Sample Plots Research Articles

A Study of the Effects of Wetland Degradation on Soil-Microbial-Extracellular Enzyme Carbon, Nitrogen, and Phosphorus and Their Ecological Stoichiometry

Due to the unique geographic location of A’er Xiang, there is a natural landscape where sandy land and lake-marsh wetlands coexist. However, the wetland degradation caused by the disturbance of anthropogenic activities has led to the change in land use. In this study, the spatial-temporal substitution method was used to select five sample plots: the original wetland converted to forest land for reuse area of five years and ten years; the original wetland converted to cropland for reuse area of five years and ten years; and the native wetland. It aims to investigate the variations in carbon, nitrogen, and phosphorus and their stoichiometric characteristics of soil-microorganisms-extracellular enzymes before and after reuse, and to analyze potential interactions among these elements. The results indicated that following wetlands degradation, changes in land use for five years did not significantly affect the content of soil organic carbon (TOC), total nitrogen (TN), or total phosphorus (TP). However, after ten years, both TOC and TN, except for TP, decreased significantly. Microbial biomass carbon (MBC) and microbial biomass nitrogen (MBN) contents in cropland were consistently higher than those in WL, showing a trend of first increasing and then decreasing with longer conversion periods. In contrast, forest land values were lower than in WL and increased as the conversion period lengthened. The microbial biomass phosphorus (MBP) content was ranked across the five sample sites as follows: 10 CL > 5 CL > 5 FL > 10 FL > WL. β-1,4-glucosidase (BG) activity was significantly increased after conversion to forest land and significantly decreased after conversion to cropland. β-1,4-N-glucosidase (NAG) and L-leucine aminopeptidase (LAP) activities were ranked as follows among the five sites: 5 FL > WL > 5 CL > 10 FL > 10 CL. Phosphatase (PHOS) activity showed no significant changes post-conversion, though it was consistently lower compared to WL.

A complex impedance spectroscopy study on PVDF/PANI/CoFe2O4 composites

Abstract The synthesis of polymer composites consisting of conducting polyaniline (PANI), cobalt ferrite (CoFe2O4), and poly(vinylidene fluoride) (PVDF) is achieved by the polymerization technique. A nanocomposite consisting of polyaniline (PANI) and CoFe2O4 is prepared using the interfacial chemical oxidation polymerization technique, resulting in a composition containing 50 wt.% of CoFe2O4. Nanoparticles of cobalt ferrite (CoFe2O4) were synthesized by using the sol–gel auto-combustion process. The PANI/CoFe2O4 (1:1) nanocomposites with weight percentages of 5, 10, 15, and 20 are incorporated into the PVDF matrix as fillers in order to alter the morphology and AC electrical conductivity of the PVDF/PANI/CoFe2O4 composite materials. The structural and morphological properties were studied using characterization techniques such as X-ray diffraction, Fourier transform infrared spectroscopy, and field emission scanning electron microscopy. At room temperature, impedance spectroscopy, dielectric measurements, and AC electrical conductivity were carried out in the frequency range of 40 Hz to 5 MHz. The dielectric permittivity has been shown to decline exponentially in low-frequency zones before remaining almost constant up to 5 MHz. When the frequency is increased up to 1 MHz, the AC conductivity of all composites remains constant; after that, it increases as the frequency increases. It was found that PANI-CoFe2O4 nanocomposites modified the overall charge transport mechanism by modulating the core grain and grain boundary. The experimental Nyquist plot for different composite samples was fitted by an appropriate RC electrical model to evaluate relaxation time. The relaxation time τ 1 (R 1 C 1) and τ 2 (R 2 C 2) were both reduced after the reinforcement of PANI-CoFe2O4 nanocomposites.

Assessing Forest Resources with Terrestrial and Backpack LiDAR: A Case Study on Leaf-On and Leaf-Off Conditions in Gari Mountain, Hongcheon, Republic of Korea

In Republic of Korea, the digital transformation of forest data has emerged as a critical priority at the governmental level. To support this effort, numerous case studies have been conducted to collect and analyze forest data. This study evaluated the accuracy of forest resource assessment methods using terrestrial laser scanning (TLS) and backpack personal laser scanning (BPLS) under Leaf-on and Leaf-off conditions in the Gari Mountain Forest Management Complex, Hongcheon, Republic of Korea. The research was conducted across six sample plots representing low, medium, and high stand densities, dominated by Larix kaempferi and Pinus koraiensis. Conventional field survey methods and LiDAR technologies were used to compare key forest attributes such as tree height and volume. The results revealed that Leaf-off LiDAR data exhibited higher accuracy in capturing tree height and canopy structures, particularly in high-density plots. In contrast, during the Leaf-on season, measurements of understory vegetation and lower canopy were hindered by foliage obstruction, reducing precision. Seasonal differences significantly impacted LiDAR measurement accuracy, with Leaf-off data providing a clearer and more reliable representation of forest structures. This study underscores the necessity of considering seasonal conditions to improve the accuracy of LiDAR-derived metrics. It offers valuable insights for enhancing forest inventory practices and advancing the application of remote sensing technologies in forest management.

The Influence of the Spatial Co-Registration Error on the Estimation of Growing Stock Volume Based on Airborne Laser Scanning Metrics

Remote sensing (RS)-based forest inventories are becoming increasingly common in forest management. However, practical applications often require subsequent optimisation steps. One of the most popular RS-based forest inventory methods is the two-phase inventory with regression estimator, commonly referred to as the area-based approach (ABA). There are many sources of variation that contribute to the overall performance of this method. One of them, which is related to the core aspect of this method, is the spatial co-registration error between ground measurements and RS data. This error arises mainly from the imperfection of the methods for positioning the sample plots under the forest canopy. In this study, we investigated how this positioning accuracy affects the area-based growing stock volume (GSV) estimation under different forest conditions and sample plot radii. In order to analyse this relationship, an artificial co-registration error was induced in a series of simulations and various scenarios. The results showed that there were minimal differences in ABA inventory performance for displacements below 4 m for all stratification groups except for deciduous sites, where sub-metre plot positioning accuracy was justified, as site- and terrain-related factors had some influence on GSV estimation error (r up to 0.4). On the other hand, denser canopy and spatially homogeneous stands mitigated the negative aspects of weaker GNSS positioning capabilities under broadleaved forest types. In the case of RMSE, the results for plots smaller than 400 m2 were visibly inferior. The BIAS behaviour was less strict in this regard. Knowledge of the actual positioning accuracy as well as the co-registration threshold required for a particular stand type could help manage and optimise fieldwork, as well as better distinguish sources of statistical uncertainty.

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 box sizes using the K-means clustering algorithm, and discarding anchor boxes 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.

МОНИТОРИНГ БИОРАЗНООБРАЗИЯ ЕЛЬНИКОВ ЕВРОПЕЙСКОЙ ЧАСТИ РОССИИ ПОСЛЕ ПРИРОДНЫХ И АНТРОПОГЕННЫХ «КАТАСТРОФ»

We analyzed the main trends of the change in the species richness of plant communities after catastrophic natural (catastrophicwindthrow, fires, large bark beetle outbreaks) and anthropogenic (clear felling) disturbances. The main factor determining the increase in species richness was the intensity of ecosystem disturbance after ca- tastrophes. The greatest disturbance was caused by clear-cut logging, when all timber was removed from the felling area by tractors. Fires also could totally destroy stands and all components of the forest ecosystem. Large- scale wind damage affected only the trees of the upper layer, while all other components of the plant communities were slightly damaged. The bark beetle outbreaks cause the death only of upper layer spruce trees, while the whole forest ecosystem remains unchanged. The degree of disturbance of stands, undergrowth, herb-dwarf scrubs layer, moss cover, litter and soil during the dieback of spruce forests as a result of ‘catastrophes’ determines the degree of vegetation recovery in disturbed areas. We studied the spruce forest in the zone of coniferous-broadleaved forests of the Moscow region, affected by an outbreak of typograph bark beetle.Dynamics of understory plant species richness was fundamentally different after stand salvage logging following beetle outbreaks and in unlogged standswith preserved deadwood. Monitor- ing observations of plant communities during 10 years on permanent sample plots revealed the main feature of the plant species succession after clear felling. For the first six years succession passed through the herb and the shrub stages with a sharp increase in species and structural diversity of plant communities. Only in the forest stage the species composition characteristic of the forest was restored. In the deadwood stand the forest communities was preserved and no differences in the dynamics of species richness of the original undisturbed forest were detected. Only dominance of species in the stand and herb-shrub layer changed. If man don't clear cut dead and wind- damaged areas of spruce forests, it will result in development of mixed forests with increased resistance to pests and forest diseases. Monocultures of spruce forests may be replaced by mixed forests, increasing the diversity of forests.

The potential of weeds in arable fields to support pollinator assemblages

AbstractTo what extent are weeds on arable land useful to pollinators in an arable‐dominated landscape? We sampled the weed flora in fields under conventional or organic farming in SE Sweden. More specifically, we noted the frequency of flowering among weeds that scored high on a pollinator index, henceforth ‘pollinator‐friendly weeds’. Furthermore, we sampled pollinating insects within cereal crops using transect walks and colour pan traps. As expected, weeds were ubiquitous and occurred in most sampling plots (0.25 m2). In the pan traps, more than 100 species of pollinators were caught, including 26 Syrphidae, 19 social bees, 37 solitary bees and 22 other Hymenoptera. In the transects walks, the probability of encountering bees increased with flowering of pollinator‐friendly weeds, and there was a similar but weaker pattern among hoverflies. Organically grown fields differed from conventional ones by having more pollinator‐friendly weeds, more flowering of such weeds, and more bees. There was also a tendency for hoverflies and other Hymenoptera to be more abundant in organic fields. Three conclusions emerged. First, pollinator‐friendly weeds made up one third to almost half of the weed occurrences recorded. Second, substantial numbers of pollinators searched for flowers within arable fields, and some increased with the abundance of flowering of pollinator‐friendly weeds. Third, flowering pollinator‐friendly weeds and some pollinators were more abundant in organic fields than in conventional ones. Overall, we showed that weeds on arable land are a potential and sought after resource among pollinators, and that even conventionally grown crops should be considered a potential habitat for bees.

Soil Organic Carbon in Three Selected Agroforestry System at Temperate Zone (2000-2800m) of North Western Himalaya, Uttarakhand, India

Climate change is largely driven by the increased concentration of greenhouse gases like carbon dioxide (CO₂) in the atmosphere. While halting climate change entirely may not be feasible at this point, mitigating its effects is crucial, and agroforestry is one of the key strategies that can help in this regard and to mitigate some of its consequences by reducing CO₂ in the atmosphere and promoting sustainable land management. Therefore, the objective of this study was to collate and synthesize the existing information on the soil organic carbon with three agroforestry system (viz.Agrihortisystem, Agrihortisilviculture system and Agrisilviculture system ) at temperate region in North Western Himalaya , Uttarakhand, India.The present study was carried out durung the year 2016-2018.Stratified random sampling techniques were used for this study. Ten sample plots (100m2) were randomly laid out in each agroforestry system in temperate zone (2000-2800m) of each developmental block. Soil organic carbon was determined at three different depth 0-10, 10-20 and 20-30 cm. Soil organic carbon was determined using the Walkley and Black method. Weighing bottle method were used for determining bulk density. The results revealed considerable that SOC of agroforestry systems goes along with the following order: Agrihortisystem (45.72 MgCha-1) >Agrihortisilviculture system (24.99 MgC ha-1) >Agrisilvi culture system (23.79 MgC ha-1) The study concluded that the agrihorticulture system at higher altitudes (2000-2800 m) had a significantly greater soil organic carbon pool. It was suggested that, if necessary, converting agricultural fields to agrihorticulture systems in the Northwestern Himalayas could enhance soil carbon sequestration.

Predicting Diameter Distributions in Mixed Forests in Southern Mexico

Understanding the diameter structure of a stand is crucial for making informed decisions regarding silviculture and forest management. This is achieved by collecting forest inventory data and applying them to probability density functions. The objective of this study was to identify the optimal probability density function for describing the diameter distribution in a mixed temperate forest in Oaxaca, Mexico. Circular sampling plots of 1000 m² were established, and the diameter at breast height (DBH) and total height of 16,863 trees of the 25 most important timber species were measured. Four probability density functions (Weibull 2P, Log-Normal, Log-Logistic, and Burr) were fitted by using maximum likelihood approach for different silvicultural treatments. The predictive ability of the functions was evaluated using bias, root mean square error, Kolmogorov-Smirnov statistic and graphical analyses. In terms of accuracy and parsimony, the Burr function demonstrated the best performance, followed by the Log-Normal and Log-Logistic functions. Consequently, the Burr function is recommended for modeling the diameter distribution of stands under different treatments in the study site.

Species Diversity and Food Potential of Orangutans in Genting Tanah Village Forest

The aim of the research is to determine the diversity of vegetation in the forests of Genting Tanah Village which has the potential to feed orangutans (Pongo pygmaeus morio). The research method is an inventory of sample plots. Data were analyzed quantitatively by calculating the Relative Density, Relative Frequency, Relative Dominance and Diversity Index values. The choice of orangutan food is determined by comparing it based on literature regarding orangutan food. The research identified 67 types of vegetation belonging to 44 families. At tree level there are 20 types of vegetation, with Shorea teysmanniana Dyer being the most important with an Importance Value Index of 78.06%. At pole level there are 32 species, with Combretocarpus rotundatus Miq. has the highest Important Value Index of 74.43%. Genting Tanah village forest also has 40 species of saplings and 41 species of undergrowth. Mallotus muticus Mull. has the highest Importance Value Index of 55.78% in the sapling group, while Hypolytrum nemorum Vahl has the highest Importance Value Index of 21.24% in the seedling and undergrowth group. The species diversity index at the seedling level of 3.24 is considered high, while the species richness index is low at the tree level and moderate at the pole level. Based on the 67 species identified, there are 34 species that have the potential to be a source of orangutan’s food.

Pattern and driver of Rosa roxburghii Tratt suitability and its effect on ecological services in karst desertification control areas

The eco-industry is an effective path to achieve ecological and economic sustainable development. However, the role played by Rosa roxburghii Tratt (R. roxburghii) industry in karst desertification control still needs to be determined. In this study, we predict the suitability probability and driving variables of R. roxburghii's distribution through a Maximum Entropy model, combining climate, population, vegetation, and soil data, and conducted a sample plot survey in a high-suitability area to explore the role of the R. roxburghii industry in ecological services by using adjacent non-R. roxburghii patches as comparisons. The results showed that South China Karst, with its simultaneous rain and heat climate characteristics, was the most suitable area for the industrialization of R. roxburghii in the world. In South China Karst, the area of no, low, medium, and high suitability zones for R. roxburghii was 4.2 × 105, 6.12 × 105, 4.64 × 105, and 4.42 × 105 km2, respectively, and population distribution was the dominant variable controlling its distribution. The net primary productivity, soil conservation, water conservation, mean species abundance, and aboveground biomass of the R. roxburghii monoculture plot in the high-suitability area were significantly lower than those of adjacent non-R. roxburghii lands and forest patches. Furthermore, the monoculture had higher trade-offs between provision, regulation, and support service. This study found that the monoculture of R. roxburghii industry, dominated by human activities, failed to improve ecological services and exacerbated the trade-offs between services. Therefore, future karst desertification control still needs to follow the agroforestry development path.

Field Validation of Remote Sensing Land Use Assessment in Kyrgyzstan

The observation and monitoring of land use and land use change (LULUC) has made extensive use of remote sensing. However, it is quite difficult for users to classify land cover and determine changes in land use if no satellite images with sufficient temporal and spatial resolution are available. Kyrgyzstan used Collect Earth to assess the historical and present LULUC trends in 2015 and 2019. Nevertheless, the availability of low spatial and temporal resolution satellite images (15%) served as the primary reason for mandatory field verification. Thus, field validation work was conducted to validate the remote sensing assessment in 2019 by visiting selected sample plots in the field, updating the database for current and historical LULUC data, and verifying or revising inconsistent or incorrect sample plots based on the field data. A total of 941 sample plots were visited and 119 misclassified sample plots were detected during the field validation work. Hence, this article reports an updated estimate of LULUC dynamics in Kyrgyzstan, based on the validation of selected sample plots in the field and reassessing and reclassifying 13,414 1-ha sample plots through the visual interpretation of satellite imagery via Collect Earth software and the Google Earth platform. The update of the database resulted in the re-classification of 1073 sample plots in Kyrgyzstan. The results reveal that in 2019, forests occupied 1.81 million ha or 9% of the total land with a 5.33% uncertainty in Kyrgyzstan.

Infection Severity of Arceuthobium oxycedri (Viscaceae) in Two Protected Areas in the Alborz Mountains, Iran: A Case Study

ABSTRACTThis study investigated the severity of mistletoe infection on junipers and the relationship between tree characteristics, physiographic factors and mistletoe infection. The study was conducted in two protected areas: Parvar and Miankouh Tash in Semnan Province, Iran. A dwarf mistletoe‐infested area of 400 ha was sampled by placing a 250 × 250 m grid over a map of each study area. A 20 × 20 m (400 m2) plot was placed at the intersection of grid lines. This resulted in a total of 110 forested plots: 45 plots in Parvar and 65 plots in Miankouh Tash. In each plot, all live junipers were measured for total height, crown area, collar diameter and crown diameter. Mistletoe severity was estimated using the 6‐class dwarf mistletoe rating system (DMR). Dead trees were also recorded and examined for evidence of past mistletoe infection. The Boruta algorithm was used for feature selection, and the selected features were utilised for predicting DMR using a random forest (RF) model. Over 40% of junipers in the sample plots were infected and 7.5% were dead. Mortality was greater in severely infested plots compared to uninfested and lightly infested plots. The results of univariate analysis demonstrated that the largest trees across plots were more severely infected. Infection severity was highest on steep terrains (slopes more than 60%) where the largest junipers were growing. Based on Boruta feature selection algorithm, collar diameter, tree height and crown area of junipers have been detected as the most important variables for the explanation of infection severity. Crown diameter, geographical aspect, density of juniper trees, altitude and juniper canopy cover were classified as tentative variables in this model. Slope percentage beside two variables related to stand characteristics including all woody species (basal area and tree density) were the rejected variables. The correlation coefficient (r) and root mean square error (RMSE) between the predictions by RF and the measured DMR were 0.83 and 0.53 respectively. These results indicate that the selected environmental variables can be used for spatial prediction of DMR in both study areas. Because increased mortality was observed for severely infected areas, more efficient management methods are needed to control the dwarf mistletoe populations in the Alborz Mountains.

Use of a Carbon Density Growth Model to Assess the Potential Carbon Sink Function of a Mongolian Pine Plantation in Heilongjiang Province, Northeast China

Accurate estimation of the potential increase in the carbon (C) sink function of forests is required for climate mitigation and C neutrality assessments. Also, accurate forest carbon density estimates are critical for understanding national- and global-level carbon cycling and storage and can inform climate change mitigation. This study established a stand C density growth model to further analyze the C sink potential of planted Mongolian pine (Pinus sylvestris var. mongolica) forests. Samples (390) from fixed plots of Mongolian pine were collected in Heilongjiang Province, Northeast China. The site index (SCI) and stand density index (SDI) were introduced to a constructed stand C density growth model, with an optimal model selected through model fitting. The effect of SDI on stand C density in different SCI grouping intervals was assessed. Total C sequestration of Mongolian pine was calculated using the established C density model. Sample plots with higher C density in each forest age stand were selected to establish a model of potential C sequestration for Mongolian pine, and the difference between this rate and the average was compared to obtain the potential increase in C sink capacity of the forest stand. Slightly different fitting accuracies among the different C density growth models were observed, with the Richards model showing the best performance, which improved through the introduction of the SCI and SDI. Stand C density was associated with an increasing trend in SCI, which within each SCI subgroup was related to the increasing SDI trend. The potential C sequestration rate of the stand was close to the average between years 5 and 13. The average C sequestration rate peaked at 3.86 Mg·ha−1·year−1 at year 13, whereas the potential C sequestration rate peaked at 4.42 Mg·ha−1·year−1 in year 15. A gap between the potential and average C sequestration rate existed between ages 13 and 45, indicating the possibility for an increased C sink function in this forest age range. The Richards growth model incorporating SCI and SDI provided a better reflection of the C density of the Mongolian pine plantation, and the established stand C sequestration rate model showed that the optimal increment in the plantation C sink function can be obtained between years 13 and 45. The results of this study can guide C sink management in the Mongolian pine plantation.

Quantitative Evaluation of Post-Tillage Soil Structure Based on Close-Range Photogrammetry

Quantitative Evaluation of Post-Tillage Soil Structure Based on Close-Range Photogrammetry

Regeneration and Diameter-Height Distribution Under Irregular Shelterwood System: A Case Study from Banke District

This study was aimed to assess the regeneration composition and status and species diversity of blocks of compartment C4S6 with 8 sub-compartment C4S1, C4S2, C4S3, C4S4, C4S5, C4S6, C4S7, And C4S8 under Irregular Shelterwood System (ISS) based scientific forest management in Samshergunj-Mathebas block forest, Banke district, Nepal. The systematic random sampling method was used for allocating circular sample plots (of radius 1.78 m for seedlings and 2.82 m for sapling) for collecting data from the field. Altogether 40 sample plots were taken to collect the required data from the blocks forest. The total regeneration per hectare in the block is 76870. In the blocks forest the total number of seedlings and saplings per hectare was 65150 and 11720 respectively. The IVI value showed that both seedling and sapling of <i>Shorea robusta</i> was dominant species and <i>Terminalia alata </i>was 1st codominant species in the blocks. Vegetation study is crucial for balancing the ecosystem as well as for biophysical environment. Forest inventories helps to determine the growing stock as well as annual increment in order to balance between harvesting and re-growth. In my study area there is abundant regeneration in the forest but their effective assessment is lacking and research on it is insufficient. Due to the lack of sustainable management, open grazing, illegal harvesting of wood, and forest fire, Productivity of forest is decreasing day by day. And at present, there can be seen Asna (<i>Terminalia alata</i>), and other Sal associates like Botdhairo instead of Sal. Graduation of seedling into sapling with time is key necessity in maintaining sustainable forest regeneration. Thus, this study was conducted for assessing regeneration composition and diameter and height class distribution after implementation of irregular Shelterwood system (ISS) in western terai. This study will be useful for policy makers working in conservation biology.

Understorey-nesting bird community responses to land use in the Korup National Park, an Afrotropical rainforest in South-Western Cameroon

Tropical forest biodiversity is under immense pressure due to a combination of global and local factors, including climate change and intensive land use. Birds play an important role in tropical forest ecosystems, contributing significantly to various ecosystem services. This study examined the impact of deforestation on the richness and abundance of understorey-nesting bird communities along a land-use intensification gradient in the peripheral zone of Korup National Park, Southwest Cameroon. We selected 30 sampling plots, each covering 4 ha, across five habitat types: near-primary forest, secondary forest, disturbed forest, cocoa/coffee plantations, and annual food crop fields. A total of 236 understorey bird nests were identified, representing 16 species. Our findings revealed that land use intensity significantly influenced bird species richness and abundance. Primary and secondary forests provided 75% of the nesting species and 71% of the nests. However, 19% of the species either appeared or increased in cocoa/coffee plantations and annual crop fields. Among the feeding groups, frugivores were the most sensitive group to deforestation. This study corroborates the adverse effects of forest destruction on birds communities and highlights the urgent need to address the rapid and extensive forest conversion occurring within Korup National Park. Allowing secondary forests to regenerate and preserving large patches of primary forest are essential strategies to help understorey-nesting bird communities mitigate the impacts of global changes.

Non-Linear Models for Tree Aboveground Biomass and Volume Estimation in Agoi-Ibami Forest Reserve, Cross River State, Nigeria

Tree biomass and volume estimation based on allometric equations is a widely used non-destructive technique for estimating biomass, sequestered carbon, and volume worldwide. Non-linear models for the biomass and volume of individual trees in Nigeria's Cross River State's Agoi-Ibami Forest Reserve were fitted and validated in this study. In this study, two parallel lines transect of 1500 meters in length, separated by 500 meters, were established using the systematic line transects sampling method. Along each transect, ten sample plots, each measuring 50 m by 50 m, were placed alternately at 100 m intervals. Twenty sample plots in all were thus marked for the study. The estimation of biomass using a non-destructive method was used. To calculate the aboveground green biomass for each, the diameter at breast height and total height were employed. Agoi-Ibami Forest Reserve had a total value of 391N ha-1 for number of stem per hectare, 14 tree families, mean dbh of 26.04cm, height of 15.9m, and basal area of 50.21m2ha-1. Conversion factors were used to estimate stand biomass, carbon sink, and sequestered carbon dioxide (CO2). Non-linear models were fitted for volume and aboveground biomass estimation in the study area. All of the models were evaluated and validated using some assessment statistical criteria and residual graphs, and models with good fit were suggested for use. Curve Expert Software was used for the development of the non-linear regression models. According to the assessment criteria, the forest reserve's best non-linear volume and aboveground biomass models were the Ratkowsky, Weibull, and Logistic models. Fitted models should therefore be employed for the forest reserve's efficient and successful management.

Modern Pollen–Vegetation Relationships: A View from the Larch Forests of Central Siberia

Understanding how pollen assemblages represent the local and regional vegetation composition is crucial for palaeoecological research. Here, we analyze 102 surface moss/soil pollen samples collected from four study regions located in various boreal forest vegetation types in Central Siberia. Despite Larix being the most prevalent tree generus in the study area, the proportion of Larix pollen can be as low as 0.6–1.5% (0.4–4.7% on average) even in localities with a high canopy density of the species. No relationship between the quantity of Larix pollen in the spectra and the abundance of Larix in the local vegetation was found. The dominant components of the pollen assemblages are Betula and Alnus fruticosa. The pollen value of Picea is low (2.6–8.2% on average), with higher proportions at sample plots where spruce is abundant in forests. Pinus is a highly prevalent pollen species within its geographical range, comprising up to 40% of pollen assemblages. Outside of the range, the ratio of Pinus pollen was higher in habitats with low canopy density and in treeless ecosystems. The composition of herbaceous pollen and spores is significantly affected by the local plant community, offering more comprehensive insights into past vegetation patterns.

Influence of Soil Texture on Carbon Stocks in Deciduous and Coniferous Forest Biomass in the Forest-Steppe Zone of Oka–Don Plain

Forests play a crucial role in climate change mitigation by acting as a carbon sink. Understanding the influence of soil properties on carbon stocks in forests is essential for developing effective forest management strategies. The aim of the study was to assess the impact of soil texture on carbon stocks in the biomass of deciduous and coniferous tree stands of a forest-steppe ecotone. Soil samples were collected from 55 soil pits, and forest inventory data were obtained from eight permanent sample plots. The results showed that the distribution of mechanical particles in soils, particularly the stocks of silt and clay, significantly influenced the accumulation of carbon in tree stands. The stock of silt and clay was shown to increase with an increase in the diversity of tree species in forests and carbon stocks in forest stands. While soil organic carbon stocks did not exhibit a clear relationship with tree stand carbon stocks, a strong positive correlation (r = 0.802, p < 0.05) was found between the stocks of fine particles in the 2 m root-inhabited soil layer and the carbon stocks in tree biomass. The study provides a classification of forest types based on soil texture, which can facilitate differentiated forest management strategies for enhancing the carbon sequestration potential of forest ecosystems in the forest-steppe zone.