Forest Cover Research Articles

Links between deforestation, conservation areas and conservation funding in major deforestation regions of South America

Abstract Land‐use change is a major driver of biodiversity loss, affecting tropical forests and savannas at an unprecedented rate. To protect these ecosystems and their biodiversity, national conservation areas and international conservation funding have increased considerably in these regions. Understanding of how conservation funding allocation relates to dynamics in deforestation and conservation areas is crucial to identifying what mobilizes and distributes conservation funding. By applying fixed‐effect models on 30 years of dynamics in forest cover, conservation areas and conservation funding in the main deforestation regions of South America, we analysed the conservation funding allocation strategies in relation to deforestation (proactive vs. reactive), conservation areas (expanding or consolidating) and previous investment (agglomerating or not). We also assessed whether allocation strategies vary across regions and the stages of deforestation and conservation. We found that funding allocation followed conservation areas and higher prior funding levels over space and over time, across all regions and deforestation stages. This highlights the important role of recognized conservation areas (including protected areas and Indigenous territories) in mobilizing financial support for conservation. The allocation strategies relating to forest dynamics, however, varied depending on the scale of analysis, whether we look at the temporal or spatial dimension, and in which regions we assessed the allocation patterns. Capturing region‐level biases and within‐region dynamics is crucial for more effective and equitable conservation. As public and private actors commit to mobilizing more conservation funding and conserving more areas of the Earth, our insights on the relations between forest change and conservation efforts can help improve the allocation of conservation areas and conservation funding. Read the free Plain Language Summary for this article on the Journal blog.

Sustainable Spatial Distribution and Determinants of Key Rural Tourism Villages in China: Promoting Balanced Regional Development

Understanding the spatial distribution and sustainable development of rural tourism is essential for promoting balanced regional growth and formulating optimized policy strategies. This study aims to provide insights into sustainable development and policy optimization. Utilizing geographic information system technology, dominance analysis, and Geodetector statistical methods, this research offers a comprehensive examination of the spatial patterns and determinants of these distributions. The findings reveal significant regional disparities and clustering, with a higher concentration of key villages in economically developed eastern and central regions and fewer in the less developed western regions. The dominance analysis highlights that provinces such as Zhejiang, Shandong, and Beijing demonstrate strong advantages across multiple dimensions, including ecological environment, economic development, tourism infrastructure, transportation accessibility, policy support, and social development. Conversely, regions such as Ningxia, Qinghai, and Tibet exhibit lower dominance scores, indicating challenges in rural tourism development due to limited resources and infrastructure. Key influencing factors include forest coverage, GDP per capita, the number of star-rated hotels, transportation network density, policy initiatives, and urbanization rates. The results underscore the importance of a multi-dimensional approach to enhance rural tourism competitiveness and suggest targeted strategies for underperforming regions. This study contributes to advancing the theoretical framework of sustainable rural tourism and provides actionable insights for policymakers to foster balanced regional development, ecological conservation, and community-centered tourism practices.

Environmental impacts of the billion tree Tsunami project in Khyber Pakhtunkhwa on the dynamics of Agro-Meteorological Droughts

Changes in forest cover are closely associated with the variability in meteorological and hydrological variables. Therefore, this study delves into investigating how forest cover changes impact the environment (i.e., hydro-meteorological variables, including precipitation, streamflow, relative humidity (RH), evapotranspiration (ET), and temperature) using Trend Projection (TP) methods during 1980–2019. The study is carried out in the Khyber Pakhtunkhwa (KP) province of Pakistan, which witnessed deforestation between 1980 and 2010 followed by afforestation (through billion tree tsunami project, BTTP) initiated in 2014. A new drought index, named as agro-meteorological drought index (AMDI), is developed in this study using the remotely sensed data to analyze the impact of forest cover on drought severity. The robust least square regression (RLSR) model is used to regress the normalized difference vegetation index (NDVI) with AMDI at various time scales to investigate the impact of forest cover on drought severity. The RLSR and paired t-test are used to quantify the impact of forest cover and BTTP, in particular, on the environment. Land use maps prepared for KP province over a span of the past four decades revealed significant deforestation during 1985–2005, transitioning gradually to afforestation in the past decade. Results indicated a decline in streamflow throughout different seasons with an increase in forest cover, particularly during the period of afforestation (i.e., 2015–2019). The precipitation, RH (maximum/minimum), and ET displayed an increasing trend over time, whereas a decrease trend is observed in Tmax/Tmin and streamflow at most of the stations. The trend analyses depicted a significant change before and after the BTTP. The paired t-test results revealed that BTTP has statistically significant impact on the environmental variables. Furthermore, the time series plots of AMDI at different time scales indicated that drought events were frequent and severe prior to 2003, whereas significant decrease in both the frequency and severity of drought was observed in the last decade (2010–2019). The RLSR results at pixel scales demonstrated the crucial role of forest covers in alleviating both the frequency and severity of drought events. The elasticities revealed that increase in the forest cover resulted in substantial increase/decrease in each hydro-meteorological variable. Overall, the results highlighted a positive and statistically significant impact of forest cover (i.e., BTTP) on both the environment and drought variability.

Ecological drivers of sexual size dimorphism in northernchamois.

Male-biased sexual size dimorphism (SSD) is common in ungulates. The dominant scenario for the evolution of ungulate SSD suggests that habitat openness leads to greater SSD by increasing group size and thus sexual selection through male-male competition for mates. At a more proximate level, adaptive changes in SSD may result from the plastic response of individuals to environmental variation. In this study, we used 161,948 body mass data from a seasonally size-dimorphic species, the northern chamois Rupicapra rupicapra, to examine the role of forest cover and other environmental variables in the expression of SSD. Data were collected from individuals hunted in the Austrian Alps, grouped into 28 mountain ranges with different forest cover, geological substrate and population density. Population-specific growth curves were fitted using monomolecular models, and SSD was calculated as the log-transformed ratio of male to female asymptotic body mass. A path model in which environmental factors indirectly influenced SSD via male or female body mass suggested that SSD increased with increasing density via reduced female body mass and decreased on siliceous substrates via reduced male body mass. Forest cover was negatively associated with body mass in both sexes, but not with variation in SSD.

Machine learning approaches for mapping and predicting landslide-prone areas in São Sebastião (Southeast Brazil)

This study employs machine learning techniques to map and predict landslide-prone areas in São Sebastião, Brazil, a region susceptible to landslides due to its steep terrain and intense rainfall. We compared five algorithms: Random Forest, Gradient Boosting, Support Vector Machine, Artificial Neural Network, and k-Nearest Neighbors, using various environmental factors as inputs. The Gradient Boosting model performed best, achieving an AUC-ROC of 0.963 and an accuracy of 99.6%. Slope degree, soil moisture index, and relief dissection emerged as the most influential factors in predicting landslide susceptibility. Analysis of land use and land cover changes between 1985 and 2021 revealed significant increases in forest cover and urban areas, with implications for landslide risk distribution. The resulting susceptibility map shows predominantly low-risk areas with scattered high-risk zones, providing crucial information for targeted risk management. This research demonstrates the effectiveness of machine learning in landslide susceptibility mapping and offers valuable insights for disaster risk reduction and urban planning in coastal mountainous regions.

Spatiotemporal analysis of wildfires and their relationship with climate and land use in the Gran Chaco and Pantanal ecoregions

The Gran Chaco and Pantanal ecoregions are the largest remaining dry forest areas in South America. Supporting diverse savanna, woodland and wetland ecosystems, these ecoregions are experiencing rapid changes in land use and fire occurrence with implications for ecosystem integrity. Our study characterizes the spatiotemporal patterns of wildfires in the Gran Chaco and Pantanal, and then examines the relationship between patterns of fire occurrence and climatic and anthropogenic drivers. We evaluated fire data of the last two decades (2001-2020) using the MODIS Collection 6.1 and the Global Fire Atlas products. Results of the fire pattern characterization were then used to model the probability of fire occurrence across each ecoregion (Random Forest, Generalized Linear Model, and Generalized Additive Model).Our results indicated that most of the total burned area belonged to the Humid Chaco, while the largest individual burned areas were mainly observed in the Pantanal. Fires primarily occurred during the dry season, with the majority of burned areas recorded during this period. Findings from the three modelling approaches consistently illustrated the spatial distribution of fire occurrence, depicting a declining probability of fire occurrence from East to West. All models underscored the importance of three variables to predict fire occurrence: temperature, livestock abundance and forest cover. Fire occurrence increased with increasing maximum temperatures and livestock presence and decreased with tree cover. This research helps to clarify the potential consequences of changes in land use, rainfall regime and temperature, and uncontrolled burning practices on the current fire activity in the Gran Chaco and Pantanal ecoregions. Understanding the spatiotemporal patterns of fire occurrence and their relationship with climatic, environmental and anthropogenic drivers can help to design more effective management strategies to mitigate fire impacts and to preserve the ecological integrity of these highly diverse regions

Distributions of non‐native and native plants are not determined by the same environmental factors

Abstract Global environmental change will cause shifts in species communities, with non‐native species likely replacing native ones at an unprecedented rate. This will have consequences for biodiversity and ecosystem services, in addition to the ecological and economic damage caused by those non‐native species that are invasive. Understanding general patterns driving distributions of native and non‐native species is therefore vital, but no study has compared yet whether environmental variables that correlate with a species' presence differ between the two groups other than at local scale and often with very limited sample size. In this study, we focus on 141 native and non‐native congeneric plant species pairs at the scale of Switzerland. In the framework of correlative species distribution models, we used newly developed methods for efficient automated selection of a parsimonious number of predictor environmental variables to determine which ones, out of a large candidate set in eight classes, have the strongest explanatory power for both species groups. Our results indicated that variables influence the two groups in significantly different ways. Climate was by far the strongest determinant of both native and non‐native species distributions, although it had significantly more explanatory power in native species models. Models for non‐native species were significantly more influenced by anthropogenic factors, land use variables and forest cover. The presence of non‐native species was also associated with habitats with a significantly lower mean naturality value than native species. These findings provide novel empirical evidence for the different environmental factors driving native and non‐native plants' distributions and guidance for non‐native species management. Practical implications: species distribution models are an increasingly frequently advised tool for conservation management and our results provide guidelines on which covariates should be specifically considered to assess the habitat suitability of non‐native versus native species. The distributions of the former group of species are particularly important to research, as, in time, they may turn invasive. In addition, areas close to infrastructure should be scanned regularly for incipient colonizations by non‐native species, especially in as yet uninvaded areas, such as high mountains.

Morphometric analysis and LULC change dynamics of Nayar watershed for the sustainable watershed management

The morphometric analysis of the watersheds is essential for the conservation of natural resources, including soil, water, and vegetation. The Morphometric analysis defines the linear, areal and relief aspects of the watershed. It involves the comprehensive analysis of various factors such as drainage network, surface water flow, and other topographical features. The aim of this study is to develop a sustainable watershed management strategy for the Nayar watershed based on morphometry and Land Use Land Cover (LULC) change dynamics. The Nayar watershed has a total area of 1956.33 km2. The multispectral satellite imagery, Digital Elevation Model (DEM) data, and Survey of India (SOI) Toposheets were used for understanding the topographical and morphological characteristics along with the LULC change dynamics. The findings of this research conclude that the Nayar watershed has parallel and dendritic drainage patterns with high relief. According to the LULC change dynamics, the Nayar watershed's area change comprising 57.60 km2 and 57.15 km2, are from Agricultural Land to Wasteland and from Forest Cover to Wasteland, respectively. This shift in the hilly region will increase the risks of landslides and erosion. Furthermore, the change of 110.03 km2 area of Forest Cover to Agricultural Land raises further challenges due to loss of natural vegetation in long run. In summary the change in LULC of Nayar watershed is vulnerable to risk of natural calamities like landslide, erosion. This work would be helpful to many experts and decision-makers for sustainable watershed management and natural resource management.

Investigating the Long-Term Effects of Anthropogenic Practices on Soil Features

Anthropogenic practices have been increasingly conducted on the rise in addressing the escalatingdemands for socioeconomic development, resulting in significant impacts on land ecosystems. This research seeks toevaluate the influence of anthropogenic activities on soil features in mountainous regions of Vietnam by focusing 84samples collected from 12 locations across Lam River Basin at seven soil profiles (0-10, 10-20, 20-30, 30-40, 40-60, 60-80 and 80-100 cm).The findings reveal notable differences in soil texture among land use types (LUTs). Forest cover lands (FCLs),showed the least amount of sand content, varying from 29.7% to 37.6%, while unplanted and bare lands (UBLs) had thehighest sand ratios, up to 53.9%. FCLs exhibited lowest bulk density (BD), soil porosity (SP) and soil electricalconductivity (EC) and C:N ratio with respective ranges of 0.93-1.29 g.cm-3, 32.7-36.5%, 0.526-0.743 mS.m-1 and6.74 -8.52, respectively.In contrast, crop cultivation lands (CCLs) demonstrated higher values for BD (1.17-1.25 g/cm3), SP (39.25-43.19%), EC (0.583-0.792 mS.m-1) and C:N ratio (11.27-15.77). UBLs, on the other hand, exhibited even highest valuesup to 1.23-1.36 g.cm-1, 43.19-49.62%, 0.437-0.619 mS.m-1 and 11.68-16.58% and displayed high levels of exchange ironsand soil organic content compared to FCLs. Other factors such as pH varied little in space between the sampled soillocations and along the soil profiles. Overall, the study indicates that anthropogenic practices have impacts on the soilfeatures across the study area.

Achieving Nepal's clean cooking ambitions: an open source and geospatial cost–benefit analysis

Cooking with traditional fuels can lead to severe health issues caused by household air pollution, and can also affect gender equality and drive environmental degradation. In Nepal, despite government efforts to promote electric cooking, more than half of the population still uses traditional fuels, with electric cooking adoption remaining below 1%. Several of the barriers to and enablers of clean cooking vary geographically; however, few studies have considered spatial explicit information in planning national-scale transitions to clean cooking. In this study we provide a spatially explicit roadmap to estimate the required investments and benefits gained from the transition across Nepal. This study uses geospatial modelling methods to evaluate strategies to achieve the Government of Nepal's vision for a national-scale transition to clean cooking. We integrate the open-source clean cooking geospatial assessment tool OnStove and a spatial multicriteria analysis model. With OnStove, we evaluate which cooking technologies and fuels maximise the net benefits of a clean-cooking transition across each km2 of the region. With the multicriteria analysis, we weigh stakeholder preferences and prioritise areas of action where policy should be implemented. We used the most up-to-date geospatial data to the year 2023, such as the High Resolution Settlement Layer, Open Street Maps' road networks, the Global Human Settlement Layer, NASA/USGS forest cover maps, and Facebook's Relative Wealth Index, among others. We also relied on data from the Nepal Oil Corporation, the Nepal Electricity Agency, the Central Bureau of Statistic's 2021 national census, and the Alternative Energy Promotion Center. We evaluate four scenarios capturing advances on clean cooking policy up to the year 2022, current market inefficiencies, and the potential effects of new policies for clean-cooking transition in Nepal. Our results show that transitional and clean cooking technologies provide higher net benefits than traditional options everywhere across Nepal in all scenarios. Our net-benefit analysis shows that around 9563 deaths could be averted yearly if benefits and externalities were perceived and valued correctly. Furthermore, substantial benefits could be achieved in regard to greenhouse gas emissions avoidance, time saved, and health-cost reductions. Our results also show that the current subsidy strategy from the Government of Nepal is well aligned with the benefits achieved under a cost-benefit analysis. In this context, electric cooking can bring the highest benefits to the largest part of the population. The analysis showed how high subsidies for liquefied petroleum gas in Nepal can present trade-offs with energy security and independence, and how this could be avoided by transferring part of the subsidy to cover differentiated electric cooking tariffs. Accounting for stakeholder preferences and sociodemographic and geographical differences to prioritise areas of focus can balance affordability constraints and target the most vulnerable people first, thus achieving integrated and inclusive planning. Using spatially explicit modelling approaches to evaluate strategies for a clean cooking transition can provide more nuanced results that have not been possible before. This approach can enable data-driven and integrated planning to help to understand which locations of a study area should be prioritised for policy application. Integrated planning can help to reduce affordability constraints on the population and design strategies for a sustainable and inclusive transition. These strategies allow financial institutions, donors, impact investors, development organisations, and government agencies to use their resources, funds, and assistance to create a large impact. Clean Cooking Alliance.

Linking changes in landscape structure to insect herbivory in forest edges and interiors of Atlantic Forest remnants.

Human activities have triggered profound changes in natural landscapes, resulting in species loss and disruption of pivotal ecological interactions such as insect herbivory. This antagonistic interaction is affected by complex pathways (e.g., abundance of herbivores and predators, plant chemical defenses, and resource availability), but the knowledge regarding how forest loss and fragmentation affect insect herbivory in human-modified tropical landscapes still remains poorly understood. In this context, we assessed multi-pathways by which changes in landscape structure likely influence insect herbivory in 20 Atlantic forest fragments in Brazil. Using path analysis, we estimated the direct effects of forest cover and forest edge density, and the indirect effect via canopy openness, number of understory plants and phenolic compounds, on leaf damage in understory plants located in the edge and interior of forest fragments. In particular, plants located in forest edges experienced greater leaf damage than interior ones. We observed that landscape edge density exerted a positive and direct effect on leaf damage in plants sampled at the edge of forest fragments. Our findings also indicated that forest loss and increase of edge density led to an increase in the canopy opening in the forest interior, which causes a reduction in the number of understory plants and, consequently, an increase in leaf damage. In addition, we detected that phenolic compounds negatively influence leaf damage in forest interior plants. Given the increasing forest loss in tropical regions, in which forest fragments become stranded in highly deforested, edge-dominated and degraded landscapes, our study highlights the pervasive enhancement in insect herbivory in remaining forest fragments-especially along forest edges and canopy gaps in the forest interior. As a result, increased herbivory is likely to affect forest regeneration and accelerate the ecological meltdown processes in these highly deforested and disturbed anthropogenic landscapes.

Urban forest cover and ecosystem service response to fire varies across California communities

Urban fires that result from wildfires are an emerging, extreme event affecting communities and urban forests globally. However, much of the fire effects on urban ecosystems literature is primarily focused on Wildland-Urban Interface (WUI) areas, correlates of building loss, risk mitigation, and wildland vegetation and fuels. Three recent urban fires in California USA provided an opportunity to explore these effects on urban forests: the 2017 Tubbs (Santa Rosa) and Thomas (Ventura) and the 2018 Camp fire that burned Paradise. Accordingly, we analyzed pre- and post-fire neighborhood level urban tree cover (nUTC) change over 5 years using Sentinel and LiDAR data (10m resolution). Then, we explored the effects of fire severity on changes in several regulating ecosystem services (e.g., carbon, air pollution, stormwater). Findings from the Rapid Assessment of Vegetation Condition after Wildfire severity processes and other geospatial datasets show that fire effects were patchy with fire severity within neighborhoods ranging from unburned to extreme. We found that ~5 years after the fires, and relative to adjacent non-fire affected neighborhoods, Ventura and Santa Rosa’s nUTC is recovering to pre-fire levels while Paradise’s nUTC is being consistently lost over time. Ventura and Santa Rosa had 20-25% of their fire affected area outside established WUI boundaries. However, local-scale urban tree cover and ecosystem service supply are lagged, and recovery time depends on the surrounding biome and socio-ecological context. In inland, higher elevation communities – like Paradise – the recovery of nUTC and the associated ecosystem services might materialize over a much longer timeframe. Our study provides a roadmap to assess the response of urban wildfire-affected tree cover and ecosystem services across space and time. It is also one of the first assessments of fire effects on urban ecosystems and communities outside of WUI boundaries that are now experiencing increased threat from wildfires globally.

Biodiversity Conservation Strategies From No Net Loss to Net Gain. A Multidimensional Accounting Method

AbstractBiodiversity credits are increasingly recognized as a potential instrument to incentivize and bolster efforts in biodiversity conservation. Nevertheless, their efficacy is impeded by a dearth of research. To mitigate these constraints, this study introduces a comprehensive and integrated framework for appraising biodiversity credits. Drawing upon the Emergy Accounting methodology, the framework encompasses four key perspectives: Emergy‐based Ecosystem Potential (EEP), Emergy‐based Ecosystem Network (EEN), Emergy‐based “Species' to Human” contributions (ESH), and Emergy‐based Species' Significance. Furthermore, this study scrutinizes the trajectory of biodiversity credits across 31 provinces spanning from 2000 to 2050, considering 220 distinct scenarios. The findings reveal that China has attained the no net loss (NNL) objective concerning conventional area‐based conservation targets, with forest cover encompassing 27% of the total land area. However, biodiversity credits at the ecosystem level exhibit an escalating trend, with growth rates ranging from 0.73% to 1.0%, while credits at the species level depict a decremental trend, with an approximate growth rate of −0.21%. Under a scenario of moderate growth, projections for the year 2030 indicate that the EEP credit is poised to accrue approximately 4.76E + 20 solar emjoules (sej), the EEN credit is forecasted to accumulate around 1.03E + 21 sej, and the ESH credit is anticipated to decline by 1.46E + 23 sej within the context of the NNL paradigm. These outcomes underscore the necessity of delineating differentiated biodiversity goals, and furnish insights into the dynamics of supply and demand pertaining to biodiversity credits within the ambit of offsetting schemes across the nation.

Prospects for Neotropical Forest Birds and Their Habitats Under Contrasting Emissions Scenarios.

Current and near future climate policy will fundamentally influence the integrity of ecological systems. The Neotropics is a region where biodiversity is notably high and precipitation regimes largely determine the ecology of most organisms. We modeled possible changes in the severity of seasonal aridity by 2100 throughout the Neotropics and used birds to illustrate the implications of contrasting climate scenarios for the region's biodiversity. Under SSP-8.5, a pessimistic and hopefully unlikely scenario, longer dry seasons (> 5%), and increased moisture stress are projected for about 75% of extant lowland forests throughout the entire region with impacts on 66% of the region's lowland forest avifauna, which comprises over 3000 species and about 30% of all bird species globally. Longer dry seasons are predicted to be especially significant in the Caribbean, Upper South America, and Amazonia. In contrast, under SSP-2.6-a scenario with significant climate mitigation-only about 10% of the entire region's forest area and 3% of its avifauna will be exposed to longer dry seasons. The extent of current forest cover that may plausibly function as precipitation-based climate refugia (i.e., < 5% change in length of dry periods) for constituent biodiversity is over 4 times greater under SSP-2.6 than with SSP-8.5. Moreover, the proportion of currently protected areas that overlap putative refugia areas is nearly 4 times greater under SSP-2.6. Taken together, our results illustrate that climate policy will have profound outcomes for biodiversity throughout the Neotropics-even in areas where deforestation and other immediate threats are not currently in play.

Drivers and Buffers of Stream Flashiness in the Mid‐Atlantic United States

ABSTRACTFlashiness is a hydrologic signature used to describe the responsiveness of streamflow to precipitation. In practise, flashiness has most often been correlated with rainfall intensity and the presence of urban land cover or imperviousness. However, a number of watershed characteristics can influence flashiness, necessitating further investigation of how this particular watershed behaviour and its associated drivers vary at regional scales. We investigated how ten measurable watershed characteristics are empirically related to flashiness of 195 gauged streams in the Mid‐Atlantic region using linear regression analysis. The selected gauges had drainage areas ranging in size from 15 to 250 km2. To explore multiple quantifications of flashiness, we determined both the Richards‐Baker flashiness index, calculated from the continuous hydrograph, and number of peaks‐over‐threshold, for all watersheds for a 10‐year period. As found in other studies, the percentage of watershed development was strongly correlated with stream flashiness, though significant inverse relationships also existed for forest cover, water and wetlands and carbonate geology. Differences in flashiness values between rural and suburban watersheds were not significant, with a significant increase in flashiness (p &lt; 0.01) occurring at 80% development or ~ 25% imperviousness. In addition, we found significant differences in relationships between watershed characteristics and flashiness when differentiated by physiographic regions. For the study area and each of its five physiographic sub‐regions, we identified the four most important predictors through multiple regression. The overall mid‐Atlantic model suggested that development, wetlands and compactness ratio as the most important predictors (adj R2 = 0.57). Significant predictors of stream flashiness varied among regions; however, wetlands served as a significant buffer of flashiness in four of five sub‐regions demonstrating the importance of wetlands in modulating flash‐flooding. In all physiographic regions, empirical models of flashiness values outperformed the Mid‐Atlantic regional model, underscoring the value of local landscape characteristics in augmenting or modulating flashy watershed responses. Our findings suggest the potential for improvements to operational flash‐flood forecasting using hydrologic characteristics of the landscape.

Phylogenetic structure and turnover between lowland and montane subtropical forests indicate differential community assembly processes, affected by successional stage and spatial gradients

Secondary forests represent an increasing proportion of global forest cover and offer a range of ecosystem services that are integral to environmental policy targets. Highly disturbed landscapes with temporal records of forest plant community composition offer key insights into the processes that shape plant communities as forests undergo secondary succession. We investigated patterns of plant community phylogenetic structure and phylogenetic beta diversity. Mean pairwise distance (MPD) and Mean Nearest Taxon Distance (MNTD) metrics were determined between co-occurring species within plant communities, in addition to phylogenetic beta diversity metrics relative to null models of random phylogenetic assembly. MPD and MNTD were compared between elevational and successional classes and modelled as products of intercommunity distance (metres) and forest community age (years). Phylogenetic nonmetric multidimensional scaling explored phylogenetic community structure between lowland and montane forest type in Hong Kong. We found that plant communities in secondary forests in Hong Kong exhibited patterns of increased basal phylogenetic clustering with increasing community elevation, while phylogenetic turnover was influenced by spatial and successional factors in lowland and montane forest. Our findings indicate differential community assembly in lowland compared to montane forest type in Hong Kong, as well as evidence for barriers to plant dispersal in secondary forest communities due to patterns of spatial phylogenetic clustering. Plant communities in montane forests were found to be phylogenetically distinct from lowland forest plant communities. Patterns of spatial phylogenetic clustering may suggest significant dispersal or post-dispersal processes causing the clustering of related species at fine spatial scales, while phylogenetic turnover with increasing successional age may indicate compositional changes during the process of passive forest regeneration. Collectively, these results emphasize the need to investigate active pathways for rewilding dispersal limited late successional forest tree species in Hong Kong.

Exploring soil erosion and reservoir sedimentation through the RUSLE model and bathymetric survey

The aim of the paper was to compare the soil erosion in a river catchment with the sediment volume accumulated at the river mouth. Firstly, the sediment yield was estimated in GIS based on the soil loss according to the RUSLE model, and then further integrated into the sediment production equation. Following this, we estimated the sediment volume accumulated at the river mouth based on the diachronic overlap of topographic and bathymetric data. This methodology was validated for the Eselnita catchment exiting into the Iron Gates I Reservoir. The LS factor has an average value of 4, with lower values for the forest cover. The C factor has an average value of 0.057 being statistically correlated with the RUSLE result. The average soil loss was estimated at approximately 1.89 t ha-1 yr -1, a value that is validated by previous studies as a low risk of erosion at national scale. The sediment transfer model indicates a distribution of cells sediment production strongly correlated with the time travel to the discharge channels. Overall, the sediment volume obtained by using the RUSLE model corresponds to about 70% of the sediment volume accumulated at the river mouth during 53 years (1970-2022). The difference in sedimentation may be due to human activities along the river mouth’s banks to extend the built-up area and to enjoy the waterscape. This paper is relevant for the topic of reservoir sedimentation and recommends the use of the RUSLE model to predict the sediment contribution, especially for small ungauged catchments.

How does regional carbon transition affect loan pricing? Evidence from China

Existing research on carbon risks mainly focuses on firm-level transition risks and regional physical risks, while the impact of regional transition risks on banks has been neglected. This study fills this gap by revealing the pricing effect of regional carbon transition risk in bank lending. Using syndicated loan data, we find a significant premium in loans in high carbon emission areas after the Paris Agreement. Moreover, the regional carbon risk premium is relatively low in areas with higher product marketization, lower legalization, and higher forest coverage levels. Our evidence suggests that banks have considered regional carbon transition risk.

Timber and carbon sequestration potential of Chinese forests under different forest management scenarios

Developing forestry action plans with the goal of carbon neutrality is a critical task to identify carbon sink potential and balance the pathways of China's forests. Current research that predicts forest biomass carbon stock and sink potential has shortcomings, such as an incomplete assessment of China's forest carbon sink range, assumptions based on unchanged forest area in the base year and insufficient consideration of natural and human disturbance factors. This study utilised the national forest inventory (NFI) data to construct a model of forest growth and consumption using a machine learning algorithm (i.e. random forest), identified suitable areas for future forest expansion by integrating multi-source data, and set up three future forest management scenarios: business as usual (BAU), enhanced policy scenario (EPS) and maximum potential scenario (MPS). In addition, changes in the area, volume stock and biomass carbon stock in China's forests between 2020 and 2060 were predicted under three forestry activities (i.e. existing forest, afforested/reforested (AR) forest and forest conversion) and three climate scenarios (i.e. SSP126, SSP370 and SSP585) based on the 9th NFI (2014–2018). According to China's relevant planning goals and suitable forest space, the area of AR forests is predicted to be 55.55 × 106 hm2 by 2060, and the forest coverage rate is predicted to increase from 23% in 2018 to 28% by 2060. Biomass carbon sequestration (BCS) between 2020 and 2060 in AR forests is predicted to be 36.00 TgC per year. By 2060, the average BCS is predicted to be approximately 140.00–287.56 TgC per year in China's forests, which is mainly owing to arbor forest management. Under the BAU and EPS scenarios, BCS in China's forests is expected to decline from 2020 to 2060. However, under the MPS, BCS in China's forests is projected to increase and be maintained at 322 TgC per year or above by 2060, with wood production reaching 4.71 × 108 m3 per year. China's forests are predicted to experience an increase in biomass carbon stock in the future and play a role as a carbon sink. By taking measures to achieve the maximum growth potential of all forest types, China's forests will achieve a win‒win situation between carbon sinks and timber production.

The Accelerating Loss of Resilience in Suburban Woodlands Can Largely Be Attributed to the Changes in Urban Precipitation Patterns.

Vegetation resilience holds significant importance for stabilizing ecosystem service functions in a changing climate. While global land surface vegetation resilience changes have been extensively studied, the impact of urbanization on the resilience of suburban woodlands remains inadequately understood. In this study, we utilized two critical slowing down (CSD) indicators, namely lag-one autocorrelation (LOA) and variance (VA), to assess the vegetation resilience, its long-term trends, and influencing factors in suburban woodlands across 1356 cities worldwide. The recovery rates estimated by LOA ( ) and VA ( ) showed close alignment in suburban woodlands with low suburban forest coverage (SFC) areas (correlation coefficient (r) = 0.95). However, a notable divergence was observed in areas with high SFC (r = 0.73). Suburban woodlands with high SFC typically exhibited lower recovery rate estimates, thus indicating greater vegetation resilience compared to areas with lower SFC. From 1986 to 2022, the recovery rates of suburban woodland areas in over 83% of the cities demonstrated a significant upward trend, with an average of 3.23 × 10-3 year-1 for both and , signifying a widespread decline in vegetation resilience. The accelerating pace of urbanization led to higher rising rates of and during 2010-2022 (5.11 × 10-3 year-1) compared to 1986-1999 (0.49 × 10-3 year-1). The notable decrease in resilience of forestland was primarily attributed to reduced precipitation in urban suburbs, which can be explained by urbanization-induced heat island and building barrier effects, causing a shift of precipitation center from urban suburbs to central cities. In summary, this study revealed that urbanization diminishes the vegetation resilience of urban suburban woodlands by altering urban precipitation patterns. These findings underscore the necessity of augmenting water availability in urban suburbs to restore resilience in these woodlands, thereby enhancing their ecosystem service value.