea Tree ring growth happens throughout the whole growing abies (L.) Karst.) in two consecutive rotations in southwestern season. Tree carbohydrate allocation to height growth Sweden. Plant Soil 154:239–247 occurs during a shorter period. The allocation of growth Frelich LE, Reich PB (2010) Will environmental changes reinforce the impact of global warming on the prairie-forest border of appears to be age dependent (Konoˆpka et al. 2010). Based central North America? Front Ecol Environ 8:371–378. doi: on our results (Fig. 3), we suggest that tree height is a more 10.1890/080191 appropriate proxy than diameter for studying long-term Grissino-Mayer HD (2001) Evaluating crossdating accuracy: manual growth changes. This is because diameter growth is more and tutorial for the computer program COFECHA. Tree-Ring Res 57:205–221 influenced by stand density, whereas height growth is more Hari P, Nikinmaa E (2008) Response of boreal forest to climate affected by long-term changes in site conditions. change. In: Hari P, Kulmala L (eds) Boreal forest and climate All of the stands studied were established on previous change. Springer, Berlin, pp 499–503 agricultural land; thereafter, the old stands represent the Hari P, Arovaara H, Raunemaa T, Hautoja¨rvi A (1984) Forest growth and the effect of energy production: a method for detecting first generation, but younger stands represent the second trends in the growth potential of trees. Can J For Res 14:437–440 forest generation on agricultural land. Eriksson and Jo- Hari P, Raïsa¨nen J, Nikinmaa E, Vesala T, Kulmala M (2008) hansson (1993) found a 40% increase of stand volume in Evaluation of the connections between boreal forest and climate second generation spruce stands in south-western Sweden. change. In: Hari P, Kulmala L (eds) Boreal forest and climate change. Springer, Berlin, pp 519–528 One explanation for the higher growth on younger stands Havimo M, Rikala J, Sirvio¨ J, Sipi M (2008) Distributions of tracheid can also be the fact that the stands established more cross-sectional dimensions in different parts of Norway spruce recently are able to adapt to site improvements triggered by stems. Silva Fenn 42:89–99

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105 Hordo, M., Metslaid, S., Kiviste, A. 2009. Response of Scots pine (Pinus sylvestris L.) radial growth to climate factors in Estonia. Baltic Forestry, 15 (2): 195205. 106 BALTIC FORESTRY RESPONSE OF SCOTS PINE /.../ RADIAL GROWTH TO CLIMATE FACTORS IN ESTONIA M. HORDO ET AL. Response of Scots Pine (Pinus sylvestris L.) Radial Growth to Climate Factors in Estonia

MARIS HORDO, SANDRA METSLAID AND ANDRES KIVISTE Department of Forest Management, Institute of Forestry and Rural Engineering, Estonian University of Life Sciences, Kreutzwaldi 5, 51014, Tartu, Estonia; * E-mail: [email protected]

Hordo, M., Metslaid, S. and Kiviste, A. 2009. Response of Scots Pine (Pinus sylvestris L.) Radial Growth to Climate Factors in Estonia. Baltic Forestry, 15 (2): 195205.

Abstract

The following research paper analyzes Scots pine (Pinus sylvestris L.) radial growth responses to climatic factors in mesotrophic and heath forest site types in Estonia. Increment cores from 889 trees from 119 plots of the network of research plots were used and chronologies for mesotrophic and heath forest site types of Scots pine were constructed. The relationship between climatic factors and the radial growth of Scots pine was characterized by correlation coefficient; also pointer year analysis, Cropper method was applied to single tree series. Cropper values were calculated; extreme negative and positive pointer years were identified. According to analyses, 1940 and 1985 were the most significant negative pointer years among different sites; and significant positive years were 1945, 1946, 1989, and 1990. Extreme Cropper values indicated significant positive correlation with the monthly mean temperature in winter (January, February) and early spring (March, April) before a growing season; also with the mean annual temperature and the mean temperature of the vegetation period (from April to September). Significant negative correlation was found between the extreme Cropper value and the precipitation of the previous year August. Therefore, temperature can be considered as the most important single factor of growth activity. Pointer year analyses confirmed that severe winters, cool springs and dry summer conditions are the main causes for the sharp decrease in the radial growth.

Key words: climate variables, pointer years, radial growth, Scots pine, tree-ring chronology

+PVTQFWEVKQP Läänelaid and Dieter Eckstein (2003), who constructed the long-term chronology for Scots pine (Pinus The dynamics of the annual radial growth of a tree sylvestris L.), covering the period of 15161998 (482 is closely related to fluctuations of climate and other years). The use of radial tree-ring data in forest re- ecological factors which encompass dendroclimatolo- search was introduced in the 1980s, when Erich Lõh- gy (Fritts 1976, Cook and Kairiukstis 1990, Schwein- mus (1992a), a researcher at the Estonian Forest In- gruber 1996). Dendroclimatological methods are wide- stitute, developed a generalized chronology for Scots ly applied in modern studies on forest dynamics, as- pine in Estonia for the period of 17801983 (203 years). sessing damages, forecasting climate changes, and Lõhmus also compiled separate chronologies for three productivity (Spiecker et al. 1996, Worbes 2004). soil moisture classes (for arid, moderate, and humid Changes in average climatic conditions, such as air sites), to enhance the sensitivity and informativeness temperature, affect the length of the growing season of general chronology by minimizing typological di- and influence site productivity (Fabian and Menzel versity. Considerable input on studying the radial 1999). Tree rings are an excellent material for study- growth of Scots pine in park forests and regions of ing climate-growth relationships, as they reflect envi- different cement dust loads has been provided by ronmental conditions and changes, and store the re- Henn Pärn (2003, 2004, 2006). action pattern over time, which can later serve as an Scots pine is the species in which tree rings are archive (Spiecker 2002). Growth response to climatic one of the main sources of chronologies used in cli- influences varies with species, provenance, competi- mate reconstruction (Lõhmus 1992a, Läänelaid 1997, tive status, and site conditions (Fitts 1976, Sweingru- Helama and Lindholm 2003, Vitas 2008) and have been ber 1996, Spiecker 2002). used successfully in dendroclimatological research. It Estonia became engaged in dendrochronological has the widest geographical distribution of all pines, research in the early 1970s, with Kalvi Aluve (Läänelaid and due to long rotation it is not problematic to find 1997, 2002) measuring and dating tree-ring widths in old trees that are considered suitable for dendrochrono- historical buildings, which was continued by Alar logical studies. Different forces control its growth in

2009, Vol. 15, No. 2 (29) ISSN 1392-1355 195

107 BALTIC FORESTRY RESPONSE OF SCOTS PINE /.../ RADIAL GROWTH TO CLIMATE FACTORS IN ESTONIA M. HORDO ET AL. climatically different regions (Cedro 2001, Helama and plots (Kiviste and Hordo 2002) similar to a Finnish Lindholm 2003), any one of which may limit or stimu- INKA system (Gustavsen et al. 1988) has been de- late their growth (Cedro 2001). signed to provide empirical data for forest growth and Scots pine is one of the most comprehensively yield modelling (Kiviste et al. 2003, Hordo 2005, Kiviste investigated tree species in Estonia and Baltic coun- et al. 2005). The network of 756 research plots with tries by using dendroclimatological techniques (Vitas more than 100,000 mapped trees was established and and Erlickytë 2007). In Estonia, pine is the most com- re-measured in 19952007. For this study, a subset of mon and economically important tree species, with 119 pine dominated sample plots was selected, which Scots pine dominating forests covering 29% (757,100 comprised 81 plots from the mesotrophic forest site ha) of total forest area (MMK 2008). Therefore, due to type and 38 plots from the heath forest site type. the changing environmental conditions and evolving According to the geographical location, the sample forest management objectives, updated information plots were grouped by regions: islands, northeast, about trees and their growth is needed (Nilson 2002). southeast and southwest (Figure 1). For growth and yield studies in Estonia, the network of forest growth research plots (Kiviste and Hordo 2002) Kunda has been established since 1995. For modelling individual -WPFC North-East diameter growth, it would be necessary to consider the 0QTVJ'CUV influence of climate factors on diameter increment dur- Ristna ing past decades by using chronological data (Mielikäin- 4KUVPC en 1985, Zahner 1988, Hynynen 1995, Gaucherela et al. 2008). Examining past relationships between tree growth and climate in Estonia will help us to understand how Tõravere tree growth (productivity) in pine forests might be af- 6ÐTCXGTG South-West fected in the future. 5QWVJ9GUV Islands+UNCPFU The aim of our study is to analyze which seasons South-East climatic variables affect the annual growth of Scots pine 5QWVJ'CUV the most, with particular attention to extreme climatic factors, like severe winters, cold springs and summer droughts, which can be the main causes for an abrupt decrease in the trees radial growth. Figure 1. Layout of permanent sample plots, used for core collection and location of EMHI stations that provided me- tereological data /CVGTKCNCPFOGVJQFU Increment cores were collected from trees outside Material collection the research plots from the North, South, East and West Increment cores from living Scots pine trees were directions, which were determined from the center of collected in Estonia during the summer/autumn of 2007. a plot (Figure 2). For each research plot, up to 8 dom- For sample tree selection, we used growth and yield inant trees without visible damages were sampled. Two research plots established in mesotrophic and heath increment cores in perpendicular radii were taken us- forest site types. Mesotrophic forests (Rhodococcum ing an increment borer from each tree at 1.30 m above and Myrtillus site types) are dry and well-lighted pine the ground. For this study, altogether 889 trees were stands, growing on moderately humid and temporari- cored from 119 sample plots, 602 trees from the mes- ly moist sandy soils (Etverk et al. 1995, Lõhmus 2004). otrophic and 287 trees from the heath forest site type. This forest type is the most widespread in Estonia, comprising 40.8% (MMK 2008) of pine dominated forest land. Heath forests (Cladonia and Calluna site types) are sparsely stocked pine stands, with slow N growth, located on poor dry sandy soils. Heath for- Figure 2. Location of ests can be found primarily in northern Estonia and sample trees (yellow on the islands and to a lesser extent in northeast, spots) on the research southeast and southwest Estonia. Heath forests are plot. Sample trees of the W E of great importance to coastal dunes where they pro- research plot were taken vide soil protection. Heath forests comprise 1.5% from the basic compass S (MMK 2008) of all pine dominated forests. points, outside the cir- An Estonian network of forest growth research cular plot

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108 BALTIC FORESTRY RESPONSE OF SCOTS PINE /.../ RADIAL GROWTH TO CLIMATE FACTORS IN ESTONIA M. HORDO ET AL.

Tree-ring measurements and cross-dating Mayer (2009), we used the residual chronology be- Annual ring-widths were measured with an accu- cause it lowers the bias inherent in tree-ring indices racy of 0.01 mm using LINTAB tree-ring measuring and therefore, provides a more rigorous assessment table with the computer program TSAP-Win Scientific of climatic influences. As a result of standardization, Version 0.59 (Rinn 2003). The measured series were chronologies for the mesotrophic and the heath for- cross-dated (Pilcher 1990) visually by comparing the est site type were compiled. graphs of ring-widths. Cross-dating and data quality As a reference chronology, we used the chronol- were assessed using the computer program COFECHA ogy for Scots pine compiled by Erich Lõhmus (1992a). (Holmes 1983, Grissino-Mayer 2001). As a result of the For his chronology, Erich Lõhmus used the detrend- cross-dating of tree ring time series, possible errors ing method of the moving average of 21 years. were eliminated and the series were verified among each other. Mean time series by trees and plots were Climate data built up with the software TSAP-Win. Sums of monthly precipitation (mm) and air tem- Descriptive statistics of a ring-width series for perature means (C) were obtained from Kunda (for mesotrophic and heath forest site types were calcu- North-East, location 59°3105N 26°3244E; period lated using the TSAP-Win program. Statistical param- 19192007), Ristna (for islands and South-West, loca- aters of the tree ring data like mean sensitivity (MS), tion 58°5514N 22°0402E; period 19452007) and standard deviation (Std), tendency changes (TC), auto Tõravere (for South-East, location 58°1550N correlation (AC), Gleichläufigkeit (Glk), t-value (TBP), 26°2742E; period 18662007) stations of the Estoni- and Cross-Date Index (CDI) were calculated. The mean an Meteorological and Hydrological Institute (EMHI). sensitivity (MS) is the mean percentage change from To illustrate climate data, average monthly tempera- each measured yearly ring value to the next (Doug- tures, the mean temperature of the vegetation period lass 1936). Standard deviation (Std) is the measure of (from April to September) and mean annual tempera- high-frequency variations (Fritts 1976). Tendency tures as well as monthly sums of precipitation, the sum changes (TC) were calculated over the points of the of precipitation in the vegetation period and the an- running window, while creating a new time series and nual sum of precipitation were calculated over the this new time series shows the variations of the cal- periods (Figure 3). culated parameter along the original series (Rinn 2003). In general, Estonia has a temperate climate, with The first order autocorrelation AR(1) was calculated warm summers and severe winters. The average annual to estimate serial correlation (Fritts 1976). temperature is 46 C. The annual sum of precipitation To express the quality of accordance between time is between 500 mm and 750 mm, about 4080 mm of series, Gleichläufigkeit (Glk), Baillie-Pilcher value (TBP) which falls down as snow. The active period of vege- (1973), and Cross-Date Index (CDI) were used. These tation growth (daily air temperature above 5C) most- parameters are characterized by different sensitivities ly lasts between 170 and 180 days per year. to tree-ring patterns. Gleichläufigkeit (Glk) represents the overall accordance of two series, while Baillie-Pilcher Analysis of climate-tree growth relationships value (TBP) presents the correlation significance, and Relationships between climate variables and the Cross-Date Index (CDI) is the combination of these two tree radial increment were evaluated using the pointer parameters (Glk and TBP), which is a date index of year analysis and correlations. The pointer year anal- possible series matches (Rinn 2003). Values of Glk great- ysis is an accepted method of showing annual growth er than 60% and values of TBP greater than 3.0 and CDI reactions due to abrupt changes in environmental = 10 were considered significant. conditions (Cropper 1979, Schweingruber 1990), especially those due to climate variations (Rolland 1993, Standardization Kroupova 2002, Neuwirth et al. 2004, Karpavièius and Measured tree-ring series were standardized us- Vitas 2006, Elferts 2007). To calculate pointer years, we ing the program ARSTAN (Cook 1985). All series were used Cropper (1979) method, where ratios among the detrended using a negative exponential curve. Index raw annual measurements for single tree series and their values were calculated as ratios between the actual and 13-year moving average were calculated: fitted values. Index values were then prewhitened Z OGCP>@YKPFQY < K using an autoregressive model selected on the basis K UVFGX>@YKPFQY of the minimum Akaike (1974) criterion and combined where: x tree-ring width in year i; mean[window] and i across all series using biweight robust estimation of stdev[window] arithmetic mean and standard devia- the mean to exclude the influence of the outliers (Cook tion of ring widths in the moving window x , x , x , i-6 i-5 i-4 1985). As recommended by Henderson and Grissino- x x , x , x , x , x , x , x , x , x . Years with a i-3 i-2 i-1 i i+1 i+2 i+3 i+4 i+5 i+6

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109 BALTIC FORESTRY RESPONSE OF SCOTS PINE /.../ RADIAL GROWTH TO CLIMATE FACTORS IN ESTONIA M. HORDO ET AL.

North-East by regions was calculated and the t-test for the com- 20 650 parison of the means of climate data between normal 18 Mean temperature 600 years and strong/extreme pointer years was performed 16 550 Sum of precipitation m

14 m 500 using SAS software (SAS 1996). C 12 450 10 400 8 350 4GUWNVU CPF FKUEWUUKQP 6 precipitation

300 temperature

4 of

250 2 Tree-ring chronologies 200 0 Sum Mean A

J F Mar A May J J A Sep O No D V While building up the general dendrochronology an un ul eb nnual pr ug egper ec -2 ct 150 v it is important to retain growth fluctuations by climate -4 100 (A 50 -6 -S factors within the chronology when larger areas are -8 ) 0 being summarized, with different growing conditions. Mo nths In this study, 889 trees from 119 plots were sampled Islands and South-W est from the Estonian network of forest growth and yield 20 650 research plots and separate chronologies were built 18 Mean temperature 600 16 Sum of precipitation 550 for mesotrophic and heath forest site types (Figure 4) 14 500

mm for different regions of Estonia (Figure 5, Figure 6).

C 12 450 The chronology for the mesotrophic forest site type 10 400 8 350 is 145 years long, and based on 602 trees, covering

6 300 precipitation

the period 17962007; whereas the chronology for the temperature of

4 250 heath forest site type is 221 years long and based on 2 200 Sum Mean 0 150 287 trees, covering the years from 1786 to 2007. J F Mar A May J J A Sep O No D V A an un ul eb pr ug egper nnual ec ct

-2 v 100 -4 50 (A

-6 -S 0 200 Months ) 180

160 South-East 20 650 140 18 Mean temperature 600 120 16 Sum of precipitation 550 100 14 Index 500 80 12 C

450 mm

60 10 400 8 40 350 6 20 300 Heat h forest site type Mesotrophic forest site type Scots pine general chronology by E. Lõhmus 4

temperature 0

precipitation

250 2 1780 1805 1830 1855 1880 1905 1930 1955 1980 2005 of Year 0 200 Mean J F Mar A May J J A Sep O No D V A an un ul eb pr ug egper nnual ec

ct 150 -2 Sum v -4 100 Figure 4. Indexed chronologies of Scots pine for heath and -6 (A 50 mesotrophic forest types and the reference chronology by -S

-8 ) 0 Months Lõhmus (1992a)

Figure 3. Mean monthly temperatures and sums of precipitation averages from Kunda (period 19192007), Ristna 200 (period 19452007), and Tõravere (period 18662007) South-West South-East Nor th- Eas t Islands 180 weather stations of EMHI 160

140 value of Z that was higher or lower than 1 or -1 (Neu- i 120 wirth et al. 2007, Pourtahmasi et al. 2007) were defined 100 Index as positive or negative pointer years, respectively. 80

Those positive and negative Cropper values were di- 60 vided into three classes by intensity: weak for 40 ,strong for and extreme for 20

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110 BALTIC FORESTRY RESPONSE OF SCOTS PINE /.../ RADIAL GROWTH TO CLIMATE FACTORS IN ESTONIA M. HORDO ET AL.

200 tive to climatic factors. The low first-order autocorre- South-East Nor th- East Islands 180 lations indicate that the influence of climate on the

160 inter-annual growth variability of pine is high (Wig-

140 ley et al. 1984, Pourtahmasi et al. 2007). We found out 120 that ring width growth was mostly influenced by the 100 previous year weather conditions, in the heath forest Index 80 site type in South-East (AR(1) = 0.78). At the same 60 time, a low influence of the previous year was detect- 40 ed in North-East (the mesotrophic forest site type) and 20 islands (the heath forest site type), with AR(1) = 0.45. 0 All these findings above confirm that the pine chro- 1797 1827 1857 1887 1917 1947 1977 2007 Year nologies from our network of sample plot data are Figure 6. Dendrochronological series for heath forest types suitable for studying the effects of climate on the ra- of Scots pine by regions dial growth.

Updated chronologies were compared to the ref- Correlation analysis erence chronology built by Lõhmus (1992a) and meas- The results of correlation analyses between the ures of accordance were calculated (Table 1). Glk radial growth indices and climate data, which include between the mesotrophic forest type and the reference monthly temperatures and precipitation from the pre- chronology ranged from 56.1 to 77.2%, and between vious growing season to this year, are presented in the heath forest type and the reference chronology Figure 7. On both forest sites, correlation analyses varied from 72.8 to 80.7%. In South-West region, Glk revealed that tree-width growth is positively correlat- was not significant. The TBP showed significant con- ed with winters prior to the growing season tempera- formity between new chronologies and then the ref- ture and the temperature during the vegetation peri- erence chronology, 7.1 for the mesotrophic and 5.0 for od. Additionally, tree growth is significantly negatively the heath forest type. Consequently, the overall con- correlated with the previous year August temperature formity between new and reference chronologies was and positively correlated with sum of the precipitation statistically significant. Intercorrelation among region- of this month. This indicates that high temperature and al chronologies within a forest site type was statisti- low amount of precipitation at that time is limiting the cally significant (p < 0.05). increment growth of pine. The analysis showed that Descriptive statistics for indexed tree-ring series in southwest Estonia in mesotrophic forest site types, were calculated. The mean sensitivity varied between precipitation in February has a significant positive 8 and 14% (Table 1), whereas the values for mes- influence on the radial growth. Considering that Feb- otrophic forests were lower than those for heath for- ruary is one of the coldest months in Estonia, with the ests, which shows that heath forests are more sensi- average temperature from 3.3 to 7.4C, higher

Table 1. Internal statistical properties of time series of Scots pine forest types by regions (generalized chronology by Lõhmus was compared to sample plots chronologies). Std = standard deviaton; AR = first-order autocorrelation (lag = 1); MS = mean sensitivity; TC = tendency changes; Glk = Gleichläu- figkeit; TBP = T-value with Baillie-Pilcher-Standardization; CDI = Cross Date Index; * 95% significance for the Glk value

0QQH .GPIVJ 4KPIYKFVJUKPFGZ (QTGUVV[RG 4GIKQP )NM 6$2 %&+ UVCPFU QHUGTKGU /5 6% /KP /GCP /CZ 5VF #4

*GCVJ )GPGTCN 5QWVJ'CUV 0QTVJ'CUV +UNCPFU $KT /GUQVTQRJKE )GPGTCN .KG 5QWVJ'CUV 0QTVJ'CUV +UNCPFU 5QWVJ9GUV

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111 BALTIC FORESTRY RESPONSE OF SCOTS PINE /.../ RADIAL GROWTH TO CLIMATE FACTORS IN ESTONIA M. HORDO ET AL.

Temperature - Mesotrophic forest site type 0.5 South-West South-East Nor th-Eas t Islands 0.4

0.3

0.2 coefficient 0.1

0.0 Jun Jul Aug Sept Oct Nov Dec Jan Feb Mar Apr Mai Jun Jul Aug Sept Oct Nov Dec Veg AnnMean

-0.1 P Correlation er -0.2

-0.3

-0.4 Months

Precipitation - Mesotrophic forest site type 0.5 South-West South-East North-East Islands 0.4

0.3

0.2 coefficient 0.1

0.0 Jun Jul Aug Sept Oct Nov Dec Jan Feb Mar Apr Mai Jun Jul Aug Sept Oct Nov Dec Veg AnnSum P -0.1Correlation er -0.2

-0.3

-0.4 Months

Temperature - Heath forest site type 0.5 South-East Nor th-Eas t Islands 0.4

0.3

0.2

0.1 coefficient

0.0 Jun Jul Aug Sept Oct Nov Dec Jan Feb Mar Apr Mai Jun Jul Aug Sept Oct Nov Dec Veg AnnMean

-0.1 P er Correlation -0.2

-0.3

-0.4 Months

Precipitation - Heath forest site type 0.4 South-East North-East Islands 0.3

0.2

0.1 coefficient 0.0 Jun Jul Aug Sept Oct Nov Dec Jan Feb Mar Apr Mai Jun Jul Aug Sept Oct Nov Dec Veg AnnMean

Figure 7. Correlation coefficients for Scots -0.1 P er pine chronologies (mesotrophic and heath Correlation-0.2 forest site types) between ring-width indices and total precipitation and the mean -0.3 monthly temperature. Dashed lines mark -0.4 the significant level (r = 0.273 or r = Months 0.273) at p = 0.05

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112 BALTIC FORESTRY RESPONSE OF SCOTS PINE /.../ RADIAL GROWTH TO CLIMATE FACTORS IN ESTONIA M. HORDO ET AL. amount of precipitation (snow) would stimulate tree 1985 and the extreme positive pointer year was 1989. growth in spring. On the islands, precipitation in April On heath forest sites significant negative extreme years has a significant negative impact on the radial incre- were detected 1906, 1931, 1932 and 1942 in the south- ment. The annual mean temperature is significantly east region, and positive years were 1921, 1923, 1945, positively correlated with tree-ring indices of pine on 1946, 1988, and 1997; in the northeast region, impor- both sites. All the findings above suggest that the tant positive extreme years were 1921, 1967, 1980, 1981, mean annual temperature significantly depends on 1989, 1990 and negative years were 1920, 1940, 1941 winter climate, and when severe winter was followed and 1985; on islands, significant positive years were by a long and cool spring, such factors limit tree 1910, 1938, 1945 and negative years were 1940, 1941, growth. and 1956. The results of the pointer year analysis are pre- Pointer year analysis sented in Table 2. The analysis of pointer years iden- The Cropper values were calculated from single tified 19 positive and 19 negative years on mesotrophic tree curves for mesotrophic and heath forest sites in forest site types, and 34 positive and 27 negative years different regions as growing conditions varied. Extreme on heath forest site types. According to analyses, 1940 negative and positive pointer years were identified and 1985 were the most significant negative years. The when the Cropper value was lower than 1.645 or high- records of EMHI prove that the most severe winters er than 1.645 and strong pointer years were below over the past century were in 1939/40, 1940/41, 1941/ 1.28 and above 1.28, respectively. On mesotrophic 42 and cold winters were in 1984/85, 1986/87, 1995/96. forest sites significant extreme negative pointer years This indicates that the cold winter prior to the grow- of 1929 and 1940 in the southwest region were detecting season and late spring (mean T MarMay +2.C) ed and 1934, a positive year; in the southeast region, affected the radial increment of pine tree growth and an extreme negative pointer year was 1940 and a pos- it was the main cause of the sharp decrease in the itive event year was 1945; in the northeast region, radial growth. Similar results were obtained in Esto- considerably negative pointer years were 1937 and nia by Läänelaid and Eckstein (2003) and in Lithuania 1985 and there were no significant positive years; on by Vitas (2008). Extreme events usually last only a few islands, significant negative years were 1901, 1940, months and the organism can survive these conditions

Table 2. Negative () and positive (+) pointer years of the radial growth of Scots pine by

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(Ahas et al. 2000), but the influence of extreme events showed a statistically significant positive response to may last for several years (Jaagus et al. 2003). The the mean annual temperature and the temperature of winter severity determines directly when the spring the growing season. Analysis revealed that the high begins. In most cases, a severe winter is followed by temperature of the previous year August had a nega- a late, cool spring and an early and warm spring fol- tive effect on the radial growth, but the precipitation lowed by a mild winter (Jaagus et al. 2003). The pointer in this month had a positive influence on the radial year analysis revealed that significant positive point- increment. This negative effect of the temperature on er years were 1945, 1946, 1989 and 1990. According to the radial growth could be explained by the promo- the EMHI, warm winters appeared in 1945 (mean T Dec- tion of bud differentiation during this time. The late Feb 3.9% OGCP6&GE(GDŌ% summer temperature affects the amount of nutrient OGCP6&GEŌ(GDŌ% CPF OGCP6&GEŌ storage which encourages sprouting in spring. This (GDŌ% 'XGPVJQWIJKPVJGCXGTCIGYKPVGT in turn affects the next growing year ratio of sprouts VGORGTCVWTGYCUDGNQYCXGTCIG NQPIRGTKQFOGCP6 and diameter increment (Lõhmus 1992b). A similar ef- Ō% KVseems that the warm winter of the previous fect is mentioned in Finland by Henttonen (1984) and year had a positive effect on increment growth. in Sweden by Jonnson (Lõhmus 1992b). In mesotrophic Additionally, growth-climate relationships were forest site types, annual precipitation had a negative tested with the correlation of strong/extreme Cropper impact, while on heath forest site types, the sum of values and climate data. The results of the analysis precipitation was important during the vegetation pe- are presented in Figure 8. The results indicate a pos- riod. The results of the analysis showed that the im- itive correlation of Cropper value with the mean tem- pact of precipitation on the radial growth of trees in perature in winter, particularly in January and Febru- comparison to temperatures was not so significant. ary, and with the temperature in the beginning of the Temperature may be considered as the most important growing season (March, April). However, no negative single factor initiating growth activity (Vaganov et al. correlation with average winter temperatures was 2006); however, low humidity can cause an earlier ter- found, but in an earlier study, Lõhmus (1992b) detect- mination of growth in a season (Fritts 1976), in our ed a high negative correlation of the radial growth with case on heath forest site types. A combination of tem- winter minimum temperature. A statistically significant perature and humidity changes in particular intervals positive correlation with temperatures in June and July of a season produces acceleration or deceleration of was detected on the mesotrophic forest site type, while growth processes (Schweingruber 1996). It is confirmed the temperature was significant for growth on heath by Jaagus et al. (2003, 2006) that beside temperature, forest site types in September and October. Scots pine precipitation has the most profound effect on the

Mesotrophic fo re st type 0.20 Temperature 0.15 Precipitation

0.10

0.05 coefficient

0.00 pJ pJ pA pS pO pN pD J F M A M J J A S O N D Vegp Ann pJ pJ pA pS pO pN pD J F M A M J J A S O N D Vegp Ann

temp prec

temp -0.05 prec Correlation

-0.10

-0.15 Month

Heath forest type 0.25 Temperature Figure 8. Correlation be- 0.20 Precipitation tween monthly and season- 0.15 al climate data (temperature 0.10 and precipitation) and 0.05 Coefficient

Cropper-values in heath 0.00 pJ pJ pA pS pO pN pD J F M A M J J A S O N D Vegp Ann pJ pJ pA pS pO pN pD J F M A M J J A S O N D Vegp Ann

prec temp and mesotrophic forest prec -0.05 temp sites; responses were simi- C orrelation-0.10 lar in all regions. indicates -0.15 statistical significance -0.20 (p<0.05) Month

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114 BALTIC FORESTRY RESPONSE OF SCOTS PINE /.../ RADIAL GROWTH TO CLIMATE FACTORS IN ESTONIA M. HORDO ET AL. growth; however, precipitation is an extremely unsta- drological Institute provided climate data. This study ble climate variable which makes long-term changes was supported by The Ministry of Education and almost impossible to predict. Research (project SF0170014s08). Finally, we used the t-test on climate data to analyze the differences between normal years and strong/ 4GHGTGPEGU extreme years. The results of the test confirmed our previous results that positive and negative pointer Ahas, R., Jaagus, J. and Aasa, A. 2000. The phenological year had a significant effect on the mean annual tem- calendar of Estonia and its correlation with mean air temperature. International Journal of Biometeorology 44 perature and the temperature of the growing season, (4): 159166. as well as on the temperature and the sum of precip- Akaike, H. 1974. A new look at the statistical model identi- itation in August of the previous year. This means that fication. IEEE Transactions on Automatic Contro 19 (6): extreme climate events are influencing the radial incre- 716723. Baillie M.G.L. and Pilcher, J.R. 1973. A sample crossdat- ment growth of pine trees. ing program for tree-ring research. Tree-Ring Bulletin 33: 714. Carrer, M. and Urbinati, C. 2004. Age-dependent tree-ring %QPENWUKQPU growth responses to climate in Larix decidua and Pinus cembra. Ecology 85 (3): 730740. In this study we compiled new chronologies for Cedro, A. 2001. Dependence of radial growth of Pinus sylv- mesotrophic and heath sites for Scots pine and ana- etris L. from western Pomerania on the rainfall and tem- lyzed the responses of the radial increment to climat- perature conditions. Geochronometria 20: 6974. Cook, E.R. 1985. A time series analysis approach to tree ring ic factors. Measures of conformity Glk between the standardization. Dissertation, University of Arizon, mesotrophic forest type and the reference chronolo- 171 p. gy ranged from 56.1 to 77.2% and between the heath Cook, E.R. and Kairiukstis, L.A. 1990. Methods of Den- forest type and the reference chronology varied from drochronology: Applications in the Environmental Sci- ences. Kluwer Academic Publischers, 394 p. 72.8 to 80.7%. Therefore, the overall conformity be- Cropper, J.P. 1979. Tree-ring skeleton plotting by compu- tween new and reference chronologies was statistically ter. Tree-Ring Bulletin 39: 4759. significant (p < 0.05). Douglass, A.E. 1936. Climatic Cycles and Tree Growth, Vol- The results of correlation analyses between radi- ume III. A Study of Cycles. Carnegie Institution of Wash- ington Publication 289: 171 p. al growth indices and climate data, on both forest sites Elferts, D. 2007. Scots pine pointer-years in northwestern revealed that tree-width growth is positively correlat- Latvia and their relationship with climatic factors. Biol- ed with temperatures in winter time and prior growing ogy, Acta Universitatis Latviensis 723: 163170. season, and temperatures during growth season (r = Etverk, I., Karoles, K., Lõhmus, E., Meikar, T., Männi, R., Nurk, T., Pikk, J., Randveer, T., Tamm, Ü., Veibri, 0.273; p > 0.05). This means that the mean annual tem- U. and Örd, A. 1995. Estonian Forests and Forestry. perature significantly depends on winter climate, and Estonian Forest Department, Tallinn, 128 p. when severe winter was followed by a long and cool Fabian, P. and Menzel, A. 1999. Change in phenology of spring, such factors limited tree growth. trees in Europe. In: Karjalainen, T., Spiecker, H. and Laroussinie, O. (eds.), Causes and Consequences of Ac- The pointer year analysis identified 19 positive celerating Tree Growth in Europe. European Forest In- and 19 negative years on mesotrophic forest site types, stitute Proceedings, 27: 4351. and 34 positive and 27 negative event years on heath Fritts, H.C. 1976. Tree Rings and Climate. Academic Press, forest site types. The analysis revealed that 1940 and London, 567 p. Gaucherela, C., Campillob, F., Missonc, L., Guiota, J. and 1985 were the most significant negative years, while Boreuxd, J.-J. 2008. Parameterization of a process-based significant positive pointer years were 1945, 1946, 1989 tree-growth model: Comparison of optimization, MCMC and 1990. Additionally, growth-climate relationships and Particle Filtering algorithms. Environmental Model- from the correlation analysis revealed the statistically ling & Software 23 (1011): 12801288. Grissino-Mayer, H.D. 2001. Evaluating crossdating accura- most significant positive response to the mean annu- cy: a manual and tutorial for the computer program al temperature and the temperature of growing season COFECHA. Tree-Ring Research 57: 205221. (p < 0.05) in both forest site types, while the monthly Gustavsen, H.G., Roiko-Jokela, P. and Varmola, M. 1988. consequence of the correlation varied. Kivennäismaiden talousmetsien pysyvat (INKA ja TIN- KA) kokeet. Suunnitelmat, mittausmenetelmät ja aineis- tojen rakenteet. Metsäntutkimuslaitoksen tiedonantoja #EMPQYNGFIGOGPVU 292: 212 p. (in Finnish). Helama, S. and Lindholm, M. 2003. Droughts and rainfall The collection of the increment core data from in south-eastern Finnland since AD 874, inferred from Scots pine ring-widths. Boreal Environment Research 8: the network of Estonian forest growth and yield re- 171183. search plots was supported by the Environmental In- Henderson, J.P. and Grissino-Mayer, H.D. 2009. Climate- vestment Centre. Estonian Meteorological and Hy- tree growth relationships of longleaf pine (Pinus palus-

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tris Mill.) in the Southeastern Coastal Plain, USA. Den- birch on the structure and development of Norway spruce drochronologia 27(1): 3143. stands]. Communicationes Instituti forestalis, Metsäntut- Henttonen, H. 1984. The dependence of annual ring indices kimuslaitos, 133: 79 p. (in Finnish). on some climatic factors. Acta Forestalia Fennica 186: MMK, 2008. Aastaraamat Mets 2007 [Yearbook Forest 2007]. 137. Tartu, 217 p. (in Estonian). Holmes, R.L. 1983. Computer-assisted quality control in tree- Neuwirth, B., Esper, J., Schweingruber, F.H. and Wini- ring dating and measurement. Research report. Tree-Ring ger, M. 2004. Site ecological differences to the climatic Bulletin 43: 6978. forcing of spruce pointer years from Lötschental, Swit- Hordo, M. 2005. Erindite ja / või mõõtmisvigade avastamise zerland. Dendrochronologia 21 (2): 6978. meetoditest puistu kasvukäigu püsiproovitükkide andmes- Neuwirth, B., Schweingruber, F.H. and Winiger, M. 2007. tikul. [Outlier and/or measurement errors on the perma- Spatial patterns of central European pointer years from nent sample plot data]. Metsanduslikud uurimused / For- 19011971. Dendrochronologia 24: 7989. estry Studies 43: 923 (in Estonian). Nilson, A. 2002. Fragmente puistu kasvu ja ehituse mudelit- Hynynen, J. 1995. Predicting the growth response to thin- est [Some fragments of stand growth and structure mod- ning for Scots pine stands using individual-tree growth els]. Metsanduslikud Uurimused / Forestry Studies 37: 9 models. Silva Fennica 29 (3): 225246. 20 (in Estonian). Jaagus, J. 2006. Climatic changes in Estonia during the sec- Pärn, H. 2003. Radial Growth Response of Scots Pine to Cli- ond half of the 20th century in relationship with chang- mate Under Dust Pollution in Northeast Estonia. Water es in large-scale atmospheric circulation. Theoretical and Air and Soil Pollution 144 (1): 343361. Applied Climatology 83 (14): 7788. Pärn, H. 2004. Hariliku männi puistute radiaalkasvu ja klii- Jaagus, J., Truu, J., Ahas, R. and Aasa, A. 2003. Spatial mategurite vaheliste seoste ajalisest varieeruvusest [Tem- and temporal variability of climatic seasons on the East poral variability in the relationships between the radial European Plain in relation to large-scale atmospheric growth of Scots pine stands and the climate]. Metsandus- circulation. Climate Research 23 (2): 111129. likud Uurimused / Forestry Studies 40: 6579 (in Esto- Karpavièius, J. and Vitas, A. 2006. Influence of environ- nian). mental and climatic factors on the radial growth of Eu- Pärn, H. 2006. Radial growth of conifers in regions of differ- ropean ash (Fraxinus excelsior L.). Ecologija 1: 19. ent cement dust loads. Ökoloogia 55 (2): 108122. Kiviste, A. and Hordo, M. 2002. Eesti metsa kasvukäigu Pilcher, J.R. 1990. Sample Preparation, Cross-dating, and püsiproovitükkide võrgustik. [Network of permanent for- Measurement. In: Cook, E. and Kairiukstis, L. 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In: Amario, A., Reed, D. and User Reference, Heidelberg, 91 p. Soares, P. (eds.), Modelling Forest Systems. CABI Pub- Rolland, C. 1993. Tree-ring and climate relationships for lishing, London, p. 169179. Abies alba in the internal Alps. Tree-ring Bulletin 53: 1 Kroupova, M. 2002. Dendroecological study of spruce growth 12. in regions under long-term air pollution load. Journal of Sachs, L. 1982. Applied statistics. Springer Series in Statis- Forest Science 48: 536548. tics, New York, 706 p. Läänelaid, A. 1997. Dendrochronological dating of the Uppsala SAS Institute Inc. 1996. SAS/STAT Software: changes and en- house in Tartu, Estonia. Dendrochronologia 15: 191198. hancements through release 6.11. Cary, North Carolina, Läänelaid, A. 2002. Tree-ring dating in Estonia. Doctoral SAS Institute Inc., 1104 p. dissertation, University of Helsinki, Helsinki, 98 p. Schweingruber, F.H. 1990. Dendroecological Information in Läänelaid, A. and Eckstein, D. 2003. Development of a Pointer Years and Abrupt Growth Changes. In: Cook, E. Tree-ring Chronology of Scots pine (Pinus sylvestris L.) and Kairiukstis, L. (eds.), Methods of Dendrochronolo- for Estonia as a Dating Tool and Climatic Proxy. Baltic gy: applications in the Environmental Sciences. Kluwer Forestry 9 (2): 7682. Academic Publishers, p. 277283. Lõhmus, E. 1992a. Eesti männikute dendrokronoloogiline Schweingruber, F.H. 1996. Tree Rings and Environment. üldskaala [Scots pine generalized chronology in Estonia]. Dendroecology. Vienna, 609 p. Metsanduslikud Uurimused / Forestry Studies 24: 103 Spiecker, H. 2002. Tree rings and forest management in 120 (in Estonian). Europe. Dendrochronologia, 20 (12): 191202. Lõhmus, E. 1992b. Hariliku männi radiaalse juurdekasvu seo- Spiecker, H., Mielikäinen, K., Köhl, M. and Skovsgaard, sest meteoroloogiliste teguritega [The dependence of Scots J.P. 1996. Growth Trends in European Forests. Europe- pine annual ring indices on some climatic factors]. Met- an Forest Institute Research Report, 5: 372 p. sanduslikud Uurimused / Forestry Studies 25: 5059 (in Vaganov, E.A., Hughes, M.K. and Shashkin A.V. 2006. Estonian). Growth dynamics of conifer tree rings: images of past and Lõhmus, E. 2004. Eesti metsakasvukohatüübid. [Estonian future environments. Springer, Berlin, 354 p. forest site types]. EPMÜ Metsanduslik Uurimisinstituut, Vitas, A. 2008. Tree-ring chronology of Scots pine (Pinus syl- 80 p. (in Estonian). vestris L.) for Lithuania. Baltic Forestry 14 (2): 110115. Mielikäinen, K. 1985 Koivusekoituksen vaikutus kuusikon Vitas, A. and Erlickytë, R. 2007 Influenceof droughts. In- rakenteeseen ja kehitykseen [Effect of an admixture of fluence of Droughts to the Radial Growth of Scots Pine

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(Pinus sylvestris L.) at Different Site Conditions. Baltic pedia of Forest Sciences, 586599. Forestry 13 (1): 1016. Zahner, R. 1988. A model for tree-ring time series to detect Wigley, T.M.L., Briffa, K.R. and Jones, P.D. 1984. On the regional growth changes in young, evenaged forest stands. Average Value of Correlated Time Series, with Applica- Tree-Ring Bulletin 48: 1320. tions in Dendroclimatology and Hydrometeorology. Jour- nal of Climate and Applied Meteorology 23: 201213. Worbes, M. 2004. Mensuration. Tree-Ring Analysis. Encyclo- 4GEGKXGF(GDTWCT[ #EEGRVGF1EVQDGT

ÂËÈßÍÈÅ ÊËÈÌÀÒÈÅÑÊÈÕ ÔÀÊÒÎÐÎÂ ÍÀ ÐÀÄÈÀËÜÍÛÉ ÏÐÈÐÎÑÒ ÑÎÑÍÛ ÎÁÛÊÍÎÂÅÍÍÎÉ (PINUS SYLVESTRIS L.) Â ÝÑÒÎÍÈÈ

Ì. Õîðäî, Ñ. Ìåòñëàéä è À. Kèâèñòå

Ðåçþìå

Â äàííîé ñòàòüå àíàëèçèðóåòñÿ âëèÿíèå êëèìàòè÷åñêèõ ôàêòîðîâ íà ðàäèàëüíûé ïðèðîñò ñîñíû îáûêíîâåííîé (Pinus sylvestris L.) â ìåçîòðîôíûõ è âåðåùàòíèêîâûõ ëåñàõ Ýñòîíèè. Õðîíîëîãèÿ ñîñíû îáûêíîâåííîé, ðàñòóùåé â ìåçîòðîôíûõ è âåðåùàòíèêîâûõ ëåñàõ, áûëà ñîñòàâëåíà íà îñíîâàíèè àíàëèçà ãîäè÷íûõ êîëåö 889 äåðåâüåâ, ïîëó÷åííûõ â ñåòè 119 ïðîáíûõ ó÷àñòêîâ. Oòíîøåíèÿ ìåæäó êëèìàòè÷åêèìè ôàêòîðàìè è ðàäèàëüíûì ïðèðîcòîì áûëè îöåíåíû, èñïîëüçóÿ êîýôôèöèåíò êîððåëÿöèè Ïèðñîíà, à òàêæå ïðè ïîìîùè àíàëèçà ðåïåðíûõ ëåò. Åäèíè÷íûå äåðåâüÿ áûëè îöåíåíû ïðè ïîìîùè ìåòîäà Êðîïïåðà. Ñîãëàñíî ðåçóëüòàòàì àíàëèçà, 1940 è 1985 áûëè íåãàòèâíûìè ðåïåðíûìè ãîäàìè, òîãäà êàê 1945, 1946, 1989 è 1990 ïîçèòèâíûìè. Ýòà òåíäåíöèÿ ñîõðàíÿåòñÿ äëÿ èçó÷åííûõ ìåñò ïðîèçðîñòàíèÿ. Ýêñòðåìàëüíûå çíà÷åíèÿ Êðîïïåðà èìåþò ïîçèòèâíóþ êîððåëÿöèþ ñî ñðåäíåé ìåñÿ÷íîé òåìïåðàòóðîé çèìîé è ðàííåé âåñíîé. Íåãàòèâíàÿ êîððåëÿöèÿ íàáëþäàåòñÿ ñ êîëè÷åñòâîì îñàäêîâ â àâãóñòå ïðîøëîãî ãîäà. Òåìïåðàòóðà ÿâëÿåòñÿ íàèáîëåå âàæíûì ôàêòîðîì, âëèÿþùåì íà èíòåíñèâíîñòü ðàäèàëüíîãî ïðèðîñòà. Àíàëèç ðåïåðíûõ ëåò ïîäòâåðäèë, ÷òî ñóðîâàÿ çèìà, ïîçäíÿÿ âåñíà, à òàêæå ñóõîå ëåòî, ÿâëÿþòñÿ îñíîâíûìè ïðè÷èíàìè ðåçêîãî ñîêðàùåíèÿ ðàäèàëüíîãî ïðèðîñòà.

Êëþ÷åâûå ñëîâà: ðàäèàëüíûé ðîñò, êëèìàò, ðåïåðíûé ãîä, ñîñíà îáûêíîâåííàÿ, õðîíîëîãèÿ ãîäè÷íûõ êîëåö

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118 Metslaid, S., Stanturf, J.A., Hordo, M., Korjus, H., Laarmann, D., Kiviste, A. 2016. Growth responses of Scots pine to climatic factors on reclaimed oil shale mined land. Environmental Science and Pollution Research, 23: 1363713652. 119

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121 Author's personal copy

Environ Sci Pollut Res DOI 10.1007/s11356-015-5647-4

HOW CAN WE RESTORE THE BIODIVERSITY AND ECOSYSTEM SERVICES IN MINING AND INDUSTRIAL SITES?

Growth responses of Scots pine to climatic factors on reclaimed oil shale mined land

Sandra Metslaid1 & John A. Stanturf2 & Maris Hordo1 & Henn Korjus1 & Diana Laarmann1 & Andres Kiviste1

Received: 15 April 2015 /Accepted: 19 October 2015 # Springer-Verlag Berlin Heidelberg 2015

Abstract Afforestation on reclaimed mining areas has high competition in the closed-canopy stands, through the thinning ecological and economic importance. However, ecosystems activities, tree sensitivity and response to climate could be established on post-mining substrate can become vulnerable manipulated. due to climate variability. We used tree-ring data and dendrochronological techniques to study the relationship between Keywords Reclamation . Post-mining area . Afforestation . climate variables and annual growth of Scots pine (Pinus Tree-ring . Scots pine . Forest management sylvestris L.) growing on reclaimed open cast oil shale mining areas in Northeast Estonia. Chronologies for trees of different age classes (50, 40, 30) were developed. Pearson’s correlation analysis between radial growth indices and monthly climate Introduction variables revealed that precipitation in June–July and higher mean temperatures in spring season enhanced radial growth of Afforestation on reclaimed mining areas has high ecological pine plantations, while higher than average temperatures in and economic importance. However, ecosystems established summer months inhibited wood production. Sensitivity of ra- on post-mining substrate can become vulnerable due to cli- dial increment to climatic factors on post-mining soils was not mate variability. homogenous among the studied populations. Older trees Reclamation of oil shale open surface-mined lands in Es- growing on more developed soils were more sensitive to pre- tonia is mainly done by afforestation with the native Scots cipitation deficit in summer, while growth indices of two other pine (Pinus sylvestris L). Since the 1960s, when systematic stand groups (young and middle-aged) were highly correlated reclamation of post-mining areas started (Korjus et al. 2007), to temperature. High mean temperatures in August were neg- more than 16,000 ha have been afforested (Yearbook Forest atively related to annual wood production in all trees, while 2014). To a large extent, the growth of forest plantations trees in the youngest stands benefited from warmer tempera- established on such reclaimed areas depends on the physical tures in January. As a response to thinning, mean annual basal (e.g., Torbet et al. 1990) and chemical properties (e.g., Hüttl area increment increased up to 50 %. By managing tree and Weber 2001) of the growth medium where tree roots are concentrated. In general, substrate conditions for plant growth Responsible editor: Philippe Garrigues on reclaimed post-mining areas are harsh due to shallow soil, high (60–100 %) content of coarse unweathered rocks * Sandra Metslaid (Reintam 2004; Roberts et al. 1988), and low organic matter [email protected] and nutrient content (especially N). Alkaline soil reaction per- sists in such substrates for decades (Reintam et al. 2002)and 1 Department of Forest Management, Institute of Forestry and Rural may inhibit natural tree establishment (Pensa et al. 2004)or Engineering, Estonian University of Life Sciences, Kreutzwaldi 5, reduce the survival of planted trees (Kaar 2002). These factors 51014 Tartu, Estonia restrict tree species selection and as a result, single-species 2 Center for Forest Disturbance Science, USDA Forest Service, 320 forest ecosystems of low stability are being established on Green Street, Athens, GA 30602, USA large areas of degraded land.

122 Author's personal copy

Environ Sci Pollut Res

Scots pine is able to grow on infertile soils and has been Material and methods widely used for reclamation of mining areas in Estonia (Kaar 2002) and throughout Europe (Hüttl and Weber 2001; Study area Baumann et al. 2006; Pietrzykowski and Socha 2011). Despite limiting soil conditions, forest plantations on mined sites in The study was carried out in Northeast Estonia (Fig. 1), in Estonia show high productivity (Reintam and Kaar 2002)and Narva (Viivikonna section) oil shale quarry (59° 16′–59° 19′ are comparable to pine growth on fertile forest sites (Korjus N, 27° 39′–27° 40′ E, elevation 40–46 m a.s.l.) that is a part of et al. 2007; Kiviste et al. 2010). Plantations on post-mining the largest commercially exploited oil shale mining area in areas are important for carbon sequestration (Karu et al. 2009) Estonia (Raukas and Punning 2009). Lowlands prevail in and play a significant ecological role in promoting soil devel- the surrounding undisturbed landscape, while the topography opment (Reintam 2001). Besides, these afforested areas are of post-mining areas has been drastically altered by large-scale managed generally for timber production. surface mining activity and mainly consists of artificial hills Previous studies carried out on the reclaimed areas of oil (leveled spoil heaps) covered with forest plantations (Sepp shale excavation have investigated tree species selection (Kaar et al. 2010). 2002), tree survival, and biomass production (Kuznetsova Although the machinery used in oil shale extraction industry et al. 2011) as well as ecosystem restoration possibilities has changed over time, there were no major differences in oil (Laarmann et al. 2015). Growth of the plantations has been shale extraction and reclamation methods in our sites. The ac- extensively studied for young trees (Vaus 1970;Kuznetsova cess to commercially valuable layers of oil shale was gained by et al. 2011), but only a few studies on growth dynamics and removing natural ecosystems, and topsoil was stockpiled and productivity (e.g., Korjus et al. 2007; Kiviste et al. 2010)are used during reclamation. The average oil shale extraction depth based on long-term observations. was 7.4 m and did not differ significantly among the sites. The Recently, changing climatic conditions have been suggested remaining surface and rock overburden was removed and to have a pronounced effect on forest growth and productivity. underwent mechanical reduction. The oil-bearing shale was Knowledge about climate-growth relationships is essential for excavated and hauled to the processing plant. After oil shale the evaluation of possible impact of climate change on the extraction, crushed waste rocks, mainly consisting of limestone productivity and vitality of pine plantations of reclaimed areas. (Reintametal.2002), were dumped at the bottom of excavated Several previous studies (Linderholm 2001; Weber et al. 2007; areas, covered with previously removed overburden, and bull- Hökkä et al. 2012) showed that climatic sensitivity of Scots dozers leveled the spoils (Pensa et al. 2004). Pine plantations pine greatly depends on local soil properties, while the influ- were subsequently established by planting 2–3-year-old Scots ence of climate variables (temperature or precipitation) differs pine seedlings with initial planting densities of 5300–6700 along the latitudinal gradient (Helama et al. 2005;Hordoetal. stems per hectare (Kaar 2002; Korjus et al. 2007). 2011; Henttonen et al. 2014). Pine trees are more sensitive to Due to drastic land disturbance, post-mining substrates are variation in temperature at high latitude sites, while this rela- characterized by locally high spatial variability in physical tionship changes, moving toward the South, where stronger properties (primarily texture), fertility, and soil water availabil- relationships between growth and precipitation are usually de- ity. Physical soil conditions restrict tree-rooting depth and water tected. In Estonia, several studies have investigated Scots pine holding in the substrate is low. Additionally, ground water radial growth response to climate variables (e.g., Läänelaid and levels are artificially lowered by pumping out the water to avoid Eckstein 2003; Hordo et al. 2009) but mainly considered trees flooding in the excavated areas. Furthermore, due to interac- growing on forest sites. Some attempts to relate growth of tions with vegetation, soil-maturing processes produce gradual young trees, growing on post-mining areas to precipitation in changes due to rock weathering and organic matter accumula- spring were described by Vaus (1970), but no thorough tion, resulting in decreased soil reaction (pH) and profile devel- dendroclimatic assessment of older trees growing on highly opment over the time (Reintam 2001;Reintam2004). degraded soils in Northeast Estonia has ever been conducted. The aim of the present study was to investigate radial growth Sampling design and stand characteristics variability and climate influence on the growth of pine mono- cultures on reclaimed areas across several combinations of sub- We selected 12 circular permanent sample plots, previous- strate and stand structural conditions. The specific aims were (a) ly established across the reclaimed area of Narva to identify the main climatic factors driving annual diameter (Viivikonna section) quarry. The plots were located in growth of Scots pine established on reclaimed oil shale mining smallgroupsatthreesites(Fig.1) and represent varying areas, (b) to examine how the relationship to climate variables is soil and stand structure conditions of reclaimed areas. affected by soil and forest stand characteristics, and (c) to assess Mean stand age decreased southwards, following the pro- possible effects of climate, site productivity, and changes in gression of excavation activities; stand age in 2010 varied stand structure on annual basal area increment. from 44 to 24 years, with around 10 years difference

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Fig. 1 Location of the study area and sampling sites (dots)

between consecutive sites. If our investigated stands grew Since plantations on reclaimed areas are grown for wood on forest sites, they would belong to a single age group, but production, management interventions occurred on two of the since continuous substrate formation occurs on post- sites. Pre-commercial thinning was conducted in 2003 in the mining areas, we divided trees into three age groups young pine populations (site 3) and first commercial thinning (50 year old, 40-year middle-aged, 30-year young). The in 2008 in the oldest stands (site 1). Middle-aged stands (site oldest trees grew on site 1; the middle-aged trees came 2) were unmanaged, with high tree numbers in the stands. from site 2 and the youngest ones were sampled on site Other dendrometric measurements for this site (Table 1) indi- 3. Stand dendrometric characteristics were calculated from cate higher competition levels (exceeding the self-thinning inventory data in 2010, gathered in circular plots (0.03– line; RD≈1), as compared to other sites. The oldest stands 0.125 ha) (Kiviste and Hordo 2002). Site index at base were the sparsest, with the lowest tree densities and greatest age of 50 years (SI50) and relative density were calculated stand basal area. However, quadratic mean diameter in the according to equations proposed by Nilson (2014), where oldest stands was not significantly different from the youngest space for tree growth is calculated based on average tree ones, where site productivity was higher as indicated by site size in the stand and mean distances between the trees. index (SI50).

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Table 1 Dendrometric characteristics of the studied Scots pine stands

−1 2 −1 3 −1 Site Sample plot Age2010 (years) Tree density (stems ha )RD SI50 (m) Dq (cm) H(m) G(m ha )V(mha )

Site 1 VK-01-01 42 828 0.71 18.8 16.2 15.1 16.7 129.2 VK-01-02 44 859 0.79 20.7 18.4 17.5 22.7 195.1 VK-01-03 44 915 0.76 19.5 16.6 16.4 19.8 163.0 VK-01-04 44 1542 0.88 19.5 14.4 16.4 24.8 205.5 VK-01-05 41 844 0.66 19.7 14.9 16.6 14.4 120.0 VK-01-06 43 1249 0.71 16.5 12.0 14.5 14.28 126.6 Mean±SD 43±1.3 1040±292 0.75±0.08 19.1±1.4 15.4±2.2 16.1±1.1 18.8±4.4 156.6±37.2 Site 2 VK-02-04 33 2674 1.06 20.6 13.0 13.5 31.3 221.2 VK-02-05 36 2483 1.07 21.3 14.0 15.7 36.8 291.2 VK-02-06 37 2674 0.99 16.9 11.3 12.2 25.1 166.5 Mean±SD 35±2.1 2610±110 1.04±0.04 19.6±2.3 12.8±1.3 13.8±1.8 31.1±5.9 226.3±62.5 Site 3 VK-03-04 24 1231 0.75 22.6 14.0 11.7 18.9 119.2 VK-03-05 24 1218 0.76 22.3 14.4 11.5 19.7 122.3 VK-03-06 24 1415 0.81 25.0 14.5 13.3 23.4 163.0 Mean±SD 24±0.0 1288±110 0.77±0.03 23.3±1.5 14.3±0.3 12.2±1.0 20.7±2.4 134.8±24.4

Site 1 50-year-old stands, site 2 40-year-old stands, site 3 30-year-old stands, Age2010 mean stand age in 2010, RD relative stand density, SI50 site index at base age of 50 years, Dq mean quadratic diameter, H mean stand height, G mean basal area, V mean standing volume

Soil characteristics station, located 15 km from the study area. Mean annual air temperature for the period of 1975–2013 was +4.9 °C (Fig. 2). To characterize the growth substrates, we sampled soils at The warmest month at Jõhvi was July (+16.9 °C), while the each permanent sample plot following procedures described coldest month was February (−6.5°C).Meanannualrainfallis in Laarmann et al. (2015). Skeletal fraction content was around 720 mm. Melting snow is the main source of water in assessed using the model developed by Laarmann et al. spring, since long-term mean monthly rainfall in early spring (2011). According to the results of soil analysis, physical months (February–April) is around 30 mm, increasing in June and chemical properties differed among the sites (Table 2). with a peak in the middle of summer (August, The thickness of the organic layer (O-hor) varied from 2.7 to 97 mm month−1). The changes in long-term climate condi- 6.4 cm and was significantly (p<0.05) thicker under the oldest tions were examined via linear regression analysis. stands as compared to the youngest stands. Depth of the fine soil fraction ranged from 7.1 to 18.0 cm (mean 10.5 cm), but Dendrochronological methods no significant trend were detected along the age gradient. The content of the skeletal fraction did not differ across all sites We sampled 12–15 dominant and co-dominant Scots pine and averaged 64 %. Clay content increased significantly trees outside each permanent sample plot at the end of (p<0.01) from the youngest site (site 3) toward the oldest (site June 2014. Only trees without visible stem damage or top 1), whereas soil alkalinity (pH) slightly decreased with stand leader change were selected. We extracted increment cores age and ranged from 7.4 to 7.7 (p<0.01) across the sites. from two radii at breast height (1.3 m above the ground). Amount of potassium (K) increased with stand age (from Cores from 18 trees with signs of healed moose damage inside 41.1 to 84.9 mg kg−1; p<0.05); while lower amounts of phos- the tree stem were excluded from further analysis because phorus (P) were detected under old stands (mean range among disturbance impulses are undesirable in assessing growth- the three sites 5.4–40.2 mg kg−1; p<0.05). Amounts of P climate relationships (Cook and Kairiukstis 1990). The re- depended on fine soil content (r=0.84; p<0.001), while maining increment cores were prepared following standard amounts of K were related to clay (r=0.87; p<0.001). Soil dendrochronological techniques (Pilcher 1990). pH decreased with increasing amount of clay (r=−0.75; Tree-ring widths were measured with the precision of p<0.05). 0.01 mm using a LINTAB® tree-ring measuring table equipped with TSAP-Win™ Scientific Version 0.59 software Climatic conditions (Rinn 2003) for graphical and statistical growth pattern matching among trees of the same site. Cross-dating accuracy The Estonian Weather Service provided daily weather data was examined with the program COFECHA (Holmes 1983) from the Jõhvi (59° 21′ 33″ N, 27° 25′ 15″ E) meteorological that calculates the correlation between individual ring-width

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Environ Sci Pollut Res ) 1 − Clay 2mm, – )K(mgkg 1 − content of phosphorus, P content of nitrogen, N content of soil with particle size 0.063 Sand (%) C (%) N (%) P (mg kg org content of total carbon, C mean fine soil thickness, Fine soil content of organic carbon, org C Sand (%) Clay (%) Silt (%) C 0.063 mm, – Fine soil depth (cm) content of soil, with particles larger than 2 cm, fraction (%) Skeletal fraction content of soil with particle size 0.002 Silt thickness of organic layer, hor - O Edaphic characteristics of the studied sites VK-01-04 7.4 4.9 64 10.2 56.8 12.9 30.3 5.6 7.2 0.10 3.6 61.8 VK-01-02VK-01-03 7.3 7.3VK-01-05VK-01-06 6.2 7.2Mean±SD 4.7 7.6 7.4±0.14VK-02-05 6.2 62 4.9±1.2VK-02-06 4.4 7.6 63Mean±SD 7.5 64.8±2.2 7.6±0.07 66VK-03-05 12.8 6.4 68 4.8±1.9VK-03-06 9.8±2.1 11.3 2.7 7.7Mean±SD 7.8 66.3±2.1 52.8±8.6 53.1 8.6 7.7±0.09 68 41.0 7.1 14.4±4.2 2.7 67 3.0±0.3 8.6±1.8 3.2 32.8±4.5 14.6 56.3 20.4 5.2±1.1 60.3±4.5 64.8±8.6 7.2 64.4 65 32.3 7.9 7.4±1.3 7.8±3.1 56 11.1 14.1±4.0 38.6 0.13±0.05 27.3±5.9 9.3 63.7 5.7 56.9±7.1 10.4±5.5 3.8±1.5 10.0 32.6 56.9 3.8 3.6±2.8 18.0 8.5±4.1 26.2 6.9 84.9±23.9 7.7 32.1±3.2 6.8 0.10±0.00 11.2 5.2 62.0 2.1±1.1 48.9 4.4 5.4±2.4 0.18 29.5 2.8±1.4 31.9 9.3 0.17 5.9 43.1±19.2 0.04±0.00 4.3 7.5 3.2 9.2 5.5 0.18 40.2±25.8 20.1 30.0 0.07 35.8 41.1±5.4 5.9 13.2 11.1 1.6 103.3 98.5 3.3 9.9 0.10 0.10 2.7 78.0 4.2 4.1 8.2 54.4 0.04 0.04 37.8 19.5 64.3 69.1 40.4 36.1 soil reaction, content of potassium K content of soil with particle size <0.002 mm, Table 2 Site Sample plot pH O-hor (cm) Skeletal Site 1 VK-01-01 7.5 3.1Site 2 VK-02-04 66 7.6Site 3 VK-03-04 8.5 5.2 7.7pH 45.0 64 3.3 17.8 10.7 60 37.2 74.0 5.1 14.2 5.3 7.5 59.9 20.7 0.10 0.5 2.6 8.3 30.5 6.3 1.3 113.7 0.10 1.5 3.9 0.05 27.0 32.0 46.9

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Fig. 2 Mean monthly air temperature (°C) and monthly sum of quartiles; line in the box depicts the median and whiskers show limits of precipitation (mm), at Jõhvi meteorological station over the period of inter-quartile range; points are outliers 1975–2013. The upper edge of the box indicates upper and lower series and master series (Grissino-Mayer 2001) and identifies Mean sensitivity is a measure of mean relative change be- mismatching segments. Ring-width series of 21 trees had low tween adjacent ring widths (Biondi and Qeadan 2008) and correlation with other tree patterns, so they were eliminated. standard deviation accounts for variability. First-order auto- The age-related trend, usually present in ring-width measure- correlation quantifies the influence of the previous year ment sequences, was removed through the standardization growth conditions on the current year’s growth. Signal process. We fitted 67 %n smoothing splines with 50 % fre- strength for each chronology was estimated by signal-to- quency cut-off on each ring-width measurement series (Cook noise ratio (SNR) and by inter-correlation of indices series. and Peters 1981; Cook et al. 1990), where n is the series The confidence of the chronologies was estimated with length, and calculated dimensionless tree-ring indices for each expressed population signal (EPS). tree by dividing the measured radial increment with values of the fitted trend. Detrended ring-width series were prewhitened to remove autocorrelation and averaged into mean Table 3 Dendrochronological statistics (mean values) of tree-ring plot-wise and site-wise tree-ring-width chronologies using measurements and residual chronologies for three sites Tukey’s biweight robust mean (Mosteller and Tukey 1977). Of the 167 trees we sampled, ring-width series from 128 Site 1 Site 2 Site 3 trees were used for chronology building (Table 3). Three mean Ring measurements site tree-ring-width chronologies covered different periods Mean tree age in 2014 (years) 47 39 28 – (Table 3;Fig.3), but shared a common period of 1993 2013 Number of trees/cores 64/128 31/62 33/66 (21 years). The residual chronology for the oldest stands was Mean TRW (mm) 2.50 2.66 3.86 based on 64 trees and covered the period of 1976–2013. The − Mean BAI (cm2 year 1) 6.84 6.01 9.60 chronology of growth indices for the middle-aged group was Standard deviation 1.18 1.08 1.48 compiled from annual growth increments of 31 trees and First-order autocorrelation 0.77 0.75 0.68 spanned the period 1985-2013. The shortest residual chronol- Mean sensitivity 0.18 0.17 0.19 ogy, covering the period of 1993–2013, was built for the 30- Residual chronologies year-old trees from site 3. It included data from 33 trees. Time span 1976–2013 1985–2013 1993–2013 Several statistics used in dendrochronology studies (Briffa Chronology length [years] 38 29 21 and Jones 1990) were calculated for un-standardized tree-ring EPS 0.967 0.944 0.987 series and for each site chronology to assess tree responsive- SNR 29.1 16.8 78.1 ness to climate and to evaluate the suitability of the chronol- Series inter-correlation 0.600 0.632 0.768 ogies for climate-growth quantification. Mean sensitivity, standard deviation in tree-ring widths, and first-order autocor- TRW tree-ring width, BAI basal area increment, EPS expressed population relation were calculated for raw ring-width series (Table 3). signal, SNR signal-to-noise ratio

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Fig. 3 Tree-ring-width residual 1.3 Site 1 Site 2 Site 3 chronologies for three sites 1.2

1.1

0.9

Growth index 0.8

0.7

0.6

Year

The rate of annual increment, expressed as annual basal variables. Mean annual temperature and total precipitation area increments (BAIs), were used to model effects of site, were calculated for the time window (previous September to competition, and climate on growth rates. To avoid bias when current August) and vegetation period, defined from April to growth rates of different size trees are compared (Biondi September. 1999), tree size has to be considered; thus we calculated an- Climate-growth correlations were calculated for the com- nual BAI from mean tree-wise ring-width measurements, as- mon growth period of 1993–2013 (21 years) in order to main- suming that rings were concentric (Biondi and Qeadan 2008). tain the same weather conditions. Correlation relationships Annual BAI was calculated from the bark to the pith for each were further validated via linear regression analysis. To inves- 2 2 tree, according to the formula: BAIt = π⋅(Rt −Rt − 1), where R tigate how a particular climate-growth relationship was influ- is tree radius and t is the year of ring formation. Subsequently, enced by stand or soil characteristics, we also calculated mean arithmetic BAI series were computed for each of the climate-growth relationships for the common growth period three studied sites. between 12 plot-wise chronologies and climate variables. Fur- thermore, the obtained correlation coefficients were used in Pearson’s correlation analysis, where relations’ strength and Statistical data analysis significance were established between correlation coefficients and stand/soil data available for each studied plot. Dendroclimatic data analyses were performed using the R software (R Core Team 2014) and the packages dplR (Bunn 2008;Bunn2010), detrendeR (Campelo et al. 2012), and Modeling response of basal area increment bootRes (Zang and Biondi 2012). Climate-growth relationships were considered significant at the 95 % confidence level. We used nonlinear modeling to investigate the factors affect- Growth-climate relationships were established between in- ing basal area growth of individual trees. Nonlinear models dices and climate factors and BAI in the nonlinear model. We are suitable to test the relationships among the variables, when used bootstrapped correlation analysis (Biondi 1999;Guiot data are unbalanced (number of observations are not equally 1991) to study the relationship between climate and radial distributed among the groups) or when the relationship cannot growth. Residual chronologies were compared against climate be linearized (Crawley 2007). Annual basal area increment variables. Considering that ring formation may take place (iba) was used as the dependent variable. According to from May through August (Jyske et al. 2014), the calculations Burkhart (2003), growth models should be as parsimonious were performed using a time window from the previous year’s as possible, to avoid over parameterization. At the same time, September to current year’s August. Climatic conditions of the important predictors for basal area growth should be consid- previous autumn and winter seasons were considered, since in ered. Thus, three components were considered: basic growth autumn of the prior year, trees allocate carbohydrates that will function, effect of thinning, and weather effect. Each compo- be used in the spring to initiate the growth flush. Restricting nent was described with a sub-model and compiled into one analysis to the period relevant for ring formation reduces the general prediction equation of multiplicative structure risk of statistically significant correlations arising by chance, expressed as follows: when numerous correlations are calculated. Climate variables iba ¼ f1ððtÞ⋅f2ðthÞ⋅f3ðavÞþε 1Þ used were monthly mean temperature and monthly rainfall sums, seasonal (autumn: September–October–November; where iba is the annual basal area increment (cm2/year) of winter: December–January–February; spring: March–April– dominant and co-dominant trees, f1(t) is a basic growth May; summer: June–July–August) and mean annual climate function showing basal area growth dynamics in

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unmanaged stands, f2 (th) is a function describing individ- associated parameter ptree, we used it further in the model ual tree response to changes in competition (stand density and tested variables of site productivity—SI50 (M5) and thick- reduction), and f3 (av) is a function expressing annual ness of fine soil (M6). Thinning effect (M7) was evaluated growth variation, depending on weather conditions during after defining the best parameters to describe basic BAI the growing period; ε is the error term. growth functions. Finally, radial increment indices (M8– Basic basal area increment model f1(t) was compiled con- M10) were added to assess the climate effect on BAI growth. sidering that basal area growth of individual trees follows We tested if there were any effects of using more generalized asymptotic pattern. We chose Weber growth function (cf. indices to avoid more laborious population-level sampling. Kiviste et al. 2002) to describe basal area growth trend in For that purpose, first, we included general growth indices unmanaged stands: into model (M8), then replaced them by site mean chronolo- ðÞ ¼ ⋅ðÞ− ðÞ− ⋅ ðÞ gies (M9) and finally tested if model fit improved by including f 1 t ptree 1 exp psite A 2 plot-level mean chronologies (M10). Statistics of each of the extended models were compared, and the model with the low- where p is the parameter indicating potential growth of the tree est RMSE and AIC and the greatest explained variance (pseu- tree and also defining the magnitude of asymptote. This pa- do-R2) was selected as the final model. rameter was related to current tree diameter at breast height After comparing observed and predicted mean BAI pat- (cm); p is the parameter indicating site fertility; A is the tree site terns by sites, we observed that the model was not able to breast height age (years). predict a smaller growth peak in growth pattern of site 3. We Response to thinning of individual trees was modeled in a hypothesized that it could be caused by one earlier manage- similar manner as proposed by Hynynen (1995). Since we did ment intervention (spacing), which was not recorded. To test not have information about stand basal area before the thin- that hypothesis, we included a hypothetical thinning in 1996 ning, years since thinning were used to describe changes in – stand densities. Studies have shown that thinning effect de- (M11 M14). Nonlinear modeling was performed using nonlinear least squares (nls) function in the R environment (R Core creases with increasing time since thinning. We assumed that Team 2014). the thinning effect would last for the same period and response would have an asymptotic shape as suggested by Hynynen (1995). We adjusted the equation to the available variables as follows: Results c th thc Changes in long-term climate conditions f ðÞ¼th 1 þ p ⋅exp − ð3Þ 2 th b b Linear regression results revealed several temporal trends in where th is the time elapsed since thinning; b and c are the long-term mean climate conditions for the period of 1975– estimates of parameters, b=13.8 and c=1.58, as proposed by 2013. Total precipitation increased for May (8.7 mm de- −1 Hynynen (1995); and pth is the thinning intensity parameter to cade ; p<0.05). Mean monthly temperature has steadily in- be estimated. creased each decade: in April (0.5 °C; p<0.05), July (0.8 °C; Annual growth variation f3 (av) was accounted for by in- p<0.001), August (0.5 °C; p<0.05), and September (0.4 °C; cluding growth indices. Stepwise inclusion of model compo- p<0.05). nents was used to estimate the parameters and to investigate the effects of possible radial growth predictors. Root of the Characteristics of tree-rings and chronologies mean squared error (RMSE) and Akaike’s Information Crite- rion (AIC) (Burnham and Anderson 2002)wereusedtoeval- The widest tree-rings (TRW=3.86 mm year−1)wereformedin uate model improvements. We also calculated the model effi- the trees growing on the youngest site and decreased with stand ciency or pseudo-R2, which describes the proportion of vari- age. Overall, year-to-year variation was not high in studied ance explained by factors included in the model. stands (0.17–0.19), which is characteristic for complacent Several candidate models were compiled (Table 5)and growth (Table 3). First-order autocorrelation ranged from 0.68 iteratively compared with the previously compiled model for to 0.77, and was slightly higher in the oldest trees (site 1), significant improvement. First, we fitted basic growth func- suggesting that the weather of the preceding year had a pro- tion with one parameter ptree (M1) on our data set, and nounced effect on the diameter growth for these trees. The most proceeded with inclusion of a second parameter psite (M2). In homogeneous tree-ring patterns were present in the youngest the following steps, either maximum tree diameter (M3)or trees (site 3) where series inter-correlation among indices was SI50 (M4) was related to the asymptote. The model with the high (0.768). Considerably, lower similarities among patterns lowest AIC and RMSE was used further in the analysis. Since were found in the middle-aged tree group on site 2 (0.632), and maximum tree diameters (Dmax) better described the the lowest (0.600) were among the oldest trees (site 1). Signal-

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Environ Sci Pollut Res to-noise ratio ranged from 16.8 to 78.1 and was more than two Similar relationship patterns (as for site 1) between precipita- times higher in the young trees than in old trees and more than tion in June–July and radial growth were found for middle-aged four times higher than in the middle-aged tree growth patterns. group (site 2): r=0.55 (June–July), r=0.55 (June), and r=0.50 Expressed population signal in all chronologies was high, sur- (July). However, growth of this age group seemed to be con- passing the recommended confidence threshold of 0.85 trolled more by variation in temperature, as indicated by a greater (Wigley et al. 1984), suggesting that chronologies are reliable number of significant correlations between indices and tempera- for examining tree growth-climate relationships. ture variables. Wood production on this site was related mostly to higher mean temperatures in spring (r=0.69), whereas temperatures in March had a pronounced effect (r=0.49). Warm winters Climate-growth relationships (r=0.38), and especially warmer weather conditions in January (r=0.39), were positively associated to pine radial growth on site Correlation analysis indicated that variation in both precipita- 2. Growth indices of middle-aged group were also positively tion and temperature had a strong relationship to Scots pine correlated with mean annual temperature (r=0.40). Mean sum- radial growth on reclaimed mining areas (Fig. 4). High pre- mer temperatures were not significant for wood production on cipitation in the June–July period was significantly positively site 2, contrary to the other two age groups. Negative relationship related to higher radial growth in all studied age groups, while between growth indices and current August temperatures was there was opposite relationship between high mean tempera- slightly stronger for the middle-aged site (r=−0.43) than for other tures in summer, especially in August and annual wood pro- two groups (r=−0.35). duction. Importance of precipitation in June–July period de- The least number of significant correlations was found creased with decreasing age (p<0.01). for the youngest age group (site 3). Radial growth rela- The strongest significant relationship between radial tionship to precipitation in June–July was significant but growth and rainfall over June–July period was detected for moderate (r=0.39), and there was no significant associa- the oldest age group (r=0.66), with precipitation in June being tion with monthly rainfall in June or July. The dominant more important (r=0.63) than in July (r=0.55). This age climatic factor controlling Scots pine radial growth in this group also benefited from higher rainfall in October of the age group was January temperature (r=0.56). High mean previous year (r=0.45). Weaker correlation coefficients were temperatures in summer (r=−0.45) were negatively relat- found between chronology of the oldest trees and monthly ed to radial growth on site 3, with mean temperatures in temperatures. Higher mean temperatures in spring season July (r=−0.39) and August (r=−0.35) being the most (r=0.50) were positively related to tree-ring width, while high influential. mean temperatures in June (r=−0.38) inhibited radial growth The greatest amount of variance (R2=0.28; p<0.05) pres- of the oldest age group. ent in radial increment of the oldest age group was associated

Fig. 4 Pearson’s correlation Temperature Site 1 Site 2 Site 3 coefficients between tree-ring- 1.0 width indices of three sites and 0.8 mean monthly, seasonal, annual, 0.6 and vegetation period (Veg.p.) 0.4 temperatures and precipitation; 0.2 pr. denotes months previous to 0.0 l l . -0.2 p v c n b r y n g l r g p vegetation period, bars show e ct e e Ju u te in Jul .S .O No D Ja F Ma Apr Ma Ju A Fa n mer g. nual r. r. m e n -0.4 pr pr p p Wi Spr u V A medians of correlation S Jun+ coefficients, and whiskers Correlation coefficient -0.6 indicate 95 % confidence -0.8 intervals. Statistically significant -1.0 correlations (p<0.05) are marked with dot Precipitation 1.0 0.8 0.6 0.4 0.2 0.0 n b r y n g ll g r -0.2 ep ov ec a a a ul a n p. al J Apr Ju J F ri g. .S .N .D Fe M M Au inter p me e nu -0.4 pr pr.Oct pr pr W S um V An S Jun+Jul

Correlation coefficient -0.6 -0.8 -1.0

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Fig. 5 Observed and predicted basal increment growth patterns of pine management activity, PT pre-commercial thinning, FT first thinning, and trees on three sites: a prediction without hypothetical thinning; b HT hypothetical thinning prediction with included hypothetical thinning. Arrows indicate time of with the variability in June–July precipitation, while fluctua- Effects of soil and stand characteristics on tree response tions in mean spring temperature were responsible for 47 % to climate (p<0.001) in annual ring-width variation at site 2. Tempera- tures in January accounted for 29 % (p<0.05) of variability in Tree responsiveness to monthly temperature regimes to growth patterns from site 3. a large extent was influenced by stand characteristics, while sensitivity to precipitation seemed to increase Growth patterns with growing tree dimensions but also depended on soil characteristics (Table 4). Moderately strong relation- – Mean annual basal area increment was greatest in the youngest ships (r=0.58 0.68) between sensitivity to spring tem- age group trees (site 3, BAI=9.60 cm2 year−1) and the smallest peratures (particularly in March) and stand density in unmanaged stands (site 2, BAI=6.01 cm2 year−1). Sudden (expressed in stand volume, relative density, and basal increases in BAI followed the pre-commercial thinning at site 3 area) were established. Thicker layers of O-hor tended in 2004, and first commercial thinning in the oldest stands (site to mitigate negative influence of temperatures in Au- 1) in 2008 demonstrated the effect of lowered competition gust, as indicated by significant negative correlation. (Fig. 5). After the thinning, mean annual BAI of dominant trees Growth sensitivity to mean temperatures in January increased 58 and 3 % in old and young trees, respectively, was related to stand age and increased with site produc- considering 5-year periods before and after the intervention. tivity (SI50). The decrease in pH inversely influenced Standardized growth patterns were quite synchronous radial growth response to precipitation in June. among all chronologies for the period of 1993–2004, but diverged in the youngest age group for the period of 2005– Thinning, site and climate effects on radial growth 2009, just after the pre-commercial thinning (Fig. 5). A sharp decrease in radial growth in the youngest age group An individual tree basal area increment model (Eq. 1)for (site 3) is evident for 2002–2003, while growth depression dominant and co-dominant Scots pine trees on reclaimed in the same period for the two other sites was considerably oil shale areas was compiled and fitted on the data set, lower. Two-year growth depression in 2005–2006 is pres- consisting of more than 3600 annual basal increment re- ent in the growth patterns of sites 1 and 2, but not for site 3. cords. The fit statistics from nonlinear procedure used to

Very low growth indices for site 2 were observed in 1984 estimate annual growth rate with alternative models (M1– and for site 1 in 1992. M14) are presented in Table 5. Correlation analysis suggested that the patterns of indi- The compiled growth model was logically consistent. ces differed slightly. The strongest correlation (r=0.74; Predicted tree basal area increment patterns followed the p<0.001) for common pair-wise length was between chro- observed ones (Fig. 5a, b). Model prediction accuracy im- nologies of sites 1 and 2. Coefficients of the other two pair- proved each time, after new variable was included, as in- wise correlations between site 1 vs. site 3 and site 2 vs. site dicated by lower RMSE and decreasing AIC (Table 5). At 3 were not statistically significant. the same time, model prediction ability remained

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Table 4 Correlation coefficients between growth-climate relationships and soil/stand characteristics. Statistically significant (p<0.05) correlations are presented in italic

Growth-climate relationship Stand characteristics Soil characteristics

Age H Dq SI50 G RD V pH O-hor Fine soil Clay C Corg NK

T1 −0.71 −045 −0.27 0.63 0.57 0.52 0.33 0.45 −0.06 0.30 −0.66 −0.17 −0.37 −0.35 −0.70 T3 0.51 0.48 0.19 −0.23 0.58 0.60 0.68 −0.34 0.38 −0.11 0.50 0.48 0.37 0.48 0.23 T8 −0.39 −0.39 0.16 0.26 −0.41 −0.39 −0.56 0.29 −0.75 0.47 0.06 −0.13 −0.07 −0.37 0.16 Tspring 0.51 0.43 0.13 −0.29 0.61 0.65 0.70 −0.33 0.37 −0.18 0.45 0.48 0.33 0.45 0.24 Tsummer 0.05 −0.42 −0.66 −0.51 0.09 0.37 −0.03 0.25 −0.50 −0.19 0.04 0.50 0.05 −0.27 −0.15 Tannual −0.57 −0.70 −0.41 0.10 −0.34 −0.29 −0.51 0.04 0.13 0.11 −0.12 0.23 0.01 0.09 −0.26 P6 0.60 0.80 0.62 −0.10 −0.18 −0.33 0.08 −0.70 0.57 −0.14 0.55 0.05 0.49 0.65 0.62 P7 0.72 0.68 0.19 −0.47 −0.14 −0.20 0.14 −0.55 0.40 −0.42 0.54 0.41 0.67 0.44 0.52 P10 0.81 0.64 0.01 −0.68 −0.27 −0.22 0.01 −0.56 0.33 −0.62 0.60 0.58 0.78 0.41 0.49

T temperature, P precipitation, numbers 1–10 denote calendar months, 1 January, 2 February etc.; Age mean stand age (years), H mean stand height (m), 2 −1 Dq mean quadratic diameter (cm), SI50 site index at base age of 50 years (m), G mean stand basal area (m ha ), RD relative density related to average tree size and distances between trees, V mean standing volume (m3 ha−1 ), O-hor thickness of organic layer (cm), pH soil reaction, Fine soil mean fine soil thickness (cm), Clay content of clay (%), C content of carbon (%), Corg content of organic carbon (%), N content of nitrogen (%), P content of phosphorus (mg kg−1 ), K content of potassium (mg kg−1 )

Table 5 Parameter estimates and fit statistics from the nonlinear regressions of annual basal area increment

Model Components Parameters RMSE AIC pseudo-R2

ptree psite pth

Estimate SE Estimate SE Estimate SE

M1 f1(A),ptree 8.59999 0.0728 ––––3.813 19916.40 0.186

M2 f1(A),ptree, psite 8.45432 0.0945 0.22339 0.0111 ––3.811 19913.33 0.187

M3 f1(A),ptree-Dmax, psite 0.42745 0.0039 0.23345 0.0100 ––3.306 18887.63 0.388

M4 f1(A),ptree-SI50, psite 0.43119 0.0047 0.20900 0.0097 ––3.673 19646.96 0.245

M5 f1(A),ptree-Dmax, psite -SI50 0.43690 0.0042 1.02682 0.0420 ––3.255 18774.91 0.407

M6 f1(A),ptree-Dmax, psite -fine soil 0.43969 0.0042 2.00520 0.0806 ––3.250 18762.74 0.409

M7 f1(A),ptree-Dmax, psite - fine soil * f2(th) 0.37288 0.0035 3.02980 0.1398 1.56969 0.0543 2.824 17749.38 0.553

M8 f1(A),ptree-Dmax, psite -fine soil* f2(th)*f3(av)- 0.37808 0.0034 2.93839 0.1284 1.46564 0.0514 2.746 17547.97 0.578 general chron M9 f1(A),ptree-Dmax, psite -fine soil* f2(th)*f3(av)- 0.38345 0.0037 2.73189 0.1159 1.07318 0.0482 2.804 17697.97 0.559 site chron M10 f1(A),ptree-Dmax, psite -fine soil* f2(th)*f3(av)-plot 0.38019 0.0034 2.89446 0.1233 1.38587 0.0501 2.723 17485.93 0.585 chron M11 f1(A),ptree-Dmax, psite -fine soil* f2(hth) 0.36497 0.0035 2.74465 0.1130 1.50165 0.0475 2.746 17113.90 0.578

M12 f1(A),ptree-Dmax, psite -fine soil* f2(hth)*f3(av)- 0.37002 0.0034 2.67500 0.1043 1.41983 0.0449 2.663 17326.93 0.603 general chron M13 f1(A),ptree-Dmax, psite -fine soil* f2(hth)*f3(av)- 0.36948 0.0032 2.68756 0.1035 1.23886 0.0438 2.664 17326.93 0.603 site chron M14 f1(A),ptree-Dmax, psite -fine soil* f2(hth)*f3(av)- 0.36890 0.0033 2.72059 0.1029 1.45914 0.0443 2.586 17329.42 0.626 plot chron

Parameters: ptree tree size parameter, depending on maximum tree diameter (diameter when trees were sampled); pth response to thinning parameter, described as years since thinning; psite site productivity parameter described by either SI50 or fine soil thickness (cm); f1(A) basic grow function, f2(th) response to thinning function, f3(av) function describing annual variation related to climate; f2(hth) response to thinning function with included hypothetical thinning. The best models are marked in italic: M10 without hypothetical thinning and M14 with hypothetical thinning considered; RMSE root mean square error, AIC Akaike’s information criterion, pseudo-R2 shows total explained variance

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Environ Sci Pollut Res considerably stable, with low variation in parameter stan- conditions and water holding in the substrate is low. Addition- dard errors. All the parameter estimates were logical and ally, ground water levels are artificially lowered by pumping highly significant (p<0.001). out the water to avoid flooding in the excavated areas. Be-

The basic models (M3–M6), where site fertility and tree size cause of this drainage, the water table can be too deep to were used as predictor variables, explained 39–41 % of the total supply the moisture for roots by capillary action. Besides that, variation in the observed basal area increments. Fine soil thick- microbial activity is also dependent on soil water availability. ness was a better predictor than SI50. Inclusion of response Microbial activity decreases with lower soil water availability, 2 function to thinning (M7) increased pseudo-R up to 55 %, with and microbial metabolism may become totally inhibited dur- a reduction in RMSE and AIC. After including climate effects ing extended drought events (Borken and Matzner 2009),

(M8–M10), the fit statistics (RMSE, AIC) showed improve- therefore suspending uptake of nutrients. In Estonia, severe ment, and maximum explained common variance increased meteorological summer droughts, when precipitation was ab- up to 59 %. The greatest effect on radial growth, according to sent or was low for longer than 20-day period, were observed our model, had site properties and thinning, while climatic var- in 2002 and 2006, with exceptionally dry summers in 1983, iation was significant, but it improved model predictions only 1992, and 1999 (Tarand et al. 2013). These dates coincide slightly. with years of extremely low radial increment in our studied Mean plot chronologies were slightly better predictors plots. In general, Scots pine is tolerant to drought, and its of basal area increment than mean site or mean general sensitivity to precipitation and water deficit is more character- chronology, proving that response to climatic factor among istic of dry regions (Allen and Breshears 1998). However, the sites are not the same. Scots pine trees become more sensitive to climate dryness, After inclusion of a hypothetical thinning into the data in the areas where water deficit becomes more frequent or lasts set, model predictions improved as indicated by lower for an extended time, eventually leading to tree mortality RMSE and AIC. Percentage of maximum explained com- (Hereş et al. 2012). 2 mon variance (pseudo-R ) increased up to 63 % (M14). No Temperature governs physiological processes, includ- evidence of dependencies was observed in bi-plots of re- ing photosynthesis and carbon sequestration, in the tree siduals against predicted values (data not presented). and determines the rate at which water evaporates from the soil (Fritts 1976). Formation of a permanent forest floor enhances moisture retention, and may also influ- Discussion ence tree response to climate (Drobyshev et al. 2010). Our results suggest that thicker layers of the organic Climate effects on radial growth horizon, which develops on reclaimed sites as vegetation develops, reduced the negative influence of temperature Dendrochronological studies in Baltic countries report that in August. Pine sensitivity to precipitation in October Scots pine, growing on dry and infertile sites, is sensitive to could be explained as due to formation of new roots cold winters (Elferts 2007; Vitas 2004; Vitas and Erlickyte (Ostonen et al. 2006) during this time. The presence of 2007) and benefits from higher temperatures in winter and higher amounts of carbon and organic carbon increased early spring (Hordo et al. 2009; Läänelaid and Eckstein tree sensitivity to precipitation in October (Table 4). With 2003). Erlickyte and Vitas (2008) found in Lithuania that a better-developed root system, water and nutrient uptake due to changes in long-term climate trends, the influence of could have been enhanced, which might be advantageous winter cold on Scots pine annual diameter growth decreased, during the ring-formation time. while sensitivity to rainfall in springtime and summer became Mean temperatures in July and August are steadily in- more important. Our results show that Scots pine radial creasing, suggesting that evaporative demand will be in- growth on the reclaimed areas is limited by water deficit in creasing and growth stress induced by high temperatures is summer, apparently as a result of low precipitation and high likely to increase in the future. This could be especially temperatures in June to August. The main water source for significant for older trees, as we found a positive relation- tree growth is precipitation and extended dry and warm pe- ship between precipitation in June and July and increasing riods may cause water deficiency in the area (Vaus 1970). tree size. Tree density reduction through thinning has been Läänelaid and Eckstein (2003) reported that rainfall in June suggested as a way to mitigate water stress effects is the main climatic factor driving radial growth in Norway (Giuggiola et al. 2013). Basal area increment patterns did spruce in Estonia. Spruce sensitivity to precipitation is usually not show growth decline, but rather was flat for dense explained by close-to-surface root system, which could be the stands and increased sharply as a response to reduced same reason for pine trees growing on reclaimed areas where competition. rooting depth is confined by the rocky substrate. Root pene- We also found that trees of different age, growing in close tration into deeper layers is restricted by physical soil vicinity and under the same climatic conditions, responded to

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Environ Sci Pollut Res climate differently. Radial growth patterns were similar within changes in competition. These variables are commonly used the sites (Table 3), but less coherent between the sites, in basal area models (Wykoff 1990; Keyser and Brown 2014; confirming spatial variability of growth response to environ- Mehtätalo et al. 2014). By applying a nonlinear modeling mental factors. Variation in tree responsiveness to climatic approach, we avoided many assumptions that are necessary variables has been previously observed between the tree spe- for using linear modeling (Zuur et al. 2009;Eastaughetal. cies on the same site and in the trees of a species between the 2013). As a result, our model was able to predict basal area sites (Fritts 1976). Furthermore, correlation coefficients, increment in managed and unmanaged Scots pine plantations established during our study between growth indices and growing on reclaimed areas. For simplicity, and because we climatic variables, were higher than the ones reported for were not interested in tree-level effects, we used tree maxi- Scots pine, growing on forest sites (e.g., Läänelaid and mum diameter to describe potential tree growth (asymptote) Eckstein 2003; Hordo et al. 2009, 2011) in the region. on particular sites. Apparently, Scots pine growing on reclaimed areas is more According to our model, site properties and thinning had sensitive to weather variability than pine trees growing on the greatest effect on the radial growth (Table 5). Our results forest sites. (Fig. 5) show that thinning on these sites reduced competition Greater growth rates were detected on the youngest site as between trees resulting in a positive effect on radial growth, compared to other two groups. Similar results were observed with trees responding immediately after treatment. for pine growing on fertile forest sites in Estonia, where youn- The first thinning increased the basal area increment of ger tree cohorts had enhanced growth as compared to the older dominant trees up to 50 %. This corresponds to the results of ones (Metslaid et al. 2011). For pine trees growing on the Mäkinen and Hynynen (2014) who found that intensive thin- mined areas, the greater BAI growth could be a result of more ning on mineral soils can increase the basal area increment of fertile soil conditions, as defined by thicker fine soil layer and remaining trees ∼50 %. Thinning on reclaimed sites has pos- more intensive management. The youngest Scots pine gener- itive effect on radial growth of dominant trees reducing the ation was more sensitive to temperature, suggesting that their competition between trees after canopy closure. Individual water needs were satisfied, possibly due to greater amount of tree response to changes in stand density was modeled as time fine soil present in the substrate. The fine soil had a higher elapsed since thinning, because stand basal area before and water holding capacity, which means that moisture could have after thinning was not available. In practice, changes in basal been stored for longer periods. Growth sensitivity to mean area are rarely recorded, and inclusion of a variable describing temperatures in spring was related to stand density. Soil under time since thinning is more convenient. It was a surprising forest cover tends to be cooler in the summer and warmer in finding that with a model assistance, we were able to detect the winter, than in the clear-felling areas (Aussenac 2000). one more thinning (on site 3), presumably a cleaning (Fig. 5b), Already during the sampling occasion, we noticed that ground since it was carried out at an early stand development stage. vegetation under theses stands was sparse, indicating lack of We refer to this management activity as hypothetical, because sunlight in these stands. It was darker, because of full canopy management activities are not well recorded for these areas, shading and the temperature felt also cooler, than at the other and we could not detect any proof for this activity. However, two sites. Due to delayed snowmelt in spring, onset of the model prediction was improved significantly after including a growing season in denser stands could have started later, hav- thinning in 1996 for that site. Thus, our finding suggests that ing considerable influence on tree annual productivity trees on reclaimed areas are highly responsive even to minor (Vaganov et al. 1999; Helama et al. 2013). The greater canopy changes in stand structure. It should be noted that only dom- cover in the middle-aged stands could explain the positive inant and co-dominant trees were investigated in this study, correlation to annual temperature (longer time span) and lower since these trees are expected to be crop trees, growing for sensitivity to temperatures in summer months as compared to many years on that site. Trees with other social status (inter- managed stands. However, relation to temperatures in August mediate or suppressed) might have been responding different- for middle-age group was stronger as compared to the other ly to increased space, since radial growth is size-mediated two groups, suggesting that there could be delay in ring for- (e.g., Gavin et al. 2008); therefore, all the findings in this study mation processes due to later onset in spring. Ring formation apply only for the largest trees in the stand (trees with crowns can be continuing in these trees, while growth processes are extending above general level and from the general level). about to cease in more sparse stands. To evaluate the effect of site productivity on radial growth, we tested traditionally used site index. Fine soil depth was Modeling basal area increment included because it showed high correlation to site index

(SI50) and because previously similar variables have been The basal area increment model developed during this study used (e.g., Schröder et al. 2002) as a direct indicator of site included three main components, which described site prop- productivity. For our data set, fine soil depth was a better erties and accounted for climatic variation and tree response to predictor than site index. Besides, it is easy to measure in

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Environ Sci Pollut Res the field. Site productivity is an important variable in basal Acknowledgments The study was conducted at the Estonian Univer- area growth modeling, describing differences in trees growing sity of Life Sciences. The study was supported by the Institutional Re- search Funding IUT21-4 of the Estonian Ministry of Education and Re- under the same climate conditions, but on different soils search, and SOO project (3.2.0801.11-0012) and the Environmental In- (Subedi and Sharma 2013). Annual basal area increment data, vestment Center supported data collection from the network of Estonian expanding over the period of 1976–2013 (37 years), was used research plots. Estonian Weather Service at Estonian Environmental to fit the model. Therefore, growth predictions may be used to Agency provided climate data. We would also like to thank Kalmer Sokman for provided information and reviewers for the valuable com- predict long-term growth on reclaimed areas and predictions ments, which helped to improve manuscript considerably. are climate sensitive.

Mitigating effects of climate change References

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138 Metslaid, S., Hordo, M., Korjus, H., Kiviste, A., Kangur, A. 20xx Spatio-temporal variability in Scots pine response to annual climate fl uctuations in hemiboreal forests of Estonia. Submitted to Agricultural and Forest Meteorology 139 Spatio-temporal variability in Scots pine response to annual climate fluctuations in hemiboreal forests of Estonia Sandra Metslaid1,*, Maris Hordo1, Henn Korjus1, Andres Kiviste1, Ahto Kangur1

1 Department of Forest Management, Institute of Forestry and Rural Engineering, Estonian University of Life Sciences, Kreutzwaldi 5, 51014 Tartu, Estonia *corresponding author

Abstract

In this study we used a comprehensive tree-ring network from Estonia and investigated Scots pine (Pinus sylvestris L.) radial growth responses to changing climate conditions, considering differences in site conditions and local climates. To assess whether climate influences Scots pine radial growth consistently across the country, we developed thirteen tree-ring width chronologies for pine populations growing in four forest site types – Cladonia, Calluna, Myrtillus, and Rhodococcum – in four sub-regions of Estonia and compared these to climate data. A correlation analysis between ring-width indexes and monthly resolved temperature and precipitation applied over the period of 19552006 revealed that in most of the cases, radial growth was related to several climatic variables, however, significant positive correlations were found with winter/early spring temperatures and the total precipitation of late summer in the year prior to growth. High mean temperatures in August, the year prior to growth were negatively related to pine growth, particularly on islands and in the Northeast of Estonia. Scots pine growth on mesic and medium fertile Myrtillus and Rhodococcum sites in the Southeast exhibited greater sensitivity to mean FebruaryApril temperatures, while high temperatures and low precipitation at the end of the summer of the previous growing season limited radial growth of pine on the islands and in the Northeastern sub-region. A hierarchical cluster analysis performed on mean index chronologies and a principal component analysis conducted on bootstrapped correlation coefficients revealed that local climate is the main driver of common growth, followed by ecological site conditions. A moving correlation analysis, performed over the period of 19552006, using 30-year windows shifted by one year showed that climate-growth relationships are not stable in changing climatic conditions. Associations between Scots pine tree-ring width and winter temperatures are getting weaker for some of the forest site types, whereas relationships with summertime water availability are gaining significance.

Keywords: dendrochronology; climate-growth relationships; PCA; ecological site conditions; moving correlation; summer water deficit.

1. Introduction

Since the middle of the last century, the growth and productivity of hemiboreal forests in Northeastern Europe have been subjects to rapid changes in climatic conditions, arising as a result of global climate warming (Serreze et al., 2000; HELCOM, 2013; Rutgersson et al., 2015). According to Giorgi (2006), Northeastern Europe is among the top hot-spot regions in Europe

1 140 where the greatest shifts in thermal and precipitation regimes are expected by the end of this century. Considering that trees are long-living organisms, concerns arise regarding the possible impact of climate change on the functioning and productivity of forest ecosystems in the region. A productivity increase is projected for boreal forests (Karjalainen and Kellomäki, 1995), while the impact of climate warming in a hemiboreal vegetation zone, which is a transition zone between boreal and temperate zones, is suggested to change and depend on site conditions and change itself (Lindner et al., 2010). In the Baltic Sea region, increases in mean annual air temperature have been observed since the beginning of the meteorological measurements in 1871, with a temperature rise of 0.08 °C per decade, which is greater than at the global scale (BACC Author Team, 2008). Due to a strong influence of large-scale circulations, particularly the North Atlantic Oscillation (NAO) during wintertime (Niedzwiedź ́ et al., 2015), which is characterized by interchanging cycles of cool (severe) and warm (mild) phases that last for several decades (Hagen and Feistel, 2005), the warming process has been fragmented, and had several cooling and warming periods (Rutgersson et al., 2015). For several recent decades all areas around the Baltic Sea experienced a distinct warming and changes in precipitation regimes, which in turn, have already altered the seasonal water balance and river run-off in the Baltic States (Kriauciuniene et al., 2012). The magnitude of the climatic change and seasonal timing in the region is not equally distributed and depend on the geographical location (Lind and Kjellström, 2009; BACC II Author Team, 2015). In Estonia, the most pronounced warming has been taking place in winter and early spring (Jaagus, 2006; Tarand et al., 2013). Winters have become milder and shorter, as the number of very cold days has decreased (HELCOM, 2013). The duration of snow cover has become shorter by 1735 days, with the greatest changes in coastal areas (Jaagus, 2006). Therefore, the growing season starts earlier (Ahas and Aasa, 2006). Projections of future climate change, based on regional climate models show that a similar warming tendency will continue during the 21st century, however, multiple uncertainties regarding precipitation patterns remain (Jaagus and Mändla, 2014). Both a precipitation increase and decrease are projected, and the results of a simulated future climate seem to depend on the used model, emission scenario and may vary by the geographical location (Lind and Kjellström, 2009; Jaagus and Mändla, 2014). A precipitation increase during the cold season is expected in most of the Baltic Sea region, including Estonia, however, much drier spring and summer seasons are also possible under future climate scenarios, especially in the southern areas of the region (Kjellström and Ruosteenoja, 2007; Rutgersson et al., 2015). Higher temperatures together with changes in precipitation patterns have already been altering seasonal hydrological regimes (Graham, 2004; Kriauciuniene et al., 2014) which may affect forest growth (Tullus et al., 2012; Rosenvald et al., 2014; Sellin et al., 2017) and productivity in the region (Lindner et al., 2010). Because the growth and wood production of trees are closely related to temperature fluctuations (Schmitt et al., 2004, Rossi et al., 2008) the question arises as to what the growth response to altered annual weather fluctuations is and what consequences it will have on the growth dynamics and functioning of forest ecosystems in the region. Therefore, a better understanding of interactions between tree growth and shifting climatic conditions is greatly needed.

2 141 Scots pine (Pinus sylvestris L.) is the most abundant (Navasaitis, 2004; Eckenwalder, 2013) and economically important (e.g. Kaimre, 2014) coniferous tree species in Northeastern Europe. In addition to wood and non-wood products, hemiboreal pine forests provide a number of other ecosystem services, including soil and water protection (Wisniewskí and Kistowski, 2015), carbon sequestration and storage (Karjalainen, 1996; Vucetich et al., 2000; Kolari et al., 2004), provision of habitats for wildlife (Marmor et al., 2013; Laarman et al., 2013), recreational opportunities (Riepšas, 2012), as well as aesthetic and cultural values. Because of great importance for the region and local communities, a profound understanding of interactions between tree growth and climate variability and responses to change is needed to promote sustainable forest development. The annual radial growth of trees, including that of Scots pine, have been extensively used to investigate growth responses to past climate (e.g. Kirchhefer, 2001; Linderholm, 2002; Friedrichs et al., 2008; Büntgen et al., 2012) and assess possible impacts of environmental change on the studied populations (e.g. Bauwe et al., 2013). An understanding has developed (based on previous spatially fragmented studies) that the radial growth of Scots pine populations in the northern latitudes (boreal forests) is limited by low temperatures during the growing season (Briffa et al., 1990; Babst et al., 2013) while the growth of pine populations at their southern range limit can be related to received precipitation. However, recent studies (e.g. Sánchez- Salguero et al., 2015; Hellmann et al., 2016) have used tree-ring networks and found that on the spatial scale responses are much more diverse. In the central part of tree species distribution, climatic responses are more complex and depend on both local climatic and local site conditions. In hemiboreal forests, cold winters (Bitvinskas, 1974; Lõhmus, 1992b) and low spring temperatures (Läänelaid and Eckstein, 2003; Hordo et al., 2009, 2011) have previously been identified as climatic factors that limit Scots pine growth. Most of the previous knowledge related to climate influence on Scots pine growth in Estonia originates from smaller-scale dendrochronological studies restricted to a certain location (Pärn, 2003, 2008; Metslaid et al., 2016) or based on general master chronologies (Lõhmus, 1992a, 1992b; Läänelaid and Eckstein, 2003). Lõhmus (1992b) used a large set of tree-ring data collected across Estonia and investigated climate influences on Scots pine growth considering the fertility gradient of sites but not focussing in more detail on regional differences in local climates. Thus, a comprehensive understanding of climate influence on Scots pine growth on the spatial scale in the country is still limited (e.g. Hordo et al., 2009, 2011). Several recently conducted spatially explicit dendrochronological studies in Estonia, which investigated Norway spruce (Helama et al., 2016), common oak (Sohar et al., 2014) and also Scots pine (Hordo et al., 2009) showed that species- specific responses to climate across the country differ, suggesting that local climatic conditions are diverse enough to result in contrasting responses, as reported for other geographical areas (e.g. Mazza et al., 2014). In addition to spatial variation in climatic conditions, the dominance of the climatic variables or seasonal timing may also shift, due to temporal changes in climatic conditions (Moir et al., 2011; Büntgen et al., 2012). To improve our understanding of climate influences on Scots pine growth on the spatial scale in Estonia, to define climatic variables which could be further used for future growth modelling, and to make models climate-sensitive, we used a tree-ring network distributed across the Estonia, which includes a range of climatic and site conditions (Kiviste et al., 2015). Since soil conditions are shown to be important in mediating climate influences (e.g. Moir et al., 2011) we studied Scots pine response to climate variability based on growing conditions as defined by classification into forest site types. A forest

3 142 site type in general terms describes edaphic and hydrological site conditions and is the main classification unit used in forest management in Estonia (Lõhmus, 2004). Therefore, the aims of this study were 1) to quantify climatic variables that are responsible for most of the annual variability in Scots pine tree-rings, considering ecological site conditions and local climates, 2) to define similarly responding populations and 3) to test if relationships remained stable over the last century when most of the warming has taken place.

2. Material and Methods

2.1. Study area

The study was conducted in Estonia, the country in Northeastern Europe that spans between 59.5° N, 28° E and 57.5° N 22° E. The area belongs to the hemiboreal vegetation zone, which is a transition area between boreal and temperate forests (Ahti et al., 1968). Coniferous tree species, including Scots pine and Norway spruce, prevail in the forests and are the most relevant tree species for local forestry, followed by birch, aspen and alder species (Raudsaar et al., 2014). The topography of Estonia is relatively flat, with more hilly landscape (max elevation 318 m a.s.l.) in the Southeastern part of the country. The climate in Estonia is temperate (Hordo et al., 2011), characterized by cold snowy winters and warm summers. The variation in air temperatures between different sub-regions of Estonia mainly arises due to the proximity of the Baltic Sea (Jaagus, 2006; Tarand et al., 2013). Along the coast and on the islands, maritime climate prevails, gradually changing to semi-continental when moving towards the inland. As a result, winters tend to be milder in the coastal areas and colder further inward the country, with the opposite effect in early spring and summer. Mean annual temperature in Estonia (in 19662010) ranged between 4.6 ºC and 6.8 ºC, being higher on the West Estonian Archipelago and lower in the eastern part of the country (Tarand et al., 2013). The coldest month is February (mean air temperature -2.5 to -6.5 ºC) and the warmest month is July (mean temperature +16.5 to +17.8 ºC). The number of sunshine hours is higher on the islands and in coastal areas than in the inland, ranging from 1,600 to 1,900 hours respectively. Accordingly, the growing season (average for Estonia 180195 days) is longer in the coastal areas. However, seasonal change is slightly delayed in the coastal areas and first approaches the Southeastern part of Estonia and then gradually spreads across the other sub-regions. The climate in the Northeast tends to be slightly cooler and the growing season is shorter than in the rest of the country. In general, the climate in Estonia is humid, with the annual average relative air humidity being around 80 percent. The mean annual precipitation varies between 570 and 750 mm (reference period 19662010; Tarand et al., 2013). It is unevenly distributed across the country and is determined by landscape topography (Jaagus, 2003). Continental Estonia, particularly uplands in the Southeast and Southwest of the country receive more precipitation than the rest of the regions, especially in autumn. Precipitation is much lower in the coastal areas and on islands (Tammets and Jaagus, 2013), especially in spring and in the beginning of summer (Fig.1 and Fig. A.1).

4 143 Fig. 1 Summary of climatic conditions in different study sub-regions for 19552006. The solid line in climographs refers to mean air temperature (°C) and the dashed line to mean precipitation sums (mm). T shows mean annual temperature (°C) and P indicates mean annual precipitation (mm) for the period of 19552006 from meteorological stations. NE – Northeast, ISL – Islands, SE – Southeast, SW – Southwest.

2.2. Tree-ring network and ecological site conditions

Sampling of increment cores of Scots pine trees was carried out in 2007 using a layout of the Estonian Network of Forest Research Plots (ENFRP; Hordo et al., 2009; Kiviste et al., 2015). The ENFRP is distributed across the country and covers a wide range of ecological and climatic conditions. Increment core sampling was restricted to plots established in Scots pine dominated forests growing on mineral soils, including the most common forest site types (Myrtillus, Rhodococcum, Calluna and Cladonia) for this tree species in Estonia (Fig. 2). For this study, we also used a set of annual radial increment data (1.3 m above ground cross-sections) gathered in 2008 (Metslaid et al., 2011) from Southeast Estonia. Since a forest site type in general terms describes edaphic and hydrological site conditions and is the main classification unit used in forest management in Estonia (Lõhmus, 2004), ecological site conditions in this study are described by the four above-mentioned forest site types. Cladonia is a nutrient-poor site type on soils consisting mainly of fine sand, containing very little clay. The organic layer is thin, decomposing slowly. The groundwater in most cases is deeper than 3 meters, therefore the site is very prone to droughts (Lõhmus, 2004; Pärn, 2009). This site type prevails in the North, Northwest, Southeast of Estonia and on islands. Calluna forest site type similarly to Myrtillus and Rhodococcum site type are mesic and more productive due to greater clay content. The level of groundwater is deeper

5 144 than 2 meters but can vary depending on topography. The top layer of the soil may dry out periodically (Lõhmus, 2004). Moisture to tree roots is better supplied on Myrtillus sites, besides that, clay content on these sites might be somewhat greater compared to Rhodococcum sites, which facilitates tree growth. Myrtillus forest site type is the most productive site among the investigated forest site types. Scots pine achieves somewhat lower productivity on Rhodococcum site type, followed by Calluna forest site type, whereas Cladonia is the least fertile forest site. All forest sites in the country are classified according to the same system, however, soils on the islands are much shallower than on mainland (Reintam and Kõlli, 2012).

Pärnu

Fig. 2 Geographical location of Scots pine increment sampling sites (dots) and meteorological stations (triangles) in Estonia. Abbreviations refer to sub-regions as follows: ISL – Islands, NE – Northeast, SE – Southeast, SW – Southwest.

2.3. Sampling, measurements and chronology development

For tree-ring analysis, at least 8 dominant or co-dominant trees were sampled outside the selected ENFRPs, by extracting double-radii core at 1.30 m above the root collar. Pine trees were sampled at 126 sample plots, but only a subset of well inter-correlating ring-width measurement series was used in this study to build chronologies and examine growth-climate relationships with regard to forest site types. Depending on the geographical location, individual sampling sites were assigned to one of four sub-regions: Southeast (SE), Southwest (SW), Northeast (NE) and Islands (ISL; Fig. 2). Samples were prepared in the laboratory by applying dendrochronological techniques (Pilcher, 1990). Tree-ring widths of the increment cores were measured with 0.01 mm measuring precision, using the measurement table LintabTM 5 equipped with a stereo microscope and TSAPWinTM software (RINNTECH, Heidelberg, Germany). Time

6 145 series of the ring-width measurements were cross-dated (Fritts, 1976) within the tree and among several other trees, to assign correctly the years of tree-ring formation. Tree-ring measurement series were visually and statistically cross-dated using TsapWinTM program (Rinn, 2003). Statistics, including Gleichläufigkeit (sign agreement; Eckstein and Bauch, 1969) and student’s t- test (shows a similarity between series) were used during the synchronization procedure. Two radii from the same tree were averaged to produce a mean curve for individual trees. Crossdating accuracy was further checked using COFECHA software (Lamont-Doherty Earth Observatory, Palisades, USA; Grissino-Mayer, 2001) by using mean tree-ring width series for each tree. Synchronous series were further individually detrended with a spline (50% of frequency response and cut-off in 67% of series length) to remove the age trend and impulses related to forest stand structure changes from the growth patterns (Cook and Peters, 1981). The tree-ring width index was calculated by dividing the raw ring-width measurement by the fitted spline value. Autoregressive modelling was used to remove autocorrelation in the individual series (Box and Jenkins, 1970). The order of the autoregressive model was defined using the first minimum Akaike information criterion (Akaike, 1974). Pre-whitened index series were further grouped according to forest site type and sub-region and averaged into mean residual chronologies using Tukey’s robust mean, which enables exclusion of outliers (Mosteller and Tukey, 1977). Finally, chronologies were truncated to the calendar year, replicated at least by five trees. To evaluate common variability and assess the chronology suitability for dendroclimatic analysis, we calculated several statistical measures, including mean sensitivity (MS), first-order autocorrelation (AC1), expressed population signal (EPS; Wigley et al., 1984) and variance captured by the first principal component (PC1; Fritts, 1976) on each set of detrended increment series, grouped by forest site type and sub-region. Standardization was achieved and dendrochronology statistics were calculated using R software (R Core Team, 2016) and package dplR (Bunn, 2008; Bunn et al., 2013).

2.4. Meteorological data

Mean monthly temperature and total precipitation data from four meteorological stations, including Kunda, Tõravere, Pärnu and Ristna, provided by the Estonian Weather Service under the Estonian Environmental Agency were used to characterize weather fluctuation in the selected sub-regions. The data from the nearest meteorological station to a sampling site were used in calculations. Therefore, data from the meteorological station in Tõravere were used for sites in the Southeast, data from Kunda meteorological station were utilized for determining growth-climate relationships in the Northeastern sub-region, data from Ristna station were used for the Islands and meteorological data from the station in Pärnu were used to describe weather variation in the Southwestern sub-region. The length of digitally available data records differed whereas a common period covered by all stations started in 1946 (Table 1).

2.5. Statistical analysis

To assess the influence of annual climate variability on Scots pine radial growth, relationships between forest site type residual chronologies and monthly resolved climatic variables (mean monthly temperature and precipitation sums) were used to calculate correlation coefficients

7 146 (Fritts, 1976). The analysis was restricted to a 52-year-long period (19552006) which was common for all forest site type chronologies and meteorological data (Tables 1 and 2). Climatic variables used in the growth-climate analysis were mean monthly temperatures and monthly precipitation sums from the previous June to the current August. The significance of the relationships at the 95th percentile was tested by applying the bootstrapping procedure (Guiot, 1991) by running 1000 iterations. The dendroclimatic analysis was conducted with R software (R Core Team, 2016), using the functions from the package treeclim (Zang and Biondi, 2015).

Table 1. Summary of mean climatic characteristics over the period of 1955–2006 for different sub-regions of Estonia.

Sub-region Islands Southwest Southeast Northeast

Station name Ristna Pärnu Tõravere Kunda Latitude 59°16′27″N 58°23′09″N 58°16′09″N 59°29′54″N Longitude 23°43′55″E 24°29′49″E 26°27′30″E 26°31′33″E

Data available since 1945 1946 1866 1919 Mean annual temperature (°C) 6.4 5.9 5.3 5.2 Mean annual precipitation (mm) 621 673 606 558 Growing period temperature (°C) 11.7 12.7 14.0 13.3 Growing period precipitation (mm) 291 351 331 331 Mean July temperature (°C) 16.5 17.6 17.1 16.6 Mean July precipitation (mm) 54 75.6 70 68 Mean February temperature (°C) -2.9 -5.1 -6.1 -5.6 Difference between mean July and 19.4 22.7 23.2 22.2 February temperatures (°C)

Previously, several approaches have been used to define similarly responding populations, including a comparison of growth patterns (e.g. Mäkinen et al., 2001; Sohar et al., 2014) or a comparison of responses to annual climate variability patterns (e.g. Nash and Kincaid, 1990; van der Maaten, 2012; Lebourgeois et al., 2013). In this study, to get a comprehensive understanding of pine responses to weather fluctuations on the spatial scale, first we compared indexed growth patterns using hierarchical cluster analysis (HCA), considering the period of 19552006 that was common for all chronologies. In HCA, dis/similarities between growth patterns were assessed by estimating Euclidean distances, according to Ward’s method. To examine the influence of ecological site conditions and local climate on growth-climate relationships, principal component analysis (e.g. Tardif et al., 2003; Mérian et al., 2011; Lebourgeois et al., 2013) was performed on previously calculated bootstrapped correlation coefficients (BCC). The input data for this analysis consisted of the BCC matrix, containing only those climate variables that were statistically significant (p<0.05) for at least one chronology. PCA analysis was conducted using the variance-covariance matrix. Temporal stability of the climate-growth relationships was examined using moving correlation analysis. Correlation coefficients between residual chronologies and climate variables over the period of 19552006 were calculated in a 30-year window, shifted by one year. The

8 147 bootstrapping method was applied and 95% confidence intervals were calculated to test the statistical significance of the correlation coefficients. Moving correlation analysis was performed with R software (R Core Team, 2016), using the treeclim package (Zang and Biondi, 2015).

3. Results

3.1. Statistical characteristics of the chronologies

Altogether, 672 Scots pine trees were used and 13 forest site chronologies for four sub-regions developed (Fig. 3). The chronologies extended between 52 to 192 years (mean 92 years) with the shortest chronologies in the Southwestern sub-region (Table 2). The mean annual tree-ring width over the 19552006 period ranged from 0.95 to 2.06 mm (overall mean 1.3 mm) and on average was slightly greater in the Southeastern and Southwestern pine populations, compared to the pine trees originating from the Islands and the Northeast. The trees sampled on the Islands and in the north of the country were somewhat older, as indicated by the mean series length compared to inland sites in the southern part. The number of samples used to build a chronology varied greatly among the sites and ranged from 14 to 173. The mean series inter- correlation (IC) ranged from 0.463 to 0.650 and was slightly greater for the Islands and the Northeastern sub-region, indicating a stronger common signal in these pine populations. Sign accordance (Glk) was in agreement with IC spatial patterns and was in the 0.570.66 range. The overall mean sensitivity (MS) was 0.18 and did not vary much among the chronologies (0.170.23), being slightly greater in the Northeastern sub-region. Standard deviation (SD) with a range of 0.200.34 followed the MS variation pattern. First-order autocorrelation (AC1) was high in the raw series (data not shown) but when calculated in the detrended series it was moderate, ranging from 0.35 to 0.55, being relatively greater for mesic sites in Northeastern and Southeastern sub-regions. The common variance, captured by the first principal component (PC1) that is assumed to show climate influence on the investigated growth series (Fritts, 2001) ranged between 27% and 54% and tended to be the lowest for sites on the Islands, with no trends observed across an ecological gradient. For most of the chronologies, except the Calluna site in the Northeast, replication of 5 trees was sufficient to reach robust signal strength, EPS ≥ 0.85, which is an accepted threshold for a reliable chronology (Wigley et al., 1985). Since replication of five series was not sufficient, for this site we truncated the chronology to the calendar year when it reached the 0.85 limit. The EPS was greater for well-replicated chronologies and for chronologies with a higher IC, however it tended to be lower for most of the chronologies of dry sites (Calluna, Cladonia). Descriptive statistics suggested that the common signal and thus climatic influences on Scots pine growth are greater on sites in the Northeast whereas annual growth variation was much lower, suggesting that growth is steady in two mesic sites (MyrtSW, RhodSW) in the Southwest.

3.2. Growth-climate relationships

Most of the forest site type chronologies were positively correlated with winter (DecemberJanuaryFebruary) and early spring (MarchApril) month temperatures (Fig. 4). The

9 148 strongest relationship between Scots pine growth and climate was observed for mean temperature in MarchApril (r=0.21–0.54). The associations with early spring temperatures were stronger for moderately mesic-fertile forest site types (Myrtillus and Rhodococcum). The relationship with February temperatures was significant for nine out of thirteen chronologies, with the strongest linkage for Myrtillus and Rhodococcum sites on the Islands. A great number of chronologies, particularly those from the Islands and the Northeast, showed positive correlations with previous August precipitation (r=0.24–0.49) in combination with an inverse correlation to the same month temperature (r=-0.40– -0.35), implying that climatic moisture availability at the end of the growing season has a significant influence on the tree-ring width of the next year. All forest site types in the Southeast, Myrtillus forest site type in the Northeast and both mesic forest sites on the Islands were significantly positively related to current year June temperature. Previous year September precipitation correlated negatively with Scots pine growth in four forest site types, including the Rhodococcum site in the Southwest (r=-0.33), Southeast (r=-0.32) and both mesic sites (MyrtISL and RhodISL) on the Islands (r=-0.36; r=-0.40). Several chronologies, from Islands and Southeast (MyrtISl, RhodISL, CladSE) and both pine chronologies from Southwest showed not a very strong (r=0.25–0.34), but a significantly positive relationship to current March precipitation. Sites in the Northeast were positively related to the total rainfall in current August (r=0.29–0.39), whereas greater precipitation at the end of summer was inversely linked (r=-0.31) to pine radial increment for RhodSW chronology.

10 149 11

bbreviations of bbreviations

depth Sample

depth Sample depth Sample depth Sample depth Sample

depth Sample

180 150 120 90 60 30 0 180 150 120 90 60 30 0

180 150 120 180 150 120 180 150 120 90 60 30 180 150 120 90 60 30 90 60 30 0 90 60 30 0 0 0 180 150 120 90 60 30 0

2005

2001

1997

1993

1989 site Forest in Estonia. cal sub-regions

1985

1981

1977

1973

1969

1965

1961

1957

1953

1949

1945

1941

1937

1933

1929

1925

1921

1917 ISL 1913

Ͳ Ͳ 1909 1905

SW ISL 1901 SE ISL

Ͳ Ͳ

ISL 1897

1893

1889 1885

Myrtillus Rhodococcum 1881 Myrtillus Cladonia Rhodococcum Myrtillus Calluna 1 1 1 1 1

1 1

1.8 1.6 1.4 1.2 0.8 0.6 0.4

1.8 1.6 1.4 1.2 0.8 0.6 0.4 RWI

1.8 1.6 1.4 1.2 0.8 0.6 0.4 RWI 1.8 1.6 1.4 1.2 0.8 0.6 0.4 RWI

RWI RWI RWI RWI

depth Sample

depth Sample depth Sample

depth Sample

180 150 120 90 60 30 0 180 150 120 90 60 30 0 180 150 120 90 60 30 0

2006

2002

1998

1994

1990

1986

1982

1978

1974

1970

1966

1962

1958

1954

1950 1946

describe moisture availability and the productivity gradient (from the driest and poorest to the most mesic and productive). A productive). mesic and poorest to the most the driest and productivity gradient (from moisture availability and the describe

1942

1938 1934

NE 1930 Ͳ

Myrtillus SE 1926 NE

1922 to 1918 SE NE

Ͳ Cladonia 1914

Ͳ 1910 1906

Cladonia 1902 Cladonia

Rhodococcum

Myrtillus Rhodococcum Calluna 1898 Residual tree-ring-width chronologies (lines) and sample sizes (grey area) of Scots pine for each forest site type by geographi forest site type for each pine of Scots area) sizes (grey sample and (lines) chronologies tree-ring-width Residual 1 1 1 1 1 1 4

1.8 1.6 1.4 1.2 0.8 0.6 0.4

1.8 1.6 1.4 1.2 0.8 0.6 0.4 1.8 1.6 1.4 1.2 0.8 0.6 0.4

1.8 1.6 1.4 1.2 0.8 0.6 0.4

1.8 1.6 1.4 1.2 0.8 0.6

1.8 1.6 1.4 1.2 0.8 0.6 0.

RWI RWI RWI RWI RWI RWI types from types from Fig. 3 sub-regions: NE – Northeast, ISL – Islands, SE – Southeast, SW – Southwest. – SW Southwest. SE – NE Southeast, ISL – Islands, – sub-regions: Northeast,

150 12 PC1 31.05 32.27 33.62 28.06 33.71 53.93 24.46 43.96 35.39 37.81 27.98 33.62 37.97 0.85 0.88 0.90 0.93 0.87 0.98 0.85 0.93 0.88 0.88 0.95 0.90 0.93 EPS 5.7 7.1 9.3 6.5 5.5 2.7 7.2 9.2 12.4 44.7 13.9 17.1 13.2 SNR 0.4 0.36 0.47 0.47 0.38 0.44 0.54 0.47 0.44 0.55 0.44 0.35 0.40 AC1 SD 0.21 0.24 0.22 0.23 0.25 0.30 0.34 0.25 0.24 0.27 0.23 0.20 0.20 ed population signal, PC1 signal, – variance ed population (mm), IC Glk – interseries correlation, – MS 0.18 0.19 0.18 0.18 0.20 0.23 0.20 0.20 0.19 0.21 0.18 0.17 0.17 Glk 0.64 0.60 0.60 0.60 0.58 0.66 0.59 0.65 0.59 0.62 0.60 0.57 0.63 IC 0.507 0.508 0.536 0.518 0.494 0.650 0.470 0.585 0.497 0.512 0.506 0.463 0.575 ring ring 1.19 0.95 1.53 1.26 0.97 1.01 1.28 0.99 1.23 1.08 1.63 2.06 1.95 Tree- (mm) width 93 97 58 74 70 97 77 52 59 101 105 126 110 length Chronology series)(n>4 sub-region. Abbreviations: RW – ring width span 1913-2006 1905-2006 1901-2006 1909-2006 1949-2007 1881-2007 1933-2007 1937-2007 1910-2007 1930-2007 1898-2008 1955-2007 1955-2007 (n>4 series) series) (n>4 Chronology (years) 17/73/96 26/58/82 52/65/73 39/67/96 20/40/54 34/45/73 41/85/134 21/65/210 20/74/135 38/89/222 27/74/127 20/67/114 35/68/205 Series length min/mean/max viation, AC1 – first-order autocorrelation, SNR – signal to noise ratio, EPS – express (cores) 19 (38) 29 (58) 21 (42) 40 (80) 24 (48) 14 (28) 24 (48) 31 (62) 48 (96) 90 (180) 77 (154) 97 (194) dual chronologies by forest site type and 173 (346) N of trees Region Islands Islands Islands Islands Southeast Southeast Southeast Northeast Northeast Northeast Northeast Southwest Southwest cipal component (eigenvector). Forest site type Calluna Calluna Cladonia Myrtillus Rhodococcum Calluna Cladonia Myrtillus Rhodococcum Cladonia Myrtillus Rhodococcum Myrtillus Rhodococcum , MS – mean sensitivity, SD – standard de Descriptive statistics of Scots pine resi name Chronology Gleichläufigkeit Table 2. CalISL CladISL MyrtISL RhodISL CalNE CladNE MyrtNE RhodNE CladSE MyrtSE RhodSE MyrtSW RhodSW (%) accounted by the(%) first prin

151

Fig. 4 Pearson’s correlation coefficients between residual ring-width chronologies and mean monthly temperature and total precipitation in 19552006. Dark bars refer to statistically significant (p<0.05) coefficients. Calendar months of the previous year (t-1) are indicated in lowercase letters and months of the current year (t) in capital letters. Climatic variables are arranged from June (j) of the previous year to August (A) of the current ring formation year. Chronology abbreviations are explained in Table 2.

13 152 3.3. Similarities in growth patterns and climatic responses

Hierarchical cluster analysis, conducted on 13 residual chronologies covering the period of 19552006, revealed a greater similarity between indexed growth patterns from the same sub- region (Fig. 5), than between chronologies of the same forest site type. Three main clusters were produced by the HCA. Chronologies from the Southeast and Southwest were compiled into one cluster, implying a greater similarity between growth patterns. The second cluster consists of forest site type chronologies from the Islands, while the last cluster, bearing least similarity to the rest of the growth patterns, consisted of pine chronologies from the Northeast. A closer resemblance in all cases was observed between Calluna and Cladonia site types and Rhodococcum and Myrtillus sites, suggesting the importance of ecological site conditions in shaping annual growth variations. MyrtNE chronology was assigned into the Southeast-Southwest cluster and was the only chronology which was included in a cluster of a sub-region different from which it actually belonged to.

Fig. 5 Dendrogram of HCA performed on 13 residual forest site chronologies, based on the period of 19552006. Chronology abbreviations are explained in Table 2. Grey lines separate three clusters.

The principal component analysis confirmed the results of the HCA and suggested that variation in Scots pine sensitivity to climate is related to special distribution and can be summarized by geographical sub-regions. Altogether the first two principal components captured 70.9% of the total variance (Fig. 6). The first principal component (PC1) resumed 48.3% of the total inertia and was associated with Scots pine sensitivity to climatic conditions of the current growing season. Temperature and precipitation in August (T8 and P8, respectively), previous year December temperature (t12), previous year September precipitation (p9) and current year March–April temperatures had the greatest loading on this dimension. As observed from the score (chronology) distribution (Fig.6), climatic sensitivity captured by this component is characteristic to most of the chronologies in Southeast, Islands and Southwest, but contradictory to pine populations (CalNE, CladNE, RhodNE and CladISL) growing on dry and infertile sites under maritime climatic conditions. The second dimension (PC2) accumulated 22.6% of the

14 153 total variance and captured Scots pine sensitivity to water availability, mainly at the end of the previous growing season. Precipitation in July, August and November (p8, p7 and p11, respectively) and temperature in August (t8) of the previous year and current July precipitation (P7) were strongly related to this principal component. Chronology scores suggested that, sensitivity to previous summer water availability was most characteristic to pine chronologies from Islands but not limiting in Southwest sub-region.

Fig. 6 PCA ordination diagram of Scots pine response to climate variability. PCA was performed on statistically significant BCC, calculated between residual chronologies and climate data. Abbreviations: ISL – Islands, NE – Northeast, SE – Southeast, SW – Southwest; Letter and number combination refers to climatic variables of a certain calendar month (1-January, 2-February, etc.). Lower case letters (p, t) refer to the previous year and capital letters to the current year climatic variables (p-precipitation and t-temperature, respectively). Abbreviations of chronologies are explained in Table 2.

3.4. Temporal stability of climate-growth relationships

According to the temporal stability analysis, relationships between growth and climate variables in 19552006 were not very stable (Fig. A.2). Statistically significant (p<0.05) linkage to winter (t12, T1, T2) and early spring (T3, T4) temperatures was strongest at the beginning of the analysis period, but the significance progressively vanished already at the end of the twentieth century (e.g. MyrtISL, MyrtSW, MyrtSE). Regarding seasonal timing, the significance of temperature in winter months for pine radial growth strongly diminished towards the end of the analysis period (e.g. CladISL, MyrtSW, RhodSW, RhodSE), with statistically significant linkage remaining only for March and April. Moving correlation analysis revealed that the radial growth of Scots pine populations in the Northeastern region was weakly linked to temperatures of winter and early spring already in the middle of the 20th century, except in Myrtillus (MyrtNE) and Calluna (CalNE) forest site types, where linkage of thermal conditions at the end of the dormant season become progressively weaker at the end of last century. The correlation to climatic conditions of the previous growing season, particularly August mean temperature (t8) and the total precipitation (p8) for pine growth on the Islands and the Northeastern region are not statistically significant for the entire period of 19552006. Positive correlation between growth

15 154 on Myrtillus (MyrtNE) and Rhodococcum (RhdNE) sites in the Northeast became stronger in recent decades. While a negative association between tree-ring width and mean August temperature of the previous year seems to get stronger for pine trees on Cladonia site in the Northeast and on the Islands.

4. Discussion

Since the Baltic countries appear in the central part of Scots pine natural distribution (Laas, 1987), optimal climatic conditions for this tree species should exist in this part of Europe. In this study, we investigated climate influence on Scots pine radial growth on the spatial scale considering ecological site conditions and found that over the period of 19552006, which is characterized by rapid warming in the region (Jaagus 2006), a clear spatially explicit variation in Scots pine responses (Fig. 4). Differentiation between growth responses to climate observed in our study seemed to be preconditioned by variability in local climatic conditions over the period of 19552006 (Table 1, Fig. 1). Thermal conditions on the coast and Islands over the studied period were warmer by around 1°C, particularly because of changes in winter. Seasonal moisture conditions in this part of Estonia also differ considerably and are characterized by very dry conditions at the beginning of the vegetation period. Likewise in other studies, investigating species-specific responses on the spatial scale (e.g. Lindholm et al., 2000), we found stronger regional coherence in growth patterns (Fig. 5). The results of PCA, conducted on the BCC matrix, confirmed that climatic variables responsible for most of the year-to-year variability in Scots pine tree-ring widths were more similar within the same study sub-region than for the same forest site type in different regions (Fig. 6). Ecological site conditions certainly played an important role in determining growth rates, however remained on the second place. Regional differentiation between pine responses to climate variability at forest type level in Estonia has been reported by Hordo et al. (2009), whereas Lõhmus (1992) studied climate influence on Scots pine growth in the country with respect to site conditions, and concluded that the main growth limiting factor in this part of Europe is low winter temperature. Recently contrasting responses to inter-annual variation have been observed for common oak (Sohar et al., 2014) and Norway spruce (Helama et al., 2016). The first two components of PCA, conducted on the BCC matrix, captured 70.9 % of the total variance, suggesting that there is a high similarity in Scots pine response to climate variability in the studied region. Nevertheless, Scots pine chronologies from the Northeast according to both HCA and PCA results, were separated from the rest of the sites (Fig. 5, Fig. 6), implying that growth patterns and responses to climate variability in these pine populations were somewhat different compared to the rest. We hypothesise that a sharp distinction between Northeastern chronologies from the rest of the growth patterns could be due to dust pollution (Pärn, 2003). Even if most of our sampling sites in this sub-region are concentrated further from pollution sources, it is known that emissions were much higher in the past (Pärn, 2003). Since tree-rings are good indicators and archives of environmental changes (Fritts, 1976; Cook and Kairiūkštis, 1990), this effect can be recorded in tree-ring series. In addition, descriptive statistics for Northeastern Scots pine chronologies indicated greater climatic sensitivity compared to other chronologies, suggesting a more pronounced influence of abiotic factors in this region. Pärn (2003) studied the impact of dust pollution on Scots pine growth and found that pine

16 155 populations growing close to a pollution source were more sensitive to macroclimate fluctuations. Considering that species-specific radial growth response to annual climate variability depends on a number of factors, including ecological site characteristics (e.g. Tardif et al., 2003; Moir et al., 2011; Hökka et al., 2012) and stand structure (Guillemot et al., 2015; Metslaid et al., 2016), we developed chronologies for forest site types rather than for a separate sampling location. By averaging growth patterns from several locations of the same forest site type, we expected to reduce the non-climatic signal related to management activities and stand structure dynamics inherent to a single stand. In that way, common variability, which is assumed to be caused by large-scale environmental factors like climate, characteristic of selected ecological conditions, would be enhanced (Fritts, 1976). Most of the forest site chronologies in our study correlated positively with February and March– April temperatures and these were among the most important climatic variables throughout the country, with weaker and usually insignificant ties in the driest forest sites (Calluna and Cladonia) and populations in the Northeast. The strong linkage to winter/early spring temperatures implies that wider rings in Scots pine are produced in years with warm winters and springs. This is in accordance with the previous studies conducted in the Baltic region (Bitvinskas, 1974; Lõhmus, 1992; Stravinskienė, 2002; Läänelaid and Eckstein, 2003; Vitas, 2008; Hordo et al., 2009, 2011). Scots pine sensitivity to winter/early spring temperatures has been observed in Southwest Finland (Helama et al., 2014), in Poland (Koprowski et al., 2010), and in West and Central Germany (Bauwe et al., 2013). Several physiological explanations could be proposed for this relationship, including frost-induced damage to fine roots during very cold winters, especially when the insulating snow cover is very thin or absent (Tuovinen et al., 2005). Severe winters may induce coniferous tree defoliation in the following spring (Kuulman, 1991), leading to lower stem biomass production due to reduced photosynthetic capacity (Ericsson et al., 1980). According to Ensminger et al. (2004), the most stressful seasons for Scots pine forests in boreal climates are winters and early springs when low temperatures restrict photosynthesis. They found that the production of pigments that regulate the amount of chlorophyll and progress of photosynthetic recovery is closely related to thermal conditions in spring, and temporal low- temperature spells during this period may even have a reversed effect on physiological recovery. Moreover, delayed snow melt and soil thaw after very cold winters may restrict water and nutrient uptake and flow in the tree and delay initiation of cambium activity (Vaganov et al., 1999). Therefore, warm springs also favour the recovery of transport capacity in xylem and phloem (Vanhatalo et al., 2015). In addition to the previously observed Scots pine sensitivity to winter/spring temperatures, a positive relationship to previous August precipitation and a negative correlation to the same month mean temperature suggested that water deficit at the end of the growing season is strongly negatively related to the next year’s tree-ring width. This relationship is characteristic of coniferous trees growing in temperate forests of central Europe (Oberhuber et al., 1998; Weber et al., 2007, Lebourgeois et al., 2013). In Estonia, associations between Scots pine radial growth and rainfall have been observed in several cases – in Scots pine plantations on reclaimed areas (Metslaid et al., 2016), pine populations growing in dust polluted areas (Pärn, 2003), but also in dry pine forests on Hiiumaa island (Hordo et al., 2009, 2011). In this study, the inverse correlations to previous year August precipitation and mean temperature were the strongest for pine populations growing on the Islands and in Northeastern sub-regions, disregarding the forest

17 156 site type. Scots pine is known as a drought-resistant tree species that during dry periods reduces water-use by closing stomata. Since carbon assimilation in pine is inhibited during such periods, dry and hot climatic conditions at the end of summer may impact root elongation (Konôpka et al., 2005), carbohydrate storage (Ericsson et al., 1980) and bud development, which may, in turn, reduce tree growth potential in the following year. As an alternative explanation for the lagged association between the growth rate and climatic conditions of the previous season, tree flowering (Sensuła et al., 2015) or seed production have been suggested (Hacket-Pain et al., 2015). Earlier research has reported that warm and dry climatic conditions at the end of the growing season promote the formation of generative buds in some tree species (la Bastide and van Vredenburch, 1970). For instance, in Scots pine, needle buds are replaced by male flower buds, which results in lower foliage in the following summer (Innes, 1994). Resource allocation during flowering, especially followed by subsequent seed production may restrict radial growth in masting tree species, including Pinus genera (Hacket-Pain et al., 2015). Moreover, radial growth in Calluna and Cladonia forest sites on the Islands were strongly related to spring precipitation, which could be explained by much dryer climatic conditions during spring-time in this part of the study area. It should be noted that common variance as described by dendrochronology statistics in these two forest site types was lower compared to more fertile and mesic Myrtillus and Rhodococcum sites; therefore, we established statistically less reliable relationships between growth and climate for these two forest sites (Fig. 5). Less coherent radial increment patterns could result from stand dynamics or pine defoliators who commonly attack pine stands on drier sites (Gedminas, 2003; Gedminas et al., 2004). Considering that climatic conditions on the Islands are warmer and dryer, pine populations growing in such conditions could have been already adapted to warmer and dryer growing conditions that could possibly emerge in the rest of the country by the end of the century (Jaagus and Mändla, 2014). Growth rates of such moisture-sensitive pine populations were very low, particularly in old trees, suggesting that trees are just maintaining basic survival functions, but little carbon is left for allocation into radial growth. Low growth rates could explain lower coherency between growth series in these sites because little variation is contained in such growth patterns. Based on previous research findings (Martin-Benito et al., 2011) less dense stands and density reduction through management activities could reduce growth decline in stands with water deficit stress and could even increase their productivity. Besides that, seeds originating from warmer and drier climates, when transferred into more favourable conditions have shown to produce wider rings and achieve greater basal area growth compared to local populations (Savva et al., 2007). Knowledge on Scots pine response to climate variability obtained in this research is recommended to be considered when preparing adaptation strategies for forest management.

5. Conclusions

We used an extensive tree-ring network distributed across Estonia and based on dendrochronological methods, determined the most influential climatic variables and seasonal timing for Scots pine radial growth in hemiboreal ecosystems of Europe. Our results suggest that growth-climate relationships are greatly influenced by local climatic conditions, followed by ecological site characteristics. The radial growth over the period of 19552006 in most studied

18 157 pine populations favoured mild winters and warm spring conditions and was restricted by warm and dry conditions at the end of the previous year summer. The relationship to water deficit at the end of summer was stronger for Scots pine stands growing under maritime climatic conditions, particularly on the Islands and Northeast coast. Pine trees growing under semi- continental climatic conditions (SE) and also in the high-precipitation sub-region (SW) showed stronger sensitivity to late winter/early spring temperatures and weaker correlations to precipitation. An analysis of temporal stability showed that relationships to temperatures of the dormant season are getting weaker, while associations with summer precipitations are becoming more significant. Temporal instability in growth-climate relationships and considerable variation on the spatial scale encumber assessment of climate warming effects on future Scots pine productivity in the region.

Acknowledgments

The study was conducted at the Estonian University of Life Sciences. The study was supported by the Institutional Research Funding IUT21-4 of the Estonian Ministry of Education and Research. The Environmental Investment Centre supported increment core sampling from the ENFRP and measurements. The Estonian Weather Service at the Estonian Environmental Agency provided meteorological data.

References

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26 165 166 167 CURRICULUM VITAE

First name: Sandra Surname: Metslaid Citizenship: Lithuanian Date of birth: 18.01.1979 Address: Institute of Forestry and Rural Engineering, Estonian University of Life Sciences, Kreutzwaldi 5, 51014 Tartu, Estonia E-mail: [email protected]

Education: 20092016 Post-graduate (PhD) Studies in Forest Management, Institute of Forestry and Rural Engineering, Estonian University of Life Sciences

20022005 Master’s (MSc) studies in Forest ecology, Faculty of For- est Sciences, Lithuanian University of Agriculture

20022003 Master’s (MSc) programme “Sustainable Forestry around the Southern Baltic Sea”, Swedish University of Agricultural Sciences, Sweden

19972002 Bachelor’s studies in Ecology and Environmental Sci- ences, Faculty of Forest Sciences, Lithuanian University of Agriculture, Lithuania

19851997 Mažeikiai Senamiesčio Secondary School, Lithuania

Professional employment: Since 2007 Estonian University of Life Sciences, Institute of Forest- ry and Rural Engineering; Senior Laboratory Assistant

20042007 Lithuanian State Forest Survey Service, Department of Statistics; Senior Engineer

168 Academic degree: 2005 MSc in Forest Ecology for the thesis “Analysis of quality criteria of forest resources” (Lithuanian University of Agriculture, Lithuania, Kaunas-Akademija)

Research interests: Tree-growth analysis and modelling based on tree-rings, dendroecology, weather variation eff ects on tree-growth.

Knowledge of foreign languages: English, Russian, Estonian

Training and special courses: 2016 Course „Statistics in DendRochRonology 2.0”, Frie- drich-Alexander University of Erlangen-Nüremberg, Erlangen, Germany, 18.09.23.09.2016

2016 Course „Multivariate statistics”, Estonian University of Life Sciences, Tartu, Estonia, 13–27.01.2016.

2015 Reconstruction of Stand Structure, Tallinn University/ Kyoto University, Tallinn, Estonia, 16.03.17.03.2015.

2014 European Dendroecological Fieldweek 2014, Oviedo (Asturias), Spain, 01.09.06.09.2014.

2013 COST Training School „Modelling drought stress responses”, Ghent University, Belgium, 26.05. 28.05.2014.

2009 PhD course „Modeling growth and yield for decision analysis”, Swedish University of Agricultural Sciences (SLU), Umeå, Sweden, 07.12.200915.01.2010.

169 20022003 Master’s programme „Sustainable Forestry around the Southern Baltic Sea”. One-year international MSc programme, Swedish University of Agricultural Sciences (SLU), Alnarp, Sweden.

Projects and cooperation: 2013–2016 8-2/T13003MIGO „Th e impact of land-related decisions on the use of land, forest and water resources”. In- vestigator.

2014 ETF8890 „Modelling Estonian forest stands growth in changing conditions on the background of European forest growth and yield models”. Principal investigator.

20082013 SF0170014s08 „Th e eff ect of changing climate on forest disturbance regimes in temperate and boreal zone”. Investigator.

20082009 8-2/T8047MIMI „Investigation of changes in forest and forest clear-cut areas, using remote sensing methods. Investigator.

20082009 8-2/T8092MIMI „Development of tree-ring chronologies for Norway spruce”. Investigator.

20082009 8-2/T8046MIMI „Re-measuring of the Estonian Network of forest monitoring plots 2008/2009”. Investigator.

2007–2008 8-2/T7023MIMI „Re-measuring of the Estonian Network of forest monitoring plots 2007/2008”. Investigator.

2007–2008 8-2/T7103MIMI „Development of tree-ring chronologies for Scots pine growing in Heath and Mesotrophic forest site types”. Investigator.

170 2007 8-2/T7131MIMI „Analysis of growth dynamics based on stem analysis applied on trees from old growth and yield permanent monitoring plots in Järvselja”. Investi- gator.

171 ELULOOKIRJELDUS

Eesnimi: Sandra Perekonnanimi: Metslaid Kodakondsus: Leedu Sünniaeg: 18.01.1979 Aadress: Metsandus- ja maaehitusinstituut, Eesti Maaülikool, Kreutzwaldi 5, 51014 Tartu, Eesti E-post: [email protected]

Hariduskäik: 20092016 Eesti Maaülikool, metsandus- ja maaehitusinstituut, metsanduse eriala, doktoriõpe

20022005 Leedu Põllumajandusülikool, metsandusteaduskond, metsaökoloogia eriala, magistriõpe

20022003 Rootsi Põllumajandusülikool, rahvusvaheline magis- triõppe programm „Sustainable Forestry around the Southern Baltic Sea”

19972002 Leedu Põllumajandusülikool, metsandusteaduskond, ökoloogia ja keskkonnateaduste eriala, bakalaureuseõpe

19851997 Mažeikiai Senamiesčio Keskkool, Leedu

Teenistuskäik: Alates 2007 Eesti Maaülikool, metsandus- ja maaehitusinstituut, metsakoralduse osakond, vanemlaborant

20042007 Leedu Riiklik Metsakorralduse Teenistus, statistika osakond, vaneminsener

Teaduskraad: 2005 Magister metsaökoloogia erialal. Magistritöö: „Analysis of quality criteria of forest resources”, Leedu Põlluma- jandusülikool 172 Teadustöö põhisuunad: puude kasvuanalüüs ja modelleerimine aastaste juurdekasvude põhjal, dendroökoloogia, kliima mõju puude kasvule

Võõrkeelte oskus: inglise, vene, eesti

Täiendkoolitused: 2016 Statistika kursus „DendRochRonology 2.0”, Erlan- gen-Nürembergi Friedrich-Alexander Ülikool, Erlan- gen, Saksamaa, 1823.09.2016.

2016 Täienduskoolituskursus „Mitmemõõtmeline statistika” (VL.4), EMÜ, Tartu, 13–27.01.2016.

2015 Kursus „Puistu struktuuri rekonstrueerimine”, Tallinna Ülikool/Kyoto Ülikool, Tallinn, Eesti, 1617.03.2015

2014 Euroopa dendroökoloogilise praktilise välitöö kursus 2014, Oviedo, Astuuria, Hispaania, 01–06.09.2014.

2013 COST-i organiseeritud kursus „Põuast tingitud stressi mõju modelleerimine” Genti Ülikool, Belgia, 2628.05.2014.

2009 Doktorikursus „Kasvukäigu modelleerimine otsuste tegemiseks”, Rootsi Põllumajandusteaduste Ülikool (SLU), Umeå, Rootsi, 07.12.200915.01.2010.

20022003 Rootsi Põllumajandusteaduste Ülikool (SLU), rahvusvaheline magistriõppe programm „Sustainable Forestry around the Southern Baltic Sea”, Alnarp, Rootsi.

Projektid: 2013–2016 8-2/T13003MIGO „Maaga seotud otsuste mõju maa-, metsa- ja veeressursside kasutamisele”. Täitja.

173 2014 ETF8890 „Eesti puistute kasvukäigu modelleerimine muutuvates kasvutingimustes Euroopa puistu kasvumudelite foonil”. Põhitäitja.

20082013 SF0170014s08 „Muutuvate kliimatingimuste mõju boreaalsete ja parasvöötme metsade häiringu režiimile”. Täitja.

20082009 8-2/T8047MIMI „Metsade pindala muutumise ja raie- te jälgimine kaugseire meetoditega”. Täitja.

20082009 8-2/T8092MIMI „Dendrokronoloogilise skaala koostamine kuusepuude andmeil”. Täitja.

2008–2009 8-2/T8046MIMI „Metsa kasvukäigu püsiproovitük- kide võrgustiku kordusmõõdistamine 2008/2009”. Täitja.

2007–2008 8-2/T7023MIMI „Metsa kasvukäigu püsiproovitük- kide võrgustiku kordusmõõdistamine 2007/2008”. Täitja.

2007–2008 8-2/T7103MIMI „Dendrokronoloogilise skaala koostamine nõmme- ja palumännikute andmeil”. Täitja.

2007 8-2/T7131MIMI „Järvselja vanade puistu kasvukäigu püsiproovitükkide puuanalüüside kogumine ja andmete töötlemine”. Täitja.

174 LIST OF PUBLICATIONS

Publications indexed in the ISI Web of Science database

Sánchez-Salguero, R., Hevia, A., Camarero, J.J., Treydte, K., Frank, D., Crivellaro, A., Domínguez-Delmás, M., Hellman, L., Kaczka, R., Kaye, M., Akhmetzyanov, L., Ashiq, W.M., Bhuyan, U., Bondarenko, O., Camisón, Á., Camps, S., García, V.C., Vaz, F.C., Gavrila, I.G., Gulbran- son, E., Huhtamaa, H., Janecka, K., Jeff ers, D., Jochner, M., Koutecky, T., Lamrani-Alaoui, M., Lebreton-Anberrée, Seijo, M.M., Matulewski, P., Metslaid, S., Miron, S., Morrisey, R., Opdebeeck, J., Ovchinnikov, S., Petres, R., Petritan, A.M., Popkova, M., Sánchez-Miranda, Á., Van der Linden, M., Vannoppen, A., Volařík. 2017. An intensive tree-ring experience. Connecting education and research during 25th European Dendroecological Fieldweek (Asturias, Spain). Dendrochronologia, 42: 80–93.

Metslaid, S., Stanturf, J. A., Hordo, M., Korjus, H., Laarmann, D., Kiviste, A. 2016. Growth responses of Scots pine to climatic factors on reclaimed oil shale mined land. Environmental Science and Pollution Research, 23(14): 1363713652.

Metslaid, S., Sims, A., Kangur, A., Hordo, M., Jõgiste, K., Kiviste, A., Hari, P. 2011. Growth patterns from diff erent forest generations of Scots pine in Estonia. Journal of Forest Research, 16(3): 237243.

Hordo, M., Metslaid, S., Kiviste, A. 2009. Response of Scots pine (Pi- nus sylvestris L.) radial growth to climate factors in Estonia. Baltic Forest- ry, 15(2): 195205.

Padari, A., Metslaid, S., Kangur, A., Sims, A., Kiviste, A. 2009. Mod- elling stand mean height in young naturally regenerated stands – A case study in Järvselja, Estonia. Baltic Forestry, 15(2): 226236.

175 Publications in other peer-reviewed research journals

Kiviste, A., Hordo, M., Kangur, A., Kardakov, A., Laarmann, D., Lillele- ht, A., Metslaid, S., Sims, A., Korjus, H. 2015. Monitoring and modelling of forest ecosystems: the Estonian Network of Forest Research Plots. Forestry Studies, 62: 2628.

Published meeting abstracts

Metslaid, S., Sims, A., Kangur, A., Hordo, M., Jõgiste, K., Kiviste, A., Hari, P., 2009. Growth patterns from diff erent generations of Scots pine dominated forests in Estonia. In: Book of Abstracts: Approaches for forest disturbance studies, VIII international meeting of SNS network Nat- ural Disturbance Dynamics Analysis for Forest Ecosystem Management, 21–25 September 2009, Vihterpalu, Estonia

Metslaid, S., Kangur, A., Sims, A., Hordo, M., Kiviste, A., 2010. Cli- matic factor infl uence on Scots pine (Pinus sylvestris L.) radial and height growth in Järvselja: an Estonian case study. In: Mielikäinen, K., Mäkin- en, H., Timonen, M., (eds.). Abstracts of Th e 8th International Confer- ence on Dendrochronology, 13–18 June 2010, Rovaniemi, Finland, pp. 258-258.

Hordo, M., Metslaid, S., Kiviste, A., 2010. Climatic signals and radial increment variation of Scots pine (Pinus sylvestris L.) and Norway spruce (Picea abies (L.) Karst.) in Estonia. In: Mielikäinen, K., Mäkinen, H., Timonen, M., (eds.) Abstracts of Th e 8th International Conference on Dendrochronology, 13–18 June 2010, Rovaniemi, Finland, pp. 258– 258.

Metslaid, S., Sims, A., Kangur, A., Hordo, M., Jõgiste, K., Kiviste, A., Hari, P., 2011. Growth patterns of diff erent forest generations of Scots pine in Estonia. In: Chubinsky, M., von Gadow, K. (eds.) ICFFI News : Ecosystem Design for Multiple Services – with an emphasis on Eurasian Boreal Forests, 9–11 November 2011, St. Peterburg, Rusia, St. Peters- burg State Forest Technical University, pp. 30−30.

Metslaid, M., Engelhart, J., Jõgiste, K., Metslaid, S., Vodde, F., Köster,

176 K., 2012. Growth response of Norway spruce advance regeneration to release: a decade of measurements on height, diameter and shoot characteristics. In: Stewart, G. (Ed.). Proceedings of the International Confer- ence “Uneven-aged silviculture: optimising timber production, ecosystem services and resilience to climate change”, 12-16 November 2012, Christchurch, New Zealand, pp. 28−29.

Motallebi, A., Hordo, M., Metslaid, S., Goodrick, S., Kiviste, A, Korjus, H., Kangur, A., 2013. Scots pine (Pinus sylvestris L.) radial and height growth relations with local climate factors and atmospheric teleconnec- tions in Estonia. In: Abstract book: Th e third international conference of Asian Dendrochronology Association, 11–14 April 2013, Tehran, Iran, pp. 20−20.

Metslaid, S., Hordo, M., Korjus, H., Laarmann, D., Promet, J., Kiviste, A. 2014. Impact of oil shale deposition depth and climatic factors on Scots pine (Pinus sylvestris L.) stand growth on reclaimed post-mining areas in Estonia. In: Kangur, A., Metslaid, M., Moser, W.K.,Trei, P. (eds.). Book of Abstracts: Forest landscape mosaics: disturbance, restoration and management at times of global change, 11-14 August 2014, Tartu, Estonia. Transactions of the Institute of Forestry and Rural Engineering, Estonian University of Life Sciences, pp. 123–123.

Metslaid, M., Engelhart, J., Jõgiste, K., Metslaid, S., Vodde, F., 2014. Potential of advance regeneration of Norway spruce in forest regeneration in Estonia. In: Kangur, A., Metslaid, M., Moser, W.K.,Trei, P. (eds.) Book of Abstracts: Forest landscape mosaics: disturbance, restoration and management at times of global change, 11-14 August 2014, Tartu, Estonia. Transactions of the Institute of Forestry and Rural Engineering, Estonian University of Life Sciences, pp. 61–61.

Metslaid, S., Kangur, A., Hordo, M., Kiviste, A. 2014. Radial growth-climate relationships along the stem in Scots pine. In: Sohar, K., (Ed.) Ma- terials of the 3rd International Conference of Dendrochronologist from the Baltic States, 25–28 August 2014, Järvselja, Estonia, pp. 17–17.

Kiviste, A., Hordo, M., Kangur, A., Kardakov, A., Korjus, H., Laar- mann, D., Lilleleht, A., Metslaid, S., Sims, A., 2014. Long-term monitoring and evaluating forest dynamics: the Estonian Network of Forest 177 Research Plots. In: Book of abstracts: International symposium “Forest and sustainable development”, 24–25 October 2014 Brașov, Romania. Transilvania University of Brașov, pp. 47–47.

Metslaid, S., Korjus, H., Kiviste, A., 2015. Forest productivity and growth variation on degraded post-mining landscapes: Assessment based on long-term observations. In: Lillemaa, T., Peterson, U., Perera, A. (eds.) Book of Abstracts: Sustaining ecosystem services in forest landscapes, IUFRO Landscape Ecology Working Group Conference 2015, 23-30 August 2015, Tartu, Estonia, pp. 196–196.

Kiviste, A., Hordo, M., Kangur, A., Kardakov, A., Korjus, H., Laar- mann, D., Lilleleht, A., Metslaid, S., Sims, A., 2015. Monitoring and evaluating forest dynamics: the Estonian Network of Forest Research Plots. In: Proceedings of the Biennial International Symposium “Forest and sustainable development”, 24–25 October 2014, Brașov, Romania, Transilvania University Press, pp. 110−117.

Metslaid, S., Hordo, K., Kiviste, A., 2016. Spatio-temporal variability in Scots pine (Pinus sylvestris L.) response to weather fl uctuations in Estonia. In: Book of Abstracts of 4th International Conference of Den- drochronologistas and Dendroecologists from Baltic Sea Region, 22–25 August 2016, Annas Tree School, Latvia, pp. 31–31.

178 SANDRA METSLAID

VIIS VIIMAST KAITSMIST

17. jaanuar 2017 Pinus sylvestris