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Decomposition dynamics of coarse woody debris of three important central European tree species

Decomposition dynamics of coarse woody debris of three important central European tree species Background: Coarse woody debris (CWD) is an important element of forest structure that needs to be considered when managing forests for biodiversity, carbon storage or bioenergy. To manage it effectively, dynamics of CWD decomposition should be known. Methods: Using a chronosequence approach, we assessed the decomposition rates of downed CWD of Fagus sylvatica, Picea abies and Pinus sylvestris, which was sampled from three different years of tree fall and three different initial diameter classes (>10 – ≤ 20 cm, >20 – ≤40 cm, >40 cm). Samples originating from wind throws in 1999 were collected along a temperature and precipitation gradient. Based on the decay class and associated wood densities, log volumes were converted into CWD mass and C content. Log fragmentation was assessed over one year for log segments of intermediate diameters (>20 – 40 cm) after 8 and 18 years of decomposition. −1 Results: Significantly higher decomposition constants (k) were found in logs of F. sylvatica (0.054 year )than in −1 −1 P. abies (0.033 year )and P. sylvestris (0.032 year ). However, mass loss of P. sylvestris occurred mainly in sapwood and hence k for the whole wood may be overestimated. Decomposition rates generally decreased with increasing log diameter class except for smaller dimensions in P. abies. About 74 % of the variation in mass remaining could be explained by decomposition time (27 %), tree species (11 %), diameter (17 %), the interactive effects between tree species and diameter (4 %) as well as between decomposition time and tree species (3 %) and a random factor (site and tree; 9.5 %), whereas temperature explained only 2 %. Wood fragmentation may play a more important role than previously thought. Here, between 14 % and 30 % of the −3 decomposition rates (for the first 18 years) were attributable to this process. Carbon (C) density (mgC · cm ), which was initially highest for F. sylvatica,followed by P. sylvestris and P. abies, decreased with increasing decay stage to similar values for all species. Conclusions: The apparent lack of climate effects on decomposition of logs in the field indicates that regional decomposition models for CWD may be developed on the basis of information on decomposition time, tree species and dimension only. These can then be used to predict C dynamics in CWD as input for C accounting models and for habitat management. Keywords: Dead wood; Carbon; Decay rate; Beech; Spruce; Pine * Correspondence: steffen.herrmann@wsl.ch Institute of Forest Sciences, Faculty of Environment and Natural Resources, University of Freiburg, Tennenbacherstr. 4, D-79106 Freiburg, Germany Swiss Federal Research Institute WSL, Zürcherstrasse 111, CH-8903 Birmensdorf, Switzerland © 2015 Herrmann et al. Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Herrmann et al. Forest Ecosystems (2015) 2:27 Page 2 of 14 Background on CWD decomposition can be explored in detail via Coarse woody debris (CWD) is structurally and function- lab incubation (see Herrmann and Bauhus 2012). ally very important for forest ecosystems, in particular for However, to capture the complex interplay of pro- biodiversity (Siitonen 2001), the energy and nutrient cycle cesses responsible for decomposition in forests, long- (Müller-Using and Bartsch 2007; Kuehne et al. 2008) and term field measurements are necessary. carbon storage (Harmon et al. 1986; Turner et al. 1995; The decomposition rate of CWD is mainly dependent on Pregitzer and Euskirchen 2004; Kahl et al. 2012). Whereas climatic (wood temperature, wood moisture) and substrate the amount of CWD may comprise up to 30 % or even specific variables (tree species, decay stage, diameter), 40 % of the total timber volume in natural beech (Com- where tree species influences chemical and physical wood marmot et al. 2013) and spruce (Ranius et al. 2003) forests, properties and the decomposer community (Mackensen et this share is typically less than 5 % in managed European al. 2003; Kahl 2008). Up to now it is not clear whether cli- forests (Bütler and Schlaepfer 2004; MCPFE 2007). This re- matic or substrate specific variables are more important for duction in the amount and related quality of dead wood the decomposition process (see also Cornwell et al. 2009; (Müller and Bütler 2010) has significant implications for its Freschet et al. 2011). Recent results indicated that climatic various functions. European lists of endangered species are variables are likely to be more important for (short term) often dominated by species depending on dead wood CWD mineralization than substrate specific variables (Grove et al. 2002). For Germany, 28 % of the saproxylic (Herrmann and Bauhus 2012). However, both factors also beetle species are listed as threatened or regionally extinct clearly interact and must therefore be considered jointly (Seibold et al. 2014). Owing to its significance for ecosys- (Herrmann and Bauhus 2012). Here, we analysed the influ- tem functioning, CWD has been recognized as an indicator ence of these factors on the field decomposition rate of of ecological sustainable forest management (MCPFE Fagus sylvatica L., Picea abies (L.) Karst. and Pinus sylves- 2003). Therefore increasing efforts have been undertaken tris L. along a climatic/altitudinal gradient (temperature, to manage CWD as a habitat component and C store in precipitation) using a chronosequence approach based on forest ecosystems. However, to manage this pool, a basic known ages of CWD logs. In addition, the influence of log understanding of patterns and rates of dead wood decom- dimension was assessed by analysing CWD pieces of differ- position in different forests is crucial. Further, for the as- ent initial diameters (in three diameter classes, i.e., >10 – sessment of C stocks in dead wood as part of National 20 cm, >20 – 40 cm, > 40 cm). Greenhouse Gas inventories, detailed information on C Our specific research questions were: stored in dead wood of different species and their relation- ship with different decay stages (which are typically cap- 1. How do decomposition rates differ between the tured in inventories) is necessary. So far this knowledge is three tree species Fagus sylvatica (hardwood), incomplete and mainly based on expert opinions (Meyer Picea abies and Pinus sylvestris (softwoods)? et al. 2003; Rock et al. 2008; Zell et al. 2009). In addition, 2. To what extent can the variation in mass and information on residence times of CWD in different decay carbon remaining in CWD be explained by tree classes would be very helpful to forecast its dynamics and species, log dimension, wood chemistry (nutrients, to calculate the input and output of different decay stages lignin) and microclimatic conditions? in order to conserve specific habitats of dead wood dependent species (Kruys et al. 2002; Ranius et al. 2003). In addition, we tested if it was possible to determine Dynamics of CWD are determined by the input through the dry density of CWD logs with the drill resistance tree mortality and the output through the decomposition method. process. The decomposition process is characterised by a decrease in dimension and mass and concomitant changes Methods in biological, physical, and chemical properties. The main Study sites process of dead wood decomposition is the loss of organic For the chronosequence approach that relates mass (i.e., material through respiration. In addition, its fragmenta- wood density × volume) to the age of CWD, logs were tion through biological activity (in particular by insects) sampled at sites where the previous stand had been and physical processes can also be important. With wind-thrown and where no or only little salvage logging ongoing decomposition and decreasing physical stabil- had taken place. Thus we could be certain about the ity of CWD, fragmentation increases in importance period that logs had undergone decomposition. The (Harmon et al. 1986). However, leaching of soluble study sites were mainly located in southern Germany, matter (including C) from CWD logs is of minor im- except for the oldest site that was situated in northern portance for mass loss (Spears et al. 2003; Kahl et al. Germany (Lower Saxony, near the city of Hannover). All 2012). The specific influence of selected environmen- selected sites were hit by severe winter storms in 1999, tal variables such as wood moisture and temperature 1990 and 1972 (Table 1). Herrmann et al. Forest Ecosystems (2015) 2:27 Page 3 of 14 Table 1 Description of the study sites a b c c Site Year of tree fall Elevation (m a.s.l.) Soil type Average temperature Precipitation per year (°C) (mm per year) Forêt de Ha-guenau (FdH) 1999 150–170 Gleyic Cambisol 10.98 645 Bienwald (Biw) 1999 100–130 Cambisol 10.0 680–700 Röttlerwald (Roe) 1999 510–590 Luvisol and Cambisol 9–10 900–1080 Lotharpfad (Lot) 1999 950 Podsolic Cambisol 7.0 1700 Hofstatt (Hof) 1990 320–340 Luvisol 8.8 800 Silbersandgrube (Sig) 1990 550 Luvisol 7.8 665 Kiekenbruch (Kib) 1972 70 Podsolic Cambisol 8.5 657 a b c according to GPS; FAO (2006); according to forest office information Sampling design were not autocorrelated according to the Durbin Downed CWD of F. sylvatica, P. abies and P. sylvestris Watson-Test (DW = 1.7). from three different years of tree fall (1972 (except for Decay class was assessed for each log and measure- beech), 1990 and 1999) was sampled in three different ini- ment position according to a 4 stage classification sys- tial diameter classes (>10 – ≤ 20 cm, >20 – ≤40 cm, tem (Albrecht 1991 for P. abies and P. sylvestris), >40 cm). Samples originating from wind throws in 1999 modified for F. sylvatica by Müller-Using and Bartsch were collected along an altitudinal gradient from the (2009). Based on the decay class and the associated Rhine valley up to the top of the black forest (Table 2). known wood densities, log volumes assessed in field in- This represents a climatic gradient with regard to ventories can be converted into CWD mass and C con- temperature and precipitation (see Table 1). Unfortu- tent (Grove et al. 2009). However, because the internal nately, no pine CWD was found at the highest elevation. log condition may not be represented by a visual decay In our design, site was not independent from year classification (Meyer 1999), large errors for assessed C since tree fall. Generally we sampled at least 5 replicates pools can result. Therefore, we also assessed the internal for each combination of species, site, and diameter. density of logs with the drill resistance method (Kahl et Where clearly different diameter classes were present al. 2009). At each point, where log diameter was mea- along the length of a single log, we selected in some sured, two drill resistance measurements were con- cases several sample-points at a minimum distance of ducted, one horizontally and one vertically. In general, at 2 m, where discs were extracted. Discs were approxi- least one stem disc per tree, representing the dominating mately 10 cm thick. On average, we extracted 2.5 sample decay class, was cut with a chainsaw at a drill resistance discs per log. These samples were treated as separate measurement position for further calibration and ana- samples, because the variations in wood density from lysis. If more than one diameter or decay class was ana- one log and within the same decay class were compar- lysed per log, the number of extracted discs increased able to the variations within the same decay class when accordingly. At the site ‘Lot’ no discs could be removed collected from a large population of logs in the field (see for most beech logs, as their position was very close to a also Müller-Using and Bartsch 2009). Separate samples hiking trail. In this case samples were taken with a wood from the same log can also differ considerably in com- corer (according to the method applied above). position of the microflora, as has been shown by others (Shigo 1986; Schwarze et al. 1999). In addition, our pre- liminary analysis showed that samples from the same log Density assessment Density was assessed gravimetrically and via drill resist- ance measurements using the Resistograph 3450S Table 2 Year of storm event (i.e., decomposition time) and elevation for the 7 study sites (RINNTECH, Heidelberg, Germany) with a resolution of 0.01 mm and a maximum drilling depth of 44 cm (for Year of Elevation (m a.s.l.) storm event further details see Kahl at al. 2009). 100 600 900 To determine density of wood samples gravimetrically, 1999 Biw (spruce, pine), Roe (beech, Lot (beech, spruce) wooden bars were cut with a band saw along the drill re- FdH (beech) spruce, pine) sistance measurement lines for each sampled stem disc. 1990 Hof (beech), Sig (spruce, pine) These were further cut into small cubes (about 2 cm thick) and dried in a fan-forced oven at 105 °C until con- 1972 Kib (spruce, pine) stant weight was achieved. Dry density was measured for Biw Bienwald, FdH Forêt de Haguenau, Roe Röttlerwald, Lot Lotharpfad, Hof Hofstatt, Sig Silbersandgrube, Kib Kiekenbruch each cube (without further sample preparation) via the Herrmann et al. Forest Ecosystems (2015) 2:27 Page 4 of 14 water displacement method and related to the drill re- value for each species and size class was then calculated sistance value for calibration. on the basis of these individual k values. The time when 50 % of the original mass would have Log fragmentation assessment been decomposed was calculated by: Log fragmentation was assessed over one year for log segments of intermediate diameters (>20 – 40 cm) of F. 0:693 t ¼ ð3Þ sylvatica, P. abies and P. sylvestris after 8 and 18 years of 0:5 decomposition (representing initial and advanced decay stages), with three replicates per species and year. Nylon To model CWD decomposition, exponential decay fabric (1 mm × 1 mm mesh, ca. 180 cm length and about functions, which assume a homogeneous substrate that 120 cm width) was placed underneath each log and decays at a constant rate, have been used in the majority metal pegs were used to hold up the material. After one of cases (Mackensen et al. 2003). This may be an over- year, the material that had fallen onto the nylon mesh simplification of the complex processes occurring during was collected and quantified. dead wood decomposition. Recent modelling approaches have also considered the different wood constituents Chemical analysis such extractives, cellulose or lignin, which undergo, owing Following density assessment, a subset of all samples to their chemical resistance, different decomposition dy- with a total of 3 replicates per species, decay stage and namics (Tuomi et al. 2011). However, we did not employ diameter class was ground and analysed for C concen- such model, because the temporal resolution of our data tration. C concentration was determined by combustion would not have permitted a robust fitting of the decom- at 950 °C with a Leco Truspec™ CN analyser (St. Joseph, position process using a model with many parameters. To MI, USA). Wood cores were taken from intact wood (1 facilitate comparison of results with that of other studies, sample per diameter class per species and site, except for we fitted the exponential decay functions for all tree the sites ‘Biw’, ‘Lot’ and ‘FdH’) to estimate initial nutrient species. concentrations. N concentration was determined according Original dry mass of undecayed wood of the three tree to the method described above for C concentration. Con- species was calculated based on original diameter and centrations of P, S, Ca, K, Mg, Na, Al, Fe, and Mn were dry density values from literature studies (Trendelenburg analysed with Inductively Coupled Plasma-Optical Emis- and Mayer-Wegelin 1964). Reference values for dry sion Spectroscopy (ICP-OES) (Spectro, Kleve, Germany), −3 density (g · cm ) were: F. sylvatica: Lot: 0.65, all other following HNO digestion in pressure chambers. Lignin sites: 0.68; P. abies: Lot: 0.4, Kib: 0.46, all other sites: concentration was measured according to the methods pro- 0.43; P. sylvestris: Kib > 40 cm: 0.45, all other sites and posed by TAPPI (1976) and Effland (1977). diameter classes: 0.49. Different values reflect site (i.e., cli- matic) differences and variation along stem height (pine), Data analysis as observed by Trendelenburg and Mayer-Wegelin (1964). To calculate decomposition constants (k) based on mass Additionally, wood cores (three replicates) were taken loss, mean dry density from gravimetric density assess- from undecomposed wood from the different sites (except ment was used if available. Otherwise we used mean dry for Lot), tree species and diameter (stratified as >30 cm density based on drill resistance. For each measurement and <30 cm, and 30 cm for pine at Roe) to confirm that position, the volume of the stem disc represented by this density values from the literature were appropriate. position was calculated based on formula 1: If the original diameter was different from the current diameter (i.e., bark and/or (sap) wood was already V ¼ L  π  ð1Þ decomposed), it was reconstructed based on the thick- ness of intact parts on the same log or adjacent logs. We determined the k values of each individual log or measurement position based on the single negative ex- Statistical analysis ponential decay model (Olson 1963): Statistical analyses were conducted using SPSS Statis- tics 17.0 (SPSS Inc., Chicago, IL, USA) and R (R Devel- MtðÞ −1n opment Core Team). All significance testing was done MðÞ 0 k ¼ ð2Þ with p < 0.05. The analysis for even distribution of the residuals was conducted graphically via normal qq-plot Here M(t) is the mass (g) remaining at time t (year) and scatter plot (residuals vs. predicted values; and vs. and M(0) is the original mass (g) at time 0 and k is the fitted values (linear regression model)), as well as via −1 decomposition rate constant (year ). The average k Kolmogorov-Smirnov test (Dormann and Kühn 2009). Herrmann et al. Forest Ecosystems (2015) 2:27 Page 5 of 14 A transformation (usually ln) was conducted, if resid- and pine, only 58 % and 50 % of the variation in density uals were not evenly distributed. were explained with drill resistance, respectively. A two-way ANOVA was conducted to test for signifi- cant differences in wood density and in C content be- Decomposition rates (k) and mass loss tween the three species as well as between the different −1 The average k value of F. sylvatica (0.054 year ;95% CI: decay stages. A one-way ANOVA followed by Fisher’s −1 0.048–0.059 year ) was significantly different from the one LSD or Dunnett-T3 post-hoc test, if variances were not −1 −1 of P. abies (0.033 year ;95%CI:0.03–0.035 year )and P. homogenous, was conducted to further analyse possible −1 −1 sylvestris (0.032 year ;95 % CI:0.028–0.035 year ) across differences in 1) k (ln-transformed) between the three all sites and diameter classes (p<0.001)(Table3). Thetwo species as well as between the different diameter classes conifer species were not significantly different from each at the species level, 2) mass remaining at the species other. The corresponding average diameters of logs were level for each period, beginning at t , for which logs had 34.8 (SD ± 14.8) cm for F. sylvatica, 33.9 (SD ± 13.9) cm for undergone decomposition, 3) wood density between spe- P. abies and 32.2 (SD ± 12.1) cm for P. sylvestris indicating cies for each decay stage and to compare the average dry that the effects of species were not biased through differ- density of the different decay stages for each species and ences in log dimensions between species. The time until 4) C concentration between the different decay stages of 50 % of the initial mass was decomposed was 13 years for each species. In the case of P. Sylvestris (only one sample F. sylvatica, 21 years for P. abies and 22 years for P. sylves- in decay stage 4, a two-sample t-test was applied for tris. The average k values until 18 years for P. abies and P. comparison between decay stages. −1 −1 sylvestris were 0.033 year ;95%CI:0.031–0.036 year The influence of substrate specific, climatic and envir- −1 −1 and 0.034 year ;95%CI:0.03–0.037 year , respectively. onmental variables on the mass of logs remaining after Owing to different decomposition rates, the patterns of different periods of decomposition in the field was ana- mass remaining differed accordingly between the species lysed based on a linear mixed-effects model. Possible in- (Fig. 2). Whereas the average mass remaining in P. sylves- fluencing variables were identified using a Spearman rho tris CWD was similar to that of P. abies until 18 years of correlation matrix. The climatic variables (temperature decomposition (about 59 % each (SD ± 12, resp. 11 %; P. and precipitation) were analysed for collinearity and cen- abies)), it was considerably higher at year 36 (P. sylvestris: tered for further use in the model. The residuals were fur- 46 % (SD ± 15), P. abies: 39 % (SD ± 18)). At this time, ther analysed for even distribution graphically via box-cox nearly all sapwood of P. sylvestris logs was decomposed transformation. To decide if a model is better than a pre- and heartwood started to decompose. Average mass vious model, we used the explained variation (r ) and the remaining decreased with increasing decomposition time st nd AIC as 1 and 2 criteria. Eta squared was used as an ef- in all species (p < 0.05, Fig. 2). fect size measure. With increasing diameter class, decomposition rates de- To assess the residence time (based on Kruys et al. creased significantly in all species, except between the 2002), in total and for each decay stage, the average dry diameter classes <20 cm and >20 – 40 cm for P. abies −3 density (g · cm ) and the average decomposition time (Table 3). (years) per decay stage were calculated from the whole dataset in a first step. For further calculations, each decay stage was defined as the interval between the mid- Prediction of mass remaining points of dry densities of decay classes (e.g., 0.5–1.49 for About 74 % of the variation in mass remaining could be decay stage 1). Afterwards a dry density value was explained by decomposition time (27 %), tree species assigned for each decay stage interval based on the ex- (11 %), diameter (17 %), the interactive effects between ponential relationship between decay stage and average tree species and diameter (4 %) as well as between de- dry density. In the same way, the decomposition time composition time and tree species (3 %) and a random was assigned, which was calculated based on the expo- factor with site and tree (9.5 %), whereas temperature nential relationship between average dry density and explained only 2 % of the variation (Table 4). If we sub- average decomposition time. Finally the residence time tract the random factor, which cannot be predicted, per decay stage was defined as the decomposition time 64 % of the variation in mass remaining may be pre- at the upper bound of each decay stage. dicted for the tree species investigated here. If initial nutrient concentrations were considered as additional independent variables, a slight model im- Results provement was achieved, when the concentration of Drill resistance measurements manganese was included. Based on a reduced data set Up to 77 % of the variation in wood density could be ex- (n = 172 instead of n = 287), an additional 1 % of the plained with drill resistance for beech (Fig. 1). For spruce total variance could be explained. Herrmann et al. Forest Ecosystems (2015) 2:27 Page 6 of 14 Fig. 1 Relationship between drill resistance (ln(DR)) and dry density for F. sylvatica, P. abies and P. sylvestris Log fragmentation originated mostly from wood in logs and was much higher The amount of fragmented material differed between tree in the former than the latter species. For P. sylvestris,bark species and age of logs, but in relation to total mass it was fragmentation dominated in older logs. Fragmentation was very low (Table 5). The maximum value was 1.8 % per very patchy between individual logs and often occurred as annum for one P. abies log (Table 5). For F. sylvatica and a single event, as indicated by the high standard deviation. P. abies, bark fragmentation dominated after the first However, when fragmentation is related to the decompos- 8 years of decomposition. In P. sylvestris, only a minor ition rate, the actual contributions are considerable. When amount of bark fragmentation and no wood fragmentation compared to the total k value for first 18 years of decom- occurred during this period of time. In older logs of F. syl- position, fragmentation over one year accounted for 27 % vatica and P. abies (18 years), fragmented material for beech, 14 % for spruce and 30 % for pine, respectively. Table 3 Annual decomposition constants k for different diameter classesin Fagus sylvatica, Picea abies and Pinus sylvestris Species Diameter class (cm) Combined <20 20–40 > 40 a b c F. sylvatica (n = 96) 0.078 (0.027) 0.055 (0.027) 0.035 (0.015) 0.054 (0.028) a a b P. abies (n = 107) 0.034 (0.011) 0.036 (0.012) 0.027 (0.013) 0.033 (0.013) a b c P. sylvestris (n = 83) 0.050 (0.015) 0.030 (0.01) 0.021 (0.01) 0.032 (0.015) Values are means with standard deviation in parentheses. Different letters indicate significant differences between diameter classes (p < 0.05) Herrmann et al. Forest Ecosystems (2015) 2:27 Page 7 of 14 beech (Table 6). In addition, with decreasing decompos- ition rate from F. sylvatica to P. sylvestris, the proportion of time, relative to the entire decay process, that logs stayed in the first decay stage became shorter, whereas the time in the last decay stage increased. Wood density, carbon content and decay stage Average dry density values differed significantly be- tween species and decay stages. Specifically, average dry densities were significantly different between F. sylvatica, P. abies and P. sylvestris for decay stage 2 (p < 0.001). For decay stage 3, dry density of F. sylva- tica was significantly different from those of P. abies and P. sylvestris (p < 0.001 and p <0.01), which were similar to each other. No significant differences be- tween species were found for decay stage 4. Average dry density decreased with increasing decay stage in all species (p < 0.001; Fig. 3). The decrease in density was highest from decay stage 2 to 3 and the density variation was highest in decay stage 3 for all three species, but in particular for F. sylva- tica. In dead wood of F. sylvatica, the decrease in density was not significant between decay stages 3 and 4. In contrast, a significant decrease between decay stages 3 and 4 was found for P. abies (p < 0.05). For P. sylvestris there was only one value in decay stage 4. Similar to dry density, carbon concentration was sig- nificantly higher for F. sylvatica, compared to P. abies and P. sylvestris, which were not different from each other (Table 7). With advancing decomposition and de- creasing dry density, an increase in C concentration was observed for all three species (except between decay stages 3 and 4 for beech and spruce). C concentration differed significantly between decay stages 1 and 2, as well as 3 for F. sylvatica and P. abies. For P. abies, decay stage 1 was significantly different also from decay stage 4, while decay stages 1 and 2 were significantly different from decay stage 3 for P. sylvestris (Table 7). The effect of decay stage on carbon (C) density was dependent on species (p = 0.000). The pattern of C dens- ity and decay stage followed the pattern of wood density and decay stage closely (Table 7 and Fig. 3). Discussion Fig. 2 Remaining mass of log segments of Fagus sylvatica (n = 96), Decomposition rates (k) and mass loss Picea abies (n = 107) and Pinus sylvestris (n = 83); boxplots display To our knowledge, this is the first study that systematic- median, lower and upper quartile, minimum and maximum values; ally assessed CWD decomposition rates and dynamics of points outside boxplots represent outliers; different letters indicate Fagus sylvatica, Picea abies and Pinus sylvestris across significant differences (p < 0.05) different sites and diameter classes in Central Europe. Average k values of F. sylvatica were significantly higher Residence time per decay class than those of P. abies and P. sylvestris across all sites The residence time per decay class increased with decay and diameter classes. class (i.e., higher degree of decomposition). This increase In other studies, decomposition rates of F. sylvatica −1 −1 was most pronounced in pine and least pronounced in varied from 0.056 year (Kahl 2008) to 0.089 year Herrmann et al. Forest Ecosystems (2015) 2:27 Page 8 of 14 Table 4 Linear mixed-effects model (ANOVA table) to predict the remaining massof Fagus sylvatica, Picea abies and Pinus sylvestris Source Sum of Squares df F Sig. Eta squared Decomp. time 12162.2 1 129.449 0.000 27.06 Tree species 5058.7 2 12.768 0.000 11.26 Diameter 7655.1 1 97.979 0.000 17.03 Temperature 789.4 1 7.273 0.009 1.76 Decomp. time × Species 1405 2 6.066 0.003 3.13 Species × Diameter 1873.8 2 10.8 0.000 4.17 Conditional R-squared: 0.739 (Marginal R : 0.644) (Müller-Using and Bartsch 2009), with intermediate extent) from different tree diameters in other studies. −1 values of 0.06–0.075 year (Christensen et al. 2005; Colonization by (cord-forming) basidiomycetes seems to based on 86 European beech forest reserves). In com- progress slower in large diameter logs (Boddy and parison to these findings, our k value for beech of Heilmann-Clausen 2008). Although this is not supported −1 0.054 year (SD ± 0.028, 52 %) is within the range of by the results of this study, different site, i.e., microcli- variation and very similar to the estimate by Kahl (2008). matic conditions might further lead to an increase or de- The difference between k values of beech may be at- crease in k values, for example through canopy removal tributed to the uncertainty over the cause of death, as (Hagemann et al. 2010; Forrester et al. 2012). Managed observed by Kahl (2008). In that study, a k value of forests may also harbor a lower diversity of wood decaying −1 0.075 year (SD ± 0.034) for logs that died naturally fungi, which may lead to different decay rates (Stenlid et (infected by Fomes fomentarius prior to death) and a k al. 2008; Purahong et al. 2014a, b). With regard to the ori- −1 value of 0.025 year (SD ± 0.012) for wind thrown logs gin of dead wood and its diameters, our data are likely was calculated (mean diameter > 40 cm). This indicates more representative of managed forests rather than strict that decomposition had advanced already in trees af- reserves. −1 fected by F. fomentarius before they died or fell. We cal- The decomposition rate of 0.033 year (SD ± 0.013, −1 culated a k value of 0.035 year (SD ± 0.015) for the 39 %) for P. abies calculated in our study was compar- diameter class > 40 cm; which is similar to the estimate able to k values in other studies (see Rock et al. 2008). by Kahl (2008). Most of the trees died “naturally” due to In detail, it was in the range of variation of the estimate −1 F. fomentarius in the study by Müller-Using and Bartsch by Kahl (2003; 0.027 year (SD±0.023)) in Central (2009), which may mainly explain the high k value in Germany and identical to k values found by Naesset −1 that study. It can be assumed that most logs investigated (1999; k = 0.033 year ; mean diameter: 13 cm) in in our study originated from trees that were still vital southeastern Norway. It was also similar to values in compared to trees that died “naturally”. In freshly fallen European (Shorohova and Kapitsa 2014) and Russian logs, colonization by (cord-forming) basidiomycetes dif- boreal forests (Krankina and Harmon 1995; Krankina fers from logs that have already been affected by fungi et al. 1999; Tarasov and Birdsey 2001; Harmon et al. before they fell and wood can be expected to decay 2000; Yatskov et al. 2003). slower (Boddy and Heilmann-Clausen 2008). Further, The conditions of the decomposition process in the tissues that have died “naturally” may be predisposed to study by Naesset (1999) were very similar to our situ- microbial colonization, whereas artificially detached liv- ation. Their starting point of the decomposition process ing tissues (i.e., due to wind breakage) may maintain was the date of cutting. All logs were free from rot at metabolic activities against decomposers (see Yin 1999). the time of cutting and the decomposition took place in In addition, different k values might result (to some open areas. Table 5 Annual fragmentation loss for bark and wood of Fagus sylvatica, Picea abies and Pinus sylvestris Species CWD age (yrs) Bark (% total fragmentation loss) Wood (% total fragmentation loss) Fragmentation (% total mass) F. sylvatica (n = 6) 8 99.6 (0.4) 0.4 (0.4) 0.27 (0.37) 18 28.5 (27.2) 71.5 (27.2) 0.02 (0.01) P. abies (n = 6) 8 98.3 (3) 1.7 (3) 0.13 (0.22) 18 0 100 0.65 (1.02) P. sylvestris (n = 6) 8 100 0 0.01 (0.01) 18 73.8 (18.8) 26.2 (18.8) 0.18 (0.20) Values are means with standard deviation in parentheses Herrmann et al. Forest Ecosystems (2015) 2:27 Page 9 of 14 Table 6 Absolute and relative residence times per decay stage Compared to beech, decomposition rates k from the for CWD logs of Fagus sylvatica, Picea abies and Pinus sylvestris literature are much less variable for spruce. One explan- Decay Residence time (Years) ation for this may be that the sensitivity to influencing stage factors (as cause of death, decomposer community or Fagus sylvatica Picea abies Pinus sylvestris differences in diameter) is less pronounced in spruce. yrs % yrs % yrs % This may partly be attributable to a higher decay resist- 1 6.7 12.2 8.6 9.8 6 6.0 ance of spruce and more uniform substrate conditions 2 10.7 19.5 15.3 17.4 14 14.0 (see also Cornwell et al. 2009; Weedon et al. 2009) when 3 15.8 28.8 25.3 28.8 28.3 28.4 compared to beech. 4 21.6 39.4 38.5 43.9 51.5 51.6 However, we observed a significant effect of diameter Relative residence times were calculated as percent of the sum of residence on k values also for spruce. Since all comparable studies times for each species in boreal regions were conducted mainly on small diam- eter trees, the variation between k values should also be small. In our study, the influence of climatic variables on −3 Fig. 3 Dry density (g cm ) in log segments of Fagus sylvatica, Picea abies and Pinus sylvestris; boxplots display median, lower and upper quartile, minimum and maximum values; points outside boxplots represent outliers; different letters indicate significant differences (p < 0.05) Herrmann et al. Forest Ecosystems (2015) 2:27 Page 10 of 14 −3 Table 7 Carbon concentrations (%) and contents (mg cm ) in relation to decay stage (DS), mean and standard deviation (SD), as well as no. of samples, of log segments of Fagus sylvatica, Picea abies and Pinus sylvestris Species Decay stage p value (Anova) 12 3 4 Mean SD n Mean SD n Mean SD n Mean SD n DS Species a a b b ab F. sylvatica % 46.3 0.52 4 47.5 0.62 39 47.5 0.73 22 47.4 0.31 2 < 0.05 < 0.001 −3 a b c c mg cm 315 3.6 238 36.8 174 48.3 161 52.7 b a b b b P. abies % 47. 0.89 7 49.9 1.51 38 50.1 1.73 34 49.8 2.73 9 < 0.01 −3 a b c c mg cm 203 9.2 170 17.4 155 18.1 117 34.1 b a a b P. sylvestris % 48.8 1.13 5 49.2 1.54 34 50.2 1.31 31 51.4 1 < 0.05 −3 a b c d mg cm 235 11.4 198 22.6 162 28.5 149 < 0.01 Small letters indicate significant differences between tree species for total C concentration as well as between the different decay stages within each species −1 −1 −1 the decomposition, also of spruce logs, was low. For ex- sylvestris of 0.067 year ,0.0525 year and 0.0575 year , ample, we found no difference between k values for the respectively. In comparison to our results the esti- sites Kib (‘cold and wet’) and Sig (‘warm and dry’), which mated k values of P. abies and in particular of P. syl- may simply reflect different types of climatic limitations vestris appear to be too high. The deviations between for wood decaying fungi when compared to the ‘warm expert estimates of decay rates and our data underpin and wet’ optimum, which was not represented in our the importance of actual measurements. In particular design. for low decay rates, small absolute errors result in −1 Our k value for P. sylvestris (0.032 year (SD ± proportionally very large errors when calculating mass −1 0.015, 47 %)) was similar to values found in the bor- loss. For example, using a k value of 0.0525 year for eal forests of Europe (Shorohova and Kapitsa 2014) Picea abies (Rock et al. 2008), which is 59 % higher −1 and Russia (Krankina and Harmon 1995; Harmon et when compared to 0.033 year (determined in this al. 2000; Wirth et al. 2000; Yatskov et al. 2003; see study) would shorten the period until 50 % of CWD also Rock et al. 2008). mass was lost by about 37 % (8 years). The k value of pine determined in our study was In contrast, the chronosequence approach used in our mainly attributable to the loss of sapwood (as heartwood study may cause an underestimation of k,because slow- was found to be still largely intact for most logs after decaying logs may have a higher probability of being in- 36 years of decomposition time). Hence, if a longer total cluded in the sampling (Kruys et al. 2002; Herrmann decomposition period was analysed, the average decom- and Prescott 2008). However, this problem increases position constant might be lower than the one we re- with the decomposition time covered and it is negli- ported here. gible, when the observation period is shorter than the Similar to spruce, variation in the decomposition con- minimum decomposition time for logs. For example, if stant k of pine logs was also very low. This may again we assume a) high decomposition rates of k =0.09, and point to a higher decay resistance and a more uniform b) that logs can still be identified in the field when they decomposition process. In the gymnosperm wood of contain 20 % of the original mass, no logs should be lost spruce and pine, there was no highly variable spatial pat- form the sample population before 18 years. tern of intact and highly decomposed patches next to In our study, decomposition rates increased with de- each other, as was observed for beech. This observation creasing diameter class, except for P. abies, where the k is in accordance with much higher variation in log res- values between 20 and 40 cm tended to be slightly higher piration rates in dead wood of F. sylvatica than in P. than in smaller logs (<20 cm) (Table 4). Lower k values for abies and P. sylvestris (Herrmann and Bauhus 2012). smaller diameters (< 10 cm compared to > 25 cm) of P. At a more general level, k values of angiosperm wood abies were also observed by Naesset (1999), who assumed are typically higher, on average 77 %, than in gymno- that it was most likely caused by branches that prevent sperm wood (Weedon et al. 2009). A similar difference the logs from soil contact. In our study, the diameters < was also found by Russell et al. (2014). For comparison, 20 cm for P. abies were most often sampled in crown sec- our decomposition rate for beech CWD was about 60 % tions of the logs, where also soil contact was less often higher than for spruce and pine. encountered than for 20–40 cm diameter logs. However, k An estimation of decomposition rates for the federal values commonly decrease with increasing diameter state of Brandenburg (Northeastern Germany) based on a (Graham and Cromack 1982; Stone et al. 1998; Chambers literature review and expert consultation (Rock et al. et al. 2000; Mackensen et al. 2003), even if sometimes 2008) produced k values for F. sylvatica, P. abies and P. inconsistent relationships have been reported (see Herrmann et al. Forest Ecosystems (2015) 2:27 Page 11 of 14 Herrmann and Prescott 2008). Hence, where decompos- In a global meta-analysis of wood decomposition rates ition models for CWD with large variation in dimensions of angiosperms and gymnosperms, significant relation- are required, it is advisable to consider log diameter. ships between wood traits and decomposition rates were We are aware that our assumption of a constant decay only observed for angiosperms (Weedon et al. 2009). For rate based on the single negative exponential decay angiosperms, positive relationships between k and the model may be questionable and that there are more so- nutrients N and P, and a negative relationship between k phisticated models, e.g., as applied in the Yasso model and C:N ratios were found. We suggest that the mass (Tuomi et al. 2011). However, our dataset was not suffi- loss of CWD of the species investigated in our study can cient in terms of the variation in decomposition periods be sufficiently well predicted for most management and of logs to fit the temporal decay dynamics of a more so- modelling tasks by the factors tree species, time since phisticated model in a robust way. Since there are only commencement of decomposition, and log diameter, very few field experiments that followed mass loss in in- which can be obtained easily from forest inventories. dividual CWD pieces over the long term, the ‘real’ tem- Predictions will likely become less accurate, when CWD poral mass loss pattern is not known. Even if, the mass originates from different processes, e.g., when trees also loss rate of some wood constituents slowed down with die standing and decay slowly for many years before logs increasing time, this may be partially compensated by hit the ground, or when the wood decomposition by fragmentation, which was found to be of increasing im- fungi commences in the living tree (e.g., Kahl 2008). In portance in later decomposition stages (see e.g., Lambert our field study, most trees were felled as living trees by et al. 1980). The mass loss rate may even increase in storms. later stages as observed in the study by Kahl (2008). In contrast to this field study, about 60 % of the vari- ation in CO flux of CWD of the same species was ex- plained by climatic variables (wood moisture and wood Prediction of mass remaining temperature) in a lab incubation experiment (Herrmann A substantial proportion of the variation (64 %) in mass and Bauhus 2012). In the same study, temperature ex- remaining could be predicted by decomposition time, plained more than 90 % of CWD respiration of individual tree species, original log diameter, and their interactive Fagus sylvatica and Picea abies logs over one year in the effects, whereas temperature had a very small, and initial field. This comparison shows that scaling up from short- nutrient concentrations had almost no influence. The term CWD respiration measurements to the long-term observed influence of manganese concentrations on dynamics of CWD mass loss is difficult, since the com- mass remaining may be explained by the relevance of bined effects of temperature, moisture, and interactions manganese for lignin degradation by white-rot fungi between substrate quality and microorganisms need to be (Hofrichter et al. 2009). considered (Herrmann and Bauhus 2008). In order to cap- Similar to our findings, a literature review on CWD de- ture the complex interplay of processes (i.e., respiration, composition had indicated that tree species had a stronger fragmentation) responsible for decomposition in forests, influence on decomposition and nutrient dynamics than long-term field measurements are necessary. the abiotic environment (Laiho and Prescott 2004). And no effect of lignin or nitrogen concentrations was ob- served in an attempt to model CWD decay based on data CWD fragmentation about 300 cases of stem, branch and root woody debris The limited study on wood fragmentation indicated that decay from North America (Yin 1999). Also, most of the this process may contribute considerably to annual mass explained variation of mass remaining in the study by loss (max. 30 % of k for pine) although values were lower Yatskov et al. (2003) could be attributed to time since than in some other studies (e.g., 63 % found by Lambert death (50 %), log position (8 %) and tree species (6 %). et al. 1980). Our logs were mainly at the beginning and However, in some studies, environmental variables did ex- in the middle of the decomposition process. Fragmenta- plain a significant proportion of CWD decomposition tion was found to be of increasing importance with ad- over time (e.g., Russell et al. 2014). About 80 % of the vari- vanced decomposition (Harmon et al. 1986; Müller- ance in the decomposition rate constant was explained by Using and Bartsch 2009) as well as in higher elevations a multiple regression model with the factors of tree spe- (Lambert et al. 1980). However, our study also showed cies, mean diameter, mean temperature in July, sum of that fragmentation was highly variable within and be- precipitation per year and a lag time (Zell et al. 2009). tween logs, and that single events, such as activities of In contrast to Zell et al. (2009), we observed no signifi- animals searching for food in the logs, may contribute cant improvement in our prediction if mean temperature substantially to mass loss. in July instead of mean annual temperature was used in Measuring fragmentation over the course of only one the model. year was certainly not sufficient to assess the significance Herrmann et al. Forest Ecosystems (2015) 2:27 Page 12 of 14 of this process in the medium to long-term. This would The absence of distinct differences in wood density be- deserve a separate study. However, it would be very diffi- tween decay classes has also been observed in other studies. cult to include fragmentation in models predicting CO Little change (respectively an overlap) in density between release from CWD decomposition, since a large propor- the two least (1 and 2) and most advanced (4 and 5) decay tion of the material lost from CWD through fragmenta- classes has been found also in the study by Yatskov et al. tion is simply transferred to a different pool, the litter (2003). Similar to our results, no significant difference in layer. And we have no information on the decompos- density of decay stages 3 and 4 of F. sylvatica were also ition rate of fragmented wood in the litter layer. found by Müller-Using and Bartsch (2009). One main char- acteristic of log sections in decay stage 4 in our study was the close proximity of highly decomposed material to areas Drill resistance measurements of relatively intact wood. Since the maximum decompos- In a study that used a similar measurement device, 65 % ition time for woody debris of F. sylvatica in our study was of the variation in wood density of P. abies could be ex- about 16 years, decay stage 4 comprised mainly log seg- plained by drill resistance (Kahl et al. 2009). The poten- ments with a diameter < 20 cm. Hence, our data may not tial to determine wood density in a large number of be representative for larger diameter CWD of beech. samples in a short period of time may compensate for Wood densities for the different tree species converged the lack of precision for individual pieces in large inven- for advanced decay stages, which is in accordance with tories. In addition, determining drill resistance may be other studies (Yatskov et al. 2003). We found no signifi- the only form to collect data on wood density in strict cant differences between species for CWD densities in forest reserves, where the collection of stem discs is not decay stage 4. permitted. The increase in C concentration with advancing de- composition observed in our study was also found by Predicting carbon density in CWD Müller-Using and Bartsch (2007) for beech. It suggests The loss of mass and thus carbon in CWD is a continu- an increase of lignin and a decrease of cellulose, as lignin ous process. Using distinct decay stages based on visual has a higher proportion of C than cellulose. The increase assessment of logs can be a useful approach to capture in C concentration cannot be explained by a decrease of the variation in CWD mass and C density in forest in- minerals in relation to C. Similar to our results, C con- ventories. However, the usefulness of this approach de- centrations were lower for angiosperm CWD when com- pends on how distinct decay stages differ in wood pared to gymnosperm CWD in a study that analysed C density and C concentration. concentrations of 60 tree species (Harmon et al. 2013). Here, we observed a high variation in CWD density Unlike our results, a decrease in C concentration with within decay classes and hence only few significant dif- increasing decay class was observed for angiosperm ferences between adjacent decay classes within a given CWD in that study. In contrast to C concentration, C species. Density variation was particularly high for F. syl- content was highest for beech, followed by pine and vatica logs in decay stages 2 and 3. This might suggest, spruce. This can be explained by the higher density of among other things, that decay stages 2 and 3 were most sound wood. −3 difficult to distinguish by the visual classification system. The pattern of C density (mgC · cm ) and decay stage In some cases, decomposition was found to be more (or was similar to that of wood density and decay stage. C less) advanced than ‘suggested’ by the tree surface (i.e., density decreased parallel with density and mass loss, as bark or sapwood condition). This could be the result of has been found by others (Müller-Using 2005). Carbon case hardening (drying out of the outer sapwood) as de- density in CWD converged across species with increas- composition took mostly place in open areas (see also ing decay (similar to the relationship between density Yin 1999). In addition, decomposition of F. sylvatica logs and decay stage). Hence for highly decayed logs, for was spatially highly variable. Intact and decomposed which it may also be difficult to determine the species patches (with different densities) separated by demarca- origin, one common wood and C density may be tion lines composed of melanin, which is characteristic of assumed. white rot fungi (see also Schwarze et al. 1999; Kahl 2008), To assess C in CWD from inventories that record were found in direct proximity within the same tree disc. decay stage, our values of dry wood and C density Owing to such patterns of log colonization by fungi, dens- per species and decay stage could be used for calcula- ity variation may initially increase with decomposition. tion purposes. However, the actual assessment of C For example, an increase in density variation with progres- concentration in inventories appears to be meaningful sing decomposition, with the maximum in decay stage 3 only for beech, where the difference between the and a decline towards decay stage 5, was also observed by measured C concentration and the default value of Yatskov et al. (2003). 50 % was 3.4 %–4.6 %. Herrmann et al. Forest Ecosystems (2015) 2:27 Page 13 of 14 Conclusions Christensen M, Hahn K, Mountford EP, Odor P, Standovar T, Rozenbergar D, Diaci J, Wijdeven S, Meyer P, Winter S, Vrska T (2005) Dead wood in European To our knowledge, this is the first study that assessed the beech (Fagus sylvatica) forest reserves. For Ecol Man 210:267–282 decomposition dynamics of CWD logs of F. sylvatica, P. Commarmot B, Brändli U-B, Hamort F, Lavnyy V (2013) Inventory of the largest abies and P. sylvestris across different sites and diameter primeval beech forest in Europe. A Swiss-Ukrainian scientific adventure. Swiss Federal Research Institute WSL, Birmensdorf; Ukrainian National Forestry classes in central Europe. In comparison to studies at indi- University, L’viv; Carpathian Biosphere Reserve, Rakhiv. p 69 vidual sites, sampling of CWD across different sites pro- Cornwell WK, Cornelissen JHC, Allison SD, Bauhus J, Eggleton P, Preston CM, Scarff F, vides a more robust estimation of decomposition rates Weedon JT, Wirth C, Zanne AE (2009) Plant traits and wood fates across the globe: rotted, burned, or consumed? Global Change Biol 15:2431–2449 because a wider range in climatic conditions and decom- Dormann CF, Kühn I (2009) Angewandte Statistik für die biologischen poser communities are captured. Wissenschaften. 2., durchgesehene, aktualisierte, überarbeitete und erweiterte Our results indicated that a reasonable prediction of Auflage. Helmholtz Zentrum für Umweltforschung-UFZ.S 245 Effland MJ (1977) Modified procedure to determine acid-insoluble in wood and the decomposition of CWD should be possible on the pulp. Tappi 60(10):143–144 basis of a few easily obtainable parameters, since vari- FAO (2006) World reference base for soil resources 2006. A framework for ables such as diameter and tree species had the greatest international classification, correlation and communication. World soil resources report No. 103. 2006 edition. Food and agriculture organization of influence on mass loss over time. Since the variation in the united nations (FAO). Rome, 2006. p 128 http://www.fao.org/ag/Agl/agll/ climatic parameters such as average annual temperature wrb/doc/wrb2006final.pdf and precipitation had only a minor effect on the decom- Forrester JA, Mladenoff DJ, Gower ST, Stoffel JL (2012) Interactions of temperature and moisture with respiration from coarse woody debris in position process, these variables might be neglected for experimental forest canopy gaps. For Ecol Man 265:124–132 the prediction of CWD mass loss in this temperature Freschet GT, Weedon JT, Aerts R, van Hal JR, Cornelissen JHC (2011) Interspecific and precipitation range. differences in wood decay rates: insights from a new short-term method to study long-term wood decomposition. J Ecol 100(1):161–170 The decomposition rates determined in our study, al- Graham RL, Cromack KJR (1982) Mass, nutrient content, and decay rate of dead though they may be further refined, provide a basis for boles in rain forests of Olympic National Parc. Can J For Res 12:511–521 the management of CWD. The decomposition constants Grove S, Meggs J, Goodwin A (2002) A review of biodiversity conservation issues relating to coarse woody debris management in the wet eucalypt will allow the development of decomposition models production forests of Tasmania. Forestry Tasmania, Hobart, p 72 based on information about the input of CWD accord- Grove SJ, Stamm L, Barry C (2009) Log decomposition rates in Tasmanian ing to species and diameter. Based on the derived C- Eucalypt obliqua determined using an indirect chronosequence approach. For Ecol Man 258:389–397 densities per decay stage, C contents in CWD can be Hagemann U, Moroni MT, Gleißner J, Makeshin F (2010) Disturbance history calculated to serve as input to C accounting of forest influences downed woody debris and soil respiration. For Ecol Man ecosystems. 260:1762–1772 Harmon ME, Franklin JF, Swanson FJ, Sollins P, Gregory V, Lattin JD, Anderson NH, Cline SP,Aumen NG,Lienkaemper GW,Cromack KJ,Cummins KW (1986) Ecologyof Competing interests coarse woody debris in temperate ecosystems. Adv Ecol Res 15:133–302 The authors declare that they have no competing interests. Harmon ME, Krankina ON, Sexton J (2000) Decomposition vectors: a new approach to estimating woody detritus decomposition dynamics. Can J For Authors’ contributions Res 30:76–84 SH planned and conducted the study including field sampling and analysis Harmon ME, Fasth B, Woodall CW, Sexton J (2013) Carbon concentration of and wrote the majority of the manuscript. TK conducted linear mixed effects standing and downed woody detritus: Effects of tree taxa, decay class, modelling and contributed to the manuscript. JB conceived and guided the position, and tissue type. For Ecol Man 291:259–267 study and co-wrote the manuscript. All authors read and approved the final Herrmann S, Bauhus J (2008) Comparison of methods to quantify respirational manuscript. carbon loss of coarse woody debris. Can J For Res 38:2738–2745 Herrmann S, Bauhus J (2012) Effects of moisture, temperature and decomposition stage on respirational carbon loss from coarse woody debris (CWD) of Acknowledgements important European tree species. Scand J For Res 28(4):346–357 We are grateful to Renate Nitschke and Germar Csapek, and the many students, Herrmann S, Prescott CE (2008) Mass loss and nutrient dynamics of coarse who helped in the field and in the lab. We thank Sarah Grimm from Seminar woody debris in three Rocky Mountain coniferous forests: 21 year results. for Statistics ETH Zurich for statistical advice. Oliver Jakoby provided helpful Can J For Res 38:125–132 comments to improve the manuscript. 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Conservation Biology. (in press) Shigo AL (1986) A new tree biology. Shigo and Trees Associates, Durham 7 Open access: articles freely available online Shorohova E, Kapitsa E (2014) Influence of the substrate and ecosystem attributes 7 High visibility within the fi eld on the decomposition rate of coarse woody debris in European boreal 7 Retaining the copyright to your article forests. For Ecol Manage 315:173–184 Siitonen J (2001) Forest management, coarse woody debris and saproxylic organisms: Fennoscandian boreal forests as an example. Ecol Bull 49:11–41 Submit your next manuscript at 7 springeropen.com http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png "Forest Ecosystems" Springer Journals

Decomposition dynamics of coarse woody debris of three important central European tree species

"Forest Ecosystems" , Volume 2 (1): 14 – Dec 1, 2015

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Springer Journals
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2015 Herrmann et al.
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2197-5620
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10.1186/s40663-015-0052-5
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Abstract

Background: Coarse woody debris (CWD) is an important element of forest structure that needs to be considered when managing forests for biodiversity, carbon storage or bioenergy. To manage it effectively, dynamics of CWD decomposition should be known. Methods: Using a chronosequence approach, we assessed the decomposition rates of downed CWD of Fagus sylvatica, Picea abies and Pinus sylvestris, which was sampled from three different years of tree fall and three different initial diameter classes (>10 – ≤ 20 cm, >20 – ≤40 cm, >40 cm). Samples originating from wind throws in 1999 were collected along a temperature and precipitation gradient. Based on the decay class and associated wood densities, log volumes were converted into CWD mass and C content. Log fragmentation was assessed over one year for log segments of intermediate diameters (>20 – 40 cm) after 8 and 18 years of decomposition. −1 Results: Significantly higher decomposition constants (k) were found in logs of F. sylvatica (0.054 year )than in −1 −1 P. abies (0.033 year )and P. sylvestris (0.032 year ). However, mass loss of P. sylvestris occurred mainly in sapwood and hence k for the whole wood may be overestimated. Decomposition rates generally decreased with increasing log diameter class except for smaller dimensions in P. abies. About 74 % of the variation in mass remaining could be explained by decomposition time (27 %), tree species (11 %), diameter (17 %), the interactive effects between tree species and diameter (4 %) as well as between decomposition time and tree species (3 %) and a random factor (site and tree; 9.5 %), whereas temperature explained only 2 %. Wood fragmentation may play a more important role than previously thought. Here, between 14 % and 30 % of the −3 decomposition rates (for the first 18 years) were attributable to this process. Carbon (C) density (mgC · cm ), which was initially highest for F. sylvatica,followed by P. sylvestris and P. abies, decreased with increasing decay stage to similar values for all species. Conclusions: The apparent lack of climate effects on decomposition of logs in the field indicates that regional decomposition models for CWD may be developed on the basis of information on decomposition time, tree species and dimension only. These can then be used to predict C dynamics in CWD as input for C accounting models and for habitat management. Keywords: Dead wood; Carbon; Decay rate; Beech; Spruce; Pine * Correspondence: steffen.herrmann@wsl.ch Institute of Forest Sciences, Faculty of Environment and Natural Resources, University of Freiburg, Tennenbacherstr. 4, D-79106 Freiburg, Germany Swiss Federal Research Institute WSL, Zürcherstrasse 111, CH-8903 Birmensdorf, Switzerland © 2015 Herrmann et al. Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Herrmann et al. Forest Ecosystems (2015) 2:27 Page 2 of 14 Background on CWD decomposition can be explored in detail via Coarse woody debris (CWD) is structurally and function- lab incubation (see Herrmann and Bauhus 2012). ally very important for forest ecosystems, in particular for However, to capture the complex interplay of pro- biodiversity (Siitonen 2001), the energy and nutrient cycle cesses responsible for decomposition in forests, long- (Müller-Using and Bartsch 2007; Kuehne et al. 2008) and term field measurements are necessary. carbon storage (Harmon et al. 1986; Turner et al. 1995; The decomposition rate of CWD is mainly dependent on Pregitzer and Euskirchen 2004; Kahl et al. 2012). Whereas climatic (wood temperature, wood moisture) and substrate the amount of CWD may comprise up to 30 % or even specific variables (tree species, decay stage, diameter), 40 % of the total timber volume in natural beech (Com- where tree species influences chemical and physical wood marmot et al. 2013) and spruce (Ranius et al. 2003) forests, properties and the decomposer community (Mackensen et this share is typically less than 5 % in managed European al. 2003; Kahl 2008). Up to now it is not clear whether cli- forests (Bütler and Schlaepfer 2004; MCPFE 2007). This re- matic or substrate specific variables are more important for duction in the amount and related quality of dead wood the decomposition process (see also Cornwell et al. 2009; (Müller and Bütler 2010) has significant implications for its Freschet et al. 2011). Recent results indicated that climatic various functions. European lists of endangered species are variables are likely to be more important for (short term) often dominated by species depending on dead wood CWD mineralization than substrate specific variables (Grove et al. 2002). For Germany, 28 % of the saproxylic (Herrmann and Bauhus 2012). However, both factors also beetle species are listed as threatened or regionally extinct clearly interact and must therefore be considered jointly (Seibold et al. 2014). Owing to its significance for ecosys- (Herrmann and Bauhus 2012). Here, we analysed the influ- tem functioning, CWD has been recognized as an indicator ence of these factors on the field decomposition rate of of ecological sustainable forest management (MCPFE Fagus sylvatica L., Picea abies (L.) Karst. and Pinus sylves- 2003). Therefore increasing efforts have been undertaken tris L. along a climatic/altitudinal gradient (temperature, to manage CWD as a habitat component and C store in precipitation) using a chronosequence approach based on forest ecosystems. However, to manage this pool, a basic known ages of CWD logs. In addition, the influence of log understanding of patterns and rates of dead wood decom- dimension was assessed by analysing CWD pieces of differ- position in different forests is crucial. Further, for the as- ent initial diameters (in three diameter classes, i.e., >10 – sessment of C stocks in dead wood as part of National 20 cm, >20 – 40 cm, > 40 cm). Greenhouse Gas inventories, detailed information on C Our specific research questions were: stored in dead wood of different species and their relation- ship with different decay stages (which are typically cap- 1. How do decomposition rates differ between the tured in inventories) is necessary. So far this knowledge is three tree species Fagus sylvatica (hardwood), incomplete and mainly based on expert opinions (Meyer Picea abies and Pinus sylvestris (softwoods)? et al. 2003; Rock et al. 2008; Zell et al. 2009). In addition, 2. To what extent can the variation in mass and information on residence times of CWD in different decay carbon remaining in CWD be explained by tree classes would be very helpful to forecast its dynamics and species, log dimension, wood chemistry (nutrients, to calculate the input and output of different decay stages lignin) and microclimatic conditions? in order to conserve specific habitats of dead wood dependent species (Kruys et al. 2002; Ranius et al. 2003). In addition, we tested if it was possible to determine Dynamics of CWD are determined by the input through the dry density of CWD logs with the drill resistance tree mortality and the output through the decomposition method. process. The decomposition process is characterised by a decrease in dimension and mass and concomitant changes Methods in biological, physical, and chemical properties. The main Study sites process of dead wood decomposition is the loss of organic For the chronosequence approach that relates mass (i.e., material through respiration. In addition, its fragmenta- wood density × volume) to the age of CWD, logs were tion through biological activity (in particular by insects) sampled at sites where the previous stand had been and physical processes can also be important. With wind-thrown and where no or only little salvage logging ongoing decomposition and decreasing physical stabil- had taken place. Thus we could be certain about the ity of CWD, fragmentation increases in importance period that logs had undergone decomposition. The (Harmon et al. 1986). However, leaching of soluble study sites were mainly located in southern Germany, matter (including C) from CWD logs is of minor im- except for the oldest site that was situated in northern portance for mass loss (Spears et al. 2003; Kahl et al. Germany (Lower Saxony, near the city of Hannover). All 2012). The specific influence of selected environmen- selected sites were hit by severe winter storms in 1999, tal variables such as wood moisture and temperature 1990 and 1972 (Table 1). Herrmann et al. Forest Ecosystems (2015) 2:27 Page 3 of 14 Table 1 Description of the study sites a b c c Site Year of tree fall Elevation (m a.s.l.) Soil type Average temperature Precipitation per year (°C) (mm per year) Forêt de Ha-guenau (FdH) 1999 150–170 Gleyic Cambisol 10.98 645 Bienwald (Biw) 1999 100–130 Cambisol 10.0 680–700 Röttlerwald (Roe) 1999 510–590 Luvisol and Cambisol 9–10 900–1080 Lotharpfad (Lot) 1999 950 Podsolic Cambisol 7.0 1700 Hofstatt (Hof) 1990 320–340 Luvisol 8.8 800 Silbersandgrube (Sig) 1990 550 Luvisol 7.8 665 Kiekenbruch (Kib) 1972 70 Podsolic Cambisol 8.5 657 a b c according to GPS; FAO (2006); according to forest office information Sampling design were not autocorrelated according to the Durbin Downed CWD of F. sylvatica, P. abies and P. sylvestris Watson-Test (DW = 1.7). from three different years of tree fall (1972 (except for Decay class was assessed for each log and measure- beech), 1990 and 1999) was sampled in three different ini- ment position according to a 4 stage classification sys- tial diameter classes (>10 – ≤ 20 cm, >20 – ≤40 cm, tem (Albrecht 1991 for P. abies and P. sylvestris), >40 cm). Samples originating from wind throws in 1999 modified for F. sylvatica by Müller-Using and Bartsch were collected along an altitudinal gradient from the (2009). Based on the decay class and the associated Rhine valley up to the top of the black forest (Table 2). known wood densities, log volumes assessed in field in- This represents a climatic gradient with regard to ventories can be converted into CWD mass and C con- temperature and precipitation (see Table 1). Unfortu- tent (Grove et al. 2009). However, because the internal nately, no pine CWD was found at the highest elevation. log condition may not be represented by a visual decay In our design, site was not independent from year classification (Meyer 1999), large errors for assessed C since tree fall. Generally we sampled at least 5 replicates pools can result. Therefore, we also assessed the internal for each combination of species, site, and diameter. density of logs with the drill resistance method (Kahl et Where clearly different diameter classes were present al. 2009). At each point, where log diameter was mea- along the length of a single log, we selected in some sured, two drill resistance measurements were con- cases several sample-points at a minimum distance of ducted, one horizontally and one vertically. In general, at 2 m, where discs were extracted. Discs were approxi- least one stem disc per tree, representing the dominating mately 10 cm thick. On average, we extracted 2.5 sample decay class, was cut with a chainsaw at a drill resistance discs per log. These samples were treated as separate measurement position for further calibration and ana- samples, because the variations in wood density from lysis. If more than one diameter or decay class was ana- one log and within the same decay class were compar- lysed per log, the number of extracted discs increased able to the variations within the same decay class when accordingly. At the site ‘Lot’ no discs could be removed collected from a large population of logs in the field (see for most beech logs, as their position was very close to a also Müller-Using and Bartsch 2009). Separate samples hiking trail. In this case samples were taken with a wood from the same log can also differ considerably in com- corer (according to the method applied above). position of the microflora, as has been shown by others (Shigo 1986; Schwarze et al. 1999). In addition, our pre- liminary analysis showed that samples from the same log Density assessment Density was assessed gravimetrically and via drill resist- ance measurements using the Resistograph 3450S Table 2 Year of storm event (i.e., decomposition time) and elevation for the 7 study sites (RINNTECH, Heidelberg, Germany) with a resolution of 0.01 mm and a maximum drilling depth of 44 cm (for Year of Elevation (m a.s.l.) storm event further details see Kahl at al. 2009). 100 600 900 To determine density of wood samples gravimetrically, 1999 Biw (spruce, pine), Roe (beech, Lot (beech, spruce) wooden bars were cut with a band saw along the drill re- FdH (beech) spruce, pine) sistance measurement lines for each sampled stem disc. 1990 Hof (beech), Sig (spruce, pine) These were further cut into small cubes (about 2 cm thick) and dried in a fan-forced oven at 105 °C until con- 1972 Kib (spruce, pine) stant weight was achieved. Dry density was measured for Biw Bienwald, FdH Forêt de Haguenau, Roe Röttlerwald, Lot Lotharpfad, Hof Hofstatt, Sig Silbersandgrube, Kib Kiekenbruch each cube (without further sample preparation) via the Herrmann et al. Forest Ecosystems (2015) 2:27 Page 4 of 14 water displacement method and related to the drill re- value for each species and size class was then calculated sistance value for calibration. on the basis of these individual k values. The time when 50 % of the original mass would have Log fragmentation assessment been decomposed was calculated by: Log fragmentation was assessed over one year for log segments of intermediate diameters (>20 – 40 cm) of F. 0:693 t ¼ ð3Þ sylvatica, P. abies and P. sylvestris after 8 and 18 years of 0:5 decomposition (representing initial and advanced decay stages), with three replicates per species and year. Nylon To model CWD decomposition, exponential decay fabric (1 mm × 1 mm mesh, ca. 180 cm length and about functions, which assume a homogeneous substrate that 120 cm width) was placed underneath each log and decays at a constant rate, have been used in the majority metal pegs were used to hold up the material. After one of cases (Mackensen et al. 2003). This may be an over- year, the material that had fallen onto the nylon mesh simplification of the complex processes occurring during was collected and quantified. dead wood decomposition. Recent modelling approaches have also considered the different wood constituents Chemical analysis such extractives, cellulose or lignin, which undergo, owing Following density assessment, a subset of all samples to their chemical resistance, different decomposition dy- with a total of 3 replicates per species, decay stage and namics (Tuomi et al. 2011). However, we did not employ diameter class was ground and analysed for C concen- such model, because the temporal resolution of our data tration. C concentration was determined by combustion would not have permitted a robust fitting of the decom- at 950 °C with a Leco Truspec™ CN analyser (St. Joseph, position process using a model with many parameters. To MI, USA). Wood cores were taken from intact wood (1 facilitate comparison of results with that of other studies, sample per diameter class per species and site, except for we fitted the exponential decay functions for all tree the sites ‘Biw’, ‘Lot’ and ‘FdH’) to estimate initial nutrient species. concentrations. N concentration was determined according Original dry mass of undecayed wood of the three tree to the method described above for C concentration. Con- species was calculated based on original diameter and centrations of P, S, Ca, K, Mg, Na, Al, Fe, and Mn were dry density values from literature studies (Trendelenburg analysed with Inductively Coupled Plasma-Optical Emis- and Mayer-Wegelin 1964). Reference values for dry sion Spectroscopy (ICP-OES) (Spectro, Kleve, Germany), −3 density (g · cm ) were: F. sylvatica: Lot: 0.65, all other following HNO digestion in pressure chambers. Lignin sites: 0.68; P. abies: Lot: 0.4, Kib: 0.46, all other sites: concentration was measured according to the methods pro- 0.43; P. sylvestris: Kib > 40 cm: 0.45, all other sites and posed by TAPPI (1976) and Effland (1977). diameter classes: 0.49. Different values reflect site (i.e., cli- matic) differences and variation along stem height (pine), Data analysis as observed by Trendelenburg and Mayer-Wegelin (1964). To calculate decomposition constants (k) based on mass Additionally, wood cores (three replicates) were taken loss, mean dry density from gravimetric density assess- from undecomposed wood from the different sites (except ment was used if available. Otherwise we used mean dry for Lot), tree species and diameter (stratified as >30 cm density based on drill resistance. For each measurement and <30 cm, and 30 cm for pine at Roe) to confirm that position, the volume of the stem disc represented by this density values from the literature were appropriate. position was calculated based on formula 1: If the original diameter was different from the current diameter (i.e., bark and/or (sap) wood was already V ¼ L  π  ð1Þ decomposed), it was reconstructed based on the thick- ness of intact parts on the same log or adjacent logs. We determined the k values of each individual log or measurement position based on the single negative ex- Statistical analysis ponential decay model (Olson 1963): Statistical analyses were conducted using SPSS Statis- tics 17.0 (SPSS Inc., Chicago, IL, USA) and R (R Devel- MtðÞ −1n opment Core Team). All significance testing was done MðÞ 0 k ¼ ð2Þ with p < 0.05. The analysis for even distribution of the residuals was conducted graphically via normal qq-plot Here M(t) is the mass (g) remaining at time t (year) and scatter plot (residuals vs. predicted values; and vs. and M(0) is the original mass (g) at time 0 and k is the fitted values (linear regression model)), as well as via −1 decomposition rate constant (year ). The average k Kolmogorov-Smirnov test (Dormann and Kühn 2009). Herrmann et al. Forest Ecosystems (2015) 2:27 Page 5 of 14 A transformation (usually ln) was conducted, if resid- and pine, only 58 % and 50 % of the variation in density uals were not evenly distributed. were explained with drill resistance, respectively. A two-way ANOVA was conducted to test for signifi- cant differences in wood density and in C content be- Decomposition rates (k) and mass loss tween the three species as well as between the different −1 The average k value of F. sylvatica (0.054 year ;95% CI: decay stages. A one-way ANOVA followed by Fisher’s −1 0.048–0.059 year ) was significantly different from the one LSD or Dunnett-T3 post-hoc test, if variances were not −1 −1 of P. abies (0.033 year ;95%CI:0.03–0.035 year )and P. homogenous, was conducted to further analyse possible −1 −1 sylvestris (0.032 year ;95 % CI:0.028–0.035 year ) across differences in 1) k (ln-transformed) between the three all sites and diameter classes (p<0.001)(Table3). Thetwo species as well as between the different diameter classes conifer species were not significantly different from each at the species level, 2) mass remaining at the species other. The corresponding average diameters of logs were level for each period, beginning at t , for which logs had 34.8 (SD ± 14.8) cm for F. sylvatica, 33.9 (SD ± 13.9) cm for undergone decomposition, 3) wood density between spe- P. abies and 32.2 (SD ± 12.1) cm for P. sylvestris indicating cies for each decay stage and to compare the average dry that the effects of species were not biased through differ- density of the different decay stages for each species and ences in log dimensions between species. The time until 4) C concentration between the different decay stages of 50 % of the initial mass was decomposed was 13 years for each species. In the case of P. Sylvestris (only one sample F. sylvatica, 21 years for P. abies and 22 years for P. sylves- in decay stage 4, a two-sample t-test was applied for tris. The average k values until 18 years for P. abies and P. comparison between decay stages. −1 −1 sylvestris were 0.033 year ;95%CI:0.031–0.036 year The influence of substrate specific, climatic and envir- −1 −1 and 0.034 year ;95%CI:0.03–0.037 year , respectively. onmental variables on the mass of logs remaining after Owing to different decomposition rates, the patterns of different periods of decomposition in the field was ana- mass remaining differed accordingly between the species lysed based on a linear mixed-effects model. Possible in- (Fig. 2). Whereas the average mass remaining in P. sylves- fluencing variables were identified using a Spearman rho tris CWD was similar to that of P. abies until 18 years of correlation matrix. The climatic variables (temperature decomposition (about 59 % each (SD ± 12, resp. 11 %; P. and precipitation) were analysed for collinearity and cen- abies)), it was considerably higher at year 36 (P. sylvestris: tered for further use in the model. The residuals were fur- 46 % (SD ± 15), P. abies: 39 % (SD ± 18)). At this time, ther analysed for even distribution graphically via box-cox nearly all sapwood of P. sylvestris logs was decomposed transformation. To decide if a model is better than a pre- and heartwood started to decompose. Average mass vious model, we used the explained variation (r ) and the remaining decreased with increasing decomposition time st nd AIC as 1 and 2 criteria. Eta squared was used as an ef- in all species (p < 0.05, Fig. 2). fect size measure. With increasing diameter class, decomposition rates de- To assess the residence time (based on Kruys et al. creased significantly in all species, except between the 2002), in total and for each decay stage, the average dry diameter classes <20 cm and >20 – 40 cm for P. abies −3 density (g · cm ) and the average decomposition time (Table 3). (years) per decay stage were calculated from the whole dataset in a first step. For further calculations, each decay stage was defined as the interval between the mid- Prediction of mass remaining points of dry densities of decay classes (e.g., 0.5–1.49 for About 74 % of the variation in mass remaining could be decay stage 1). Afterwards a dry density value was explained by decomposition time (27 %), tree species assigned for each decay stage interval based on the ex- (11 %), diameter (17 %), the interactive effects between ponential relationship between decay stage and average tree species and diameter (4 %) as well as between de- dry density. In the same way, the decomposition time composition time and tree species (3 %) and a random was assigned, which was calculated based on the expo- factor with site and tree (9.5 %), whereas temperature nential relationship between average dry density and explained only 2 % of the variation (Table 4). If we sub- average decomposition time. Finally the residence time tract the random factor, which cannot be predicted, per decay stage was defined as the decomposition time 64 % of the variation in mass remaining may be pre- at the upper bound of each decay stage. dicted for the tree species investigated here. If initial nutrient concentrations were considered as additional independent variables, a slight model im- Results provement was achieved, when the concentration of Drill resistance measurements manganese was included. Based on a reduced data set Up to 77 % of the variation in wood density could be ex- (n = 172 instead of n = 287), an additional 1 % of the plained with drill resistance for beech (Fig. 1). For spruce total variance could be explained. Herrmann et al. Forest Ecosystems (2015) 2:27 Page 6 of 14 Fig. 1 Relationship between drill resistance (ln(DR)) and dry density for F. sylvatica, P. abies and P. sylvestris Log fragmentation originated mostly from wood in logs and was much higher The amount of fragmented material differed between tree in the former than the latter species. For P. sylvestris,bark species and age of logs, but in relation to total mass it was fragmentation dominated in older logs. Fragmentation was very low (Table 5). The maximum value was 1.8 % per very patchy between individual logs and often occurred as annum for one P. abies log (Table 5). For F. sylvatica and a single event, as indicated by the high standard deviation. P. abies, bark fragmentation dominated after the first However, when fragmentation is related to the decompos- 8 years of decomposition. In P. sylvestris, only a minor ition rate, the actual contributions are considerable. When amount of bark fragmentation and no wood fragmentation compared to the total k value for first 18 years of decom- occurred during this period of time. In older logs of F. syl- position, fragmentation over one year accounted for 27 % vatica and P. abies (18 years), fragmented material for beech, 14 % for spruce and 30 % for pine, respectively. Table 3 Annual decomposition constants k for different diameter classesin Fagus sylvatica, Picea abies and Pinus sylvestris Species Diameter class (cm) Combined <20 20–40 > 40 a b c F. sylvatica (n = 96) 0.078 (0.027) 0.055 (0.027) 0.035 (0.015) 0.054 (0.028) a a b P. abies (n = 107) 0.034 (0.011) 0.036 (0.012) 0.027 (0.013) 0.033 (0.013) a b c P. sylvestris (n = 83) 0.050 (0.015) 0.030 (0.01) 0.021 (0.01) 0.032 (0.015) Values are means with standard deviation in parentheses. Different letters indicate significant differences between diameter classes (p < 0.05) Herrmann et al. Forest Ecosystems (2015) 2:27 Page 7 of 14 beech (Table 6). In addition, with decreasing decompos- ition rate from F. sylvatica to P. sylvestris, the proportion of time, relative to the entire decay process, that logs stayed in the first decay stage became shorter, whereas the time in the last decay stage increased. Wood density, carbon content and decay stage Average dry density values differed significantly be- tween species and decay stages. Specifically, average dry densities were significantly different between F. sylvatica, P. abies and P. sylvestris for decay stage 2 (p < 0.001). For decay stage 3, dry density of F. sylva- tica was significantly different from those of P. abies and P. sylvestris (p < 0.001 and p <0.01), which were similar to each other. No significant differences be- tween species were found for decay stage 4. Average dry density decreased with increasing decay stage in all species (p < 0.001; Fig. 3). The decrease in density was highest from decay stage 2 to 3 and the density variation was highest in decay stage 3 for all three species, but in particular for F. sylva- tica. In dead wood of F. sylvatica, the decrease in density was not significant between decay stages 3 and 4. In contrast, a significant decrease between decay stages 3 and 4 was found for P. abies (p < 0.05). For P. sylvestris there was only one value in decay stage 4. Similar to dry density, carbon concentration was sig- nificantly higher for F. sylvatica, compared to P. abies and P. sylvestris, which were not different from each other (Table 7). With advancing decomposition and de- creasing dry density, an increase in C concentration was observed for all three species (except between decay stages 3 and 4 for beech and spruce). C concentration differed significantly between decay stages 1 and 2, as well as 3 for F. sylvatica and P. abies. For P. abies, decay stage 1 was significantly different also from decay stage 4, while decay stages 1 and 2 were significantly different from decay stage 3 for P. sylvestris (Table 7). The effect of decay stage on carbon (C) density was dependent on species (p = 0.000). The pattern of C dens- ity and decay stage followed the pattern of wood density and decay stage closely (Table 7 and Fig. 3). Discussion Fig. 2 Remaining mass of log segments of Fagus sylvatica (n = 96), Decomposition rates (k) and mass loss Picea abies (n = 107) and Pinus sylvestris (n = 83); boxplots display To our knowledge, this is the first study that systematic- median, lower and upper quartile, minimum and maximum values; ally assessed CWD decomposition rates and dynamics of points outside boxplots represent outliers; different letters indicate Fagus sylvatica, Picea abies and Pinus sylvestris across significant differences (p < 0.05) different sites and diameter classes in Central Europe. Average k values of F. sylvatica were significantly higher Residence time per decay class than those of P. abies and P. sylvestris across all sites The residence time per decay class increased with decay and diameter classes. class (i.e., higher degree of decomposition). This increase In other studies, decomposition rates of F. sylvatica −1 −1 was most pronounced in pine and least pronounced in varied from 0.056 year (Kahl 2008) to 0.089 year Herrmann et al. Forest Ecosystems (2015) 2:27 Page 8 of 14 Table 4 Linear mixed-effects model (ANOVA table) to predict the remaining massof Fagus sylvatica, Picea abies and Pinus sylvestris Source Sum of Squares df F Sig. Eta squared Decomp. time 12162.2 1 129.449 0.000 27.06 Tree species 5058.7 2 12.768 0.000 11.26 Diameter 7655.1 1 97.979 0.000 17.03 Temperature 789.4 1 7.273 0.009 1.76 Decomp. time × Species 1405 2 6.066 0.003 3.13 Species × Diameter 1873.8 2 10.8 0.000 4.17 Conditional R-squared: 0.739 (Marginal R : 0.644) (Müller-Using and Bartsch 2009), with intermediate extent) from different tree diameters in other studies. −1 values of 0.06–0.075 year (Christensen et al. 2005; Colonization by (cord-forming) basidiomycetes seems to based on 86 European beech forest reserves). In com- progress slower in large diameter logs (Boddy and parison to these findings, our k value for beech of Heilmann-Clausen 2008). Although this is not supported −1 0.054 year (SD ± 0.028, 52 %) is within the range of by the results of this study, different site, i.e., microcli- variation and very similar to the estimate by Kahl (2008). matic conditions might further lead to an increase or de- The difference between k values of beech may be at- crease in k values, for example through canopy removal tributed to the uncertainty over the cause of death, as (Hagemann et al. 2010; Forrester et al. 2012). Managed observed by Kahl (2008). In that study, a k value of forests may also harbor a lower diversity of wood decaying −1 0.075 year (SD ± 0.034) for logs that died naturally fungi, which may lead to different decay rates (Stenlid et (infected by Fomes fomentarius prior to death) and a k al. 2008; Purahong et al. 2014a, b). With regard to the ori- −1 value of 0.025 year (SD ± 0.012) for wind thrown logs gin of dead wood and its diameters, our data are likely was calculated (mean diameter > 40 cm). This indicates more representative of managed forests rather than strict that decomposition had advanced already in trees af- reserves. −1 fected by F. fomentarius before they died or fell. We cal- The decomposition rate of 0.033 year (SD ± 0.013, −1 culated a k value of 0.035 year (SD ± 0.015) for the 39 %) for P. abies calculated in our study was compar- diameter class > 40 cm; which is similar to the estimate able to k values in other studies (see Rock et al. 2008). by Kahl (2008). Most of the trees died “naturally” due to In detail, it was in the range of variation of the estimate −1 F. fomentarius in the study by Müller-Using and Bartsch by Kahl (2003; 0.027 year (SD±0.023)) in Central (2009), which may mainly explain the high k value in Germany and identical to k values found by Naesset −1 that study. It can be assumed that most logs investigated (1999; k = 0.033 year ; mean diameter: 13 cm) in in our study originated from trees that were still vital southeastern Norway. It was also similar to values in compared to trees that died “naturally”. In freshly fallen European (Shorohova and Kapitsa 2014) and Russian logs, colonization by (cord-forming) basidiomycetes dif- boreal forests (Krankina and Harmon 1995; Krankina fers from logs that have already been affected by fungi et al. 1999; Tarasov and Birdsey 2001; Harmon et al. before they fell and wood can be expected to decay 2000; Yatskov et al. 2003). slower (Boddy and Heilmann-Clausen 2008). Further, The conditions of the decomposition process in the tissues that have died “naturally” may be predisposed to study by Naesset (1999) were very similar to our situ- microbial colonization, whereas artificially detached liv- ation. Their starting point of the decomposition process ing tissues (i.e., due to wind breakage) may maintain was the date of cutting. All logs were free from rot at metabolic activities against decomposers (see Yin 1999). the time of cutting and the decomposition took place in In addition, different k values might result (to some open areas. Table 5 Annual fragmentation loss for bark and wood of Fagus sylvatica, Picea abies and Pinus sylvestris Species CWD age (yrs) Bark (% total fragmentation loss) Wood (% total fragmentation loss) Fragmentation (% total mass) F. sylvatica (n = 6) 8 99.6 (0.4) 0.4 (0.4) 0.27 (0.37) 18 28.5 (27.2) 71.5 (27.2) 0.02 (0.01) P. abies (n = 6) 8 98.3 (3) 1.7 (3) 0.13 (0.22) 18 0 100 0.65 (1.02) P. sylvestris (n = 6) 8 100 0 0.01 (0.01) 18 73.8 (18.8) 26.2 (18.8) 0.18 (0.20) Values are means with standard deviation in parentheses Herrmann et al. Forest Ecosystems (2015) 2:27 Page 9 of 14 Table 6 Absolute and relative residence times per decay stage Compared to beech, decomposition rates k from the for CWD logs of Fagus sylvatica, Picea abies and Pinus sylvestris literature are much less variable for spruce. One explan- Decay Residence time (Years) ation for this may be that the sensitivity to influencing stage factors (as cause of death, decomposer community or Fagus sylvatica Picea abies Pinus sylvestris differences in diameter) is less pronounced in spruce. yrs % yrs % yrs % This may partly be attributable to a higher decay resist- 1 6.7 12.2 8.6 9.8 6 6.0 ance of spruce and more uniform substrate conditions 2 10.7 19.5 15.3 17.4 14 14.0 (see also Cornwell et al. 2009; Weedon et al. 2009) when 3 15.8 28.8 25.3 28.8 28.3 28.4 compared to beech. 4 21.6 39.4 38.5 43.9 51.5 51.6 However, we observed a significant effect of diameter Relative residence times were calculated as percent of the sum of residence on k values also for spruce. Since all comparable studies times for each species in boreal regions were conducted mainly on small diam- eter trees, the variation between k values should also be small. In our study, the influence of climatic variables on −3 Fig. 3 Dry density (g cm ) in log segments of Fagus sylvatica, Picea abies and Pinus sylvestris; boxplots display median, lower and upper quartile, minimum and maximum values; points outside boxplots represent outliers; different letters indicate significant differences (p < 0.05) Herrmann et al. Forest Ecosystems (2015) 2:27 Page 10 of 14 −3 Table 7 Carbon concentrations (%) and contents (mg cm ) in relation to decay stage (DS), mean and standard deviation (SD), as well as no. of samples, of log segments of Fagus sylvatica, Picea abies and Pinus sylvestris Species Decay stage p value (Anova) 12 3 4 Mean SD n Mean SD n Mean SD n Mean SD n DS Species a a b b ab F. sylvatica % 46.3 0.52 4 47.5 0.62 39 47.5 0.73 22 47.4 0.31 2 < 0.05 < 0.001 −3 a b c c mg cm 315 3.6 238 36.8 174 48.3 161 52.7 b a b b b P. abies % 47. 0.89 7 49.9 1.51 38 50.1 1.73 34 49.8 2.73 9 < 0.01 −3 a b c c mg cm 203 9.2 170 17.4 155 18.1 117 34.1 b a a b P. sylvestris % 48.8 1.13 5 49.2 1.54 34 50.2 1.31 31 51.4 1 < 0.05 −3 a b c d mg cm 235 11.4 198 22.6 162 28.5 149 < 0.01 Small letters indicate significant differences between tree species for total C concentration as well as between the different decay stages within each species −1 −1 −1 the decomposition, also of spruce logs, was low. For ex- sylvestris of 0.067 year ,0.0525 year and 0.0575 year , ample, we found no difference between k values for the respectively. In comparison to our results the esti- sites Kib (‘cold and wet’) and Sig (‘warm and dry’), which mated k values of P. abies and in particular of P. syl- may simply reflect different types of climatic limitations vestris appear to be too high. The deviations between for wood decaying fungi when compared to the ‘warm expert estimates of decay rates and our data underpin and wet’ optimum, which was not represented in our the importance of actual measurements. In particular design. for low decay rates, small absolute errors result in −1 Our k value for P. sylvestris (0.032 year (SD ± proportionally very large errors when calculating mass −1 0.015, 47 %)) was similar to values found in the bor- loss. For example, using a k value of 0.0525 year for eal forests of Europe (Shorohova and Kapitsa 2014) Picea abies (Rock et al. 2008), which is 59 % higher −1 and Russia (Krankina and Harmon 1995; Harmon et when compared to 0.033 year (determined in this al. 2000; Wirth et al. 2000; Yatskov et al. 2003; see study) would shorten the period until 50 % of CWD also Rock et al. 2008). mass was lost by about 37 % (8 years). The k value of pine determined in our study was In contrast, the chronosequence approach used in our mainly attributable to the loss of sapwood (as heartwood study may cause an underestimation of k,because slow- was found to be still largely intact for most logs after decaying logs may have a higher probability of being in- 36 years of decomposition time). Hence, if a longer total cluded in the sampling (Kruys et al. 2002; Herrmann decomposition period was analysed, the average decom- and Prescott 2008). However, this problem increases position constant might be lower than the one we re- with the decomposition time covered and it is negli- ported here. gible, when the observation period is shorter than the Similar to spruce, variation in the decomposition con- minimum decomposition time for logs. For example, if stant k of pine logs was also very low. This may again we assume a) high decomposition rates of k =0.09, and point to a higher decay resistance and a more uniform b) that logs can still be identified in the field when they decomposition process. In the gymnosperm wood of contain 20 % of the original mass, no logs should be lost spruce and pine, there was no highly variable spatial pat- form the sample population before 18 years. tern of intact and highly decomposed patches next to In our study, decomposition rates increased with de- each other, as was observed for beech. This observation creasing diameter class, except for P. abies, where the k is in accordance with much higher variation in log res- values between 20 and 40 cm tended to be slightly higher piration rates in dead wood of F. sylvatica than in P. than in smaller logs (<20 cm) (Table 4). Lower k values for abies and P. sylvestris (Herrmann and Bauhus 2012). smaller diameters (< 10 cm compared to > 25 cm) of P. At a more general level, k values of angiosperm wood abies were also observed by Naesset (1999), who assumed are typically higher, on average 77 %, than in gymno- that it was most likely caused by branches that prevent sperm wood (Weedon et al. 2009). A similar difference the logs from soil contact. In our study, the diameters < was also found by Russell et al. (2014). For comparison, 20 cm for P. abies were most often sampled in crown sec- our decomposition rate for beech CWD was about 60 % tions of the logs, where also soil contact was less often higher than for spruce and pine. encountered than for 20–40 cm diameter logs. However, k An estimation of decomposition rates for the federal values commonly decrease with increasing diameter state of Brandenburg (Northeastern Germany) based on a (Graham and Cromack 1982; Stone et al. 1998; Chambers literature review and expert consultation (Rock et al. et al. 2000; Mackensen et al. 2003), even if sometimes 2008) produced k values for F. sylvatica, P. abies and P. inconsistent relationships have been reported (see Herrmann et al. Forest Ecosystems (2015) 2:27 Page 11 of 14 Herrmann and Prescott 2008). Hence, where decompos- In a global meta-analysis of wood decomposition rates ition models for CWD with large variation in dimensions of angiosperms and gymnosperms, significant relation- are required, it is advisable to consider log diameter. ships between wood traits and decomposition rates were We are aware that our assumption of a constant decay only observed for angiosperms (Weedon et al. 2009). For rate based on the single negative exponential decay angiosperms, positive relationships between k and the model may be questionable and that there are more so- nutrients N and P, and a negative relationship between k phisticated models, e.g., as applied in the Yasso model and C:N ratios were found. We suggest that the mass (Tuomi et al. 2011). However, our dataset was not suffi- loss of CWD of the species investigated in our study can cient in terms of the variation in decomposition periods be sufficiently well predicted for most management and of logs to fit the temporal decay dynamics of a more so- modelling tasks by the factors tree species, time since phisticated model in a robust way. Since there are only commencement of decomposition, and log diameter, very few field experiments that followed mass loss in in- which can be obtained easily from forest inventories. dividual CWD pieces over the long term, the ‘real’ tem- Predictions will likely become less accurate, when CWD poral mass loss pattern is not known. Even if, the mass originates from different processes, e.g., when trees also loss rate of some wood constituents slowed down with die standing and decay slowly for many years before logs increasing time, this may be partially compensated by hit the ground, or when the wood decomposition by fragmentation, which was found to be of increasing im- fungi commences in the living tree (e.g., Kahl 2008). In portance in later decomposition stages (see e.g., Lambert our field study, most trees were felled as living trees by et al. 1980). The mass loss rate may even increase in storms. later stages as observed in the study by Kahl (2008). In contrast to this field study, about 60 % of the vari- ation in CO flux of CWD of the same species was ex- plained by climatic variables (wood moisture and wood Prediction of mass remaining temperature) in a lab incubation experiment (Herrmann A substantial proportion of the variation (64 %) in mass and Bauhus 2012). In the same study, temperature ex- remaining could be predicted by decomposition time, plained more than 90 % of CWD respiration of individual tree species, original log diameter, and their interactive Fagus sylvatica and Picea abies logs over one year in the effects, whereas temperature had a very small, and initial field. This comparison shows that scaling up from short- nutrient concentrations had almost no influence. The term CWD respiration measurements to the long-term observed influence of manganese concentrations on dynamics of CWD mass loss is difficult, since the com- mass remaining may be explained by the relevance of bined effects of temperature, moisture, and interactions manganese for lignin degradation by white-rot fungi between substrate quality and microorganisms need to be (Hofrichter et al. 2009). considered (Herrmann and Bauhus 2008). In order to cap- Similar to our findings, a literature review on CWD de- ture the complex interplay of processes (i.e., respiration, composition had indicated that tree species had a stronger fragmentation) responsible for decomposition in forests, influence on decomposition and nutrient dynamics than long-term field measurements are necessary. the abiotic environment (Laiho and Prescott 2004). And no effect of lignin or nitrogen concentrations was ob- served in an attempt to model CWD decay based on data CWD fragmentation about 300 cases of stem, branch and root woody debris The limited study on wood fragmentation indicated that decay from North America (Yin 1999). Also, most of the this process may contribute considerably to annual mass explained variation of mass remaining in the study by loss (max. 30 % of k for pine) although values were lower Yatskov et al. (2003) could be attributed to time since than in some other studies (e.g., 63 % found by Lambert death (50 %), log position (8 %) and tree species (6 %). et al. 1980). Our logs were mainly at the beginning and However, in some studies, environmental variables did ex- in the middle of the decomposition process. Fragmenta- plain a significant proportion of CWD decomposition tion was found to be of increasing importance with ad- over time (e.g., Russell et al. 2014). About 80 % of the vari- vanced decomposition (Harmon et al. 1986; Müller- ance in the decomposition rate constant was explained by Using and Bartsch 2009) as well as in higher elevations a multiple regression model with the factors of tree spe- (Lambert et al. 1980). However, our study also showed cies, mean diameter, mean temperature in July, sum of that fragmentation was highly variable within and be- precipitation per year and a lag time (Zell et al. 2009). tween logs, and that single events, such as activities of In contrast to Zell et al. (2009), we observed no signifi- animals searching for food in the logs, may contribute cant improvement in our prediction if mean temperature substantially to mass loss. in July instead of mean annual temperature was used in Measuring fragmentation over the course of only one the model. year was certainly not sufficient to assess the significance Herrmann et al. Forest Ecosystems (2015) 2:27 Page 12 of 14 of this process in the medium to long-term. This would The absence of distinct differences in wood density be- deserve a separate study. However, it would be very diffi- tween decay classes has also been observed in other studies. cult to include fragmentation in models predicting CO Little change (respectively an overlap) in density between release from CWD decomposition, since a large propor- the two least (1 and 2) and most advanced (4 and 5) decay tion of the material lost from CWD through fragmenta- classes has been found also in the study by Yatskov et al. tion is simply transferred to a different pool, the litter (2003). Similar to our results, no significant difference in layer. And we have no information on the decompos- density of decay stages 3 and 4 of F. sylvatica were also ition rate of fragmented wood in the litter layer. found by Müller-Using and Bartsch (2009). One main char- acteristic of log sections in decay stage 4 in our study was the close proximity of highly decomposed material to areas Drill resistance measurements of relatively intact wood. Since the maximum decompos- In a study that used a similar measurement device, 65 % ition time for woody debris of F. sylvatica in our study was of the variation in wood density of P. abies could be ex- about 16 years, decay stage 4 comprised mainly log seg- plained by drill resistance (Kahl et al. 2009). The poten- ments with a diameter < 20 cm. Hence, our data may not tial to determine wood density in a large number of be representative for larger diameter CWD of beech. samples in a short period of time may compensate for Wood densities for the different tree species converged the lack of precision for individual pieces in large inven- for advanced decay stages, which is in accordance with tories. In addition, determining drill resistance may be other studies (Yatskov et al. 2003). We found no signifi- the only form to collect data on wood density in strict cant differences between species for CWD densities in forest reserves, where the collection of stem discs is not decay stage 4. permitted. The increase in C concentration with advancing de- composition observed in our study was also found by Predicting carbon density in CWD Müller-Using and Bartsch (2007) for beech. It suggests The loss of mass and thus carbon in CWD is a continu- an increase of lignin and a decrease of cellulose, as lignin ous process. Using distinct decay stages based on visual has a higher proportion of C than cellulose. The increase assessment of logs can be a useful approach to capture in C concentration cannot be explained by a decrease of the variation in CWD mass and C density in forest in- minerals in relation to C. Similar to our results, C con- ventories. However, the usefulness of this approach de- centrations were lower for angiosperm CWD when com- pends on how distinct decay stages differ in wood pared to gymnosperm CWD in a study that analysed C density and C concentration. concentrations of 60 tree species (Harmon et al. 2013). Here, we observed a high variation in CWD density Unlike our results, a decrease in C concentration with within decay classes and hence only few significant dif- increasing decay class was observed for angiosperm ferences between adjacent decay classes within a given CWD in that study. In contrast to C concentration, C species. Density variation was particularly high for F. syl- content was highest for beech, followed by pine and vatica logs in decay stages 2 and 3. This might suggest, spruce. This can be explained by the higher density of among other things, that decay stages 2 and 3 were most sound wood. −3 difficult to distinguish by the visual classification system. The pattern of C density (mgC · cm ) and decay stage In some cases, decomposition was found to be more (or was similar to that of wood density and decay stage. C less) advanced than ‘suggested’ by the tree surface (i.e., density decreased parallel with density and mass loss, as bark or sapwood condition). This could be the result of has been found by others (Müller-Using 2005). Carbon case hardening (drying out of the outer sapwood) as de- density in CWD converged across species with increas- composition took mostly place in open areas (see also ing decay (similar to the relationship between density Yin 1999). In addition, decomposition of F. sylvatica logs and decay stage). Hence for highly decayed logs, for was spatially highly variable. Intact and decomposed which it may also be difficult to determine the species patches (with different densities) separated by demarca- origin, one common wood and C density may be tion lines composed of melanin, which is characteristic of assumed. white rot fungi (see also Schwarze et al. 1999; Kahl 2008), To assess C in CWD from inventories that record were found in direct proximity within the same tree disc. decay stage, our values of dry wood and C density Owing to such patterns of log colonization by fungi, dens- per species and decay stage could be used for calcula- ity variation may initially increase with decomposition. tion purposes. However, the actual assessment of C For example, an increase in density variation with progres- concentration in inventories appears to be meaningful sing decomposition, with the maximum in decay stage 3 only for beech, where the difference between the and a decline towards decay stage 5, was also observed by measured C concentration and the default value of Yatskov et al. (2003). 50 % was 3.4 %–4.6 %. Herrmann et al. Forest Ecosystems (2015) 2:27 Page 13 of 14 Conclusions Christensen M, Hahn K, Mountford EP, Odor P, Standovar T, Rozenbergar D, Diaci J, Wijdeven S, Meyer P, Winter S, Vrska T (2005) Dead wood in European To our knowledge, this is the first study that assessed the beech (Fagus sylvatica) forest reserves. For Ecol Man 210:267–282 decomposition dynamics of CWD logs of F. sylvatica, P. Commarmot B, Brändli U-B, Hamort F, Lavnyy V (2013) Inventory of the largest abies and P. sylvestris across different sites and diameter primeval beech forest in Europe. A Swiss-Ukrainian scientific adventure. Swiss Federal Research Institute WSL, Birmensdorf; Ukrainian National Forestry classes in central Europe. In comparison to studies at indi- University, L’viv; Carpathian Biosphere Reserve, Rakhiv. p 69 vidual sites, sampling of CWD across different sites pro- Cornwell WK, Cornelissen JHC, Allison SD, Bauhus J, Eggleton P, Preston CM, Scarff F, vides a more robust estimation of decomposition rates Weedon JT, Wirth C, Zanne AE (2009) Plant traits and wood fates across the globe: rotted, burned, or consumed? Global Change Biol 15:2431–2449 because a wider range in climatic conditions and decom- Dormann CF, Kühn I (2009) Angewandte Statistik für die biologischen poser communities are captured. Wissenschaften. 2., durchgesehene, aktualisierte, überarbeitete und erweiterte Our results indicated that a reasonable prediction of Auflage. Helmholtz Zentrum für Umweltforschung-UFZ.S 245 Effland MJ (1977) Modified procedure to determine acid-insoluble in wood and the decomposition of CWD should be possible on the pulp. Tappi 60(10):143–144 basis of a few easily obtainable parameters, since vari- FAO (2006) World reference base for soil resources 2006. A framework for ables such as diameter and tree species had the greatest international classification, correlation and communication. World soil resources report No. 103. 2006 edition. Food and agriculture organization of influence on mass loss over time. Since the variation in the united nations (FAO). Rome, 2006. p 128 http://www.fao.org/ag/Agl/agll/ climatic parameters such as average annual temperature wrb/doc/wrb2006final.pdf and precipitation had only a minor effect on the decom- Forrester JA, Mladenoff DJ, Gower ST, Stoffel JL (2012) Interactions of temperature and moisture with respiration from coarse woody debris in position process, these variables might be neglected for experimental forest canopy gaps. For Ecol Man 265:124–132 the prediction of CWD mass loss in this temperature Freschet GT, Weedon JT, Aerts R, van Hal JR, Cornelissen JHC (2011) Interspecific and precipitation range. differences in wood decay rates: insights from a new short-term method to study long-term wood decomposition. J Ecol 100(1):161–170 The decomposition rates determined in our study, al- Graham RL, Cromack KJR (1982) Mass, nutrient content, and decay rate of dead though they may be further refined, provide a basis for boles in rain forests of Olympic National Parc. Can J For Res 12:511–521 the management of CWD. The decomposition constants Grove S, Meggs J, Goodwin A (2002) A review of biodiversity conservation issues relating to coarse woody debris management in the wet eucalypt will allow the development of decomposition models production forests of Tasmania. Forestry Tasmania, Hobart, p 72 based on information about the input of CWD accord- Grove SJ, Stamm L, Barry C (2009) Log decomposition rates in Tasmanian ing to species and diameter. Based on the derived C- Eucalypt obliqua determined using an indirect chronosequence approach. For Ecol Man 258:389–397 densities per decay stage, C contents in CWD can be Hagemann U, Moroni MT, Gleißner J, Makeshin F (2010) Disturbance history calculated to serve as input to C accounting of forest influences downed woody debris and soil respiration. For Ecol Man ecosystems. 260:1762–1772 Harmon ME, Franklin JF, Swanson FJ, Sollins P, Gregory V, Lattin JD, Anderson NH, Cline SP,Aumen NG,Lienkaemper GW,Cromack KJ,Cummins KW (1986) Ecologyof Competing interests coarse woody debris in temperate ecosystems. Adv Ecol Res 15:133–302 The authors declare that they have no competing interests. Harmon ME, Krankina ON, Sexton J (2000) Decomposition vectors: a new approach to estimating woody detritus decomposition dynamics. Can J For Authors’ contributions Res 30:76–84 SH planned and conducted the study including field sampling and analysis Harmon ME, Fasth B, Woodall CW, Sexton J (2013) Carbon concentration of and wrote the majority of the manuscript. TK conducted linear mixed effects standing and downed woody detritus: Effects of tree taxa, decay class, modelling and contributed to the manuscript. JB conceived and guided the position, and tissue type. For Ecol Man 291:259–267 study and co-wrote the manuscript. All authors read and approved the final Herrmann S, Bauhus J (2008) Comparison of methods to quantify respirational manuscript. carbon loss of coarse woody debris. Can J For Res 38:2738–2745 Herrmann S, Bauhus J (2012) Effects of moisture, temperature and decomposition stage on respirational carbon loss from coarse woody debris (CWD) of Acknowledgements important European tree species. Scand J For Res 28(4):346–357 We are grateful to Renate Nitschke and Germar Csapek, and the many students, Herrmann S, Prescott CE (2008) Mass loss and nutrient dynamics of coarse who helped in the field and in the lab. We thank Sarah Grimm from Seminar woody debris in three Rocky Mountain coniferous forests: 21 year results. for Statistics ETH Zurich for statistical advice. Oliver Jakoby provided helpful Can J For Res 38:125–132 comments to improve the manuscript. 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Conservation Biology. (in press) Shigo AL (1986) A new tree biology. Shigo and Trees Associates, Durham 7 Open access: articles freely available online Shorohova E, Kapitsa E (2014) Influence of the substrate and ecosystem attributes 7 High visibility within the fi eld on the decomposition rate of coarse woody debris in European boreal 7 Retaining the copyright to your article forests. For Ecol Manage 315:173–184 Siitonen J (2001) Forest management, coarse woody debris and saproxylic organisms: Fennoscandian boreal forests as an example. Ecol Bull 49:11–41 Submit your next manuscript at 7 springeropen.com

Journal

"Forest Ecosystems"Springer Journals

Published: Dec 1, 2015

Keywords: Ecology; Ecosystems; Forestry

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