Sample Plots Research Articles

Climate interacts with the trait structure of tree communities to influence forest productivity

Abstract Tree functional diversity can increase forest productivity by enhancing species interactions and providing greater growth stability. However, very few studies have examined the influence of tree community trait structure on survivor growth, recruitment and mortality simultaneously, which are the main drivers of forest population dynamics. Here, we explore the interactions among functional diversity, productivity and climate to investigate the role of the trait structure of communities on forest productivity and to determine under what circumstances functional diversity should be promoted to ensure forest adaptive capacity under future climate. Using random‐forest modelling and a network of permanent sample plots covering a broad gradient of climatic conditions, we isolated the effects of functional diversity—described as the distribution of trait values in a community—and climate variables on net forest productivity (NFP), survivor growth, recruitment and mortality. Based on our findings, community‐level trait structure affects forest productivity in different ways. NFP was influenced by three traits from three different plant strategy dimensions, whereas survivor growth and recruitment were strongly correlated with leaf and resource acquisition traits, and tree mortality with a mix of traits reflecting various plant strategies. We also observed climate interactions with the functional trait structure of tree communities. For instance, we observed an interaction between drought tolerance and mean annual temperature: At low temperatures, NFP biomass accumulation increased with the value of the drought tolerance trait; however, at higher temperatures, the opposite pattern was observed. However, we found contrasting patterns of population response to climate variability, depending on their functional diversity. Greater functional diversity does not necessarily increase biomass accumulation under different climatic conditions. Synthesis. As all components of forest productivity contribute to NFP, studies on forest productivity should consider not only survivor growth but also recruitment and mortality. Each component responds differently in terms of biomass changes in climatic variation, according to the trait structure of tree communities. This study provides a framework to identify the trait structure that should be targeted under different climate scenarios to anticipate change and help strengthen forest response capacity to climate change.

Spatial Prediction of Diameter Distributions for the Alpine Protection Forests in Ebensee, Austria, Using ALS/PLS and Spatial Distributional Regression Models

A novel Bayesian spatial distributional regression model is presented to predict forest structural diversity in terms of the distributions of the stem diameter at breast height (DBH) in the protection forests in Ebensee, Austria. The distributional regression approach overcomes the limitations and uncertainties of traditional regression modeling, in which the conditional mean of the response is regressed against explanatory variables. The distributional regression addresses the complete conditional response distribution, instead. In total 36,338 sample trees were measured via a handheld mobile personal laser scanning system (PLS) on 273 sample plots each having a 20 m radius. Recent airborne laser scanning (ALS) data were used to derive regression covariates from the normalized digital vegetation height model (DVHM) and the digital terrain model (DTM). Candidate models were constructed that differed in their linear predictors of the two gamma distribution parameters. In the distributional regression approach, covariates can enter the model in a flexible form, such as via nonlinear smooth curves, cyclic smooths, or spatial effects. Supported by Bayesian diagnostics DIC and WAIC, nonlinear smoothing splines outperformed linear parametric slope coefficients, and the best implementation of spatial structured effects was achieved by a Gaussian process smooth. Model fitting and posterior parameter inference was achieved by using full Bayesian methodology and MCMC sampling algorithms implemented in the R-package BAMLSS. With BAMLSS, spatial interval predictions of the DBH distribution at any new geo-locations were enabled via straightforward access to the posterior predictive distributions of the model terms and by offering simple plug-in solutions for new covariate values. A cross-validation analysis validated the robustness of the proposed method’s parameter estimation and out-of-sample prediction. Spatial predictions of stem count proportions per DBH classes revealed that regeneration of smaller trees was lacking in certain areas of the protection forest landscape. Therefore, the intensity of final felling needs to be increased to reduce shading from the dense, overmature shelter trees and to promote sunlight for the young regeneration trees.

Keanekaragaman Serangga Musuh Alami Pada Pertanaman Kakao Di Kecamatan Leihitu Kabupaten Maluku Tengah

The research aims to identify the types of natural enemy insects of cocoa fruit borer pests and analyze the diversity index and calculate the index of dominance of natural enemy insects in cocoa cultivation (Theobroma cacao L.) in Mamala and Hila Villages, Leihitu district, Central Maluku regency. Random sample method with sampling techniques with certain criteria (cocoa plants that have borne fruit). Each village was taken 3 farmers then each farmer determined 3 sample plots with a plot size of 20 x 20 m2 then determined 3 sample crops. Insect catching uses 4 traps namely sweep net, pit fall traps, yellow traps, and leaf traps. The dataanalyzed in the form of qualitative data is the type of insects found and quantitative data, namely the diversity index and the dominance index. Based on the results of the study obtained the results of the study obtained 5 orders and 9 Families that have the potential as natural enemies, namely the Order Hymenoptera, Dermaptera, Ortopthera, Diptera and Ordo Coleoptera while the Families found are the Family Formicidae, Chelisochidae, Carcinophoridae, Forficulidae, Mantidae, Dolichopodidae, Stratiomydae, Coccinelidae, and the Family Chrysomelidae. The natural enemy diversity index in mamala village cocoa plantation (0.0771) and Hila (0.0884) is low and the dominance index in Mamala (0.97600557) and Hila (0.97362747) is high

Age changes of birch stands on the permanent sample plots of the Kologrivsky Forest Nature Reserve

The actual patterns of forest stand dynamics can be revealed only if observational data on permanent sample plots are available. The aim of the study is to identify the characteristics of the age changes of stand indicators in permanent birch sampleplots of the Kologrivsky Forest Nature Reserve (Kostroma Region). The object of the study was forest stands in birch stands located in the core of the Kologrivsky Forest Nature Reserve. At present they are represented by mature and over-mature birch stands with the predominance of spruce undergrowth in narrow clearings of 1928 with a width of 100‑150 m. In the permanent sample plots trees were counted according to individual forest elements with the distribution of 4 cm in thickness steps. For 20‑25 trees, heights were measured in the range of varying diameters with further graphical alignment of the values. The stock was calculated using stem volume tables, and the biomass of the stand fractions was calculated using allometric equations. An analysis of the age changes of stand indicators (average height and diameter, number of trees, basal area, stock, biomass fractions, carbon content in biomass) for the period from 1981‑1983 to 2018‑2022 was carried out. In the sample plots considered, the dominant forest element in each age interval is birch. At present, however, the forest stands are approaching the stage of initial decay. Instead of pure birch stands, mixed spruce-linden stands will develop in the future. Comparing the changes of the stand indicators on the plots with a model of the growth of birch stands in the sorrel spruce forest type, it was found that the changes of the average heights of the actual stands are synchronous with the curve obtained from the model, while for other indicators there are significant discrepancies.

Assessment and mapping of soil salinity and groundwater quality in Nijhum Island, Hatiya Upazila, Noakhali

Crop yield is heavily influenced by soil salinity. The objective of the research is to assess the depth wise soil salinity distribution pattern and groundwater quality by measuring physico-chemical parameters in the agricultural land of Nijhum Island, Noakhali, Bangladesh. This study also explicates the spatial variation of soil salinity in the study area. Soil samples were collected from three different depths to estimate the Electrical Conductivity (EC) using a paste of 1:5 soil and distilled water suspension (1:5 weight-to-volume method). Spatial analyst tool of ArcGIS was used and observed that the land of Nijhum Island was affected with severe salinity in almost 85% of sampling plots and 15% of sampling plots were extremely saline. The soil salinity process has been noticed due to seawater intrusion from the Bay of Bengal through the Meghna estuary which threatens crop production and soil fertility, threatening the population's livelihood. This study provides a baseline understanding of soil salinity in Nijhum Island to help decision-makers and smallholder farmers improve their livelihoods. Thus, groundwater depth did not affect soil salinity significantly. The result contradicts most studies that found a strong influence on soil salinity. Because the study area is coastal, most of the salt comes from seawater inundation and capillary rise, which reduces groundwater availability.

Short Communication: Estimation of aboveground biomass and carbon sequestration in KGPAA Mangkunagoro I Grand Forest Park, Central Java, Indonesia

Abstract. Indriyani S, Sari SP, Negari SIT, Prambudi SA, Nur AAI, Kusumaningrum L, Indrawan M, Sunarto, Sugiyarto, Budiharta S, Setyawan AD. 2024. Short Communication: Estimation of aboveground biomass and carbon sequestration in KGPAA Mangkunagoro I Grand Forest Park, Central Java, Indonesia. Biodiversitas 25: 2257-2263. Forests play an important role in climate change mitigation by absorbing carbon dioxide in the atmosphere and store it in the form of biomass. Valuation of carbon storage potential of a forest will provide insight on the importance of preserving it. This study aims to assess plant diversity and estimate aboveground biomass, carbon stocks, carbon dioxide sequestration potential, and the economic value of ecosystem services from carbon sequestration in KGPAA Mangkunagoro I Grand Forest Park, Central Java, Indonesia. We used a non-destructive method (i.e. allometric equation) in estimating aboveground biomass and carbon stock. We established 50 sampling plots to identify and measure trees (DBH >20 cm) and poles (10<DBH<20 cm). The results showed that there were 30 species from 20 families found in the observation plots. The potential aboveground biomass at the tree and pole level was 297.92 tons/ha and 57.22 tons/ha, respectively, while the total carbon stock for tree and pole was 140.02 tons/ha and 26.89 tons/ha, respectively. This was equivalent to carbon dioxide sequestration of 513.88 tons/ha and 98.70 tons/ha for trees and poles. The economic value of environmental services from carbon stock in the whole area of KGPAA Mangkunagoro I Grand Forest Park was estimated at IDR 21,289,040,253. The results of this research show that apart from playing a role in mitigating climate change by absorbing carbon, KGPAA Mangkunagoro I Grand Forest Park also has valuable economic value.

Comparison of Deep and Machine Learning Approaches for Quebec Tree Species Classification Using a Combination of Multispectral and LiDAR Data

Accessing tree species information is required for making appropriate decisions in forest management. 3D photo-interpretation using high spatial resolution aerial imagery is used to provide information on tree species in the province of Quebec. However, the shortage of qualified interpreters and the increasing costs of 3D photo-interpretation have affected the production of the forest inventory. In this study, we employed deep and machine learning models to classify nine tree species (i.e., paper birch, yellow birch, red maple, poplar, black spruce, white spruce, tamarack, jack pine, and balsam fir). We used a combination of spectral and vertical structural information extracted from 30-cm aerial imagery and airborne light detection and ranging (LiDAR) data in a 9 , 100 km 2 forested area in Quebec, Canada. The results indicated that Dense Convolution Network (DenseNet) achieved the best overall accuracy of 78%, outperforming machine learning methods by 5%. In addition, the models’ performance was independently assessed using permanent and temporary ground sample plots, acknowledging the superiority of DenseNet in terms of overall accuracy (73%) in predicting the dominant species. Our results suggest that the combination of aerial imagery and airborne LiDAR data, using deep learning approaches, can be applied to accurately map tree species.

Крупные древесные остатки в коренных и вторичных среднетаежных ельниках

Restoration of the pool of coarse woody debris after disturbances is one of the mechanisms for maintaining the stability of forest biogeocenoses. The studies of coarse woody debris have been carried out in the “Vepssky Forest” Reserve in the Leningrad Region on 8 sample plots established in primary forests (4 sample plots) and in secondary forests of the 1st generation after logging in 1973–1974 (4 sample plots), where the composition and structure of the stand, as well as the site conditions have been identical to those in primary forests. The coarse woody debris has been inventoried on transects. The stocks of coarse woody debris in primary stands have ranged from 104 to 233 m3 ha–1. Windfall and leaning trees have prevailed. The proportion of deadwood in both primary and secondary forests has been low. Clear cutting has significantly changed not only the stock of coarse woody debris, but also its distribution by decay classes and substrate categories. The stocks of coarse woody debris in secondary forests have ranged from 8 to 40 m3 ha–1, and have been mainly represented by stumps. The coarse woody debris of the 4th and 5th decay classes has almost been absent in primary forests, while in secondary forests the proportion of highly decomposed wood remaining after cutting has been about 50 %. The ratio of coarse woody debris and growing stocks has been on average 1:1 and 1:5 in the biogeocenoses of primary and secondary forests, respectively. The annual carbon balance of coarse woody debris (the difference in fluxes due to the loss of growing forest and the decay of coarse woody debris) has ranged from 0,40 to 2,80 t C ha–1 year–1, averaging 1,75 and 0,63 t C ha–1 year–1 in secondary and primary forests, respectively. The positive annual carbon balance in the coarse woody debris is due to the predominance of the rate of the loss of growing forest over the rate of the decay of coarse woody debris in primary forests as a result of wind disturbances and as a result of self-thinning of the stand in secondary forests.

Multi-objective early-stage design optimization of multifamily residential projects

The aim of this study is to find a building form and plan layout that can be used in the early stages of architectural design, where criteria such as daylight, view, sun-hour, sales area, and cost are optimized according to the different expectations of different housing type users. This study proposes a multi-objective early-stage design optimization for a real estate development project based on the NSGA2 genetic algorithm, considering weighted user preferences for different housing types. The framework is implemented using the platforms Rhino and Grasshopper; Wallacei is used for NSGA2, and Viktor.ai is used to deploy the app. Tested on six sample plots, the model was able to find architecturally optimized results that respond to different user expectations. While the model successfully demonstrated responsiveness to parameters, its focus on Pareto-optimal solutions limited the diversity of unit mixes generated. The model has been tested by professionals on a sample plot and is found to be important for architects and investors to generate ideas at an early stage of architectural design.

Biodiversity and Soil Properties of Saranda Sal Forest: Implications for Conservation and Management

Aims: This study is significant as it elucidates the relationship between floristic composition and soil nutrient availability of India's Saranda Sal (Shorea robusta Gaertn.) forest. Study Design: Vegetation sampling was done following a grid of 5 km x 5 km, and soil sampling was done in each sampling plot using a Z-pattern. Place and Duration of Study: The study was conducted in Saranda Forest of West Singhbhum district, Jharkhand, Eastern India, during 2021-2022 Methodology: Sampling has been designed as grid methods (5 km X 5 km) following a forest survey of India, and vegetation sampling was done by quadrat method. The soil samples were collected from three depths (i.e., surface: 0–30 cm, sub-surface: 30–60 cm and inner: 60-90 cm from each selected site. ANOVAs were used to compare the chemical properties of soil samples from various forests. Pearson correlation analyses to examine the effects of climatic variables on chemical properties of the soil of selected Sal forest as well as their relation with plant diversity. Results: A comprehensive analysis of 5432 vascular plants from 65 species and 34 families was conducted across the 17 sites. Fabaceae is the most dominant family with 07 species. The study also examined soil chemical parameters and micronutrients in different sites and established their relations with vegetation dynamics. Notably, tree density showed a significant positive correlation with soil pH (r=0.59, p<0.05), but a significant negative correlation with Shannon diversity (H') (r=-0.53, p<0.05). Similarly, there was a significant positive correlation of Organic carbon with copper (r=0.59, p<0.05) and iron (r=0.61, p<0.00); however, there was a significant negative correlation with Available Phosphorus (r=-0.52, p<0.05). Conclusion: Research findings underscore the importance of soil nutrients in promoting forest health and growth. Importantly, they can guide the formulation of practical and effective soil-forest management strategies for S. robusta and its associated forests, directly benefiting the forestry and environmental science community.

Модель формирования березово-еловых насаждений в условиях южной тайги Русской равнины

The results of 30 years or research on the formation and growth of birch forests from the subcanopy spruce population in the conditions of the Russian Plain southern taiga are presented. The studies have been carried out on 19 permanent sample plots (PSPs). They were laid in regionally representative sorrel-blueberry birch forests aged 15–115 years with high density and productivity. The birch plantations on the PSPs belong to the same natural age series. The majority of spruce trees appeared under the canopy of birch forests up to 40 years old. As the birch trees aged, the age structure of spruce trees changed from even-aged to conditionally even-aged. Based on the study of the dynamics of the number of trees, the vertical structure of stands, the growing stock and the total stem wood stock, the age stages of development of the spruce population in the birch forests up to 120 years old have been determined. They are regeneration, young growth, polewood and maturation. The end of the maturation stage occurs at the birch tree age of 120 years. At the same time, the average spruce tree age in the first and second layers is 90 years. The duration of the stages varies from 10 years (the young growth stage) to 40 years (the regeneration and maturation stages). To determine the time of onset and end of other age stages of the subcanopy spruce population, the studies in birch forests older than 120 years are needed. The work performed has made it possible to propose a model of the joint formation of birch forests and the subcanopy spruce population, in which the main taxation characteristics are given for all elements of the stand: age, number of trees, the sum of the cross-section of the stems, the stock of the growing and dead wood, as well as total productivity. According to the model, by the age of a birch of 120 years, the total productivity of modal plantations will be about 950 m3/ha and the growing stock of the stand will be about 490 m3/ha. The share of spruce wood will be 28 % or about 150 m3/ha. After the birch forest decay, a low-density spruce forest of the 3rd quality class can form in its place.

Characterizing Canopy Structure Variability in Amazonian Secondary Successions with Full-Waveform Airborne LiDAR

Full-waveform LiDAR (FWF) offers a promising advantage over other technologies to represent the vertical canopy structure of secondary successions in the Amazon region, as the waveform encapsulates the properties of all elements intercepting the emitted beam. In this study, we investigated modifications in the vertical structure of the Amazonian secondary successions across the vegetation gradient from early to advanced stages of vegetation regrowth. The analysis was performed over two distinct climatic regions (Drier and Wetter), designated using the Maximum Cumulative Water Deficit (MCWD). The study area was covered by 309 sample plots distributed along 25 LiDAR transects. The plots were grouped into three successional stages (early—SS1; intermediate—SS2; advanced—SS3). Mature Forest (MF) was used as a reference of comparison. A total of 14 FWF LiDAR metrics from four categories of analysis (Height, Peaks, Understory and Gaussian Decomposition) were extracted using the Waveform LiDAR for Forestry eXtraction (WoLFeX) software (v1.1.1). In addition to examining the variation in these metrics across different successional stages, we calculated their Relative Recovery (RR) with vegetation regrowth, and evaluated their ability to discriminate successional stages using Random Forest (RF). The results showed significant differences in FWF metrics across the successional stages, and within and between sample plots and regions. The Drier region generally exhibited more pronounced differences between successional stages and lower FWF metric values compared to the Wetter region, mainly in the category of height, peaks, and Gaussian decomposition. Furthermore, the Drier region displayed a lower relative recovery of metrics in the early years of succession, compared to the areas of MF, eventually reaching rates akin to those of the Wetter region as succession progressed. Canopy height metrics such as Waveform distance (WD), and Gaussian Decomposition metrics such as Bottom of canopy (BC), Bottom of canopy distance (BCD) and Canopy distance (CD), related to the height of the lower forest stratum, were the most important attributes in discriminating successional stages in both analyzed regions. However, the Drier region exhibited superior discrimination between successional stages, achieving a weighted F1-score of 0.80 compared to 0.73 in the Wetter region. When comparing the metrics from SS in different stages to MF, our findings underscore that secondary forests achieve substantial relative recovery of FWF metrics within the initial 10 years after land abandonment. Regions with potentially slower relative recovery (e.g., Drier regions) may require longer-term planning to ensure success in providing full potential ecosystem services in the Amazon.

An Investigation of the Magnitude of the Role of Different Plant Species in Grassland Communities on Species Diversity, China.

In this study, we selected four grassland plots in Altai forest area and used the field survey method of "two-valued occurrence" to obtain the occurrence data of each plant species in the plots so as to calculate the species diversity index value of the community as a whole and the species diversity index value of each plant species not present in the community and to make use of the difference between these two diversity indices to determine the role of each plant species in the overall species diversity of the community. The difference between these two diversity indices was used to investigate the role of each plant species in the overall species diversity of the community. The results show the following: (1) In the grassland of the Altai forest area in Xinjiang, Asteraceae, Poaceae, Fabaceae, Polygonaceae, and Rosaceae are the dominant families, among which the genera Puccinellia Parl, Taraxacum, Pharbitis, Lactuca, Geranium, and Alchemilla are the dominant genera. (2) The plant species with the greatest contribution to species diversity in the four grassland samples was not the first dominant species of the community, but rather the plant species whose dominance was in the second to sixth position. (3) The first dominant species was overwhelmingly dominant in the four sample plots, and it served to increase the overall diversity of the community. (4) The overall trend in the size of the role of species in diversity is unimodal, i.e., logarithmically increasing to a maximum as species dominance decreases and then exponentially or linearly decreasing and eventually converging to zero. The synthesis showed that it was not the first dominant species that played the largest role in species diversity in the different grassland communities and that the overwhelmingly dominant species reduced the species diversity of the community.

The effect of pre- and post-processing techniques on tree detection in young forest stands from images of snow cover using YOLO neural networks

A neural network model for individual tree detection was developed based on the YOLOv4 architecture, which underwent additional preprocessing and postprocessing steps. The preprocessing step involved expanding the dataset by randomly cutting fragments from images, calculating anchor frame sizes using the K-means clustering algorithm, and discarding anchor frames that were too small a priori. The existing post-processing block of the YOLO architecture was modified by giving more weight to false positives in the error function and using the non-maximum suppression algorithm. Baseline neural networks from the YOLOv4 and YOLOv5 architectures, each in two versions (pre-trained and not pre-trained on the MS COCO dataset), were used for comparison without any additional modifications. In the overgrown experimental field, multi-season aerial copter surveys and ground counts were conducted on several sample plots to gather data. Comparison of multi-season aerial photographs with ground-count data showed that the best images in terms of the percentage of visually identifiable trees were those taken during the snowy season and when there was no foliage. Using these images and some additional images, we manually created a dataset on which we trained and tested neural network models. The model we developed showed significantly better results (2 to 10 times better) on the mAP 0.5 metric compared to the alternatives we considered.

Integration of field measurements with unmanned aerial vehicle to predict forest inventory metrics at tree and stand scales in natural pure Crimean pine forests

ABSTRACT Inventorying forest ecosystems is an essential part of forest management planning. However, it is quite costly and time-consuming, particularly for larger areas. Recently, significant developments have been made in unmanned aerial vehicle (UAV) technology to improve the cost and time efficiency in forest inventory. Therefore, UAV images have become one of the inventory tools that produces data with high spatial resolution in determining forest resources. This study aims to investigate the contribution of UAV data to forest inventory in a case study area with a total of 30 sample plots located in pure and natural Crimean pine (Pinus nigra J.F. Arnold ssp. pallasiana (Lamb.) Holmboe) stands in the Black Sea backward region of Türkiye. Total tree height (h) and stem volume (v) were recorded at individual tree level (n = 367), and the number of trees (N), mean height (hmean), top height (htop), stand basal area (BA) and stand volume (V) were calculated at sample plot level (n = 30) from both the field and UAV-based data. Pearson’s correlation coefficients (r) for h and v were 0.96 and 0.72, respectively, the highest correlation at the sample plot level was observed for the hmean - htop (r = 0.96), while the lowest correlation was found for BA (r = 0.54). The suitability of the observation and prediction values was assessed using a t-test at both individual tree and sample plot levels. According to the t-test results, the observation and prediction values for h, v, hmean, htop, BA and V metrics were found to be compatible (p > 0.05), but not for N (p < 0.05). Overall results indicated that UAV technology has a potential to be used in forest inventory and can contribute to the determination of individual tree and stand metrics. Thereby, it saves cost and time in forest inventory studies and helps monitoring the dynamic structure of the forest ecosystem with an effective approach in forest inventory.

Square-grid sampling to address the vegetation patterns of declined Mediterranean forest ecosystems

Palo Laziale Wood is a relatively small biotope (129 ha) situated along the coast of the Metropolitan Area of Rome, Lazio region, Italy. Despite being one of the region's remaining patches of an ancient oak floodplain broad-leaved forest, it conserves numerous priority habitats and species of high conservation interest. The vegetation consists mainly of Turkey oak stands with small temporary ponds and flooded meadows. The forest underwent a dieback in 2003, triggered by a significantly hot and dry summer. In 2018, an ecological restoration project (LIFE PRIMED LIFE17 NAT/GR/000511) was initiated to restore Palo Laziale’s ecosystems. This paper presents the methodological approach employed to assess the vegetation ecology of a degraded forest ecosystem. Such an investigation was a key component of the Adaptative Restoration Plan of the Project. It provided the baseline necessary for designing and calibrating the planned direct conservation actions on the target habitat types (91M0: Pannonian-Balkanic turkey oak-sessile oak forests, 3170*: Mediterranean temporary ponds, 5230*: Arborescent matorral with Laurus nobilis, etc.) and establishing reference values to enable long-term monitoring. Plant taxa comparison from 1 ha square-grid sampling and multivariate analyses were carried out to group species and identify environmental and Ellenberg-based drivers. Six ecologically distinct units were found, eventually confirming the distinctive ecological heterogeneity of Mediterranean ecosystems. Amongst these, the hygrophilous vegetation has resulted to be the one mainly affected by the dieback outbreak. Due to the high heterogeneity, introduced by the massive tree mortality, the method of regular 1 ha squares turned out to be a reliable alternative to random vegetation sampling plots (e.g., phytosociological relevés) to disentangle ecological patterns of fragmented and disturbed habitats.

Pattern of plant biomass and carbon stock along different elevational forests in eastern Nepal

The primary aim of this investigation was to determine the biomass and carbon stock distribution pattern among different forest stands of diverse elevations in the Morang district of East Nepal. It is noteworthy to estimate carbon stock and biomass of relatively least underexplored forests in east Nepal. The data for estimating the biomass and carbon stocks of the five different forest sites, viz. Bhaunne, Raja-Rani, Murchungi, Adheri, and Sagma located between 100-1300m above the mean sea level, were acquired through the measurement of inventory plots selected randomly. Altogether, 50 sample plots were established within five forest stands located on different elevational zone; within each forest site, 10 sample plots of 20m × 20m size, were laid out for the measurement of trees. In the case of shrubs and herbs, nested plots of 5m × 5m and 1m ×1m, respectively were established. Calculation of the biomass of trees and shrubs was facilitated through the application of an allometric equation, while the biomass of herbs was determined by the harvest method. The carbon concentration in the plant materials was estimated using ash content method. The comprehensive analysis of the stand biomass in the Bhaunne, Raja-Rani, Murchungi, Adheri, and Sagma forest sites were: 815.86 Mg ha-1, 414.19 Mg ha-1, 606.81 Mg ha-1, 519.20 Mg ha-1, and 299.96 Mg ha-1, respectively, with minimum at the Sagma site (high-altitude forest) and maximum at the Bhaunne site (low-altitude forest). As per the variation in stand biomass, the carbon stocks in the forest sites also showed the same trend, but the values ranged from 140.19 Mg C ha-1 to 333.63 Mg C ha-1, with the minimum in the Sagma site and the maximum in the Bhaunne site. The application of the Friedman Test revealed statistically significant variation in the tree biomass between the Murchungi and Sagma sites and also in the shrub biomass between the Adheri and Sagma sites. Similarly, noteworthy variations were observed in the herb biomass of the Bhaunne, Raja-Rani, Murchungi, and Adheri sites as compared to that of the Sagma site. The present study contributes to the understanding of forest ecosystems in context to carbon management.

New Zealand's planted forests–Carbon stocks and yield in fast growing exotic tree plantations of the Southern Hemisphere

Every year New Zealand measures 1/5th of the permanent sample plots of the National Planted Forest Inventory administered by the Ministry for the Environment (MFE). This means that all plots are measured once during a five-year cycle to provide the full inventory, while each measurement year in its own right provides a random nationally-representative sample of New Zealand's plantation forests.Land-use change and planted forest area are highly dynamic in New Zealand compared to forests in many northern hemisphere countries and every year new plots are added to the inventory due to new afforestation areas. Over time, old plot data representing stands that have been recently harvested will be replaced with measurements of the new forests growing on these sites or will drop out altogether due to improvements in mapping or deforestation. This means that estimates from a single year's plot measurements can give different results from the previous year due to the changes that the plantation forest estate experiences from year to year.In this paper we analyse results for the latest measurement cycle (Panels 2016 - 2020) and identify any issues or trends in carbon stocks. We use all panel data plus data from previous periodic forest inventories to generate a fully representative yield table for all plantation forests in the sub-categories of Kyoto Protocol-compliant afforestation and other planted forests (“non-Kyoto”). Furthermore, we use an imputation approach to provide a statistically accurate representation of stock changes over time. The yield tables generated are used in MFE's Land Use Carbon Analysis System (LUCAS) to generate estimates of carbon stocks and stock changes for international reporting.The estimated mean carbon sequestration rate over the typical rotation for New Zealand's dominant plantation species is higher than the default range provided by the IPCC (Intergovernmental Panel on Climate Change). Predicted carbon sequestration in non-Kyoto planted forests (10.5 tC ha−1 yr−1) exceeds the estimated carbon sequestration in Kyoto-compliant planted forests (9.8 tC ha−1 yr−1) with both at the upper range of estimates for temperate plantation forests (IPCC). Current mean carbon stocks are significantly larger in Kyoto-compliant plantation forests (169 tC ha−1) than in non-Kyoto planted forests (108 tC ha−1) across New Zealand, largely because they are on average five years older. Estimated Mean annual increment, m3 ha−1 yr−1 expressed as a comparable index (“300Index”) indicates that Kyoto-compliant forest land has slightly higher productivity than non-Kyoto planted forests, but because the latter are composed of younger stands, greater carbon sequestration rates but lower current stocks are estimated. Plots with slower growing tree species such as Douglas-fir, cypresses and coastal redwood are now an increasing part of the Kyoto-compliant estate, reducing the overall sequestration rates for this forest type.

Population structure and regeneration of Tsuga dumosa and Abies spectabilis across altitudinal gradient in Rasuwa district, central Nepal

In Nepal, Tsuga dumosa thrives well between 2,100-3,600 m altitudes above the mean sea level, mostly in the temperate region while Abies spectabilis occurs between 3,000-4,200 m altitude above the mean sea level in the sub-alpine region. Research on mature T. dumosa-A. spectabilis forests in Nepal is limited. This study aimed to analyze the population structure and regeneration status of T. dumosa and A. spectabilis in the high-altitude mixed forests of Rasuwa, central Nepal. Conducted in 2023, a total of 61 concentric circular sample plots were laid out following a stratified systematic sampling method. The population-structure-curve displayed a consistent upward trend showing abnormality. The abnormal population structure with a lack of young trees and poor regeneration status in both species points towards potential threats like grazing and wildfires. The highest DBH class ranged from 90 cm to 120 cm DBH for T. dumosa while from 60 cm to 90 cm DBH for A. spectabilis. The study found that the seedling condition of T. dumosa species was ‘fair’ in the lower (2800-3100 m) and middle stratum (3100-3400 m), while it was ‘poor’ in the upper-elevation stratum (3400-3600 m). On the other hand, the seedling condition of A. spectabilis was found to be ‘poor’ in all the three elevation strata. Furthermore, the sapling condition of both the species were found to be ‘poor’. Therefore, the studied forest requires sustainable management along with a comprehensive strategy combining controlled grazing, zonation, monitoring, community engagement, regulation enforcement, restoration, ongoing research, and public awareness.

Weed Biology and Ecology Studies: Diversity, Dominance and Prediction of Yield Loss of Corn (Zea mays L.) Due to Broadleaf Weeds Competition in Dryland

The aim of the research was to determine the diversity, distribution, dominance, and yield loss of corn due to competition from broadleaf weeds. Descriptive research method with direct survey techniques of research objects. Data were collected using 10 sample plots measuring 1 m2, which were placed using random sampling. Observation parameters include the population and dry biomass weight of weeds and corn. The data was analyzed descriptively and comparatively by calculating density, frequency, and dominance to obtain an important value index and summation diminance ratio (SDR). The results of the research showed that 14 species of broad-leaved weeds were found with high species diversity, distribution, dominance, and abundance indices. The competitiveness and relative weighted dominance of six weed species, namely Amaranthus spinusus, Amaranthus gracilis, Synedrella nodiliflora, Acalypha indica, Ageratum conyzoides, and Alternanthera philoxeroides, are higher than those of other weed species, causing corn yield losses of 5.56%–21.90%. Therefore, these six weed species must be controlled.