1. bookVolume 75 (2021): Issue 1 (December 2021)
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1736-8723
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Composition of live, dead and downed trees in Järvselja old-growth forest

Published Online: 04 Jun 2022
Volume & Issue: Volume 75 (2021) - Issue 1 (December 2021)
Page range: 15 - 40
Received: 02 Dec 2021
Accepted: 28 Dec 2021
Journal Details
License
Format
Journal
eISSN
1736-8723
First Published
24 Mar 2011
Publication timeframe
2 times per year
Languages
English
Introduction

Human activity and influence on forests has been occurring for thousands of years and most forests have a long history of exploitation and alteration (Bölöni et al., 2021). Old-growth forests can be defined as forests where human impact is minimal or negligible (FAO, 2020). Old-growth forests cover 0.7% of Europe’s forest area (excluding Russia, (Sabatini et al., 2018)). There are a few remnants of old-growth forests in Estonia; one is the Järvselja old-growth forest. The structure of old-growth forests differs from managed forests considerably; the main differences include spatial variability, size differentiation and species composition of trees, large amounts of standing and downed dead trees and continuous tree regeneration and deadwood input.

Stemwood volume is an important measure in forest management and ecology. It is widely recognized that stemwood volume correlates with woody biomass and carbon content (further on C) in managed and natural forests. However, there is little evidence regarding the quantities of live and dead wood in hemiboreal old-growth forest ecosystems. Wood volume assessment in such ecosystems is rather complicated as these stands vary considerably in the number of trees and cohorts by species, age, dimensions, spatial patterns, and deadwood in all decay stages occurs throughout the forest. In addition, the taper form of trees in old-growth forests may differ from trees in managed forests.

Stem lateral surface area of trees is a valuable trait for evaluating the overall ecological quality of forest stands because it is directly related to niches and habitats for different vertebrates, arthropods, fungi, lichens and bryophytes. Estimating the surface area of a log or tree trunk poses some distinctive challenges not found in the estimation of other tree attributes (Williams et al., 2005). The first challenge is that the surface of a trunk usually has bark that can be heavily furrowed; such concavities in the surface are difficult to measure and vary by species, so this component of the surface area is often ignored. An additional concern is that in field conditions so far it has not been possible to take the necessary measurements; now with the growing capabilities of terrestrial Lidars this possibility is slightly coming to life. Unbiased estimates of individual tree characteristics are necessary for developing appropriately fitted models for accurate prediction of these characteristics for other trees or for simulating dynamics.

It is evident that the prognosis of any stand descriptive variable in mixed multi-aged and multicohort conditions, as in an old-growth forest, is a lot complex than in the case of pure stands (Fabrika et al., 2018). The growing conditions in old-growth uneven-aged mixed stands are vertically lot more structured due to different growth rates of individual trees with different tree species (Bravo et al., 2019; Kängsepp et al., 2015). Therefore even the simplest and most studied tree allometric relationships between tree height and tree diameter become a complex issue (Nigul et al., 2021).

The aim of this study is to describe and analyse the methodology for calculating single tree height, tree stem lateral surface area (hereafter SLSA), tree volume and carbon content for standing live trees, standing dead trees and for downed deadwood of the main tree species in the stand that formed under natural conditions. As a study question we aim to determine whether the Järvselja old-growth forest is moving into the multiaged condition, where we can expect increasing dominance by shade-tolerant species with the maintenance of high biomass and high but variable amounts of dead wood (Bormann & Likens, 1994).

Materials and Methods
Study site

The study area is in Järvselja Training and Experimental Forest Centre and is a part of the Järvselja nature conservation area. In 1924, an area of 12.8 ha (Raukas, 1967) in the southern part of a 19-hectare compartment was set aside to allow an experiment where all management actions were abandoned (Mathiesen, 1940). In 1936, the area was enlarged to cover the whole compartment and the site was included in the nature conservation registry, obtaining a state-controlled protection status. In 1959, the whole old-growth forest compartment was registered as a botanical-zoological conservation area (Krigul, 1971). Today the old-growth forest and its surrounding compartments are part of the Järvselja nature conservation area that covers altogether 188 ha. According to the site’s conservation plan, this area is divided into two special management zones (Ürgmets and Järvselja) and one, partially limited, management zone (Apna) (Laas et al., 2007).

The protected old-growth forest (known as compartment JS226) (Kollom, 2011; Laas et al., 2007) is the most important, scientifically interesting and most studied area in the conservation area. The old-growth forest compartment is divided into 15 sub-compartments, altogether covering 19.4 ha according to the forest inventory in 2016. There are three different Natura 2000 habitat types apparent in this compartment: 91D0 (bog woodlands), 9020 (Fennoscandian hemiboreal natural old broadleaved deciduous forests) and 9050 (Fennoscandian herb-rich forests with Picea abies (L.) H. Karst.). Several registered, protected habitats are registered in the compartment with records for the following species: Epipactis helleborine (L.) Crantz, Skeletocutis odora (Peck ex Sacc.) Ginns, Cypripedium calceolus L., Cinna latifolia (Trevir.) Griseb., Corallorhiza trifida Châtel., Corydalis intermedia (L.) Mérat, Amylocystis lapponica (Romell) Singer, Leucopaxillus salmonifolius M.M. Moser & Lamoure, and Tricholoma apium Jul. Schäff.

Compartment JS226 (58.28 N and 27.32 E) is divided according to three dominating vegetation types (Lõhmus, 1993): on the eastern side, fresh boreo-nemoral forests with rich groundcover species like Oxalis acetosella L., Hepatica nobilis Schreb., Rubus saxatilis L. and Maianthemum bifolium (L.) F.W. Schmidt. The western side is dominated by drained swamp forests, dominated by Scots pine (Pinus sylvestris L.) and Norway spruce (Picea abies) in the tree layer. The central part of the compartment is a fen, with a constantly high ground water table and very lush plant cover. Dominant tree species are black alder (Alnus glutinosa (L.) Gaertn.) and silver birch (Betula pendula Roth).

Former vegetation studies in Järvselja old-growth forest

One of the earliest studies about the old-growth forest ground vegetation was carried out by Gustav Vilbaste in 1929. In that study three dominating forest site types were defined and described in the old-growth forest compartment: fresh boreo-nemoral forests, minerotrophic mobile water swamp forest and mixotrophic bog forests (Sepp & Rooma, 1993; Vilbaste, 1929).

There are numerous subsequent descriptive studies about the old-growth forest compartment. Mathiesen published an overview about the compartment in 1940 and Masing wrote about the old-growth forest conditions in 1960 (Masing & Rebane, 1995). Krigul carried out several studies in Järvselja old-growth forest from 1939 to 1971 (see Table 1). While earlier studies focused on volume description of living trees and coarse woody debris (CWD) (Krigul, 1940), later studies focused more extensively on stand health conditions and also presented additional stand-wide inventory data (Kollom, 2011).

Earlier studies on Järvselja old-growth forest.

Author(s) Publishing year Measurement year/period Inventory type* Citation
Kollom, M. 2011 1922–2010 3 (Kollom, 2011)
Kasesalu, H. 2001 2001 1 (Kasesalu, 2001)
Kasesalu, A. 1985, 1993 1982, 1983 1 (Kasesalu, 1985), (Kasesalu, 1993)
Irdt, E., Rebane, H. 1985 1954–1985 1; 2 (Irdt & Rebane, 1985)
Pettai, T. 1985 1922–1984 3 (Pettai, 1985)
Raukas, A. 1967 1958–1959 1; 3 (Raukas, 1967)
Krigul, T. 1961 1958, 1959 3 (Krigul, 1961)
Krigul, T. 1940 1939 3 (Krigul, 1940)
Visnapuu, M. 1927 1927 3 (Krigul, 1940)

Inventory type: 1) Plot-wide inventory; 2) Transect inventory; 3) Stand-wise inventory.

Irdt and Rebane conducted one of the most important research studies in the period of 1954–1985 (Irdt & Rebane, 1985). They established a complex measurement plot transect in the old-growth forest and remeasured it regularly from 1954 until 1985. The measurements on this transect were carried out on 10 × 10 m plots. They assessed standing live tree diameters, heights, tree layer, geographical location, ground vegetation and soil microsites. Based on those descriptions, Irdt & Rebane (1985) partly described the stand dynamics. Heino Kasesalu published several studies about the old-growth forest, also describing the stand dynamics since 1984 up to 2001 (Kasesalu, 1993; Kasesalu, 2004).

In addition to the studies presented in Table 1, there are stand-wise forest inventory data available, covering a period of 94 years (from 1922 to 2016). Long-term dynamics based on the stand-wise forest management inventory data were analysed by Kollom (2011). In her study, the standing gross volume of all tree species in old-growth forest compartments increased up to 1962, and was then followed by a period of decrease up to 1983. Similar trends were reported by Pettai (1985) and Kasesalu (2004). In the period of 1983–2010, the average volume of the dominating layer showed low fluctuations, but a clear increase in the volume of the supressed spruce layer can be seen starting from 1993 (Kollom, 2011).

Data collection

From spring until autumn 2013, an inventory was carried out with the aim of covering the study area with tree mapping data. To avoid potentially disruptive extensive measurements on more fragile sites and close to habitats under protection, two methodologies were followed: whole surface inventory (in sub-compartments 6, 9, 10, 11, 12, 13, 14, 15) and partial surface mapping based on permanent sample plots (in sub-compartments 1, 2, 3, 4, 5, 7, 8). To facilitate mapping, additional measurements on tree positions on the site were carried out to establish a network of geodetically accurate reference points (approx. 50 × 50 m interval, RTK GNSS method, with the location accuracy of 0.05–0.5 meters). This resulted in a 72-point grid and the grid points were marked on the ground with metallic rods (see Appendix A). Whole surface mapping covered 9.34 ha and partial mapping covered 10.03 ha. The study area included five different site types: Oxalis drained swamp site type 1.52 ha, Alder fen site type 2.31 ha, Myrtillus drained swamp site type 6.07 ha, Aegopodium site type 6.62 ha and Transitional bog forest site type 2.84 ha (forest site types described following Lõhmus (2004).

FieldMap technology (IFER-Monitoring and Mapping Solutions Ltd.) was used in conjunction with geodetically accurate reference points (see Appendix A), where tree distance was measured with a ForestPro impulse laser rangefinder. For tree positioning the Mapstar TruAngle angle encoder together with ForestPro impulse laser rangefinder was used. Tree heights to the live crown base and heights of the lowest dry branch on the tree stem were measured with a Vertex hypsometer to the nearest 0.1 m. Data were collected and stored on site with the FieldMap data collector software. Partial surface stem mapping was carried out on wetter and more fragile sites in parallel with full surface stem mapping elsewhere. Circular plots of various radii (15–20 m) were used in different sub-compartments. Permanent plots were located similarly with FieldMap measurements using the geodetically accurate reference points as shown in Appendix A.

Both full surface and partial surface tree mapping followed the design of the survey protocol of the Estonian Network of Forest Research Plots (ENFRP) (Kiviste et al., 2015). The measurement protocol was amended so that all tree heights were measured, where possible (in ENFRP a sub-sample of trees, e.g., every fifth tree, had tree height recorded). Live standing trees: all trees with DBH larger than 7 cm were measured. For every tree, measurements included tree position, tree species, tree height, live crown length, and diameter (in two perpendicular directions). The tree layer was divided according to tree classes into an upper storey (dominant layer and secondary cohort) and a lower storey (under-storey cohort and regeneration layer). For all trees, tree foliage vitality (vital, normal, poor) and dominance (dominant, co-dominant, suppressed) were determined. Standing and broken dead trees (snags: all standing dead or broken trees with DBH larger than 7 cm) were measured.

Other tree measurements included position, species, height, and diameter (in two perpendicular directions). If the height of the broken dead tree was under 1.3 m, then the diameter was measured 10 cm below the breakage point or from the root collar (marked accordingly). Standing and broken dead trees were also described according to five decay stages following studies by Fridman & Walheim (2000) and Köster et al. (2015): 1) Hard wood, recently dead; 2) Slightly decayed wood, outer layer of wood starts to soften; 3) Medium decayed, outer layers of stem wood soft, core still hard; 4) Well-decayed wood, wood soft through the tree stem; 5) Almost decomposed, wood soft, fragmented. Logs: All downed dead wood and tree stem parts with a top diameter larger than 6 cm were measured. Other measurements for every tree or stem included: top and trunk position, tree species, stem length or the length of a stem part, top and trunk diameter (in two perpendicular directions). The five decay stages of downed stems were also described according to the classification as described above for standing dead trees. The spatial arrangement of measured standing, dead and downed trees in Järvselja old-growth forest is shown in Figure 2.

Modelling of H-D relationship

Assessing the tree height and diameter (H-D) relation of old-growth forests using models that have been developed for managed forest stands has its own challenges (Nigul et al., 2021). Here, we employed several models and developed a model selection procedure, mostly to tackle problems like a very small number of measured trees per species and structural cohort, linearization and bias removal, or mean tree height dependent scaling for very small or very large trees (Figure 1). Our basic tool is the Näslund function with a controlled starting point at breast height (initiated 1.3, 0) (Näslund, 1936; Schmidt et al., 2011): h=1.3+(da+bd)3, h = 1.3 + {\left( {{d \over {a + b \cdot d}}} \right)^3}, where h refers to tree height, d is diameter at breast height (DBH), a and b are model parameters determining the location of the curve’s inflection point and the convergence behavior towards the height asymptote. Transforming Equation 1 by linearization yields: dh1.33=a+bd. {d \over {\root 3 \of {h - 1.3} }} = a + b \cdot d. To avoid systematic bias in the parameter estimation, introduced by the transformation to the linear form, we applied adjusted weight functions as suggested by Artur Nilson (Nilson, 2002). We applied the following weight function: W=(h1.3)w, W = {\left( {h - 1.3} \right)^w}, where W = weight and h = tree height. w is a searchable parameter and can be chosen so that the regression residuals are minimized, and either are near or equal to 0. This criterion is achieved when w lies in the interval between 0.6 and 0.75.

Figure 1

H-D model calculation scheme based on the Näslund function.

We introduced an additional parameter c to obtain a scalable version of Equation 1: h=1.3+c(da+bd)3, {\rm{h}} = 1.3 + {\rm{c}} \cdot {\left( {{{\rm{d}} \over {{\rm{a}} + {\rm{b}} \cdot {\rm{d}}}}} \right)^3}, where the variables and parameters are as given as in Equation 1. The new parameter c is either set to 1 in the case of a sample size n of measured trees is ≥ 9 (Figure 1 left side) or it is calculated from raw data by fixing the parameters a and b, using the default parameters from Appendix B, in the case that n < 9 (Figure 1 right side).

The selection procedure in the case of n ≥ 9 is as follows: from the weight function W, the scaling procedure creates a polynomial form cc. If cc = 0, the asymptotic height is set to 55 m and scaled parameters a and b are calculated including the weight function W. If cc ≠ 0, new parameters a and b are determined as given in Figure 1 and in the case of a < 0.08 new scaled parameters a and b are calculated where b depends on the mean height only. In all other cases, the calculated parameters a and b are then applied to Equation 4. The whole selection and scaling process is iterated until the sum of residuals of the regression is minimized and approaches 0. In short, the procedure either calculates the scaling parameter c or it scales the parameters a and b with respect to the weight function W.

Following the model selection procedure, H-D ratios were assessed, and model solutions calculated by using the measured data per stand element (Figure 1) – a combination of the tree species in a particular cohort in a particular site type and sub-compartment. There were 5 site types and 15 sub-compartments in the Järvselja old-growth forest compartment. Similar calculations were carried out for deciduous and coniferous trees, which resulted in 129 different height curves.

For comparison, we used the two best-performing height curve models from an earlier study by Nigul and colleagues (Nigul et al., 2021):

Chapman-Richards function with fitted parameters per species (Table 2): h=1.3+α(1eβd)γ, h = 1.3 + \alpha \cdot {\left( {1 - {e^{ - \beta d}}} \right)^\gamma }, where h= tree height; d= DBH; α, β and γ are constants per tree species;

Näslund function with fitted parameters (Table 2; see Equation 1).

H-D model parameters from Nigul et al. (2021) study.

Tree species Chapman-Richards function (Eq. 5) Näslund function (Eq. 1)

a b c a b
SP 30.67342 0.032552 0.80312 1.59472 0.30835
NS 39.02975 0.033294 1.18444 2.16428 0.27970
BI 33.57059 0.023891 0.62549 1.19810 0.31378
CA 37.72567 0.043817 0.89476 1.28707 0.28380
BAR 30.46570 0.036539 0.85649 1.43745 0.30966
LI 33.70126 0.040448 1.07656 1.69100 0.29469
NM 33.17741 0.030304 0.76981 1.26131 0.31403
Other 34.06000 0.035000 0.91100 1.51900 0.30000

Tree species and tree species codes are as follows: SP – Scots pine; NS – Norway spruce; BI – silver birch and downy birch; CA – common aspen; BAR – black alder; LI – linden family; NM – Norway maple

To achieve comparable results, a scaling factor x, like c in Equation 4, was introduced: h=1.3+xα(1eβd)γ,wherex=(h1.3)[α(1eβd)γ], h = 1.3 + x \cdot \alpha \cdot {\left( {1 - {e^{ - \beta d}}} \right)^\gamma },\,{\rm{where}}\,x = {{\sum {\left( {h - 1.3} \right)} } \over {\sum {\left[ {\alpha \cdot {{\left( {1 - {e^{ - \beta d}}} \right)}^\gamma }} \right]} }}, h=1.3+x(da+bd)3,wherex=(h1.3)[(da+bd)3], h = 1.3 + x \cdot {\left( {{d \over {a + b \cdot d}}} \right)^3},\,{\rm{where}}\,x = {{\sum {\left( {h - 1.3} \right)} } \over {\sum {\left[ {{{\left( {{d \over {a + b \cdot d}}} \right)}^3}} \right]} }}, where h is tree height; d is DBH; α, β, γ, a, and b are constants for tree species (Table 4).

All three H-D curves were used to estimate single tree heights and to make comparisons with measured tree heights. Root mean square errors were calculated to assess the goodness of fit (Figure 3).

Calculation of tree volume and tree stem lateral surface area

General information about measured live and dead standing trees is presented in Table 5, and the spatial allocation can be followed in Figure 2 (B). The measured tree height was used for the modelling of the tree stem volume (m3) and the tree stem lateral surface area (SLSA, m2). Missing tree heights were calculated using Equation 4. The calculation principle is described in more detail by Padari (2020). For species-specific tree functions a modification of Ozolinš taper curve formula (Ozolinš, 2002; Silava, 1988) was used for calculating diameter at any tree height: γ(x)=1+(x20.01)(p(hh0)+q(d1.3d0)), \gamma \left( x \right) = 1 + \left( {{x^2} - 0.01} \right) \cdot \left( {p \cdot \left( {h - {h_0}} \right) + q \cdot \left( {{d_{1.3}} - {d_0}} \right)} \right), dl=d1.3γ(lh)(a0+a1(lh)+a2(lh)2+a3(lh)3+a4(lh)4+a5(lh)5+a6(lh)6)γ(1.3h)(a0+a1(1.3h)+a2(1.3h)2+a3(1.3h)3+a4(1.3h)4+a5(1.3h)5+a6(1.3h)6), {d_l} = {d_{1.3}} \cdot {{\gamma \left( {{l \over h}} \right) \cdot \left( {{a_0} + {a_1} \cdot \left( {{l \over h}} \right) + {a_2} \cdot {{\left( {{l \over h}} \right)}^2} + {a_3} \cdot {{\left( {{l \over h}} \right)}^3} + {a_4} \cdot {{\left( {{l \over h}} \right)}^4} + {a_5} \cdot {{\left( {{l \over h}} \right)}^5} + {a_6} \cdot {{\left( {{l \over h}} \right)}^6}} \right)} \over {\gamma \left( {{{1.3} \over h}} \right) \cdot \left( {{a_0} + {a_1} \cdot \left( {{{1.3} \over h}} \right) + {a_2} \cdot {{\left( {{{1.3} \over h}} \right)}^2} + {a_3} \cdot {{\left( {{{1.3} \over h}} \right)}^3} + {a_4} \cdot {{\left( {{{1.3} \over h}} \right)}^4} + {a_5} \cdot {{\left( {{{1.3} \over h}} \right)}^5} + {a_6} \cdot {{\left( {{{1.3} \over h}} \right)}^6}} \right)}}, where γ(x) = perturbation coefficient, dl = diameter at the distance from stem base l, cm; p, h0, q, and d0 are parameters for the perturbation coefficient; d1.3 = breast height diameter, cm; l = distance from stem base, m; h = tree height, m; a0, a1, …, a6 are coefficients of the taper curve.

Figure 2

The measured areas (light green colour) in Järvselja old-growth forest (compartment JS226) following the numbered sub-compartments. The locations of mapped downed trees and stems (A) and standing live and dead trees (B) are presented (green dots - live trees, blue dots - dead standing trees, red dots - standing snags, blue lines – downed trees and stems).

A volumetric transformation is needed for the tree stem volume calculation based on the taper curve. In the current study it was calculated using the following formula: v=π400000ldl2dl, v = {\pi \over {40000}}\int\limits_0^l {d_l^2dl,} where v = above bark stem volume up to the distance l from stem base (m3); l distance from stem base (for live tree equal to tree height) (m); dl =diameter at the distance l from stem base, cm (estimated by Equation 8 and 9).

SLSA above bark was calculated as follows: S=π1000ldldl, S = {\pi \over {100}}\int\limits_0^l {{d_l}\,dl,} where S = above bark SLSA up to the distance l from stem base 0, m2; l = distance from stem base (for live and standing dead trees) the tree height h, m; dl = diameter at the distance l from stem base l, cm (Equation 8 and 9).

Calculations of snag volume and SLSA

The measured standing snags (see Table 5 for general statistics and Figure 2 (B) for spatial arrangement) were divided into two groups: snags that were at least 1.3 m or taller in height and snags lower in height than 1.3 m (stratification needed due to different calculation methods). For taller snags h= or ≥1.3 m, breast height diameter was measured. For shorter snags, the diameter was measured below the breakage point and needed to have DBH calculated. The breast height diameter was calculated by fitting the species-specific stem taper curve (Equation 8 and 9) to the measured diameter at snag height. For all measured snags the modelled tree height was further calculated using Equation 4 and applying the selection procedure as described before (see also Figure 1) to obtain the species-specific H-D curve. For species-specific tree taper functions in Estonian conditions, a modified Ozolinš taper curve (Equation 8 and 9) was used for calculating the diameters in the measured snag height above the bark. The calculation principle is described in more detail by Padari (2020). Snag volume (m3) was calculated based on the snag breast height diameter d1.3, predicted height h and snag height l by Equation 10. Stem section lateral surface area above bark was calculated by Equation 11.

Calculation of downed deadwood volume and stem sections’ lateral surface area

For CWD on the site, both co-ordinates and diameters of the bottom and top sides of tree stem sections were measured (see Table 5 for general statistics and Figure 2 (A) for spatial placement). For spatial assessment, a tree stem section was cut if the section was placed outside a particular plot or sub-compartment using GIS analysis. The diameters were interpolated for the cut point and only the sections inside a particular plot or sub-compartment were used for volume calculation. For stem section volume calculations, a truncated cone formula was used: v=πl(d12+d1d2+d22)12000, v = {{\pi \cdot l \cdot \left( {d_1^2 + {d_1} \cdot {d_2} + d_2^2} \right)} \over {12000}}, where v = stem section volume (m3), l = stem length (m), d1 and d2 = diameters for the stem, cm; accordingly, d1 = diameter at the base of the stem section and d2 = diameter at the top of the stem section.

The stem sections’ lateral surface area was calculated using the stem lateral surface area of a truncated cone as follows: S=πl2+(d1d2)240000d1+d2200, S = \pi \cdot \sqrt {{l^2} + {{{{\left( {{d_1} - {d_2}} \right)}^2}} \over {40000}}} \cdot {{{d_1} + {d_2}} \over {200}}, where S = stem section lateral surface area from section base to section top, m2; l = distance from stem section base to the section top, m; d1 = diameter at stem section base, cm and d2 = diameter at stem section top, cm.

Calculation of accumulated C in woody biomass

For the estimation of wood density (wood dry mass per m3) and calculation of the respective C content of fresh (growing trees) timber and timber in different decomposition stages (dead and downed trees), data from different studies were employed. For the estimation of fresh (growing trees) wood densities in different tree species most of the densities used are available from Saarman & Veibri (2006); for grey alder the fresh wood density was available from (Uri et al., (2014) (see Table 4). Fresh wood C relative content was available for pine (Uri et al., 2019), birch (Uri et al., 2017) and black alder (Uri et al., 2014). In the case of fresh wood C relative content, we applied the pine value for spruce, the birch value for aspen and the black alder value for grey alder.

For the assessment of C content in dead and downed stems along the different decay classes (fresh wood and five decay classes), we used available measures on wood density and the relative C content for six tree species. Those are Scots pine, Norway spruce, silver and downy birch, black alder, common alder and common aspen measured by Köster et al. (2015). We then used these measurements (Köster et al., 2015) to derive the wood density dynamics as depending on the wood decay classes. The results of the regression analysis with parameter estimates are presented in Table 3.

Wood density and related model parameters for Equation 14.

Species Fresh wood basic density d0, g/cm3 Equation constants

a0 a1
Pedunculate oak 584.4 529.58 −88.546
European ash 574.7 529.58 −88.546
Elm family 550.4 529.58 −88.546
Larch family 547.7 433.39 −55.003
Fir family 317.0 431.07 −56.649
Willow family 387.0 435.23 −58.931

Wood density along different decay classes was estimated as follows: dDecay=d0+a1d0Decaya0, {d_{Decay}} = {d_0} + {{{a_1} \cdot {d_0} \cdot Decay} \over {{a_0}}}, where dDecay = basic density in decay class, g/m3; d0 – fresh wood basic density, g/m3 (Table 3); Decay – decay class (1 to 5); a0, a1 – model constants (Table 4).

Wood density and C relative content according to tree species and decay class (Köster et al., 2015; Saarman & Veibri, 2006; Uri et al., 2019, 2017, 2014).

Variable Tree species Decay class

fresh 1 2 3 4 5
Basic density, g/cm3 SP 422.71 381.12 337.22 258.82 233.72 141.82
NS, DF 375.01 410.72 354.22 280.72 191.32 124.82
BI, ER 538.21 466.62 326.52 230.02 175.92 112.12
CA, LI, NA 419.21 391.32 330.62 230.62 161.12 60.72
BAR 440.51 422.42 289.42 212.92 158.92 95.62
CAR, CH, BC, OD 396.03 426.92 345.32 220.72 184.92 153.62
PO, NM 584.41 486.7 389.0 291.3 193.5 95.8
AH 574.71 478.6 382.5 286.4 190.3 94.2
EL 550.41 458.4 366.3 274.3 182.3 90.2
WI 387.01 334.6 282.2 229.8 177.4 125.0
LF 547.71 478.2 408.7 339.2 269.7 200.1
FF 317.01 275.3 233.7 192.0 150.4 108.7

Carbon relative content, % SP, LF 47.464 49.032 49.262 49.562 49.582 50.212
NS, DF, FF 47.46 48.352 48.312 47.932 49.602 51.332
BI, ER, WI, PO, AH, NM, EL 47.005 47.162 47.692 47.452 48.802 50.122
CA, LI, NA 47.00 47.192 47.372 47.382 46.562 46.312
BAR 47.30 47.892 48.242 48.072 48.352 48.112
CAR, CH, BC, OD 47.303 48.022 48.072 48.712 47.952 48.042

Tree species and tree species codes are as follows: CA – common aspen; EL – elm family; BI – silver birch and downy birch; NS – Norway spruce; DF – Douglas fir; FF – fir family; BAR – black alder; CAR – common alder; SP – Scots pine; LF – larch family; ER – European rowan; LI – linden family; WI – willow family; AH – European ash; BC – bird cherry; NM – Norway maple; CH – Common hazel; PO – Pedunculate oak; OD – other deciduous; NA – species undeterminable.

The original values from earlier studies are indicated as follows:

(Saarman & Veibri, 2006)

(Köster et al., 2015)

(Uri et al., 2014)

(Uri et al., 2019)

(Uri et al., 2017)

For the estimation of the C content for all tree species represented in Järvselja old-growth forest the following substitutions were used: for rowan, the birch values were used, for linden and unknown species the aspen values were used, for hazel, bird cherry and other broadleaved species grey alder values were used and for fir the spruce values were used. The wood density estimations are calculated using Equation 14 and presented in Table 4.

Stem wood C relative content was estimated as follows: C=VdC%100, C = {{V \cdot d \cdot C\% } \over {100}}, where C is carbon mass, kg; V is tree/snag/laying tree volume, m3; d is wood density (dry weight/raw volume), kg/m3; C% is carbon relative content, %.

Results

With this study we captured the data from 6205 live trees, 1119 snags, 270 standing dead trees (see Table 5) and 2983 dead wood trunks in Järvselja old-growth forest. The most common tree species in the woody cohorts were Norway spruce and linden (Tilia cordata Mill.). The most common decay classes for standing dead trees were 1 and 2, and decay classes 4 and 5 for snags and downed trees.

Summary statistics of the measured data.

Variable Live trees Standing dead wood Snags Downed dead wood
Number of measured trees, (N) 6205 270 1119 2983
Number of estimated trees in the compartment JS226 16227 1073 2749 6112
Dominating species* NS, SP, WI, LI, BAR NS, SP NS, SP, CA, BAR NS, SP
Mean diameter, D (cm) 22.5 22.8 31.3 26.9
Mean height/length, H/L (m) 21.3 21.8 3.9 8.6
Mean decay class - 1.91 2.60 3.03
Mean stem volume, V (m3) 0.43 0.43 0.21 0.34
Mean stem lateral surface area, SLSA (m2) 7.61 7.95 2.62 5.15

Tree species codes are as follows: NS = Norway spruce; SP = Scots pine; WI = willow family; LI = linden family; BAR = black alder; CA = common aspen.

Table 6 condenses the major results of the main stand characteristics as given in different sub-compartments by the measured structural stand cohorts. With a couple of exceptions all structural cohorts (live trees, dead standing trees, snags and downed trees) were recorded in all sub-compartments. Only live trees and snags were recorded in the sub-compartments 1 and 14. All the stands in Järvselja old-growth forest were mixed species stands and the most abundant tree species in all cohorts is Norway spruce (NS). Spruce is also the most common species in the current structural cohorts of dead trees, snags and downed stems.

Stand characteristics in sub-compartments.

Sub-compartment Area, ha Cohort Stand composition1 N G M S C

per hectare2
1 0.60 Live 50NS25BAR19SP2BI2WI1AH1LI 1505 21.2 198.0 4696 38.263
Snags 87NA7NS6BAR 127 6.4 7.5 119 0.623

Downed 51NS2SP+BAR47NA 281 137.8 1916 21.968

2 0.74 Live 62SP38NS 905 31.4 339.5 6585 65.224
Dead 57NS26BI17SP 151 4.4 44.7 1038 6.918
Snags 74SP22NS4NA 82 1.5 9.9 246 1.579

Downed 30SP27BI13NS30NA 164 23.5 499 3.795

3 1.13 Live 58SP29NS9BAR4BI 952 31.9 309.0 6509 60.798
Dead 59NS41SP 167 1.3 8.8 340 1.464
Snags 35SP34BI30NS1NA 134 2.8 10.3 264 1.595

Downed 70NS10BI1SP17NA 184 16.8 441 2.545

4 3.37 Live 59SP26NS9BI6BAR 921 33.2 330.2 6656 66.027
Dead 80SP20NS 63 2.6 24.6 500 4.319
Snags 44SP38NA10BI8NS 183 7.8 37.7 655 5.999

Downed 28NS17SP16BI2CA37NA 310 53.2 1197 7.167

5 1.11 Live 33WI22LI11AH11BI10NM5RE1CA1DF 1435 36.6 360.3 7063 77.277
Dead 100NS 14 2.8 37.4 406 6.401
Snags 67CA29NS3NA1RE 225 35.3 105.3 805 11.803

Downed 40NS9BI3NM1BAR+WI+EL47NA 394 318.0 3650 37.188

6 0.25 Live 68LI28NS3NM1AH 501 38.1 497.6 7276 97.085
Dead 100NS 4 0.1 0.6 22 0.123
Snags 62NS22NA12LI4BAR 61 8.8 7.8 71 1.070

Downed 59NS7LI4BAR3BI27NA 151 85.8 1044 11.600

7 1.37 Live 56NS41SP2BAR1LI 1032 29.9 310.8 6170 58.481
Dead 55NS45SP 146 6.8 74.0 1480 11.615
Snags 99NS1NA 167 7.4 19.4 303 3.035

Downed 87NS7SP6NA 375 56.7 1325 7.928

8 1.71 Live 43NS33SP13BAR10BI1OD 828 37.1 369.5 6886 72.923
Dead 62NS38SP 79 3.6 35.1 665 6.179
Snags 62NS18SP10BAR4NA4FF2BI 152 6.0 19.8 367 3.120

Downed 76NS15BI5SP4NA 308 48.2 1012 6.774

9 2.52 Live 56NS15LI14BAR7BI5CA2NM1SP 711 40.0 497.7 7249 96.452
Dead 97NS2NM1LI 26 1.3 16.4 235 3.033
Snags 44NS17BI10NA9BAR7CA6LI6CAR1RE 131 16.6 40.2 367 5.717

Downed 56NS9BI6BAR3LI+CA+NM+SP+AH26NA 268 160.8 1883 18.707

10 0.64 Live 68NS15BAR8BI5SP1NM1CA1BC1OD 819 25.5 260.6 5449 49.877
Dead 80NS15SP4BAR1NM 50 2.0 20.8 401 3.514
Snags 76NS11NA7BI4BAR2SP 233 19.0 33.0 452 3.806

Downed 75NS10BI6FF3BAR2SP4NA 439 138.5 2213 15.252

11 0.89 Live 71NS11SP10BAR5BI1NM1FF1OD 661 26.0 296.7 5288 56.064
Dead 91NS6SP3AH 34 2.0 23.6 397 4.135
Snags 81NS8SP6NA2BI1BAR1FF1AH 157 15.6 23.4 272 3.106

Downed 73NS8BI6SP2AH1BAR+FF10NA 384 151.8 2334 17.569

12 2.31 Live 50BAR31NS7BI6SP5LI1OD 578 32.7 361.9 5530 72.801
Dead 66NS15BAR9BI8SP1LI1AH 20 1.2 12.7 201 2.266
Snags 36BAR28NA22NS10BI1CAR1AH1OD 83 7.1 20.7 195 2.393

Downed 48NS14BI11BAR4SP2LI2AH1NM+FF18NA 394 98.4 1807 14.534

13 0.95 Live 61LI29NS5NM2AH2BAR1BI 661 38.0 467.8 7076 92.608
Dead 99NS1BAR 15 0.6 7.1 120 1.161
Snags 59NS24NA11LI4BAR2OD 77 10.7 13.1 132 1.686

Downed 57NS8BAR7LI3SP1BI1AH1NM+EL+FF+ER+BC22NA 256 154.0 1768 20.462

14 0.15 Live 38BAR26LI17NS10BI4AH2BC2FF1NM 907 23.5 214.5 4877 44.431
Snags 69NS21NM5BI5NA 172 16.1 16.6 219 1.735

Downed 56NS20BI5BAR2NM+BC+EL+LI17NA 687 163.7 2395 15.524

15 1.64 Live 48NS23LI11BAR6CA4NM4BI3AH1OD 562 31.4 386.6 5784 76.092
Dead 45NS20BI18AH13NM4LI 12 1.2 15.0 179 3.005
Snags 56NS13BI9NA8LI8BAR2AH1CA1RE1OD 111 11.4 24.5 257 3.467

Downed 50NS6LI5BI5NM4BAR3AH3CA+FF+BC+WI+ER+EL24NA 308 136.6 1898 19.182

Compartment 19.38 Live 46NS20LI18BAR6SP5BI2NM2CA 1AH+WI+ER+FF+EL+BC+DF+OD 837 33.3 363.5 6375 71.717
Dead 75NS10SP6BI3BAR3NM2AH1LI 55 2.3 24.0 440 4.115
Snags 49NS11BAR10BI5CA4LI3SP1AH1RE2OD14NA 142 10.9 29.7 372 4.084

Downed 53NS9BI4BAR3SP2LI1NM1AH1CA+FF+WI+EL+BC+ER26NA 315 107.6 1624 13.889

Tree species and tree species codes are as follows: CA – common aspen; BI – silver birch and downy birch; NS – Norway spruce; BAR – black alder; SP – Scots pine; WI – willow family; AH – European ash; LI – linden family; ER – European rowan; BC – bird cherry; NM – Norway maple; DF – Douglas fir; FF – fir family; OD – other deciduous species; NA – species undeterminable.

Variables as follows: N – number of trees, G – basal area at DBH height, m2, M – standing volume, m3, S – cumulative stem lateral surface area, m2, C – carbon content, Mg.

The live tree basal area ranged between 21.3 and 38.1 m2 per hectare indicating that the stands growing in Järvselja old-growth forest have maintained high basal area despite moving beyond the even-aged, stem-exclusion stage of development. The standing volume of live trees was 203–498 m3 per hectare, indicating a generally high stocking density. The standing dead wood volume ranged from 7 to 73 m3 per hectare. The largest standing volume was registered in the Aegopodium site type sub-compartments.

When the average diameter of standing live and dead trees was similar (22.5 and 22.8, accordingly), then in the case of standing snags and downed stems (31.3 and 26.9, accordingly) it is considerably higher and indicating a break-down of larger trees. The downed stems’ dead wood volume ranged from 16.8 to 163.7 with an average of 107.6 m3 per hectare.

The three H-D curves (1) Näslund function with iterated regression analysis, (2) Chapman-Richards function with previously fitted parameters and (3) Näslund function, also using the previously fitted parameters were used to calculate the tree heights which were subsequently compared to actual measured tree heights. For the Näslund function with the iterated regression (Equation 4) and the scaled parameters the lowest root mean squared error (RMSE) was found. Only for two tree species (grey alder and common hazel) the Chapman-Richards function with previously fitted parameters showed better results (see Figure 3).

Figure 3

Model residual comparison according to main tree species. Tree species and tree species codes are as follows: CA – common aspen; EL – elm family; BI – silver birch and downy birch; NS – Norway spruce; BAR – black alder; CAR – common alder; SP – Scots pine; WI – willow family; ER – European rowan; LI – linden family; AH – European ash; CH – Common hazel; BC – bird cherry; NM – Norway maple.

The share of standing live and dead trees and the share of downed dead wood in different stands in the sub-compartments are presented in Table 7. The lowest share of live trees was 43.9% in sub-compartment 5 and the highest 89.6 in sub-compartment 3; the average share for the compartment was 69.3%. The share of downed dead wood ranged from 4.9% to 41.5 % and 20.5% on average for the compartment. The occurrence of high live tree biomass and considerably high downed wood volume indicate that with a couple of exceptions there is no strong evidence of structural stand break-down in Järvselja old-growth forest. Only in the case of sub-compartments 1, 5, and 14 a clear heavy disturbance or breakdown effect is recorded.

Volume and C content of live and dead trees in sub-compartments.

Sub-compartment Volume, m3/ha Live, % Dead standing, % Dead laying, % C content MgC/ha Live, % Dead standing, % Dead laying, %
1 343.4 57.7 2.2 40.1 60.85 62.9 1.0 36.1
2 417.5 81.3 13.1 5.6 77.52 84.1 11.0 4.9
3 345.0 89.6 5.5 4.9 66.40 91.6 4.6 3.8
4 445.7 74.1 14.0 11.9 83.51 79.1 12.3 8.6
5 820.9 43.9 17.4 38.7 132.67 58.3 13.7 28.0
6 591.7 84.1 1.4 14.5 109.88 88.4 1.1 10.5
7 460.9 67.4 20.3 12.3 81.06 72.1 18.1 9.8
8 472.6 78.2 11.6 10.2 89.00 81.9 10.5 7.6
9 715.2 69.6 7.9 22.5 123.91 77.8 7.1 15.1
10 452.8 57.5 11.9 30.6 72.45 68.8 10.1 21.1
11 495.4 59.9 9.5 30.6 80.87 69.3 9.0 21.7
12 493.7 73.3 6.8 19.9 91.99 79.1 5.1 15.8
13 641.9 72.9 3.1 24.0 115.92 79.9 2.5 17.6
14 394.7 54.3 4.2 41.5 61.69 72.0 2.8 25.2
15 562.7 68.7 7.0 24.3 101.75 74.8 6.4 18.8

Compartment 524.9 69.3 10.2 20.5 93.81 76.5 8.7 14.8
Discussion

Our results of species occurrence in structural cohorts (live trees, dead standing trees, snags and downed trees) in all sub-compartments indicate that Järvselja old-growth forest is moving into the old multiaged condition (Frelich, 2002) with maintained or slightly elevated tree species richness. Although the increase in dominating and co-dominating tree species is desired and is expected after the stand was set aside from anthropogenic disturbances, there can be complex reasons for such a development (Jõgiste et al., 2018). The most straight-forward reason is that the late-successional tree species move in or increase the extent of areas they occupy (Bormann & Likens, 1994; Kern et al., 2021). In the old multiaged condition it is often related to gap size variation and where the large gaps allow persistence or re-entry of species mid-tolerant of shade, or in a few cases, even shade-intolerant species can be maintained at low levels when colonizing gaps (Bormann & Likens, 1994; Frelich, 2002).

The long-term dynamics of live tree species composition in Järvselja old-growth forest is shown in Figure 4. During the last 100 years, the number of tree species has slightly increased with the inclusion of linden and black alder (Kasesalu, 2004; Kollom, 2011; Krigul, 1940). Birch, common aspen and Scots pine have been slightly but gradually declining (Kollom, 2011). In late succession the shade-intolerant species decline in abundance, compared to stand initiation and stem exclusion conditions (Frelich, 2002), but the regeneration is not totally excluded. This also coincides well with the concept of cumulative disturbance severity, where the vertical direction of disturbance (i.e., starting in the canopy and then to the understory or vice versa) is intermixed with the degree of tree mortality (Kern et al., 2021). In Järvselja old-growth forest we see the dominance of shade-tolerant species (e.g., Norway spruce and linden species) that maintain high stem biomass volume and during demographic transition to old multiaged stages, they move into the co-dominating or overstory layer (Kern et al., 2021).

Figure 4

The long-term dynamics of stand composition and average standing live stem-wood volume. Tree species and tree species codes are as follows: CA – common aspen; EL – elm family; BI – silver birch and downy birch; NS – Norway spruce; BAR – black alder; SP – Scots pine; LI – linden family; AH – European ash; NM – Norway maple; DF – Douglas fir; FF – fir family; OD – other deciduous.

It is interesting to see Norway spruce presence in all structural cohorts, including in the dead wood and downed trees strata. This can be seen in the long-term tree species composition dynamics in the forest management inventory data (Figure 4 A) where spruce slowly catches up and overtakes aspen and birch as stands age. Depending on the stand age and forest disturbance dynamics we can expect the spruce to be favoured by patch dynamics caused by gap formation, with shifting mosaic development. Still, it is difficult to say based on the current study if it is a shifting mosaic with changing composition in each gap (neutral dynamics) or shifting mosaic with gaps holding the similar composition as before (positive dynamics) (Frelich, 2016) or it could be a combination of both conditions. Earlier studies from more boreal conditions by Sirén (1955) indicated that spruce acquired dominance approximately 80 years after stand replacing disturbances and when the stand variables were getting close to secondary succession levels (Shorohova et al., 2009). The results of the latest revisited study of Sirén’s work (Aakala & Keto-Tokoi, 2011) coincide with our results that Norway spruce is still able to replace other species as well as itself in successional dynamics.

A separate question arises when trying to set the average standing volume results of the current study into context with the long-term forest management inventory data (see Figure 4 B) for comparison on the whole-compartment level. A clear difference becomes visible in the average standing volume estimate of the live trees structural cohort, where, in comparison with the latest forest management inventory results in 2010 and 2016, the average standing volume in our study indicates a considerably higher average standing volume in 2013. This situation, where in fully stocked conditions a systematic deviation in comparison with forest management inventory data becomes evident, has been reported by Arumäe & Lang (2016). There can be several contributing reasons for this systematic deviation, but the main origin of the difference is likely to be in the differences of methods used: single-tree sampling versus stand-wise variable-size point-based sampling (known also as Bitterlich sampling, or angle-count sampling). The variable-size point-based sampling has its hidden sets of field sampling based shortcomings that were reported by multiple authors (Eastaugh & Hasenauer, 2013; Packard & Radtke, 2007; Piqué et al., 2011; Yamada & Ohno, 2016). In the use of variable-size point-based sampling methods in Estonia we can add a methodological difference in the volume assessment to the possible shortcomings in field sampling. In the volume calculation the stem form factor and tree height are estimated following the stem form of the single tree species growing in monocultures. This does not agree with the variable growing conditions in old-growth multi-species and multiaged stands (Nigul et al., 2021). The deviation is high in spite of the fact that in assessing volume in the current study stem form was calculated following Ozolinš (2002) where stem volume was overestimated up to four percent (Padari, 2020).

Based on different studies carried out in the last few decades, measured volumes of dead wood in naturally developing forests in Estonia vary greatly. There are several contributing factors: a) the time since the protection status (stand measurements often occur on protected sites), b) the current offset of dominant tree species and site conditions, c) stand age and/or d) the imprints of forest disturbance history (Jõgiste et al., 2018). From earlier studies in southern Estonia (Table 8, A) the amount of dead wood was found to be moderate, around 30 m3/ha; whereas in central Estonia (Table 8, B) the amount of dead wood was very low – less than 10 m3/ha. In northern Estonia, (Table 8, B) the amount in strictly protected nature reserves was in average around 85 m3/ha and in restricted management zones around 65 m3/ha. Studies conducted on the data collected throughout Estonia (Table 8, D and E) showed that the volume of dead wood on protected sites varies from 10 to almost 200 m3/ha, depending on different site characteristics and management regimes. The measurements of dead wood occurrence in the current study fall in the range of previous studies in Järvselja old-growth forests. Krigul (1961) reported dead wood amounts in 1960 in broadleaved species dominated stands as from 17.4 m3 in black alder stands and up to 146.8 m3 in common aspen dominated stands and in conifer dominated stands from 36.4 in Scots pine stands up to 177.2 in Norway spruce dominated stands.

Studies carried out in Estonia containing information about the volume of coarse woody debris (CWD) in unmanaged forests (located often in nature protection areas), or locating in both, unmanaged and commercial forests.

Location Citation Site description Dominant tree species1 Protection/management regime2 Average amount of dead wood, m3/ha
A Karula National Park (Köster et al., 2005) water swamp forest, dry boreal forest NS, BI, SP, BAR NR and RMZ 27.6
B Alam-Pedja Nature Reserve (Lõhmuset al., 2005) mostly forests on wet soils BI, SP, NS, CA, CAR, BAR, other MAN 9.0
NR 6.2
C Lahemaa National Park (Köster et al., 2005) dry boreal, heath and ombro-trophic bog forests NS, BI, SP NR 85.9
RMZ 63.2
D Estonia (Lõhmus & Kraut, 2010) dry boreal forest SP mature MAN 10
meso-eutrophic forest conifer/deciduous mixtures 62
eutrophic boreo-nemoral forest conifer/deciduous mixtures 43
water swamp forest BAR, BI 52
dry boreal forest SP NAT 36
meso-eutrophic forest conifer/deciduous mixtures 144
eutrophic boreo-nemoral forest conifer/deciduous mixtures 198
water swamp forest BAR, BI 140
(Põldveer et al., 2020) mixed oligo-mesotrophic and mesotrophic forests on mineral soils SP MAN 28.9
REC 33.9
NAT 49.3
NS MAN 15.3
REC 46.6
NAT 85.1

Tree species and tree species codes are as follows: CA – Common aspen; BI – silver birch and downy birch; NS – Norway spruce; BAR – black alder; CAR – common alder; SP – Scots pine.

Protection/management regime are as follows: NR – in nature reserve, RMZ – in special or restricted management zone, NAT – in natural forest without visible signs of direct human influence, REC – in recovering forest with possible signs of past management, yet the present human impact on forest structure is insignificant, MAN – commercial forest.

Most of the studies presented in Table 8 did not provide information about the decay stages of dead wood; the exceptions were Lõhmus et al. (2005) study where the decay stages of snags and downed trees, and Lõhmus & Kraut (2010) study where the decay stages of downed trees were estimated. The results of the first study found that throughout the study area, dead wood was present in most decay stages; however, well-decayed wood was often more abundant in commercial forests. In the current study the decaying wood was mostly in the decay classes 1, 3 and 5. The existence of dead wood in all decay stages in forest stands indicated either gap dynamics or the cascading effect of windthrow in the case of slowly progressing cohort dynamics (Shorohova et al., 2009).

Tree canopy structure and tree mortality patterns are related to certain site conditions, tree age and diameter distribution, dominant species, severity of disturbance, time since the last disturbance, and spatial structure in natural boreal forests (Shorohova et al., 2009). Stand dynamic types “even-aged dynamics”, “cohort dynamics” and “fine-scale gap dynamics” can be considered as typical dynamic patterns in old-growth forests (Shorohova & Kapitsa, 2015). The structure and dynamics of Järvselja primeval forest are more likely referred to as cohort dynamics than to fine-scale gap dynamics. However, an ecosystem dominated by small-scale gap dynamics would involve the merger of heterogeneous patches that may be communicated through tree diameter distributions, which often converge on a rotated sigmoid or reverse-J distribution (Zenner et al., 2018; Zenner et al., 2020). For better understanding of forest successional development in the Järvselja old-growth forest compartment, an in-depth analysis of tree structural cohort size distributions is needed in combination with the analysis of the distribution of dead wood decay stages.

Figure 1

H-D model calculation scheme based on the Näslund function.
H-D model calculation scheme based on the Näslund function.

Figure 2

The measured areas (light green colour) in Järvselja old-growth forest (compartment JS226) following the numbered sub-compartments. The locations of mapped downed trees and stems (A) and standing live and dead trees (B) are presented (green dots - live trees, blue dots - dead standing trees, red dots - standing snags, blue lines – downed trees and stems).
The measured areas (light green colour) in Järvselja old-growth forest (compartment JS226) following the numbered sub-compartments. The locations of mapped downed trees and stems (A) and standing live and dead trees (B) are presented (green dots - live trees, blue dots - dead standing trees, red dots - standing snags, blue lines – downed trees and stems).

Figure 3

Model residual comparison according to main tree species. Tree species and tree species codes are as follows: CA – common aspen; EL – elm family; BI – silver birch and downy birch; NS – Norway spruce; BAR – black alder; CAR – common alder; SP – Scots pine; WI – willow family; ER – European rowan; LI – linden family; AH – European ash; CH – Common hazel; BC – bird cherry; NM – Norway maple.
Model residual comparison according to main tree species. Tree species and tree species codes are as follows: CA – common aspen; EL – elm family; BI – silver birch and downy birch; NS – Norway spruce; BAR – black alder; CAR – common alder; SP – Scots pine; WI – willow family; ER – European rowan; LI – linden family; AH – European ash; CH – Common hazel; BC – bird cherry; NM – Norway maple.

Figure 4

The long-term dynamics of stand composition and average standing live stem-wood volume. Tree species and tree species codes are as follows: CA – common aspen; EL – elm family; BI – silver birch and downy birch; NS – Norway spruce; BAR – black alder; SP – Scots pine; LI – linden family; AH – European ash; NM – Norway maple; DF – Douglas fir; FF – fir family; OD – other deciduous.
The long-term dynamics of stand composition and average standing live stem-wood volume. Tree species and tree species codes are as follows: CA – common aspen; EL – elm family; BI – silver birch and downy birch; NS – Norway spruce; BAR – black alder; SP – Scots pine; LI – linden family; AH – European ash; NM – Norway maple; DF – Douglas fir; FF – fir family; OD – other deciduous.

Wood density and related model parameters for Equation 14.

Species Fresh wood basic density d0, g/cm3 Equation constants

a0 a1
Pedunculate oak 584.4 529.58 −88.546
European ash 574.7 529.58 −88.546
Elm family 550.4 529.58 −88.546
Larch family 547.7 433.39 −55.003
Fir family 317.0 431.07 −56.649
Willow family 387.0 435.23 −58.931

Earlier studies on Järvselja old-growth forest.

Author(s) Publishing year Measurement year/period Inventory type* Citation
Kollom, M. 2011 1922–2010 3 (Kollom, 2011)
Kasesalu, H. 2001 2001 1 (Kasesalu, 2001)
Kasesalu, A. 1985, 1993 1982, 1983 1 (Kasesalu, 1985), (Kasesalu, 1993)
Irdt, E., Rebane, H. 1985 1954–1985 1; 2 (Irdt & Rebane, 1985)
Pettai, T. 1985 1922–1984 3 (Pettai, 1985)
Raukas, A. 1967 1958–1959 1; 3 (Raukas, 1967)
Krigul, T. 1961 1958, 1959 3 (Krigul, 1961)
Krigul, T. 1940 1939 3 (Krigul, 1940)
Visnapuu, M. 1927 1927 3 (Krigul, 1940)

Stand characteristics in sub-compartments.

Sub-compartment Area, ha Cohort Stand composition1 N G M S C

per hectare2
1 0.60 Live 50NS25BAR19SP2BI2WI1AH1LI 1505 21.2 198.0 4696 38.263
Snags 87NA7NS6BAR 127 6.4 7.5 119 0.623

Downed 51NS2SP+BAR47NA 281 137.8 1916 21.968

2 0.74 Live 62SP38NS 905 31.4 339.5 6585 65.224
Dead 57NS26BI17SP 151 4.4 44.7 1038 6.918
Snags 74SP22NS4NA 82 1.5 9.9 246 1.579

Downed 30SP27BI13NS30NA 164 23.5 499 3.795

3 1.13 Live 58SP29NS9BAR4BI 952 31.9 309.0 6509 60.798
Dead 59NS41SP 167 1.3 8.8 340 1.464
Snags 35SP34BI30NS1NA 134 2.8 10.3 264 1.595

Downed 70NS10BI1SP17NA 184 16.8 441 2.545

4 3.37 Live 59SP26NS9BI6BAR 921 33.2 330.2 6656 66.027
Dead 80SP20NS 63 2.6 24.6 500 4.319
Snags 44SP38NA10BI8NS 183 7.8 37.7 655 5.999

Downed 28NS17SP16BI2CA37NA 310 53.2 1197 7.167

5 1.11 Live 33WI22LI11AH11BI10NM5RE1CA1DF 1435 36.6 360.3 7063 77.277
Dead 100NS 14 2.8 37.4 406 6.401
Snags 67CA29NS3NA1RE 225 35.3 105.3 805 11.803

Downed 40NS9BI3NM1BAR+WI+EL47NA 394 318.0 3650 37.188

6 0.25 Live 68LI28NS3NM1AH 501 38.1 497.6 7276 97.085
Dead 100NS 4 0.1 0.6 22 0.123
Snags 62NS22NA12LI4BAR 61 8.8 7.8 71 1.070

Downed 59NS7LI4BAR3BI27NA 151 85.8 1044 11.600

7 1.37 Live 56NS41SP2BAR1LI 1032 29.9 310.8 6170 58.481
Dead 55NS45SP 146 6.8 74.0 1480 11.615
Snags 99NS1NA 167 7.4 19.4 303 3.035

Downed 87NS7SP6NA 375 56.7 1325 7.928

8 1.71 Live 43NS33SP13BAR10BI1OD 828 37.1 369.5 6886 72.923
Dead 62NS38SP 79 3.6 35.1 665 6.179
Snags 62NS18SP10BAR4NA4FF2BI 152 6.0 19.8 367 3.120

Downed 76NS15BI5SP4NA 308 48.2 1012 6.774

9 2.52 Live 56NS15LI14BAR7BI5CA2NM1SP 711 40.0 497.7 7249 96.452
Dead 97NS2NM1LI 26 1.3 16.4 235 3.033
Snags 44NS17BI10NA9BAR7CA6LI6CAR1RE 131 16.6 40.2 367 5.717

Downed 56NS9BI6BAR3LI+CA+NM+SP+AH26NA 268 160.8 1883 18.707

10 0.64 Live 68NS15BAR8BI5SP1NM1CA1BC1OD 819 25.5 260.6 5449 49.877
Dead 80NS15SP4BAR1NM 50 2.0 20.8 401 3.514
Snags 76NS11NA7BI4BAR2SP 233 19.0 33.0 452 3.806

Downed 75NS10BI6FF3BAR2SP4NA 439 138.5 2213 15.252

11 0.89 Live 71NS11SP10BAR5BI1NM1FF1OD 661 26.0 296.7 5288 56.064
Dead 91NS6SP3AH 34 2.0 23.6 397 4.135
Snags 81NS8SP6NA2BI1BAR1FF1AH 157 15.6 23.4 272 3.106

Downed 73NS8BI6SP2AH1BAR+FF10NA 384 151.8 2334 17.569

12 2.31 Live 50BAR31NS7BI6SP5LI1OD 578 32.7 361.9 5530 72.801
Dead 66NS15BAR9BI8SP1LI1AH 20 1.2 12.7 201 2.266
Snags 36BAR28NA22NS10BI1CAR1AH1OD 83 7.1 20.7 195 2.393

Downed 48NS14BI11BAR4SP2LI2AH1NM+FF18NA 394 98.4 1807 14.534

13 0.95 Live 61LI29NS5NM2AH2BAR1BI 661 38.0 467.8 7076 92.608
Dead 99NS1BAR 15 0.6 7.1 120 1.161
Snags 59NS24NA11LI4BAR2OD 77 10.7 13.1 132 1.686

Downed 57NS8BAR7LI3SP1BI1AH1NM+EL+FF+ER+BC22NA 256 154.0 1768 20.462

14 0.15 Live 38BAR26LI17NS10BI4AH2BC2FF1NM 907 23.5 214.5 4877 44.431
Snags 69NS21NM5BI5NA 172 16.1 16.6 219 1.735

Downed 56NS20BI5BAR2NM+BC+EL+LI17NA 687 163.7 2395 15.524

15 1.64 Live 48NS23LI11BAR6CA4NM4BI3AH1OD 562 31.4 386.6 5784 76.092
Dead 45NS20BI18AH13NM4LI 12 1.2 15.0 179 3.005
Snags 56NS13BI9NA8LI8BAR2AH1CA1RE1OD 111 11.4 24.5 257 3.467

Downed 50NS6LI5BI5NM4BAR3AH3CA+FF+BC+WI+ER+EL24NA 308 136.6 1898 19.182

Compartment 19.38 Live 46NS20LI18BAR6SP5BI2NM2CA 1AH+WI+ER+FF+EL+BC+DF+OD 837 33.3 363.5 6375 71.717
Dead 75NS10SP6BI3BAR3NM2AH1LI 55 2.3 24.0 440 4.115
Snags 49NS11BAR10BI5CA4LI3SP1AH1RE2OD14NA 142 10.9 29.7 372 4.084

Downed 53NS9BI4BAR3SP2LI1NM1AH1CA+FF+WI+EL+BC+ER26NA 315 107.6 1624 13.889

H-D model parameters from Nigul et al. (2021) study.

Tree species Chapman-Richards function (Eq. 5) Näslund function (Eq. 1)

a b c a b
SP 30.67342 0.032552 0.80312 1.59472 0.30835
NS 39.02975 0.033294 1.18444 2.16428 0.27970
BI 33.57059 0.023891 0.62549 1.19810 0.31378
CA 37.72567 0.043817 0.89476 1.28707 0.28380
BAR 30.46570 0.036539 0.85649 1.43745 0.30966
LI 33.70126 0.040448 1.07656 1.69100 0.29469
NM 33.17741 0.030304 0.76981 1.26131 0.31403
Other 34.06000 0.035000 0.91100 1.51900 0.30000

Summary statistics of the measured data.

Variable Live trees Standing dead wood Snags Downed dead wood
Number of measured trees, (N) 6205 270 1119 2983
Number of estimated trees in the compartment JS226 16227 1073 2749 6112
Dominating species* NS, SP, WI, LI, BAR NS, SP NS, SP, CA, BAR NS, SP
Mean diameter, D (cm) 22.5 22.8 31.3 26.9
Mean height/length, H/L (m) 21.3 21.8 3.9 8.6
Mean decay class - 1.91 2.60 3.03
Mean stem volume, V (m3) 0.43 0.43 0.21 0.34
Mean stem lateral surface area, SLSA (m2) 7.61 7.95 2.62 5.15

Studies carried out in Estonia containing information about the volume of coarse woody debris (CWD) in unmanaged forests (located often in nature protection areas), or locating in both, unmanaged and commercial forests.

Location Citation Site description Dominant tree species1 Protection/management regime2 Average amount of dead wood, m3/ha
A Karula National Park (Köster et al., 2005) water swamp forest, dry boreal forest NS, BI, SP, BAR NR and RMZ 27.6
B Alam-Pedja Nature Reserve (Lõhmuset al., 2005) mostly forests on wet soils BI, SP, NS, CA, CAR, BAR, other MAN 9.0
NR 6.2
C Lahemaa National Park (Köster et al., 2005) dry boreal, heath and ombro-trophic bog forests NS, BI, SP NR 85.9
RMZ 63.2
D Estonia (Lõhmus & Kraut, 2010) dry boreal forest SP mature MAN 10
meso-eutrophic forest conifer/deciduous mixtures 62
eutrophic boreo-nemoral forest conifer/deciduous mixtures 43
water swamp forest BAR, BI 52
dry boreal forest SP NAT 36
meso-eutrophic forest conifer/deciduous mixtures 144
eutrophic boreo-nemoral forest conifer/deciduous mixtures 198
water swamp forest BAR, BI 140
(Põldveer et al., 2020) mixed oligo-mesotrophic and mesotrophic forests on mineral soils SP MAN 28.9
REC 33.9
NAT 49.3
NS MAN 15.3
REC 46.6
NAT 85.1

j.fsmu-2021-0009.apptab.002

Site type Tree species1 N a b x R2 RMSE p-value
Oxalis drained swamp NS 707 2.01436 0.284572 0.72 0.8727 2.4486 <0.0001
SP 20 2.16767 0.284568 0.66 0.2679 3.1509 0.0706
AC 738 2.00648 0.285206 0.71 0.8771 2.4693 <0.0001

BI 110 1.15131 0.310943 0.67 0.7346 2.5354 <0.0001
BAR 147 1.24516 0.316697 0.75 0.7501 2.5076 <0.0001
ER 15 0.57528 0.442944 0.65 0.2384 1.2038 0.1951
LI 11 1.47998 0.342744 0.69 0.4544 2.51 0.0886
NM 40 0.9162 0.347308 0.65 0.7481 1.5143 <0.0001
AD 345 1.24048 0.315907 0.69 0.6945 2.7383 <0.0001

Alder fen NS 475 2.25391 0.290099 0.65 0.8603 2.2956 <0.0001
SP 34 1.21007 0.324187 0.62 0.2978 2.2989 0.0042
AC 513 2.18471 0.293724 0.64 0.8618 2.3175 <0.0001

BI 40 2.01465 0.301563 0.67 0.4511 2.6922 <0.0001
DAR 346 1.48036 0.310482 0.68 0.8368 2.7971 <0.0001
CAR 12 1.51185 0.316172 0.78 0.8124 2.3561 0.0005
LI 57 1.84125 0.300121 0.72 0.7165 3.1607 <0.0001
AH 10 1.36098 0.305205 0.77 0.885 1.9276 0.0005
AD 476 1.52893 0.30999 0.71 0.8227 2.8396 <0.0001

Myrtillus drained swamp NS 95 1.91242 0.296379 0.65 0.8897 2.2089 <0.0001
SP 21 0.1853 0.346608 0.68 0.0349 1.5099 0.7264
AC 117 1.85238 0.298818 0.6 0.9137 2.1902 <0.0001

BI 10 1.44643 0.302228 1.12 0.8936 1.8118 0.0004
DAR 22 1.18785 0.32696 0.5 0.9197 1.4993 <0.0001
AD 48 1.34335 0.319018 0.61 0.872 1.8657 <0.0001

Aegopodium NS 1088 2.08325 0.277529 0.68 0.8852 2.9114 <0.0001
AC 1091 2.0843 0.277633 0.69 0.8834 2.9281 <0.0001

CA 28 1.11678 0.287548 0.52 0.7055 3.4818 <0.0001
SE 22 1.29345 0.347246 0.63 0.6247 2.2132 0.0001
BI 49 1.84504 0.29027 0.82 0.565 4.3707 <0.0001
DAR 209 1.6667 0.297854 1.03 0.8081 3.218 <0.0001
WI 13 1.74562 0.30235 0.75 0.9448 1.7704 <0.0001
ER 31 1.3131 0.331834 0.66 0.5718 2.8427 <0.0001
LI 1155 1.68554 0.29393 0.67 0.7871 3.2555 <0.0001
AH 58 1.30368 0.300885 0.7 0.7939 3.1191 <0.0001
CH 38 1.04191 0.366278 0.67 0.1382 2.7214 0.0741
BC 13 0.52782 0.389661 0.45 0.4197 2.1269 0.0658
NM 257 1.36259 0.30576 0.9 0.7314 2.7437 <0.0001
AD 1870 1.64682 0.294705 0.73 0.7861 3.3405 <0.0001

Transitional bog NS 122 1.95335 0.30006 0.66 0.7957 2.4476 <0.0001
SP 29 1.24269 0.324479 0.73 0.3303 1.9695 0.0055
AC 159 1.84773 0.306233 0.64 0.8308 2.3759 <0.0001

BI 18 1.28842 0.325971 0.73 0.6217 2.5596 0.0007
DAR 35 1.48154 0.310051 0.66 0.8894 1.9883 <0.0001
AD 59 1.40218 0.316723 0.71 0.8469 2.2027 <0.0001

Wood density and C relative content according to tree species and decay class (Köster et al., 2015; Saarman & Veibri, 2006; Uri et al., 2019, 2017, 2014).

Variable Tree species Decay class

fresh 1 2 3 4 5
Basic density, g/cm3 SP 422.71 381.12 337.22 258.82 233.72 141.82
NS, DF 375.01 410.72 354.22 280.72 191.32 124.82
BI, ER 538.21 466.62 326.52 230.02 175.92 112.12
CA, LI, NA 419.21 391.32 330.62 230.62 161.12 60.72
BAR 440.51 422.42 289.42 212.92 158.92 95.62
CAR, CH, BC, OD 396.03 426.92 345.32 220.72 184.92 153.62
PO, NM 584.41 486.7 389.0 291.3 193.5 95.8
AH 574.71 478.6 382.5 286.4 190.3 94.2
EL 550.41 458.4 366.3 274.3 182.3 90.2
WI 387.01 334.6 282.2 229.8 177.4 125.0
LF 547.71 478.2 408.7 339.2 269.7 200.1
FF 317.01 275.3 233.7 192.0 150.4 108.7

Carbon relative content, % SP, LF 47.464 49.032 49.262 49.562 49.582 50.212
NS, DF, FF 47.46 48.352 48.312 47.932 49.602 51.332
BI, ER, WI, PO, AH, NM, EL 47.005 47.162 47.692 47.452 48.802 50.122
CA, LI, NA 47.00 47.192 47.372 47.382 46.562 46.312
BAR 47.30 47.892 48.242 48.072 48.352 48.112
CAR, CH, BC, OD 47.303 48.022 48.072 48.712 47.952 48.042

j.fsmu-2021-0009.apptab.001

Point x y Point x y
1 6 464 594,87 694 981,72 37 6 464 890,85 694 924,91
2 6 464 586,41 694 931,92 38 6 464 877,95 694 876,23
3 6 464 577,35 694 882,21 39 6 464 869,38 694 828,03
4 6 464 567,75 694 833,29 40 6 464 861,24 694 778,04
5 6 464 558,40 694 784,62 41 6 464 854,12 694 727,95
6 6 464 548,39 694 737,17 42 6 464 843,08 694 680,60
7 6 464 643,70 694 972,09 43 6 464 939,49 694 916,71
8 6 464 635,09 694 923,09 44 6 464 929,00 694 868,38
9 6 464 624,73 694 873,60 45 6 464 913,88 694 814,16
10 6 464 616,65 694 826,37 46 6 464 891,17 694 763,67
11 6 464 604,62 694 776,79 47 6 464 902,60 694 723,56
12 6 464 597,74 694 725,18 48 6 464 890,52 694 673,42
13 6 464 690,72 694 962,87 49 6 464 988,72 694 907,52
14 6 464 683,63 694 914,06 50 6 464 980,91 694 868,03
15 6 464 673,81 694 863,77 51 6 464 967,85 694 810,65
16 6 464 666,04 694 817,53 52 6 464 960,56 694 757,21
17 6 464 657,10 694 765,99 53 6 464 947,26 694 710,16
18 6 464 648,22 694 716,03 54 6 464 948,30 694 659,10
19 6 464 743,26 694 954,20 55 6 465 037,07 694 895,42
20 6 464 735,45 694 907,09 56 6 465 035,31 694 856,40
21 6 464 722,88 694 855,44 57 6 465 009,53 694 810,44
22 6 464 715,35 694 807,84 58 6 465 014,67 694 752,37
23 6 464 706,62 694 755,87 59 6 464 998,40 694 719,68
24 6 464 695,19 694 709,06 60 6 464 984,42 694 657,38
25 6 464 793,81 694 943,93 61 6 465 093,76 694 887,58
26 6 464 779,58 694 894,90 62 6 465 075,21 694 841,74
27 6 464 772,83 694 845,93 63 6 465 062,54 694 782,18
28 6 464 766,20 694 797,11 64 6 465 061,73 694 739,59
29 6 464 755,54 694 748,05 65 6 465 047,56 694 695,83
30 6 464 745,35 694 699,82 66 6 465 039,32 694 648,56
31 6 464 839,77 694 936,05 67 6 465 136,17 694 877,23
32 6 464 832,38 694 886,11 68 6 465 128,02 694 829,38
33 6 464 822,55 694 838,31 69 6 465 122,07 694 782,05
34 6 464 809,65 694 791,79 70 6 465 122,45 694 743,17
35 6 464 806,20 694 734,42 71 6 465 103,39 694 671,84
36 6 464 794,40 694 690,46 72 6 465 086,71 694 631,43

Volume and C content of live and dead trees in sub-compartments.

Sub-compartment Volume, m3/ha Live, % Dead standing, % Dead laying, % C content MgC/ha Live, % Dead standing, % Dead laying, %
1 343.4 57.7 2.2 40.1 60.85 62.9 1.0 36.1
2 417.5 81.3 13.1 5.6 77.52 84.1 11.0 4.9
3 345.0 89.6 5.5 4.9 66.40 91.6 4.6 3.8
4 445.7 74.1 14.0 11.9 83.51 79.1 12.3 8.6
5 820.9 43.9 17.4 38.7 132.67 58.3 13.7 28.0
6 591.7 84.1 1.4 14.5 109.88 88.4 1.1 10.5
7 460.9 67.4 20.3 12.3 81.06 72.1 18.1 9.8
8 472.6 78.2 11.6 10.2 89.00 81.9 10.5 7.6
9 715.2 69.6 7.9 22.5 123.91 77.8 7.1 15.1
10 452.8 57.5 11.9 30.6 72.45 68.8 10.1 21.1
11 495.4 59.9 9.5 30.6 80.87 69.3 9.0 21.7
12 493.7 73.3 6.8 19.9 91.99 79.1 5.1 15.8
13 641.9 72.9 3.1 24.0 115.92 79.9 2.5 17.6
14 394.7 54.3 4.2 41.5 61.69 72.0 2.8 25.2
15 562.7 68.7 7.0 24.3 101.75 74.8 6.4 18.8

Compartment 524.9 69.3 10.2 20.5 93.81 76.5 8.7 14.8

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