# How do forest structure and composition near trees with black woodpecker (Dryocopus martius L.) cavities differ from the surrounding forest? A multisite approach in French forests

Vincent Breton, Marie-Emelie Guignot, Lucie Vincenot, Aurélie Husté, Laureline Leclerc, Marc Fuhr, Yoan Paillet

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Vincent Breton, Marie-Emelie Guignot, Lucie Vincenot, Aurélie Husté, Laureline Leclerc, et al. How do forest structure and composition near trees with black woodpecker (Dryocopus martius L.) cavities differ from the surrounding forest? A multisite approach in French forests. Annals of Forest Science, 2025, 82 (1), pp.39. 10.1186/s13595-025-01313-7. hal-05340656

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# How do forest structure and composition near trees with black woodpecker (*Dryocopus martius* L.) cavities differ from the surrounding forest? A multisite approach in French forests

Vincent Breton¹*, Marie-Emelie Guignot², Lucie Vincenot², Aurélie Husté², Laureline Leclerc³, Marc Fuhr¹ and Yoan Paillet¹

**Abstract**

**Key message** In French temperate lowland and mountain forests, the black woodpecker (*Dryocopus martius* L.) excavates preferably trees in beech-dominated sites, with a lower fraction of small-diameter trees than in the surrounding areas, while the presence of deadwood in the immediate vicinity does not matter.

**Context** The black woodpecker (*Dryocopus martius* L. 1758) is the largest primary tree cavity excavator in Europe providing nest sites for numerous secondary cavity users. It is seen both as an ecosystem engineer and as a keystone species, providing nesting and roosting habitat for many species and having a positive role for forest biodiversity. As such, this bird has an important conservation role, and its cavities are conserved by foresters in their management. However, the characteristics of its habitat have seldom been quantified from a forestry point of view.

**Aim** By analysing the characteristics of a wide range of temperate forest stands (uneven-aged and even-aged, mountain and lowland), we sought to highlight differences between plots containing or not black woodpecker cavities.

**Methods** We characterised the stand structure (tree diameter, density, composition), the abundance of the understorey (density of saplings) and the midstorey (density of small trees), and the volume of deadwood (standing and lying), and compared the results obtained in plots with black woodpecker cavities (n = 40) with those obtained in paired control plots without cavities (n = 40).

**Results** We found a higher proportion of beech (*Fagus sylvatica* L.) and a smaller proportion of small-diameter trees in the plots with cavity-trees. Apart from the cavity-tree, we failed to highlight a difference in large-diameter trees. The amount of deadwood and the number of saplings were not decisive.

**Conclusion** To favour the black woodpecker, forest management methods must maintain a sufficient number of large trees for cavity excavation, as well as a relatively open midstorey layer.

**Keywords** Nest site, Tree cavities, Tree-related microhabitats, Conservation, Forest management

Handling editor: Andreas Bolte

*Correspondence:
Vincent Breton
vincent.breton@inrae.fr

Full list of author information is available at the end of the article

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© The Author(s) 2025. **Open Access** This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
# 1 Introduction

Sustainable forest management must combine objectives of wood production with habitat and biodiversity conservation (Krumm et al. 2013; Bollmann et al. 2020) in a context of global changes and very high societal expectations (e.g. carbon sinks, production of renewable energy and materials, protection of property and people, well-being of the population). Given the complexity of these issues, forest managers need to rely on integrative methods and indicators that take into account the wide range of ecological factors involved at multiple spatial scales. To this end, the use of indicator species to assess forest biodiversity can be of great interest, once the relationship between potential indicator species and overall biodiversity has been established.

Being a primary cavity excavator, the black woodpecker (Dryocopus martius L. 1758) is considered an ecosystem engineer, since its cavities can serve as habitat for a variety of cavity-nesting species, thereby promoting forest biodiversity (Mikusiński and Angelstam 1997; Garmendia et al. 2006; Kosiński et al. 2011). Generally speaking, tree cavities represent keystone vegetation structures inhabited by numerous animal species, such as birds (9–18% of bird species) (Newton 1998), mammals, and also host many different invertebrate and fungi (Conner et al. 1976; Cockle et al. 2012); primary excavators such as the black woodpecker therefore play an essential role in the abundance and diversity of many other species.

Over the last century, its range has expanded considerably, both in France (Cuisin 1986) and in central and western Europe (Mikusiński 1995). In south-eastern Europe, this extension appears to be slightly more recent (Yatsiuk and Viter 2024). In some regions in the Mediterranean or Nordic European countries, its population has tended to decrease (Mikusiński 1995; Fernandez and Azkona 1996; Rolstad et al. 1998). In both cases, explanations linked to management methods or forest expansion have been put forward, although it was not possible to identify a general framework to explain population trends (Mikusiński 1995). The gradual disappearance of hunting from the end of the nineteenth century and measures to protect the black woodpecker may also explain an increase in its populations (Schmidt et al. 2016). In addition, we can expect that the effects of ongoing climate changes on populations will probably be more marked at the range margins of the species (Hampe and Petit 2005). Although some authors have suggested the possibility of such changes for the black woodpecker (De Rosa et al. 2016; Oparin et al. 2021), to our knowledge they have not yet been clearly demonstrated.

The black woodpecker is either considered as a forest generalist (Angelstam et al. 2002) or a forest specialist (Mikusiński et al. 2018). This difference in assessment

is probably due to different parameters being taken into account. Saporetti et al. (2016) suggested that the black woodpecker may be considered as a habitat specialist for the nesting site and feeding preferences, and as a habitat generalist for its home range (i.e. on a landscape and land-use scale). The species is able to adapt to different forest landscapes (Rolstad et al. 2000; Angelstam et al. 2002; Saporetti et al. 2016) and to live in both managed and unmanaged forests (Zawadzki and Sławski 2023). However, several studies have shown higher cavity densities in unmanaged than in managed forests (Remm and Löhmus 2011; Andersson et al. 2018; Pakkala et al. 2024): heterogeneous forest structure may favour excavation by this bird. Based on existing literature, Olano et al. (2015) estimated the species' breeding home covers about 250 ha. It requires large patches of mature forest (Garmendia et al. 2006; Khanaposhtani et al. 2012; Olano et al. 2015), although it can also be observed in young or fragmented forests (Tjernberg et al. 1993; Rolstad et al. 1998). The black woodpecker seems to prefer large trees (Hågvar et al. 1990; Fernandez and Azkona 1996), or trees larger than those in the vicinity (Basile et al. 2020), allowing cavities to be positioned at greater heights, thereby reducing the risk of predation (Zahner et al. 2017). Beech (Fagus sylvatica L.) is recognised as most preferred the tree species by black woodpecker in western Europe, both for excavating cavities and as nesting conditions that reduce the risk of predation (Fernandez and Azkona 1996; Olano et al. 2015; Saporetti et al. 2016; Zahner et al. 2017). But the black woodpecker can also adapt to different forest contexts with other dominant species like aspen (Populus tremula L.) in northern Europe (Rolstad et al. 2000; De Rosa et al. 2016; Yatsiuk and Viter 2024), Scots pine (Pinus sylvestris L.) in Eastern Europe (Rolstad et al. 2000; Bocca et al. 2007; Zawadzki and Sławski 2023) or silver fir (Abies alba L.) in the Alps (Pirovano and Zecca 2014). To avoid the risk of predation, selection criteria do not only concern the cavity tree, but also the surrounding environment. Indeed, it is potentially quicker and easier for black woodpecker to seek shelter when the cavity environment is less crowded (Puverel et al. 2019). Finally, the black woodpecker habitat is also characterised by foraging and substrate resources. With its wide beak and large size, the black woodpecker is particularly well suited to searching for food in woody substrates (Mikusiński and Angelstam 1997) such as beetles or their larvae under the bark (Mikusinski 1997). Ants are also particularly sought-after by this species (Mikusiński 1995), both in stumps and in deadwood (Rolstad et al. 2000).

Despite this knowledge, most of the studies carried out on the topic of black woodpecker habitat preferences have focused on the description of the cavity tree only (Puverel et al. 2019; Zawadzki 2023) or on the
environment nearby the cavity tree at the stand scale or in local forest contexts (Fernandez and Azkona 1996; Rolstad et al. 2000; Garmendia et al. 2006; Saporetti et al. 2016). The structural attributes beneficial to cavity excavation were never quantified from a forestry point of view. For the purposes of forest management and black woodpecker conservation, it is necessary to rely on large-scale data and to have indicators and thresholds characterising the species' ecological preferences (Fernandez and Azkona 1996).

Here, we worked on a wide range of forest stands in mainland France, both in mountains and lowlands, and with different types of management (uneven-aged or even-aged). We compared the characteristics of forest plots centred on a cavity tree with control plots without cavity trees (40 each) and looked for differences in forest structure between these two modalities. The parameters taken into account reflect the stand structure (basal area, diameter at breast height, diameter distribution, tree density), the composition (percentage of beech), the abundance of woody understorey (density of saplings) and midstorey (density of small trees) as well as the deadwood volume.

Our aim was to identify general trends in the listed parameters that explain the presence of black

woodpecker cavities across French forests. We hypothesised that:

i) Plots with cavity trees would have a greater proportion of beech trees.

ii) Plots with cavity trees would present more attributes of mature forest (larger diameter of living trees and more deadwood).

iii) The local environment around the cavity tree would be less crowded by the understorey and midstorey.

# 2 Material and methods

## 2.1 Study sites and sampling design

Seven forest sites where black woodpecker cavities had been inventoried by forest managers and naturalists (Fig. 1, see Puverel et al. (2019) for a subset) were selected. Two are located in lowlands (90 to 500 m a.s.l), the other five in mountains (900 to 1400 m a.s.l). The field surveys were carried out between November 2021 and March 2023. For each site, a set of plots with a cavity tree was selected according to the following criteria: (1) size of the cavity (i.e. Ø > 10 cm) specific to black woodpecker regardless of current use (nesting, roosting, abandoned or inhabited by another nesting species), (2) minimum distance of 400 m from other cavity trees

<figure><img src="image_6.png" /><figcaption>**Fig. 1** Location of the mountain (Chaîne des Puys, Aigoual and Alpine sites) and lowland (Loches and Auberive) sites</figcaption></figure>
to reduce spatial autocorrelation. Then, 5 or 10 plots per forest site (depending on the forested area and altitudinal context) were selected randomly amongst plots meeting these criteria (Table 1). Each cavity-tree plot corresponds to a circular area with the cavity tree at its centre. For each cavity-tree plot, we paired a control plot (also characterised by a central point and a circular area) in an equivalent stand. The criteria for selecting it were (1) no cavity tree within a 50 m radius at least, (2) topographical parameters (elevation, slope, aspect) and forest conditions (structure, composition) overall comparable to those of the paired cavity-tree plot, (3) close to the cavity-tree plot (within an 850 m radius or 250 ha, i.e. the surface area of the home range of black woodpecker) and 4) with the presence of at least one mature beech tree on the control plot to minimise the selection bias in sites not dominated by beech (i.e. mountain sites).

## 2.2 Stand structure measurements

We used the protocol for the national forest reserve monitoring programme in France (Paillet et al. 2015; Cateau et al. 2024). It assesses all stages in the life cycle of trees, from seedling to deadwood (Cateau et al. 2024). For either live trees or deadwood, the protocol was based on the position relative to the central point of the plot.

For living trees, the protocol combined several sampling methods depending on the size of the trees. In the case of trees with a diameter at breast height (DBH) above 30 cm, the sampling distance was proportional to the diameter of the tree. We used a fixed-angle method to select and measure individuals within a relascopic angle of 3%. All living trees from 7.5 to 30 cm DBH were

measured within a fixed radius of 10 m (314 m²) around the plot centre. Finally, in 3 subplots with a radius of 1.5 m located 10 m away from the plot centre located at 3 different directions (0 [North], 120 and 240 degrees from the central point), we counted seedlings and saplings higher than 1.5 m and with DBH > 2.5 cm and < 7.5 cm.

For dead trees, we distinguished two categories: standing dead trees (i.e. snags and stumps with a height ≤ 1.3 m) and deadwood on the ground (lying logs). Within a 20 m radius (1256 m²), we measured the volume of all lying deadwood logs, stumps and snags with a diameter ≥ 30 cm. Within a fixed 10 m (314 m²) radius around the plot centre, we measured the volume of snags and stumps with a diameter from 7.5 to 30 cm. Finally, we measured logs between 5 and 30 cm on a total of length of 60 m by line intersect sampling.

Whenever possible, we identified all trees, both alive and dead, to species level.

The dataset has been uploaded to the Recherche Data Gouv multidisciplinary repository (Breton et al. 2025).

## 2.3 Statistical analysis

We performed the analyses in R version 4.3.2 (R Core Team 2020). We used generalised linear mixed-effect models to compare nine response variables (stand structure parameters) between the two modalities (cavity-tree plot and control plot), the two elevation classes (lowland and mountain) and interactions between these two parameters, with forest site and pair of plots as random effects on the intercept (function *glmmTMB* in the *glmmtMB* package, Magnusson et al. (2017)). Amongst the nine variables, six describe stand structure (total basal

<table><caption>**Table 1** Main stand characteristics of the sampling sites: <sup>a</sup>means per site calculated over all plots (cavity and control plots combined), standard errors (s.e.) and <sup>b</sup>ranges (minimum and maximum)</caption><thead><tr><th rowspan="2"></th><th colspan="2">Lowland</th><th colspan="3">Mountain</th></tr><tr><th>Auberive</th><th>Loches</th><th>Chaine-des-Puys</th><th>Alpine sites</th><th>Aigoual</th></tr></thead><tbody><tr><td>Number of plots</td><td>20</td><td>10</td><td>10</td><td>20</td><td>20</td></tr><tr><td>Location</td><td>47° 48' 28" N<br>5° 5' 40" E</td><td>47° 8' 45" N<br>1° 5' 59" E</td><td>45° 46' 27" N<br>2° 57' 39" E</td><td>45° 19' 54" N<br>5° 43' 47" E</td><td>44° 4' 12" N<br>3° 31' 14" E</td></tr><tr><td>Management type</td><td>Uneven-aged</td><td>Even-aged</td><td>Uneven-aged</td><td>Uneven-aged</td><td>Uneven-aged</td></tr><tr><td>Dominant tree species</td><td>Fagus sylvatica (46%)<br>Quercus sp. (16%)<br>Other broadleaves (31%)</td><td>Quercus sp. (51%)<br>Fagus sylvatica (42%)</td><td>Fagus sylvatica (61%)<br>Abies alba (26%)</td><td>Fagus sylvatica (35%)<br>Abies alba (33%)<br>Picea abies (17%)<br>Other broadleaves (14%)</td><td>Fagus sylvatica (90%)<br>Abies alba (7%)</td></tr><tr><td>Altitude (m)<sup>a</sup></td><td>444 ± 5</td><td>126 ± 2</td><td>1027 ± 18</td><td>1297 ± 37</td><td>1251 ± 5.5</td></tr><tr><td>Slope (°)<sup>a</sup></td><td>11.3 ± 2.7</td><td>2.8 ± 1.4</td><td>33.6 ± 4.0</td><td>18.1 ± 3.1</td><td>29.0 ± 2.8</td></tr><tr><td>Deadwood (m³/ha)<sup>a</sup></td><td>35.1 ± 5.7</td><td>24.6 ± 9.4</td><td>40.1 ± 4.9</td><td>81.2 ± 12.9</td><td>33.1 ± 5.4</td></tr><tr><td>Basal area (m²/ha)<sup>a</sup></td><td>18.6 ± 1.2</td><td>32.4 ± 2.4</td><td>38.4 ± 3.6</td><td>33.9 ± 2.2</td><td>33.3 ± 2.9</td></tr><tr><td>Mean DBH (cm)<sup>a</sup></td><td>34.2 ± 2.3</td><td>41.3 ± 3.6</td><td>34.5 ± 1.6</td><td>33.7 ± 2.2</td><td>30.0 ± 1.9</td></tr><tr><td>DBH of cavity trees (cm)<sup>b</sup></td><td>55.9 [38–79]</td><td>52.4 [44–62]</td><td>51.5 [43–61]</td><td>51.7 [43–77]</td><td>45.0 [40–53]</td></tr></tbody></table>
area, mean DBH, percentage of beech, total density, diameter distribution and diameter heterogeneity), two young trees (density of seedlings with DBH ≤ 2.5 cm, density of small trees with DBH from 7.5 to 22.5 cm) and two deadwood (mean lying deadwood volume, mean standing deadwood volume). The diameter distribution is based on the Skewness index (moments R package, Komsta and Novomestky (2012)) calculated on the DBH, i.e. the asymmetry with respect to a normal distribution, with negative values when the distribution curve is shifted to the right (predominance of large diameters) and positive values when it is shifted to the left (predominance of small diameters). The diameter heterogeneity is quantified as the coefficient of variation of the DBH (CVD), using the formula: $CVD = 100 * SD_{DBH} / \mu_{DBH}$ where $SD_{DBH}$ was the standard deviation of the DBH distribution and $\mu_{DBH}$ the mean DBH.

Since the total basal area, mean DBH, total density, diameter heterogeneity and density of small trees have strictly positive continuous values, we chose the gamma error distribution with a log link. For deadwood and sapling density, we chose the zero-inflated gamma error distribution with a log link, in order to account for zeros not taken into account in a classical gamma distribution. Finally, since the percentage of beech is limited on the interval [0, 100], we used the beta distribution with a logit link.

Adjusted means were used to illustrate the effect of modality (cavity tree or control) and elevation (mountain or lowland) on each response variable. Post hoc Tukey tests allowed to compare estimated means between modalities and elevation (*emmeans* function; *emmeans* package v1.8.5, Russell (2018)).

# 3 Results

## 3.1 Forest structure and composition

Forest structure varied significantly between lowlands and mountain sites. Basal area was significantly higher in mountain sites than in lowland sites (+49%, *p* = 0.028), but no difference was detected between cavity-tree plots and control plots (Fig. 2), and tree density was significantly lower (−71%) on lowland sites compared to mountain sites (*p* < 10⁻³).

Regarding cavity-tree plots, values differed greatly between sites and ranged from 14 to 100% for the percentage of beech, 17.1 to 56.4 cm for the DBH and 81 to 784 individuals for the total density.

Cavity-tree plots showed significantly higher diameters (+20%) and a higher proportion of beech than in the control plots (respectively *p* = 0.041 and *p* = 0.005, Table 2). The percentage of beech was significantly higher (+41%) on average in cavity-tree plots compared with control plots, while the total tree density was significantly lower (−42%) on cavity-tree plots compared with control plots (*p* = 0.014, Fig. 2).

DBH heterogeneity was slightly higher (*p* = 0.048) in cavity-tree plots. DBH distribution also differed significantly between modalities, with positive asymmetry (shifted towards smaller diameters) more marked in the control plots (*p* = 0.041, Table 2). Logically, the asymmetry values were by far the most negative for the Loches plots, the only even-aged site (Table 1).

## 3.2 Woody understorey and midstorey density

Sapling density appeared greater in cavity-tree plots (Fig. 3), but no difference with control plots was detected (*p* = 0.119) despite a large magnitude (+26% in lowlands

<figure><img src="image_7.png" /><figcaption>**Fig. 2** Comparison of structural stand characteristics between modalities (cavity-tree plot in grey and control plot in white) and elevation (lowland and mountain). Error bars represent 95% confidence intervals. Letters represent significant differences between modalities (type × elevation) according to the Tukey post hoc test for multiple comparisons. Associated tables display the significance of each parameter in each model: type (cavity plot vs. control plot), elevation (lowland vs. mountain) and type:elevation interaction, with significance thresholds (*** *p* < 0.001; ** *p* < 0.01; * *p* < 0.05; \*\* *p* < 0.1)</figcaption></figure>
<figure><img src="image_8.png" /><figcaption>**Fig. 3** Comparison of the understorey density between modalities (cavity-tree plot in grey and control plot in white) and elevation (lowland and mountain). Error bars represent 95% confidence intervals. Letters represent significant differences between modalities (type × elevation) according to the Tukey post hoc test for multiple comparisons. Associated tables display the significance of each parameter in each model: type (cavity plot vs. control plot), elevation (lowland vs. mountain) and type:elevation (the interaction between type and elevation), with significance thresholds (***p < 0.001; **p < 0.01; *p < 0.05; (\*)p < 0.1)</figcaption></figure>

and +80% in mountains). It should be noted that in a majority of plots (48 out of 80) no sapling was observed, hampering the detection of differences between cavity-tree plots and control plots (Table 2).

The density of small trees was 76% larger in control plots than in cavity-tree plots, although this difference was not significant (p=0.068). No effect of elevation was detected.

## 3.3 Deadwood volume

The results did not indicate any effect on lying deadwood volume, whereas standing deadwood volume was 36.7% higher (p=0.034) in mountain than in lowland sites (Fig. 4). Zero values were rare: only six plots had no lying deadwood, and four had no standing deadwood. The detailed analysis for each site (results not presented) did not show any contrast or amplitude that would suggest any particular trend between cavity-tree plots and control plots (Table 2).

## 4 Discussion

Using controlled pairwise comparisons between cavity-tree and control plots, we sought to highlight the structural parameters of the stand that are specific to the presence of the cavities. Based on a set of 40 pairs of plots spread over several French regions and representing a wide range of forest situations, we show that beech tend to dominate places where the black woodpecker excavates (41% more), with 42% less trees and trees with a mean diameter approximately 6 cm higher than the control counterpart. These results validate our first hypothesis (larger proportion of beech trees), and partially the second (larger diameter of living trees) and the third (less crowded environment around the cavity tree). However, we did not fully validate the fact that cavity-tree sites were more mature since deadwood

volume and proportion of large trees was not significantly higher in cavity-tree than in control plots.

### 4.1 The importance of the composition and the size of the trees

In accordance with previous studies (Fernandez and Azkona 1996; Olano et al. 2015; Zahner et al. 2017), the importance of beech-dominated stands was clearly observed in our study. We found that cavity-tree plots displayed 23% (in mountain sites) to 88% (in lowland sites) higher beech tree proportion than in the forests nearby. We thus confirmed and quantified the results observed elsewhere in western Europe. Indeed, beech trees in temperate forests are particularly suitable for excavation for a variety of reasons including smooth bark and moderate wood density (Puverel et al. 2019). It is important to note that the magnitude of this effect is probably underestimated since control plots contained at least one mature beech tree, which would not have been the case if control plots have been chosen totally at random. Without these precautions, the importance of beech in the cavity-tree plots would probably have been even larger than observed in our study, especially in mountains, where the proportion of conifers in the landscape is large. At a landscape scale, the effects of composition may then be even higher than at the local scale (Cabrera et al. 2025).

Cavity-tree plots displayed a larger mean DBH than controls, but a similar basal area than control plots. These structural differences were confirmed by differences in diameter distribution overall (see below). This suggests that spatial distribution of trees at plot scale may have a larger role than size per se. The higher mean diameters we observed in the cavity-tree plots were mainly due to a lower proportion of small-diameter trees, but not to a higher proportion of large
<table><caption>**Table 2** Comparisons between structural attributes between cavity-tree plots and control plots. Results are based on generalised linear mixed models with adapted error distributions (see text for details) and pairs nested in site as a random effect</caption><thead><tr><th>Structure parameters</th><th>Fixed effect</th><th>Estimate</th><th>Std. error</th><th>z value</th><th>p</th></tr></thead><tbody><tr><td rowspan="4">Total basal area</td><td>Intercept</td><td>3.14</td><td>0.14</td><td>22.66</td><td>&lt;0.001</td></tr><tr><td>Modality (control)</td><td>0.06</td><td>0.093</td><td>0.067</td><td>0.506</td></tr><tr><td>Elevation (mountain)</td><td>0.39</td><td>0.18</td><td>2.19</td><td>0.028</td></tr><tr><td>Modality x elevation</td><td>-0.051</td><td>0.12</td><td>-0.43</td><td>0.664</td></tr><tr><td rowspan="4">Percentage of beech trees</td><td>Intercept</td><td>0.82</td><td>0.74</td><td>1.11</td><td>0.268</td></tr><tr><td>Modality (control)</td><td>-1.47</td><td>0.33</td><td>-4.48</td><td>0.005</td></tr><tr><td>Elevation (mountain)</td><td>0.31</td><td>0.96</td><td>0.32</td><td>0.756</td></tr><tr><td>Modality x elevation</td><td>0.72</td><td>0.41</td><td>1.73</td><td>0.081</td></tr><tr><td rowspan="4">Total density</td><td>Intercept</td><td>5.53</td><td>0.12</td><td>46.35</td><td>&lt;0.001</td></tr><tr><td>Modality (control)</td><td>0.39</td><td>0.16</td><td>2.47</td><td>0.014</td></tr><tr><td>Elevation (mountain)</td><td>0.58</td><td>0.15</td><td>3.92</td><td>&lt;0.001</td></tr><tr><td>Modality x elevation</td><td>-0.050</td><td>0.20</td><td>-0.25</td><td>0.800</td></tr><tr><td rowspan="4">Diameter at breast height (DBH)</td><td>Intercept</td><td>3.70</td><td>0.077</td><td>48.32</td><td>&lt;0.001</td></tr><tr><td>Modality (control)</td><td>-0.20</td><td>0.096</td><td>-2.04</td><td>0.041</td></tr><tr><td>Elevation (mountain)</td><td>-0.13</td><td>0.095</td><td>-1.39</td><td>0.164</td></tr><tr><td>Modality x elevation</td><td>0.014</td><td>0.12</td><td>0.11</td><td>0.911</td></tr><tr><td rowspan="4">DBH distribution asymmetry</td><td>Intercept</td><td>-0.34</td><td>0.16</td><td>-2.07</td><td>0.039</td></tr><tr><td>Modality (control)</td><td>0.47</td><td>0.23</td><td>2.04</td><td>0.041</td></tr><tr><td>Elevation (mountain)</td><td>0.26</td><td>0.21</td><td>1.24</td><td>0.216</td></tr><tr><td>Modality x elevation</td><td>-0.05</td><td>0.29</td><td>-0.18</td><td>0.858</td></tr><tr><td rowspan="4">DBH heterogeneity</td><td>Intercept</td><td>3.80</td><td>0.08</td><td>46.68</td><td>&lt;0.001</td></tr><tr><td>Modality (control)</td><td>0.20</td><td>0.10</td><td>1.98</td><td>0.048</td></tr><tr><td>Elevation (mountain)</td><td>-0.02</td><td>0.10</td><td>-0.15</td><td>0.878</td></tr><tr><td>Modality x elevation</td><td>-0.13</td><td>0.13</td><td>-1.01</td><td>0.315</td></tr><tr><td rowspan="4">Small tree density</td><td>Intercept</td><td>4.94</td><td>0.20</td><td>24.8</td><td>&lt;0.001</td></tr><tr><td>Modality (control)</td><td>0.50</td><td>0.27</td><td>1.82</td><td>0.068</td></tr><tr><td>Elevation (mountain)</td><td>0.36</td><td>0.25</td><td>1.46</td><td>0.143</td></tr><tr><td>Modality x elevation</td><td>0.08</td><td>0.34</td><td>0.25</td><td>0.807</td></tr><tr><td rowspan="4">Sapling density</td><td>Intercept</td><td>10.11</td><td>0.23</td><td>43.34</td><td>&lt;0.001</td></tr><tr><td>Modality (control)</td><td>-0.48</td><td>0.31</td><td>-1.56</td><td>0.119</td></tr><tr><td>Elevation (mountain)</td><td>0.27</td><td>0.32</td><td>0.85</td><td>0.395</td></tr><tr><td>Modality x elevation</td><td>-0.10</td><td>0.44</td><td>-0.23</td><td>0.814</td></tr><tr><td rowspan="4">Lying deadwood volume</td><td>Intercept</td><td>2.63</td><td>0.28</td><td>9.34</td><td>&lt;0.001</td></tr><tr><td>Modality (control)</td><td>0.062</td><td>0.33</td><td>0.19</td><td>0.850</td></tr><tr><td>Elevation (mountain)</td><td>0.12</td><td>0.34</td><td>0.36</td><td>0.719</td></tr><tr><td>Modality x elevation</td><td>-0.15</td><td>0.42</td><td>-0.36</td><td>0.716</td></tr><tr><td rowspan="4">Standing deadwood volume</td><td>Intercept</td><td>2.46</td><td>0.37</td><td>6.61</td><td>&lt;0.001</td></tr><tr><td>Modality (control)</td><td>0.15</td><td>0.27</td><td>0.53</td><td>0.594</td></tr><tr><td>Elevation (mountain)</td><td>1.01</td><td>0.48</td><td>2.13</td><td>0.034</td></tr><tr><td>Modality x elevation</td><td>-0.21</td><td>0.35</td><td>-0.60</td><td>0.551</td></tr></tbody></table>

Here, it is important to distinguish the diameter distribution at plot scale and the diameter of the cavity tree itself. Regarding the former, we did not observe any difference between the two modalities in the density of large and medium-sized trees. For the latter, our results seem to point at a general threshold around

38 cm (Table 1) for the minimum diameter of a cavity tree that allows the cavity to be rather long lasting (the walls are not too thin so the tree is still strong) and well insulated from temperature variations due to wall thickness (Remm et al. 2006). Numerous studies have indicated the average diameter of cavity trees but few
<figure><img src="image_9.png" /><figcaption>**Fig. 4** Comparison of the deadwood volume between modalities (cavity-tree plot in grey and control plot in white) and elevation types (lowland and mountain). Error bars represent 95% confidence intervals. Letters represent significant differences between modalities (type × elevation) according to the Tukey post hoc test for multiple comparisons. Associated tables display the significance of each parameter in each model: type (cavity plot vs. control plot), elevation (lowland vs. mountain) and type:elevation (the interaction between type and elevation), with significance thresholds (***p < 0.001; **p < 0.01; *p < 0.05; (\*)p < 0.1)</figcaption></figure>

gave minimum threshold values. These include Rolstad et al. (2000), Kosinski and Kempa (2007) and Pirovano and Zecca (2014) who indicated, for other tree species, threshold values similar to ours: respectively (i) 36 cm for aspen and 40 for Scots pine, (ii) 38 cm for stands dominated by beech and (iii) 35 cm for silver fir and larch. Finally, our results do not contradict the importance of large trees for the black woodpecker, but it seems to apply mainly to the cavity tree rather than surrounding trees at the stand scale, as has been shown by Bocca et al. (2007), Puverel et al. (2019) and Zawadzki (2023), unlike suggested by Fernandez and Azkona (1996). In other words, providing that the location is suitable, one or a few large trees (DBH > 38 cm) in a mature beech forest is enough for the black woodpecker to excavate. This result is consistent with the work of Kosinski and Kempa (2007) who, using a different formulation, came to a similar conclusion: "...thick (dbh > 40 cm) and even scatter-distributed groups of beech trees seem to be crucial for supporting [the] species long-term".

This result was confirmed by the positive DBH distributions linked to a dominance of small-diameter trees we observed in uneven-aged stands. It might seem more surprising to find a lower negative asymmetry in cavity-tree plots than in control plots. The explanation lies in the lower density of small trees in the cavity-tree plots, as indicated above, and a posteriori shows a consistency between the DBH distributions and the densities of the different diameter classes.

## 4.2 There is a more open environment around the cavity trees

The importance of the understorey and midstorey may be linked to the need for a clear environment to prevent and minimise the risk of nest predation. Comparing cavities visited by predators and cavities undetected by potential

predators, Zahner et al. (2017) did not find any factor driving cavity selection in order to reduce predation risk. In our study, we found fewer small trees in cavity-tree plots compared to control plots. This low abundance of small trees in the cavity-tree plots is particularly marked when compared the values observed by Paillet et al. (2015) in different managed stands in France: the number of small trees was at least twice to three times lower compared with lowland stands, and 1.7 times to twice lower compared with mountain stands studied by these authors, whereas the values observed in the control plots are close to it. Thus, in accordance with Rolstad et al. (2000), Saporetti et al. (2016) and Puverel et al. (2019), our results showed that cavity trees occur in relatively less crowded areas.

The larger density of saplings (diameter < 7.5 cm) in the cavity-tree plots sounds counter-intuitive. The location of cavity trees is generally well known by the managers, who may tend to preserve the tranquillity around the site and avoid any intervention as far as possible. These conditions, observed at certain mountain sites (J. Lhuillier, personal communications), may explain the larger understorey in cavity-tree plots in some cases. In addition, saplings have probably a lower height than cavities and do not impede access by the black woodpecker.

## 4.3 Deadwood characteristics do not differ between cavity-tree sites and the surrounding forest

In the immediate environment around cavity trees, we did not find any particular conditions in terms of quantity of deadwood. According to the global review of Burrascano et al. (2013), the volume of deadwood (lying and standing combined) we observed were lower than the values given for old-growth forests in the context of European or deciduous forests, but very close to those for mature forest in the same contexts. Moreover, Paillet et al. (2015), using the same protocol as ours, highlighted
significant differences in the quantity of deadwood between mountain and lowland stands, comparable to those we observed. Based on our results, it is not possible to state that the black woodpecker finds an adequate foraging resource in the deadwood in the immediate vicinity of the cavity tree, as we did not find any significant differences between the cavity-tree plots and the control plots. Rather, it is possible that the black woodpecker finds adequate quantities of deadwood to forage within its vital range, and that some deadwood nuggets may suffice. The species is also capable of changing its foraging habits according to the seasons (Mikusinski 1997). Finally, it cannot be ruled out that the amount of deadwood as a foraging resource observed on the scale of our plots is sufficient for the black woodpecker and that, under the conditions of our study, this criterion is simply not a limiting factor.

Nielsen et al. (2024) recent study of the black woodpecker's daily movements highlighted its great capacity for movement, including in fragmented habitats (up to 700 m crossing between forest patches), well beyond the size of our study plots. More than the accuracy of the protocol used, these results raise methodological questions on the spatial scale of our analysis, and the difficulty of assessing foraging resources for a species such as the black woodpecker, which has a very large home range. Despite this, our results show that it is not necessary to have all maturity attributes at the same place to favour cavity excavation.

## 5 Conclusions and implications for forest management

We showed that cavity-tree plots had a higher proportion of beech trees with few small trees and, consequently, a higher mean diameter. Our results also showed that, overall, the parameters associated with the presence of cavity trees could vary considerably and could represent a wide range of forest situations. We also know that the preference for beech is not exclusive, as numerous other studies have shown that the black woodpecker can accommodate many other tree species for cavity excavation in its choice of habitat in various forest conditions. It therefore seems to be accepted that this species has generalist behaviour and an ability to adapt to a wide range of situations in terms of management, structure and species composition.

In terms of forest management, the number of large-diameter trees the black woodpecker needs for excavation seems limited and appears to be easily achieved over time by many silvicultural practices. Our results can be used by managers to set silvicultural objectives based on indices of composition (e.g. proportion of

beech or other favourable species) and forest structure (e.g. density of small trees and number of large trees with DBH > 38 cm) favourable to black woodpecker excavation. The quantified references we provide here are a first step towards this integration. However, it would probably be inappropriate, or even dangerous, to recommend a single management system that would be favourable to the black woodpecker, especially at landscape scale (Schall et al. 2018). This homogenisation of the forest mosaic and silvicultural practices poses the question of the potential impact on biodiversity (Bunnell and Huggard 1999; Nagel et al. 2024), including extensive management systems based on selection silviculture. It seems preferable to seek a wide diversity of forest landscape features, including stand types related to traditional sylviculture, as suggested by Redon et al. (2014). Such a diversity of landscape can be beneficial to the black woodpecker which, thanks to its ability to adapt and move around, should be able to benefit from favourable conditions in a diversified landscape, thus providing habitat for other cavity-dwelling species.

Evidence of ongoing climate change also raises concerns on the adaptability of black woodpecker and its habitat. More than direct effects of climate change on the species survival, it is above all the indirect effects on its habitat that are to be feared. Numerous studies have highlighted the impacts of climate change on certain key forest species, involving an increase in tree mortality and a reduction in growth and/or a shift in their geographical range (Allen et al. 2010, 2015, Ruiz-Benito et al. 2017), particularly in beech (Leuschner 2020; Schuldt et al. 2020; Axer et al. 2021; Martinez del Castillo et al. 2022). Our study provides a basis to assume that the black woodpecker habitats may be affected by beech dieback, reduced tree growth and, as a result, a smaller proportion of large-diameter trees (Bennett et al. 2015). Silviculture aiming at reducing the risk of dieback by shorter rotations and lower tree diameter targets may even reinforce this detrimental effect.

### Acknowledgements

The authors especially thank Eric Mermin, Margaux Kurek, Lise Balaguer and Delia Bensussan for their help in the field. They would thank also field managers for their support on site and help in selecting the study sites and facilitating access to them. We are also indebted to all forest managers, naturalists and other Woodpecker lovers who contributed to cavity mapping. Without their huge work and dedication, this study would not have been possible.
B. Algoët, J-J. Boutteaux, H. Caroff, P. Denis, B. Doutau, D. Joud, A. Savine, A. Porte, L. Belenger and B. Guérin especially facilitated the data exchange and interpretation.

### Authors' contributions

YP and LV conceived and designed the study. YP, VB, LV, MG, LL and MF carried out the data collection with help from. AH and MG wrote the primary R codes for analyses, and the analyses were carried forward by VB and YP. VB wrote the first draft of the manuscript with inputs from YP, LV and LL. All authors read and approved the final manuscript.
**Data availability**

The dataset can be accessed through the Recherche Data Gouv multidisciplinary repository https://recherche.data.gouv.fr/fr (DOI: https://doi.org/10.57745/JCEOEA).

**Declarations**

**Competing interests**

The authors declare no competing interests.

**Author details**

¹Univ. Grenoble Alpes, INRAE, LESSEM, Grenoble, France. ²Univ. Rouen - Normandie, INRAE, ECODIV, Rouen, France. ³Univ. Aix Marseille, IMBE - UMR CNRS 7263, Marseille, France.

Received: 28 February 2025 Accepted: 14 October 2025

Published online: 30 October 2025

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