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125 results for “national assessment”
2007 Environmental Protection Agency (EPA) National Lakes Assessment dataset plus derived data and additional spatially explicit ancillary environmental data.
Lake water quality is known to be affected by local and regional drivers, including lake physical characteristics, hydrology, landscape position, land cover, land use, geology, and climate. Here, we demonstrate the utility of hypothesis testing within the landscape limnology conceptual framework using a random forest algorithm on large, national-scale, spatially explicit dataset, the United States Environmental Protection Agency 2007 National Lakes Assessment. For 1026 lakes, we tested the relative importance of water quality drivers across spatial scales, the importance of hydrologic connectivity in mediating water quality drivers, and how the importance of both spatial scale and connectivity differ across response variables for five important in-lake water quality metrics (total phosphorus, total nitrogen, dissolved organic carbon, turbidity, and conductivity).
Geographically paired lake-reservoir dataset derived from the 2007 USA EPA National Lakes Assessment
Climate change poses a significant threat to lake and reservoir ecosystems, though the exact nature of these threats may differ between lakes and reservoirs. To assess differences between lakes and reservoirs that may influence their response to climate change, we compared catchment and waterbody attributes of 132 geographically paired lakes and reservoirs from the 2007 United States Environmental Protection Agencys National Lakes Assessment (NLA) dataset. The data include the NLA IDs of each waterbody and their elevation, catchment area, surface area, perimeter, maximum depth, residence time, Secchi disk depth, surface temperature, and bottom temperature. Residence time data was collected from estimates generated by Brooks, J.R., J.J. Gibson, S.J. Birks, M.H. Weber, K.D. Rodecap, J.L. Stoddard. 2014. Stable isotope estimates of evaporation: inflow and water residence time for lakes across the United States as a tool for national lake water quality assessments. Limnology and Oceanography 59(6):2150-2165.
Comparative Bird Community Assessments in Grassland, Shrubland, and Woodland Habitats at the Sevilleta National Wildlife Refuge, New Mexico (1991-1997 and 2022-2023)
Across North America, avifauna abundance has declined by 30% since 1970 (Rosenberg, K.V. et al. 2019). Direct mortality from anthropogenic sources (pets, cars, collisions with building, power lines, wind turbines, etc.) and indirect mortality (habitat loss, disturbance, climate change, etc.) have both been major contributors to these declines (Loss, S.R. et al. 2015 and Calvert, A.M. et al. 2013). Variables such as migration patterns, family, breeding and non-breeding biomes show differing rates of decline (Rosenberg, K.V. et al. 2019). In New Mexico, there are three breeding biomes all classified with declining avian abundance. Avian abundance in grasslands has declined by 53.3% since 1970, western forests by 29.5% and arid lands by 17.0% (Rosenberg, K.V. et al. 2019). All three of these biomes also occur at the Sevilleta National Wildlife Refuge thus temporal declines in species richness and abundance are expected. This project was originally designed to sample the species richness and abundance of birds on the Sevilleta National Wildlife Refuge in three types of habitat: grassland, creosote shrubland and pinyon-juniper woodland. Surveys were conducted between January 1991 and May 1997 (Parmenter, R. 2016). Surveys were re-established in 2022 to document current species richness and abundance and to capture any temporal changes from the 90s data. Avian point count survey stations in grassland, creosote and pinyon-juniper habitats run through existing study sites which have all been subjected to intense research activity. Literature Cited A. M. Calvert, C. A. Bishop, R. D. Elliot, E. A. Krebs, T. M. Kydd, C. S. Machtans, G. J. Robertson, A synthesis of human-related avian mortality in Canada. Avian Conserv. Ecol. 8, art11 (2013). https://www.ace-eco.org/vol8/iss2/art11/ Loss, S. R., Will, T., Marra, P. P. 2015. Direct Mortality of Birds from Anthropogenic causes. Annu. Rev. Ecol. Evol. Syst. 46, 99–120. https://www.annualreviews.org/doi/10.1146/annurev-ecolsys-1124
Dataset linking to the publication "An assessment of data sources, data quality and changes in national forest monitoring capacities in the Global Forest Resources Assessment 2005–2020"
<p>This dataset links to the study “An assessment of data sources, data quality and changes in national forest monitoring capacities in the Global Forest Resources Assessment 2005–2020”. This study is published in the journal “Environmental Research Letters” which can be found at <a href="https://iopscience.iop.org/article/10.1088/1748-9326/abd81b">https://iopscience.iop.org/article/10.1088/1748-9326/abd81b</a>. The dataset contains two files, one csv file, and one shape file. The two files contain the same data to meet the different users' needs. The dataset contains variables for assessing national forest monitoring data sources i.e., RS and/or NFI. Separate indicators namely 'Use of RS', and 'Use of NFI' were used to analyze the two data sources (RS and NFI). The description of each variable for these two indicators contained in the dataset is given in the Table below.</p> <table> <caption><strong>The description of the variables in the datase</strong>t <strong>for country capacity assessment</strong></caption> <tbody> <tr> <td><strong>Variables Name</strong></td> <td><strong>Description of the variables</strong></td> </tr> <tr> <td>Country</td> <td>Country</td> </tr> <tr> <td>ISO_A3_CODE</td> <td>ISO A3 Code for country</td> </tr> <tr> <td>ADM0_CODE</td> <td>ADMO Code for country</td> </tr> <tr> <td>CONTINENT</td> <td>Continent</td> </tr> <tr> <td>Region</td> <td>Region</td> </tr> <tr> <td>RSInd_05</td> <td>Use of remote sensing (RS) for forest area (change) monitoring 2005 Indicator</td> </tr> <tr> <td>RSSc_05</td> <td>Use of RS for forest area (change) monitoring 2005 Score</td> </tr> <tr> <td>RSInd _10</td> <td>Use of RS for forest area (change) monitoring 2010 Indicator</td> </tr> <tr> <td>RSSc _10</td> <td>Use of RS for forest area (change) monitoring 2010 Score</td> </tr> <tr> <td>RSInd_15</td> <td>Use of RS for forest area (change) monitoring 2015 Indicator</td> </tr> <tr> <td>RSSc _15</td> <td>Use of RS for forest area (change) monitoring 2015 Score</td> </tr> <tr> <td>RSInd_20</td> <td>Use of RS for forest area (change) monitoring 2020 Indicator</td> </tr> <tr> <td>RSSc _20</td> <td>Use of RS for forest area (change) monitoring 2020 Score</td> </tr> <tr> <td>DRS05_20</td> <td>Difference ‘use of RS’ 2005-2020</td> </tr> <tr> <td>NFIInd_05</td> <td>Use of national forest inventories (NFI) for forest monitoring 2005 Indicator</td> </tr> <tr> <td>NFISc_05</td> <td>Use of NFI for forest monitoring 2005 Score</td> </tr> <tr> <td>NFIInd _10</td> <td>Use of NFI for forest monitoring 2010 Indicator</td> </tr> <tr> <td>NFISc _10</td> <td>Use of NFI for forest monitoring 2010 Score</td> </tr> <tr> <td>NFIInd_15</td> <td>Use of NFI for forest monitoring 2015 Indicator</td> </tr> <tr> <td>NFISc _15</td> <td>Use of NFI for forest monitoring 2015 Score</td> </tr> <tr> <td>NFIInd_20</td> <td>Use of NFI for forest monitoring 2020 Indicator</td> </tr> <tr> <td>NFISc _20</td> <td>Use of NFI for forest monitoring 2020 Score</td> </tr> <tr> <td>DNFI05_20</td> <td>Difference ‘Use of NFI’ 2005-2020</td> </tr> </tbody> </table> <p>Indicators and Scores in the above Table for showing the use of RS and NFI data for forest monitoring in Figure 1 (1a, 1b, and 2a, 2b) are related in the following way.</p> <table> <caption><strong>The indicator values and scores of the country capacity assessment</strong></caption> <tbody> <tr> <td><strong>Indicator</strong></td> <td><strong>Score</strong></td> </tr> <tr> <td>Low</td> <td>0</td> </tr> <tr> <td>Limited</td> <td>1</td> </tr> <tr> <td>Intermediate</td> <td>2</td> </tr> <tr> <td>Good</td> <td>3</td> </tr> <tr> <td>Very Good</td> <td>4</td> </tr> </tbody> </table> <p>The capacity changes from 2005 to 2020 in Figure 1 (1c & 2c) are related in the following way.</p> <table> <caption><strong>The indicator values and levels for country capacity changes</strong></caption> <tbody> <tr> <td><strong>Capacity change values</strong></td> <td><strong>Capacity change levels</strong></td> </tr> <tr> <td>1,2,3,4</td> <td>Increase</td> </tr> <tr> <td>0</td> <td>No change</td> </tr> <tr> <td>-1,-2,-3,-4</td> <td>Decrease</td> </tr> </tbody> </table> <p> </p>
Using Biodiversity Data to Assess Species-Habitat Relationships in Glacier National Park, Montana
Biodiversity surveys are becoming increasingly popular. However, standard analysis techniques for these data have not yet been developed. This paper explores the use of multivariate ordination techniques for assessing species-habitat relationships using biodiversity data. The research was conducted in Glacier National Park, Montana, and birds and butterflies were chosen as the taxonomic groups of study. Biodiversity assessment sites were established through a range of habitats and monitored from 1987 through 1989. Presence/absence sampling over the total number of sampling sites was used to classify species commonness and rarity. Approximately 86% of the historically recorded butterflies and 70% of the historically recorded bird species have been observed in the 3 yr of sampling. During the 3 yr of this study there was a striking continuity of species richness per site. There was also a striking overlap between the sites that support high species diversity and sites that support rare species. Principal components analysis and cluster analysis worked well in discerning species-habitat relationships. Elevation, structural diversity of the site, and moisture were the major factors explaining species distributions. A chi-square analysis also provided some insights into species-habitat relationships, showing birds were more habitat specific than butterflies. Habitat diversity analyses demonstrated a positive but non-significant correlation between remotely sense spectral-class diversity of a site and species richness for both birds and butterflies. Aspect, slope and elevation diversity had a negative or negligible relationship with species richness.
Decarbonization scenario database for the Fifth National Climate Assessment (NCA5)
<p>The decarbonization scenario database for the U.S. Fifth National Climate Assessment (NCA5) forms part of the evidence base for the Mitigation chapter of the assessment. The database contains scenarios that were submitted in response to an open call for contributions for U.S. scenarios that reach net-zero emissions across the economy by midcentury. These scenarios inform the figures and text in the Mitigation chapter about decarbonization pathways for the U.S.</p><p>Visit the interactive NCA5 Scenario Explorer at <a href="https://data.ece.iiasa.ac.at/nca5">https://data.ece.iiasa.ac.at/nca5</a>.</p>
Fig. 6 in Hornets (Vespidae: Vespinae) of Singapore: ecology, identification, and national conservation assessment
Fig. 6. Nests of Vespa affinis in Singapore. A & B, mature nest in a tree and on a metal beam of a plant nursery respectively, C, lateral view of an embryo nest, D, multiple queens in an early embryo nest. (Photographs: Kam-Yung Soh, John Lee, and Zestin Soh).
Fig. 8 in Hornets (Vespidae: Vespinae) of Singapore: ecology, identification, and national conservation assessment
Fig. 8. Vespa analis in Singapore. A: dorsal view of a worker visiting flowers of Syzygium zeylanicum; B: mature nest built on a Caryota mitis palm; C: lateral view of a worker. (Photographs: Zestin Soh and Janice Ang).
Fig. 1 in Hornets (Vespidae: Vespinae) of Singapore: ecology, identification, and national conservation assessment
Fig. 1. Provespa anomala in Singapore. A, worker resting in a public lavatory in a forested park; B, worker capturing a small insect at a light trap at night; C, a swarm congregating under a shelter in a probable swarm-founding event; D, unusual occurrence of a worker attracted to shrimp paste in the day. (Photos: Marcus Ng, John Lee).
Fig. 3 in Hornets (Vespidae: Vespinae) of Singapore: ecology, identification, and national conservation assessment
Fig. 3. Vespa affinis in Singapore. A: worker capturing a flower-visiting sweat bee (Lasioglossum sp.); B: workers scavenging from a dead fish left behind at a fishing ground; C: worker with an insect caught in a spider's web; D: worker nectaring from flowers of Leea rubra. (Photos: Lim Yu Jun, Marcus Ng and Zestin Soh).
Fig. 7. The hoverfly Milesia vespoides, a in Hornets (Vespidae: Vespinae) of Singapore: ecology, identification, and national conservation assessment
Fig. 7. The hoverfly Milesia vespoides, a Batesian mimic of Vespa affinis and V. tropica, at Windsor Nature Park in January 2022. (Photograph by Yue Teng Lee).
Fig. 10 in Hornets (Vespidae: Vespinae) of Singapore: ecology, identification, and national conservation assessment
Fig. 10. Vespa multimaculata feeding on inflorescences of a palm (Arecaceae) in Selangor, Malaysia in September 2022. (Photographs: Mike Hooper).
Fig. 4 in Hornets (Vespidae: Vespinae) of Singapore: ecology, identification, and national conservation assessment
Fig. 4. Comparison of the clypeus of Vespa affinis (A) and V. tropica (B). The white arrows point to the clypeal apices, bordering a medial emargination, which are semi-circular protuberances in V. affinis but more strongly produced as triangular projections in V. tropica. (Photographs: John Lee).
National SDG-7 performance assessment to support achieving sustainable energy for all within planetary limits
<p>Supplementary materials for publication Gebara et al. 2022, "National SDG-7 performance assessment to support achieving sustainable energy for all within planetary limits". </p>
Fig. 1 in Conservation status of Amphibians of Argentina: An update and evaluation of national assessments
Fig. 1. Map of the ecoregions of Argentina from Burkart et al. 1999. Numbers indicate ecoregions divisions as follows: (1) Puna, (2) High Andean, (3) Yungas Forest, (4) Dry Chaco, (5) Humid Chaco, (6) Delta and Islands of the Parana River, (7) Esteros of Ibera, (8) Paranaen Forest, (9) Campos and Malezales, (10) Espinal, (11) Pampas, (12) Monte of Sierras and Bolsones, (13) Monte de Llanuras and Mesetas, (14) Patagonian Steppe, (15) Patagonian Woodlands. Arrows indicate the four overthreatened ecoregions.
Figure 3. A & B in A rapid biodiversity assessment of Lesotho's first proposed Biosphere Reserve: a case study of Bokong Nature Reserve and Tšehlanyane National Park
Figure 3. A & B, the spectacular scenery of mountaineous landscapes forming part of the proposed BR; C, the endemic spiral aloe; D, the near-endemic Lesotho lily; E, the Lesotho red-hot poker; F, locally uncommon fern – bracken; G, endemic Maloti minnow; H, common eland; and I, its presence on rock paintings; J, some of the wetlands in the BNR; K, some of them damaged by diggings of Sloggett's ice rat (Source: K. Kobisi).
Figure 1. A & B in A rapid biodiversity assessment of Lesotho's first proposed Biosphere Reserve: a case study of Bokong Nature Reserve and Tšehlanyane National Park
Figure 1. A & B, Lesotho's first proposed Biosphere Reserve, showing the core (Tšehlanyane National Park and Bokong Nature Reserve), buffer and transition zones (Source: T. Leballo).
Figure 2 in A rapid biodiversity assessment of Lesotho's first proposed Biosphere Reserve: a case study of Bokong Nature Reserve and Tšehlanyane National Park
Figure 2. Transect walks covered during the different phases of the biodiversity survey (data collection) in the proposed Biosphere Reserve (Source: T. Leballo).
Assessing Negative Carbon Dioxide Emissions from the Perspective of a National 'Fair Share' of the Remaining Global Carbon Budget: Supplementary Material
<p>Detailed calculations supporting the results in the published paper, <em>Assessing Negative Carbon Dioxide Emissions from the Perspective of a National 'Fair Share' of the Remaining Global Carbon Budget</em>, <a href="https://link.springer.com/journal/11027">Mitigation and Adaptation Strategies for Global Change</a>, DOI: <a href="https://doi.org/10.1007/s11027-019-09881-6">10.1007/s11027-019-09881-6</a>.</p> <ul> <li><strong>IE-CO2-Quota-2015.ods</strong>: Spreadsheet/workbook in <a href="http://opendocumentformat.org/">Open Document</a> format. Includes table and charts as presented in the paper. Prepared using <a href="http://www.libreoffice.org">LibreOffice</a> (v 5.0+). Should also be accessible also in Microsoft Excel, but some formatting or functionality may be lost.</li> <li><strong>IE-CO2-Quota-2015.ipynb</strong>: Mathematical background and cross-check of detailed calculations in interactive <a href="https://jupyter.org/">Jupyter notebook</a> format (coding in <a href="https://www.python.org/">python</a>).</li> <li><strong>IE-CO2-Quota-2015-ipynb.html</strong>: Static HTML version of the <strong>IE-CO2-Quota-2015.ipynb</strong> suitable for simple viewing/printing.</li> <li><strong>IE-CO2-Quota-2015-ipynb.pdf</strong>: Static version of the <strong>IE-CO2-Quota-2015.ipynb</strong> suitable for simple viewing/printing.</li> </ul>
Linked collectors and determiners for: Aquatic Insects from the Caatinga: checklists and diversity assessments of Ubajara (Ceará State) and Sete Cidades (Piauí State) National Parks, Northeastern Brazil.
Natural history specimen data linked to collectors and determiners held within, "Aquatic Insects from the Caatinga: checklists and diversity assessments of Ubajara (Ceará State) and Sete Cidades (Piauí State) National Parks, Northeastern Brazil". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/0f243a11-1cef-442e-98be-a743ace6085f">https://bionomia.net/dataset/0f243a11-1cef-442e-98be-a743ace6085f</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/0f243a11-1cef-442e-98be-a743ace6085f">https://gbif.org/dataset/0f243a11-1cef-442e-98be-a743ace6085f</a>. Formatted as a Frictionless Data package.
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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.