Restorative selective cutting maintains tree-related
microhabitats in mixed oak-dominated forests

M. Trinidad Torres-García, Magnus Löf, Laurent Larrieu, Julia Schmucker, Björn Nordén, Adam Felton

► To cite this version:

M. Trinidad Torres-García, Magnus Löf, Laurent Larrieu, Julia Schmucker, Björn Nordén, et al. Restorative selective cutting maintains tree-related microhabitats in mixed oak-dominated forests. Forest Ecology and Management, 2026, 608, pp.123598. 10.1016/j.foreco.2026.123598. hal-05534009

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# Restorative selective cutting maintains tree-related microhabitats in mixed oak-dominated forests

M. Trinidad Torres-García<sup>a,b,*</sup>, Magnus Löf<sup>a,●</sup>, Laurent Larrieu<sup>c,d</sup>, Julia Schmucker<sup>a,●</sup>, Björn Nordén<sup>e,●</sup>, Adam Felton<sup>a,●</sup>

## ARTICLE INFO

Keywords:

Biodiversity conservation

Hemiboreal region

Habitat tree

Partial cutting

TreM

## ABSTRACT

Mixed-species forests in southern Fennoscandia have been subject to deforestation and degradation by land-use changes and intensive forest management. Given the importance of mixed forests for biodiversity and climate change adaptation, it is crucial to conserve remaining patches and manage them in ways that sustain or restore their associated habitats for biodiversity. We evaluated whether restorative selective cutting contributes to forest diversity by fostering tree-related microhabitats (TreMs). We inventoried TreMs on 2346 trees from 42 mixed oak-dominated forests in Sweden and Norway and compared the composition and diversity of TreMs at the plot and tree scale in selective cutting stands (25 % basal area removed 7 or 21 years ago, cutting conifers preferably and opening the space around large deciduous trees) and control stands (no recent intervention). At the tree scale, higher TreM diversity was observed on trees from selective cutting plots, but only in stands cut 7 years ago. Moreover, selective cutting had a positive effect on the epiphyte group that includes bryophytes and lichens. At the plot scale, however, there was no difference in TreM diversity between treatments. At both scales, diameter at breast height was the prevalent factor that positively affected TreM diversity, together with tree species and living status: conifers had a negative effect on TreMs, whereas higher proportion of standing dead trees explained higher TreM diversity. Our results indicate that selective cutting with clear ecological-restoration goals is a favourable approach to maintaining TreMs in mixed forests of hemiboreal regions, although its long-term implications need further research.

## 1. Introduction

Production forests play a key role in providing society with an array of ecosystem services like timber and non-timber products (e.g., berries and mushrooms), climate and water regulation, and environments for recreation (Brockerhoff et al., 2017). However, intensive forest management for wood production has transformed many ecologically diverse forests into compositionally and structurally homogeneous even-aged stands (Messier et al., 2022). The promotion of monospecific conifer plantations, short rotations, and intensive and repeated interventions are commonly applied management practices that standardize forest ecosystems and their disturbance regimes (Aszalós et al., 2022; Felton et al., 2024). This form of intensive management can limit

the contribution of these forests to biodiversity conservation and the breadth of ecosystem services provided (Messier et al., 2022), as well as potentially reducing their general resilience to disturbances (Felton et al., 2024).

Mixed-species forest are proposed as one production forest management alternative that instead helps to ensure the provision of an array of ecosystem goods and services while concurrently promoting forest diversity and enhancing resilience to disturbance (Gamfeldt et al., 2013; Messier et al., 2022). Despite this capacity, in many regions of the world, and especially in Europe, mixed-species forests have been severely degraded by anthropogenic activities (Venter et al., 2016). For example, in Fennoscandia the area of mixed-species forests dominated by broadleaves has been dramatically reduced, with remaining forest

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<footnote><sup>a</sup> Southern Swedish Forest Research Centre, Swedish University of Agricultural Sciences (SLU), PO Box 190, Lomma 234 22, Sweden</footnote>

<footnote><sup>b</sup> Andalusian Center for Global Change – Hermelindo Castro (ENGLOBA), Almería, Spain</footnote>

<footnote><sup>c</sup> Université de Toulouse, INRAE, UMR DYNAFOR, Castanet-Tolosan, France</footnote>

<footnote><sup>d</sup> CNPF-CRPF Occitanie, Auzeville Tolosane, France</footnote>

<footnote><sup>e</sup> Norwegian Institute for Nature Research, Oslo, Norway</footnote>

<footnote>* Corresponding author at: Southern Swedish Forest Research Centre, Swedish University of Agricultural Sciences (SLU), PO Box 190, Lomma 234 22, Sweden. E-mail address: maria.trinidad.torres.garcia@slu.se (M.T. Torres-García).</footnote>
patches often further degraded by human disturbance (Lindbladh et al., 2014, 2007). As mixed forests still have the highest ratio of red-listed species for any forest category found in this region (Artdatabanken, 2020; Henriksen et al., 2015), particularly those dominated by temperate broadleaf tree species, pathways for sustainably managing remaining areas of mixed forest are actively considered and sought after (Götmark, 2013).

The diversity of habitats provided by mixed forests mostly consisting of different broadleaved species (hereafter mixed forests) can be further enhanced or complemented by increasing the structural complexity of these stands. One pathway for doing so is selective cutting, which by harvesting single trees over the whole stand area, can maintain an uneven forest structure with trees of different ages and sizes. However, the use of selective cutting can be driven by different priorities and produce different forest outcomes. If used to improve habitat availability and restore ecosystem functions, selective cutting can be implemented to promote compositional, structural, and functional diversity (i.e., restorative selective cutting) (Götmark, 2013; Stanturf et al., 2014). This can be achieved by, for example, promoting the regeneration of native and light-demanding tree species like oaks (Götmark, 2007; Liepina et al., 2024; Pohl et al., 2025), retaining old trees with high ecological values (Götmark, 2013; Mölder et al., 2019), and promoting forest heterogeneity (Koivula et al., 2025). In contrast, other types of selective cutting (e.g., production-oriented cutting) may be focused primarily on promoting economically desired tree species by e.g. reducing competition. Restorative selective cutting is being increasingly applied in places like Sweden (Götmark, 2013), and there are now corresponding opportunities to test its effect on regional forest biodiversity (e.g. Nordén et al., 2021). One means of assessing this effect is through the systematic evaluation of tree-related microhabitats (TreMs), a framework of indicators used to evaluate the potential of individual trees and forests to host taxonomic diversity (Asbeck et al., 2021; Paillet et al., 2018).

TreMs are structures like cavities, dead branches, or excrescences, that develop on living or standing dead trees, which are regularly found to provide habitat resources to an array of species or communities that belong to a wide range of taxonomic groups (Bütler et al., 2013; Larrieu et al., 2018). Several studies have investigated the relationship between the presence or diversity of TreMs and the corresponding presence or diversity of forest-dwelling taxa (e.g. Bouget et al., 2014; Wesolowski, 2007). A recent review of the field finds that for the most widely studied forest taxa (birds, saproxylic beetles, and bats), their occurrence in forest plots is often associated with the presence of specific categories of TreM (Dutta et al., 2025), with more research needed to better quantify the prevalence and strength of such associations for additional forest taxa and specific TreM categories within different forest settings (Asbeck et al., 2021; Dutta et al., 2025). In parallel to these efforts, multiple studies are establishing the relationship between specific tree characteristics and the abundance and diversity of TreMs. For example, broadleaf trees, and often particular species of broadleaf, usually bear a higher number of TreMs than conifer species (Asbeck et al., 2019; Courbaud et al., 2022; Larrieu and Cabanettes, 2012; Vuidot et al., 2011). Similarly, living status (i.e., living or standing dead) (Larrieu and Cabanettes, 2012; Paillet et al., 2019; Vuidot et al., 2011), tree size (Großmann et al., 2018; Vuidot et al., 2011), and tree age (Kozák et al., 2023; Torres-García et al., 2024) are factors that often explain the occurrence and diversity of TreMs.

There is increasing evidence that the occurrence and diversity of TreMs is also determined by stand characteristics such as the surrounding basal area (Torres-García et al., 2024), tree spatial distribution (Larrieu et al., 2025), tree-species composition (Acloque et al., 2023) and forest management history (Asbeck et al., 2019; Larrieu et al., 2012; Paillet et al., 2017). For example, production forest management that is strictly oriented towards intensive timber production likely reduces TreM density, recruitment, and functionality because the trees are harvested before they reach the minimum size or age for TreM development to take place (Martin and Raymond, 2019), and because trees bearing

TreMs may be actively removed due to their lower wood quality or safety concerns (Lindenmayer and Laurance, 2017). In contrast, integrative forest management (Kraus and Krumm, 2013) preserves habitat trees (i.e., large, old, and standing dead or living trees that bear TreMs) to promote forest diversity (Bütler et al., 2013). What remains unclear is the extent to which alternative types of silvicultural intervention can be used to enhance the abundance and diversity of different categories of TreMs.

It is within this context that we investigated the extent to which restorative selective cutting affects the provision and diversity of TreMs in mixed forests. We hypothesized that applying this approach can support, or even enhance, the occurrence and diversity of TreMs. This is because 1) some tree species that are promoted by selective cutting may be more likely to develop TreMs (e.g., oaks, Vuidot et al., 2011), and 2) the formation of some TreM groups is related to disturbances (e.g. exposed sapwood is likely to occur during harvest) (Larrieu et al., 2022). However, the prevalence of different groups of TreMs can be expected to vary with the type and intensity of silvicultural disturbance regimes, resulting in likely complex trade-offs and synergies in the promotion of different communities of TreMs with different specific forms of forest management. Specifically, we focused on mixed oak-dominated forests located along a European transitional biogeographical gradient from the northern temperate to the boreal. Given that the majority of TreM studies are concentrated in temperate forests (Martin et al., 2022), with few conducted in boreal and hemiboreal forests (but see Bütler et al., 2025; Körkjas et al., 2021; Martin et al., 2021; Torres-García et al., 2024), we were also interested in disentangling how specific drivers affect TreM occurrence and diversity at the plot and individual tree scale in this hemiboreal context. Our final aim was to inform future management regimes intended to enhance the habitat provision of mixed forests.

## 2. Methodology

### 2.1. Study forest sites

Our study was conducted in 42 mixed oak-dominated forest sites located in the hemiboreal climatic region of southern Sweden and Norway (Fig. 1); a transition zone between boreal and temperate regions (Ahti et al., 1968). The sites are situated 5–230 m above sea level and were natural regenerated on abandoned pasture woodlands or agricultural fields between 50 and 180 years ago (Nordén et al., 2004). The forest composition consists of a mixture of broadleaf trees, particularly pedunculate and sessile oak (*Quercus robur* L. and *Q. petraea* (Matt.) Liebl.), ash (*Fraxinus excelsior* L.), birch (*Betula pendula* Roth. and *B. pubescens* Ehrh.), and aspen (*Populus tremula* L.) and the conifer species Norway spruce (*Picea abies* (L.) H. Karst) and Scots pine (*Pinus sylvestris* L.) (see Supplementary Material Table S1 for details on species composition). At each site, we compared two 1-ha stands: one selective cutting stand (treatment) where about 25 % of the basal area (BA) was removed, and one control stand with no recent interventions during the last 50 years (Supplementary material Fig. S1). The mean BA before cutting was 27.5 m²/ha. The main aim of the selective cutting was habitat restoration to create more open and diverse forests, and not optimisation of wood production (although the harvested wood was used for biomass production). Therefore, most broadleaf trees from all size classes were maintained, whereas conifers, birch trees, and woody shrubs were removed, especially around large oaks to promote regeneration. For the selective cutting treatment, 21 of the stands were cut during winter 2002/2003, meaning that the selective cutting stands were cut 21 years before this study (Fig. 1). For the other 21 stands, management occurred during the winter of 2016/2017, thus the selective cutting occurred 7 years before TreM inventory (see Nordén et al. 2004; Nordén et al. 2019 respectively for further details).
<figure><img src="image_3.png" /><figcaption>Fig. 1. Location of the 42 mixed forest sites in the hemiboreal region of southern Sweden and Norway. Orange circles refer to the sites where selective cutting in the treatment stands occurred during winter 2002/2003 (21 years before TreM inventory) and turquoise diamonds correspond to the sites where it took place during winter 2016/2017 (7 years before TreM inventory). Numbers indicate site ID (see Supplementary Material Table S1 for further details).</figcaption></figure>

**Table 1**

Main characteristics of the living and standing dead trees inventoried in the control and selective cutting stands. The number of trees per species and mean diameter at breast height (DBH) ± standard deviation, and range [minimum – maximum DBH] is provided, as well as the number of stands of each treatment. The total number (and share) of trees with TreMs is also shown.

<table><thead><tr><th rowspan="2"></th><th colspan="3">Control (N = 21)</th><th colspan="3">Selective cutting (N = 21)</th><th colspan="4">Total</th></tr><tr><th>N</th><th>DBH ± SD (cm)</th><th>DBH Min - Max (cm)</th><th>N</th><th>DBH ± SD (cm)</th><th>DBH Min - Max (cm)</th><th>N</th><th>With TreMs</th><th>DBH ± SD (cm)</th><th>DBH ± SD (cm)</th></tr></thead><tbody><tr><td><strong>Living trees</strong></td><td><strong>1170</strong></td><td><strong>42.3 ± 21.0</strong></td><td></td><td><strong>1045</strong></td><td><strong>43.3 ± 22.0</strong></td><td></td><td><strong>2212</strong></td><td></td><td></td><td></td></tr><tr><td>Acer platanoides</td><td>27</td><td>30.9 ± 12.8</td><td>[10.1 – 59.2]</td><td>15</td><td>30.1 ± 12.1</td><td>[13.7 – 54.0]</td><td>42</td><td>31 (73.8 %)</td><td>33.6 ± 26.7</td><td>33.6 ± 26.7</td></tr><tr><td>Alnus glutinosa</td><td>13</td><td>26.3 ± 8.6</td><td>[17.1 – 44.2]</td><td>19</td><td>33.8 ± 8.1</td><td>[22.2 – 51.0]</td><td>32</td><td>31 (96.9 %)</td><td>30.8 ± 9.0</td><td>30.8 ± 9.0</td></tr><tr><td>Betula spp.</td><td>109</td><td>36.7 ± 13.2</td><td>[12.4 – 78.0]</td><td>102</td><td>40.3 ± 14.2</td><td>[15.0 – 81.2]</td><td>211</td><td>193 (91.5 %)</td><td>38.4 ± 13.8</td><td>38.4 ± 13.8</td></tr><tr><td>Fagus sylvatica</td><td>28</td><td>35.6 ± 14.4</td><td>[12.8 – 59.8]</td><td>25</td><td>45.3 ± 14.0</td><td>[16.1 – 75.4]</td><td>51</td><td>46 (90.2 %)</td><td>40.0 ± 15.9</td><td>40.0 ± 15.9</td></tr><tr><td>Fraxinus excelsior</td><td>21</td><td>38.3 ± 17.6</td><td>[15.0 – 83.8]</td><td>34</td><td>32.0 ± 10.3</td><td>[14.3 – 53.1]</td><td>55</td><td>54 (98.2 %)</td><td>34.4 ± 13.7</td><td>34.4 ± 13.7</td></tr><tr><td>Picea abies</td><td>160</td><td>38.9 ± 15.8</td><td>[10.2 – 92.3]</td><td>19</td><td>38.1 ± 13.4</td><td>[17.4 – 64.7]</td><td>178</td><td>140 (78.7 %)</td><td>38.8 ± 15.6</td><td>38.8 ± 15.6</td></tr><tr><td>Pinus sylvestris</td><td>71</td><td>45.7 ± 14.5</td><td>[21.1 – 99.2]</td><td>22</td><td>52.5 ± 9.1</td><td>[38.8 – 73.9]</td><td>93</td><td>85 (91.4 %)</td><td>47.3 ± 13.7</td><td>47.3 ± 13.7</td></tr><tr><td>Populus tremula</td><td>115</td><td>35.0 ± 12.5</td><td>[11.1 – 69.7]</td><td>143</td><td>37.9 ± 11.7</td><td>[10.5 – 85.3]</td><td>258</td><td>239 (92.7 %)</td><td>36.6 ± 12.1</td><td>36.6 ± 12.1</td></tr><tr><td>Quercus spp.</td><td>522</td><td>48.2 ± 20.2</td><td>[12.8 – 145.8]</td><td>550</td><td>48.1 ± 19.4</td><td>[10.8 – 213.3]</td><td>1072</td><td>1035 (96.5 %)</td><td></td><td>48.1 ± 19.8</td></tr><tr><td>Sorbus aucuparia</td><td>12</td><td>32.4 ± 20.8</td><td>[12.0 – 90.0]</td><td>19</td><td>15.7 ± 5.3</td><td>[10.0 – 28.6]</td><td>31</td><td>30 (96.8 %)</td><td></td><td>22.2 ± 15.6</td></tr><tr><td>Tilia cordata</td><td>55</td><td>41.9 ± 33.2</td><td>[15.0 – 241.0]</td><td>53</td><td>36.2 ± 14.1</td><td>[12.5 – 78.3]</td><td>108</td><td>90 (83.3 %)</td><td></td><td>39.1 ± 25.7</td></tr><tr><td>Other broadleaf spp.<sup>a</sup></td><td>37</td><td>20.0 ± 9.1</td><td>[10.0 – 46.7]</td><td>44</td><td>29.6 ± 12.4</td><td>[10.0 – 68.6]</td><td>81</td><td>74 (91.4 %)</td><td></td><td>25.2 ± 12.0</td></tr><tr><td><strong>Standing dead trees</strong></td><td><strong>57</strong></td><td><strong>48.1 ± 59.4</strong></td><td></td><td><strong>74</strong></td><td><strong>35.4 ± 13.3</strong></td><td></td><td><strong>131</strong></td><td></td><td></td><td></td></tr><tr><td>Quercus spp.</td><td>29</td><td>47.3 ± 18.7</td><td>[15.8 – 95.4]</td><td>42</td><td>39.5 ± 13.1</td><td>[12.2 – 78.9]</td><td>71</td><td>71 (100 %)</td><td></td><td>42.7 ± 16.0</td></tr><tr><td>Other broadleaf spp.<sup>b</sup></td><td>18</td><td>29.3 ± 13.1</td><td>[10.0 – 70.0]</td><td>26</td><td>29.2 ± 10.1</td><td>[12.7 – 59.1]</td><td>44</td><td>44 (100 %)</td><td></td><td>29.2 ± 11.3</td></tr><tr><td>Conifer spp.<sup>c</sup></td><td>10</td><td>42.0 ± 17.3</td><td>[18.1 – 68.0]</td><td>6</td><td>34.6 ± 17.9</td><td>[12.4 – 58.4]</td><td>16</td><td>16 (100 %)</td><td></td><td>39.2 ± 17.3</td></tr></tbody></table>

<footnote>a Alnus incana, Corylus avellana, Malus sylvestris, Salix caprea, Ulmus glabra</footnote>

<footnote>b non-identified broadleaf trees</footnote>

<footnote>c Picea abies and Pinus sylvestris</footnote>
## 2.2. TreM inventory

TreM data collection was performed by one trained expert (the first author) between November 2023 and May 2024, in the leafless and snow free season. One circular plot of variable size (hereafter, relascope plot) was randomly placed in each 1-ha stand (Supplementary Material Fig. S1) applying an angle-count sampling with a dendrometer and counting factor 1. This method, also known as Bitterlich approach (Bitterlich, 1984), commonly used for BA estimation, is also applied in many TreM studies (e.g., Bouget et al., 2023; Larrieu and Cabanettes, 2012; Larrieu et al., 2014; Mamadashvili et al., 2023) with complex forest structure (i.e., variety of tree sizes and density). By using relascope plots, trees are included with a probability that is proportional to their size, thus allowing a high sampling rate of larger trees with higher probability of hosting TreMs (e.g., Larrieu et al., 2014; Winter and Möller, 2008), while also allowing smaller or suppressed trees to be sampled. All living and standing dead trees within each relascope plot with a diameter at breast height (DBH) higher than 10 cm were included and observed individually for TreMs. If a minimum of 20 trees per relascope plot was not reached, another random plot was established, not overlapping the first one, in order to balance the number of trees observed in each treatment (control vs. selective cutting) (Pommerening and Sterba, 2024). The trunks of the trees were carefully examined from the ground to the crown to detect TreMs, using binoculars for the upper parts of the canopy. TreM types were recorded as presence/absence following the classification described by Larrieu et al. (2018) which includes 47 TreM types belonging to 15 groups with shared morphological characteristics and taxonomic assemblages (See Supplementary Material Table S2 for further details). In total, 2346 trees were inventoried (Table 1, Supplementary material Table S1). We estimated the diversity of TreMs at plot and tree scale as the number of different TreM types per plot or tree. This is a relevant metric since each TreM type gets its own potential taxonomic assemblage (i.e., each typology is based both on the TreM morphology and its associated community). To detect if selective cutting affects the occurrence of some TreM groups we also considered the number of TreM types per tree within each group. We also collected tree species identity, living status (live or standing dead), and DBH for each tree (Table 1). Some tree species only occurred in limited numbers during surveys and were grouped as “other broadleaf species” (Table 1). These tree species were grey alder (Alnus incana (L.) Moench), common hazel (Corylus avellana L.), European wild apple (Malus sylvestris (L.) Mill.), goat willow (Salix caprea L.), Wych elm (Ulmus glabra Huds.), and non-identified standing dead trees (please see Supplementary Material Table S1 for further details).

## 2.3. Stand characterization

Three 10-meter-radii circular plots were established in each stand (treatment and control) for periodically stand density measurements (tree-inventory plots, Supplementary Material Figure S1) as part of other study. All trees with a minimum DBH of 5 cm were measured, from which we calculated basal area (BA, Eq. 1) and the stand density index (SDI, Eq. 2), as measurements of stand openness and tree density (Reineke, 1933); and the Gini coefficient of heights (GCH) and diameters (GCD) (Eq. 3), as estimates of structural diversity (Kramer, 1988; Pretzsch and Hilmers, 2024).

$$BA = \frac{\sum_{i=1}^{N} ba_i}{A} \qquad (1)$$

$$SDI = N \times \left(\frac{25}{d_q}\right)^{-1.605} \qquad (2)$$

$$GC = \frac{\sum_{i=1}^{n} \sum_{j=1}^{n} |x_i - x_j|}{2n(n-1) \times \bar{x}} \qquad (3)$$

In Eq. 1, $ba_i$ refers to the basal area of all individual trees (calculated as the area of a circle with the diameter DBH, m²) for one hectare; A is the area of the plot in hectares (ha). The SDI (Eq. 2) describes the expected number of trees in a stand with an index diameter of $dq = 25$ cm, thus N refers to the tree number per hectare and $dq$ the quadratic mean diameter (i.e., DBH² / N) (Reineke, 1933). Although originally developed for even-aged stands, the SDI has been successfully used for uneven-aged stands (Woodall et al., 2003) and in TreM studies (Winter et al., 2015). In Eq. 3, $x_i$ and $x_j$ are the size for the i'th and j'th tree in the stand, n refers to the number of trees, and the $\bar{x}$ is the mean value of the measured characteristic (i.e., height or diameter) across all trees. The Gini coefficients quantifies if a tree size distribution, either height (GCH) or diameter (GCD) is homogenous (GC = 0), uneven (GC = 1), or in between (GC = 0.5). Here, we tested both density and structural metrics to find out whether and which one best explains the occurrence and diversity of TreMs in our study stands.

## 2.4. Statistical analyses

All statistical analyses were carried out with the R software environment (RStudio version 2024.12.1). First, to identify overall differences in TreM diversity between control and selective cutting, we ran a non-parametric Kruskal-Wallis test with the function `kruskal.test` from the `stats` package and the Dunn post-hoc test (function `dunn.test`). We tested differences by treatment (control vs. selective cutting) and time since intervention (7 years and 21 years) to detect a treatment or time effect, or possible biases related to the sites.

Second, to further investigate differences between treatments and analyse the specific drivers that contribute to explaining TreM diversity at the plot and the individual tree scale, we used generalised linear models (GLMMs). We fitted one general model and one specific model per treatment (control vs. selective cutting) using the function `glmmTMB` from the same name package. As potential predictors at the plot scale we tested mean DBH of the trees in the plot, number of tree species, proportion of the most abundant species (i.e., oaks, aspen, birch) and proportion of tree groups (i.e., conifers, broadleaves, standing dead trees, and living trees), as well as stand characteristics (BA, SDI, GCD, and GCH) and treatment (control vs. selective cutting, only in the general model). Since the plots were managed at different times (7 or 21 years before this study), we initially included ‘time since intervention’ as a random factor. However, as ‘time since intervention’ did not contribute to the variance, it was removed from the final model.

At the tree scale, we analysed the relationships between TreM diversity and the predictor variables DBH, species identity, living status, treatment (in the general model), BA, SDI, GCD, and GCH. To avoid overparameterization, particularly in the specific models, due to the high number of tree species recorded we maintained the most abundant tree species (i.e. Quercus spp., Populus tremula, Betula spp., Picea abies, Pinus silvestris, Tilia cordata) and the rest were pulled in the category of “other broadleaf species”. We also focused on Quercus spp, the most dominant tree species present in all the sites (Supplementary material, Table S1), to analyse TreM diversity for this subset of trees and detect underlying treatment effects that could otherwise be overlooked. To analyse the occurrence of TreM types within groups we focused on those groups that were more frequently found in the plots (i.e., more than 70 observations per treatment, see Supplementary Material Table S2). These were rot holes, insect galleries, exposed sapwood, concavities, dead branches and epiphytes. We used the same predictors and random factors as for the diversity analysis. Site identity nested within ‘time
since intervention' was initially included as a random factor (1|Time/Site), both in the general and the specific models. In the case that 'time since intervention' did not contribute to the variance, site alone was included as a random factor.

Prior to model set-up, we assessed continuous predictor variables and checked for possible collinearities. We performed a Spearman correlation analysis using the *corrplot* function from the same name package. Since BA and SDI were highly correlated, as well as GCH and GCD (see Supplementary Material Table S3), we ran duplicated full models. We fitted the models with negative binomial distribution (nbinom2, log link function) or, in case of under-dispersion issues, with a Conway-Maxwell Poisson distribution (compois, log link function). To analyse insect galleries, the model was fitted with a binomial distribution (logit link function) since only one TreM type belongs to this group. Interactions were avoided in the models, both at the tree and plot scales, to prevent over-parameterization and since they are shown to have lower explanatory power in TreM studies compared to single predictors (Asbeck et al., 2019; Paillet et al., 2019).

To find the best models among all possible combinations of predictors, we used the function *dredge* (MuMln package) that ranks the candidate models based on their lower Akaike Information Criterion (AIC). We tested the selected models for over-dispersion and zero-inflation with the *DHARMa* package and evaluated their performance by analysing the distribution of the residuals. Multi-collinearity was also analysed with a variance inflation analysis using the *vif* function of the *car* package. Variables with a VIF< 2 were accepted (Zuur et al., 2010). The overall goodness of fit of the models was assessed by calculating the coefficients of determination with the function *r2* from the *performance* package.

Finally, we carried out a constrained-ordination redundancy analysis (RDA) to assess the effects of treatment, habitat tree characteristic (DBH and species), and stand characteristics (BA or SDI and GCD or GCH) on the occurrence (presence or absence) of TreM types at the tree level. We

gathered tree species and living status in one variable because of the reduced number of standing dead trees observed compared with living ones (Table 1). We used the function *rda* from the *vegan* package to perform the full models and the *ordistep* function from the same package for a backward selection of the best model according to the lowest AIC value – after 9999 permutations.

RDA selected model: TreM types ~ Treatment + DBH + Tree sp + Time since intervention + GCD

We used the function *anova* (stats package) to compute an analysis of variance of the terms of the selected model (see Supplementary Material Table S5 and S6 for further details of the selected model).

## 3. Results

### 3.1. Differences in TreM diversity between control and selective cutting plots

The analysis of the diversity of TreMs at the plot scale did not reveal significant differences between the control and selective cutting plots (Fig. 2A), which showed on average 14.11 ± 3.06 TreM types. In contrast, when analysing the differences between sites that were managed at different time (7 vs. 21 years ago), we found significant differences (Fig. 2B). Sites managed 21 years ago showed higher diversity of TreM types (15.86 ± 2.82) compared to sites managed 7 years ago (12.36 ± 2.17). However, we did not observe differences between treatments within these sites (Fig. 2C, D).

At the tree scale, we found significant differences in the diversity of TreMs between control and selective cutting (Fig. 2E) as well as between time since intervention (Fig. 2F). The mean diversity was 2.67 ± 1.77 TreM types per tree, which was slightly higher on trees from the selective cutting plots (2.78 ± 1.75 TreM types per tree) compared to the

<figure><img src="image_4.png" /><figcaption>**Fig. 2.** Diversity of TreM types at the plot scale (panels A-D) and at the tree scale (panels E-H). Panels A and E show the general comparison between the control and selective cutting stands, whereas panels B and F refer to the comparison between stands that were managed at different time (7 and 21 years before the study). The specific comparison between treatments within 7- and 21-year-since-intervention stands are also shown. Inset numbers refer to the significant differences (p-value) or non-significant (n.s.) between factors according to the Kruskal-Wallis test.</figcaption></figure>
control plots (2.56 ± 1.78 TreM types per tree). This difference between treatments was also maintained between trees from the 7-year-since-intervention sites (Fig. 2G), but not in the 21-year-since-intervention sites (Fig. 2H). Like at the plot scale, trees in the 21-year-since-intervention stands showed higher diversity of TreMs (2.82 ± 1.92 TreM types per tree) than trees at the 7-year-since-intervention stands (2.51 ± 1.58 TreM types per tree) (Fig. 2F).

## 3.2. Drivers of the diversity of TreMs at the plot and tree scale

The main drivers that contributed to explain the diversity of TreMs at the plot scale in mixed forests of the hemiboreal region were mean DBH, proportion of standing dead trees, and BA (Table 2). When separating between treatments, BA remained relevant for selective cutting plots, but not for control plots. Moreover, it is predicted that the diversity of TreMs increased with mean DBH (Fig. 3), although both the slope and the intercept are quite similar, not revealing differences between treatments (according to the 95 % confidence interval of the prediction).

At the tree scale, status (living or standing dead), tree species identity, and DBH were the factors that contributed to explaining the diversity of TreM types, both in the control and the selective cutting plots (Table 3). Living status contributed the most to explain the diversity of TreMs at the tree scale, showing standing dead trees had a significant positive effect. For species identity, the conifer species *Picea abies* and *Pinus sylvestris* and the broadleaf tree *T. cordata* negatively affected TreM diversity at the tree scale in all models. *Picea abies* specially affected TreM diversity in the control plots. When the species effect was removed and we focused on the most abundant species (*Quercus* spp.), we found similar results (Supplementary Material Table S4). In this case, treatment was included in the model but did not show an effect on TreM diversity.

As for the plot scale, TreM type diversity increased significantly with increasing mean DBH (Fig. 4). When comparing the specific models, diversity was predicted to be slightly higher in trees from the selective cutting plots for a DBH up to approximately 70 cm, according to their 95 % confidence interval. For trees larger than 70 cm, the overlap of their confidence interval shows similar trends for trees in the control and the selective cutting plots. However, these differences are very narrow, and the treatment effect was not included in the selected models that better explained the diversity of TreM types at the tree scale (Table 3). Regarding stands characteristics, BA was found to have a positive effect on the general model, although it was not included in the final specific models.

DBH, together with species identity and status, were also the prevalent factors that explained the occurrence of all TreM groups assessed (Table 4). Our analyses revealed that DBH had a positive effect on all TreM groups. When separating between treatments, we observed that larger trees (higher DBH) are more likely to develop rot holes in the control than in the selective cutting plots (Fig. 5A), although the treatment effect was not included in the selected models that contributed to

explain the occurrence of this TreM group (Table 4). In contrast, epiphytes were predicted to be more abundant on selective cutting trees below 120 cm of DBH (Fig. 5F). This difference between treatments was also detected for epiphytes in the general model (Table 4), indicating that selective cutting positively contributed to their occurrence.

As for diversity, *Picea abies* and *Pinus sylvestris* negatively affected most of the TreM groups, an effect that was particularly pronounced for rot holes and epiphytes (Table 4). However, our models revealed that *P. sylvestris* had a positive effect on the occurrence of crown deadwood in the selective cutting but also in the general model. We also observed that *P. tremula* had a negative effect on epiphytes and concavities, although positively contributed to the occurrence of crown deadwood. *Quercus* spp., which were the predominant trees in our stands, had a positive effect on the occurrence of crown deadwood, epiphytes, exposed sapwood, and insect galleries, and negatively affected rot holes and concavities. Status (refers here to standing dead trees) had a positive effect on the occurrence of most TreM groups, particularly pronounced for insect galleries and exposed sapwood for which it was the strongest effect. For epiphytes, however, standing dead trees showed a negative effect. Regarding stand characteristics, BA only showed a minor negative effect for rot holes and positive effect for exposed sapwood (control model) and crown deadwood (general model).

## 3.3. Drivers of the composition of TreMs at the tree scale

The composition of TreM types at the tree scale was significantly related to the attributes of the habitat tree, namely DBH, species identity and status, as well as the treatment (control vs selective cutting, and 7 years and 21 years since cutting), and the stands characteristics GCD (Fig. 6 and Supplementary material Table S5). These variables explained 18.3 % of the variation in the composition of TreMs (Supplementary material Table S6), with 11.2 % on the two first axes (Fig. 6).

We observed a marked difference between some tree species in their TreM composition, and in particular between living and standing dead trees (Fig. 6A, B). Among living trees, we observed four groups of tree species. First, *Quercus* spp. (bottom right quadrant) showed a different composition of TreM types with a reduced confidence interval (the smallest ellipse). *Quercus* trees were characterised by the presence of rot holes (RH2 and RH3), remnants of broken limbs (CD3), and dead branches (CD1) whose TreMs were positively related to increasing DBH and GCD (Fig. 5B). In a second group, and on the opposite side of the ordination plot, we found *P. abies* (top-left quadrat) (Fig. 6A, B). The assemblage of TreM types of *P. abies* was dominated by the presence of buttress-root concavities (Con4). In a third group (middle, top-left quadrat) we observed the other conifer species, *P. sylvestris*, that showed a composition of TreMs more similar to broadleaf species like *S. aucuparia*, and *Betula* spp. These tree species were also associated with the occurrence of ephemeral fruiting bodies (EF1 and EF2), and stem breakage (ESH1). In the fourth group at the bottom left quadrant, that is only somewhat distinguishable from the third group, we found *F. excelsior*, *F. sylvatica*, *A. glutinosa*, and the other broadleaf species that were characterised by the development of lichens (Epi2). At the top right quadrat and totally separated from the other groups we found standing dead trees, that included *Quercus* spp., other broadleaves, and the conifers *Picea abies* and *Pinus sylvestris*. Their TreM composition was characterised by the presence of insect galleries (IG), and exposed sapwood, particularly bark shelters (ESap3) and pockets (ESap4) (Fig. 6B).

The RDA analysis also revealed slight differences in the composition of TreMs at tree scale between the treatments (Fig. 6C, D). Trees in the control plots and in the plots that were cut 21 years ago showed a similar composition of TreM types (Fig. 6D) which were characterised by the occurrence of trunk-base rot holes (RH1), woodpecker breeding cavities (WpC), and pulpy agaric (EF2). The TreM composition was different in trees where selective cutting occurred 7 years ago, according to the 95 % confidence interval ellipse (Fig. 6D).

<caption>**Table 2**</caption>

Results of the selected models for the diversity of TreM types at plot scale. Scaled estimates, significance of the predictors (***: p-value < 0.001; **: p-value <0.01; *: p-value <0.05) and goodness of the fit (R² for GLM) are shown. DBH: diameter at breast height; BA: basal area.

<table><thead><tr><th>Model</th><th>General</th><th>Selective cutting</th><th>Control</th></tr></thead><tbody><tr><td><strong>Predictors</strong></td><td></td><td></td><td></td></tr><tr><td>(Intercept)</td><td>2.64</td><td>2.62</td><td>2.66</td></tr><tr><td>Mean DBH</td><td>0.09 ***</td><td>0.12 ***</td><td>0.08 **</td></tr><tr><td>Dead trees<sup>1</sup></td><td>0.05 **</td><td>0.06 *</td><td>0.06 *</td></tr><tr><td>BA</td><td>0.06 **</td><td>0.08 **</td><td></td></tr><tr><td>Observations</td><td>84</td><td>42</td><td>42</td></tr><tr><td>R<sup>2</sup></td><td>0.38</td><td>0.51</td><td>0.28</td></tr></tbody></table>

<footnote>a Proportion of standing dead trees</footnote>
<figure><img src="image_5.png" /><figcaption>Fig. 3. Predicted (lines) and observed (circles) diversity of TreM types at the plot scale by mean DBH of the trees in the plot, from the GLM models. Green dashed lines and circles represent selective cutting plots and pink solid line and circles, control plots. Shaded area shows 95 % confidence interval of the prediction.</figcaption></figure>

<caption>**Table 3**</caption>

Results of the selected generalized linear mixed models (GLMM) for the diversity of TreM types at tree scale. Scaled estimates, significance of the predictors (***: p-value < 0.001; **: p-value < 0.01; *: p-value < 0.05) and goodness of the fit (conditional and marginal R²) are shown. DBH: diameter at breast height; Species: species identity; Status: living vs. standing dead trees; SDI: stand density index.

<table><thead><tr><th>Model</th><th>General</th><th>Selective cutting</th><th>Control</th></tr></thead><tbody><tr><td><strong>Predictors</strong></td><td></td><td></td><td></td></tr><tr><td>(Intercept)<sup>a</sup></td><td>0.94***</td><td>0.88***</td><td>1.04***</td></tr><tr><td>DBH</td><td>0.25***</td><td>0.26***</td><td>0.25***</td></tr><tr><td>Species</td><td>***</td><td>***</td><td>***</td></tr><tr><td>Broadleaves<sup>b</sup></td><td>0.01</td><td>0.08</td><td>-0.11</td></tr><tr><td>Picea abies</td><td>-0.66***</td><td>-0.41***</td><td>-0.80***</td></tr><tr><td>Pinus sylvestris</td><td>-0.63***</td><td>-0.54***</td><td>-0.69***</td></tr><tr><td>Populus tremula</td><td>-0.06</td><td>0.01</td><td>-0.22**</td></tr><tr><td>Quercus spp.</td><td>0.03</td><td>0.06</td><td>-0.06</td></tr><tr><td>Tilia cordata</td><td>-0.22**</td><td>-0.24*</td><td>-0.22**</td></tr><tr><td>Status<sup>c</sup></td><td>0.80***</td><td>0.82***</td><td>0.81***</td></tr><tr><td>BA</td><td>0.04**</td><td></td><td></td></tr><tr><td>Observations</td><td>2286</td><td>1089</td><td>1197</td></tr><tr><td>R<sup>2</sup> (cond /marg)</td><td>0.23 / 0.18</td><td>0.20 / 0.15</td><td>0.26 / 0.21</td></tr></tbody></table>

# 4. Discussion

## 4.1. Selective cutting can maintain the provision of TreMs

Our results revealed that restorative selective cutting did not affect the overall occurrence and diversity of TreMs in mixed forests of the hemiboreal region. However, under some circumstances (e.g., in the stands where the intervention occurred 7 years ago) and for some TreM groups (e.g., epiphytes, including bryophytes and lichens), selective cutting can increase the diversity and occurrence of TreMs at the tree scale. These results partially confirm our hypotheses since they seem to be connected to retaining tree species with a high potential to bear TreMs (e.g., broadleaf and standing dead trees). In this regard, our results are in line with Vuidot et al. (2011) who observed in temperate mixed forests that, at the tree level, forest management did not affect TreMs, but tree characteristics did.

Despite not finding clear differences in TreM diversity between control and selective cutting at the plot scale, we nevertheless identified a positive effect of this type of forest management on the occurrence of epiphytes, which includes bryophytes and lichens in our study sites (no other epiphytic structure included in this TreM group was observed). These results agree with numerous studies that found that a low-intensity management like selective cutting can promote epiphytic lichens and bryophytes (Nascimbene et al., 2013; Nordén et al., 2012; Paltto et al., 2008). This is supported by the hypotheses that lichen communities particularly, are shaped by light availability, having its optimum under intermediate light conditions in European temperate forests (Barkman, 1958), and that selective cutting can maintain semi-opened forests while preserving large old trees (Nascimbene et al., 2013). These characteristics are essential for the development of TreMs that grow on the tree trunk (Larrieu et al., 2018). In this regard, our results support that trees in selective cutting stands can provide better conditions for the occurrence of this TreM group than in recently abandoned forest where no selective cutting (or any other type of management) takes place.

At the plot level, however, an extensive body of research have shown higher occurrence of TreMs in long-term unmanaged forests compared to managed stands (Asbeck et al., 2022; Courbaud et al., 2022; Larrieu et al., 2012; Paillet et al., 2017). Our results differ from this literature. Traditional management practices in production forests aim to reduce the number of trees with “defects” as they are less valuable for timber (Martin and Raymond, 2019), whereas the restoration type of selective cutting applied here can be used to promote these habitat-trees. Our results thereby highlight the potential to apply silvicultural practices that maintains the diversity of TreMs at the plot scale both in the short- (7-year-since-intervention plots) and the mid-term (21-year-since-intervention plots).

## 4.2. DBH as the prevalent factor that drives TreM diversity

The positive effect of tree size on the occurrence and diversity of TreMs has been widely explained in temperate forests by the occurrence of larger and often older trees having a higher exposure to abiotic and biotic disturbances (Asbeck et al., 2019; Larrieu and Cabanettes, 2012). In contrast, Bütler et al. (2025) recently showed this is not necessarily the case for boreal forests, where the occurrence and prevalence of TreMs can depend more on local environmental conditions and tree species than on tree size. In the hemiboreal mixed forests studied here,

<footnote>a The intercept represents *Betula spp*.</footnote>

<footnote>b It includes other broadleaf species: *A. platanoides*, *A. glutinosa*, *F. sylvatica*, *F. excelsior*, and *S. aucuparia* as well as *A. incana*, *C. avellana*, *M. sylvestris*, *S. caprea*, and *U. glabra* (See Supplementary Material Table S1 for further details).</footnote>

<footnote>c Status estimate refers here to standing dead trees</footnote>
<figure><img src="image_6.png" /><figcaption>Fig. 4. Predicted (lines) and observed (circles) diversity of TreM types at the tree scale by DBH from the GLMM models. Green dashed lines and circles represent selective cutting and pink solid line and circles, control. Shaded area shows 95 % confidence interval of the prediction.</figcaption></figure>

we observed a pattern more similar to the temperate than to the boreal study developed by Bütler et al. (2025), as increasing DBH was associated with higher TreM diversity at the tree and plot scale. Moreover, at the tree scale, we observed that with the same DBH, trees from the selective cutting plots can have slightly higher TreM diversity than from the control plots; a difference that is observed up to a threshold DBH of approximately 70 cm (Fig. 4). This DBH threshold, which includes small, medium and large trees following Larrieu and Cabanettes (2012) terminology, is at the upper range for most of the living trees found in the selective cutting plots (Table 1). According to Larrieu and Cabanettes (2012), very large trees (DBH > 70 cm), which occurred more often in the control plots, bear far more TreMs than smaller trees. The lower abundance of very large trees in the treatment plots may have thereby contributed to the lack of an observed difference between control and selective cutting plots for trees above 70 cm in DBH (Fig. 4). This raises the question as to whether the promotion of very large trees and trees over 90–100 cm will still have a significant enhancing effect on TreM diversity in the future, as observed by Larrieu and Cabanettes (2012). In either regard, previous research has highlighted the importance of old and very large trees as providers of stable microclimates for threatened and rare species, such as rare lichens and mosses (Zemanová et al., 2017).

Although DBH was found to be the main driver of TreM diversity, the models failed to provide predictors that explained most of the variability in the plots. Additional factors that were not explored in this study, and that were not related to the tree species composition or stand structure, seem to be driving much of the diversity of TreMs at the plot scale. Some studies have pointed out that site-specific environmental conditions, like soil type and fertility, or the disturbance history of the site, can shape the diversity of TreMs (Bütler et al., 2025; Larrieu et al., 2022; Zemlerová et al., 2023). In this sense, further research is needed to unravel the additional mechanisms that drive TreM diversity in the mixed forests of hemiboreal regions.

### 4.3. The role of standing dead trees in providing a different TreM profile in mixed forests

Numerous studies from different contexts have already shown that standing dead trees can host a higher diversity and different profile of TreMs than living trees (Grossmann et al., 2018; Larrieu and Cabanettes, 2012; Paillet et al., 2017; Vuidot et al., 2011). In our study, we observed that all dead trees with DBHs that ranged from 10 to 95.4 cm, bore at

least one TreM (Table 1), indicating the great importance of the wood decay process for TreM occurrence. Moreover, standing dead trees provided a composition of TreMs that differed from living trees, and which is particularly characterised by the development of exposed sapwood (bark pockets and bark shelter). It is the higher occurrence of standing dead trees, combined with larger ones, that contributed to the higher diversity of TreMs at the plot scale (Table 2); especially in the selective cutting plots where dead trees were found to be more abundant (Table 1). These results can be explained by the natural decay process of standing dead trees, combined with tree falls or stem breakage that also contribute to bark loss (Larrieu et al., 2012, 2022).

The presence and proportion of standing dead trees also contributed to increased TreM diversity at the tree and plot scales respectively. The slower decay rates of hardwood species like oaks, the dominant tree species in the study sites, may have promoted standing dead trees and a higher diversity of TreMs. For instance, some TreMs are often related to softened and drier wood, like e.g. woodpecker cavities (Remm and Lohmus, 2011). The presence of dead or decaying trees may increase the probability of cavity formation by avian excavators that would otherwise avoid resinous tree species (Cooke and Hannon, 2012; Remm and Lohmus, 2011), as well as the probability of occurrence of bark shelters and fruiting bodies of saproxylic fungi (Larrieu and Cabanettes, 2012) and myxomycetes. In contrast, bryophytes, as well as foliose and fruticose lichens, generally depend on tree bark and are often difficult to find on snags (Paillet et al., 2019). In summary, these results lend further support to the substantial contribution that large and standing dead trees can make to the diversity of specific TreMs in mixed forests. And since the restoration type of selective cutting applied here maintains high levels of dead wood, it can be expected that it will contribute to maintain high TreM diversity.

### 4.4. The importance of tree species selection

Tree species identity also had a key role in TreM diversity, as observed in numerous studies conducted in both managed and unmanaged forests (Asbeck et al., 2019; Courbaud et al., 2022; Larrieu and Cabanettes, 2012; Vuidot et al., 2011). Our results showed that conifers had a lower average diversity of TreMs at the tree scale, given that at the same DBH conifers generally have fewer TreMs than broadleaves (Larrieu and Cabanettes, 2012; Vuidot et al., 2011). This was particularly evident for *P. abies* in the control plots, indicating that this species might be the least favourable for the diversity of TreM types among the
<table><caption>**Table 4**
Results of the selected models for the occurrence of TreM groups (see Supplementary Material Table S2 for group details). Scaled estimates, significance of the predictors (**; p-value < 0.001; **; p-value < 0.01; *; p-value < 0.05) and goodness of the fit (conditional and marginal R²) are shown. DBH: diameter at breast height; BA: basal area; GCD: Ginni coefficient of diameter; GCH: Ginni coefficient of height.</caption><thead><tr><th rowspan="2">Models</th><th colspan="2">Rot holes</th><th colspan="2">Insect galleries</th><th colspan="2">Concavities</th><th colspan="2">Exposed sapwood</th><th colspan="2">Epiphytes</th><th colspan="2">Crown deadwood</th></tr><tr><th>General</th><th>Selective cutting</th><th>General</th><th>Selective cutting</th><th>General</th><th>Selective cutting</th><th>General</th><th>Selective cutting</th><th>General</th><th>Selective cutting</th><th>General</th><th>Selective cutting</th><th>Control</th></tr></thead><tbody><tr><td><strong>Predictors</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>(Intercept)<sup>a</sup></td><td>-1.91***</td><td>-1.67***</td><td>-2.51***</td><td>-2.75***</td><td>-2.33***</td><td>-0.69***</td><td>-0.81***</td><td>-0.54***</td><td>-1.92***</td><td>-1.95***</td><td>-1.87***</td><td>-0.72***</td><td>-0.57**</td><td>-0.64***</td><td>-0.35***</td><td>-0.57***</td><td>-0.14*</td></tr><tr><td>DBH</td><td>0.51***</td><td>0.46***</td><td>0.52***</td><td>0.79***</td><td>0.84***</td><td>0.24***</td><td>0.31***</td><td>0.20***</td><td>0.28***</td><td>0.28**</td><td>0.29***</td><td>0.07***</td><td>0.10***</td><td>0.05</td><td>0.17***</td><td>0.17***</td><td>0.17**</td></tr><tr><td>Species</td><td>***</td><td>*</td><td>***</td><td>***</td><td>***</td><td>***</td><td>***</td><td>***</td><td>***</td><td>***</td><td>***</td><td>***</td><td>***</td><td>***</td><td>***</td><td>***</td><td>***</td><td>***</td></tr><tr><td>Broadleaves<sup>b</sup></td><td>0.24</td><td>0.00</td><td>0.56</td><td>0.43</td><td>0.33</td><td>0.26</td><td>-0.64***</td><td>-0.64***</td><td>-0.80***</td><td>0.50**</td><td>0.53*</td><td>0.35</td><td>0.28***</td><td>0.27**</td><td>0.19</td><td>-0.05</td><td>0.15</td><td>-0.20*</td></tr><tr><td>P. abies</td><td>-1.85***</td><td>-18.74</td><td>-1.58**</td><td>-1.34**</td><td>-0.89</td><td>-1.65**</td><td>0.02</td><td>-0.08</td><td>0.01</td><td>-0.35</td><td>0.40</td><td>-0.74*</td><td>-1.88***</td><td>-1.76**</td><td>-1.97**</td><td>-0.35***</td><td>0.26</td><td>-0.61***</td></tr><tr><td>P. sylvestris</td><td>-2.81**</td><td>-2.00</td><td>-19.45</td><td>-0.86</td><td>-0.55</td><td>-1.25</td><td>-1.89***</td><td>-1.38*</td><td>-2.19***</td><td>-0.05</td><td>-0.03</td><td>-0.21</td><td>-2.04***</td><td>-1.44**</td><td>-2.31***</td><td>0.30***</td><td>0.40*</td><td>0.17</td></tr><tr><td>P. tremula</td><td>-0.28</td><td>-0.44</td><td>-0.37</td><td>-0.27</td><td>0.19</td><td>-1.22</td><td>-0.77***</td><td>-0.74***</td><td>-1.06***</td><td>0.07</td><td>0.06</td><td>0.14</td><td>-0.6***</td><td>-0.70***</td><td>-0.54**</td><td>0.28***</td><td>0.46***</td><td>0.11</td></tr><tr><td>Quercus spp.</td><td>-0.63**</td><td>-0.72**</td><td>-0.53</td><td>1.21***</td><td>1.18**</td><td>1.02*</td><td>-1.12***</td><td>-1.23***</td><td>-1.30***</td><td>-0.24</td><td>-0.14</td><td>-0.41</td><td>0.22***</td><td>0.23**</td><td>0.15</td><td>0.41***</td><td>0.59***</td><td>0.23***</td></tr><tr><td>T. cordata</td><td>0.31</td><td>-0.09</td><td>0.61</td><td>-0.27</td><td>-0.55</td><td>-0.38</td><td>-1.00***</td><td>-0.50</td><td>-1.30***</td><td>-0.24</td><td>-0.14</td><td>-0.41</td><td>0.22***</td><td>0.23**</td><td>0.16</td><td>-0.43***</td><td>-0.45*</td><td>-0.46**</td></tr><tr><td>Status<sup>c</sup></td><td>0.67**</td><td>0.97**</td><td>5.39***</td><td>5.51***</td><td>5.63***</td><td>-0.26</td><td>-0.41*</td><td>1.74***</td><td>1.79***</td><td>1.73**</td><td>0.12*</td><td>0.6</td><td>-0.63***</td><td>-0.66**</td><td>-0.57*</td><td>0.57***</td><td>0.67***</td><td>0.44***</td></tr><tr><td>BA</td><td>-0.19*</td><td>-0.28*</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>GCH</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Treatment<sup>d</sup></td><td></td><td></td><td>-0.25</td><td></td><td></td><td></td><td></td><td>0.09</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>R<sub>2</sub> (cond / marg)<sup>e</sup></td><td>0.28 / 0.24</td><td>0.79 / 0.77</td><td>0.90 / 0.90</td><td>0.51 / 0.44</td><td>0.51 / 0.42</td><td>0.54 / 0.47</td><td>0.72 / 0.44</td><td>0.79 / 0.28</td><td>0.74 / 0.57</td><td>0.58 / 0.52</td><td>0.61 / 0.51</td><td>0.64 / 0.60</td><td>0.81 / 0.53</td><td>0.76 / 0.31</td><td>0.84 / 0.63</td><td>0.41 / 0.34</td><td>0.40 / 0.35</td><td>0.44 / 0.37</td></tr></tbody></table>

<sup>a</sup> The intercept represents *Betula* spp.

<sup>b</sup> Includes other broadleaf species: A. platanoides, A. glutinosa, F. sylvatica, F. excelsior, and S. aucuparia as well as A. incana, C. avellana, M. sylvestris, S. caprea, and U. glabra (See Supplementary Material Table S1 for individual details).

<sup>c</sup> Status refers here to standing dead trees.

<sup>d</sup> Treatment refers here to selective cutting.

tree species observed in our study sites. *Pinus sylvestris*, in contrast, as a light-demanding species, is prone to bear more TreMs than *Picea abies* when there is competition between trees.

The lower diversity of TreMs on conifers is particularly relevant for specific TreM groups like rot holes that are rarely formed on these tree species (Courbaud et al., 2022), as confirmed by the strong negative effect they show in our hemiboreal context. However, we must be aware of other TreMs like resin flows that are strictly related to conifers and therefore rarely observed in our mixed forests mostly consisting of different broadleaves. Although few species are associated with resin flows, their occurrence provides valuable habitat of direct relevance to the conservation of some taxa (about 20 species of ascomycetes, one species of myxomycete and four species of diptera Cecidomyiidae; Beimforde et al., 2020; Götzsche et al., 2025; Nijveldt, 1987).

We also observed that oaks were associated with the occurrence of several TreM groups. As previously observed in studies of TreMs on retention trees in southern Sweden (Torres-García et al., 2024), oaks were associated with a higher abundance of insect galleries and crown deadwood, two TreM groups that are influenced by tree age and size (Kozák et al., 2023). In fact, oaks are long-lived species of usually large size that can exceed 500 years in hemiboreal regions (Drobyshev and Niklasson, 2010). Their potential long-life span and their importance as TreM-bearing trees (Bouget et al., 2011; Torres-García et al., 2024; Vuidot et al., 2011; Wetherbee et al., 2020), are arguments that contribute to the preservation of *Quercus* spp. in mixed forests.

### 4.5. From theory to practice. How to enhance habitat provision in mixed forests?

Today, climate adaptation measurements often emphasise the need to transition from monospecific stands to mixed forests, and from coniferous to broadleaved trees, in order to increase forest resilience to perturbances like drought or forest pests (Felton et al., 2024; Yao et al., 2025). Moreover, it is known that mixed forests not only contribute to tree species diversity, but also to functional and structural diversity, particularly when conservation-oriented cutting is implemented (Götmark, 2013). Because our experimental design included sites that were managed at different time periods in the past and on a large spatial scale, and because the inherent variability of mixed species forests in terms of structure and composition, this introduced sources of variation that could have complicated the analysis and interpretation of the results. However, we consider that this natural variability has also enriched this study, as it provided an array of forest contexts for which the effect of selective cutting on TreMs can be assessed. In this sense, we have detected that selective cutting can increase the diversity of TreM types at the tree scale over relatively short time periods since the silvicultural intervention.

The potential benefits to the occurrence, prevalence, and diversity of TreMs from the use of restorative selective cutting can likely be enhanced by paying particular attention to tree species selection and tree characteristics during harvesting. For example, by promoting the preservation wherever possible of large trees, and in some cases, by selectively removing otherwise regionally abundant late-successional conifer species that, depending on circumstances, can suppress the growth of high conservation value broadleaf trees. For example, in this region the promotion of dense Norway spruce dominated stands has led to darker forests that can suppress tree genera like *Quercus* (Petersson et al., 2019; Pohl et al., 2025), and is known to actively increase in several of our study sites if left unmanaged (Nordén et al., 2021). However, because broadleaves and conifers host different types and combinations of TreMs, and even the same type of TreM borne by a broadleaf or a conifer can host different species assemblages (Speight, 2020), maintaining both broadleaves and conifers can be very important for sustaining forest biodiversity when these trees are naturally present. Therefore, preserving large broadleaves and conifers, and promoting an array of tree species, including both living and standing dead
<figure><img src="image_7.png" /><figcaption>**Fig. 5.** Predicted (lines) and observed (circles) occurrence of TreM groups (A-F) at the tree scale by DBH from the GLMM models. Green dashed lines and circles represent selective cutting and pink solid line and circles the control. Shaded area shows 95 % confidence interval of the prediction. Groups are based on Larrieu et al. (2018) typologies (See Supplementary Material Table S2 for TreM group details).</figcaption></figure>

individuals, is a proactive means of increasing the capacity of mixed forest stand to host a greater diversity of TreM-dwelling taxa.

This type of selective cutting, focused on ecological restoration, can also be combined with integrative management strategies (Kraus and Krumm, 2013; Krumm et al., 2020) to achieve even better outcomes for TreM dependent forest biodiversity. These strategies can include (i) providing set-asides for species requiring disturbance features that are not recreated by forestry operations (Aszalós et al., 2022), (ii) providing the large amounts of deadwood required by high-demanding saproxylic taxa (Bässler and Müller, 2010; Parisi et al., 2018), (iii) allowing higher

BA in some areas that provide shaded conditions for taxa such as mosses or molluscs (Rancka et al., 2015), (iv) promoting complex structures that favour spiders, birds, bats and orthopterans, and (v) promoting natural spatial patterns of habitat-trees (Larrieu et al., 2025).

We hope that our results, combined with previous studies that highlight the economic (Nordén et al., 2019), social, and ecological (Götmark, 2007) importance of sustainable forest management in mixed forest, contributes to the improved management and restoration of the remaining patches of these forest habitats, that are of such high potential value for forest biodiversity in this region. We also hope that further
<figure><img src="image_8.png" /><figcaption>**Fig. 6.** Ordination plot from the RDA model, representing the bidimensional space defined by the two first ordination axes. Panel A shows individual trees (dots) whose colour refer to different taxa. Colour ellipses represent 95 % confidence intervals around the taxa centroids in ordination space. The black arrows represent continuous explanatory variables: BA: basal area; GCD: Ginni coefficient of diameter; DBH: diameter at breast height. Panel B represents the detailed distribution of TreM types (grey codes) belonging to different groups (in bold in the legend) in the bidimensional space together with the taxa confidence intervals. Panel C and D zooms in to show the ellipses that represent the treatment (control vs. selective cutting) and the time since intervention (control, 21 y and 7 y ago) respectively, as well as the distribution of TreM types.</figcaption></figure>

research will shed light on the extent to which this type of selective cutting can be further refined to promote TreMs and thereby better conserve the forest biodiversity of mixed forests.

### CRediT authorship contribution statement

**Björn Nordén:** Writing – review & editing, Conceptualization.
**Adam Felton:** Writing – review & editing, Methodology, Conceptualization.
**M. Trinidad Torres-García:** Writing – review & editing, Writing – original draft, Methodology, Investigation, Formal analysis, Data curation, Conceptualization.
**Magnus Löf:** Writing – review & editing,

Funding acquisition, Conceptualization.
**Laurent Larrieu:** Writing – review & editing, Methodology.
**Julia Schmucker:** Writing – review & editing, Investigation.

### Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
## Acknowledgments

The authors would like to thank Emil Svensson for his help with fieldwork and Nora S. Pohl for her advice and knowledge about the sites. The authors also thank the reviewers for their constructive comments, as their comments have allowed them to further improve this manuscript. This work was supported by the Swedish Research Council Formas (grant 2022-02070). Björn Nordén was financed by Norwegian research council project 336381 SynForest, Enabling synergies among value creation, climate adaptation, biodiversity, and sustainable use of temperate broadleaf forests.

## Appendix A. Supporting information

Supplementary data associated with this article can be found in the online version at doi:10.1016/j.foreco.2026.123598.

## Data availability

Data will be made available on request.

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