Search Results (15)

The present research was carried out in stands of Scots pine and black pine, pure or mixed with deciduous trees, installed on degraded lands from the Curvature Subcarpathian area, Romania, in a representative network of permanent research plots and followed the analysis of the structural diversity and stability indicators of these stands at different ages and in different conditions of degraded lands. The relationships between the quantitative variables with reference to the structure were established by analyzing the significance of the Pearson correlation coefficient (r) and also including datasets of slenderness indexes, which were classed into three domains of vulnerability to abiotic factors (like wind and snow). The compositional diversity of pine stands (pure or mixed with deciduous ones) is different in relation to age and is correlated with the structural diversity. The obtained correlation coefficients (r Pearson) express very strong and significant relationships between biometric parameters (h x Dbh, h x Lc%, Dc x Dbh, and Lc% x Dbh) of the structural diversity (r = 0.800–0.930), which is important for the analysis of the stability and vulnerability of pine forests. The strong correlation between the analyzed variables expresses a weak vulnerability to the action of harmful abiotic factors and the increase in the stability and resilience of the studied stands, especially of over 50 years old. In the old pine stands, the low-vulnerability domain (I < 0.80) is the best represented one, with an average of 64.01% from the total number of trees. At this age, trees with DBH > 22 cm fall into the low-vulnerability category. The explanation is that the stands were affected in their youth by the action of snow and wind, which, combined with the silvotechnical works performed, led to their compositional and structural diversification and increased stability. The young (<45 years) and pure-pine stands with higher consistency (>0.8) and even-aged structure are the most vulnerable to abiotic factors due to the fact that a large number of trees are passing gradually into the higher cenotic classes. Full article

Forest site productivity estimates are crucial for making informed forest resource management decisions. These estimates are valuable both for the tree species currently growing in the stands and for those being considered for future stands. Current models are generally designed for pure stands and do not account for the influence of climate on tree growth. Consequently, site index (SI) conversion equations were developed specifically for jack pine (Pinus banksiana Lamb.) and trembling aspen (Populus tremuloides Michx.) trees grown in naturally originated mixed stands. This work involved sampling 186 trees (93 of each species) from 31 even-aged mixed stands (3 trees per species per site) across Ontario, Canada. Stem analysis data from these trees were utilized to develop stand height growth models by incorporating climate variables for each species. The models were developed using a mixed effects modelling approach. The SI of one species was correlated with that of the other species and climate variables to establish SI conversion equations. The effect of climate on site productivity was evaluated by projecting stand heights at four geographic locations (east, center, west, and far west) in Ontario from 2022 to 2100 using the derived stand height growth models. Height projections were made under three emissions scenarios reflecting varying levels of radiative forcing by the end of the century (2.6, 4.5, and 8.5 watts m⁻²). Climate effects were observed to vary across different regions, with the least and most pronounced effects noted in the central and far western areas, respectively, for jack pine, while effects were relatively similar across all locations for trembling aspen. Stand heights and SIs of jack pine and trembling aspen trees grown in naturally originated mixed stands can be estimated using the height growth models developed here. Similarly, SI conversion equations enable the estimation of the SI for one species based on the SI of another species and environmental variables. Full article

Societies in the global North face a future of accelerated ageing. In this context, advanced technology, especially that involving artificial intelligence (AI), is often presented as a natural counterweight to stagnation and decay. While it is a reasonable expectation that AI will play important roles in such societies, the manner in which it affects the lives of older people needs to be discussed. Here I argue that older people should be able to exercise, if they so choose, a right to refuse AI-based technologies, and that this right cannot be purely negative. There is a public duty to provide minimal conditions to exercise such a right, even if majorities in the relevant societies disagree with skeptical attitudes towards technology. It is crucial to recognize that there is nothing inherently irrational or particularly selfish in refusing to embrace technologies that are commonly considered disruptive and opaque, especially when the refusers have much to lose. Some older individuals may understandably decide that they indeed stand to lose a whole world of familiar facts and experiences, competencies built in decades of effort, and autonomy in relation to technology. The current default of investigating older people’s resistance to technology as driven by fear or exaggerated emotion in general, and therefore as something to be managed and extinguished, is untenable. Full article

The stem CO₂ efflux (Es) plays an important role in the carbon balance in forest ecosystems. However, a majority of studies focus on ecosystem flux, and little is known about the contribution of stem respiration to ecosystem respiration (Reco) for rubber (Hevea brasiliensis) plantations. We used a portable CO₂ analyzer to monitor the rate of Es in situ at different heights (1.5 m, 3.0 m and 4.5 m) in an even-aged rubber plantation from 2019 to 2020. Our results showed that Es exhibited a significant seasonal difference with a minimum value in April and a maximum in September. The mean annual rate of Es at 3.0 m in height (1.65 ± 0.52 μmol·m⁻²·s⁻¹) was slightly higher than Es at 4.5 m in height (1.56 ± 0.59 μmol·m⁻²·s⁻¹) and Es at 1.5 m in height (1.51 ± 0.48 μmol·m⁻²·s⁻¹). No obvious differences in vertical variations were found. An area-based method (Ea) and a volume-based method (Ev) were used to estimate stem respiration at stand levels. One-way ANOVA showed that Ea had no obvious differences in vertical variation (p = 0.62), and Ev indicated differences in vertical variation (p < 0.05). Therefore, the Ea chamber-based measurements at breast height were reasonable and practical extrapolation proxies of stem respiration in an even-aged rubber plantation. With the use of the area-based method, the stem carbon values released from a mature rubber forest were estimated to be 1.214 t C·hm⁻²·a⁻¹ in 2019 and 1.414 t C·hm⁻²·a⁻¹ in 2020. Ea/Reco and Ev/Reco showed seasonal changes, with a minimum value in April and a maximum value in December. The leaf area index (LAI) and soil volumetric moisture content (VWC) were the major impact factors of Ea/Reco in an even-aged pure rubber plantation. Full article

The mild climate and, in recent decades, the increased demand for timber have favoured the establishment of extensive plantations of fast-growing species such as Pinus radiata in Galicia (a fire-prone region in northwestern Spain). This species is characterised by very poor self-pruning; unmanaged pine stands have a worrying vertical continuity of fuels after crown closure because the dead lower branches accumulate large amounts of fine dead biomass including twigs and suspended needles. Despite the important contribution of these dead ladder fuels to the overall canopy biomass and to crown-fire hazards, equations for estimating these fuels have not yet been developed. In this study, two systems of equations for estimating dead ladder fuel according to size class and the vertical distribution in the first 6 m of the crown were fitted: a tree-level system based on individual tree and stand variables and a stand-level system based only on stand variables. The goodness-of-fit statistics for both model systems indicated that the estimates were robust and accurate. At the tree level, fuel biomass models explained between 35% and 59% of the observed variability, whereas cumulative fuel biomass models explained between 62% and 81% of the observed variability. On the other hand, at the stand level, fuel-load models explained between 88% and 98% of the observed variability, whereas cumulative fuel-load models explained more than 98% of the total observed variability. These systems will therefore allow managers to adequately quantify the dead ladder fuels in pure Pinus radiata stands and to identify the treatments required to reduce crown-fire hazard. Full article

Modelling is essential in forest management as it enables the prediction of productions and yields, and to develop and test alternative models of silviculture. The allometry of trees depends on a set of factors, which include species, stand structure, density and site. Several mathematical methods and techniques can be used to model the individual tree allometry. The variability of tree allometry results in a wide range of functions to predict diameter at breast height, total height and volume. The first functions were developed for pure even-aged stands from crown closure up to the end of the production cycle. However, those models originated biased predictions when used in mixed, uneven-aged, young or older stands and in different sites. Additionally, some modelling methods attain better performances than others. This review highlights the importance of species, stand structure and modelling methods and techniques in the accuracy and precision of the predictions of diameter at breast height, total height and volume. Full article

Forest managers need site productivity estimates for tree species growing in mixed stands. Models developed in the past are generally for pure stands and don’t factor in the effects of climate change on site productivity. Therefore, site index (SI) models were developed for black spruce (Picea mariana Mill. B.S.P.) and trembling aspen (Populus tremuloides Michx.) trees grown in natural origin mixed stands. For this, 186 trees (93 black spruce and trembling aspen each) were sampled from 31 even-aged natural mixed stands (sites) (3 trees/species/site) across Ontario, Canada. Stand height growth models were developed by incorporating climate variables during growth for each species. Stem analysis data collected from sampled trees were used to develop these models. A mixed effects modelling approach was used to fit the models. The relationship between SIs of black spruce and trembling aspen grown in mixed stands was analyzed by calculating correlation coefficients and plotting black spruce SIs against those of trembling aspen. Climate effects on site productivity were evaluated by predicting stand heights for 4 geographic areas of Ontario for the period 2021 to 2080. Three emissions scenarios reflecting different amounts of heat at the end of the century (i.e., 2.6, 4.5, and 8.5 watts m⁻²) were used in the stand height growth models developed here for evaluation. Climate effects were more pronounced for trembling aspen than black spruce only in the far west. The relationship between SIs of black spruce and trembling aspen trees grown in natural origin mixed stands could not be described using a linear/nonlinear mathematical function. The models developed here can be used to estimate stand height and SI of black spruce and trembling aspen trees grown in natural origin mixed stands in a changing climate. In the absence of climate data, models fitted without climate variables can be used to estimate SI of both species. Full article

In this study, we used data from a thinning trial conducted on 34 different sites and 102 sample plots established in pure and even-aged Pinus radiata and Pinus pinaster stands, to test the potential use of low-density airborne laser scanning (ALS) metrics and terrestrial laser scanning (TLS) metrics to provide accurate estimates of variables related to surface and canopy fires. An exhaustive field inventory was carried out in each plot to estimate the main stand variables and the main variables related to fire hazard: surface fuel loads by layers, fuel strata gap, surface fuel height, stand mean height, canopy base height, canopy fuel load and canopy bulk density. In addition, the point clouds from low-density ALS and single-scan TLS of each sample plot were used to calculate metrics related to the vertical and horizontal distribution of forest fuels. The comparative performance of the following three non-parametric machine learning techniques used to estimate the main stand- and fire-related variables from those metrics was evaluated: (i) multivariate adaptive regression splines (MARS), (ii) support vector machine (SVM), and (iii) random forest (RF). The selection of the best modeling approach was based on a comparison of the root mean square error (RMSE), obtained by optimizing the parameters of each technique and performing cross-validation. Overall, the best results were obtained with the MARS techniques for data from both sensors. The TLS data provided the best results for variables associated with the internal characteristics of canopy structure and understory fuel but were less reliable for estimating variables associated with the upper canopy, due to occlusion by mid-canopy foliage. The combination of ALS and TLS metrics improved the accuracy of estimates for all variables analyzed, except the height and the biomass of the understory shrubs. The variability demonstrated by the combined use of both types of metrics ranged from 43.11% for the biomass of duff litter layers to 94.25% for dominant height. The results suggest that the combination of machine learning techniques and metrics derived from low-density ALS data, drawn from a single-scan TLS or a combination of both metrics, may represent a promising alternative to traditional field inventories for obtaining valuable information about surface and canopy fuel variables at large scales. Full article

Forests assume a great socioeconomic and environmental importance, requiring good management decisions to value and care for these natural resources. In Portugal, forest land use accounts for 34.5% of the continental area. The softwood species with the highest representation is maritime pine (Pinus pinaster Ait.). Traditionally, the species is managed as pure and even-aged stands for timber production, with a rotation age of 45 to 50 years. Depending on the initial stand density, the stands are thinned 2 to 4 times during the rotation period. Disturbances associated with forest fires have a negative impact on the age structure of stands over time, as they result in a narrow range of stand ages. This age homogenization over large forest areas increases with the recurrence and size of forest fires, bringing new challenges to forest management, namely the difficulty in ensuring the long-term sustainability of the wood supply. The problem aggravates with the increasing demand pressure on pine wood. This article aims to suggest a framework of DSS for Pinus pinaster that can effectively support the management of forest areas under these circumstances, i.e., narrow age ranges and high demand of harvested timber volume. A communal woodland area in the Northern region of Portugal affected by forest fires was selected as a study case. The Modispinaster model was used as the basis of the DSS, to simulate growth scenarios and interventions along the optional rotation period. Two clear-cut ages were considered: 25 and 40 years. The results obtained were the input data for an integer linear programming (ILP) model to obtain the plan that maximizes the volume of timber harvested in the study area, during the planning horizon. The ILP model has constraints bounding the area of clearings, and sustainability, operational and forestry restrictions. The computational results are a powerful tool for guidance in the decision-making of scheduling and forecasting the execution of interventions determining the set of stands that are exploited according to the different scenarios and the period in which the clear-cut is made throughout the planning horizon. Considering all constraints, the solution allows a balanced extraction of a total of 685 m³·ha⁻¹, over the 50-year horizon, as well as the representation of all age classes at the end of the planning period. Full article

Previous research cross-sectionally characterised eight morphological systolic blood pressure (SBP) active stand (AS) patterns using a clinical clustering approach at Wave 1 (W1) of the Irish Longitudinal Study on Ageing. We explored the longitudinal stability and clinical associations of these groupings at Wave 3 (W3), four years later. Eight AS groups had their clinical characteristics and AS patterns at W3 compared to W1. We explored longitudinal associations (new cognitive decline, falls, syncope, disability, and mortality) using multivariate logistic regression models. In total, 2938 participants (60% of Wave 1 sample) had adequate AS data from both W1 and 3 for analysis. We found no longitudinal stability of the eight AS groups or their morphological patterns between the waves. A pattern of impaired stabilisation and late deficit seemed more preserved and was seen in association with new cognitive decline (OR 1.63, 95% CI: 1.12–2.36, p = 0.011). An increase in antihypertensive usage seemed associated with reduced immediate SBP drops, improved AS patterns, and reduced orthostatic intolerance (OI). In pure longitudinal groups, AS patterns were not preserved after 4 years. AS patterns are longitudinally dynamic, and improvements after 4 years are possible even in the presence of higher antihypertensive burden. Full article

Forests provide a range of ecosystem services essential for human wellbeing. In a changing climate, forest management is expected to play a fundamental role by preserving the functioning of forest ecosystems and enhancing the adaptive processes. Understanding and quantifying the future forest coverage in view of climate changes is therefore crucial in order to develop appropriate forest management strategies. However, the potential impacts of climate change on forest ecosystems remain largely unknown due to the uncertainties lying behind the future prediction of models. To fill this knowledge gap, here we aim to provide an uncertainty assessment of the potential impact of climate change on the forest coverage in Italy using species distribution modelling technique. The spatial distribution of 19 forest tree species in the country was extracted from the last national forest inventory and modelled using nine Species Distribution Models algorithms, six different Global Circulation Models (GCMs), and one Regional Climate Models (RCMs) for 2050s under an intermediate forcing scenario (RCP 4.5). The single species predictions were then compared and used to build a future forest cover map for the country. Overall, no sensible variation in the spatial distribution of the total forested area was predicted with compensatory effects in forest coverage of different tree species, whose magnitude and patters appear largely modulated by the driving climate models. The analyses reported an unchanged amount of total land suitability to forest growth in mountain areas while smaller values were predicted for valleys and floodplains than high-elevation areas. Pure woods were predicted as the most influenced when compared with mixed stands which are characterized by a greater species richness and, therefore, a supposed higher level of biodiversity and resilience to climate change threatens. Pure softwood stands along the Apennines chain in central Italy (e.g., Pinus, Abies) were more sensitive than hardwoods (e.g., Fagus, Quercus) and generally characterized by pure and even-aged planted forests, much further away from their natural structure where admixture with other tree species is more likely. In this context a sustainable forest management strategy may reduce the potential impact of climate change on forest ecosystems. Silvicultural practices should be aimed at increasing the species richness and favoring hardwoods currently growing as dominating species under conifers canopy, stimulating the natural regeneration, gene flow, and supporting (spatial) migration processes. Full article

Background: Crown fires are often intense and fast spreading and hence can have serious impacts on soil, vegetation, and wildlife habitats. Fire managers try to prevent the initiation and spread of crown fires in forested landscapes through fuel management. The minimum fuel conditions necessary to initiate and propagate crown fires are known to be strongly influenced by four stand structural variables: surface fuel load (SFL), fuel strata gap (FSG), canopy base height (CBH), and canopy bulk density (CBD). However, there is often a lack of quantitative data about these variables, especially at the landscape scale. Methods: In this study, data from 123 sample plots established in pure, even-aged, Pinus radiata and Pinus pinaster stands in northwest Spain were analyzed. In each plot, an intensive field inventory was used to characterize surface and canopy fuels load and structure, and to estimate SFL, FSG, CBH, and CBD. Equations relating these variables to Sentinel-2A (S-2A) bands and vegetation indices were obtained using two non-parametric techniques: Random Forest (RF) and Multivariate Adaptive Regression Splines (MARS). Results: According to the goodness-of-fit statistics, RF models provided the most accurate estimates, explaining more than 12%, 37%, 47%, and 31% of the observed variability in SFL, FSG, CBH, and CBD, respectively. To evaluate the performance of the four equations considered, the observed and estimated values of the four fuel variables were used separately to predict the potential type of wildfire (surface fire, passive crown fire, or active crown fire) for each plot, considering three different burning conditions (low, moderate, and extreme). The results of the confusion matrix indicated that 79.8% of the surface fires and 93.1% of the active crown fires were correctly classified; meanwhile, the highest rate of misclassification was observed for passive crown fire, with 75.6% of the samples correctly classified. Conclusions: The results highlight that the combination of medium resolution imagery and machine learning techniques may add valuable information about surface and canopy fuel variables at large scales, whereby crown fire potential and the potential type of wildfire can be classified. Full article

Crown size estimations are of vital importance in forest management practice. This paper presents nonlinear models that were developed for crown width prediction of Black pine (Pinus nigra Arn.) natural, pure, even-aged stands in Olympus Mountain, central Greece. Using a number of measured characteristics at tree and plot level from 66 sample plots as independent variables, an attempt was made to predict crown width accurately, initially based on Least Square Analysis. At the second stage, nonlinear mixed effect modeling was performed in order to increase the fitting ability of the proposed models and to deal with the lack of between observations independence error assumption. Based on the same form, a generalized crown width model was developed by including six main regressors, such as the diameter at breast height, the total height, the canopy base height, the basal area, the relative spacing index and the diameter to quadratic mean diameter ratio, while at the final stage, the same model was expanded to mixed-effect. The proposed models were evaluated against independent crown width sample observations that were also obtained from the study area. The results showed that the two types of mixed-effect models performed equally well and, therefore, we propose those for use in forestry practice. Furthermore, the exact contribution of each inherent variable in crown width allometry was evaluated, thus providing a framework to facilitate field measurements for forest management predictions. Full article

There is worldwide interest in managing forests to improve biodiversity, enhance ecosystem services and assure long-term sustainability of forest resources. An increasingly important goal of forest management is to increase stand diversity and improve wildlife and aquatic habitat. Well-planned silvicultural systems containing a mixture of broadleaf-conifer species have potential to enhance stand diversity and provide other ecosystem services earlier than typical even-aged conifer plantations. Here, we use the example of mixed Sitka spruce/western hemlock and red alder in young, managed stands in southeast Alaska to achieve these goals. We briefly describe the silvics of Sitka spruce, western hemlock and red alder plantations as pure conifer stands or pure broadleaf stands. Then, we synthesize studies of mixed red alder-Sitka spruce/western hemlock stands in southeast Alaska and present their potential for improving stand structural complexity, biodiversity and other ecosystem services over pure conifer forests. Finally, we discuss some of the opportunities and potential tradeoffs for managing mixed broadleaf-conifer stands for providing a number of natural resources and the influence of these broadleaf-conifer forests on ecosystem linkages and processes. Full article

The main aim was to develop models for predicting diameter at breast height (DBH), merchantable tree volume (V), and aboveground biomass (AGB) of individual black pine (Pinus nigra Arn.) trees grown in Sub-Mediterranean Croatian pure even-aged forests, which will be suitable for remote sensing based forest inventories. In total, eight variables obtained from field measurement, existing database, and digital terrain model were candidates for independent variables in regression analysis. DBH, V, and AGB were modeled as linear function of each of the independent variables, and all possible linear combinations thereof. Goodness of fit of every model was then evaluated using R² statistic. Comparison between selected models showed that the variability of all dependent variables are explained best by models which include both crown diameter and tree height as independent variables with coefficients of determination of 0.83, 0.89, 0.82 for DBH, V, and AGB, respectively. Consequently, these models may be recommended as the most suited for DBH, V and AGB estimation of black pine trees grown in pure Sub-Mediterranean forest stands using high-resolution aerial images or high-density airborne laser scanning data. This assumption should be further validated by conducting remote sensing inventory and comparing the obtained results with field measurement results. Full article