Forest Ecosystem Management Research Articles

Effects of single-tree selective harvest method on ecosystem services in a mixed temperate broadleaf forest in Iran

IntroductionThis study examines the effects of the single-tree selective harvesting method on ecosystem services in a mixed temperate broadleaf forest in Iran. Key indicators such as carbon sequestration, tree species diversity, soil nutrient cycling, and stand volume are analyzed, emphasizing their significance for sustainable forest management.MethodsThe research was conducted in four districts, each comprising two parcels: one managed using the single-tree selective harvesting method and the other serving as a control. Data on ecological and biodiversity parameters were collected, and statistical analyses, including two-way ANOVA and Principal Component Analysis (PCA), were performed to assess the impact of management practices.ResultsThe findings reveal that the single-tree selective harvesting method significantly influences regulating and supporting ecosystem services. Carbon storage varied with elevation, affecting both soil and forest floor litter. Tree species diversity increased, with more species present and a reduction in dominance by certain species. However, this method also resulted in reduced stand volume in the managed areas. Elevation significantly impacted diversity indices, litter carbon storage, available potassium, and stand volume. Additionally, the interaction between management and elevation was significant for soil carbon storage, richness, diversity, total nitrogen, available potassium, and stand volume.DiscussionThe single-tree selective harvesting method appears to be a viable forest management strategy for preserving ecosystem services in mixed temperate broadleaf forests, maintaining ecosystem health without significant negative effects on soil. However, careful consideration of site-specific ecological conditions and trade-offs between provisioning and regulating services is crucial. These findings have important implications for sustainable forest management in Iran and similar forest ecosystems globally.

Enhancing Forest Canopy Height Mapping in Kaziranga National Park, Assam, by Integrating LISS IV and SAR data with GEDI LiDAR data Using Machine Learning

Abstract. This study investigates the integration of spaceborne Global Ecosystem Dynamics Investigation (GEDI) LiDAR data with optical imagery from Linear Imaging Self Scanning Sensor (LISS IV), Sentinel-1 Synthetic Aperture Radar (SAR) and PALSAR data for continuous forest canopy height mapping in Kaziranga National Park (KNP), Assam for the years 2018 and 2022. Four machine learning models were trained and evaluated to assess the predictive ability of LISS IV data in conjunction with SAR variables. The mean canopy height was measured at approximately 8.58 m in 2018, which increased to about 9.07m in 2022. Results reveal Extreme Gradient Boosting (XGB) as the top-performing model, achieving an RMSE of 5.47m and an R2 of 0.55. In comparison, Random Forest (RF), Support Vector Machine (SVM), and k-Nearest Neighbors (kNN) achieved RMSE values of 5.49, 5.51, and 5.73, respectively. Analysis shows a prevalent occurrence of canopy heights below 5 meters in KNP (more than 35% of the area), while taller canopies beyond 20m can be found in less than 5% of the area. This finding underscores the importance of integrating satellite data and machine learning and highlights the novel application of LISS IV data in enhancing canopy height mapping. Furthermore, it represents the first comprehensive attempt to map canopy height in KNP, laying the groundwork for further research on biomass assessment and carbon sequestration in this vital biodiversity hotspot. Overall, the study highlights the potential of leveraging advanced remote sensing technologies and machine learning approaches for improved understanding and management of forest ecosystems.

Afforestation Reduces Deep Soil Carbon Sequestration in Semiarid Regions: Lessons From Variations of Soil Water and Carbon Along Afforestation Stages in China's Loess Plateau

AbstractAfforestation represents an effective approach for ecosystem restoration and carbon (C) sequestration. Nonetheless, it poses notable challenges concerning water depletion and soil drought in (semi)arid regions. The underlying mechanisms regulating the influence of afforestation on soil carbon‐water dynamics, particularly how deep soil C reacts to afforestation‐induced soil drying, remain largely unclear. This study examined the variations of soil water content (SWC), soil organic carbon (SOC), and soil inorganic carbon (SIC) in 500 cm depth along four afforestation stages: abandoned grasslands, shrublands, and 20‐year and 40‐year Robinia pseudoacacia forests (RP20 and RP40) in the semiarid Loess Plateau, China. The results indicated that afforestation has significantly increased SWC (+26.6%), SOC (+44.5%), and SIC (+6.5%) in the shallow layer (0–100 cm) but caused evident soil drying (−60.8%), decrease in SOC (−37.8%), and slight reduction in SIC (−0.3%) in the deep layer (300–500 cm) when compared with grasslands. The seriously decline in the coupling coordination between soil C and SWC in the middle and deep layers indicates the unsustainability of afforestation especially for RP40. Structural equation model showed that the negative impact of afforestation on deep SOC through soil water depletion (−0.38) outweighed the direct positive impact of increased aboveground biomass (AGB) (+0.33). The negative impacts of decreased SWC and increased pH on deep SIC was close to the positive impacts of AGB. Afforestation has different effects on SOC and SIC across shallow and deep layers, and its negative effects on deep soil C should be fully integrated into future forest ecosystem restoration and management efforts.

The Effects and Contributions of Ecological Factors on Soil Carbon, Water and Nutrient Storages Under Long-Term Vegetation Restoration on the Eastern Loess Plateau

Vegetation restoration plays a crucial role in conserving soil and water, as well as rehabilitating ecosystems, by enhancing soil properties and vegetation attributes. The evaluation of the ecological consequences among different vegetation restoration types can be achieved by clarifying the impacts on carbon, water and nutrient storages. In this study, we selected four typical vegetation restoration types (Pinus tabuliformis forest (PTF), Platycladus orientalis forest (POF) and Robinia pseudoacacia forest (RPF) as typical planted forests, and the natural secondary forest (NSF) as the control treatment) in the eastern Loess Plateau of China. The soil properties (at 0–200 cm depth) and vegetation attributes (including arborous, shrubs and herbaceous plants) were measured, as well as calculated soil carbon, water and nutrient storages, with a total of 1600 soil samples and 180 vegetation survey plots. The partial redundancy analysis (pRDA) and correlation analysis were also used to analyze the contributions and relationships among environmental factors, soil eco-hydrology and nutrient supplement services in different forestry ecosystems. The results indicate the following: (1) NSF has the lowest soil bulk density (1.21 ± 0.184 g·cm−3). Soil properties varied significantly at vertical scales, and had obvious surface accumulation. (2) Soil moisture storages were better in natural forests than those in planted forests, with more drastic changes in soil moisture dynamics. (3) The soil carbon, nitrogen, and phosphorus storages significantly differed among four vegetation types, with the highest carbon storages in PTF (207.75 ± 0.674 t·ha−1), the highest nitrogen storages in POF (5.54 t·ha−1), and the highest phosphorus storages in RPF (4.33 t·ha−1), respectively. (4) Soil carbon storages depend primarily on the coupling effect of soil properties and precipitation, while nutrient storage is mainly influenced by soil properties. The results quantify the significant differences in soil water, carbon, and nutrient storage across various vegetation restoration types, and reveal the individual and combined contributions of environmental factors, providing new insights into the mechanisms driving these differences. These findings offer practical guidance for the sustainable management of forest ecosystems and the optimization of ecological restoration strategies on the Loess Plateau.

Daily activity rhythm of sympatric ungulate species in Fanjingshan Reserve, China

Sympatric species inhabiting similar ecological niches can coexist by partitioning aspects of available ecological resources, such as temporal, spatial, and nutritional niches. Ungulates often coexist in the same habitat, resulting in temporal partitioning and divergence in foraging strategies and spatial utilization. To understand the temporal patterns of ecological differentiation and coexistence mechanisms among sympatric ungulates, we analyzed the monitoring data in Fanjingshan National Nature Reserve, Guizhou, China, from 2017 to 2022. Kernel density estimation was used to scrutinize disparities in diurnal activity patterns among four ungulates [tufted deer (Elaphodus cephalophus), Reeves' muntjac (Muntiacus reevesi), wild boar (Sus scrofa), and mainland serow (Capricornis milneedwardsii)] as well as the interspecies variances during different seasons, at diverse altitudes, and between breeding and non-breeding periods. Daily activity patterns differed significantly among four species, with those of tufted deer, Reeves' muntjac, and wild boar affected significantly by season and breeding cycle. In addition, the daily activity rhythms of four species were affected by altitude. Thus, the daily activity rhythms of these ungulates were resulted from a combination of environmental factors and interspecific competitive interactions, as well as the reproductive cycle of each species. Our results provided a scientific reference for the effective management of wildlife and forest ecosystems, and we suggest that knowledge of temporal dynamics should be integrated into conservation planning for these sympatric species.

Eucalyptus and Native Broadleaf Mixed Cultures Boost Soil Multifunctionality by Regulating Soil Fertility and Fungal Community Dynamics.

The growing recognition of mixed Eucalyptus and native broadleaf plantations as a means of offsetting the detrimental impacts of pure Eucalyptus plantations on soil fertility and the wider ecological environment is accompanied by a clear and undeniable positive impact on forest ecosystem functions. Nevertheless, the question of how mixed Eucalyptus and native broadleaf plantations enhance soil multifunctionality (SMF) and the mechanisms driving soil fungal communities remains unanswered. In this study, three types of mixed Eucalyptus and native broadleaf plantations were selected and compared with neighboring evergreen broadleaf forests and pure Eucalyptus plantations. SMF was quantified using 20 parameters related to soil nutrient cycling. Partial least squares path modeling (PLS-PM) was employed to identify the key drivers regulating SMF. The findings of this study indicate that mixed Eucalyptus and native broadleaf plantations significantly enhance SMF. Mixed Eucalyptus and native broadleaf plantations led to improvements in soil properties (7.60-52.22%), enzyme activities (10.13-275.51%), and fungal community diversity (1.54-29.5%) to varying degrees compared with pure Eucalyptus plantations. Additionally, the mixed plantations exhibit enhanced connectivity and complexity in fungal co-occurrence networks. The PLS-PM results reveal that soil properties, fungal diversity, and co-occurrence network complexity directly and positively drive changes in SMF. Furthermore, soil properties exert an indirect influence on SMF through their impact on fungal diversity, species composition, and network complexity. The findings of this study highlight the significant role of mixed Eucalyptus and native broadleaf plantations in enhancing SMF through improved soil properties, fungal diversity, and co-occurrence network complexity. This indicates that incorporating native broadleaf species into Eucalyptus plantations can effectively mitigate the negative impacts of monoculture plantations on soil health and ecosystem functionality. In conclusion, our study contributes to the understanding of how mixed plantations influence SMF, offering new insights into the optimization of forest management and ecological restoration strategies in artificial forest ecosystems.

Classification and Mapping of Fuels in Mediterranean Forest Landscapes Using a UAV-LiDAR System and Integration Possibilities with Handheld Mobile Laser Scanner Systems

In this study, we evaluated the capability of an unmanned aerial vehicle with a LiDAR sensor (UAV-LiDAR) to classify and map fuel types based on the Prometheus classification in Mediterranean environments. UAV data were collected across 73 forest plots located in NE of Spain. Furthermore, data collected from a handheld mobile laser scanner system (HMLS) in 43 out of the 73 plots were used to assess the extent of improvement in fuel identification resulting from the fusion of UAV and HMLS data. UAV three-dimensional point clouds (average density: 452 points/m2) allowed the generation of LiDAR metrics and indices related to vegetation structure. Additionally, voxels of 5 cm3 derived from HMLS three-dimensional point clouds (average density: 63,148 points/m2) facilitated the calculation of fuel volume at each Prometheus fuel type height stratum (0.60, 2, and 4 m). Two different models based on three machine learning techniques (Random Forest, Linear Support Vector Machine, and Radial Support Vector Machine) were employed to classify the fuel types: one including only UAV variables and the other incorporating HMLS volume data. The most relevant UAV variables introduced into the classification models, according to Dunn’s test, were the 99th and 10th percentile of the vegetation heights, the standard deviation of the heights, the total returns above 4 m, and the LiDAR Height Diversity Index (LHDI). The best classification using only UAV data was achieved with Random Forest (overall accuracy = 81.28%), with confusion mainly found between similar shrub and tree fuel types. The integration of fuel volume from HMLS data yielded a substantial improvement, especially in Random Forest (overall accuracy = 95.05%). The mapping of the UAV model correctly estimated the fuel types in the total area of 55 plots and at least part of the area of 59 plots. These results confirm that UAV-LiDAR systems are valid and operational tools for forest fuel classification and mapping and show how fusion with HMLS data refines the identification of fuel types, contributing to more effective management of forest ecosystems.

A growth-effective age-based periodic site-index for the estimation of dynamic forest site productivity under environmental changes

Key messageA novel periodic site index is introduced for the quantification of dynamic forest site productivity. The measure is age-independent, sensitive to environmental changes and efficient for the estimation and prediction of stand height and stand volume increment.ContextAccurate and up-to-date prediction of site productivity is crucial for the sustainable management of forest ecosystems, especially under environmental changes.AimsThe aim of this study was to introduce a novel concept: a periodic site index based on growth-effective age for the quantification of dynamic forest site productivity.MethodsThe growth-effective age based periodic site index is estimated from repeated or multi-temporal measurements of stand dominant height. Furthermore, a recursive procedure to update the underlying site index model is presented by using repeated measurements of stand dominant height. The database used in this study comprised repeated measurements of 945 Norway spruce (Picea abies L.) experimental plots at 508 different locations in Southwest Germany.ResultsThe evaluation shows that periodic site index is statistically superior to the conventional site index, based on chronological stand age, for estimating stand height and stand volume increment. The analysis of temporal differences between growth-effective stand age and chronological stand age and between periodic site index and conventional site index in the period 1900 to 2020 reveals trends referring to stand age and site productivity, which corroborate earlier regional studies on forest growth trends due to environmental changes.ConclusionsThe periodic site index is a better indicator for site productivity than conventional site index. Under conditions of environmental changes, conventional site index is biased, whereas the growth-effective age based site index provides an unbiased estimate of stand height development. With the more widespread application of remote sensing techniques, such as airborne laser scanning, the availability of multi-temporal stand height data will increase in the near future. The novel concept provides an adaptive modeling approach perfectly suited to these data for an improved estimation and prediction of forest site productivity under environmental changes and can straightforwardly be applied also to uneven-aged and multi-species stands.

Scale-dependent effects of vegetation attributes and soil fertility on productivity in two temperate forests in Northeast China

Forests are rich in biodiversity and high in productivity, and understanding the relationship between biodiversity and productivity is critical to the sustainable management of forest ecosystems. Although biodiversity and other factors contribute to forest productivity to varying degrees, it is unclear whether spatial scale (or plot size) influences this relationship. In this study, we analyzed the impacts of biodiversity, trait composition, structural attributes, and soil fertility on forest productivity at three scales in two temperate old-growth forests. We found that structural attributes, including basal area and stand density, were positively correlated with productivity and were consistently the strongest drivers of productivity changes in both forest types. Trait composition had a weak direct positive correlation with productivity. Soil nitrogen had positive effects on productivity, and soil moisture content had negative effects, both of which increased with increasing plot size. Different results for the effect of biodiversity on productivity were found between the two forests. In the mixed broadleaved-Korean pine forest, diversity correlated positively with productivity, and the strength of the relationship increased with increasing plot size. In contrast, in the spruce-fir valley forest, diversity had a direct positive correlation with productivity at 10 m × 10 m, no significant effect at 20 m × 20 m, and a negative correlation at 30 m × 30 m. Our results suggest that the contribution of vegetation attributes and soil fertility to forest productivity varies with scale. Despite the fact that both the niche complementarity hypothesis and the mass ratio hypothesis act simultaneously in forests, they have limited effects on productivity. Stand structural attributes play a key role in elucidating the biodiversity-productivity relationships, and they may serve as a more significant regulator of forest productivity than diversity. At the same time, the role of soil fertility in regulating productivity cannot be ignored.

Shifts in fungal community diversity and potential function under natural forest succession and planted forest restoration in the Kunyu Mountains, East China.

Soil fungi participate in various ecosystem processes and are important factors driving the restoration of degraded forests. However, little is known about the changes in fungal diversity and potential functions under the development of different vegetation types during natural (secondary forest succession) and anthropogenic (reforestation) forest restoration. In this study, we selected typical forest succession sequences (including Pinus densiflora Siebold & Zucc., pine-broadleaf mixed forest of P. densiflora and Quercus acutissima Carruth., and Q. acutissima), as well as natural secondary deciduous broadleaved mixed forests and planted forests of Robinia pseudoacacia on Kunyu Mountain for analysis. We used ITS rRNA gene sequencing to characterize fungal communities and used the FUNGuild database to predict fungal functional groups. The results showed that forest succession affected fungal β-diversity, but not the α-diversity. There was a significant increase in Basidiomycota and a decrease in Ascomycota in the later successional stage, accompanied by an increase in the functional groups of ectomycorrhizal fungi (ECM). Conversely, planted forests exhibited decreased fungal α-diversity and altered community compositions, characterized by fewer Basidiomycota and more Ascomycota and Mucoromycota. Planted forests led to a decrease in the relative abundances of ECM and an increase in animal pathogens. The TK content was the major factor explaining the distinction in fungal communities among the three successional stages, whereas pH, AP, and NH4 + were the major factors explaining community variations between natural and planted forests. Changes in vegetation types significantly affected the diversity and functional groups of soil fungal communities during forest succession and reforestation, providing key insights for forest ecosystem management in temperate forests.

Scale effect of landscape characteristics on undergrowth vegetation variance with different ecological traits

Herbaceous understory vegetation plays a significant role in temperate forested ecosystems, with its diversity strongly dependent on both local and landscape conditions. However, the contribution of these factors across different spatial scales (e.g., scale effect) remains inadequately understood. The objective of this study was to determine the most important landscape and local characteristics influencing the species richness of the herb layer. We surveyed and sampled 53 sites in a mountainous forest within the Baihuashan National Nature Reserve of Beijing. Using generalized linear models, we investigated the simultaneous effect of local (e.g. tree density, average tree height, and cover of tree, shrub, and ground vegetation) and landscape (e.g. Area-weighted Mean Patch Size, Area-weighted Mean Euclidean Nearest Neighbor Distance, Area-weighted Mean Fractal Dimension Index, forest area, and shrubbery area) variables on the richness of species in the understory herb layer. Variation partitioning was used to examine the individual contribution of local and landscape variables. Our results identified 366 plant species from 95 families across 53 sampling sites. Significant differences in species richness were found among life forms, seed dispersal types, and habitat preferences (P < 0.001). Parsimonious models indicated that combining local and landscape factors at a 2 km scale explained the most variation in phanerophytes (R2 = 0.658). Locally, average tree height significantly influenced the species richness of biotic dispersal, phanerophytes, and geophytes. At the landscape level, variables such as the Area-weighted Mean Fractal Dimension Index and farmland area significantly impacted plant community traits across different scales. Understanding these effects is crucial for developing effective sustainable forest ecosystem management strategies and for establishing robust ecological conservation policies.

Sustainable governance of natural gas exploration: A coastal forest ecosystem perspective

AbstractCoastal forest ecosystems can benefit from an empirical study of the sustainable governance of natural gas exploration. But, policymakers and practitioners cannot make informed decisions in the absence of thorough and comprehensive research. This study examines empirical research on the sustainable governance of natural gas exploration within the coastal forest ecosystem, identifies research gaps and outlines future directions. Researchers utilised the theories, context, characteristics, and methodologies (TCCM) analysis in the systematic narrative review of nine studies. The study findings revealed that sustainable governance of natural gas exploration to lessen coastal forest ecological consequences is questionable since most of the study indicated negative impacts on coastal forests. Furthermore, the study findings contributed by identifying the most prominent theories, such as economic theory and efficient taxation, which are thought to maximise long‐term economic benefits while minimising the degradation of forest ecosystems. Natural gas exploration‐related taxes have the potential to support conservation initiatives and coastal forest ecosystem restoration. Furthermore, the study acknowledges that most studies do not have guidelines for evaluating the concepts of sustainable governance. Moreover, the research suggests the need for sustainable governance that strikes a balance between the goals of natural gas development and the protection of coastal forest ecosystems, enacts laws and regulations that promote good governance towards the Green House Gases protocol, and sets up monitoring and control mechanisms. Furthermore, the government should provide incentives, such as research grants, to encourage research and dissemination, as well as sustainable management techniques for natural gas extraction and coastal forest ecosystem management and conservation.

The Expanding Thread of Ungulate Browsing—A Review of Forest Ecosystem Effects and Management Approaches in Europe

The Expanding Thread of Ungulate Browsing—A Review of Forest Ecosystem Effects and Management Approaches in Europe

Research Trends in Wildland Fire Prediction Amidst Climate Change: A Comprehensive Bibliometric Analysis

Wildfire prediction plays a vital role in the management and conservation of forest ecosystems. By providing detailed risk assessments, it contributes to the reduction of fire frequency and severity, safeguards forest resources, supports ecological stability, and ensures human safety. This study systematically reviews wildfire prediction literature from 2003 to 2023, emphasizing research trends and collaborative trends. Our findings reveal a significant increase in research activity between 2019 and 2023, primarily driven by the United States Forest Service and the Chinese Academy of Sciences. The majority of this research was published in prominent journals such as the International Journal of Wildland Fire, Forest Ecology and Management, Remote Sensing, and Forests. These publications predominantly originate from Europe, the United States, and China. Since 2020, there has been substantial growth in the application of machine learning techniques in predicting forest fires, particularly in estimating fire occurrence probabilities, simulating fire spread, and projecting post-fire environmental impacts. Advanced algorithms, including deep learning and ensemble learning, have shown superior accuracy, suggesting promising directions for future research. Additionally, the integration of machine learning with cellular automata has markedly improved the simulation of fire behavior, enhancing both efficiency and precision. The profound impact of climate change on wildfire prediction also necessitates the inclusion of extensive climate data in predictive models. Beyond conventional studies focusing on fire behavior and occurrence probabilities, forecasting the environmental and ecological consequences of fires has become integral to forest fire management and vital for formulating more effective wildfire strategies. The study concludes that significant regional disparities in knowledge exist, underscoring the need for improved research capabilities in underrepresented areas. Moreover, there is an urgent requirement to enhance the application of artificial intelligence algorithms, such as machine learning, deep learning, and ensemble learning, and to intensify efforts in identifying and leveraging various wildfire drivers to refine prediction accuracy. The insights generated from this field will profoundly augment our understanding of wildfire prediction, assisting policymakers and practitioners in managing forest resources more sustainably and averting future wildfire calamities.

Temporal Variations in Aboveground Biomass, Nutrient Content, and Ecological Stoichiometry in Young and Middle-Aged Stands of Chinese Fir Forests.

Understanding the ecological dynamics of forest ecosystems, particularly the influence of forest age structure on soil carbon (C), nitrogen (N), and phosphorus (P) content, is crucial for effective forest management and conservation. This study aimed to investigate the nutrient storage and ecological stoichiometry across different-aged stands of Chinese fir forests. Soil samples were collected from various depths (0-15 cm, 15-30 cm, and 30-45 cm) across four age groups of Chinese fir forests (8-year-old, 12-year-old, 20-year-old, and 25-year-old) in the Forest Farm, Pingjiang County, China. Soil organic carbon (SOC), total nitrogen (TN), and total phosphorus (TP) were measured, and their stoichiometries were calculated. The results showed that both individual tree biomass and stand biomass, along with SOC, TN, and TP content, increased with stand age, highlighting the significant importance of stand age on biomass production and nutrient accumulation in forests. Specifically, soil C and P contents significantly increased as the forest aged, while variation in N content was relatively minor. Soil C/N and C/P ratios exhibited variation corresponding to forest age, suggesting alterations in the ecological stoichiometry characteristics of the forests over time. These findings are crucial for understanding the dynamics of ecosystem functioning and nutrient cycling within Chinese fir forests and provide a solid scientific basis for the effective management and conservation of these vital forest ecosystems.

Seed Availability and Small Mammal Populations: Insights from Mediterranean Forests

Plant–animal interactions play a crucial role in ecosystem functioning, especially through seed dispersal mechanisms. Understanding how small mammal populations respond to seed availability is essential for ecosystem management and biodiversity conservation, especially in the context of habitat loss and climate change. We conducted a 10-year study in mixed Mediterranean oak–beech forests to investigate the population dynamics of common small mammal species in response to seed availability. Our findings revealed distinct responses among species, influenced by life history traits, foraging behaviour, and diet. Wood mice (Apodemus sylvaticus) showed a rapid population increase with seed availability both in the same year of seed fall and the following year, suggesting a flexible foraging strategy and a dependence on arboreal seed producers. Yellow-necked mice (Apodemus flavicollis) revealed immediate population increases in response to seed availability in the autumn, probably because of their arboreal habits and preference for exploiting seeds prior to maturation. Bank voles (Clethrionomys glareolus) showed responses with population peaks in years following high seed availability, indicating a slower demographic response to resource abundance. Surprisingly, the greater white-toothed shrew (Crocidura russula) responded indirectly to seed availability in Mediterranean forests, suggesting complex interactions with seed-associated invertebrates or dependence on other variables not considered. Our findings highlight the importance of understanding how changes in seed availability influence the population ecology of small mammals, with significant implications for the conservation and management of Mediterranean forest ecosystems in the context of climate change and recurrent droughts. These results emphasise the need to consider species interactions, resource availability, and climate change in the conservation and management of evolving ecosystems.

Multi-Agent Reinforcement Learning for Stand Structure Collaborative Optimization of Pinus yunnanensis Secondary Forests

This study aims to investigate the potential and advantages of multi-agent reinforcement learning (MARL) in forest management, offering innovative insights and methodologies for achieving sustainable management of forest ecosystems. Focusing on the Pinus yunnanensis secondary forests in Southwest China, we formulated the objective function and constraints based on both spatial and non-spatial structural indices of the forest stand structure (FSS). The value of the objective function (VOF) served as an indicator for assessing FSS. Leveraging the random selection method (RSM) to select harvested trees, we propose the replanting foreground index (RFI) to enhance replanting optimization. The decision-making processes involved in selection harvest optimization and replanting were modeled as actions within MARL. Through iterative trial-and-error and collaborative strategies, MARL optimized agent actions and collaboration to address the collaborative optimization problem of FSS. We conducted optimization experiments for selection felling and replanting across four circular sample plots, comparing MARL with traditional combinatorial optimization (TCO) and single-agent reinforcement learning (SARL). The findings illustrate the superior practical efficacy of MARL in collaborative optimization of FSS. Specifically, replanting optimization based on RFI outperformed the classical maximum Delaunay generator area method (MDGAM). Across different plots (P1, P2, P3, and P4), MARL consistently improved the maximum VOFs by 54.87%, 88.86%, 41.34%, and 22.55%, respectively, surpassing those of the TCO (38.81%, 70.04%, 41.23%, and 18.73%) and SARL (54.38%, 70.04%, 41.23%, and 18.73%) schemes. The RFI demonstrated superior performance in replanting optimization experiments, emphasizing the importance of considering neighboring trees’ influence on growth space and replanting potential. Following selective logging and replanting adjustments, the FSS of each sample site exhibited varying degrees of improvement. MARL consistently achieved maximum VOFs across different sites, underscoring its superior performance in collaborative optimization of logging and replanting within FSS. This study presents a novel approach to optimizing FSS, contributing to the sustainable management of Pinus yunnanensis secondary forests in southwestern China.

Differential Responses of Bacterial and Fungal Community Structure in Soil to Nitrogen Deposition in Two Planted Forests in Southwest China in Relation to pH

Increased nitrogen deposition profoundly impacts ecosystem nutrient cycling and poses a significant ecological challenge. Soil microorganisms are vital for carbon and nutrient cycling in ecosystems; however, the response of soil microbial communities in subtropical planted coniferous forests to nitrogen deposition remains poorly understood. This study carried out a four-year nitrogen addition experiment in the subtropical montane forests of central Yunnan to explore the microbial community dynamics and the primary regulatory factors in two coniferous forests (P. yunnanensis Franch. and P. armandii Franch.) under prolonged nitrogen addition. We observed that nitrogen addition elicited different responses in soil bacterial and fungal communities between the two forest types. In P. yunnanensis Franch. plantations, nitrogen supplementation notably reduced soil bacterial α-diversity but increased fungal diversity. In contrast, P. armandii Franch. forests showed the opposite trends, indicating stand-specific differences. Nitrogen addition also led to significant changes in soil nutrient dynamics, increasing soil pH in P. yunnanensis Franch. forests and decreasing it in P. armandii Franch. forests. These changes in soil nutrients significantly affected the diversity, community structure, and network interactions of soil microbial communities, with distinct responses noted between stands. Specifically, nitrogen addition significantly influenced the β-diversity of fungal communities more than that of bacterial communities. It also reduced the complexity of bacterial interspecies interactions in P. yunnanensis Franch. forests while enhancing it in P. armandii Franch. forests. Conversely, low levels of nitrogen addition improved the stability of fungal networks in both forest types. Using random forest and structural equation modeling, soil pH, NH4+-N, and total nitrogen (TN) were identified as key factors regulating bacterial and fungal communities after nitrogen addition. The varied soil nutrient conditions led to different responses in microbial diversity to nitrogen deposition, with nitrogen treatments primarily shaping microbial communities through changes in soil pH and nitrogen availability. This study provides essential insights into the scientific and sustainable management of subtropical plantation forest ecosystems.

Biodiversity monitoring for the jarrah (Eucalyptus marginata) forest in south-west Western Australia: An extension to ten-year findings of Forestcheck

Long-term research and monitoring programs can provide important insights into biodiversity patterns to inform sustainable management of multi-use forest ecosystems. This study investigated biodiversity patterns in two forest ecosystems in the southwest region of Australia, as part of a long-term monitoring program, Forestcheck, established to track changes and trends in forest biodiversity associated with management activities. We assessed the effects of forest ecosystem, silvicultural treatment types (gap release and shelterwood/selective harvesting) and time since fire, on taxonomic richness and assemblage patterns across six taxonomic groups (terrestrial vertebrates, birds, macro-invertebrates, vascular plants, macro-fungi and cryptogams). Further, temporal comparisons in taxonomic richness and assemblage were completed for vascular plants and terrestrial vertebrates using data collected in a second round of monitoring. There was a low-level effect of harvesting and prescribed burning disturbance on combined community assemblage, but forest ecosystem had a greater influence on biodiversity patterns. Plant assemblages changed significantly according to ecosystem, silvicultural treatment type and monitoring round when two rounds of monitoring data were considered. These findings support previous assessments of Forestcheck sites, in that the strongest determinant of biodiversity patterns was forest ecosystem, and the greatest effects of disturbance were on assemblage patterns. These results provide some insight into post disturbance patterns and may assist with decision-making in relation to silviculture and prescribed burning regimes, and post-silviculture management, to provide increased opportunity for restoration of diversity.

Impact of Deforestation on Water Quality in Cameroon

Purpose: The aim of the study was to analyze the impact of deforestation on water quality in Cameroon. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: Deforestation in Cameroon significantly impacts water quality, as evidenced by increased sedimentation, higher pollutant levels, and elevated risks of waterborne diseases. Loss of forest cover reduces natural filtration capacities, leading to higher concentrations of contaminants in water sources. Alterations in hydrological patterns and changes in land use further exacerbate these issues, affecting water availability and aquatic habitats. Unique Contribution to Theory, Practice and Policy: Hydrological cycle theory, land use/land cover change (LU/LC) theory &amp; ecosystem services theory may be used to anchor future studies on impact of deforestation on water quality in Cameroon. Implement land-use planning measures that prioritize the conservation and sustainable management of forest ecosystems, including protected area designation, forest restoration initiatives, and sustainable forest management practices. Develop and enforce policies that incentivize forest conservation and sustainable land management practices, such as payment for ecosystem services schemes, land-use zoning regulations, and carbon offset programs.