The need to preserve mature trees colonised by wood decay fungi is increasing with the rising awareness of the value of trees in the urban environment (Sterken
2005). In natural woodlands, wood decay fungi rarely affect the mechanical behaviour of standing trees (Luchi et al.
2016; Boddy
2021). Nevertheless, trees outside the forest ecosystem are more often exposed to various negative abiotic and biotic factors (Ordóñez-Barona et al.
2018), and the balanced relationship between wood decay fungi and host trees is altered by stressful anthropogenic conditions (Deflorio
2006). Therefore, fungi colonisation can irreversibly compromise the host tree’s stability through the degradation of external intact sound-wood layers, causing the collapse of the tree’s structure (Schwarze et al.
2004). In many cases, wood decay is not detectable by visual assessment, and the use of advanced risk assessment methods is necessary (Koeser et al.
2017). These methods include acoustic tomography (AT), which detects defects in a cross-section of the assessed stem, using the velocity of stress-wave propagation, and creates a spatial (2D/3D) estimation of the defect (Turpening et al.
1999; Liang et al.
2008; Wang
2013). Stress-wave propagation velocity is higher in sound wood than in degraded wood (Divos and Szalai
2002). In addition to the matrix of measured velocities among all sensors, the main result comprises an image reconstruction obtained from the interpolation of the measured velocities (Feng et al.
2014; Du et al.
2018), which shows the theoretical integrity of the wood on a cross-section at the chosen height on the stem (Maurer et al.
2006). Because of its low cost, portability and low invasiveness, this technique attracts the attention of many professionals in the arboriculture sector (Du et al.,
2018). By comparing visual assessments of decay in cross-sections with tomography results, Gilbert and Smiley (
2004) proved that the average accuracy of degraded wood detection was approximately 89%. Ostrovský et al. (
2017) assessed the accuracy and reliability of the acoustic tomography technique for detecting internal structural defects and discovered that irregularity of the cross-section shape does not affect the final accuracy of the tomographic assessment. With a reliable representation of cavities and degraded wood, and a high correlation between the dynamic parameters and the static mechanical properties (Chauhan and Sethy
2016), AT can predict the loss of the load-bearing capacity of trees with internal defects (Burcham et al.
2019). However, due to a wide range of factors that can influence stress-wave propagation velocities, such as the natural heterogeneity in standing trees (Palma et al.
2018), moisture content (Divos and Divos
2005; Montero et al.
2015; Kumar et al.
2016) and wood density (de Oliveira and Sales
2006; Baar et al.
2016), results of AT can be altered (Socco et al.
2004). AT outputs can also vary according to the used device (Cristini et al.
2021). Regarding the presence of wood-decaying fungi on standing trees (Schmidt
2006; Guglielmo et al.
2012; Zhou
2014), one crucial factor to consider is wood degradation caused by fungal enzymatic activity (Schwarze et al.
1995). Fungal degradation of wood is generally described by a loss of mass (Witomski et al.
2016), where most mechanical properties are influenced by wood density, including green wood (Niklas and Spatz
2010). Nevertheless, there can be a decrease in strength even without an observable loss of mass (Brischke et al.
2008). For example, with a small weight loss (up to 5%), a sharp decrease in strength (35–50%) can occur (Wilcox
1978). Curling et al. (
2002) showed that the ratio of strength to weight loss was 4:1 on average. Humar et al. (
2008) reported changes in the modulus of elasticity of Norway spruce (
Picea abies) and Scots pine (
Pinus sylvestris). These changes are affected by wood decay fungi causing brown rot and blue stain, where the colonised wood showed a slight increase in the modulus of elasticity. Yang et al. (
2017) tested the static bending and stress wave propagation properties of Elliot pine (
Pinus elliotii) wood samples artificially inoculated with white and brown rot fungi, which showed a significant correlation between the static and dynamic bending moduli of elasticity (
MOE and
MOED). Bader et al. (
2012) investigated changes in the longitudinal elastic moduli and stiffness data for all anatomical directions of Scots pine (
P. sylvestris) sapwood that was degraded by
Gloeophyllum trabeum and
Trametes versicolor for up to 28 weeks. Schwarze et al. (
1995) investigated the acoustic and mechanical properties of artificially inoculated samples with various wood-decaying fungi, which showed that the classic relationship between density, modulus of elasticity and sound propagation in sound wood does not apply to degraded wood. Deflorio et al. (
2008) investigated changes in the acoustic properties of wooden bodies after 2, 16 and 27 months of exposure to various wood decay fungi occurring on living trees. In some cases, involving more advanced stages of decomposition, an increase in the speed of sound propagation in wood was detected. Nevertheless, the mechanical properties of degraded wood in standing trees, in relation to results obtained from non-destructive testing, still need further investigation. This study presents a description of the relationship between the dynamic and static mechanical parameters of the green intact and degraded wood of standing beech trees in relation to AT results taking into consideration the spatial distribution of fungal colonisation.