Contrasting Regeneration Patterns in Abies alba-Dominated Stands: Insights from Structurally Diverse Mountain Forests across Europe

Abstract

To maintain the ecosystem resilience to large-scale disturbances in managed forests, it is essential to adhere to the principles of close-to-nature silviculture, adapt practices to the traits of natural forest types, and utilize natural processes, including natural regeneration. This study examines the natural regeneration patterns in silver fir (Abies alba Mill.)-dominated forests, analyzing how the stand structure—tree size diversity, species composition, and stand density—affects the regeneration. We analyze the data from four sites in Poland, Germany, and Italy, employing generalized linear and zero-inflated models to evaluate the impact of the management strategies (even- vs. uneven-aged) and forester-controlled stand characteristics (structural diversity, broadleaf species admixture, and stand density) on the probability of regeneration, its density, and the developmental stages (seedling, small sapling, and tall sapling) across a climatic gradient. Our results indicate a significantly higher probability of regeneration in uneven-aged stands, particularly in areas with lower temperatures and lower overall regeneration density. The tree size diversity in the uneven-aged stands favors advancement from juveniles to more developed stages (seedling to sapling) in places with higher aridity. A denser stand layer (higher stand total basal area) leads to a lower density of natural regeneration for all the present species, except silver fir if considered separately, signifying that, by regulating the stand growing stock, we can selectively promote silver fir. A higher admixture of broadleaf species generally decreases the regeneration density across all the species, except in a water-rich site in the Bavarian Alps, where it had a strong positive impact. These findings underscore the complex interactions of forest ecosystems and provide a better understanding required for promoting silver fir regeneration, which is essential for a close-to-nature silviculture under climate change.

1. Introduction

The continuity of forest cover is a key element of sustainable forest management and strongly depends on the effective tree regeneration resulting from the successful completion of a series of interconnected events. Any disruption in the sequence of these events can lead to the failure of the entire process, which is highly undesirable in places where the socio-ecological role of forests is incredibly high [1,2]. Mountain forests serve as primary buffers on slopes, playing a crucial role in protecting against soil erosion, water runoff, avalanches, landslides, and torrential floods. The integrity of mountain forests is crucial for their ecological functioning as habitats for wildlife and biodiversity hotspots. Mountain forests also play an increasingly important role in the provisioning of social functions like places for tourism and connectivity with nature in today’s fast-paced world [3]. While the idea of protecting these forests through strict protection or minimal interventions is widely recognized by society, the reality is ominously different.

The current state of the European mountain forests is largely shaped by human activity and timber demands. Global warming-driven changes will further alter this landscape significantly, yet the demand for timber will persist, making the strict protection of large areas unfeasible. A notable example is the widespread Norway spruce (Picea abies (L.) Karst.) dieback in monodominant, monolayered plantations, which have replaced the mostly naturally diverse mixed mountain forests. There is a pressing need to restore these ecosystems to their “pre-management” diversity and support the species adversely affected by human activities. It is essential to develop sustainable management practices based on uneven-aged silviculture principles to promote a continuously high level of ecosystem functioning to satisfy the growing demand for timber and non-timber ecosystem services [4,5,6,7].

Silver fir (Abies alba Mill.), one of the species impacted by the artificial expansion of Norway spruce, could reclaim its place in the European mountain forests. More resilient to warm climates and drought than spruce [8], silver fir thrives in the montane zone and is found even in the flatlands in Poland and Ukraine [9,10]. Alongside European beech (Fagus sylvatica L.), it is extremely shade-tolerant and can be classified as a competitive stress tolerator, making it well-suited for uneven-aged silviculture [11,12,13]. The continuity and the demographic stability of silver fir-dominated forests hinge on a robust bank of natural regeneration. Recent decades have witnessed a decline in mature silver fir stands, but also in the natural regeneration layer at some places, even often despite the presence of sufficient seed production [14]. The key issues include suboptimal microsite conditions and stand characteristics, but also browsing by ungulates [15,16].

Paluch and Jastrzębski [14] observed the highest regeneration in the pure fir stands, with the regeneration reducing in the mixed stands as the proportion of silver fir decreased. Similarly, Dobrowolska [17] emphasized that the increase in the fir percentage in a stand correlates with enhanced regeneration quantity and height increments. While a higher percentage of silver fir in a stand composition appears to be beneficial for the regeneration quantity in the short term, long-term issues like allelopathic auto-intoxication in pure silver fir stands are well-documented [18]. Furthermore, Paluch and Jastrzębski [14] found that the survival rate of the fir regeneration in nearly mono-specific stands (90% of silver fir) with an admixture of Norway spruce, European beech, and Scots pine (Pinus sylvestris L.) was higher than in pure fir stands. However, the benefits offered by the admixture of other tree species might not be sufficient to offset the reduced seed availability.

The impact of the stand-level tree size diversification on the success of forest stand regeneration remains ambiguous. It is posited that uneven-aged stands exhibit greater resilience to disturbances such as windthrows at the stand level, enabling rapid regeneration. This resilience is attributed partly to the presence of multiple layers, particularly the understory, as well as to the existing regeneration [19]. Hence, as the introductory step to our research, we would like to confirm whether uneven-aged silver fir-dominated stands show a higher self-regeneration capacity compared to even-aged stands, which is gauged by the likelihood of seedling and sapling emergence to ensure forest continuity and maintain the demographic balance of silver fir under varying climatic conditions. Thus, our first hypothesis (H1) is that the probability of regeneration (for all the species and specifically for silver fir) is higher in uneven-aged stands than in even-aged stands (Figure 1, H1). Following the verification of the first hypothesis, we aim to explore in detail how the structural (tree size diversity) and compositional (admixture of broadleaved tree species) characteristics of uneven-aged stands (i.e., stands with at least two age classes or layers) influence the density of regeneration, focusing particularly on silver fir. Hence, our second and third hypotheses (H2) posit that improved stand characteristics (increased tree size diversity, admixture of broadleaf tree species, and reduced total basal area) positively influence the total density of natural regeneration (Figure 1, H2) and (H3) increase the dominant developmental stage of natural regeneration (seedlings, small saplings, and tall saplings) of silver fir (Figure 1, H3). The presence of large overstory trees is considered to be a good forestry practice, leading to increased biodiversity and other socio-ecological functions. For instance, in Poland, a minimum of five large trees per hectare should remain [20]. Considering that large trees can also produce an excessive amount of seeds and are not inherent in the tree size diversity, we are interested in whether the (H4) presence of large overstory silver fir trees contributes to a higher density of silver fir regeneration (Figure 1, H4). Understanding how silver fir at the youngest age responds to different management strategies and stand characteristics, which foresters can influence, is vital for active protection and sustainable management.

2. Materials and Methods

2.1. Research Area

The study was conducted across four sites in Poland, Germany, and Italy, covering a wide climatic gradient and a large part of the silver fir natural range (Figure 2;

Table 1. The general characteristics of the research sites.

Site	Elevation	Average Annual Temp. (C) *	Sum of Annual Precipitation (mm) *	Total Basal Area (m2 ha−1)	Admixture of Broadleaf Species (%)
Tissens-Laurein, Italy (IT1)	1050–1750 (1320)	3.1–8.8 (6.8)	642–1336 (894)	29.8–90.5 (53.4)	0–34.3 (13.9)
Inzel, Germany (GE1)	820–1140 (920)	6.2–8.5 (7.6)	1471–2081 (1805)	40.7–70.1 (54.4)	0–35.7 (11.5)
Zagnansk, Poland (PL1)	320–400 (350)	7.3–9.8 (8.7)	531–816 (685)	21.1–46.9 (34.1)	0–36.5 (13.0)
Nawojowa, Poland (PL2)	580–820 (650)	8.1–10.5 (9.4)	702–1087 (905)	31.0–62.9 (45.4)	0–58.8 (15.3)

* For IT1 and GE1, climatic grid data from ECAD with a resolution of 0.1 degrees were used. For PL1 and PL2, due to the tendency of ECAD E-OBS data to underestimate precipitation in central Poland, data from the nearby local climatic station in Bartków for PL1 and Nowy Sącz for PL2 were used. Climatic data are a product of the last 20 years.

).

Table 1 is located in the Zagnańsk Forest District, within the Małopolska Upland, on the outskirts of the Świętokrzyskie Mountains range. This district marks the northeastern boundary of the silver fir’s distribution, presenting a unique opportunity to study the marginal populations facing environmental stress and explore regeneration dynamics at the species’ range limit [22]. The second site (PL2) is in the Nawojowa Forest District, situated in the western part of the Low Beskid Mountains. This area lies on the east of the Carpathian flysch, featuring a geological formation of alternating sandstone and shale layers, and is a part of the Sądecki Beskids [23]. Contrary to Zagnańsk, Nawojowa provides optimal conditions for fir growth and development, serving as an uninterrupted habitat for silver fir.

IT in the Tisens-Laurein region is nestled within the Southern Alps in South Tyrol, Italy. IT1 experiences heavy snowfall during the winter, leading to prolonged snow cover that significantly affects local hydrology and ecosystem dynamics. Plots in IT1 are located in the mountain mixed forest zone, close to the upper boundary, as evidenced by the limited vertical growth of broadleaf species (Figure S1). Lastly, the site in Inzel (GE1), situated in the Bavarian Alps, Germany, is an exceptionally water-rich area (Table 1) with pre-Alpine climatic conditions marked by distinct seasonal changes. Cold winters with steady snowfall and mild, moist summers define the climate, with snowpack playing a crucial role in the regional water cycle. The research plots in GE1 are in the middle of the mountain mixed forest zone. All four study sites were severely affected by ungulate browsing, with silver fir being the most heavily impacted among silviculturally important tree species on our plots.

2.2. Data Collection

At each site, 34–36 circular plots were established, with an area of 0.05 hectares, using Field-Map technology [24], to have at least 3-4 replications for each stand type (Figure 3). By having 3-4 replications per stand type, we ensure that our dataset is robust, captures the variability within each type, and meets the minimum requirement for regression analysis. These plots span a gradient from simple, pure, even-aged to complex, mixed, uneven-aged silver fir-dominated forest stands, with the fir constituting ≥ 40% of the stand’s basal area. For even-aged stands, a pair of pre-mature (40–80 years old) and mature (80–120 years old) were included per site. The admixture tree species predominantly include European beech and Norway spruce. The research plots are distributed across stands with varying tree size diversity and admixtures of broadleaf tree species (Figure 3; Figure S2). The positioning of plots within each site is random and confined within a maximum 10 km radius.

All trees larger than 7 cm in diameter at the breast height (DBH) within the plots were mapped in a 3D local coordinate system. Following, standard dendrometric measurements were performed, including DBH in mm, species identification, and documentation of any significant damages (such as wind breakage, decay, and beetle damage). For selected trees, additional measurements were taken, including total height, height to crown base—the height to the lowest living branch forming the continuous crown—and crown projection in four directions. Height curves were fitted to estimate the heights of the non-measured trees (Figure S1).

Natural regeneration was measured within concentric subplots. In the first subplot, with a radius of 1.26 m, seedlings aged 2+ years and under 50 cm in height (after seedlings) were recorded, with the total count of seedlings per species noted. The second concentric plot, with a radius of 2.52 m, focused on small saplings taller than 50 cm but with a DBH of less than 2 cm (after small saplings). The third subplot, with a radius of 3.99 m, included measurements for specimens with a DBH between 2 and 7 cm (after tall saplings) (Figure 4).

2.3. Tree Size Diversity and Admixture of Broadleaf Tree Species

To address H1, the initial classification of forest stands into even-aged and uneven-aged was performed directly in the field and later validated by measuring age at breast height (from increment cores) to mitigate bias. The number of even-aged plots per site was around 10–12. To answer hypotheses H2–H4 and to numerically describe the continuous tree size diversity, we used the Shannon diversity index (ShD) with the 4-m height classes and the sum of the basal area (BA) of all trees larger than 7 cm per class as a proxy of the class share. For instance, in class (4,8], we summed up the basal area of all trees taller than 4 m but equal to or shorter than 8 m and used it as a proportion of trees in the class (4,8]. The generalized formula is based on the proportion of observed objects in the selected class to the total across all classes (Equation (1)).

$$ShD=-\sum _{i=1}^N{p}_i\times \ln (p_i)$$

(1)

where

• N is the number of classes;
• $p_i$ is the proportion of trees in the i-th class.

Subsequently, to facilitate comparison across different locations, the Shannon diversity index values were normalized to a 0–1 scale, with each site’s diversity score divided by the maximum observed value for the site. The share of broadleaf tree species within each plot was determined by calculating the proportion of the total BA represented by broadleaf species.

2.4. Developmental Stage of Fir Regeneration

To estimate the dominant developmental stage (DS) of silver fir regeneration (seedling, small sapling, and tall sapling) within each plot, we employed a straightforward approach (Equation (2)) based on the adjusted formula of the weighted mean [25].

$$DS=\frac{1\times \mathrm{s}\mathrm{e}\mathrm{e}\mathrm{d}\mathrm{l}\mathrm{i}\mathrm{n}\mathrm{g}+3\times \mathrm{s}\mathrm{m}\mathrm{a}\mathrm{l}\mathrm{l} \mathrm{s}\mathrm{a}\mathrm{p}\mathrm{l}\mathrm{i}\mathrm{n}\mathrm{g}\mathrm{s}+5\times \mathrm{t}\mathrm{a}\mathrm{l}\mathrm{l} \mathrm{s}\mathrm{a}\mathrm{p}\mathrm{l}\mathrm{i}\mathrm{n}\mathrm{g}\mathrm{s}}{\mathrm{d}\mathrm{e}\mathrm{n}\mathrm{s}\mathrm{i}\mathrm{t}\mathrm{y} \mathrm{a}\mathrm{l}\mathrm{l} \mathrm{s}\mathrm{t}\mathrm{a}\mathrm{g}\mathrm{e}\mathrm{s}}$$

(2)

where

• seedlings, small saplings, and tall saplings represent the per hectare density of silver fir regeneration in each respective developmental stage;
• density of all stages denotes the total density per hectare of silver fir regeneration across all developmental stages;
• 1, 3, 5—weights that signify the increasing importance of saplings with increasing developmental stage.

This approach was selected to provide a simple yet effective representation of the predominant developmental stage of silver fir regeneration across the plots. The weighting scheme (1, 3, 5) was empirically tested against an alternative conventional scheme (1, 2, 3) that is typically the first choice for such purposes. The results showed that the DS calculated with weights 1, 3, 5 produced residuals that were more favorable for the fitted model’s performance. Specifically, the residuals with the 1, 3, 5 weighting scheme were smaller and more uniformly distributed, indicating a better fit and more accurate representation of the predominant developmental stages.

2.5. Climatic Data

To account for climatic differences between regions, we utilized different data sources. For IT1 and GE1, climate grid data from ECAD E-OBS were used, featuring a high resolution of 0.1 degrees with daily resolution [21]. However, for PL1 and PL2, data from the Bartków and Nowy Sącz climatic stations were used due to the observed tendency of ECAD E-OBS data to underestimate precipitation in central Poland.

To more accurately assess the impact of precipitation and temperature on natural regeneration processes, we calculated the mean de Martonne aridity index for the last 20 years [26] as a product of these two variables (Equation (3)). The lower the Martonne index, the more arid the climate is.

$$Martonne=\frac{P}{T+10}$$

(3)

where

• P—annual sum of precipitation in millimeters;
• T—average annual temperature in degrees.

2.6. Density of Ungulates

Ungulates, particularly red deer (Cervus elaphus L.) and roe deer (Capreolus capreolus L.), play a significant role in shaping forest ecosystems through browsing habits, often favoring more palatable tree species such as silver fir. This interaction can lead to changes in forest composition. Despite similar average densities of red deer across Poland, Italy, and Germany, their spatial distribution within these countries shows distinct patterns. In Poland and Germany, red deer are relatively uniformly distributed, with some areas of concentration, while, in Italy, about 75% are found in the central and eastern Alps [27,28,29].

Data on ungulate populations were sourced locally due to the lack of uniform data across different regions. For site IT1, red deer density data were obtained from the Autonomous Province of Bolzano—South Tyrol, Department of Forestry Services, Office for Wildlife Management. The mean density of red deer per hectare over the past 10 years was calculated from this source. Similarly, for sites PL1 and PL2 in Poland, data on red and roe deer densities over the previous 10 years were provided by the Zagnansk and Nawojowa forestry districts, respectively. In Germany, for site GE1, official red deer density data were unavailable; instead, data systemized by Suzanne T. S. van Beeck Calkoen [30] were used. To estimate the red deer density in GE1, two points from [30] nearest to the GE1 study site were selected, and the distance-weighted mean was calculated.

2.7. Statistical Analysis

2.7.1. Probability of the Regeneration

To test the first hypothesis, a logistic regression analysis was performed using the glm function from the “stats” package as part of the core R [31], with the binomial distribution family and logit link, focusing on binary outcomes of the probability of regeneration influenced by two predictors, namely structure type (even/uneven-aged) and site (Equation (4)). Due to the presence of regeneration on all the plots, and thus the insignificance of the data from GE1 for this regression, they were not used. Variables explaining climatic differences were not found to be significant.

$$\mathit{log}\left(\frac{p}{1-p}\right)={\beta }_0+{\beta }_1\times Structure+{\beta }_2\times Site$$

(4)

where

• $p$ denotes the probability of the regeneration (all species);
• coefficients ${\beta }_0$, ${\beta }_1$, and ${\beta }_2$ correspond to the intercept and the effects of the two predictors on the log odds of the event, respectively;
• Structure—structure type (even/uneven-aged);
• Site—study site.

Following, a second logistic regression was fitted, with the probability of silver fir regeneration as a dependent variable, mean annual temperature and stand structure (even- and uneven-aged) as dependent variables, and site as a random effect using the data from all sites.

$$\mathit{log}\left(\frac{p}{1-p}\right)={\beta }_0+{\beta }_1\times Structure+{\beta }_2\times Temp+{\mathsf{\alpha }}_{Site}$$

(5)

where

• $p$ denotes the probability of the regeneration (silver fir);
• coefficients ${\beta }_0$, ${\beta }_1$, and ${\beta }_2$ correspond to the intercept and the effects of the two predictors on the log odds of the event, respectively;
• Structure—structure type (even/uneven-aged);
• Temp—mean annual temperature for the last 20 years;
• ${\mathsf{\alpha }}_{Site}$—random intercept for each site.

2.7.2. Regeneration Density

To answer the second hypothesis of our research using the “glmmTMB” R package version 1.1.9 [32], we fitted two zero-inflated (ZI) models with the truncated negative binomial distribution (TNB), chosen to address overdispersion and an abundance of zero observations (

Table 2. Overview of ZI models’ components and variables used and tested for the regeneration density; those tested and not included were found to be statistically insignificant.

Component	Variable	Description	Part of the Model/Model
Model 1 *—all species
Dependent Variable	Density_all_ha	The density of natural regeneration of all species per hectare
Fixed Effect	Martonne	Martonne aridity index	Main Model and ZI Part
Fixed Effect	Admixture_broad	Admixture of broadleaf tree species	Main Model and ZI Part
Fixed Effect	TBA	Normalized by maximum total BA of living trees per hectare	Main Model
Interaction Term	Site × Admixture_broad	Interaction between site and admixture of broadleaf tree species	Main Model
Random Effect	Site	The factor for grouping plots by site	Main Model
Model 2—silver fir
Dependent Variable	Density_fir_ha	Density of natural regeneration of silver fir per hectare
Fixed Effect	Martonne	Martonne aridity index	Main Model and ZI Part
Fixed Effect	Admixture_broad	Admixture of broadleaf tree species	Main Model
Interaction Term	Site × Admixture_broad	Interaction between site and admixture of broadleaf tree species	Main Model
Random Effect	Site	The factor for grouping plots by site	Main Model
Tested variables that were not included
	Tree_size_div	Tree size diversity expressed as the normalized Shannon diversity index	Models 1 and 2
	Ungulates_density	Density of the ungulates by site	Models 1 and 2
	Admixture	Admixture of tree species other than fir by species	Models 1 and 2
	TBA	Normalized by maximum total BA of living trees per hectare	Model 2

* For model 1, the bootstrapping was applied with 1000 iterations using “stats” base R package [31].

). Despite the final similarity in our case, in contrast to two-step models, ZI models assume that there are two kinds of zeros in the data: structural zeros and zeros that occur as the result of the count process. The ZI models simultaneously calculate the probability of an excessive amount of structural zeros (a logit model for the ZI part) and, in the case of the TNB, only the count model for non-zero data [32]. A simultaneous ZI model was selected over the classic hurdle model to check whether count zeros are crucial for the model or not without drastically changing the computation behind it (negative binomial distribution vs. truncated negative binomial).

2.7.3. Dominant Developmental Stage of Silver Fir Regeneration

To answer the third hypothesis, we evaluated the Gamma and Inverse-Gaussian distributions within the framework of generalized linear models (GLMs) to accommodate the right-skewed nature of our response variable using the same “stats” base R package [31]. The choice of an appropriate link function was crucial for linearizing the relationship between predictors and the response variable. To this end, we explored a variety of link functions, including the identity, logarithmic, inverse, square, square root, and beta distribution functions. The selection of the optimal model was based on minimizing the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC), and also considering the normality of the simulated residuals. This process led us to select the GLM with a Gamma distribution paired with a log link function as the best-fitting model (

Table 3. Table of GLM components and variables used and tested for the regeneration-dominant developmental stage.

Component	Variable	Description
Dependent Variable	DS	Dominant developmental stage (DS) of silver fir regeneration
Fixed Effect	Martonne	Martonne aridity index
Fixed Effect	Tree_size_div	Tree size diversity expressed as the normalized Shannon diversity index
Interaction Term	Martonne × Tree_size_div	Interaction between site and tree size diversity
Random Effect	Site	A random intercept for each site
Tested variables that were not included
	Admixture_broad	Admixture of broadleaf tree species
	TBA	Normalized by maximum total BA of living trees per hectare
	Ungulates_density	Density of the ungulates by site

).

2.7.4. Presence of Large Overstory Trees

To answer our last hypothesis (H4), we initially defined a criterion for what constitutes an overstory tree, describing an overstory tree as any tree reaching at least 80% of the height of the tallest tree within a given site. The influence of these overstory trees on regeneration density was then analyzed using a binary variable (0 for absence; 1 for presence).

3. Results

3.1. Probability of the Regeneration Appearance

The regeneration density of all the species exhibits considerable variation across the sites, with silver fir consistently emerging as the dominant species in the regeneration layer. In GE1 and IT1, the mountain environment and higher elevations contribute to a greater share of spruce within the regeneration layer. Conversely, at lower altitudes, beech—and hornbeam only in PL1—predominantly complement the regeneration layer (Figure 5).

The likelihood of regeneration across various plots, regardless of the developmental stages (seedlings, small saplings, and tall saplings) and species involved, was significantly higher in the plots managed under uneven-aged forestry practices (

Table 4. Logistic regression summary on the influence of forest structure on the likelihood of regeneration (all species and fir) across four sites.

Predictors	Regeneration Presence
	All Species			Silver Fir
	Odds Ratios	CI	p	Odds Ratios	CI	p
(Intercept)	3.75	1.14–16.95	0.047	0.00	0.00–0.69	0.037
Structure [Uneven-aged]	6.91	2.09–26.26	0.002	3.42	1.29–9.06	0.014
Site [IT1]	0.13	0.02–0.51	0.007
Site [PL1]	1.75	0.26–14.62	0.568
Temp				2.43	1.16–5.08	0.018
Random Effects
σ2				3.29
τ00				0.42 Site
ICC				0.11
N				4 Site
Observations	105			139
R2 Tjur	0.252			0.255/0.340

; Figure 6). We observed a consistent trend where the probability of fir regeneration decreased, as well as a widening disparity between the even- and uneven-aged stands with a decrease in the average annual temperature (Figure 6). This trend was especially notable in IT1, where the average annual temperature and the overall density of regeneration were significantly lower compared to the other sites (Figure 5; Figure 6). Similarly, a difference in the regeneration of all the species between the even- and uneven-aged stands was observed. However, for all the species, the influence of the average annual temperature on the regeneration appeared to be insignificant.

3.2. Regeneration Density

Across all the sites, higher total basal area (TBA) is associated with a decrease in the density of natural regeneration of all the species regardless of the tree size diversity and the presence of a broadleaf species admixture in the stand layer (Figure 7). Interestingly, while the inclusion of broadleaf species generally tended to slightly (but statistically significantly, Tables S1 and S2) decrease the density of the natural regeneration in places with a higher aridity index (PL1, PL2, and IT1), it notably increased the regeneration density in water-rich GE1 (Figure 7).

When considering solely silver fir regeneration, the findings diverge slightly. The density of silver fir regeneration does not appear to be influenced by TBA. Yet, similar to the overall trend, an increasing admixture of broadleaf species is associated with a decrease in the silver fir regeneration density in PL1, PL2, and IT1, whereas, in GE1, with a lower aridity index (high water sufficiency), it is associated with a significant increase (Figure 8).

3.3. Dominant Developmental Stage of Silver Fir Regeneration

The tree size diversification has demonstrated a clear positive impact on the advancement of silver fir in the regeneration layer, indicating that vertical diversification significantly contributes to the progression of silver fir from the early growth stages (seedlings) to more developed stages (saplings) (

Table 5. GLM summary on the influence of forest tree size diversity on the DS of silver fir.

Predictors	sqrt(DS)
	Estimates	CI	p
(Intercept)	0.96	0.68–1.35	0.813
Martonne	1.00	1.00–1.01	0.185
Tree_size_div	2.19	1.31–3.68	0.003
Martonne × Tree_size_div	0.99	0.98–1.00	0.035
Random Effects
σ2	0.07
τ00 Site	0.01
ICC	0.07
N Site	4
Observations	77
Marginal R2/Conditional R2	0.119/0.179

; Figure 9). Notably, neither the TBA nor the admixture of broadleaf tree species within the stand layer has a discernible effect on the developmental stages of silver fir regeneration.

4. Discussion

4.1. The Likelihood of Regeneration Success—To Be or Not to Be?

The principle of fostering a robust bank of natural regeneration is anchored in the objective of establishing a resilient ecosystem capable of maintaining a stable level of ecosystem services provisioning, species demographic equilibrium, and prioritizing naturally favored species [33,34,35]. This is especially critical in the face of large-scale disturbances, which have become more frequent during the last decade [36]. Creating favorable conditions for the rapid restoration of ecosystem functioning by carefully selecting a forest management approach is paramount, even more so under the challenges posed by climate change [37,38].

Our findings support the implicit idea, resonating in numerous publications, e.g., [39,40,41], that uneven-aged stands are more likely to undergo natural regeneration than even-aged stands with a simplified vertical structure and closed canopy. This holds for all the species collectively and specifically for silver fir. Moreover, the disparity in the regeneration probability between the even-aged and uneven-aged stands is more pronounced for all the species than for silver fir alone (Figure 6).

For the germination of most forest tree species’ seeds, three fundamental conditions are required: sufficient water, oxygen, and an optimal temperature range [42]. However, for the successful establishment and survival of regenerations beyond their first years, the substrate and the level of light availability become critical factors [43,44] in addition to the possible disturbance by browsing. Different species employ diverse life strategies and follow distinct recruitment patterns, resulting in varying resource requirements. Consequently, the environmental heterogeneity at the stand level, especially concerning the light availability, microsites, and soil conditions that partly arise from small-scale disturbances of uneven-aged forestry, is creating multiple niches for varied species regeneration [45,46]. The uneven-aged silviculture primarily involves modulating the competition for light across the forest’s vertical profile, including the natural regeneration layer [19]. The increased light availability facilitates more optimal conditions through enhanced solar radiation reaching the forest floor, creating a favorable environment for regeneration development in general. For instance, Scheller and Mladenoff [47] compared the even-, uneven-aged, and old-growth forests in terms of the understory plant communities, including the regeneration of wooden plants. Scheller and Mladenoff [47] found that the understory species richness was lower in old-growth forests compared to even-aged forests, and, most importantly for us, lower in even-aged stands compared to uneven-aged stands, and attributed the difference to the available light and deadwood debris.

Certain tree species, including silver fir, can derive benefits from the limited light under the horizontally closed canopy of even-aged stands and degraded microsite conditions. Such trees experience reduced competition from other tree species in the regeneration layer, shrubs, and grasses [48]. Particularly in their early developmental stages, seedlings of extremely shade-tolerant species exhibit a remarkable degree of tolerance. Despite the minimal photosynthesizing area, which restricts growth, the small silver fir seedlings under the canopy can maintain their current stage for an extended period of time [49]. A study conducted in Comelico (Italian eastern Alps) in Norway spruce–silver fir stands showed that, in the gap system, the fir saplings were more abundant in the understory and less in the gaps, as compared with spruce [50]. Furthermore, even though the age structure of the regeneration in the gap showed that most of it appeared after the formation of the gap, saplings taller than 2 m were predominantly already present at the moment of the gap harvest [50], underscoring the importance of the understory presence for a rapid post-disturbance recovery. However, the advantage gained from the reduced competition does not outweigh the benefits derived from light availability. In even-aged stands, natural regeneration predominantly remains at the seedling stage, with the saplings that manage to develop often exhibiting a suboptimal “silvicultural” quality mainly due to insufficient light (Figure S2).

In addressing the rhetorical question of whether natural regeneration is more viable in uneven-aged stands, our research affirms its feasibility. Following this, we focused on identifying the forester-controlled stand characteristics only within the class of uneven-aged stands that could further enhance natural regeneration.

4.2. The Complexity of Choice: The Impact of Stand Density and Tree Size Diversity on the Natural Regeneration

We found that the tree size diversity does not affect the regeneration density, suggesting that any level of diversification in the vertical profile, which enables increased light penetration to the forest floor and/or improved microsite conditions, is sufficient to create an optimal environment for seed germination and the initial establishment of regeneration. Further diversification, even with improved microsite conditions and the availability of light, is not needed at the early developmental stages. However, as seedlings grow and progress and the demand for resources increases, a higher stratification in the vertical profile could theoretically partition the competition not only for light but also for other resources [46,51]. This provides a window of opportunity for seedlings to grow and develop, thus enhancing the average developmental stage of fir regeneration. This is supported by our findings that, in IT1, PL1, and PL2, where the main resource of competition is most likely water, the average developmental stage of regeneration increases with increasing tree size diversity, whereas, in the water-rich GE1, the tree size diversity appears to have no significant effect. While the quantity of regeneration may not increase, its quality, in terms of being in a more advanced developmental stage, improves, potentially enabling faster post-disturbance recovery. Moreover, reducing the TBA significantly boosted the density of all the species, although it did not notably influence the silver fir density alone. Thus, when the goal is to promote the natural regeneration of silver fir, selecting the locally optimal stand density emerges as an effective strategy.

The process of formation and further management of stands with high tree size diversity, such as those managed under the renowned “Plenterwald” system, is recognized for its complexity, time-intensive nature, and associated initial costs [52,53,54,55]. The maintenance and stable functioning of these systems necessitate frequent interventions [19,56]. While on a long-term scale such practices have proven to be economically viable in regions with well-developed infrastructure and accessible terrain, the feasibility of frequent interventions becomes questionable in remote areas with less developed infrastructure or challenging terrain [57,58,59]. In these contexts, in practice, foresters often resort to the less frequent single-tree selection system or shelterwood, creating simpler stand structures like two-layered stands [60]. Nevertheless, the critical question persists: do the advantages of an improved regeneration developmental stage, which acts as a form of ecological insurance for the quick recovery of ecosystem functions, outweigh the extra costs in areas where frequent management actions are economically demanding? This question, while beyond the scope of our current research, opens the field for further research.

4.3. Influence of Broadleaf and Conifer Interplay on the Natural Regeneration

The impact of species mixtures on the natural regeneration patterns of tree species is a topic of ongoing research within forest ecology, reflecting a limited understanding of this area. The high level of uncertainty in this field is often linked to the complexity of forested ecosystems, especially those characterized by a high level of diversity, which leads to increased entropy in terms of microsite conditions [61,62,63]. Generally, the introduction of broadleaved species at the stand level is observed to decrease the density of silver fir regeneration, with a belief that low levels of admixture might positively influence the fir regeneration density [14,17]. However, the threshold at which the benefits of improved microsite conditions offset the drawbacks of a reduced seed source remains ambiguous. Our findings in places with a comparatively higher aridity index (PL1, PL2, and IT1) align with the commonly observed trend: a small but overall reduction in the density of silver fir regeneration within stands dominated by fir. Contrary to expectations, the situation significantly diverged with increasing water availability (GE1). The GE1 is characterized by the presence of large overstory fir trees, which can produce a substantial amount of seeds and potentially offset the drawbacks of a reduced number of seed sources. Yet, our models failed to confirm our H4 and such a relationship in general. The possibility of allelopathic effects or soil acidification influencing the regeneration patterns was considered unlikely given the long history of mixed stand management in this region. We assume that the distinct outcomes observed in GE1 are predominantly the result of unique local climatic conditions, signifying that the climate plays a crucial role in shaping the impact of species mixtures on regeneration. This underlines the complexity inherent in forest ecosystem dynamics and emphasizes the need for continued research to elucidate these complex interactions with greater clarity and precision [64,65,66].

4.4. Influence of Ungulates on the Regeneration of Silver Fir

Our study found no significant impact regarding the differences in ungulate density and thus browsing pressure on the regeneration success, density, and developmental stage of silver fir and other tree species. This is not in line with the expectations based on previous case studies that have consistently demonstrated that ungulates preferably browse silver fir trees [51,67]. The presence of red deer has been specifically linked to changes in the regeneration composition, heavily affecting silver fir regeneration.

The role of the ungulates in silver fir regeneration dynamics is complex and influenced by multiple other factors [68]. The differences in data sources and methodologies to estimate the ungulate density and the resulting browsing pressure contribute to high uncertainty, potentially explaining the discrepancies with previous studies. The broad spatial and temporal scale has inherent high variability; thus, more precise long-term local observations of ungulate density and direct measures of ungulate browsing (including control plots without browsing) are needed to fully understand the natural regeneration and wild game interplay.

5. Conclusions

Keeping our initial goal in mind, it is fitting to conclude with recommendations for foresters from the perspective of natural regeneration. (1) Adopt uneven-aged silviculture: silver fir-dominated forests, when managed under an uneven-aged system, possess an advanced self-regeneration capacity expressed as a higher probability of regeneration compared to even-aged stands. (2) Balance the tree size diversity: while adding more tree size diversity to these uneven-aged stands helps silver fir progress to more advanced developmental stages, it does not boost the overall density of regeneration, which leads us to an important consideration: the need to balance the ecological benefits of enhanced regeneration quality against the required frequent interventions that are crucial to increasing and maintaining a high level of tree size diversity. (3) Moderate the broadleaf species admixture considering specific growing conditions: including an admixture of broadleaved species into uneven-aged stands in places with a higher aridity index (PL1, PL2, and IT1) results in the reduced density of the natural regeneration of all the species and silver fir specifically. This reduction is statistically significant, although it is negligible in absolute terms. To avoid soil degradation and allelopathy, reaching an equilibrium between the negative and positive effects of the admixture of broadleaf tree species and maintaining a sensible proportion of broadleaf species to favor silver fir regeneration are crucial. This is also important when we consider places with water abundance (GE1), where we noted a significant increase in the natural regeneration density with increasing admixture. (4) Retain large overstory trees: large overstory fir trees did not significantly enhance the density of the fir regeneration. However, given the critical role these trees play in ecosystem functioning and biodiversity conservation, the fact that their presence does not detract from the quality and density of the natural regeneration underlines the importance of retaining them in some places. (5) Use the stand density as a tool to promote desired species: the total BA played a crucial role in boosting the density of all the species, even though it was not significantly influential for silver fir separately. In scenarios where promoting silver fir natural regeneration is a key objective, regulating and carefully selecting the locally appropriate stand density appears to be a suitable tool. Furthermore, considering the importance of the findings, we would like to underline that there is room for further research that incorporates direct measures of light availability and ungulate browsing.