Forest Structure Research Articles

The extent and impact of exotic vine invasions in fragmented mesic forests in Eastern Australia

AbstractExotic vines threaten biodiversity causing damage to forest structure. We investigated the distribution of exotic vines across different patchily distributed plant communities. We surveyed patches of 12 threatened, mesic forest communities along the coastal plain in New South Wales, Australia to determine how exotic and native vine distribution and density were influenced by characteristics of forest patches and neighbouring land use. Vine density and stem widths were measured in quadrats in the edge and interior of patches. Canopy cover, number of dead trees, area to perimeter ratio and surrounding land use were recorded for each patch. Our results show that exotic vine assemblages were influenced by anthropogenic land use surrounding patches but not influenced by community type. Most exotic vine species were present across the whole region where surveys were undertaken. Exotic vines species were sometimes at high densities but had smaller stem widths than native species and there was no change in density from the edges into the interior. Araujia sericifera and Ipomoea cairica were the most prevalent exotic species and together with the fast edge growing Anredera cordifolia are of key concern. In contrast, native vine assemblages were species rich, with some individuals having large stem widths suggesting greater age and we found species composition varied with plant community type. Native vines showed continual recruitment and appeared more specialized to the attributes of each plant community. Surrounding anthropogenic land uses (residential, agricultural and industrial) were the most important factors predicting invasion of exotic vines and were more important than the length of edges. This suggests that the surrounding matrix characteristics were influencing degradation of the interior of these patches, rather than exotic vines invading along an invasion front from the edge. Exotic vines need to be controlled across the entirety of patches, rather than a focus on edge control practices.

Bitemporal Radiative Transfer Modeling Using Bitemporal 3D-Explicit Forest Reconstruction from Terrestrial Laser Scanning

Radiative transfer models (RTMs) are often used to retrieve biophysical parameters from earth observation data. RTMs with multi-temporal and realistic forest representations enable radiative transfer (RT) modeling for real-world dynamic processes. To achieve more realistic RT modeling for dynamic forest processes, this study presents the 3D-explicit reconstruction of a typical temperate deciduous forest in 2015 and 2022. We demonstrate for the first time the potential use of bitemporal 3D-explicit RT modeling from terrestrial laser scanning on the forward modeling and quantitative interpretation of: (1) remote sensing (RS) observations of leaf area index (LAI), fraction of absorbed photosynthetically active radiation (FAPAR), and canopy light extinction, and (2) the impact of canopy gap dynamics on light availability of explicit locations. Results showed that, compared to the 2015 scene, the hemispherical-directional reflectance factor (HDRF) of the 2022 forest scene relatively decreased by 3.8% and the leaf FAPAR relatively increased by 5.4%. At explicit locations where canopy gaps significantly changed between the 2015 scene and the 2022 scene, only under diffuse light did the branch damage and closing gap significantly impact ground light availability. This study provides the first bitemporal RT comparison based on the 3D RT modeling, which uses one of the most realistic bitemporal forest scenes as the structural input. This bitemporal 3D-explicit forest RT modeling allows spatially explicit modeling over time under fully controlled experimental conditions in one of the most realistic virtual environments, thus delivering a powerful tool for studying canopy light regimes as impacted by dynamics in forest structure and developing RS inversion schemes on forest structural changes.

Timber Harvest Planning Using Reinforcement Learning: A Feasibility Study

This study developed a forest management plan model using reinforcement learning (Q-learning) to optimize both the economic and ecological functions of forests. Management objectives for national forests were established, and forest conditions were analyzed using GIS spatial data and administrative records. A 60-year forest management plan was formulated to predict timber production and management performance across different regions and time periods. Our analysis revealed that Scenario 3 (Carbon Storage Priority) demonstrated the highest economic value, starting at approximately KRW 576.2 billion in the initial period and escalating to KRW 775.7 billion over six 10-year periods, totaling 60 years. In addition to its economic performance, Scenario 3 effectively improved forest age class structure and ensured a stable timber supply, making it the most balanced approach for sustainable forest management. By focusing on carbon storage as a key management goal, this approach highlights the potential for achieving both economic and environmental benefits concurrently. These results suggest that reinforcement learning is a powerful tool for developing long-term forest management strategies that address multiple objectives, including economic viability, ecological sustainability, and resource optimization.

Coexistence of territorial competitor ants in fragmented boreal forest landscape

The distribution of species in a patchy habitat may be influenced by competitive interactions. The dominant and highly competitive boreal ant species belong to the Formica rufa group. A pair of species, Formica aquilonia and Formica polyctena, require extensive territories due to their multi-nest breeding habits. The coexistence and habitat patterns of these two wood ant species in the boreal forest landscape were investigated. Forest characteristics in the vicinity of nests in forest patches were similar for both species, but they did not coexist in the same sampling plots of 0.79 ha in forest patches, indicating competitive exclusion. The sampling plots in large forest patches were more occupied by F. aquilonia, while no such association was found for F. polyctena. At a larger spatial scale (78.5 ha), we found that F. polyctena was more tolerant of smaller forest patches than F. aquilonia suggesting that these two ant species can coexist in moderately fragmented forest landscapes. However, forest habitat loss, fragmentation and climate-induced changes in forest tree structure may shift the species balance in favour of F. polyctena over F. aquilonia in the future.

Effectiveness of pre-fire forest management on post-fire forest conditions in southeastern Arizona

BackgroundWestern forests in the United States are facing multiple threats that have the potential to permanently alter forest composition and structure. In particular, wildfire can either have beneficial or adverse effects on overall forest health and resilience. Monitoring and assessing the effectiveness of existing forest treatment plans for meeting forest management goals is becoming more critical to increase the capacity for managers to prepare for and accommodate uncertainty associated with changing disturbance regimes. We used a combination of fine-scale vegetation and microclimate surveys on 57 plots, active remotely sensed data (light detection and ranging: LiDAR), and high-resolution satellite imagery to evaluate the effectiveness of an existing management strategy to increase disturbance resistance and resilience of an isolated mixed-conifer forest following a recent large-scale wildfire in southeastern Arizona, USA. We specifically assessed the effectiveness of forest overstory live tree thinning treatments (silviculture) as well as understory fuel reduction treatments (fuel) for influencing post-fire abiotic and biotic conditions, reducing direct post-fire tree mortality, and increasing resilience as compared to untreated forest stands.ResultsWe found that forest silviculture and fuel reduction treatments implemented prior to a large wildfire had mixed results on post-fire fine-scale vegetation composition and structure, microclimate conditions, tree mortality, and tree resilience. Fine-scale vegetation characteristics within silviculture- and fuel-treated forest units displayed higher herbaceous diversity and decreased density of new tree snags as compared to untreated units post-fire. Relevant to seedling emergence, we found that variance in spring soil moisture content was lower overall in treated units; however, units that received overstory thinning (silviculture) treatments were also associated with higher average summer high soil temperatures as compared to untreated units. Additionally, direct tree mortality and rate of recovery of trees post-fire differed between two treatment types (silviculture and fuel reduction) when compared to untreated units and among contrasting levels of burn severity. Post-fire tree mortality and tree resilience did not differ between control and silviculture units; however, these characteristics did differ between control and fuel units. Unlike control units, probability of tree mortality changed little between burn severity categories in fuel treatments (53.4% of mortality occurring in unburned/low vs. 46.7% in moderate/high severity) and resilience increased an average of 2.04% for trees from unburned/low to moderate/high-severity burn categories.ConclusionsOur methodology could be applied to any forested system experiencing increasing intensity and frequency of wildfire. Our results indicate that post-fire forest conditions and resilience are influenced by forest management strategies, particularly fuel reduction treatments. To accommodate uncertainty associated with changing disturbance regimes and climate change, implementing post-fire and post-treatment assessments and monitoring as presented in this study will be essential for developing attainable goals and for maintaining desired forest conditions.

A global dataset of forest regrowth following wildfires

Wildfires result in forest loss or degradation and release substantial emissions into the atmosphere. Forest regrowth following these fires allows for ecosystem repair and carbon replenishment. However, there is a lack of datasets explicitly characterizing the forest regrowth after fires. Here we employed multiple remotely sensed datasets to generate the first global maps of forest structure regrowth including forest height, aboveground biomass (AGB), leaf area index (LAI), and fraction of photosynthetically active radiation (FPAR) following wildfires at a 30 m spatial resolution. The regrowth index for each structural parameter includes regrowth ratio and rate at 5-year intervals, primarily from 2000 to 2020. The dataset developed in this study provides detailed insights into the characteristics of global forest regrowth following forest fires in both spatial and temporal dimensions, contributing to the assessment of forest ecology equilibrium and the quantification of forest carbon dynamics.

Chronosequence and Temporal Changes in Soil Conditions, Vegetation Structure and Leaf Traits in a Tropical Dry Forest in Brazil

Chronosequence and Temporal Changes in Soil Conditions, Vegetation Structure and Leaf Traits in a Tropical Dry Forest in Brazil

Historical pyrodiversity in Douglas-fir forests of the southern Cascades of Oregon, USA

Our understanding of forest dynamics and successional pathways in coastal Douglas-fir (Pseudotsuga menziesii var menziesii) forests with relatively frequent mixed-severity fires is limited by a lack of annually precise dendroecological reconstructions that combine records of historical fires and tree establishment. The processes by which old-forest heterogeneity developed under historical fire regimes with recurrent low- and moderate-severity fires has not been well studied at fine temporal scales and across spatial scales. We developed crossdated multi-century records of fire and tree establishment histories in old forests (170 – 550 years) at 34 plots distributed across six sites. Study sites include warm-dry to cool-moist Douglas-fir forest types found in the southern west Cascades of Oregon, USA. Spatial variability in historical fire frequency and fire effects resulted in tremendous diversity in forest developmental histories, age structure, and forest conditions. Most historical fire intervals were very frequent (<10 years) to frequent (<25 years) in dry Douglas-fir forests. Exceptionally high fire frequency and an abrupt decrease in fire frequency after European colonization in dry Douglas-fir forests adds to growing evidence and recognition of Indigenous fire stewardship in montane Douglas-fir forests. In moist forests where Douglas-fir is seral to western hemlock, fire intervals were frequent to moderately frequent (<50 years), but intervals varied substantially over time. Relatively young moist forests burned frequently while mature moist forests had long fire intervals (50–160 years). Nearly all tree establishment cohorts were preceded by either stand-replacing (28 %) or non-stand-replacing fires (64 %). However, tree cohorts only provided evidence of 16 % of historical fire events that we reconstructed from cambial fire scars. This study demonstrates that frequent fire can be an important driver of forest development and in some contexts shapes the structure of coastal old-growth Douglas-fir forests, which are often characterized as developing from endogenous disturbances during long fire-free periods. The high level of pyrodiversity we observed was associated with variation in and interactions of micro-climate, topography, fuels, and Indigenous fire stewardship. We recommend rigorous dendroecological reconstructions across the coastal Douglas-fir region to refine our understanding of the geography of fire-mediated forest developmental dynamics in this important forest type, to inform forest management, conservation, and ecocultural restoration.

Effects of Thinning on the Diversity, Composition, and Spatial Structure in a Mixed Temperate Forest

structure in a temperate mixed forest. Design/methodology/approach: Data were collected in a 1 ha research plot. Five scenarios were evaluated through computer simulations: no thinning (T1), thinning with removal of 25% of basal area (Gha-1), (T2), thinning with removal of 25% of Gha-1 (T3), thinning with removal of 45% of Gha-1 (T4), and thinning with removal of 70% of Gha-1 (T5). The importance value index, alpha diversity, Pretzsch's A index and structural complexity index were estimated. A spatial distribution analysis was performed using the pair-correlation function g(r). Results: Pinus douglasiana and Quercus resinosa were the species of highest ecological value. Due to the removal effect, no significant changes in tree diversity were observed in the applied thinning scenarios. However, as thinning became more intense, at least one species (Quercus candicans) was lost. Thinning from below affected the oaks and thinning from above affected the pine species, which is also reflected in the spatial distribution of the remaining trees. Limitations on study/implications: The analysis is static; therefore, it is recommended that a long-term study be conducted under varying ecological conditions. Findings/conclusions: The effect of thinning on forest diversity, composition and structure depends on the type of thinning, condition of the structure, initial composition and intensity of removal. Thinning of less than 25% of the basal area, in the immediacy, allows timber harvesting without generating changes in the diversity, structure and composition of the temperate mixed forest under study.

Forest Management and Tree Species Diversity in a Temperate Forest of the Meseta Purhépecha, Michoacán

Objective: To evaluate how traditional forest management practices impact the diversity and structure of temperate forests in the Nuevo San Juan Parangaricutiro Indigenous Community (ICNSJP), with the goal of providing information on the sustainable management of these forests. Design/methodology/approach: Forest inventory data were collected and analyzed in stands treated with four thinning (A4) through the Silvicultural Development Method (SDM) and contrasted with stands that have not been intervened for more than 30 years (A0). Diversity indices and stand variables were used to statistically compare the effects of the treatments. Results: In total 20 species were recorded, 13 in A0 and 18 in A4. The true diversity (q1) recorded a value of 3.3±1.6 in A4 and 1.9±0.8 in A0, although this difference was not statistically significant. The species with highest importance value were Pinus douglasiana Martinez (30%) for A0 and P. pseudostrobus Lindl (27%) for A4. Limitations on study/implications: According to the SDM, the cutting cycle in A4 has not yet ended since the release and regeneration treatments are missing, therefore, monitoring is recommended to evaluate the effect of the SDM more comprehensively on diversity and structure in the forests of the ICNSJP. Findings/conclusions: Forest management in the ICNSJP has shown a positive effect in terms of species diversity and due to its commercial value, the development of P. pseudostrobus has been favored. In general, forest management has contributed to socioeconomic development by promoting species diversity, a key element for sustainable development.

Vegetation Structure, Composition and Plant Community types in Tropical Dry Deciduous Forests of Jaipur, Rajasthan, India

The research was carried out in the Aravalli Forests of Nahargarh (NWLS) and Jamwa Ramgarh Wildlife Sanctuaries (JRWLS) of Jaipur, Rajasthan, India. The vegetation structure, composition, biodiversity and plant community types were assessed across the five forest stands viz Nahargarh biological park (S1), Nahargarh Sanctuary (S2) in NWLS and Ramgarh (S3), Raisar (S4), and Digota (S5) of JRWLS, respectively. A total of 164 qaudrats of 20*20 m2 were laid to assess the vegetation data including frequency, basal area, density, importance value index (IVI), biodiversity indices (Shannon-weiner, Simpson, Pielou and Margalef’s) and plant community types. The tree basal area ranged between 18.56 -34.36 m2/ha while stand density was 923-1433 tree/ha. Our study indicated the dominance of family Fabaceae with 20 species and 11 genera with Anogeissus pendula showing the maximum frequency, density, mean basal area and IVI. Further, the agglomerative hierarchal clustering showed five types of plant communities with Anogeissus-Adhatoda as the most widely spread community in these forests. Among the two sanctuaries, species diversity was higher for JRWLS (2.83) than NWLS (2.80). The current study provides important baseline data for forthcoming research on monitoring and preserving these forest’s biological diversity. The significance of present study will provide insight into the structure and variety of tree species in tropical dry deciduous forests.

A global analysis of the effects of forest thinning on soil N stocks and dynamics

Forest thinning is a critical management measure for changing the forest community structure and improving the soil microclimate and nitrogen (N) cycle. However, differences in forest types, thinning intensity, recovery time, and climate conditions make the accumulation of soil N under forest thinning uncertain; especially the soil N accumulation rate is still lacking. This study systematically discusses the effects of forest thinning on the rate of soil N stock change (RNC) and analyzes the relationship between the RNC and the rate of soil carbon (C) stock change (RCC) by synthesizing 387 global data points. Forest thinning significantly increased the soil N stock, with the RNC being +0.07 Mg/ha yr−1. The RNCs in coniferous and mixed forests (+0.07 and +0.09 Mg/ha yr−1, respectively) were higher than that in broadleaf forest (−0.03 Mg/ha yr−1). Heavy forest thinning inhibited soil N accumulation; however, light forest thinning and long-term recovery were more conducive to improving it. The RNC decreased with increasing precipitation and temperature under forest thinning. Additionally, the RNC was positively correlated with the RCC. Overall, heavy forest thinning decreases the soil N accumulation rate in the global forest ecosystem; conversely, long-term recovery increases it. Reducing the forest thinning intensity and extending the recovery time can accelerate soil N accumulation and thus reduce greenhouse gas emissions.

Insect seedling herbivory is influenced by multiple factors, but the plant apparency theory is more supported than other hypotheses – A case study in a subtropical forest

Insect herbivory is ubiquitous in various ecosystems, and directly influences the growth and survival of individual plants, especially during their vulnerable early life stages like the seedling phase. This, in turn, exerts a significant influence on forest community diversity and structure, as well as ecosystem function and stability. Notable variation in herbivory has been detected both among and within plant species. For decades, many hypotheses have been proposed to explain such variations, including both biotic and abiotic variables. However, most studies have considered only one or several of these hypotheses by focusing on a few potential variables, and their results were usually inconsistent; thus, the factors driving herbivory remain unclear. In this study, we examined leaf herbivory by insects of woody species seedlings in a subtropical forest in southwestern China over two seasons. In total, 24 potential variables that represented abiotic resource availability, characters of individual seedlings, conspecific and heterospecific species, and the whole seedling community were selected to test several commonly discussed alternative herbivory hypotheses. Overall, our findings showed that the plant apparency hypothesis was more supported than the other hypotheses in explaining insect seedling herbivory. Our results further indicated that the mechanisms and causes of insect herbivory are complex, multifactorial, species-specific and vary with seasons, indicating that there may be no uniform rules in explaining herbivory for all seedlings. Consequently, such complexity may play an important role in promoting species coexistence and biodiversity maintenance in seedling communities, which may further translate into the following generation of saplings or even adult communities. Changes in the community of insect herbivores and/or variables influencing insect herbivory, may disrupt stability of the original seedling community, thus affecting the regeneration and development of the entire forest community. Therefore, we suggest that issues related to insect herbivory should be considered when developing forest management and conservation.

Forest Canopy Height Estimation Combining Dual-Polarization PolSAR and Spaceborne LiDAR Data

Forest canopy height data are fundamental parameters of forest structure and are critical for understanding terrestrial carbon stock, global carbon cycle dynamics and forest productivity. To address the limitations of retrieving forest canopy height using conventional PolInSAR-based methods, we proposed a method to estimate forest height by combining single-temporal polarimetric synthetic aperture radar (PolSAR) images with sparse spaceborne LiDAR (forest height) measurements. The core idea of our method is that volume scattering energy variations which are linked to forest canopy height occur during radar acquisition. Specifically, our methodology begins by employing a semi-empirical inversion model directly derived from the random volume over ground (RVoG) formulation to establish the relationship between forest canopy height, volume scattering energy and wave extinction. Subsequently, PolSAR decomposition techniques are used to extract canopy volume scattering energy. Additionally, machine learning is employed to generate a spatially continuous extinction coefficient product, utilizing sparse LiDAR samples for assistance. Finally, with the derived inversion model and the resulting model parameters (i.e., volume scattering power and extinction coefficient), forest canopy height can be estimated. The performance of the proposed forest height inversion method is illustrated with L-band NASA/JPL UAVSAR from AfriSAR data conducted over the Gabon Lope National Park and airborne LiDAR data. Compared to high-accuracy airborne LiDAR data, the obtained forest canopy height from the proposed approach exhibited higher accuracy (R2 = 0.92, RMSE = 6.09 m). The results demonstrate the potential and merit of the synergistic combination of PolSAR (volume scattering power) and sparse LiDAR (forest height) measurements for forest height estimation. Additionally, our approach achieves good performance in forest height estimation, with accuracy comparable to that of the multi-baseline PolInSAR-based inversion method (RMSE = 5.80 m), surpassing traditional PolSAR-based methods with an accuracy of 10.86 m. Given the simplicity and efficiency of the proposed method, it has the potential for large-scale forest height estimation applications when only single-temporal dual-polarization acquisitions are available.

Principle, structure and application progress of the forest landscape model FireBGCv2.

Forest landscape model can quantitatively simulate the spatiotemporal variations in forest structure and function at the landscape scale based on traditional field survey data and mathematical models, providing a reference for the formulation of scientific forest management strategies. FireBGCv2 is one of the representative models currently used in the research area of forest landscape changes. It can simulate ecological processes at various scales, including trees scale (tree growth, establishment, and mortality), stand scale (carbon and nitrogen pools, fuel treatment, decomposition), site scale (resource competition and species phenology), and landscape scale (seed dispersal and wildfire disturbances), and the effects of those processes on forest landscape structure and function. The advantage of this model lies in its ability to simulate multiple ecological processes while considering the diversity and complexity of ecosystems. However, it also has drawbacks, such as high computational demands and complexity of use. We summarized the basic principles and structure of FireBGCv2 and introduced its application progress in forest landscape research and management. Currently, the application of the FireBGCv2 model, both domestically and internationally, mainly focused on exploring the interactions between fire, climate, and vegetation, quantifying the spatial and temporal dynamics of fires, and describing potential fire dynamics under future climate scenarios and land management strategies. With the in-depth development of forest landscape model theories and applications, the future prospects of FireBGCv2 include improving and updating the model's algorithms, adding new functional modules to explore fire management issues, and meeting the needs of different users.

Spatial structure and individual competition characteristics of secondary Mongolian oak mature forests in the mountainous area of eastern Liaoning Province, China.

The community structure of natural mature forests is determined by long-term forest succession, characterized by rational structure, rich biodiversity, and high ecological function. Understanding the spatial structure and formation mechanisms of mature forests is a fundamental prerequisite for forest management. We analyzed four structure parameters, including diameter structure, angular scale, size ratio, and mixture degree, as well as the Hegyi competition index, of secondary Quercus mongolica (Mongolian oak) mature forests in the mountainous area of eastern Liaoning Province. The results showed that Q. mongolica predominated the tree layer. In the sapling layer, Q. mongolica, Tilia amurensis, and Acer pictum were the dominant species. In the seedling layer, Acer pseudosieboldianum, T. amurensis, and A. amurensis dominated, with very few Q. mongolica seedlings. The overall diameter distribution of the stand showed an inverse "J" shape, while the diameter distribution of Q. mongolica, the dominant tree species, followed a normal distribution. The horizontal spatial structure of the stand was generally randomly distributed, with an average angle scale of 0.505, size ratio of 0.219, and mixture degree of 0.670 for Q. mongolica. From the perspective of spatial structure binary distribution, Q. mongolica individuals which had a random distribution exhibited greater growth advantages and higher levels of mixing, in comparison to other distribution types. Randomly distributed dominant and subdominant individuals made up nearly half individuals in the stand, and showed a high degree of mixing with surrounding trees. The stand-level individual tree competition index decreased with increasing diameter classes. When the diameter at breast height exceeded 20 cm, the competition index tended to stabilize (ΔCI<2). The competitive radius of individual Q. mongolica trees was 8 m, with intraspecific competition as the main pressure. Other species experienced competition pressure primarily from interspecific sources. Our results suggested that competition played an important role in shaping the spatial structure of secondary Q. mongolica mature forests.

Early spectral dynamics are indicative of distinct growth patterns in post‐wildfire forests

AbstractWestern North America has seen a recent dramatic increase in large and often high‐severity wildfires. After forest fire, understanding patterns of structural recovery is important, as recovery patterns impact critical ecosystem services. Continuous forest monitoring provided by satellite observations is particularly beneficial to capture the pivotal post‐fire period when forest recovery begins. However, it is challenging to optimize optical satellite imagery to both interpolate current and extrapolate future forest structure and composition. We identified a need to understand how early spectral dynamics (5 years post‐fire) inform patterns of structural recovery after fire disturbance. To create these structural patterns, we collected metrics of forest structure using high‐density Remotely Piloted Aircraft (RPAS) lidar (light detection and ranging). We employed a space‐for‐time substitution in the highly fire‐disturbed forests of interior British Columbia. In this region, we collected RPAS lidar and corresponding field plot data 5‐, 8‐, 11‐,12‐, and 16‐years postfire to predict structural attributes relevant to management, including the percent bare ground, the proportion of coniferous trees, stem density, and basal area. We compared forest structural attributes with unique early spectral responses, or trajectories, derived from Landsat time series data 5 years after fire. A total of eight unique spectral recovery trajectories were identified from spectral responses of seven vegetation indices (NBR, NDMI, NDVI, TCA, TCB, TCG, and TCW) that described five distinct patterns of structural recovery captured with RPAS lidar. Two structural patterns covered more than 80% of the study area. Both patterns had strong coniferous regrowth, but one had a higher basal area with more bare ground and the other pattern had a high stem density, but a low basal area and a higher deciduous proportion. Our approach highlights the ability to use early spectral responses to capture unique spectral trajectories and their associated distinct structural recovery patterns.

Spatial patterns of damage and tree mortality in a selectively logged Atlantic Forest

Selective logging, a common disturbance in mixed-species and uneven-aged forests, can cause substantial collateral stand damage and tree mortality. Here we explore damage patterns and some mechanisms that increase post-harvest tree mortality in a selectively logged subtropical Atlantic Forest in Argentina. We investigate the spatial relations of felled and damaged trees through spatial point pattern analysis and evaluate the relationships between mortality and different endogenous (size - diameter at breast height: DBH; and wood density: WD) and exogenous (damage and neighboring basal area: NBA) factors. The permanent plots were logged in 1999, and the fates of all pre-logging live trees ≥10 cm DBH were evaluated 20 years later. Of the monitored 3973 trees, 381 with damaged concentrated within 10.5 m of felled tree stumps. Over the next twenty years mean mortality was higher and more variable for damaged than undamaged trees (47 % ± 10 % SE and 39 % ± 2 % SE, respectively), and the presence of damage interacted with the other analyzed factors. For undamaged trees, the probability of mortality declined with DBH and NBA but not with WD. For damaged trees, instead, the probability of mortality was related to an interaction between DBH, WD, and NBA. For damaged trees <30 cm DBH, mortality increased with WD and NBA, whereas for damaged trees ≥30 cm DBH, mortality peaked at both extremes of the WD range. For these large trees with low WD, the probability of mortality decreased with NBA, whereas for trees with high WD, the opposite was observed. Our findings suggest that selective logging affects the dynamics of forests by spatially concentrating damage and may alter subsequent tree deaths. This could have, long-term effects on forest structure. Increases in logging intensity would increase overall damage and spatially isolate trees (i.e., lower NBA), rendering them more vulnerable to wind damage and other external factors. Increased tree mortality will reduce forest carbon stocks and thereby jeopardize global efforts to mitigate climate change.

USE OF MACHINE LEARNING AND DEEP LEARNING METHODS IN HOUSING PRICE INDEX ESTIMATION: AN ANALYSIS ON ANKARA AND ISTANBUL

Factors such as supply chain difficulties, rising energy and oil prices, economic recession and production loss due to the pandemic have increased costs and inflation. All these factors have also seriously affected the construction sector. This study aims to create a deep learning and machine learning focused forecasting system based on Istanbul and Ankara monthly housing price index data for the period of January 2010 to June 2023. The system was created using approximately 13 years of housing interest rates, Consumer Price Index, XGMYO, Monthly Average Dollar and XAU data as the basis of the Istanbul and Ankara Housing Price Index forecasting process. During the research process, different RNN structures (Long and Short Term Memory, Gated Recurrent Unit) and machine learning (Random Forest) structures were tested and the effectiveness of these structures in housing price index forecasting was compared. The performances of the models were evaluated using RMSE, MSE, MAE, MAPE and R2 statistics. According to the results obtained, the method that gave the best performance for both provinces is the RF model. This is followed by LSTM and GRU models, respectively

Sparse large trees in secondary and planted forests highlight the need to improve forest conservation and management

Large trees are essential for carbon storage and biodiversity conservation. While an increasing number of studies have focused on large trees in primary forests, little is known about them in secondary and planted forests. We surveyed 86,936 trees in secondary forests and 91,294 trees in planted forests in Zhejiang, China, to investigate the distribution patterns and determinants of large trees in these forests. We found a mean density of large trees (DBH ≥ 30 cm) of 15 ± 13 stems ha−1 in secondary forests and 11 ± 9 stems ha−1 in planted forests. Moreover, the mean density of trees with DBH ≥ 60 cm was 0.36 stems ha−1, indicating that large trees are particularly rare in secondary and planted forests. These large trees were primarily occurred in secondary forests that living in high-elevation area with less human exploitation and colder and wetter climates, and in planted forests with higher species richness and lower tree density. In addition, the density of large trees in these forests significantly increased with tree species richness and decreased with increasing tree density. These results indicate that the sparse large trees were the legacy of historical human activities in the studied area, but currently, the development of large trees is still limited by the improper forest structure characterized by low species diversity and high tree density. To better conserve large trees, there is an urgent need for enhanced conservation policies for secondary forests, such as establishing forest parks for forests with large trees, and implementing near-natural forest management practices for planted forests, which include planting mixed native tree species and maintaining moderate tree density.