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Fig. 3 in Amphibians and reptiles of Parsa National Park, Nepal
Fig. 3. Duttaphrynus stomaticus. Photograph by Santosh Bhattarai.
Fig. 13 in Amphibians and reptiles of Parsa National Park, Nepal
Fig. 13. Nesting female of Sitana fusca. Photograph by Santosh Bhattarai.
Fig. 10 in Amphibians and reptiles of Parsa National Park, Nepal
Fig. 10. Polypedates maculatus. Photograph by Santosh Bhattarai.
Fig. 12 in Amphibians and reptiles of Parsa National Park, Nepal
Fig. 12. Calotes versicolor. Photograph by Santosh Bhattarai.
Fig. 7 in Amphibians and reptiles of Parsa National Park, Nepal
Fig. 7. Microhyla cf. nilphamariensis. Photograph by Santosh Bhattarai.
Fig. 9 in Amphibians and reptiles of Parsa National Park, Nepal
Fig. 9. Uperodon taprobanicus. Photograph by Santosh Bhattarai.
Fig. 5 in Amphibians and reptiles of Parsa National Park, Nepal
Fig. 5. Hoplobatrachus tigerinus. Photograph by Santosh Bhattarai.
Fig. 14 in Amphibians and reptiles of Parsa National Park, Nepal
Fig. 14. Eutropis carinata. Photograph by Kapil Pokharel/ NTNC-BCC.
Fig. 6 in Amphibians and reptiles of Parsa National Park, Nepal
Fig. 6. Sphaerotheca breviceps. Photograph by Santosh Bhattarai.
Fig. 15 in Amphibians and reptiles of Parsa National Park, Nepal
Fig. 15. Eutropis dissimillis. Photograph by Kapil Pokharel/ NTNC-BCC.
Data from: Harvest of transboundary gray wolves from Yellowstone National Park is largely additive
<p>Large carnivores are globally threatened due to habitat fragmentation and loss, prey depletion, and human exploitation. Human exploitation includes both legal and illegal hunting and trapping. Protected areas can create refugia from hunting and trapping, however, hunting can still threaten wide-ranging large carnivores when they leave these areas. Large carnivore reintroductions to protected areas are often motivated to restore ecological processes, including wolf reintroduction to Yellowstone National Park (YNP). Determining if harvest is compensatory or additive is essential for informed conservation strategies, as it influences the overall impact on wolf populations and their ecosystems. If the harvest was compensatory, then increasing harvest pressure outside YNP should not decrease overall survival for transboundary wolves. Alternatively, if increasing harvest was additive, then increasing harvest pressure outside YNP should decrease overall survival for transboundary wolves. We tested the effects of variable harvest pressure following delisting on the survival of YNP gray wolves (<em>Canis lupus</em>) from 1995 to 2022. We defined three harvest levels: no harvest, harvest with limited quotas, and unlimited harvest. We used Cox-proportional hazards models and cumulative incidence functions to estimate survival rates, factors affecting survival, and cause-specific mortality between these three harvest periods to test predictions of the additive mortality hypothesis. Most wolves that primarily lived in YNP were harvested adjacent to the park border. Cox-proportional hazards models revealed that mortality was highest during years of unlimited harvest during winter outside YNP. Cause-specific mortality analyses showed that natural mortality from other wolves and harvest were the two leading causes of death, but that harvest mortality had additive effects on wolf mortality. Wolf survival decreased with increased harvest mortality, while natural mortality remained relatively unchanged. High rates of additive harvest mortality of wolves could negatively impact wolf survival in YNP. Harvest mortality of transboundary wolves is additive possibly due to source-sink dynamics of uneven spatial susceptibility of wolves to harvest mortality across protected area borders, as well as effects of harvest on complex social dynamics of wolves in YNP. Transboundary management of large carnivores is challenging, yet cooperation between agencies is vital for wolf management in and around Yellowstone National Park. Our results support the use of small quota zones surrounding protected areas, that minimize transboundary mortality impacts on large carnivores living primarily inside protected areas.</p>
Fig 6A in Diversity and distribution of scleractinian corals from Mandapam group of Islands in Gulf of Mannar marine national park, South East coast of India
Fig 6A: D Acropora sp. E - G Montipora sp. H - Tubastreae coccinea
Burned areas dataset for the enclaves of grasslands and savannah of the Mapinguari National Park (Amazonas, Brazil)
<p><span>The present dataset includes annual mapping of fire scars for the enclaves of grasslands and savannah of the Mapinguari National Park (Amazonas, Brazil), at 30-meter spatial resolution, for the period 2000-2023. The enclave areas occupy a total of 241,000 hectares.</span></p> <p><span>The detection of burned areas was carried out using Burned Area Mapping (BAMS) algorithm (Bastarrika et al, 2014), followed by the performance of visual supervision processes and the use of active fire products to optimize the detection date of each fire event. The algorithm is applied to the Surface Reflectance series of Landsat (TM, ETM+, OLI and OLI-2) – Collection II, accessed using Google Earth Engine (Gorelick et al, 2017). Data from active fire products MCD14DL V006, VIIRS-NPP, VIIRS-NOA20 and GOES16, as well as burned area data from product MCD64A1 v006, were used to optimize the detection date of each fire scar.</span></p> <p><span>_________________________________________________________________________________________________________</span></p> <p><span>July 08, 2023 – The version 1.0 includes the period 2000-2023, with a total of 356,688.5 hectares of fire affected areas, distributed across 453 fire scars.</span></p> <p><span>_________________________________________________________________________________________________________</span></p> <p><span>The files available include: </span></p> <p><span>i) “fire_scars_dataset.rar”: annual burned areas vector files, in shapefile format (*.shp), projected at WGS84 UTM 20S. Each observation is an individual fire scar, with his attribute table indicating:</span></p> <p><span>- ID: Identification number for each;</span></p> <p><span>- area_ha - area of each fire scar, calculated in hectares.</span></p> <p><span>- date - detection date of the fire scar</span></p> <p><span>- date_preci - precision flag of the fire detection date:</span></p> <p><span>0 - fire date detected using only Landsat data</span></p> <p><span>1 - fire date optimized based on active fires dataset (MCD14DL V006; VIIRS-NPP; VIIRS-NOA20; or GOES16)</span></p> <p><span>ii) “enclaves_Mapinguari_National_Park.rar”: vector file of study area location, in shapefile format (*.shp), projected at WGS84 UTM 20S. Includes 8 enclaves of grasslands and savannah situated inside Mapinguari National Park.</span></p> <p><span>iii) “dataset_description.pdf”: description of the dataset.</span></p> <p><span>_________________________________________________________________________________________________________</span></p> <p><span>We thank the Conselho Nacional de Pesquisa e Desenvolvimento (CNPq) and the Instituto Chico Mendes de Conservação da Biodiversidade (ICMBIO) (process number 126772/2022-3) for the grant conceded to the second and third authors.</span></p> <p><span>_________________________________________________________________________________________________________</span></p> <p> </p> <p><span>References</span></p> <p> </p> <p><span>Bastarrika, Aitor, Maite Alvarado, Karmele Artano, Maria Pilar Martinez, Amaia Mesanza, Leyre Torre, Rubén Ramo, and Emilio Chuvieco. 2014. “BAMS: A Tool for Supervised Burned Area Mapping Using Landsat Data.” Remote Sensing 6: 12360–80. https://doi.org/10.3390/rs61212360.</span></p> <p><span>Gorelick, Noel, Matt Hancher, Mike Dixon, Simon Ilyushchenko, David Thau, and Rebecca Moore. 2017. “Google Earth Engine: Planetary-Scale Geospatial Analysis for Everyone.” Remote Sensing of Environment 202: 18–27. https://doi.org/10.1016/j.rse.2017.06.031.</span></p>
Microhabitat selection of the big-headed turtle Platysternon megacephalum in the Hainan Tropical Rainforest National Park, China
<p>Understanding species habitat requirements is vital for ensuring the success of targeted conservation and habitat restoration measures. The big-headed turtle (<em>Platysternon megacephalum</em>)<em> </em>is a freshwater species which is distributed across Southeast Asia. Due to the human threats posed by illegal pet trade and overharvesting for food and medicinal purposes, the species has undergone rapid decline. Furthermore, limited research has been conducted on this species, particularly regarding habitat preferences on Hainan Island, China. Therefore, this study examined the microhabitat selection of <em>P. megacephalum</em> using cage and sample plot methods in the Diaoluo Mountain area of the Hainan Tropical Rainforest National Park. Our results indicated that big-headed turtles select stream microhabitats at higher altitudes, in proximity to rocky substrates, several caves, and high diversity of food sources. Microhabitat utilization did not differ significantly between adults and juveniles. This suggests that protecting microhabitats and main food sources is important for the conservation of <em>P. megacephalum</em>. Our findings provide a reference for the protection of this species in Jianfeng, Yingge Ridge, and other areas in the Hainan Tropical Rainforest National Park.</p>
Lemonade Creek, Yellowstone National Park, USA - Microbial Community Analysis - Metabolomics Data
<p>Polar metabolomics data (targeted and untargeted) used for analysis of microbial community function over a diurnal cycle in Lemonade Creek, Yellowstone National Park, USA.</p> <p> </p> <p><code>GNPS_positive-2.xlsx</code> Comparison of GNPS data used for main metabolite analysis with targeted metabolite features. Done to support the accuracy of the GNPS results for metabolites identified outside the targeted set.</p> <p> </p> <p><code>NEG_506963_FinalEMA-HILIC_Identifications.xlsx</code> Negative ionization targeted metabolite identification quality and confidence results (prepared by the Joint Genome Institute, USA).</p> <p><code>NEG_msms_mirror_plots.tar.gz</code> Negative ionization targeted metabolite mirror plots.</p> <p><code>NEG_peak_height.tab</code> Negative ionization targeted metabolite peak height file (main results file used for abundance analysis).</p> <p><code>POS_506963_FinalEMA-HILIC_Identifications.xlsx</code> Positive ionization targeted metabolite identification quality and confidence results (prepared by the Joint Genome Institute, USA).</p> <p><code>POS_msms_mirror_plots.tar.gz</code> Positive ionization targeted metabolite mirror plots.</p> <p><code>POS_peak_height.tab</code> Positive ionization targeted metabolite peak height file (main results file used for abundance analysis).</p> <p> </p> <p><code>NEG_peak_height.csv</code> Negative ionization untargeted metabolite peak height file (main results file used for abundance analysis).</p> <p><code>POS_peak_height.csv</code> Positive ionization untargeted metabolite peak height file (main results file used for abundance analysis).</p>
Figure 5 in Seasonal distribution and habitat use preference of Barking deer (Muntiacus vaginalis) in Murree-Kotli Sattian-Kahuta National Park, Punjab Pakistan
Figure 5. Slopes of occupied locations of Barking deer habitat.
Figure 1 in Seasonal distribution and habitat use preference of Barking deer (Muntiacus vaginalis) in Murree-Kotli Sattian-Kahuta National Park, Punjab Pakistan
Figure 1. Distribution of Barking deer in study area.
Figure 7 in Seasonal distribution and habitat use preference of Barking deer (Muntiacus vaginalis) in Murree-Kotli Sattian-Kahuta National Park, Punjab Pakistan
Figure 7. Aspects of occupied locations of Barking Deer in study area.
Figure 3 in Seasonal distribution and habitat use preference of Barking deer (Muntiacus vaginalis) in Murree-Kotli Sattian-Kahuta National Park, Punjab Pakistan
Figure 3. Plant species recorded in summer from habitat of Barking deer.
Figure 6 in Seasonal distribution and habitat use preference of Barking deer (Muntiacus vaginalis) in Murree-Kotli Sattian-Kahuta National Park, Punjab Pakistan
Figure 6. Coarse topography / habitat characteristics at occupied locations of Barking deer.
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.