Casuarina Research Articles

Modeling Initial Recruitment of Mediterranean Pine Forests Using an Artificial Neural Network

Artificial neural networks (ANNs) have been recognized as powerful tools able to automatically learn complex relationships in data. To the best of our knowledge, ANNs have not yet been applied to forest regeneration modeling. Such models are essential to evaluate the effects of reforestation techniques. To fill this gap, the capacity of ANNs to simulate the initial recruitment of pine species in Mediterranean forests has been evaluated in this study. A feed-forward multilayer neural network has been applied to a case study of pine forests of Castilla La Mancha (Central–Eastern Spain), where seed germination and seedling survival, with or without seed protection, of four pine species ( Pinus nigra, Pinus pinaster, Pinus halepensis, and Pinus sylvestris) were observed throughout 10 years under three soil conditions (scalped, wildfire-affected, and unaltered soils). The results we obtained have witnessed the good capacity of ANNs to predict both stages of pine initial recruitment. This may be of help in predicting the success of natural regeneration in Mediterranean pine forests under different tree species, soil characteristics, and management strategies.

Unraveling the Impact of Diverse Vegetative Covers on Soil Carbon Fractions

Evaluating SOC lability is crucial for sustainable agriculture policies and environmental quality. This study assessed the impact of well-stocked forest cover on SOC pools and lability compared to shrubby soil. Geo-referenced soils under Red pine (Pinus brutia), Black pine (Pinus nigra), Cedar (Cedrus libani), Fir (Abies cilicia), Juniper (Juniperus excelsa), Oak (Quercus L.), Carob (Ceratonia siliqua), and degraded shrubs were collected from a depth of 0 to 30 cm in the Seyhan River Basin, Mediterranean Turkey. The analysis of soil organic carbon (SOC), soil AC (active carbon), and PC (passive carbon) were analyzed to understand soil carbon management across diverse vegetation types. Juniper forests have the highest SOC (27.98 g/kg) and PC (27.35 g/kg), followed by Cedar (SOC: 27.64 g/kg, PC: 27.05 g/kg) and Fir (SOC: 26.44 g/kg, PC: 25.85 g/kg). Shrubby areas have the lowest SOC (4.06 g/kg) and PC (3.61 g/kg). The Oak soil had the highest CLI (1.16), suggesting a relatively higher proportion of labile carbon than other forests. CPI indicates forests have a greater carbon storage capacity (1.09) compared to shrublands (0.18), with forests also having a higher CMI (0.83). The findings emphasize the critical role of forests, especially Juniper forests, in carbon sequestration and climate change mitigation within the Seyhan River Basin in Turkey.

Improving European Black Pine Stem Volume Prediction Using Machine Learning Models with Easily Accessible Field Measurements

Reliable prediction of tree stem volume is crucial for effective forest management and ecological assessment. Traditionally, regression models have been applied to estimate forest biometric variables, yet they often fall short when handling the complex, non-linear patterns typical of biological data, potentially introducing biases and errors. Tree stem volume, a critical metric in forest biometrics, is generally estimated through easily measured parameters such as diameter at breast height (d) and total tree height (h). This study investigates advanced machine learning (ML) techniques—Extreme Gradient Boosting (XGBoost), epsilon-Support Vector Regression (ε-SVR), and Random Forest regression (RFr)—to predict the stem volume of European black pine (Pinus nigra) on Mount Olympus, Greece, using basic field measurements. Machine learning (ML) approaches demonstrated substantial improvements in prediction accuracy compared to traditional non-linear regression-based models (RMs). Notably, XGBoost significantly enhanced predictive performance by reducing the Furnival index (FI) by as much as 42.3% (from 1.1859 to 0.1056) and 21.3% (from 0.1475 to 0.1161) in the test and fitting datasets, respectively, for the single-entry model. For the double-entry model, XGBoost achieved FI reductions of 40.5% (from 0.1136 to 0.0676) and 41.3% (from 0.1219 to 0.0715) in the test and fitting datasets, respectively. These findings highlight the potential of ML models to improve the accuracy of forest inventory predictions, thereby supporting more effective and data-driven forest management strategies.

Post-fire succession of black pine (Pinus nigra) forest vegetation under different fire regimes

Post-fire succession of black pine (<i>Pinus nigra</i>) forest vegetation under different fire regimes

Carbon storage potentiality in successional and secondary old growth forests

AbstractAs the capacity of old-growth forests to store carbon until very old ages has been proved, their conservation has become a mitigation strategy to reduce net CO2 emissions and moderate climate warming. We investigated the effect of tree age, competition and climate on aboveground standing biomass and C stocks over a 50-years period in two Spanish forest stands (successional Pinus pinea forests with old-growth attributes, OGFA and secondary Pinus nigra old-growth Forest, SOGF), combining dendroecological methods with forest inventory data, using semiparametric modeling. P. nigra SOGF stored 69.9 t C ha−1 in standing volume, while P. pinea OGFA stored 58.2 t C ha−1. Carbon stored during the last 50 years increased in both forests, with a steeper increase in P. pinea OGFA. The fraction of annual C stored by the oldest trees was 20–25% in P. nigra SOGF and 17–23% in P. pinea OGFA. The different patterns of biomass growth increment in the two forests were explained by different biophysical environments, climate and history effects. The response to contrasted climate events was forest-specific. Results for P.nigra showed a similar response to climate irrespective of tree age, while biomass growth in P. pinea increased in humid periods compared to dry periods in trees older than 100-years. The negative effect of drought is evidenced in P. pinea trees over 100-years old, while P. nigra showed a cumulative negative effect of drought for all ages. A lower effect of competition was recorded in the oldest trees in both forests. The interaction of competition with climate and tree age showed attenuated climate-mediated differences when competition was high.

Tree growth variability in Pinus nigra plantations modulated by climate and soil properties

Tree growth variability in Pinus nigra plantations modulated by climate and soil properties

High Desertification Susceptibility in Forest Ecosystems Revealed by the Environmental Sensitivity Area Index (ESAI)

This study evaluated the desertification vulnerability of an Anatolian black pine forest in Türkiye using the Environmental Sensitivity Area Index (ESAI). Desertification Risk (DR) and ESAI values were calculated for 90 sampling plots, incorporating key indicators such as vegetation cover, soil depth, rock fragment presence, soil texture, slope gradient, parent material, mean annual precipitation, aridity index, land use intensity, and policy enforcement. These indicators were processed through the Desertification Indicator System for Mediterranean Europe (DIS4ME). Spatial patterns of DR and ESAI were analysed using semivariograms and Kriging-interpolated maps. The mean DR (4.850; range = 2.310–8.090) and ESAI (1.46; range = 1.390–1.580) values indicated significant vulnerability to desertification. DR showed moderate spatial dependence, while ESAI exhibited strong spatial dependence. Ordinary kriging maps revealed critical desertification hotspots within the forest. ESAI values varied with soil organic matter (SOM) content, which was moderately and significantly correlated with ESAI (n = 90, r = −0.58, p &lt; 0.01). These findings provide actionable insights for sustainable land management. Interventions such as improving SOM content through afforestation, enhancing soil conservation practices, and promoting sustainable water use are critical to mitigating desertification and fostering ecosystem resilience. This study identifies high-risk areas and demonstrates how DR and ESAI can guide targeted strategies to restore degraded lands and ensure forest sustainability. This aligns with SDG 15 (Life on Land), which emphasizes the need to combat desertification, restore degraded ecosystems, and promote the sustainable management of forests. Integrating ESAI into regional policy planning highlights its potential as a practical tool for achieving long-term environmental and socioeconomic sustainability in vulnerable forest ecosystems like those in Türkiye.

Dyeing of banana-silk union fabrics with cochineal using different concentrations of bio-mordant

Banana (Musa × paradisiaca L.) is a lignocellulosic natural fiber that can be processed to produce eco-friendly and biodegradable hybrid fabrics when combined with other fibers. Natural-dyed banana fiber can be utilized in the production of sustainable materials for wearable products, household linens, technical textiles, and apparel. In this study, banana-silk union fabrics, pre-treated with bio-mordant at concentrations of 1%, 2%, 3%, 4%, and 5%, were dyed separately using cochineal insect extract (Dactylopius coccus Costa). To assess the dyeability of the banana-silk union fabric, Anatolian black pine cones (Pinus nigra subsp. pallassiana) were utilized as a bio-mordant, with varying quantities. The CIELab values of the dyeings were measured and compared. Additionally, the chemical composition of the cochineal extract, pinecone mordant, raw fabric, and selected dyed fabrics was analyzed using Fourier Transform Infrared (FTIR) spectroscopy. The washing, light, and rubbing fastness properties of the dyed banana-silk union fabrics were also evaluated and compared. This study developed a method for weaving union fabric by blending two distinct natural yarns, namely banana and silk, to reduce dependence on a single fiber and promote the utilization of agricultural waste.

Infestation Rates, Seasonal Abundance of Aphids and Relationships with Some Environmental Factors in Different Forest Habitats in Southwestern Türkiye

Aim of study: It was to determine the infestation rate and seasonal abundance of aphids and their relationships with some environmental factors in forests. Area of study: The study was conducted in Pinus nigra, P. brutia, Cedrus libani, Abies cilicica, Juniperus spp., Quercus spp. and Robinia pseudoacacia forests (34 sampling areas) in Isparta. Materials and methods: Systematic sampling was carried out monthly throughout April-October in 2019-2020. As aphid density and infestation rates were determined, 30 cm shoots were taken from 10 trees. The slope, aspect, elevation, forest characteristics, daily minimum, average, and maximum temperature and total precipitation were recorded. Main results: Abundance were highest in P. nigra, Quercus and Robinia pseudoacacia forests in both years. There was a significant correlation between abundance and development stages (p

Response of black pine (Pinus nigra) in southwestern Anatolia to climate change

Global climate change is one of the most critical challenges facing natural ecosystems. As sedentary organisms, plant communities are especially vulnerable, with climate change significantly impacting their development, productivity, and distribution. This study focuses on modelling and mapping the potential distribution of black pine (Pinus nigra) within the Antalya Regional Forest Directorate under the SSP1 2.6 climate scenario for the years 2040, 2060, 2080, and 2100. The MaxEnt modelling method, combined with the HadGEM3-GC31-LL climate model from WorldClim, was used to assess the impact of climate change on black pine. The current distribution model results showed “good” predictive performance, with AUC values of 0.855 for training data and 0.851 for testing data. Key variables influencing the model included precipitation during the wettest quarter, annual precipitation, maximum temperature of the warmest month, and elevation. Projections indicate that the black pine’s distribution will shrink by 2040, become increasingly fragmented by 2060, and decline further by 2080, with near-total disappearance by 2100. In conclusion, this study highlights the urgent need for adaptive management strategies to address the effects of climate change on black pine forests.

New insights into the mechanisms of plant isotope fractionation from combined analysis of intramolecular 13C and deuterium abundances in Pinus nigra tree-ring glucose.

Understanding isotope fractionation mechanisms is fundamental for analyses of plant ecophysiology and paleoclimate based on tree-ring isotope data. To gain new insights into isotope fractionation, we analysed intramolecular 13C discrimination in tree-ring glucose (Δi', i = C-1 to C-6) and metabolic deuterium fractionation at H1 and H2 (εmet) combinedly. This dual-isotope approach was used for isotope-signal deconvolution. We found evidence for metabolic processes affecting Δ1' and Δ3', which respond to air vapour pressure deficit (VPD), and processes affecting Δ1', Δ2', and εmet, which respond to precipitation but not VPD. These relationships exhibit change points dividing a period of homeostasis (1961-1980) from a period of metabolic adjustment (1983-1995). Homeostasis may result from sufficient groundwater availability. Additionally, we found Δ5' and Δ6' relationships with radiation and temperature, which are temporally stable and consistent with previously proposed isotope fractionation mechanisms. Based on the multitude of climate covariables, intramolecular carbon isotope analysis has a remarkable potential for climate reconstruction. While isotope fractionation beyond leaves is currently considered to be constant, we propose significant parts of the carbon and hydrogen isotope variation in tree-ring glucose originate in stems (precipitation-dependent signals). Asbasis for follow-up studies, we propose mechanisms introducing Δ1', Δ2', Δ3', and εmet variability.

Adsorption of Methylene Blue and Eriochrome Black T onto Pinecone Powders (Pinus nigra Arn.): Equilibrium, Kinetics, and Thermodynamic Studies

In this study, methylene blue (MB) and eriochrome black T (EBT) dyes were removed with the waste Pinus nigra Arn. powders from Anatolian black pinecone (PC-PnA) within the framework of sustainability. UV–Vis spectroscopy, X-ray diffraction (XRD), scanning electron microscope (SEM), energy dispersive X-ray (EDX), fourier transform infrared spectroscopy (FTIR), thermogravimetry–differential thermal analysis (TGA-DTA), Brunauer–Emmett–Teller (BET) surface area, and point of zero charge (pHpzc) analyses were performed for the characterization of PC-PnAs. The effects of pH, amount of adsorbent, time, initial concentration and temperature were determined by batch adsorption experiments. Four kinetic and isotherm models were examined, and error function tests were used for the most suitable model. According to this, the average pore diameters, mass losses at 103.9 and 721.6 °C and pHpzc values of PC-PnAs were found as 61.661 Å, 5.9%, 30%, and 5.77, respectively. Additionally, the most suitable kinetic and isotherm models for the removal of both dyes were Langmuir and pseudo-second-order. The maximum removal efficiencies (qmax) for MB and EBT dyes was calculated as 91.46 and 15.85 mg/g, respectively and the adsorption process was found to be endothermic. As a result, PC-PnA particles can be used as an alternative sorbent for the removal of MB and EBT dyes.

Comparative Study of the Coastal Geomorphology of Kajirbhati Sand-Spit Using Google Earth Pro Images (2005, 2011, 2023) and Landsat Satellite Data (2009, 2016, 2023): An Approach Towards the Impact of the Casuarina Plantation

Coastal geomorphology undergoes constant transformation due to a combination of coastal and fluvial processes and human intervention. Remote sensing and GIS tools are invaluable in identifying and studying these changes. Due to the dynamic interaction between land and sea, the coastline requires careful monitoring to detect hotspots and understand the spatial and temporal impacts of climate change on the coastal environment. Remote sensing data offers a comprehensive means of studying coastal landform changes. In a recent study, the focus was to analyze the impact of the Casuarina plantation on the coastal geomorphology of Kajirbhati spit. This involved studying various depositional features such as sand bars, sand spits, dunes, and beaches. The study utilized Google Earth Pro images from 2005, 2011, and 2023 to digitize 13 classes each year, enabling the identification of changing geomorphic classes. These include agriculture, swash zones, dunes on sand spits, berm lines, Casuarina and coconut plantations, estuary, mangroves, mudflats, open land, sea, settlement, and vegetation. The results revealed dramatic changes in land use and land cover classes from 2005 to 2023. In the years from 2011 to 2023, the area of sandspit covered by Casuarina plants expanded rapidly. To analyze the changes, Landsat 5, 8, and 9 data were used to check the intensity of NDVI (Normalized Difference Vegetation Index) in 2009, 2016, and 2023. NDVI is a critical factor in this research. The mean NDVI values for 2009, 2016, and 2023 were -0.002, 0.02, and 0.04 respectively. The changes in higher NDVI values from 2009 to 2023 were -0.10, 0.27, and 0.28, while the lower values were -0.10, -0.10, and -0.07 for the same years. The correlation calculation between the mean NDVI values and the years indicates a strong positive correlation with an R-value of 0.99. Casuarina plants play a crucial role in protecting the coast from strong winds and dynamic sea waves. They help protect mudflats, mangroves, settlements, and estuary land use and cover classes along the coast of Kajirbhati.

Thinning followed by slash burning enhances growth and reduces vulnerability to drought for Pinus nigra.

Increasingly frequent severe drought events are pushing Mediterranean forests to unprecedented responses. Lack of management leads to dense forests that are highly susceptible to drought stress, potentially resulting in extensive dieback and increased vulnerability to other disturbances. Forest treatments like thinning and slash burning reduce competition for resources and have the potential to enhance tree growth and vigor and minimize tree vulnerability to drought. Here, we used tree rings to study the growth and physiological response of black pine (Pinus nigra) to drought in northeastern Spain under different treatments, including two thinning intensities (light and heavy, with 10% and 40% basal area reduction, respectively) followed by two understory treatments (clearing alone and in combination with slash burning), resulting in a research design of four treatments plus an untreated control with three replicates. Specifically, we studied basal area increment (BAI), resilience indices, and intrinsic water use efficiency (iWUE) using carbon and oxygen isotope composition (δ13C and δ18O in tree-ring cellulose) before and after treatments. Our results showed that BAI and resistance to drought increased in the heavy-thin (burned and unburned) and light-thin burned units. Resilience increased in the burned units regardless of the thinning intensity, while recovery was not affected by treatment. Slash burning additionally increased BAI in the light-thin and resistance and resilience in the heavy-thin units compared with clearing alone. The stable isotope analysis revealed a minor effect of treatments on δ13C and δ18O. No change in iWUE among treatments was presumably linked to a proportional increase in both net CO2 assimilation and stomatal conductance, which particularly increased in the heavy-thin (burned and unburned) and light-thin burned units, indicating that these trees were the least affected by drought. This study shows that management approaches aimed at reducing wildfire hazard can also increase the vigor of dominant trees under drought stress. By reducing competition both from the overstory and the understory, thinning followed by clearing alone or in combination with slash burning promotes tree growth and vigor and increases its resistance and resilience to drought.

Effect of the mechanical densification process in wood material on the surface adhesion strength of varnishes

This research aimed to determine the impact of the mechanical densifying process of wood material on the varnish surface adhesion strength. Specimens from black pine (Pinus nigra) and Uludag fir (Abies bornmuelleriana Mattf.) were subjected to densification in a hydraulic press at 140 °C to the extent of 25% and 50% in the radial direction. While densification increased the surface adhesion strength of the varnish layer in black pine, the value decreased in fir. Regarding the interaction between densification ratio, surface treatment, and wood type, the highest surface adhesion strength of the varnish layer was found in black pine + unsanded surface + 25% densification, and the lowest was in Uludag fir + unsanded surface + 50% densification. It can be stated that the densification process creates high adhesion values for the polyurethane varnish in the black pine wood type. The sanding process has an intensifying effect on these values, and the products that were obtained from the polyurethane varnished samples do not require sanding. Considering these situations can provide significant advantages in projects with wood materials subjected to the densification process.

Comparative analysis of taper models for Pinus nigra Arn. using terrestrial laser scanner acquired data

Comparative analysis of taper models for Pinus nigra Arn. using terrestrial laser scanner acquired data

Disentangling how the opportunistic parasitic fungus Armillaria affects the flammability of coarse deadwood in exotic pine plantations

Parasitic wood fungi are important to forest carbon cycles globally. However, whether or how they affect the flammability of coarse deadwood is poorly understood. Given the predicted climate-driven increase in wildfires and associated carbon emissions into the atmosphere, potentially amplifying climate warming, filling this knowledge gap should have high priority. We thereto investigated coastal plantations of the exotic black pine, Pinus nigra J.F. Arnold, in the Netherlands, which are widely suffering from Armillaria infection. We hypothesized that branches from forest stands with a visible Armillaria infection will burn differently compared with branches from stands without a visible Armillaria infection, due to Armillaria infection having an additional effect on the branch traits. We tested this hypothesis by burning coarse Pinus nigra branches across a range of densities from infected and uninfected forest patches under standardized conditions in a fire lab and by measuring Armillaria biomass (via ddPCR), deadwood traits and key flammability parameters. Armillaria infection did enhance the flammability of Pinus nigra branches (e.g. more ignitable, longer flame duration and higher percentage mass loss). This higher flammability originated from both direct Armillaria influences, e.g. via changing wood structure (before and/or after wood death), and indirect influences, e.g. by facilitating nitrogen fixation in wood, thereby increasing wood decomposability and consequently reducing wood density. Our findings also have important implications for understanding the role of pathogens in fire regimes more broadly.

Propagation technology of Pinus pallasiana (L.) for agroforestry in southern regions

Propagation technology of Pinus pallasiana (L.) for agroforestry in southern regions

Atmospheric deposition patterns in bulk open field precipitation and throughfall in Aleppo pine forest and black pine forest on the eastern Adriatic coast

The Mediterranean region, with its unique ecological characteristics, is particularly sensitive to global environmental changes, including climate change and impact of air pollution.Although Aleppo pine and black pine forests are the most abundant on the eastern Adriatic coast, atmospheric deposition in these forests is poorly studied. Changes in the chemical composition of precipitation as it passes through the tree canopy can lead to soil and groundwater eutrophication, and soil acidification, which affects plant vitality. In this study, the dynamics of ion deposition in Aleppo pine forest (Pinus halepensis Mill.) and black pine forest (Pinus nigra Arnold) on the eastern Adriatic coast are investigated, focusing on throughfall and bulk open field depositions.The aim of our research was to fill the gaps in understanding the influence of tree canopies on deposition fluxes in two different Mediterranean pine stands and to compare total inorganic nitrogen loads with critical loads. Over a period of two years, bulk open field precipitation and throughfall were sampled, measured and analysed using the ICP Forest methodology.The results indicate significant differences in ion deposition between bulk open field and throughfall, with throughfall showing higher values for almost all ions. The highest enrichment ratio was determined for K+. The comparison of the actual inorganic nitrogen load with the critical nitrogen load for Mediterranean pine forests revealed that the inorganic nitrogen load exceeded the critical load in the Aleppo pine forest. Ion deposition increased in the throughfall compared to bulk precipitation, which can be attributed to the seasonality of precipitation, including leaching and long dry periods. These findings enhance our understanding of ion deposition fluxes in vulnerable Mediterranean pine ecosystems and emphasize the need for long-term research on this topic in the actual changing environmental conditions.

Fungal symbiotic interactions of Ips sexdentatus (Boerner, 1767) in Pinus nigra Arn. in the Western Mediterranean Region of Türkiye

Fungal symbiotic interactions of Ips sexdentatus (Boerner, 1767) in Pinus nigra Arn. in the Western Mediterranean Region of Türkiye