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The Kunming-Montreal Global Biodiversity Framework (GBF) is the most ambitious multilateral agreement on biodiversity to date. It calls for a whole-of-government and whole-of-society approach to halt and reverse biodiversity loss worldwide. The GBF's monitoring framework lays out how Parties to the Convention on Biological Diversity are expected to report on their progress. An expert group convened by the Convention on Biological Diversity, the Ad Hoc Technical Expert Group (AHTEG) on Indicators, provided guidance on its implementation, including a gap analysis to identify the strengths and limitations of the indicators in the monitoring framework. We present the results of the AHTEG gap analysis and provide recommendations on implementing and improving monitoring of the GBF. We compare three implementation scenarios, from worst-case to best-case: (1) Parties only report on required headline and binary indicators; (2) Parties also report on all headline indicator disaggregations and (3) Parties additionally report on all optional component and complementary indicators. In each case, the monitoring framework covers (1) between 19-40%, (2) 22-41% and (3) 29-47% of the elements in the GBF's goals and targets. Even in the best-case scenario (3), no indicators are available for 12% of the GBF's elements. In practice, the coverage and thus effectiveness of the monitoring framework will depend on which indicators (required and optional) and disaggregations countries apply. Substantial investment is required to collect the necessary data to compute indicators, infer change and effectively monitor progress. We highlight important next steps to progressively improve the efficacy of the monitoring framework.
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http://dx.doi.org/10.1038/s41559-025-02718-3 | DOI Listing |
PLoS One
September 2025
College of Information Engineering, Sichuan Agricultural University, Ya'an, Sichuan Province, China.
Animals communicate information primarily via their calls, and directly using their vocalizations proves essential for executing species conservation and tracking biodiversity. Conventional visual approaches are frequently limited by distance and surroundings, while call-based monitoring concentrates solely on the animals themselves, proving more effective and straightforward than visual techniques. This paper introduces an animal sound classification model named SeqFusionNet, integrating the sequential encoding of Transformer with the global perception of MLP to achieve robust global feature extraction.
View Article and Find Full Text PDFPLoS One
September 2025
School of Health & Society, University of Salford, Salford, Greater Manchester, United Kingdom.
Background: Velocity-Based Training (VBT) is an emerging method in resistance training for objectively prescribing and monitoring training intensity and neuromuscular function. Given its growing popularity, assessing the validity and reliability of VBT devices is critical for strength and conditioning coaches.
Objective: The primary purpose of this review was twofold: (1) to identify and address methodological gaps in current assessments of VBT device validity and reliability, and (2) to propose and apply a novel, multi-layered, criterion-based framework-developed in collaboration with statisticians and domain experts-for evaluating these devices.
J Vis Exp
August 2025
Marianne Bernadotte Centrum, Department for Clinical Neuroscience, Karolinska Institutet; St Erik Eye Hospital.
The present protocol evaluates the relative impact of visual and vestibular inputs during roll plane rotations using optokinetic, vestibular, and combined visuovestibular stimulations. Subjects underwent isolated visual rotations, whole-body vestibular rotations in darkness, and visuovestibular stimulations combining static visual scenes with head rotations. Dynamic and static eye movement gains, absolute amplitudes, velocities, and accelerations were measured alongside perceptual responses.
View Article and Find Full Text PDFIEEE J Biomed Health Inform
September 2025
Multi-modal brain tumors segmentation is a critical step for diagnosing and monitoring brain-related disease. Many studies have developed models for this task, but two challenges remain, i.e.
View Article and Find Full Text PDFIEEE Trans Biomed Eng
September 2025
Signal complexity analysis plays a crucial role in biomedical research, particularly in electroencephalography (EEG), for early disease diagnosis and cognitive monitoring. However, traditional entropy-based methods lack robustness, suffer from limitations such as sensitivity to noise, and fail to capture the multi-frequency structure of brain signals. To address these challenges, this study introduces Multivariate Multiscale Multi-Frequency Entropy (M3FrEn), a novel complexity metric that simultaneously incorporates multiscale dynamics, multichannel dependencies, and multi-frequency structure into a unified entropy-based framework.
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