Pollution-induced slowdown of coarse woody debris decomposition differs between two coniferous tree species

Forest Ecology and Management 448 (2019) 312–320

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Pollution-induced slowdown of coarse woody debris decomposition differs between two coniferous tree species

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Olesya V. Dulya , Igor E. Bergman, Vladimir V. Kukarskih, Evgenii L. Vorobeichik, Georgii Yu. Smirnov, Vladimir S. Mikryukov Institute of Plant and Animal Ecology, Ural Branch of the Russian Academy of Sciences, 8 Marta Str. 202/3, Ekaterinburg 620144, Russian Federation

A R T I C LE I N FO

A B S T R A C T

Keywords: Downed logs Wood decay classes Rate of organic matter decomposition Residence time Air pollution Copper smelter Heavy metals Dendrochronology Wood density Dynamic probing Penetration resistance

Boreal forests store a large portion of the planet’s terrestrial carbon. A significant portion of this carbon is stored in coarse woody debris (CWD). Industrial pollution greatly inhibits organic matter decomposition and thus enhances carbon sequestration in the soil. However, little is known about the decomposition of CWD in polluted areas. In this work, by means of dendrochronological cross-dating, we determined the death dates of 90 downed Siberian spruce (Picea obovata Ledeb.) and Siberian fir (Abies sibirica Ledeb.) logs in the latest stages of decay in an undisturbed boreal forest and in two industrially polluted forests with 10-fold and more than 100-fold higher copper content in the soil. We found that, in the unpolluted area, the mean halftime of fir and spruce decomposition was 26 years and 23 years, respectively. In polluted areas, this time increased by approximately 16 years for fir and 5 years for spruce. Based on an exponential decay model, pollution caused a 16–60% decrease in the wood decomposition rate constant. Copper concentrations in CWD were similar between tree species and were about 10–20 times lower than in the surrounding soil. These values are comparable with the concentrations tolerated by fungi in laboratory tests, indicating that heavy metal excess cannot be considered a primary inhibitor of wood decomposer activity. The greater pollution-induced delay in the decomposition of fir trees, which are lighter and smaller in size than spruce trees, suggests that dead tree leaning is an important factor in the wood decomposition slowdown in polluted areas where stand density increases due to intensive forest regeneration because of the reduced competition of tree seedlings with toxically inhibited herbaceous vegetation. We also evaluated dynamic probing as a rapid, low-cost, and nondestructive surrogate of wood density measuring. Dynamic probing performed in the field on heavily decayed CWD explained about 40% of wood density variability and about 70% of variability when taking into account the gravimetrically measured wood moisture content. With negligible modifications, this method can be applied in the field monitoring of wood density loss. For the purpose of between-study comparisons, we recommend transforming the widely used measure of penetration depth to specific resistance to penetration and encourage the elaboration of reference tables that relate specific resistance to penetration and wood density for different tree species across distinct ecological zones.

1. Introduction Due to the substantial stocks of coarse woody debris (CWD) in forest biomes, the wood decay rate is an important predictor of the global carbon balance (Russell et al., 2015). Estimates of the CWD decay rate vary substantially among research methods, tree species, CWD forms and sizes, and environmental conditions (Harmon et al., 1986; Garrett et al., 2007; Weedon et al., 2009; Freschet et al., 2012). Therefore, it is important to consider these sources of variability in the predictive

modeling of the contribution of wood to carbon flows. The effect of climate, microclimate, forest type, management regime, elevation, and other environmental conditions on the wood decay rate have been previously evaluated (Janisch et al., 2005; Weedon et al., 2009; Zell et al., 2009; Russell et al., 2013; Shorohova and Kapitsa, 2014; Crockatt and Bebber, 2015; Fravolini et al., 2016; Yuan et al., 2017). However, the effect of pollution on the rate of wood decomposition has not yet been directly assessed. Few efforts have indirectly estimated the CWD decay rate in industrially polluted areas (Stavishenko, 2010; Bergman

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Corresponding author. E-mail addresses: [email protected] (O.V. Dulya), [email protected] (I.E. Bergman), [email protected] (V.V. Kukarskih), [email protected] (E.L. Vorobeichik). https://doi.org/10.1016/j.foreco.2019.06.026 Received 16 March 2019; Received in revised form 9 June 2019; Accepted 15 June 2019 Available online 20 June 2019 0378-1127/ © 2019 Elsevier B.V. All rights reserved.

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of pollution near a large industrial enterprise that has been operating since 1940. It mainly emits SO2 and heavy metals. We aimed to assess the decomposition time of Siberian spruce (Picea obovata Ledeb.) and Siberian fir (Abies sibirica Ledeb.) CWD at the latest stages of decay in spruce-fir forests growing along the pollution gradient. Based on the cellulose decomposition rates (Vorobeichik, 1991; Vorobeichik and Pishchulin, 2011) and CWD census data (Bergman and Vorobeichik, 2017), it is reasonable to expect that CWD resides significantly longer in the vicinity of the pollution source compared to the unpolluted territory. We also evaluated the potential of dynamic probing, proposed by Larjavaara and Muller-Landau (2010), as a cheap and labor-saving surrogate for wood density during the latter stages of CWD decomposition.

et al., 2015; Bergman and Vorobeichik, 2017), although numerous efforts have explored the decomposition of leaf litter and cellulose (Strojan, 1978; Freedman and Hutchinson, 1980; Berg et al., 1991; McEnroe and Helmisaari, 2001; Kozlov and Zvereva, 2015; Lukina et al., 2017; Killham and Wainwright, 1981; Vorobeichik, 1991; Ohtonen et al., 1994; Vorobeichik and Pishchulin, 2011). Globally, there are at least 330 large non-ferrous enterprises (Vorobeichik and Kozlov, 2012). Their vicinities are characterized by 10–100-fold excess of heavy metals in the upper soil horizons above regional values (Dudka and Adriano, 1997). Toxic load mainly caused by heavy metal excess is considered the primary driver of soil biota deterioration. Consequently, it breaks the cycle of organic matter decomposition in these areas. In addition, the pollution-induced thinning of tree and herbaceous vegetation alters the forest microclimate, increasing daily and seasonal fluctuations in temperature and humidity on and under the soil surface, which additionally inhibit soil biota (Kozlov and Haukioja, 1998; Vorobeichik et al., 2014; Zolotarev and Nesterkov, 2015). As a result, a decrease in the activity of soil decomposers by several times (up to 10-fold) is usually registered in areas that have been exposed to industrial emissions, mining, or sewage sludge for a long period of time (Rühling and Tyler, 1973; Strojan, 1978; Freedman and Hutchinson, 1980; Berg et al., 1991; Vorobeichik, 1991; Chew et al., 2001; McEnroe and Helmisaari, 2001; Vorobeichik and Pishchulin, 2011; Kozlov and Zvereva, 2015; Smorkalov and Vorobeichik, 2016; Lukina et al., 2017; but see Fritze, 1988). Direct censuses of large soil saprophages even illustrate their complete elimination from heavily polluted areas, leading to the absence of zoogenic signs of leaf litter decomposition in the topsoil horizons (Korkina and Vorobeichik, 2016, 2018). Data on the response of wood-decomposing biota to industrial pollution are scarce. Existing data have evidenced the decrease in the frequency of wood-degrading fungal fruit bodies in the vicinity of metal smelters (Bryndina, 2000; Stavishenko and Kshnyasev, 2013). Similarly, the CWD decomposition rate in polluted areas can also be considered significantly inhibited compared to undisturbed areas. However, the actual toxic load on the biota of decaying wood in industrially polluted territories has not been assessed except for the work of Esenin and Ma (2000), which was performed in a slightly polluted area. Until now, the concentrations of heavy metals in wood from forests experiencing strong gradients of industrial emissions have only been estimated for living trees (Lukaszewski et al., 1988, 1993; Koptsik et al., 2008; Shcherbenko et al., 2008; Koroteeva et al., 2015). These concentrations are generally substantially lower compared to those observed in soil and forest litter. This means that, at least in the initial decay stages, the toxic load on wood inhabitants may not be as severe as that on soil biota, and the decomposition rate may not necessarily decrease. The assessment of dead wood stock or carbon release during wood decomposition involves the estimation of wood density and its loss (IPCC, 2006). However, density measuring is invasive and laborious, especially at the latest stages of CWD decay when the deformation of a log impedes the geometric calculation of the wood volume. Also, wood at these stages is so fragile that the water displacement approach is nearly unfeasible. Accordingly, there is a great practical interest in the development of cheap, rapid, accurate, and noninvasive instruments for the assessment of wood properties that can be used as density surrogates (Creed et al., 2004; Larjavaara and Muller-Landau, 2010; Mäkipää and Linkosalo, 2011; Oberle et al., 2014). Methods exhibiting all of these characteristics have been proposed for the wood industry and civil engineering purposes (Gao et al., 2017). However, their effectiveness in the prediction of wood density was only tested on wood free from decay. Fragile, highly heterogeneous, and water saturated wood that is strongly decomposed does not fulfill the initial requirements of these methods, so the search for density surrogates applicable to CWD of all decay classes must be continued. The present work was carried out in along a strong 30-km gradient

2. Materials and methods 2.1. Study sites and material collection The studied territory is located in the southern taiga on the western slope of the Middle Urals (Fig. A1a). It has a mean annual temperature of 2.4 °C and an annual precipitation of 561 mm. The study was carried out in the surroundings of the Middle Ural Copper Smelter (Revda, Sverdlovsk region), which emitted 250,000 tons/year of SO2 and heavy metals in 1980, 65,000–100,000 tons/year in 1990–2000, 20,000–60,000 tons/year in 2001–2009, and 3000–5000 tons/year since 2010 (Vorobeichik et al., 2014). Notably, the reduction in smelter emissions over time has not yet resulted in a decrease in the heavy metal concentrations of the upper soil horizons (Vorobeichik and Kaigorodova, 2017). Material was collected in three areas (Fig. A1b): unpolluted (UP), moderately polluted (MP), and heavily polluted (HP). The areas were chosen to be as similar as possible in biotope and soil characteristics but strongly different in the degree of pollution and ecosystem damage (Table 1). In the UP and HP areas, fir trees prevailed in the stand, whereas spruce dominated the MP area. Because of the reduced competition among tree seedlings as a result of the toxic inhibition of herbaceous vegetation, the tree stand has become denser in the HP area over time (in details see (Vorobeichik and Khantemirova, 1994; Veselkin, 2004; Bergman, 2011; Usoltsev et al., 2012). On 7–29 September 2017, three sampling sites were established in each area approximately 100–800 m apart from one another. In each site, all autochthonous, fir, and spruce downed logs of the 4th–5th decay classes (as per Storozhenko, 1990; Table A1) were considered; we randomly selected ten logs (90 logs in total, corresponding with 10 logs × 3 areas × 3 sites). Specific criteria for CWD selection included base diameter of at least 10 cm, full or almost full contact with the ground, absence of pronounced fragmentation (large animal foraging signs), indicators of the long-term standing of trees after death (woodpecker foraging signs), and ant colonization. Species identification (fir or spruce) of heavily decayed CWDs was performed in the laboratory by means of the microscopic inspection of resin canals and wood structure (Fig. A2). We measured the length of each log with a laser distance measurer Disto A3 (Leica Geosystems, Switzerland). The circumference of the top and base (right above the root collar if present) of each log was also measured using a measuring tape with an accuracy of 0.5 cm. On each log, we selected three sampling points more than 1 m apart from each other. At each point, we measured the stem circumference and performed wood testing with a dynamic penetrometer. At a distance of 1.3–15 m from each log (more than 1.5 m from any other decaying log), we collected three samples from the O-horizon and bulked them into one sample for chemical analysis. 2.2. Wood testing with a dynamic penetrometer A dynamic penetrometer was custom constructed (Fig. A3) following the specifications of Larjavaara and Muller-Landau (2010). The 313

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* , stand data are taken from Bergman (2011). Stand composition (portions of tree species were weighted with basal area) and density (for trees of not less than 4 cm in diameter at 1.3 m height) were estimated in mensuration plots (0.0625 ha) located in the vicinity of studied CWD; herbaceous vegetation data are taken from Mikryukov and Dulya (2017). ** , data from Vorobeichik and Kaigorodova (2017).

4.70 ± 0.23 n = 30 2405.10 ± 883.93 134.13 ± 96.83 n = 30 HP

Stagnic Retisol (Cutanic, Toxic) Cu-, Zn-, Pb-, and Cd-contaminated, unsaturated, medium-humus, slightly skeletal, heavy loamy-clayey, fine chemozem on gleyic soddy-podzolic soil 1760.0 ± 439.1 n=8 10–70% of A. sibirica, 20–70% of P. obovata with up to 40% of Betula spp. and 20% of P. tremula. Agrostis capillaris absolutely dominates herbaceous layer.

4.96 ± 0.21 n = 29 959.42 ± 306.07 22.37 ± 10.52 n = 30 Stagnic Retisol (Cutanic, Toxic) Chemically contaminated, unsaturated, high-humus, slightly skeletal, clayey, heavy loamy, medium-fine, gleyic soddy-podzolic soil 1180.8 ± 585.4 n=5 MP

10–60% of A. sibirica, 20–80% of P. obovata with up to 20% of Betula spp. and 10% of Populus tremula.C. arundinacea and O. acetosella dominate herbaceous layer.

5.69 ± 0.44 n = 29 Albic Retisol (Cutanic) Medium-humus, moderately- fine, heavy loamy-clayey, unsaturated, typical soddy-podzolic soil. 1104.0 ± 261.9 n=5 60–80% of A. sibirica, 20–40% of P. obovata with up to 20% of Betula spp. Oxalis acetosella, Dryopteris spp., Calamagrostis arundinacea, Aegopodium podagraria and Ajuga reptans dominate herbaceous layer

Spruce-fir forest on the watershed between rs. Bol’shaya Talitsa and Belyy Atig 346–404 m a.s.l. 30 km from the smelter N56.801° E59.425° Fir-spruce forest on the western midslope of Shaitan Ridge 338–412 m a.s.l. 4 km from the smelter N56.850° E59.827° Spruce-fir forest on the eastern midslope of Shaitan Ridge 380–419 m a.s.l. 1–2 km from the smelter N56.848° E59.863° UP

22.80 ± 6.17 0.64 ± 0.18 n = 30

O-horizon pHwater [Cu] in O-horizon, µg g−1 Soil description** Stand density, tree/ha* Stand and herbaceous layer description* Landscape description Area

Table 1 Characteristics of the studied areas. Means ± standard deviations are shown for stand density, concentrations of acid-soluble (in numerator) and exchangeable (in denominator) forms of copper and pH in the O-horizon collected near the studied CWD.

O.V. Dulya, et al.

mass of the tool without hammer was 273 g, the hammer falling height was 26.5 cm. The stylus had a diameter of 0.5 cm with the pointed end angle of 20˚. Replaceable moving hammers with the mass of 1000, 582, and 250 g were at our disposal. In preliminary tests with heavily decayed logs, we opted for 15 hits with 250 g hammer, which provided suitable resolution of penetration depth. Finally, dynamic probing was performed according to the following protocol. 1. On the upside of the test log, choose three representative sampling points of 1–5 cm2 that are free from wood knots and large holes. 2. At each sampling point, clean the wood of irrelevant cover (moss, lichens, plants, soil, etc.) and bark remnants. 3. In case of a thin hanging log, the log must be fixed securely before measurement; otherwise, it may be resilient to probing due to vibration. 4. Within the sampling point, position the pointed end of the penetrometer on the upside of the log, avoiding holes. Aim the stylus at the log’s core, at an angle approximately 90˚ to the wood fibers. According to Larjavaara and Muller-Landau (2010), a slight deviation from vertical orientation of the stylus is not critical for measurement. 5. Raise the moving hammer to the stopper and then release it to hit the anvil. During subsequent hits, the hammer must be raised gently in order not to pull out the stylus from the wood due to impact with the stopper. 6. After 15 hits, record penetration depth, i.e., the distance the stylus traveled through the wood. When dealing with heavily decayed logs, a smaller number of hits may be employed. Count them until the stylus almost completely traverses the log (for smaller logs) or travels about 15 cm into the log (for larger logs), then record the number of hits and penetration depth. If a crack extends through the wood during probing, Oberle et al., (2014) recommends interrupting the measurement and recording the number of hits and penetration depth. In contrast to the protocol used in this and previous works, we recommend probing until a standard depth (chosen beforehand) is reached using as many hits as needed. 7. Carefully pull the stylus from the wood, avoiding deformation of the tool. 8. Because of wood heterogeneity, the measurement must be performed three times at each sampling point. Each time, the penetrometer must be positioned beyond the previously gouged hole. Finally, the data were converted into specific penetration resistance (RP, J/cm3) (ISO22476-2:2005, 2015) according to equation (1):

RP = En ∗ n/ h

(1)

where n is the number of hits, h is the distance that the stylus traveled through the wood (cm), and En is the specific work per hit (J/cm2), calculated by equation (2):

En = m ∗ H ∗ g / A ∗ m /(m + m′)

(2)

where m is the mass of hammer (0.250 kg), H is the falling height of the hammer (0.265 m), g is the acceleration due to gravity (9.806 m/s2), A is the base area of the stylus (0.196 cm2 for 0.5 cm diameter), and m′ is the mass of the tool without hammer (0.273 kg). Using m′ in the equation considers the energy absorbed by the tool (Vaz and Hopmans, 2001; Vanags et al., 2004). 2.3. Measurement of wood density, moisture, pH, and metal content After the dynamic probing of each CWD, a disc was cut out with a chain saw. Then, the volumetric moisture content (MC) of wood was determined using an electronic wood moisture and temperature meter Hydromette M2050 (GANN, Gerlingen, Germany) with the drive-in electrode applied four times to each cross section of the stem. Then, the 314

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However, the chronosequences of the cores extracted from several available healthy firs matched well with those of the spruce chronologies. CWD samples were cross-dated with the corresponding site chronology using the COFECHA 6.06P software (Holmes, 1983; Grissino-Mayer, 2001). If uncertainties regarding the correct year of the tree death remained, the tree ring sequences were visually compared with other sequences of the nearest CWD at the same study site.

cut-out wood samples were collected and combined into a bulk sample of 98–780 g (for a total of 90 samples). Samples were weighed, dried at 65 °C for 4 days, and weighed again. Thus, gravimetric MC was expressed as the mass of water per wet wood mass. The samples of wood and O-horizon were homogenized with laboratory grinder MF 10 basic (IKA, Germany) and sieved through 2 mm mesh. Acid-soluble and exchangeable metal forms were extracted with 20 ml of 5% HNO3 and 0.5H CaCl2 solutions, respectively, from 1 g of each sample (HR-120 laboratory balance, A&D, Japan, with accuracy of 0.1 mg). After one hour shaking and 24 h resting at room temperature, the extracts were filtered through the paper filter with 8–12 mm pore size (Melior XXI, Russia). Copper concentrations were measured with atomic-absorption spectrometer AAS Vario 6 (Analytik Jena, Germany) following USEPA Method 7000B (USEPA, 2007). O-horizon pH (soil: water, 1:25) was analyzed with pH-meter InoLab 740 (Germany). To analyze the relationship between wood mechanical properties, of the 90 studied CWDs, we randomly chose 27 “model” CWDs (nine from each area). From these CWDs, we took an additional three discs of approximately 10-cm thickness at each sampling point (in most cases, the discs were fragmented). For each of these samples, wood density (g/ cm3) was calculated based on the “green volume.” Each fresh wood fragment was weighed with a laboratory balance (Shimadzu UW 2200H, Japan) with 0.01-g accuracy. Then, each fragment was covered with melted paraffin wax and submerged carefully in water to determine its displacement volume at an accuracy of 0.01 cm3. Bulk density (g/cm3) or, in other words, dry mass per “green volume,” was calculated using the wood shrinkage factor obtained in the measurements of gravimetric MC.

2.5. Statistical analysis The data analysis was conducted using R software v.3.5.1 (R Core Team, 2018). Data were visualized with ggplot2 package v.3.1.0 (Wickham, 2016). First, we were interested in testing whether specific RP and MCs could be used as predictors of wood density and whether the volumetric MC was a reliable estimate of the gravimetric MC of the logs. Multiple measurements of specific RP, MCs and wood density per CWD generally resulted in the correlated errors, violating the basic assumptions of traditional methods. Therefore, we used a mixed-effects model (Bolker et al., 2009), that is, varying intercept linear model considering the CWD identifier as a group-level (random) effect. Second, we were interested in testing whether CWD decay time differed between pollution areas, while controlling for several potentially biasing factors such as RP, tree species, and CWD diameter. For this analysis we used the following model: log(time after tree death) ∼ RP + pollution area × species + base diameter. Within each CWD, the RP was averaged across all sampling points. Model-based predictions for CWD decay time were then estimated for “typical logs,” which were considered to have an average base diameter of 20 cm and a specific RP of 3.9 and 2.2 J/cm3 for fir and spruce, respectively. All models were fitted with the brms package v.2.6.0 (Bürkner, 2017) and Stan v.2.18 (Carpenter et al., 2017) using seven Markov chains of Hamiltonian Monte Carlo, with 15,000 sampling iterations and 2000 warm-up iterations for each chain. A weakly informative lognormal prior distribution was specified for the “pollution area” parameters. Chain convergence was evaluated visually and also with the Gelman-Rubin diagnostic test (Gelman, 2014). Model diagnostics were performed with graphical posterior predictive checks using the bayesplot package v.1.6.0 (Gabry and Mahr, 2018). The coefficients of determination (R2) for the regression models were estimated following (Gelman et al., 2018). The effects of the predictors on the dependent variables were visualized with marginal effect plots wherein uncertainty was quantified using the 95% highest posterior density intervals (HPDI95).

2.4. Dendrochronological cross-dating To determine time after tree death, one to three cross-sections were taken from the most intact parts of each CWD for chronological sequence analysis. If it was impossible to take the material from heavily decayed logs, cores were taken from the nearby trees decidedly damaged by the fallen tree of interest. Coring was performed using an increment borer of 400/5.15-mm (Haglöf, Sweden). Wood discs and cores were polished with a razor blade. Wet chalk was used to increase the visibility of tree ring boundaries. Ring widths were measured with LINTAB 6 station (Rinntech, Germany) to the nearest 0.01 mm using MZ6 stereomicroscope (Leica, Germany) and the TSAP-Win software package (both RINNTECH, Germany). Tree ring widths were measured for one to three of the most well-preserved radii. For each CWD, the radius with the longest tree-ring sequence was subjected to further analysis. Site chronologies were compiled from the wood cores of living spruce trees collected in 2014 (50–75 cores from each area). It was mostly impossible to collect fir cores because of the high incidence of heart rot in this species in the studied territory (Stavishenko, 2010).

3. Results The samples from the UP and HP areas were mainly represented by fir logs (Table 2), while the samples from the MP area were mostly

Table 2 Characteristics of fir and spruce CWD at the latest stages of decomposition in areas with different levels of industrial pollution; mean values and standard deviations are shown. Wood characteristic

Sample size (n) Base diameter, cm Length, m Gravimetric MC, % of dry matter Penetration depth per hit, mm Specific RP, J/cm3 Acid-soluble Cu concentration in wood, µg g−1 Exchangeable Cu concentration in wood, µg g−1

A. sibirica

P. obovata

UP area

MP area

HP area

UP area

MP area

HP area

19 20.51 ± 7.02 12.92 ± 4.10 77.80 ± 6.26 1.74 ± 1.65 3.73 ± 2.02 5.72 ± 2.92a 0.95 ± 0.78a

12 20.08 ± 5.56 14.04 ± 4.40 75.91 ± 6.91 0.94 ± 0.73 4.14 ± 2.58 51.87 ± 45.97b 5.86 ± 8.04b

18 16.92 ± 3.03 14.64 ± 3.54 73.12 ± 5.90 1.65 ± 2.04 3.89 ± 2.51 211.14 ± 188.79c 23.44 ± 27.24c

11 24.05 ± 7.00 13.24 ± 3.39 81.38 ± 3.32 1.88 ± 2.09 3.83 ± 2.14 7.37 ± 4.82a 0.83 ± 0.60a

18 23.13 ± 6.04 15.53 ± 3.78 81.55 ± 2.35 2.11 ± 1.13 1.44 ± 0.65 42.13 ± 38.91b 4.33 ± 4.33b

12 24.80 ± 12.74 16.61 ± 5.91 79.57 ± 2.64 1.86 ± 1.88 2.01 ± 0.88 153.62 ± 107.41c 23.27 ± 22.23c

Values followed by different letter indicate significant differences between areas (FDR-adjusted p < 0.05 according to the Tukey’s test on log-transformed variables), the absence of letters denotes that the variables are not significantly different. 315

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Fig. 1. Mechanical properties of spruce and fir wood at the latest stages of decomposition: (a) raw specific RP (on a logarithmic scale) and raw wood density; (b, c) relationship between specific RP and wood density (WD); (d) raw resistance-based volumetric MC (MCv on a square-transformed scale) and raw gravimetric MC (MCg); (e, f) relationship between resistance-based volumetric MC and gravimetric MC.

represented by spruce logs, roughly reflecting the forest stand composition in the studied sites (see Table 1). Fir logs were characterized by a smaller base diameter than spruce logs, especially in the HP area. Fir wood was harder and was less saturated with water than spruce wood. The copper concentrations in CWD from the MP and HP areas were 5–9 and 21–37 times higher than in the UP area, respectively.

under resistance-based principle equaled 100% (Fig. 1d). A resistancebased MC of 100% was recorded for samples with a gravimetric MC of 52.2–88.0%. Taking into account tree species, the resistance-based MC explained 30.7% of gravimetric MC variability (Fig. 1e). The moisture meter performed better for denser wood but was completely undescriptive when working with strongly decayed wood (Fig. 1f).

3.1. Evaluation of dynamic probing and resistance-based MC measuring

3.2. Wood decomposition rate

We assessed the adequacy of dynamic probing for predicting wood density using samples from 9 spruce logs and 18 fir logs that were visually assigned in the field to the 4th and 5th decay classes (Fig. 1a). Mean wood density in the “model” logs was 0.175 ± 0.087, 0.189 ± 0.071, and 0.172 ± 0.045 g/cm3 for fir in the UP, MP, and HP areas, respectively, and 0.131 ± 0.021, 0.141 ± 0.020, and 0.152 ± 0.020 g/cm3 for spruce. The within-log variability of wood density was higher in fir. Wood density significantly increased with distance from root collar in firs but not in spruces: F(1, 225.204) = 0.16, p = 0.89 for “relative distance from the root” effect and F(1, 228.542) = 5.34, p = 0.02 for the “tree species × relative distance from the root” interaction (Fig. A4). The relationship between wood RP and density was positive and did not depend on the pollution level, tree species or, the log diameter at the sampling point (likelihood ratio χ2(2) = 2.39, p = 0.30 for area, χ2(1) = 0.74, p = 0.39 for species, and χ2(1) = 0.99, p = 0.32 for log diameter; in details see Table A2). The inclusion of gravimetric MC significantly improved model fit (χ2(1) = 21.63, p < 0.001). Thus, the analysis was performed for a pooled sample without considering log diameter. Dynamic probing alone explained 39% of density variation. The gravimetric MC of wood was negatively related to density and explained 51% of its variation. The full model including both predictors explained 71% of density variation (Fig. 1b). the performance of the dynamic penetrometer differed for wood with different gravimetric MC (Fig. 1c). Approximately 65% of volumetric MC measurements obtained

According to the results of dendrochronological cross-dating, we deal with the trees, which died after the smelter launching (Fig. 2), though the time elapsed from the death substantially varied between the trees. The logs of the same decay stage collected in the same area differed in the duration of decomposition by 48–58 years. The relationship between the duration of decomposition and wood-specific RP was negative and nearly flat (F(1, 85) = 0.78, p = 0.38), which is indicative for the lower plateau in the relationship between time and the remaining CWD fraction (Harmon et al., 2000; Zell et al., 2009). For each study area, we estimated the mean time required by a typical log with a diameter of 20 cm to reach a penetration resistance of 3.9 J/cm3 and 2.2 J/cm3 for fir and spruce, respectively. In the UP area, the required time was 27.4 years for fir and 29.8 for spruce (Fig. 3). In the MP and HP areas, the duration of fir decomposition was approximately 15.1 and 16.6 years longer, respectively, than in the UP area, whereas the duration of spruce decomposition was 4.8 and 7.0 years longer, respectively. Based on the HPDI95, 95% of typical fir logs in the UP area had been decomposed for 12.3–48.1 years (Fig. 3, Fig. A5), whereas in the MP and HP areas, 95% of typical fir logs had been decomposed for 18.1–72.1 years and 19.2–75.2 years, respectively. For spruce, the between-area difference in decomposition time was less pronounced: the HPDI95 was 13.3–53.9 years in the UP area, 15.2–61.0 years in the MP area, and 16.4–65.8 years in the HP area. According to our previous measurements in the studied areas, the wood density of living trees with a 20-cm base diameter was 0.34 g/cm3 316

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Fig. 2. Time elapsed from the death of fir (a) and spruce (b) trees in areas with different pollution levels (UP, unpolluted; MP, moderately polluted; HP, heavily polluted). The size of the circles indicates the area of the stem cross-sections.

wood density because of the higher moisture capacity of strongly decayed porous wood (Oberle et al., 2014; Gao et al., 2017). However, gravimetric MC does not comply with the non-invasiveness criterion, and in our opinion, it was so effective because of the wet and cool weather before and during most of the field work (10 days of the 23-day sampling period were rainy). In more xeric conditions, using this predictor alone could be unproductive. Dynamic probing explained less of the variation in wood density than in previous laboratory and field experiments, although it did explain more than in observations of naturally decayed wood (Oberle et al., 2014). The usage of both gravimetric MC and RP provided a quite high explanatory power of 71%. Therefore, similar to Larjavaara and Muller-Landau (2010), we encourage the use of the dynamic penetrometer as an instrument for rapid field measurements of the degree of wood decomposition. A 250-g hammer was the smallest at our disposal, and in 10% of our measurements, the stylus broke through the log in just 1–3 hits, resulting in the lower resolution of dynamic probing for heavily decayed logs. In these cases, an analysis of the censored regression model can be useful (Fox et al., 2015), although it may be better to have several hammers at hand and to use one lighter than 250 g when working with heavily decayed wood in order to increase the resolution of probing. Reducing the falling height, using adjustable collars proposed by Herrick and Jones (2002), could also be helpful for this purpose. CWD with low wood density had higher gravimetric MC. Dynamic probing performed more poorly for such logs, and it was impossible to partition the effect of MC and wood density for probing effectiveness. Considering the experimental data obtained with paired measurements, the penetrometer stylus traveled 35% further through wood with a 27% MC compared to wood with a 14% MC (Oberle et al., 2014). Thus, theoretically, the explanatory power of specific RP in our observations could depend on MC. And, it is reasonable to suppose that the sensitivity of dynamic probing can be enhanced after periods of dry weather when wood is less moisturized. Wood moisture meters based on the resistance measuring principle are unable to adequately measure MC when working with decayed wood. This may be due to the specific composition of the electrolytic solution saturating the wood (see James, 1963; Bieker and Rust, 2010) on the impact of wood electrolyte composition on resistance measurement). Also, these meters are ab initio inaccurate above the fiber

Fig. 3. Time of CWD decomposition adjusted for stem diameter and specific RP. Thin lines denote HPDI95 and bold lines denote HPDI50, markers indicate average time of decomposition.

for fir and 0.38 g/cm3 for spruce (Table A3). Based on the parameters of the relationship between RP and wood density (see Fig. 1b), the wood density of a typical log in our sample is 0.165 g/cm3 for fir and 0.155 g/ cm3 for spruce. These values approximately correspond with 51% and 59% decrease of green wood density in fir and spruce, implying that the calculated average time of typical CWD decomposition represents T51 for fir and T59 for spruce. To compare our estimates with the published data, we used these values in an exponential decay model (Olson, 1963) and calculated decomposition rate constants (k). For fir CWD, k equaled 0.026, 0.017, and 0.016 year−1 in the UP, MP, and HP areas, respectively. For spruce CWD, k equaled 0.030, 0.026, and 0.024 year−1 in the UP, MP, and HP areas, respectively. Based on k values, calculated T50 comprises 26.3, 40.7, and 42.2 years for fir in UP, MP and HP areas, respectively, and 23.0, 26.7, and 28.4 years for spruce in UP, MP and HP areas, respectively. 4. Discussion 4.1. Dynamic probing appraisement In line with other works, gravimetric MC was a good predictor of 317

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delay in decomposition of tree tops compared to basal parts. Additionally, the delay in CWD colonization and the stagnation in decay may result from the decreased abundance and diversity of wooddegrading biota. A few observational studies have shown the decreased diversity and frequency of fruiting bodies of wood-degrading fungi in polluted areas (Bryndina, 2000; Stavishenko, 2010; Stavishenko and Kshnyasev, 2013). So far, the data on the response of saproxylic insects to pollution are still insufficient for generating conclusions. Revealed 1.16–1.60 decrease in k mean values in MP and HP areas indicates that the effect of strong pollution on the wood decomposition rate is less pronounced than the local effects of forest edges and canopy thinning (Forrester et al., 2012; Vanderhoof et al., 2013; Crockatt and Bebber, 2015). Previously, in the HP area, we recorded a 3.14-fold decrease in forest litter respiration (Mikryukov and Dulya, 2017); in MP and HP areas, the specific forest litter respiration decreased by 2.00and 2.88-fold (Smorkalov and Vorobeichik, 2012). Also, the cellulose decomposition rate decreased by 1.38- and 2.83-fold in MP and HP areas, respectively (Vorobeichik and Pishchulin, 2011), and up to 10.3fold in the HP area (Vorobeichik, 1991). Thus, pollution induced a smaller decrease in wood decomposer activity than in soil biota activity. The relatively mild effect of pollution on the rate of wood decomposition may partially be explained by the very low rate of adsorption of pollutants by wood from the soil and precipitation. To some extent, CWD from industrially polluted areas can be regarded as analogous to wood treated with metal-based preservatives. At recommended levels of wood retention with preservatives, metal content in newly treated wood may be extremely high. For example, in wood saturated with chromated copper arsenate (CCA), total copper content exceeds 2000 µg/g (AWPA, 1996). Such high concentration is recommended due to leaching of the preservative from wood over time (Lebow et al., 2003) and by the high tolerance of some wood-degrading organisms to metal excess. According to metal tolerance tests, fungi (including wood-destroying species) can tolerate up to 25.4 µg/g of water soluble copper salts (Englander and Corden, 1971; Colpaert and van Assche, 1987; Daniel and Nilsson, 1988; Woodward and De Groot, 1999), which is higher than the concentrations of exchangeable forms of copper registered in the logs of the HP area (importantly, water soluble forms of heavy metals represent a minor part of the exchangeable forms in polluted soils, e.g. Reichman, 2002; Dulya et al., 2013). This threshold, however, is exceeded by several times in nearby soil (see Table 1). Apart from its lower toxicity, CWD may be more attractive for saprotrophic biota than the upper soil horizons, because of its more favorable microclimate (Gray and Spies, 1998; Marra and Edmonds, 2005; Pichler et al., 2012; Goldin and Hutchinson, 2015). The favorable conditions of decayed wood for soil saprophages was directly demonstrated in the HP area in our recent work showing that earthworms more intensively colonize heavily decayed birch and linden CWD compared to nearby soil (Vorobeichik et al., 2019).

saturation point or, in other terms, above a gravimetric MC of 25–30%, which is typical for downed CWD in the field. No need to recalibrate, simple design, and low cost are additional advantages of the tool. It can be produced in a machine shop for approximately $100, including labor (see also Larjavaara and MullerLandau, 2010; Herrick and Jones, 2002). For those who require wood density surrogates and wish to use rapid dynamic probing, we recommend transforming penetration depth, expressed as millimeters per hit (e.g. in Larjavaara and Muller-Landau, 2010; Oberle et al., 2014), to a standard geotechnical variable such as specific RP. These two variables (related with Eq. (1)) are inversely proportional to each other; therefore, specific RP does not need to be log transformed prior to analyzing its relationship with density. The usage of specific work per hit calculated with Eq. (2) will enable the obtained data to be standardized for different hammer masses and falling heights and will also allow for the comparison of data between studies. The standardization of the stylus cone angle would also be a great advantage for the purpose of between-study comparisons. More importantly, in contrast to the protocol described in Section 2.2 (item 6) and in other works (Larjavaara and Muller-Landau, 2010; Oberle et al., 2014), we recommend standardizing the depth of probing instead of the number of hits. Otherwise, varying probing depths may increase the variability of RP between and within logs due to radial heterogeneity, caused by the difference in sapwood and heartwood structure or decomposition degree. 4.2. Wood decomposition time The wood density values and obtained in our work corresponded those recorded by other researchers for Siberian spruce and Siberian fir in the latest stages of decay (Harmon et al., 1986; Yatskov et al., 2003). Our estimates of k in the UP area are close to the minimum values published for downed CWD in different taiga subzones (0.026–0.079 year−1 in Yatskov et al., 2003; Shorohova et al., 2009; Kapitsa et al., 2012; Shorohova and Kapitsa, 2014). In polluted areas, logs of both tree species decompose even slower. Theoretically, the slowdown of CWD decomposition may occur over the entire course of decay, although many researchers emphasize the importance of the time lag from tree death to wood decomposer colonization (Harmon et al., 1986; Shorohova et al., 2009). The importance of the time lag is indirectly confirmed by our results of the CWD censuses of 2009 and 2014, which revealed that the proportion of CWD in the initial stages of decay is much higher in polluted areas compared to the UP area (Bergman et al., 2015; Bergman and Vorobeichik, 2017). We suppose that the prime cause of the increase in time lag is such an important factor of CWD decomposition as contact with the ground. This hypothesis is supported by the significantly more pronounced slowdown of fir CWD decomposition compared to spruce. Previously and in the present study, we show that the mean diameter of fir logs is smaller than that of spruce logs, especially in the HP area (see Table 2; Table A3). In other studies performed in this territory, we also revealed that the mass of fir stems, branches, and needles was lower than that of spruce trees with the same height and diameter at breast height (Bergman, 2011). In addition, tree stands in polluted areas are denser than in the UP area, at least for the last 29 years (see Table 1, Table A4; Vorobeichik and Khantemirova, 1994; Vorobeichik et al., 1994). Therefore, near the smelter, heavy spruce trees that fall may more easily come into contact with the ground, whereas lighter fir trees may lean upon their branches or on nearby trees for a long time. Log position has already been suggested as influential for the decomposition time of Siberian fir and Siberian spruce as well as other species because the wood of leaning trees has a lower moisture content and, accordingly, is less favorable for wood-degrading biota (Shorohova and Kapitsa, 2014; Song et al., 2017). The importance of leaning for firs is also evidenced by the slight increase in wood density with increasing distance from the root collar (see Section 3.1), which illustrates the

5. Conclusions Long-term industrial pollution decreases the decomposition rate of A. sibirica and P. obovata CWD. Considering the tolerable concentrations of heavy metals in decaying logs of these species and the differences in the effect of pollution on the decomposition rate on these species (which also differ in size and mass), we suppose that substrate toxicity may not be the primary factor in the slowdown of the decay of CWD. The primary factors of decomposition slowdown can be further explored through time series analysis of woody debris or chronosequence analysis of the full spectrum of decay stages in polluted and unpolluted areas. Also, understanding which groups of wood decomposers are most vulnerable to pollution, which would represent the weak links in the sequential process of wood decomposition in polluted areas, is of particular interest. The guidelines for national greenhouse gas inventories recommend using the gain-loss method for the estimation of changes in the dead 318

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wood stock over a specified period. Based on the analysis of the relationship between wood density and RP, we suppose that the dynamic penetrometer may be used as a noninvasive, cheap, pocket-sized, and flexible tool in gain-loss surveys. Undoubtedly, this approach needs further development. Specifically, reference tables relating RP to wood density for different tree species across distinct ecological zones would have great practical value. One example to follow is the standard penetration test globally used in geotechnical engineering. Acknowledgements We thank Makar V. Modorov for the help in material collection, Anton V. Schepetkin for the technical assistance in laboratory, Viktor V. Dulya for dynamic penetrometer construction and equipment maintenance, and anonymous reviewers whose comments helped improve this manuscript. The work was funded by the Russian Foundation for Basic Research with grants nos. 19-04-00921, 18-29-05042 (penetrometer construction), and 18-04-00160 (analysis of copper in soil). Paper preparation was supported by the State Contract of the Institute of Plant and Animal Ecology, UB RAS. Declaration of Competing Interest None. Appendix A. Supplementary material Supplementary data to this article can be found online at https:// doi.org/10.1016/j.foreco.2019.06.026. References AWPA, 1996. Book of Standards. American Wood Preservers' Association, Selma, AL. Berg, B., Ekbohm, G., Söderström, B., Staaf, H., 1991. Reduction of decomposition rates of scots pine needle litter due to heavy-metal pollution. Water Air Soil Pollut. 59, 165–177. Bergman, I.E., 2011. Biological productivity of spruce and fir in the gradient of atmospheric pollution in the Urals: comparative analysis and compilation of inventory tables. In. Ural State Forest Engineering University, Yekaterinburg, p. 156. Bergman, I.E., Vorobeichik, E.L., 2017. The effect of a copper smelter emissions on the stock and decomposition of coarse woody debris in spruce and fir woodlands. Contemp. Probl. Ecol. 10, 790–803. https://doi.org/10.1134/S1995425517070022. Bergman, I.E., Vorobeichik, E.L., Usoltsev, V.A., 2015. The structure of spruce-fir tree stands mortality under impact of the Middle Ural copper smelter emissions. Siberian J. Forest Sci. 20–32. https://doi.org/10.15372/SJFS20150202. Bieker, D., Rust, S., 2010. Electric resistivity tomography shows radial variation of electrolytes in Quercus robur. Can. J. For. Res. 40, 1189–1193. https://doi.org/10. 1139/X10-076. Bolker, B.M., Brooks, M.E., Clark, C.J., Geange, S.W., Poulsen, J.R., Stevens, M.H., White, J.S., 2009. Generalized linear mixed models: a practical guide for ecology and evolution. Trends Ecol. Evol. 24, 127–135. https://doi.org/10.1016/j.tree.2008.10.008. Bryndina, E.V., 2000. Effects of discharges of a copper smelting plant on communities of wood-decaying fungi of Southern taiga. Siberian J. Ecol. 679–684. Bürkner, P.-C., 2017. brms: An R package for bayesian multilevel models using stan. J. Stat. Softw. 80, 28. https://doi.org/10.18637/jss.v080.i01. Carpenter, B., Gelman, A., Hoffman, M.D., Lee, D., Goodrich, B., Betancourt, M., Brubaker, M., Guo, J., Li, P., Riddell, A., 2017. Stan: A probabilistic programming language. J. Stat. Softw. 76, 32. https://doi.org/10.18637/jss.v076.i01. Chew, I., Obbard, J.P., Stanforth, R.R., 2001. Microbial cellulose decomposition in soils from a rifle range contaminated with heavy metals. Environ. Pollut. 111, 367–375. https://doi.org/10.1016/S0269-7491(00)00094-4. Colpaert, J.V., van Assche, J.A., 1987. Heavy metal tolerance in some ectomycorrhizal fungi. Funct. Ecol. 1, 415–421. https://doi.org/10.2307/2389799. Creed, I.F., Webster, K.L., Morrison, D.L., 2004. A comparison of techniques for measuring density and concentrations of carbon nitrogen in coarse woody debris at different stages of decay. Can. J. For. Res. 34, 744–753. https://doi.org/10.1139/x03212. Crockatt, M.E., Bebber, D.P., 2015. Edge effects on moisture reduce wood decomposition rate in a temperate forest. Glob. Change Biol. 21, 698–707. https://doi.org/10.1111/ gcb.12676. Daniel, G.F., Nilsson, T., 1988. Studies on preservative tolerant Phialophora species. Int. Biodeteriorat. 24, 327–335. https://doi.org/10.1016/0265-3036(88)90018-8. Dudka, S., Adriano, D.C., 1997. Environmental impacts of metal ore mining and processing: a review. J. Environ. Qual. 26, 590–602. https://doi.org/10.2134/jeq1997. 00472425002600030003x. Dulya, O.V., Mikryukov, V.S., Vorobeichik, E.L., 2013. Strategies of adaptation to heavy

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