Ecological differentiation in Pinus nigra subspecies predicts differential responses to climate change

Understanding the causes of geographic variation in traits is the focus of much research. Prior studies on multiple species of cone-bearing conifers have discovered that geographic variation in cone structure is related to variation in the intensity and form of natural selection exerted by seed predators. Here, we used data on the intensity of pre-dispersal seed predation by common crossbills (Loxia curvirostra) in relation to black pine (Pinus nigra) cone traits, along with seed predation and cone trait data for nine sites in the Iberian Peninsula to determine whether patterns of differential seed predation could account for among-site variation in cone traits. The tree and cone preferences of crossbills within a site were similar to those found in other studies of crossbills and conifers: crossbills preferentially foraged on trees producing many small cones with small scales. In contrast, the variation in the intensity of seed predation among the nine sites appeared to be negatively influenced by the proportion of empty seeds. The preferences for small, thin-scaled cones within a site have the potential to cause the evolution of larger and thicker scaled cones, because such cone traits are often heritable. However, the different intensities of seed predation among sites that we document are unlikely to lead to concordant trait differences among sites because the proportion of empty seeds appears related to environmental factors (e.g., precipitation) affecting pollination success that vary from year to year. Controlling such among-site variation is key for inferring whether selection can account for geographic variation.

Background and Aims This study investigated the variation in physical properties of black pine (Pinus nigra Arnold) needles, according to elevation, stand development stage, needle age, and seasonal factors. Method Needle samples were collected seasonally (May 2024, July 2024, September 2024, November 2024) from 12 stands across two elevation ranges (750–1000 m and 1000–1250 m) and two stand development stages (b–bc and c–cd). Needles were classified into four age groups (current year 0, 1st, 2nd, and 3rd year needles), and needle length, sheath length, mean diameter, and 1,000-needle weight were measured. Results Analysis results revealed that needle age had a statistically significant effect on all physical properties. Current-year needles (0) exhibited the lowest length (7.71 cm) and lowest weight (76.38 g), while one-year-old needles (1) reached maximum values (11.01 cm; 135.28 g). Sheath length decreased systematically with needle age (0: 1.41 cm; 3: 0.35 cm). Seasonally, needle characteristics (except for the sheath length) developed throughout the summer season, reaching maximum values in autumn. In terms of elevation, only the average diameter and the weight needles showed significant differences. The stand development stage affected all needle properties, with longer and heavier needles observed in the c–cd stage stands. Correlation analysis detected a strong positive relationship between needle length and weight and a strong negative relationship between sheath length and needle age. Conclusion These findings demonstrate that black pine needle traits are sensitive to elevation, stand development stage and seasonal gradients, and that needle properties can be used as an indicator of site quality and forest health.

The selection of wood materials in accordance with the place of use and their processing under the most appropriate conditions will ensure the utilisation of natural resources with high efficiency. In the utilisation of wood material, it is known that the characteristics of the place where it grows and the place where it grows affect the properties of the log and timber obtained. In this study, the surface roughness (Ra) values of larch (Pinus nigra Arnold) and eastern beech (Fagus orientalis Lipsky) wood species growing in northern and southern aspects were determined under various machining parameters. The specimens were grooved using a Scilled 2040 CNC 3-axis milling machine at 3 different feeds of 1000 mm/min, 1500 mm/min and 2000 mm/min, 12000 rpm, 15000 rpm and 18000 rpm, using a 12 mm diameter HSS (High-speed steel) cutter. The surface roughness parameter Ra was determined on the surfaces obtained, and the data obtained was evaluated. When evaluated in general, the lowest Ra value for both wood species was obtained at 18000 rpm at 1000 mm/min for larch and 1000 mm/min or 1500 mm/min for beech at a feed of 1000 mm/min or 1500 mm/min for the specimens obtained from the southern aspect. A linear decrease in Ra value occurred for both wood species with the increased number of revolutions. The lowest Ra values were obtained at 18000 rpm. Generally, the samples obtained from the southern aspect direction gave smoother surfaces for both wood species.

Knowledge of the reduction in tree stem diameter with increasing height is considered significant for reliable tree taper prediction. Tree taper modeling offers a comprehensive framework that connects tree form to growth processes, enabling precise estimates of volume and biomass. In this context, machine learning modeling approaches offer strong potential for predicting difficult-to-measure field biometric variables, such as tree stem diameters. Two promising machine learning approaches, temporal convolutional networks (TCNs) and extreme gradient boosting (XGBoost), were evaluated for their ability to accurately predict trees’ stem profiles, suggesting a powerful and safe strategy for predicting tree stem sectional volume with minimal ground-truth measurements. The comparative analysis of TCN- and XGBoost-constructed models showed their strong ability to capture the taper trend of the trees. XGBoost proved particularly well adapted to the stem profile of black pine (Pinus nigra) trees in the Karya forest of Mount Olympus, Greece, by summarizing its spatial structure, substantially improving the accuracy of total stem volume up to RMSE% equal to 3.71% and 7.94% of all ranges of the observed stem volume for the fitting and test data sets. The same trend was followed for the 1 m sectional mean stem-volume predictions. The tested machine learning methodologies provide a stable basis for robust tree stem volume predictions, utilizing easily obtained field measurements.

Rapid human population growth accelerates biodiversity loss through urban habitat fragmentation, yet ecologically informed urban planning can mitigate these effects. This study evaluates whether and how vegetation characteristics, as captured by Earth observation data varies across forest habitats in a small Mediterranean city in Italy. The Normalized Difference Vegetation Index (NDVI), the Normalized Difference Moisture Index (NDMI), and Land Surface Temperature (LST) for the Functional Urban Area of Campobasso were derived from multitemporal Landsat 8 imagery (2020–2023) acquired during the growing season and combined with elevation data to account for topographic gradients. Different forest habitats were identified using the regional coeval Carta della Natura (Map of Nature) and were sampled by a random stratified strategy yielding more than 900,000 observations. A linear mixed-effects model was used to model NDVI as a function of NDMI, LST, elevation, and habitat type, while accounting for temporal and spatial dependencies. The model explained a large proportion of NDVI variability (marginal R2 = 0.75; conditional R2 = 0.85), with NDMI emerging as the strongest predictor, followed by weaker effects of LST and elevation. Habitat differences were also evident: oak-dominated forests (i.e., Quercus frainetto, Q. cerris, and Q. pubescens dominated habitats) exhibited the highest NDVI values, while coniferous plantations (i.e., Pinus nigra dominated habitat) had the lowest; forests dominated by Robinia pseudoacacia and riparian Salix alba showed intermediate vegetation greenness values. These results highlight the ecological importance of oak forests in Mediterranean urban landscapes and demonstrate the value of satellite-based monitoring for capturing habitat variability. The reproducible workflow applied here provides a scalable tool to support habitat conservation and planning in urban environments, also accounting for impending climate change scenarios.

We conducted a molecular phylogenetic analysis of the abiotic stress response in 13 key European forest species (Fagus sylvatica L., Quercus robur L., Quercus ilex L., Quercus pubescens Willd., Quercus suber L., Quercus lobata L., Juglans regia L., Populus trichocarpa L., Pinus taeda L., Pinus nigra J.F. Arnold, Pinus pinea L., Pinus pinaster Aiton and Abies alba Mill.) to clarify how different abiotic stressors have influenced their adaptation. The study on the evolution of abiotic stress responses in these species, seeks to uncover the factors driving their distinct evolutionary pathways of adaptation. We created the dataset by collecting data from genomic dataset on genes relevant to the response to abiotic stress in the target species dataset. Then, we used the data in the dataset to search for possible orthologs in the studied species dataset. A matrix was created with sequences of each identified ortho-group, closely related to the analyzed genes, and phylogenetic relationships were reconstructed using the maximum likelihood (ML) method. Pairwise estimates of synonymous and nonsynonymous substitutions per site (Ks and Ka, respectively) were calculated using the ML method. Analysis of 616 genes associated with abiotic stress response revealed 347 genes in angiosperms species, with F. sylvatica having the highest count, and 269 genes in conifers, where A. alba contributing the most. Drought stress exhibited the highest number of shared genes, while freezing stress showed the least. Substitution rate analysis indicated higher average values in angiosperms species, with a stronger signature of adaptive evolution in conifers, as suggested by the higher Ka/Ks ratio. The study unveils distinctive patterns in the evolutionary dynamics of molecular responses to abiotic stresses between the 13 key forest tree species. Lower substitution rates in conifers suggest unique constraints, likely influenced by larger genomes and ancient lineage divergence. The prevalence of Ka/Ks values below unity emphasizes strong selective constraints, highlighting the conservation of abiotic stress response mechanisms across diverse lineages.

Climate change is intensifying drought conditions in the Eastern Mediterranean, posing a significant threat to its unique forest ecosystems. While residual water loss from leaves (i.e., minimal leaf conductivity) after stomatal closure has been identified to play an important role in drought susceptibility across different tree species worldwide, the role of bark as an additional source of residual transpiration (i.e., bark conductivity - gbark) still remains largely underexplored. This study investigates gbark and bark structural traits in two co-occurring Mediterranean pine species Pinus brutia and Pinus nigra in Cyprus. Since P. brutia typically occurs in hotter and drier areas, we expected a lower gbark associated with thicker outer bark. Contrary to our initial hypothesis, P. brutia exhibited significantly higher gbark and thinner outer bark than P. nigra on branches of similar diameter (~1 cm). Along with its higher gbark, P. brutia also showed traits associated with an acquisitive growth strategy, including thicker inner bark and potentially greater bark photosynthetic capacity. Contrary to species-specific relationships, gbark showed a negative relationship with outer bark thickness across species level. These findings suggest that bark structure and function are intricately linked to species-specific growth strategies.

The sustainability of apiculture within Mediterranean forest ecosystems is contingent upon the extent and health of melliferous tree habitats. This study outlines a five-year initiative (2020–2024) aimed at mapping and monitoring four principal honey-producing tree species—pine (Pinus halepensis and Pinus nigra), Greek fir (Abies cephalonica), oak (Quercus ithaburensis subsp. macrolepis), and chestnut (Castanea sativa)—across Evia, Greece. This is achieved through the utilization of high-resolution Sentinel-2 satellite imagery in conjunction with a hierarchical deep learning framework. Distinct from prior vegetation mapping endeavors, this research introduces an innovative application of a hierarchical framework for species-level semantic segmentation of apicultural flora, employing a U-Net convolutional neural network to capture fine-scale spatial and temporal dynamics. The proposed framework first stratifies forests into broadleaf and coniferous types using Copernicus DLT data, and subsequently applies two specialized U-Net models trained on Sentinel-2 NDVI time series and DEM-derived topographic variables to (i) discriminate pine from fir within coniferous forests and (ii) distinguish oak from chestnut within broadleaf stands. This hierarchical decomposition reduces spectral confusion among structurally similar species and enables fine-scale semantic segmentation of apicultural flora. Our hierarchical framework achieves 92.1% overall accuracy, significantly outperforming traditional multiclass approaches (89.5%) and classical ML methods (76.9%). The results demonstrate the framework’s efficacy in accurately delineating species distributions, quantifying the ecological and economic impacts of the catastrophic 2021 forest fires, and projecting long-term habitat recovery trajectories. The integration of a novel hierarchical approach with Deep Learning-driven monitoring of climate- and disturbance-driven changes in honey-producing habitats marks a significant step towards more effective assessment and management of four major beekeeping tree species. These findings highlight the significance of such methodologies in guiding conservation, restoration, and adaptive management strategies, ultimately supporting resilient apiculture and safeguarding ecosystem services in fire-prone Mediterranean landscapes.

ABSTRACT Pinus nigra Ten. (black pine) bark, a by‐product of the timber industry, is rich in bioactive compounds with high antioxidant activity and various health benefits. This study investigated the effects of the addition of pine bark extract (PBE) into bread at different concentrations (0%, 0.25%, 0.50%, and 1.00%; w/w) on the hydrolysis index (HI), predicted glycemic index (pGI), antioxidant activity (DPPH and FRAP), total phenolic content (TPC), in vitro phenolic digestibility, mineral composition, textural properties, and sensory attributes. Bioaccessibility levels of the control bread were 6.79% for TPC, 8.99% for FRAP activity, and 6.44% for DPPH radical scavenging activity, whereas 1.00% PBE‐enriched bread exhibited significantly higher values of 28.63%, 30.14%, and 22.75%, respectively. Incorporation of 0.50% and 1.00% of PBE into bread also reduced their pGI significantly; thereby these breads were classified as medium (pGI = 66.28 and 59.29, respectively) glycemic index. A strong linear correlation was observed between TPC and antioxidant activities. Mineral analysis (mg/100 g) confirmed the presence of essential macro‐ (Ca [19.79–38.95], K [4.80–25.72], Mg [5.78–12.38], Na [7.12–28.29]) and microelements (Zn [2.62–3.61], Cu [< 2.5], Fe [< 2.5], Mn [< 2.5]) in especially PBE‐enriched breads. Texture analysis showed that the breads became softer (304.43–394.09 N) in proportion to the PBE level while sensory evaluations did not show any negative effect of PBE addition. Overall, these findings highlighted the potential of PBE as a valuable functional ingredient for enhancing the nutritional and technological properties of bakery products.

Accurate detection of machinery-induced strip roads after forest operations is fundamental for assessing soil disturbance and supporting sustainable forest management. However, in Mediterranean pine forests where canopy openings after boom-corridor thinning are moderate, the effectiveness of different remote sensing techniques remains uncertain. Previous studies have shown that LiDAR-based methods can reliably detect logging trails in different forest stands, but their direct transfer to structurally simpler, even-aged Mediterranean stands has not been validated. This study addresses this gap by testing whether UAV-derived RGB imagery can achieve comparable accuracy to LiDAR-based methods under the canopy conditions of boom-corridor thinning. We compared four approaches for detecting strip roads in a black pine (Pinus nigra Arn.) plantation on Mount Amiata (Tuscany, Italy): one based on high-resolution UAV RGB imagery and three based on LiDAR data, namely Hillshading (Hill), Local Relief Model (LRM), and Relative Density Model (RDM). The RDM method was specifically adapted to Mediterranean conditions by redefining its return-density height interval (1–30 cm) to better capture areas of bare soil typical of recently trafficked strip roads. Accuracy was evaluated against a GNSS-derived control map using nine performance metrics and a balanced subsampling framework with bootstrapped confidence intervals and ANOVA-based statistical comparisons. Results confirmed that UAV-RGB imagery provides reliable detection of strip roads under moderate canopy openings (accuracy = 0.64, Kappa = 0.27), while the parameter-tuned RDM achieved the highest accuracy and recall (accuracy = 0.75, Kappa = 0.49). This study demonstrates that RGB-based mapping can serve as a cost-effective solution for operational monitoring, while a properly tuned RDM provides the most robust performance when computational resources are sufficient to work on large point clouds. By adapting the RDM to Mediterranean forest conditions and validating the effectiveness of low-cost UAV-RGB surveys, this study bridges a key methodological gap in post-harvest disturbance mapping, offering forest managers practical, scalable tools to monitor soil impacts and support sustainable mechanized harvesting.

The Corsican pine (Pinus nigra subsp. laricio (Poir.) Maire), a subspecies of black pine endemic to southern Italy, is widely known for the quality of its valuable timber, and the parts of the plant that are not used for this purpose are considered unusable production waste. In this study, we investigated the phytochemical profile and a series of biological activities of extracts from the female and male pine cones. The extracts were prepared by maceration with ethanol and subsequently fractionated using liquid-liquid separation. The total phenolic and flavonoid content, antioxidant potential (DPPH and β-carotene bleaching tests), anti-inflammatory activity (nitric oxide inhibition in RAW 264.7 cells), and enzymatic inhibition against pancreatic lipase and α-amylase were determined. The female cones showed a higher crude extract yield and total phenolic content (76.4 mg GAE/g) than the male cones, while the latter were richer in flavonoids. The extracts from the female cones showed higher antioxidant and pancreatic lipase inhibitory activities. On the contrary, extracts from male cones showed greater activity against α-amylase, with the dichloromethane fraction proving to be the most potent (IC50 = 35.28 ± 3.08 µg/mL). The hexane fraction of female cones also showed significant anti-inflammatory activity (IC50 = 107.50 ± 15.22 µg/mL). Our results reveal that the pine cones of Pinus nigra subsp. laricio (Poir.) Maire are a rich source of bioactive compounds. These results provide the first scientific evidence of the potential of extracts from this still poorly studied part of the plant for further investigation of their antioxidant and anti-inflammatory capabilities.

Abstract. The Mediterranean basin, a recognized climate change hotspot, faces increasing hydroclimatic pressures, particularly from severe drought and precipitation events. To assess contemporary changes and potentially manage future impacts, it is crucial to understand the long-term context of this variability beyond the relatively short instrumental record. This study utilizes tree-ring records to reconstruct past hydroclimate in the Iberian Range of eastern Spain, a water-sensitive Mediterranean environment. We present a well-replicated tree-ring width chronology from Pinus sylvestris and Pinus nigra trees that calibrates and verifies significantly against cumulative instrumental precipitation over a 320 d period ending in June (r = 0.749; p < 0.01). The resulting 520-year reconstruction reveals substantial multi-centennial variability in precipitation and reveals an increase in the frequency and intensity of hydroclimatic extremes (both wet and dry) during the late 20th and early 21st centuries compared to the longer-term baseline. The reconstruction has a spatial representativeness centred over eastern and central Iberia and covaries with independent historical drought indices derived from rogation ceremony records during the late 18th and early 19th centuries. The documented intensification of hydroclimatic extremes is consistent with climate change projections and provides a baseline for evaluating ecosystem resilience and water resource vulnerability.

Experimental and numerical investigation of burning characteristics of Australian pine trees for wildfire modelling

Abstract Deadwood is a crucial component of the global carbon budget, storing a substantial amount of carbon in forests. Understanding factors influencing deadwood turnover is therefore vital for predicting carbon cycling under climate change. While climate is an important driver, biotic factors, including wood and fungal traits, also play significant roles in deadwood decomposition. How parasitic fungi affect deadwood decomposition is likely important for global forest carbon turnover but is poorly understood. As opportunistic parasitic fungi, Armillaria species are capable of degrading wood components and altering wood traits. However, how (much) Armillaria drives deadwood decomposition remains unclear. To address this, we studied black pine ( Pinus nigra ), susceptible to Armillaria infection. We hypothesized that branches from infected stands would exhibit higher decay rates, even at given similar wood density. We collected naturally fallen Pinus nigra branches from infected and uninfected stands, sorted them by wood density and incubated them for 1 to 1.6 years under standardized conditions in a common garden experiment. Mass loss was measured and decomposition dynamic was modelled using a relative wood density approach. Our findings support the hypothesis that branches from infected Pinus nigra stands experience higher decay rates, backed up by molecular evidence. This difference may arise from Armillaria itself being a wood decay agent and from its effects via wood traits. These findings have broader implications for estimating deadwood stocks in forests impacted by climate‐induced fungal pathogens. Read the free Plain Language Summary for this article on the Journal blog.

The present study is focused on a newly isolated Fomitopsis strain obtained from black pine (Pinus nigra) from the Sredna Gora Mountains, Bulgaria. Molecular identification, based on ITS1-5.8S-ITS2 region sequencing, confirmed the strain as Fomitopsis pinicola with 99.84 BLAST percent identity. Phylogenetic analysis verified that the new fungal isolate belongs to the European F. pinicola clade. The morphological analysis of the strain revealed several distinctive structures that further support its identification. The influence of culture media composition on fungal development was evaluated by analyzing the mycelial growth kinetics using both the logistic growth model and the reversible autocatalytic model. Submerged cultivation was employed to produce fungal biomass, which was subsequently lyophilized and used for the assessment of the antimicrobial potential of the fungal strain. The results demonstrated notable antimicrobial effects against all tested bacterial strains. The most significant activity was observed for the aqueous extract against Escherichia coli and the hexane extract against Salmonella enteritidis, both with a minimum inhibitory concentration of 312.5 µg/mL. These findings highlight the promising potential of the newly isolated F. pinicola strain for future applications in the medical and pharmaceutical industries, particularly in developing drugs to combat multidrug resistance, based on the promising results of its water extracts.

Understanding carbon concentration in different tree components is essential for accurate forest carbon accounting and climate change mitigation efforts. This study investigated the carbon concentrations of wood, bark, needles, and roots in natural black pine (Pinus nigra subsp. pallasiana) forests located in the central part of Türkiye. Samples were collected from ten pure mature stands between 1000 and 1600 m elevation, considering both aspect and slope position. Carbon content was analyzed using an elemental analyzer. Results showed significant differences in carbon concentration among tree components, with the highest mean carbon concentration found in wood (54.96%), followed by bark (53.90%) and needles (52.89%), while the lowest was found in roots (51.75%). Above-ground and total tree biomass-weighted carbon concentrations were calculated to be 54.68% and 54.19%, respectively. Carbon content in needles and roots was significantly influenced by aspect and slope position. The study highlights the importance of using component-based and site-specific carbon coefficients, rather than default coefficients, to improve the precision of national carbon inventories and forest-based carbon credit projects.

Exploring vitamin D3 profile of epiphytic lichen forming fungi in forest ecosystems: Influence of habitat-dependent ecological variables.

ABSTRACT Throughfall plays a significant role in hydrological processes, defining the effective rainfall available for soil moisture and runoff generation under vegetation. This study presents the first cross‐climate comparison of the drivers of throughfall under black pine trees ( Pinus nigra Arnold) in urban environments. Open rainfall and throughfall were measured at two experimental sites in the city of Ljubljana, Slovenia (temperate continental), and Sopron, Hungary (humid continental). To determine which of the climatic and canopy‐related variables influence the event throughfall percentage (Tf) across three periods (i.e., whole study, growing and dormant periods), regression tree (RT) and boosted regression tree (BRT) models were used. The total rainfall and mean Tf recorded during the study period from September 2023 to September 2024 were 1591.2 mm and 45% ± 21.5% in Slovenia, respectively. Conversely, Hungary experienced 767.2 mm and 50% ± 27.2% . Both models confirmed rainfall (RA) as the primary driver of Tf across the three examined periods in both sites. Furthermore, the BRT model confirmed rainfall intensity as the secondary influential variable, specifically during Slovenia's growing and Hungary's dormant periods. Contrarily, the RT model showed relative humidity (RH) and leaf area index as the secondary variables defining the Tf over the whole study period at both sites, with variations across the growing and dormant periods. Under a scenario that included only the atmospheric variables, the BRT model identified RH as the most significant driver across all periods and both sites. Similarly, the RT model recognised RH as the primary variable during the three periods in Hungary and the entire study period in Slovenia. Moreover, air temperatures influenced Tf in Slovenia's growing and dormant periods. The findings indicated throughfall as a climate‐sensitive parameter, emphasising the significance of these results in hydrological models susceptible to climate variability and in areas characterised by comparable climatic conditions.

Aim: Pine cone syrups are widely used to treat various diseases or to strengthen the immune system. The aim of this study was to determine the antimicrobial efficacy of black pine (Pinus nigra) cone syrup traditionally used in the western Black Sea region against important pathogenic microorganisms under in vitro conditions, to compare sugared and sugar-free forms, and to investigate its efficacy against standard antibiotics used in routine practise. Methods: In this study, the antimicrobial activity of sweetened (3%) and sugar-free pinecone syrup against Staphylococcus aureus (ATCC 29213), Enterococcus faecalis (ATCC 10876), Escherichia coli (ATCC 25922), Pseudomonas aeruginosa (ATCC 27853) and Candida albicans (ATCC 10231) strains was investigated. The diameters of the inhibition zones were determined using the agar disc-diffusion method and the minimum inhibitory concentration (MIC) was determined using the microdilution method. The susceptibility profiles of the test microorganisms to standard antibiotics (ampicillin (AM10), erythromycin (E15), gentamicin (CN10), cefixime (CFM5), oxacillin (OX1), penicillin (P10), ceftriaxone (CRO30), amoxicillin/clavulanic acid (AMC30) and itraconazole (ITC10) were analysed comparatively. Results: Pine cone syrup showed antimicrobial activity against P. aeruginosa and C. albicans among the microorganisms tested. The diameters of the inhibition zones were determined to be 20mm and 22mm, respectively, and the MIC values were 125 mg/ml for both microorganisms. The relationship between pine cone syrup and gentamicin (21mm) against P. aeruginosa and between pine cone syrup and itraconazole (21mm) against C. albicans was found to be statistically significant (p<0.05). No significant difference was found between sugared and sugar-free forms in terms of antimicrobial activity (p>0.05). Conclusion: Black pine cone syrup was found to have limited spectrum antimicrobial activity and was particularly effective against P. aeruginosa and C. albicans which have multidrug resistance problems. In vivo studies and tests against a broader spectrum of microorganisms are required to determine clinical efficacy.