Strength loss caused by fungal degradation is an important factor to be considered during tree-stability assessment. Detailed information on the relationship between static mechanical properties in relation to the heterogeneity of density and dynamic mechanical parameters of wood degraded by the soft-rot fungus Kretzschmaria deusta can improve the understanding of its decay process and the interpretation of results obtained from stress-wave-based non-destructive methods used for tree-stability assessment. This research presents density profiles of artificially inoculated samples with K. deusta and static mechanical properties of green beech wood in relation to physical parameters (density, moisture content, vibroacoustic parameters). A statistically relevant difference (p < 0.01) in the variability of density distribution between degraded and intact samples was proved. Relevant correlations were proved among modulus of rupture ( ), mass loss and variability longitudinal density distribution. A strong linear relationship between and static modulus of elasticity ( ) of degraded and intact specimens was presented. A strong relationship was also proved between and dynamic parameters (dynamic modulus of elasticity ( ) and stress-wave velocity in longitudinal direction ( )). showed a stronger correlation to than proving the importance of density in assessing strength loss through non-destructive methods.
Degradation caused by wood-decaying fungi is one of the most important factors influencing tree stability (Assefa 2020; Kobza et al. 2021). Due to climate changes and high biological potential of degraded wood, the need to preserve mature urban trees colonised by wood decay fungi is increasing (Fröhlich and Ciach 2020; Sterken 2005). Nevertheless, high target value in the urban environment and the impact of wood degradation on tree stability require detailed periodic visual and device-supported assessments (Allison et al. 2020; Koeser et al. 2017). Depending on the enzymes they produce, wood-decaying fungi can break down various components of the wood cell wall (hemicelluloses, cellulose and lignin) into simple carbohydrates, which are then absorbed and metabolised (Schmidt 2006). Wood-decaying fungi are generally classified according to their degradation process of the wood cell wall into white, brown and soft rot fungi (Rayner and Boddy 1988). The degradation of each cell wall component reduces mechanical properties (Ibach and Lebow 2014; Winandy and Rowell 2005; Winandy et al. 2001). The degree of wood degradation caused by wood-decaying fungi is often described by weight loss (Witomski et al. 2016). Unfortunately, this parameter does not always correlate with the degree of strength reduction, which is, from a practical point of view, one of the most important parameters concerning tree stability assessment and public safety in the urban environment. Strength loss can occur even without observable weight loss (Brischke et al. 2008; Witomski et al. 2016). For example, even with a minimal weight loss (up to 5%), a sharp drop in strength properties can occur (35–50%) (Wilcox 1978). Curling et al. (2002) demonstrated that the ratio of strength loss to weight loss is on average 4:1. Winandy et al. (2001) investigated the relationship between weight loss and strength loss in pine wood (Pinus spp.) caused by brown-rot fungi. Measurable weight loss manifested itself from a 40% strength loss. Humar et al. (2008) reported changes in the modulus of elasticity of wood of Norway spruce (Picea abies) and Scots pine (Pinus sylvestris) colonised by wood-staining fungi, showing a slight increase in the modulus of elasticity. More reliable methods to assess the change in mechanical properties of wood caused by wood degradation are non-destructive testing (NDT) techniques (Hering et al. 2012). A common group of NDT techniques are vibroacoustic methods, which can be used to obtain a reliable estimation of static mechanical properties of wood (Chauhan and Sethy 2016). Unlike classic mass-loss estimation, these techniques can identify alterations in mechanical properties even during incipient stages of decay (Machek et al. 2001). Two main techniques representing this group are the resonant frequency technique, which describes more parameters simultaneously (Bucur 2006), and the measurement of time of flight (TOF) of stress waves for a known distance (Wang et al. 2001). The latter method is used in acoustic tomography, a technique used to detect defects in a cross-section of the assessed stem, using interpretation of the velocity of stress-wave propagation (c) and creating a spatial (2D/3D) estimation of the defect (Cristini et al. 2021; Liang et al. 2008; Wang 2013). Using acoustic tomography, it is possible to evaluate dynamic mechanical properties in the transverse directions (radial and tangential velocities of stress-wave propagation). However, mechanical properties in the longitudinal direction are more representative in evaluating tree stability and stress analysis (Giambastiani et al. 2017). Yang et al. (2017) tested Elliot pine (Pinus elliottii) samples artificially inoculated with different wood-decaying fungi using static and stress-wave propagation techniques and showed a significant correlation between static and dynamic modulus of elasticity (MOE and MOED) and modulus of rupture (MOR). Schwarze et al. (1995) measured the acoustic and mechanic properties of artificially inoculated samples with various wood-decaying fungi and showed that the classical relationship between density, modulus of elasticity, and sound propagation in intact wood does not apply to degraded wood. Deflorio et al. (2008a) investigated changes in the acoustic properties of wooden samples after 2, 16, and 27 months of exposure to various wood-decaying fungi. In some cases of more advanced stages of degradation, an increase in the velocity of stress-wave propagation in wood occurred. Generally, bending properties are considered relevant parameters representing strength loss caused by fungal degradation (Zabel and Morrell 1992). The test sample should have a homogeneous structure for static bending testing results to be meaningful (de Almeida et al. 2018). Considering the fact that wood-decaying fungi propagate preferentially in the longitudinal direction (Rayner and Boddy 1988), it can be assumed that the most intense degradation of the samples takes place near the faces of the wooden sample. Most of the abovementioned publications describe the action of fungi on construction wood, and none of them consider the possible heterogeneity of decomposition caused by a specific wood-decaying fungus, mostly because wood degradation below 10% weight loss is identifiable only microscopically (Ibach and Lebow 2014). Computed tomography (CT) can be used to measure the heterogeneity of fungal deterioration, a NDT technique for non-destructive imaging of wood (Bucur 2003; Kobayashi et al. 2019). CT can also be used to obtain a detailed assessment of wood density (Freyburger et al. 2009; Fromm et al. 2001; Longuetaud et al. 2016). Nevertheless, the resolution of CT scanning results of degraded wood and their correlation with density values gravimetrically measured were not yet presented.
Kretzschmaria deusta is one of the most important wood-decaying fungi of urban trees (Schwarze et al. 2004). It often causes stem failures in the basal zone of different common species for the urban environment (Fagus sp., Tilia sp., Acer sp., etc.) (Deflorio 2006; Terho and Hallaksela 2008). It has a prevalent heart-rot mode of expansion, with different modes of arrival and colonisation (wound and root infection) (Guglielmo et al. 2012). Under optimal habitat conditions, a common beech tree can well compartmentalise its infection. However, this pattern does not apply in the urban environment, where due to anthropogenic stressors, this species is much more susceptible to colonisation and further degradation caused by K. deusta (Schwarze et al. 2004). This wood-decaying fungus causes soft rot, a specific degradation pattern typical for Ascomycetes. Soft rot is represented by two morphologically different decay patterns known as Type I (cavity formation) and Type II (cell wall erosion) (Daniel 2014). Type I soft rot consists of a preferred hyphal growth in the S2 layer of the secondary wood cell wall (Findlay and Savory 1954; Schmidt 2006). This method of decay does not reduce the speed of stress-wave propagation in wood even in advanced stages of degradation, so results of tomographic assessments can be distorted (Schwarze et al. 1995, 2004). One of the main objectives of this paper is to find a parameter enabling a more detailed prediction of mechanical properties of wood degraded by K. deusta during acoustic device-supported assessement of tree stability. Mechanical properties of intact green wood have been investigated in the past (King et al. 2006; Niklas 1992; Niklas and Spatz 2010). Nevertheless, the impact of biomechanically relevant wood-decaying fungi on mechanical properties of green wood is still insufficiently explored. A detailed description of the degradation of beech wood caused by K. deusta and its influence on physical and mechanical properties (and their mutual relations), considering the density variability, is still missing. Obtained results can be used to implement device-supported methods for non-destructive assessment of tree stability.
2 Materials and methods
2.1 Sample preparation
Small orthotropic bending samples (7 × 7 × 100 mm) of European beech (Fagus sylvatica L.) were crafted during January 2021 from four wood boards obtained from different fresh timber felled during the previous two weeks. Chosen wood came from the University Forest Enterprise Masarykův les – Křtiny (Czech Republic). These dimensions were chosen to fit the specimens to fungal culture in Kolle flasks and preserve the dimension ratio of bending specimens for mechanical testing according to the testing standards (20 × 20 × 300 mm). For samples’ crafting, only intact sapwood was used. To obtain oven-dry mass, samples were completely dried (moisture content (MC) = 0%) in a kiln for two days at 103 °C and weighed after approaching steady condition. Before vibroacoustic measurement and the samples’ inoculation, a higher moisture content (MC = 60 ± 10%) was established through vacuum impregnation with demineralised water (20 kPa, 3 min).
The moisture content (MC), green density ( ) and conventional density ( – ratio of oven-dried wood mass and the volume of the same sample with a moisture range over the fibre saturation point) were calculated from sample dimensions and dry/wet (before and after degradation) masses.
In order to calculate a coefficient ratio to obtain the longitudinal dynamic modulus of elasticity ( ) from the bending on , larger intact specimens with the same dimensions’ ratio (40 × 40 × 600 mm and 60 × 60 × 900 mm) were crafted and conserved in clean water to reach a moisture range over the fibre saturation point (MC = 60 ± 10%).
2.2 Fungal exposure
Each Kolle flask (400 ml) was filled with 70 ml of sterile malt extract agar base (malt extract = 30 g/l; mycological peptone = 5 g/l; agar = 15 g/l; final medium concentration = 5%). Flasks were inoculated with a K. deusta strain obtained in May 2020 from the wood of a mature standing beech tree located at the edge of a forest in Bilovice, Czech Republic. Before flask inoculation, K. deusta mycelium was cultivated on small beech wood blocks in Petri dishes to stimulate enzymatic activity. After two weeks of incubation at 22 °C and 70% relative humidity, mycelium completely covered the medium surface, and flasks were prepared for samples’ insertion. After the first vibroacoustic measurements using the frequency-resonance method, all the wood bending specimens were sterilised by steam (10 min, 120 °C). Both faces of half of the samples destined for fungal deterioration were covered with pure paraffin wax to try to limit the hyphal propagation in longitudinal direction (Figure 1A). All samples were laid on the radial face (Figure 1B). After 12 weeks of fungal exposure (Figure 1C), all specimens were removed from Kolle flasks, cleaned from superficial mycelium, and assessed with vibroacoustic techniques and static mechanical testing immediately afterwards.
2.3 Vibroacoustic measurements
Resonant frequency technique was used to determine MOED. Mallet strikes on radial and tangential sides caused oscillations. Vibrations of longitudinal-tangential and longitudinal-radial bending modes (Figure 2A) and longitudinal modes (Figure 2B) were sensed using Doppler’s laser vibrometer PDV-100 (Polytec GmbH, Germany) and recorded using dynamic signal acquisition module DEWE-41-T-DSA with DEWESoft (DEWETRON, Inc., USA) at sampling frequency 20 kHz. Records of vibrations were transformed from time domain to frequency domain using the Fast Fourier Transform (FFT) processed using MATLAB® (The MathWorks, Inc., USA). Obtained frequency was used for the calculation of MOED in tangential (t) and radial (r) directions using the following equation:
where f is the resonant frequency of the first bending mode, m is the sample mass, l is the sample length, and I is the moment of inertia. was calculated for both small degraded and larger intact samples. Due to the small length samples, it was not possible to measure the longitudinal mode frequency from which dynamic modulus of elasticity in the longitudinal direction ( ) and c were calculated. Therefore, the conversion coefficient ratio from to was determined on larger intact samples (40 × 40 × 600 mm and 60 × 60 × 900 mm), where their size allowed the measurement of longitudinal mode frequency (Figure 2B). was calculated from the frequency of the first longitudinal mode shape of vibrations:
where is the green density, f is the resonant frequency of the first bending mode, and l is the sample length. According to data obtained from the measurement of the bigger specimens, the conversion coefficient was calculated through multiple linear regression (function ‘regress’ – MATLAB) from the mean of and to . To determine c, dynamic modulus of elasticity in the longitudinal direction ( ) was used:
where is the calculated dynamic modulus of elasticity in longitudinal direction and is the green density. To evaluate the effect of the drying and sterilisation treatment process on , a group of 30 specimens was tested before and after the preparation process. After each operation, samples were acclimatised for one week (20 °C, 65% relative humidity) and tested. Results after the whole process were then compared with results obtained before treatment.
2.4 Static mechanical testing
Static three-point bending tests were carried out on a universal testing machine (ZWICK® Z050, 50 kN) (Figure 3A), where the force (F) applied to the sample in tangential direction was obtained. Bending stress in the tangential direction ( ) was calculated according to the following equation:
where is the bending stress, is the applied force, is the distance between the two supports (12 times the sample’s height), and , are the base and the height of the specimen’s cross-section. The span/depth ratio was 12. For comparison, 30 reference intact specimens were also tested. Half of the reference group went through the same drying, sterilisation, and impregnation process (MC = 60 ± 10%); the remainder only underwent impregnation. Modulus of rupture ( ) was calculated as the bending stress at the highest applied force ( ). Bending deflection ) was evaluated by a point-probe optical displacement measurement based on the Digital Image Correlation (DIC) technique. Two cameras (AVT Stingray Copper F504B, cell size: 3.45 µm, resolution: 5 MPx, image-capture frequency: 2 fps) were used to acquire the images (Figure 3A). Images were processed in Mercury software (Sobriety s.r.o., Czech Republic), where the DIC technique allows the measurement of displacement at the point of applied force (Figure 3B). The static bending moduli of elasticity in tangential direction ( ) were established using a least-squares method fitting data from the zone of linear elastic behaviour from the stress-strain diagram of each sample. The data was processed using MATLAB® .
2.5 Computed tomography assessment
After static mechanical testing, all samples were stored in a climatised room (20 °C, 65% relative humidity) for one week, together with 11 intact (reference) specimens. Afterwards, all samples were scanned with a computed tomography (CT) system DeskTom (RX Solutions, France) with a resolution of 80 μm. For each sample, 1000 cross-sections in the longitudinal direction were obtained. Because of the limited dimension of the chamber, all specimens were scanned in three different groups to reach a higher resolution (Figure 4A). Cross-sections of each sample were afterwards cropped in MATLAB® (Figure 4B). During image cropping, approximately 0.5 mm from each side was cut off from each sample. To obtain the longitudinal density distribution of each specimen, all cross-sections were imported into MATLAB® as numerical matrixes, and the mean value was calculated for each of them (Figure 4D). To eliminate the influence of cracks produced during static testing on the density distribution, mean values obtained from cross-sections where the crack was present were deleted and replaced with values computed through a linear regression between the first intact cross-sections before and after the crack. The density obtained by CT scanning ( ) was calculated for each specimen as the mean of the numerical vector used for the longitudinal density distribution.
2.6 Scanning electron microscopy
A scanning electron microscope (SEM) was used to conduct microscopy analysis. Small samples (4 × 4 × 4 mm) were sectioned from three different specimens: one intact (reference), one degraded with paraffin wax, and one degraded without. Three small samples were prepared for each specimen: one taken from the centre and two taken 2 cm from the edges. The samples were softened with water, hand-cut with a razor blade to ensure a smooth surface, and dried at room conditions. The samples were sputter-coated with gold using a Luxor gold coater (Aptco Group, Belgium). The layer thickness was 15 nm. The cross-section and radial section were observed with SEM Tescan Vega 4 (TESCAN ORSAY HOLDING a.s., Czech Republic). The scans were prepared in RESOLUTION scanning mode in a high vacuum using a detector of secondary electrons (SE detector). The images were obtained with these settings: landing energy 7 keV, beam current 7 pA.
Relationships between measured parameters were statistically investigated in MATLAB® (Spearman’s correlation coefficient (Sc) at \alpha=\ 0.05, and linear regression). Because of the higher variability of measured data (natural variability of wood material and high variability caused by fungal deterioration) and the sample size, the ANOVA significance level (\alpha) was set at 0.01, which is commonly used in biostatistics (Marusteri and Bacarea 2010; van Duong and Matsumura 2018).
3 Results and discussion
3.1 Variability of density caused by fungal degradation
According to the results shown in Figure 5D, there was very high variability between density distribution of degraded specimens. According to ANOVA, no statistically relevant difference was proved among the variability of specimens with paraffin wax and without (p > 0.01). Nevertheless, compared with the degraded specimens, reference intact samples showed a very low variability and a statistically significant difference (p < 0.01) (Figure 5A). The graphical representation of each density profile obtained from CT scanning for both degraded and intact specimens supports this statement (Figure 5B). According to these results, the application of pure paraffin wax on the faces of specimens to limit the longitudinal propagation in the longitudinal direction through vessels (Rayner and Boddy 1988; Schmidt 2006) and consequent variability of degradation was not effective. The skewness of each sample group shown in Figure 5C shows a higher amount of lower local densities (calculated for each cross-section) influencing the mean.
Observing the different density profiles showed in Figure 5A, there is no specific trend in differences suggesting that the deterioration was primarily concentrated in the extremities of the degraded specimens. The absence of any trend can be caused by the ununiform surface of the medium in the Kolle flasks caused by the growth of K. deusta’s mycelium. Each specimen was positioned on the cultivated mycelium only in some points and not along its whole length, so fungal growth and specimens’ degradation could have been influenced. Figure 5D shows a significant relationship between and . According to their strong linear regression (r 2 = 0.81), densities obtained from CT scanning can be considered representative. This result is supported by Fromm et al. (2001), who showed a strong relationship between densities assessed with CT scanning and gravimetric-volumetric method (r 2 = 0.89 for spruce; r 2 = 0.71 for oak). Freyburger et al. (2009) demonstrated a very strong linear relationship between CT scanning and gravimetric method (r 2 > 0.99) using a calibration data set which consisted in tropical wood samples.
Figure 6 shows a different level of degradation in different samples obtained from degraded specimens (6B/C/D) and one reference intact sample (6A). Incipient decay shown in Figure 6B presents different stages of soft rot Type I with mostly isolated cavities in the cell walls of libriform fibres – typical for soft-rot fungi as K. deusta. Different authors have already described this kind of cell wall degradation (Levy 1966; Morrell and Zabel 1985; Schmidt 2006; Schwarze et al. 1995, 2004). After longer exposure to fungal deterioration, singular pits in the S2 layer showed a significant thinning of the cell wall (6C). In a stage of progressed degradation (6D), the only intact part of the lignified cell wall was the compound-middle lamella. Schwarze et al. (1995) stated that its preservation is not only caused by its high lignin content but also by the high percentage of Guayacil lignin. These different levels of deterioration were found on the sides (2 cm from the edges) of the degraded samples and in the middle of the specimens. Obtained results from SEM scanning supported the results presented in Figure 5B, where no specific degradation trend among the specimens’ length was observed.
3.2 Influence of mass loss on MOR
Figure 7A shows the mass losses for the degraded samples with paraffin wax and without. According to the Kruskal-Wallis test, no statistically significant difference between these two groups was proved (p > 0.01). The average mass loss was 31%, with the maximum and minimum 54.6 and 17.2%, respectively. Schwarze et al. (1995) presented the mass loss caused by K. deusta on beech and large-leaved linden wood. After 12 weeks of incubation, beech wood presented an average mass loss of 4.5%, while for inden wood was 8.3%. The lower mass loss was probably caused by the different specimen dimensions (45 × 20 × 20 mm), presenting a lower specific surface contact area. Deflorio et al. (2008b) showed that the degradation caused by K. deusta on standing beech trees felled 28 months after artificial inoculation caused a 12.9% mass loss. These results showed that the degradation in standing trees is relatively slower compared to in-vitro deterioration. This difference is caused by the presence of parenchyma cells and their task in the compartmentalisation of decay in standing trees, retarding the process of wood degradation (Schwarze et al. 2004).
According to Figure 7B, considering the biological origin of measured values and the even higher variability caused by the degradation process, a relevant moderate linear relationship between and was proved (r 2 = 0.54). A strong negative correlation was also proved between mass loss and (Sc = −0.77). No difference in MOR was proved between sterilised and non-sterilised reference specimens (p > 0.01). Curling et al. (2002) showed the relationship between mass loss and in southern pine caused by Gleophyllum trabeum and Rhodonia placenta, where a linear trend was observed till 20% mass loss. At higher mass losses (up to 40%), MOR reduction dropped. Xu et al. (2019) also proved a strong linear relationship (r 2 = 0.75) between mass loss and compressive strength of Chinese poplar blocks exposed for 90 days to G. trabeum. Lundström et al. (2007) also showed a strong relationship (r 2 = 0.92) between and of green Norway spruce wood. According to our data, the variance of density among each specimen correlates with the average density (Sc = −0.69).
The weaker linear relationship proved in Figure 7B can be explained by the higher variability of density among the length of the degraded specimens, showing the influence of the heterogeneity of degradation caused by K. deusta on bending testing results. With a decrease in density (progression in the degradation process), an increase in the variability of the density profile in longitudinal direction occurred. The variation in density among each specimen also had a relevant correlation with MOR (Sc = −0.58), which can be explained by the strong correlation between density and coefficients of variance. According to these results, we can state that the variability of density caused by fungal degradation should be considered during the assessment of reduction.
3.3 Relationship between static/dynamic elastic parameters and MOR
During the assessment of vibroacoustic properties, no significant influence of drying and sterilisation treatment processes was proved (p > 0.01). Therefore, all changes in mechanical and vibroacoustic properties were caused only by fungal degradation. Figure 8 shows the relationship between and different elastic and dynamic properties ( , , and ). The coefficient ratio obtained from the vibroacoustic measurements of big bending specimens and used to calculate from was 1.1166. According to Figure 8B, a strong linear relationship between and was proved (r 2 = 0.79). The strong correlation between and contradicts the conclusion proposed by Schwarze et al. (1995), who stated that wood degraded by K. deusta during incipient decay loses strength faster than stiffness.
Figure 8A shows a strong linear regression between (calculated from the vibroacoustic measurement) and , which is also confirmed by the strong correlation between and (Sc = 0.92). These results concur with Baar et al. (2016), who proved strong linear relationships between and (r 2 = 0.73) and and measured with a flexural resonance method (r 2 = 0.87) of different tropical wood samples at 8% . As shown in Table 1, values for static and dynamic measured and calculated properties for degraded samples with paraffine have higher variability than values obtained for degraded specimens without wax. Nevertheless, as for the variability of measured density for degraded samples shown in Figure 5A, these differences are not considered statistically relevant (p > 0.01).
|Samples||MOR (MPa)||MOE t (MPa)||MOED t (MPa)||MOED r (MPa)||C l (m/s)|
|Wax||46||24.2 (47.9%)||2505 (49.9%)||3984 (42.6%)||4401 (49.3%)||2530 (21.9%)|
|No wax||46||26.8 (41%)||3123 (38.4%)||4508 (36.9%)||5340 (39.4%)||2568 (19.3%)|
|Intact||30||63.4 (10.6%)||7992 (10.7%)||10882 (10.2%)||11423 (10.6%)||3632 (4.9%)|
Comparing the relationships of and against (Figure 8C and D), it is possible to see a relevant correlation of both parameters with . Nevertheless, had a higher r 2 value (r 2 = 0.72). To that, some degraded samples with similar to the reference group, showed a pronounced strength loss and the relationship between and for intact specimens did not have the same trend as the degraded specimens (Figure 8D). On the other side, the linear regression calculated for and was also applicable to intact samples (Figure 8C). Obtained results agree with previous investigations, which could not detect soft rot decay by K. deusta with a laboratory stress-wave timer (Schwarze et al. 1995). Deflorio et al. (2008a) showed decreased stress-wave velocity in standing beech trees caused by the artificial inoculation with K. deusta. The authors showed a statistically proven difference (α = 0.05) comparing two measurements, 2 and 27 months after inoculation. Nevertheless, they did not consider the strength loss caused. The stronger relationship between and suggests a strong influence of on strength-loss prediction. According to the above-mentioned results, utilization of calculated dynamic moduli of elasticity trough measured velocities and approximately known absolute densities can lead to an improvement in results interpretation of instrumental methods (e.g. acoustic tomography) used for non-destructive tree stability assessement. of degraded samples was highly variable ( / = 58/149%; coefficient of variation = 27.89%). Nevertheless, no significant correlation between and was proved (Sc = −0.15). This result supports the conclusions proposed by Niklas and Spatz (2010), who proposed mechanical testing for green wood above 50% to eliminate its influence on wood strength.
The relationships between green beech wood’s physical, mechanical, and acoustic/dynamic parameters degraded by K. deusta were proved. According to the presented results, the following conclusive statements can be presented:
A statistically relevant difference between density variability of degraded and intact samples was proved.
The application of pure paraffin wax on the specimens’ faces did not influence the heterogeneity of fungal degradation (p > 0.01).
A strong correlation (Sc = −0.77) between mass loss and strength loss caused by K. Deusta was proved.
With a higher mass loss, increased density variability among the sample length occurred. Thus, higher heterogeneity can have an impact on strength loss.
A strong relationship (r 2 = 0.79) between and proved a linear decrease in stiffness and strength of wood degraded by K. deusta.
of degraded samples was between 58 and 149%. Even with its high variability, did not influence (Sc = −0.15).
Relevant linear relationships were proved between all dynamic parameters and . shows a stronger correlation (r 2 = 0.72) than (r 2 = 0.58) proving the strong influence of density in strength-loss prediction.
Based on the relationships among the investigated material parameters in this study, heterogeneity of fungal degradation should be considered during strength-loss assessment. In contrast to results published in the past, soft rot caused by K. deusta caused a decrease in both strength and stiffness. The strong relationship between strength-loss and shows the importance of for interpreting acoustic-based assessments of degraded wood. Presented results can be used to improve the accuracy of device supported methods used for tree-stability assessment.
Funding source: Ministerstvo Školství, Mládeže a Tělovýchovy
Award Identifier / Grant number: CZ.02.2.69/0.0/0.0/19_073/0016670
Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
Research funding: This outcome was supported by the Internal Grant Schemes of Mendel University in Brno, registration no.: CZ.02.2.69/0.0/0.0/19_073/0016670, funded by the ESF.
Conflict of interest statement: The authors declare no conflicts of interest regarding this article.
Assefa, A. (2020). Estimation of strength loss and decay severity of Juniperus procera by Juniper pocket rots fungus, P. demidoffii in Ethiopian forests. J. For. Environ. Sci. 36: 143–155, https://doi.org/10.7747/JFES.2020.36.2.143.Search in Google Scholar
Brischke, C., Welzbacher, C.R., and Huckfeldt, T. (2008). Influence of fungal decay by different basidiomycetes on the structural integrity of Norway spruce wood. Holz als Roh- Werkst. 66: 433–438, https://doi.org/10.1007/s00107-008-0257-1.Search in Google Scholar
Chauhan, S. and Sethy, A. (2016). Differences in dynamic modulus of elasticity determined by three vibration methods and their relationship with static modulus of elasticity. Maderas Cienc. Tecnol. 18: 373–382, https://doi.org/10.4067/S0718-221X2016005000034.Search in Google Scholar
Cristini, V., Tippner, J., Vojáčková, B., and Paulić, V. (2021). Comparison of variability in results of acoustic tomographs in pedunculate oak (Quercus robur L.). BioResources 16: 3046–3058, https://doi.org/10.15376/biores.16.2.3046-3058.Search in Google Scholar
Curling, S.F., Clausen, C.A., and Winandy, J.E. (2002). Relationships between mechanical properties, weight loss and and chemical composition of wood during incipient brown-rot decay. For. Prod. J. 52: 34–39.Search in Google Scholar
Daniel, G. (2014). Fungal and bacterial biodegradation: white rots, brown rots, soft rots, and bacteria. In: Deterioration and protection of sustainable biomaterial. American Chemical Society, Washington, DC, pp. 24–58.10.1021/bk-2014-1158.ch002Search in Google Scholar
de Almeida, T.H., de Almeida, D.H., Christoforo, A.L., and Lahr, F.A.R. (2018). Static bending loading diagram in wood. Int. J. Mater. Eng. 8: 1–4, https://doi.org/10.5923/j.ijme.20180801.01.Search in Google Scholar
Deflorio, G. (2006). Wood decay dynamics in the sapwood of trees – in vitro and in vivo studies on the role of the wood substrate in decay development. Cuvillier Verlag, Göttingen.Search in Google Scholar
Deflorio, G., Fink, S., and Schwarze, F.W.M.R. (2008a). Detection of incipient decay in tree stems with sonic tomography after wounding and fungal inoculation. Wood Sci. Technol. 42: 117–132, https://doi.org/10.1007/s00226-007-0159-0.Search in Google Scholar
Deflorio, G., Franz, E., Fink, S., and Schwarze, F.W.M.R. (2008b). Host responses in the xylem of trees after inoculation with six wood-decay fungi differing in invasiveness. Botany 87: 26–35, https://doi.org/10.1139/B08-113.Search in Google Scholar
Findlay, W.P.K. and Savory, J.G. (1954). Moderfäule. Die Zersetzung von Holz durch niedere Pilze. Holz als Roh- und Werkstoff. Eur. J. Wood Wood Ind. 12: 293–296, https://doi.org/10.1007/BF02606055.Search in Google Scholar
Freyburger, C., Longuetaud, F., Mothe, F., Constant, T., and Leban, J.M. (2009). Measuring wood density by means of X-ray computer tomography. Ann. For. Sci. 66: 804, https://doi.org/10.1051/forest/2009071.Search in Google Scholar
Fröhlich, A. and Ciach, M. (2020). Dead wood resources vary across different types of urban green spaces and T depend on property prices. Landsc. Urban Plann. 197: 103747, https://doi.org/10.1016/j.landurbplan.2020.103747.Search in Google Scholar
Fromm, J.H., Sautter, I., Matthies, D., Kremer, J., Schumacher, P., and Ganter, C. (2001). Xylem water content and wood density in spruce and oak trees detected by high-resolution computed tomography. Plant Physiol. 127: 416–425, https://doi.org/10.1104/pp.010194.Search in Google Scholar
Giambastiani, Y., Preti, F., Errico, A., and Sani, L. (2017). On the tree stability: pulling tests and modelling to assess the root anchorage. Procedia Environ. Sci. Eng. Manag. 4: 207–218.Search in Google Scholar
Guglielmo, F., Michelotti, S., Nicolotti, G., and Gonthier, P. (2012). Population structure analysis provides insights into the infection biology and invasion strategies of Kretzschmaria deusta in trees. Fungal Ecol. 5: 714–725, https://doi.org/10.1016/j.funeco.2012.06.001.Search in Google Scholar
Hering, S., Saft, S., Resch, E., Niems, P., and Kaliske, M. (2012). Characterisation of moisture-dependent plasticity of beech wood and its application to a multi-surface plasticity model. Holzforschung 66: 373–380, https://doi.org/10.1515/hf.2011.162.Search in Google Scholar
Humar, M., Vek, V., and Bucar, B. (2008). Properties of blue-stained wood. Drv. Ind. 59: 75–79.Search in Google Scholar
King, D.A., Davies, S.J., Tan, S., and Noor, N.S.M. (2006). The role of wood density and stem support costs in the growth and mortality of tropical trees. J. Ecol. 94: 670–680, https://doi.org/10.1111/j.1365-2745.2006.01112.x.Search in Google Scholar
Kobayashi, K., Hwang, S.W., Okochi, T., Lee, W., and Sugyiama, J. (2019). Non-destructive method for wood identification using conventional X-ray computed tomography data. J. Cult. Herit. 38: 88–93, https://doi.org/10.1016/j.culher.2019.02.001.Search in Google Scholar
Kobza, M., Ostrovský, R., Adamčíková, K., and Pastirčáková, K. (2021). Stability of trees infected by wood decay fungi estimated by acoustic tomography: a field survey. Trees Struct. Funct. 36: 103–112, https://doi.org/10.1007/s00468-021-02185-w.Search in Google Scholar
Koeser, A.K., Hauer, R.J., Klein, R.W., and Miesbauer, J.W. (2017). Assessment of likelihood of failure using limited visual, basic, and advanced assessment techniques. Urban For. Urban Green. 24: 71–79, https://doi.org/10.1016/j.ufug.2017.03.024.Search in Google Scholar
Liang, S., Wang, X., Wiedenbeck, J., Cai, Z., and Fu, F. (2008). Evaluation of acoustic tomography for tree decay detection. In: 15th international symposium on nondestructive testing of wood. Forest Products Society, Duluth, Minnesota, USA, pp. 49–54.Search in Google Scholar
Longuetaud, F., Mothe, F., Fournier, M., Dlouha, J., Santenoise, P., and Deleuze, C. (2016). Within-stem maps of wood density and water content for characterization of species: a case study on three hardwood and two softwood species. Ann. For. Sci. 73: 601–614, https://doi.org/10.1007/s13595-016-0555-4.Search in Google Scholar
Lundström, T., Heiz, U., Stoffel, M., and Stöckli, V. (2007). Fresh-wood bending: linking the mechanical and growth properties of a Norway spruce stem. Tree Physiol. 27: 1647.10.1093/treephys/27.9.1229Search in Google Scholar PubMed
Machek, L., Militz, H., and Sierra-Alvarez, R. (2001). The use of an acoustic technique to assess wood decay in laboratory soil-bed tests. Wood Sci. Technol. 34: 467–472.10.1007/s002260000070Search in Google Scholar
Morrell, J.J. and Zabel, R.A. (1985). Wood strength and weight losses caused by soft rot fungi isolated from treated southern pine utility poles. Wood Fiber Sci. 17: 132–143.Search in Google Scholar
Niklas, K.J. (1992). Plant biomechanics – an engineering approach to plant form and function. The University of Chicago Press, Chicago, Illinois, USA.Search in Google Scholar
Niklas, K.J. and Spatz, H.C. (2010). Worldwide correlations of mechanical properties and green wood density. Am. J. Bot. 97: 1587–1594, https://doi.org/10.3732/ajb.1000150.Search in Google Scholar PubMed
Rayner, A.D.M. and Boddy, L. (1988). Fungal decomposition of wood – its biology and ecology. John Wiley & Sons, Hoboken, New Jersey, USA.Search in Google Scholar
Schmidt, O. (2006). Wood and tree fungi – biology, damage, protection and use. Springer, Berlin/Heidelberg.Search in Google Scholar
Schwarze, F.W.M.R., Engels, J., and Mattheck, C. (2004). Fungal strategies of wood decay in trees. Springer, Berlin/Heidelberg.Search in Google Scholar
Schwarze, F.W.M.R., Lonsdale, D., and Mattheck, C. (1995). Detectability of wood decay caused by Ustulina deusta in comparison with other tree-decay fungi. Eur. J. For. Pathol. 25: 327–341, https://doi.org/10.1111/j.1439-0329.1995.tb01348.x.Search in Google Scholar
Sterken, P. (2005). A guide for tree-stability analysis, Available at: <https://www.researchgate.net/publication/242418928_A_Guide_For_Tree-Stability_Analysis>.Search in Google Scholar
Terho, M. and Hallaksela, A.M. (2008). Decay characteristics of hazardous Tilia, Betula, and Acer trees felled by municipal urban tree managers in the Helsinki City Area. Forestry 81: 151–159, https://doi.org/10.1093/forestry/cpn002.Search in Google Scholar
van Duong, D. and Matsumura, J. (2018). Transverse shrinkage variations within tree stems of Melia azedarach planted in Northern Vietnam. J. Wood Sci. 64: 720–729, https://doi.org/10.1007/s10086-018-1756-2.Search in Google Scholar
Wang, X., Ross, R.J., McClellan, M., Barbour, R., Erickson, R., Forsman, R., and McGinnis, G. (2001). Nondestructive evaluation of standing trees with a stress wave method. Wood Fiber Sci. 33: 522–533.Search in Google Scholar
Wilcox, W.W. (1978). Review of literature on the effects of early stages of decay on wood strength. Wood Fiber Sci. 9: 252–257.Search in Google Scholar
Winandy, J., Clausen, C.C., and Curling, S. (Eds.) (2001). Proceedings of the 2nd annual conference on durability and disaster mitigation in wood-frame housing, November 6–8, 2000: predicting the effects of decay on wood properties and modeling residual service-life. Forest Product Society, Madison.Search in Google Scholar
Winandy, J. and Rowell, R.M. (2005). The chemistry of wood strength. In: Handbook of wood chemistry and wood composites. CRC Press, Boca Raton, Florida, USA, pp. 211–255.10.1021/ba-1984-0207.ch005Search in Google Scholar
Witomski, P., Olek, W., and Bonarski, J.T. (2016). Changes in strength of Scots pine wood (Pinus silvestris L.) decayed by brown rot (Coniophora puteana) and white rot (Trametes versicolor). Construct. Build. Mater. 102: 162–166, https://doi.org/10.1016/j.conbuildmat.2015.10.109.Search in Google Scholar
Xu, H., Di, Y., Cappellazzi, J., and Morrell, J.J. (2019). Effect of brown rot degradation on mass loss and compressive strength of Chinese poplar (Populus Simonii). Maderas Cienc. Tecnol. 21: 341–346, https://doi.org/10.4067/S0718-221X2019005000306.Search in Google Scholar
Yang, Z., Jiang, Z., Hse, C.Y., and Liu, R. (2017). Assessing the impact of wood decay fungi on the modulus of elasticity of slash pine (Pinus elliottii) by stress wave non-destructive testing. Int. Biodeterior. Biodegrad. 117: 123–127, https://doi.org/10.1016/j.ibiod.2016.12.003.Search in Google Scholar
Zabel, R.A. and Morrell, J.J. (1992). Wood microbiology – decay and prevention, 2nd ed. Elsevier Academic Press, London, UK.Search in Google Scholar
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