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Article

Development of Forest Tree Species Composition: Selected Results of the National Forest Inventory of Lithuania

by
Raimundas Petrokas
1,*,
Michael Manton
2,
Gintaras Kulbokas
3 and
Milda Muraškienė
4
1
Department of Forest Genetics and Tree Breeding, Institute of Forestry, Lithuanian Research Centre for Agriculture and Forestry, Liepų Str. 1, LT-53101 Girionys, Lithuania
2
Bioeconomy Research Institute, Vytautas Magnus University, Studentų Str. 13, LT-53362 Akademija, Lithuania
3
Lithuanian State Forest Service, Pramonės Str. 11A, LT-51327 Kaunas, Lithuania
4
Department of Silviculture and Ecology, Institute of Forestry, Lithuanian Research Centre for Agriculture and Forestry, Liepų Str. 1, LT-53101 Girionys, Lithuania
*
Author to whom correspondence should be addressed.
Plants 2025, 14(5), 667; https://doi.org/10.3390/plants14050667
Submission received: 17 December 2024 / Revised: 10 February 2025 / Accepted: 19 February 2025 / Published: 21 February 2025
(This article belongs to the Collection Selected Papers from Lithuanian Research Centre for Agriculture and Forestry)
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Versions Notes

Abstract

:
Forest development forms the foundation for the advancement of sustainable forest management that integrates the knowledge of natural and anthropogenic processes with ecological and biological insights. This study aims to emphasize the role of assisted natural regeneration and balanced forest development phases in fostering closer-to-nature management approaches, contributing to resilient forest ecosystems capable of self-regulation and biodiversity support in the face of anthropogenic and climatic challenges. This study focuses on forest development in Lithuania based on five National Forest Inventories (NFIs) from 2002 to 2022. We examine the tree volume structure of the growing stock by stand type and forest type series from the point of view of stand age and forest development phases. This is performed by applying the standardized methodologies of the Lithuanian National Forest Inventory. Our analysis focuses on broader patterns derived from the selected NFI data rather than stand-level details. Our findings demonstrate that long-term observation of dynamic National Forest Inventories can aid in the development of closer-to-nature forest management methods for different forest type series. In order to implement the European Union’s strategy and policy for closer-to-nature forest management, we call for the use of “assisted succession” methods in commercial forests, promoting the formation of mixed-species forest stands with multi-cohort age profiles, including old-growth all-aged forest patches of >121 years.

1. Introduction

The European Union has been progressively developing its closer-to-nature forest management policy as part of its broader sustainability agenda [1]. This approach, stressed in the EU Forest Strategy for 2030 and the EU Biodiversity Strategy for 2030, aims to enhance biodiversity, improve forest resilience against climate change, and promote multifunctional forest ecosystems [2,3]. Closer-to-nature forest management seeks to integrate the ecological, social, and economic dimensions of forestry, by aligning with the principles of sustainable forest management (SFM) established in earlier EU policies [4].
The implementation of closer-to-nature forest management calls on each EU country to develop their own practices that involve adapting forestry practices to mimic natural processes, thereby fostering healthier ecosystems and enhancing carbon sequestration capabilities [5,6]. This paradigm shift is crucial as it addresses the building pressures on forest resources due to the rising demand for biomass and timber, while also mitigating biodiversity loss [7]. Furthermore, it provides opportunities for forest managers to maintain and re-create ecosystems that are not only productive but also resilient to climate change, thereby ensuring their long-term sustainability [8,9]. However, to achieve this requires the impartial understanding of natural forest patterns and processes, such as forest stand development, on the one hand, and the access to relevant forest data that can deliver current forest characteristics and trends, such as the National Forest Inventory (NFI) data, on the other [10,11].
Forest stand development and tree species composition are influenced by a range of factors, including ecological, climatic, and anthropogenic elements, which interact with the life history characteristics of the tree species [12,13]. Ecologically, the presence of shade-intolerant species is enhanced through practices like group selection, which allows for greater regeneration success by providing necessary light conditions [14]. The composition of undergrowth, particularly in fertile soils, indicates a transition from pioneer deciduous species to more stable deciduous hardwoods [15]. This can reflect both natural succession and management practices [16]. Climatic factors, i.e., temperature and precipitation patterns, also play a critical role in shaping species distribution and forest dynamics, as they affect growth rates and species interactions [17]. Natural disturbances such as windthrow, insects, disease, and fire can significantly alter forest stand structure and species composition, necessitating adaptive management strategies to maintain biodiversity and ecosystem health [18,19]. Furthermore, human activities, including forest management practices, land-use changes, drainage, and forest planting, disrupt natural processes, leading to shifts in species composition and forest integrity and resilience [20,21]. As climate change continues to alter the frequency and intensity of disturbances, it becomes increasingly important to understand the dynamics of forest stand development and to incorporate these dynamics into the management practices to ensure the sustainability of hemiboreal forests [18,22]. Thus, the understanding the life histories and adaptive strategies of tree species in response to competition, stress, and disturbance are vital for forest stand development and ecosystem resilience [2,9].
To capture the changes and trends in forest development and tree species composition as well as defining closer-to-nature forest management practices requires long-term monitoring, such as National Forest Inventories (NFIs) [23]. NFIs provide accurate, comprehensive, and up-to-date information data on forest resources, including tree species composition, age structure, biomass, and carbon stocks [24,25]. As climate change continues to pose significant challenges and opportunities for forest growth, the importance of NFIs in providing reliable data for closer-to-nature forest management strategies cannot be overstated. For example, NFI data can help identify changes in forest stand structure and composition over time, allowing for the evaluation of the impacts of both climate change and human activities on forest health [26,27]. Thus, NFIs are promising tools for assessing the health and dynamics of forest ecosystems and aiding in an informed decision-making process for closer-to-nature forest management.
Therefore, the aim of this study is to analyse the tree species compositions of hemiboreal forests in the context of forest development and sustainability, in order to support the transition to closer-to-nature forest management. To achieve this, we first examined the tree volume structure of the growing stock across different stand types and forest type series, as well as stand age and forest development phases, and second, we identified trends in forest composition, structural changes, and succession processes that influence long-term forest resilience. Finally, we discussed the future research needs of the sustainable management and evaluation of forest development to ensure that the physical and biological conditions of forest ecosystems self-regulate to support forest-dependent biodiversity.

2. Materials and Methods

2.1. Lithuania as a Case Study

We selected Lithuania, which lies completely within the European hemiboreal forest zone, as the case study. This forest zone forms a distinctive ecological region that serves as a transitional zone between boreal and temperate forests and is characterized by a rich diversity of flora and fauna, shaped by a combination of climatic conditions, soil types, and historical land use practices [9,28].
According to the Lithuanian Forestry Statistics [29], the area of forest land covers 33.7% of the country’s territory. Of this forest land, 74.9% is covered by commercial forests, in which the main objective of the economic activities is to form productive forest stands for a permanent timber supply. Strict reserve forests are distinguished in the forest land of the state only. Coniferous stands prevail in Lithuania, covering 55.7% of the forest area; they are followed by softwood deciduous forests, at 40.9%, while hardwood deciduous forests occupy 3.4% [29].
The mixture of Lithuanian forests is relatively stable, but the very obvious and constant tendencies of the increasing areas of softwood deciduous trees and decreasing areas of coniferous tree species cannot be ignored [30]. According to the Lithuanian NFI data, 55% of permanent forest inventory plots are single-species-dominated stands (i.e., >75%) based on growing stock volume (Figure 1). This can be accounted for by the dominant even-aged clearfell rotation system used throughout Lithuania’s forests [29,31].
Most of the changes in the tree species composition of Lithuanian forests are due to changes in the volume and age structure of timber harvests, depending on the growth of wood and the sanitary condition of the stands [32]. For instance, to prevent the natural loss of trees that die naturally due to competition, diseases, pests, or are destroyed by storms, snow, ice, drought, etc., the stands are thinned out or cut down in advance, and the target species composition is formed by taking high-quality living wood [30]. However, pre-harvesting high-quality timber or low-quality biodiversity trees does not guarantee a healthy forest. In Lithuania, the number of trees dying spontaneously or due to natural factors, pests, and diseases does not decrease (and makes up, on average, 17–19% of the gross volume increment), which shows the insufficient impact of silviculture measures on increasing the resilience and sustainability of the stands [30].
Thus, the long-term resilience of Lithuanian forests is increasingly challenged by climate change, pests, and human activities. Furthermore, the observed transitions in forest composition, such as the increasing share of Norway spruce (Picea abies), may lead to vulnerabilities, as Norway spruce-dominated forests are highly susceptible to drought, windthrow, and bark beetle infestations [33]. Ash dieback has been found to cause a large amount of damage, especially in humid forests where European ash (Fraxinus excelsior) grows densely [34,35]. Nevertheless, subsequent forest re-establishment practices with scarification and planting of singular tree species continues to decrease forest resilience in Lithuania. The situation is aggravated by the increase in the volume of logging in Lithuanian forests, which has doubled from 3.0 to 6.7 million cubic metres of timber between 1990 and 2020.
International processes individually emphasize some but not all the benefits from forests, with some variation over time [36]. Some current problems at the national level are related to poor links between information supply and demand. For example, in Lithuania, the need for discussion and information at the national level is currently due to climate change, biodiversity issues and carbon sequestration, while at the local level the emphasis is on wood supply. Furthermore, Lithuania’s current focus on forest area and area change provides a very poor evaluation of forest benefits, as most of them depend on other parameters. In conclusion, it should be noted that a national forest assessment should examine not only the biophysical resources of forests and trees, but also the sustainable management and use of these resources, with particular attention to ecosystem integrity [36,37].

2.2. Lithuanian National Forest Inventory

The Lithuanian National Forest Inventory (NFI) is a systematic approach that systematically collects, analyses, and reports data on the country’s forest resources, focusing on aspects like forest extent, composition, health, biomass, and biodiversity [30,38]. The main indicators that are analysed are the growing stock volume, volume increment, and stand density, overall and broken down by tree size. The widespread system of permanent forest sample plots (N = 6154) contains the geographical location of each tree, which ensures the identification of trees during subsequent inventories and delivers high data confidence [30]. According to the Lithuanian NFI, one permanent sample plot represents 400 ha of Lithuania’s forest area. The growing stock volume of all Lithuanian forest stands per 1 ha according to permanent sample plots is estimated with an accuracy of 1.0% (with a probability of 0.68). In all forests of Lithuania, the total growing stock volume is estimated with an error of 1.5%. This is especially important to objectively assess the impact of forest management activities on the formation of the stand [30].
Conducted at regular intervals, the Lithuanian NFI employs standardized methodologies to monitor and assess various parameters, such as tree species diversity, age classes, growth rates, and forest stand health. It uses tree-level inventory reporting, using objective systematic sampling with a random starting point. The main NFI sampling unit consists of a permanent circular plot, which consists of several elements for different measurement objectives (see Žemaitis et al. [39]). The main circular sample plot (500 m2 in size, horizontal radius equals 12.62 m) is used to measure all trees with a diameter greater than 14 cm at a height of 1.3 m. Basal area, species composition, age, and growing stock volume of the stand are assessed throughout the stand in a horizontal radius of 12.62 m, and the forest type series, site type, as well as soil type in a horizontal radius of 30 m [39,40]. Wood resources in Lithuanian forests are expressed in terms of tree stem volume, in cubic meters (solid) per unit area (1 ha) or in the total forest stand area [41,42,43].
This study analysed Lithuanian NFI data from five 5-year remeasurement cycles spanning 1998–2002 (NFI 2002), 2003–2007 (NFI 2007), 2008–2012 (NFI 2012), 2013–2017 (NFI 2017), and 2018–2022 (NFI 2022), covering all regions of Lithuania. Using this dataset, we examined growing stock volume, volume increment, stand density, tree distribution, and growing stock characteristics by diameter classes and age profiles. A tree volume stratification model was employed to explore how biodiversity mechanisms known to enhance ecosystem biomass production are linked to forest development [44,45]. Standardized methodologies of the Lithuanian NFI were applied; the volume structure of the growing stock (I and II storey) was determined using species-specific indices. Individual tree species’ stem volumes were further converted into relative values following Lithuania’s NFI standards [41].
The study of the relationship between tree age structure and species composition across different stand types and forest type series is an important area of our current and future research. It focuses on broader patterns derived from the selected NFI data rather than stand-level details. While stand-level data, such as the proportion of real monocultures, even-aged, and uneven-aged stands, would indeed provide valuable insights, such data were beyond the scope of our current study due to the structure of the available NFI dataset.

2.3. Forest Vegetation and Soil Dynamics

Forest development is simply the secondary succession or the orderly and predictable change in the dominant species of forest plants [46]. It is jointly determined by multiple factors pertaining to three dimensions, i.e., climatic, edaphic, and biotic [47]. The mosaic of natural hemiboreal forest communities is largely the result of ecological succession leading ultimately to climatic climax vegetation [14]. In some parts of the hemiboreal forest zone, there are areas where local peculiarities of the soil or topography make the development of the climatic climax forest impossible, so the potential natural forest communities here are edaphic climaxes on immature sand soils or biotic climaxes on hydromorphic soils associated with marshes, swamps, or poorly drained uplands [21].
Multiple edaphic factors influence different aspects of the hemiboreal forest stand structure, including different tree life stages and species composition. The distribution, richness, and structure of forest species are related to both edaphic and topographic characteristics; the spatial dependence of forest–plant diversity on soil diversity indicates the integrity of the community with the bedrock [48,49]. Soils whose fertility is determined by bedrock are less diverse, while soils that are not associated with bedrock in their mineral composition are more diverse. Soil fertility differentiates the ways in which interactions between plants and bedrock develop [48]. Forest species composition is primarily influenced by the type of bedrock (especially its base saturation), soil reaction, and nutrients [50]. If there are no differences in base saturation, then the second most important variables are those related to the terrain (especially slope), which affect the availability of light and moisture. For example, peat thickness is strongly related to topographical features at macroscales, which can explain more than 40% of the peat thickness spatial variability [51].
Forest ecosystems create soil heterogeneity through organisms, litter, and disturbance. However, modern soil classification uses soil morphology aligned with parent material, climate, and time, and assumes continuous soil formation at landscape and millennial scales [52]. In mainstream forestry (age-class management with pure stands), the different classifications of forest development and algorithms used to assign a forest patch to a specific forest development phase does not take into account soil dynamics at the temporal and spatial scales of trees and stand structures [52,53,54]. However, soil series can be useful when combined with other characteristics to delineate stands. The Lithuanian NFI does not include the Food and Agriculture Organization (FAO) soil classification system but continues to assess site types based on soil typological groups [55,56]. To be consistent with FAO approach, we have classified the main forest habitats of Lithuania into forest type series, site types, and soil types (Table 1), including 13 Natura 2000 forest habitat types of European Community importance [57,58,59]. Table 1 was used to select the NFI data, which were further analysed in the results. Nevertheless, since the Lithuanian NFI data on soil typological groups are based on the old soil classification system, our study does not include an analysis of forest soils.

2.4. Forest Development

In this study, the phases of secondary forest development are characterized using a general scheme of patch development [62]. Although there is no consensus among scientists on how many phases a forest life cycle has [53], we applied a general classification of forest development phases (Table 2) that defined the phases of stand initiation, stem exclusion, understorey reinitiation, and steady state [46,63]. The stand initiation phase starts following the cessation of the disturbance [46]. An example of such a disturbance is clear-cutting, which resets the successional process to the beginning. Stand initiating disturbances are sufficiently intense and widespread that large amounts of sunlight are available at or near the soil of the forest floor. The early successional dominants, such as Scots pine (Pinus sylvestris), silver and downy birch (Betula pendula and Betula pubescens), Eurasian aspen (Populus tremula), and grey and black alder (Alnus incana and Alnus glutinosa), form the early canopy. The duration of this phase is the shortest, perhaps 15 to 25 years. The stem exclusion phase starts as soon as the tree vegetation forms a closed canopy. For many pioneer species in this phase, self-thinning takes place, allowing sunlight to penetrate deeper and eventually promoting the development of post-pioneer species in the understorey, in other words, the establishment of the understorey. Due to the difference in the moment of death of individual trees, the release of the post-pioneer understorey also occurs differently [62]. For fast-growing species, this phase can last 20 to 30 years. Meanwhile, the duration of the understorey reinitiation phase is highly dependent on the longevity of the dominant species. For example, the lifespan of early dominant Eurasian aspen and silver birch can be 60 to 100 years, whereas if Scots pine and English oak (Quercus robur) are dominant, the lifespan might be well over 120 years. The steady state phase represents the mature forest. The early successional dominants may have died out; the forest regenerates itself among longer-lived “pre-climax” trees, such as European white elm (Ulmus laevis), wych elm (Ulmus glabra), small-leaved lime (Tilia cordata), Norway maple (Acer platanoides), and European beech (Fagus sylvatica), which can survive in the sunlight available through small openings in the canopy as single trees die. In a forest development context, this final phase occurs when all trees in the forest are “climax” species established in the shade of other trees [64].
Forests that have been recently killed or altered by fire, insects, disease, wind or logging are still forests because of the biological and physical legacies from the previous forest [65]. Under a regime of sustainable forest management, many or most of these legacies persist during the period between forest disturbance and the redevelopment of tree cover. Therefore, to ensure the resilience and sustainability of the stands, it is important to retain structural forest legacies and apply assisted natural regeneration using a variety of species [14]. Natural reforestation and artificial reforestation are two distinct approaches to forest restoration. Natural reforestation relies on the spontaneous regeneration of native species through natural processes, often influenced by existing seed banks and surrounding vegetation, leading to diverse ecosystems [66,67]. In contrast, artificial reforestation involves human intervention, typically through planting specific tree species, which may not always replicate the ecological complexity of natural forests [68,69]. Whereas natural afforestation occurs when native species colonize non-forest land through land abandonment, artificial afforestation involves the deliberate planting of trees in these areas [70]. Both methods aim to enhance biodiversity and ecosystem services, but their ecological outcomes can differ significantly due to the methods employed and species selected [71].
All potential trees should be provided the opportunities to develop throughout the four phases (see Table 2) to continue delivering forest resilience and legacy [62]. Therefore, we examined the tree volume structure of the growing stock by stand type and forest type series from the point of view of the stand age and forest development phases. The initial step in comprehending stand successional pathways under various disturbance regimes is age structure [72]. Indirect evidence of stand-replacing disturbances can be seen in even-aged stands. Following partial cohort-replacing disturbances, uneven-aged stands form; the severity of the disturbance is indicated by the ratio of younger to older tree cohorts. In the absence of major disturbances, all-aged stands develop over several centuries of succession.
In a coupled human–nature system, where local adaptations guide the ecosystem toward local and global optimums, anthropogenic modification can be detected by the distributions of correlated patches [37,73]. However, it was not possible to achieve this in our study. A stand-wise forest inventory in Lithuania is carried out by recording the actual condition of the stands, so the full history of thinning and fellings under the established forest management regime is not visible [74]. Thus, we aimed to identify unsustainable forestry practices that need immediate attention [75].
Table 2. A conceptual ecological framework for the development phases of the hemiboreal forest. The background colour indicates the development phase characteristic of the successional status of a group of tree species. Tree species are listed in descending order of light need [14].
Table 2. A conceptual ecological framework for the development phases of the hemiboreal forest. The background colour indicates the development phase characteristic of the successional status of a group of tree species. Tree species are listed in descending order of light need [14].
Potential Natural Forest Vegetation
(Forest Type Series) *		Forest Development Phases: Successional Status of Tree Species
Stand Initiation (0–20 Years): PioneerStem Exclusion and Understorey Initiation (20–60 Years): Post-PioneerUnderstorey Reinitiation (60–120 Years): Pre-ClimaxSteady State (>120 Years): Climax
Climatic
(aeg cmh hox mox ox oxn)		Pinus sylvestris (hox mox ox)
Betula pubescens (aeg cmh oxn)
Betula pendula (aeg cmh hox mox ox oxn)
Salix fragilis (aeg)
Populus tremula (aeg cmh hox mox ox oxn)
Alnus incana (aeg cmh hox oxn)
Alnus glutinosa (aeg cmh oxn)		Pinus sylvestris (hox mox ox)
Betula pubescens (aeg cmh oxn)
Betula pendula (aeg cmh hox mox ox oxn)
Salix fragilis (aeg)
Populus tremula (aeg cmh hox mox ox oxn)
Alnus incana (aeg cmh hox oxn)
Alnus glutinosa (aeg cmh oxn)
Quercus robur (aeg cmh hox mox ox oxn)
Fraxinus excelsior (aeg cmh oxn)
Carpinus betulus (aeg hox)
Picea abies (aeg hox mox ox oxn)		Pinus sylvestris (hox mox ox)
Betula pendula (aeg cmh hox mox ox oxn)
Alnus glutinosa (aeg cmh oxn)
Quercus robur (aeg cmh hox mox ox oxn)
Fraxinus excelsior (aeg cmh oxn)
Carpinus betulus (aeg hox)
Picea abies (aeg hox mox ox oxn)
Ulmus glabra (aeg cmh hox oxn)
Ulmus laevis (aeg cmh hox)
Tilia cordata (aeg cmh hox oxn)
Acer platanoides (aeg cmh hox ox oxn)
Fagus sylvatica (hox)		Pinus sylvestris (hox mox ox)
Quercus robur (aeg cmh hox mox ox oxn)
Fraxinus excelsior (aeg cmh oxn)
Carpinus betulus (aeg hox)
Picea abies (aeg hox mox ox oxn)
Ulmus glabra (aeg cmh hox oxn)
Ulmus laevis (aeg cmh hox)
Tilia cordata (aeg cmh hox oxn)
Acer platanoides (aeg cmh hox ox oxn)
Fagus sylvatica (hox)
Edaphic
(cal cl fil m ur v vm)		Pinus sylvestris (v cl vm m)
Betula pubescens (cal fil ur)
Betula pendula (cal fil m ur v vm)
Salix fragilis (ur)
Salix alba (ur)
Populus tremula (m vm)
Alnus incana (fil)
Alnus glutinosa (cal fil ur)		Pinus sylvestris (v cl vm m)
Betula pubescens (cal fil ur)
Betula pendula (cal fil m ur v vm)
Salix fragilis (ur)
Salix alba (ur)
Populus tremula (m vm)
Alnus incana (fil)
Alnus glutinosa (cal fil ur)
Quercus robur (cal)
Fraxinus excelsior (fil ur)
Carpinus betulus (ox)
Picea abies (cal fil m ur vm)		Pinus sylvestris (v cl vm m)
Betula pendula (cal fil m ur v vm)
Alnus glutinosa (cal fil ur)
Quercus robur (cal)
Fraxinus excelsior (fil ur)
Carpinus betulus (ox)
Picea abies (cal fil m ur vm)		Pinus sylvestris (cl m v vm)
Quercus robur (cal)
Fraxinus excelsior (fil ur)
Carpinus betulus (ox)
Picea abies (cal fil m ur vm)
Biotic
(c cir csp lsp msp)		Pinus sylvestris (csp lsp msp)
Betula pubescens (c cir csp msp)
Alnus glutinosa (c cir)		Pinus sylvestris (csp lsp msp)
Betula pubescens (c cir csp msp)
Alnus glutinosa (c cir)
Picea abies (c cir)		Pinus sylvestris (csp lsp msp)
Alnus glutinosa (c cir)
Picea abies (c cir)		Pinus sylvestris (csp lsp msp)
Picea abies (c cir)
* Forest type series (ground layer codes) are indicated in brackets [76]: aeg—Aegopodiosa, c—Caricosa, cal—Calamagrostidosa, cir—Carico-iridosa, cl—Cladoniosa, cmh—Carico-mixtoherbosa, csp—Carico-sphagnosa, fil—Filipendulo-mixtoherbosa, hox—Hepatico-oxalidosa, lsp—Ledo-sphagnosa, m—Myrtillosa, mox—Myrtillo-oxalidosa, msp—Myrtillo-sphagnosa, ox—Oxalidosa, oxn—Oxalido-nemorosa, ur—Urticosa, v—Vacciniosa, vm—Vaccinio-myrtillosa.

3. Results

3.1. Growing Stock Volume of Lithuanian Forest Stands

Currently, the largest share of the volume of tree stems is made up of Scots pine and Norway spruce (Table 3, Figure 2), which are considered the most valuable tree species. There is a different distribution between the stem volumes of individual tree species in mature stands, taking into account the participation of each tree species in the species composition of stands of other dominant tree species [30]. According to the Lithuanian NFI data for 2022, the percentage of growing stock volume of predominant tree species from the growing stock volume of all tree species is 82% for Scots pine and 74% for Norway spruce stands. Scots pine is the least common admixture in forest stands of other dominant tree species. Stands of silver and downy birch, as well as Eurasian aspen are mostly mixed stands, where the volume of the predominant species is 62%, as are English oak (57%) and European ash (44%) stands. The most widespread admixture in forest stands of other tree species is Norway spruce.
The changes in tree species composition by volume in Lithuania from 2002 to 2022 showed that Scots pine and Norway spruce exhibit relatively stable trends, with Scots pine consistently maintaining the highest share (around 35%), whereas Norway spruce has increased from 20.14% to 22.18% (Figure 2). Silver and downy birch and black alder display slight declines over the study period. Minor increases are observed in grey alder and Eurasian aspen, while less common species like English oak and small-leaved lime exhibit a gradual increase. In contrast, European ash has significantly declined from 2.43% in 2003 to 0.56% in 2022. Changes in the other species, such as such as Norway maple, crack and white willow (Salix fragilis and Salix alba), and European hornbeam (Carpinus betulus), were negligible. Overall, these trends indicate a stable dominance of a few species, with slight shifts in diversity towards the less abundant tree species.
The natural change in forest stand types in Lithuania over the last 5-year period (2017–2022) showed that the most significant transitions occurred in mixed stands, where species like silver and downy birch (16.3%), Norway spruce (10.6%), and black alder (5.6%) contributed substantially to the changes (Figure 3). Scots pine stands decreased significantly, transitioning towards Norway spruce (8.3%) and silver and downy birch (5.1%) shifts. Silver and downy birch stands showed large transitions to other species, ranging from 16.3% for Norway spruce to 7.4% and 7.0% towards Scots pine and Eurasian aspen, respectively. The other less dominant tree species accounted for minor contributions, reflecting their lower prevalence. Overall, silver and downy birch and Norway spruce were the primary species driving natural stand changes, while Scots pine showed consistent replacement trends across the analysed period. This highlights the dynamic nature of forest composition in response to environmental and successional processes.

3.2. Growing Stock Composition and Structure of Lithuanian Forest Stands

The tree volume structure of the growing stock (I and II storey) was studied by stand type and forest type series, depending on the stand age and forest development phases (see Table 2). According to the Lithuanian NFI, the proportion of Norway spruce growing stock in stands of Scots pine, silver and downy birch, Eurasian aspen, and black alder is increasing with age (Table 4). In early successional phases (0–60 years), silver birch and downy birch are the main secondary species in all other tree stands. Norway spruce is the main companion in stands of other tree species at the phases of understorey reinitiation and steady state (≥61 years). European ash stands do not follow the general pattern of forest development.
Silver and downy birch stands are found in almost all series of forest types. Mixed Norway spruce forests (i.e., Oxalidosa, Myrtillo-oxalidosa, Hepatico-oxalidosa, and Oxalido-nemorosa forest type series) have stands of all tree species (Table 4). The predominant stand type in the Oxalidosa series was found to be Scots pine with Norway spruce (Table 5); however, it is well known that the main forest type in this series is the Oxalidosa-Piceetum with Scots pine and English oak [21]. European ash stands have survived in only one series of forest types, the Aegopodiosa. It is necessary to note that there is no mature or old-growth forest in the Aegopodiosa, Carico-mixtoherbosa, and Urticosa series of forest types (see Table 5). However, it is well known that the main forest types in these series (i.e., Aegopodio-Quercetum/Fraxinetum, Carico-mixtoherbo-Fraxinetum/Quercetum, and Urtico-Alnetum glutinosae/Fraxinetum) are dominated by long-living “post-pioneer” English oaks and European ashes [21].
The analysis of the tree volume structure of growing stock across various forest types and development phases, as indicated by the Lithuanian National Forest Inventory data for 2022, reveals distinct patterns in species prevalence (Table 5). The Aegopodiosa forest type exhibited the highest dominance of a single species (English oak, 100% for the youngest cohort), while the Vacciniosa type showed remarkable stability with near-complete dominance across all age classes with Scots pine. Forest types such as Myrtillo-oxalidosa and Oxalidosa displayed notable variability in species (silver birch, Norway spruce, Scots pine, and English oak) composition across different age classes, suggesting a dynamic growth structure. In contrast, the Cladoniosa lacked sufficient data for analysis. Overall, the findings highlight the diverse ecological characteristics of Lithuanian forests, with variations in tree species volume distribution influenced by both age and forest type.

3.3. Stand Regeneration

Comparing species stand regeneration composition and area changes of forest regeneration over the past 20 years showed a variation between natural reforestation (NR), artificial reforestation (AR), natural afforestation (NA), and artificial afforestation (AA). NR predominantly features black alder (34.4 thousand ha) and grey alder (24.1 thousand ha), Eurasian aspen (43.7 thousand ha), and Norway spruce (13.9 thousand ha). AR is dominated by Norway spruce (102.6 thousand ha), followed by Scots pine (32.9 thousand ha), and black alder (16.8 thousand ha), accounting for the most substantial contribution to forest recovery. For NA, grey alder(19.1 thousand ha), Scots pine (17.6 thousand ha), and black alder(10.5 thousand ha) play leading roles, with lesser contributions from Eurasian aspen. AA showed a smaller overall area, with Norway spruce (12.1 thousand ha) and Scots pine (6.8 thousand ha) as the primary species.
The temporal trends (2007–2022) revealed a steady area increase in NR, particularly after 2017, while AR fluctuates but remains significant. NA demonstrates moderate growth, whereas AA has declined. This indicates that artificial interventions, particularly in reforestation, have been crucial for large-scale forest recovery, while natural processes are increasingly contributing to afforestation (see Figure 4).

4. Discussion and Conclusions

4.1. Implications of Current Forest Management

The results highlight a stable dominance of Scots pine and Norway spruce, accounting for significant shares of the growing stock volume—82% and 74%, respectively. However, there is a gradual increase in less common species such as English oak, small-leaved lime, and grey alder, alongside a concerning decline in European ash. These trends underscore the limited diversity of dominant forest stand species in Lithuania and the vulnerability of certain tree species to environmental or biotic stressors.
The natural transition of forest stands between 2017 and 2022, particularly the replacement of Scots pine with Norway spruce and silver birch, reflect the dynamic nature of forest ecosystems. These changes are influenced by successional processes, competition, environmental conditions, loss of natural processes (e.g., [13]), and a push for faster-growing timber [77]. However, the dominance of a few species like Norway spruce in the later phases of forest development (≥61 years) suggests a lack of structural diversity in premature stands [78,79]. The increase in grey alder and Eurasian aspen could be due to increased harvesting as well as land abandonment, which leads to the formation of a younger forest age profile (0–60 years), since both tree species are pioneers and do not tolerate shade (see Figure 4).
Lithuania’s forests, dominated by Scots pine and Norway spruce, provide high timber value but may compromise other ecosystem services, such as biodiversity support, and soil protection [80]. Early successional species like silver and downy birch are critical for soil stabilization and nutrient cycling, while long-lived species like English oak provide habitats for diverse fauna and flora [81]. The decline of European ash, a keystone species in certain deciduous forest ecosystems, further highlights the need to balance timber production with ecological considerations [82].
Although the individual results of the Lithuanian NFI, presented for stand types, forest type series, and age classes, show good tree species diversity for all units studied, the absence of mature and old-growth stands in certain forest type series, such as Aegopodiosa, Carico-mixtoherbosa, and Urticosa, further highlights the gaps in forest management strategies. Old-growth all-aged forests serve as benchmarks for natural ecosystem functions [83]; they provide a variety of niches and habitats, supporting a higher number of plant, fungus, and animal species [84]. Unfortunately, the Lithuanian NFI does not analyse the actual condition of the old-growth all-aged forests. Moreover, in Lithuanian forestry, old-growth stands are considered overmature and require renewal [85]. Although the locations of permanent NFI sample plots are not published and their locations are kept secret, foresters may actively protect them and thus promote over- and underestimations. For instance, NFI data showed a significant increase in the number of large trees greater than 50 cm in diameter from 2.9 to 5.5 trees/ha−1 (https://amvmt.lrv.lt/lt/naujienos/ar-stambiu-medziu-miskuose-mazeja/ (accessed on 23 January 2025)). Nevertheless, Mozgeris et al. [86] forecast that the availability of large trees for large nesting birds will be decreased, thus indicating a loss in old large trees. So, EU policy requires consolidation in Lithuanian forest legislation. Currently, the final felling of natural forest habitats of European Community importance is halted until national forest management laws are amended, in line with the EC. However, the NFI still provides a valid dataset for broad national trends.
In summary, it can be said that Lithuanian forestry is focused on the wood resources of healthy stands using clear-cut forestry that reduces tree species age diversity, by locally favouring dominant tree species and typically restricting harvesting age to less than 121 years. In Lithuania, strict nature reserves are the only areas where forests have the potential to turn into ancient woods; as of now, they make up only 1.3% of Lithuanian forest territory and are located on nonproductive peatlands [29,87]. However, according to the NMI data, almost 10% of the forest area in Lithuania is classified as natural forest habitats of EC importance and must have closer-to-nature forest management practices developed.

4.2. A Case for Closer-to-Nature Forest Management

Paradigm shifts are required in traditional European forestry practices to reconcile human activities with the preservation of natural ecosystems [88]. Traditional forest management has prioritized economic outputs, leading to practices that diminish biodiversity and disrupt ecological natural patterns and processes. For instance, timber-oriented management has been linked to declining populations of forest species, [20,89]. This conflict between anthropogenic interests and ecological integrity emphasizes the need for a more integrated approach to forest governance that incorporates ecological, social, and economic dimensions [5,90]. Recent studies advocate for sustainable multifunctional forest management, which emphasizes the importance of diverse management practices that enhance ecosystem resilience and biodiversity [91]. The current trajectory of forest management in Europe, characterized by a historical focus on maximizing timber yield, is unsustainable and poses the risk of a “war with nature”, where ecosystems are pushed beyond their capacity to recover [92,93]. Therefore, a shift towards governance models that prioritize ecological health, such as those outlined in the pan-European criteria for sustainable forest management, is imperative [94]. This shift aims to mitigate the adverse impacts of human activities on forest ecosystems and seeks to foster a harmonious coexistence between human needs and the natural environment, thereby reducing the inevitability of conflict with nature.
All sustainable forest management requirements must include the maintenance, conservation, and enhancement of ecosystem biodiversity, the protection of ecologically important forest areas, and the prohibition of forest conversions [95,96]. Closer-to-nature forest management, with its emphasis on mixed-species stand structures that can be found in forests under natural dynamics, can address the implications of current forestry by fostering biologically integrated communities together with the local soils and climates with which they interact [1,65]. Mixed stands are more resilient to pests, diseases, and climate change, providing a buffer against catastrophic losses and maintaining ecosystem functions [97]. For instance, promoting the integration of deciduous broadleaved trees in appropriate habitats can help restore their populations while enhancing structural complexity and biodiversity [98]. This would also lead towards a dynamic forest composition and adaptability of forest ecosystems that prioritizes the emulation of natural processes, allowing for more gradual and adaptive stand transitions [99]. This approach encourages the development of multilayered canopies, uneven-aged stands, and natural regeneration, which aligns closely with Lithuania’s observed complex dynamics of forest ecosystems [14]. For instance, allowing silver and downy birch, Norway spruce, and other species to regenerate together can create more resilient and self-sustaining forests.
Another aspect of closer-to-nature management is to enhance the delivery of ecosystem services. This can be achieved through the maintenance of forest-dependent species that fulfil key ecological roles [100]. Practices such as selective thinning, natural and assisted regeneration, and the preservation of deadwood can enhance key ecosystem functions such as water regulation and habitat provision, contributing to the multifunctionality of forests [3]. However, there may be trade-offs between the benefits that less-modified forests offer (such as regulatory functions) and those production services that require some anthropogenic modification (e.g., timber production) [37]. Overall, forests largely free of significant modification, i.e., forests having high ecosystem integrity, typically provide higher levels of many benefits than modified forests of the same type [101].
Closer-to-nature management can also address the need for a diverse forest age structure by extending rotation periods, applying different harvesting techniques, and prioritizing the restoration of mature and old-growth characteristics in appropriate forest types [102]. This includes retaining legacy trees and promoting long-lived native species and long-term naturally developing stand structures to recreate the ecological integrity of natural forests [37,74,103]. Modern management still aims towards single species and age stand profiles which lack variation, whilst natural forming stand communities are generally comprised mixed species with various ontogenetic age dynamics [14]. According to the Lithuanian NFI [30], the implementation of the sustainable forest management principle means the constant renewal of stands as the main precondition for maintaining a stable wood increment. The natural development of forest patches can be fostered through targeted management activities, in particular by carrying out intermediate felling aimed at maintaining a natural light balance within multiple canopies in order to achieve a mosaic of near-natural forming stand communities of different ontogenetic age profiles and, as a result, a more sustainable forest use [14,30,73,104].
To promote the implementation of more nature-friendly forest management methods, forest soil assessment should become an integral part of long-term monitoring, such as national forest inventories. Evaluating soil typology can help identify more sustainable forestry practices. Generally, forest soils can lose their resilience through large-scale homogenization of forests and soils via salvage logging, agriculture-like soil preparation, and seedling planting in plantation forestry [52]. For instance, previous studies [105,106] have indicated that sandy soils are particularly sensitive to intensive forest harvesting due to their low capacity to retain organic matter.
Finally, closer-to-nature management helps mitigate climate and anthropogenic pressures by fostering forest-dependent biodiversity and habitat heterogeneity [107]. Many species benefit from increased tree diversity, which also boosts other essential ecosystem services including productivity and disease and pest resistance [108]. Incorporating broadleaves and pioneer species into conifer-dominated forests enhances their resilience to environmental stressors [109]. Adaptive management, guided by long-term monitoring such as the National Forest Inventory can ensure that commercial forests remain robust and capable of withstanding future uncertainties [110].

5. Final Remarks

In conclusion, the stand development of European hemiboreal forests is a complex interplay of natural and anthropogenic factors. The integration of nature-based forestry practices into EU forest policy represents a significant step towards sustainable forest management, addressing the challenges posed by climate change and biodiversity loss. This study showed that National Forest Inventories serve as a vital tool in this context, providing essential data for informed decision-making and adaptive management.
Using data from the Lithuanian NFI, this study showed that more than half of Lithuanian forest stands are dominated by single tree species; Scots pine (24%), Norway spruce (9%), and individual deciduous species (23%), applying a 75% dominant single-species threshold limit. This highlights the need for a paradigm shift in forest management. Nature-based forestry can enhance forest-dependent biodiversity, long-term resilience, and ecosystem services while maintaining timber production. By emulating natural processes, diversifying native species, and prioritizing ecological integrity, Lithuania can create productive, self-sustaining forests. This transition requires policy support, stakeholder engagement, and investment in research and monitoring. The long-term ecological and economic benefits make it essential for Lithuania’s forests. Sustainable management should focus on “assisted succession”, respecting the ontogenetic age dynamics of stand-forming tree species and diversifying harvest ages. Letting nature lead is a core principle of sustainable forest management.

Author Contributions

Conceptualization, R.P. and M.M. (Michael Manton); methodology, R.P., G.K., M.M. (Michael Manton), and M.M. (Milda Muraškienė); software, G.K.; formal analysis, R.P., G.K., M.M. (Michael Manton), and M.M. (Milda Muraškienė); investigation, R.P., M.M. (Michael Manton), G.K. and M.M. (Milda Muraškienė); data curation, G.K.; writing—original draft preparation, R.P., M.M. (Michael Manton), G.K. and M.M. (Milda Muraškienė); writing—review and editing, R.P., M.M. (Michael Manton), G.K. and M.M. (Milda Muraškienė); visualization, M.M. (Michael Manton) and G.K. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

Data were obtained from the Lithuanian State Forest Service. Requests to access the datasets should be directed to Gintaras Kulbokas, Lithuanian State Forest Service.

Acknowledgments

We would like to thank the Lithuanian Research Centre for Agriculture and Forestry for providing funding for this publication.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Larsen, J.B.; Angelstam, P.; Bauhus, J.; Carvalho, J.F.; Diaci, J.; Dobrowolska, D.; Gazda, A.; Gustafsson, L.; Krumm, F.; Knoke, T. Closer-to-Nature Forest Management. From Science to Policy 12; EFI European Forest Institute: Joensuu, Finland, 2022; Volume 12. [Google Scholar]
  2. Motta, R.; Larsen, J.B. Un Nuovo Paradigma per La Gestione Forestale Sostenibile: La Selvicoltura “Più” Prossima Alla Natura. For. J. Silvic. For. Ecol. 2022, 19, 52. [Google Scholar] [CrossRef]
  3. European Commission. Guidelines on Closer-to-Nature Forest Management; Publications Office of the European Union: Brussels, Belgium, 2023; ISBN 978-92-68-00222-3. [Google Scholar]
  4. Kaliszewski, A. Forest Policy Goals in Poland in Light of the Current Forestry Aims in Europe Part 2. Forest Policy Priorities in Europe. Leśne Pr. Badaw. 2018, 79, 169–179. [Google Scholar] [CrossRef]
  5. Carnus, J.-M.; Hengeveld, G.; Mason, B. Sustainability Impact Assessment of Forest Management Alternatives in Europe: An Introductory Background and Framework. Ecol. Soc. 2012, 17, 49. [Google Scholar] [CrossRef]
  6. Rosa, F.; Di Fulvio, F.; Lauri, P.; Felton, A.; Forsell, N.; Pfister, S.; Hellweg, S. Can Forest Management Practices Counteract Species Loss Arising from Increasing European Demand for Forest Biomass under Climate Mitigation Scenarios? Environ. Sci. Technol. 2023, 57, 2149–2161. [Google Scholar] [CrossRef]
  7. Sierota, Z.; Miścicki, S. Is It Possible to Compromise Forest Conservation with Forest Use? Earth 2022, 3, 1059–1075. [Google Scholar] [CrossRef]
  8. Kuusk, A.; Pisek, J.; Lang, M.; Märdla, S. Estimation of Gap Fraction and Foliage Clumping in Forest Canopies. Remote Sens. 2018, 10, 1153. [Google Scholar] [CrossRef]
  9. Petrokas, R.; Baliuckas, V.; Manton, M. Successional Categorization of European Hemi-Boreal Forest Tree Species. Plants 2020, 9, 1381. [Google Scholar] [CrossRef] [PubMed]
  10. McRoberts, R.E.; Tomppo, E.O.; Næsset, E. Advances and Emerging Issues in National Forest Inventories. Scand. J. For. Res. 2010, 25, 368–381. [Google Scholar] [CrossRef]
  11. Fridman, J.; Holm, S.; Nilsson, M.; Nilsson, P.; Ringvall, A.H.; Ståhl, G. Adapting National Forest Inventories to Changing Requirements—The Case of the Swedish National Forest Inventory at the Turn of the 20th Century. Silva Fenn. 2014, 48, 1095. [Google Scholar] [CrossRef]
  12. Berglund, H.; Kuuluvainen, T. Representative Boreal Forest Habitats in Northern Europe, and a Revised Model for Ecosystem Management and Biodiversity Conservation. Ambio 2021, 50, 1003–1017. [Google Scholar] [CrossRef]
  13. Manton, M.; Ruffner, C.; Kibirkštis, G.; Brazaitis, G.; Marozas, V.; Pukienė, R.; Makrickiene, E.; Angelstam, P. Fire Occurrence in Hemi-Boreal Forests: Exploring Natural and Cultural Scots Pine Fire Regimes Using Dendrochronology in Lithuania. Land 2022, 11, 260. [Google Scholar] [CrossRef]
  14. Petrokas, R.; Manton, M.; Kavaliauskas, D. Tree Regeneration and Ontogenetic Strategies of Northern European Hemiboreal Forests: Transitioning towards Closer-to-Nature Forest Management. PeerJ 2024, 12, e17644. [Google Scholar] [CrossRef] [PubMed]
  15. Gough, C.M.; Curtis, P.S.; Hardiman, B.S.; Scheuermann, C.M.; Bond-Lamberty, B. Disturbance, Complexity, and Succession of Net Ecosystem Production in North America’s Temperate Deciduous Forests. Ecosphere 2016, 7, e01375. [Google Scholar] [CrossRef]
  16. Šilingas, M.; Suchockas, V.; Varnagirytė-Kabašinskienė, I. Evaluation of Undergrowth under the Canopy of Deciduous Forests on Very Fertile Soils in the Lithuanian Hemiboreal Forest. Forests 2022, 13, 2172. [Google Scholar] [CrossRef]
  17. Petrokas, R.; Ibanga, D.-A.; Manton, M. Deep Ecology, Biodiversity and Assisted Natural Regeneration of European Hemiboreal Forests. Diversity 2022, 14, 892. [Google Scholar] [CrossRef]
  18. Jõgiste, K.; Korjus, H.; Stanturf, J.A.; Frelich, L.E.; Baders, E.; Donis, J.; Jansons, A.; Kangur, A.; Köster, K.; Laarmann, D.; et al. Hemiboreal Forest: Natural Disturbances and the Importance of Ecosystem Legacies to Management. Ecosphere 2017, 8, e01706. [Google Scholar] [CrossRef]
  19. Batcheler, M.; Smith, M.M.; Swanson, M.E.; Ostrom, M.; Carpenter-Boggs, L. Assessing Silvopasture Management as a Strategy to Reduce Fuel Loads and Mitigate Wildfire Risk. Sci. Rep. 2024, 14, 5954. [Google Scholar] [CrossRef] [PubMed]
  20. Wade, A.S.I.; Barov, B.; Burfield, I.J.; Gregory, R.D.; Norris, K.; Butler, S.J. Quantifying the Detrimental Impacts of Land-Use and Management Change on European Forest Bird Populations. PLoS ONE 2013, 8, e64552. [Google Scholar] [CrossRef] [PubMed]
  21. Petrokas, R.; Manton, M. Adaptive Relationships in Hemi-Boreal Forests: Tree Species Responses to Competition, Stress, and Disturbance. Plants 2023, 12, 3256. [Google Scholar] [CrossRef] [PubMed]
  22. Schelhaas, M.-J.; Nabuurs, G.-J.; Hengeveld, G.; Reyer, C.; Hanewinkel, M.; Zimmermann, N.E.; Cullmann, D. Alternative Forest Management Strategies to Account for Climate Change-Induced Productivity and Species Suitability Changes in Europe. Reg. Environ. Change 2015, 15, 1581–1594. [Google Scholar] [CrossRef]
  23. Corona, P.; Chirici, G.; McRoberts, R.E.; Winter, S.; Barbati, A. Contribution of Large-Scale Forest Inventories to Biodiversity Assessment and Monitoring. For. Ecol. Manag. 2011, 262, 2061–2069. [Google Scholar] [CrossRef]
  24. Santoro, M.; Eriksson, L.E.B.; Fransson, J.E.S. Reviewing ALOS PALSAR Backscatter Observations for Stem Volume Retrieval in Swedish Forest. Remote Sens. 2015, 7, 4290–4317. [Google Scholar] [CrossRef]
  25. Armolaitis, K.; Varnagirytė-Kabašinskienė, I.; Žemaitis, P.; Stakėnas, V.; Beniušis, R.; Kulbokas, G.; Urbaitis, G. Evaluation of Organic Carbon Stocks in Mineral and Organic Soils in Lithuania. Soil Use Manag. 2022, 38, 355–368. [Google Scholar] [CrossRef]
  26. Bradshaw, C.J.A.; Warkentin, I.G. Global Estimates of Boreal Forest Carbon Stocks and Flux. Glob. Planet. Change 2015, 128, 24–30. [Google Scholar] [CrossRef]
  27. Pereira, P.; Mierauskas, P.; Novara, A. Stakeholders’ Perceptions about Fire Impacts on Lithuanian Protected Areas. Land Degrad. Dev. 2016, 27, 871–883. [Google Scholar] [CrossRef]
  28. Brukas, V. New World, Old Ideas—A Narrative of the Lithuanian Forestry Transition. J. Environ. Policy Plan. 2015, 17, 495–515. [Google Scholar] [CrossRef]
  29. State Forest Service Lithuanian Forestry Statistics 2021. Available online: https://amvmt.lrv.lt/lt/atviri-duomenys-1/misku-statistikos-leidiniai/misku-ukio-statistika/2021-m-1/ (accessed on 23 January 2025).
  30. Kuliešis, A.; Kulbokas, G.; Kasperavičius, A.; Kazanavičiūtė, V.; Kvalkauskienė, M. Lithuanian National Forest Inventory, 1998–2017. From Measurements to Decision Making; Lututė: Kaunas, Lithuania, 2021; ISBN 978-9955-37-234-9. [Google Scholar]
  31. Mason, W.L.; Diaci, J.; Carvalho, J.; Valkonen, S. Continuous Cover Forestry in Europe: Usage and the Knowledge Gaps and Challenges to Wider Adoption. For. Int. J. For. Res. 2022, 95, 1–12. [Google Scholar] [CrossRef]
  32. Babelytė, A. Assessment of the Risk of Unsustainable Production of Forest Biomass for Lithuania—SURE System; Lithuanian Biomass Energy Association: Vilnius, Lithuania, 2023; p. 52. [Google Scholar]
  33. Hallas, T.; Steyrer, G.; Laaha, G.; Hoch, G. Two Unprecedented Outbreaks of the European Spruce Bark Beetle, Ips Typographus L. (Col., Scolytinae) in Austria since 2015: Different Causes and Different Impacts on Forests. Cent. Eur. For. J. 2024, 70, 263–274. [Google Scholar] [CrossRef]
  34. Pliūra, A.; Bakys, R.; Suchockas, V.; Marčiulynienė, D.; Gustienė, A.; Verbyla, V.; Lygis, V. Ash Dieback in Lithuania: Disease History, Research on Impact and Genetic Variation in Disease Resistance, Tree Breeding and Options for Forest Management. In Dieback of European Ash (Fraxinus spp.): Consequences and Guidelines for Sustainable Management; Swedish University of Agricultural Sciences: Uppsala, Sweden, 2017; pp. 150–165. ISBN 978-91-576-8696-1. [Google Scholar]
  35. Marçais, B.; Kosawang, C.; Laubray, S.; Kjær, E.; Kirisits, T. Chapter 13—Ash Dieback. In Forest Microbiology; Asiegbu, F.O., Kovalchuk, A., Eds.; Academic Press: Cambridge, MA, USA, 2022; pp. 215–237. [Google Scholar]
  36. Persson, R.; Holmgren, P. Evolution and Prospects of Global Forest Assessments. Available online: http://www.fao.org/docrep/005/y4001e/Y4001E02.htm (accessed on 24 January 2025).
  37. Grantham, H.S.; Duncan, A.; Evans, T.D.; Jones, K.R.; Beyer, H.L.; Schuster, R.; Walston, J.; Ray, J.C.; Robinson, J.G.; Callow, M.; et al. Anthropogenic Modification of Forests Means Only 40% of Remaining Forests Have High Ecosystem Integrity. Nat. Commun. 2020, 11, 5978. [Google Scholar] [CrossRef] [PubMed]
  38. Lithuanian State Forest Service On the approval of the Forest Management Work. Approved by Order No 11-10-V of the Director of the Lithuanian State Forest Service. 14 January 2010. Available online: https://e-seimas.lrs.lt/portal/legalAct/lt/TAD/TAIS.370142/asr (accessed on 14 December 2024).
  39. Žemaitis, P.; Armoška, E.; Stakėnas, V.; Kulbokas, G. Wood Decay and Norway Spruce Vulnerability to Wind-Inflicted Mortality in Monospecific and Mixed Stands in Hemiboreal Forests. For. Ecol. Manag. 2024, 569, 122163. [Google Scholar] [CrossRef]
  40. Kuliešis, A.; Kasperavičius, A.; Kulbokas, G.; Brukas, V.; Petrauskas, E.; Mozgeris, G. Lithuania Forest Inventory-Based Projection Systems for Wood and Biomass Availability; Barreiro, S., Schelhaas, M.-J., McRoberts, R.E., Kändler, G., Eds.; Managing Forest Ecosystems; Springer International Publishing: Cham, Switzerland, 2017; Volume 29, pp. 223–239. ISBN 978-3-319-56199-8. [Google Scholar]
  41. Kuliešis, A.; Kulbokas, G.; Kasperavičius, A.; Kuliešis, A.A. Miškų inventorizacijos sistema ir jos tobulinimas pagal intensyvios miškininkystės reikmes. Miškininkystė 2010, 68, 61–77. [Google Scholar]
  42. Kuliešis, A.; Kasperavičius, A.; Kulbokas, G. Lithuania. In National Forest Inventories; Vidal, C., Alberdi, I.A., Hernández Mateo, L., Redmond, J.J., Eds.; Springer International Publishing: Cham, Switzerland, 2016; pp. 521–547. ISBN 978-3-319-44014-9. [Google Scholar]
  43. Kuliešis, A.; Kasperavičius, A.; Kulbokas, G. Brandžių medynų tūrių kaitos ir pagamintos medienos apskaitos iššūkiai. Mūsų Girios 2016, 3, 9–10. [Google Scholar]
  44. Madrigal-González, J.; Ruiz-Benito, P.; Ratcliffe, S.; Calatayud, J.; Kändler, G.; Lehtonen, A.; Dahlgren, J.; Wirth, C.; Zavala, M.A. Complementarity Effects on Tree Growth Are Contingent on Tree Size and Climatic Conditions across Europe. Sci. Rep. 2016, 6, 32233. [Google Scholar] [CrossRef] [PubMed]
  45. Wang, S.; Isbell, F.; Deng, W.; Hong, P.; Dee, L.E.; Thompson, P.; Loreau, M. How Complementarity and Selection Affect the Relationship between Ecosystem Functioning and Stability. Ecology 2021, 102, e03347. [Google Scholar] [CrossRef] [PubMed]
  46. Smallidge, P. Forest Succession and Management—Cornell Small Farms. Available online: https://smallfarms.cornell.edu/2016/04/forest-succession-and-management/ (accessed on 16 November 2024).
  47. Anyomi, K.A.; Neary, B.; Chen, J.; Mayor, S.J. A Critical Review of Successional Dynamics in Boreal Forests of North America. Environ. Rev. 2022, 30, 563–594. [Google Scholar] [CrossRef]
  48. Samec, P.; Volánek, J.; Kučera, M.; Cudlín, P. Effect of Soil Diversity on Forest Plant Species Abundance: A Case Study from Central-European Highlands. Forests 2021, 12, 534. [Google Scholar] [CrossRef]
  49. Sellan, G.; Thompson, J.; Majalap, N.; Brearley, F.Q. Soil Characteristics Influence Species Composition and Forest Structure Differentially among Tree Size Classes in a Bornean Heath Forest. Plant Soil 2019, 438, 173–185. [Google Scholar] [CrossRef]
  50. Reczyńska, K. Diversity and Ecology of Oak Forests in SW Poland (Sudetes Mts.). Phytocoenologia 2015, 45, 85–106. [Google Scholar] [CrossRef]
  51. Li, Y.; Henrion, M.; Moore, A.; Lambot, S.; Opfergelt, S.; Vanacker, V.; Jonard, F.; Van Oost, K. Factors Controlling Peat Soil Thickness and Carbon Storage in Temperate Peatlands Based on UAV High-Resolution Remote Sensing. Geoderma 2024, 449, 117009. [Google Scholar] [CrossRef]
  52. Stutz, K.P.; Lang, F. Forest Ecosystems Create Pedogenic Patchworks through Woody Debris, Trees, and Disturbance. Geoderma 2023, 429, 116246. [Google Scholar] [CrossRef]
  53. Krotiuk, A.; Bedernichek, T. Concept of Forest Development Phases: Identification and Classification Issues. Environ. Sci. Proc. 2020, 3, 9. [Google Scholar] [CrossRef]
  54. Spathelf, P.; Bolte, A. Continuous-Cover Forestry—The Appropriate Concept for Climate Change Adaptation of Forests in Germany? Ciência Florest. 2025, 35, e90715. [Google Scholar] [CrossRef]
  55. Buivydaitė, V. Classification of Soils of Lithuania Based on FAO-Unesco Soil Classification System and WRB. In Proceedings of the 17 World Congress of Soil Science, Bangkok, Thailand, 14–20 August 2002; pp. 2189-1–2189-13. [Google Scholar]
  56. Anjos, L.; Gaistardo, C.C.; Deckers, J.; Dondeyne, S.; Eberhardt, E.; Gerasimova, M.; Harms, B.; Jones, A.; Krasilnikov, P.; Reinsch, T.; et al. World Reference Base for Soil Resources 2014 International Soil Classification System for Naming Soils and Creating Legends for Soil Maps; World Soil Resources Reports; FAO: Rome, Italy, 2015. [Google Scholar]
  57. Vaičys, M.; Mažvila, J. The Influence of Soil Characteristics on Plant Productivity and Ecological Stability. Ekologija 2009, 55, 99–106. [Google Scholar] [CrossRef]
  58. Brazaitis, G.; Marozas, V.; Augutis, D.; Preikša, Ž.; Šaudytė-Manton, S. Lithuanian Forest Habitat Management Recommendations—“Guidelines for the Management of Natural Forest Habitat Types of EC Importance”; Naturalit: Vilnius, Lietuva, 2021. [Google Scholar]
  59. Petrokas, R.; Kavaliauskas, D. Concept for Genetic Monitoring of Hemiboreal Tree Dynamics in Lithuania. Land 2022, 11, 1249. [Google Scholar] [CrossRef]
  60. Vaičys, M. Miško Augaviečių Tipai; Ozolinčius, R., Ed.; Lututė: Kaunas, Lietuva, 2006; ISBN 978-9955-692-41-6. [Google Scholar]
  61. Buivydaitė, V.; Vaičys, M.; Juodis, J.; Motuzas, A.-J. Lietuvos Dirvožemių Klasifikacija. Kn. 34; Lietuvos Mokslas: Vilnius, Lietuva, 2001; ISBN 9986795118. [Google Scholar]
  62. Vester, H.F. Forest development as a basis for management: Tree architecture and tree temperaments. In Ecology and Management of Tropical Secondary Forest: Science, People, and Policy, Proceedings of a Conference Held at CATIE, Turrialba, Costa Rica, 10–12 November 1997; Guariguata, M.R., Finegan, B., Eds.; Serie Técnica: Reuniones Técnicas; Centro Agronómico Tropical de Investigación y Enseñanza: Turrialba, Costa Rica, 1998; Volume 4, pp. 35–48. ISBN 9977-57-318-2. [Google Scholar]
  63. Oliver, C.D.; Larson, B.C. Brief Notice: Forest Stand Dynamics (Update Edition). For. Sci. 1996, 42, 397. [Google Scholar] [CrossRef]
  64. Gray, A.N.; Pelz, K.; Hayward, G.D.; Schuler, T.; Salverson, W.; Palmer, M.; Schumacher, C.; Woodall, C.W. Perspectives: The Wicked Problem of Defining and Inventorying Mature and Old-Growth Forests. For. Ecol. Manag. 2023, 546, 121350. [Google Scholar] [CrossRef]
  65. Kimmins, J.P. Forest Ecosystem Management: An Environmental Necessity, but Is It a Practical Reality or Simply an Ecotopian Ideal. In Proceedings of the XII World Forestry Congress, Québec, QC, Canada, 21–28 September 2003. [Google Scholar]
  66. Taki, H.; Yamaura, Y.; Okochi, I.; Inoue, T.; Okabe, K.; Makino, S. Effects of Reforestation Age on Moth Assemblages in Plantations and Naturally Regenerated Forests. Insect Conserv. Divers. 2010, 3, 257–265. [Google Scholar] [CrossRef]
  67. Fang, Z.; Bao, W.; Yan, X.; Liu, X. Understory Structure and Vascular Plant Diversity in Naturally Regenerated Deciduous Forests and Spruce Plantations on Similar Clear-Cuts: Implications for Forest Regeneration Strategy Selection. Forests 2014, 5, 715–743. [Google Scholar] [CrossRef]
  68. Bremer, L.L.; Farley, K.A. Does Plantation Forestry Restore Biodiversity or Create Green Deserts? A Synthesis of the Effects of Land-Use Transitions on Plant Species Richness. Biodivers. Conserv. 2010, 19, 3893–3915. [Google Scholar] [CrossRef]
  69. Dobrowski, S.Z.; Aghai, M.M.; Chichilnisky du Lac, A.; Downer, R.; Fargione, J.; Haase, D.L.; Hoecker, T.; Kildisheva, O.A.; Murdoch, A.; Newman, S.; et al. ‘Mind the Gap’—Reforestation Needs vs. Reforestation Capacity in the Western United States. Front. For. Glob. Change 2024, 7, 1402124. [Google Scholar] [CrossRef]
  70. Song, S.; Ding, Y.; Li, W.; Meng, Y.; Zhou, J.; Gou, R.; Zhang, C.; Ye, S.; Saintilan, N.; Krauss, K.W.; et al. Mangrove Reforestation Provides Greater Blue Carbon Benefit than Afforestation for Mitigating Global Climate Change. Nat. Commun. 2023, 14, 756. [Google Scholar] [CrossRef] [PubMed]
  71. de Araújo, G.J.; Storck-Tonon, D.; Dáttilo, W.; Izzo, T.J. Is Being Green What Matters? Functional Diversity of Cavity-Nesting Bees and Wasps and Their Interaction Networks with Parasites in Different Reforestation Types in Amazonia. Insect Conserv. Divers. 2021, 14, 620–634. [Google Scholar] [CrossRef]
  72. Shorohova, E.; Kuuluvainen, T.; Kangur, A.; Jõgiste, K. Natural Stand Structures, Disturbance Regimes and Successional Dynamics in the Eurasian Boreal Forests: A Review with Special Reference to Russian Studies. Ann. For. Sci. 2009, 66, 201. [Google Scholar] [CrossRef]
  73. Lansing, J.S.; Thurner, S.; Chung, N.N.; Coudurier-Curveur, A.; Karakaş, Ç.; Fesenmyer, K.A.; Chew, L.Y. Adaptive Self-Organization of Bali’s Ancient Rice Terraces. Proc. Natl. Acad. Sci. USA 2017, 114, 6504–6509. [Google Scholar] [CrossRef] [PubMed]
  74. Šilingas, M.; Šilingienė, G. The Lower Storeys of Main Tree Species in Deciduous Pioneer Tree Stands of Fertile Sites: Case of Lithuania. Balt. For. 2022, 28, 208–216. [Google Scholar] [CrossRef]
  75. Smith, C.; Thwaites, R. ForesTIM: Evaluating Plantation Forest Land Management by Identifying Unsustainable Practices. Aust. For. 2013, 61, 89–102. [Google Scholar] [CrossRef]
  76. Karazija, S. Forest Types of Lithuania; Mokslas: Vilnius, Lietuva, 1988; ISBN 978-5-420-00421-0. [Google Scholar]
  77. Kuuluvainen, T.; Gauthier, S. Young and Old Forest in the Boreal: Critical Stages of Ecosystem Dynamics and Management under Global Change. For. Ecosyst. 2018, 5, 26. [Google Scholar] [CrossRef]
  78. Brassard, B.W.; Chen, H.Y.H. Stand Structural Dynamics of North American Boreal Forests. Crit. Rev. Plant Sci. 2006, 25, 115–137. [Google Scholar] [CrossRef]
  79. Zeller, L.; Pretzsch, H. Effect of Forest Structure on Stand Productivity in Central European Forests Depends on Developmental Stage and Tree Species Diversity. For. Ecol. Manag. 2019, 434, 193–204. [Google Scholar] [CrossRef]
  80. Makrickas, E.; Manton, M.; Angelstam, P.; Grygoruk, M. Trading Wood for Water and Carbon in Peatland Forests? Rewetting Is Worth More than Wood Production. J. Environ. Manag. 2023, 341, 117952. [Google Scholar] [CrossRef] [PubMed]
  81. Esseen, P.-A.; Ehnström, B.; Ericson, L.; Sjöberg, K. Boreal Forests. Ecol. Bull. 1997, 46, 16–47. [Google Scholar]
  82. Pautasso, M.; Aas, G.; Queloz, V.; Holdenrieder, O. European Ash (Fraxinus Excelsior) Dieback–A Conservation Biology Challenge. Biol. Conserv. 2013, 158, 37–49. [Google Scholar] [CrossRef]
  83. Barredo, J.I.; Brailescu, C.; Teller, A.; Sabatini, F.M.; Mauri, A.; Janouskova, K. Mapping and Assessment of Primary and Old-Growth Forests in Europe; Amt Fur Veroffentlichungen der EU; Publications Office of the European Union: Luxemburg, 2021. [Google Scholar]
  84. Hämäläinen, A.; Runnel, K.; Ranius, T.; Strengbom, J. Diversity of Forest Structures Important for Biodiversity Is Determined by the Combined Effects of Productivity, Stand Age, and Management. Ambio 2024, 53, 718–729. [Google Scholar] [CrossRef] [PubMed]
  85. Kuliešis, A.; Kasperavičius, A.; Kulbokas, G.; Kuliešis, A.A.; Pivoriūnas, A.; Aleinikovas, M.; Šilinskas, B.; Škėma, M.; Beniušienė, L. Using Continuous Forest Inventory Data for Control of Wood Production and Use in Large Areas: A Case Study in Lithuania. Forests 2020, 11, 1039. [Google Scholar] [CrossRef]
  86. Mozgeris, G.; Mörtberg, U.; Pang, X.-L.; Trubins, R.; Treinys, R. Future Projection for Forest Management Suggests a Decrease in the Availability of Nesting Habitats for a Mature-Forest-Nesting Raptor. For. Ecol. Manag. 2021, 491, 119168. [Google Scholar] [CrossRef]
  87. Survila, M. Protecting Old-Growth Forests as an Environmental Investment—Nordic Investment Bank. Available online: https://www.nib.int/cases/protecting-old-growth-forests-as-an-environmental-investment/ (accessed on 21 November 2024).
  88. Duncker, P.; Barreiro, S.; Hengeveld, G.; Lind, T.; Mason, W.; Ambrozy, S.; Spiecker, H. Classification of Forest Management Approaches: A New Conceptual Framework and Its Applicability to European Forestry. Ecol. Soc. 2012, 17, 51. [Google Scholar] [CrossRef]
  89. Paillet, Y.; Bergès, L.; Hjältén, J.; Ódor, P.; Avon, C.; Bernhardt-Römermann, M.; Bijlsma, R.-J.; De Bruyn, L.; Fuhr, M.; Grandin, U.; et al. Biodiversity Differences between Managed and Unmanaged Forests: Meta-Analysis of Species Richness in Europe. Conserv. Biol. 2010, 24, 101–112. [Google Scholar] [CrossRef] [PubMed]
  90. Sarvašová, Z.; Cienciala, E.; Beranová, J.; Vančo, M.; Ficko, A.; Pardos, M. Analysis of Governance Systems Applied in Multifunctional Forest Management in Selected European Mountain Regions/Analýza Systémov Governancie Využívaných Pri Multifunkčnom Manažmente Lesov vo Vybraných Európskych Horských Oblastiach. Cent. Eur. For. J. 2014, 60, 159–167. [Google Scholar] [CrossRef]
  91. Nagel, T.A.; Rodríguez-Recio, M.; Aakala, T.; Angelstam, P.; Avdagić, A.; Borowski, Z.; Bravo-Oviedo, A.; Brazaitis, G.; Campagnaro, T.; Ciach, M.; et al. Can Triad Forestry Reconcile Europe’s Biodiversity and Forestry Strategies? A Critical Evaluation of Forest Zoning. Ambio 2024, 12, 1–10. [Google Scholar] [CrossRef]
  92. Felton, A.; Löfroth, T.; Angelstam, P.; Gustafsson, L.; Hjältén, J.; Felton, A.M.; Simonsson, P.; Dahlberg, A.; Lindbladh, M.; Svensson, J.; et al. Keeping Pace with Forestry: Multi-Scale Conservation in a Changing Production Forest Matrix. Ambio 2020, 49, 1050–1064. [Google Scholar] [CrossRef] [PubMed]
  93. Křenová, Z.; Janík, T.; Romportl, D. One Park, Two Owners—Inconsistencies in Forest Stewardship. Conserv. Sci. Pract. 2022, 4, e12834. [Google Scholar] [CrossRef]
  94. Böttcher, H.; Verkerk, P.J.; Gusti, M.; HavlÍk, P.; Grassi, G. Projection of the Future EU Forest CO2 Sink as Affected by Recent Bioenergy Policies Using Two Advanced Forest Management Models. GCB Bioenergy 2012, 4, 773–783. [Google Scholar] [CrossRef]
  95. European Commission. New EU Forest Strategy for 2030. COM (2021) 572 Final. In Communication from the Commission to the European Parliament, the Council, the European Economic and Social Committee and the Committee of the Regions; Publications Office of the European Union: Brussels, Belgium, 2021. [Google Scholar]
  96. PEFC. What Is Sustainable Forest Management? Available online: https://www.pefc.org/what-we-do/our-approach/what-is-sustainable-forest-management (accessed on 27 November 2024).
  97. Bauhus, J.; Forrester, D.I.; Gardiner, B.; Jactel, H.; Vallejo, R.; Pretzsch, H. Ecological Stability of Mixed-Species Forests. In Mixed-Species Forests; Pretzsch, H., Forrester, D.I., Bauhus, J., Eds.; Springer: Berlin/Heidelberg, Germany, 2017; pp. 337–382. ISBN 978-3-662-54551-5. [Google Scholar]
  98. Vasseur, L. Restoration of Deciduous Forests. Available online: https://www.nature.com/scitable/knowledge/library/restoration-of-deciduous-forests-96642239/ (accessed on 20 December 2024).
  99. Spathelf, P.; Stanturf, J.; Kleine, M.; Jandl, R.; Chiatante, D.; Bolte, A. Adaptive Measures: Integrating Adaptive Forest Management and Forest Landscape Restoration. Ann. For. Sci. 2018, 75, 55. [Google Scholar] [CrossRef]
  100. Ding, J.; Delgado-Baquerizo, M.; Wang, J.-T.; Eldridge, D.J. Ecosystem Functions Are Related to Tree Diversity in Forests but Soil Biodiversity in Open Woodlands and Shrublands. J. Ecol. 2021, 109, 4158–4170. [Google Scholar] [CrossRef]
  101. Watson, J.E.M.; Evans, T.; Venter, O.; Williams, B.; Tulloch, A.; Stewart, C.; Thompson, I.; Ray, J.C.; Murray, K.; Salazar, A.; et al. The Exceptional Value of Intact Forest Ecosystems. Nat. Ecol. Evol. 2018, 2, 599–610. [Google Scholar] [CrossRef]
  102. Bergeron, Y.; Irulappa Pillai Vijayakumar, D.B.; Ouzennou, H.; Raulier, F.; Leduc, A.; Gauthier, S. Projections of Future Forest Age Class Structure under the Influence of Fire and Harvesting: Implications for Forest Management in the Boreal Forest of Eastern Canada. For. Int. J. For. Res. 2017, 90, 485–495. [Google Scholar] [CrossRef]
  103. Pasques, O.; Munné-Bosch, S. Ancient Trees Are Essential Elements for High-Mountain Forest Conservation: Linking the Longevity of Trees to Their Ecological Function. Proc. Natl. Acad. Sci. USA 2024, 121, e2317866121. [Google Scholar] [CrossRef] [PubMed]
  104. Vangi, E.; Dalmonech, D.; Cioccolo, E.; Marano, G.; Bianchini, L.; Puchi, P.F.; Grieco, E.; Cescatti, A.; Colantoni, A.; Chirici, G.; et al. Stand Age Diversity (and More than Climate Change) Affects Forests’ Resilience and Stability, Although Unevenly. J. Environ. Manag. 2024, 366, 121822. [Google Scholar] [CrossRef] [PubMed]
  105. Durante, S.; Augusto, L.; Achat, D.L.; Legout, A.; Brédoire, F.; Ranger, J.; Seynave, I.; Jabiol, B.; Pousse, N. Diagnosis of Forest Soil Sensitivity to Harvesting Residues Removal–A Transfer Study of Soil Science Knowledge to Forestry Practitioners. Ecol. Indic. 2019, 104, 512–523. [Google Scholar] [CrossRef]
  106. Pare, D.; Manka, F.; Barrette, J.; Augustin, F.; Beguin, J. Indicators of Site Sensitivity to the Removal of Forest Harvest Residues at the Sub-Continental Scale: Mapping, Comparisons, and Challenges. Ecol. Indic. 2021, 125, 107516. [Google Scholar] [CrossRef]
  107. Hekkala, A.-M.; Jönsson, M.; Kärvemo, S.; Strengbom, J.; Sjögren, J. Habitat Heterogeneity Is a Good Predictor of Boreal Forest Biodiversity. Ecol. Indic. 2023, 148, 110069. [Google Scholar] [CrossRef]
  108. Nabuurs, G.J.; Begemann, A.; Linser, S.; Paillet, Y.; Pettenella, D.; Ermgassen, S. Sustainable Finance and Forest Biodiversity Criteria; From Science to Policy; European Forest Institute: Joensuu, Finland, 2024; Volume 16, ISBN 978-952-7426-83-8. [Google Scholar]
  109. Pardos, M.; del Río, M.; Pretzsch, H.; Jactel, H.; Bielak, K.; Bravo, F.; Brazaitis, G.; Defossez, E.; Engel, M.; Godvod, K.; et al. The Greater Resilience of Mixed Forests to Drought Mainly Depends on Their Composition: Analysis along a Climate Gradient across Europe. For. Ecol. Manag. 2021, 481, 118687. [Google Scholar] [CrossRef]
  110. Murray, C.; Marmorek, D.R. Adaptive Management: A Science-Based Approach to Managing Ecosystems in the Face of Uncertainty. In Proceedings of the Making Ecosystem-Based Management Work: Proceedings of the Fifth International Conference on Science and Management of Protected Areas, Victoria, BC, Canada, 11–16 May 2003. [Google Scholar]
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Figure 1. Forest stand species mixtures according to tree growing stock volume in Lithuania’s National Forest Inventory plots (N = 6113) for 2022. Pure stands are represented as dominant single species, containing >75%. Pure deciduous stands are represented by individual dominant tree species (except silver and downy birch, which were estimated together). Mixed stands include all possible tree species.
Figure 1. Forest stand species mixtures according to tree growing stock volume in Lithuania’s National Forest Inventory plots (N = 6113) for 2022. Pure stands are represented as dominant single species, containing >75%. Pure deciduous stands are represented by individual dominant tree species (except silver and downy birch, which were estimated together). Mixed stands include all possible tree species.
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Figure 2. Three levels of tree species biodiversity in Lithuanian forests according to the National Forest Inventory data from five 5-year remeasurement cycles. Numbers show the percentages of the growing stock volume of the tree species from the growing stock volume of all tree species: (1) more than 3%, (2) 0.3–3%, and (3) 0.03–0.3%.
Figure 2. Three levels of tree species biodiversity in Lithuanian forests according to the National Forest Inventory data from five 5-year remeasurement cycles. Numbers show the percentages of the growing stock volume of the tree species from the growing stock volume of all tree species: (1) more than 3%, (2) 0.3–3%, and (3) 0.03–0.3%.
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Figure 3. Natural change in forest stand type over 5 years (2017–2022), excluding clear-cut areas. The label shows the change area per 1000 ha.
Figure 3. Natural change in forest stand type over 5 years (2017–2022), excluding clear-cut areas. The label shows the change area per 1000 ha.
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Figure 4. The comparison of species composition and changes in area of natural and artificial reforestation/afforestation over the last 20 years. NR—natural reforestation, AR—artificial reforestation, NA—natural afforestation, and AA—artificial afforestation.
Figure 4. The comparison of species composition and changes in area of natural and artificial reforestation/afforestation over the last 20 years. NR—natural reforestation, AR—artificial reforestation, NA—natural afforestation, and AA—artificial afforestation.
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Table 1. The typology of the hemiboreal forests of Lithuania.
Table 1. The typology of the hemiboreal forests of Lithuania.
Forest Habitats
(NATURA 2000)		Forest Type Series *Site Types **Soil Types ****
Scots pine forests
(9010 9060 91D0 91T0)		Cladoniosa (cl)Šae, NaeAR, RG
Vacciniosa (v)Šal, NalAR, PZ, RG
Vaccinio-myrtillosa (vm)Šbl, Šbp, Nbl, Nbp AR, PZ
Myrtillosa (m)Lal, Lbl, LbpAR, PL, PZ
Myrtillo-sphagnosa (msp msps)Ual, Ubl, Ubp, Ma, Mb ***GL, PZ
Carico-sphagnosa (csp csps)Pb, MbHSf-s
Ledo-sphagnosa (lsp lsps)Pa, MaHSf
Mixed Norway spruce forests
(9050 9160 9180 9190 9070)		Oxalidosa (ox)Šcl, Šcp, Šcs, Ncl, Ncp, Ncs AR, LV, RT, PL, CM, FL
Myrtillo-oxalidosa (mox)Lcl, Lcp, LcsAR, LV, RT, PL, CM
Hepatico-oxalidosa (hox)Šdl, Šdp, Šds, Ndl, Ndp, NdsAR, LV, RT, PL, CM, FL
Oxalido-nemorosa (oxn)Ldl, Ldp, LdsLV, CM, FL
Mixed broadleaved forests
(9020 9080 91F0 91E0)		Aegopodiosa (aeg)Šfp, Šfs, Nfp, Nfs LV, CM
Carico-mixtoherbosa (cmh)Lfl, Lfp, LfsLV, CM
Calamagrostidosa (cal cals)Ucl, Ucp, Ucs, Mc ***GL
Filipendulo-mixtoherbosa (fil fils)Udl, Udp, Uds, Md ***GL, FL
Urticosa (ur)Ufl, Ufp, UfsGL
Carico-iridosa (cir cirs)Pd, MdHSs
Caricosa (c cs)Pc, McHSs
* Dominant ground vegetation types: s in the ground layer code is the index of drained (siccatum) forest sites. ** Hydrotope codes in Lithuania: Š and N—slopes and normal water regime; L—temporarily overmoistured; U—overmoistured; P—peatland, thickness of peat layer: Pa, Pb ≥ 60 cm; Pc, Pd ≥ 40 cm; M—drained peatland. Trophotope and soil structure codes in Lithuania: a—very oligotrophic, b—oligotrophic, c—mesotrophic, d—eutrophic, f—very eutrophic, e—degraded, l—light soils sand–gravel, s—heavy soils clays, and p—light soil on heavy soil [60]. *** Thickness of peat layer in mineral soils: Ma, Mb, Mc, Md—up to 40 cm [61]. **** RT—Retisols, AR—Arenosols, CM—Cambisols, FL—Fluvisols, GL—Gleysols, HSf—Fibric Histosols, HSf-s—Terri-Fibric Histosols, HSs—Pachiterric Histosols, LV—Luvisols, PL—Planosols, PZ—Podzols, and RG—Regosols [56,57,61].
Table 3. Tree volume structure of Lithuanian forest stands according to the National Forest Inventory data from five five-year remeasurement cycles. The numbers show the percentage of growing stock volume of the tree species designated by the letter from the growing stock volume of all tree species. The bold text in brackets is the stand type area percentage (%).
Table 3. Tree volume structure of Lithuanian forest stands according to the National Forest Inventory data from five five-year remeasurement cycles. The numbers show the percentage of growing stock volume of the tree species designated by the letter from the growing stock volume of all tree species. The bold text in brackets is the stand type area percentage (%).
Stand Type *NFI 2022NFI 2017NFI 2012NFI 2007NFI 2002
Scots pine (p)82p 10e 5b (31.5)83p 10e 5b (32.0)83p 10e 5b (32.9)84p 9e 5b (33.5)84p 9e 5b (34.1)
Norway spruce (e)74e 8b 6p (18.6)74e 8b 7p (17.8)73e 9b 7p (17.5)72e 9b 7p (18.2)71e 10b 7p (17.8)
Silver and downy birch (b)62b 15e 5j (19.9)62b 15e 6j (20.3)62b 15e 6j (20.5)61b 14e 6j (20.3)61b 14e 6j (19.6)
Eurasian aspen (d)62d 12e 10b (7.3)62d 12e 11b (6.9)61d 13e 11b (6.7)60d 13e 12b (6.4)60d 13e 12b (6.3)
Black alder (j)72j 11b 7e (11.3)71j 12b 8e (11.1)70j 13b 8e (10.2)69j 13b 8e (9.3)67j 13b 8e (9.1)
Grey alder (bt)70bt 8b 5e (6.3)71bt 8b 6e (6.4)73bt 8b 5e (6.6)74bt 8b 4e (6.4)73bt 9b 5e (6.9)
English oak (a)57a 14e 8b (2.6)56a 14e 8b (2.5)55a 16e 7b (2.4)55a 16e 7b (2.2)56a 14e 8b (2.4)
European ash (u)44u 16k 9e (0.5)51u 10k 10e (0.7)58u 7e 6d (1.2)59u 7e 7d (2.1)61u 7e 7d (2.4)
Other species26l 17k 7bl (2.1)27l 14k 7b (2.3)28l 13k 8bl (2.0)27l 12k 10a (1.5)22l 11k 10bl (1.5)
All stands36p 22e 16b (100)36p 22e 16b (100)37p 21e 17b (100)37p 20e 17b (100)36p 20e 17b (100)
* Tree species in descending order of light need (ID): p—Pinus sylvestris, b—Betula pubescens and Betula pendula, d—Populus tremula, bt—Alnus incana, j—Alnus glutinosa, a—Quercus robur, u—Fraxinus excelsior, e—Picea abies, g—Ulmus glabra, l—Tilia cordata, and k—Acer platanoides [14].
Table 4. Tree volume structure of the growing stock (I and II storey) by stand type according to the Lithuanian NFI data for 2022. The numbers show the percentage of the volume of the dominant tree species and the subsequent two tree species. The appointed letter refers to the tree species.
Table 4. Tree volume structure of the growing stock (I and II storey) by stand type according to the Lithuanian NFI data for 2022. The numbers show the percentage of the volume of the dominant tree species and the subsequent two tree species. The appointed letter refers to the tree species.
Stand Type * Forest Development Phases/Stand Age (Years) **Forest Type Series ***
≤2021–4041–6061–8081–100101–120≥121
Scots pine (p)88p85p86p84p80p79p80pcal cals cl cmh csp csps hox lsp lsps m mox msp msps ox oxn v vm
4b7b6b8e14e14e15e
4e5e5e5b4b4b3b
Norway spruce (e)47e77e81e73e71e66e64ec cs cal cals cir cirs cmh csp csps fil fils hox m mox msp msps ox oxn vm
13a8b8b8b8p11p13p
12p4a3p8p8b9b7b
Silver and downy birch (b)60b69b67b59b54b49b-aeg c cs cal cals cir cirs cmh csp csps fil fils hox lsp lsps m mox msp msps ox oxn v vm
9e10e11e19e21e29e
9p5j5j6d7j7j
Eurasian aspen (d)65d64d60d62d60d--aeg c cs cal cals cir cirs cmh csp csps fil fils hox mox ox oxn ur
11b10b13b16e20e
9e7e12e9b10b
Black alder (j)71j74j78j69j57j60j62jc cs cal cals cir cirs cmh csp csps fil fils hox mox ox oxn ur urs
10b13b10b13b19e27e36e
8e4e4e9e11b10b2a
Grey alder (bt)62bt72bt71bt69bt---aeg c cs cal cals cir cirs cmh fil fils hox mox ox oxn
14b8b8b14e
5j5e5j5j
English oak (a)48a43a57a58a56a58a56aaeg c cs cmh fil fils hox mox ox oxn
18b28b17e17e13e9e16e
17l11e11b11b10b8l8l
European ash (u)48u47gl46k60u48u46u44eaeg c cs cir cirs fil fils hox mox ox oxn
24e35u33u13k20l38a32u
10bt8bt8a10a13b13b14b
Other species18a20bl22l35l54l30k75laeg c cs cal cals cmh fil fils hox mox ox oxn ur urs
16gl12gl13k23k14k27l25e
13bl10k10gl8a7u14sb
* Tree species (ID): p—Pinus sylvestris, e—Picea abies, b—Betula pendula and Betula pubescens, d—Populus tremula, j—Alnus glutinosa, bt—Alnus incana, a—Quercus robur, u—Fraxinus excelsior, k—Acer platanoides, l—Tilia cordata, bl—Salix caprea, gl—Salix fragilis and Salix alba, and sb—Carpinus betulus. ** Stand initiation (0–20 years), stem exclusion and understorey initiation (20–60 years), understorey reinitiation (60–120 years), and steady state (>120 years). *** aeg—Aegopodiosa, c—Caricosa, cal—Calamagrostidosa, cir—Carico-iridosa, cl—Cladoniosa, cmh—Carico-mixtoherbosa, csp—Carico-sphagnosa, fil—Filipendulo-mixtoherbosa, hox—Hepatico-oxalidosa, lsp—Ledo-sphagnosa, m—Myrtillosa, mox—Myrtillo-oxalidosa, msp—Myrtillo-sphagnosa, ox—Oxalidosa, oxn—Oxalido-nemorosa, ur—Urticosa, v—Vacciniosa, and vm—Vaccinio-myrtillosa (s—index of drained (siccatum) forest types; the underlined series of forest types includes stands of all tree species).
Table 5. Tree volume structure of the growing stock (I and II storey) by forest type series according to the Lithuanian NFI data for 2022. The numbers show the percentage of the volume of the dominant tree species and the subsequent two tree species. The appointed letter refers to the tree species.
Table 5. Tree volume structure of the growing stock (I and II storey) by forest type series according to the Lithuanian NFI data for 2022. The numbers show the percentage of the volume of the dominant tree species and the subsequent two tree species. The appointed letter refers to the tree species.
Forest Type Series *Forest Development Phases/Stand Age (Years) **NFI 2022
≤2021–4041–6061–8081–100101–120≥121
Cladoniosa (cl)NEDNEDNEDNEDNEDNEDNEDNED
Vacciniosa (v)100p78p97p98p98p99p99p97p
17b3b2b1b1pk1b2b
2pb 1pk
Vaccinio-myrtillosa (vm)77p66p78p87p85p84p87p84p
12b18e12e8e12e13e10e11e
9e14b9b4b3b3b2b5b
Myrtillosa (m)45p53e49e56p59p69p66p55p
30e24b31p30e31e25e27e32e
18b21p9b12b9b5b5b10b
Myrtillo-sphagnosa (msp msps)37p44b39p65p62p53p64p54p
35b35e33b27e18e37e29e27e
7a17p28e7b15b9b6b17b
Carico-sphagnosa (csp csps)71b55b43b50p65p64p69p53p
10p16d25p23b19e25e21e22b
8e13p19e20e14b10b7b19e
Ledo-sphagnosa (lsp lsps)57p67b80p88p94p91p95p82p
43b27p17b12b4b5e4b15b
4j3e 1d4b 2e
Oxalidosa (ox)28b31e43p51p47p56p59p44p
20e21b21e22e32e23e22e24e
14d19p17b13b9b8b11a14b
Myrtillo-oxalidosa (mox)28b39e39e41e54e47e44e42e
22e23b23b22b16p22p36p20b
15d10bt10j14p12b14b13a11p
Hepatico-oxalidosa (hox)23b26d19e17b30e29e29e21e
18a22bt14bt16e19b24a24a14b
18bt18e13b15p15a13l23p13d
Oxalido-nemorosa (oxn)26d25e30e27b35b33e40a26e
17b20bt19bt25e23e19b25e22b
14bt18b17b20d13d12a8d16d
Aegopodiosa (aeg)100a ***66bt36bt34u38d47b-25bt
11bl25e24b32a32a15u
8b17u23bt14k10e14b
Carico-mixtoherbosa (cmh)23b26bt32b42b22e36l-25b
17e24e23bt26d17a35b17bt
17bt19d15j11j13d21a17d
Calamagrostidosa (cal cals)53j36b37j36j30b31e62e35j
25b32j31b30e21e26b18b28b
8e15e20e19b21j25j15d22e
Filipendulo-mixtoherbosa (fil fils)60j38j56j51j37j37e53e48j
14b19b22b20b27b28b30u21b
11bt13bt8bt11e22e26j10b11e
Urticosa (ur)68j32gl29bt82j41j--53j
22u29j27d8b40b13b
10b19b16j5bt8u10bt
Carico-iridosa (cir cirs)90j37j89j62j45j76j-71j
9b24b7b27b32e22e15b
1a17e3e10e24b3b10e
Caricosa (c cs)60j50j51j33j30b39e-42j
18b35b31b31b28e22j31b
7p5e8e20e27j19b14e
* Scots pine forests: cl, v, vm, m, msp, msps, csp, csps, lsp, and lsps; mixed Norway spruce forests: ox, mox, hox, and oxn; mixed broadleaved forests: aeg, cmh, cal, cals, fil, fils, ur, cir, cirs, c, and cs; s—index of drained (siccatum) forest types. ** Stand initiation (0–20 years), stem exclusion and understorey initiation (20–60 years), understorey reinitiation (60–120 years), and steady state (>120 years). *** Tree species (ID): p—Pinus sylvestris, pb—Pinus banksiana, pk—Pinus mugo, e—Picea abies, b—Betula pendula and Betula pubescens, d—Populus tremula, j—Alnus glutinosa, bt—Alnus incana, a—Quercus robur, u—Fraxinus excelsior, k—Acer platanoides, l—Tilia cordata, bl—Salix caprea, and gl—Salix fragilis and Salix alba. NED—Not Enough Data.
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Petrokas, R.; Manton, M.; Kulbokas, G.; Muraškienė, M. Development of Forest Tree Species Composition: Selected Results of the National Forest Inventory of Lithuania. Plants 2025, 14, 667. https://doi.org/10.3390/plants14050667

AMA Style

Petrokas R, Manton M, Kulbokas G, Muraškienė M. Development of Forest Tree Species Composition: Selected Results of the National Forest Inventory of Lithuania. Plants. 2025; 14(5):667. https://doi.org/10.3390/plants14050667

Chicago/Turabian Style

Petrokas, Raimundas, Michael Manton, Gintaras Kulbokas, and Milda Muraškienė. 2025. "Development of Forest Tree Species Composition: Selected Results of the National Forest Inventory of Lithuania" Plants 14, no. 5: 667. https://doi.org/10.3390/plants14050667

APA Style

Petrokas, R., Manton, M., Kulbokas, G., & Muraškienė, M. (2025). Development of Forest Tree Species Composition: Selected Results of the National Forest Inventory of Lithuania. Plants, 14(5), 667. https://doi.org/10.3390/plants14050667

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