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Article

Species-Specific Growth Responses to Climate in a Multi-Site Study, NE Poland

by
Agnieszka Tokarska-Osyczka
1,*,
Marek Ksepko
2,
Magdalena Terlecka
3,
Łukasz Kolendo
2,
Szymon Chmur
2,
Martyna Lasek
3 and
Grzegorz Iszkuło
1,3
1
Institute of Biological Sciences, University of Zielona Góra, Prof. Z. Szafrana 1, 65-516 Zielona Góra, Poland
2
Bureau for Forest Management and Geodesy, ul. Lipowa 51, 15-424 Białystok, Poland
3
Institute of Dendrology, Polish Academy of Sciences, Parkowa 5, 62-035 Kórnik, Poland
*
Author to whom correspondence should be addressed.
Forests 2025, 16(9), 1447; https://doi.org/10.3390/f16091447
Submission received: 12 August 2025 / Revised: 8 September 2025 / Accepted: 9 September 2025 / Published: 11 September 2025
(This article belongs to the Section Forest Ecology and Management)
Browse Figures
Versions Notes

Abstract

In the context of intensifying climate change, dendroclimatic research provides insight into tree responses to environmental variability. This study assessed relationships between temperature, precipitation, and radial growth of four major forest species in Central Europe: Scots pine (Pinus sylvestris L.), Norway spruce (Picea abies (L.) H. Karst), pedunculate oak (Quercus robur L.), and black alder (Alnus glutinosa (L.) Gaertn.). Analyses were conducted at three independent sites to account for local variability and enhance robustness. We tested three hypotheses: (1) replication improves reliability in dendrochronological studies, (2) multi-species analysis strengthens interpretation of climate–growth relationships, and (3) black alder exhibits distinct precipitation sensitivity. Results showed species- and site-specific responses. The first hypothesis was supported, as replication enhanced the robustness of climate–growth signals in Scots pine, Norway spruce, and pedunculate oak showed broadly consistent responses, while black alder maintained its distinct pattern. Scots pine responded strongly to summer rainfall (June–August, including July of the previous year), Norway spruce to summer rainfall (June–July of the current and previous year), and pedunculate oak to summer rainfall (June–July, with additional effects in August). Black alder exhibited positive correlations with winter precipitation (December–January of the previous year) and negative with summer rainfall (May, June, August; September–November of the previous year), suggesting moisture-related stress. Temperature sensitivity occurred in winter and early spring (December–April, especially February–March) for most species, except black alder, which also responded positively to summer temperatures (May, July–September). These findings highlight the importance of species traits and site conditions in dendroclimatic studies and support replicated multi-species approaches to guide adaptive forest management under climate change.

1. Introduction

In the face of dynamic climate change, including rising average temperatures and the increasing frequency of extreme weather events, the study of tree radial growth has gained particular importance. Annual tree rings record variable environmental signals within their structure, serving as a sensitive indicator of responses to climatic conditions [1,2,3]. Understanding how radial increments are formed provides insight into the growth mechanisms influenced by external factors [4]. Dendroclimatology enables the identification of relationships between climate and tree development. Species-specific and site-dependent climate–growth relationships have been widely reported across Europe and globally [2,5,6]. However, the effectiveness of this method depends on study design, particularly the consideration of local variability through field replication.
A key limitation of many dendroclimatic analyses is the lack of replication, which hinders the identification of true relationships and reduces the validity of generalisations [7]. Replication, i.e., analysing growth increments at independent but comparable sites, allows separation of climatic signals from local environmental influences [8,9]. In this context, genetic differences among trees, which determine their varying sensitivity to environmental factors, even within the same species, also gain importance [10]. This further justifies the use of replication in order to capture the full spectrum of individual responses.
In this study, we analysed four key forest-forming species of Central Europe: Scots pine (Pinus sylvestris L.), Norway spruce (Picea abies (L.) H. Karst), pedunculate oak (Quercus robur L.), and black alder (Alnus glutinosa (L.) Gaertn.), representing both conifers and broadleaf trees. These species differ in habitat requirements, growth physiology, and sensitivity to stress factors, making them suitable for dendroclimatic comparisons under Central European conditions.
The response of black alder is particularly interesting, as preliminary findings indicate that it stands out in both its reaction to precipitation and the duration of cambial activity compared to the other species studied. Understanding this divergence may be important for predicting future stand structures under conditions of ongoing warming and hydrological change.
In this study, the following research hypotheses were formulated: (1) the use of replication in dendrochronological studies strengthens the analytical power of this method; (2) conducting analyses across a wide range of tree species is valuable for identifying general relationships between climate and radial growth; (3) Alnus glutinosa exhibits a distinct growth response to precipitation compared to the other species, reflecting its specific habitat preferences and water use physiology.
In light of previous studies [11,12], which indicate species-specific climate response models and predict northward range shifts, understanding the dynamics of tree growth in relation to local climatic parameters is particularly important. The results of this work may be of significant value for modelling future changes in forest productivity and for adaptive forest habitat management under climate change. This includes providing data to support species selection for regeneration, optimising silvicultural strategies, and protecting water resources under increasing thermal and precipitation stress.
Therefore, the aim of this study was to assess the impact of climatic conditions—monthly mean temperature and total precipitation—on the radial growth of selected tree species (Pinus sylvestris, Picea abies, Quercus robur, and Alnus glutinosa) in Wigry National Park, with particular focus on the following: (1) evaluating the sensitivity of radial to changing climatic conditions; (2) identifying key months and seasons critical for the formation of annual tree rings; (3) comparing growth responses among the studied tree species to determine their differentiated climate sensitivity.

2. Materials and Methods

2.1. Study Area

The research material was collected in Wigry National Park, located in north-eastern Poland, in the Lithuanian Lakeland, of the Mazurian-Podlasian region (Figure 1). The park lies on the northern edge of the Augustów Primeval Forest, the largest continuous forest complex of the North European Plain, encompassing a geomorphologically diverse part of the Eastern Suwałki Lakeland. The park was established on 1 January 1989 to protect a unique complex of ribbon lakes, rivers, and associated wetlands, Wigry National Park covers more than 15,000 hectares, of which approximately 63% comprises forest ecosystems [13,14]. The dominant forest types are pine and mixed coniferous stands, with some preserved in near-natural condition. Narrow strips of alder and riparian forests occur along river valleys and around lakes, while hornbeam forests are less common. The primeval character of many forest fragments, high habitat diversity, and the presence of ecotones make the area particularly rich in flora and fauna, including numerous relict and protected species [14,15].
The forest habitat types in which the study plots were established include fresh mixed forest, swampy mixed forest, and ash–alder swamp forest. These are typical habitats of the Eastern Suwałki Lakeland region, differing in soil moisture and edaphic conditions, and they correspond to the ecological requirements of the analysed tree species. Kliczkowska et al. [16] characterised them as follows: (1) fresh mixed forest is a moderately moist and moderately fertile habitat developed on brown and rusty glaciofluvial soils, which favour the growth of Pinus sylvestris, Quercus robur, and Picea abies; (2) swampy mixed forest, a strongly waterlogged habitat with peat soils, limited water drainage, and an organic layer >30 cm. This habitat is typically found in transitional zones between alder woods and swampy coniferous forests and is preferred by Alnus glutinosa; (3) ash–alder swamp forest, a wet habitat associated with floodplain terraces and spring areas, with periodic flooding and shallow groundwater. It provides favourable conditions for black alder and, locally, ash (Fraxinus excelsior L.). Comparable dendroclimatic studies in other parts of Europe have also highlighted the role of site conditions in shaping growth responses [17,18].

2.2. Species Characteristics

Pinus sylvestris is a species with a broad ecological amplitude, dominant on poor podzolic soils and often occupying extreme habitats [19]. It exhibits high tolerance to site-related stress, but its radial growth is sensitive to severe winter frosts and spring droughts, which more strongly limit growth than summer precipitation deficits [20,21]. Picea abies prefers cool and moist habitats, and its shallow root system limits water uptake during dry periods. In lowland environments, its growth is strongly dependent on precipitation during the growing season, while temperature has a weaker influence [22]. Quercus robur is characterised by a deep root system and good tolerance to water deficits. The growth of younger trees is constrained by high temperatures in June, while older individuals respond negatively to winter and summer warming and positively to summer rainfall [23]. Alnus glutinosa is a pioneer species typical of wet and waterlogged habitats, widespread in Central Europe, especially in river valleys and peatlands [24,25]. It exhibits high tolerance to periodic flooding and can fix atmospheric nitrogen through symbiosis with Frankia alni bacteria [25,26]. Despite its adaptation to water-saturated soils [27], alder is sensitive to prolonged flooding and sudden fluctuations in groundwater levels, which may lead to growth inhibition and tree mortality [28,29,30,31]. Since black alder predominantly grows in wet and waterlogged habitats, its climate–growth relationships may also be shaped by these conditions, which distinguishes it from other co-occurring forest species.

2.3. Sampling and Processing of Tree-Ring Data

Increment cores from Pinus sylvestris, Picea abies, Quercus robur, and Alnus glutinosa, were collected in July 2024 using a Pressler increment borer, with one sample per tree. The extracted cores were glued into wooden holders and sanded with sandpaper of various grit sizes to ensure good visibility of the tree rings. The cores were then properly labelled and scanned at a standard resolution of 1200 dpi. In cases where the tree rings were too faint or poorly visible, samples were re-sanded and re-scanned at a higher resolution of 2400 dpi.
Following processing and scanning, each increment core was analysed for annual ring widths using the software CooRecorder and its companion program CDendro (Cybis Elektronik & Data AB 2024, version 9.8.4). CooRecorder was used to measure annual ring widths, while CDendro facilitated the aggregation of measured cores into collections, the construction of reference chronologies, and a preliminary quality assessment of the data through the analysis of basic sample statistics, time-series plots, and cross-dating correlations. Any inconsistencies identified during analysis in CDendro prompted verification of the original data in CooRecorder. Cross-dating quality was evaluated in CDendro.
For each species and groups, complete datasets were compiled, containing ring-width values for all analysed individuals. These datasets were saved in files compatible with Microsoft Excel.
Climatic data, including monthly mean air temperatures and total precipitation for the period 1901–2023, were obtained via Google Earth Pro (2024, version 7.3.6) using an overlay containing CRU TS (Climatic Research Unit gridded Time Series) data, version 4.08 [32]. Data were extracted for the area covering the Wigry Natura 2000 site within Wigry National Park (54°01′ N, 23°05′ E).
Both the climatic data (monthly mean temperatures and precipitation totals) and the tree-ring width measurements were analysed in the RStudio (ver. 2025.05.1-513) environment (R Core Team 2024). The analyses employed functions from dendrochronology-specific packages: dplR [33], treeclim [34], and dendroTools [35]. For data visualisation, the tidyverse package was used [36]. The analytical procedures included descriptive statistics, detrending of time series, and the construction of growth chronologies for individual species. Detrending was performed using a 30-year spline. Alternative spline lengths (20 and 50 years) were also tested and yielded comparable results; therefore, the 30-year spline was applied consistently across all species to ensure methodological uniformity. Of particular importance was the application of the treeclim package, which enabled correlation analyses with climatic variables to identify significant positive and negative relationships between annual ring width and monthly temperature and precipitation values for the years 1901–2023. In addition, moving-window correlation analyses were performed using 25-year windows shifted by 1 year, in order to evaluate temporal stability of climate–growth relationships across the study period.
For the purpose of this study, three discrete core groups (replicates) were selected for each analysed tree species. These groups were located at a minimum distance of 500 m from one another and were composed of trees of comparable age and growth under similar site conditions (Table 1, Figure 1).

2.4. Statistical Analysis

Statistical significance was assessed using the Pearson correlation coefficient (r), calculated for each combination of month–group–species, using monthly mean air temperature and total precipitation in relation to the width of detrended tree rings (spline 30). Correlations were considered statistically significant at the p < 0.05 (two-tailed t-test for r). To evaluate reproducibility of the climate signal, the number of groups (out of three) showing a significant correlation in a given month was also recorded.
To simplify result presentation, summary tables were created listing the months and indicating significant results (i.e., significance in at least one group). Replication strength was encoded as +, ++, +++ (for positive correlations) and −, − −, − − − (for negative correlations), depending on the number of replicates with significant results. Significant correlations occurring in 10%–29% of the replicates were marked in black, whereas in the ≥30% category were marked in red.
For each collection, a summary of basic dendrochronological statistics was compiled, including the number of cores, series length and temporal range of the series, number of measurements, and key data quality indicators such as the expressed population signal (EPS), average inter-series correlation ( r ¯ ), and signal-to-noise ratio (SNR). EPS was calculated using the average number of trees as n × rbar.eff/((n − 1) × rbar.eff + 1), while SNR was calculated as n × rbar.eff/(1 − rbar.eff), where n is the average number of trees and rbar.eff is the effective mean inter-series correlation. All parameters were calculated for the entire detrended series (spline30). Chronologies were not truncated based on EPS thresholds, but dendroclimatic analyses were limited to the overlap period with the CRU climate dataset (1901–2023). For each species, visual comparison was generated between raw chronology (rwr) and the detrended chronology (rwi, spline30) across the three groups. This comparison enabled an evaluation of the effect of detrending on data structure and a comparative assessment of growth dynamics among groups. Chronologies were constructed as biweight robust mean chronologies using the chron() function in the dplR package [33] and plots were prepared in R for both raw and detrended (spline30) data. These standardised chronologies formed the basis for all dendroclimatic analyses.
Pearson correlation coefficients were also calculated between indexed chronologies (spline30) to quantify agreement within and across species. Two variants were reported: (i) correlations for the common period 1961–2024 and (ii) pairwise–overlap correlations for each chronology pair. For each estimate, the correlation coefficient (r), two-tailed p-value, and number of overlapping years (N) were provided.
Finally, for each species, a composite indexed chronology was constructed by pooling all detrended series from the three groups and calculating the biweight robust mean (dplR::chron). Data-quality metrics (SNR, rbar, EPS) were summarised for both replicate and composite chronologies (Table A2 in Appendix A).

3. Results

Analysis of dendrochronological parameters (Table 2) revealed variation in climate signal quality among the studied tree species and their respective groups. Among the evaluated indicators, Quercus robur demonstrated the highest data quality, with mean inter-series correlations ( r ¯ = 0.435–0.513), consistently high expressed population signal values (EPS = 0.960–0.966), and the highest signal-to-noise ratio (SNR = 23.884–28.462). These results indicate excellent coherence and strong representativeness of the oak data for climate–growth analyses.
Picea abies also exhibited good dendrochronological properties, with r ¯ values ranging from 0.443 to 0.504 and EPS values between 0.924 and 0.934. The SNR values (12.175–14.251) were moderate but sufficient for further analysis.
Alnus glutinosa reached r ¯ values between 0.342 and 0.380, high EPS values (0.934–0.963), and more variable SNR values (13.710–25.789), indicating generally good data quality, though slightly less consistent than that of oak or spruce.
The lowest indicator values were observed for Pinus sylvestris, particularly in group 3 ( r ¯ = 0.294, EPS = 0.853, SNR = 5.823), which indicates weaker coherence among samples and a less pronounced climate signal. Although the remaining pine groups showed slightly higher values, the overall data quality for this species was lower compared to the other taxa. It should also be emphasised that the relatively low values for P. sylvestris partly reflect both the limited number of series (13 cores) and the weaker inter-series correlation.
Figure 2, Figure 3, Figure 4 and Figure 5 present comparative chronologies for the four studied tree species, showing both raw and detrended data using a 30-year spline (spline30). Detrending was applied to reduce the influence of individual growth trends and to enhance the clarity of the shared environmental signal, which is particularly important for subsequent dendroclimatic analyses.
In Pinus sylvestris (Figure 2), the raw chronology shows marked amplitude fluctuations and a noticeable growth trend, whereas the detrended series exhibits a more stable course, revealing a shared environmental signal among samples. In Picea abies (Figure 3), the differences between raw and detrended chronologies are less pronounced than in pine, suggesting a smaller influence of biological trends and a relatively strong climate signal already present in the raw data. Quercus robur (Figure 4) is characterised by a well-defined and coherent chronology after detrending, which corresponds with previously observed high r ¯ and EPS values, indicating a strong and distinct climatic signal. In Alnus glutinosa (Figure 5), the raw data show relatively high variability, which detrending effectively reduced. The resulting spline30 chronology reveals clear, synchronous fluctuations characteristic of environmental influence.
Since within-species correlations between chronologies from the three sampling plots were generally high and statistically significant across all species (see Table A1 in Appendix A for details), the data were combined, and subsequent inter-species correlation analyses were performed on the merged groups. The analysis of the indexed chronologies (spline30) revealed strong and significant positive relationships between pine and spruce (r = 0.667, p ≤ 0.001) and moderate, significant correlations between pine and oak (r = 0.531, p ≤ 0.001) as well as spruce and oak (r = 0.556, p ≤ 0.001), whereas correlations involving alder were weak, negative, or near zero, and not statistically significant (p > 0.05); see Table 3, Figure A1 in Appendix A. This suggests that pine, spruce, and oak partly share a common climatic signal, whereas alder follows a distinct growth pattern. The results confirm significant agreement within conifers and between conifers and oak, while alder showed weaker and non-significant relationships.
Composite chronologies increased sample depth (N) and generally yielded higher SNR (and thus higher EPS) than individual replicate chronologies (Table A2 in Appendix A), confirming the added value of replication at the species level.

3.1. Temperature

The analysed groups of pine, spruce, and oak exhibited very similar patterns of radial growth response to temperature (Table 4, Figure A2, Figure A3 and Figure A4 in Appendix A). Winter and early spring temperatures, from December of the previous year through April of the current year, proved particularly important for growth. All species responded positively to high temperatures in February and March (Table 4, Figure A2, Figure A3, Figure A4 and Figure A5 in Appendix A). In spruce and alder, additional positive correlations were observed in October of the previous year at two sites (Table 4, Figure A3b,c and Figure A5a,b in Appendix A). For alder, the influence of temperatures during the growing season, especially in May, July, August, and September, was particularly noteworthy (Table 4, Figure A5 in Appendix A).
Negative effects of high temperatures, present in all three analysed groups, were observed only in spruce (Table 4, Figure A3 in Appendix A), specifically in June and in September of the year prior to ring formation. For oak, negative correlations were also recorded in September of the previous year for oak at two of the three sites (Table 4, Figure A4b,c in Appendix A), while in alder, they were recorded in June of the previous year (Table 4, Figure A5a,c in Appendix A).

3.2. Precipitation

The analysed groups of oak, pine, and spruce (Table 5, Figure A6, Figure A7 and Figure A8 in Appendix A) exhibited a similar pattern of radial growth response to precipitation. High rainfall in June and July proved particularly beneficial for growth. In two out of three sites, both Pinus sylvestris and Quercus robur showed additional positive correlations with August precipitation (Table 5, Figure A6b,c and Figure A8b,c in Appendix A). For Picea abies, positive correlations were also observed in July and June of the previous year (Table 5, Figure A7 in Appendix A), while for pine, a positive response occurred in July of the preceding year (Table 5, Figure A6a,b in Appendix A).
In Alnus glutinosa, positive correlations with precipitation were recorded in all three replicates for December of the previous year (Table 5, Figure A9 in Appendix A), in two replicates for January (Table 5, Figure A9a,b in Appendix A), and in single replicates for March (Figure A9a in Appendix A), April (Figure A9c in Appendix A), and October of the previous year (Figure A9a in Appendix A).
Negative effects of precipitation were observed primarily in alder. These included negative correlations in September and November of the previous year (Table 5, Figure A9a,c in Appendix A), as well as in May (Figure A9a in Appendix A), June (Figure A9a,b in Appendix A), and August of the current growth year (Figure A9 in Appendix A).

4. Discussion

The study conducted in Wigry National Park confirms the value of replication in dendrochronological analyses. Replication strengthens the robustness of inference, making results more reliable and suitable for drawing general conclusions [37] as evidenced by the greater consistency of climate–growth signals across replicate sites in our data. The use of three replicates, each located at least 500 metres apart, proved sufficient, particularly when a statistically significant influence of a given climatic factor on radial growth was observed.
To identify general relationships between climatic conditions and radial growth, it is especially valuable to conduct dendrochronological studies across multiple tree species. This approach enhances the validity of conclusions regarding the overall influence of climatic variables on growth dynamics [1]. In the present study, Pinus sylvestris, Picea abies, and Quercus robur displayed a broadly similar pattern of response to climatic conditions. In contrast, Alnus glutinosa clearly diverged from this pattern. It responded positively not only to winter and early spring temperatures but also, unlike the other species, to late spring and summer temperatures. Additionally, its negative response to precipitation during this period further distinguished alder’s growth behaviour from the positive growth responses observed in oak, pine, and spruce.

4.1. Temperature

The analysis indicates that Pinus sylvestris, Picea abies, and Quercus robur display a comparable climatic response pattern, in which winter and early spring temperatures play a decisive role in determining tree-ring width. These findings are consistent with global observations on cambial phenology [38,39]. Elevated temperatures during these periods are positively correlated with increased radial growth, suggesting that thermal conditions significantly influence the onset and rate of cambial activity. This pattern, frequently reported in the literature, underscores the importance of accumulated heat as a factor promoting earlier cambial reactivation [17,40,41].
Early-season reactivation of the cambium can extend the period of wood formation and under favourable moisture conditions, resulting in enhanced xylem production and wider tree rings. Literature reports indicate that early-season temperatures influence cambial reactivation and wood formation, with timing and rate varying among species and sites [38,42,43].
In conifer species, April temperatures of the current growth year had a particularly strong effect, likely corresponding to the initiation of cambial activity and early ring formation, preceding full development of the photosynthetic apparatus. In addition, spruce showed unique positive correlations with temperatures in January and October of the previous year, which may reflect its sensitivity to climatic conditions beyond the main growing season and could be linked to winter hardening processes or the conclusion of the assimilation period.
Unlike the other analysed species, Alnus glutinosa exhibits a strong and prolonged response to temperature throughout the entire growing season. The most pronounced positive correlations were observed during peak vegetation months, confirming the importance of high temperatures in the main growth period for this species. Such sensitivity may be linked to alder’s high phenological plasticity and its capacity to efficiently utilise thermal resources across the entire growing season [44]. This ability is likely supported by the consistently high water availability typical of wetland habitats which limits the impact of drought stress during warmer months and enables continuous growth [25]. Consequently, alder may respond strongly to ongoing climate warming, which could result in enhanced radial growth and increased productivity. Additionally, its range is expected to shift northward, indicating the potential for expansion into higher latitudes [12]. Moreover, as observed in spruce, positive correlations with temperature were recorded in January and October of the previous year, possibly reflecting shared adaptive mechanisms between the two species.
Although negative correlations occurred less frequently, their presence warrants attention. In spruce, a significant negative relationship with June temperatures of the current year was detected, likely indicating heat stress during the peak of the growing season [39]. In alder, a negative correlation with June temperatures of the previous year suggests that thermal conditions during the preceding season may adversely affect physiological status and preparation for the subsequent growth cycle, including cambial activation and resource use efficiency [45].
September of the previous year was the month in which both spruce and oak exhibited negative correlations with temperature. In spruce, this may be related to the disruption of dormancy processes and limited resource accumulation prior to the next growing season [46], while in oak, the response may reflect sensitivity to warm late-summer conditions, potentially delaying the onset of dormancy [18].
Overall, our analyses indicate the dominant role of winter and early spring temperatures in shaping radial growth in most species, with an additional strong influence of summer temperatures in Alnus glutinosa. These findings highlight the need for further research in the context of climate change and its impact on the dynamics of annual ring formation.

4.2. Precipitation

Similar to temperature responses, the precipitation analysis reveals clear patterns of similarity among oak, pine, and spruce. The key factor contributing to enhanced radial growth in these species was precipitation in June and July, coinciding with the peak period of wood formation. The importance of precipitation for radial growth has been well documented, particularly for deciduous and coniferous species in temperate climate zones [47,48]. A comparable dependence on summer precipitation has also been found in spruce and oak populations across Europe [22,23,48]. Under water-deficit conditions, stomatal closure limits the influx of CO2 into chloroplasts, reducing photosynthetic efficiency [49,50,51]. This, in turn, negatively affects plant growth, development, and overall vitality, since photosynthesis is the primary process through which plants generate energy and build biomass.
Pinus sylvestris is known to close its stomata early under warm and dry conditions [52], which significantly reduces its photosynthetic activity [53,54,55,56]. Water deficiency is further intensified by elevated temperatures, which increase evapotranspiration. As a result, summer droughts following spring dry spells may be especially detrimental to tree growth [12,57].
Spruce also showed strong dependence on summer precipitation in the year preceding ring formation (June and July). Typically, trees complete radial growth by September, followed by a period of intensive synthesis and storage of non-structural carbohydrates [58]. It is plausible that soil water retention or moisture availability during winter months support cambial activity and the initial rate of ring formation in the subsequent growing season [59].
The coniferous species, Pinus sylvestris and Picea abies, belong to the group of boreal plants adapted to cold climates [19,60]. Both are classified as light-seeded species [61]; their winged seeds [19] are easily dispersed by wind, enabling colonisation of exposed areas at distances exceeding 100 m from the parent tree [62]. Although Picea abies is partly shade-tolerant [63,64], it typically germinates on acidic, open substrates under moderate light conditions [65]. These characteristics, including light seeds, long dispersal range, pine’s preference for open sites, and spruce’s affinity for moist microsites with partial light, fit both species within a pioneer (r-selected) strategy, effective in colonizing dynamic environments.
In the context of climate change, these traits take on particular importance. Pioneer species with light seeds and a preference for sites without water limitations during the growing season are expected to be especially vulnerable to the negative effects of global warming [11], particularly due to their limited capacity for northward migration [12]. Climate models indicate that both Pinus sylvestris and Picea abies may lose a substantial portion of their climatic suitability in temperate regions, with the exception of Western Europe and mountainous areas. A similar reduction (approximately 20%) is projected for deciduous species such as Quercus robur and Alnus glutinosa, both considered moderately threatened [12]. Among all taxa analysed in this study, conifers appear most at risk due to their insufficient drought resistance and limited ability to cope with rising temperatures [66,67].
Oak and pine showed positive correlations with August precipitation, suggesting extended cambial activity under sufficient moisture. In contrast to the other species analysed, black alder showed predominantly positive correlations with winter precipitation—particularly in December of the previous year and, at some sites, also in January. This pattern may suggest that winter snow accumulation improves early spring soil moisture conditions, thus enhancing the onset of the growing season. However, alder also exhibited significant negative correlations with precipitation during parts of the growing season, specifically in May, June, and August, as well as in September and November of the previous year. This response likely reflects the species’ ecological specificity. Alnus glutinosa naturally occupies wetlands and periodically flooded areas, particularly riparian habitats, where short-term rises in groundwater pose little threat due to its high tolerance of excess water [68]. However, prolonged inundation, lasting several weeks, can result in growth inhibition, disruption of root function, and even tree mortality [69,70,71].
This species produces adventitious roots that serve as microhabitats for other organisms, rapidly colonises bare substrates, is intolerant of shade, and forms symbiosis with nitrogen-fixing microorganisms [68,72]. These traits may explain why black alder benefits from high soil moisture in early spring but is adversely affected by excessive precipitation during the peak growing season, likely due to reduced oxygen availability in the root zone.
Although Alnus glutinosa is typically associated with wet habitats and naturally grows under conditions of high groundwater levels, excessive precipitation may deteriorate soil oxygen conditions, and lead to hypoxic stress. Restricted oxygen availability in such circumstances can impair tree physiology and limit growth activity.
The literature indicates that alder growth is sensitive to flooding and hydrological variability, which can reduce growth even in wetland-adapted sites. Sudden and prolonged changes in water level, particularly flooding, can considerably limit growth, although their effect on wood properties and xylem conductivity appears relatively minor [31,73].
In summary, summer precipitation plays a crucial role in shaping the annual growth of coniferous species and oak. In contrast, alder exhibits a more complex response. These differences emphasise the importance of distinct ecological strategies among the studied species and indicate the need for further research that considers both habitat specificity and species-specific growth physiology.

5. Conclusions

This study, conducted in Wigry National Park, assessed climate–growth relationships of four temperate tree species using replicated, multisite dendrochronological analyses. The results demonstrated that winter and early spring temperatures are the primary factors promoting cambial activity in Pinus sylvestris, Picea abies, and Quercus robur. In contrast, Alnus glutinosa exhibited a distinctive growth strategy, with extended cambial activity and strong responses to both intra-seasonal temperatures and precipitation.
Among the examined taxa, P. abies showed vulnerability to summer heat stress, confirming its sensitivity to ongoing climate warming. Oak, pine, and spruce benefited from summer precipitation, whereas alder responded positively to winter rainfall but negatively to high summer precipitation, reflecting its adaptation to wetland habitats.

Author Contributions

Conceptualisation, A.T.-O., M.K. and G.I.; methodology, A.T.-O., M.K., M.T. and G.I.; software, Ł.K. and M.T.; validation, A.T.-O., M.K., Ł.K., S.C., M.T. and G.I.; formal analysis, A.T.-O., M.T. and M.L.; investigation, A.T.-O., M.L., M.T., Ł.K., S.C. and G.I.; resources, A.T.-O., M.K., Ł.K., S.C. and M.L.; data curation, A.T.-O., M.T. and G.I.; writing—original draft preparation, A.T.-O. and G.I.; writing—review and editing, M.K. and G.I.; visualisation, A.T.-O., M.T. and Ł.K.; supervision, M.K. and G.I.; project administration, A.T.-O.; funding acquisition, M.K. and G.I. All authors have read and agreed to the published version of the manuscript.

Funding

This research was co-financed by the Forest Fund provided by the Polish State Forests under the Agreement No. EZ.0290.1.21.2024 (Action No. 20) concluded between the Directorate General of the State Forests and the Wigry National Park. The sponsors had no role in the design, execution, interpretation, or writing of the study.

Data Availability Statement

Data are contained within the article.

Acknowledgments

We would like to express our sincere gratitude to the staff of Wigierski National Park for their assistance in planning and conducting the research. We also thank Marcin Koźniewski, Rafał Snarski, Kamil Wojtas, and Jakub Iszkuło for their technical and substantive support. During the preparation of this manuscript, the authors used ChatGPT (version 4.0, OpenAI) to assist with translation. The authors have reviewed and edited the output and take full responsibility for the content of this publication.

Conflicts of Interest

The authors declare no conflicts of interest.

Appendix A

Table A1. Within-species correlation matrices of indexed chronologies (spline30) from three sampling plots per species. Above the diagonal: correlations in the format r (p) for the fixed period 1961–2024. Below the diagonal: pairwise–overlap correlations in the format r (p). Diagonal entries are “—”.
Table A1. Within-species correlation matrices of indexed chronologies (spline30) from three sampling plots per species. Above the diagonal: correlations in the format r (p) for the fixed period 1961–2024. Below the diagonal: pairwise–overlap correlations in the format r (p). Diagonal entries are “—”.
G1G2G3
P. sylvestris
G1—0.846 (p < 0.001)0.762 (p < 0.001)
G20.682 (p < 0.001)—0.770 (p < 0.001)
G30.531 (p < 0.001)0.578 (p < 0.001)—
P. abies
G1—0.874 (p < 0.001)0.873 (p < 0.001)
G20.805 (p < 0.001)—0.934 (p < 0.001)
G30.811 (p < 0.001)0.876 (p < 0.001)—
Q. robur
G1—0.847 (p < 0.001)0.856 (p < 0.001)
G20.843 (p < 0.001)—0.963 (p < 0.001)
G30.856 (p < 0.001)0.881 (p < 0.001)—
A. glutinosa
G1—0.797 (p < 0.001)0.807 (p < 0.001)
G20.797 (p < 0.001)—0.864 (p < 0.001)
G30.582 (p < 0.001)0.864 (p < 0.001)—
Table A2. Sample depth (N) and quality metrics (SNR, rbar, EPS) for replicate chronologies (G1–G3) and the species-level composite chronology (COMPOSITE) for each species.
Table A2. Sample depth (N) and quality metrics (SNR, rbar, EPS) for replicate chronologies (G1–G3) and the species-level composite chronology (COMPOSITE) for each species.
SpeciesChronologyNSNR** rbar* EPS
Pinus sylvestrisPINE_G12912.3210.2980.925
PINE_G21711.1670.3960.918
PINE_G3145.8230.2940.853
PINE_COMPOSITE6023.0750.2780.958
Picea abiesSPRUCE_G11612.7370.4430.927
SPRUCE_G21614.2510.4710.934
SPRUCE_G31312.1750.4840.924
SPRUCE_COMPOSITE4536.5040.4480.973
Quercus roburOAK_G13123.8840.4350.960
OAK_G22728.4620.5130.966
OAK_G32825.7890.4790.963
OAK_COMPOSITE8670.6690.4510.986
Alnus glutinosaALDER_G12413.7100.3640.932
ALDER_G22814.5590.3420.936
ALDER_G32314.1040.3800.934
ALDER_COMPOSITE7538.6540.3400.975
EPS and rbar were derived from SNR, * EPS = SNR/(SNR + 1), ** rbar = SNR/(SNR + N).
Forests 16 01447 g0a1
Figure A1. Comparison of composite chronologies of the four studied species: Alnus glutinosa, Picea abies, Pinus sylvestris, Quercus robur, chronologies after detrending using a 30-year smoothing spline.
Figure A1. Comparison of composite chronologies of the four studied species: Alnus glutinosa, Picea abies, Pinus sylvestris, Quercus robur, chronologies after detrending using a 30-year smoothing spline.
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Figure A2. Correlations between mean monthly temperatures and annual ring widths for dendrochronological groups: no. 1 (a), no. 2 (b) and no. 3 (c) of Pinus sylvestris. Blue indicates positive correlations, red indicates negative correlations, and statistical significance is marked with asterisks. Values on the x-axis represent the starting year of a 25-year moving window (e.g., “1902” corresponds to the period 1902–1926, “1904” to 1904–1928, etc.). For clarity, not all consecutive years are shown on the x-axis.
Figure A2. Correlations between mean monthly temperatures and annual ring widths for dendrochronological groups: no. 1 (a), no. 2 (b) and no. 3 (c) of Pinus sylvestris. Blue indicates positive correlations, red indicates negative correlations, and statistical significance is marked with asterisks. Values on the x-axis represent the starting year of a 25-year moving window (e.g., “1902” corresponds to the period 1902–1926, “1904” to 1904–1928, etc.). For clarity, not all consecutive years are shown on the x-axis.
Forests 16 01447 g0a2
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Figure A3. Correlations between mean monthly temperatures and annual ring widths for dendrochronological groups: no. 1 (a), no. 2 (b), and no. 3 (c) of Picea abies. Blue indicates positive correlations, red indicates negative correlations, and statistical significance is marked with asterisks (e.g., “1904” corresponds to the period 1904–1928, “1906” to 1906–1930, etc.). For clarity, not all consecutive years are shown on the x-axis.
Figure A3. Correlations between mean monthly temperatures and annual ring widths for dendrochronological groups: no. 1 (a), no. 2 (b), and no. 3 (c) of Picea abies. Blue indicates positive correlations, red indicates negative correlations, and statistical significance is marked with asterisks (e.g., “1904” corresponds to the period 1904–1928, “1906” to 1906–1930, etc.). For clarity, not all consecutive years are shown on the x-axis.
Forests 16 01447 g0a3
Forests 16 01447 g0a4
Figure A4. Correlations between mean monthly temperatures and annual ring widths for dendrochronological groups: no. 1 (a), no. 2 (b), and no. 3 (c) of Quercus robur. Blue indicates positive correlations, red indicates negative correlations, and statistical significance is marked with asterisks. Values on the x-axis represent the starting year of a 25-year moving window (e.g., “1959” corresponds to the period 1959–1983, “1960” to 1957–1984, etc.). For clarity, not all consecutive years are shown on the x-axis in (b,c).
Figure A4. Correlations between mean monthly temperatures and annual ring widths for dendrochronological groups: no. 1 (a), no. 2 (b), and no. 3 (c) of Quercus robur. Blue indicates positive correlations, red indicates negative correlations, and statistical significance is marked with asterisks. Values on the x-axis represent the starting year of a 25-year moving window (e.g., “1959” corresponds to the period 1959–1983, “1960” to 1957–1984, etc.). For clarity, not all consecutive years are shown on the x-axis in (b,c).
Forests 16 01447 g0a4
Forests 16 01447 g0a5
Figure A5. Correlations between mean monthly temperatures and annual ring widths for dendrochronological groups: no. 1 (a), no. 2 (b), and no. 3 (c) of Alnus glutinosa. Blue indicates positive correlations, red indicates negative correlations, and statistical significance is marked with asterisks. Values on the x-axis represent the starting year of a 25-year moving window (e.g., “1947” corresponds to the period 1947–1971, “1948” to 1948–1972, etc.). For clarity, not all consecutive years are shown on the x-axis in (c).
Figure A5. Correlations between mean monthly temperatures and annual ring widths for dendrochronological groups: no. 1 (a), no. 2 (b), and no. 3 (c) of Alnus glutinosa. Blue indicates positive correlations, red indicates negative correlations, and statistical significance is marked with asterisks. Values on the x-axis represent the starting year of a 25-year moving window (e.g., “1947” corresponds to the period 1947–1971, “1948” to 1948–1972, etc.). For clarity, not all consecutive years are shown on the x-axis in (c).
Forests 16 01447 g0a5
Forests 16 01447 g0a6
Figure A6. Correlations between mean monthly precipitation sums and annual ring widths for dendrochronological groups: no. 1 (a), no. 2 (b), and no. 3 (c) of Pinus sylvestris. Blue indicates positive correlations, red indicates negative correlations, and statistical significance is marked with asterisks. Values on the x-axis represent the starting year of a 25-year moving window (e.g., “1902” corresponds to the period 1902–1926, “1904” to 1904–1928, etc.). For clarity, not all consecutive years are shown on the x-axis.
Figure A6. Correlations between mean monthly precipitation sums and annual ring widths for dendrochronological groups: no. 1 (a), no. 2 (b), and no. 3 (c) of Pinus sylvestris. Blue indicates positive correlations, red indicates negative correlations, and statistical significance is marked with asterisks. Values on the x-axis represent the starting year of a 25-year moving window (e.g., “1902” corresponds to the period 1902–1926, “1904” to 1904–1928, etc.). For clarity, not all consecutive years are shown on the x-axis.
Forests 16 01447 g0a6
Forests 16 01447 g0a7
Figure A7. Correlations between mean monthly precipitation sums and annual ring widths for dendrochronological groups: no. 1 (a), no. 2 (b), and no. 3 (c) of Picea abies. Blue indicates positive correlations, red indicates negative correlations, and statistical significance is marked with asterisks. (e.g., “1904” corresponds to the period 1904–1928, “1906” to 1906–1930, etc.). For clarity, not all consecutive years are shown on the x-axis.
Figure A7. Correlations between mean monthly precipitation sums and annual ring widths for dendrochronological groups: no. 1 (a), no. 2 (b), and no. 3 (c) of Picea abies. Blue indicates positive correlations, red indicates negative correlations, and statistical significance is marked with asterisks. (e.g., “1904” corresponds to the period 1904–1928, “1906” to 1906–1930, etc.). For clarity, not all consecutive years are shown on the x-axis.
Forests 16 01447 g0a7
Forests 16 01447 g0a8
Figure A8. Correlations between mean monthly precipitation sums and annual ring widths for dendrochronological groups: no. 1 (a), no. 2 (b), and no. 3 (c) of Quercus robur. Blue indicates positive correlations, red indicates negative correlations, and statistical significance is marked with asterisks. Values on the x-axis represent the starting year of a 25-year moving window (e.g., “1959” corresponds to the period 1959–1983, “1960” to 1957–1984, etc.). For clarity, not all consecutive years are shown on the x-axis in (b,c).
Figure A8. Correlations between mean monthly precipitation sums and annual ring widths for dendrochronological groups: no. 1 (a), no. 2 (b), and no. 3 (c) of Quercus robur. Blue indicates positive correlations, red indicates negative correlations, and statistical significance is marked with asterisks. Values on the x-axis represent the starting year of a 25-year moving window (e.g., “1959” corresponds to the period 1959–1983, “1960” to 1957–1984, etc.). For clarity, not all consecutive years are shown on the x-axis in (b,c).
Forests 16 01447 g0a8
Forests 16 01447 g0a9
Figure A9. Correlations between mean monthly precipitation sums and annual ring widths for dendrochronological groups: no. 1 (a), no. 2 (b), and no. 3 (c) of Alnus glutinosa. Blue indicates positive correlations, red indicates negative correlations, and statistical significance is marked with asterisks. Values on the x-axis represent the starting year of a 25-year moving window (e.g., “1947” corresponds to the period 1947–1971, “1948” to 1948–1972, etc.). For clarity, not all consecutive years are shown on the x-axis in (c).
Figure A9. Correlations between mean monthly precipitation sums and annual ring widths for dendrochronological groups: no. 1 (a), no. 2 (b), and no. 3 (c) of Alnus glutinosa. Blue indicates positive correlations, red indicates negative correlations, and statistical significance is marked with asterisks. Values on the x-axis represent the starting year of a 25-year moving window (e.g., “1947” corresponds to the period 1947–1971, “1948” to 1948–1972, etc.). For clarity, not all consecutive years are shown on the x-axis in (c).
Forests 16 01447 g0a9

References

  1. Speer, J.H. Some basic principles and concepts in dendrochronology. In Fundamentals of Tree-Ring Research, 1st ed.; University of Arizona Press: Tucson, AZ, USA, 2010; pp. 10–27. [Google Scholar]
  2. Babst, F.; Poulter, B.; Trouet, V.; Tan, K.; Neuwirth, B.; Wilson, R.; Carrer, M.; Grabner, M.; Tegel, W.; Levanic, T.; et al. Site- and species-specific responses of forest growth to climate across the European continent. Glob. Ecol. Biogeogr. 2013, 22, 706–717. [Google Scholar] [CrossRef]
  3. Babst, F.; Bodesheim, P.; Charney, N.; Friend, A.D.; Girardin, M.P.; Klesse, S.; Moore, D.J.P.; Seftigen, K.; Björklund, J.; Bouriaud, O.; et al. When tree rings go global: Challenges and opportunities for retro- and prospective insight. Quat. Sci. Rev. 2018, 197, 1–20. [Google Scholar] [CrossRef]
  4. Gričar, J.; Vedenik, A.; Skoberne, G.; Hafner, P.; Prislan, P. Timeline of leaf and cambial phenology in relation to development of initial conduits in xylem and phloem in three coexisting Sub-Mediterranean deciduous tree species. Forests 2020, 11, 1104. [Google Scholar] [CrossRef]
  5. Allen, C.D.; Macalady, A.K.; Chenchouni, H.; Bachelet, D.; McDowell, N.; Vennetier, M.; Kitzberger, T.; Rigling, A.; Breshears, D.D.; Hogg, E.H.; et al. A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests. For. Ecol. Manag. 2010, 259, 660–684. [Google Scholar] [CrossRef]
  6. Cavin, L.; Mountford, E.P.; Peterken, G.F.; Jump, A.S. Extreme drought alters competitive dominance within and between tree species in a mixed forest stand. Funct. Ecol. 2013, 27, 1424–1435. [Google Scholar] [CrossRef]
  7. Filazzola, A.; Cahill, J.F., Jr. Replication in field ecology: Identifying challenges and proposing solutions. Methods Ecol. Evol. 2021, 12, 1780–1792. [Google Scholar] [CrossRef]
  8. Fritts, H.C. Interpretation of climatic calibrations, reconstruction, and verification. In Tree Rings and Climate; Elsevier: Amsterdam, The Netherlands, 1976; pp. 376–433. [Google Scholar] [CrossRef]
  9. Biondi, F. Comparing tree-ring chronologies and repeated timber inventories as forest monitoring tools. Ecol. Appl. 1999, 9, 216–227. [Google Scholar] [CrossRef]
  10. Vitas, A.; Erlickytė, R. Influence of droughts to the radial growth of Scots pine (Pinus sylvestris L.) at different site conditions. Balt. For. 2007, 13, 10–16. [Google Scholar]
  11. Dyderski, M.K.; Paź, S.; Frelich, L.E.; Jagodziński, A.M. How much does climate change threaten European forest tree species distributions? Glob. Change Biol. 2018, 24, 1150–1163. [Google Scholar] [CrossRef]
  12. Dyderski, M.K.; Paź-Dyderska, S.; Jagodziński, A.M.; Puchałka, R. Shifts in native tree species distributions in Europe under climate change. J. Environ. Manag. 2025, 373, 123504. [Google Scholar] [CrossRef] [PubMed]
  13. Rozporządzenie Rady Ministrów z dnia 27 czerwca 1988 r. w sprawie utworzenia Wigierskiego Parku Narodowego [Regulation of the Council of Ministers of 27 June 1988 on the establishment of Wigry National Park]. Dz. Ustaw 1988, 25, 173. (In Polish)
  14. Krzysztofiak, L. Wstęp [Introduction]. In Śluzowce Myxomycetes, Grzyby Fungi i Mszaki Bryophyta Wigierskiego Parku Narodowego; Krzysztofiak, L., Ed.; Stowarzyszenie “Człowiek i Przyroda”: Suwałki, Poland, 2010; pp. 5–9. (In Polish) [Google Scholar]
  15. Staniaszek-Kik, M.; Górski, P.; Fałtynowicz, W.; Fałtynowicz, H.; Halama, M.; Kowalewska, A.; Patejuk, K.; Pencakowski, B.; Piegdoń, A.; Romański, M. Nowe gatunki we florze mchów Wigierskiego Parku Narodowego [New species in the moss flora of Wigry National Park]. Steciana 2020, 24, 17–20. (In Polish) [Google Scholar] [CrossRef]
  16. Bańkowski, J.; Cieśla, A.; Czerepko, J.; Czępińska-Kamińska, D.; Kliczkowska, A.; Kowalkowski, A.; Krzyżanowski, A.; Mąkosa, K.; Sikorska, E.; Zielony, R. Charakterystyka cech geologiczno-glebowych [Characteristics of geological and soil features]. In Siedliskowe Podstawy Hodowli Lasu. Załącznik nr 1 do Zasad Hodowli i Użytkowania Lasu Wielofunkcyjnego; Kliczkowska, A., Zielony, R., Czępińska-Kamińska, D., Kowalkowski, A., Sikorska, E., Krzyżanowski, A., Cieśla, A., Czerepko, J., Eds.; Dyrekcja Generalna Lasów Państwowych: Warsaw, Poland, 2003; pp. 27–58. (In Polish) [Google Scholar]
  17. Lebourgeois, F.; Breda, N.; Ulrich, E.; Granier, A. Climate-tree-growth relationships of European beech (Fagus sylvatica L.) in the French Permanent Plot Network (RENECOFOR). Trees 2005, 19, 385–401. [Google Scholar] [CrossRef]
  18. Nechita, C.; Chiriloaei, F. Interpreting the effect of regional climate fluctuations on Quercus robur L. trees under a temperate continental climate (southern Romania). Dendrobiology 2017, 79, 77–89. [Google Scholar] [CrossRef]
  19. Boratyński, A. Systematyka i geograficzne rozmieszczenie [Systematics and geographic distribution]. In Biologia Sosny Zwyczajnej, 1st ed.; Białobok, S., Boratyński, A., Bugała, W., Eds.; Sorus: Poznań, Poland, 1993; pp. 45–69. (In Polish) [Google Scholar]
  20. Cedro, A. Dependence of radial growth of Pinus sylvestris L. from western Pomerania on the rainfall and temperature conditions. Geochronometria 2001, 20, 69–74. [Google Scholar]
  21. Vitas, A. Sensitivity of Scots pine trees to winter colds and summer droughts: Dendroclimatological investigation. Balt. For. 2006, 12, 220–226. [Google Scholar]
  22. D’Andrea, G.; Šimůnek, V.; Pericolo, O.; Vacek, Z.; Vacek, S.; Corleto, R.; Olejár, L.; Ripullone, F. Growth response of Norway spruce (Picea abies [L.] Karst.) in Central Bohemia (Czech Republic) to climate change. Forests 2023, 14, 1215. [Google Scholar] [CrossRef]
  23. Rozas, V. Dendrochronology of pedunculate oak (Quercus robur L.) in an old-growth pollarded woodland in northern Spain: Tree-ring growth responses to climate. Ann. For. Sci. 2005, 62, 209–218. [Google Scholar] [CrossRef]
  24. Prieditis, N. Alnus glutinosa–dominated wetland forests of the Baltic Region: Community structure, syntaxonomy and conservation. Plant Ecol. 1997, 129, 49–94. [Google Scholar] [CrossRef]
  25. Claessens, H.; Oosterbaan, A.; Savill, P.; Rondeux, J. A review of the characteristics of black alder (Alnus glutinosa (L.) Gaertn.) and their implications for silvicultural practices. Forestry 2010, 83, 163–175. [Google Scholar] [CrossRef]
  26. McVean, D.N. Ecology of Alnus glutinosa (L.) Gaertn: IV. Root system. J. Ecol. 1956, 44, 219–225. [Google Scholar] [CrossRef]
  27. Glenz, C.; Schlaepfer, R.; Iorgulescu, I.; Kienast, F. Flooding tolerance of Central European tree and shrub species. For. Ecol. Manag. 2006, 235, 1–13. [Google Scholar] [CrossRef]
  28. Bönsel, A. Schnelle und individuenreiche Besiedlung eines revitalisierten Waldmoores durch Leucorrhinia pectoralis (Odonata: Libellulidae). Libellula 2006, 25, 151–157. [Google Scholar]
  29. Laganis, J.; Pečkov, A.; Debeljak, M. Modeling radial growth increment of black alder (Alnus glutinosa (L.) Gaertn.) tree. Ecol. Model. 2008, 215, 180–189. [Google Scholar] [CrossRef]
  30. Tulik, M.; Grochowina, A.; Jura-Morawiec, J.; Bijak, S. Groundwater level fluctuations affect the mortality of black alder (Alnus glutinosa Gaertn.). Forests 2020, 11, 134. [Google Scholar] [CrossRef]
  31. Anadon-Rosell, A.; Scharnweber, T.; von Arx, G.; Peters, R.L.; Smiljanić, M.; Weddell, S.; Wilmking, M. Growth and wood trait relationships of Alnus glutinosa in peatland forest stands with contrasting water regimes. Front. Plant Sci. 2022, 12, 788106. [Google Scholar] [CrossRef]
  32. Harris, I.; Osborn, T.J.; Jones, P.; Lister, D. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset. Sci. Data 2020, 7, 109. [Google Scholar] [CrossRef]
  33. Bunn, A.G. A dendrochronology program library in R (dplR). Dendrochronologia 2008, 26, 115–124. [Google Scholar] [CrossRef]
  34. Zang, C.; Biondi, F. treeclim: An R package for the numerical calibration of proxy-climate relationships. Ecography 2015, 38, 431–436. [Google Scholar] [CrossRef]
  35. Jevšenak, J. New features in the dendroTools R package: Bootstrapped and partial correlation coefficients for monthly and daily climate data. Dendrochronologia 2020, 63, 125753. [Google Scholar] [CrossRef]
  36. Wickham, H.; Averick, M.; Bryan, J.; Chang, W.; D’Agostino McGowan, L.; François, R.; Grolemund, G.; Hayes, A.; Henry, L.; Hester, J.; et al. Welcome to the Tidyverse. J. Open Source Softw. 2019, 4, 1686. [Google Scholar] [CrossRef]
  37. Fritts, H.C. Dendrochronology and dendroclimatology. In Tree Rings and Climate; Elsevier: Amsterdam, The Netherlands, 1976; pp. 1–54. [Google Scholar] [CrossRef]
  38. Rahman, M.H.; Kudo, K.; Yamagishi, Y.; Nakamura, Y.; Nakaba, S.; Begum, S.; Nugroho, W.D.; Arakawa, I.; Kitin, P.; Funada, R. Winter–spring temperature pattern is closely related to the onset of cambial reactivation in stems of the evergreen conifer Chamaecyparis pisifera. Sci. Rep. 2020, 10, 14341. [Google Scholar] [CrossRef]
  39. Way, D.; Oren, R. Differential responses to changes in growth temperature between trees from different functional groups and biomes: A review and synthesis of data. Tree Physiol. 2010, 30, 669–688. [Google Scholar] [CrossRef] [PubMed]
  40. Zajączkowski, S.; Bogaciński, B.; Wodzicki, T.J. Zmienność długości okresu aktywności kambium w sezonie a liczba produkowanych cewek w populacjach drzewostanowych Pinus sylvestris L. [Variation in the length of cambial activity period during the season and the number of tracheids produced in Scots pine (Pinus sylvestris L.) populations]. Sylwan 1988, 132, 27–40. (In Polish) [Google Scholar]
  41. Bogaciński, B.; Zajączkowski, S.; Wodzicki, T.J. Zmienność inicjacji i kończenia sezonowej aktywności kambium w populacjach drzewostanowych Pinus sylvestris L. [Variation in initiation and cessation of seasonal cambial activity in Scots pine (Pinus sylvestris L.) populations]. Sylwan 1998, 132, 17–26. (In Polish) [Google Scholar]
  42. Swidrak, I.; Gruber, A.; Oberhuber, W. Cambial activity and xylem cell development in Pinus cembra and Pinus sylvestris at their climatic limits in the eastern Alps in 2007. Phyton 2011, 51, 299–313. [Google Scholar] [PubMed]
  43. Čufar, K.; Prislan, P.; Gricar, J. Cambial activity and wood formation in beech (Fagus sylvatica) during the 2006 growth season. Wood Res. 2008, 53, 1–12. [Google Scholar]
  44. Kramer, K.; Leinonen, I.; Loustau, D. The importance of phenology for the evaluation of impact of climate change on growth of boreal, temperate and Mediterranean forests ecosystems: An overview. Int. J. Biometeorol. 2000, 44, 67–75. [Google Scholar] [CrossRef]
  45. Dittmar, C.; Zech, W.; Elling, W. Growth variations of common beech (Fagus sylvatica L.) under different climatic and environmental conditions in Europe-a dendroecological study. For. Ecol. Manag. 2003, 173, 63–78. [Google Scholar] [CrossRef]
  46. Anfodillo, T.; Carrer, M.; Rento, S.; Urbinati, D. Long and short term growth dynamics of Picea abies (L.) Karst, Larix decidua Mill., Pinus cembra (L.) and climatic factors: First results of an integrated study at the timberline in eastern Italian Alps. Écologie 1998, 29, 253–259. [Google Scholar]
  47. García González, I.; Eckstein, D. Climatic signal of earlywood vessels of oak on a maritime site. Tree Physiol. 2003, 23, 497–504. [Google Scholar] [CrossRef] [PubMed]
  48. Biondi, F.; Rossi, S. Plant-water relationships in the Great Basin Desert of North America derived from Pinus monophylla hourly dendrometer records. Int. J. Biometeorol. 2015, 59, 939–953. [Google Scholar] [CrossRef]
  49. Stepniewska, Z.; Przywara, G.; Bennicelli, R.P. Reakcja roślin w warunkach anaerobiozy [Plant responses under anaerobic conditions]. Acta Agrophysica. Rozpr. I Monogr. 2004, 7, 1–86. (In Polish) [Google Scholar]
  50. Sakowska, K.; Juszczak, R.; Uździcka, B.; Olejnik, J. Zmienność dobowa strumieni CO2 wymienianych między atmosferą a różnymi uprawami rolniczymi [Daily variability of CO2 fluxes exchanged between the atmosphere and different agricultural crops]. Woda-Sr.-Obsz. Wiej. 2012, 12, 221–244. (In Polish) [Google Scholar]
  51. Turbiak, J. Ocena produktywności ekosystemów łąkowych na podstawie pomiarów fotosyntezy brutto [Assessment of meadow ecosystem productivity based on gross photosynthesis measurements]. Woda-Sr.-Obsz. Wiej. 2015, 15, 57–68. (In Polish) [Google Scholar]
  52. Zweifel, R.; Rigling, A.; Dobbertin, M. Species-specific stomatal response of trees to drought—A link to vegetation dynamics? J. Veg. Sci. 2009, 20, 442–454. [Google Scholar] [CrossRef]
  53. Ciais, P.; Reichstein, M.; Viovy, N.; Granier, A.; Ogée, J.; Allard, V.; Aubinet, M.; Buchmann, N.; Bernhofer, C.; Carrara, A.; et al. Europe-wide reduction in primary productivity caused by the heat and drought in 2003. Nature 2005, 437, 529–533. [Google Scholar] [CrossRef]
  54. Rennenberg, H.; Loreto, F.; Polle, A.; Brilli, F.; Fares, S.; Beniwal, R.S.; Gessler, A. Physiological responses of forest trees to heat and drought. Plant Biol. 2006, 8, 556–571. [Google Scholar] [CrossRef] [PubMed]
  55. Granier, A.; Reichstein, M.; Bréda, N.; Janssens, I.A.; Falge, E.; Ciais, P.; Grünwald, T.; Aubinet, M.; Berbigier, P.; Bernhofer, C.; et al. Evidence for soil water control on carbon and water dynamics in European forests during the extremely dry year: 2003. Agric. For. Meteorol. 2007, 143, 123–145. [Google Scholar] [CrossRef]
  56. Eilmann, B.; Rigling, A. Tree-growth analyses to estimate tree species’ drought tolerance. Tree Physiol. 2012, 32, 178–187. [Google Scholar] [CrossRef]
  57. McDowell, N.G.; Allen, C.D.; Anderson-Teixeira, K.; Aukema, B.H.; Bond-Lamberty, B.; Chini, L.; Clark, J.S.; Dietze, M.; Grossiord, C.; Hanbury-Brown, A.; et al. Pervasive shifts in forest dynamics in a changing world. Science 2020, 368, eaaz9463. [Google Scholar] [CrossRef] [PubMed]
  58. Wilczyński, S.; Szymański, N.; Wertz, B.; Muter, E. Wpływ wieku na odpowiedź przyrostową drzew na czynnik klimatyczny na przykładzie modrzewia europejskiego [Effect of age on tree growth response to climatic factors: A case study of European larch (Larix decidua)]. Stud. I Mater. CEPL W Rogowie 2014, 16, 256–264. (In Polish) [Google Scholar]
  59. Lie, M.H.; Asplund, J.; Göhl, M.; Ohlson, M.; Nybakken, L. Similar growth responses to climatic variations in Norway spruce (Picea abies) and European beech (Fagus sylvatica) at the northern range limit of beech. Eur. J. For. Res. 2023, 142, 1059–1068. [Google Scholar] [CrossRef]
  60. Caudullo, G.; Tinner, W.; De Rigo, D. Picea abies in Europe: Distribution, habitat, usage and threats. In European Atlas of Forest Tree Species; San-Miguel-Ayanz, J., De Rigo, D., Caudullo, G., Houston Durrant, T., Mauri, A., Eds.; Publications Office of the European Union: Luxembourg, 2016; pp. 114–116. [Google Scholar]
  61. Bobiec, A. What is the use of the research carried out on the permanent plots in the Białowieża National Park? For. Res. Pap. 2016, 77, 296–301. [Google Scholar] [CrossRef]
  62. Andrzejczyk, T. Wpływ odległości od ściany drzewostanu na zagęszczenie i przeżywalność nalotów sosny zwyczajnej (Pinus sylvestris L.) na zrębach zupełnych i gniazdach [Effect of distance from the forest stand edge on density and survival of Scots pine (Pinus sylvestris L.) seedlings in clear-cuts and shelterwood gaps]. Sylwan 2000, 144, 27–42. (In Polish) [Google Scholar]
  63. Gorzelak, A. Mikroklimat-środowisko glebowe-roślinność chwastowa szkółek leśnych [Microclimate–soil environment–weed vegetation in forest nurseries]. Sylwan 1998, 142, 15–33. (In Polish) [Google Scholar]
  64. de Chantal, M.; Leinonen, K.; Kuuluvainen, T.; Cescatti, A. Early response of Pinus sylvestris and Picea abies seedlings to an experimental canopy gap in a boreal spruce forest. For. Ecol. Manag. 2003, 176, 321–336. [Google Scholar] [CrossRef]
  65. Dyderski, M.K.; Gazda, A.; Hachułka, M.; Horodecki, P.; Kałucka, I.L.; Kamczyc, J.; Malicki, M.; Pielech, R.; Smoczyk, M.; Skorupski, M.; et al. Impacts of soil conditions and light availability on natural regeneration of Norway spruce Picea abies (L.) H. Karst. in low-elevation mountain forests. Ann. For. Sci. 2018, 75, 91. [Google Scholar] [CrossRef]
  66. DeSoto, L.; Cailleret, M.; Sterck, F.; Jansen, S.; Kramer, K.; Robert, E.M.R.; Aakala, T.; Amoroso, M.M.; Bigler, C.; Camarero, J.J.; et al. Low growth resilience to drought is related to future mortality risk in trees. Nat. Commun. 2020, 11, 545. [Google Scholar] [CrossRef]
  67. Thom, D.; Buras, A.; Heym, M.; Klemmt, H.-J.; Wauer, A. Varying growth response of Central European tree species to the extraordinary drought period of 2018–2020. Agric. For. Meteorol. 2023, 338, 109506. [Google Scholar] [CrossRef]
  68. Kaelke, C.M.; Dawson, J.O. Seasonal flooding regimes influence survival, nitrogen fixation, and the partitioning of nitrogen and biomass in Alnus incana ssp. rugosa. Plant Soil 2003, 254, 167–177. [Google Scholar] [CrossRef]
  69. Pallardy, S.G.; Kozlowski, T.T. Introduction. In Physiology of Woody Plants, 3rd ed.; Elsevier: Amsterdam, The Netherlands, 2008; pp. 1–7. [Google Scholar]
  70. Pallardy, S.G.; Kozlowski, T.T. Photosynthesis. In Physiology of Woody Plants, 3rd ed.; Elsevier: Amsterdam, The Netherlands, 2008; pp. 107–167. [Google Scholar]
  71. Pallardy, S.G.; Kozlowski, T.T. Mineral nutrition. In Physiology of Woody Plants, 3rd ed.; Elsevier: Amsterdam, The Netherlands, 2008; pp. 255–285. [Google Scholar]
  72. Bjelke, U.; Boberg, J.; Oliva, J.; Tattersdill, K.; McKie, B.G. Dieback of riparian alder caused by the Phytophthora alni complex: Projected consequences for stream ecosystems. Freshw. Biol. 2016, 61, 565–579. [Google Scholar] [CrossRef]
  73. Siebel, H.N.; Blom, C.W.P.M. Effects of irregular flooding on the establishment of tree species. Acta Bot. Neerl. 1998, 47, 231–240. [Google Scholar]
Forests 16 01447 g001
Figure 1. Location of the study area and distribution of the sampling plots. The black line indicates the boundaries of the study area within Wigry National Park. Map source: OpenStreetMap. Species abbreviations: PO—pedunculate oak, BA—black alder, SP—Scots pine, NS—Norway spruce.
Figure 1. Location of the study area and distribution of the sampling plots. The black line indicates the boundaries of the study area within Wigry National Park. Map source: OpenStreetMap. Species abbreviations: PO—pedunculate oak, BA—black alder, SP—Scots pine, NS—Norway spruce.
Forests 16 01447 g001
Forests 16 01447 g002
Figure 2. Comparison of chronologies for Pinus sylvestris groups based on raw (RWR—raw ring width) and detrended (RWI—ring width index) data.
Figure 2. Comparison of chronologies for Pinus sylvestris groups based on raw (RWR—raw ring width) and detrended (RWI—ring width index) data.
Forests 16 01447 g002
Forests 16 01447 g003
Figure 3. Comparison of chronologies for Picea abies groups based on raw (RWR—raw ring width) and detrended (RWI—ring width index) data.
Figure 3. Comparison of chronologies for Picea abies groups based on raw (RWR—raw ring width) and detrended (RWI—ring width index) data.
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Forests 16 01447 g004
Figure 4. Comparison of chronologies for Quercus robur groups based on raw (RWR—raw ring width) and detrended (RWI—ring width index) data.
Figure 4. Comparison of chronologies for Quercus robur groups based on raw (RWR—raw ring width) and detrended (RWI—ring width index) data.
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Forests 16 01447 g005
Figure 5. Comparison of chronologies for Alnus glutinosa groups based on raw (RWR—raw ring width) and detrended (RWI—ring width index).
Figure 5. Comparison of chronologies for Alnus glutinosa groups based on raw (RWR—raw ring width) and detrended (RWI—ring width index).
Forests 16 01447 g005
Table 1. Characteristics of the study groups for each analysed species.
Table 1. Characteristics of the study groups for each analysed species.
SpeciesGroupSite TypeMean AgeCover
Period *		Number of Trees
P. sylvestris1Fresh mixed forest123.11896–202429
2Fresh mixed forest146.21831–202417
3Fresh mixed forest213.11794–202414
P. abies1Fresh mixed forest110.91904–202416
2Fresh mixed forest86.21932–202416
3Fresh mixed forest110.41878–202413
Q. robur1Fresh mixed forest59.61959–202431
2Fresh mixed forest81.21925–202427
3Fresh mixed forest76.51925–202428
A. glutinosa1Ash–alder swamp forest54.81947–202424
2Fresh mixed forest56.71961–202428
3Fresh mixed forest/swampy mixed forest59.71940–202423
* Cover period—the earliest and latest calendar years represented in the samples.
Table 2. Characteristics of the dendrochronological collections and quality of detrended data (spline30) for the analysed species.
Table 2. Characteristics of the dendrochronological collections and quality of detrended data (spline30) for the analysed species.
SpeciesGrouprbar *EPS **SNR ***
Pinus sylvestris10.2980.92512.321
20.3960.91811.167
30.2940.8535.823
Picea abies10.4430.92712.737
20.4710.93414.251
30.5040.92412.175
Quercus robur10.4350.96023.884
20.5130.96628.462
30.4790.96325.789
Alnus glutinosa10.3640.93213.710
20.3420.93614.559
30.3800.93414.104
* rbar—the mean inter-series correlation, ** EPS—expressed population signal, *** SNR—signal-to-noise ratio.
Table 3. Correlation matrix of indexed composite chronologies (spline30). Above the diagonal: correlations in the format r (p) for the fixed period 1961–2024. Below the diagonal: pairwise–overlap correlations in the format r (p). Diagonal entries are “—”. Significance levels have been standardised.
Table 3. Correlation matrix of indexed composite chronologies (spline30). Above the diagonal: correlations in the format r (p) for the fixed period 1961–2024. Below the diagonal: pairwise–overlap correlations in the format r (p). Diagonal entries are “—”. Significance levels have been standardised.
P. sylvestrisP. abiesQ. roburA. glutinosa
P. sylvestris—0.667 (p < 0.001)0.531 (p < 0.001)−0.218 (p = 0.083)
P. abies0.637 (p < 0.001)—0.622 (p < 0.001)−0.208 (p = 0.100)
Q. robur0.493 (p < 0.001)0.556 (p < 0.001)—−0.149 (p = 0.240)
A. glutinosa−0.166 (p = 0.145)−0.090 (p = 0.434)0.028 (p = 0.807)—
Table 4. Statistically significant correlations between monthly temperature and radial growth response in selected dendrochronological groups of the studied species. A plus sign indicates a positive correlation; a minus sign indicates a negative correlation. The frequency of significant correlations in moving 25-year windows is expressed as percentage classes: 10%–29% (black) and ≥30% (red), which indicate the stability of climate–growth relationships over time (see Figure A2, Figure A3, Figure A4 and Figure A5 in Appendix A for details). The notation: + or − indicates significance in one group, + + or − − in two groups, and + + + or − − − in all three groups.
Table 4. Statistically significant correlations between monthly temperature and radial growth response in selected dendrochronological groups of the studied species. A plus sign indicates a positive correlation; a minus sign indicates a negative correlation. The frequency of significant correlations in moving 25-year windows is expressed as percentage classes: 10%–29% (black) and ≥30% (red), which indicate the stability of climate–growth relationships over time (see Figure A2, Figure A3, Figure A4 and Figure A5 in Appendix A for details). The notation: + or − indicates significance in one group, + + or − − in two groups, and + + + or − − − in all three groups.
MonthP. sylvestrisP. abiesQ. roburA. glutinosa
Previous yearJune− +− −
July −
August− −
September−− − − − −
October + +++ +
November −
December++ ++ ++ +
Current yearJanuary + + + +
February+ + ++ + ++ +
March+ ++ ++ ++ + +
April+ ++ +
May−+++ + +
June − − −
July+ + + +
August + +
September + +
Table 5. Statistically significant correlations between monthly precipitation and radial growth response in selected dendrochronological groups of the studied species. A plus sign indicates a positive correlation, a minus sign a negative correlation. The frequency of significant correlations in moving 25-year windows is expressed as percentage classes: 10%–29% (black) and ≥30% (red), which indicate the stability of climate–growth relationships over time (see Figure A6, Figure A7, Figure A8 and Figure A9 in Appendix A for details). The notation + or − indicates significance in one group, + + or − − in two groups, and + + + or − − − in all three groups.
Table 5. Statistically significant correlations between monthly precipitation and radial growth response in selected dendrochronological groups of the studied species. A plus sign indicates a positive correlation, a minus sign a negative correlation. The frequency of significant correlations in moving 25-year windows is expressed as percentage classes: 10%–29% (black) and ≥30% (red), which indicate the stability of climate–growth relationships over time (see Figure A6, Figure A7, Figure A8 and Figure A9 in Appendix A for details). The notation + or − indicates significance in one group, + + or − − in two groups, and + + + or − − − in all three groups.
MonthP. sylvestrisP. abiesQ. roburA. glutinosa
Previous yearJune + + +
July+ ++ + +
August −
September − − −
October +
November − −
December + + + +
Current yearJanuary− − + +
February
March −++
April +
May +−
June+ + ++ + ++ + +− −
July+ + ++ + ++ + +
August+ + + +− − −
September
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Tokarska-Osyczka, A.; Ksepko, M.; Terlecka, M.; Kolendo, Ł.; Chmur, S.; Lasek, M.; Iszkuło, G. Species-Specific Growth Responses to Climate in a Multi-Site Study, NE Poland. Forests 2025, 16, 1447. https://doi.org/10.3390/f16091447

AMA Style

Tokarska-Osyczka A, Ksepko M, Terlecka M, Kolendo Ł, Chmur S, Lasek M, Iszkuło G. Species-Specific Growth Responses to Climate in a Multi-Site Study, NE Poland. Forests. 2025; 16(9):1447. https://doi.org/10.3390/f16091447

Chicago/Turabian Style

Tokarska-Osyczka, Agnieszka, Marek Ksepko, Magdalena Terlecka, Łukasz Kolendo, Szymon Chmur, Martyna Lasek, and Grzegorz Iszkuło. 2025. "Species-Specific Growth Responses to Climate in a Multi-Site Study, NE Poland" Forests 16, no. 9: 1447. https://doi.org/10.3390/f16091447

APA Style

Tokarska-Osyczka, A., Ksepko, M., Terlecka, M., Kolendo, Ł., Chmur, S., Lasek, M., & Iszkuło, G. (2025). Species-Specific Growth Responses to Climate in a Multi-Site Study, NE Poland. Forests, 16(9), 1447. https://doi.org/10.3390/f16091447

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