Search Results (34)

The Andean walnut (Juglans neotropica Diels), locally known as tocte, is a keystone tree species of major socio-ecological importance in South American mountain ecosystems, facing severe anthropogenic pressure associated with genetic erosion, habitat fragmentation, and unregulated selective logging. This article presents a case study applying a qualitative phenomenological approach to examine the power relations and institutional failures shaping the sustainable management of its value chain in Imbabura Province, Ecuador. Drawing on 21 in-depth semi-structured interviews with key actors (including woodcarvers, sawyers, traders, and environmental authorities) conducted between March and September 2025 until theoretical saturation was achieved, and supported by thematic analysis in ATLAS.ti, we identified five thematic categories revealing the tension between cultural valuation and market pressure. The findings confirm the existence of a value paradox, whereby high timber demand paradoxically accelerates resource depletion rather than incentivizing conservation, as premature harvesting of young trees undermines the viability of non-timber forest products such as nuts and accelerates the loss of local genetic resources. We conclude that the long-term conservation of the species requires a transition toward polycentric stewardship, community forestry enterprises, and integrated landscape management in which the standing tree is formally recognized as carrying greater ecological and economic value than harvested timber. Full article

The Montado, a traditional Mediterranean agro-silvopastoral system, has historically sustained ecological and economic functions through the integration of trees, livestock, and crops. Today, its multifunctionality is increasingly threatened by climate variability, market volatility, and evolving policy frameworks. While previous research has examined Montado dynamics at landscape or plot scales, less attention has been paid to sustainability trajectories at the farm level, where management decisions are made. This study bridges that gap by assessing the sustainability dynamics of farms through a participatory, typology based, scenario approach grounded in a regional typology. We characterized three representative farm archetypes (forestry-focused, mixed agro-silvopastoral, and livestock-focused) and evaluated their trajectories under plausible future scenarios driven by climate, market, and policy pressures. Scenario outcomes were assessed using expert-based scoring (five-point scale), revealing score differences of up to two points across sustainability dimensions between farm archetypes and scenarios. Findings reveal marked trade-offs: Tree-focused farms maintain high environmental value but remain vulnerable to market and labor constraints, while livestock-specialized farms achieve higher economic output at the expense of ecological integrity. Mixed systems demonstrate greater resilience through diversification but face significant labor intensity challenges. We conclude that current “one-size-fits-all” policies generate contradictory incentives. Therefore, adaptive governance frameworks (e.g., results-based payment schemes) are essential to realign farm economics with ecological stewardship. Beyond the Montado, the approach provides insights relevant to other Mediterranean agroforestry systems facing similar sustainability challenges. Full article

Background/Objectives: Klebsiella pneumoniae is an opportunistic pathogen increasingly resistant to carbapenems and broad-spectrum antibiotics, complicating timely infection management. In critical cases like septic shock, where initiating effective antibiotics within 3 h improves survival, culture-based resistance testing is often too slow. This study evaluates machine learning (ML) algorithms for faster antimicrobial resistance prediction than conventional methods. Methods: In this retrospective study, antibiogram results of 607 Klebsiella pneumoniae isolates collected between 2017 and 2024 were combined with demographic and clinical information of the patients from whom the isolates were obtained. Four different ML algorithms, namely Decision Tree (DT), Support Vector Classifier (SVC), K-Nearest Neighbors (KNN) and Random Forest (RF), were applied to classify the resistance status for 22 antibiotics. Model performances were evaluated using accuracy, precision, recall, F-score, AUC and feature importance metrics. Results: The RF model showed the highest overall performance in accurately predicting resistance to 22 antibiotics, achieving an average AUC value of 0.96. In particular, it predicted resistance to treatment-critical antibiotics such as Ertapenem (100%), Imipenem (93%) and Meropenem (95%) with high accuracy. Conclusions: ML models, especially RF, offer a powerful tool for rapid antibiotic resistance prediction, supporting accurate empirical treatment decisions and antimicrobial stewardship. Full article

Maple syrup production has the potential to promote sustainable rural economic development in regions with suitable forest and climate conditions. Kentucky emerges as a promising candidate due to its extensive maple tree inventory and favorable seasonal patterns. However, the broader economy-wide implications of developing a maple syrup industry in the state remain underexplored. To fill this knowledge gap, this study employs a customized static single-region computable general equilibrium (CGE) modeling approach for Kentucky under nine scenarios based on production capacities and potential levels. The results consistently show positive impacts on net household income, social welfare (measured by equivalent variation), government revenues, and state GDP across all scenarios. Medium production capacities generate the most balanced and efficient outcomes, while high-potential scenarios, especially under small and large scales produce the largest absolute gains. These results underscore the viability of maple syrup production as an economic development strategy and highlight the role of production scale in maximizing benefits. Furthermore, expanding maple syrup production can enhance rural livelihoods by diversifying forest-based income and promoting long-term stewardship. As a non-timber forest product, maple syrup tapping provides economic incentives to maintain healthy forests, strengthening rural sustainability and resilience. Our findings indicate that developing this industry beyond traditional regions can generate meaningful economic benefits while encouraging sustainable resource use when appropriately scaled and managed. Full article

Vegetation in residential areas plays a crucial role in biodiverse and sustainable cities as it enhances biological diversity, environmental quality, and the human well-being of city residents. However, the distribution of vegetation among these areas is often unequal, leading to disparities in access to its benefits. To promote a more biodiverse and environmentally just city, we investigated how woody plants (trees, shrubs and vines) vary with socioeconomic level in residential streetscapes of Santiago de Chile. Across the city, we sampled woody plants in 120 plots (11 m radius) located in residential streetscapes of three socioeconomic levels: low, medium, and high. A total of 557 woody plants were identified and measured. Of these, only 9.7% corresponded to native species, whereas 90.3% were introduced species. Wealthier residential areas had higher species richness and abundance of woody plants, as well as plants with greater structural size (revealed by height and crown area). In addition, we found that the composition of woody plants differed among socioeconomic levels: Liquidambar styraciflua, Platanus x hispanica, and Pittosporum tobira were more abundant in high socioeconomic areas; Prunus cerasifera, Citrus limon, and Ailanthus altissima were more abundant in medium socioeconomic areas; Robinia pseudoacacia, Acer negundo, and Schinus areira were more abundant in low socioeconomic areas. Our research highlights that woody plant diversity, abundance, structure, and composition vary with socioeconomic level in residential streetscapes. Key insights for reducing these inequalities and achieve a more environmentally just city include: (a) governance and equity-based investment; (b) prioritizing local native species; (c) promoting the use of non-tree woody plants; and (d) empowering communities through capacity building and stewardship. Full article

Tree-covered urban green spaces, including streets, parks, and other public areas, are vital for urban sustainability and people’s well-being. However, such trees face threats from the occurrence of extreme weather. In this study, we investigated wind damage to urban trees in the city of Debrecen, Hungary, during two severe windstorms in July 2025. Field surveys were conducted across three distinct urban zones, covering approximately 515,000 m² in total. We assessed 201 damaged and 325 undamaged trees and recorded the species, size, damage type, and contextual landscape features associated with them being damaged or not. Damage type to trees consisted primarily of broken branches, whilst uprooting and trunk breakage were recorded less often. Most tree characteristics (trunk circumference, height, systematic position, nativity) and the proximity and height of buildings upwind of focal trees were significant predictors of their vulnerability to windstorms. In addition, we surveyed 150 residents in person and received comments from 54 people via online questionnaires and explored their perceptions of storm frequency, the causes of storms, and mitigation measures. Most respondents noted increased storm frequency and attributed that to climate change, and they suggested mitigation measures focused on urban tree management and environmental protection. Some people expressed scepticism about the presence of climate change and/or their ability to address such damage on an individual basis. Our study is the first to integrate assessments of storm-related impacts on urban trees with the opinions of residents living in proximity to them. Our findings highlight the need for climate-adaptive and mechanically robust urban forestry planning and offer insights that guide the management of trees in urban areas globally. Specifically, we propose to undertake the following: (1) Prioritise structurally resilient, stress-tolerant tree species adapted to extreme weather conditions when planting new trees. (2) Integrate wind dynamics, microclimatic effects and artificial stabilisation techniques into urban design processes to optimise tree placement and their long-term stability. Urban planners, builders, developers, and homeowners should be informed about these stabilising practices and incorporate the needs of trees early in the design process, rather than as decorative additions. (3) Develop regionally calibrated risk models and early-warning systems to support proactive and data-driven tree management and public safety. (4) Promote climate literacy and public participation to strengthen collective stewardship and resilience of urban trees. Full article

In many hardwood forests, resprouting is a common response to disturbance and basal resprouts may represent a substantial component of the forest understory, especially post-wildfire. Despite this, resprouts are often overlooked in biomass assessments and drivers of resprouting responses in certain species are still unknown. These knowledge gaps are problematic as the contribution of resprouts to understory fuel loads are needed for wildfire risk modeling and effective forest stewardship. Here, we validated the handheld mobile laser scanning (HMLS) of basal resprout volume and field measurements of stem count and clump height as methods to estimate the mass of California Bay Laurel (Umbellularia californica) basal resprouts at Pepperwood and Saddle Mountain Preserves, Sonoma County, California. In addition, we examined the role of tree size and wildfire severity in predicting post-wildfire resprouting response. Both field measurements (clump height and stem count) and remote sensing (HMLS-derived volume) effectively estimated dry mass (total, leaf and wood) of U. californica resprouts, but underestimated dry mass for a large resprout. Tree size was a significant factor determining post-wildfire resprouting response at Pepperwood Preserve, while wildfire severity significantly predicted post-wildfire resprout size at Saddle Mountain. These site differences in post-wildfire basal resprouting predictors may be related to the interactions between fire severity, tree size, tree crown topkill, and carbohydrate mobilization and point to the need for additional demographic and physiological research. Monitoring post-wildfire changes in U. californica will deepen our understanding of resprouting dynamics and help provide insights for effective forest stewardship and wildfire risk assessment in fire-prone northern California forests. Full article

Carbapenem-resistant Klebsiella pneumoniae (CR-Kp) is eroding therapeutic options for urinary tract infections. We isolated a multidrug-resistant strain from the urine of a chronically bacteriuric patient and confirmed its identity by Vitek-2 and MALDI-TOF MS. Initial disk-diffusion profiling against 48 antibiotics revealed susceptibility to only 5 agents. One month later, repeat testing showed that tetracycline alone remained active, highlighting the strain’s rapidly evolving resistome. Given the scarcity of drug options, we performed an “aromatogram” with seven pure essential oils, propolis, and two commercial phytotherapeutic blends. Biomicin Forte^® produced a 30 mm bactericidal halo, while thyme, tea tree, laurel, and palmarosa oils yielded clear inhibition zones of 11–22 mm. These in vitro data demonstrate that carefully selected plant-derived products can target CR-Kp where conventional antibiotics fail. Integrating aromatogram results into One Health’s stewardship plans may therefore help preserve last-line antibiotics and provide adjunctive options for persistent urinary infections. Full article

Climate-amplified extreme heat events are particularly dangerous for city dwellers. Nature-based solutions such as urban greening may serve as an effective preventative strategy against extreme heat. Driven by historical injustices such as redlining, disadvantaged communities often face limited green space and a heightened risk of vulnerability to extreme heat in urban environments. This paper investigates community engagement strategies for heat-vulnerable community participation in urban greening research as a part of a broader transdisciplinary environmental research praxis focused on multistakeholder co-creation. We conducted semi-structured interviews with community leaders in heat-vulnerable neighborhoods in New York City to explore community co-creation in the design and implementation of tree planting, and compared these themes with interviews with urban tree professionals and other community groups. Overall, the participants agreed on broad themes of environmental justice, intergenerational engagement, community building, and socioecological relationships, although community leaders differed in both a greater emphasis of experiential knowledge and reduced focus on volunteer community stewardship. The findings inform our research process and associated community engagement, including building online resources and addressing community-specific concerns during the research process. We conclude by recommending future steps for facilitating multistakeholder conversations to build inclusive and equitable urban greening heat-adaptive strategies. Full article

Background: Antimicrobial resistance (AMR) poses a growing threat to veterinary medicine and food safety. This study examines Escherichia coli antibiotic resistance patterns in ducks, focusing on multidrug-resistant (MDR) strains. Understanding resistance patterns and predicting MDR occurrence are critical for effective intervention strategies. Methods: E. coli isolates were collected from duck samples across multiple regions. Descriptive statistics and resistance frequency analyses were conducted. A decision tree classifier and a neural network were trained to predict MDR status. Cross-resistance relationships were visualized using graph-based models, and Monte Carlo simulations estimated MDR prevalence variations. Results: Monte Carlo simulations estimated an average MDR prevalence of 79.6% (95% CI: 73.1–86.1%). Key predictors in MDR classification models were enrofloxacin, neomycin, amoxicillin, and florfenicol. Strong cross-resistance associations were detected between neomycin and spectinomycin, as well as amoxicillin and doxycycline. Conclusions: The high prevalence of MDR strains underscores the urgent need to revise antibiotic usage guidelines in veterinary settings. The effectiveness of predictive models suggests that machine learning tools can aid in the early detection of MDR, contributing to the optimization of treatment strategies and the mitigation of resistance spread. The alarming MDR prevalence in E. coli isolates from ducks reinforces the importance of targeted surveillance and antimicrobial stewardship. Predictive models, including decision trees and neural networks, provide valuable insights into resistance trends, while Monte Carlo simulations further validate these findings, emphasizing the need for proactive antimicrobial management. Full article

Background/Objectives: Infections in dogs caused by methicillin-resistant staphylococci (MRS) present limited treatment options. This study’s objective was to investigate the molecular epidemiology of Staphylococcus spp. cultured exclusively from clinical canine skin and ear samples in Queensland, Australia, using whole-genome sequencing (WGS). Methods: Forty-two Staphylococcus spp. isolated from clinical canine skin and ear samples, from an unknown number of dogs, were sourced from two veterinary diagnostic laboratories between January 2022 and May 2023. These isolates underwent matrix-assisted laser desorption ionisation– time of flight bacterial identification, minimum inhibitory concentration testing using Sensititre^TM plates and WGS. Phylogenetic trees and core genome multilocus sequence typing (cgMLST) minimum spanning trees (MSTs) were constructed. Results: The isolates included methicillin-resistant and -sensitive S. pseudintermedius (MRSP: 57.1%, 24/42; and MSSP: 19.1%, 8/42), methicillin-resistant and -sensitive S. coagulans (MRSC: 14.3%, 6/42; and MSSC: 2.4%, 1/42) and methicillin-resistant coagulase-negative staphylococci (MR-CoNS: 7.1%, 3/42). Thirty-nine isolates were included after WGS, where all MRS harboured the mecA gene. Eighteen sequence types (STs) were identified, including three novel MRSP and six novel MSSP STs. MRSP ST496-V-VII (23%; 9/39) and MRSP ST749-IV-(IVg) (12.8%; 5/39) were commonly isolated. Phylogenetic analysis of single nucleotide polymorphisms showed that MRSP, MRSC and MSSC were similar to globally isolated staphylococci from canine skin and ear infections. Using cgMLST MSTs, MRSP isolates were not closely related to global strains. Conclusions: Our findings revealed a genotypically diverse geographical distribution and phylogenetic relatedness of staphylococci cultured from clinical canine skin and ear samples across Queensland. This highlights the importance of ongoing surveillance to aid in evidence-based treatment decisions and antimicrobial stewardship. Full article

Forests contain just under 58% of all known species globally, with approximately 61% of these found in countries with Forest Stewardship Council (FSC)-certified forests. Few studies have directly analyzed the association between biodiversity and certification, often focusing on limited geographical or temporal scopes. There is a lack of understanding regarding the socio-political and ecological drivers of FSC certification’s relationship with biodiversity conservation. We assess the associations between country-level characteristics and the relationship between FSC’s forest management (FSC-FM) certification area and independently made biodiversity estimates from the International Union for Conservation of Nature (IUCN). Specifically, using generalized random forests (GRF) and data from 91 countries between 2008 and 2019, we examine the associated factors that govern FSC-FM certifications relationship with percentage threatened species. The results indicate that increasing FSC-FM-certified areas is linked to a reduction in the percentage of threatened species by 0.1 to 0.15. Moreover, FSC-FM certifications show a positive relationship with biodiversity in regions with high population densities and significant tree cover loss. Enhanced perception of corruption control and reduced extraction of non-forestry resources further strengthen this association. These findings provide the FSC with strategic insights to expand FM certification, contributing to biodiversity conservation through sustainable forest management. Full article

Within the U.S., there is a growing need to integrate environmentally friendly practices into conventional agriculture. Agroforestry enhances environmental and resource stewardship in agricultural landscapes while offering potential economic benefits to farmers. Despite rising interest, limited information on its application in the U.S. hinders development efforts. A spatiotemporal analysis of current farm operations can provide crucial insights. This study examined patterns of agroforestry and tree crop adoption in the U.S. Corn Belt using USDA Census data (2012, 2017, and 2022) and spatial tools (Global Moran’s I, Local Moran’s I, and Moran scatterplots). The tree crops included in the analysis were chestnut (Castanea spp.), hazelnut (Corylus spp.), improved northern pecan (Carya illinoinensis), elderberry (Sambucus spp.), and pawpaw (Asimina triloba). The results showed increasing farm operations with agroforestry and tree crops over time for all census periods. Agroforestry had the strongest spatial cluster patterns, with Local Moran’s I revealing R2 values rising from 0.30 to 0.35 between 2017 and 2022. Chestnut, hazelnut, and improved pecan had clustered spatial patterns, but had decreasing spatial autocorrelations from 2012 to 2022, while elderberry clustered in 2017 but not 2022. This study reveals an upward trend in agroforestry adoption and the spatial expansion of certain tree crops in the U.S. Corn Belt, highlighting potential for region-specific agroforestry development. The findings offer insights to guide strategies and programs supporting sustainable agricultural practices. Full article

Background/Objectives: Inexperienced dentists and dental students are especially prone to misdiagnosis, and this represents a huge problem regarding antimicrobial stewardship. We aimed to develop a mobile app for rational antibiotic prescribing in dentistry based on local–systemic symptoms and patient factors, rather than solely on diagnosis, to tackle misdiagnosis. Methods: The study involved 64 participants, 50 of which were third-year dental students attending a pharmacology course focusing on antimicrobials, comprising lectures and practical sessions without (noAPP group, n = 22) or with (APP group n = 28) the assistance of a mobile application. The other 14 participants were practicing dentists who decided to register and use the application. All registered users of the application were asked to take a feedback survey, while learning outcomes were evaluated via a pharmacology quiz. Results: A decision tree was used for application development. In total, 76 impressions were collected on the application. The majority of the impressions were related to odontogenic–endodontic infections. Multiple linear regression analysis did not reveal differences in survey responses between practicing dentists and undergraduate students in the feedback survey responses. There was a significant difference in the mean pharmacology test scores between the noAPP and APP groups (5.50 ± 1.80 vs. 7.21 ± 1.03, p = 0.0001). Conclusions: The dentalantibiotic.com application was developed to support rational antibiotic prescribing, in view of tackling misdiagnosis, among inexperienced dentists, as well as to assist in undergraduates’ pharmacology learning, and the current study shows its large impact as an educational tool. The majority of participants considered it easy to use, efficient in facilitating the right antibiotic choice, and useful for everyday decision-making. Full article

This work conducts a rigorous examination of the economic influence of tourism in Saudi Arabia, with a particular focus on predicting tourist spending patterns and classifying spending behaviors during the COVID-19 pandemic period and its implications for sustainable development. Utilizing authentic datasets obtained from the Saudi Tourism Authority for the years 2015 to 2021, the research employs a variety of machine learning (ML) algorithms, including Decision Trees, Random Forests, K-Neighbors Classifiers, Gaussian Naive Bayes, and Support Vector Classifiers, all meticulously fine-tuned to optimize model performance. Additionally, the ARIMA model is expertly adjusted to forecast the economic landscape of tourism from 2022 to 2030, providing a robust predictive framework for future trends. The research framework is comprehensive, encompassing diligent data collection and purification, exploratory data analysis (EDA), and extensive calibration of ML algorithms through hyperparameter tuning. This thorough process tailors the predictive models to the unique dynamics of Saudi Arabia’s tourism industry, resulting in robust forecasts and insights. The findings reveal the growth trajectory of the tourism sector, highlighted by nearly 965,073 thousand tourist visits and 7,335,538 thousand overnights, with an aggregate tourist expenditure of SAR 2,246,491 million. These figures, coupled with an average expenditure of SAR 89,443 per trip and SAR 9198 per night, form a solid statistical basis for the employed predictive models. Furthermore, this research expands on how ML and AI innovations contribute to sustainable tourism practices, addressing key aspects such as resource management, economic resilience, and environmental stewardship. By integrating predictive analytics and AI-driven operational efficiencies, the study provides strategic insights for future planning and decision-making, aiming to support stakeholders in developing resilient and sustainable strategies for the tourism sector. This approach not only enhances the capacity for navigating economic complexities in a post-pandemic context, but also reinforces Saudi Arabia’s position as a premier tourism destination, with a strong emphasis on sustainability leading into 2030 and beyond. Full article