Forest Expansion Research Articles

Bryophyte literature records database of Aysén, Chilean sub-Antarctic ecoregion

The Chilean sub-Antarctic ecoregion hosts the largest expanse of temperate forests, wetlands and peatlands, as well as the largest proportion of protected areas in the southern hemisphere. Bryophytes are highly diverse and ecologically essential in sub-Antarctic ecosystems and are considered as biodiversity loss indicators caused by the current socio-ecological crisis. However, knowledge about their biodiversity is rather limited. Integrating the available information on bryophyte diversity in regional platforms such as SIB-Aysén can be useful to acknowledge their ecological importance and remarkable biodiversity. This article integrates 345 records of 273 bryophyte taxa known in the region of Aysén and emphasizes the need to include citizen science as a tool to increase observations in lesser-known taxonomic groups.

Rapid Expansion of Coastal Mangrove Forest in Guangxi Beibu Gulf: Patterns, Drivers, and Impacts

Rapid Expansion of Coastal Mangrove Forest in Guangxi Beibu Gulf: Patterns, Drivers, and Impacts

Spatio-temporal analysis of vegetation dynamics in new protected areas: Exploring the partial nature reserve of Aghien (southern Côte d'Ivoire)

The Aghien Partial Nature Reserve, located in the district of Abidjan, is a new protected area that requires special monitoring because it is subject to strong urbanization pressure. To analyse the spatio-temporal dynamics of land use at this site, field data was first collected, followed by processing of Landsat OLI/TIRS images of the reserve from 2020 and 2024. Parameters such as the average annual rate of spatial expansion (T), the conversion rate (Tc), the transition matrix and the conversion intensity were then calculated. Finally, a forecast was made for land-use dynamics up to 2032. A total of ten land-use units were identified within the administrative boundaries of the reserve. Among these units, old rubber plantations, fallow land and secondary forests are the most represented plant formations. Over the period from 2020 to 2024, vegetation dynamics are marked by the expansion of secondary forests (Tc = 9.48%; T = 2.26%), young palm plantation (Tc = 15.01%; T = 3.50%) and bare soil (Tc = 18.50%; T = 4.24%). However, the forecast to 2032 shows a significant expansion of fallow land (Tc = 11.66%; T = 0.92%) and secondary forest (Tc = 26.46%; T = 1.96%). This reflects the gradual restoration of floristic diversity in the reserve's landscape. However, given the persistence of certain human activities, the site needs to be monitored.

Correlation of Bio-physicochemical Factors with the Expansion of Mangrove Forests in Laikang Bay, Indonesia

The bio-physicochemical conditions of seawater are critically important in the rate of expansion of mangrove forests. This study aims to assess the driving factors of mangrove forest expansion with bio-physicochemical water quality analysis using the Maximum Entropy (MaxEnt) method in Laikang Bay, Indonesia. Water quality analysis included measurements of NO3, PO4, kH, salinity, current speed, brightness (D3), NO2, pH, and chlorophyll-a levels (bio-physicochemical factors). This research adopts quantitative methods, with data collected from 42 specific locations between 12:00 a.m. and 3:00 p.m. The observation data was gathered using the stratified random sampling method. Spatial distribution mapping of mangroves and observation data were analyzed using Euclidean nearest neighbor distance with ArcGIS software version 8.1. The MaxEnt method was applied to investigate the percentage contribution of water quality on the distribution of mangroves. The results of this study indicate that the most significant factor contributing to the growth and expansion of mangrove forests in Laikang Bay is the PO4 content, with a contribution value of 47.4%. The PO4 concentration ranges from 0.10 to 1.40 mg.100g-1, with a concentration of approximately 0.10 mg.100g-1 having the greatest impact. Meanwhile, the less influential factor is brightness (D3), with a contribution value of 0.3%. These results indicate that to maintain the growth and expansion of mangrove forests in Laikang Bay, it is necessary to maintain the levels of these influential variables.

Climate and land-cover controls of aquatic carbon dynamics since the last glacial maximum: Evidence from stable carbon isotopes of subfossil Cladocera

Lake metabolism and associated emissions of CO2 in lakes are heavily subsidized by terrestrial carbon. However, how land-cover change and long-term climate interact to influence landscape biogeochemistry remains unclear. A ∼26,000-year sediment record from a lake in Southwest China shows how terrestrial-aquatic carbon dynamics responded to climate changes, atmospheric CO2 levels, and changing land-cover (vegetation composition) prior to cultural disturbances. Decoupled and coupled variations in the δ13C of Zooplankton (Bosmina) and sedimentary organic carbon from the Last Glacial Maximum tracked changes in atmospheric CO2 and the δ18O records of monsoonal intensity (Dykoski et al., 2005; Wang et al., 2005), highlighting a primary climatic control on coupled terrestrial-aquatic carbon dynamics. Zooplankton and algal production, alongside Bosmina δ13C-inferred lake CO2 concentrations, exhibited synchronous variations with the intensification of the southwest monsoon from ∼10 cal kyr BP, reflecting both increased aquatic production and enhanced terrestrial carbon export driven by forest expansion. These results highlight the critical role of monsoon-driven hydrological changes in regulating terrestrial organic matter inputs to lakes and shaping aquatic carbon dynamics at timescales of 102–103 year.

Effects of Environmental Changes on Flood Patterns in the Jing River Basin: A Case Study from the Loess Plateau, China

Human activities and climate change have significantly influenced the water cycle, impacting flood risks and water security. This study centers on the Jing River Basin in the Chinese Loess Plateau, analyzing hydrological patterns and flood progression using the HEC-HMS model under changing conditions. The findings indicate that climate change substantially affects flood predictions, increasing peak flows and volumes by up to 10.9% and 11.1%, respectively. It is essential to recognize that traditional flood models may underestimate the risks posed by these changes, emphasizing the necessity for updated methods incorporating climatic and human factors. Changes in land use, such as the expansion of grasslands and forests, have reduced peak discharges and flood volumes. Consequently, the combined impacts of climate and land use changes have intensified flood frequencies, necessitating updated strategies to manage risks effectively. The dynamics of flooding are significantly impacted by changes in climate and land use, particularly in minor floods that occur frequently, highlighting the influence of climate change on flooding trends. Within the Jing River Basin, hydrological patterns have been shaped by both climatic variations and human activities, leading to an increase in extreme hydrological events and concerns regarding water security. Using the HEC-HMS model, this study examines the hydrology of the Jing River Basin, focusing on the design of storm events and analyzing various flood characteristics under different scenarios. Climate change has resulted in higher peak discharges and volume surges ranging from 6.3% to 10.9%, while shifts in land use, such as decreases in farmland and the expansion of grasslands, have caused declines ranging from 7.2% to 4.7% in peak flows and volumes. The combined effects of climate variation and land utilization have complex implications for flood patterns, with milder to moderate floods showing a more significant impact and shorter return periods facing increased consequences. These findings underscore the interconnected nature of climate change, land use, and flooding dynamics in the Jing River Basin, highlighting the need for comprehensive strategies to address these challenges and ensure sustainable water management in the region.

Traffic flow prediction based on machine learning

The rapid development of intelligent transportation systems requires accurate prediction of short-term traffic flow. However, the nonlinearity, uncertainty, and spatiotemporal variability of traffic flow make it difficult for traditional traffic flow prediction methods to achieve ideal results. This study uses machine learning methods to improve the accuracy of prediction and expand features by evaluating the clustering effect of the k-means algorithm through data dimensionality reduction, interpolation of missing data, and the silhouette coefficient method. The 5-fold cross-validation and grid search methods were used to optimize the hyperparameters, and the Lasso regression model, ridge regression model and random forest regression model were compared. It was found that the feature expansion of the random forest had the highest fitting coefficient and the smallest error. This improves prediction accuracy, provides a valuable reference for intelligent traffic flow prediction, and has the potential for further optimization in feature selection, model design, and traffic control strategies.

(Invited) Performance As Electrode for Electric Double-Layer Capacitor of Activated Carbon Derived from Bamboo Residue after Steam Treatment Activated By Alkaline Metal Hydroxide

Introduction Biomass-derived activated carbon can be the electrode material for high performance EDLCs, owing to its high surface area, porosity, and excellent conductivity1). The performance of EDLC electrode can be presumed to depend on pore size distribution from the theory of electric double layer. In Japan, the disorderly expansion of bamboo forest is serious problem because it causes economical damage. Therefore, we have already proposed “cascading usage of bamboo”, the extraction of saccharides by hydrolysis and the usage of solid residue as the precursor for EDLC electrode, as the promising candidate of the solution for the bamboo problem2). The alkaline metal hydroxide, such as KOH and NaOH, is known to be the activator for preparing activated carbon, and the pore size distribution could be presumed to depend on the kind of alkaline metal hydroxide from the proposed activation mechanism. In this study, the solid residue of steam-treated bamboo is used as a precursor for activated carbon for the activation by KOH or NaOH, and the performance as EDLC electrode of the activated carbons is investigated in detail. Experimental Moso bamboo powder and water were separately set in a container for autoclave (HU-100, Sanai Science Co., Ltd.). The container was heated at 200°C for 2.5 hours, which was chosen based on previous work3). The treated bamboo was dispersed in distilled water at room temperature for 1 hour for the extraction of sugars. The solid residue after filtration was heated at 500oC for 1 hour with N2 gas flowing for carbonization. The carbonized sample was mixed with KOH or NaOH, and then the mixture was heated at 800°C for 1 hour with N2 gas flowing. The activated sample washed with heated distilled water for several times and then dried. Raman Spectroscopy (NRS-5100, JASCO Corporation) utilizing a 532 nm laser for excitation was utilized to examine the degree of graphitization within the sample structures. The morphology of the samples was examined by Field Emission Scanning Electron Microscope (FE-SEM) from Japan Electronics Co., Ltd (Model JSM-6701F). The adsorption isotherms of the samples for N2 (at 77 K) and CO2 (at 298 K) was measured by a commercial apparatus (BELSORP-miniII, MicrotracBEL). The electrochemical performance, such as cyclic Voltammetry (CV), Electrochemical Impedance Spectroscopy (EIS), and galvanostatic charge/discharge measurement, of the sample was evaluated by using two-electrode cell with 1 M (C2H5)4NBF4 (TEABF4)/PC as electrolyte. Results and Discussions The yield, which is (weight after activation)/(weight before activation), decreased at higher weight ratios of KOH or NaOH, and the yield for NaOH activation was lower than that for KOH activation. The Raman spectra of the samples indicated that the samples have amorphous structure. The SEM images showed that the activated samples kept the microstructure derived from plant tissue. The N2 adsorption isotherms at 77 K for all the activated samples exhibited type I, indicating a microporous structure. However, the NaOH-activated samples displayed gentler slopes at low relative pressure, suggesting wider micropore size distribution. That is, the micropore size distribution depended on the kind of alkaline metal hydroxide.The capacitance values calculated from discharge process at 10 mA g-1 from 2.5-0 V was ca. 25 F g-1, which is comparable to the reported values. The capacitance values calculated from CV curves of NaOH-activated sample, 37.6 F g-1 at 1 mV s-1, was slightly higher than that of KOH-activated sample. Moreover, the decrement of the capacitance value at higher scan rate was trend to be suppressed for NaOH-activated samples. The capacitive contribution calculated according to the Dunn method4) indicated that NaOH-activated carbon showed a higher degree of capacitive contribution (74 %). The wider pore size distribution of NaOH-activated sample could contribute the suppression of the decrement of the capacitance value at higher scan rate. Conclusions The chemical activation by hydroxide of alkaline metal, such as KOH and NaOH, was performed to the solid residue after the extraction of steam-treated bamboo. From the results of the capacitance value, the suppression of the decrement of the capacitance value, and the capacitive contribution, the performance as EDLC electrode of NaOH-activated sample was better than that of KOH-activated sample. The wider pore size distribution for NaOH-activated sample could be one of the reasons for the higher performance. References 1) K.Ö. Köse et al. (2018) International Journal of Hydrogen Energy, 43:18607–16.2) T. Tsubota et al., (2018) Journal of Porous Materials, 25: 1541-1548.3) D.A. Khuong et al. (2023) Materials Chemistry and Physics, 304:127853.4) D. Dubey et al. (2023) Journal of Energy Storage, 58:106441. Figure 1

Allelochemicals from Moso Bamboo: Identification and Their Effects on Neighbor Species

Moso bamboo, which is essential to China’s economy, is currently facing significant threats due to declining profits. Inadequate management of moso bamboo can negatively impact the surrounding ecosystems. This study investigated allelopathy in moso bamboo forests by identifying potential allelochemicals and their effects on coexisting plants. Fresh leaves and litter from moso bamboo were collected to examine allelochemicals released through natural processes such as rainwater leaching and litter decomposition. Seven substances with potential allelopathic effects were identified using liquid chromatography–mass spectrometry (LC–MS). Four of these substances—DBP, PHBA, citric acid, and CGA—were selected for a detailed analysis of their effects on the photosynthetic and antioxidant systems of two naturally coexisting plants, Phoebe chekiangensis and Castanopsis sclerophylla. The results indicated that the four chemicals influenced P. chekiangensis and C. sclerophylla through different patterns of interference. DBP, PHBA, and citric acid negatively impacted the transfer of electrons during photosynthesis in both plants but had a lesser effect on the antioxidant system-related indicators in P. chekiangensis. In C. sclerophylla, these four chemicals led to a significant accumulation of reactive oxygen species (ROS) and increased malondialdehyde (MDA) content and catalase (CAT) activity to varying degrees. Furthermore, the relative abundance of fungi and bacteria in the soil was also affected by the DBP treatment. The identification of allelochemicals from moso bamboo, along with the investigation of their mechanisms, provides valuable insights into competitive interactions among plant species, particularly between moso bamboo and other species, along with the expansion of moso bamboo forests.

The Expanding Truffle Environment: A Study of the Microbial Dynamics in the Old Productive Site and the New Tuber magnatum Picco Habitat.

Truffles are valuable underground mushrooms with significant economic importance. In recent years, their cultivation has achieved satisfactory results, but not for all species. The harvesting of white truffles (Tuber magnatum Picco) is still dependent on natural production, which is at risk due to various issues, such as improper forest management. A useful practice to protect natural resources is to promote the expansion of productive forests. In this study, we investigate the dynamics of the microbiome in an old and new truffle forest using an amplicon sequencing approach of the fungal ITS region and the prokaryotic 16S rRNA gene. We monitor the soil biological community's development to compare differences and similarities between the primary productive forest and the expanding area over a two-year sampling period. In particular, we observed the colonization of vacant ecological niches by certain fungi, such as those belonging to the genus Mortierella. Additionally, we examined the competitive interactions between saprotrophs and ectomycorrhizal fungi (ECM). In both study areas, the bacterial community was dominated by Pseudomonadota, Planctomycetota, and Actinomycetota. The behavior of the Tuber genus differed significantly from other ECMs and displayed positive correlations with bacterial taxa such as Ktedonobacter, Zavarzinella, and Sphingomonas. The present work provides an initial overview of expanding white truffle habitats. Further, more specific research is needed to explore potential connections between individual taxa.

Vegetation response to Early Holocene cooling events in the Moervaart region (northwestern Belgium)

A high‐resolution palynological record from northwest Belgium is presented. The record encompasses the second part of the Younger Dryas and the Early Holocene. The basal part of the pollen record reflects a relatively open landscape, characteristic for the Younger Dryas. The transition to the Holocene is marked by an expansion of birch woodland, followed by an expansion of pine in the Late Preboreal (~11.2–10.7 cal. ka BP). Subsequently, the record shows an expansion of mixed deciduous forest with hazel, elm and oak, characteristic for the Boreal (~10.7–8.6 cal. ka BP). Early Holocene forest expansion was interrupted by a number of short‐lived fluctuations, presumably driven by climatic changes. The most distinct event is the Rammelbeek phase (~11.4–11.2 cal. ka BP), during which forest development was temporarily interrupted in favour of grasses, while wildfires increased. Following this climatic event, hunter‐gatherers returned to the area after a period of almost 1.5 millennia of low population density. They most likely were attracted by the increased temperatures and abundance of edible plants present in the birch‐pine forests and on the banks of the river Kale/Durme. A temporary expansion of pine during the Boreal (~10.7–8.6 cal. ka BP) may correspond with the 9.3‐ka event. At this time, superimposed on a trend of gradual infilling of the channel, a temporary change to drier conditions is observed. The significant drop in the number of prehistoric sites in the Moervaart region clearly cannot be attributed to this short‐term climatic event alone but was most likely caused by a combination of environmental changes, such as the decreasing availability of hazelnuts as well as freshwater and edible (semi‐)aquatic plants as the Kale/Durme river gradually turned dry. The study provides insight into, partly climate‐driven, Early Holocene environmental changes and the effect this may have had on human occupation.

Greening of India and revival of the South Asian summer monsoon in a warmer world

While it is accepted that the tropical hydrological cycle has intensified during past interglacial periods due to changes in insolation, greenhouse gases and ice volume, their respective influences are uncertain. Here we present a pollen record from Bengal Bay to reconstruct vegetation changes in India’s core monsoon zone during two warm periods, the current and last interglacial, comparing the data with numerical model simulations to assess the influence of different forcing mechanisms. Results show tropical forest expansion between 11.7-5 ka and 127-120 ka, defining two Indian humid periods, with the last interglacial showing the strongest monsoon activity, consistent with salinity reconstructions. Model-data comparison highlights boreal summer insolation as the primary driver of vegetation dynamics and monsoon intensity during interglacial periods, with CO2 and ice-sheets having a limited effect. Vegetation remains unaffected by pre-industrial CO2 variations above 250 ppmv, a threshold value that characterizes most interglacials of the last million years.

The Implications of Plantation Forest-Driven Land Use/Land Cover Changes for Ecosystem Service Values in the Northwestern Highlands of Ethiopia

In the northwestern Highlands of Ethiopia, a region characterized by diverse ecosystems, significant land use and land cover (LULC) changes have occurred due to a combination of environmental fragility and human pressures. The implications of these changes for ecosystem service values remain underexplored. This study quantifies the impact of LULC changes, with an emphasis on the expansion of plantation forests, on ecosystem service values in monetary terms to promote sustainable land management practices. Using Landsat images and the Random Forest algorithm in R, LULC patterns from 1985 to 2020 were analyzed, with the ecosystem service values estimated using locally adapted coefficients. The Random Forest classification demonstrated a high accuracy, with values of 0.97, 0.98, 0.96, and 0.97 for the LULC maps of 1985, 2000, 2015, and 2020, respectively. Croplands consistently dominated the landscape, accounting for 53.66% of the area in 1985, peaking at 67.35% in 2000, and then declining to 52.86% by 2020. Grasslands, initially the second-largest category, significantly decreased, while wetlands diminished from 14.38% in 1985 to 1.87% by 2020. Conversely, plantation forests, particularly Acacia decurrens, expanded from 0.4% of the area in 2000 to 28.13% by 2020, becoming the second-largest land cover type. The total ecosystem service value in the district declined from USD 219.52 million in 1985 to USD 39.23 million in 2020, primarily due to wetland degradation. However, plantation forests contributed USD 17.37 million in 2020, highlighting their significant role in restoring ecosystem services, particularly in erosion control, soil formation, nutrient recycling, climate regulation, and habitat provision. This study underscores the need for sustainable land management practices, including wetland restoration and sustainable plantation forestry, to enhance ecosystem services and ensure long-term ecological and economic sustainability.

Land covers associated with forest expansion hot spots in the Nepal Himalaya

Abstract Many regions of the Himalaya are experiencing forest expansion, signifying a widespread forest transition across this fragile mountain system. In the Nepal Himalaya, forest expansion is increasingly attributed to the narrative of cropland abandonment driven by rural outmigration. Understanding forest change dynamics as consequences of land use changes is critical in the context of rural mountain societies such as Nepal, which exhibit complex interactions between forest, agriculture, and livelihoods. We analysed forest gain in Nepal from 2000–2019 to inquire where and when forests have expanded and what land covers transitioned into forests. We integrated spatiotemporal analysis of national land cover maps and photointerpretation of very high resolution (VHR) imagery to improve analytical estimates. The Middle Mountain physiographic region experienced the strongest hot spots of forest gains relative to the high mountains or lowlands. Visual interpretation of VHR showed that 68.8% of forest gains since 2000 occurred on shrubland, and 26.5% on cropland. The dominance of shrubland-forest transitions implied that shrubland is an intermediate stage in a multi-decadal succession process, or a semi-permanent state such as arrested succession. Notably however, we also detected cropland-forest transitions occurring as fast as 6–10 years, indicating the potential for rapid forest succession if biophysical conditions permit. Thus, variations in land cover transitions to forests in the hills implicated multiple pathways of forest regeneration, reflecting a variety of spatiotemporally explicit drivers. Our results differ from previous studies because VHR image analysis accounted for land cover classification errors and improved land cover quantification. Further research on spatially explicit mechanisms and drivers of forest gain are needed to understand the synergies of forest, agriculture, and livelihoods to inform land use land cover policies that could be leveraged to enhance rural mountain livelihoods.

Incorporating modelling uncertainty and prior knowledge into landslide susceptibility mapping using Bayesian neural networks

ABSTRACT In landslide susceptibility (LS) mapping, the reliance on deep learning (DL) models within the frequentist paradigm often leads to an oversight of modelling uncertainties and priors, undermining reliability. This study applied Bayesian neural network (BNN) using variational inference and Bayes backpropagation to LS mapping in Lin’an, China. We developed two BNN configurations: one employing a zero-mean Gaussian distribution as priors (BNN-normal) and another grounded in domain knowledge (BNN-knowledge), and benchmarked them against deep neural networks (DNNs). The study further investigates the models’ performance against random noise impacts on conditional factors and challenges posed by incomplete landslide inventories. Results indicated although BNNs were comparable to the DNNs in generalisation, they demonstrated superior calibration accuracy, resistance to noise and overfitting. Notably, BNN-knowledge effectively mitigated the adverse effects of incomplete data. A landslide confidence map was constructed by integrating the LS and epistemic entropy. Compared to LS maps without uncertainty analysis, the landslide densities in very high and very low LS area with low uncertainty increased by 8.34 and decreased by 0.1, respectively, providing more information for scientific and effective decision-making. Our results further confirmed that sparse vegetation, anthropogenic disturbances and economic forest expansion were main factors of high LS in the region.

The impacts of income inequality, forest area, and technology innovations on ecological footprint in Indonesia: ARDL and ML approach

Reducing humans’ ecological footprint (ECF) is critical for guaranteeing a sustainable future and sustaining the planet's health for future generations. Consequently, sustainability policies and development aim to minimize ECF and guarantee a sustainable future. This study analyzes the effects of gross domestic product (GDP), financial development, renewable energy, share of global forest area, and technological innovations on the ECF of the Indonesian economy from 1990 to 2020. The Autoregressive Distributed Lag approach is applied to observe the varying levels of influence across variables. The findings show significant short-term links between GDP, income inequality, technological advancements, and ECF, and statistically significant long-term relationships between GDP, share of forest area, and technological innovation. The machine learning approach uses neural networks and regression as its parametric models to analyze the data for prediction. Both models can predict how the parameters interacted with ECF, with neural networks making more accurate predictions. The study reveals that economic growth intensifies ECF, whereas income equality decreases it. Technological advancements and forest expansion benefit the environment by reducing the footprint. These insights can provide policy recommendations to minimize ECF in Indonesia and strengthen the efforts to achieve a sustainable future.

Spatiotemporal Variability of Soil Erosion in the Pisha Sandstone Region: Influences of Precipitation and Vegetation

The Pisha sandstone area, situated in the upper and middle reaches of the Yellow River in China, is characterized by severe soil and water erosion, making it one of the most critical regions on the Loess Plateau. The rugged terrain and exposed bedrock complicate management efforts for this area, posing challenges for accurate forecasting using soil erosion models. Through an analysis of terrain, vegetation, and precipitation impacts on soil erosion, this study offers theoretical support for predicting soil erosion within the exposed Pisha sandstone area of the Loess Plateau. This has substantial implications for guiding water and soil conservation measures in this region. Focusing on China’s exposed sandstone area within the Geqiugou watershed, temporal and spatial changes in vegetation cover and land use from 1990 to 2020 were analyzed. The result shows that, from 1990 to 2020, the grassland area has exhibited a consistent downward trend, with successive reductions of 64.86% to 59.46%. The area of low vegetation cover witnessed a significant decline of 59.29% in 2020 compared to that in 1990. The moderate erosion area decreased from 84.52 to 57.17 km2. The significant reduction in soil and water loss can be attributed to the expansion of forest and grassland areas, with the implementation of the Grain for Green project serving as a key policy driver for facilitating this expansion. This study provided a good example of combining rainfall with vegetation coverage to fast estimation soil erosion. A mathematical relationship between the vegetation rainfall coupling index (RV) and soil erosion was established with strong fitting effects, enabling estimation of the soil erosion volume under varying slope conditions within Pisha sandstone areas. The main focus of future soil and water conservation in the Pisha sandstone area should be on effectively managing the channel slope and minimizing exposed bedrock areas through a combination of slope cutting, the application of anticorrosive materials, and the implementation of artificial vegetation planting.

Highland forest dynamics across equatorial East Africa during the end of the African humid period

Tropical mountain ecosystems hold immense ecological and economic importance, yet they face disproportionate risks from shifting tropical climates. For example, present-day montane vegetation of East Africa is characterized by different plant species that grow in and are restricted to certain elevations due to environmental tolerances. As climate changes and temperature/rainfall zones move on mountains, these species must rapidly adjust their ranges or risk extinction.Paleoenvironmental records offer valuable insights into past climate and ecosystem dynamics, aiding predictions for ongoing climate change impacts. In particular, warming and wetting in tropical East Africa during the mid-Holocene resulted in both lowland and highland forest expansion. However, the relative impacts of rainfall and temperature change on montane ecosystems along with the influence of lowland forest expansion on montane communities is not completely understood. We use fossil pollen to study the vegetation changes in two lakes at different altitudes in the Rwenzori Mountains, Uganda: Lake Mahoma (Montane Forest belt) and Upper Kachope Lake (Afroalpine belt). Further, using the newly relaunched African Pollen Database and recent temperature reconstructions, we provide a regional synthesis of vegetation changes in the Rwenzori and then compare this with changes observed from other equatorial East African montane sites (particularly Mt Kenya).In the early to mid-Holocene in the Rwenzori Mountains, trees common today in lowland forests dominated, driven largely by warmer temperatures. After 4000 years ago (4ka), Afromontane forest trees along with grasses progressively replaced lowland trees. Not all sites experienced identical transitions. For instance, at Lake Rutundu on Mt Kenya at the same elevation as Lake Mahoma, bamboo expansion preceded Afromontane forest growth, likely influenced by variations in fire. Variance partitioning indicates that each site responded differently to changes in temperature and rainfall. Therefore, these site-specific ecological responses underscore the importance of considering biogeographic legacies as management strategies are developed, despite similarities in modern ecology.

Vegetation and environmental changes on the Northeast China Plain during warm periods since MIS 3

Understanding past terrestrial ecosystem responses to climate changes is vital for future predictions, but research in densely populated plains is limited due to insufficient materials. This study focuses on vegetation and environmental changes in the Northeast China Plain since Marine Isotopic Stage (MIS) 3 using pollen analysis from core WCZK03. The findings reveal significant shifts in vegetation that correspond to climatic events. During MIS 3 (52–29 cal ka BP), the region was predominantly a lake environment with vegetation transitioning from grassland dominated by Poaceae, Chenopodiaceae and Artemisia to conifer-broadleaf mixed forests as the climate ameliorated. The Last Glacial Maximum (29–17 cal ka BP) was characterized by loess deposits, followed by drought-tolerant grassland (17.0–11.3 cal ka BP) dominated by Artemisia and Chenopodiaceae in the plain. The onset of the Holocene witnessed the expansion of conifer and broad-leaved deciduous forests in hilly areas and the retreat of grassland in the plain. The sedimentary sequence shows transitions from fluvial-lacustrine deposits to loess-like and black soil deposits, showing significant environmental changes. This study suggests that changes in vegetation on the Northeast China Plain were closely related to regional climate patterns and were more responsive to climate changes than the surrounding mountainous areas.

How the characteristics of land cover changes affect vegetation greenness in Guangdong, a rapid urbanization region of China during 2001-2022.

To evaluate the quantitative impacts of land cover change on vegetation greenness in the significantly human-impacted subtropical region, the characteristics of land cover change were explored by land use dynamic degree, transition matrix and normalized entropy. Various methods including Standardized coefficient, LMG (Lindeman-Merenda-Gold), GEN (Genizi measure) and CAR (Correlation-Adjusted Marginal Correlation) were employed to estimate the contributions of land cover changes on vegetation greenness using MODIS data during 2001-2022 in Guangdong. The conclusions revealed that land cover changes exhibited obvious temporal characteristics in Guangdong with a significantly increasing trend of normalized entropy indicating a more balanced distribution of land cover types under human intervention. NDVI (Normalized Difference Vegetation Index) tended to increase likely due to the large-scale increase in evergreen forest. With regard to the contributions of impact factors on vegetation greenness, the contributions evaluated by LMG, GEN and CAR showed that the natural variation of NDVI accounted for the major contribution (> 33%), while the changes of evergreen forest and grassland had the highest contribution (> 37%) according to Standardized coefficient. These differences were mainly due to the characteristics of land cover changes in Guangdong, the correlations among impact factors and the inherent attributions of the methods. Moreover, the expansions of evergreen forest and urban at the expense of the reductions of grassland and cropland also had significant impacts on NDVI (> 10%) according to LMG, GEN and CAR indicating that human-induced land cover changes had remarkable influences on NDVI.