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706 results for “protected area”
Burnt forest area and CO2 emissions from fires in Russian forests by fire protection zones in 2010-2020
<p>The dataset is Supplementary Materials for the article ''<em>Reassessment of carbon emissions from fires and a new estimate of net carbon uptake in Russian forests in 2010-2020</em>'' in the Carbon Balance and Management journal. It contains files with burnt forest area and carbon dioxide emissions from fires data in Russia 2010-2020.<br> Article Supplementary materials are stored in the file Supplementary Tables and contains:</p> <p>- Table 1. Burnt forest area from NIR and MODIS (MCD64A1) in 2010-2020;</p> <p>- Table 2. Burnt forest area in the ground, aviation and the control (no fire protection) zones in 2010-2020 using MCD64A1;</p> <p>- Table 3. Carbon emissions from forest fires from National Inventory Report (NIR) and Copernicus Atmosphere Monitoring Service (CAMS) in 2010-2020</p> <p>Also, there is Supplementary Figure 1 with the Federal Districts of Russia schematic map.</p> <p>There are 22 files in GeoTIFF format for every year: </p> <p>1. Burnt forest area obtained using MODIS product MCD64A1 (250 m pixel, ESRI:102025). Coverage: -4064059.5401764437556267,1967242.6686790268868208 : 3658440.4598235562443733, 6012242.6686790268868208</p> <p>2. CO2 emissions using Copernicus Atmosphere Monitoring System (CAMS) (0.1 degrees, VGS 84). Coverage: 27.9493818283081055,42.9493612670349520 : 190.0498617200859712,78.0494651794433594<br> <br> In addition, we share Shapefiles:</p> <p>1. Russian borders (necessary to cut Russia from CO2 GeoTIFFs), EPSG:4326. Coverage: -180.0000000000000000,41.1888656599999976 : 180.00000000000000000,81.8562469499999992;</p> <p>2. Forest Fire Protection zoning in 2019: ground zone, aviation zone, the so-called control zone (no fire protection), EPSG:4326. Coverage: 27.4019779002987676,41.3483353426717599 : 173.8255532772949721,72.6575707670955353.</p>
Fig. 2 in Changes In The Structure Of Assemblages Of Three Liolaemus Lizards (Iguania, Liolaemidae) In A Protected Area Of South-Central Chile Affected By A Mixed-Severity Wildfire
Fig. 2. Species of Liolaemus lizards recorded in the study area. A — L. tenuis (© G. Zúñiga); B — L. pictus (© A. H. Zúñiga); C — L. lemniscatus (© A. H. Zúñiga).
Fig. 3 in Changes In The Structure Of Assemblages Of Three Liolaemus Lizards (Iguania, Liolaemidae) In A Protected Area Of South-Central Chile Affected By A Mixed-Severity Wildfire
Fig. 3. Percentages of microhabitat use by lizards in study area according to severity of damage caused by fire.
Fig. 1 in Changes In The Structure Of Assemblages Of Three Liolaemus Lizards (Iguania, Liolaemidae) In A Protected Area Of South-Central Chile Affected By A Mixed-Severity Wildfire
Fig. 1. Study area: A — Geographical context; B — Mosaic of areas of different degrees of severity (modified from CONAF, 2014, 2015).
Research data: Counterfactual assessment of protected area avoided deforestation in Cambodia version 4
<p>This dataset includes the data, the R scripts used for analysis and results that are the basis of the journal article: Black, B., Anthony, B. In review. Counterfactual assessment of protected area avoided deforestation in Cambodia: Trends in effectiveness, spillover effects and the influence of establishment date. Global Ecology and Conservation.</p> <p>Each folder includes a specific readme file in .txt format which includes metadata and instructions for reproducing the research.</p> <p> </p>
Data generated from: Functional connectivity of the world's protected areas
<p>Here, we provide the two primary global connectivity datasets generated in the study titled "Functional connectivity of the world's protected areas", including the protected area isolation (PAI) metric for all included protected areas (i.e., effective resistance), provided as a csv file, and the map of global mammal movement probability (i.e., electrical current density), provided as a tif. We also include the nationally aggregated PAI values in National_PAI.csv. National PAI represents the median PAI value for each country, after excluding values equal to -1.</p> <p>Generation of these datasets relied on the following three external data sources:</p> <p>- Observed mammal movement data (0.95 quantile displacement distances over 10-days), predictor variables and the linear mixed effects model presented in: M. A. Tucker <em>et al.</em>, <em>Science</em>. <strong>359</strong>, 466–469 (2018). </p> <p>- The 2009 Global Human Footprint map presented in: O. Venter <em>et al.</em>, <em>Nat. Communications.</em> <strong>7</strong>, 1–11 (2016). </p> <p>- The May 2020 and April 2018 versions of the World Database on Protected Areas, found at: UNEP-WCMC and IUCN, Protected Planet: the World Database on Protected Areas (WDPA), Cambridge, UK, (available at www.protectedplanet.com). </p> <p>Please read the Readme.txt for file details and cite the following paper if you use these data: Brennan, A., R. Naidoo, L. Greenstreet, Z. Mehrabi, N. Ramankutty and C. Kremen. Functional connectivity of the world's protected areas. Science (2022).</p> <p> </p>
Data and analysis code for: Global protected areas seem insufficient to safeguard half of the world's mammals from human-induced extinction
<div> <p class="normal">Protected areas (PAs) are a cornerstone of global conservation and central to international plans to minimize global extinctions. During the coming century, global ecosystem destruction and fragmentation associated with increased human population and economic activity could make the <span class="PI"></span>long-term<span class="PI"></span> survival of most terrestrial vertebrates even more dependent on PAs. However, the capacity of the current global PA network to sustain species for the long term is unknown. Here, we explore this question for all <span class="PI"></span>nonvolant terrestrial mammals<span class="ins cts-1"> for which we found sufficient data</span>, ∼4,000 species. We first estimate the potential population size of each such mammal species in each PA and then use three different criteria to estimate if solely the current global network of PAs might be sufficient for their <span class="PI"></span>long-term<span class="PI"></span> survival. Our analyses suggest that current PAs may fail to provide robust protection for about half the species analyzed, including most species currently listed as threatened with extinction and a third of species not currently listed as threatened. Hundreds of mammal species appear to have no viable protected populations. Underprotected species were found across all body sizes, taxonomic groups, and geographic regions. <span class="PI"></span>Large-bodied<span class="PI"></span> mammals, endemic species, and those in <span class="PI"></span>high-biodiversity<span class="PI"></span> tropical regions were particularly poorly protected by existing PAs. As<span class="ins cts-1"> new</span> international biodiversity targets are formulated, our results suggest that the global network of PAs must be <span class="PI"></span>greatly expanded and most importantly that PAs must be located in diverse regions that encompass species not currently protected and must be large enough to ensure that protected species can persist for the long term.</p> </div> <p class="kwd-group"></p>
Data from: Human impacts on mammals in and around a protected area before, during, and after COVID‐19 lockdowns
<p>The dual-mandate for many protected areas (PAs) to simultaneously promote recreation and conserve biodiversity may be hampered by negative effects of recreation on wildlife. However, reports of these effects are not consistent, presenting a knowledge gap that hinders evidence-based decision-making. We used camera traps to monitor human activity and terrestrial mammals in Golden Ears Provincial Park and the adjacent Malcolm Knapp Research Forest near Vancouver, Canada, with the objective of discerning relative effects of various forms of recreation on cougars (Puma concolor), black bears (Ursus americanus), black-tailed deer (Odocoileus hemionus), snowshoe hares (Lepus americanus), coyotes (Canis latrans), and bobcats (Lynx rufus). Additionally, public closures of the study area associated with the COVD-19 pandemic offered an unprecedented period of human-exclusion through which to explore these effects. Using Bayesian generalized mixed-effects models, we detected negative effects of hikers (mean posterior estimate = -0.58, 95% credible interval (CI) -1.09 to -0.12) on weekly bobcat habitat use and negative effects of motorized vehicles (estimate = -0.28, 95% CI -0.61 to -0.05) on weekly black bear habitat use. We also found increased cougar detection rates in the PA during the COVID-19 closure (estimate = 0.007, 95% CI 0.005 to 0.009), but decreased cougar detection rates (estimate = -0.006, 95% CI -0.009 to -0.003) and increased black-tailed deer detection rates (estimate = 0.014, 95% CI 0.002 to 0.026) upon reopening of the PA. Our results emphasize that effects of human activity on wildlife habitat use and movement may be species- and/or activity-dependent, and that camera traps can be an invaluable tool for monitoring both wildlife and human activity, collecting data even when public access is barred. Further, we encourage PA managers seeking to promote both biodiversity conservation and recreation to assess trade-offs between these two goals in their PAs.</p>
Hypothetical landscapes to evaluate connectivity metrics of protected area networks.
<p>This repo contains the raw datasets (as GIS shapefiles) useful to evaluate connectivity metrics of protected area networks. Please suggest if additional landscapes could be added that would be useful to evaluate an additional class or characteristic of protected area networks. They were created using Google Earth Engine script: <strong><a href="https://code.earthengine.google.com/d1a8dfa3202ac8b4e55657bd3b5a1160">https://code.earthengine.google.com/d1a8dfa3202ac8b4e55657bd3b5a1160</a>.</strong></p> <p>Two shapefiles are provided: (1) ProNet_connectivity_library_L1_26pa -- this contains polygons that represent the size and shape of protected areas (PAs); (2) ProNet_connectivity_library_L1_26pae -- this contains polylines that represent "edges" that do not represent any protected area but denotes that two PAs are connected. Note that these landscapes are fictitious, and represented at the global origin (i.e. 0.0 degrees latitude and 0.0 degrees longitude) -- and are quite small so zooming in will be required to see them in GIS software.</p>
Fig. 3 in Pteromalidae Of Lagodekhi Protected Areas With The Description Of A New Psilocera Species From Sakartvelo (Georgia)
Fig. 3. Lateral view of species belonging to the genus Trigonoderus Westwood, 1832 collected in the Lagodekhi protected areas: a = Trigonoderus cyanescens (Förster, 1841), b = T.
Fig. 2 in Pteromalidae Of Lagodekhi Protected Areas With The Description Of A New Psilocera Species From Sakartvelo (Georgia)
Fig. 2. Dorsal and lateral views of Cleonyminae species collected in the Lagodekhi protected areas: a–b = Cleonymus brevis Bouček, 1972, c–d = Oodera formosa (Giraud, 1863)
Fig. 5 in Pteromalidae Of Lagodekhi Protected Areas With The Description Of A New Psilocera Species From Sakartvelo (Georgia)
Fig. 5. Psilocera kartveli sp. n. paratype male: a = head in lateral view, b = antenna, c =, head in frontal view d = right side wings, e = mesosoma in dorsal view, f = mesosoma in lateral
Fig. 1 in Pteromalidae Of Lagodekhi Protected Areas With The Description Of A New Psilocera Species From Sakartvelo (Georgia)
Fig. 1. Phenology of the species Asaphes vulgaris (Hymenoptera, Pteromalidae) in the Lagodekhi Nature Reserve in year 2014, based on Malaise-trap data. Trap ID: H1 = low-altitude forest (alt. 666 m), H2 = mid-altitude forest (alt. 847 m), H3 = high-altitude forest (alt. 1351 m), H4 = subalpine forest (alt. 1841 m), H4-5 = subalpine forest (alt. 1902 m), H5 =
Fig. 4 in Pteromalidae Of Lagodekhi Protected Areas With The Description Of A New Psilocera Species From Sakartvelo (Georgia)
Fig. 4. Psilocera kartveli sp. n. holotype female: a = head in lateral view, b = head in frontal view, c = antenna, d = head in dorsal view, e = propodeum, f = mesosoma in lateral view, g = mesosoma in dorsal view, h = right side wings, i = gaster in dorsal view, j = habitus in lateral view
Measuring protected-area effectiveness using vertebrate distributions from leech iDNA
<p>Protected areas are key to meeting biodiversity conservation goals, but direct measures of effectiveness have proven difficult to obtain. We address this challenge by using environmental DNA from leech-ingested bloodmeals to estimate spatially-resolved vertebrate occupancies across the 677 km<sup>2</sup> Ailaoshan reserve in Yunnan, China. From 30,468 leeches collected by 163 park rangers across 172 patrol areas, we identify 86 vertebrate species, including amphibians, mammals, birds and squamates. Multi-species occupancy modelling shows that species richness increases with elevation and distance to reserve edge. Most large mammals (e.g. sambar, black bear, serow, tufted deer) follow this pattern; the exceptions are the three domestic mammal species (cows, sheep, goats) and muntjak deer, which are more common at lower elevations. Vertebrate occupancies are a direct measure of conservation outcomes that can help guide protected-area management and improve the contributions that protected areas make towards global biodiversity goals. Here, we show the feasibility of using invertebrate-derived DNA to estimate spatially-resolved vertebrate occupancies across entire protected areas.</p>
Рис. 3. КоΛичество фактически учтенных жиΛых гнезΑ в 2018–2019 гг. на особо охраняемых прироΑных территориях Амурской обΛасти Fig. 3. The actual number of inhabited nests in 2018–2019 in protected natural areas of the Amur region in Oriental stork (Ciconia boyciana Swinhoe) breeding population survey in the Amur region in 2018-2019
Рис. 3. КоΛичество фактически учтенных жиΛых гнезΑ в 2018–2019 гг. на особо охраняемых прироΑных территориях Амурской обΛасти Fig. 3. The actual number of inhabited nests in 2018–2019 in protected natural areas of the Amur region
FIGURE 2 in The effectiveness of protected areas in the Paraná-Paraguay basin in preserving multiple facets of freshwater fish diversity under climate change
FIGURE 2 | Paraná-Paraguay basin and the 17% of the area with the highest values of species richness (SR), functional richness (FRic), and phylogenetic diversity (PD), as well as the protected areas (PAs). A. SR, FRic, and PD, as well as their individual distribution for the current and future scenarios of climate change; B. the overlap between SR, FRic, and PD, as well as the protected areas in the Paraná-Paraguay basin, for the current and future scenarios of climate change C. The Venn diagrams showing the percentage of overlap between the components of fish diversity and the protected areas currently in the basin.
FIGURE 1 in The effectiveness of protected areas in the Paraná-Paraguay basin in preserving multiple facets of freshwater fish diversity under climate change
FIGURE 1 | Paraná-Paraguay basin showing countries' boundaries, topography, hydrographic features, and protected areas. 1. Upper Paraná River basin; 2. Middle Paraná River basin; 3. Lower Paraná basin; 4. Upper Paraguay basin; 5. Middle Paraguay basin; 6. Lower Paraguay basin.
Fig. 2 in Patterns of growth and natural mortality in Lysapsus bolivianus (Anura, Hylidae, Pseudae) in an environmental protection area in the estuary of the Amazon River
Fig. 2. Weight-length relationships in adult male and female Lysapsus bolivianus from the Rio Curiaú EPA on the estuary of the Amazon River, in northern Brazil.
Fig. 4 in Patterns of growth and natural mortality in Lysapsus bolivianus (Anura, Hylidae, Pseudae) in an environmental protection area in the estuary of the Amazon River
Fig. 4. Von Bertalanffy's growth curves for (A) male and (B) female Lysapsus bolivianus from the Rio Curiaú EPA in Amapá, Brazil.
ScienceDex guides
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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.