Search Results (96)

Artificial intelligence (AI) offers several opportunities in wildfire management, particularly for improving short- and long-term fire occurrence forecasting, spread modeling, and decision-making. When properly adapted beyond research into real-world settings, AI can significantly reduce risks to human life, as well as ecological and economic damages. However, despite increasingly sophisticated research, the operational use of AI in wildfire contexts remains limited. In this article, we review the main domains of wildfire management where AI has been applied—susceptibility mapping, prediction, detection, simulation, and impact assessment—and highlight critical limitations that hinder practical adoption. These include challenges with dataset imbalance and accessibility, the inadequacy of commonly used metrics, the choice of prediction formats, and the computational costs of large-scale models, all of which reduce model trustworthiness and applicability. Beyond synthesizing existing work, our survey makes four explicit contributions: (1) we provide a reproducible taxonomy supported by detailed dataset tables, emphasizing both the reliability and shortcomings of frequently used data sources; (2) we propose evaluation guidance tailored to imbalanced and spatial tasks, stressing the importance of using accurate metrics and format; (3) we provide a complete state of the art, highlighting important issues and recommendations to enhance models’ performances and reliability from susceptibility to damage analysis; (4) we introduce a deployment checklist that considers cost, latency, required expertise, and integration with decision-support and optimization systems. By bridging the gap between laboratory-oriented models and real-world validation, our work advances prior reviews and aims to strengthen confidence in AI-driven wildfire management while guiding future research toward operational applicability. Full article

Real-time insect monitoring is essential for sustainable agriculture and biodiversity conservation. The traditional method of attracting insects to colored glue traps and manually counting the catch is time-intensive and requires specialized taxonomic expertise. Moreover, these traps are often lethal to pests and beneficial insects alike, raising both ecological and ethical concerns. Camera-based trap designs have recently emerged to lower the amount of manual labor involved in determining insect species, yet they are still deadly to the catch. This study presents the design and evaluation of a non-lethal acoustic monitoring system capable of detecting and classifying insect species based on their sound signatures. A first prototype was developed with a focus on low self-noise and suitability for autonomous field deployment. The system was initially validated through laboratory experiments, and subsequently tested in six rapeseed fields over a 25-day period. More than 3400 h of acoustic data were successfully collected without system failures. Key findings highlight the importance of carefully selecting each component to minimize self-noise, as insect sounds are extremely low in amplitude. The results also underscore the need for efficient data and energy management strategies in long-term field deployments. This paper aims to share the development process, design decisions, technical challenges, and practical lessons learned over the course of building our IoT sensor system. By outlining what worked, what did not, and what should be improved, this work contributes to the advancement of non-invasive insect monitoring technologies. Full article

The Mediterranean Sea, a biodiversity hotspot with over 8500 marine species, faces escalating threats from climate change, pollution, overfishing, and habitat degradation. This study introduces a novel Decision Support System (DSS) integrating a modified PESTEL framework (BESTEL: Biological, Economic, Social, Technological, Environmental, Legal) with Fuzzy Cognitive Mapping (FCM) to assess and mitigate risks to marine species. Leveraging expert knowledge from 34 specialists, we identified 30 key factors influencing extinction risk, analyzed through Principal Component Analysis (PCA) to reduce dimensionality. The resulting FCM model simulated four policy scenarios, evaluating the impacts of climate change and dam proliferation on biodiversity. Findings reveal that mitigating both drivers significantly reduces extinction risk (−0.14), while unchecked climate change offsets gain from dam removal. The DSS highlights the dominance of climate stressors, with pollution and temperature shifts (−0.45, −0.42) as critical variables. Biological traits like reproductive frequency and longevity respond strongly to environmental improvements. This integrative approach bridges qualitative expertise and quantitative modeling, offering actionable insights for conservation planning. The study underscores the need for holistic strategies combining climate mitigation and habitat restoration to safeguard Mediterranean marine ecosystems. The FCM-based DSS provides a scalable tool for policymakers to prioritize interventions and assess trade-offs in complex socio-ecological systems. Full article

Genealogy is the study of family history and ancestral lineage, tracing relationships across generations through records and narratives. The digital revolution has shifted genealogical research from traditional archives to online platforms. Grounded in knowledge co-creation theory, this study examined the role of social media communities and their managers as knowledge hubs within the genealogical ecosystem. Its central innovation lies in identifying two emerging actors in modern genealogical knowledge ecology: the online community as a hub of expertise and the community manager as a key figure in knowledge creation. Drawing on interviews with fifteen Facebook managers of genealogical communities from diverse Jewish backgrounds worldwide, the study explored their perceptions of online genealogical spaces and their roles as facilitators of knowledge. Participants demonstrated a high level of professionalism and thoughtful engagement with sources; however, verifying the accuracy of genealogical claims was not within the scope of this study. Interviews were conducted in English and Hebrew based on participant preference. Thematic analysis revealed five key areas: two focused on the community’s role as a knowledge hub for both members and outsiders, and three on the manager’s role through self-perception, member engagement, and strategic initiatives. As part of a broader dissertation, this chapter deepens understanding of collaborative, community-driven genealogical knowledge in the age of social media. Full article

Geospatial sensing and data-driven technologies (GSDDTs) are playing an increasingly important role in transforming (agro)forestry practices across the Western Balkans 6 region (WB6). This review critically examines the current state of GSDDT application in six WB countries (also known as the WB6 group)—Albania, Bosnia and Herzegovina, Kosovo*, Montenegro, North Macedonia, and Serbia—with a focus on their contributions to sustainable (agro)forest management. The analysis explores the use of unmanned aerial vehicles (UAVs), light detection and ranging (LiDAR), geographic information systems (GIS), and satellite imagery in (agro)forest monitoring, biodiversity assessment, landscape restoration, and the promotion of circular economy models. Drawing on 25 identified case studies across WB6—for example, ALFIS, Forest Beyond Borders, ForestConnect, Kuklica Geosite Survey, CREDIT Vibes, and Project O₂ (including drone-assisted reforestation in Kosovo*)—this review highlights both technological advancements and systemic limitations. Key barriers to effective GSDDT deployment across WB6 in the (agro)forestry sector and its cross-border cooperation initiatives include fragmented legal frameworks, limited technical expertise, weak institutional coordination, and reliance on short-term donor funding. In addition to mapping current practices, this paper offers a comparative overview of UAV regulations across the WB6 region and identifies six major challenges influencing the adoption and scaling of GSDDTs. To address these, it proposes targeted policy interventions, such as establishing national LiDAR inventories, harmonizing UAV legislation, developing national GSDDT strategies, and creating dedicated GSDDT units within forestry agencies. This review also underscores how GSDDTs contribute to compliance with seven European Union (EU) acquis chapters, how they support eight Sustainable Development Goals (SDGs) and their sixteen targets, and how they advance several EU Green Agenda objectives. Strengthening institutional capacities, promoting legal alignment, and enabling cross-border data interoperability are essential for integrating GSDDTs into national (agro)forest policies and research agendas. This review underscores GSDDTs’ untapped potential in forest genetic monitoring and landscape restoration, advocating for their institutional integration as catalysts for evidence-based policy and ecological resilience in WB6 (agro)forestry systems. Full article

The ongoing degradation of global forests has severely weakened ecosystem service functions, and traditional judicial remedies have struggled to quantify intangible ecological losses. China has become an important testing ground for restorative justice through the establishment of specialized environmental courts and the practice of environmental public interest litigation. Since 2015, China has actively explored and institutionalized the application of the concept of restorative justice in its environmental justice reform. This concept emphasizes compensating environmental damages through actual ecological restoration acts rather than relying solely on financial compensation. This shift reflects a deep understanding of the limitations of traditional environmental justice and an institutional response to China’s ecological civilization construction, providing critical support for forest ecosystem restoration and enabling ecological restoration activities, such as replanting and re-greening, habitat reconstruction, etc., to be enforced through judicial decisions. This study conducts a qualitative analysis of judicial rulings in forest restoration cases to systematically evaluate the effectiveness of restorative justice in compensating for losses in forest ecosystem service functions. The findings reveal the following: (1) restoration measures in judicial practice are disconnected from the types of ecosystem services available; (2) non-market values and long-term cumulative damages are systematically underestimated, with monitoring mechanisms exhibiting fragmented implementation and insufficient effectiveness; (3) management cycles are set in violation of ecological restoration principles, and acceptance standards lack function-oriented indicators; (4) participation of key stakeholders is severely lacking, and local knowledge and professional expertise have not been integrated. In response, this study proposes a restorative judicial framework oriented toward forest ecosystem services, utilizing four mechanisms: independent recognition of legal interests, function-matched restoration, application of scientific assessment tools, and multi-stakeholder collaboration. This framework aims to drive a paradigm shift from formal restoration to substantive functional recovery, providing theoretical support and practical pathways for environmental judicial reform and global forest governance. Full article

Machine learning (ML) has established itself as an innovative tool in sustainable forest management, essential for tackling critical challenges such as deforestation, biodiversity loss, and climate change. Through the analysis of large volumes of data from satellites, drones, and sensors, machine learning facilitates everything from precise forest health assessments and real-time deforestation detection to wildfire prevention and habitat mapping. Other significant advancements include species identification via computer vision and predictive modeling to optimize reforestation and carbon sequestration. Projects like SILVANUS serve as practical examples of this approach’s success in combating wildfires and restoring ecosystems. However, for these technologies to reach their full potential, obstacles like data quality, ethical issues, and a lack of collaboration between different fields must be overcome. The solution lies in integrating the power of machine learning with ecological expertise and local community engagement. This partnership is the path forward to preserve biodiversity, combat climate change, and ensure a sustainable future for our forests. Full article

Tainan’s Cigu, located on Taiwan’s southwestern coast, is a prominent aquaculture hub known for its extensive ponds, tidal flats, and lagoons. This study explored the novel integration of kayaking within aquavoltaic (APV) aquaculture ponds, creating a unique hybrid tourism landscape that merges industrial land use (aquaculture and energy production) with nature-based recreation. We investigated the relationships among facility maintenance and safety professionalism (FM), the perceived value of kayaking training (PV), and green energy and sustainable development recognition (GS) within these APV systems in Cigu, Taiwan. While integrating recreation with renewable energy and aquaculture is an emerging approach to multifunctional land use, the mechanisms influencing visitors’ sustainability perceptions remain underexplored. Using data from 613 kayaking participants and structural equation modeling, we tested a theoretical framework encompassing direct, mediated, and moderated relationships. Our findings reveal that FM significantly influences both PV (β = 0.68, p < 0.001) and GS (β = 0.29, p < 0.001). Furthermore, PV strongly affects GS (β = 0.56, p < 0.001). Importantly, PV partially mediates the relationship between FM and GS, with the indirect effect (0.38) accounting for 57% of the total effect. We also identified significant moderating effects of APV coverage, guide expertise, and operational visibility. Complementary observational data obtained with underwater cameras confirm that non-motorized kayaking causes minimal ecological disturbance to cultured species, exhibiting significantly lower behavioral impacts than motorized alternatives. These findings advance the theoretical understanding of experiential learning in novel technological landscapes and provide evidence-based guidelines for optimizing recreational integration within production environments. Full article

Live music can afford novel, transformative aesthetic interactions for individual audience members. Nevertheless, concert research tends to focus on shared experience. In this paper we offer an account of exploratory listening that foregrounds embodied–enactive engagement and affective resonance through close analysis of the music, physiological measurements, and reflections from interviews. Our analysis centres on data collected from two musician audience members about one specific piece out of a larger interdisciplinary project involving concerts given by the Stavanger Symphony Orchestra and The Norwegian Radio Orchestra in March and June of 2024. Through the combination of in-depth phenomenological interviews with musically skilled audience members and measurements of breathing and body motion, we explore aesthetic enactment beyond common patterns of ‘synchronised’ response, focusing on audience members’ experiences of Harald Sæverud’s ‘Kjempeviseslåtten’ (The Ballad of Revolt) (1943). We find forms of absorbed, both imaginative and embodied involvement, of listeners enacting meaningful contact with, and pathways through, the music that in some ways corroborate crowd patterns but also reveal exploratory expertise and idiosyncratic affective orientations. Full article

Historical archives hold untapped potential for understanding long-term biodiversity change. This study introduces computational approaches to historical ecology, combining archival research, text analysis, and spatial mapping to reconstruct past biodiversity patterns. Using the 1845 Bavarian Animal Observation Dataset (AOD1845), a comprehensive survey of vertebrate species across 119 districts, we transform 5400 prose records into structured ecological data. Our analyses reveal how species distributions, habitat associations, and human–wildlife interactions were shaped by land use and environmental pressures in pre-industrial Bavaria. Beyond documenting ecological baselines, the study captures early perceptions of habitat loss and species decline. We emphasise the critical role of historical expertise in interpreting archival sources and avoiding anachronisms when integrating historical data with modern biodiversity frameworks. By bridging the humanities and environmental sciences, this work shows how digitised archives and computational methods can open new frontiers for conservation science, restoration ecology, and Anthropocene studies. The findings advocate for the systematic mobilisation of historical datasets to better understand biodiversity change over time. Full article

Before the Anthropocene, Bedouin communities in Southern Israel were based on a clan structure—a kin-based social network; clans were culturally and socially homogenous communities with a strong authority structure. Work consisted of subsistence activities necessary for physical survival. Group-based authority and cooperative problem solving were adaptive in this ecology. Throughout the Anthropocene, the Bedouin of Southern Israel have had to adapt to diverse urban environments, expanded educational opportunity, and exposure to media emanating from different cultures. Our study explored the implications of these ecological shifts for epistemic thinking by comparing three generations of 60 Bedouin families: adolescent girls, their mothers, and their grandmothers (N = 180). Families were evenly divided among three residence types differing in degree of urbanization and degree of population homogeneity: unrecognized Bedouin villages consisting of single clans; recognized Bedouin villages, towns, or cities, consisting of multiple clans; and ethnically diverse cities. Results: Across the generations, media exposure and formally educated parents have weakened the epistemic authority of family elders, in turn weakening clan identity. Ethnically diverse cities have weakened extended family identity. At the same time, personal knowledge and professional expertise have gained new cultural importance. These changes in epistemology and identity are adaptive in the ecological environments that have multiplied in the Anthropocene era. Local identity was strongest both in diverse cities, with their many attractions, and in unrecognized villages, where the population continues to occupy ancestral lands. Full article

The preservation of paleontological heritage is relevant for paleobiological and geological research, as well as for scientific dissemination. Fossils are indicative of the evolutionary processes, biodiversity, ecology, and geography of the past and require specific geoconservation actions. In Italy, fossils are protected as cultural heritage, and their unauthorized collection and trading are counteracted by a regulatory framework. The local superintendence and law enforcement provide control over the territory, often resorting to the expertise of university paleontologists. In the Calabria region (southern Italy), the collaboration among institutions is yielding interesting results in research, preservation, and public awareness of paleontological heritage. This paper highlights the different approaches to the conservation and promotion of fossils by three museums in Calabria. At the National Archeological Museum of Reggio Calabria, fossils are included in the Prehistory section. The history of life on Earth, with a focus on regional paleontology, characterizes the Museum of the University of Calabria. The Civic Museum of Ricadi exhibits local fossils. Collaboration among these museums and their institutional referents contributes to public awareness and scientific understanding of paleontological heritage, reinforcing the need for conservation and promotion strategies. Full article

One major environmental concern is the lead (Pb) pollution from automobile traffic, especially in highway-side grazing areas. Sheep grazing in Pb-contaminated areas are particularly vulnerable because Pb exposure from soil, water, and feed can have harmful effects that impair their general health, reproductive capability, and immune systems. Long-term hazards to cattle from persistent Pb exposure include neurotoxicity, hematological abnormalities, reproductive health problems, and immunosuppression. These can have serious consequences, such as reduced productivity and even mortality. Additionally, through the food chain, Pb bioaccumulation in lamb tissues directly endangers human health. Pb poisoning is caused by a variety of intricate mechanisms, including disturbances in calcium-dependent processes, oxidative stress, and enzyme inhibition. To mitigate these risks, an interdisciplinary approach is essential, combining expertise in environmental science, toxicology, animal husbandry, and public health. Effective strategies include rotational grazing, alternative foraging options, mineral supplementation, and soil remediation techniques like phytoremediation. Additionally, the implementation of stringent regulatory measures, continuous monitoring, and community-based initiatives are vital. This review emphasizes the need for comprehensive and multidisciplinary methodologies to address the ecological, agricultural, and public health impacts of Pb pollution. By integrating scientific expertise and policy measures, it is possible to ensure the long-term sustainability of grazing systems, protect livestock and human health, and maintain ecosystem integrity. Full article

The automation of environmental assessment processes aimed at reducing the ecological footprint of industrial facilities, buildings, and infrastructure is one of the main challenges currently faced by the construction sector. In this context, Building Information Modeling (BIM) is a comprehensive methodology that enables the creation of digital models, facilitating the analysis of environmental performance throughout the life cycle of built assets. In addition to the capabilities offered by BIM, specialized tools for environmental impact analysis implement the standardized life cycle assessment (LCA) methodology. However, one of the current limitations is the integration of BIM models and LCA tools. Few software solutions enable automated data transfer, complicating the environmental assessment process. The objective of this study is to evaluate the reliability of data transfer from BIM models to LCA tools, using an industrial warehouse as a case study. The research compares two LCA tools: Athena Impact Estimator, specialized in the construction sector, and SimaPro, a professional tool with advanced capabilities. This study is structured in two phases: (i) the development of a BIM model in Revit, including the definition of structural and functional components and the export of data to LCA tools, and (ii) the execution of the life cycle assessment in compliance with ISO 14040 and EN 15804 standards. The results show methodological and interoperability differences between the two tools, highlighting their strengths and limitations in terms of the precision of the results, resource consumption, training and expertise requirements, scope of environmental impact calculations, and adaptability to the construction sector in the integration of an LCA with a BIM model. Full article

Freshwater fish community surveys are an important component of aquatic ecosystem management. However, the standard method for taxonomic identification currently used for these surveys, wherein fishes are manually identified in the field by a taxonomic expert, has several shortcomings. These include handling-related fish injury and mortality, the need for a fish-identification expert to be present during field sampling, and additional fish mortality due to physical voucher collection. These shortcomings may be overcome using new methods such as environmental DNA (eDNA) or image analyses. While eDNA can provide fish community data through metabarcoding, it is costly and provides little ecological information. A novel, image-based method for taxonomic identification (“batch-image identification”), which addresses the shortcomings of standard and eDNA methods, was tested in this study. Fishes were captured in the field and photographed in small groups (“batches”) within fish viewers for subsequent identification by taxonomic experts. Comparing taxonomist-based identifications from batch images to specimen-based identification, batch-image identification yielded an overall species-level correct-identification rate (CIR) of 49.7%, and an overall genus-level CIR of 61.2%. CIR increased with taxonomist expertise, reaching 83% when identification was performed by expert taxonomists. Batch-image identification data also produced rarefaction curves and fish-length measurements comparable to those obtained through standard methods. Potential methodological improvements to batch-image identification, including procedural adjustments and alternative identification methods, provide direction for the continued testing and improvement of this method. Full article