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Article

The Effect of Hydrometeorological Factors on Tree Growth (Abies borisii-regis Mattf.) in Mountainous Watersheds (Central Greece)

by
Aristeidis Kastridis
1,*,
Dimitrios Koutsianitis
2 and
Dimitrios Stathis
1,†
1
Laboratory of Mountainous Water Management Control, School of Forestry and Natural Environment, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
2
Department of Forestry and Natural Environment Management, Agricultural University of Athens, 36100 Karpenisi, Greece
*
Author to whom correspondence should be addressed.
†
Deceased author.
Forests 2025, 16(5), 750; https://doi.org/10.3390/f16050750 (registering DOI)
Submission received: 4 April 2025 / Revised: 24 April 2025 / Accepted: 26 April 2025 / Published: 27 April 2025
(This article belongs to the Section Forest Meteorology and Climate Change)
Browse Figures
Versions Notes

Abstract

:
Tree ring chronologies (tree ring width—TRW, earlywood—EW, latewood—LW) were constructed to investigate fir’s (Abies borisii-regis Mattf.) response to key hydrometeorological factors, namely precipitation, temperature and drought (12-month Standardized Precipitation Evapotranspiration Index, SPEI-12). There has been only one previously published study conducted in the northern area of the species’ expansion (Albania). The current study was conducted in the southern area of the species’ expansion (Central Greece). Precipitation was the most important factor that affected tree growth. May precipitation was positively correlated with LW, while June and July precipitation was positively correlated with both EW and LW. Previous September precipitation was positively correlated with EW and LW. Interestingly, the current September precipitation was negatively correlated with EW. High temperatures in April showed a positive relation with LW, high temperatures in June negatively affected all chronologies, while high temperatures July and August were negatively related with LW. High autumn temperatures in the previous year significantly (negatively) influenced all tree ring chronologies. The SPEI index revealed that wet conditions during May and June positively correlated with high tree growth for both EW and LW, while wet conditions in July and August significantly affect LW formation. Wet conditions in the previous September also had a positive effect on tree growth. SPEI showed similar behavior with precipitation, showing that precipitation is the driving factor in fir growth. The results highlight the importance of summer rainfall and temperature in controlling tree growth in Mediterranean regions. The study revealed significant knowledge on the susceptibility of Abies borisii-regis Mattf. to climate variability and highlighted its consequences for future forest management plans.

1. Introduction

Parameters, such as temperature, precipitation, the type of vegetation, biotic activity, slope, aspect, soil conditions, etc., influence forest ecosystems’ productivity rate and biomass production [1]. Consequently, the combined effect of internal and external factors—including the climate—has a substantial impact, which leads to the synthesis of the tree ring width [2,3]. Tree rings offer long-term data relevant to forest growth and how tree-growth respond to climate variabilities. The frequency, intensity, and duration of severe climatic occurrences are potentially intensified by global climate change [4], resulting in droughts, flood events, and soil erosion [5], where trees are typically negatively affected [6]. Under such circumstances, trees may become vulnerable to the destructive effects of microbes’ action [7,8,9] and present significant mortality rates [10]. In addition to sudden variations in temperature and rainfall, other elements including biotic stress-generating factors, air pollution, and unnatural or deliberate disruptions may have an impact on the structure of wood and anatomical characteristics within the various growth rings [11,12,13].
The cambium can be active for almost the whole year when ideal growing conditions prevail [14]. However, because of the unfavorable climate conditions that appear in many Mediterranean areas (Figure 1), the cambium typically stops dividing temporarily during the summer due to drought phenomena [14], as well as during the long, dry winter in high altitudes or in mountainous places characterized by severely low temperatures [15]. During exceptionally warm temperatures, drought stress usually results in low growth rates, with springtime water deficits being the most common cause [16]. When the water supply is reestablished, the cambium zone’s activity is revived [10]. In Mediterranean regions, the tree-growing season typically begins in late April and lasts until late September [17,18,19,20,21]. It is a common practice, particularly in research activities undertaken in the Mediterranean region, to adopt the 12-month “hydrological” year, which runs from October of one year until September of the next [3,22,23]. Intense hydrological events, hot and dry summers, and mild and rainy winters are the main characteristics of Mediterranean hydro-climatic conditions in lower altitudes [24,25,26]. An important role in tree ring formation is played by the autumn–winter precipitation that occurs prior to growing, as well as the total spring precipitation amount that occurs during the tree ring synthesis. In general, wide tree rings appear in each species in years of high precipitation due to the high amount of water being stored in the ground [3]. Furthermore, spring precipitation is considered essential since there are more plant-physiological processes that require water, such as the intense cambial reactivation.
The opposite relationship between spring temperature and ring width has been observed. The positive correlation between precipitation amount and growth, as well as the inverse association between evapotranspiration and tree growth, may assist in this tendency comprehension. In years with a substantial decrease in precipitation, smaller tree ring widths could be observed [3]. In general, the increase in ring width seems to be higher when the water excess is higher, and vice versa.
Abies alba Mill. and Abies cephalonica Loudon can hybridize, resulting in the endemic hybrid species Abies borisii-regis Mattf., which is found throughout northern central regions of Greece [27] and has a regular distribution between the latitudes of 38 and 40 N. The species grows in less compact rocks, especially flysch, and in the humid microclimate regions [28]. Fir has historically been used in dendroclimatological research conducted in the Mediterranean area [29], including Greece [30,31], due to its remarkable adaptation to a variety of Mediterranean climatic and soil conditions. Fir is an ideal species to examine when assessing the tree ring width response to climate variability, because of its rapid growth and the availability of high-age trees in relation to the available meteorological data. Abies borisii-regis Mattf. fir exhibits wider rings than Abies cephalonica Loudon at the same age and under the same climate conditions, respectively [28]. Papadopoulos et al. have reported that, in several Mediterranean regions, climate has been found to be a significant factor in this species’ growth-ring width variability [3]. Growth-ring width has been found to be positively correlated with winter and spring temperatures and negatively correlated with high summer temperatures. Temperatures in April appear to have a significant influence on the determination of the growing season duration and, consequently, the width of the growth rings for Abies alba Mill. grown in Central Italy, according to Manetti and Cutini [32]. Other research articles indicated that the late spring and early summer rainfalls, which were also observed in studies involving other Mediterranean plant species, appear to be the key climatic factors influencing the growth-ring width of fir [33,34]. However, a significant correlation between growth-ring width and precipitation between July and August has also been observed [3,35]. It appears that the species’ ability to adapt to climatic–soil variations also constitutes a key factor in the growth ring to climate relationship in Central Greece [28] There is only one previously published study concerning the Abies borisii-regis Mattf. [36], which investigated the relation between TRW, EW, LW, and hydrometeorological factors. It is clear that there is inadequate information provided so far in the literature related to the way in which A. borisii-regis responds to climatic variability, the rate of wood synthesis, EW and LW wood ring width, and the level of potential adaptability of this species against the anticipated, forthcoming climate change.
Growth patterns across the various fir populations vary geographically (for example, from south to north in terms latitude, as well as north–south aspect and high–low altitude). Further experiments are needed that can elucidate the way in which fir forests around the world are currently affected and will be affected in future by climate variability; such studies considered to be extremely vital and would certainly contribute to the creation of the required tools needed for reasonable forest management strategies and future plans.
The purpose of this research is to analyze the variability in fir tree ring width and its relationship to critical hydrometeorological variables. The species of A. borisii-regis was investigated, as there is a very limited number of studies and lack of information about how it responds to climate factor variation, particularly in the areas of southern Europe and Greece. In this study, the response of fir trees to extreme and average climatic conditions (min and max temperatures) was thoroughly studied, as well as the year-to-year variability in tree ring width. To the best of our knowledge, this is only the second study to measure and analyze the TRW, EW and LW ring width and correlate the tree ring chronologies with monthly precipitation, temperature (min, max, mean), and SPEI. As the only previous study [36] on that species took place in the northern area of the species’ expansion (Albania), the present work acquires great importance as it was conducted in one of the southernmost species expansion areas, and under different climatic conditions. In the current study, the tested hypothesis was that the “Southern population of Abies borisii-regis Mattf. may present different response and adaptability to climate variability in comparison to North populations”.

2. Materials and Methods

2.1. Study Area, Sampling Process, and Statistical Analysis

In this study, a population of Abies borisii-regis Mattf. was studied. The study area is located in Pertouli University Forest, Pindos Mountain in Central Greece (39.542484°–21.464476°). The altitude of the forest ranges between 1000–1900 m. The main aspect of the forest is south-easterly, the mean annual precipitation is 1471.4 mm, and the mean annual temperature is 9.1 °C (Figure 1). The last decade’s (2009–2018) forest management plan was used to evaluate the site’s quality (pp. 65–74, [37]).
The sample site (Figure 2) was chosen based on two criteria, namely a relatively similar altitude and close proximity to the meteorological station and homogenous site characteristics (slope, aspect, altitude, crown density, and no anthropogenic interactions). The forest section with the number 820 was chosen (https://uniforest.auth.gr/en/web-gis-en/, accessed on 23 February 2025). The main characteristics of section 820 are available online (https://uniforest.auth.gr/gis/perUnitPDFs/pertouli_pdfs/820.pdf, accessed on 23 February 2025, https://uniforest.auth.gr/gis/perUnitPDFs/pertouli_pdfs/820X.pdf, accessed on 23 February 2025). The forest is single-species and uneven-aged with understory vegetation. It is managed under selective logging, the average slope is 50%, the altitude range is between 1200–1340 m, the aspect is S–SE, the crown density is above 85%, the dominant rock is flysch, and the soil type is chromic luvisol [38], with a mean depth of 55 cm.
Where possible, two sample cores were taken at about breast height from each of the trees that were used as samples (1.3 m). A total of 39 cores were acquired, using a 50 cm long, three-threaded Pressler’s increment borer. The conditioning process of wood samples was implemented under constant laboratory conditions (20 °C, 65% relative humidity) until the achievement of a constant weight. To accurately perform the cross-dating process, the radial increments were mounted on a supporting substrate and gently sanded until the growth rings were clearly visible and identified. The EW and LW ring widths were measured using the LignoStation system, which was provided by the Department of Forestry, Wood Sciences, and Design. LignoStation was used to obtain information about the EW and LW ring growth of the dry increment cores, using a high-frequency (HF) probe attached to a very thin tip. The dielectric constant of wood is proportional to the spatial density, which is measured by the HF probe. The cross-dating process was conducted and evaluated using COFECHA software [39,40]. The ARSTAN program was used to build an index chronology in order to assess the relationship between climate and growth rings [41]. A two-step detrending procedure was performed, maintaining 50% variance at a wavelength of 30 years with a cubic smoothing spline function applied after a negative exponential or linear regression [42]. This common practice resulted in the conversion of unprocessed tree ring widths into indices. For these indices, first-order temporal autocorrelation was reduced using autoregressive modeling [43]. Typical and widely used dendrochronological statistics [2,44] were calculated. The mean sensitivity (MS), standard deviation, average growth-ring width, and first-order auto-correlation coefficient (AC1) for the raw chronologies were determined. The mean sensitivity (MS) is a measure of mean relative changes between consecutive years of ring widths and increases during drought years. The average correlation between all series (RBAR) and the expressed population signal (EPS) were determined in respect to the residual data. From 0 to 1, the RBAR scale represents the perfect common variance (1). RBAR is an objective measurement of common signal strength that is independent of sample size [41,43,45].
The EPS was used to measure how well a finite-sample chronology represented a theoretical population chronology of an unlimited number of trees [42,43,46]. On a scale of 0 to 1, 1 represents complete agreement with the population chronology in terms of EPS. A tree ring series’ chronology signal intensity typically evolves over time in response to variations in RBAR variation and sample depth, which finally affect the EPS. Therefore, it is crucial to investigate the properties of the RBAR and EPS in order to gain a thorough understanding of the dates at which the chronologies may become questionable during the series analysis. A 50-year moving frame with 25-year overlaps was used in order to estimate the running RBAR and EPS values.

2.2. Climatic Information and Trend Analysis

The Faculty of Forestry (Aristotle University of Thessaloniki) developed and maintains the regional meteorological station that provides the relevant climatic information, including temperature and precipitation. The 60-year time series (1961–2020) was continuous without any missing values and provided daily observations for temperature (mean, max, min) and precipitation (rain and snow). Precipitation and temperature annual and seasonal time series trends have been thoroughly investigated in previously published studies by our institution [23,47]. In these studies, it was found that there was a statistically significant increase in annual temperature, while the precipitation remained relatively stable. To ascertain whether extreme climatic conditions had an impact on the growth of firs in the research area, the minimum and maximum mean monthly temperatures were employed to find any significant relationship with tree ring growth. This is the first time in the literature that this specific species has been investigated in terms of correlating tree ring width (EW, LW, TRW) with min and max temperatures. To identify drought months and their potential impact on tree ring width, the 12-month SPEI [48] was calculated using the available (Institutional Meteorological Station) precipitation and temperature monthly time series. SPEI provides the ability to define the beginning and end of a drought phenomenon and helps quantify the strength or severity of a dry spell in relation to its duration and intensity. SPEI is a multi-scalar drought index that can estimate drought on various time scales while considering both temperature and precipitation. Considering that evapotranspiration is among the most important types of water loss during droughts and hot weather, SPEI appears to be a helpful measure of drought because of its capacity to incorporate both components of temperature and precipitation [49].

2.3. Tree Ring Width Correlation Analysis Under Different Climatic Conditions

Pearson correlations [50,51,52] were used to assess the relationships between climate and tree ring growth. The tree ring chronologies (TRW, EW, and LW) were taken into account for this analysis as the dependent variables, and the monthly climatic parameters (precipitation, mean, min, and max temperatures) from September of the previous year (SEP-1) until September of the next year were taken as the independent variables. In the Mediterranean basin, this specific duration is frequently used in dendroclimatological techniques [53].

3. Results

3.1. Tree Ring Time Series Statistical Analysis

After an initial examination of the 39 tree cores (from 28 trees), the cores from 25 trees were included in the sample for further processing and statistical analysis, while the tree cores that showed ambiguous tree ring formation were excluded from the sample and from further analysis. The length of the time series utilized in the research was 188 years, ranging from 1833 to 2020, while the time series with two or more trees covered 151 years, ranging from 1870 to 2020. The mean width of the raw tree ring (TRW) time series was 3.004 mm, with a corresponding standard deviation value of 1.099, mean sensitivity (MS) of 0.15, and mean value of the AC1 of 0.76 (Table 1). The latewood (LW) time series presented a lower AC1 and a slightly higher MS than the TRW and earlywood (EW) time series. However, the influence of LW on the final TRW time series seems to be low.
Figure 3 demonstrates the non-standardized chronologies of fir (A. borisii-regis). The red line corresponds to the 10-year moving average, which helps to detect long-term variations in the tree ring chronologies. There are a lot of variations in tree ring width, with sharp increases (i.e., 1955–1959) and decreases (2000–2003). Figure 3 was constructed to better understand the potential low frequency variations in the raw data, which have been removed by employing the detrending methodology and procedures, in comparison with the standardized chronologies (Figure 4). In Figure 4, the standardized tree ring chronologies (TRW, EW, and LW) are presented, accompanied by the TRW 10-year average and the tree core sample depth. In Figure 4, it is evident that, between 1833–1910, the fluctuation of the chronologies is intensely high, compared with the 1910–2020 period. This could be mainly explained by the low sample depth during the period of 1833–1910.
Figure 5 depicts the running series of RBAR and EPS for TRW, EW, and LW. The mean RBAR values for TRW, EW, and LW are presented in Table 1. Figure 5 shows that the RBAR fluctuated during the period of 1885–1945. The EPS appears to be steady and over 0.85 during most periods, except for the period of 1885–1930, in which it was below the threshold of 0.85 especially concerning the EW and LW chronologies. The EPS of the TRW chronology was just below the threshold before the year of 1915.

3.2. Relationships Between Tree Ring Chronologies and Climatic Variables

Correlation analysis between TRW, EW, LW, and monthly precipitation (Figure 6) revealed that precipitation values in May, June, and July are statistically significant factors that positively affect the tree ring width. Precipitation in June and July is especially highly influential on tree growth, as the p values were less than 0.01. The other months of the current year do not have any significant effect on tree ring chronologies, except for September of the current year, which showed an unexpected statistically significant effect, negatively influencing EW formation. In contrast, September of the previous year (pS) demonstrated a strong, positive, and statistically significant effect on all tree ring chronologies (TRW, EW, and LW).
Figure 6 presents the correlations between tree ring width chronologies (TRW, EW, and LW) and mean monthly values of temperature (mean, minimum (min), and maximum (max)). It is evident that only April’s temperatures (mean, min, max) have a positive effect on tree ring width, and particularly on LW. June’s high temperatures have a strong negative effect on tree ring chronologies, while high August temperatures mainly influence the formation of LW. The high autumn temperatures of the previous year (especially November) significantly and negatively impact on the wood formation of the next growing season.
Figure 7 depicts the combined influence of monthly precipitation and temperature (using the monthly SPEI) on the tree ring chronologies (TRW, EW, and LW). As was expected, May, June, and July are the most important months during the growing season for the examined tree species. Wet conditions during these months significantly promote tree growth, while also positively affecting LW formation during July and August. On the other hand, wet conditions prevailing in April could negatively influence LW growth, while wet conditions in September could negatively influence the formation of EW. The same negative effects can be observed in the precipitation correlations (Figure 6). On the contrary, wet conditions in September of the previous year could positively influence tree growth. All the above results are discussed further in the Section 4.

4. Discussion

4.1. Tree Ring Chronologies’ (TRW, EW, and LW) Variability

The calculated time series statistics revealed some differences between TRW, EW and LW, while EW drives the statistics of TRW. It appears that low-frequency variance predominates in the research area based on the low mean sensitivity (MS) and high AC1 values. The low mean sensitivity (MS) can be explained by the fact that climate does not change much during the year or from year to year [2,28,54]. The summer drought period, the high average annual precipitation, the generally low annual temperatures, and the good site quality of the forest are factors that do not considerably fluctuate year by year. Wood biosynthesis and tree ring formation were balanced throughout successive years, suggesting that drought-related stress was maintained at a low level. The results showed that this balance agreed with the higher first-order autocorrelation values (AC1). The running RBAR and EPS of the constructed time series (TRW, EW, and LW) revealed that the chronologies could not be considered reliable in full length series. Although RBAR decreased during the 1975–2005 period, in general, the values of RBAR remained relatively high during the recent period of 1945–2005. Additionally, the EPS was found to remain constant and above 0.85 for all chronologies, except for the period of 1885–1930, in which it was below the established threshold [44]. These variations were potentially caused by the low sample depth during this period. High EPS and RBAR values show an evident sensitivity to stand and environmental conditions. Similar findings were reported in previously published research in Albania by Pasho et al. [36] and Papadopoulos [28] in Greece. A strong common signal across the TRW, EW and LW, chronologies was observed in relation to climatic conditions, primarily in response to autumn and summer temperatures and summer rainfall. These relations resemble those identified in other species that flourish in environments characterized by a Mediterranean climate and comparable site characteristics [55,56,57,58].

4.2. Relations Between Climate Variability and Tree Growth

4.2.1. Precipitation

The correlation between precipitation and TRW, EW, and LW chronologies revealed a strong and statistically significant effect during May, June, and July. This is something usual for Mediterranean sites and has been reported in previous studies in Greece (Papadopoulos [28] and Koutavas [59]) and in other Mediterranean regions [34,60]. Pasho et al. [36] found favorable relationships between the A. borisii-regis species in south Albania with precipitation during the summer months. In addition, Papadopoulos et al. [3] noted that, in the Mediterranean, fir tree ring width variability is highly influenced by climate, emphasizing the positive correlation between the tree ring width and June precipitation. This trend is linked to the significant tree-growth activity that occurs in June, coinciding with the onset of the Mediterranean dry period.
Interestingly, May precipitation was positively correlated with LW and not EW, which would be more expected, as May is the month of EW formation. This finding suggests that wet conditions in May could lead to additional stored water in the ground and/or in the axial parenchyma and sapwood (as nutrients and starch) (https://doi.org/10.3390/plants10061247, Accessed on 3 April 2025, which could be utilized during the LW formation period. In a previous study implemented in Albania dealing with the same species [36], the researchers did not find any statistically significant correlation between May (or spring months) precipitation and tree growth. This difference between the two regions could be attributed to the mean temperature differences in spring. As our study area is located much further south than Albania, the growing season starts earlier due to the higher mean temperature in spring. Mainly for that reason, our study covered more months to present statistically significant relations between tree ring chronologies and precipitation.
The previous year’s September’s (pS) precipitation proved to be statistically significant and was positively correlated with TRW growth. This is in agreement with previous findings from studies investigating the same species [28,36], with the difference that in our study a positive relationship was observed for both EW and LW formation. The positive correlation with the previous September suggests that the precipitation depth of the previous autumn can affect water reserves and, consequently, the tree growth during the next year [55,61,62,63,64].
Another quite interesting finding is the statistically significant inverse correlation of September precipitation and EW formation in the same year. There are previous studies in the literature which have reported similar tendency, though they did not attempt to interpret or explain the causes of this trend [65,66,67].
This finding could be considered as unexpected, since it is widely known that in September, the EW formation period has already ended (normally by late July). To explain this unusual behavior, a hypothesis was examined, suggesting that a dry growing period (May, June, July, and August (MJJA)) is usually followed by a wet September, assuming that the dry MJJA period causes the narrow EW rings and not the wet September. The hypothesis was tested comparing the years of the narrowest and widest EW rings. The comparison revealed at least 20% higher precipitation in September and 22% lower precipitation in the MJJA period during the narrow EW years. To conclude, the negative correlation between September precipitation and EW could be induced by the preceding dry period (MJJA), rather than the wet September.

4.2.2. Temperature

Generally, the correlation between temperatures (min, max, and mean) and the TRW, EW, and LW chronologies was in agreement with previous studies undertaken in Mediterranean area and dealing with the same species or other coniferous species [3,28,34,60,68]. Temperatures (min, max, mean) showed a strong and statistically significant negative impact on tree ring chronologies (TRW, ER, and LW). High autumn temperatures in the previous year, especially pS max and pN max and mean, could highly influence the tree growth of the following year. High temperatures in autumn could increase evapotranspiration, causing the depletion of the water reserves that would be available for the following tree growth period [69,70,71]. June’s max and mean temperatures revealed an extremely negative effect on all tree ring chronologies (TRW, ER, and LW), a fact which is very important, since June is the most crucial month as regards EW formation. Additionally, high max and mean August temperatures can negatively influence the LW, since, during this month, LW formation is active. These interactions correspond to those observed in other species that flourish in environments of a Mediterranean climate and comparable site characteristics [55,56,57,58,72]. The only statistically significant positive correlation that was found was the relationship between April temperatures (min, max, mean) and the LW chronology. This could be explained by the fact that high temperatures in April may trigger an earlier start for the growing season, consequently lengthening the period of LW formation.

4.2.3. Standardized Precipitation Evapotranspiration Index (SPEI)

SPEI is a multi-scalar drought index which combines both precipitation and temperature measurements. Thus, it was expected to observe similar correlations with those of precipitation and temperature. The correlation between SPEI and tree ring chronologies (TRW, EW, and LW) revealed that a wet September in the previous year (pS) with low temperatures could significantly influence and increase the tree growth of the next year. For the same reasons explained above, wet Septembers with low temperatures can efficiently store and preserve valuable water which can be used in the following growing season [73,74]. Additionally, high values of SPEI (wet conditions) during May, June, and July were found to be correlated with increased tree ring growth. Especially for July and August, it is evident that wet conditions can positively influence the formation of LW [75,76,77]. September’s SPEI follows the same behavior as September’s precipitation, showing a negative correlation with EW formation, probably for the same reasons explained above for September precipitation. Generally, a variety of studies [17,78,79] have identified that tree species’ susceptibility to temperature and precipitation fluctuations may result in varied geographical responses, revealing the complicated nature of the Mediterranean climate, which is characterized by considerable variability across various locations and catchments. According to the present study findings, the growing season in high elevations of the study area begins mostly in late April and early May and can last until late September. However, earlier investigations on Abies species under comparable conditions revealed that the growth season might be extended until late September or October depending on species’ sensitivity to temperature and water availability, as well as climatic and geomorphological factors [17,18,20,21,28,36].

5. Conclusions

The current study examined Abies borisii-regis Mattf. tree ring chronologies in relation to climate factors and dendrochronological statistics, offering important new information on the species’ growth processes in Mediterranean climates. Low mean sensitivity (MS) and high first-order autocorrelation (AC1) were found in the statistical analysis, indicating stable environmental conditions with low year-to-year fluctuation in the research region. Although there was a variation in EPS and RBAR, especially before 1915, the overall tree ring chronologies showed consistency in describing patterns of tree ring growth across time.
An important finding of this study was the robust relationship between precipitation in May, June, and July and tree growth chronologies (TRW, EW, and LW), with June being particularly important for the creation of earlywood (EW). Consistent with the results from earlier research, these relationships highlighted the significance of summer rainfall for tree development in Mediterranean environments. Furthermore, the positive influence of the previous year’s September precipitation on tree growth highlights the significance of stored water reserves in increasing tree growth in the following growing season. Though uncommon, the negative correlation between September precipitation in the same year and the EW formation implies that dry summers are generally followed by wet Septembers and that this has an impact on the relationship.
High temperatures, especially in June and August, have an adverse effect on tree development, with June temperatures being particularly unsuited to the creation of EW. In contrast, it seems that April’s warm weather promoted an earlier start of the growth season, which increased the creation of latewood. The species’ adaptation to the Mediterranean climate, where summer drought stress is a major limiting factor, is consistent with its sensitivity to temperature.
The study concludes by showing that temperature and precipitation have a significant impact on tree ring growth in Abies borisii-regis Mattf., especially during the critical growth months of May through August. These findings provide important information for evaluating future climate change implications on the region’s forest ecosystems and further our understanding of how fluctuations in the Mediterranean climate affect tree growth patterns. On the basis of these results, future studies could investigate the effects of extended drought periods and severe weather events on ecosystem stability and tree growth.

Author Contributions

Conceptualization, A.K. and D.S.; methodology, A.K. and D.S.; software, A.K. and D.K.; validation, A.K. and D.K.; formal analysis, A.K.; investigation, A.K. and D.S.; resources, D.S.; data curation, A.K. and D.K.; writing—original draft preparation, A.K. and D.K.; writing—review and editing, A.K. and D.K.; visualization, A.K.; supervision, D.S.; funding acquisition, D.S. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the University Forest Administration and Management Fund (Aristotle University of Thessaloniki, Greece). Fund number: 72075.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors on request.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Walter and Lieth climate diagram. Distribution of monthly precipitation and temperature (mean, min, and max) in the study area. The blue areas indicate the wet period, the blue hatched areas indicate the humid period, and the red hatched area indicates the potential dry period.
Figure 1. Walter and Lieth climate diagram. Distribution of monthly precipitation and temperature (mean, min, and max) in the study area. The blue areas indicate the wet period, the blue hatched areas indicate the humid period, and the red hatched area indicates the potential dry period.
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Figure 2. The first study on Abies borisii-regis Mattf. [36] was conducted in Albania (left panel, the green dot). The current study took place in Pertouli University Forest (red polygon).
Figure 2. The first study on Abies borisii-regis Mattf. [36] was conducted in Albania (left panel, the green dot). The current study took place in Pertouli University Forest (red polygon).
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Figure 3. Raw tree ring chronologies of the Pertouli University Forest area. The plot shows the annual variation in the non-standardized chronologies of the tree ring width (TRW), earlywood (EW), and latewood (LW). The red line superimposed on tree ring lines depicts the TRW 10-year moving average.
Figure 3. Raw tree ring chronologies of the Pertouli University Forest area. The plot shows the annual variation in the non-standardized chronologies of the tree ring width (TRW), earlywood (EW), and latewood (LW). The red line superimposed on tree ring lines depicts the TRW 10-year moving average.
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Figure 4. A. borisii-regis standardized tree ring chronologies of the Pertouli University Forest area. The plot shows the standardized annual tree ring index for tree ring width (TRW), earlywood (EW), and latewood (LW). The red line superimposed on the tree ring lines depicts the TRW 10-year moving average and the black line depicts the sample depth.
Figure 4. A. borisii-regis standardized tree ring chronologies of the Pertouli University Forest area. The plot shows the standardized annual tree ring index for tree ring width (TRW), earlywood (EW), and latewood (LW). The red line superimposed on the tree ring lines depicts the TRW 10-year moving average and the black line depicts the sample depth.
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Figure 5. Running RBAR and EPS plot for TRW, EW, and LW chronologies, based on a 30-year window with 15-year overlaps.
Figure 5. Running RBAR and EPS plot for TRW, EW, and LW chronologies, based on a 30-year window with 15-year overlaps.
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Figure 6. Correlation coefficients calculated between the constructed chronologies (EW, LW, and TRW) and climatic factors (precipitation, max, mean, and min temperature (level of significance: * p < 0.05, ** p < 0.01, and “p” in x axis indicates months in the previous year).
Figure 6. Correlation coefficients calculated between the constructed chronologies (EW, LW, and TRW) and climatic factors (precipitation, max, mean, and min temperature (level of significance: * p < 0.05, ** p < 0.01, and “p” in x axis indicates months in the previous year).
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Figure 7. Response of tree ring width (TRW, EW, and LW) to the monthly SPEI in the Pertouli University Forest (level of significance: * p < 0.05, ** p< 0.01, “p” in x axis indicates the previous year).
Figure 7. Response of tree ring width (TRW, EW, and LW) to the monthly SPEI in the Pertouli University Forest (level of significance: * p < 0.05, ** p< 0.01, “p” in x axis indicates the previous year).
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Table 1. Dendrochronological statistics of tree ring width (TRW), earlywood (EW), and latewood (LW) A. borisii-regis (Mattf.) chronologies (period 1833–2020).
Table 1. Dendrochronological statistics of tree ring width (TRW), earlywood (EW), and latewood (LW) A. borisii-regis (Mattf.) chronologies (period 1833–2020).
Growth ChronologiesTreesPeriodMW (mm)SD (mm)AC1MSRBAREPSAC1st
TRW251833–20203.0041.0990.760.150.340.900.37
EW251833–20202.1090.7390.710.150.240.850.28
LW251833–20200.8950.4160.510.240.250.840.11
Note: Raw data tree ring width: mean width (MW); standard deviation (SD); first-order autocorrelation (AC1). Standardized tree ring series: mean sensitivity (MS); mean correlation between all series (RBAR); expressed population signal (EPS); first-order autocorrelation (AC1st).
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Kastridis, A.; Koutsianitis, D.; Stathis, D. The Effect of Hydrometeorological Factors on Tree Growth (Abies borisii-regis Mattf.) in Mountainous Watersheds (Central Greece). Forests 2025, 16, 750. https://doi.org/10.3390/f16050750

AMA Style

Kastridis A, Koutsianitis D, Stathis D. The Effect of Hydrometeorological Factors on Tree Growth (Abies borisii-regis Mattf.) in Mountainous Watersheds (Central Greece). Forests. 2025; 16(5):750. https://doi.org/10.3390/f16050750

Chicago/Turabian Style

Kastridis, Aristeidis, Dimitrios Koutsianitis, and Dimitrios Stathis. 2025. "The Effect of Hydrometeorological Factors on Tree Growth (Abies borisii-regis Mattf.) in Mountainous Watersheds (Central Greece)" Forests 16, no. 5: 750. https://doi.org/10.3390/f16050750

APA Style

Kastridis, A., Koutsianitis, D., & Stathis, D. (2025). The Effect of Hydrometeorological Factors on Tree Growth (Abies borisii-regis Mattf.) in Mountainous Watersheds (Central Greece). Forests, 16(5), 750. https://doi.org/10.3390/f16050750

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