Generating societal value from natural capital on corporate-owned land: a real estate case study from Mauritius.

Widespread positive priming of dissolved organic carbon decomposition in inland waters.

Are intertidal habitats keeping up with nutrient export? Insights from modelling climate and management scenarios.

Integrating sufficiency-oriented lifestyles and socio-political acceptance of land-use changes in urban sustainability transformations

Climate Change and Infectious Diseases in Dermatology.

Microplastic contaminations in edible seaweed Caulerpa racemosa across Indonesian tropical seawaters related to land uses: Implication on human health.

Climate impacts of radiative forcing driven by agricultural land-use and land-cover changes in northern Canada.

Understanding deforestation dynamics in Amazonian protected areas through land-use change models informed by conservation discourses

Recurring struggle in managing water hyacinth invasion and water quality in a tropical lake: Case study of Lake Rawa Pening, Indonesia.

Wetlands are essential components of biological systems that provide several ecological services. Urban Wetland (UW)settings enhance city resilience by enhancing water purity, sequestering carbon, offering homes for animals, mitigating heat-island(HI)impacts, and offering recreational possibilities. Nonetheless, the preservation of UWenvironments encounters several obstacles, including diminished hydrological processes, altered water cycles resulting from barriers, pollution from waterways, habitat degradation owing to land-use changes, and biodiversity decline due to the introduction of invasive species. The article examines the theoretical framework of UW, including its ecological, economic, and cultural significance (EECS) andsusceptibility. This biodiversity governs the local environment, carbon storage, fuelwood supply, fishery-related enterprises, and other ecological,social,and economic factors. Furthermore, UWprovidesother biological functions, such as preserving the purity of water via sedimentation and nutrient retention. Globally, wetlands face several challenges, both man-made and natural.

The tapayaxin (Phrynosoma orbiculare) is a lizard endemic to Mexico, with a small, flattened, spiny body, horns on its head, and a specialized diet of ants. In the Sierra de Santa Catarina (SSC) of the Mexico City, its populations are threatened by urbanization, land-use change, fires, and illegal logging. This study documents the existence of a wild population in the SSC, confirming its presence despite ecological isolation. Beyond its ecological value, the orbicularis has a deep historical significance: it was described by colonial naturalists as early as the 16th century and is present in the oral traditions of indigenous peoples. Proposing it as a flagship species seeks to protect its habitat, strengthen territorial identity, and foster environmental awareness. The orbicularis represents a link between the memory of the landscape and the sustainable future of the city.

Urban air pollution has evolved into one of the most severe environmental problems in Asia, driven by rapid industrialization, urbanization and complex climate interactions. This study is a synthesis of the spatiotemporal patterns, driving forces and governing responses of air quality in major Asian cities. Distinct interannual and seasonal trends were identified: air quality is improving in East Asia, stagnating in South Asia and fluctuating in Southeast Asia; pollution peaks in dry or winter months and eases in wet or summer seasons. These variations arise from the combined effects of emissions intensity, meteorological dynamics, and regional climate regimes of different regions. Socioeconomic factors such as energy structure, land-use change and urban morphology influence local patterns of pollutant production and exposure inequalities. Despite the continued implementation of policies aimed at improving air quality, persistent ozone pollution and transboundary haze exposure highlight deficiencies in co-governance. Improving air quality will ultimately depend on greater cooperation between the Asian cities and regions in the 2030 agenda, synergistic PM.-O control interactions, optimized monitoring networks, and more inclusive public participation. This review, drawing on 577 studies provides a detailed overview of how Asian megacities have changed, the mechanistic and management processes at play. It provides insights into designing multi-pollutant management approaches.

Human-monkey conflict is increasing in most parts of Nepal due to rapid land-use change, habitat loss, and expanding human settlements. This study assessed the patterns, drivers, and community responses to HMC in Bheemdatta and Bedkot Municipalities of Kanchanpur District. The types of conflict, perceived drivers, local mitigation strategies, and community attitudes towards monkeys were documented through household surveys (160 households (80 per municipality), key informant interviews and field observations. Most of the respondents reported frequent crop damage, food theft, property destruction, and safety concerns. Our results indicated that habitat degradation and food scarcity are the major drivers of HMC. Bedkot, characterized by forest-adjacent farmlands, reported higher crop losses and more frequent encounters, while Bheemdatta experienced predominantly urban-related incidents such as food snatching and household intrusion. Communities primarily relied on reactive measures (stone-throwing, guarding), which were often ineffective. Despite economic losses, cultural and religious values promoted tolerance toward monkeys, although expectations from local authorities varied across municipalities. The present study recommends community-based mitigation, improved land-use planning, and future integration of ecological and spatial data for hotspot identification to guide management strategies.

Abstract Global trade in agricultural and livestock commodities plays a significant role in driving deforestation, as land-use changes in producer countries often supply consumption in distant regions. Although numerous studies have quantified deforestation embodied in traded products, much less is known about the underlying economic and institutional factors that explain why some countries export or import more of it than others. This study addresses these questions by examining the determinants of embodied deforestation in international trade flows. Drawing on Factor Endowment Theory, we test whether income levels, land availability, and institutional quality influence the distribution of embodied forest loss between exporting and importing countries. Using a gravity model estimated through Poisson Pseudo Maximum Likelihood (PPML), we assess how GDP, distance, trade composition, and governance shape embodied deforestation patterns in agricultural and livestock commodities. Results highlight a prominent role of GDP per capita as a driver of deforestation: a 1% increase in per capita GDP is associated, on average, with a 0.28% decrease in deforestation embodied in exports, but with a 0.88% increase in embodied deforestation imports, reflecting an outsourcing of land-use pressures. While developing countries embody comparatively more deforestation in their exports, higher-income importing countries increasingly shift the environmental burden of their consumption to other regions through imports. Domestic consumption factors, such as higher per capita meat demand, are also positively associated with embodied deforestation inflows: a 1 kg increase in yearly meat consumption per capita is linked to a 1.8% rise in embodied deforestation inflows. Overall, the findings highlight the structural asymmetries of global trade and underscore the responsibility of affluent nations in driving deforestation beyond their borders. They also suggest that both production- and consumption-side policies are needed to mitigate embodied deforestation in high-impact commodity sectors.

Climate change poses a serious threat to Sri Lanka’s Dry Zone, where agriculture relies heavily on rainfall. As a result, farming communities in this region are among the most severely affected. Climate change has increased the complexity of water resource management, particularly in areas with fragmented institutions and competing sectoral demands. Addressing these challenges requires transformative adaptation that reshapes governance structures, power relations, and institutional frameworks. Understanding existing water governance mechanisms—and their limitations—is therefore critical for enhancing the region’s climate resilience and socio-economic development. This study focused on water governance in Sri Lanka’s Dry Zone, with particular attention to the village tank cascade system—a traditional hydrological and social-ecological network that has supported irrigated agriculture for centuries. The research employed a combined methodology of literature review, field observations, and open-ended discussions with farmers, community organizations, and government officials across three cascade systems in the Kurunegala and Anuradhapura districts. This approach enabled an assessment of the functionality of Village Tank Cascade Systems (VTCS), their governance structures, water management practices, and associated limitations. The literature analysis examined polycentric governance principles and the evolution of water governance within VTCS. Findings indicate that VTCS represent deeply rooted community-based water management systems. However, these multifunctional systems face increasing threats from land-use changes, population pressures, and fragmented institutional arrangements, all of which weaken their resilience and integrated performance. The study highlights the importance of strengthening the hydrological and ecological connectivity of VTCS, which can be achieved through cascade-level management and polycentric approaches to enhance the climate resilience of Dry Zone farming communities.

Urban flooding has emerged as one of the most critical challenges in rapidly developing cities, driven by climate change, intense rainfall events, and increasing land-use pressures. Accurate flood hazard mapping is essential for informed urban planning and disaster risk reduction, yet traditional approaches often face limitations in capturing complex hydrodynamic processes and ensuring interpretability for decision-makers. This study presents an integrated methodology that combines two-dimensional HEC-RAS hydrodynamic modelling with explainable machine learning (XAI) techniques for flood hazard zonation in urban watersheds. The HEC-RAS model successfully simulated flood depths and flow velocities, validated against observed data with a strong correlation coefficient (R2 = 0.92) and low error indices. Machine learning models were tested using rainfall intensity, land use, slope, and proximity to rivers as predictors, with Random Forest achieving the highest performance (91% accuracy). To address the ‘black-box’ limitation, Shapley Additive Explanations (SHAP) were applied, identifying rainfall intensity and river proximity as the most significant drivers of flood risk. An integrated hazard map was developed by combining hydrodynamic outputs with Random Forest predictions. Validation against historical flood records yielded an overall accuracy of 89% and a Kappa statistic of 0.84, confirming the robustness of the approach. Sensitivity and statistical analyses further highlighted the impacts of rainfall variability and land-use change on flood susceptibility. The findings demonstrate that integrating hydrodynamic modelling with explainable AI enhances the accuracy, interpretability, and practical utility of flood hazard mapping, offering a valuable framework for urban planners and policymakers in managing flood risks under dynamic climate and land-use scenarios.

Landscape fragmentation and habitat loss are major drivers of global biodiversity decline. Understanding how animals adjust their behavior in response to these threats and adapt to human-altered environments is essential for developing effective conservation strategies. In this study, we apply Bayesian models to a unique movement dataset to explore how landscape transformation influences behavioral patterns in the giant anteater (Myrmecophaga tridactyla; Pilosa; Mammalia), a vulnerable species with limited physiological thermoregulatory capacity and strong behavioral responses to environmental changes. We used an extensive GPS tracking dataset from 41 giant anteaters in the Brazilian savanna-a biodiversity hotspot facing extensive landscape alteration due to land-use and land-cover change. We used the Time-Explicit habitat selection model to investigate giant anteater sex-based differences in habitat selection. Additionally, we evaluated how habitat types influence the species' activity patterns using a nonparametric Bayesian Hidden Markov model. Our study reveals that giant anteaters change their movement patterns in response to land use and land cover change. Individuals spent more time in and selected native habitats compared to human-modified habitats, regardless of sex. The time spent on, and selection for, native habitats are likely due to greater resources such as food, shelter, and protection from predation. Additionally, giant anteaters are more likely to rest in native habitats while being more active in human-disturbed areas. Our research reveals the negative impact of human-disturbed landscapes on animal movement. Consistent with our expectations, giant anteaters spend more time in native habitats and tend to avoid areas with high human disturbance, underscoring the urgent need for conservation efforts in degraded landscapes. Understanding how different land use and land cover classes influence animal habitat selection and activity patterns is essential for assessing the species' adaptability and ecological requirements in human-modified environments. Our findings can guide the prioritization of critical areas for conservation and restoration, offering valuable insights for policymakers and supporting the effective management of this vulnerable species and the Brazilian savanna.

Urban catchments are increasingly vulnerable to hydrologic extremes driven by land-use change and climate variability, challenging the traditional assumption of stationarity. This study develops a computational framework to assess the nonstationary behavior of peak flow, volume, and duration in an urban catchment in the Philippines using 39 years of daily flow records (June 1984–November 2022). Missing observations (~8% of the series) were reconstructed using multiple linear regression (MLR) and artificial neural networks (ANNs) with four predictors: daily rainfall, antecedent rainfall, antecedent flow, and built-up area index. MLR with all predictors yielded the most accurate reconstructions. Nonstationarity was detected using the Mann–Kendall test, Sen slope estimator, Pettitt test, and variance change test. Flood events were extracted using block maxima (BM) and peak-over-threshold (POT) methods. BM-based results showed stationary peak flow and volume, while duration increased by 1.78 h/year. POT analyses revealed nonstationarity across all variables, without significant shifts in variance. These findings demonstrate that methodological choices strongly influence nonstationary detection. The framework underscores the importance of reliable data reconstruction and robust statistical testing for nonstationary analysis of flood events. POT-based approaches more effectively capture evolving trends in peak flow, volume, and duration. These can be used in designing resilient infrastructure and flood risk management in urbanizing catchments.

Ecosystem services play a vital role in supporting human life and environmental sustainability. However, tourism activities in Badung Regency, Bali, have led to significant changes in land cover and use, impacting the function of ecosystem services. This study integrates remote sensing, machine learning, and InVEST technology to understand the impact of Land Use/Land Cover (LULC) changes on ecosystem services in Badung Regency. The results show a decrease in non agricultural vegetation area from 17659.65 hectares in 2014 to 11405.84 hectares in 2024. Meanwhile, built-up land experienced a drastic increase from 15074.47 hectares in 2014 to 22134.06 hectares in 2024. In addition, the InVEST model shows a decrease in carbon stock by 1379,841.68 tons in the period 2014 to 2024. Meanwhile, water yield, nitrogen export, and sediment export increased, reflecting a relationship between tourism development and the decline in ecosystem services. Correlation analysis shows a consistent negative correlation between water yield and carbon stock, as well as a positive correlation between nitrogen export and sediment export. The results of this study are expected to serve as a reference for further studies on the dynamics of ecosystem services and support sustainable environmental management efforts in areas with rapidly growing tourism activity.

Abstract. Urban expansion in India has accelerated significantly over the past two decades, leading to widespread changes in land use and land cover patterns. This study examines the spatiotemporal dynamics of urban expansion and land cover changes from 2001 to 2022 using a dual-resolution geospatial framework. MODIS MCD12Q1 (500 m) data were used for national-scale assessment across twenty Tier- I urban growth centres, while GLC_FCS30D (30 m) data supported high-resolution assessment in the Hyderabad metropolitan region. At the national level, the study revealed a steady increase in built-up areas, often exceeding 30–80% growth across key urban growth centres. Croplands were identified as the primary land category converted into urban use, followed by losses in grasslands and shrublands. The Hyderabad case study demonstrated the limitations of coarse-resolution datasets in detecting fragmented growth and peri-urban development. In contrast, the high-resolution GLC_FCS30D data enabled more detailed mapping of edge expansion, spatial fragmentation, and heterogeneous growth morphology. Unlike prior studies limited to either national or local focus, this work develops a unified, dual-resolution LULC analysis framework with pixel-level transition tracking enables cross-scale insights into urban expansion patterns in India. The integration of both datasets facilitated a comprehensive understanding of urban land changes, combining long-term trend detection with local-level spatial clarity. This approach underscores the importance of resolution–aware methods in urban monitoring and supports evidence–based decision–making in sustainable urban planning, infrastructure development, and land governance. The findings highlight the need for scalable geospatial strategies to address the challenge of rapid urbanization in India, particularly in developing countries undergoing intense land transformation.