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1. Niche Distribution Pattern of Rüppell's Vulture ( Gyps rueppellii ) and Conservation Implication in Kenya

   https://doi.org/10.1002/ece3.70371  

   Chepkirui, Purity; Chiawo, David; James, Jumbe; Jemimah, Simbauni; Ellwood, Elizabeth; Mugo, Jane; Ngila, Peggy Mutheu (December 2024, Ecology and Evolution)

   Rüppell's vultures are critically endangered, primarily due to anthropogenic activities such as habitat degradation, climate change, and intentional and unintentional poisoning, which have led to the loss of nesting and breeding sites. To aid in the conservation and protection of these species, habitat evaluation and niche mapping are crucial. Species distribution modeling (SDM) is a valuable tool in conservation planning, providing insights into the ecological requirements of species under conservation concerns. This study employed an ensembling modeling approach to assess the habitat suitability and distribution of Rüppell's vultures across Kenya. We utilized four algorithms; Gradient Boosting Machine, Generalized Linear Model, Generalized Additive Model, and Random Forest. Data on Rüppell's vultures were sourced from the Global Biodiversity Information Facility, while key environmental variables influencing the species' distribution were obtained from WorldClim. The resultant species distribution map was overlaid with a conservation area map to evaluate the overlap between suitable habitats and existing protected areas. Our analysis identified suitable habitats in regions such as the Masai Mara Game Reserve, Mount Kenya National Park, Nairobi National Park, Tsavo East National Park, and Hell's Gate National Park, with the majority of these habitats located outside protected areas, except those within Hell's Gate National Park. Precipitation and elevation emerged as the primary environmental predictors of the distribution of Rüppell's vultures. Based on these findings, we recommend establishing vulture sanctuaries in suitable habitats and hotspots to enhance the conservation of Rüppell's vultures outside the protected areas. 

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2. A Dataset of Amphibian Species in U.S. National Parks

   https://doi.org/10.1038/s41597-023-02836-2  

   LaFrance, Benjamin J.; Ray, Andrew M.; Fisher, Robert N.; Grant, Evan H.; Shafer, Charles; Beamer, David A.; Spear, Stephen F.; Pierson, Todd W.; Davenport, Jon M.; Niemiller, Matthew L.; et al (December 2024, Scientific Data)

   Abstract National parks and other protected areas are important for preserving landscapes and biodiversity worldwide. An essential component of the mission of the United States (U.S.) National Park Service (NPS) requires understanding and maintaining accurate inventories of species on protected lands. We describe a new, national-scale synthesis of amphibian species occurrence in the NPS system. Many park units have a list of amphibian species observed within their borders compiled from various sources and available publicly through the NPSpecies platform. However, many of the observations in NPSpecies remain unverified and the lists are often outdated. We updated the amphibian dataset for each park unit by collating old and new park-level records and had them verified by regional experts. The new dataset contains occurrence records for 292 of the 424 NPS units and includes updated taxonomy, international and state conservation rankings, hyperlinks to a supporting reference for each record, specific notes, and related fields which can be used to better understand and manage amphibian biodiversity within a single park or group of parks. 

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3. Rust HUBB: DNA barcode-based identification of Pucciniales

   https://doi.org/10.1186/s43008-023-00132-7  

   Kaishian, Patricia; Layug, Christopher_R_K; Anderson, Mark; Berg, Diane_R; Aime, M_Catherine (February 2024, IMA Fungus)

   Abstract Rust fungi (Pucciniales, Basidiomycota) are a species-rich (ca. 8000 species), globally distributed order of obligate plant pathogens. Rust species are host-specific, and as a group they cause disease on many of our most economically and/or ecologically significant plants. As such, the ability to accurately and rapidly identify these fungi is of particular interest to mycologists, botanists, agricultural scientists, farmers, quarantine officials, and associated stakeholders. However, the complexities of the rust life cycle, which may include production of up to five different spore types and alternation between two unrelated host species, have made standard identifications, especially of less-documented spore states or alternate hosts, extremely difficult. The Arthur Fungarium (PUR) at Purdue University is home to one of the most comprehensive collections of rust fungi in the world. Using material vouchered in PUR supplemented with fresh collections we generated DNA barcodes of the 28S ribosomal repeat from > 3700 rust fungal specimens. Barcoded material spans 120 genera and > 1100 species, most represented by several replicate sequences. Barcodes and associated metadata are hosted in a publicly accessible, BLAST searchable database called Rust HUBB (Herbarium-based Universal Barcode Blast) and will be continuously updated. 

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4. Subfossil birds from a submerged cave in southwestern Madagascar

   Rasolonjatovo, H.A M.; Muldoon, K.M.; Ranivoharimanana, L.; Rakotoarijaona, M.; Goodman, S. M. (December 2021, Malagasy nature)

   The Mahafaly karst of southwestern Madagascar is rich with subfossil deposits. Vintany Cave (also known as Aven Cave), a submerged cave at Tsimanampesotse National Park, is the most subfossil-dense submerged cave known in the world. In particular, the cave has yielded abundant remains of birds, including some that are extinct. Among 1077 bird specimens recovered under water from the cave floor and from excavated sediments at this site, 35 different taxa were identified. Taxonomic attributions were made through comparative morphological analysis, using comparative osteological museum collections. The majority of these species still occur inside the park. Five extinct taxa were recovered from the cave, including one species of elephant bird (Aepyornithidae, Mullerornis modestus), two species of giant endemic ground couas (Cuculidae, Coua cf. berthae and C. cf. primaeva), a shelduck (Alopochen sirabensis, Anatidae), and a lapwing (Charadriidae, Vanellus madagascariensis). Two extant taxa, Haliaeetus vociferoides (Accipitridae) and Threskiornis bernieri (Threskiornithidae) are locally extirpated, but exist at other localities in Madagascar. Remains of Greater Vasa Parrots (Psittaculidae, Coracopsis vasa) are predominant. Some of the identified extinct and locally extirpated taxa from Vintany Cave have an aquatic dependence, most specifically freshwater, that suggests that there has been environmental modification such as reduction of the important water sources in the region of Tsimanampesotse, and wetter conditions in the area in the past. 

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5. Deep learning models map rapid plant species changes from citizen science and remote sensing data

   https://doi.org/10.1073/pnas.2318296121  

   Gillespie, Lauren E; Ruffley, Megan; Exposito-Alonso, Moises (September 2024, Proceedings of the National Academy of Sciences)

   Anthropogenic habitat destruction and climate change are reshaping the geographic distribution of plants worldwide. However, we are still unable to map species shifts at high spatial, temporal, and taxonomic resolution. Here, we develop a deep learning model trained using remote sensing images from California paired with half a million citizen science observations that can map the distribution of over 2,000 plant species. Our model—Deepbiosphere—not only outperforms many common species distribution modeling approaches (AUC 0.95 vs. 0.88) but can map species at up to a few meters resolution and finely delineate plant communities with high accuracy, including the pristine and clear-cut forests of Redwood National Park. These fine-scale predictions can further be used to map the intensity of habitat fragmentation and sharp ecosystem transitions across human-altered landscapes. In addition, from frequent collections of remote sensing data,Deepbiospherecan detect the rapid effects of severe wildfire on plant community composition across a 2-y time period. These findings demonstrate that integrating public earth observations and citizen science with deep learning can pave the way toward automated systems for monitoring biodiversity change in real-time worldwide. 

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