World Wide Fund for Nature

Abstract Human–wildlife conflict (HWC) is a growing threat to conservation and human well‐being. It is increasingly recognized that HWC is largely human–human conflict over wildlife, which is frequently characterized by deep‐rooted differences among stakeholder values, identities, cultures, and perceptions about wildlife. Such conflicts cannot be resolved in a lasting manner with superficial dispute resolution and conflict management approaches. Therefore, conflict transformation, which addresses deeper, systemic differences and causes of conflict, such as differences in cultural approaches and perceptions through iterative creative change processes, has gained increased prominence in conservation. Over the last decade, mental models—representations in people's minds of how parts of the world work—have received increasing attention in conservation, as they enable the structured elicitation and discussion of differences among stakeholder views and their underlying assumptions to enable participatory reframing. However, the potential contribution of mental models to HWC and to navigating and transforming conflicts over wildlife has received little attention. We present a framework for how mental models can be elicited and used to support the transformation of conflicts over wildlife.

ABSTRACTThe blue shark (Prionace glauca) is a widely distributed pelagic predator that undergoes ontogenetic morphological changes that reflect ecological and functional adaptations. Investigating this aspect could provide powerful tools for elucidating the patterns of trait variation and their significance in relation to environmental conditions. This study explores morphometric variation and its ecomorphological implications in the Mediterranean blue shark population using linear and geometric morphometric analyses. A total of 119 specimens (64 females, 55 males), spanning from juvenile (total lengths [TLs] 85.4–149.8 cm in females and 82.9–121.2 cm in males) to adult (TLs 150.4–333.1 cm in females and 133.2–206.8 cm in males) stages, were analysed through a set of 30 linear measurements. Linear morphometric analysis identified significant differences between the juvenile and subadult/adult groups, particularly in the anterior body region and pectoral fin measurements. Geometric morphometric results revealed that juveniles have a more heterocercal caudal fin shape, which transitions to a less heterocercal form in adults, with a proportionally larger ventral lobe. The first dorsal fin showed positive allometry, becoming higher. The observed ontogenetic differences could improve energy efficiency and manoeuvrability. This aligns with the diel vertical migrations (DVM) of larger individuals and their need for sustained swimming during long‐distance migrations, as well as the enhanced manoeuvrability required by juveniles, notably for predator avoidance. These morphological changes likely reflect adaptations to changing energy requirements and locomotion needs throughout life stages. Although blue sharks maintain a generalist diet across ontogeny, these adaptations may optimise feeding efficiency and swimming performance, particularly in adults. This study highlights the interplay between ontogeny, morphology and ecological function, shedding light on the selective pressures that shape the life history strategies of blue sharks. The findings emphasise the importance of integrating ontogenetic perspectives into ecomorphological studies to better understand the evolutionary adaptations of pelagic sharks.

Abstract. In recognition of the importance of inland waters, numerous datasets mapping their extents, types, or changes have been created using sources ranging from historical wetland maps to real-time satellite remote sensing. However, differences in definitions and methods have led to spatial and typological inconsistencies among individual data sources, confounding their complementary use and integration. The Global Lakes and Wetlands Database (GLWD), published in 2004, with its globally seamless depiction of 12 major vegetated and non-vegetated wetland classes at 1 km grid cell resolution, has emerged over the last few decades as a foundational reference map that has advanced research and conservation planning addressing freshwater biodiversity, ecosystem services, greenhouse gas emissions, land surface processes, hydrology, and human health. Here, we present a new iteration of this map, termed GLWD version 2, generated by harmonizing the latest ground- and satellite-based data products into one single database. Following the same design principle as its predecessor, GLWD v2 aims to avoid double counting of overlapping surface water features while differentiating between natural and non-natural lakes, rivers of multiple sizes, and several other wetland types. The classification of GLWD v2 incorporates information on seasonality (i.e., permanent vs. intermittent vs. ephemeral); inundation vs. saturation (i.e., flooding vs. waterlogged soils), vegetation cover (e.g., forested swamps vs. non-forested marshes), salinity (e.g., salt pans), natural vs. non-natural origins (e.g., rice paddies), and stratification of landscape position and water source (e.g., riverine, lacustrine, palustrine, coastal/marine). GLWD v2 represents 33 wetland classes and – including all intermittent classes – depicts a maximum of 18.2 ×106 km2 of wetlands (13.4 % of the global land area excluding Antarctica). The spatial extent of each class is provided as the fractional coverage within each grid cell at a resolution of 15 arcsec (approximately 500 m at the Equator), with cell fractions derived from input data at resolutions as small as 10 m. The upgraded GLWD v2 offers an improved representation of inland surface water extents and their classification for contemporary conditions (∼ 1984–2020). Despite being a static map, it includes classes that denote intrinsic temporal dynamics. GLWD v2 is designed to facilitate large-scale hydrological, ecological, biogeochemical, and conservation applications, aiming to support the study and protection of wetland ecosystems around the world. The GLWD v2 database is available at https://doi.org/10.6084/m9.figshare.28519994 (Lehner et al., 2025).

Polarized optical signals traveling through optical networks are sensitive to environmental changes, making them suitable for capturing valuable data about their surroundings. This enables the entire optical infrastructure to function as a sensing and localization grid for earthquake early detection. In this paper, we experimentally validate the co-existence of a sensing detector with WDM traffic generators, making the proposed approach both practical and robust for real-world applications. Offline data post-processing and fine-tuning of a pretrained machine learning method are employed to identify the pattern of polarization changes caused by the primary earthquake wave, which precedes the destructive wave by tens of seconds. To assess the system's effectiveness, we replicate, in a laboratory setup, the dynamics of a real M4.3 earthquake as a function of strain, propagating along 38 km of deployed fiber in a ring topology. The system captures two different earthquakeinduced polarization datasets, showcasing that the machine learning model, pretrained on one modality, successfully adapts to another. The model still achieves high accuracy in detecting the primary earthquake wave despite propagation challenges, demonstrating the potential for real-world integration of optical fiber networks as both high-speed communication channels and smart environmental sensors.

BackgroundCoronavirus 2019 (COVID-19) disease progression evidence and viral clearance time remain limited in tropical settings. Understanding this is crucial for public health control measures at community-level. We evaluated the viral dynamics of SARS-CoV-2 infection and factors associated with positivity duration in COVID-19 cases in Cameroon.Material and MethodsWe conducted a retrospective cohort-study of SARS-CoV-2 positive cases from the first to third wave (March 2020-October 2021) in Yaounde-Cameroon. RT-PCR was carried out on the participants using nasopharyngeal swabs after every 7 days. SARS-CoV-2 positivity duration was evaluated from the first to last positive PCR-test before a negative result. Epi-info V.7.0 was used for data analyses with p < 0.05 considered statistically significant.ResultsA total of 282 participants were enrolled. The mean age was 41 ± 14 years, with male predominant (62.1%). We had 15.6% symptomatic participants of which 59% had cough. The overall median positivity duration was 15[IQR: 9–23] days with 15[IQR: 13–22] in the first, 17[IQR: 12–26] in the second and 9[IQR: 6–12] in the third wave (p < 0.0001). Positivity duration was significantly higher in males (16 versus 14 days, p = 0.03) those aged [35–45] years (16 days) followed by those ≥ 46 years(15 days); p = 0.008). Positivity duration was not affected by presence or absence of symptoms (p = 0.80). No significant correlation was found with viral load (r = 0.03; p = 0.61). Considering baseline (24.7 ± 7.2Ct) and last viral load (29.3 ± 5.9 Ct), the ΔCt (4.6 ± 1.3) and positivity duration (15 days) revealed a kinetic in viral decay of 0.3 ± 0.087 Ct/day.ConclusionsA median positivity duration of 15 days is in accordance with viral clearance around 2 weeks for optimal confinement at community-level. Men and/or the elderly stand at higher risk of prolonged infection. Given the viral decay (0.3 Ct daily), we suggest personalized confinement periods. The variability of positivity duration according to waves could be function of strains which could be a factor influencing positivity duration.

Correction to: Nature Foodhttps://doi.org/10.1038/s43016-024-01109-4, published online 14 January 2025. In the version of the article initially published, an incorrect version of Fig. 5 was included and has now been replaced in the HTML and PDF versions of the article, as seen in Fig. 1. (Figure presented.) Original and corrected Fig. 5.

Trade in wood and forest products spans the global supply chain. Illegal logging and associated trade in forest products present a persistent threat to vulnerable ecosystems and communities. Illegal timber trade has been linked to violations of tax and conservation laws, as well as broader transnational crimes. The United States is the largest importer globally of wood and forest products, such as pulp, paper, flooring, and furniture-importing $78 billion in 2021. Transaction-level data such as shipping container manifests and bills of lading provide a comprehensive data source that can be used to detect and disrupt trade that may be suspected of containing illegally harvested or traded forest products. Owing to the volume, velocity, and complexity of shipment data, an automated decision support system is required for the purposes of detecting suspicious forest product shipments. We present a proof of concept framework using machine learning and big data approaches-combining domain expertise with automation-to achieve this objective. We formulated the underlying machine learning problem as an anomaly detection problem and collected and collated forest sector-specific domain knowledge to filter and target shipments of interest. In this work, we provide the overview of our framework, with the details of domain knowledge extraction and machine learning models, and discuss initial results and analysis of flagged anomalous and potentially suspicious records to demonstrate the efficacy of this approach. The proof of concept work presented here provides the groundwork for an actionable and feasible approach to assisting enforcement agencies with the detection of suspicious shipments that may contain illegally harvested or traded wood.

Background/Objectives: The domestication of the grey wolf (Canis lupus) and subsequent creation of modern dog breeds have significantly shaped the genetic landscape of domestic canines. This study investigates the genomic effects of hybridization and breeding management practices in two hybrid wolfdog breeds: the Czechoslovakian Wolfdog (CSW) and the Saarloos Wolfdog (SAW). Methods: We analyzed the genomes of 46 CSWs and 20 SAWs, comparing them to 12 German Shepherds (GSHs) and 20 wolves (WLFs), which served as their ancestral populations approximately 70-90 years ago. Results: Our findings highlight that hybridization can increase genetic variability and mitigate the effects of inbreeding, as evidenced by the observed heterozygosity levels in both wolfdog breeds. However, the SAW genome revealed a higher coefficient of inbreeding and longer runs of homozygosity compared to the CSW, reflecting significant inbreeding during its development. Discriminant Analysis of Principal Components and fixation index analyses demonstrate that the CSW exhibits closer genetic proximity to the GSH than the SAW, likely due to differences in the numbers of GSHs used during their creation. Maximum likelihood clustering further confirmed clear genetic differentiation between these hybrid breeds and their respective ancestors, while shared ancestral polymorphism was detectable in all populations. Conclusions: These results highlight the role of controlled hybridization with captive-bred wolves and peculiar breeding strategies in shaping the genetic structure of wolfdog breeds. To ensure the long-term genetic health of these breeds, it is recommended to promote adequate and sustainable breeding practices to maintain genetic diversity, minimize inbreeding, and incorporate the careful selection of unrelated individuals from diverse lineages, while avoiding additional, uncontrolled crossings with wild wolves.

The G-res tool shows only a part of the climate impact of tropical hydropower reservoirs because several emission pathways are not included and it does not address the change in surface albedo.