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Article

Forest Stand Changes Drive Conservation of Understory Composition and Biomass in the Boreal Forest of the Southern Urals †

by
Natalya Ivanova
Institute Botanic Garden Ural Branch of RAS, 8 Marta Street, 202a, 620144 Yekaterinburg, Russia
†
This article is a revised and expanded version of a paper entitled Predictive Models of the Species Diversity and Biomass of the Herb Layer During Changes in the Stands of Dark Coniferous Forests, which was presented at [The 3rd International Electronic Conference on Diversity, 15 October 2024].
Diversity 2025, 17(10), 672; https://doi.org/10.3390/d17100672
Submission received: 25 August 2025 / Revised: 23 September 2025 / Accepted: 23 September 2025 / Published: 25 September 2025
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Abstract

The health of ecosystems, their functionality and the fulfilment of ecosystem functions are all dependent on biodiversity and productivity. The ongoing transformation of forests is intensifying the need for conservation. At the same time, the herbaceous layer has not yet been studied enough by researchers. The aim of the study is to ascertain the impact of the composition and age of the stand of primary and secondary forests on the biomass and species diversity of the herbaceous layer in the most prevalent forest type of the Western Macroscline of the Southern Urals: moss spruce forests. The methodological basis was chosen to be genetic forest typology and generally accepted methods of studying forest vegetation. We studied primary dark coniferous forests, as well as secondary birch and aspen forests of different compositions and ages. Positive correlations with the age of the stand were found to be most pronounced for Oxalis acetosella L. and Lycopodium clavatum L., while negative correlations were found to be most pronounced for Deschampsia caespitosa (L.) P. Beauv., Brachypodium pinnatum (L.) Beauv., and Dactylis glomerata L. The positive correlations with the proportion of birch and aspen in the stand composition are most pronounced for Dactylis glomerata L., Geum rivale L., Aegopodium podagraria L., Aconitum septentrionale Koelle, and Prunella vulgaris L. The research results clearly demonstrate the length of time that changes in species composition and productivity of the herbaceous layer of mountain forests take place over. This must be considered when planning forest management and nature conservation in mountain forests in the Urals. On the one hand, our study is certainly regional, but on the other, similar forests, forest degradation, regenerative succession and the plant species studied are widespread in the boreal zone. Therefore, the research results will be of interest to many researchers whose work relates to forest resources, biodiversity conservation and forest succession. To expand the scope of the research, further studies are planned in other types of forest in the Ural Mountains.

1. Introduction

An understanding of the biospheric role of forests in mitigating the effects of global warming situates the problem of their conservation and restoration among the most pressing issues of our time [1]. In numerous countries [2,3,4,5], including the Russian Federation [6], the investigation of biodiversity, productivity, and the dynamics of forests represents a principal focus within the domains of forest ecology and forest science. It has been demonstrated that the primary source of greenhouse gas emissions is forest degradation and the subsequent reduction in forest area. This phenomenon occupies the second place among all sources of greenhouse gases [7]. The consequences of forest disturbance are manifested at both the regional and global levels, whereby a decrease in ecosystem services, the economic value of forest resources, and an increase in the threats of natural disasters are expressed [7,8,9]. Furthermore, the adaptive abilities of ecosystems are not always sufficient to compensate for the influence of external factors, which results in a loss of stability and an exacerbation of degradation [5,7,10]. Positive trends remain exceedingly uncommon [3,4,11,12]. For instance, an enhancement in land use and reforestation practices has been observed in specific regions of China [13]. Additionally, some positive developments have been documented in select EU countries [5]. At the same time, information on forest quality, succession status, biodiversity and sustainability is usually lacking. It is repeatedly emphasized in the literature that there is still an acute lack of information on regional and forest typological characteristics for accurate estimation of forest degradation and prediction of the success of reforestation [11,14,15].
The rapidly developing methods of remote sensing of territories make it possible to organize the receipt of a continuous stream of information on the structure of a stand over large areas [16,17,18]. At the same time, the understory—consisting of saplings, woody plants, undergrowth and herbaceous layers—remains insufficiently studied [19,20]. Nevertheless, it is the competition between woody plant seedlings and the herbaceous layer that largely determines the success of the natural regeneration of woody plants under the forest canopy. This is evidenced by the findings of studies [21,22,23]. An alternative perspective posits that species composition is contingent on bioclimatic conditions. The vulnerability of plant species may increase due to the transformation of habitat conditions caused by gaps in the canopy of trees or shrubs. These alterations in habitat conditions can lead to an increase in evapotranspiration, which in turn can result in a loss of water and humidity within the microclimatic environment [24]. The current state of knowledge regarding these aspects in the Ural Mountains is insufficient. It is imperative to examine the regional manifestations of these processes. Moreover, the species diversity of taiga plant communities is largely contingent upon the herbaceous layer, which is characterized by a significantly markedly greater species diversity than that observed in the stand itself [22,25]. It is important to acknowledge that herbaceous layer is highly susceptible to external disturbances, both natural and anthropogenic [26]. They can serve as a sensitive indicator of forest degradation and reforestation. Additionally, they can provide valuable insights into the structure and sustainability of forests, as well as the nature of land use in the past [22,27]. Furthermore, the herbaceous layer’s vegetation plays a significant role in forest biomass and carbon deposition. [28,29,30,31]. It is therefore evident that in order to develop predictive models of forest dynamics and biodiversity conservation, it is essential to have access to accurate and reliable information on the regional and forest typological features of the interrelationships between the stand and subordinate tiers. This is a conclusion that has been reached by a number of researchers, including [20,32]. In this regard, further research on this topic is highly pertinent.
The forests of the Ural Mountains are acknowledged as a significant constituent of the Northern Hemisphere’s forest zone. Boreal forests play an important role in stabilizing the climate not only for the territories of the Russian Federation, rather they have a global effect. However, for approximately three centuries, the Ural forests have been subjected to intensive forest management. The implementation of long-term intensive forest management has resulted in significant alterations to the structure and functionality of mountain ecosystems across vast expanses, a process that is still ongoing [33]. The extent of primary forests began to diminish. Only those primary forests that are situated in specially protected areas have been preserved. This process needs to be monitored. Both monitoring forest degradation and assessing reforestation potential are important. Much attention is currently being paid to these problems: A number of papers are devoted to the anthropogenic transformation of landscapes [34], to restorative successions [35,36], and to the study of the influence of climatic factors on forest ecosystems [37,38]. The creation of Vegetation Database Forest of Southern Ural (GIVD ID 00-RU-001) that contains geobotanical relevés of primeval zonal forest of South-Ural region and their mountain analogues is a major achievement in the study of the vegetation of the Southern Urals [39]. This development initiated a programme of in-depth analytical studies of the region’s flora and forest vegetation, and of investigations into relationships at the species and ecosystem levels. Nevertheless, the information provided in this database is inadequate for the development of predictive models. There is still a lack of information on the relationships between the stand and the lower levels, the correlation of their dynamics in the process of anthropogenic transformation and restoration changes. To address this gap, it is imperative to utilize more rigorous quantitative data. In the context of these objectives, the collection of data pertaining to the bio-productivity of species is of paramount importance. However, data concerning forest productivity in the Ural Mountains is only available for forest stands [40]. Research into the productivity of the subordinate tiers has hitherto been extremely limited.
The study addresses two gaps in modern research on forest dynamics: the absence of undergrowth biomass estimates in boreal succession studies, and the underrepresentation of the Southern Urals in research.
Our objective was to clarify how overstory changes influence understory biomass and diversity in the most common forest type of the Western Macroscline of the Southern Urals: moss spruce forests.
In order to achieve this aim, the following specific research objectives were established: firstly, to identify the relationship between the stand structure and such general characteristics of the herbaceous layer as species richness, total biomass and species diversity expressed by various diversity indices; secondly, to analyze the relationship between the structure of the stand and the biomass of individual species of the herbaceous layer and identify the species most susceptible to the transformation of the stand.
Null hypothesis: There is no significant relationship between stand transformation and understory composition or biomass. Alternative hypothesis: Stand transformation significantly alters understory composition and biomass.

2. Materials and Methods

2.1. Study Area

In order to achieve the objectives of the research and ensure its success, it was imperative to identify a region where both primary dark coniferous forests and a variety of secondary forest communities, representing different ages and compositions, were adequately represented. Concurrently, it is imperative to ensure that no substantial anthropogenic pollution is detected. In the Russian Federation, the forests of the Southern Urals meet these requirements. The study area is situated on the western mountain slope of the Southern Ural Mountains in the Chelyabinsk region (Russia) between 54°33′–54°40′ N latitude and 57°48′–57°55′ E longitude (see Figure 1). According to the climatic geography of the Southern Urals, they are included in the continental Atlantic forest area of the temperate zone.
It is evident that bioclimatic variables offer a comprehensive representation of the climate. In this study, the second version of the CHELSA (Climate Hazards and Exposure Atlas) (accessed on 1 July 2025) data set was utilized, encompassing the period from 1980 to 2010 [41,42]. This data set, which encompasses 19 bioclimatic variables, was employed to analyze the climate within the specified study area, specifically the Chelyabinsk region in Russia. The detailed values of these bioclimatic variables can be found in Table 1. The growing degree-days heat sum above 10 °C does not exceed 121 days. The number of days with snow cover is 198. The maximum snow cover is recorded in March and amounts to 56–64 cm. The number of frost-free days is 204.
In the context of the Southern Ural Mountains in the Chelyabinsk region (Russia), the most prevalent forest-forming species are the Siberian spruce (Picea obovata Ledeb.) and the Siberian fir (Abies sibirica Ledeb.). Tilia cordata Mill is a prevalent species in the second layer of the forest understorey and undergrowth. White birch (Betula pubescens Ehrh.) is also a customary element of the forest ecosystem in the Southern Ural Mountains [8]. The syntaxonomy of the Southern Urals forests has been the subject of detailed study and is presented in numerous publications [43,44,45].
The intricate interplay of atmospheric dynamics and the topographical complexity of the Ural Mountain range significantly influence the climatological variations within this region, with the elevation of the terrain acting as a pivotal factor in modulating these patterns. This results in a markedly pronounced altitudinal zonation. The upper elevation zone of the mountains, situated between 700 and 900 m above sea level, is characterized by a distinct contrast in temperature conditions. Soil moisture is inherently unstable and wholly contingent upon precipitation. The topographic position under consideration is distinguished by the presence of spruce forests, which are characterized by a sparse tree canopy. Picea obovata Ledeb. is the most prevalent species, constituting the bulk of the stand. As a consequence of the temperature regime, Abies sibirica Ledeb. is practically non-existent in these habitats [46].
The middle elevation zone (500–700 m above sea level) is the warmest due to the occurrence of temperature inversions. The presence of productive spruce-fir forests, characterized by an abundance of Tilia cordata Mill. in the second tree layer, is indicative of this elevation zone. Acer platanoides L., Ulmus glabra Huds., Quercus robus L. have been observed with a high frequency. As Ivanova [46] demonstrate, the dominant species in the herbaceous layer are Dryopteris filix-mas (L.) Schott., Athyrium filix-femina (L.) Roth. and Dryopteris austriaca (Jacq.). The forest in question is evidently a temperate, supra-temperate humid forest of the Querco-Fagetea class.
The lower elevation zone, situated between 400 and 500 m above sea level, is distinguished by the presence of long, gentle mountain slopes and thick loamy soils, which collectively facilitate stable humidification conditions. Gentle mountain slopes, located in the lower parts of the mountains, are the most common habitat group. The spruce forests within these habitats have been found to be the most productive, with some studies reporting yields of up to 330 m3/ha. The age and spatial structure of the forest stand vary in terms of complexity and diversity, with the age of the trees ranging from 85–95 to 160–300 years, forming 2–3 layers [46]. The primary forest stand is characterized by a predominance of Picea obovata Ledeb. and Abies sibirica Ledeb. Participation in the Betula pubescens Ehrh. has been observed to occur in up to 20% of cases. Pinus sylvestris L. has been shown to be exclusively associated with fires, thus serving as a reliable indicator of this phenomenon in this type of forest. The second layer of trees is constituted by the following species: Tilia cordata Mill., Sorbus aucuparia L., Rubus idaeus L., and Padus avium Mill. The projective coverage of mosses has been determined to exceed 60%. The following species are commonly encountered: Pleurozium schreberi (Brid.) Mitt., Hylocomium splendens (Hedw.) B. S. G., and Dicranum sp. The most abundant species are Oxalis acetosella L., Lycopodium clavatum L. and Lycopodium annotinum L. The following species have been recorded with high frequency: Calamagrostis arundinacea (L.) Roth, Carex pilosa Scop., Carex rhizina Blytt. ex Lindbl., Athyrium filix-femina (L.) Roth, Dryopteris expansa (C. Presl) Fraser-Jenk. & Jermy, and Dryopteris carthusiana (Vill.) H.P. Fuchs.

2.2. Sampling and Measurements

The study of Southern Urals forests was initiated in 1991, thereby constituting a continuation of the long-term forest typological research that was initiated by Elena Filroze [47]. The methodological basis of this study is formed by two elements: firstly, the genetic forest typology [48,49]; and secondly, the generally accepted methods of studying forest vegetation [50]. The research was conducted on the gentle mountain slopes. We have studied the most widespread and productive forest type in the Southern Urals in the Chelyabinsk region (Russia): Moss spruce forest (Asaro europaei-Abietetea sibiricae according to the Braun-Blanquet classification). The topography of the land, characterized by a slope and the presence of gravel, contributes to the effective drainage of the soils. The study focused on primary dark coniferous forests, as well as secondary birch and aspen forests of varying composition and age. A total of 17 plots were established and surveyed, with each plot subdivided into four sections. Total 68 with an area of 50 m × 50 m (Figure 2).
The size of the plot was chosen so that at least 200 main-generation trees would grow on it. This is one of the key requirements for sampling boreal forests in order to obtain reliable information about their structure [8,50].
The soil is characterized by elevated levels of nutrient content, with a range of 50–70 cm. The topography of the land, characterized by a slope and the presence of gravel, contributes to the effective drainage of the soils. The height and diameter of all the trees in these plots were measured. The stand age was determined from the tree rings of 10–15 model trees. The number of saplings was determined on 25 survey plots of 5 m × 5 m, which had been established on each of the 68 plots. The survey plots were arranged in four parallel rows. The composition of the shrub species and the extent of their coverage were determined. Furthermore, in order to account for the species structure and biomass of the herbaceous layer, a total of 10–16 survey plots of 1 m × 1 m were established on each of the 68 plots. The measurement of biomass was conducted at the point in time when the herbaceous layer had attained maximum growth, which corresponded to the month of July. The plants were harvested at ground level and their species were identified. The material was then dried at 105 °C to a constant mass (absolute dry state). The plants that were harvested from the survey plots, classified according to species and dried, were weighed with an accuracy of 0.1 g. Figure 3 illustrates the research scheme.

2.3. Data Processing

In our study, the sample areas were selected to minimize variation in environmental factors such as temperature, structure and soil moisture. At the same time, the species composition and age of the stand were varied as much as possible. The age of the stands in our study ranged from 5 to 160 years. The proportion of Siberian spruce and Siberian fir in the stand composition ranges from almost 0 to 95%. The proportion of birch and aspen also varies from almost 0 to 100% (Figure 3). Such an organization of research makes it possible to consider the composition and age of the forest stand as the most important factors and to study their influence on the lower levels of the plant community structure.
Our analytical objective was to comprehensively understand the relationship between the transformation of the stand and the herbaceous layer and to present the results in a clear intuitive form for users.
DCA analysis was utilized in order to identify and evaluate the main factors determining the biodiversity and biomass of the herbaceous layer. “Detrended Correspondence Analysis” (DCA) has been demonstrated to be a more reliable and useful tool for data research in community ecology than PCA or CA [51]. The DCA algorithm is well described in the literature [52]. The method is implemented in a number of software application packages: CANOCO [53], DECORANA [54]. DCA was performed using the popular R package (vegan) Version 2.2-1 [55,56]. The vegan package is one of the most widely used packages for ecological data analysis in R. It provides a variety of functions for multivariate analysis, including ordination methods such as Principal Component Analysis (PCA), Correspondence Analysis (CA), and Non-metric Multidimensional Scaling (NMDS). The main reasons for choosing DCA are, on the one hand, its convenience and, on the other hand, the absence of any restrictions on the data. Another important positive feature is the possibility to use it when working with very large amounts of data and to obtain comprehensive information about the structure of the analyzed object. The analysis was conducted using biomass as the primary data source. The utilization of this indicator is less subjective than coverage. The rarest species, which were only found in a single sample area and exhibited a low biomass, were excluded prior to the DCA. This accounts for approximately one third of all recorded plant species. Species abundance data are often hetero-elastic and have extreme values (outliers). Prior to DCA, it is common practice to perform a logarithmic transformation or a scaling operation to normalize the data. In this study, DCA was performed in two variants: with logarithmic data transformation and without transformation. Both variants gave good results. However, in our opinion, the results of DCA in the variant without data transformation proved to be more informative. Therefore, this paper presents the results of the ordination without data transformation. The selection of biomass for the calculation of indices and DCA was driven by the objective nature and reliability of this criterion. Species biomass is the resulting indicator and the result of all factors. In addition, the subjective factor has less influence on its definition (for example, compared to the abundance and coverage).
In order to identify the relationships between the stand and the herbaceous layer, correlation analyses were also carried out. Spearman’s rank correlation coefficient was utilized to assess the relationships. Spearman’s rank correlation coefficient, denoted by Spearman’s ρ, is a non-parametric measure of rank correlation, i.e., the statistical dependence between the rankings of two variables. The Spearman correlation coefficient is employed to assess monotonic relationships, which may be linear or non-linear in nature. In the absence of repeated data values, a perfect Spearman correlation of +1 or −1 occurs when each variable is a perfect monotone function of the other [57]. Correlations were calculated for all plant species of the herbaceous layer. The ecological niche of plants can be assessed using ecological indicator values. This method is recognized as effective and is widely used by researchers [58,59,60]. Ecological niche characteristics of the studied species according to new European indicator values [61] are given in Table A1. The European Indicator Values are by far the most complete and accurate system of environmental indicators for European vascular plants [61].

3. Results

Primary forests having the structure corresponding to natural conditions have largely disappeared. Their separate rests are found in hard-to-reach areas only. The stand of primary forests is multi-aged and multi-layered. Against the background of the dominance of Siberian spruce and Siberian fir, birch (up to 10–15%) and single aspen are present in the stand. The spatial structure of primary forests is characterized by mosaic. The closed canopy of the stand alternates with the gaps.
The DCA has shown that all secondary forests differ from primary spruce forests. In Figure 4, after-cutting spruce and fir forests are closest to primary forests. However, these two categories of dark coniferous forests are clearly separated in Figure 4.
Short- and long-term secondary forests are even more different from primary spruce forests and lie behind post-harvest spruce and fir forests in the DCA axes. Another interesting conclusion is the lack of differentiation between short- and long-term secondary birch forests and their dynamic stages. Stable-term secondary aspen forests and logging areas are as far away from primary forests as possible. Furthermore, unlike short and long-term secondary forests, their age stages are differentiated in the DCA axes, which is a sign of a change in composition and structure. The logging areas occupy a separate area in the ordination diagram. The Table A2 shows the core DCA indicators: the eigenvalues and the percentage of variance explained. The DCA1 axis explains more than 41% of the variance, and the second axis explains more than 28% of the variance. To test the hypothesis of a close relationship between regenerative successions and the structure of the emerging stand, we applied the vectors characterizing the tree layer to Figure 4. The age of the stand and the proportion of Picea obovata Ledeb., Abies sibirica Ledeb. in the stand composition were taken as the main characteristics of the role of tree species in the ecosystem. Figure 4 clearly shows that our null hypothesis was confirmed. The characteristics of the position of the plant communities on the ordination diagram allow us to conclude that the first DCA axis is related to the composition of the stand and the second DCA axis is related to the age of the stand. These correlations are statistically significant (Table 2).
Changes in the forest stand are accompanied by changes in the dominant species in the herbaceous layer (Table 3).
These differences reflect successional trajectories and the influence of the participation of birch and aspen in stand composition. In older stands dark coniferous forests (aged 140–160 years), the herbaceous layer was dominated by shade-tolerant perennial species such as Lycopodium clavatum and Equisetum sylvaticum, with respective biomass values of 13.62 and 12.54 g/m2. Despite their ecological persistence, these species exhibited very high coefficients of variation (123–169%), indicating heterogeneous distribution within the forest mosaic. The presence of Calamagrostis arundinacea and Athyrium filix-femina as subdominants also indicates the presence of gaps in the canopy.
By contrast, mid-aged dark coniferous forests (aged 50–100 years) exhibited a more diverse dominance pattern, featuring Calamagrostis arundinacea, Carex pilosa and Filipendula ulmaria. The notably low coefficient of variation for Filipendula ulmaria (10%) indicates its stable and consistent establishment under these conditions, suggesting a competitive advantage in moderately aged, mixed forests containing 20–40% birch and aspen.
In the youngest deciduous stands (5–20 years), Calamagrostis arundinacea and Brachypodium pinnatum reached maximum biomass levels, reflecting these fast-growing species’ ability to dominate disturbed or early successional habitats. Subdominant species, such as Phalaroides arundinacea and Rubus saxatilis, also exhibited relatively high biomass levels. The ability of Carex pilosa to co-dominate alongside Brachypodium pinnatum and Calamagrostis arundinacea highlights its ecological plasticity and potential role in shaping the structure of secondary deciduous forests.
A distinctive pattern emerged in almost pure deciduous stands (99% birch and aspen). Here, dominance shifted towards Aconitum septentrionale and Stachys sylvatica. This indicates a significant transformation in the composition of the herbaceous layer under conditions of long-term deciduous dominance.
Next, a correlation analysis was performed. The results are shown in Table 4 and Table A3. The application of correlation analysis yielded a statistically significant relationship between the biomass of the herbaceous layer, number of species and the two characteristics of the stand under study that were selected for investigation: the proportion of birch and aspen in the composition of the stand, and the age of the stand (Table 4).
Oxalis acetosella L. exhibits the strongest positive correlation with the age of the stand. This suggests that this species exhibits a substantial biomass in primary dark coniferous forests and in the concluding stages of regenerative successions. Conversely, Deschampsia caespitosa (L.) P. Beauv. shows a slight negative correlation with stand age. This suggests that this species achieves higher productivity levels in both deforestation and young forests. Dactylis glomerata L. exhibited the strongest positive correlation with birch and aspen in the stand. This suggests that this species is more productive in birch and aspen forests than in dark coniferous forests. Lycopodium clavatum L. is predominantly located in dark coniferous forests. Furthermore, the plant demonstrates a marked preference for old-growth dark coniferous forests. This preference is substantiated by the findings of Spearman’s correlation coefficients (Table 4). This confirms the vulnerability of this plant to deforestation and the replacement of coniferous forests by deciduous forests, and to the associated reduction in stand age. Brachypodium pinnatum (L.) Beauv. has a negative relationship with the stand age. Spearman’s correlation coefficient is −0.50 (Table 4). This plant species reaches maxi-mum productivity in deforestation and young forests. The research carried out has shown that there is a strong positive relationship between the biomass of Geum rivale L. and the proportion of birch and aspen in the composition of the stand. Spearman’s correlation coefficient is 0.68 (Table 4). Thus, the transformation of dark coniferous forests into birch and aspen forests contributes to an increase in the Geum rivale participation in the herbaceous layer biomass of the mountain forests of the Southern Urals. Aegopodium podagraria L., Aconitum septentrionale Koelle, Prunella vulgaris L. have also been found to have a fairly strong relationship between their biomass and stand age.
One of the null hypotheses was that there was a statistically significant relationship between the values of various diversity indices and the characteristics of the stand. The values of the diversity indices are shown in Table 5.
In primary dark coniferous forests (140–160 years old), species richness was relatively low (36–41 species). Shannon values ranged between 1.94 and 2.01, and the Pielou index between 0.53 and 0.57, indicating moderate diversity and evenness. Simpson index values (0.19–0.22) pointed to the presence of dominant species, a typical feature of late-successional, shade-tolerant communities.
After-cutting spruce and fir forests demonstrated substantially higher species richness (48–69 species). Diversity peaked at 70 years with 69 species, accompanied by Shannon values of 2.73–2.87 and Pielou indices of 0.57–0.70. Simpson values were low (0.08–0.16), reflecting more even species distributions and reduced dominance. In short-term secondary birch forests, species richness and diversity reached their maximum. Even at later successional stages (80–100 years), richness remained high (59–66 species) with Shannon indices of 2.80–3.28.
Long-term secondary birch forests showed more variable patterns. While early stages (8 years) maintained high richness (72 species) and Shannon values (2.69), older stands (35–100 years) revealed declines in richness (43–51 species) and diversity (Shannon 1.97–2.49). Higher Simpson values (0.21–0.27) and lower Pielou indices (0.51–0.53) indicated increasing dominance of competitive species and declining evenness.
In stable-term secondary aspen forests, richness ranged between 48 and 55 species. Shannon indices were consistently moderate to high (2.28–3.06), while Pielou values (0.59–0.77) confirmed relatively balanced community structures. Low Simpson values (0.08–0.13) indicated weak dominance throughout the successional stages studied. However, no statistically significant correlations were found between the diversity indices and the age and composition of the forest stand (Table 6). Statistically significant correlations were found only for the total biomass of the herbaceous layer and species richness.
Next, we tested the null hypothesis that the formation and dynamics of species composition and structure are unrelated to the dynamics of environmental factors transformed by emerging stands during regenerative succession. The obtained correlation coefficients were found to be in good agreement with the ecological characteristics of the species, as estimated using new European indicator values (Table 7). A close negative relationship has been revealed between the age of the stand and the plants’ attitude to light (EIVEres-L). It has also been established that EIVEres-N and EIVEres-R have the strongest correlation with the proportion of birch and aspen in the stand’s composition. This relationship is positive (Table 7).

4. Discussion

The research we conducted is the first complete or basic study of the relationship between stand dynamics and herbaceous layer of the unique dark coniferous forests of the Southern Urals, Russia, conducted on the basis of quantitative indicators. The forests of the Ural Mountains play an essential role in the protection of soil from erosion and the conservation and management of water resources. The ecological functions of these forests are important not only in the Urals, but throughout Eurasia. However, it is evident that degradation of the mountain forest has occurred across all the criteria considered. The DCA ordination diagram clearly showed the uniqueness and vulnerability of primary dark coniferous forests, separating them from secondary forests. Even spruce and fir forests, which are the most structurally similar, were clearly separated. This proves that there has been a high degree of transformation in both the species composition and the abundance ratios of species in secondary plant communities. Our previous studies [62] showed that only 8% of the forested area remained dark coniferous forest. The structure of the forest fund is dominated by long-term and sustainable secondary forests, where the restoration of the dominance of Siberian spruce and Siberian fir will take a long time. The share of short-term secondary stands is only 5%: 4% of them are short-term secondary birch forests and 1% are short-term secondary aspen forests. The anthropogenic transformation not only affects the tree cover but also changes the species composition and structure of the lower layers. To date, there have been many examples of changes in both species composition and quantitative relationships be-tween species in the herbaceous layer [35]. However, this is the first time that we have analyzed the relationships between stand structure and the biodiversity and productivity of lower strata at a quantitative level. The analysis was carried out not only for the general criteria (total biomass of the herbaceous layer, diversity indices), but also to the relationships between the structure of the stand and the biomass of individual species of the herbaceous layer. These results highlight the complexity and variability of regenerative trajectories, with implications for forest management and biodiversity conservation. This is the first positive result in this area for the most widespread forest type, mountain forests in the Southern Urals. From an ecological perspective, the results obtained emphasize the vulnerability of herbaceous plants in dark coniferous forests to logging and the capacity of competitive grasses and sedges to proliferate in secondary forests. These findings undoubtedly have implications for nutrient cycling and biodiversity conservation in boreal forest ecosystems. On the one hand, our study is certainly regional, but on the other hand, similar forests, forest degradation, regenerative successions and the plant species studied are widespread in the boreal zone. Therefore, the research results will be of interest to many researchers whose interests are related to forest resources, biodiversity conservation and forest succession. In light of the global trend of primary forest decline and the spread of secondary plant communities, the research results are particularly relevant.
The main difference in our research is the minimization of subjective judgements, achieved through a rigorous methodology of data collection and analysis. Biomass itself is also recognized as one of the most important characteristics of a plant community [63,64]. Furthermore, it is a more objective reflection of the quantitative relationship between plant species than, for example, visual abundance on the Braun-Blanquet scale or coverage. Nevertheless, the acquisition of data regarding species productivity necessitates the utilization of specific expertise and is inherently labour intensive. Consequently, there is a paucity of data pertaining to the productivity of forest vegetation in the Ural region. The available data pertains primarily to the tree stand [65,66,67]. The dearth of data for modelling the productivity of herbaceous layer represents a persisting challenge that has yet to be fully addressed. This issue is not exclusive to the Ural forests; it is also prevalent in other regions and countries [68,69]. As a result, our team’s research is of significant value to the advancement of forestry and forest ecology. The relevance, methodological basis and scientific level of our study are comparable to those of other studies in the field [70,71,72].
Crucially, correlation analysis revealed statistically significant relationships between stand characteristics and the biomass and species composition of the herbaceous layer. These findings provide empirical verification of the hypothesis that overstory dynamics, particularly in terms of species composition and stand age, are the main drivers of understory biodiversity and productivity. Several species exhibited strong correlations with specific stand attributes. For instance, Oxalis acetosella was positively associated with older stands, confirming its status as an indicator species of late-successional coniferous ecosystems. In contrast, Deschampsia caespitosa and Brachypodium pinnatum displayed higher biomass in early successional or disturbed forests, reflecting their preference for open, light-rich environments. The results obtained confirm the vulnerability of dark coniferous forest species and suggest that changes to species composition are increasing due to greater anthropogenic impact on forests.
Primary dark coniferous forests, although structurally complex, support relatively low species richness, with dominance by a limited number of shade-tolerant taxa. By contrast, secondary birch stands, particularly at early stages, serve as biodiversity hotspots. Their high richness and evenness suggest that partial canopy openness favour coexistence of a wide spectrum of species. However, as birch forests mature, competitive exclusion processes lead to lower diversity and stronger dominance, reflecting a gradual homogenization of the understory. Aspen forests displayed a different trend, sustaining moderate to high diversity across their development. Their ability to maintain relatively high evenness, even in older stages, distinguishes them from birch stands. The research results are in good agreement with those of other researchers who have conducted studies in the Urals Mountains [21,22,35,73,74] and other regions. These studies confirm that changes in the composition of the tree layer and the stage of succession strongly impact the diversity of the understory. The patterns observed are similar to those in our study. What makes our study unique and novel is the identification of a correlation between herbaceous plants biomass and the age and composition of the forest stand. Our study enhances our understanding of common mechanisms operating at a wide geographical level.
Interestingly, while individual species responses were significantly associated with stand variables, traditional diversity indices (Shannon, Pielou, Simpson) showed no statistically significant correlation with stand age or composition. This suggests that species richness and evenness may remain relatively stable, even as community structure and functional composition undergo substantial shifts. However, the study did not analyze functional diversity. The hypothesis that functional diversity is sensitive to forest stand transformations is yet to be tested in future studies.
Furthermore, incorporating European Indicator Values (EIVs) into the analysis revealed that shifts in forest structure are closely linked to light demand (EIVEres-L) and nutrient and soil reaction preferences (EIVEres-N and EIVEres-R). The negative correlation between stand age and EIVEres-L reflects the degree to which the stand’s canopy intercepts light. Our study is in line with the findings of other researchers who have demonstrated that herbaceous layer biomass is more limited by light availability when the canopy is closed [21,22]. Of course, indicator species of late successional stages, such as Oxalis acetosella and Lycopodium clavatum, benefit in dark coniferous forests. This confirms the conclusions previously obtained by other researchers [21,22]. Meanwhile, the positive relationship between birch and aspen dominance and species with high nitrogen and pH requirements highlights the importance of tree species composition in shaping forest soils. However, initially, based on studies conducted in other climatic zones and other forest types [24], it was hypothesized that the main factors limiting the bioproductivity of the studied plants under the forest canopy would be light, temperature, and moisture. This research result is of interest as it demonstrates that the set of drivers is regional and forest-typological in nature and can vary significantly. It is imperative that this is given due consideration when making forecasts.
In general, this study provides new data on the relationships between the structure of forest stands and species composition in digressive and regenerative successions in boreal dark coniferous forests. The use of multidimensional ordination methods, correlation analysis and environmental indicator values provides a basis for understanding how biodiversity at lower levels of forest vegetation reacts to disturbances and stand restoration. In light of the accelerating pace of global climate change and the growing impact of human activities on ecosystems, our research findings are crucial for the planning of biodiversity conservation, restoration and sustainable forest management. Our results also confirm the importance of preserving primary forests and emphasize the need to consider not only the composition of tree species, but also the integrity of the entire plant community at all stages of succession. It is recommended to strengthen the protection of mountain conditionally indigenous dark coniferous forests and, if possible, exclude logging in them.

5. Conclusions

Thus, the studies conducted in the most widespread forest type (Moss spruce forest (Asaro europaei-Abietetea sibiricae according to the Braun-Blanquet classification)) of the western macroscline of the Southern Ural Mountains confirmed our null hypothesis about the existence of relationships between stand structure and herbaceous layer biomass and individual plant species. The positive correlations with the age of the stand are most pronounced for Oxalis acetosella L. and Lycopodium clavatum L. The negative correlations with the age of the stand are most pronounced for Deschampsia caespitosa (L.) P. Beauv., Brachypodium pinnatum (L.) Beauv. and Dactylis glomerata L. The positive correlations with the proportion of birch and aspen in the composition of the stand are most pronounced for Dactylis glomerata L., Geum rivale L., Aegopodium podagraria L., Aconitum septentrionale Koelle and Prunella vulgaris L. On the one hand, our study is certainly regional, but on the other hand, similar forests, forest degradation, regenerative successions and the plant species studied are widespread in the boreal zone. Therefore, the research results will be of interest to many researchers whose interests are related to forest resources, biodiversity conservation and forest succession. In order to expand the scope of the research results, further research is planned for other types of forests in the Ural Mountains.

Funding

The studies are carried out as a part of the state assignment of the Institute Botanic Garden, the Ural Branch of the Russian Academy of Sciences (state registration no. 123112700125-1).

Data Availability Statement

The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.

Acknowledgments

I would like to express my gratitude to Vladimir Evdokimov for his assistance in calculating the diversity indices and to George Andreev for his help in conducting field research and providing data on the age and composition of the stand.

Conflicts of Interest

The author declares no conflicts of interest.

Appendix A

Table A1. Ecological niche characteristics of the studied species according to new European indicator values.
Table A1. Ecological niche characteristics of the studied species according to new European indicator values.
EIVEres-MEIVEres-NEIVEres-REIVEres-LEIVEres-T
Aegopodium podagraria L.5.17.96.54.13.9
Ajuga reptans L.55.75.65.34.1
Asarum europaeum L.4.76.17.32.44.3
Galium odoratum (L.) Scop4.65.46.21.74.1
Athyrium filix-femina (L.) Roth.5.65.752.84
Brachypodium pinnatum (L.) Beauv.3.647.35.74.2
Calamagrostis arundinacea (L.) Roth.4.44.644.93.6
Carex atherodes Spreng.75.56.773.8
Carex pallescens L.53.746.53.5
Carex pilosa Scop.4.555.13.14.3
Cirsium heterophyllum (L.) Hill.66.456.52.6
Cirsium oleraceum (L.) Scop.6.167.45.73.9
Crepis paludosa (L.) Moench.6.45.66.66.23
Dactylis glomerata L.4.36.16.16.84.5
Dryopteris expansa (C. Presl) Fraser-Jenk. & Jermy4.93.534.33.2
Dryopteris filix-mas (L.) Schott4.85.85.433.9
Equisetum sylvaticum L.5.74.242.93
Filipendula ulmaria (L.) Maxim.6.45.75.86.43.4
Fragaria vesca L.4.45.35.75.94
Geranium sylvaticum L.56.55.95.22.8
Geum rivale L.6.15.15.85.83.3
Impatiens noli-tangere L.5.86.86.83.24
Juncus effusus L.6.24.24.16.94.2
Lathyrus vernus (L.) Bernh.4.54.77.33.54.2
Lycopodium annotinum L.5.12.82.42.62.9
Lycopodium clavatum L.4.11.61.86.93.2
Milium effusum L.4.85.75.43.23.8
Oxalis acetosella L.55.74.51.23.9
Phalaroides arundinacea (L.) Rauschert6.87.16.974.2
Bistorta officinalis Delarbre5.85.45.16.63
Prunella vulgaris L.4.94.75.86.64.1
Pulmonaria obscura Dumort5.177.53.23.9
Rubus saxatilis L.4.64.16.65.13.2
Solidago virgaurea L.4.34.24.453.7
Stachys sylvatica L.5.67.36.83.74
Stellaria bungeana Fenzl.5 3.72.8
Stellaria holostea L.4.65.25.54.44.5
Succisa pratensis Moench.5.82.65.66.63.9
Trollius europaeus L.5.95.66.17.72.5
Urtica dioica L.58.76.65.44.1
Vaccinium myrtillus L.4.72.724.82.9
Table A2. Core DCA indicators: the eigenvalues and the percentage of variance explained.
Table A2. Core DCA indicators: the eigenvalues and the percentage of variance explained.
DCA1DCA2
Eigenvalues41.2628.36
Percentage of variance explained0.600.41
Table A3. Spearman’s rank correlation coefficient between stand characteristics and species biomass of the herbaceous layer.
Table A3. Spearman’s rank correlation coefficient between stand characteristics and species biomass of the herbaceous layer.
Age, YearsBirch and Aspen in the Stand, %
Aconitum septentrionale Koelle0.090.53 *
Aegopodium podagraria L.−0.220.54 *
Ajuga reptans L.0.03−0.18
Alchemilla sp−0.29 *0.22
Asarum europaeum L.−0.140.19
Galium odoratum (L.) Scop0.150.21
Athyrium filix-femina (L.) Roth.0.030.02
Betonica officinalis L.−0.48 *0.17
Brachypodium pinnatum (L.) Beauv.−0.50 *0.27 *
Calamagrostis arundinacea (L.) Roth.−0.34 *0.03
Calamagrostis epigeios (L.) Roth.−0.23 *0.27 *
Carex atherodes Spreng.−0.35 *0.29 *
Carex pallescens L.−0.200.13
Carex pilosa Scop.−0.070.04
Cirsium heterophyllum (L.) Hill.−0.36 *0.49 *
Cirsium oleraceum (L.) Scop.0.140.47 *
Crepis paludosa (L.) Moench.0.070.20
Dactylis glomerata L.−0.50 *0.70 *
Deschampsia caespitosa (L.) P. Beauv.−0.61 *0.14
Dryopteris expansa (C. Presl) Fraser-Jenk. & Jermy0.14−0.38 *
Dryopteris filix-mas (L.) Schott−0.28 *0.24 *
Equisetum sylvaticum L.0.21−0.39 *
Festuca gigantea (L.) Vill.−0.26 *0.15
Filipendula ulmaria (L.) Maxim.−0.230.45 *
Fragaria vesca L.−0.08−0.07
Geranium sylvaticum L.−0.200.46 *
Geum rivale L.−0.200.68 *
Impatiens noli-tangere L.0.210.48 *
Juncus effusus L.−0.31 *−0.04
Lathyrus vernus (L.) Bernh.−0.24 *0.32 *
Lycopodium annotinum L.0.21−0.28 *
Lycopodium clavatum L.0.46 *−0.50 *
Milium effusum L.−0.060.38 *
Oxalis acetosella L.0.54 *−0.36 *
Phalaroides arundinacea (L.) Rauschert−0.40 *0.01
Bistorta officinalis Delarbre−0.40 *0.31 *
Prunella vulgaris L.−0.080.53 *
Pulmonaria obscura Dumort 0.04−0.38 *
Rubus saxatilis L.−0.10−0.05
Solidago virgaurea L.−0.41 *−0.17
Stachys sylvatica L.−0.220.52 *
Stellaria bungeana Fenzl.0.36 *0.17
Stellaria holostea L.0.040.50 *
Succisa pratensis Moench.−0.200.24 *
Trollius europaeus L.−0.15−0.09
Urtica dioica L.0.27 *0.34 *
Vaccinium myrtillus L.0.02−0.12
Note: * correlations are significant at the level of p < 0.05.

References

  1. Chen, S.; Woodcock, C.; Dong, L.; Tarrio, K.; Mohammadi, D.; Olofsson, P. Review of drivers of forest degradation and deforestation in Southeast Asia. Remote Sens. Appl. Soc. Environ. 2024, 3, 101129. [Google Scholar] [CrossRef]
  2. Kacic, P.; Kuenzer, C. Forest Biodiversity Monitoring Based on Remotely Sensed Spectral Diversity—A Review. Remote Sens. 2022, 14, 5363. [Google Scholar] [CrossRef]
  3. 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]
  4. Casas, G.G.; Baselly-Villanueva, J.R.; Limeira, M.M.C.; Torres, C.M.M.E.; Leite, H.G. Classifying the risk of forest loss in the Peruvian amazon rainforest: An alternative approach for sustainable forest management using artificial intelligence. Trees For. People 2023, 14, 100440. [Google Scholar] [CrossRef]
  5. Santoro, A.; Piras, F.; Fiore, B.; Bazzurro, A.; Agnoletti, M. Forest-Cover Changes in European Natura 2000 Sites in the Period 2012–2018. Forests 2024, 15, 232. [Google Scholar] [CrossRef]
  6. Ivanova, N.; Fomin, V.; Kusbach, A. Experience of Forest Ecological Classification in Assessment of Vegetation Dynamics. Sustainability 2022, 14, 3384. [Google Scholar] [CrossRef]
  7. Mitchell, A.L.; Rosenqvist, A.; Mora, B. Current remote sensing approaches to monitoring forest degradation in support of countries measurement, reporting and verification (MRV) systems for REDD+. Carbon Balance Manag. 2017, 12, 9. [Google Scholar] [CrossRef]
  8. Maiti, R.; Rodriguez, H.G.; Ivanova, N.S. Autoecology and Ecophysiology of Woody Shrubs and Trees: Concepts and Applications; John Wiley & Sons: Oxford, UK, 2016; 352p. [Google Scholar] [CrossRef]
  9. Sparey, M.; Williamson, M.S.; Cox, P.M. Machine Learning for Global Bioclimatic Classification: Enhancing Land Cover Prediction through Random Forests. Atmosphere 2024, 15, 700. [Google Scholar] [CrossRef]
  10. Zavyalov, K.; Ivanova, N.; Potapenko, A.; Ayan, S. Influence of soil fertility on the ability of Scots pine (Pinus sylvestris L.) to adapt to technogenic pollution. Cerne 2019, 25, 326–331. [Google Scholar] [CrossRef]
  11. Liu, B.; Liao, C.; Chang, Y. Changing Dynamic of Tree Species Composition and Diversity: A Case Study of Secondary Forests in Northern China in Response to Climate Change. Forests 2024, 15, 322. [Google Scholar] [CrossRef]
  12. Qi, Y.; Hu, Y. Spatiotemporal Variation and Driving Factors Analysis of Habitat Quality: A Case Study in Harbin, China. Land 2024, 13, 67. [Google Scholar] [CrossRef]
  13. Luo, X.; Luo, Y.; Le, F.; Zhang, Y.; Zhang, H.; Zhai, J. Spatiotemporal Variation and Prediction Analysis of Land Use/Land Cover and Ecosystem Service Changes in Gannan, China. Sustainability 2024, 16, 1551. [Google Scholar] [CrossRef]
  14. Zevallos, J.; Lavado-Casimiro, W. Climate Change Impact on Peruvian Biomes. Forests 2022, 13, 238. [Google Scholar] [CrossRef]
  15. Ivanova, N.; Zolotova, E. Vegetation Dynamics Studies Based on Ellenberg and Landolt Indicator Values: A Review. Land 2024, 13, 1643. [Google Scholar] [CrossRef]
  16. Huete, A.R. Vegetation Indices, Remote Sensing and Forest Monitoring. Geogr. Compass 2012, 6, 513–532. [Google Scholar] [CrossRef]
  17. Torres, P.; Rodes-Blanco, M.; Viana-Soto, A.; Nieto, H.; García, M. The Role of Remote Sensing for the Assessment and Monitoring of Forest Health: A Systematic Evidence Synthesis. Forests 2021, 12, 1134. [Google Scholar] [CrossRef]
  18. Fassnacht, F.E.; White, J.C.; Wulder, M.A.; Næsset, E. Remote sensing in forestry: Current challenges, considerations and directions. For. Int. J. For. Res. 2024, 97, 11–37. [Google Scholar] [CrossRef]
  19. Liu, B.; Yu, P.; Wang, X.; Zhang, X.; Yu, Y.; Wan, Y.; Wang, Y.; Liu, Z.; Xu, L. Tradeoffs between Stand Volume and Understory Vegetation Diversity in Quercus wutaishanica Forests under Climate Change. Forests 2024, 15, 1750. [Google Scholar] [CrossRef]
  20. Liang, W.; Wei, X. Relationships between ecosystems above and below ground including forest structure, herb diversity and soil properties in the mountainous area of Northern China. Glob. Ecol. Conserv. 2020, 24, e01228. [Google Scholar] [CrossRef]
  21. Légaré, S.; Bergeron, Y.; Paré, D. Influence of Forest Composition on Understory Cover in Boreal Mixedwood Forests of Western Quebec. Silva Fenn. 2002, 36, 353–366. [Google Scholar] [CrossRef]
  22. Gilliam, F.S. The ecological significance of the herbaceous layer in temperate forest ecosystems. BioScience 2007, 57, 845–858. [Google Scholar] [CrossRef]
  23. Sannikova, N.S.; Sannikov, S.N.; Petrova, I.V.; Mishchikhina, Y.D.; Cherepanova, O.E. Competition factors of edificator tree stand: Quantitative analysis and synthesis. Russ. J. Ecol. 2012, 43, 426–432. [Google Scholar] [CrossRef]
  24. Piñar Fuentes, J.C.; Cano-Ortiz, A.; Musarella, C.M.; Quinto Canas, R.; Pinto Gomes, C.J.; Spampinato, G.; del Río, S.; Cano, E. Bioclimatology, Structure, and Conservation Perspectives of Quercus pyrenaica, Acer opalus subsp. Granatensis, and Corylus avellana Deciduous Forests on Mediterranean Bioclimate in the South-Central Part of the Iberian Peninsula. Sustainability 2019, 11, 6500. [Google Scholar] [CrossRef]
  25. Dauber, J.; Hirsch, M.; Simmering, D.; Waldhardt, R.; Otte, A.; Wolters, V. Landscape structure as an indicator of biodiversity: Matrix effects on species richness. Agric. Ecosyst. Environ. 2003, 98, 321–329. [Google Scholar] [CrossRef]
  26. Suchar, V.A.; Crookston, N.L. Understory cover and biomass indices predictions for forest ecosystems of the Northwestern United States. Ecol. Indic. 2010, 10, 602–609. [Google Scholar] [CrossRef]
  27. Zhigunova, S.N.; Martynenko, V.B.; Fedorov, N.I.; Shendel, G.V. The influence of climatic and topographic parameters on the distribution of forest communities of the Ufa plateau. IOP Conf. Ser. Earth Environ. Sci. 2021, 6, 012085. [Google Scholar] [CrossRef]
  28. Deng, L.; Han, Q.-S.; Zhang, C.; Tang, Z.-S.; Shangguan, Z.-P. Above-Ground and Below-Ground Ecosystem Biomass Accumulation and Carbon Sequestration with Caragana korshinskii Kom Plantation Development. Land Degrad. Dev. 2017, 28, 906–917. [Google Scholar] [CrossRef]
  29. Ma, H.; Mo, L.; Crowther, T.W.; Maynard, D.S.; van den Hoogen, J.; Stocker, B.D.; Terrer, C.; Zohner, C.M. The global distribution and environmental drivers of aboveground versus belowground plant biomass. Nat. Ecol. Evol. 2021, 5, 1110–1122. [Google Scholar] [CrossRef]
  30. Zhou, L.; Zhou, X.; He, Y.; Fu, Y.; Du, Z.; Lu, M.; Sun, X.; Li, C.; Lu, C.; Liu, R.; et al. Global systematic review with meta-analysis shows that warming effects on terrestrial plant biomass allocation are influenced by precipitation and mycorrhizal association. Nat. Commun. 2022, 13, 4914. [Google Scholar] [CrossRef]
  31. Zhang, Z.; Wang, K.; Li, G.; Xie, X.; Chang, X.; Zheng, J. Beyond shrub dieback: Understory plant diversity, soil water and soil carbon storage were improved in a semi-arid region. For. Ecol. Manag. 2023, 545, 121267. [Google Scholar] [CrossRef]
  32. Marefat, S.; Rad, J.E.; Khanalizadeh, A. Effects of mixed beech and hornbeam stands on soil properties and plant species diversity indices in hyrcanian forests of Iran. Madera Bosques 2020, 26, 1–14. [Google Scholar] [CrossRef]
  33. Ivanova, N.S. Differentiation of forest vegetation after clear-cuttings in the Ural Mountains. Mod. Appl. Sci. 2014, 8, 195–203. [Google Scholar] [CrossRef]
  34. Latipova, Z.B.; Davletberdin, D.D. Geo-ecological assessment of anthropogenic changes to mountain and forest landscapes in Bashkortostan. Probl. Reg. Ekol. 2022, 6, 102–105. (In Russian) [Google Scholar] [CrossRef]
  35. Shirokikh, P.S.; Martynenko, V.B.; Baisheva, E.Z.; Bikbayev, I.G. Vegetation dynamics on felling in the southern ural region: Main results of studies of the Ufa geobotanical school. Fitoraznoobraziye Vostochnoy Yevropy 2018, 12, 17–30. (In Russian) [Google Scholar] [CrossRef]
  36. Martynenko, V.B.; Shirokikh, P.S.; Mirkin, B.M.; Naumova, L.G. Syntaxonomic analysis of restorative succession after cutting down light coniferous forests of South Ural region. Biol. Bull. Rev. 2014, 75, 478–490. (In Russian) [Google Scholar]
  37. Fedorov, N.I.; Martynenko, V.B.; Zhigunova, S.N.; Shendel, G.V.; Mikhailenko, O.I.; Naumova, L.G. Changes in the distribution of broadleaf tree species in the central part of the Southern Urals since the 1970s. Russ. J. Ecol. 2021, 52, 118–125. [Google Scholar] [CrossRef]
  38. Kucherov, S.E.; Kucherova, S.V.; Martynenko, V.B.; Velmovskiy, P.V. Climatic signal in tree rings of the common oak (Quercus robur L.) on the south-eastern border of its range in the Ural River basin, Russia. South Russ. Ecol. Dev. 2023, 18, 34–45. (In Russian) [Google Scholar] [CrossRef]
  39. Martynenko, V.; Shirokikh, P.; Solomeshch, A.; Muldashev, A. Vegetation Database Forest of Southern Ural. Biodivers. Ecol. 2012, 4, 289. [Google Scholar] [CrossRef][Green Version]
  40. Usoltsev, V.A. Sample Tree Biomass Data for Eurasian Forests. CD-Version in English and Russian; Yekaterinburg Ural State Forest Engineering University: Yekaterinburg, Russia, 2015; ISBN 978-5-94984-521-9. [Google Scholar] [CrossRef]
  41. Karger, D.N.; Conrad, O.; Böhner, J.; Kawohl, T.; Kreft, H.; Soria-Auza, R.W.; Zimmermann, N.E.; Linder, P.; Kessler, M. Climatologies at high resolution for the Earth land surface areas. Sci. Data 2017, 4, 170122. [Google Scholar] [CrossRef]
  42. Brun, P.; Zimmermann, N.E.; Hari, C.; Pellissier, L.; Karger, D. CHELSA-BIOCLIM+ A novel set of global climate-related predictors at kilometre-resolution. EnviDat 2022. [Google Scholar] [CrossRef]
  43. Martynenko, V.B. Syntaxonomy of native forests of the South Ural region and their mountain analogues. In Diversity and Dynamics of Forest Ecosystems of Russia; Isaev, A.S., Ed.; KMK: Moscow, Russia, 2013; Volume 2, pp. 67–129. [Google Scholar]
  44. Shirokikh, P.S.; Baisheva, E.Z.; Martynenko, V.B.; Muldashev, A.A.; Alibaev, F.H.; Gorichev, Y.P.; Davydychev, A.N. Flora and Vegetation of the South Ural State Nature Reserve; Mirkin, B.M., Ed.; Gilem: Ufa, Russia, 2008; 528p. [Google Scholar]
  45. Martynenko, V.; Shirokikh, P.; Baisheva, E.; Muldashev, A.; Fedorov, N.; Zhigunova, S.; Naumova, L. Phytosociological Analysis of the Boreal Oligotrophic Pine Forests in the Southern Ural Region (Russia). Forests 2024, 15, 1461. [Google Scholar] [CrossRef]
  46. Ivanova, N.S. Biodiversity of Old-growth Coniferous Forests in the Ural Mountains. Int. J. Bio-Resour. Stress Manag. 2019, 10, 251–256. [Google Scholar] [CrossRef]
  47. Filroze, E.M. Scheme of genetic classification of forest types in the Southern Urals. In Ecological–Geographical and Genetic Principles of Forest Study; UNC of the USSR Academy of Sciences: Sverdlovsk, Russia, 1983; pp. 53–60. [Google Scholar]
  48. Kolesnikov, B.P. The genetic stage in forest typology and its tasks. Russ. For. Sci. 1974, 2, 3–20. [Google Scholar]
  49. Fomin, V.; Mikhailovich, A.; Zalesov, S.; Popov, A.; Terekhov, G. Development of ideas within the framework of the genetic approach to the classification of forest types. Balt. For. 2021, 27, 1–14. [Google Scholar] [CrossRef]
  50. Ivanova, N. Research Methods of Timber-Yielding Plants (in the Example of Boreal Forests). In Biology, Productivity and Bioenergy of Timber-Yielding Plants; Springer: Cham, Switzerland, 2017; pp. 121–137. [Google Scholar] [CrossRef]
  51. Ter Braak, C.J.F.; Prentice, I.C. A Theory of Gradient Analysis. Adv. Ecol. Res. 1988, 18, 271–317. [Google Scholar] [CrossRef]
  52. Gauch, H.G., Jr. Multivariate Analysis in Community Structure; Cambridge University Press: Cambridge, UK, 1982; 298p. [Google Scholar]
  53. Gilliam, F.S.; Saunders, N.E. Making more sense of the order: A review of Canoco for Windows 4.5, PC–ORD version 4/F.S. Gilliam. J. Veg. Sci. 2003, 14, 297–304. [Google Scholar] [CrossRef]
  54. Hill, M.O.; Gauch, H.G., Jr. Detrended Correspondence Analysis: An improved ordination technique. Vegetatio 1980, 42, 47–58. [Google Scholar] [CrossRef]
  55. Oksanen, J. Multivariate Analysis of Ecological Communities in R: Vegan Tutorial. 2013. Available online: https://www.researchgate.net/publication/313074248_Multivariate_analysis_of_ecological_communities_in_R_vegan_tutorial (accessed on 1 July 2025).
  56. Oksanen, J.; Blanchet, F.; Kindt, R.; Legendre, P.; Minchin, P.; O’Hara, R.; Simpson, G.; Solymos, P.; Stevens, M.; Wagner, H. Vegan: Community Ecology. R Package Version 2.2-1. 2015. Available online: http://CRAN.R-project.org/package=vegan (accessed on 1 July 2025).
  57. Myers, J.L.; Well, A.D.; Lorch, R.F. Research Design and Statistical Analysis, 3rd ed.; Taylor and Francis: Abingdon, UK, 2013; 809p. [Google Scholar] [CrossRef]
  58. Tichý, L.; Axmanová, I.; Dengler, J.; Guarino, R.; Jansen, F.; Midolo, G.; Nobis, M.P.; Van Meerbeek, K.; Attorre, F.; Bergmeier, E.; et al. Ellenberg-type indicator values for European vascular plant species. J. Veg. Sci. 2023, 34, e13168. [Google Scholar] [CrossRef]
  59. Zolotova, E.; Ivanova, N.; Ivanova, S. Global Overview of Modern Research Based on Ellenberg Indicator Values. Diversity 2023, 15, 14. [Google Scholar] [CrossRef]
  60. Ivanova, N.; Zolotova, E. Landolt Indicator Values in Modern Research: A Review. Sustainability 2023, 15, 9618. [Google Scholar] [CrossRef]
  61. Dengler, J.; Jansen, F.; Chusova, O.; Hüllbusch, E.; Nobis, M.P.; Van Meerbeek, K.; Axmanová, I.; Bruun, H.H.; Chytrý, M.; Guarino, R.; et al. Ecological Indicator Values for Europe (EIVE) 1.0. Veg. Classif. Surv. 2023, 4, 7–29. [Google Scholar] [CrossRef]
  62. Andreev, G.V.; Ivanova, N.S. Short-term restorative successions in spruce forests of South Urals. Biosfera 2024, 14, 183–248. [Google Scholar]
  63. Zhang, Y.; Liang, S.; Yang, L. A Review of Regional and Global Gridded Forest Biomass Datasets. Remote Sens. 2019, 11, 2744. [Google Scholar] [CrossRef]
  64. Tian, L.; Wu, X.; Tao, Y.; Li, M.; Qian, C.; Liao, L.; Fu, W. Review of Remote Sensing-Based Methods for Forest Aboveground Biomass Estimation: Progress, Challenges, and Prospects. Forests 2023, 14, 1086. [Google Scholar] [CrossRef]
  65. Dyderski, M.K.; Paź, S.; Frelich, L.E.; Jagodziński, A.M. How much does climate change threaten European forest tree species distributions? Glob. Change Biol. 2018, 24, 1150–1163. [Google Scholar] [CrossRef]
  66. Usoltsev, V.A.; Piernik, A.; Osmirko, A.A.; Tsepordey, I.S.; Chasovskikh, V.P.; Zukow, W. Forest stand biomass of Picea spp.: An additive model that may be related to climate and civilisational changes. Bull. Geography. Socio-Econ. Ser. 2019, 45, 133–147. [Google Scholar] [CrossRef]
  67. Hordijk, I.; Maynard, D.S.; Hart, S.P.; Lidong, M.; ter Steege, H.; Liang, J.; de-Miguel, S.; Nabuurs, G.J.; Reich, P.B.; Abegg, M.; et al. Evenness mediates the global relationship between forest productivity and richness. J. Ecol. 2023, 111, 1308–1326. [Google Scholar] [CrossRef]
  68. Shugart, H.H.; Wang, B.; Fischer, R.; Ma, J.; Fang, J.; Yan, X.; Huth, A.; Armstrong, A.H. Gap models and their individual–based relatives in the assessment of the consequences of global change. Environ. Res. Lett. 2018, 13, 033001. [Google Scholar] [CrossRef]
  69. Landuyt, D.; Maes, S.L.; Depauw, L.; Ampoorter, E.; Blondeel, H.; Perring, M.P.; Brūmelis, G.; Brunet, J.; Decocq, G.; Den Ouden, J.; et al. Drivers of above-ground understorey biomass and nutrient stocks in temperate deciduous forests. J. Ecol. 2020, 108, 982–997. [Google Scholar] [CrossRef]
  70. Groote, S.D.; van Schrojenstein, L.I.M.; Sercu, B.K.; Lens, L. Tree species identity outweighs the effects of tree species diversity and forest fragmentation on understorey diversity and composition. Plant Ecol. Evol. 2017, 150, 229–239. [Google Scholar] [CrossRef]
  71. Pires, A.P.F.; Srivastava, D.S.; Marino, N.A.C.; MacDonald, A.A.M.; Figueiredo-Barros, M.P.; Farjalla, V.F. Interactive effects of climate change and biodiversity loss on ecosystem functioning. Ecology 2018, 99, 1203–1213. [Google Scholar] [CrossRef]
  72. Li, M.; Zhang, X.; Niu, B.; He, Y.; Wang, X.; Wu, J. Changes in plant species richness distribution in Tibetan alpine grasslands under different precipitation scenarios. Glob. Ecol. Conserv. 2020, 21, e00848. [Google Scholar] [CrossRef]
  73. Kuuluvainen, T.; Aakala, T. Natural forest dynamics in boreal Fennoscandia: A review and Classification. Silva Fenn. 2011, 45, 823–841. [Google Scholar] [CrossRef]
  74. Kryshen, A.; Genikova, N. Changes in the Number of Vascular Plant Species during Reforestation of Clearcut Forests. Forests 2023, 14, 2395. [Google Scholar] [CrossRef]
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Figure 1. Location of the study area in Chelyabinsk region (Russia).
Figure 1. Location of the study area in Chelyabinsk region (Russia).
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Figure 2. Forests under investigation.
Figure 2. Forests under investigation.
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Figure 3. Research scheme.
Figure 3. Research scheme.
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Figure 4. Differentiation of forest vegetation within one primary type of forest: moss spruce forests (Asaro europaei-Abietetea sibiricae according to the Braun-Blanquet classification): F1—Proportion of Picea obovata Ledeb., Abies sibirica Ledeb. in the composition of the stand; F2—Age of the stand (years): The numbers on the figure indicate the successional status of the forest community: 1—Primary dark coniferous forests (140–160 years old), 2—After-cutting spruce and fir forests (50–70 years old); 3—Short-term secondary birch forests (20 years old); 4—Short-term secondary birch forests (80 years old); 5—Short-term secondary birch forests (100 years old); 6—Logging area (Short-term secondary young birch forests; 5 years of harvesting); 7—Long-term secondary birch forests (20 years old); 8—Long-term secondary birch forests (35 years old); 9—Long-term secondary birch forests (65–100 years old); 10—Stable-term secondary aspen forests (65–110 years old); 11—Stable-term secondary young aspen forests (7–8 years old); 12—Stable-term secondary aspen forests (20 years old).
Figure 4. Differentiation of forest vegetation within one primary type of forest: moss spruce forests (Asaro europaei-Abietetea sibiricae according to the Braun-Blanquet classification): F1—Proportion of Picea obovata Ledeb., Abies sibirica Ledeb. in the composition of the stand; F2—Age of the stand (years): The numbers on the figure indicate the successional status of the forest community: 1—Primary dark coniferous forests (140–160 years old), 2—After-cutting spruce and fir forests (50–70 years old); 3—Short-term secondary birch forests (20 years old); 4—Short-term secondary birch forests (80 years old); 5—Short-term secondary birch forests (100 years old); 6—Logging area (Short-term secondary young birch forests; 5 years of harvesting); 7—Long-term secondary birch forests (20 years old); 8—Long-term secondary birch forests (35 years old); 9—Long-term secondary birch forests (65–100 years old); 10—Stable-term secondary aspen forests (65–110 years old); 11—Stable-term secondary young aspen forests (7–8 years old); 12—Stable-term secondary aspen forests (20 years old).
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Table 1. Bioclimatic variables for the study area in Chelyabinsk region (Russia).
Table 1. Bioclimatic variables for the study area in Chelyabinsk region (Russia).
Bioclimatic VariablesDescriptionValue
BIO1Annual Mean Temperature2.25
BIO2Mean Diurnal Range (Mean of monthly (max temp − min temp))7.3
BIO3Isothermality (BIO2/BIO7)0.19
BIO4Temperature Seasonality (standard deviation × 100)1109.8
BIO5Max Temperature of Warmest Month22.35
BIO6Min Temperature of Coldest Month−15.95
BIO7Temperature Annual Range (BIO5-BIO6)38.3
BIO8Mean Temperature of Wettest Quarter16.45
BIO9Mean Temperature of Driest Quarter−4.65
BIO10Mean Temperature of Warmest Quarter16.45
BIO11Mean Temperature of Coldest Quarter−12.15
BIO12Annual Precipitation575.7
BIO13Precipitation of Wettest Month68.7
BIO14Precipitation of Driest Month25.1
BIO15Precipitation Seasonality (Coefficient of Variation)31.5
BIO16Precipitation of Wettest Quarter201.5
BIO17Precipitation of Driest Quarter85.6
BIO18Precipitation of Warmest Quarter201.5
BIO19Precipitation of Coldest Quarter102.1
Table 2. Evaluation of the statistical significance of the correlation between the values of the ordination axes (site scores) and the age and composition of the stand.
Table 2. Evaluation of the statistical significance of the correlation between the values of the ordination axes (site scores) and the age and composition of the stand.
DCA1DCA2r2p-Value
F1—Proportion of Picea obovata Ledeb., Abies sibirica Ledeb. in the composition of the stand0.996−0.130.670.001
F2—Age of the stand (years)0.59−0.810.240.001
Table 3. Biomass (absolutely dry condition) of the dominant herbaceous species in the studied forests.
Table 3. Biomass (absolutely dry condition) of the dominant herbaceous species in the studied forests.
Age, YearsBirch and Aspen in the Stand, %Dominant Species, Biomass, g/m2 (Coefficient of Variation)Subdominant Species, Biomass, g/m2 (Coefficient of Variation)
16010Lycopodium clavatum L., 13.62 (123)Calamagrostis arundinacea (L.) Roth, 5.98 (206)
14020Equisetum sylvaticum L., 12.54 (169)Athyrium filix-femina (L.) Roth, 6.9 (346)
5040Ajuga reptans L., 3.32 (121)Calamagrostis arundinacea (L.) Roth, 2.77 (131)
6540Calamagrostis arundinacea (L.) Roth, 20.75 (152)Carex pilosa Scop., 11.47 (141)
7020Filipendula ulmaria (L.) Maxim., 32.64 (10)Carex pilosa Scop., 17.45 (140)
550Calamagrostis arundinacea (L.) Roth, 35.30 (80)Phalaroides arundinacea (L.) Rauschert, 17.62 (205)
2070Carex pilosa Scop., 13.9 (87)Rubus saxatilis L., 5.12 (144)
8050Dryopteris filix-mas (L.) Schott, 3.44 (310)Brachypodium pinnatum (L.) Beauv., 2.42 (369)
10040Stellaria bungeana Fenzl, 8.58 (165)Calamagrostis arundinacea (L.) Roth, 6.68 (199)
880Brachypodium pinnatum (L.) Beauv., 35.47 (49)Carex pilosa Scop., 8.77 (93)
3590Carex pilosa Scop., 35.85 (49)Calamagrostis arundinacea (L.) Roth, 17.63 (92)
5090Brachypodium pinnatum (L.) Beauv., 7.55 (120)Carex pilosa Scop., 6.29 (100)
10090Carex pilosa Scop., 21.38 (125)Carex pallescens L., 12.76 (180)
899Calamagrostis arundinacea (L.) Roth, 17.79 (106)Geum rivale L., 16.28 (92)
2099Calamagrostis arundinacea (L.) Roth, 18.20 (84)Brachypodium pinnatum (L.) Beauv., 9.92 (113)
6599Aconitum septentrionale Koelle, 32.99 (99)Stachys sylvatica L., 30.18 (176)
11099Aconitum septentrionale Koelle, 32.44 (112)Stachys sylvatica L., 22.89 (97)
Table 4. Spearman’s rank correlation coefficient between stand characteristics and species biomass of the herbaceous layer.
Table 4. Spearman’s rank correlation coefficient between stand characteristics and species biomass of the herbaceous layer.
Age, YearsBirch and Aspen in the Stand, %
Aconitum septentrionale Koelle0.090.53 *
Aegopodium podagraria L.−0.220.54 *
Alchemilla sp−0.29 *0.22
Betonica officinalis L.−0.48 *0.17
Brachypodium pinnatum (L.) Beauv.−0.50 *0.27 *
Calamagrostis arundinacea (L.) Roth.−0.34 *0.03
Calamagrostis epigeios (L.) Roth.−0.23 *0.27 *
Carex atherodes Spreng.−0.35 *0.29 *
Cirsium heterophyllum (L.) Hill.−0.36 *0.49 *
Cirsium oleraceum (L.) Scop.0.140.47 *
Dactylis glomerata L.−0.50 *0.70 *
Deschampsia caespitosa (L.) P. Beauv.−0.61 *0.14
Dryopteris expansa (C. Presl) Fraser-Jenk. & Jermy0.14−0.38 *
Dryopteris filix-mas (L.) Schott−0.28 *0.24 *
Equisetum sylvaticum L.0.21−0.39 *
Festuca gigantea (L.) Vill.−0.26 *0.15
Filipendula ulmaria (L.) Maxim.−0.230.45 *
Geranium sylvaticum L.−0.200.46 *
Geum rivale L.−0.200.68 *
Impatiens noli-tangere L.0.210.48 *
Juncus effusus L.−0.31 *−0.04
Lathyrus vernus (L.) Bernh.−0.24 *0.32 *
Lycopodium annotinum L.0.21−0.28 *
Lycopodium clavatum L.0.46 *−0.50 *
Milium effusum L.−0.060.38 *
Oxalis acetosella L.0.54 *−0.36 *
Phalaroides arundinacea (L.) Rauschert−0.40 *0.01
Bistorta officinalis Delarbre−0.40 *0.31 *
Prunella vulgaris L.−0.080.53 *
Pulmonaria obscura Dumort 0.04−0.38 *
Solidago virgaurea L.−0.41 *−0.17
Stachys sylvatica L.−0.220.52 *
Stellaria bungeana Fenzl.0.36 *0.17
Stellaria holostea L.0.040.50 *
Succisa pratensis Moench.−0.200.24 *
Urtica dioica L.0.27 *0.34 *
Note: * correlations are significant at the level of p < 0.05.
Table 5. Diversity indices of the forests studied.
Table 5. Diversity indices of the forests studied.
Age, YearsBirch and Aspen in the Stand, %Species RichnessSimpson Diversity IndexShannon Diversity IndexPielu Diversity Index
Primary dark coniferous forests16010360.222.010.57
14020410.191.940.53
After-cutting spruce and fir forests5040480.082.870.70
6540540.162.420.57
7020690.132.730.63
Short-term secondary birch forests550620.182.320.56
2070740.093.000.70
8050660.053.280.79
10040590.102.800.69
Long-term secondary birch forests880720.152.690.63
3590430.271.970.53
5090490.132.490.65
10090510.212.000.51
Stable-term secondary aspen forests899490.132.280.59
2099550.083.060.77
6599530.122.540.65
11099480.082.800.73
Table 6. Spearman’s correlation coefficient between stand characteristics and diversity indices.
Table 6. Spearman’s correlation coefficient between stand characteristics and diversity indices.
Age, YearsBirch and Aspen in the Stand, %
Total herbaceous layer biomass−0.46 *0.66 *
Number of species−0.38 *0.45 *
Shannon diversity index−0.240.24
Pielu diversity index−0.230.32
Simpson diversity index0.15−0.31
Note: * correlations are significant at the level of p < 0.05.
Table 7. Relationship between a plant species’ ecological niche and its response to stand structure.
Table 7. Relationship between a plant species’ ecological niche and its response to stand structure.
Age, YearsBirch and Aspen in the Stand, %
EIVEres-M−0.020.16
EIVEres-N−0.060.45 *
EIVEres-R−0.150.44 *
EIVEres-L−0.46 *0.17
EIVEres-T−0.140.25
Note: * correlations are significant at the level of p < 0.05.
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Ivanova, N. Forest Stand Changes Drive Conservation of Understory Composition and Biomass in the Boreal Forest of the Southern Urals. Diversity 2025, 17, 672. https://doi.org/10.3390/d17100672

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Ivanova N. Forest Stand Changes Drive Conservation of Understory Composition and Biomass in the Boreal Forest of the Southern Urals. Diversity. 2025; 17(10):672. https://doi.org/10.3390/d17100672

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Ivanova, Natalya. 2025. "Forest Stand Changes Drive Conservation of Understory Composition and Biomass in the Boreal Forest of the Southern Urals" Diversity 17, no. 10: 672. https://doi.org/10.3390/d17100672

APA Style

Ivanova, N. (2025). Forest Stand Changes Drive Conservation of Understory Composition and Biomass in the Boreal Forest of the Southern Urals. Diversity, 17(10), 672. https://doi.org/10.3390/d17100672

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