Currently, human activity is recognized as an important factor in recent climate change, which is often called global warming (Anderegg et al., 2010; Climate Change, 2014; Haunschild et al., 2016). Most researchers believe that advancing climate change can significantly affect the forest ecosystem biomass and species composition (Kellomaki, 2016; Schaphoffa et al., 2016; Ochuodho, 2016; Murray et al., 2017). Many researchers use anthropogenic impact scenarios to describe and predict climate change (Casajus et al., 2016). This leads to a rapid increase in the number of publications on this topic; every 5-6 years of publication becomes two times more (Grieneisen and Zhang, 2011; Haunschild et al., 2016). The most numerous are the studies devoted to the problem of bioproductivity. Climate change modeling is second in the publication rate. The search for adaptation mechanisms is a new and rapidly developing scientific field, the publications of which are actively read and quoted (Haunschild et al., 2016). Climate change management is an extraordinarily challenging task that is being addressed by researchers from various countries (Kellomaki, 2016).

The general approach of determination of productivity (Net Primary Prodcutivity, NPP) relies on the use of land cover maps in combination with bioproductivity based on GIS technologies (Jiang et al., 1999). LAI index (the ratio of leaf area to the area occupied by plantation) is a highly informative characterization of forest canopy associated with its energy and mass transfer, and is evaluated with satellite sensors with high resolution in wide areas (Running et al., 1986).

It is introduced in the model as the main independent variable for calculating the processes of light interception of the canopy, transpiration, photosynthesis, growth, and carbon sequestration. However, large databases make it difficult to use generally accepted methods of statistical analysis. Lankin and Ivanova (2015) propose to use dynamic neural networks, which are extremely flexible and efficient tool for data analysis. High flexibility, precision, and high simulation efficiency are important features of neural networks which allow solving wide range of tasks using the same mathematical algorithms (Lankin et al., 2012). Modeling and forecasting using artificial neural networks is based on samples of the source data required for training neural networks. There are examples that show a good ratio of empirical and theoretical data (Lankin et al., 2012).

Kellomaki (2016) explores the carbon cycle and he comes to the conclusion that the patterns found will help in solving the problem of climate change mitigation. His studies unite experimental and modeling approaches to discuss how to use climate change to one’s advantage and optimize forest management in the boreal zone.

Ecological studies are focused on the analysis of plant habitats and transformation of growing conditions, which are initiated by factors of a changing climate (Lawler, 2013). However, various researchers obtained conflicting results using different methods (Thuiller, 2004). Therefore, new approaches are being tested to solve this problem. Chai et al. (2016) use a synthesis of traditional risk assessments with habitat suitability modeling to disseminate and introduce new species. The proposed approach is promising to use for the analysis of ecological niche species and the transformation of the species structure.

Murray et al. (2017) use large-scale environmental niche models. They found that climatic factors have a significant impact on the distribution and growth of most species in North America.

D’Orangeville et al. (2016) predict the effects of climate warming on natural complexes in North America. The study of the amiual growth of trees allowed researchers to identify habitats in which current climate change will favorably affect the growth of woody plants.


Currently, most boreal forests are used to produce wood. The sustainability of ecosystems is on the verge of disaster (Gauthier et al., 2015). Wildfires are of particular concern and can cause enormous economic damage (Rodriguez- Baca et al., 2016). In boreal forests, fire is a force that can influence forest succession and structure (Li et al., 2013). Forecast and observations warn that the number of wildfires will increase with climate warming (Krawchuk et al., 2009). These forecasts are valid for both Russia and Canada (Flannigan et al., 2005). Therefore, a lot of publications are devoted to the development of sustainable forestry, forest protection from fires, and reforestation (Gunn, 2007; Li et al., 2013; Kuuluvainen, 2016).

The greatest difficulties in the organization of sustainable forestry are complicated by the problem of uncertainty (Rodriguez-Васа et al., 2016). The use of risk and uncertainty accounting methods is a good basis for decision-making concerning natural resource management (Hildebrandt and Клоке, 2011). Therefore, further development of risk assessment methods in forestry is extremely important and should be strengthened in the near future (Yousefpour et al., 2012; Rodriguez-Васа et al., 2016).

A. Komarov and his colleagues developed system of forest ecosystems models EFIMOD (Komarov et al., 2003). The model allows you to calculate the growth of a single tree with the help of maximum net biological productivity per unit mass of photosynthetic organs. Further, the potential growth is reduced depending on the shading of the tree and availability of soil nitrogen. Annual growth of biomass is allocated to organs of the tree (trunk, branches, and roots). Researchers are focused on interactions between trees like competition for light and competition for available nitrogen in the soil, which allows simulating various reforestation and logging.

The next achievement was the program the EFIMOD 2 that described and predicted the growth not only of individual woody plants, but also of the whole planting as a whole. Further, it led to the opportunity to analyze the circulation of elements in the plants (Komarov et al., 2003). The model can work with the following wood species: Punts sylvestris L., Picea abies

L. Karst, and Betula pendnla L. The structure and biodiversity of modern boreal forests in Eurasia is the result of long-term economic use (Kalyakin et al., 2016; Korotkov, 2017).


Ural Mountains (Russia) are located in the center of Eurasia (Fig. 3.1) (Komar and Chikushev, 1968).

Location of the Ural Mountains

FIGURE 3.1 Location of the Ural Mountains.

The Ural Mountains are among the oldest mountains. Their history is long and complex. It begins in the Proterozoic era with the breach of the crust. This period lasted 2 billion years. At this tune, on the site of the mountains settled the ocean. The fonnation of the Ural Mountains began 300 million years ago (Komar and Chikushev, 1968).

Now the Ural Mountains are a whole system of mountain ridges, which extend parallel to one another in the meridional direction. The Ural Mountains are represented by low ridges of great length. The highest of them have a height of 1200-1640 m above sea level. The height of the Middle Ural Mountains is not more than 600-650 m above sea level (Komar and Chikushev, 1968).

The long time of fonnation is contributed to the increase in the diversity of natural complexes. Forests are the most common type of vegetation. The forests stretch along the mountain slopes of the Ural Mountains continuous strip. Dark coniferous forests prevail on the western slopes. Spruce forests and fir forests are dominant ecosystems. Pine forests prevail on the eastern slopes. The geographical location, a large distance from north to south, extreme heterogeneity of landscapes, and long-term intensive economic activities make the Ural Mountains a unique object for studying forest dynamics (Komar and Chikushev, 1968).

The dynamics of vegetation due to climate is most clearly seen in the mountains at the upper limit of the distribution of woody plants. This problem has been studied for many years by Russian researchers (Shiyatov, 1995; Mazepa, 2005; Kapralov et al., 2006; Hagedorn et al., 2014). A survey of highland areas revealed the emergence of abundant undergrowth of conifers above the forest boundary. The cause of this phenomenon is climate warming. Long-term studies in the Polar Urals have established the beginning of the active invasion of woody vegetation into the mountain tundra. This process actively proceeds about 100 years. Probably, this tendency speaks of mitigating the factors limiting the distribution of woody plants. Stepana Shiyatov proposed a unique method for monitoring the state of vegetation. This method consists in photographing landscapes from one point, but after many years and decades. He received several thousands of landscape photos. He compared the photos which are taken at different tunes in the Southern, North, Subpolar, and Polar Urals. His study found that forest boundary rose to 4-8 m in all four regions over the past decade, and the forests became more dense (Hagedom et al., 2014).

Typological studies of forests are of particular importance for scientific research and silviculture, since on their basis continual vegetation cover is divided into discrete units with which to work. The large-scale anthropogenic destruction of climax forests and the invasion of dynamic secondary plant communities necessitated the reflection of this process in forest classification schemes. Kolesnikov et al. (1973) have done a great deal of research on forest dynamics. The most detailed forest type schemes are the result of their hard work of many years. However, detailed quantitative characteristics of the structure of vegetation were not enough in these schemes until recently. Therefore, E. S. Zolotova and N. S. Ivanova (Ivanova and Zolotova, 2013) conducted a comprehensive study of the characteristics of plants and soils from 12 types of forest and 11 species of cuttings in the Urals. A database was created with data on plant structure and physical and chemical characteristics of natural forests and clear fellings within a unified topoecological profile. The scientists have found that each forest type and cutting type has its unique vegetation structure and dynamics, as well as unique patterns of soil evolution within soil profiles (Zolotova, 2013).

Forest syntaxonomy develops parallel to forest typology (Mirkin et al., 2009, 2014; Mirkin, Ermakov, 2010). Initially, this scientific direction was intended to study the diversity of plant communities (Mirkin et al., 2014). Currently, forest syntaxonomy has been tested to compare climax forests with secondary communities (Mirkin et al., 2015). Ural researchers attempted to analyze successions using the floristic approach methodology (Martynenko et al., 2014).

Research on the biomass of the Ural forests has received close attention for many years. Many years of research by Usoltsev (2007) are of interest from the point of view of the collected databases, unique techniques, and thoughtful in-depth analysis. He focused on the uncertainties that lead to risks in forestry. The formation of the most complete database, which includes almost all available information on the biomass of woody plants, can be considered an outstanding achievement of this researcher (Usoltsev, 2001). Identifying dependencies based on multiple regression is a second important result. Conduct drawing detailed maps of the carbon pool of the Ural Federal district is another valuable direction of research.

Thus, despite the huge number of publications that have been made within the framework of this scientific direction, satisfactory solutions have not been obtained at present. The effects of various natural and human factors are interrelated very often. Their actions overlap and reinforce the final effect, which is likely to lead to even greater environmental problems. Therefore, the study of adaptive strategies of woody plants is extremely important (Schaphoffa et al., 2016).

A comprehensive analysis that includes all plant species can be extremely useful for objectively assessing the extent of climate change (Murray et al., 2017). Understanding these processes is critical to developing a strategy for sustainable management of resources (Lankin and Ivanova, 2015; Murray et al., 2017).

Research objective: study of the patterns of the impact of climate change, timber harvesting, and fires on boreal forests with a view to predicting their natural and anthropogenic dynamics.

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