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31 results for “protection status”
OpenStreetMap+ Protected nature areas in continental Europe (IUCN status + Natura 2000)
<p>Twelve maps of continental Europe indicating the protected nature area status in 2019 according to <a href="https://ec.europa.eu/environment/nature/natura2000/index_en.htm">Natura 2000</a> and the <a href="https://www.iucn.org/">International Union for Conservation of Nature</a> (IUCN). The IUCN status was extracted from crowdsourced data obtained from OpenStreetMap through geofabrik.de.</p> <p>This dataset contains:</p> <ul> <li>3 raster maps representing Natura 2000 protection status (A, B and C), named <strong>Natura2000_[status].tif</strong></li> <li>8 raster maps representing OSM-derived IUCN protection status (1a, 1b, 2, 3, 4, 5, 6, and 'other'), named <strong>OSM_IUCN_[status].tif</strong></li> <li>1 aggregated map (<strong>adm_protected.area_natura2000.osm_p_30m_0..0cm_2019..2021_eumap_epsg3035_v0.1</strong>) where each of the 11 protection statuses, as well as pixels where multiple statuses apply, are assigned a unique value. This map can also be accessed interactively at <a href="https://maps.opendatascience.eu/?base=OpenStreetMap%20(grayscale)&layer=Natura2000-OSM%20Protected%20areas&zoom=4&eye=5000000&center=53.7139,17.0066&opacity=45">maps.opendatascience.eu</a>.</li> </ul> <p>All files are provided as <a href="https://gdal.org/drivers/raster/cog.html">Cloud Optimized GeoTIFFs</a> and projected in the Coordinate Reference System ETRS89 / LAEA Europe (= EPSG code 3035). Styling files for the aggregated raster are provided in both <strong><em>SLD</em></strong> and <strong><em>QML</em></strong> format.</p>
Figure. 1 in Stoneflies (Plecoptera) of the Czech Republic: species checklist, distribution and protection status
Figure. 1. Map of the Czech Republic showing 8 districts based on main river basins. Published data are marked by circle, new unpublished data by × mark. Main mountains are marked by numbers, their Czech, English and German names are listed: 1 – Doupovské hory (Duppauer Gebirge) Mts., 2 – Slavkovský les Mts., 3 – Krušné hory (Ore/Erzgebirge) Mts., 4 – Jizerské hory (Isergebirge) Mts., 5 – Krkonoše (Giant/Riesengebirge) Mts., 6 – Orlické hory (Adlergebirge) Mts., 7 – Kralický Sněžník Mt.; 8 – Hrubý Jeseník (Altvatergebirge/Hohes Gesenke) Mts., 9 – Šumava (Bohemian Forest/Böhmerwald) Mts., 10 – Novohradské hory Mts., 11 – Českomoravská vrchovina (Böhmisch-Mährische Höhe), 12 – Moravskoslezské Beskydy Mts., 13 – Bílé Karpaty Mts.
Data from: Protection status, human disturbance, snow cover and trapping drive density of a declining wolverine population in the Canadian Rocky Mountains
<p>Protected areas are important in species conservation, but high rates of human-caused mortality outside their borders and increasing popularity for recreation can negatively affect wildlife populations. We quantified wolverine (<em>Gulo gulo</em>) population trends from 2011 to 2020 in >14 000 km2 protected and non-protected habitat in southwestern Canada. We conducted wolverine and multi-species surveys using non-invasive DNA and remote camera-based methods. We developed Bayesian integrated models combining spatial capture-recapture data of marked and unmarked individuals with occupancy data. Wolverine density and occupancy declined by 39 percent, with an annual population growth rate of 0.925. Density within protected areas was 3 times higher than outside and declined between 2011 (3.6 wolverines/1000 km2) and 2020 (2.1 wolverines/1000 km2). Wolverine density and detection probability increased with snow cover and decreased near development. Detection probability also decreased with human recreational activity. The annual harvest rate of 13% was above the maximum sustainable rate. We conclude that humans negatively affected the population through direct mortality, sub-lethal effects and habitat impacts. Our study exemplifies the need to monitor population trends for species at risk – within and between protected areas - as steep declines can occur unnoticed if key conservation concerns are not identified and addressed.</p>
Fig. 4 in Stag Beetle Lucanus Cervus (Linnaeus, 1758) In Latvia (Coleoptera: Lucanidae): Faunal Status, Protection And Use In Folk Art
Fig. 4. The sweater knitted and embroidered by Nadežda Harlamova with an image of a stag beetle. Photo: Nadežda Harlamova.
Fig 2 in Stag Beetle Lucanus Cervus (Linnaeus, 1758) In Latvia (Coleoptera: Lucanidae): Faunal Status, Protection And Use In Folk Art
Fig 2. Distribution map showing known sites of records for Lucanus cervus in Latvia. Red dots are marked with higher confidence (all more than 40 years old), but with black squares – low reliability or erroneously published information.
Fig. 3 in Stag Beetle Lucanus Cervus (Linnaeus, 1758) In Latvia (Coleoptera: Lucanidae): Faunal Status, Protection And Use In Folk Art
Fig. 3. The mittens knitted by Laura Riekstiņa with an image of a stag beetle on the palm part of the mitten. Photo: Arvīds Barševskis.
Fig. 1 in Stag Beetle Lucanus Cervus (Linnaeus, 1758) In Latvia (Coleoptera: Lucanidae): Faunal Status, Protection And Use In Folk Art
Fig. 1. Lucanus cervus specimen caught in 1969, in Ieriķi. Currently kept in Alūksne Museum. Photo: Mārtiņš Kalniņš
Figure 2 in Protected taxonomic status and lectotype designation for Holochila albosericea Miskin, 1891 (Lepidoptera: Lycaenidae)
Figure 2. Type material of Holochila caeruleolactea T.P. Lucas, 1891 showing dorsal and ventral views and labels: (A–C) lectotype male in SAMA (31-001721); (D–F) paralectotype female in SAMA (31-001722). Scale bars = 10 mm. Photo credits: 2A, C–F by Ben Parslow; 2B by Ted Edwards.
Figure 1 in Protected taxonomic status and lectotype designation for Holochila albosericea Miskin, 1891 (Lepidoptera: Lycaenidae)
Figure 1. Type material of Holochila albosericea Miskin, 1891 showing dorsal and ventral views and labels: (A–C) lectotype male in QM (T.12401); (D–F) paralectotype female in AMS (KL21390). Scale bars = 10 mm. Photo credits: 1A–C by Geoff Thompson; 1D–F by Natalie Tees.
Fig. 1 in Conservation status of the herpetofauna, protected areas, and current problems in Valle del Cauca, Colombia
Fig. 1. Political map of Valle del Cauca (Colombia). North: El Águila (AGL), El Cairo (CR), Ansermanuevo (ASN), Argelia (ARG), Cartago (CTG), Ulloa (ULA), Alcalá (ACL), Toro (TR), Versalles (VRSL), Obando (OBD), La Unión (UN), El Dovio (DV), Roldanillo (RDNL), La Victoria (VTR), Zarzal (ZRZ), Bolívar (BLV); East: Sevilla (SVL), Caicedonia (CDN); Middle: Bugalagrande (BGG), Trujillo (TJL), Andalucía (ADL), Rio Frio (RF), Tuluá (TL), San Pedro (S/PD), Yotoco (YTC), Darién (DR), Buga (BG), Guacarí (GCR), Ginebra (GNB), Vijes (VJ), Restrepo (RTP), Cumbre (CMB), El Cerrito (CRT); South: Palmira (PMR), Yumbo (YMB), Cali (CL), Candelaria (CDR), Pradera (PDR), Florida (FRD), Jamundí (JMD); West: Buenaventura (B/tura), Dagua (DG).
Fig. 6 in Conservation status of the herpetofauna, protected areas, and current problems in Valle del Cauca, Colombia
Fig. 6. Endemic species listed according to (A) threat category, and (B) by protected areas in Valle del Cauca.
Fig. 3 in Conservation status of the herpetofauna, protected areas, and current problems in Valle del Cauca, Colombia
Fig. 3. Distribution maps for the richness of herpetofauna in the most documented areas in Valle del Cauca. (A) richness, (B) NT species, (C) VU species, (D) EN species, (E) CR species, and (F) DD species.
Fig. 5 in Conservation status of the herpetofauna, protected areas, and current problems in Valle del Cauca, Colombia
Fig. 5. The herpetofauna of Valle del Cauca according to (A) the type of protected area, and (B) species with some degree of threat in each type of protected area.
Fig. 2 in Conservation status of the herpetofauna, protected areas, and current problems in Valle del Cauca, Colombia
Fig. 2. Threat status of the percentage of herpetofaunal species in Valle de Cauca: (A) = Amphibians, (B) = Reptiles.
Fig. 4 in Conservation status of the herpetofauna, protected areas, and current problems in Valle del Cauca, Colombia
Fig. 4. Municipalities in Valle del Cauca with the greatest number of species in the threat categories: (A) Buenaventura, (B) Dagua, (C) Cali, (D) Darién, (E) El Cairo, and (F) La Cumbre.
Data from: Protection status, human disturbance, snow cover and trapping drive density of a declining wolverine population in the Canadian Rocky Mountains
Open the record for dataset details and reuse information.
Data from: Robust inference on large-scale species habitat use with interview data: the status of jaguars outside protected areas in Central America
Evaluating range-wide habitat use by a target species requires information on species occurrence over broad geographic regions, a process made difficult by species rarity, large spatiotemporal sampling domains, and imperfect detection. We address these challenges in an assessment of habitat use for jaguars (Panthera onca) outside protected areas in Central America. Occurrence records were acquired within 12 putative corridors using interviews with knowledgeable corridor residents. We developed a Bayesian hierarchical occupancy model to gain robust inference, allowing for heterogeneity introduced in the sampling process over space and time, using records of jaguar occurrence prone to false positives and false negatives. Probability of false detection of jaguars increased with the number of interviews conducted per unit (from 5.42% to 7.74% given <4 and ≥4 observers per unit). True probability of detection (mean=0.58) increased with the number of days interviewees spent in a survey unit per year. Failing to account for false positives biased predicted habitat use high (˜1.8x), especially where occurrence records were sparse. Probability of site use by jaguars increased with greater forest cover, prey richness, and distance from human settlements, and decreased with greater agricultural cover, elevation, and distance from protected areas. Site use probabilities averaged 0.15-0.97 by corridor, providing relatively fine-scale resolution of predicted jaguar occurrence consistent with known patterns of jaguar gene flow across Central America. Model validation, accounting for both false positives and negatives in the observation process, indicated moderate correspondence between model-predicted observations and actual observations for withheld data (0.65, 95% CRI 0.59–0.71), with sensitivity and specificity rates of 0.69 (0.61 – 0.77) and 0.59 (0.50 – 0.68), respectively. These results demonstrate that reliable predictions can be achieved despite the complexity of large-scale, interview-based analyses of species occurrence. Synthesis and applications. Our Bayesian hierarchical occupancy model accommodated heterogeneity caused by typical sampling inequities and idiosyncrasies associated with interview data, yielding robust estimates of jaguar habitat use. Our approach is applicable to any wide-ranging and readily identifiable species and has particular utility for rare species in human-dominated landscapes where traditional survey techniques (e.g., camera traps) may be impractical.
Relationships between a common Caribbean corallivorous snail and protected area status, coral cover, and predator abundance
<p>As coral populations decline across the Caribbean, it is becoming increasingly important to understand the forces that inhibit coral survivorship and recovery. Predation by corallivores, such as the short coral snail <i>Coralliophila abbreviata</i>,are one threat to the health of reefs worldwide, but understanding of the factors controlling corallivore populations, and therefore corallivore predation pressure, remains limited. To examine the extent to which bottom-up (i.e., coral prey) and top-down (i.e., predators) forces relate to <i>C. abbreviata </i>distributions, we surveyed <i>C. abbreviata </i>abundance, percent coral cover, and the abundance of potential snail predators across six protected and six unprotected reefs in the Florida Keys. We found that <i>C. abbreviata </i>abundance was lower in protected areas, which had more diverse predator assemblages, and that across all sites snail abundance generally increased with coral cover. <i>C. abbreviata </i>abundance had strong, negative relationships with two gastropod predators in particular - the Caribbean spiny lobster (<i>Panulirus argus</i>) and the grunt black margate (<i>Anisotremus surinamensis</i>). Further, we found the size of <i>C. abbreviata </i>was also related to reef protection status, with larger <i>C. abbreviata </i>on average in protected areas, suggesting these gape-limited predators may alter size distributions by targeting small snails. Combined, these results provide preliminary evidence that marine protection in the Florida Keys may be a mechanism available to preserve critical trophic interactions that indirectly promote coral success via control of local populations of the common corallivore <i>C. abbreviata.</i></p>
FIGURE 3 in Taxonomic status of Podabrus albocaudatus Krefft, 1872 and declaration of Sminthopsis granulipes Troughton, 1932 (Marsupialia: Dasyuridae) as a protected name for the White-tailed Dunnart from Western Australia
FIGURE 3. Holotype alcoholic body of Sminthopsis granulipes, PA.669, with metal registration tag attached to right manus. Scale rule in cms. (Photo: Anja Divljan).
FIGURE 2 in Taxonomic status of Podabrus albocaudatus Krefft, 1872 and declaration of Sminthopsis granulipes Troughton, 1932 (Marsupialia: Dasyuridae) as a protected name for the White-tailed Dunnart from Western Australia
FIGURE 2. Holotype skull and mandible of Sminthopsis granulipes, PA.669. Scale bar = 5 mm. (Photo: Sally Cowan).
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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)
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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.