Increase In Forest Cover Research Articles

Impacts of Participatory Forest Management on Land Use/Land Cover of Adaba-Dodola Forest in South Eastern Ethiopia and its Implication to REDD+ Implementation.

Despite various interventions to protect forests, many developing countries, including Ethiopia, continue to face substantial forest conservation challenges, particularly where local communities heavily rely on forests for their livelihoods. Recognizing the urgency of this issue, the government of Ethiopia introduced Participatory Forest Management (PFM) and devolved forest management responsibilities to enhance forest conservation. Therefore, this assessment examines the impacts of PFM on forest cover based on an analysis of the Land Use/Land Cover Change (LULCC) over the last 23 years in Adaba-Dodola, and its implications for REDD+ implementation. The study involved determining the LULCC of the Adaba-Dodola forest after the introduction of PFM from 2000 to 2023. Landsat images of 2000, 2012, and 2023 were analyzed to detect LULCC. The study result showed that the Adaba-Dodola forest cover increased by 1.83% since the PFM was introduced. The decreased agricultural land by 0.87% was the main factor attributed to the increase in shrub cover, while shrubland attributed to the rise in forest cover. Net areas of about 148 ha/year of shrublands were converted into forest land owing to significant forest regeneration, while shrublands had a net gain of 110.5 ha/year from agriculture and grasslands between 2000 and 2023. The increase in forest cover is attributed to the effectiveness of PFM in halting deforestation and promoting forest conservation. Thus, the PFM approach is a tool for preserving forest ecosystems and mitigating the adverse effects of deforestation and forest degradation, therefore would be used as an umbrella for implementing REDD+.

Global rise in forest fire emissions linked to climate change in the extratropics.

Climate change increases fire-favorable weather in forests, but fire trends are also affected by multiple other controlling factors that are difficult to untangle. We use machine learning to systematically group forest ecoregions into 12 global forest pyromes, with each showing distinct sensitivities to climatic, human, and vegetation controls. This delineation revealed that rapidly increasing forest fire emissions in extratropical pyromes, linked to climate change, offset declining emissions in tropical pyromes during 2001 to 2023. Annual emissions tripled in one extratropical pyrome due to increases in fire-favorable weather, compounded by increased forest cover and productivity. This contributed to a 60% increase in forest fire carbon emissions from forest ecoregions globally. Our results highlight the increasing vulnerability of forests and their carbon stocks to fire disturbance under climate change.

Влияние насаждений Quercus robur L. и Juglans rupestris Engelm. на свойства степных почв

The aim of the study has been to determine the condition of Quercus robur L. and Juglans rupestris Engelm. and their influence on the properties of segregated chernozems. The study was conducted in 2020–2021 in the steppe Crimea. The control has been a perennial grass fallow. To characterize the general condition of the trees, a 4-point scale for assessing the condition of the introduced species has been used. Standard methods have been used for soil analysis. A better preservation and a greater number of 50-year old plants in excellent and good condition have been established for Q. robur (67 и 56 %, respectively) compared to J. rupestris (48 и 42 %, respectively) in these environmental conditions, which is explained by the greater adaptive capabilities of oak as an indegenous species. Both tree crops have influenced soil properties. In their phytocenoses, a litter has been formed, more significant under J. rupestris. The dark humus horizon has also turned out to be thicker under the J. rupestris plantation (80 cm) than under the Q. robur plantation and the fallow (72 and 70 cm, respectively). Under Q. robur the soil density has been lower than under J. rupestris by 0.02–0.18 g/cm3. Under the woody plants, the soil structure has improved: the number of lumpy aggregates has decreased, and the number of agronomically valuable and granular particles has increased. The maximum coefficient of structure has been in the soil under the oaks (4.4–6.6), which exceeded the control by 2.5–4.9 times. Under the woody plants, carbonates have been significantly washed out of the soil profile, especially under J. rupestris. The pHH2O value of the alkaline soil under the woody plants has decreased by 0.2–0.4 and has been closely related to carbonate content (r = 0.83–0.91). The humus content under both species has been significantly higher than under the fallow – by 0.30–0.86 %. Afforestation has resulted in the accumulation of total forms of nutrients, to a greater extent under J. rupestris, and the C:N ratio has decreased. Tree species have stored more labile forms of nutrients in the soil. Soil desalination has been noted in the tree cenoses. Thus, J. rupestris and Q. robur are ecologically stable in the conditions of the central steppe of Crimea and can be used to increase forest cover in steppe areas. This will help improve soil fertility.

Social and Ecological Contributions of the Taungya Agroforestry System in the Restoration of Mount Elgon National Park, Uganda

Balancing conservation objectives with the livelihood needs of local communities remains a significant challenge in the management of protected areas. The Taungya agroforestry system, introduced in Mount Elgon National Park, Uganda, in 2009, aims to address this issue by integrating forest restoration with agricultural production for food security. This study assesses the social and ecological contributions of the Taungya system in Kapkwata sub-county, Kapchorwa district, focusing on its impact on forest cover restoration, food security, income generation, and the provision of ecosystem services. Using a mixed-methods approach, data were collected from 240 participants selected through random sampling, supplemented by key informant interviews. Quantitative data were analyzed using SPSS, and qualitative insights were analyzed through thematic analysis. The results indicate that the Taungya system significantly improved vegetation cover, with the majority of participants reporting an increase in forest cover from 0-25% to 51-75%. The program also contributed to household food security and income generation, with participants cultivating crops like Irish potatoes, maize, and onions for both consumption and sale. Non-crop benefits, including access to firewood and clean water, further enhanced household resilience. However, challenges such as small land allocations, crop pests, and wildlife conflicts were identified as barriers to maximizing the program's impact. To optimize outcomes, the study recommends increased land allocations per household and the introduction of integrated pest management strategies. The findings underscore the potential of agroforestry systems like Taungya to achieve sustainable conservation and community development in protected areas

The last 20,000 years of climate change in the Iberian Peninsula characterized by the small-mammal assemblages

Climate and environments have undergone significant changes throughout the Earth's history, which are reflected in the faunal record. In that sense, small mammals provide valuable palaeoecological insights due to their close relationship with their associated environments. Our research investigates the dynamics of small mammal communities in the Iberian Peninsula from the Late Pleistocene to the Holocene, comparing regional environmental variations as well as anthropogenic impacts. Our review of the small mammal assemblages from 26 archaeological sites across the Iberian Peninsula revealed a differential response of the small mammal communities between the northern and southern areas. The colder climatic conditions which characterized the Late Pleistocene favoured the presence of mid-European species (e.g., Alexandromys (Oecomicrotus) oeconomus, Chionomys (Chionomys) nivalis) in northern regions until the Mid Holocene, while other taxa today restricted to northern Iberia (e.g., Arvicola amphibius, Microtus gr. (Euarvicola) arvalis, Sorex araneus-coronatus) reached southern regions during the Last Glacial Maximum but disappeared at the beginning of the Holocene period. Palaeoenvironmental reconstructions indicate predominantly open landscapes and colder climates in the Late Pleistocene, with increased forest cover during the Early-Mid Holocene in northern areas. In contrast, the southern regions exhibited more stable environments compared to the northern ones. Our results underscore how the great environmental diversity and interactions between natural and anthropogenic factors affected the composition of Iberian small mammal communities during the last 20,000 years. Most specifically, during the Late Holocene, biogeographical patterns of the small mammals are mostly affected by anthropic activities, with changes in the distribution of some species (Microtus (Iberomys) cabrerae), and the entrance of new ones (synanthropic species).

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.

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.

Mining-induced forest cover change of Paschim Bardhaman, a mining-based district of India

Mining activities are a recognized factor for Forest Cover Loss (FCL) worldwide. Huge forest cover areas are lost due to mining activities worldwide and in India. This study is conducted to identify the villages that experienced more FCL as a result of mining activities and also focuses on identifying the role of individual coal mines on FCL. Results indicate that the mining area increased to 70.79 km2 in 2020 from 25.56 km2 in 1990, and the vegetation area reduced to 149.22 km2 from 271 km2 at the same time. Mostly Jamuria, Barabani, Raniganj, and Pandabeswar blocks have lost large amounts of forest cover due to mining activities. Results also indicate that mining areas have increased nearly threefold and influenced the rate of FCL in the district. Village-level analysis of mining-induced FCL identified that more than ten villages had lost more than 10% of the total forest cover areas due to coal mine expansion resulting in environmental degradation. Analysis of spatial matrices indicates a fragmentation nature of vegetation cover areas of the selected coal mines and indicates that available vegetation areas are concentrated in some pocket areas. Local Indicators of Spatial Autocorrelation (LISA) based spatial patterns of mining-induced FCL show high cluster location in and around major coal mines of the area proving the role of open-cast coal mines on FCL and forest fragmentation. The analysis results may help the planners maintain the healthy environment of the affected villages by formulating alternative ways of forest cover increase.

Global and regional hydrological impacts of global forest expansion

Abstract. Large-scale reforestation, afforestation, and forest restoration schemes have gained global support as climate change mitigation strategies due to their significant carbon dioxide removal (CDR) potential. However, there has been limited research into the unintended consequences of forestation from a biophysical perspective. In the Community Earth System Model version 2 (CESM2), we apply a global forestation scenario, within a Paris Agreement-compatible warming scenario, to investigate the land surface and hydroclimate response. Compared to a control scenario where land use is fixed to present-day levels, the forestation scenario is up to 2 °C cooler at low latitudes by 2100, driven by a 10 % increase in evaporative cooling in forested areas. However, afforested areas where grassland or shrubland are replaced lead to a doubling of plant water demand in some tropical regions, causing significant decreases in soil moisture (∼ 5 % globally, 5 %–10 % regionally) and water availability (∼ 10 % globally, 10 %–15 % regionally) in regions with increased forest cover. While there are some increases in low cloud and seasonal precipitation over the expanded tropical forests, with enhanced negative cloud radiative forcing, the impacts on large-scale precipitation and atmospheric circulation are limited. This contrasts with the precipitation response to simulated large-scale deforestation found in previous studies. The forestation scenario demonstrates local cooling benefits without major disruption to global hydrodynamics beyond those already projected to result from climate change, in addition to the cooling associated with CDR. However, the water demands of extensive forestation, especially afforestation, have implications for its viability, given the uncertainty in future precipitation changes.

Impacts of climate and land-use change on flood events with different return periods in a mountainous watershed of North China

Study regionLuanhe River Basin in North China Study focusGlobal warming exacerbates hydrological cycles, leading to increased intensity and frequency of floods. Regional studies on the impacts of climate change and land cover changes on flood events in recent decades are particularly important for flood prevention and risk assessment. In this study, the Peak Over Threshold- Generalized Pareto Distribution (POT-GPD) method is used to fit extreme runoff values in the Luanhe River Basin from 1961 to 2018 to identify floods with different return periods. Six flood behavior indicators are adopted to analyze the flood characteristics and extreme climate indices are also used for breakpoint testing and trend analysis. Various scenarios are established as the input of the SWAT model to study the impacts of climate change and land-use change on the flood events. New hydrological insights for the regionThe results indicate that the 2-year and 20-year return period floods are primarily rapid-rise single-peak floods, dominated by short-duration intense precipitation. The 5-year and 10-year return period floods are mainly multi-peak floods, primarily caused by long-duration precipitation. Floods in the Luanhe River Basin are primarily influenced by climate change, however, flood events do not directly correspond to the return periods of precipitation events. Forest is the most effective land-use type for flood retention. It could reduce flood magnitudes, especially for the 20-year return period floods. However, increasing forest cover does not effectively reduce the occurrence of the 5-year or 10-year floods, i.e. multi-peak floods. With more extreme rainfall events triggered by future climate change, particular attention should be paid to long-duration precipitation events, which may result in more severe disasters and economic losses.

Long-Term Landscape Changes in the Ojców National Park (Poland) and Its Surroundings: Implications for the Effectiveness of Buffer Zones

Protected areas (PAs) serve as crucial elements in biodiversity conservation but are in danger of becoming isolated islands in human-dominated landscapes. It is related to landscape changes, especially changes in land use and land cover (LULC). Over the past decades, most research on the effectiveness of nature conservation has focused mainly on PAs, while the areas surrounding PAs are of key importance for maintaining ecological connectivity and biodiversity. Therefore, the main objective of this study was to determine the long-term changes in LULC within the selected national park in Poland and its surroundings and to assess the effectiveness of the BZ based on these changes. We hypothesized that, despite restrictions within the buffer zone, land development has intensified and increased, in the nearest surroundings of the analyzed national park. For the analysis, we selected Ojców National Park (southern Poland), one of the oldest national parks in Poland. We analyzed landscape changes before (since the 1930s) and after establishing the park and its BZ. We conducted a comprehensive quantitative analysis of the landscape structure and LULC. We used historical maps and the contemporary national LULC database. Our results showed that almost 40% of the study area consisted of lands with non-persistent LULC. The main changes include a three-fold increase in built-up areas and an increase in forest cover, mainly on abandoned agricultural land. We also found that land development around the national park is at a level similar to the general rate for the area outside the BZ. It suggests the ineffectiveness of the buffer zone in preventing land development. The identified long-term landscape changes the basis for sustainable development land management from the nature conservation perspective.

Coupling Coordination of Multi-Dimensional Urbanization and Ecological Security in Karst Landscapes: A Case Study of the Yunnan–Guizhou Region, China

Globally, karst regions face the dual challenges of urbanization and ecological protection, with the coupling coordination of multi-dimensional urbanization (MDU) and ecological security (ECS) being a necessary condition for achieving sustainable development. This study, based on statistical data on MDU and ECS in the Yunnan–Guizhou Region (the YGR) in China, employs the entropy weight TOPSIS model, degree of coupling coordination (CCD) model, and panel Tobit regression model to explore the coupling relationship between MDU and ECS. The main conclusions are as follows. (1) MDU in the YGR increased from 0.299 to 0.305, indicating low-level and sluggish development. Spatially, it is characterized by a “dual-core” structure centered on Kunming and Guiyang. (2) ECS decreased from 0.456 to 0.423, with a spatial pattern of “high in the east, low in the west”. The impact of human activities on ECS increased from 0.579 to 0.631 due to the increase in social and economic activities. (3) CCD increased to 0.579, achieving moderate coordination. The spatial feature evolved into a tri-cluster pattern of “high–low–high” across the “eastern–central–northwestern” regions. (4) Regression results indicate that annual average precipitation has a “both promoting and limiting” dual effect on CCD. The coefficient for the proportion of afforested land area is 0.205, with a significance level of 5%, suggesting that increasing forest cover is a key measure for improving CCD. The study reveals the factors influencing the evolution of MDU and ECS from a negative to a positive correlation, providing a basis for decisions related to sustainable development for urban and ecological management in karst landscapes globally.

Impact of urban-rural development and its industrial elements on regional economic growth: An analysis based on provincial panel data in China

Urban-rural development is an important driving force for regional economic growth. The existing researches have studied this issue from various perspectives, but they ignore the impact of big data on the economy. In the post pandemic era, big data, as an emerging production factor, has a significant indicative effect in promoting urban-rural economic recovery and fostering new business forms. Therefore, fully considering the factor of big data can help reveal its impact mechanism on urban-rural economic growth in the post-epidemic period. Based on the data of 30 provinces and cities in China, this paper introduced big data on the basis of traditional models and constructed a multi-dimensional factor indicator system. At the same time, the panel regression model was established by using unit root test, Hausman test and precision test. Through benchmark regression and heterogeneity analysis, the impact of urban-rural development factors on economic growth was discussed. The results showed that the panel model passed all tests, and its regression error was stable below 5 %. Transportation, technology, and the three major industries can all promote positive economic growth, with a significance of 1 %. The three industries' contribution to economic growth ranks the third, second and first industries in order. In addition, the good ecological environment contributes to the benign economic growth during the study period. A 1 % increase in forest cover would drive economic growth by 0.215 %. But the impact of public's attention on the overall economy was an indirect effect manifested through its physical industries.The regional heterogeneity indicated that each element had different effects on economic development in eastern, central and western regions. Based on its results, this paper proposed suggestions for each region. In addition, this study found that the Internet attention reflected by big data did not directly drive economic growth, but affected economic growth through indirect channels such as information flow and resource allocation of real industries. This study provided data support for the existing theoretical review, and provided policy reference for the rational planning and industrial layout of China's regional economy.

Negative impacts of future forest succession on three threatened butterfly species

The dynamic interplay between anthropogenic activities and biodiversity conservation necessitates a nuanced understanding of habitat change, especially in contexts marked by transitions from grasslands to forested areas. This investigation utilised three threatened butterfly species—the Marsh Fritillary, Apollo, and Large Blue—as models to explore how grassland associated species respond to altered environmental conditions. The methodology encompassed extensive field surveys and statistical analyses with ecological niche modelling to determine their current and future distributions on the Island of Gotland. The species were surveyed under distinct years from 2017 to 2020 in a total of 3333 hectares in a 6000 hectare area—the Marsh Fritillary in 2017 (1232 hectares), Apollo in 2019 (2346 hectares), and Large Blue in 2020 (2256 hectares). Results revealed that the estimated current extents of suitable habitats were 49,104 hectares for the Marsh Fritillary (15.6% of the island), 45,646 hectares for Apollo (14.5%), and 33,089 hectares for Large Blue (10.5%). In general, increased forest and shrub cover and decreased heterogeneity negatively affected butterfly occupancy, but each species exhibited unique habitat preferences. The predictive modelling demonstrated that continued succession would reduce the amount of habitats predicted to be suitable and generated alarming forecasts—a twofold increase in forest and shrub cover suggests habitat declines of 41%, 47%, and 65% for the Marsh Fritillary, Apollo, and Large Blue, respectively. Given these findings, proactive measures are imperative for strategically managing these habitats to preserve landscape heterogeneity and accommodate diverse ecological needs. This study is important to conservation management providing, crucial insights amid anthropogenic and ecological changes.

Long-term annual estimation of forest above ground biomass, canopy cover, and height from airborne and spaceborne sensors synergies in the Iberian Peninsula

The Mediterranean Basin has experienced substantial land use changes as traditional agriculture decreased and population migrated from rural to urban areas, which have resulted in a large forest cover increase. The combination of Landsat time series, providing spectral information, with lidar, offering three-dimensional insights, has emerged as a viable option for the large-scale cartography of forest structural attributes across large time spans. Here we develop and test a comprehensive framework to map forest above ground biomass, canopy cover and forest height in two regions spanning the most representative biomes in the peninsular Spain, Mediterranean (Madrid region) and temperate (Basque Country). As reference, we used lidar-based direct estimates of stand height and forest canopy cover. The reference biomass and volume were predicted from lidar metrics. Landsat time series predictors included annual temporal profiles of band reflectance and vegetation indices for the 1985–2023 period. Additional predictor variables including synthetic aperture radar, disturbance history, topography and forest type were also evaluated to optimize forest structural attributes retrieval. The estimates were independently validated at two temporal scales, i) the year of model calibration and ii) the year of the second lidar survey. The final models used as predictor variables only Landsat based metrics and topographic information, as the available SAR time-series were relatively short (1991–2011) and disturbance information did not decrease the estimation error. Model accuracies were higher in the Mediterranean forests when compared to the temperate forests (R2 = 0.6–0.8 vs. 0.4–0.5). Between the first (1985–1989) and the last (2020–2023) decades of the monitoring period the average forest cover increased from 21 ± 2% to 32 ± 1%, mean height increased from 6.6 ± 0.43 m to 7.9 ± 0.18 m and the mean biomass from 31.9 ± 3.6 t ha−1 to 50.4 ± 1 t ha−1 for the Mediterranean forests. In temperate forests, the average canopy cover increased from 55 ± 4% to 59 ± 3%, mean height increased from 15.8 ± 0.77 m to 17.3 ± 0.21m, while the growing stock volume increased from 137.8 ± 8.2 to 151.5 ± 3.8 m3 ha−1. Our results suggest that multispectral data can be successfully linked with lidar to provide continuous information on forest height, cover, and biomass trends.

Assessment of forest cover change, key drivers of change and perception of locals in Birendranagar Municipality, Surkhet District, Nepal

Land use and land cover change has an immense impact on the global environment and ecosystem and is mostly connected with human activities such as conversion of forest into agricultural land and settlements. Forests are changing globally in quantity and quality, and in both increasing and decreasing trends. For the Birendra Nagar Municipality, this study evaluated LULC dynamics, the conversion of forest cover from 2000 to 2020, as well as community perceptions on forest cover change, its drivers. Landsat images of 2000 and 2020 were used for LULC dynamics and for forest cover change detection Using software GIS 10.8. Questionnaire survey, key informant survey, field observation and secondary data were acquired from journal articles, reports, District Forest office, books, and various other sources to find out drivers of forest cover change and perception of locals. It was found that 1146 ha of forest area, 280.53 ha of water bodies, 752.62 ha of barren land were lost between 2000 and 2020. However, 1147.74 ha of settlement area and 1032.40 ha of agricultural land was gained.Net forest cover decreased overall by 4.67%, however, some areas within the municipality experienced an increase in forest cover namely Ward 11 and ward 16 of Municipality shows Highest positive and Negative Forest cover change in terms of Area. Expansion of agriculture land and settlements with the encroachment of forest area, haphazard infrastructure development, illegal timber harvesting and low regeneration capacity of land responsible for the reduction of forest, along with Community forestry approach, plantations programs, promotion of agroforestry practices, fuel transition were responsible drivers for improvements of forest cover. Water cycle disruption, Increases Natural hazard, Temperature rises, shortage of wood for fuel, extinction of indigenous species were the most perceived negative impacts of the forest cover reduction whereas income generation Activities, support biodiversity, easy to collect fuel wool and fodder and others including purifying air quality, increasing aesthetic beauty were the most perceived positive impacts of the forest cover improvement in the study area. Most of the locals have a holistic understanding of forest cover change. Strengthening forest protection, improving soil and water conservation structures, awareness creation, enrichment planting, Promote Alternate Energy Resources and Equitable benefit-sharing mechanism are possible solutions perceived by locals.

Impact of Land Use and Climate Change on Streamflow: An Assessment Using a Semi-Empirical Model in the Guishui Watershed of North China

Land-use change may significantly influence streamflow. The semi-empirical model PhosFate was used to analyze the impact of land use and climate change on streamflow by choosing the Guishui watershed as a pilot site and then expanding, applying it to all of North China. The Guishui watershed (North Beijing, China) has experienced a dramatic decline in its streamflow in recent decades. Parallel to this, significant land-use change has happened in this area; afforestation programs have increased forest cover from 41% (1980) to 59% (2013) and a similar increase in forest cover can also be observed in North China. Managing flow decline requires separating climatic and direct human-influenced effects. The results showed the following: (1) Afforestation is a major factor that decreased total flow in the Guishui watershed from 1996 to 2014; total flow increased by around 24% more than the actual dataset in the constant scenario (no afforestation) and decreased by 5% more than the actual dataset in the forest scenario (all agriculture land use transferred to forests). (2) When forest coverage increases, the Qinghai–Tibet Plateau and the Loess Plateau are the most sensitive areas regarding total flow in North China; the total flow change rate increased by up to 25% in these two areas when land use shifted from sparse vegetation to mixed forests. After analyzing the contributions of these two factors, we formulated recommendations on future afforestation practices for North China. In the central–north and northwest districts, the annual precipitation is under 520 mm and 790 mm, respectively, and the practice of afforestation should be more carefully planned to prevent severe damage to streams. This research also proved that the PhosFate model can be used in North China, which would be a practical tool for watershed management.

Recent changes in tropical-dry-forest connectivity within the Balsas Basin Biogeographic Province: potential effects on endemic-bird distributions

Maintaining landscape connectivity is a conservation priority for biodiversity as it may mitigate the adverse effects of forest degradation, fragmentation, and climate change by facilitating species dispersal. Despite their importance for biodiversity conservation, Mexican tropical dry forests (TDFs) face high fragmentation rates due to anthropogenic activities. In this study, we analyzed the connectivity dynamics of TDFs in the Balsas Basin Biogeographic Province (BBBP) between 2013 and 2018, focusing on old-growth and secondary TDF covers, including Protected Areas and Important Bird Areas. We evaluated the effects of connectivity loss and gain on the distribution areas of 30 endemic bird species with ecological associations with TDFs in the BBBP. We found expansion in TDFs accounting for a total increase of 227,905 ha due to secondary forest increase (12%). In contrast, old-growth forests experienced a reduction of 66,576 ha in the study area (8%). We also found a decrease in areas with high and very-high connectivity, coupled with an increase in low connectivity, except for TDFs inside Protected Areas, which increased by 3,000 ha, leading to higher connectivity. There was an increase in total forest cover in 27 species’ potential distribution, highlighting the possible role of secondary forests in promoting connectivity between old-growth forest patches. Our results reveal the complex dynamics between forest types, connectivity, and bird-species distributions. Despite an overall increase in forested areas, most TDFs continue to have low connectivity, likely impacting biodiversity, particularly for species that rely on highly conserved ecosystems. This study underscores the importance of integrated conservation strategies considering connectivity, forest recovery, and the dynamics of species-ecosystem interactions.

Monitoring the impact of forest changes on carbon uptake with solar-induced fluorescence measurements from GOME-2A and TROPOMI for an Australian and Chinese case study

Abstract. Reliable and robust monitoring tools are crucial to assess the effectiveness of land mitigation techniques (LMTs) in enhancing carbon uptake, enabling informed decision making by policymakers. This study, addressing one of the scientific goals of the EU Horizon 2020 Land Use Based Mitigation for Resilient Climate Pathways (LANDMARC) project, examines the feasibility of using satellite solar-induced fluorescence (SIF) in combination with other satellite data as a monitoring proxy to evaluate the effects of LMTs on carbon uptake. Two distinct cases are explored: (1) instantaneous vegetation destruction caused by a 2019 eucalyptus wildfire in southeast Australia and (2) gradual forest gain resulting from reforestation efforts in northern China in 2007–2012. The cases are monitored using SIF from the TROPOspheric Monitoring Instrument (TROPOMI) and Global Ozone Monitoring Experiment-2A (GOME-2A), respectively. Comparing the temporal variability in SIF across the affected areas and nearby reference areas reveals that vegetation dynamics changed as a consequence of the land-use changes in both cases. Specifically, in the Australia case, TROPOMI demonstrated an immediate reduction in the SIF signal of 0.6 mW m−2 sr−1 nm−1 (−72 %) over the eucalypt forest right after the fire. Exploiting the strong correspondence between TROPOMI SIF and gross primary productivity (GPP) at the nearby representative eddy covariance Tumbarumba site and through the FluxSat product, we estimate that the Australian fire led to a loss in GPP of 130–200 GgC in the first 8 months after the fire. Over the northern Chinese provinces of Gansu, Shaanxi, Sichuan, Chongqing, and Shanxi, we report an increase in GOME-2A summertime SIF of 0.1–0.2 mW m−2 sr−1 nm−1, coinciding with reforestation efforts between 2007 and 2012. This increase in the SIF signal is likely driven by a combination of increasingly favourable natural conditions and by the reforestation effort itself. A multivariate model that takes into account growth factors such as water availability and maximum temperature as well as satellite-derived forest-cover data explains the observed variability in GOME-2A SIF in the Chinese case reasonably well (R2=0.72). The model suggests that increases in both forest cover and soil moisture have led, in step, to the observed increase in vegetation activity over northern China. In that region, for every 100 km2 of additional forest cover, SIF increases by 0.1 mW m−2 sr−1 nm−1 between 2007 and 2012. Our study highlights that the use of satellite-based SIF, together with supporting in situ, modelled, and satellite data, allows us to monitor the impact of LMT implementation on regional carbon uptake as long as the scale of the LMT is of sufficient spatial extent.

Ecological Requirements for Abundance and Dispersion of Brazilian Yellow Fever Vectors in Tropical Areas.

In the Americas, wild yellow fever (WYF) is an infectious disease that is highly lethal for some non-human primate species and non-vaccinated people. Specifically, in the Brazilian Atlantic Forest, Haemagogus leucocelaenus and Haemagogus janthinomys mosquitoes act as the major vectors. Despite transmission risk being related to vector densities, little is known about how landscape structure affects vector abundance and movement. To fill these gaps, we used vector abundance data and a model-selection approach to assess how landscape structure affects vector abundance, aiming to identify connecting elements for virus dispersion in the state of São Paulo, Brazil. Our findings show that Hg. leucocelaenus and Hg. janthinomys abundances, in highly degraded and fragmented landscapes, are mainly affected by increases in forest cover at scales of 2.0 and 2.5 km, respectively. Fragmented landscapes provide ecological corridors for vector dispersion, which, along with high vector abundance, promotes the creation of risk areas for WYF virus spread, especially along the border with Minas Gerais state, the upper edges of the Serra do Mar, in the Serra da Cantareira, and in areas of the metropolitan regions of São Paulo and Campinas.