Search Results (48)

The pepper weevil Anthonomus eugenii is a devastating pest native to Central America that can cause severe damage to over 35 pepper varieties. Global trade in peppers has significantly increased the risk of its spread and expansion. Moreover, future climate change may add more uncertainty to its distribution, resulting in considerable ecological and economic damage globally. Therefore, we employed an ensemble model combining Random Forests and CLIMEX to predict the potential global distribution of A. eugenii in historical and future climate scenarios. The results indicated that the maximum temperature of the warmest month is an important variable affecting global A. eugenii distribution. Under the historical climate scenario, the potential global distribution of A. eugenii is concentrated in the Midwestern and Southern United States, Central America, the La Plata Plain, parts of the Brazilian Plateau, the Mediterranean and Black Sea coasts, sub-Saharan Africa, Northern and Southern China, Southern India, Indochina Peninsula, and coastal area in Eastern Australia. Under future climate scenarios, suitable areas in the Northern Hemisphere, including North America, Europe, and China, are projected to expand toward higher latitudes. In China, the number of highly suitable areas is expected to increase significantly, mainly in the south and north. Contrastingly, suitable areas in Central America, northern South America, the Brazilian Plateau, India, and the Indochina Peninsula will become less suitable. The total land area suitable for A. eugenii under historical and future low- and high-emission climate scenarios accounted for 73.12, 66.82, and 75.97% of the global land area (except for Antarctica), respectively. The high-suitability areas identified by both models decreased by 19.05 and 35.02% under low- and high-emission scenarios, respectively. Building on these findings, we inferred the future expansion trends of A. eugenii globally. Furthermore, we provide early warning of A. eugenii invasion and a scientific basis for its spread and outbreak, facilitating the development of effective quarantine and control measures. Full article

The European automotive industry, particularly in Germany, faces a significant crisis, heavily impacting suppliers reliant on OEMs. To enhance resilience, participation in the platform economy has been proposed as a solution. This study employs a qualitative research approach by conducting 18 expert interviews with automotive companies in the Northern Black Forest region to assess their awareness, perceived potential, and support needs regarding platform-based business models. The findings reveal diverse perspectives: smaller firms perceive higher risks, while larger companies recognize potential but struggle with limited expertise. The results highlight the novelty of the platform economy within the supplier industry and the need for increased awareness, strategic guidance, and tailored support measures. This study provides original insights into regional supplier engagement with platform ecosystems, contributing to the limited research on this topic and offering a foundation for future industry adaptation strategies. Full article

Mediterranean chestnut forests represent ecologically and economically important systems that support biodiversity while providing timber, non-timber forest products, and cultural services. However, traditional management practices are undergoing substantial shifts, with potential implications for forest structure and biodiversity. This study investigates how variation in forest structure and management intensity influences woodpecker communities in chestnut-dominated forests on Mount Paiko, northern Greece. Standardized surveys were conducted at 26 sites stratified by management intensity, and structural forest characteristics such as tree diameter, basal area, and deadwood volume were quantified. Species richness, abundance, and habitat use (feeding and nesting signs) were assessed in relation to these variables. Seven woodpecker species were detected, with distinct differences in species composition across management types. Feeding activity was positively associated with mean tree diameter, while basal area showed a significant negative correlation with woodpecker diversity. Canonical correspondence analysis revealed species-specific preferences along structural gradients, highlighting the association of the Black and Lesser Spotted woodpeckers with larger-diameter trees and deadwood-rich stands. Our results underscore the role of structural heterogeneity in supporting diverse woodpecker assemblages and highlight the need to integrate biodiversity conservation into chestnut forest management, particularly through selective retention of large trees and deadwood elements. Full article

Chinese forests, particularly the coniferous forest ecosystems represented by pines, play a crucial role in the global carbon cycle, significantly contributing to mitigating climate change, regulating regional climates, and maintaining ecological balance. However, pine wilt disease (PWD), caused by the pine wood nematode (PWN), has become a major threat to forest carbon stocks in China. This study evaluates the impact of PWN invasion on forest carbon stocks in China using multi-source data and an optimized MaxEnt model, and the study analyzes this invasion’s spread trends and potential risk areas. The results show that the high-suitability area for PWN has expanded from 68,000 km² in 2002 to 184,000 km² in 2021, with the spread of PWN accelerating, especially under warm and humid climate conditions and due to human activities. China’s forest carbon stocks increased from 111.34 billion tons of carbon (tC) to 168.05 billion tC, but the carbon risk due to PWN invasion also increased from 87 million tC to 99 million tC, highlighting the ongoing threat to the carbon storage capacity. The study further reveals significant differences in tree species’ sensitivity to PWN, with highly sensitive species such as Masson’s pine and black pine mainly concentrated in the southeastern coastal regions, while less sensitive species such as white pine and larch show stronger resistance in the northern and southwestern areas. This finding highlights the vulnerability of high-sensitivity tree species to PWN, especially in high-risk areas such as Guangdong, Guangxi, and Guizhou, where urgent and effective control measures are needed to reduce carbon stock losses. To address this challenge, the study recommends strengthening monitoring in high-risk areas and proposes specific measures to improve forest management and policy interventions, including promoting cross-regional joint control, enhancing early warning systems, and utilizing biological control measures, while encouraging local governments and communities to actively participate. By strengthening collaboration and implementing control measures, the health and sustainable development of forest ecosystems can be ensured, safeguarding the forests’ important role in climate regulation and carbon sequestration and contributing to global climate change mitigation. Full article

One of the priorities in European policy is the greater use of wood. In this context, it is important to know the total amount of CO₂ absorbed by the tree and accumulated in the wood. In the timber industry, butt logs are mainly processed. The aim of this study is to analyze diameter at breast height (DBH), wood density (WD), and the amount of CO₂ in grand fir (GF), Douglas fir (DF), northern red oak (NRO), and black locust (BL) wood. The DBH and bark thickness were measured, and cores were taken to study WD and calculate the amount of CO₂. Analyses were conducted in three age classes of trees. It was found that in the youngest age class, DF had a significantly larger DBH compared to NRO and BL, and GF had a significantly larger DBH compared to NRO. The wood density of coniferous species was significantly lower compared to broadleaved species. DF absorbed the most CO₂. In Class III, DF had significantly larger DBH and significantly lower wood density compared to NRO and BL. DF absorbed significantly more CO₂ compared to NRO. In Classes IV and V, DF had larger DBH compared to NRO and lower wood density. The amount of CO₂ absorbed by both species was similar. Taking into account the amount of absorbed CO₂, the durability of the wood, and aspects related to sustainable forest management of the four studied non-native tree species, Douglas fir seems to be the best choice for cultivation in Polish forests. Full article

Nevertheless, there is a lot to know about air pollutants in Mexico’s largest cities, like San Luis Potosi City, which is one of the 12 most crowded cities and is expected to grow in the next years; however, there is little information about air pollutant levels mainly particulate matter in their regulated size fractions (PM₁₀ or PM_2.5), and its main component of the Organic fraction: Black Carbon (BC), which is especially important because of its chemical properties and their effects on human health, air pollution, and climate change. This work presents a one-year BC monitoring in the northern part of the city (2018–2019) and another one-year BC monitoring in the southern area (2019–2020) during the health contingency situation due to the SARX-CoV-2 virus to obtain direct equivalent black carbon (eBC) concentrations and their main fractions related to fossil fuel and biomass burning using aethalometer AE-33, as well as other air pollutants concentrations measured at the same periods by the governmental local monitoring network (SEGAM). At the North, BC mass annual average concentration was (1.11 µg m⁻³), divided into seasonal stations, the cold season was the highest with (1.44 µg m⁻³), followed by the dry season (1.23 µg m⁻³), rainy season (0.94 µg m⁻³) and finally warm dry season (0.83 µg m⁻³). In the south, BC annual average concentration was (1.96 µg m⁻³); divided into seasons, the highest was the dry season with (2.73 µg m⁻³), followed by the cold season (2.37 µg m⁻³), dry warm season (1.61 µg m⁻³) and the rainy season (1.28 µg m⁻³). One of the main findings was the dominance of annual mean concentrations of BC originating from fossil fuels (BCff) on the north site in the city was 0.97 and on the south site (BCff) was 0.91 due to some forest fires during the monitoring period. This study presented information from two zones of a growing city in Mexico to generate new air pollutant indicators to have a better understanding of pollutant interactions in the city, to decrease the emission precursor sources, and reduce the health risks in the population. Full article

Resolving the status of soil carbon with land cover is critical for addressing the impacts of climate change arising from land cover conversion in boreal regions. However, many conventional inferential approaches inadequately gauge statistical significance for this issue, due to limited sample sizes or skewness of soil properties. This study aimed to address this drawback by adopting inferential approaches suitable for smaller samples sizes, where normal distributions of soil properties were not assumed. A two-step inference process was proposed. The Kruskal–Wallis (KW) test was first employed to evaluate disparities amongst soil properties. Generalized estimating equations (GEEs) were then wielded for a more thorough analysis. The proposed method was applied to soil samples (n = 431) extracted within the southern transition zone of the boreal forest (49°–50° N, 80°40′–84° W) in northern Ontario, Canada. Sites representative of eight land cover types and seven dominant tree species were sampled, investigating the total carbon (C), carbon-to-nitrogen ratio (C:N), clay percentage, and bulk density (BD). The KW test analysis corroborated significance (p-values < 0.05) for median differences between soil properties across the cover types. GEEs supported refined robust statistical evidence of mean differences in soil C between specific tree species groupings and land covers, particularly for black spruce (Picea mariana) and wetlands. In addition to the proposed method, the results of this study provided application for the selection of appropriate predictors for C with digital soil mapping. Full article

The Chattogram Hill Tracts (CHTs) in Bangladesh have a rich biodiversity, hosting a wide range of wild mammal species, underscoring the importance of systematic monitoring focused on conservation efforts. This study aims to assess the diversity and abundance of small- and medium-sized wild mammal species in Pittachhara Forest in the CHTs. A comprehensive wildlife monitoring survey was conducted in this forest using strategically placed camera traps to identify the wild species diversity, and we evaluated the activity patterns and seasonal variations for the period of February 2023 to August 2024. The camera trap identified eight species of small- and medium-sized wild mammals: the Bengal slow loris, northern pig-tailed macaque, leopard cat, large Indian civet, common palm civet, crab-eating mongoose, northern tree shrew, and black rat. The activity patterns showed distinct temporal behaviors among these wild mammals, with nocturnal activity dominating for the Bengal slow loris and leopard cat, while the northern pig-tailed macaque exhibited diurnal activity. Seasonal variations demonstrated a significant difference in increased activity among most wild mammal species throughout the summer, particularly the large Indian civet and crab-eating mongoose, likely due to favorable environmental conditions, and decreased activity in the winter (p < 0.05). The findings suggest an intricate connection between species-specific behaviors and environmental factors that influence activity patterns, with increased activity in summer and a decrease in winter, indicating that summer conditions may enhance the movement and foraging behaviors of mammals. This study underscores the necessity for continuous biodiversity monitoring followed by conservation efforts in Pittachhara Forest to reduce the threats of habitat fragmentation, human disturbances, and inadequate protection, thus protecting the survival of endangered wild mammal species and maintaining the environmental harmony of this small, protected area. Full article

With the intensification of climate change and human activities, the impacts of land use shifts on hydrological processes are becoming more pronounced, especially in regions with complex geographic, geological, and climatic conditions such as the Northeast Black Soil Region, China. This study quantitatively examines the variations in various land use types from 1980 to 2020 by means of a land use transfer matrix, and it incorporates the multi-year average runoff value to mitigate the interference of short-term climate fluctuations on the runoff trend, thereby enhancing the representativeness and stability of the simulation outcomes. The SWAT (Soil and Water Assessment Tool) model is employed to simulate land use alterations in different periods. The findings indicate that the area of farmland increased by 5.34% and the area of grassland decreased by 5.36% over 40 years. The areas of forest land and wetland have fluctuated significantly due to policy interventions and population growth. This study discovers that LUCC has resulted in a marginal increase in annual water yield. For instance, the water yield of paddy fields in 2020 amounts to 92.26 mm/year, which is 0.52–9.42% higher than the historical scenario and exhibits a notable upward trend in summer. Spatial analysis discloses regional disparities, with substantial changes in the hydrological behavior of northern watersheds (such as the Huma River) and southeastern regions (such as the Toudao River). The augmentation of wetland and forest coverage has effectively mitigated peak runoff, especially during extreme rainfall events. Wetlands have manifested strong water regulation capabilities and alleviated the impact of floods. This study quantitatively discloses the complex response pattern of LUCC to runoff by introducing a multi-scale analysis approach, which furnishes a scientific basis for flood risk assessment, land use optimization, and water resource management, and demonstrates the potential for extensive application in other countries and regions with similar climatic and topographic conditions. Full article

Soil fungal communities are critical for forest ecosystem functions in the Central Hardwood Region (CHR) of the USA. This evaluation, which took place in 2022–2023, investigates the influence of Juglans nigra (BW, black walnut) and Quercus rubra (NRO, Northern red oak) on soil properties and fungal community structures across three CHR sites. The objectives of this study are to investigate how the fungal communities identified beneath J. nigra and Q. rubra serve to influence biodiversity and soil health within hardwood plantations. Soils from two locations in Indiana and one in Michigan were examined and assessed for variations in fungal composition and diversity. Soil fungal communities were characterized using Illumina high-throughput sequencing while multivariate analysis was applied to analyze patterns in these fungal communities. These data provided insights into how environment, location, and tree species affect fungal community structure. Results indicate that J. nigra soils exhibited higher carbon (0.36%, 1.02%, 0.72%), nitrogen (25%, 29%, 56%), and pH (0.46, 1.08, 1.54) levels than Q. rubra soils across all three sites and foster greater fungal diversity. Specifically, J. nigra was associated with increased Ascomycota diversity, whereas Q. rubra supported a higher prevalence of Basidiomycota. Basidiomycota were negatively correlated with carbon and pH, while Ascomycota showed positive correlations with these variables. These findings highlight how crucial it is to understand how different tree species influence fungal communities and, consequently, how they influence forest soil health. Our findings serve to improve forest management practices by emphasizing the importance of fungal communities in maintaining the function and resilience of an ecosystem. Our study underscores that grasping these specific interactions is essential for effective forest management, especially when considering how to use fungal communities to boost plant growth. This work focuses on hardwood plantations rather than either agricultural ecosystems, monocultures, or native forests, thus filling a gap in the current literature where many studies are limited to specific fungal groups such as mycorrhizae. In future research, it is important to examine a wider range of tree species. This will deepen our understanding of fungal community dynamics and their impact on maintaining healthy forest ecosystems. Our hardwood plantation focus also notes the potential for adaptive forest management as environmental conditions change. Full article

The effect of climate change on forest dynamics is likely to increase in importance in the forthcoming decades. For this reason, it is essential to predict the extent to which changes in temperature, precipitation, and atmospheric CO₂ might affect the development of forest ecosystems and successional pathways. The gap model ZELIG-CFS was used to simulate the potential long-term effects of climate change on species-specific annual change in mean basal area and stand density under two scenarios of representative concentration pathways (RCP), 4.5 and 8.5, for the boreal forest region of Ontario, Canada, where mean temperature, precipitation, and atmospheric CO₂ are expected to increase. Forest ecosystems in this boreal region included pure and mixed stands of black spruce (Picea mariana [Mill.] B.S.P.), paper birch (Betula papyrifera Marsh.), balsam fir (Abies balsamea [L.] Mill.), jack pine (Pinus banksiana Lamb.), trembling aspen (Populus tremuloides Michx.), white spruce (Picea glauca [Moench] Voss), northern white cedar (Thuja occidentalis L.), American larch (Larix laricina [Du Roi] K. Koch), and balsam poplar (Populus balsamifera L.). Simulation results under climate change generally predicted a decline in the basal area and stand density for black spruce, balsam fir, jack pine, and white spruce, but an increase for paper birch, trembling aspen, American larch, and balsam poplar. However, the extent of change differed regionally among species. Forest composition is expected to change over the long term. Simulation results indicated that shade-intolerant deciduous and conifer species will increase their dominance over the 100-year time horizon. This transition toward the increasing presence of deciduous forests is likely explained by more favorable temperature conditions for their growth and development. Full article

Over the years, the Cape Peninsula has seen a rise in the number of fires that occur seasonally. This study aimed to investigate the extent of fire spread and associated damages during the 2023/2024 Cape Peninsula fire events. Remote sensing datasets from Sentinel-5P, Sentinel-2, Moderate Resolution Imaging Spectroradiometer (MODIS), and Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) were used. Most of the fires on the northern side of the Cape Peninsula had a short burning span of between 6 and 12 h, but fires with a duration of 12–24 h were minimal. The northern area is composed of low forests and thickets as well as fynbos species, which were the primary fuel sources. Excessive amounts of carbon monoxide (CO) and black carbon (BC) emissions were observed. High speeds were observed during the period of the fires. This is one of the factors that led to the spread of the fire. Relative humidity at 60% was observed, indicating slightly dry conditions. Additionally, the Leaf Water Content Index (LWCI) indicated drier vegetation, enhancing fire susceptibility. High temperatures, low moisture and strong winds were the main drivers of the fire. The Normalized Burn Ratio (NBR) values for the targeted fires showed values close to −1, which signifies presence of a fire scar. The study can be of use to those in the fire management agencies and biodiversity conservation in the region. Full article

The transition to battery electric vehicles (BEVs) poses significant challenges for automotive suppliers, particularly in the Northern Black Forest Region, Germany’s largest industrial area. This study examines the risk of falling into the commodity trap and the impact of platform economics on these suppliers. A VUCA (volatility, uncertainty, complexity, ambiguity) analysis is used to assess the consequences of the open platform approach promoted by the Mobility in Harmony (MIH) Consortium. The methodology is based on a comprehensive literature review on commodity traps and platform economies, as well as an analysis of the MIH Consortium’s strategies for standardizing BEV components. The results show that while the MIH Consortium’s modularization and standardization efforts can reduce costs and facilitate mass production, they also intensify competition and limit differentiation, threatening the profit margins of smaller suppliers. The study highlights the importance of strategic positioning and innovation to counter these risks. It concludes with recommendations on how automotive suppliers can adapt to the evolving environment and be agile in responding to new technological standards and market dynamics. Full article

Wetlands are integral components of agricultural landscapes, providing a wide range of ecological, economic, and social benefits essential for sustainable development and rural livelihoods. Globally, they are vulnerable ecological assets facing several significant threats including water extraction and regulation, land clearing and reclamation, and climate change. Classification and mapping of wetlands in agricultural landscapes is crucial for conserving these ecosystems to maintain their ecological integrity amidst ongoing land-use changes and environmental pressures. This study aims to establish a robust framework for wetland classification and mapping in intensive agricultural landscapes using time series of Sentinel-2 imagery, with a focus on the Gwydir Wetland Complex situated in the northern Murray–Darling Basin—Australia’s largest river system. Using the Google Earth Engine (GEE) platform, we extracted two groups of predictors based on six vegetation indices time series calculated from multi-temporal Sentinel-2 surface reflectance (SR) imagery: the first is statistical features summarizing the time series and the second is phenological features based on harmonic analysis of time series data (HANTS). We developed and evaluated random forest (RF) models for each level of classification with combination of different groups of predictors. Our results show that RF models involving both HANTS and statistical features perform strongly with significantly high overall accuracy and class-weighted F1 scores (p < 0.05) when comparing with models with either statistical or HANTS variables. While the models have excellent performance (F-score greater than 0.9) in distinguishing wetlands from other landcovers (croplands, terrestrial uplands, and open waters), the inter-class discriminating power among wetlands is class-specific: wetlands that are frequently inundated (including river red gum forests and wetlands dominated by common reed, water couch, and marsh club-rush) are generally better identified than the ones that are flooded less frequently, such as sedgelands and woodlands dominated by black box and coolabah. This study demonstrates that HANTS features extracted from time series Sentinel data can significantly improve the accuracy of wetland mapping in highly fragmentated agricultural landscapes. Thus, this framework enables wetland classification and mapping to be updated on a regular basis to better understand the dynamic nature of these complex ecosystems and improve long-term wetland monitoring. Full article

Sandstone-hosted uranium deposits are indeed significant sources of uranium resources globally. They are typically found in sedimentary basins and have been extensively explored and exploited in various countries. They play a significant role in meeting global uranium demand and are considered important resources for nuclear energy production. Erlian Basin, as one of the sedimentary basins in northern China, is known for its uranium mineralization hosted within sandstone formations. In this research, machine learning (ML) methodology was applied to mineral prospectivity mapping (MPM) of the metallogenic zone in the Manite depression of the Erlian Basin. An ML model of 92% accuracy was implemented with the random forest algorithm. Additionally, the confusion matrix and receiver operating characteristic curve were used as model evaluation indicators. Furthermore, the model explainability research with post hoc interpretability algorithms bridged the gap between complex opaque (black-box) models and geological cognition, enabling the effective and responsible use of AI technologies. The MPM results shown in QGIS provided vivid geological insights for ML-based metallogenic prediction. With the favorable prospective targets delineated, geologists can make decisions for further uranium exploration. Full article