University of Daugavpils (Latvian: Daugavpils Universitāte, DU) is a public university in Daugavpils, Latvia, and the largest regional university in the country.
Biological invasions, driven by the spread of non-native species, have become a critical global issue because of their far-reaching ecological and socioeconomic impacts. Effective communication of the risks of biological invasions is essential for implementing robust policy and legislation and gaining public support for conservation efforts. However, current policies often suffer from fragmentation and ineffectiveness, largely due to inadequate risk communication and complex multi-level governance. To address this challenge, we develop a global framework designed to enhance clearer communication about biological invasion risks. The framework contextualizes key terms across three domains in invasion science: species invasiveness, risk analysis, and decision support tools. Using both diffusion-of-English and ecology-of-language paradigms, and following a three-step process involving preliminary consensus, AI querying, and ground-truthing with final consensus, we validate the framework in 70 non-English languages which, together with English, have official status in at least one country and collectively cover all 195 countries worldwide. Our findings reveal that while terminology for risk analysis is well established, terminology for species invasiveness and, especially, for decision support tools remains underdeveloped in many languages, hindering effective communication and policy implementation. Our framework underscores the importance of cultural and political neutrality. By promoting clearer risk communication among scientists, policymakers, and the public globally, we aim to reduce policy fragmentation and foster enhanced collaboration in risk mitigation. We recommend expanding multilingual decision support tools to include the full risk analysis process: risk identification, risk assessment, and risk management. This will support intergovernmental mitigation efforts and promote a unified global response to biological invasions.
A revision of all the species of the genus Thopeutica described or cited from the island of Mindanao (Philippines) is presented. Four species are described as new: T. (s. str.) cassolai sp. nov., T. (s. str.) intermedia sp. nov., T. (s. str.) maindai sp. nov. and Wiesner, 1992 syn. nov., and T. (s. str.) davaoensis Cassola & Ward, 2004 = T. (s. str.) anichtchenkoi Wiesner, 2015 syn. nov. In total, 12 species are confirmed for the fauna of this island; the previous citations of 4 other species from Mindanao are discussed and considered erroneous. A key to the species is provided. Color photographs of the habitus, the main diagnostic characters, and the variability of all species are presented.
A review of the Philippine species of Campsosternus Latreille, 1834 (Coleoptera: Elateridae) is presented. Campsosternus mindorensissp. nov. is described from Mindoro. It differs from all previously known Philippine species of Campsosternus by the presence of dense pubescence on both dorsal and ventral parts of the thorax, a feature not observed in any previously described species from the archipelago. Besides that, two new subspecies are described and illustrated: C. rutilans mindanaoensisssp. nov. (Mindanao) and C. r. cupreusssp. nov. (Panay, Negros). Morphological variation among island populations for these taxa is interpreted as intraspecific geographic differentiation. Two new synonymies are proposed: C. rutilans Chevrolat, 1841 = C. proteus Hope, 1843, syn. nov.; C. leachii Hope, 1843 = C. eschscholtzii Hope, 1843, syn. nov. A key to the Philippine species and subspecies is included.
The rapid expansion of sensor-based monitoring systems in smart cities has intensified debates about the balance between technological efficiency, privacy protection, and social trust. This study explores the interplay between aggregated behavioral data and citizens’ perceptions to identify the ethical conditions under which data-driven monitoring can be considered socially acceptable in intelligent urban environments. The research focuses on the innovation district of Ülemiste City (Tallinn, Estonia), where mobility flows, building activity, and public transport usage are continuously monitored through digital infrastructure. The empirical dataset combines a survey of 195 respondents with multiple sensor-based urban datasets. Mobility flows were obtained from Telia Crowd Insights mobile-network analytics and the Fyma computer vision system, which provided anonymized origin–destination indicators and automated vehicle counts. Building occupancy and spatial activity patterns were compiled using infrared entry sensors and parking monitoring systems, enabling the identification of operational mobility patterns within the district. The methodological approach integrates descriptive statistical analysis, temporal pattern analysis, and cross-validation of independent measurement systems, allowing the comparison of behavioral indicators with survey-based perceptions. The results demonstrate a clear relationship between perceived transparency of monitoring practices and citizens’ acceptance of aggregated data collection. Survey analysis shows that most respondents support the use of sensor-based monitoring when data are anonymized and clearly linked to public benefits such as traffic management and service optimization, while a smaller but notable group expresses concerns regarding privacy risks and potential misuse of data. Cross-validation with behavioral mobility indicators confirms that aggregated monitoring systems generate operational insights that directly support congestion management, infrastructure planning, and public transport optimization. The convergence between perception-based and sensor-derived data suggests that social trust in smart-city monitoring increases when monitoring practices are transparent, anonymized, and visibly associated with improvements in urban governance. The findings provide practical insights for policymakers, urban planners, and smart-city administrators seeking to design ethical, transparent, and privacy-respecting data governance systems capable of improving urban mobility management without undermining public trust. The study contributes to the understanding of how behavioral analytics and citizen perceptions can be integrated to support responsible data governance in digitally monitored urban environments.
