Forest stand biomass of Picea spp.: an additive model that may be related to climate and civilisational changes

Vladimir А. Usoltsev CDFMR 1 , 2 , Agnieszka Piernik CDFMR 3 , Anna A. Osmirko CDFMR 1 , Ivan S. Tsepordey CDFMR 2 , Viktor P. Chasovskikh CDFMR 1 , and Walery Zukow CDFMR 3
  • 1 Ural State Forest Engineering University, Sibirskii trakt str, 37, Yekaterinburg, 620100, Yekaterinburg, Russian Federation
  • 2 Russian Academy of Sciences, Ural Branch, Botanical Garden, 8 Marta str, 202a, Yekaterinburg, 620144, Yekaterinburg, Russian Federation
  • 3 Nicolaus Copernicus University, Department of Geobotany and Lanscape Planning, Toruń, Poland
Vladimir А. Usoltsev CDFMR
  • Ural State Forest Engineering University, Sibirskii trakt str, 37, Yekaterinburg, 620100, Yekaterinburg, Russian Federation
  • Russian Academy of Sciences, Ural Branch, Botanical Garden, 8 Marta str, 202a, Yekaterinburg, 620144, Yekaterinburg, Russian Federation
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, Agnieszka Piernik CDFMR
  • Nicolaus Copernicus University, Department of Geobotany and Lanscape Planning, Toruń, Poland
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, Anna A. Osmirko CDFMR
  • Ural State Forest Engineering University, Sibirskii trakt str, 37, Yekaterinburg, 620100, Yekaterinburg, Russian Federation
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, Ivan S. Tsepordey CDFMR
  • Russian Academy of Sciences, Ural Branch, Botanical Garden, 8 Marta str, 202a, Yekaterinburg, 620144, Yekaterinburg, Russian Federation
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, Viktor P. Chasovskikh CDFMR
  • Ural State Forest Engineering University, Sibirskii trakt str, 37, Yekaterinburg, 620100, Yekaterinburg, Russian Federation
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and Walery Zukow CDFMR
  • Corresponding author
  • Nicolaus Copernicus University, Department of Geobotany and Lanscape Planning, Toruń, Poland
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Abstract

Since ancient times, climate change has largely determined the fate of human civilisation, which was related mainly to changes in the structure and habitats of forest cover. In the context of current climate change, one must know the capabilities of forests to stabilise the climate by increasing biomass and carbon-depositing abilities. For this purpose, the authors compiled a database of harvest biomass (t/ha) in 900 spruce (Picea spp.) sample plots in the Eurasian area and used the methodology of multivariate regression analysis. The first attempt at modelling changes in the biomass additive component composition has been completed, according to the Trans-Eurasian hydrothermal gradients. It is found that the biomass of all components increases with the increase in the mean January temperature, regardless of mean annual precipitation. In warm zonal belts with increasing precipitation, the biomass of most of the components increases. In the process of transitioning from a warm zone to a cold one, the dependence of all biomass components upon precipitation is levelled, and at a mean January temperature of ˗30°C it becomes a weak negative trend. With an increase in temperature of 1°C in different ecoregions characterised by different values of temperature and precipitation, there is a general pattern of decrease in all biomass components. With an increase in precipitation of 100 mm in different ecoregions characterised by different values of temperature and precipitation, most of the components of biomass increase in warm zonal belts, and decrease in cold ones. The development of such models for the main forest-forming species of Eurasia will make it possible to predict changes in the productivity of the forest cover of Eurasia due to climate change.

1 Introduction

In recent decades climate change and the possibility of mitigating its consequences for humanity have been recognised as one of the leading problems (Toman et al., 1996). Since ancient times, climate change has largely determined the fate of human civilisation (Behrensmeyer, 2006; Miles-Novelo and Anderson, 2019), the great migrations of peoples (Laczko and Aghazarm, 2009), and changes in the structure and habitats of biota and their main terrestrial component – forest cover (Emanuel et al., 1985; Halofsky et al., 2018; Kosanic et al., 2018). Although it is believed that ancient civilisations died out largely as a result of forest harvesting, “still in no way and by no human device could the wood be exhausted” (Radkau, 2008: 133), and the desertification of once-forested areas of ancient civilisations was the result of climate change.

Nikolay Danilevskiy noted that unlike steppe nomadism, which “indulges in laziness”, the forest promoted the settled lifestyle of the Slavs, a development of primary culture, and hence had a “strong cultural and tribal power”. The forest also had another influence, “forest with its mystic grounds and shade brings a poetic spirit to the people living in it”. He believed that this distinctive culture could originate without any outside influence other than in forest country (Usoltsev, 2019: 9). Thus, forests play an important role both in the history of civilisation and in its future, and therefore any changes in the areas, structure, biological diversity and biological productivity of forests under the influence of climate should be in the focus of world scientists.

One of the most important ecological properties of forest cover is its biomass. However, R. A. Houghton et al. (2009: 9) believe that:

“Our knowledge of distribution and amount of terrestrial biomass is based almost entirely on ground measurements over an extremely small and possibly biased sample, with many regions still unmeasured. Our understanding of change in terrestrial biomass is even more rudimentary, although changes in land use, largely tropical deforestation, are estimated to have reduced biomass, globally”

In the context of climate change, this state of our knowledge seems to be even more depressing.

Temperature and precipitation are the most informative climatic factors that determine not only the radial annual growth of tree stems, but also the biological productivity of forest stands (DeLucia et al., 2000; Ni et al., 2001; Stegen et al., 2011; D’Aprile et al., 2015; Fang et al., 2016). Analysis of the results of such studies revealed significant contradictions and uncertainties about the assessment of these indices’ dependence on temperature and precipitation using both empirical and functional models (Eggers et al., 2008; Shuman, Shugart, 2009; Poudel et al., 2011; Han et al., 2018). Contradictory results were obtained even within a single region, while the influence of the same climatic factors on the biological production of stands of certain tree species (genera) in the Trans-Eurasian climatic gradients of temperature and precipitation is still unknown, since the available information is fragmentary and contradictory (Strömgren, Linder, 2002; Wilmking et al., 2004; Stegen et al., 2011; Fu et al., 2017), and is obtained in narrow temperature and precipitation ranges shifted in magnitude in different ecoregions.

Moreover, numerous studies of stochastic relationships of biological productivity of stands with temperature and precipitation were carried out for indices, depersonalised by age and morphology of stands, and even without considering the species composition (Lieth, 1974; Anderson et al., 2006; Keeling, Phillips, 2007; Huston, Wolverton, 2009). However, it is known that the adaptive capacity of forests is species-specific (Spathelf et al., 2018), and it is not correct to identify global patterns of changes in their biological productivity without stratification by species composition. Spruce is one of the most common and socially significant species in Eurasia, and this report is dedicated to this tree species.

The genus Picea spp. includes about 45 species, but a relatively intensive hybridisation complicates even their simple description. Usually Meyer’s classification scheme was applied, which splits the genus into three sections – Morinda, Casicta and Omorica (Bobrov, 1978). Common spruce (P. abies [L.] Karst.), Siberian spruce (P. obovata Ldb.), Caucasian spruce (P. orientalis [L.] Link), Schrenk’s spruce (P. schrenkiana F. et M.) and Jezo spruce (P. jezoensis [Siebold and Zucc.] Carrière) are the most widely distributed in Eurasia. The common spruce area is fragmented and set before with three local areas – Alpine, Carpathian and Baltic – that independently developed in the postglacial time (Il’inskiy, 1937). The distribution of common spruce to the south of Western Europe and almost to the Arctic Ocean shows that its climate adaptation has a wide range. It is frost-resistant but sensitive to high temperatures and air aridity. Its lateral root system conditions signify a soil moisture dependence. Common spruce is characterised by high shade tolerance; needle longevity reaches from 5 to 7 years in the middle taiga subzone and up to 12–18 years in the Khibiny forest tundra. If common spruce reaches up to 50 metres in height and 2 metres in diameter in Western Europe, then eastward its height unsurprisingly gets shorter and in the European part of Russia it is already only 30 metres tall (Sukachev, 1938). By biological characteristics is resembles the Siberian spruce.

In the development of trivial empirical models, the additivity of component composition is not provided, according to which the total biomass of components (stems, branches, needles, roots) obtained by component equations would be equal to the value of biomass obtained by the common equation (Dong et al., 2015). According to Sanquetta et al. (2015), independent (without additivity) fitting of coefficients for biomass components and total biomass is not satisfactory, but this is not observed when simultaneous fitting is used accounting the additivity principle, which results in more effective estimators. The influence of climatic changes on the biomass of a tree species in the format of additive models in accordance with transcontinental hydrothermal gradients has not been studied at all.

Hence, the purpose of this study is to develop a model of changes in the additive component composition of the Picea spp. stand biomass on the Trans-Eurasian gradients of the mean January temperature and mean annual precipitation. The database compiled by Usoltsev (2013) makes it possible to perform the analysis of biological productivity (potentially for both biomass and carbon) at the trans-continental level. The ranges of variation of mean temperatures and precipitation in Eurasia (which covers a lot of natural biomes) are large, and using multiple regression analysis, it seems promising to relate the biomass structure of spruce stands described by an additive system of equations to two hydrothermal indices – mean January temperature and mean annual precipitation across Eurasia.

2 Materials and methods

The database on biomass of forest-forming species of Eurasia (Usoltsev, 2013) was used in the modelling process. The data are taken from it in the amount of 900 sample plots with the tree harvesting and biomass estimated for the Picea spp. forest stands (Fig. 1 and 2). Data on 10 species of the genus Picea spp. obtained from 200 published sources in 24 countries (See: Usoltsev et al., 2019). Sample trees were usually taken on each of the sample plots in 5 to 10 copies, and then samples were taken from each biomass component to determine the dry matter content (and, for wood and bark of stems, also to determine the basic density) and after drying the samples at the temperature of 80– 100°C the results were recalculated for the whole tree. The quantity of each biomass component per 1 ha was determined by regression method. Not all biomass components are equally presented in the database: if the mass of stems above bark, branches and needles is determined on all 900 plots, the mass of the stem bark is determined on 400 plots and the mass of roots on 520. Nevertheless, some sampling procedures for estimating biomass of tree components differed between the studies, since they were performed by representatives of different scientific fields in forestry. But these small methodological differences do not play the determining role in the level of accuracy of biomass estimates, because there is only one definite variant of biomass component structure corresponding to a given structure of taxonometric parameters (morphological structure) of a tree stand (Usoltsev, 2007).

Fig. 1
Fig. 1

Distribution of biomass harvest data of 900 Picea spp. sample plots on the map of the mean January temperature, °C. (World Weather Maps, 2007). https://www.mapsofworld.com/world-maps/currents-and-temperature-jan-enlarge-map.html

Citation: Bulletin of Geography. Socio-economic Series 45, 45; 10.2478/bog-2019-0029

Fig. 2
Fig. 2

Distribution of biomass harvest data of 900 Picea spp. sample plots on the map of the mean annual precipitation, mm sample trees on the map of the mean annual precipitation, mm. (World Weather Maps, 2007): (https://eldoradoweather.com/climate/world-maps/world-annual-precip-map.html)

Citation: Bulletin of Geography. Socio-economic Series 45, 45; 10.2478/bog-2019-0029

Each sample plot on which the biomass determination was performed is positioned relative to the isolines of the mean January temperature (Fig. 1) and relative to isolines of mean annual precipitation (Fig. 2). The matrix of harvest data is then compiled in which values of the biomass component and morphological indices of forest stands are conjugated with the corresponding values of mean January temperature and precipitation. This matrix is then included in the regression analysis procedure.

According to the structure of the disaggregation three-step additive biomass model (Tang et al., 2000; Dong et al., 2015), the total biomass estimated from the initial equation is divided into its components according to the scheme presented in Fig. 3 and Table 1. We used a schematic map of the contours of the mean January temperature (stage of deep winter dormancy in forest trees), rather than the mean annual temperature, as warming is most pronounced in the cold half of the year (Golubyatnikov, Denisenko, 2009; Laing, Binyamin, 2013; Felton et al., 2016). In his time, F. Schwarz (1899) showed that, for the growth of trees, air temperature from January to March is twice as decisive as precipitation from May to June. Furthermore, to ensure the maximum stability of the model, each of the selected factors (independent variables) should be presented in the maximum range of its variation (Usoltsev, 2004). In our example, mean January temperatures range from ˗40°C in the forest–tundra of North-Eastern Siberia to +15°C in the subtropics of Nepal, and mean annual precipitation from 190 mm in the permafrost regions of North-Eastern Siberia and the steppe zone of Eurasia to 1,140 mm in the territory of Nepal.

Fig. 3
Fig. 3

The pattern of disaggregating three-step proportional weighting additive model. Designation: Pt, Pr, Pa, Pc, Ps, Pf, Pb, Pw and Pbk are forest stand biomass respectively: total, underground (roots), aboveground, crown (foliage and branches), stems above bark (wood and bark), foliage, branches, stem wood and stem bark correspondingly, t per ha

Citation: Bulletin of Geography. Socio-economic Series 45, 45; 10.2478/bog-2019-0029

3 Results

Calculated initial regression equations

lnPi=a0i+a1i(lnA)+a2i(lnV)+a3i(lnN)+a4i[ln(Tm++40)]+a5i(lnPRm)+a6i[ln(Tm+40)](lnPRm),

where:

Pi – mass of the i-th fraction, t / ha;

A – the age of the stand, years;

V – wood supply, m3 / ha;

N – stand density, thousand ind./ha;

i – phytomass fraction index: total (t), aboveground (a), roots (r), crowns (c), stem above bark (s), foliage (f), branches (b), stem wood (w) and stem bark (bk);

Tm – average January temperature, °С;

РRm – average annual rainfall, mm.

Since the mean January temperature in the northern limit of Eurasia has negative values, the corresponding independent variable is modified to the form (Тm + 40) which may be subjected to logarithmic procedure.

The coefficients of Eq. (1) are calculated using the Statgraphics software for multiple regression analysis and their characteristics are obtained. Equations after correcting on logarithmic transformation by G. L. Baskerville (1972) and anti-log transforming are given in Table 2. They are characterised by a significance level of not lower than 0.05. The equations are adequate to the harvest data presented in the above-mentioned database. The equations obtained are modified to the additive form according to the above-mentioned algorithm (Fig. 3 and Table 1), and the final form of the transcontinental additive model of component composition of Picea spp. biomass is shown in Table 3.

Table 1

The structure of the three-step additive model sold under proportional weighting supposed by Dong et al. (2015). Symbols here and further as per Fig. 3 and Eq. (1)

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Table 2

Characteristics of initial model (1) after its anti-log transforming

Biomass componentsThe initial model characteristics
Pt1.6868A-0.0631V0.8917N0.0303
Step 1
Pa1.84E-01A-0.0528V0.8846N0.0456
Pr7.34E+01A0.0144V0.8453N0.0528
Step 2
Pc1.37E-05A-0.2301V0.7464N0.2057
Ps2.98E-01A0.0435V0.9822N0.0235
Step 3а
Pf1.83E-10A-0.2702V0.6718N0.2592
Pb1.84E-04A-0.1596V0.8371N0.2046
Step 3b
Pw7.05E-02A0.0950V0.9731N-0.0138
Pbk3.11E-01A0.1663V0.7954N0.1152
Biomass componentsThe initial model characteristicsadjR2*SE*
Pt(Tm+40) 0.4910(Tm+40) -0.0924 ln(PRm)PRm0.04870.9741.25
Step 1
Pa(Tm+40) 0.8254(Tm+40) -0.1201 ln(PRm)PRm0.27440.9741.21
Pr(Tm+40) -0.7995(Tm+40) 0.0772 ln(PRm)PRm-0.69340.9131.49
Pc(Tm+40) 3.9073Step 2 (Tm+40) -0.6237 ln(PRm)PRm1.83940.8121.50
Ps(Tm+40) 0.3354(Tm+40) -0.0491 ln(PRm)PRm0.02400.9861.17
Step
Pf(Tm+40) 6.6083(Tm+40) -1.0477 ln(PRm)PRm3.53740.7001.63
Pb(Tm+40) 2.8783(Tm+40) -0.4661 ln(PRm)PRm1.24220.8171.60
Step 3b
Pw(Tm+40) 0.3654(Tm+40) -0.0489 ln(PRm)PRm0.19410.9891.16
Pbk(Tm+40) -0.6774(Tm+40) 0.0880 ln(PRm)PRm-0.18670.9341.36

* adjR2 – coefficient of determination adjusted for the number of parameters; SE – equation standard error

For comparison of the adequacy of the initial (see Table 2) and additive (see Table 3) equations, those and others are tabulated on the experimental mass-forming indices, and the predicted biomass values obtained are compared with the observed ones according to adjR2 (Table 4). The results of the comparison shown in Table 4 indicate that the adequacy indices of the two systems of equations are close to each other. The ratio of harvest biomass data and values obtained by the calculation of initial and additive models of Picea spp. biomass (Fig. 4) show the degree of correlation of these values and the absence of visible differences in the structure of residual dispersions obtained from the two models.

Table 3

Final three-step additive model of Picea forest biomass

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Table 4

Comparison of coefficients of determination of the initial and additive equations of Picea spp. forest biomass

Biomass components
PtPaPrPsPwPbkPcPbPf
Initial equations
0.8830.8980.5950.9120.9560.5330.4100.3630.249
Additive equations
0.8830.8990.5950.9190.9470.5410.4480.4430.349

The problem of transforming the model (1) into a tabular variant is that we can specify only the indicators of the stand age, as well as temperature and precipitation, while the values of stem volume and tree density can be entered into the table in the form of calculated values obtained by a system of auxiliary recursive equations having the general form:

N=f[A,1/A,(Tm+40),PRm],
V=f[1/A,N,(Tm+40),PRm].

The results of calculating (2) and (3) are given in Table 5.

Table 5

Characteristics of the recursive system of auxiliary equations for mass-forming indices of spruce stands

Mass-forming indicesAuxiliary equation characteristics
lnN-6.9760-1.1359 lnA-9.0578 (1/A)-3.9808 ln(Tm+40)
lnV26.64760.4128 lnN-55.2965 (1/A)-0.0821 (lnN)(lnA)-6.4361 ln(Tm+40)
Mass-forming indicesAuxiliary equation characteristicsadjR2SE
lnN2.4072 lnPRm-0.7650 [ln(Tm+40)] ln(PRm)0.5670.59
lnV-4.2439 lnPRm1.3318 [ln(Tm+40)] ln(PRm)0.7790.64

The results of tabulating the equations in the sequence (2), (3) and (1) represent a rather cumbersome table. We took from it the component biomass for the age of 100 years and built graphs of their dependence on temperature and precipitation (Fig. 5).

Fig. 5
Fig. 5

Dependence of Picea spp. biomass on mean January temperatures (Tm) and annual precipitation (PRm). Designation: Pt, Ps, Pa, Pf, Pr, and Pb – respectively, total biomass, stems, aboveground, foliage, roots and branches, t/ha

Citation: Bulletin of Geography. Socio-economic Series 45, 45; 10.2478/bog-2019-0029

The obtained additive models of spruce stand biomass make it possible to establish quantitative changes in the biomass structure due to climatic changes, in particular, mean January temperature and mean annual precipitation. Let us pay attention to the fact that in Fig. 5 both temperature and precipitation trends of biomass have positive dependences. The exception is the foliage biomass, which during the transition from the regions of insufficient moisture (PRm = 300 mm) to the regions of increased moisture (PRm = 900 mm) in the warm zone (Тm = +10°C) does not increase, but decreases.

Figure 6 shows the change (Δ, %) in the Picea spp. forest biomass with an increase in temperature of 1°C in different ecoregions characterised by different values of temperature and precipitation. It is assumed that climate change does not affect precipitation, which changes only geographically (by ecoregions), while as a result of the expected climate change the temperature increases by 1°C at different territorial (zonal) temperature levels, designated as -30Δ ... +10Δ. Thus, Fig. 6 shows the common pattern of the increase (the location of the increment surface above the zero plane) in both the total biomass and all its components, including the roots, of spruce stands with a temperature increase of 1°C in all the temperature zones of Eurasia and in all the regions with different precipitation levels.

Fig. 6
Fig. 6

Changes in Picea spp. stand biomass with a temperature increase of 1°C due to the expected climate change at different territorial levels of temperature and precipitation. Tm – mean January temperature, °C; PRm – mean annual precipitation, mm. On the axes of ordinates the biomass components consistently marked, changing with increasing temperature by 1°C ( % ): total Δt, aboveground Δa, roots Δr, stems Δs, foliage Δf and branches Δb. 1 – the plane corresponding to zero change in biomass at the expected temperature increase of 1°C

Citation: Bulletin of Geography. Socio-economic Series 45, 45; 10.2478/bog-2019-0029

Figure 7 shows the change (Δ, %) in the Picea spp. forest biomass with an increase in precipitation of 100 mm in different ecoregions. It is assumed that January temperature changes only geographically, while as a result of the expected climate change the precipitation increases by 100 mm at different territorial precipitation levels designated as 300Δ ... 800Δ. In warm zones (0°C ... 10°C), with an increase in precipitation of 100 mm, biomass total, aboveground, stems and roots increase (the location of the increment surface above the zero plane), and in cold ones (˗20°C ... ˗30°C) they decrease (the location of the increment surface under the zero plane). Biomass components of the crown respond ambiguously to changes in precipitation.

Fig. 7
Fig. 7

Changes in Picea spp. stand biomass with a precipitation increase of 100 mm due to the expected climate change at different territorial levels of temperature and precipitation. On the axes of ordinates the biomass components consistently marked, changing with increasing precipitation by 100 mm (% ): total Δt, aboveground Δa, roots Δr, stems Δs, foliage Δf and branches Δb. 1 – the plane corresponding to zero change of biomass at the expected precipitation increase by 100 mm; 2 – the line of differing of positive and negative changes in biomass (Δ,%) with an expected increase in precipitation of 100 mm

Citation: Bulletin of Geography. Socio-economic Series 45, 45; 10.2478/bog-2019-0029

Foliage biomass decreases in cold zones (˗10°C ... ˗30°C), but branch biomass increases, regardless of regional levels of current precipitation. In warm zones (0°C ... 10°C), the foliage and branch biomass remain unchanged with an increase in precipitation of 100 mm at the current levels of precipitation in all the ecoregions (all the increment surface is under the zero plane). A more accurate geographical localisation of these dependences is not yet possible, as will be discussed below.

4 Discussion

As we can see on these graphs (Fig. 5), all the Picea spp. biomass components, with some exceptions for foliage, change in one general scheme, but in different ratios. The biomass of all components increases with the increase in the mean January temperature from ˗30°С to +10°С, regardless of precipitation. In warm zonal belts (Тm = +10°С) with precipitation increasing from 300 to 900 мм the biomass of all components increases, except for the foliage biomass, but the latter decreases under the same conditions. With the transition from warm zones (Тm = +10°C) to cold (Tm = -30°C), the dependence upon precipitation of the biomass of all components except for foliage is levelled, i.e. it becomes less pronounced, and at the temperature Tm equal to ˗30°C acquires a weak negative trend.

Almost full compliance with our data, though obtained for the other production index (NPP), can be seen in the graph presented by T. Luo; according to his results of modelling 167 spruce–fir forest stands, represented throughout China by species

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of Picea wilsonii Masters, P. jezoensis (Siebold and Zucc.), and P. koraiensis Nakai in dependence of temperature and precipitation. There is only some difference from the obtained regularity for biomass in the regularity of the reduction of NPP in the cold zone (Tm = ˗4°C) as precipitation increases from 400 to 1400 mm: it occurs in China more intensively than in our Eurasian gradient.

There are more significant differences in our regularities for the dark-conifer genus Picea spp. when compared to the same Trans-Eurasian patterns obtained for the two-needled subgenus Pinus L. where the pine models manifested propeller-shaped surfaces (Usoltsev et al., 2018). If the decrease in the spruce stand biomass on the moisture-poor ecoregions (PRm = 300 mm) occurs less intensively compared to moisture-rich (PRm = 900 mm) ones as the transition from warm (Тm = +10°C) to cold (Tm = -30°C) zones, then the pine stand biomass in such moisture-poor ecoregions in the same temperature gradient significantly increases. A possible reason is that the ecoregions where pine grows are represented by a steppe zone where spruce cannot grow. Some sample plots with estimations of spruce biomass fall on the pole of continentality in Yakutia in the permafrost region, where precipitation, as in the steppe zone, is about 300 mm.

However, this trend does not correspond to the results obtained by other scientists. For example, at the local level in the marsh forests of the Siberia, at the maximum amounts of temperature sums above 10°C (2200°C) there is an increase in the radial growth of stems by 30–50% with an increase in precipitation from 400 to 600 mm, and at the minimum amounts of temperature sums (1600°C) the radial growth is reduced by 4–9% with an increase in precipitation in the same range. Correspondingly, at the level of precipitation of 400 mm the radial growth is reduced by 14–20% with an increase in the sum of temperatures from 1600 to 2200°C, and it increases by 14–33% in the same temperature range at the level of precipitation of 600 mm (Glebov, Litvinenko, 1976). According to the results obtained by A. A. Molchanov (1976), in the North of Eurasia the greatest influence on the growth of the annual tree ring comes from air temperature, and in the conditions of the southern forest-steppe the dominant role is played by precipitation.

The third type of the investigated regularity was shown by the example of fir (Abies spp.) stands of Eurasia: our modelling suggested that the biomass of all components grows as precipitation total increases from 300 to 900 mm, as well as due to the increase in the mean January temperature from -30°C to +10°C (Usoltsev et al., 2019a). The finding is consistent with the increase in relative radial increment of boreal forests in Canada if both mean annual temperature and annual precipitation increase (Miao and Li, 2011).

Finally, the fourth type of investigated pattern was detected on the example of larch (Larix spp.) forests of Eurasia: the unified regularity to all the biomass components of wood story is the following: increase with raising temperature in the range from -40°C to 0°C at invariable precipitation, but decrease with raising precipitation in the range from 200 to 900 mm at constant temperature (Usoltsev et al., 2019b).

These uncertainties can be explained by differing species-specific hydrothermal optima between the studied species, and by the insufficient representation of empirical biomass data to date.

Of course, the solution to each new problem and the corresponding removal of associated uncertainty generates several new unknowns. In our case, there are at least three uncertainties that have arisen.

  1. The patterns shown are hypothetical: they reflect the long-term adaptive responses of forest stands to regional climatic conditions and do not take into account the rapid trends in current environmental changes, which place serious constraints on the ability of forests to adapt to new climatic conditions (Alcamo et al., 2007; Pastore et al., 2019).
  2. Since the database used has a lot of “gaps” and does not completely cover the territory of Eurasia, the shown patterns are preliminary. Therefore, today there is no sense in using more advanced climate maps, for example, by Köppen (1918) or Geiger (1954), or the maps taking into account the topography of the territories, but having some shifts in the time of their designing and the time period of establishing our sample plots. As the database will be supplemented and climate maps will be updated, these patterns will be modified.
  3. The patterns shown in accordance with the database used are related to fir forests, pure or with slight admixture of other tree species. However, most of the forest area is represented by mixed-tree coenoses. There is a lot of evidence that mixed-tree coenoses are more resistant to stresses caused by abiotic factors and are more productive than pure coenoses (Liang et al., 2016). However, a recent study of net primary production (NPP) of aboveground biomass from forest phytocoenoses in Spain and Canada over a wide range of biodiversity index, mean temperature and moisture conditions (Paquette et al., 2018) led to a paradoxical conclusion. It was found that in pure forest stands (biodiversity index is zero), NPP reacts to temperature rise up to 1–2°C in different climatic zones in different ways: it grew in forests of the temperate zone, remained stable in boreal forests and declined in the Mediterranean forests, but as the biodiversity index increases in different zones, these trends gradually transformed into a unified negative trend common for all zones (Paquette et al., 2018). This result calls into question all previously obtained regularities of changes in biomass and NPP of pure (or almost pure) forest communities in variable climate conditions: in forests with an increased biodiversity index, previously established patterns can be significantly modified and even reversed.

5 Conclusion

The first attempt was made at modelling changes in the additive component composition of 900 spruce (Picea spp.) plots’ biomass (t/ha), according to the trans-Eurasian hydrothermal gradients of Eurasia using a database compiled on the structure of harvest biomass. It is found that the biomass of all components increases with an increase in mean January temperature, regardless of mean annual precipitation. In warm zonal belts with increasing precipitation the biomass of all components increases, except for foliage mass, which decreases under the same conditions. In the process of transition from a warm zone to a cold one, the dependence of all biomass components upon precipitation is levelled, and at a mean January temperature of -30°C it becomes a weak negative trend. With an increase in temperature of 1°C in different ecoregions characterised by different values of temperature and precipitation, there is a general pattern of decrease in total biomass, and all its components, including roots. With an increase in precipitation of 100 mm in different ecoregions characterised by different values of temperature and precipitation, in warm zonal belts (0°C ... 10°C) biomass total, aboveground, stems and roots increases, and decreases in cold ones (-20°C ... ˗30°C). The foliage biomass in cold zones (-10°C ... -30°C) increases regardless of regional levels of current precipitation, and decreases in warm zones (0°C ... 10°C). The branch biomass decreases in all ecoregions. The development of such models for the main forest-forming species of Eurasia will make it possible to predict changes in the productivity of the forest cover of Eurasia due to climate change.

Acknowledgements

We thank the anonymous referees for their useful suggestions. This paper was completed according to the programmes of current scientific research of the Ural Forest Engineering University, Botanical Garden of the Ural Branch of Russian Academy of Sciences (Yekaterinburg, Russia) and Nicolaus Copernicus University (Toruń, Poland).

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    • Export Citation
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    • Export Citation
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    • Export Citation
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    • Export Citation
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  • Fu L. Lei X. Hu Z. Zeng W. Tang Sh. Marshall P. Cao L. Song X. Yu Li. and Liang J. (2017). Integrating regional climate change into allometric equations for estimating tree aboveground biomass of Masson pine in China. Annals of Forest Science. 74(42): 1-15. DOI https://doi.org/10.1007/s13595-017-0636-z

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  • Han S.H. Kim S. Li G. Chang H. Yun S.J. Jiae An J. and Son Y. (2018). Effects of warming and precipitation manipulation on fine root dynamics of Pinus densiflora Sieb. et Zucc. Seedlings. Forests. 9: 14. doi: https://doi.org/10.3390/f9010014

  • Huston M.A. and Wolverton S. (2009). The global distribution of net primary production: resolving the paradox. Ecological Monographs. 79(3): 343–377. DOI: https://doi.org/10.1890/08-0588.1

    • Crossref
    • Export Citation
  • Il’inskiy A.P. (1937).The vegetation of the globe. Moscow; Leningrad: Academy of Sciences of USSR. 458 pp. (Rus.).

  • Keeling H.C. and Phillips O.L. (2007). The global relationship between forest productivity and biomass. Global Ecology and Biogeography. 16: 618-631. DOI: https://doi.org/10.1111/j.1466-8238.2007.00314.x

    • Crossref
    • Export Citation
  • Köppen W. (1918). Klassification der Klimate nach Temperatur, Niederschlag and Jahreslauf. Petermanns Geographische Mitteilungen. 64: 193–203, 243–248.

  • Kosanic A. Anderson K. Harrison S. Turkington T., and Bennie J. (2018). Changes in the geographical distribution of plant species and climatic variables on the West Cornwall Peninsula (South West UK). PLoS ONE. 13 (2): e0191021 https://doi.org/10.1371/journal.pone.0191021

    • Crossref
    • PubMed
    • Export Citation
  • Laczko F. and Aghazarm Ch. (2009). Migration, Environment and Climate Change: Assessing the Evidence. Geneva, International Organization for Migration. 441 pp. https://publications.iom.int/system/files/pdf/migration_and_environment.pdf

  • Laing J. and Binyamin J. (2013). Climate change effect on winter temperature and precipitation of Yellowknife, Northwest Territories, Canada from 1943 to 2011. American Journal of Climate Change. 2: 275-283. DOI: https://doi.org/10.4236/ajcc.2013.24027

    • Crossref
    • Export Citation
  • Liang J. Crowther T.W. Picard N. Wiser S. Zhou M. Alberti G. Schulze E.-D. McGuire A. D. Bozzato F. Pretzsch H. de-Miguel S. Paquette A. Hérault B. Scherer-Lorenzen M. Barrett C. B. Glick H. B. Hengeveld G.M. Nabuurs G.-J. Pfautsch S. Viana H. Vibrans A.C. Ammer C. Schall P. Verbyla D. Tchebakova N.M. Fischer M. Watson J.V. Chen H.Y.H. Lei X. Schelhaas M.-J. Lu H. Gianelle D. Parfenova E.I. Salas C. Lee E. Lee B. Kim H.S. Bruelheide H. Coomes D.A. Piotto D. Sunderland T. Schmid B. Gourlet-Fleury S. Sonké B. Tavani R. Zhu J. Brandl S. Vayreda J. Kitahara F. Searle E.B. Neldner V.J. Ngugi M.R. Baraloto C. Frizzera L. Bałazy R. Oleksyn J. Zawiła-Niedźwiecki T. Bouriaud O. Bussotti F. Finér L. Jaroszewicz B. Jucker T. Valladares F. Jagodzinski A.M. Peri P.L. Gonmadje C. Marthy W. O’Brien T. Martin E.H. Marshall A.R. Rovero F. Bitariho R. Niklaus P. A. Alvarez-Loayza P. Chamuya N. Valencia R. Mortier F. Wortel V. Engone-Obiang N.L. Ferreira L.V. Odeke D.E. Vasquez R.M. Lewis S.L. and Reich P.B. (2016). Positive biodiversity - productivity relationship predominant in global forests. Science. 354 (6309): 196-208. DOI: https://doi.org/10.1126/science.aaf/8957

  • Lieth H. (1974). Modeling the primary productivity of the world. International Section for Ecology Bulletin. 4: 11-20.

  • Luo T.Х. (1996). Patterns of biological production and its mathematical models for main forest types of China. Ph.D. Dissertation. Committee of Synthesis Investigation of Natural Resources, Chinese Academy of Sciences. Beijing. 211. (in Chinese with English abstract).

  • Miao Z. and Li C. (2011). Predicting tree growth dynamics of boreal forest in response to climate change. In: C. Li et al. (eds.). Landscape Ecology in Forest Management and Conservation. Berlin, Heidelberg: Higher Education Press, Beijing and Springer-Verlag, 176-205.

    • Crossref
    • Export Citation
  • Miles-Novelo A. and Anderson C. A. (2019). Climate Change and Psychology: Effects of Rapid Global Warming on Violence and Aggression. Current Climate Change Reports, 5: 36–46. https://doi.org/10.1007/s40641-019-00121-2

    • Crossref
    • Export Citation
  • Molchanov, А.А. (1976). Dendro-climatic fundamentals of weather forecasts. Мoscow, Nauka Publ., 168 pp. (Rus.).

  • Ni J. Zhang X.-S. and Scurlock J.M.O. (2001). Synthesis and analysis of biomass and net primary productivity in Chinese forests. Annals of Forest Science. 58: 351-384 (www.edpsciences.org)

    • Crossref
    • Export Citation
  • Paquette A. Vayreda J. Coll L. Messier C. and Retana J. (2018). Climate change could negate positive tree diversity effects on forest productivity: A study across five climate types in Spain and Canada. Ecosystems. 21 5 960–970 DOI: https://doi.org/10.1007/s10021-017-0196-y

    • Crossref
    • Export Citation
  • Pastore M. Lee T. Hobbie S.E. and Reich P.B. (2019). Strong photosynthetic acclimation and enhanced water‐use efficiency in grassland functional groups persist over 21 years of CO2 enrichment, independent of nitrogen supply. Global Change Biology. May. DOI: https://doi.org/10.1111/gcb.14714

  • Poudel B.C. Sathre R. Gustavsson L. Bergh J. Lundström A. and Hyvönen R. (2011). Effects of climate change on biomass production and substitution in north-central Sweden. Biomass and Bioenergy. 35 (10): 4340–4355.

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  • World Weather Maps (2007). URL: https://www.mapsofworld.com/referrals/weather/

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    Distribution of biomass harvest data of 900 Picea spp. sample plots on the map of the mean January temperature, °C. (World Weather Maps, 2007). https://www.mapsofworld.com/world-maps/currents-and-temperature-jan-enlarge-map.html

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    Distribution of biomass harvest data of 900 Picea spp. sample plots on the map of the mean annual precipitation, mm sample trees on the map of the mean annual precipitation, mm. (World Weather Maps, 2007): (https://eldoradoweather.com/climate/world-maps/world-annual-precip-map.html)

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    The pattern of disaggregating three-step proportional weighting additive model. Designation: Pt, Pr, Pa, Pc, Ps, Pf, Pb, Pw and Pbk are forest stand biomass respectively: total, underground (roots), aboveground, crown (foliage and branches), stems above bark (wood and bark), foliage, branches, stem wood and stem bark correspondingly, t per ha

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    Dependence of Picea spp. biomass on mean January temperatures (Tm) and annual precipitation (PRm). Designation: Pt, Ps, Pa, Pf, Pr, and Pb – respectively, total biomass, stems, aboveground, foliage, roots and branches, t/ha

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    Changes in Picea spp. stand biomass with a temperature increase of 1°C due to the expected climate change at different territorial levels of temperature and precipitation. Tm – mean January temperature, °C; PRm – mean annual precipitation, mm. On the axes of ordinates the biomass components consistently marked, changing with increasing temperature by 1°C ( % ): total Δt, aboveground Δa, roots Δr, stems Δs, foliage Δf and branches Δb. 1 – the plane corresponding to zero change in biomass at the expected temperature increase of 1°C

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    Changes in Picea spp. stand biomass with a precipitation increase of 100 mm due to the expected climate change at different territorial levels of temperature and precipitation. On the axes of ordinates the biomass components consistently marked, changing with increasing precipitation by 100 mm (% ): total Δt, aboveground Δa, roots Δr, stems Δs, foliage Δf and branches Δb. 1 – the plane corresponding to zero change of biomass at the expected precipitation increase by 100 mm; 2 – the line of differing of positive and negative changes in biomass (Δ,%) with an expected increase in precipitation of 100 mm