Quadratic Mean Diameter Research Articles

Effects of site productive capacity and stand density management on the maximum density line for Eucalyptus pilularis (blackbutt)

ABSTRACT Data were available from two measurements of each stand of a set of stands in native regrowth and plantation forests of Eucalyptus pilularis (blackbutt) in subtropical New South Wales and Queensland. Measurements were made after competition-induced mortality had started in each stand. Most of the regrowth forest had been thinned some time before the measurements. Based on the Reineke model that relates stand stocking density to quadratic mean diameter, a maximum density line was determined for each stand based on methods developed by Vanclay and Sands. It was found that the intercept of the maximum density line increased both with increasing stand site productive capacity and in the thinned regrowth stands. The latter finding does not seem to have been reported for forests before; it is hypothesised to be a result of the delayed redevelopment of inter-tree competitive processes following the major reduction in stand density resulting from thinning. Both effects will have consequences for the structure of density management diagrams commonly used to assist in forest management practice.

A Novel Approach to Modelling Stand-Level Growth of an Even-Aged Forest Using a Volume Productivity Index with Application to New Zealand-Grown Coast Redwood

Empirical growth models are widely used to predict the growth and yield of plantation tree species, and the precise estimation of site quality is an important component of these models. The most commonly used proxy for site quality in growth models is Site Index (SI), which describes the mean height of dominant trees at a specified base age. Although SI is widely used, considerable research shows significant site-dependent variation in height for a given volume, with this latter variable more closely reflecting actual site productivity. Using a national dataset, this study develops and describes a stand-level growth and yield model for even-aged New Zealand-grown coast redwood (Sequoia sempervirens). We used a novel modelling approach that quantifies site quality using SI and a volume-based index termed the 300 Index, defined as the volume mean annual increment at age 30 years for a reference regime of 300 stems ha−1. The growth model includes a number of interrelated components. Mean top height is modelled from age and SI using a polymorphic Korf function. A modified anamorphic Korf function is used to describe tree quadratic mean diameter (Dq) as a function of age, stand density, SI and a diameter site index. As the Dq model includes stand density in its formulation, it can predict tree growth for different stand densities and thinning regimes. The mortality model is based on a simple attritional equation improved through incorporation of the Reineke stand density index to account for competition-induced mortality. Using these components, the model precisely estimates stand-level volume. The developed model will be of considerable value to growers for yield projection and regime evaluation. By more robustly describing the site effect, the growth model provides researchers with an improved framework for quantifying and understanding the causes of spatial and temporal variation in plantation productivity.

Stand Volume Growth Modeling with Mixed-Effects Models and Quantile Regressions for Major Forest Types in the Eastern Daxing’an Mountains, Northeast China

The relative growth rate (RGRnv) is the standardized measurement of forest growth, whereby excluding the size differences between individuals allows their performance to be compared equally. The RGRnv model was developed using the National Forest Inventory (NFI) data on the Daxing’an Mountains, in Northeast China, which contain Dahurian larch (Larix gmelinii Rupr.), white birch (Betula platyphylla Suk.), and mixed coniferous–broadleaf forests. Four predictor variables—i.e., quadratic mean diameter (Dq), stand basal area (G), average tree height (Ha), and altitude (A)—and four different methods—i.e., the nonlinear mixed-effects models (NLME), three nonlinear quantile regression (NQR3), five nonlinear quantile regression (NQR5), and nine nonlinear quantile regression (NQR9) models—were used in this study. All the models were validated using the leave-one-out method. The results showed that (1) the mixed coniferous–broadleaf forest presented the highest RGRnv; (2) the RGRnv was negatively correlated with the four predictors, and the heteroscedasticity reduced significantly after the weighting function was integrated into the models; and (3) the quantile regression models performed better than NLME, and NQR9 outperformed both NQR3 and NQR5. To make more accurate predictions, parameters of the adjusted mixed-effects and quantile regression models should be recalculated and localized using sampled RGRnv in each region and then applied to predict all the other RGRnv of plots. MAPE% indicates the mean absolute percentage error. The values were stable when the sample numbers were greater than or equal to six across the three forest types, which showed relatively accurate and lowest-cost prediction results.

Predicting parameters of Weibull probability density function for diametric distributions in A. melanoxylon, E. camaldulensis, and E. nitens bioenergy plantation

Precise modeling of stand diameter distributions is required to provide accurate estimates of volume per diameter class and unit area. Therefore, it is necessary to obtain the most accurate probability density functions parameters estimates to predict stand diameter distribution in time. We evaluate two methods to estimate the parameters of the Weibull probability density function in the modeling of diameter distributions of bioenergy plantations. The methods considered a direct method of parameter prediction based on regression models (PPRM) and an indirect method of parameter recovery through the determination of percentiles (PRDP). Both methods are considered systems of linear equations and are adjusted through simultaneous estimation of parameters using stand variables. The greatest precision was obtained with PPRM. The PRDP method was not effective in the prediction of diameter distributions due to the high level of truncation of the observed distributions showing an overestimation of the distribution for the largest diameter classes. Estimated parameters of the Weibull PDF are directly related to mean height, quadratic mean diameter, and crop age; and are inversely related to stocking.

Estimating Primary Forest Attributes and Rare Community Characteristics Using Unmanned Aerial Systems (UAS): An Enrichment of Conventional Forest Inventories

The techniques for conducting forest inventories have been established over centuries of land management and conservation. In recent decades, however, compelling new tools and methodologies in remote sensing, computer vision, and data science have offered innovative pathways for enhancing the effectiveness and comprehension of these sampling designs. Now with the aid of Unmanned Aerial Systems (UAS) and advanced image processing techniques, we have never been closer to mapping forests at field-based inventory scales. Our research, conducted in New Hampshire on complex mixed-species forests, used natural color UAS imagery for estimating individual tree diameters (diameter at breast height (dbh)) as well as stand level estimates of Basal Area per Hectare (BA/ha), Quadratic Mean Diameter (QMD), Trees per Hectare (TPH), and a Stand Density Index (SDI) using digital photogrammetry. To strengthen our understanding of these forests, we also assessed the proficiency of the UAS to map the presence of large trees (i.e., >40 cm in diameter). We assessed the proficiency of UAS digital photogrammetry for identifying large trees in two ways: (1) using the UAS estimated dbh and the 40 cm size threshold and (2) using a random forest supervised classification and a combination of spectral, textural, and geometric features. Our UAS-based estimates of tree diameter reported an average error of 19.7% to 33.7%. At the stand level, BA/ha and QMD were overestimated by 42.18% and 62.09%, respectively, while TPH and SDI were underestimated by 45.58% and 3.34%. When considering only stands larger than 9 ha however, the overestimation of BA/ha at the stand level dropped to 14.629%. The overall classification of large trees, using the random forest supervised classification achieved an overall accuracy of 85%. The efficiency and effectiveness of these methods offer local land managers the opportunity to better understand their forested ecosystems. Future research into individual tree crown detection and delineation, especially for co-dominant or suppressed trees, will further support these efforts.

Modelling height-diameter relationships in complex tropical rain forest ecosystems using deep learning algorithm

Modelling tree height-diameter relationships in complex tropical rain forest ecosystems remains a challenge because of characteristics of multi-species, multi-layers, and indeterminate age composition. Effective modelling of such complex systems required innovative techniques to improve prediction of tree heights for use for aboveground biomass estimations. Therefore, in this study, deep learning algorithm (DLA) models based on artificial intelligence were trained for predicting tree heights in a tropical rain forest of Nigeria. The data consisted of 1736 individual trees representing 116 species, and measured from 52 0.25 ha sample plots. A K-means clustering was used to classify the species into three groups based on height-diameter ratios. The DLA models were trained for each species-group in which diameter at beast height, quadratic mean diameter and number of trees per ha were used as input variables. Predictions by the DLA models were compared with those developed by nonlinear least squares (NLS) and nonlinear mixed-effects (NLME) using different evaluation statistics and equivalence test. In addition, the predicted heights by the models were used to estimate aboveground biomass. The results showed that the DLA models with 100 neurons in 6 hidden layers, 100 neurons in 9 hidden layers and 100 neurons in 7 hidden layers for groups 1, 2, and 3, respectively, outperformed the NLS and NLME models. The root mean square error for the DLA models ranged from 1.939 to 3.887 m. The results also showed that using height predicted by the DLA models for aboveground biomass estimation brought about more than 30% reduction in error relative to NLS and NLME. Consequently, minimal errors were created in aboveground biomass estimation compared to those of the classical methods.

Forest productivity and associated soil ecosystem services remain altered 15years after mechanized site preparation for reforestation with Pinus radiata

The projected increase in the worldwide demand for forest products over the coming decades may lead to more intensive forest management. This will involve extension of the area of planted forests, increased mechanization and greater pressure to extract timber from sensitive zones such as steep slopes, wet and heavy soils. However, such intensification may place soil protection and ecosystem services provided by forests at risk. In this study, the impacts of mechanized forest site preparation (shearing and ripping) and manual site preparation were evaluated during the first 2.5 years and 15 years after plantation establishment in a soil susceptible to erosion and compaction in a temperate humid climate. Site productivity, soil carbon storage, soil fertility, soil hydrological properties and protection against erosion and run-off were evaluated as ecosystem services provided by forest soils. Soil loss due to manual site preparation was 25 g m−2 yr-1, while annual soil loss ranged from 98 to 3128 g m−2 in sheared sites and from 345 to 4652 g m−2 in ripped sites. However, soil loss was negligible 2.5 years after site preparation. By contrast, soil compaction persisted during 15 years and was significantly higher in mechanically prepared plots than in manually prepared plots. The changes in soil water holding capacity persisted 15 years after site preparation. The available water content was significantly lower in mechanically prepared plots (by shearing and ripping) than in manually prepared plots. During the period 2002–2004, tree height was between 13 and 52 % higher in manually prepared sites than in sheared sites, while in ripped sites tree height was between 91 % higher and 62 % lower than in the manually prepared plots. However, no significant differences in tree height were observed 15 years after site preparation. By contrast, significant differences in the quadratic mean diameter of trees were observed 15 years after site preparation, with tree volumes being highest in manually prepared plots. The findings show that manual site preparation preserves forest ecosystem services such as site productivity and water regulation and can be considered a means of conserving soil and water and a “climate smart” approach in plantation forestry.

Identification of Significative LiDAR Metrics and Comparison of Machine Learning Approaches for Estimating Stand and Diversity Variables in Heterogeneous Brazilian Atlantic Forest

Data collection and estimation of variables that describe the structure of tropical forests, diversity, and richness of tree species are challenging tasks. Light detection and ranging (LiDAR) is a powerful technique due to its ability to penetrate small openings and cracks in the forest canopy, enabling the collection of structural information in complex forests. Our objective was to identify the most significant LiDAR metrics and machine learning techniques to estimate the stand and diversity variables in a disturbed heterogeneous tropical forest. Data were collected in a remnant of the Brazilian Atlantic Forest with different successional stages. LiDAR metrics were used in three types of transformation: (i) raw data (untransformed), (ii) correlation analysis, and (iii) principal component analysis (PCA). These transformations were tested with four machine learning techniques: (i) artificial neural network (ANN), ordinary least squares (OLS), random forests (RF), and support vector machine (SVM) with different configurations resulting in 27 combinations. The best technique was determined based on the lowest RMSE (%) and corrected Akaike information criterion (AICc), and bias (%) values close to zero. The output forest variables were mean diameter at breast height (MDBH), quadratic mean diameter (QMD), basal area (BA), density (DEN), number of tree species (NTS), as well as Shannon–Waver (H’) and Simpson’s diversity indices (D). The best input data were the new variables obtained from the PCA, and the best modeling method was ANN with two hidden layers for the variables MDBH, QMD, BA, and DEN while for NTS, H’and D, the ANN with three hidden layers were the best methods. For MDBH, QMD, H’and D, the RMSE was 5.2–10% with a bias between −1.7% and 3.6%. The BA, DEN, and NTS were the most difficult variables to estimate, due to their complexity in tropical forests; the RMSE was 16.2–27.6% and the bias between −12.4% and −0.24%. The results showed that it is possible to estimate the stand and diversity variables in heterogeneous forests with LiDAR data.

Modeling diameter distribution of Aleppo pine (Pinus halepensis Mill.) natural forest in the Aures (Algeria) using the Weibull, Beta and Normal distributions with parameters depending on stand variables

230 temporary plots located in Aleppo pine ( Pinus halepensis Mill.) stands in the Aures (Algeria) were used for modeling its structure with three theoretical distributions, i.e., the Weibull; the normal and the Beta one. Parameters of the Weibull distribution were estimated using two methods: the maximum likelihood and the method of moments. Diameter distribution models were obtained by estimation of each distribution parameters and by their prediction using stand variables. Results revealed the efficiency of the Weibull distribution estimated with the method of moments. The parameter estimation method is more accurate compared to parameter recovery method despite the existence of strong correlations between parameters of the theoretical distributions and some population variables such as arithmetic or quadratic mean diameter and dominant height. Plot characteristics revealed the existence of several distribution shapes: symmetrical; dissymmetrical with left asymmetry and reverse I or J -shaped distributions.

Assessment of natural regeneration of longleaf pine (Pinus palustris) 15 years post-regeneration control

We assessed natural regeneration of longleaf pine (Pinus palustris Mill.) using the data collected from the Escambia Experimental Forest in southern Alabama. Fifteen years after the regeneration control, natural regeneration of longleaf pine remained patchy across a wide range of site and stand conditions; slightly more than half of all plots contained regeneration, but the density of seedlings and saplings varied significantly. The abundance of seedlings ≤1-year-old was positively related to stand age and time since last fire, but negatively related to overstory basal area. The abundance of seedlings and saplings was positively related to stand age, but negatively related to time since last fire and overstory basal area. The probability of achieving at least 15 000 seedlings·ha–1 that are older than 1 year but less than 1 m tall and at least 1250 saplings·ha–1 that are over 1 m tall was, respectively, positively related to the ratio of time since last fire to overstory basal area and the ratio of quadratic mean diameter to site index. A longer fire interval (> 2 to 3 years) should be adopted to naturally regenerate longleaf. We did not find clear zones of exclusion present in natural regeneration even though overstory trees, seedlings, and saplings tended to be repulsive spatially and >80% grass stage seedlings and saplings occurred outside tree crowns.

Evaluation of planting design for cajuput development (Melaleuca cajuputi Powel) in KPH Bojonegoro

Development of cajuput plantation currently becomes the most important activity in KPH Bojonegoro since it provides an essential contribution to maintain the future viability of company. However, the growth performance of cajuput stand relatively varies one of which is influenced by planting design. This study aims to determine the optimum planting design for supporting cajuput stand development. It was conducted by evaluating two different planting designs that generally used for cajuput establishment, namely C1 (spacing 3 m x 1 m) and C2 (spacing 5 m x 1.5 m x 1.5 m). Five parameters were used to evaluate the growth performance and oil production of cajuput stand for each planting design, i.e., survival rate, quadratic mean diameter, basal area, harvesting biomass, and cajuput oil production. Data were collected by field measurement using a sampling plot 50 m x 50 m with six replications for each planting design. Comparison mean of cajuput growth performance between two planting designs were analyzed separately for each parameter using t-test. Results documented there were not a significant different for all parameters, except survival rate. The planting design C2 generated higher survival rate (94.9%) than planting design C1 (64.7%). The planting design C2 was recommended for cajuput development in KPH Bojonegoro. Besides requiring the lower planting density, the use of planting design C2 can optimize land utility with a similar productivity to planting design C1.

Managing plantation density through initial spacing and commercial thinning: yield results from a 60-year-old red pine spacing trial experiment

We report on a 60-year-old red pine (Pinus resinosa Aiton) spacing trial experiment located in Ontario, Canada, that included the combinations between six initial spacings (from 1.2 to 3.0 m) and the presence or absence of a commercial thinning (CT) regime, as well as their impacts on quadratic mean diameter (QMD) and stand volume yield. The CT regime, initiated at age 30, targeted a residual basal area (BA) of 38 m2·ha−1after each of four entries. Without thinning, as initial spacing increased, QMD increased; gross and net volume production peaked in the 2.1–2.4 m spacings. With thinning, similar trends with spacing were evident for QMD, although piece sizes were larger and differences between spacings were lower. The immediate increase of mean tree size caused by tree selection explained most of the differences in QMD between thinned and unthinned plots. Thinning to a common target BA resulted in similar standing volume across spacings. Cumulative gross yield was similar between spacings of <2.1 m for both thinned and unthinned stands and decreased for thinned plots for wider spacings. Greater net volume production in thinned stands with the narrower spacings confirmed that mortality was captured. Lower gross and net production for wider spacings suggested that thinning resulted in underutilized growing space.

Comparison of two parameter recovery methods for the transformation of Pinus sylvestris yield tables into a diameter distribution model

• Key message We successfully transformed Pinus sylvestris yield tables into diameter distribution models. The best results were obtained with the parameter recovery method based on both mean and quadratic mean diameter, which explained 70% of the variability of frequencies by diameter classes and provided better results in the analysis of errors. On the other hand, the method based on stand density, dominant diameter and quadratic mean diameter explained less variability of frequencies by diameter classes (64.4%). • Context Old datasets used to develop yield table models can be recovered to transform those yield tables into diameter distribution models that provide a more detailed description of size variability and forest structure. • Aims We used archived measurements collected to develop yield table models for Pinus sylvestris L in central Spain, to transform those yield tables into a diameter distribution model by using parameter recovery methods. • Methods We compared two different parameter recovery methods, one based on both mean and quadratic mean diameter and another one based on dominant diameter, stand density and quadratic mean diameter and used a set of 104 even aged plots to analyze the performance of the said methods for the transformation of Pinus sylvestris L yield tables in central Spain into a diameter distribution model. • Results The parameter recovery method based on both mean and quadratic mean diameter explained 70% of the variability of frequencies by diameter classes and provided better results than the method based on stand density, dominant diameter and quadratic mean diameter that explained 64.4% of the variability of frequencies by diameter classes. However, more important than the method itself were the errors that propagated from the models predicting the different variables used in the parameter recovery. • Conclusion Based on the results from the analysis of errors by diameter classes, the method using both mean and quadratic mean diameter outperformed the method using dominant diameter, stand density and quadratic mean diameter and is the best option to transform P. sylvestris yield tables into diameter distribution models.

Long-term effects of catastrophic wind on southern US coastal forests: Lessons from a major hurricane.

Threats posed by windstorms are an increasing concern to forest managers in the southern United States (US). Studies suggest that the southern US will experience an increase in the occurrence as well as the intensity of windstorms, such as hurricanes, in the future. However, forest managers may have difficulty preparing for this future because there is limited understanding of how windstorms affect the structure and composition of forests over the long term. In this study, we evaluated the impacts of Hurricane Ivan, which made landfall in September 2004 near Gulf Shore, Alabama, impacting forests in the western Florida Panhandle and southwestern Alabama. We acquired the United States Department of Agriculture Forest Inventory and Analysis (FIA) plot data available for the period from 2002 to 2018 for the Ivan-affected area and classified the plots into 4 categories: (1). ND (No Disturbance), (2). NDBH (No Disturbance but Harvested), (3). ID (Disturbance caused by Hurricane Ivan), and (4). IDAH (Disturbance caused by Hurricane Ivan and Harvested). The plots that were damaged by Hurricane Ivan (ID and IDAH plots) had significantly (α = 0.05) (1) higher basal area, (2) higher quadratic mean diameter and height, (3) more diverse tree species composition (species richness and Shannon diversity index), (4) denser stocking of seedling and saplings, (5) lower proportion of dead trees or saplings, and (6) higher live aboveground biomass than the plots that were not damaged by the hurricane (ND and NDBH plots). Diverse stands were not necessarily more windstorm resistant. Species diversity in the overstory may not improve forest resistance to hurricane damage but may improve its resilience following the hurricane. The study suggests that managing stand structure through density management and stand improvement could be critical to windstorm resilience and resistance in the southern US forests.

Short- and long-term growth response to climate in mixed and monospecific forests of Pinus pinea and Pinus pinaster

Over recent decades, climate change has been particularly severe in the Mediterranean basin, where the intensity and frequency of drought events have had a significant effect on tree growth and mortality. In this context, differences in structural and physiological strategies between tree species could help to mitigate the damage inflicted by climate variability and drought events. Here, we used dendroecological approaches to observe common associations (synchrony) between indexed ring width in Pinus pinea and P. pinaster, as a measure of degree of dependence on climate variation or growth sensitivity to climate, as well as to analyze species growth responses to drought events through the Lloret’s indices of resistance, recovery and resilience. Based on data from 75 mixed and pure plots installed in the Northern Plateau of Spain, we used modeling tools to detect the effect of the mixture, along with climate and stand-related variables, on the short-term responses and long-term growth sensitivity to climate. Our results showed a trade-off between resistance and recovery after the drought episodes. In addition, different attributes of tree species, such as age and size as well as stand density seemed to act synergistically and compensate drought stress in different ways. The presence of age and quadratic mean diameter as covariates in the final synchrony model for P. pinaster reflected the influence of other variables as modulators of growth response to climate. Furthermore, differences in growth synchrony in mixed and monospecific composition suggested the existence of interactions between the two species and some degree of temporal niche complementarity. In mixed stands, P. pinaster exhibited a lower sensitivity to climate than in monospecific composition, whereas P. pinea enhanced its resistance to extreme droughts. These results allowed us to identify the species-specific behavior of P. pinea and P. pinaster to mitigate vulnerability to climate-related extremes.

Assessing single photon LiDAR for operational implementation of an enhanced forest inventory in diverse mixedwood forests

Airborne laser scanning (ALS; LiDAR) data are an increasingly common data source for forest inventories, and approaches integrating ALS data with field plot measurements have become operational in several jurisdictions. As technology continues to evolve, different LiDAR sensors can provide new opportunities to incorporate LiDAR data into forest inventory workflows. Single photon LiDAR (SPL) enables efficient, large area data acquisition and merits further investigation for forest inventory applications. Herein, we investigated the capacity of leaf-on SPL data, combined with 269 field plots, for estimating forest inventory attributes in the Great Lakes–St. Lawrence mixedwood forests of southern Ontario, Canada. Inventory attribute estimates were validated at the stand level using independent reference data acquired for 27 intensively sampled stands. Top height, Lorey’s height, gross total volume for merchantable stems, merchantable stem volume, basal area, quadratic mean diameter, and total aboveground biomass were estimated with a relative RMSE of 13.52%, 7.24%, 14.61%, 16.27%, 14.42%, 12.25%, and 11.72%, respectively. Relative bias was < 1% for all attributes except top height (10.34%), merchantable volume (3.37%), and basal area (1.68%). Accuracy and bias varied by forest type and stand-level validation was important for assessing model performance in different stand conditions. SPL data can be used to generate accurate, area-based forest inventories in mixedwood forests that have a multitude of tree species and complex forest management histories.

Mixing effects on Scots pine (Pinus sylvestris L.) and Norway spruce (Picea abies (L.) Karst.) productivity along a climatic gradient across Europe

Mixed-species stands have been found to be more productive than would be expected from the performance of their component species in monocultures due to facilitation and complementarity between species, although these interactions depend on the combination of species present. Our study focuses on monospecific and mixed-species stands of Scots pine and Norway spruce using 20 triplets established in nine countries along a climatic gradient across Europe. Differences in mean tree and stand characteristics, productivity and stand structure were assessed. Basal area increment in mixed stands was 8% higher than expected while volume increment was only 2% greater. Scots pine trees growing in mixed-species stands showed 11% larger quadratic mean diameter, 7% larger dominant diameter, 17% higher basal area and 25% higher stand volume than trees growing in monospecific stands. Norway spruce showed only a non-significant tendency to lower mean values of diameters, heights, basal area, as well standing volume in mixtures than monocultures. Stand structure indices differed between mixed stands and monocultures of Scots pine showing a greater stratification in mixed-species stands. Furthermore, the studied morphological traits showed little variability for trees growing in monospecific stands, except for diameter at breast height, crown length and crown length ratio. For trees growing in mixed stands, all the morphological traits of the trees were identified as different. Some of these morphological traits were associated with relative productivity. Nevertheless, relative productivity in mixed-species stands was not related to site conditions.

Analyzing the Joint Effect of Forest Management and Wildfires on Living Biomass and Carbon Stocks in Spanish Forests

Research Highlights: This is the first study that has considered forest management and wildfires in the balance of living biomass and carbon stored in Mediterranean forests. Background and Objectives: The Kyoto Protocol and Paris Agreement request countries to estimate and report carbon emissions and removals from the forest in a transparent and reliable way. The aim of this study is to forecast the carbon stored in the living biomass of Spanish forests for the period 2000–2050 under two forest management alternatives and three forest wildfires scenarios. Materials and Methods: To produce these estimates, we rely on data from the Spanish National Forest Inventory (SNFI) and we use the European Forestry Dynamics Model (EFDM). SNFI plots were classified according to five static (forest type, known land-use restrictions, ownership, stand structure and bioclimatic region) and two dynamic factors (quadratic mean diameter and total volume). The results were validated using data from the latest SNFI cycle (20-year simulation). Results: The increase in wildfire occurrence will lead to a decrease in biomass/carbon between 2000 and 2050 of up to 22.7% in the medium–low greenhouse gas emissions scenario (B2 scenario) and of up to 32.8% in the medium–high greenhouse gas emissions scenario (A2 scenario). Schoolbook allocation management could buffer up to 3% of wildfire carbon loss. The most stable forest type under both wildfire scenarios are Dehesas. As regards bioregions, the Macaronesian area is the most affected and the Alpine region, the least affected. Our validation test revealed a total volume underestimation of 2.2% in 20 years. Conclusions: Forest wildfire scenarios provide more realistic simulations in Mediterranean forests. The results show the potential benefit of forest management, with slightly better results in schoolbook forest management compared to business-as-usual forest management. The EFDM harmonized approach simulates the capacity of forests to store carbon under different scenarios at national scale in Spain, providing important information for optimal decision-making on forest-related policies.

A unified system for tree- and stand-level predictions

The unified system developed in this study comprises two components, one to predict survival and the other to predict diameter, both at tree and stand levels. The tree survival model defaults to the stand survival model when tree diameter reaches a certain value. Similarly, the tree diameter growth model defaults to the quadratic mean diameter model when tree diameter is equal to quadratic mean diameter. The annual growth approach was used to accommodate growth intervals of different lengths. Parameters of the unified system can be estimated together (Simultaneous Estimation) or separately in two phases (Sequential Estimation). Also investigated was the Disaggregation method in which tree attributes (survival probabilities and diameters) were adjusted to match outputs from the stand-level models (number of trees and basal area per unit area).Results indicated that, overall, the Disaggregation approach gave a better performance than did the Unadjusted approach. On the other hand, the Sequential Estimation narrowly surpassed the Simultaneous Estimation method (overall rank of 1.00 versus 1.10). Parameters of the unified system should be estimated by use of the Sequential Estimation method because it is a simpler method which involves only a subset of the parameters at each estimation phase.

Growth and structure of italian alder (Alnus cordata /Loisel./ Duby) linear plantation at age 11 and 16 years at Fruška gora (Serbia)

Growth and structure of Italian alder (Alnus cordata /Loisel/ Duby) trees were analyzed in a linear plantation established by planting two-year-old seedlings at Fruška Gora (Serbia). The aim of this paper is to point out the growth characteristics and the structure of the Italian alder linear plantation at age 11 and 16 years and contribute to the knowledge of adaptive and productive potential of the species in the available plantation in Serbia. The spacing between the trees was 7 m (200 trees per hectare). The plantation is located on anthropogenically changed pedunculate oak and hornbeam site at 125 m above sea level. On the basis of 35 measured trees at age 11 and 16 years, the top height was 15 and 21 m, and the Lorey’s mean height 13.4 and 19.5 m. The dominant diameter was 32.4 cm at age 11 and 59.4 cm at age 16 years. The mean quadratic diameter was 25.1 and 47 cm.The productivity of the plantation is high. At age 11 years, the basal area was 9.9 m2∙ha−1, and the standing volume 107.2 m3∙ha−1 while at age 16 years, it was 34.7 m2∙ha−1 and 305.1 m3∙ha−1, respectively. In the period from 11 to 16 years of age, the periodic annual increment in diameter was 4.4 cm∙year−1, height 1.22 m∙year−1, and in basal area and volume 4.9 m2∙ha−1·year−1and 39.6 m3·ha−1·year−1, respectively, pointing to fast growth of Italian alder. Despite the limits due to a small sample and the fact that the linear plantation was analyzed, we generalise the ­following conclusion: the measured growth characteristics at age 11 and 16 years of Italian alder trees show that the species can grow fast and could be usable in similar areas.