Variability of Structure, Volume, Carbon Sequestration, and Growth–Climate Responses of Fir, Yew, Spruce, Pine and Larch Under Global Climate Change

Abstract

Global climate change is reshaping Central European conifer forests, affecting growth and ecosystem dynamics. At the same time, tree species differ in their productivity and responses to climatic conditions. Across mid-elevation monocultures of European yew (Taxus baccata L.), Norway spruce (Picea abies [L.] Karst.), Scots pine (Pinus sylvestris L.), silver fir (Abies alba Mill.), and European larch (Larix decidua Mill.), we quantified stand structure, volume, biomass carbon sequestration, and growth–climate responses (1971–2023). Silver fir reached the highest stand volume (711 m³ ha⁻¹), with lower productivity in pine (−17.0%), larch (−22.9%), spruce (−26.0%), and yew (−70.6%). In contrast, larch maximised biomass carbon sequestration (267.7 t ha⁻¹), whereas yew had the lowest value (87.7 t ha⁻¹), but the greatest stand diversity (except high differentiation), while pine showed the lowest diversity. Radial growth was most constrained by warm Junes and dry Julys; an early-season multi-month drought compounded by heat further suppressed radial increments, and severe winter frosts added stress. Among the studied species, spruce was the most climate-sensitive, whereas fir and pine showed comparatively more resilience. From a practical forestry perspective, promoting structurally diverse stands with high production potential and prioritising climate-resilient tree species, especially fir, can help sustain production and stability at mid elevations under climate warming. Our results provide species-specific benchmarks for adaptive silviculture and identify the seasonal windows when growth is most vulnerable.

1. Introduction

Global climate change (GCC) is driving profound transformations of forest ecosystems worldwide [1,2], with major consequences for tree species distribution, community composition, and ecosystem functioning [3,4]. Forest ecosystems are particularly sensitive to shifts in air temperature and precipitation [5], and tree species in mid- and high latitudes respond with heightened vulnerability [6]. Climatic warming induces range shifts, including upward and northward migrations and local extinctions, thereby reducing biodiversity, ecosystem stability, and long-term sustainability [7,8]. Forestry must therefore address new challenges that call for adaptive management strategies to ensure resilience [9,10,11]. A sound understanding of GCC impacts, particularly those associated with increasing air temperature and long-term droughts, is essential for developing sustainable silvicultural approaches [12,13].

In recent years, research has increasingly focused on how GCC alters tree growth patterns and changes in species composition [6,14,15,16,17]. In European forests, long-term warming, irregular precipitation, and more frequent extreme events are accompanied by a higher incidence of secondary pest outbreaks, together creating pressures that require a reorganisation of stand structure and species diversity [18,19]. Determining the most suitable tree species composition is a long-term process complicated by considerable uncertainties [20]. It is well established that GCC has strong impacts on tree growth processes, vitality, and interspecific competition [21,22].

Impacts of GCC vary considerably with elevation and are often species- and site-specific: in higher mountains, tree growth may be enhanced where temperature limits productivity, whereas in lowlands or moisture-limited sites, it is typically reduced [23,24]. Documented declines or increases in growth and distribution, especially in coniferous species, include Scots pine (Pinus sylvestris L.) in Germany [25], silver fir (Abies alba Mill.) across southern and central Europe [26,27], and Norway spruce (Picea abies [L.] Karst.) in Austria and the Czech Republic [28,29]. Among these, Norway spruce appears especially sensitive, as evidenced by large-scale disturbances in Central Europe [28,30,31]. Norway spruce has a shallow root system and a dense, conical crown, making it particularly susceptible to long-term drought and windthrow [32]. Silver fir shows more variable responses, retreating in lowlands but expanding in humid mountain environments [33,34], yet projections of its dynamics remain inconsistent [21]. This shade-tolerant species has a deep, tapering root system and a tall, narrow crown, which enhances drought resilience but limits regeneration in open, dry sites [35]. Scots pine, a pioneer species with a deep taproot and sparse crown, seems comparatively less vulnerable due to higher drought tolerance and adaptability to poor soils, and shows potential for northward shifts in distribution [36,37]. European larch (Larix decidua Mill.) is considered a promising species due to its pioneer character, deep roots and tolerance to summer heat [38], although in the lowlands it is increasingly threatened by drought stress [39,40]. By contrast, information on yew (Taxus baccata L.), a slow-growing, shade-tolerant species with dense evergreen foliage and a deep, spreading root system, is still limited [41]. Available evidence, however, points to negative effects of hot and dry summers, with a tendency for the species to retreat from the southern margins of its distribution [42,43].

The successful silviculture of these Central European native conifers under GCC requires a comprehensive understanding of their ecological requirements, stand structure, and growth dynamics. Despite numerous studies on individual species, comparative assessments of multiple native conifers growing under identical environmental conditions remain limited. Such comparisons are essential for identifying species-specific sensitivities and adaptive capacities under GCC. We hypothesise that the studied conifer species (Picea abies, Abies alba, Pinus sylvestris, Larix decidua, Taxus baccata) differ significantly in their growth and sensitivity to climatic drivers due to their contrasting ecological strategies and physiological tolerances. In particular, species traditionally considered climatically sensitive, such as Norway spruce, are expected to exhibit stronger growth limitations under warming and drought conditions than more tolerant species such as Scots pine or European larch. The present study, therefore, aimed to assess (i) production potential including carbon sequestration, (ii) stand diversity and structural attributes, (iii) radial growth dynamics, and (iv) the influence of climatic drivers (air temperature, precipitation, extreme events) on the growth of monocultures of five conifers in mid-elevation forests of the Czech Republic. The inclusion of the rarely studied yew, a legally protected species in the Czech Republic, provides important new insights into its responses to GCC and highlights the relevance of this study for ecological research, conservation practice and forest management.

2. Materials and Methods

2.1. Study Area

The study area is managed by the private owner Colloredo-Mannsfeld and lies at an elevation of 405–545 m above sea level. Slope gradients range from 2 to 13°. For data collection and the establishment of permanent research plots (PRPs), stands located within the coordinate ranges 49.8057–49.8283 N and 13.6916–13.9169 E were selected. The investigated stands consist predominantly of monocultures of the target tree species (silver fir, European yew, Norway spruce, Scots pine, and European larch), with admixtures of European hornbeam (Carpinus betulus L.), silver birch (Betula pendula Roth), Scots elm (Ulmus glabra Huds.), and sessile oak (Quercus petraea [Matt.] Liebl.), collectively accounting for 0%–5% of the stand composition. All investigated forest stands in the study area were established through artificial regeneration, using the minimum number of seedlings prescribed by law, ranging from 4000 for European larch to 9000 for Scots pine. These stands are managed using uniform silvicultural practices, including consistent thinning intensity and other management interventions. According to the Forest Management Plan, the age of the monitored stands ranges between 62 and 73 years.

Climatic conditions correspond to a humid continental climate (Dfb) according to the updated Köppen–Geiger classification [44]. In the early 21st century (2000–2024), the mean annual air temperature is approximately 8.5 °C, peaking in July at 18.4 °C, and the annual precipitation totals 775 mm, with the maximum in June. The average number of days with snow cover is 51 per year, the number of ice days (Tmax < 0 °C) reaches up to 19, and the maximum snow depth observed did not exceed 40 cm. (Czech Hydrometeorological Institute). Typologically, the stands are classified as Acidic Oak-Beech (Querceto-Fagetum acidophilum) and Nutrient-medium Oak-Beech (Querceto-Fagetum oligo-mesotrophicum) forest site types [45]. Soil conditions are represented by modal cambisols, developed predominantly on volcanic bedrock (rhyolite, dacite, and andesite).

2.2. Data Collection

To investigate stand structure and productivity, the Field-Map system (IFER-Monitoring and Mapping Solutions Ltd., Jílové u Prahy, Czech Republic) was employed. A total of 25 permanent research plots (25 × 25 m each) were established, i.e., five plots for each tree species. In August 2024, the spatial positions of individual trees with a diameter at breast height (DBH) ≥ 7 cm were recorded [40]. For each tree, crown projection measurements were taken in at least four cardinal directions. DBH was determined using a Mantax Blue calliper (Haglöf, Långsele, Sweden) with a precision of 1 mm, measured along two perpendicular axes. Total tree height and the height to the base of the live crown were recorded with a Vertex Laser hypsometer (Haglöf, Långsele, Sweden) with an accuracy of 0.1 m.

To analyse radial growth, increment cores were extracted at 1.3 m above ground using a Pressler auger (Haglöf, Långsele, Sweden), oriented along the slope gradient. From each tree species, 30 vigorous dominant or co-dominant trees according to [46] were randomly selected (RNG function, Excel). This selection was made to represent the primary growth response to climatic conditions, while minimising the influence of competition; sub-dominant and suppressed trees were excluded [47]. Cores were evenly distributed across the PRPs (5 per plot) for each species. Increment cores of yew were collected under a permit issued by the Nature Conservation Agency of the Czech Republic in accordance with the Act No. 114/1992 Coll., on Nature and Landscape Protection.

Annual ring widths were measured with 0.01 mm precision using a LINTAB measuring table and an Olympus binocular microscope (Olympus Corporation, Tokyo, Japan), and the data were processed in TSAPWin software (version 4.81, Rinntech, Heidelberg, Germany). Climate influences on growth were assessed using long-term meteorological records from the Zbiroh–Švabín station (476 m a.s.l.) for the period 1971–2023 (range defined by the start of meteorological measurements and the end of tree sampling), which corresponds to the time span used for the dendrochronological analyses. Monthly average air temperature and monthly accumulated precipitation were analysed to examine their relationship with tree-ring development. Historical data on solar radiation were also available for the study area.

2.3. Data Analyses

Tree layer structure, diversity, and production attributes were quantified using the SIBYLA Triquetra 10 software, based on spatially explicit, tree-level measurements [48,49]. All dendrometric parameters recorded in the field (see Section 2.2) were employed as input data, including species identity, spatial coordinates, total height, DBH, crown width, live crown base height, and age. Tree volumes were estimated using species-specific allometric equations [50]. Stand-level parameters, including crown closure [51] and the relative stand density index [52], were derived from measured density indicators. The relative species-specific SDI was calculated as the ratio of the stand density index to its maximum value. The stand density index represents the theoretical number of trees per hectare, if the mean quadratic diameter of the stand component were equal to 25 cm [52]. The maximum SDI value for each tree species was derived from the yield-table model. Above-ground tree biomass (stem, branches and needles) was derived from models provided by [50]. The biomass of tree roots was calculated using a model [53]. The carbon sequestration (C_BIO) in coniferous trees was calculated according to [54], based on the carbon fraction in dry matter.

Structural diversity was evaluated through both horizontal and vertical perspectives (

Table 1. Overview of indices describing the stand diversity and their common interpretation.

Criterion	Quantifiers	Label	Reference	Evaluation
Horizontal structure	Aggregation index	R (C&Ei)	[55]	mean value R = 1; aggregation R < 1; regularity R > 1
Vertical structure	Arten-profile index	A (Pri)	[56]	range 0–1; balanced vertical structure A < 0.3; selection forest A > 0.9
	Vertical div.	S (J&Di)	[57]	low S < 0.3, medium S = 0.3–0.5, high S = 0.5–0.7, very high diversity S > 0.7
Structure differentiation	Diameter dif.	TMd (Fi)	[58]	range 0–1; low TM < 0.3; very high differentiation TM > 0.7
	Height dif.	TMh (Fi)
	Crown dif.	K (J&Di)	[57]	low K < 1.0, medium K = 1.0–1.5, high K = 1.5–2.0, very high differentiation K > 2.0
Complex diversity	Stand diversity	B (J&Di)	[57]	monotonous structure B < 4; uneven structure B = 6–8; very diverse structure B > 9

). Horizontal structure was quantified by [55] aggregation index, whereas vertical structure was assessed using the Arten-profile index [56] and the vertical diversity index [57]. Stand structural differentiation was further characterised using diameter and height differentiation indices [58] and crown differentiation metrics [57]. Finally, a complex stand diversity index reflecting overall biodiversity was calculated [57], integrating tree species diversity, vertical stratification, spatial distribution, and crown differentiation. Detailed methodology for the computation of these indices is provided by [59].

Dendrochronological data from coniferous tree species were processed using R software (version 4.3.2, R Core Team, Vienna, Austria) with the “dplR” package [60,61]. Each tree series was detrended using a negative exponential function combined with a fitted spline, following the standard procedures outlined for dplR [62]. Detrending removes the age-related growth trend while preserving high-frequency climatic signals [63,64]. For the detrended data, the Expressed Population Signal (EPS) was calculated. EPS represents the reliability of a chronology as the proportion of the variance shared with a hypothetical infinite tree population. Only data series exceeding the EPS threshold of 0.85 were considered suitable for comparison with climatic records [62]. Furthermore, Rbar (mean interseries correlation) and the GINI index (average interannual growth variability in the series) were calculated. The identification of negative pointer years (NPY) followed the approach outlined by [65,66]. For each cored tree, a pointer year was defined as a year in which the annual ring width fell below 40% of the mean increment of the preceding four years. A negative pointer year was considered significant for a plot if at least 20% of the trees exhibited such a pronounced reduction in radial growth. To investigate the relationships between climatic variables (monthly air temperatures and precipitation totals) and radial growth, analyses were performed using the DendroClim software (version 1.0) [67].

Statistical analyses of diversity and production parameters among individual tree species were conducted using Statistica (version 14.1.0.8, TIBCO, Palo Alto, CA, USA). Data were first tested for normality using the Shapiro–Wilk test and then for homogeneity of variance using the Bartlett test. When both requirements were met, the differences among the examined parameters were tested using analysis of variance (ANOVA). If the normality and variance assumptions were not met, the investigated characteristics were tested using the nonparametric Kruskal–Wallis test. For post hoc pairwise comparisons, Tukey’s Honestly Significant Difference (HSD) test was used following ANOVA, whereas the procedure of multiple comparisons described by [68] was implemented after the Kruskal–Wallis test. Significant differences among groups are indicated by distinct lowercase letters; groups labelled differently are considered statistically distinct. Principal component analysis (PCA) was performed in the CANOCO programme (version 5.15) [69] to evaluate the relations between the stand structure, production parameters, and tree species on 25 research plots. Before analysis, the data were standardised and centralised. The results of PCA were presented as a diagram of species and environmental variables.

3. Results

3.1. Productive Potential

Significant differences among the studied tree species were observed across all measured dendrometric parameters (

Table 2. Basic stand characteristics differentiated by tree species in 2024; significant differences are indicated by different letters, and p-values are underlined.

Species	dbh	h	f	v	N	BA	V	HDR	CC	SDI	CBIO
	(cm)	(m)		(m3)	(trees ha−1)	(m2 ha−1)	(m3 ha−1)		(%)
Fir	38.2 b	26.8 b	0.45 bc	1.54 b	650 ab	58.8 c	711 b	72.5 b	0.91 c	87.0 bc	238.8 bc
Larch	33.8 b	28.0 b	0.36 a	0.91 a	606 ab	54.0 bc	548 b	83.4 bc	0.98 c	83.6 bc	267.7 c
Pine	31.5 ab	28.1 b	0.45 bc	1.01 b	594 ab	45.8 ab	590 b	89.2 c	0.86 bc	80.2 ab	243.1 bc
Spruce	32.9 b	27.2 b	0.43 b	1.01 b	531 b	44.5 ab	526 b	82.7 bc	0.67 a	72.8 a	177.7 b
Yew	21.5 a	12.2 a	0.47 c	0.21 a	1031 a	36.3 a	209 a	57.1 a	0.71 ab	90.7 c	87.7 a
test	ANO	KW	KW	ANO	KW	KW	ANO	KW	ANO	ANO	ANO
Statistic	5.0	17.2	24.0	4.7	9.8	16.5	14.7	18.0	11.5	10.4	21.3
p-value	0.009	<0.001	<0.001	0.012	0.048	<0.001	<0.001	<0.001	<0.001	<0.001	<0.001

Notes: dbh—quadratic mean diameter at breast height; h—mean tree height; f—form factor; v—mean stem volume; N—number of trees per hectare; BA—basal area; V—stand volume; HDR—height-to-diameter ratio; CC—canopy closure; SDI—stand density index; C_BIO—carbon sequestration in biomass; ANO—Analysis of Variance; KW—Kruskal–Wallis test; Statistic—H or F according to tests.

), with the smallest variation detected in tree density (p = 0.048). Yew exhibited the lowest mean DBH (21.5 cm) and tree height (12.2 m), whereas fir had the largest diameters (38.2 cm) and pine reached the greatest heights (28.1 m; p < 0.01). No significant difference in height was found among all tree species except for yew (p < 0.05). Fir also showed the highest mean stem volume (1.54 m³) compared to yew (0.21 m³). Total stand volume was maximal in fir (711 m³ ha⁻¹). Pine, larch, and spruce showed 17.0%, 22.9%, and 26.0% lower productivity, respectively, and differed significantly from one another. Yew had the lowest stand volume (70.6%) and formed a separate homogenous group. Similarly, stand basal area reached its maximum in fir (58.8 m² ha⁻¹). Canopy closure was most pronounced in larch (0.98), and the stand density index (SDI) was highest for yew (90.7). Yew showed the lowest height-to-diameter ratio (HDR = 57.1) compared to pine (89.2) and larch (83.4). From the perspective of GCC, the highest carbon sequestration in biomass was significantly higher in larch (267.7 t ha⁻¹) than in yew stands (87.7 t ha⁻¹).

3.2. Diversity of the Tree Layer

In terms of stand diversity, significant differences among tree species were identified for most parameters, except for the aggregation index, vertical diversity, and overall stand diversity (

Table 3. Basic indicators of stand diversity differentiated by tree species in 2024; significant differences are indicated by different letters, and p-values are underlined.

Species	R (C&Ei)	V (J&Di)	Ap (Pi)	S (J&Di)	TMd (Fi)	TMh (Fi)	K (J&Di)	B (J&Di)
Fir	1.101 ns	0.714 ab	0.451 abc	0.630 ns	0.272 ab	0.197 abc	0.496 a	3.862 ns
Larch	1.118 ns	0.724 ab	0.320 ab	0.597 ns	0.223 ab	0.136 ab	0.735 ab	3.961 ns
Pine	0.992 ns	0.764 ab	0.268 a	0.618 ns	0.181 a	0.123 a	0.578 ab	3.849 ns
Spruce	1.065 ns	0.532 a	0.577 bc	0.588 ns	0.291 ab	0.235 c	0.818 ab	3.535 ns
Yew	1.014 ns	0.910 b	0.588 c	0.662 ns	0.304 b	0.220 bc	1.290 b	4.764 ns
Test	KW	KW	KW	ANO	ANO	ANO	KW	KW
Statistic	4.1	11.1	11.5	0.9	3.8	4.2	9.7	5.0
p-value	0.403	0.020	0.013	0.502	0.036	0.017	0.048	0.343

Notes: R—aggregation index; V—horizontal structure; Ap—vertical Arten-profile index; S—vertical diversity; TM_d—diameter differentiation; TM_h—height differentiation; K—crown differentiation; B—overall stand diversity; ANO—Analysis of Variance; KW—Kruskal–Wallis test; Statistic—H or F according to tests; ns—not significant.

). In terms of horizontal structure, a significant (p < 0.05) difference was found only between spruce and yew. The most pronounced difference (p = 0.013) was found in vertical structure, where yew stands reached significantly higher values (Ap = 0.588), reflecting their marked vertical heterogeneity, in contrast to pine, which exhibited a high degree of homogeneity (Ap = 0.268). Similarly, aside from height differentiation, which was highest in spruce, yew consistently achieved the highest diversity indices (six out of seven). Regarding crown differentiation, fir showed a significantly (p < 0.05) lower value compared to yew. Spatial distribution of trees was generally random with a slight tendency towards regularity, except in Scots pine, which displayed a mildly aggregated horizontal pattern. Regarding overall diversity, all stands of the studied species exhibited relatively uniform, monotone structures (B = 3.535–3.961), except for yew, which showed a markedly heterogeneous stand structure (B = 4.764).

3.3. Dynamics of Radial Growth

Dendrochronological analyses indicated that all tree species reached EPS threshold (0.927–0.965), confirming the reliability of the chronologies and their suitability for further analyses (

Table 4. Characteristics of basic tree-ring chronologies of dominant and co-dominant trees on permanent research plots differentiated according to the tree species (in years 1971–2023).

Species	Mean RW	SD RWI	Age	Cores	Rbar	EPS	GINI	NPY
	(mm)		(y)	(n)
Fir	2.72	0.158	70	30	0.357	0.945	0.135	-
Larch	1.34	0.243	68	29	0.487	0.965	0.138	1992, 2020
Pine	1.87	0.134	72	25	0.459	0.953	0.134	-
Spruce	1.96	0.262	59	29	0.362	0.943	0.090	1990, 1998, 2007, 2017
Yew	1.18	0.248	66	27	0.321	0.927	0.081	1976, 2003, 2007, 2017

Notes: Mean RW—mean tree-ring width; SD RWI—standard deviation of ring width index; Age—age of core samples, Cores—number of used core samples in the series; Rbar—mean interseries correlation, EPS—expressed population signal; GINI—average interannual growth variability in the series; NPY—negative pointer years (years with significantly extreme low radial growth).

). Fir exhibited the significantly highest mean radial increment over the period 1971–2023 (2.72 mm), followed by spruce (1.96 mm) and pine (1.87 mm), while the lowest growth rates were observed in larch (1.34 mm) and particularly in yew (1.18 mm). The average interannual growth variability in the series (GINI index) was comparably high in fir, larch, and pine (0.134–138), whereas markedly lower values were found in yew (0.081) and subsequently in spruce (0.090). The mean interseries correlation (Rbar) ranged from 0.321 in yew to 0.487 in larch, indicating moderate common growth variability among the individual series. Annual growth variability (standard deviation of ring width index) was greatest in spruce and yew. In contrast, pine and fir showed the lowest variation (Figure 1). With respect to NPY, spruce and yew recorded the highest number (four NPY each), larch showed two, while no NPY were detected in fir and pine. Fir and yew shared two common NPY (2007 and 2017). The year 2007 was characterised by the lowest recorded April precipitation (3 mm compared to the long-term mean of 32 mm) and unusually warm early-season air temperatures. In contrast, 2017 was defined by extremely low solar radiation during the growing season (up to a 50% reduction relative to average) and exceptionally severe winter conditions, with monthly temperature deviations of up to −5.2 °C from the norm.

3.4. Effect of Climate on Growth

Across all examined tree species, monthly air temperature had a more pronounced influence on radial growth (26 significant months) than precipitation sums (18 significant months). Except for spruce, where air temperature and precipitation contributed equally, air temperature generally had a stronger effect on growth (Figure 2). In most species, air temperature showed a predominantly negative effect on radial increment. In contrast, pine was the only species exhibiting a prevailing positive relationship, most notably in February (r = 0.45). In terms of specific months influencing growth, the strongest negative correlation was observed in September of the previous year (r = −0.45 to −0.30), and in the current year, it was June (r = −0.40 to −0.20). The correlation between radial growth and air temperature was highest in spruce and larch (6 months each) and the lowest in fir (4 months). For larch and fir, growth was more strongly influenced by temperatures in the preceding year, whereas in pine, spruce, and yew, the current-year air temperatures played a more critical role. Growth responses to mean annual temperature were negative, with the strongest correlation observed in spruce (r = −0.48, p < 0.001), while fir showed the weakest relationship (r = −0.18, p > 0.05).

The correlation between radial growth and monthly precipitation sums was highest in spruce (6 significant months), followed by yew (4 months). Larch and pine had moderate correlation (3 months), while fir showed the lowest correlation (only 2 months). Precipitation generally exerted a positive influence on growth in all species, except for fir, where a negative effect was observed in the previous year, but a positive one in the current year (Figure 3). For all coniferous species except larch, precipitation in the current year had a stronger effect on radial growth compared to the previous year. Among individual months, September of the preceding year was the most influential (r = 0.27), while in the current year, July had the strongest impact (r = 0.28–0.42), as seen consistently across all studied species. In contrast to temperature, annual precipitation totals showed generally positive relationships with growth, most pronounced in spruce (r = 0.41, p < 0.01), while larch exhibited only a weak association (r = 0.12, p > 0.05).

3.5. Interaction Among Production, Diversity and Species

The results of the PCA, illustrating the relationships between production potential, stand structure, diversity, and different tree species, are presented as an ordination diagram in Figure 4. The first axis explained 51.6% of the total variance, the first two axes together accounted for 67.4%, and the four axes combined explained 85.5% of the data variability. The x-axis reflects stand volume, mean height, and carbon sequestration. Production-related characteristics such as stand volume, basal area, and carbon sequestration were positively correlated with each other, but negatively associated with parameters of structural differentiation. Increasing stand density was accompanied by higher overall diversity and canopy closure. The least explanatory variable was the aggregation index. Among the studied species, fir showed the highest variability across PRPs, whereas larch was characterised by relatively homogeneous data. Yew was associated with high stand density and diversity (right part of the diagram), spruce with low stocking and high height differentiation (upper part of the diagram), while the remaining species generally exhibited relatively high production potential.

4. Discussion

4.1. Production Potential of Tree Species

In terms of DBH, mean stem volume, basal area, and stand volume, fir ranked as the most productive species among the five assessed taxa in our study (V = 711 m³ ha⁻¹). In the following section, we focus primarily on stand volume, as it represents the most important indicator of the production potential of forest tree species [70,71]. In the case of fir, similar findings about stand volume have been reported by [35,72,73]. In contrast, ref. [74] documented substantially lower stand volumes of fir stands across different regions of Europe, ranging between 220 and 398 m³ ha⁻¹.

Comparable stand volumes within our study were found for larch (548 m³ ha⁻¹), Scots pine (590 m³ ha⁻¹), and Norway spruce (526 m³ ha⁻¹). Reference [75] reported similar values for first-generation larch stands established on former agricultural land in the Orlické Mountains, Czech Republic (466 m³ ha⁻¹). The production potential of larch is further reflected in its mean annual increment (MAI), which may reach 10 m³ ha⁻¹ or more depending on site conditions [76,77,78]. According to [71], larch achieved MAI of 8.0 m³ ha⁻¹ on former agricultural sites and 6.7 m³ ha⁻¹ on historically permanent forest sites. On nutrient-rich sites, however, larch can attain values as high as 20 m³ ha⁻¹, as documented in Lithuania [76].

The productivity of pine was lower than in our study in most other cases, but highly variable (236–500 m³ ha⁻¹), largely due to its occurrence on poor substrates typical of natural pine sites [79,80]. In pine stands aged 40–46 years, [81] recorded stand volumes of 318–371 m³ ha⁻¹ on reclaimed sites and 370–500 m³ ha⁻¹ on original forest sites, i.e., 22.4% higher. The MAI on reclaimed sites reached 8.0 m³ ha⁻¹, while on original forest land it was 25% higher. Lower values were reported from Estonia, where pine stands on oil shale spoil heaps reached a MAI of only 6.3 m³ ha⁻¹ [82].

In contrast to our findings, significantly lower spruce volumes were reported by [83] in the Krkonoše Mts. (180–407 m³ ha⁻¹), reflecting the harsher site and elevation conditions. Similar results from mountain spruce forests were presented by [24,84,85,86,87,88,89]. Reference [90] also observed lower stand volumes of spruce monocultures in lowland regions of Central Bohemia, where stands have been severely affected by ongoing GCC. A wide range of spruce stand volumes (364–685 m³ ha⁻¹) across different altitudes in the Czech Republic was reported by [30].

The stand volume of yew in our study was 209 m³ ha⁻¹, at a density of 1032 trees ha⁻¹. Reference [87] reported a mixed stand dominated by yew in the “V Horách” Nature Reserve, Czech Republic, where a 75-year-old stand reached 220 m³ ha⁻¹ with a density of 1225 trees ha⁻¹. The MAI there was 2.9 m³ ha⁻¹ [91]. In another reserve in the Křivoklátsko region, a mixed stand with abundant yew reached only 108 m³ ha⁻¹ [92]. In Poland, higher volumes were observed in natural yew communities (165–301 m³ ha⁻¹), although yew itself represented only a minor component of the stand structure [93].

The observed differences in productivity among the studied species support our first hypothesis that conifer species differ significantly in their productivity, growth and sensitivity to climatic drivers due to their contrasting ecological strategies and physiological tolerance [94,95]. Silver fir showed the highest stand volume in our study, which can be attributed to its high growth potential and efficient use of available soil moisture [96,97]. In contrast, European yew exhibited the lowest stand volume, which reflects its inherently slow growth rate and shade-tolerant strategy, typically associated with understory or sub-canopy positions rather than dominant canopy formation [98,99].

4.2. Diversity of the Tree Layer

Tree layer diversity was highest for yew across nearly all parameters except for height differentiation. Subsequently, the focus is placed on the total stand diversity (B) index, which integrates vertical diversity, horizontal structure, and crown differentiation in our case. The B for yew reached 4.76, indicating a relatively uniform stand structure. Other studied species displayed more monotonous structures, with fir (B = 3.86), larch (B = 3.96), pine (B = 3.85), and spruce (B = 3.54). Stand biodiversity is strongly influenced by management practices [92]. Had these stands been managed under close-to-nature approaches, overall stand diversity would likely be higher, particularly in fir and yew, which often develop heterogeneous stand structures [35,92,93,100]. For instance, [89] reported high diversity across all surveyed yew reserves. Fir and yew are key species for maintaining high forest biodiversity due to their shade tolerance, ability to survive in the understory, respond to improved light conditions, ecological plasticity, and coexistence with multiple tree species [35,72,73,101]. Fir stands in other studies also show a wide range of B values. Reference [94] classified fir plots with B = 9.20–10.52 as highly diverse, while [68] reported B = 3.51–3.92 for mid-aged fir stands. The unmanaged fir stand further demonstrated higher tree species richness, greater deadwood presence, and distinct tree and understory layer structures, indicating that fir woodlands abandoned for over 50 years undergo spontaneous structural changes [102].

Stand diversity is influenced not only by management practices but also by historical soil development. In the case of larch, higher overall diversity was observed on forest soils (B = 4.14), whereas former farmland (B = 3.78), despite often having higher nutrient content, exhibited lower total diversity [75]. Pine stands show substantial variability depending on site fertility, B ranging from 2.45 to 8.02 [79,80,81]. Managed and semi-natural pine stands in the Czech Republic also display variable B values [79,103]. In our study, pine stands generally reached the lowest diversity across the analysed indices. Lower diversity is typically observed on reclaimed post-mining soils [81]. Spruce stands in our study show lower B values than fir and yew, but comparable to other mid-elevation or lowland sites, while higher values have been recorded in natural mountain spruce forests [29,83]. Similarly, when comparing our study of managed spruce stands, autochthonous mountain Norway spruce in protected areas exhibited higher vertical diversity as well as a greater tendency toward aggregation [104]. Historical air pollution in the 1980s negatively affected the mountain spruce stand biodiversity [105,106], consistent with findings that soil acidification, or eutrophication, can reduce overall biodiversity [107].

Generally, monocultures show lower biodiversity than mixed stands, reducing adaptive capacity under GCC, since structural and functional diversity enhances ecosystem resilience [108,109,110,111]. Promoting species diversity and structural heterogeneity is recommended as a strategic measure for climate adaptation in forest management [112,113,114,115]. Ecosystem biodiversity encompasses species composition, function, and structure [116], and can be defined taxonomically (species diversity), functionally (functional diversity), or structurally (stand layer differentiation). Numerous studies report positive relationships between tree diversity and forest productivity connected with carbon sequestration, and diversity also increases resilience against pest outbreaks [29,117,118,119].

Overall, yew exhibited the highest total stand diversity and also showed the greatest differentiation in most structural parameters, including vertical structure and crown differentiation, reflecting its shade-tolerant ecology and ability to persist across multiple canopy layers. In contrast, spruce displayed the lowest overall diversity, likely due to its more uniform stand structure typical of managed spruce stands. Pine also showed relatively low structural differentiation, which may be related to its light-demanding growth strategy. This is consistent with findings from the German National Forest Inventory, where spruce and pine forests exhibited the lowest stand diversity across most parameters [120]. These results indicate that species-specific ecological traits, together with management practices, play a key role in shaping the structural diversity of forest stands [121,122].

4.3. Dynamics of Radial Growth and Climate

In our study, pine and fir exhibited the most stable radial growth, whereas spruce and yew showed the greatest variability. Regarding NPY, characterised by pronounced reductions in radial growth, pine and fir showed no NPY, even during extremely stressful periods such as severe droughts in 2003 and 2015–2018. This stability in fir growth is consistent with previous findings showing no significant short-term reductions after extreme droughts; under sufficient soil moisture and moderate climatic conditions, fir can maintain stable growth even in warm environments [123]. Larch experienced two negative years (1992 and 2020). The highest fluctuations in radial growth were recorded for spruce (1990, 1998, 2007, 2017) and yew (1976, 2003, 2007, 2017), each exhibiting four NPY. The absence of NPY in pine and fir is explained by the standard detection procedure applied for all species, which included a 20% threshold and smoothing/detrending of growth series, following commonly used protocols. This approach ensures that observed NPY reflect genuine reductions in growth rather than methodological artefacts.

Across all species examined, monthly air temperatures exerted a stronger influence on radial growth than total precipitation, except for spruce, where both factors had comparable effects in the studied altitudes. The most critical months influencing growth were June for temperature and July for precipitation in our study. These months are important for cambium formation and radial increment, as documented in several studies on conifer growth dynamics in other research [83,124,125].

Among individual tree species, elevated February temperatures were identified as the most influential factor affecting radial growth in pine. Reference [126] reported geographic differences in Scots pine radial growth, with the highest values in the UK (mean annual ring width 2.22 mm), the lowest in the Czech Republic (0.95 mm), and intermediate in Spain (1.33 mm) and Poland (1.25 mm). In our study, Scots pine exhibited a higher mean ring width (1.87 mm) than in the three previously mentioned countries, which can be attributed to a lower stand age and more favourable site conditions. Climatic factors, particularly high air temperatures and low rainfall, increasingly influenced pine growth in Central Europe during 1986–2016 compared to 1951–1985. Critical periods are June and July, when high air temperatures and low precipitation can limit growth. Drought and rising temperatures are major abiotic factors driving pine dieback [127,128,129].

Fir exhibited the significantly highest mean radial growth in our study (2.72 mm). For this tree species, summer precipitation positively affects radial growth, while elevated air temperatures often reduce growth due to increased evapotranspiration. This pattern has been confirmed in the Czech Republic and other European regions [27,130,131]. Fir demonstrates high plasticity, buffering temperature and moisture extremes in mixed stands [132]. In addition, this tree species had the lowest sensitivity to precipitation effects on growth among the five studied conifers.

Larch radial growth is sensitive to late summer temperatures and precipitation during key months [133,134]. Cold and wet Septembers in the previous year can prematurely end radial growth in northern and mountainous provenances [135,136]. In our research, positive temperature effects during May–June and summer precipitation were particularly important, whereas extreme dry (especially in July) or cold conditions reduced radial growth.

In our study, spruce was identified as the most sensitive tree species, with radial growth strongly negatively affected by elevated air temperatures and, in particular, by precipitation deficits during the growing season. Similarly, a study from Germany demonstrated that spruce is strongly limited by water availability during the summer period [137]. This pattern is consistent with previous studies showing that the positive effects of summer temperatures are observed only when water availability is adequate [83,138,139]. Drought also promotes bark beetle (Ips typographus L.) outbreaks, increasing spruce mortality in Central Europe [140]. The highest growth of spruce was observed on soils with better water retention (cambisols) compared to rendzinas, confirming spruce preference for cool, moist conditions [60,63,141].

European yew showed the lowest and most variable radial growth (1.18 mm) in our study. This is likely due to the pronounced competitive interactions experienced by yews, as well as their specific growth ecology [93]. Moreover, male and female trees differ in growth due to higher reproductive costs in females [142,143] as yew is dioecious, whereas spruce, fir, pine, and larch are monoecious, carrying both male and female reproductive structures on the same tree. Yew is shade- and drought-tolerant [144,145], with winter temperature being critical for radial growth in the Caucasus [146]. In Poland, winter and early spring temperatures, combined with summer drought, largely determine yew growth, while populations near the southern range margin are particularly sensitive to dry periods [147,148,149].

With respect to the second hypothesis, which was only partially confirmed. Pine with fir (not as in the case of larch in the hypotheses) showed the most stable radial growth with no negative pointer years, reflecting high resistance to climatic stress. Spruce and yew had the greatest variability and most negative years, indicating strong sensitivity to temperature and precipitation extremes. Similarly, studies from Central Europe indicate that spruce is more sensitive to climate change—particularly to extreme drought events—compared to fir [150,151] and pine [152], reflecting its higher vulnerability to climate stress. Growth was generally more influenced by temperature than precipitation, with June and July being critical months. These patterns reflect species-specific strategies: fir and pine are stable and plastic, while spruce and yew are more sensitive or variable due to their ecological traits. Similarly, other studies comparing multiple conifer species across Europe have reached consistent conclusions, identifying silver fir as the least sensitive species of the comparison [153].

4.4. Limitations and Scope

Our inference is constrained by a limited number of research plots and the absence of repeated long-term inventories, although radial growth series from increment cores provided a multi-decadal perspective (1971–2023), stand-level structural dynamics through time could not be fully captured. In addition, all plots are monocultures sampled within a single mid-elevation region in the Czech Republic, and stands were relatively even-aged (62–73 years). These design choices minimise disruptive factors but may limit generalisability to mixed stands, other elevations and soil conditions, and broader age structures. Climatic responses were assessed with monthly aggregates; therefore, sub-monthly heatwaves, compound hot-dry events, and lagged carry-over effects can be underestimated. Above- and below-ground carbon estimates rely on allometric equations; thus, species-specific parameter uncertainty and wood-density variability introduce additional uncertainty. One factor that may influence the results is the initial planting density at stand establishment; however, this was determined by legal requirements and the ecological demands of each tree species. Although this may introduce some variation in growth outcomes, the data still reliably reflect species-specific growth patterns and structural responses under the conditions at the studied site.

To address these caveats, future work should extend the network to mixed-species stands and multiple regions, span wider age classes, and couple ring-width analyses with mechanistic water-balance metrics, such as Standardised Precipitation Evaporation Index (SPEI), and event-scale extremes. Importantly, the rank ordering observed in our study, specifically fir highest in volume, larch highest in biomass carbon, and spruce the most climate-sensitive, was consistent across plots and robust in sensitivity checks, supporting the management implications despite these limits.

5. Conclusions

At mid elevations in Central Europe, studied coniferous species differ markedly in production and climate sensitivity. Silver fir attained the highest stand volume (711 m³ ha⁻¹), European larch maximised biomass carbon (267.7 t ha⁻¹), and yew combined low production (V 209 m³ ha⁻¹, C_BIO 87.7 t ha⁻¹) with the greatest structural diversity, while pine showed the lowest diversity. Radial growth was most constrained by warm Junes and dry Julys. In the mid-elevation sites, growth was influenced significantly more by monthly air temperature than by precipitation. Norway spruce emerged as the most climate-sensitive species, consistent with its documented sensitivity under recent warming, whereas fir and pine were comparatively more resilient. Fir also exhibited the significantly highest mean radial increment (2.72 mm), while yew showed the lowest growth rate (1.18 mm). Structural diversity, particularly pronounced in yew stands, may enhance production and resilience, whereas low-diversity stands, such as pine stands, may be more vulnerable. Managing for structural diversity and prioritising climate-robust species, especially fir, offers a pragmatic pathway to sustain production and stability under GCC.