Complex Forest Ecosystems Research Articles

Maastrichtian palaeoenvironments and palaeoclimate reconstruction in southern South America (Patagonia, Argentina) based on fossil fungi and algae using open data resources

ABSTRACT The use of non-pollen palynomorphs (NPP), particularly fossil fungi and algae, as palaeobiological proxies for Late Cretaceous palaeoenvironmental and palaeoclimatic reconstructions of warm-to-hot greenhouse conditions, can enhance our understanding of climate change impacts on modern Patagonian environments. This study aimed to reconstruct the Maastrichtian palaeoenvironment and palaeoclimate in the Cañadón Asfalto Basin (CAB, Chubut Province) by testing these NPPs as proxies using the Nearest Living Relative method (NLR). Moreover, using modern ecological requirements from open-source databases, such as GBIF and processing it with an open-source, cross-platform tool like QGIS, linked with Köppen-Geiger shapefiles, provided evidence of climate-driven palaeo-distribution patterns of fungal and algal diversity at CAB. Applying modern ecological requirements and biogeographic distribution data, we reconstructed the palaeoclimate as temperate with evenly distributed precipitation and warm summers, corresponding to the Cfb climate zone in Köppen-Geiger classifications. Additionally, our methodology produced reliable results regarding Cenozoic floras’ physiognomies based on fossil fungi, revealing a transition from sparsely wooded areas with palms and prairies to complex forest ecosystems with palms, deciduous trees, and shrubland. Furthermore, testing Cretaceous algae with the NLR method, for the first time, provided comprehensive insights into past water body characteristics, including trophic state and water quality.

New forest fire assessment model based on artificial neural network and analytic hierarchy process or fuzzy-analytic hierarchy process methodology for fire vulnerability map

Forest fires significantly disrupt global ecosystems. Many forecasting techniques predict fire activity and allocate prevention resources, but various factors are missing from the assessments, and multi-criteria decision approaches alone are insufficient. This work introduced a novel methodology combining artificial neural networks (ANN) with the analytical hierarchy process (AHP), fuzzy AHP multi-criteria methods, and spatial data to detect potential forest fire vulnerability areas using twenty variables. The results from AHP or fuzzy AHP, were processed using a multilayer perceptron with a backpropagation algorithm. The final ANN model has two objectives: first, to create a forest fire vulnerability with four classes (low, moderate, high, and very high), and second, to classify burned area sizes in regions highly or very highly vulnerable to fire. Evaluation metrics were also applied for validation. The fire model was tested using both literature review data and in situ observations. Comparative analysis showed that the burned area size model performed better than other machine learning methods, achieving an accuracy score of 89%. Meanwhile, the fire vulnerability model scored 82%. The study addresses the problem of prediction and provides an algorithm for classifying fire risk based on historical data in the Czech Republic, offering a master model for future forest management. Therefore, the ANN model, once trained, validated, and tested, does not require resetting and is effective for estimating forest fire vulnerability. Moreover, it produces results quickly, providing rapid insights into complex forest ecosystems, saving time and enhancing understanding for decision-makers.

Detection of forest disturbance across California using deep-learning on PlanetScope imagery

California forests have recently experienced record breaking wildfires and tree mortality from droughts, However, there is inadequate monitoring, and limited data to inform policies and management strategies across the state. Although forest surveys and satellite observations of forest cover changes exist at medium to coarse resolutions (30–500 m) annually, they remain less effective in mapping small disturbances of forest patches (<5 m) occurring multiple times a year. We introduce a novel method of tracking California forest cover using a supervised U-Net deep learning architecture and PlanetScope’s Visual dataset which provides 3-band RGB (Red, Green, and Blue) mosaicked imagery. We created labels of forest and non-forest to train the U-Net model to map tree cover based on a semi-unsupervised classification method. We then detected changes of tree cover and disturbance with the U-Net model, achieving an overall accuracy of 98.97% over training data set, and 95.5% over an independent validation dataset, obtaining a precision of 82%, and a recall of 74%. With the predicted tree cover mask, we created wall to wall monthly tree cover maps over California at 4.77 m resolution for 2020, 2021, and 2022. These maps were then aggregated in a post-processing step to develop annual maps of disturbance, while accounting for the time of disturbance and other confounding factors such as topography, phenological and snow cover variability. We compared our high-resolution disturbance maps with wildfire GIS survey data from CALFIRE, and satellite-based forest cover changes and achieved an F-1 score of 54% and 88% respectively. The results suggest that high-resolution maps capture variability of forest disturbance and fire that wildfire surveys and medium resolution satellite products cannot. From 2020 to 2021, California maintained 30,923.5 sq km of forest while 5,994.9 sq km were disturbed. The highest observed forest loss rate was located at the Sierra Nevada mountains at 21.4% of the forested area being disturbed between 2020 and 2021. Our findings highlight the strong potential of deep learning and high-resolution RGB optical imagery for mapping complex forest ecosystems and their changes across California, as well as the application of these techniques on a national to global scale.

Effects of drought, disturbance, and biotic neighborhood on experimental tree seedling performance.

Forest biodiversity is likely maintained by a complex suite of interacting drivers that vary in importance across both space and time. Contributing factors include disturbance, interannual variation in abiotic variables, and biotic neighborhood effects. To probe ongoing uncertainties and potential interactions, we investigated tree seedling performance in a temperate mid-Atlantic forest ecosystem. We planted seedlings of five native tree species in mapped study plots, half of which were subjected to disturbance, and then monitored seedling survival, height growth, and foliar condition. The final year of data collection encompassed a drought, enabling comparison between intervals varying in water availability. Seedling performance was analyzed as a function of canopy cover and biotic neighborhood (conspecific and heterospecific abundance), including interactions, with separate generalized linear mixed models fit for each interval. All species exhibited: (a) pronounced declines in height growth during the drought year, (b) detrimental effects of adult conspecifics, and (c) beneficial effects of canopy openness. However, despite these consistencies, there was considerable variation across species in terms of the relevant predictors for each response variable in each interval. Our results suggest that drought may strengthen or reveal conspecific inhibition in some instances while weakening it or obscuring it in others, and that some forms of conspecific inhibition may manifest only under particular canopy conditions (although given the inconsistency of our findings, we are not convinced that conspecific inhibition is critical for diversity maintenance in our study system). Overall, our work reveals a complex forest ecosystem that appears simultaneously and interactively governed by biotic neighborhood structure (e.g., conspecific and/or heterospecific abundance), local habitat conditions (e.g., canopy cover), and interannual variability (e.g., drought).

Integrated forest protection

In forestry science and profession, continuous efforts are being made to implement forest protection, the most perfect, most efficient and most complex dynamic systems of interconnected and inextricably linked parts of living and non-living nature, against all harmful abiotic and biotic factors, integrally and in an organized manner. Integral forest protection implies versatile and maximum, continuous, application of protective measures in order to ensure unhindered growth and growth of trees, and the creation of the highest quality wood volume. The dangers that threaten mature forest trees are numerous. Maintaining the vitality of forest plant species, from seedlings to trees of different age classes, is a long-term process during which they can be exposed for a longer or shorter time, to harmful biotic or abiotic factors, which sometimes cause long-term pathological processes, with unforeseeable environmental and economic consequences. Their sources lie, on the one hand, in the ecosystem itself, and on the other, in the activities of man, who consciously or unconsciously stimulates them. Any intervention in natural forests must take into account that they are regulated communities of the living beings and that the laws of its maintenance and renewal must be respected, if its regulatory mechanisms are not to be disrupted. Integral forest protection, as well as its most important part related to integrated pest management (IPM), requires a conceptual approach to knowing the complexity of forest ecosystems and the role of man in maintaining their good health and vitality. Integral protection must be used in a broad context, as a set of all available activities that prevent economic damage, with as little disturbance of biocenotic balance, as little environmental pollution and as low costs as possible, that is, as much economy as possible. This implies the unification of numerous activities, aimed at eliminating the causes of damage and preserving their stability, and through a strategy of protection against all adverse factors, not just the currently most aggressive. Prevention in IPM, requires an ecological approach to problems in the forestry business. The protection of natural and anthropogenic stands should be based primarily on preventive measures. Prevention in IPM is an ecological approach to problems in the forestry business. In order to decide on the necessity of applying repressive measures, it is necessary to determine, for each species and area, specific harmfulness thresholds, as well as risk assessment, which usually includes characterization of biological control means, definition of possible ecological and health risk and efficiency. The application of pesticides in forests has always been the subject of, sometimes very heated, discussion by the scientific and professional public. The main controversy was over whether natural enemies are powerful enough to keep a certain type of harmful organism in natural numbers, that is, below the threshold of harmfulness. Management of pesticides and their use in the fight against the most economically important forest pests, includes a detailed analysis of the benefits and risks of this method of IPM. Pesticides are used only when this is the only solution, to prevent the occurrence of absolutely unacceptable damage. The use of pesticides cannot be justified if the costs of control, or potential damage to the environment, are greater than the estimated loss of wood mass. Therefore, monitoring, whose task is to detect harmful organisms and assess the infestation of forest ecosystems, should be part of the overall forest management plan. Long-term monitoring of individual species and taking appropriate forestry measures reduce or eliminate the need for repressive pesticide control measures.

Individual tree-based forest species diversity estimation by classification and clustering methods using UAV data

Monitoring forest species diversity is essential for biodiversity conservation and ecological management. Currently, unmanned aerial vehicle (UAV) remote sensing technology has been increasingly used in biodiversity monitoring due to its flexibility and low cost. In this study, we compared two methods for estimating forest species diversity indices, namely the spectral angle mapper (SAM) classification approach based on the established species-spectral library, and the self-adaptive Fuzzy C-Means (FCM) clustering algorithm by selected biochemical and structural features. We conducted this study in two complex subtropical forest areas, Mazongling (MZL) and Gonggashan (GGS) National Nature Forest Reserves using UAV-borne hyperspectral and LiDAR data. The results showed that the classification method performed better with higher values of R2 than the clustering algorithm for predicting both species richness (0.62 > 0.46 for MZL and 0.55 > 0.46 for GGS) and Shannon-Wiener index (0.64 > 0.58 for MZL, 0.52 > 0.47 for GGS). However, the Simpson index estimated by the classification method correlated less with the field measurements than the clustering algorithm (R2 = 0.44 and 0.83 for MZL and R2 = 0.44 and 0.62 for GGS). Our study demonstrated that the classification method could provide more accurate monitoring of forest diversity indices but requires spectral information of all dominant tree species at individual canopy scale. By comparison, the clustering method might introduce uncertainties due to the amounts of biochemical and structural inputs derived from the hyperspectral and LiDAR data, but it could acquire forest diversity patterns rapidly without distinguishing the specific tree species. Our findings underlined the advantages of UAV remote sensing for monitoring the species diversity in complex forest ecosystems and discussed the applicability of classification and clustering methods for estimating different individual tree-based species diversity indices.

Soil organic carbon distribution and factors affecting carbon accumulation in natural and plantation forests in tropical China

Tropical rainforests are the most complex forest ecosystem in the world, and play an important role in the global carbon cycle. To investigate the accumulation characteristics of soil organic carbon (SOC) in tropical rainforests of different origins, soil samples from plantation and natural forests of tropical mountain rainforests in Wuzhi Mountain (WM), Qixian Mountain (QM) and Diaoluo Mountain (DM) in eastern Hainan Island, China, were collected at various depths (0–10, 10–30 and 30–50 cm). SOC concentrations of different-forest-origin soils were analysed and compared, and the effects of soil physicochemical parameters on SOC concentration in natural and plantation forests were investigated. SOC concentrations in plantation forests in WM, QM and DM were higher than those in natural forests, although the difference was not statistically significant. In the 0–50 cm soil depth, the SOC concentrations of different-forest-origin soils decreased with increasing soil depth, with SOC concentrations in the surface layer (0–10 cm) of natural forests being significantly higher than those in the middle layer (10–30 cm) and bottom layer (30–50 cm) (P < 0.05). Natural forests have lower pH than plantation forests. Correlation analysis revealed significant negative correlation (P < 0.05) between SOC concentration in both forest origins and soil pH. The proportion of clay and silt is greater in natural forests than in plantation forests, but the proportion of sand is greater in plantation forests.

The Influence of Stand Structure on Understory Herbaceous Plants Species Diversity of Platycladus orientalis Plantations in Beijing, China

Species diversity is a crucial index used to evaluate the stability and complexity of forest ecosystems. Studying the relationship between stand structure and understory herbaceous plants species diversity is useful for managers to formulate the best forest structure optimization method with the goal of improving herbaceous species diversity. In this research, Platycladus orientalis plantations in Beijing were taken as the research object. Pearson’s correlation analysis was used to explore the single-factor correlation between stand structure and understory herbaceous plants species diversity; furthermore, a typical correlation analysis and multiple linear regression were used to explore the multi-factor correlation and analyze the dominant stand structure parameters affecting understory herbaceous plants species diversity. In the range of stand structures studied, the results showed that canopy density was negatively correlated with the Shannon–Wiener index and Simpson index (p &lt; 0.01), and tree density was negatively correlated with the Shannon–Wiener index (p &lt; 0.05). In terms of stand spatial structure, the mingling degree was positively correlated with the Shannon–Wiener index, Simpson index, Margalef richness index and Pielou evenness index (p &lt; 0.05), while the uniform angle was negatively correlated with the Pielou evenness index (p &lt; 0.05). The correlation coefficient of the first group of typical variables in the typical correlation analysis was 0.90 (p &lt; 0.05); from this group of typical variables, it can be concluded that canopy density is the most influential indicator affecting the comprehensive index of understory herbaceous plants species diversity, with a load of −0.690, and the Shannon–Wiener index and Simpson index are the most responsive indicators of changes in the comprehensive index of stand structure, with loads of 0.871 and 0.801, respectively. In the process of the management of Platycladus orientalis plantations under a low altitude, south slope, thin soil layer and hard soil parent material, in order to improve the herbaceous species diversity, the canopy density of the overstory and tree density should be appropriately reduced. Additionally, it is necessary to regulate the horizontal spatial structure of stands. When the trees are randomly distributed and the mingling degree is high, the species diversity of herbs can be increased.

Pollen R-values in arid central Asia for quantitative palaeo-vegetation reconstruction

Establishing the relationship between surface pollen assemblages and related modern vegetation provides a bridge for reconstructing the palaeo-vegetation succession and related climate changes. Numerous contributions have demonstrated an inconsistency between the relative abundance values of surface pollen assemblages and corresponding modern vegetation for some taxa, which is attributed to differences in their pollen production, dispersal mode and preservation potential. Consequently, the concept of R-value was introduced as a correction coefficient to counterbalance this discrepancy. Unlike most of the complex forest and grassland ecosystems, desert vegetation with fewer dominant taxa provides a simpler model to demonstrate how to use the R-value to correct this deviation. Here, we propose a scheme of Rrel median values from modern desert vegetation types based on calculated R and Rrel adjustment coefficients of the dominant taxa using surface pollen and related modern vegetation data from 145 sample plots along an east-west desert transect in the eastern arid central Asia (ACA). These data revealed that 1) taking Nitraria as a reference, Chenopodiaceae, Artemisia and Ephedra are highly represented, while Tamaricaceae, Zygophyllaceae, Asteraceae (excluding Artemisia), Calligonum and Poaceae are poorly represented. 2) the pollen representation was constrained by their pollination strategies. For example, anemophilous plants (e.g., Ephedra, Artemisia and Chenopodiaceae) are mostly over-represented, while those of entomophilous taxa (e.g., Nitraria, Tamaricaceae, Zygophyllaceae and Calligonum) are under-represented. The scheme and findings mentioned above cast a new light on the future of quantitative reconstruction of palaeo-vegetation succession in the ACA.

Predicting forest stand attributes using the integration of airborne laser scanning and Worldview-3 data in a mixed forest in Turkey

The aim of this study is to examine the capability of the combined LiDAR/WorldView-3 data in estimating the plot-level stand attributes (stem number-N, mean diameter-D, mean height-H, basal area-BA and volume-V) in a complex forest located in the northwest of Turkey. Total 135 plots were measured to determine the forest attributes. Prediction models were developed at three levels which are: i) the general level for all stands (including all plots), ii) forest type level (coniferous forest, broad-leaved forest), and iii) tree species level (Black pine stands, Maritime pine stands, Oak stands, Mixed stands). Multiple Linear Regression (MLR) and Random Forest (RF) modelling approaches were tested to predict stand attributes. The MLR regression modelling showed that the stand attributes were estimated with R 2 ranging from 0.71 (N and V in Mixed) to 0.94 (H in Maritime pine) at tree species level, from 0.73 (BA in Broadleaved) to 0.95 (H in Conifer) at forest types level and from 0.77 (V) to 0.89 (H) at general level. The RF modelling indicated that the stand attributes were estimated with R 2 ranging from 0.69 (V in Mixed and Oak) to 0.94 (H in Maritime pine) at tree species level, from 0.72 (N in Broadleaved) to 0.95 (H in Conifer) at forest types level and from 0.81 (N and V) to 0.88 (D) at general level. The mean height had the highest prediction accuracy for almost all levels in both approaches. However, the stem number and basal area were generally estimated with the lower accuracies. The homogeneous coniferous stands provided the higher estimation accuracy than the broadleaved stands. Our results showed that the modelling approaches used here provide different performance for predicting different stand attributes. While the MLR approach performed better in estimating the stand attributes at the tree species level, the RF approach towards the general level provided higher accuracy estimation. In conclusion, the combination of aerial laser scanning and high resolution satellite data has high potential for predicting stand attributes in complex forest ecosystems.

Aboveground Biomass of Living Trees Depends on Topographic Conditions and Tree Diversity in Temperate Montane Forests from the Slătioara-Rarău Area (Romania)

The study zone includes one of the largest montane old-growth forests in Europe (Slatioara UNESCO site), and understanding the structure and functioning of sill intact forests in Europe is essential for grounding management strategies for secondary forests. For this reason, we set out to analyze the dependencies between aboveground biomass (AgB), tree species and size diversity and terrain morphology, as well as the relationship between biomass and diversity, since neither of these issues have been sufficiently explored. We found that tree species diversity decreases with increased solar radiation and elevation. Tree size heterogeneity reaches its highest mean values at elevations between 1001 and 1100 m, on slopes between 50 and 60 degrees. AgB is differentiated with elevation; the highest mean AgB (293 tonnes per hectare) is recorded at elevations between 801 and 900 m, while it decreases to 79 tonnes per hectare at more than 1500 m a.s.l. It is also influenced by tree species diversity and tree size heterogeneity, with the highest AgB reached in the most complex forest ecosystems in terms of structural diversity. We showed that intact temperate montane forests develop maximum biomass for optimum species diversity and highest size heterogeneity; all three are modulated mainly by elevation.

Mediterranean forest policy beyond the Paris Climate Agreement

The nature and complexity of forest ecosystems in the Mediterranean region imply the necessity to implement robust and integrated policies, strategies, and special measures to meet the commitments set in Article 5 of the Paris Agreement. This study aims to present the existing literature on forest policy directions on land use change, forestry, and the utilisation of forests as carbon sinks for Mediterranean countries. Another objective is to review current trends and identify challenging issues that lead to policy gaps. A scoping review and analysis were conducted based on a pre-determined research question. Scopus was used as the search database, and descriptive statistics were applied for data processing. The study finds that the forestry sector is most cited for its high potential in climate change mitigation and resilience planning. However, forest industry models aiming at climate neutrality are the least cited schemes in the literature. If the main goal for Mediterranean countries is to efficiently meet the current and future targets for their international pledges and to ensure a responsible position in a greener planet, forest policy re-construction should be listed at the top of the agenda of policy-makers.

In Situ Live Imaging of Gut Microbiota.

ABSTRACTMost studies of gut microbiota have focused on relationships between a specific disease and the presence/abundance of one or a few bacterial species/genera. Whether the spatial and temporal distribution of gut microbiota, as a whole, affects or correlates with health is unknown, largely due to the absence of tools for dynamically monitoring the overall gut microbiota landscape inside living subjects. Here, we describe a novel, noninvasive, live imaging method for gut microbiota using 2-deoxy-2-[18F]fluoro-d-sorbitol (18F-FDS), a compound that specifically labeled gut bacteria in mice and hamsters following oral administration. Positron emission tomography-computed tomography (PET-CT) scanning showed that the radiolabel signal was concentrated in the gut (especially the large intestine), was absent when mice gut microbiota was depleted by antibiotic treatment, and was restored after transplanting antibiotic-treated mice with a fecal or probiotic bacterial mixture. Thus, 18F-FDS images microbiota, not gut tissue. The tissue distribution of 18F-FDS was the highest in the gut (∼3-fold higher than average), in contrast to 2-deoxy-2-[18F]fluoro-d-glucose, which concentrated in brain and many other organs. 2-[18F]fluoro-aminobenzoic acid, another bacterium-specific radioactive tracer, was unsuited for gut microbiota imaging due to unexpected stomach retention following oral administration. When similar gut microbiota imaging was done with hamsters, the spatial resolution increased significantly over that with mice, suggesting that even higher spatial resolution can be achieved with humans or large animals. Thus, our work establishes a new tool for noninvasive, live imaging of gut microbiota; the new tool may enable exploration of relationships between gut microbiota landscape and diseases in clinical settings.IMPORTANCE Gut microbiota dysbiosis correlates with many diseases, but such correlations derive mostly from relationships between one or a few bacteria and a particular disease. Since microbiota resemble complex forest ecosystems more closely than individual patches of trees, the overall landscape (spatial and temporal distribution) of gut bacteria may also affect/reflect disease development. Such a possibility has not been explored due to a lack of tools for directly visualizing natural landscape patterns of gut microbiota. The present work identified 2-deoxy-2-[18F]fluoro-d-sorbitol as a gut microbiota-specific radioactive tracer and developed a novel PET-CT scan-based imaging method that enables noninvasive, real-time imaging of the overall gut bacterial landscape. The method showed increased spatial resolution when hamsters replaced mice, suggesting that even higher spatial resolution could be achieved with larger animals such as humans. This novel technology establishes the feasibility of investigating spatial-temporal distribution dynamics of gut microbiota with many human diseases.

Low- to Moderate-Level Forest Disturbance Effects on Plant Functional Traits and Associated Soil Microbial Diversity in Western Himalaya

This study quantifies the effect of low- to moderate-intensity anthropogenic activities and natural activities affecting less than 40% of the canopy cover on major plant functional traits and associated soil microbial diversity in western Himalayan temperate forests. Plots of 0.1 ha were placed in the temperate forests along disturbance gradient and were categorized based on lopping intensity, deadwood counts, grazing, and litter removal. Plots were classified into three classes, i.e., low disturbance intensity (LDI) and moderate disturbance intensities (MDI1 and MDI2) based on lopping activities and canopy cover. The study was conducted on functional traits related to growth and survival strategies of a species in a complex forest ecosystem. Furthermore, DNA was extracted and metagenome of soil samples was performed using Illumina MiSeq platform from three disturbance classes to study the effect of disturbance and plant traits on microbial diversity. Tree basal area was found to be most significantly affected by disturbance intensity. Total density was found to be greater for the LDI site. Specific leaf area (SLA) and crown cover (CC) were the most affected traits in the moderately disturbed sites, whereas maximum plant height (HT) and seed mass (SM) were least affected by disturbance. Soil microbial diversity was found to be negatively associated with disturbance index. Microbial biomass carbon (MBC) and microbial biomass nitrogen (MBN) were found to be significantly higher in LDI sites. Disturbance level elicited variation in microbial community composition.Proteobacteriawas the most abundant phylum andPhenylobacterium,DA101, andCandidatus solibacterwere mainly abundant at the genus level. A decreasing level of disturbance due to the absence of human residences in the LDI site led to the dominance ofPhenylobacterium(27%) which reduced to 18% in MDI1 site. Shannon alpha bacterial diversity and plant species diversity (H′) were found to be greatest for MDI2 site. In forests with varying levels of management, treefall gaps due to low levels of logging intensity might have a similar effect to those of reduced lopping intensities and deadwood count (due to natural disturbances) in the study sites. The study concludes that moderate disturbance is important for promoting species diversity and species richness (SR), but species having conservative ecological strategies would be more prone to continued disturbance intensity. Therefore, low- to moderate-level disturbance in such forests can be used as a model for natural treefall gaps, and moderate-level disturbance intensity plays a powerful role in buffering ecosystem processes.

Comparative Analysis of Wood Fuels Consumption in Households in the Federation of Bosnia and Herzegovina

Analysis of the consumption of wood energy from wood-based fuels in households in the Federation of Bosnia and Herzegovina was the main purpose of this paper. A survey on wood fuel consumption was first conducted using the Wood fuel Integrated Supply/Demand Overview Mapping (WISDOM) methodology of the Food and Agriculture Organization (FAO) of the United Nations (UN). These results were compared to the results published by official statistics. The survey was conducted in ten cantons in the Federation of Bosnia and Herzegovina, namely in 5,475 households, 2,669 urban and 2,806 other households. The results of the research showed that wood fuels were used in 516,334 or 71.59% of the total number of households. Firewood was used in 497,139 households or in 96.28% in relation to the total number of households in the Federation of Bosnia and Herzegovina that used wood fuels. The surrounding countries showed similar trends. The estimated consumption of firewood in households using the WISDOM methodology is 4.10 (when compared to the official statistical data on total felled timber in and outside public and private forests) to 4.93 times higher than the official statistical data (when compared to the official statistical data on the sale of firewood in state forests), i.e. 4.52 times higher in average. This research indicates the need to apply a new methodological approach for the collection and analysis of data on the consumption of wood fuels in households for energy purposes. Some of the shortcomings of the existing official statistics are the neglect of statistically unregistered production of firewood from private forests and from areas not categorized as forests and forest land, as well as incomplete coverage in terms of conducting a survey on consumption of all categories of wood fuels and all major consumer categories. All the aforementioned, in addition to the economic consequences, has a negative impact on the fulfilment of commitments arising from international agreements in terms of the share of energy from renewable sources and greenhouse gas emissions in total energy consumption in Bosnia and Herzegovina. In addition, incomplete knowledge of the realistic supply and consumption of wood fuels can affect the reliability of planning documents in forestry, with many negative consequences for sustainability of a complex forest ecosystem.

Characterizing reflectance anisotropy of background soil in open-canopy plantations using UAV-based multiangular images

The canopy bidirectional reflectance distribution function (BRDF) plays a pivotal role in estimating the biophysical parameters of plants, whereas soil background anisotropy creates challenges for their retrieval. Soil optical properties affect canopy anisotropic characteristics, especially in open-canopy areas. However, the remote sensing of background anisotropy is challenging due to the difficulties of information extraction in complex forest ecosystems and varying illumination conditions. This study develops an efficient photogrammetric technique to extract the background soil bidirectional reflectance factor (BRF) from unmanned aerial vehicle (UAV)-based multiangular images and to verify the need for accurate soil anisotropy information in canopy radiative transfer modeling. Soil BRF profiles were measured over three open-canopy sample plots from multiangular remotely sensed multispectral images collected with a hexacopter. As validation, reference soil BRF profiles were synchronously acquired by a ground-based multiangular imaging system.A high level of consistency between the ground- and UAV-measured soil BRF was observed with an RMSE of less than 0.012. Uncertainty analysis of the measured soil BRF showed that multiple scattering between sunlit soil in large sunflecks and foliage elements contributed less than 5%. Both results demonstrated that soil anisotropy can be accurately extracted from UAV multiangular measurements. To explicitly demonstrate that the use of soil anisotropy can reduce uncertainties in canopy radiative transfer simulations, we simulated the canopy BRF with Lambertian soil and with anisotropic soil using a three-dimensional (3D) radiative transfer model under different soil moisture content (SMC) levels, canopy cover (CC) levels and solar zenith angles (SZAs) with simulated realistic forest scenes. We found that less CC, lower SZAs and less SMC lead to a more significant influence of soil anisotropy on canopy reflectance; e.g., the reflectance bias reaches up to 0.3 in the hotspot direction. This illustrates that neglecting soil anisotropy can cause considerable errors in the modeling of the canopy BRF of open forests (i.e., CC levels of less than 0.5). The proposed technique facilitates the characterization of anisotropic forest background soil, which is important for advancing canopy radiative transfer modeling and validation and for the retrieval of vegetation parameters.

Assessing Biotic and Abiotic Effects on Biodiversity Index Using Machine Learning

Forest ecosystems play multiple important roles in meeting the habitat needs of different organisms and providing a variety of services to humans. Biodiversity is one of the structural features in dynamic and complex forest ecosystems. One of the most challenging issues in assessing forest ecosystems is understanding the relationship between biodiversity and environmental factors. The aim of this study was to investigate the effect of biotic and abiotic factors on tree diversity of Hyrcanian forests in northern Iran. For this purpose, we analyzed tree diversity in 8 forest sites in different locations from east to west of the Caspian Sea. 15,988 trees were measured in 655 circular permanent sample plots (0.1 ha). A combination of machine learning methods was used for modeling and investigating the relationship between tree diversity and biotic and abiotic factors. Machine learning models included generalized additive models (GAMs), support vector machine (SVM), random forest (RF) and K-nearest–neighbor (KNN). To determine the most important factors related to tree diversity we used from variables such as the average diameter at breast height (DBH) in the plot, basal area in largest trees (BAL), basal area (BA), number of trees per hectare, tree species, slope, aspect and elevation. A comparison of RMSEs, relative RMSEs, and the coefficients of determination of the different methods, showed that the random forest (RF) method resulted in the best models among all those tested. Based on the results of the RF method, elevation, BA and BAL were recognized as the most influential factors defining variation of tree diversity.

Modelling of three-dimensional, diurnal light extinction in two contrasting forests

The three-dimensional (3D) distribution of light within forest ecosystems is a major driver for species competition, coexistence, forest ecosystem functioning, productivity, and diversity. However, accurate knowledge about the 3D distribution of light within the canopy is difficult to obtain. Recent advances in 3D forest reconstruction as well as the use of radiative transfer modelling provide new insights into spatio-temporal variations of light distribution within a forest canopy.We used high resolution laser scanning data coupled with in-situ leaf optical properties (LOP) measurements to parameterize the DART radiative transfer model for a temperate deciduous forest on the Laegern mountain, Switzerland, and for a tropical rain forest located in the Lambir Hills national park, Borneo, Malaysia. Combining terrestrial and unmanned aerial vehicle (UAV) laser scanning acquisitions allowed a high detailed, 3D reconstruction of forest canopies.We analyse the impact of the two contrasting forest canopies, both in terms of structure as well as optical properties, on the 3D extinction of photosynthetic active radiation (PAR, 400 nm - 700 nm) for a whole diurnal cycle. We show that PAR extinction is mainly driven by the canopy structure, resulting in an exponential light extinction profile for the temperate and a more linear extinction profile in the tropical site. The larger 3D heterogeneity in canopy structure for the tropical site also resulted in larger variability in light extinction throughout the whole canopy. We found that contrasting LOPs between the two forests had a minor influence on light extinction. However, approximating light extinction profiles with layered Beer-Lambert or Big-Leaf models only poorly represented the 3D heterogeneity of light extinction within the canopy, illustrating the need for more detailed 3D modelling of light distribution within forest ecosystems. This can give us important insights into light-related mechanisms driving species coexistence, competition and diversity in complex forest ecosystems.

Ribbonized sap flow: an emerging technology for the integration of sap flow sensor components onto a single platform

AbstractTrees play an important role in both the water and carbon cycles. Sensors that measure the flow rate of sap in individual trees provide critical insight into water use dynamics, and these data are vital for the understanding of ecosystem functions. Current sap flux sensors typically require the installation of multiple needle‐like probes into the sapwood of the tree. Despite the scientific value of high‐density measurements, the high cost and complexity of existing methods prevents the deployment of large networks of sensors. This study presents a new sap flow sensor design where all sensor components are on a single integrated circuit board (ribbon) thereby reducing cost and complexity. This system also eliminates misalignment errors, which are a problem inherent in some sap flow sensor designs. The new ribbonized sap flow (RSF) sensor was compared to gravimetric measurements of water flow to quantify the accuracy of the method and the tree wounding response. In a pair of field experiments, we tested the general performance of the sensor design over a growing season, and among several tree species. The laboratory calibration showed that the RSF sensor has similar accuracy and wounding response as traditional three‐probe designs. At a flow rate of 15 cm/h, the sensor underreported rates by 68% with a 19‐d wounding response, compared to other sensor designs, which underreport rates by 20–60%. Collectively, our field experiments show that the RSF sensor can produce reliable field data in a variety of species across full growing seasons. The RSF sensor platform is compact, robust, and versatile. Ribbonized sap flow sensors can be quickly and easily manufactured at low cost, which makes them ideal for the dense network of observations needed for the study of complex forest ecosystems and their response to global change.

Estimation of Forest Structural Attributes Using ICESat/GLAS-Spaceborne Laser Altimetry Data in the Western Ghats Region of India

Three-dimensional structural information of a forest plays a significant role in global forest ecosystem management practices. Optical remote sensing successfully provides abundant spectral information regarding the horizontal distribution of forests. However, measuring the vertical structural attributes of the forest ecosystems for highly complex and heterogeneous forests such as Western Ghats (India) is typically challenging for even these technologies. The measurements of the forest stand-level structural parameters in the Western Ghats of India have not been reported to date. Therefore, the objective of the present study was the estimation of the structural attributes of Mudumalai and Sholayar reserved forests in the Western Ghats region of India using spaceborne ICESat GLAS data. The forest structural attributes estimated in the present study were as follows: canopy height, canopy density, and understory vegetation height. The results obtained indicated a strong correlation with field measurements. The ICESat/GLAS data used in the present study enabled the analysis of structural parameters, contributing significantly to the better understanding of the heterogeneous complex forest ecosystem of the Western Ghats.