The question of subspecies identity matters far more than a taxonomic technicality. Globally, the brown bear is listed as Least Concern by the IUCN, but that sweeping label conceals sharp regional declines. In Nepal, the species is classified as Critically Endangered, with fewer than 20 mature individuals estimated nationwide and a distribution restricted to the northern high Himalayas. The species enjoys full legal protection under Nepal’s National Parks and Wildlife Conservation Act of 1973, yet conservation planning has been hampered by a fundamental problem: nobody knew for certain which bear they were protecting. Historical records relied on indirect signs, local reports, and photographs, none of which could reliably distinguish the Tibetan brown bear from its Himalayan relative, whose range is centered on northern Pakistan and northwestern India.

Previous mitochondrial DNA studies had shown that the two subspecies represent genetically distinct evolutionary lineages despite their geographic proximity. The Tibetan brown bear occupies the Tibetan Plateau and adjoining high-altitude regions, while the Himalayan brown bear is largely confined to the northwestern Himalaya. But no genetic study of brown bears had ever been conducted within Nepal itself. The new research, led by Rishi Baral of Hokkaido University and colleagues, set out to close that gap by sampling two key landscapes: Mustang District within the Annapurna Conservation Area and Dolpa District within Shey-Phoksundo National Park, both characterized by cold arid climates, rugged terrain, and alpine steppe vegetation.

Between 2022 and 2024, field teams working with experienced local personnel collected ten non-invasive samples putatively attributed to brown bears, seven fecal samples and three hair samples, located using associated field signs such as tracks, digging marks, and feeding sites. The researchers swabbed the outer mucosal layer of feces with sterile swabs preserved in buffer, collected hair with sterilized forceps, and geo-referenced every sample with handheld GPS units before storage at minus 20 degrees Celsius. In the laboratory, DNA was extracted under sterile conditions with negative controls to monitor contamination, and a short fragment of the mitochondrial control region, the D-loop, was amplified using bear-specific primers designed for subspecies discrimination.

The genetic results were strikingly clear. Seven of the ten samples yielded successful mitochondrial sequences, and every one of them carried the same haplotype, one that phylogenetic analysis placed firmly within the Tibetan brown bear lineage. The Nepalese sequences formed a well-supported clade with reference samples from the Tibetan Plateau, while the Himalayan brown bear emerged as a distinct sister lineage, clearly separate. Bootstrap support values for the major nodes exceeded 70 percent, and the sloth bear served as a divergent outgroup. Haplotype diversity and nucleotide diversity were both zero, meaning no mitochondrial variation was detectable among the samples analyzed, a pattern consistent with historical bottlenecks, long-term isolation, and restricted female dispersal reported for Tibetan brown bears across their core range.

Microsatellite genotyping, though successful in only 40 percent of samples, a common limitation with degraded non-invasive material, identified two unique individuals, both male. One was detected in Upper Mustang from a single hair sample, and the other from three fecal samples collected in Dolpa. The male-only detection pattern fits a well-documented trend in bear genetics: males disperse farther and hold larger home ranges, so they are more frequently encountered in surveys, while females tend to remain near their birthplaces. That sex bias raises the possibility that female-mediated gene flow into Nepal is particularly limited, potentially deepening the genetic isolation of any resident bears or linking them only through seasonal movement across the Tibetan border.

The camera-trap data provided independent ecological corroboration. As part of a broader mammalian community survey, the team deployed 96 motion-triggered infrared camera stations, 64 in Mustang and 32 in Dolpa, arranged on a systematic 4-by-4-kilometer grid across elevations of roughly 3,500 to 5,600 meters, with stations separated by at least 2 kilometers and no bait or scent lures used. Over 9,336 camera-trap nights of effort, the traps recorded four independent brown bear capture events at three stations in Upper Mustang between April 2023 and June 2024, at elevations from 3,834 to 4,816 meters across alpine steppe, shrubland, and grassland. One bear was photographed twice in a single day at Lomanthang, moving upslope at dawn and downslope in the evening, apparently after concealing itself from five feral dogs detected nearby. Another individual spent the night of June 5, 2023 playing and jumping in front of a camera at Damodar Kunda. In Dolpa, cameras set for long-term snow leopard monitoring captured four additional brown bear detections in 2023 and 2024, including one animal that paused to inspect the lens at nearly 5,000 meters.

Independent experts from the IUCN Species Survival Commission’s Bear Specialist Group reviewed the photographs and consistently judged the recorded animals consistent with the Tibetan brown bear, citing pale to dark brown pelage, prominent shoulder humps, and concave facial profiles. The study also compiled a verified photograph of a live bear taken by a local horse owner above Samdo village in the Manaslu Conservation Area, showing an individual feeding on a carcass, evidence of either scavenging or predation. Synthesizing these records with published reports spanning 2008 to 2025, the authors mapped confirmed brown bear occurrence across several Trans-Himalayan landscapes, including Humla, Dolpa, Mustang, Manaslu, and the Kanchenjunga Conservation Area, all along the southern periphery of the species’ global range.

The authors are careful to note the limits of their data. With only two genetically confirmed individuals and no detectable mitochondrial variation, the study cannot yet demonstrate a resident breeding population in Nepal. Some detected animals may be dispersing or transient males from larger populations on the Tibetan Plateau. Whether Nepal supports permanent residents, seasonal migrants, or a movement corridor connecting Himalayan and Plateau populations remains an open question that will require expanded non-invasive sampling targeting females, nuclear genomic markers to assess connectivity and effective population size, and long-term monitoring across the fragmented alpine habitat.

Still, the repeated detections across multiple years and locations suggest that Nepal’s Trans-Himalayan landscapes are regularly used by brown bears rather than visited by accident. For a nationally Critically Endangered carnivore at the edge of its range, that distinction carries real weight. Peripheral populations often show reduced genetic diversity and heightened vulnerability to environmental change, infrastructure development, livestock grazing, and human-bear conflict, but they can also serve as stepping stones or refugia under future climate scenarios. The researchers argue that maintaining landscape connectivity across Trans-Himalayan corridors and mitigating conflict in pastoral communities should be immediate priorities, alongside transboundary collaboration with counterparts in China. The study establishes the first molecular baseline for Tibetan brown bears in Nepal and demonstrates how genetics, camera trapping, and local ecological knowledge can be woven together to illuminate the lives of species that dwell where few observers can follow.

Subject of Research: Genetic and camera-trap confirmation of the Tibetan brown bear (Ursus arctos pruinosus) in central Nepal

Article Title: Confirmation of the Tibetan Brown Bear (Ursus arctos pruinosus) in Central Nepal Based on Genetic and Camera‐Trap Evidence

Article References: Baral, R., Gurung, R. B., Sapkota, A., Subedi, N., Giordano, A. J., Paudel, R. P., Kadariya, R., Khanal, G., Kodzue, K., Tsubota, T., & Shimozuru, M. (2026). Confirmation of the Tibetan Brown Bear ( Ursus arctos pruinosus ) in Central Nepal Based on Genetic and Camera‐Trap Evidence. Ecology and Evolution, 16(10), Article e74400. https://doi.org/10.1002/ece3.74400

Image Credits: AI Generated

DOI: 10.1002/ece3.74400

Keywords: Tibetan brown bear, Ursus arctos pruinosus, Nepal, mitochondrial DNA, camera trapping, non-invasive genetics, Trans-Himalaya, conservation, phylogeography, Annapurna Conservation Area, critically endangered, wildlife monitoring

The research team, working with the conservation organisation Rewilding Apennines as part of the LIFE ESC360 project, assembled 69 spatially explicit wildcat occurrence records collected between 2009 and 2023. The records spanned the full length of the country, from Friuli-Venezia Giulia in the northeast to the slopes of Mount Etna in Sicily, and drew on three independent datasets: the national ‘Progetto Gatto Selvatico’ archive, new camera-trapping surveys in Abruzzo, and previously published records from Etna Regional Park. To ensure data reliability, records from the national project were classified under the SCALP scheme, a verification framework originally developed for large-carnivore monitoring, yielding 11 genetically or morphologically confirmed records and 24 supported by unambiguous camera-trap photographs of wild-type individuals.

With those occurrences in hand, the team built a species distribution model using MaxEnt 3.4.3, a maximum-entropy algorithm designed for presence-only data. Environmental predictors were deliberately kept parsimonious to avoid overfitting: eight land-cover variables derived from the CORINE Land Cover 2018 dataset, aggregated to reflect habitat types relevant to wildcats, plus average elevation from the SRTM digital elevation model. Because camera traps and carcass recoveries are not distributed evenly across the landscape, the researchers generated a kernel density surface of sampling effort and used it to bias the selection of 10,000 background points, so that the model would not simply relearn where humans had looked. Model tuning via the ENMeval package selected hinge features and a regularisation multiplier of 3, and the final model was validated against a distribution of 100 null-model iterations.

The results were strikingly clear. The model achieved an AUC of 0.81, well above the null-model median of 0.50, and two variables dominated the prediction: forest and grassland cover contributed 62.5 percent of the model’s explanatory power, with a permutation importance of 78.6 percent, while average elevation contributed a further 36.1 percent. The response curves showed that the probability of wildcat occurrence rose sharply with increasing forest and grassland cover, levelling off at roughly 70 to 75 percent cover, and climbed with elevation up to about 1,000 metres above sea level before plateauing. Urban fabric, roads, agriculture, and water bodies contributed essentially nothing to the model. Mapped across Italy, the highest suitability values traced the spine of the Apennine chain and the Alpine region, with lower but non-negligible suitability in lowland and coastal zones.

To test whether these predictions held up on the ground, the researchers collected 23 new wildcat occurrences in the Central–Southern Apennines, none of which had been used to calibrate the model. Sixteen tissue samples came from carcasses, mostly roadkill and a few accidental hunting bycatch, recovered by rangers in the Abruzzo, Lazio and Molise National Park and the Regional Natural Park of the Simbruini Mountains. Seven further occurrences came from non-invasive monitoring combining camera traps with hair traps, wooden poles wrapped in Velcro and laced with valerian tincture, deployed in the Monte Genzana–Alto Gizio Regional Nature Reserve and the Sirente–Velino Regional Natural Park. When the sampling locations were overlaid on the prediction map, most fell within areas of relatively high predicted occurrence probability, an encouraging sign that the model captures real ecological signal rather than statistical artefact.

The genetic analysis, however, delivered the study’s most provocative twist. The team sequenced an 835-base-pair fragment of the mitochondrial MT-ND5 gene, a marker favoured in felid phylogenetics because it carries low homoplasy and is free of nuclear mitochondrial pseudogenes, trimming the sequences to 669 base pairs for comparison with a European reference dataset. Among 21 successfully sequenced individuals, all of which had been phenotypically classified as wildcats based on diagnostic pelage traits, the researchers found just four haplotypes grouped into three haplogroups. Only two individuals, roughly 10 percent, carried a ‘w’ haplotype reported exclusively in European wildcats. The remaining 90 percent carried maternal lineages shared with domestic cats: the dw1 haplotype accounted for 71.4 percent of samples and dw4 for 14.3 percent, while a single individual carried the domestic-type d3 haplotype.

One finding in particular stands out. The w12 haplotype, detected in two Apennine individuals, had previously been recorded in only one animal across a broad European dataset: a wildcat from Spain assigned to Felis silvestris on the basis of nuclear markers, where it was interpreted as a private Iberian lineage potentially reflecting historical isolation south of the Pyrenees. Its appearance in central Italy extends the known geographic range of this rare maternal lineage well beyond Iberia and adds to the mitochondrial diversity documented for Italian wildcats. The authors are careful, however, to note that because mitochondrial DNA is maternally inherited and represents a single non-recombining locus, this detection should not by itself be read as evidence of a direct phylogeographic connection between Italian and Iberian populations.

What, then, do the domestic-type maternal lineages mean? The answer is more nuanced than a simple hybridisation alarm. Recent genome-wide work suggests that contemporary interbreeding between wildcats and domestic cats is limited despite centuries of sympatry across Europe, which makes historical introgression a plausible contributor to the mitochondrial patterns observed here. The study’s ND5 data alone cannot establish the timing or extent of admixture; distinguishing recent hybridisation from ancient gene flow would require nuclear or genome-wide markers. There are also caveats of scale: successful sequences came from only 21 samples, and because individual identity could not be genetically confirmed for all hair samples, the apparent dominance of the dw1 haplotype may be inflated, since three hair samples sharing it were collected at the same location within a short timeframe. The haplotype frequencies should therefore be treated as preliminary.

The mismatch between pelage-based identification and mitochondrial assignment also carries a methodological warning. Diagnostic coat characteristics, such as the thick ringed tail and distinctive dorsal stripe described in classic identification keys, remain a practical field tool, but phenotypic overlap between wildcats, domestic cats, and admixed individuals means some misclassification is inevitable. That uncertainty propagates into the occurrence records underlying the distribution model itself, and the authors recommend interpreting habitat suitability patterns with this in mind. Interestingly, statistical comparison of predicted occurrence probabilities across sample categories showed that phenotypically wild individuals without genetic data occupied significantly more suitable habitat than any genetic category, hinting that the most remote, least disturbed areas may host the least admixed animals.

For conservation planners, the practical message is concrete. The Apennine chain emerges as the critical connective infrastructure for Italian wildcats, and protecting forest and grassland across suitable elevational gradients should be the priority, alongside ecological corridors and buffer zones that maintain landscape connectivity. The authors also point to precautionary measures in areas of sympatry, including responsible pet ownership and monitoring of feral cat populations to reduce wildcat–domestic cat interactions. Set against global biodiversity targets such as protecting 30 percent of terrestrial areas by 2030, the study offers a template: combine habitat modelling, updated field occurrence data, and genetic monitoring, supported by citizen science, to build an evidence base strong enough to guide action. For a species so adept at vanishing into the Mediterranean maquis, that combination of eyes and molecules may be the only reliable way to keep track of Europe’s last truly wild cat.

Subject of Research: Conservation genetics and habitat suitability modelling of the European wildcat in Italy

Article Title: Checking Wilderness Through Conservation Genetics: The Case of the European Wildcat in the Central Apennines, Italy

Article References: Maini, M., Polce, C., Bernini, F., Longeri, M., Milanesi, R., Caniglia, R., Mattucci, F., Velli, E., & Gatti, R. C. (2026). Checking Wilderness Through Conservation Genetics: The Case of the European Wildcat in the Central Apennines, Italy. Ecology and Evolution, 16(9), Article e74418. https://doi.org/10.1002/ece3.74418

Image Credits: AI Generated

DOI: 10.1002/ece3.74418

Keywords: European wildcat, Felis silvestris, conservation genetics, species distribution model, MaxEnt, mitochondrial DNA, Apennines, Italy, hybridisation, habitat suitability, camera trapping, wildlife monitoring

The research team, led by Badreldin A. A. Mohamed of the Wildlife Research Center in Khartoum and Salwa Mansour Abdelhameed of the Sudanese National Commission for UNESCO, carried out systematic field surveys during the wet seasons of June and July and the dry seasons of January and February in 2021 and 2022. Their toolkit combined standardized line transects along locally known footpaths running from the foothills to the summit, camera traps deployed for roughly 720 hours across representative sites in the Fawas habitat, and nocturnal spotlight surveys along vehicle-accessible routes. In total, 64 transects of 100 by 10 meters were walked on foot, covering a cumulative sampled area of 64,000 square meters. Records included direct sightings as well as indirect evidence such as tracks, feces, burrows, feathers, and bones, with strict measures to avoid double counting.

The tally that emerged is remarkable for a semi-arid reserve facing intensifying human pressure. The 191 species comprised 42 mammals, 112 birds, 19 reptiles, 4 amphibians, 12 insects, and 2 mollusks, representing six major taxonomic groups and an array of ecological niches. Birds dominated the assemblage, accounting for 60.4 percent of all observations, followed by mammals at 18.2 percent and insects at 15.6 percent. Crucially, the numbers were not static. Overall abundance in the wet seasons reached 2,510 individuals, roughly double the 1,256 records gathered during the dry seasons, a difference that held consistently across both study years.

Statistical analyses confirmed what the raw counts suggested. Because abundance data deviated significantly from normality, the researchers applied the Kruskal–Wallis H test followed by Dunn’s post hoc comparisons with Bonferroni adjustment. Seasonal differences were highly significant (H = 83.63, df = 3, P < 0.001), with each wet season differing from each dry season, while the two dry years and the two wet years were statistically indistinguishable from one another. This pattern implies a stable, predictable seasonal rhythm rather than random year-to-year fluctuation. Diversity indices told the same story: bird Shannon–Wiener diversity ranged from 3.61 to 4.19 and peaked during Wet 2021, accompanied by very low Simpson’s dominance and high Pielou’s evenness, while mammals reached their highest wet-season diversity with H′ values between 3.24 and 3.28.

Species richness shifted just as dramatically. Bird species climbed from 55 to 59 during the dry seasons of 2021 and 2022 to 90 and 89 during the corresponding wet seasons, and amphibians showed perhaps the sharpest response of any group, with 71 individuals recorded in the wet seasons against only 9 in the dry ones. The mechanism is well understood in savanna ecology: rainfall triggers pulses of primary productivity, expands temporary water sources, and cascades through food webs as insects, seeds, and fruits become abundant. For amphibians, whose breeding depends on ephemeral pools, the wet season is essentially the only viable window. Birds, including migratory and seasonal visitors that made up 48 percent of recorded species, ride the same resource wave. Mammals, more buffered by mobility and physiology, showed a smaller but still clear seasonal rise.

Perhaps the most ecologically revealing finding concerns the reserve’s internal geography. The study distinguished six habitat types: four natural ones—Higher Elevation, Fawas (the enclosed plains wedged between rocky hills), Upland Valleys and Streams, and Lower Slopes—and two human-modified ones, Farm Areas and Residential Areas. The Fawas habitat supported the highest overall abundance with 941 individuals and the greatest species richness across most taxonomic groups, while the Higher Elevation habitat recorded the fewest animals, only 214. Kruskal–Wallis testing confirmed significant differences among habitats (χ² = 95.386, df = 5, P < 0.001), with Higher Elevation differing significantly from every other habitat and the Fawas differing from Upland Valleys and Streams, Lower Slopes, and Residential Area. Notably, Farm Areas came second in abundance at 851 individuals, showing that even agricultural mosaic can sustain substantial wildlife when it offers crops, residues, and insect prey.

Behind the aggregate numbers lie individual species of considerable conservation weight. The mammal inventory included the olive baboon as the most abundant species at 95 individuals, the greater kudu recorded consistently across seasons and habitats, and rarely seen residents such as the aardvark, Egyptian mongoose, and Egyptian tomb bat, each represented by a single individual. Three mammals—the straw-coloured fruit bat, the patas monkey, and the striped hyaena—were classified as Near Threatened by the IUCN Red List. Among birds, the reserve hosts six globally threatened or Near Threatened species, including the Critically Endangered white-backed vulture (Gyps africanus) and hooded vulture (Neophron monachus), alongside Endangered steppe and tawny eagles, the lappet-faced vulture, and the pallid harrier. The reptile fauna included the Endangered African spurred tortoise (Centrochelys sulcata) and two Near Threatened python species, underscoring the reserve’s value far beyond its national borders.

To ensure their inventory was not an artifact of under-sampling, the team employed individual-based rarefaction and extrapolation using the iNEXT framework, and the species accumulation curves approached an asymptote for all major taxonomic groups, suggesting the surveys captured most detectable species within the surveyed habitats. The authors are candid about limitations: abundance estimates did not correct for detection probability, remote areas were inaccessible during the rains, and invertebrates were sampled through opportunistic hand collection that likely understates true diversity. Even so, the dataset represents a leap forward. Previous work at Jebel Al-Dair had focused on management planning, vegetation, or localized observations of birds and snakes, but never before had a single study quantified wildlife diversity across multiple taxonomic groups with standardized surveys and inferential statistics.

The conservation implications are pointed. Sudan’s protected area network remains under-resourced and under-representative of many ecosystems, and Jebel Al-Dair faces mounting threats from agricultural expansion, overgrazing, fuelwood harvesting, settlement growth, and illegal hunting. The study’s recommendations are correspondingly practical: prioritize protection of the Fawas and Upland Valleys and Streams, safeguard dry-season refuges and permanent water sources when animals concentrate into limited areas, restore degraded habitats, strengthen community-based conservation, and embed long-term standardized monitoring into an adaptive management plan. Because wet-season wildlife disperses broadly while dry-season animals cluster near water and shelter, patrol and monitoring schedules should track these temporal shifts rather than assume static distributions.

Beyond its local significance, the study positions Jebel Al-Dair as a reference site for the wider Sahel, where dryland biodiversity is declining under climate stress and land-use change and where ecological data are chronically scarce. The occurrence of both resident and migratory birds confirms the reserve’s role along the Afro-Palearctic flyway, and its inselberg-like mountain habitats mirror patterns seen at rocky outcrops across Africa where topographic complexity generates ecological niches that persist despite harsh conditions. In documenting 191 species in a single reserve, the researchers have supplied not just a species list but a baseline against which future change—whether from climate, development, or conservation action—can be measured, and a demonstration that even a semi-arid mountain in western Sudan can rank among the region’s most important biodiversity refuges.

Subject of Research: Seasonal and habitat-based patterns of wildlife diversity in a Sudanese semi-arid biosphere reserve

Article Title: Seasonal and habitat based patterns of wildlife diversity in Jebel Al Dair Biosphere Reserve, Sudan

Article References: Mohamed, B. A. A., & Abdelhameed, S. M. (2026). Seasonal and habitat based patterns of wildlife diversity in Jebel Al Dair Biosphere Reserve, Sudan. Discover Animals, 3(1), Article 92. https://doi.org/10.1007/s44338-026-00251-6

Image Credits: AI Generated

DOI: 10.1007/s44338-026-00251-6

Keywords: wildlife diversity, Jebel Al Dair Biosphere Reserve, Sudan, biodiversity, seasonal variation, habitat heterogeneity, conservation biology, semi-arid ecosystem, species richness, IUCN Red List, camera trapping, protected areas