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Article

Climate Sensitivity and Tree Growth Patterns in Subalpine Spruce-Dominated Forests of the North-Western Dinaric Alps

by
Marko Orešković
1,
Domagoj Trlin
1,
Igor Anić
1,2,
Milan Oršanić
1,
Luka Prša
1 and
Stjepan Mikac
1,*
1
Department of Forest Ecology and Silviculture, Faculty of Forestry and Wood Technology, University of Zagreb, Svetošimunska cesta 23, 10000 Zagreb, Croatia
2
Croatian Academy of Sciences and Arts, Zrinski trg 11, 10000 Zagreb, Croatia
*
Author to whom correspondence should be addressed.
Forests 2024, 15(11), 1972; https://doi.org/10.3390/f15111972
Submission received: 8 October 2024 / Revised: 31 October 2024 / Accepted: 6 November 2024 / Published: 8 November 2024
(This article belongs to the Special Issue Effects of Climate Change on Tree-Ring Growth)
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Abstract

:
The mountain forests in Europe, especially the ecosystems dominated by Norway spruce [Picea abies (L.) Karst], are facing major challenges due to climate change. Climatic stress factors such as increased temperatures and drought contribute to reduced growth and increased mortality, especially at lower altitudes. In this study, which was conducted in the northern Velebit region, the growth dynamics and climate sensitivity of Norway spruce were analyzed using standard dendrochronological methods. The focus was on samples collected at altitudes between 1135 and 1545 m. The results show two different growth trends: a positive trend from 1950 to 1977, followed by a negative trend from 1977 to 2013. Precipitation proved to be a key factor for the stability of spruce growth, while the high summer temperatures of the previous year correlated negatively with growth increment. In addition, trees at higher altitudes showed greater resistance to climatic stress. These results underline the crucial role of precipitation and site-specific conditions in maintaining the vitality of spruce forests in mountainous regions, and suggest that climate change could further destabilize spruce ecosystems in the Dinaric Alps.

1. Introduction

The mountain forests in Europe, especially those dominated by spruce [Picea abies (L.) Karst.], are very sensitive to climate change [1,2]. Their survival is increasingly threatened, which is a cause for concern due to their ecological, economic and cultural importance. In order to make informed management decisions, it is important to understand the future development of these ecosystems, especially to improve their resilience. Environmental changes resulting from recent climate change are exerting considerable pressure on all tree species, especially spruce [3,4]. This pressure is primarily manifested by increased mortality rates due to more frequent natural disturbances such as droughts, wind damage and bark beetle infestations [5,6,7].
Species with a limited geographical range are more sensitive to climate variability, which consequently reduces the suitability of their habitat [8] or weakens their competitiveness compared to other tree species, such as European beech [9]. Acclimatization, adaptation and migration can partially mitigate the negative effects of climate change [10], while the complex topography of mountain areas can help to mitigate some of these effects by creating climate refugia [11]. Species resilience and the ability to adapt, as well as understanding the effects of climate on tree physiology and growth, are critical mechanisms for the conservation of existing mountain forest ecosystems [12,13,14].
In general, climate is the most important factor limiting tree growth in cold and humid environments such as mountain regions [15]. However, microhabitat conditions and local topographic factors can significantly alter climate sensitivity [16,17]. High air temperatures without significant changes in precipitation patterns can lead to drought stress that limits tree growth or weakens their vigour [5,6], although in some situations they can have a positive effect on productivity [16,18].
Norway spruce is one of the most widespread conifer species in Europe. Its natural range extends from the Arctic Circle in the north to the southwestern and western areas of the Alps, the Carpathians and the Balkan Peninsula, reaching the northernmost part of Greece [19]. In view of its wide geographical distribution and its importance for European forestry, numerous dendroclimatological studies have been carried out on Norway spruce in Central and Western Europe.
An important ecological question related to global change therefore considers the comprehensive understanding of tree growth along elevational gradients [20,21] and landscape diversity [22], which can shed light on how mountain forests might respond to warming. Spruce growth shows a positive relationship with air temperatures in March, April, June and July of the current year. With increasing altitude, the influence of spring temperatures (March–April) on growth decreases, while the positive influence of summer temperatures (June–July) is more pronounced [23]. At lower altitudes, spruce growth is restricted by high summer temperatures, while at higher altitudes it reacts positively to warmer conditions [5,13,24]. Similar results were found by Obladen et al. [25] in central Germany, where annual spruce growth at lower elevations was strongly correlated with summer drought and soil moisture, while at higher elevations spruce growth was positively correlated with summer temperatures.
Given the shift in warming attributed to climate change, numerous studies have focused on analyzing the responses of spruce before and after these observed changes [26]. Rising spring and summer temperatures, lower precipitation and more frequent droughts have increased the sensitivity of spruce to drought in Central Europe since the 1970s [5,25]. Its shallow root system makes Norway spruce particularly susceptible to drought [6,27,28], which poses a serious threat to its survival in the face of climate change [29,30]. Due to frequent heatwaves and droughts, the resilience of spruce populations is predicted to decline, especially at the edge of their range [31,32,33].
In this context, particular attention is paid to spruce stands at the edge of their range, such as in the Dinaric Alps, where Norway spruce reaches its southernmost distribution (Figure 1). The spruce stands on the northern Velebit are monotypic and occur in small fragments within subalpine beech forests. Although anthropogenic influences on Dinaric ecosystems are relatively negligible compared to Central Europe [34], climate change has also led to an increase in the frequency and intensity of natural disturbances here [35]. The causes of forest dieback in northern Velebit are not yet fully understood, but it is assumed that they result from the complex interplay of unfavorable ecological factors. In the last ten years, natural spruce stands in this region have been significantly affected by forest dieback. According to estimates [36], almost 40% of the spruce forest area has died completely since 2005. A large part of this area has now been taken over by beech (Figure S1). This alarming trend underlines the need for detailed research to identify the specific causes of species extinction and to develop strategies for the conservation of these vital ecosystems.
Studies on the climate sensitivity of Norway spruce in the Dinaric Alps show a clear negative influence of summer temperatures and a positive influence of precipitation and humidity, suggesting that the growth of spruce stands in this region is limited by the availability of moisture [37,38].
It can be assumed that the growth and development of spruce forests in the Dinaric Alps will come under increasing pressure in the future due to climate change, leading to a higher mortality rate, especially with more frequent heat waves and droughts. In order to better understand the relationship between climate and spruce growth and to predict future developments, this study pursues the following objectives:
(i)
Investigate and analyze historical trends of radial growth of Norway spruce.
H—The radial growth of Norway spruce will exhibit significant variations over time, reflecting the influence of climatic and environmental changes;
(ii)
Explore the relationship between radial growth and climate variables and identify the main climatic factors influencing Norway spruce growth in the study area.
H—There is a significant correlation between the radial growth of Norway spruce and specific climate variables (temperature, precipitation and SPEI);
(iii)
Assess the influence of altitude on the direction and intensity of climate sensitivity along the local altitudinal gradient.
H—The sensitivity of Norway spruce to climatic factors will vary with altitude, showing increased sensitivity to high temperature at lower elevations.

2. Materials and Methods

2.1. Research Area

The study was carried out in the north-western Dinaric Mountains, on the massif of northern Velebit. The study area lies within the boundaries of the Northern Velebit National Park (NPSV, 45.24° N, 17.07° E). The western border of the park marks the boundary between two biogeographical regions—the Mediterranean and the continental regions (Figure 1). The 10,900-hectare park is dominated by pure beech forests, mixed beech–fir forests, submontane beech forests, Norway spruce forests and, at the highest altitudes, dwarf mountain pine forests (Pinus mugo). Pure mountain Norway spruce forests, covering around 2200 hectares [39], have a stand density ranging from 360 to 820 trees per hectare and occur at altitudes between 1000 and 1600 m. The survival of spruce in this area compared to the competing beech is due to the specific karst topography, which includes cooler, more humid sinkholes, hollows, frost depressions, and north-facing slopes that significantly change the macroclimatic conditions of the region. The Northern Velebit National Park is the youngest national park in Croatia, established in 1999, and no economic activities have been carried out in the area since its designation. The studied stands were previously extensively managed and are thought to have been created by secondary succession on former pastures and meadows. Structurally, the stands are pure (monotypic) with the complete dominance of spruce, and they show an even distribution of breast-height diameters, which are distributed in smaller units within the subalpine beech forest belt.
The study area is characterized by a typical mountain climate [40]. The average annual air temperature is 3.9 °C and the total annual precipitation is 1937 mm. July is the driest and warmest month with an average precipitation of 92 mm. The absolute minimum temperature during the observation period is −28.6 °C, while the absolute maximum temperature is 28.3 °C [41]. In terms of seasonal distribution, most of the precipitation falls in winter (approx. 45%). Due to its location at the intersection of two biogeographical regions, NPSV is a crossroads of continental and maritime climates, characterized by frequent strong winds such as the Bora, which can reach speeds of over 150 km/h [40].

2.2. Sampling Location and Data Collection

The data for this study were collected in pure Norway spruce stands at altitudes between 1115 and 1560 m. Along this selected altitudinal gradient, 10 circular plots with a radius of 21.84 m (area = 1500 m2) were established. Plots P01 and P10 are located on northern slopes, which are characterized by a cooler microclimate and less solar radiation. In contrast, plots P02, P03, P05, P07 and P09 are located on the edges of larger valleys, while plots P04, P06 and P08 are located at the bottom of sinkholes, where the particular topography can lead to unique hydrological and thermal conditions.
In each plot, the diameter at breast height (DBH, cm) and height (h, m) of all trees were measured. For trees with a DBH greater than 10 cm, samples were taken with a 4.9 mm Pressler borer at approximately 1.30 m above the ground. Two samples were taken from each tree opposite to each other and perpendicular to the slope of the terrain to avoid the sampling of reaction wood. A total of 529 individual tree ring width series of 291 Norway spruce trees were precisely dated. The collected samples were air-dried, mounted on wooden holders and sanded with sandpaper of different grits (from 100 to 600).

2.3. Measurement of the Tree Ring Width

Tree ring width (TRW) measurements were performed using CooRecorder 9.6 software [42] on digital images captured with the Advanced Tree Ring Image Capturing System (ATRICS) [43]. The system comprises an automated measuring stage (Isel MS200HT), a binocular microscope (Zeiss Stemi 305, Zeiss, Munich, Germany) and a digital camera (Infinity Lumenera 1, Teledyne Vision Solutions, Ottawa, ON, Canada), all connected to a computer. The “Distance to pith” tool in CooRecorder 9.6 was used to estimate the age of each series. The tree ring series were synchronized both visually and statistically using the TSAP-Win™ software (version 4.81) [44]. The tree ring series were dated using t-values of correlation coefficients [45] and Gleichläufigkeit coefficients [46]. The quality of the dated series was checked using the COFECHA software (version 6.06P) [47]. Finally, the well-dated individual tree ring series were averaged for each tree (TRWt).

2.4. Development of the Chronology

Standardization, i.e., the removal of non-climatic signals and noise, was performed using various methods, including standard (traditional) statistical methods such as the following: (1) negative exponential function (NEXP) applied to stabilized series via a power transformation [48] to create standard (SDT) and residual (RES) chronologies, (2) a 60-year spline applied to the previously calculated basal area increment (BAIspl.), and (3) regional curve standardization (RCS), using the dplR package in R (version 1.7.7) [49]. Index values were calculated as residuals between the measured tree ring widths and the applied functions. The final chronologies were calculated using Tukey’s biweight robust mean [50]. The quality of the chronology was assessed using standard dendrochronological indicators, such as the following: mean sensitivity (MS), which measures the variability of tree growth from year to year [15] and is calculated as the difference between successive rings divided by their mean [51]; first-order autocorrelation (AC1), which explains the variance in growth in the current year compared to the previous year [52]; expressed population signal (EPS), which assesses the reliability of the chronology, with an EPS value above 0.85 indicating the degree to which the chronology represents the hypothesized population [53]; and the mean correlation between series (Rbar). These resulting chronologies (RWI) were used for further analyses of climate sensitivity.
Long-term growth trends were analyzed using a time series of annual basal area increment (BAI). BAI was calculated using the bai.in function in the dplR package [54]. BAI values for each tree were derived from DBH and tree ring width (TRWt) series. The individual series were then compiled to create BAI chronologies for each plot. Differences in BAI dynamics were analyzed across three periods defined by changes in summer air temperature (JAS) for three half-periods: 1942 to 1976 (P1-light blue), 1977 to 2018 (P2-grey), and more recently, 2010 to 2018 (P3-dark blue) (Figure 2b).

2.5. Climate Data

The climate data used to analyze the relationship between the indexed chronologies and the climate were obtained from the nearby state hydrometeorological station Zavižan (44.81° N, 14.97° E, altitude: 1594 m a.s.l.), which is the highest station in Croatia and has been recording data continuously since 1954. Monthly precipitation and mean monthly air temperature data from this station were used. These data were compared with publicly available spatially explicit data for temperature and precipitation: E-OBS v28.0e with a spatial resolution of 0.25° (local values interpolated for the study area and regional averages [55]) and data from the nearby meteorological station Gospić (44.53° N, 15.38° E, altitude: 573 m a.s.l.), which has been measuring long-term climate data since 1872. In addition, precipitation data from the GPCC v2020 analysis dataset (land) with a spatial resolution of 0.25° [56] were used. The slopes of the linear trend for summer temperature were analyzed for two periods: from 1942 to 1976 and from 1977 to 2022 (Figure 1b).
To analyze the impact of drought, the Standardized Precipitation Evapotranspiration Index (SPEI) was calculated using the R-Package SPEI [57]. As the available climate data are limited, the SPEI values were calculated using the Thornthwaite method [58]. SPEI is a multi-scalar index that is used in addition to temperature and precipitation variations to assess drought. Positive SPEI values indicate a water surplus, while negative values indicate a deficit (drought).

2.6. Climate–Growth Analysis

To determine the influence of climate factors on the variability of radial growth of Norway spruce, simple linear (Pearson’s) correlation coefficients were calculated between indexed tree ring width chronologies (RWI) and selected monthly climate variables (precipitation sum, average temperature and SPEI for 1 and 3 months) from June of the previous year to October of the current year for the period 1954 to 2022. The correlation coefficients were calculated using the treeclim package in R (version 2.0.6.0.) [59]. In addition, correlation coefficients between average seasonal climate variables were calculated for three-month periods. The moving correlations were calculated for a period of 35 years with a shift of one year for the climatic elements that proved to be most important in the analysis of climatic sensitivity: the sum of precipitation over 3 months (July–September) and the average air temperature over 3 months (July–September). The moving correlations were calculated using three sources of climate data—local data, E-OBS for temperature and precipitation, and GPCC for precipitation only—to gain insights into the stability of the climate signal over a longer historical period.
Principal component analysis (PCA) was performed with indexed chronologies (RWI) using the standardization method to identify the dominant climatic factor. The first two components (PC1, PC2) explain most of the total variance, regardless of the standardization method.

3. Results

3.1. Structural Characteristics of the Studied Stands

The experimental plots are located at an altitude of 1135 to 1545 m. The studied stands are exclusively monotypic, with Norway spruce being the dominant tree species. Other species, such as beech (Fagus sylvatica) and silver fir (Abies alba), are present, but to a much lesser extent. The stand density varies between 360 and 820 trees per hectare. The average diameter at breast height (DBH) is 44.12 cm and ranges from 33.0 cm to 68.1 cm, while the average height across all plots is 26.9 m, with values ranging from 21.8 m to 35.2 m (Table 1). The frequency distribution of tree age shows a unimodal pattern, with the average stand age ranging from 117 to 174 years, with an average age of 150 years. The oldest recorded tree is 210 years old.

3.2. Basic Statistical Indicators of the Measured Tree Ring Width (TRW) Series

For the purpose of dendrochronological analysis, a total of 529 individual tree ring width series of 291 Norway spruce trees are precisely dated. The average tree ring widths range from 0.98 to 2.49 mm per year. The mean sensitivity (MS) varies between 0.15 and 0.20, with a significant decrease with increasing tree age (R = −0.63, p = 0.00495). The first-order autocorrelation (AR1) of the raw series is between 0.76 and 0.88. The mean inter-series correlation (Rbar) is between 0.70 and 0.90 and is significantly influenced by the average tree age in the plots. With increasing tree age, the inter-series correlation also increases significantly (R = 0.81, p = 0.0044). The time periods suitable for climate analysis, in which 85% of the series have an expressed population signal (EPS), are listed in Table 2. The common time period suitable for analyzing climate sensitivity across the studied plots is from 1887 to 2018.

3.3. Climatic Characteristics

An analysis of the climate data for the area studied, combining different data sources, shows considerable fluctuations in summer air temperatures (average from July to September, JAStemp), especially in the period from 1940 to the present (Figure 1). From 1942 to 1976, average summer air temperatures decreased significantly by −0.09 °C per year, while from 1977 they increased significantly by +0.06 °C per year. The coldest summer was recorded in 1976. Trends for the time series of summer precipitation totals are not significant.
A comparison of the time series of air temperature and precipitation between the Zavižan meteorological station and the available data shows that the best correlation for temperature was obtained with the E-OBSL data, resulting in a correlation coefficient of 0.93. Conversely, the precipitation data show the highest correlation with GPCC (0.93) and E-OBSL (0.88).

3.4. Long-Term Trends in Basal Area Increment (BAI)

Cluster analysis (using the Ward method and Euclidean distance) has identified four groups (clusters) based on the similarities of the transformed values (z-scores) of the basal area increment (BAI) chronologies for the period from 1887 to 2018 (Figure 2a). The chronology of basal area increment (BAI) in the studied plots shows similar values and trends, with the exception of plot P08, where the increment values are significantly higher than in all other plots (Figure S2). The average BAI values for all plots except P08 are 10.27 cm2 (between 3.02 and 27.05 cm2), while the increment in plot P08 is significantly higher, with an average of 27.06 cm2 (between 2.10 and 69.43 cm2). In general, a monotonic upward trend in basal area increment (BAI) can be seen across all plots in the period from 1950 to 1980, followed by a decline in increment in the period from 1980 to 2013, after which positive growth resumed after 2013 (Figure 2b).

3.5. Tree Ring Width Chronologies (RWIs)

The standardized values of the indexed tree ring width chronologies (RWIs) are shown in Figure 3 for the period in which EPS > 0.85 (from 1887 to 2018). When comparing the chronologies for the period from 1887 to 2018 using correlation coefficients and cluster analysis (Figure 3b), we find that the highest similarity exists between the RCS and BAI chronologies (R = 0.96), while the BAI and RES chronologies have the lowest correlation (R = 0.57). As expected, the average values of the first-order autocorrelation (AR1) are highest for the standard chronology (SDT) at 0.64, while the lowest values are found for the residual chronology (RES) at −0.04.
Taking into account the variability of the chronology values, we identify years and periods with above- and below-average growth. The lowest below-average values were recorded in the recent period, particularly in 2009 and 2013, as well as in several other years such as 1988, 1980, 1975, 1950, 1929, 1923, 1906 and 1891 (Figure 3a). We also observe several years with above-average growth, including 2001, 1990, 1981, 1977, 1976, 1969, 1934 and 1919. The shape of the trend variability of the chronologies shows clear periods with a positive trend from 1950 to 1977 and a negative trend from 1977 to 2013. The trend analysis revealed that all chronologies show a significant (p < 0.05) declining trend from 1977 to 2013, while in the period from 1950 to 1977, only the residual chronology does not show a statistically significant trend (Figure 3c).

3.6. Influence of Climatic Factors on Tree Growth

The results of the analysis between climate variables and chronologies (using the standardization method) show a significant (p < 0.05) positive correlation of growth with precipitation (Prec) and drought index (SPEI) and a negative correlation with air temperatures (Temp) in the summer of the previous and current year. Statistically significant correlations (p < 0.05) were found at almost all studied sites for temperature (Temp), precipitation (Prec) and the drought index (SPEI) in individual monthly and seasonal values for the 3-month interval in the period from 1954 to 2018. Regardless of the standardization method, all chronologies show similar values of the correlation coefficients with the climate factors (Figure 4). The highest values of the correlation coefficients were found when using the standard chronology (SDT) with the air temperature in summer (jasT) of the previous year (R = −0.43, average for all sites). Meanwhile, some sites, such as P08, show the lowest correlation values with temperature at R = −0.72. In general, among the individual months, August of the previous year has the lowest correlation values, although significant correlations do not exceed values higher than R = 0.44 for precipitation, R = −0.52 for air temperature and R = 0.53 for SPEI. The values of the correlation coefficients are higher when considering the 3-month seasonal values in the summer period of the previous year (jja-jas).

3.7. Stability of Climate Sensitivity

From the analysis performed, it is evident that the climate sensitivity is not stable across all plots during the observed period (Figure 5). In other words, during the observed period, spruce growth increasingly depends on the previous year’s summer precipitation and is limited by high air temperatures. When analyzing the stability of the climatic signal between the different clusters, we find that the values of the moving correlations show a similar trend but differ in magnitude. Cluster C3 has the highest values of correlation coefficients, while C1 and C2 have lower values.

3.8. Common Climatic Signal

The highest explained variance from the first two principal components in the residual chronologies (RES) is 79.8% (PC1 70.7%, PC2 9.1%), in the standard chronology (SDT) 72.33% (PC1 61.66%, PC2 10.67%), in the RCS chronologies 71.58% (PC1 62.3%, PC2 9.28%) and in the BAI_spl 76.95% (PC1 63.54%, PC2 13.41%).
Principal component 1 (PC1) explains the highest percentage of the total variance and correlates significantly with the chronologies of all studied sites (Figure 6b). Principal component 2 (PC2) explains on average about 9% of the total variance and correlates significantly with site P08 (Figure 6b). The high variance explained by principal component 1 (PC1) indicates the consistent presence of a common signal that significantly influences spruce growth in the studied area.
Simple linear (Pearson) correlation coefficients between selected climate factors (total precipitation, average air temperature and SPEI) and principal component 1 (PC1) for the period from 1954 to 2018 show that tree growth is significantly positively influenced by higher precipitation in the summer months of the previous and current years from July to September (JAS), while it is significantly restricted by high air temperatures in the same period (Figure 6c). Among the individual months, August stands out with the highest values of the correlation coefficients. The most significant negative correlation coefficients for PC1 in standard chronologies (SDT) were found for the average air temperature from June to August (jja, R = −0.64) and from July to September (jasT, R = −0.55) of the previous year.
The analysis of the moving correlations (period of 35 + 1 years) and principal component 1 (PC1) using three climate data sources, namely, the local data from the nearby Zavižan meteorological station, E-OBS and GPCC (for precipitation only), confirmed the temporal stability of the dominant climate signal (jasT and jasP) over different periods of climatological data (Figure 6d). Regardless of the data source, the relationship between climate and spruce growth was pronounced in the period after the 1940s. On a long-term scale (GPCC from 1891), we can observe that the positive influence of the previous summer’s precipitation (jasP) has become increasingly significant over the last century. At the same time, on a slightly shorter time scale (EOBS from 1920), the high air temperatures in the previous summer (jasT) increasingly restricted the growth of Norway spruce from the 1940s onwards (Figure 6d).

3.9. The Influence of Altitude on the Strength of the Climate Signal

In addition to the temporal stability of the climate signal, we analyzed the influence of altitude on the climatic sensitivity of Norway spruce trees in relation to the prevailing climatic factors—precipitation and air temperature-in the summer of the previous year (from July to September) (Figure 7). For this purpose, the correlation coefficients (R) between the spruce growth chronologies, determined by moving correlations with E-OBS data, were compared with the altitude of the studied sites, which was considered a fixed factor. The analysis showed that the effects of high air temperatures on Norway spruce growth decrease with increasing altitude. In other words, trees growing at lower altitudes show a stronger (negative) correlation with air temperature (Figure 7c), regardless of the chosen standardization method (Figure 7a).

4. Discussion

In this study, chronologies of radial growth of Norway spruce were developed using samples collected from ten experimental plots in northern Velebit at altitudes between 1135 and 1545 m above sea level. The resulting chronologies cover the period from 1834 to 2022.
When examining the variability of the chronologies, two distinct periods with different growth trends were identified. A positive growth trend was observed from 1950 to 1977, while a negative trend persisted from 1977 to 2013 (Figure 3c). These changes in growth patterns can be attributed to trends in precipitation and air temperature in the respective periods. From 1942 to 1976, a significant decrease in the trend of average summer air temperatures was observed (−0.09 °C per year), followed by a trend reversal indicating an increase in summer air temperatures (0.06 °C per year) after 1977 (Figure 1b). A similar pattern of climatic influence on spruce growth was found in the study by Čermak et al. [60], which analyzed changes in tree growth in two periods: 1915–1964 and 1964–2012. The authors linked these changes to the above-average temperatures in the latter period, and emphasized that precipitation becomes a decisive factor in spruce growth dynamics with increasing temperatures.
The average basal area increment (BAI) series in the studied plots showed uniform growth values, with the exception of plot P08, where the absolute growth values were significantly higher. This deviation is due to the specific habitat conditions, as plot P08 is located in a deep sinkhole with a very cold microclimate. Large sinkholes facilitate the retention of humidity and lower air temperatures, as well as a higher concentration of nutrients, which are crucial for spruce growth [27,28,61,62].
The analyzed trends in basal area increment (BAI) indicate significant changes during the entire observation period, with a clear upward trend from 1950 to 1980, followed by a decline from 1980 to 2013 and a subsequent recovery from 2013 onwards (Figure 2b). These changes in the trend of basal area increment can be linked to climatic fluctuations during the observed period (Figure 1b).
Cluster analysis identified four groups based on the similarity of transformed values (z-scores) of basal area increment (BAI) chronologies for the period 1887–2018. The differences between the groups did not show clear altitudinal patterns and could not be explained by the different forms of microrelief in which the studied spruces occur (Figure 2a), or by structural characteristics of the stands or their age (Table 1). We hypothesize that the observed clustering is due to different management histories, which could influence the specific responses and subsequent classification of the plots. Such a result indicates that regardless of the specific microrelief conditions, high air temperatures are the dominant factor influencing spruce growth. In particular, increasing air temperatures due to climate change prove to be the primary limiting factor for the growth of the studied populations, as confirmed by other studies [2,5,23].
The results of the correlation analysis between climatic variables and spruce index chronologies show a significant positive influence of the precipitation (Prec) and the Standardized Precipitation-Evapotranspiration Index (SPEI), which is consistent with studies from Central [25,63,64] and Southeastern Europe [37,38,65]. The SPEI is a standardized drought index that uses the monthly difference between precipitation and potential evapotranspiration (PET) and represents a simple climatic water balance [57,58]. This underlines the importance of spring and summer precipitation as key factors for spruce growth, and highlights its sensitivity to moisture availability and drought.
High air temperatures from the previous year have a negative effect on radial increment, a result confirmed by other studies in Southeastern and Central Europe [25,37,63,65,66]. Conversely, in northern Europe, where summers are cooler, higher temperatures can have a positive effect on spruce growth, which indicates regional differences in climate sensitivity [5,25,67].
Moving correlations indicate instability in the climate signal throughout the observation period (Figure 5), with fluctuations following the fluctuations in annual precipitation and average temperatures (Figure 1b,c). These results are consistent with previous studies showing that rising temperatures and increased evapotranspiration increase the sensitivity of spruce to precipitation [60,68]. This means that precipitation will play a crucial role in maintaining the stable growth and vitality of spruce forests under the conditions of future climate change.
All analyzed sites indicate that summer temperatures are the most important limiting climate factor, as confirmed by the principal component analysis (PCA). The first principal component (PC1) explains most of the total variance and shows a significant correlation with the chronologies of all sites (Figure 6b). The second component (PC2), which accounts for about 9% of the variance, is mainly associated with site P08. The Pearson correlations for the period 1954–2018 show that tree growth is significantly positively correlated with higher precipitation in the summer months (July–September) of the previous year and the current year, while higher temperatures in the same period have a negative effect on growth. These results confirm that precipitation is a key factor for spruce growth, while high temperatures limit growth.
With increasing altitude, the average temperature decreases and precipitation increases [69]. It is therefore assumed that spruce populations at lower altitudes are more sensitive to higher temperatures compared to those at higher altitudes. Norway spruce populations along elevation gradients in Central Europe have exhibited climate-induced stress, particularly at mid-elevations where rising temperatures and moisture limitations have substantially impacted growth. Current climatic conditions now indicate that lower and middle elevations are increasingly unsuited to sustaining Norway spruce [2]. Studies confirm that the negative effects of high summer temperatures on spruce growth decrease with increasing altitude [25]. At higher altitudes, the trees show a lower sensitivity to high temperatures, while the trees at lower altitudes show a more pronounced negative correlation with the summer temperatures of the previous year [13,24,35]. Similar patterns have been observed in the Dinaric Alps, where spruce trees at lower elevations suffer stronger negative effects of summer temperatures, while these effects are milder at higher elevations due to cooler conditions [37,65]. Our research also shows that spruce growth patterns at lower altitudes vary under the influence of climatic factors, with temperatures being the main limiting factor, while this influence is less pronounced at higher altitudes. This pattern varies over time and correlates with fluctuations in temperatures within the period studied (Figure 1b).

5. Conclusions

This study presents a comprehensive analysis of growth trends and climatic factors and their effects on the growth of Norway spruce stands in the southwestern Dinarides. The study shows that Norway spruce stands are subject to multiple changes in climate sensitivity over time, with response patterns varying according to altitude.
Numerous studies have shown that certain regions and tree species are more sensitive to climatic fluctuations than others, indicating varying degrees of adaptation to climate change [3,8]. Our results suggest that climate change, in particular the rising air temperatures in the previous summer, has become the dominant factor significantly limiting the growth and development of Norway spruce stands in the studied area.
Although the specific microhabitat conditions in this study did not show a significant influence on the strength and direction of the climate signal, they could play a crucial role in the climate sensitivity of Norway spruce and the long-term sustainability of its populations in this area. This requires additional and more detailed research.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/f15111972/s1, Figure S1: The succession of beech in pure spruce stands following spruce dieback; Figure S2: Long-term growth of basal area increment (BAI) across four sites; Figure S3: Values of the simple linear (Pearson) correlation coefficient (R) between Standardized Precipitation-Evapotranspiration Index (SPEI); Figure S4. Walter-Lieth Climate diagram. Table S1: Linear trend values of basal area increment (BAI) chronologies across sites and different time periods.

Author Contributions

Conceptualization and methodology, M.O. (Marko Orešković) and S.M.; software, D.T.; validation, M.O. (Marko Orešković), S.M., D.T., M.O. (Milan Oršanić), L.P. and I.A.; field work M.O. (Marko Orešković), D.T. and L.P.; writing—original draft preparation, M.O. (Marko Orešković); writing—review and editing, S.M.; visualization, M.O. (Marko Orešković) and S.M.; M.O. (Milan Oršanić) and I.A. helped supervise the project. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Croatian Forests ltd (Hrvatske šume d.o.o.), grant number DIR/19-01/4080;00-04-02/01-19-01.

Data Availability Statement

The original contributions presented in this study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding authors.

Acknowledgments

M.O. (Marko Orešković), S.M. and D.T. were supported by Ministry of Economy and Sustainable Development (Croatia) under the project KK.05.1.1.02.0009 (MEMORIE).

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. (a) The location of the experimental plots (P1–P10) in the Northern Velebit National Park. The black areas indicate pure mountain spruce forests. The red line marks the boundary between the Mediterranean and continental biogeographical regions. (b) Z-score of average air temperature values, (c) sum of precipitation in summer (July to September, JAS), for different climate data sources. Pearson correlation coefficients between time series of summer air temperatures (average from July to September, JJA) from local meteorological stations Zavižan and Gospić, and spatially explicit gridded data series, where meteorological stations are ZAV—Zavižan, GOSP—Gospić, EobL—local value of E-OBS v28.0e, and EobR—regional average for [15-20E, 40-45N] of E-OBS v28.0e.
Figure 1. (a) The location of the experimental plots (P1–P10) in the Northern Velebit National Park. The black areas indicate pure mountain spruce forests. The red line marks the boundary between the Mediterranean and continental biogeographical regions. (b) Z-score of average air temperature values, (c) sum of precipitation in summer (July to September, JAS), for different climate data sources. Pearson correlation coefficients between time series of summer air temperatures (average from July to September, JJA) from local meteorological stations Zavižan and Gospić, and spatially explicit gridded data series, where meteorological stations are ZAV—Zavižan, GOSP—Gospić, EobL—local value of E-OBS v28.0e, and EobR—regional average for [15-20E, 40-45N] of E-OBS v28.0e.
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Figure 2. (a) Results of the cluster analysis of the transformed values (Z-scores) of the basal area increment (BAI) of the chronologies for the period from 1887 to 2018 (a). (b) Time series of average values for each cluster (C1–C4) and (c) moving correlations (over a period of 30 years) between the average values of the time series of basal area increment (BAI) by cluster.
Figure 2. (a) Results of the cluster analysis of the transformed values (Z-scores) of the basal area increment (BAI) of the chronologies for the period from 1887 to 2018 (a). (b) Time series of average values for each cluster (C1–C4) and (c) moving correlations (over a period of 30 years) between the average values of the time series of basal area increment (BAI) by cluster.
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Figure 3. (a) Regional chronology (average of all studied site chronologies) according to standardization method (a), where RCS—regional curve standardization, RES—residual chronology, SDT—standard chronology, BAI_s—basal area increment combined with a 60-year spline and BAI—basal area increment. (b) Dendrogram of the cluster analysis between regional chronologies and (c) linear trends for the periods from 1950 to 1977 and from 1977 to 2013, together with the corresponding statistical indicators.
Figure 3. (a) Regional chronology (average of all studied site chronologies) according to standardization method (a), where RCS—regional curve standardization, RES—residual chronology, SDT—standard chronology, BAI_s—basal area increment combined with a 60-year spline and BAI—basal area increment. (b) Dendrogram of the cluster analysis between regional chronologies and (c) linear trends for the periods from 1950 to 1977 and from 1977 to 2013, together with the corresponding statistical indicators.
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Figure 4. Values of the simple linear (Pearson) correlation coefficient (R) between selected climate factors: air temperature (TEMP) and precipitation totals (PREC) according to the standardization method for each plot separately for the period from 1954 to 2018. * indicates the highest and most important correlation coefficient values.
Figure 4. Values of the simple linear (Pearson) correlation coefficient (R) between selected climate factors: air temperature (TEMP) and precipitation totals (PREC) according to the standardization method for each plot separately for the period from 1954 to 2018. * indicates the highest and most important correlation coefficient values.
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Figure 5. Moving correlations between chronologies and the dominant climate factor (total precipitation Psum (JAS) and average air temperature Tavg (JAS) in the summer of the previous year from July to September) using different sources of climate data. Correlations with precipitation (solid line) and air temperature (dashed line). Values of the standardized 35-year average of precipitation and air temperature for the same months.
Figure 5. Moving correlations between chronologies and the dominant climate factor (total precipitation Psum (JAS) and average air temperature Tavg (JAS) in the summer of the previous year from July to September) using different sources of climate data. Correlations with precipitation (solid line) and air temperature (dashed line). Values of the standardized 35-year average of precipitation and air temperature for the same months.
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Figure 6. (a) Spatial correlation between principal component 1 (PC1) from the PCA of the standard chronologies (SDT) and the average air temperature from July to September of the previous year (jasT) from the EOBS for the period from 1954 to 2018. The black square represents the geographic location of the study area. (b) Correlations (R) of the chronologies with the first (PC1) and second (PC2) principal components according to the standardization method. (c) Simple linear correlation coefficients between PC1 and the total precipitation (Prec.), the average air temperature (Temp.) and the SPEI index for the months from June of the previous year to October of the current year (OCT) and the average seasonal values over a period of 3 months. (d) Moving correlations with dominant climate variables (total precipitation and average air temperature from July to September of the previous year) using different sources of climate data. The gray shaded area indicates the significance threshold for p > 0.05.
Figure 6. (a) Spatial correlation between principal component 1 (PC1) from the PCA of the standard chronologies (SDT) and the average air temperature from July to September of the previous year (jasT) from the EOBS for the period from 1954 to 2018. The black square represents the geographic location of the study area. (b) Correlations (R) of the chronologies with the first (PC1) and second (PC2) principal components according to the standardization method. (c) Simple linear correlation coefficients between PC1 and the total precipitation (Prec.), the average air temperature (Temp.) and the SPEI index for the months from June of the previous year to October of the current year (OCT) and the average seasonal values over a period of 3 months. (d) Moving correlations with dominant climate variables (total precipitation and average air temperature from July to September of the previous year) using different sources of climate data. The gray shaded area indicates the significance threshold for p > 0.05.
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Figure 7. (a) Values of the simple linear (Pearson) moving correlation coefficient (R) between selected climate factors: air temperature (TEMP), (b) total precipitation (PREC) and different chronologies for the period from 1920 to 2018 using the E-OBS data sets. (The gray area represents a period of unexpected lower correlation. (c) shows an example for only one year of the relationship between altitude and air temperature (top) and precipitation (bottom).
Figure 7. (a) Values of the simple linear (Pearson) moving correlation coefficient (R) between selected climate factors: air temperature (TEMP), (b) total precipitation (PREC) and different chronologies for the period from 1920 to 2018 using the E-OBS data sets. (The gray area represents a period of unexpected lower correlation. (c) shows an example for only one year of the relationship between altitude and air temperature (top) and precipitation (bottom).
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Table 1. Descriptive data of the experimental plots, where Elev.—elevation, Age—age in years, DBH—diameter at breast height, H—height and N—tree density.
Table 1. Descriptive data of the experimental plots, where Elev.—elevation, Age—age in years, DBH—diameter at breast height, H—height and N—tree density.
IDLat.Long.Elev.
(m a.s.l.)		Age
(Years)		DBH
(cm)		H
(m)		N
(pcs.ha−1)
P0144.80°14.97°1545117 (61–149)33.0 (12–60.9)21.8 (15.1–27.5)560
P0244.77°15.00°1340169 (122–190)36.5 (17–58.7)25.2 (10.8–32.9)440
P0344.74°15.01°1295148 (88–190)46.5 (21–75)28.5 (15.9–38.7)560
P0444.75°14.98°1455147 (98–180)41.0 (16.4–57.3)23.0 (9.1–29.9)420
P0544.77°15.00°1358168 (49–194)45.3 (15–70.5)29.0 (9.1–37.2)430
P0644.76°14.97°1350158 (92–186)49.6 (24.2–74.9)31.4 (14.7–38.8)620
P0744.74°15.01°1315126 (100–147)40.0 (19.1–72.2)27.3 (8.7–37.7)580
P0844.69°15.05°1135135 (107–210)68.1 (53.0–91.2)35.2 (32.8–38.8)360
P0944.70°15.02°1325174 (161–186)33.4 (10.4–58.9)20.5 (8.7–27.5)820
P1044.78°15.02°1470158 (102–171)47.8 (15.3–72.3)27.8 (5.9–35.3)560
Table 2. Basic statistical data on tree ring width (TRW) series, where trees/series—sample size of trees and series, Span—length of series, mean—average ring width (mm), MS—mean sensitivity, AR—first-order autocorrelation, Rbar—within-series correlation, EPS—expressed population signal.
Table 2. Basic statistical data on tree ring width (TRW) series, where trees/series—sample size of trees and series, Span—length of series, mean—average ring width (mm), MS—mean sensitivity, AR—first-order autocorrelation, Rbar—within-series correlation, EPS—expressed population signal.
PlotsTrees/SeriesSpanMeanMSARRbarEPS > 0.85
P0133 (59)1651.390.210.790.721860–2018
P0226 (49)1880.980.180.890.901837–2018
P0332 (58)1841.500.180.820.761854–2020
P0417 (31)1711.420.170.810.811864–2020
P0536 (63)1911.190.160.840.831834–2020
P0640 (67)1941.540.170.850.811839–2021
P0743 (81)1441.480.180.760.721883–2021
P0816 (32)1922.490.200.880.701887–2020
P0924 (42)1791.020.180.820.801851–2022
P1024 (47)1681.830.160.870.861862–2021
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Orešković, M.; Trlin, D.; Anić, I.; Oršanić, M.; Prša, L.; Mikac, S. Climate Sensitivity and Tree Growth Patterns in Subalpine Spruce-Dominated Forests of the North-Western Dinaric Alps. Forests 2024, 15, 1972. https://doi.org/10.3390/f15111972

AMA Style

Orešković M, Trlin D, Anić I, Oršanić M, Prša L, Mikac S. Climate Sensitivity and Tree Growth Patterns in Subalpine Spruce-Dominated Forests of the North-Western Dinaric Alps. Forests. 2024; 15(11):1972. https://doi.org/10.3390/f15111972

Chicago/Turabian Style

Orešković, Marko, Domagoj Trlin, Igor Anić, Milan Oršanić, Luka Prša, and Stjepan Mikac. 2024. "Climate Sensitivity and Tree Growth Patterns in Subalpine Spruce-Dominated Forests of the North-Western Dinaric Alps" Forests 15, no. 11: 1972. https://doi.org/10.3390/f15111972

APA Style

Orešković, M., Trlin, D., Anić, I., Oršanić, M., Prša, L., & Mikac, S. (2024). Climate Sensitivity and Tree Growth Patterns in Subalpine Spruce-Dominated Forests of the North-Western Dinaric Alps. Forests, 15(11), 1972. https://doi.org/10.3390/f15111972

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