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136 results for “rangeland”
Insights on global rangeland ecosystem services shaped by grazing and fertilization (2007-2021)
The Nutrient Network (NutNet) is a globally coordinated research initiative aimed at investigating the impacts of human-induced changes in nutrient availability and consumer presence on grassland ecosystems. In this study, we used data from 79 grassland sites participating in NutNet, which includes a factorial experiment involving herbivory exclusion and/or nutrient addition. Standardized methodologies were applied across all sites to facilitate direct comparisons of response variables. We used ecosystem variables to quantify three provisioning ecosystem services (forage quantity, forage chemical quality, and forage physical quality), three supporting services (forage stability, soil fertility, and soil stability), and eight regulating services (erosion control, control of soil acidification, regulation of water quantity and quality, carbon storage, resistance to plant invasion, pest control, and pollination). Additionally, we identified three plant biodiversity variables that are closely related to the provisioning of ecosystem services (alpha richness, beta diversity, and native diversity). Using this data, we quantified key ecosystem services provided by rangelands, assessed both short- and long-term impacts of grazing exclusion and fertilization on these services, and identified synergies and trade-offs between them.
Compost Amendments up to One Inch Restore Dry Rangeland Soil Health and Plant Productivity in New Mexico, 2020-2022
Dry rangelands are important systems for coproducing food and other ecosystem services, but degradation of productivity, diversity, and water holding capacity may require active intervention to restore. Use of compost amendments on grasslands has been shown to improve many outcomes related to carbon, water, and nutrients unless excessive amounts are used, but practitioners lack guidance of optimal and cost-effective use to meet their management goals. We compared compost additions from 0-2.54 cm at two ranches in New Mexico and measured plant composition and biomass, soil characteristics such as bulk density, infiltration rate, aggregate stability, and total carbon content under baseline conditions and one- and two years after addition.
WER01 Elevated CO2 counteracts effects of water stress on woody rangeland-encroaching species at Konza Prairie
Woody plants are increasing prevalence and dominance in many rangelands around the world. The reason for their increase is various but two common drivers that have changed are an increase in CO2 concentrations and alteration to precipitation dynamics. We asked what the physiological growth dynamics of four juvenile woody plant species (Cornus drummondii, Rhus glabra, Gleditsia triacanthos and Juniperus osteosperma) when grown in elevated CO2 and chronically water stressed. We found that elevated CO2 counteracts much of the physiological effects of chronic water stress in the four different woody plant species measured. The alleviation of water stress from increased CO2 concentrations will result in juvenile woody plants continuing to expand and establish in North American rangelands. This information will aid land managers in making long-term management objectives for reducing woody plants in rangelands.
Amendments and seeding did not augment erosion control structure effectiveness in dry rangelands, 2021-2023
This study investigates the effectiveness of combining rock structures with organic amendments (wood mulch or compost) and native perennial grass seed addition to address erosion on rangelands. The study was conducted across five cattle ranches in New Mexico with 9-18 active head cuts studied at each ranch. Rock rundown structures were built at each headcut and a plot above each structure received an organic amendment treatment (compost, mulch, or control) and seed addition treatment (seeded or control).
Data for 'Weak latitudinal gradients in insect herbivory for dominant rangeland grasses of North America'
Data for Kent et al. Accepted manuscript in Ecology and Evolution, with abstract: Patterns of insect herbivory may follow predictable geographical gradients, with greater herbivory at low latitudes. However, biogeographic studies of insect herbivory often do not account for multiple abiotic factors (e.g. precipitation, soil nutrients) that could underlie gradients. We tested for latitudinal clines in insect herbivory as well as climatic, edaphic, and trait-based drivers of herbivory. We quantified herbivory on five dominant grass species over 23 sites across the Great Plains, USA. We examined the importance of climate, edaphic factors, and traits as correlates of herbivory. Herbivory increased at low latitudes when all grass species were analysed together and for two grass species individually, while two other grasses trended in this direction. Higher precipitation was related to more herbivory for two species but less herbivory for a different species, while higher specific root length was related to more herbivory for one species and less herbivory for a different species. Taken together, results highlight that climate and trait-based correlates of herbivory can be highly contextual and species specific. Patterns of insect herbivory on dominant grasses supports the hypothesis that herbivory increases towards lower latitudes, though weakly, and indicates that climate change may have species-specific effects on plant-herbivore interactions.
Map of ecological sites and ecological states for pastures 1, 4, 14, and 15 on the Chihuahuan Desert Rangeland Research Center, New Mexico
This data package includes an ArcMap geodatabase for the Chihuahuan Desert Rangeland Research Center (CDRRC) pastures 1, 4, 14, and 15: one polygon feature class, one point feature class, associated attribute tables and metadata. The spatial data, CDRRC1_4_14_15_StateMap_v1.gdb.zip, represents the ecological sites and states on Pastures 1, 4, 14 and 15 on the Chihuahuan Desert Rangeland Research Center, and includes field traverse data. CDRRC1_4_14_15_StateMapMetadata.pdf and TraversePointsMetadata.pdf contain the geospatial metadata provided by ArcMap. CDRRC1_4_14_15_StateMap_v1.csv is the attribute table associated with the state map’s polygon feature class, and TraversePoints.xlsx is the attribute table associated with the traverse points feature class and includes a sheet containing detailed attribute metadata.
Data from: A burning issue: Savanna fire management can generate enough carbon revenue to help restore Africa's rangelands and fill Protected Area funding gaps
<p>Many savanna-dependent species in Africa including large herbivores and apex predators are at increasing risk of extinction. Achieving effective management of protected areas (PAs) in Africa where lions live will cost an estimated USD >$1-2 B/year in new funding. We explored the potential for fire management-based carbon-financing programs to fill this funding gap and benefit degrading savanna ecosystems. We demonstrated how introducing early dry season fire management programs could produce potential carbon revenues (PCR) from either a single carbon-financing method (avoided emissions) or from multiple sequestration methods ranging from USD $59.6-$655.9 M/year (at USD $5/ton) or USD $155.0 M–$1.7 B/year (at USD $13/ton). We highlighted variable but significant PCR for savanna PAs from USD $1.5–$44.4 M/year per PA. We suggest investing in fire management programs to jump-start the United Nations Decade of Ecological Restoration to help restore degraded African savannas and conserve imperiled keystone herbivores and apex predators. <br> <br> Open Access article: <a href="https://doi.org/10.1016/j.oneear.2021.11.013">https://doi.org/10.1016/j.oneear.2021.11.013</a></p>
UAV outputs and associated field measurement of the herbaceous of a Sahelian Rangeland during the wet season in Northern Senegal
<p>This dataset contains UAV outputs (mosaic and digital surface model) and field measurement of vegetation (shapefile) that were made in northern Senegal.</p> <p><strong>Site gradient measurement</strong></p> <p>The data was collected on a plot of the Centre of Zootechnical Researches of Dahra / ISRA during 2020 rainy season (from July 19, 2020, to September 17, 2020). The average rainfall for the period 1981-2018 was ranging from 221 mm.y-1 to 468 mm. y-1. The vegetation in the field is a herbaceous savannah where <em>Vachellia tortilis</em> and <em>Balanites aegyptiaca</em> are the dominant trees.</p> <p><strong>Field measurement.</strong></p> <p><strong>UAV flight plan</strong></p> <p>We used two different drones : Bluegrass and Anafi of Parrot. The Bluegrass of Parrot was used from 19/07/2020 to 04/08/2020. The Bluegrass flights were done at 60 meters of altitude, with a speed of 2 m s<sup>-1</sup>, and 90% of overlap rate between images, on a double grid of 100 m x 100 m. Anafi of Parrot was used for the rest of the season. The Anafi flights were done at 60 meters of altitude, with a speed of 2 m s<sup>-1</sup>, and 90% of overlap rate between images, on a double grid of 100 m x 100 m and the angle of inclination of the camera fixed at 80°. The flights have been done with PIX4D capture application at earlier in the day every two days. A total of 61 drone flights were conducted over the rainy season.</p> <p><strong>Herbaceous Biomass</strong></p> <p>Every two days , after drone flight, herbaceous measurements were carried out, in three plots of 1 m² distributed respectively under the crown of a tree, at the edge of the crown, and at a distance from the edge of the crown equal to the height of the tree. These plots were rotated among the trees in the field until all four azimuths of trees were covered.We collected Fresh mass and dry mass.</p> <p><strong>Image analysis.</strong></p> <p>The drone images taken for each day of collect, were analyzed in the software PIX4DMapper (Pix4D SA, Lausanne, Switzerland) by the Structure from Motion method. We used precisely the 3D mapping option of the software. Then for each flight we computed and exported an orthophotograph and a digital surface model.</p> <p><strong>Data organization</strong></p> <p>The data contains :</p> <ul> <li>DSM that contains the surface model in tiff</li> <li>Mosaic that the orthomosaic in tiff.</li> <li>Data that contains the shapefile with the position and table with the field measurements</li> </ul>
Herbaceous production lost to tree encroachment in United States rangelands
<p>Data products and modeling code supporting the publication:</p> <p><strong>Herbaceous production lost to tree encroachment in United States rangelands</strong> in the <em>Journal of Applied Ecology</em>.</p> <p>Manuscript DOI: 10.1111/1365-2664.14288</p> <p><strong>Abstract</strong></p> <ol> <li>Rangelands of the United States provide ecosystem services that benefit society and rural economies. Native tree encroachment is often overlooked as a primary threat to rangelands due to the slow pace of tree cover expansion and the positive public perception of trees. Still, tree encroachment fragments these landscapes and reduces herbaceous production, thereby threatening habitat quality for grassland wildlife and the economic sustainability of animal agriculture. </li> <li>Recent innovations in satellite remote sensing permit the tracking of tree encroachment and the corresponding impact on herbaceous production. We analyzed tree cover change and herbaceous production across the western United States from 1990 to 2019.</li> <li>We show that tree encroachment is widespread in U.S. rangelands; absolute tree cover has increased by 50% (77,323 km<sup>2</sup>) over 30 years, with more than 25% (684,852 km<sup>2</sup>) of U.S. rangeland area experiencing tree cover expansion. Since 1990, 302 ± 30 Tg of herbaceous biomass have been lost. Accounting for variability in livestock biomass utilization and forage value reveals that this lost production is valued at between $4.1 - $5.6 billion U.S. dollars.</li> <li>Synthesis and applications: The magnitude of impact of tree encroachment on rangeland loss is similar to conversion to cropland, another well-known and primary mechanism of rangeland loss in the U.S. Prioritizing conservation efforts to prevent tree encroachment can bolster ecosystem and economic sustainability, particularly among privately-owned lands threatened by land-use conversion.</li> </ol> <p><strong>Description</strong></p> <p>This archive contains data products and modeling code for production loss and tree cover change estimates provided in the accompanying refereed publication. The easiest way to view and use these data is in Google Earth Engine:</p> <ul> <li><a href="https://smorford.users.earthengine.app/view/yield-gap">https://smorford.users.earthengine.app/view/yield-gap</a></li> <li><a href="https://code.earthengine.google.com/8ad19c7f7a6e04b377953326b274f98d">https://code.earthengine.google.com/8ad19c7f7a6e04b377953326b274f98d</a></li> </ul> <p>Summary data products are included in the <em>data-products</em> folder, and include links and scripts to download all annual data discussed in the manuscript. The full dataset is roughly 660GB and cannot be achieved on Zonodo as of summer 2022.</p> <p>Similarly, the <em>model</em> directory contains the primary codebase for processing raw tree cover data and running XGBoost modeling training and inference for the production loss model. To recreate the production loss data will require downloading approximately 900GB of biomass data and 250GB of tree cover data; total project size will be approximate 1.8 TB after inference.</p> <p>Data can also be downloaded directly from the University of Montana web servers:</p> <ul> <li><a href="http://rangeland.ntsg.umt.edu/data/rap/rap-vegetation-biomass/v2/">http://rangeland.ntsg.umt.edu/data/rap/rap-vegetation-biomass/v2/</a></li> <li><a href="http://rangeland.ntsg.umt.edu/data/rap/rap-derivatives/yield-gap/v1/">http://rangeland.ntsg.umt.edu/data/rap/rap-derivatives/yield-gap/v1/</a></li> </ul> <p><strong>Journal citation:</strong></p> <p>Morford, S.L., Allred, B.W., Twidwell, D., Jones, M.O., Maestas, J.D., Roberts, C.P. and Naugle, D.E., <em>Accepted</em>. Herbaceous production lost to tree encroachment in United States rangelands. <em>Journal of Applied Ecology</em>, August 2022.</p>
Dry perennial herbaceous community data of Castril, Santiago and Pontones high-altitude rangelands in Andalusia (Spain)
<p>Data generated from vegetation monitoring in Castril, Santiago and Pontones rangelands situated in Sierra de Segura and Sierra de Castril in North-Eastern Andalusia (Spain).</p> <p>These data and R Script are linked to the article <em>How transhumance and pastoral commons shape plant community structure and composition. </em></p> <p>The plant community sampled belongs to <em>Festuco hystricis-Ononidetea striatae</em> class, consisting of <em>Coronillo minimae-Astragaletum nummularioidis</em> and <em>Seseli granatensis-Festucetum hystricis</em> plant associations. Plant data community consists in 72 vegetation transects. For further details see the article or contact S.A. Parra (santiago.parra-bulacio@etu.univ-amu.fr).</p> <p>File “Read_me.txt” details the databases and R script published.</p>
Landsat-derived rangeland condition indicators in East Africa from 2000 to 2022
<p>Tracking environmental change is important to ensure efficient and sustainable natural resources management. East Africa is dominated by arid and semi-arid rangeland systems, where extensive grazing of livestock represents the primary livelihood for most of the human population. Despite several mapping efforts, East Africa lacks accurate and reliable high-resolution rangeland health maps necessary for management, policy, and research purposes. Earth Observations offer the opportunity to assess spatiotemporal dynamics in rangeland health conditions at much higher spatial and temporal coverage than conventional approaches that rely on in-situ methods, while complimenting their certainty. Using machine learning-based classification and linear unmixing, this paper produced Landsat-based time series at 30 m spatial resolution for mapping of land cover classes (LCC) and vegetation fractional cover (VFC, including photosynthetic vegetation PV, non-photosynthetic vegetation NPV, and bare ground BG), two major data assets to derive metrics for rangeland health in East Africa. Due to scarcity of in-situ measurements in a large, remote and highly heterogeneous landscape, an algorithm was developed to combine very high-resolution WorldView-2 and -3 satellite imagery at < 2 m resolutions with a limited set of ground observations to generate reference labels across the study region. The LCC analysis yielded an overall accuracy of 0.856 using our validation dataset, with Kappa of 0.832; VFC, yielded R<sup>2</sup> = 0.801, <em>p</em> < 2.2e-16, normalized root mean squared error (nRMSE) = 0.123. Our products represent the first multi-decadal high-resolution dataset specifically designed for mapping and monitoring rangelands health in East Africa including Kenya, Ethiopia and Somalia, covering a total area of 745,840 km<sup>2</sup>, dominated by arid and semi-arid extensive rangeland systems. These data can be valuable to a wide range of development, humanitarian, and ecological conservation efforts and are available at https://doi.org/10.5281/zenodo.7106166 and Google Earth Engine (GEE; details in data availability section).</p>
Supplementing enhanced weathering with organic amendments accelerates the net climate benefit in rangeland soils
Open the record for dataset details and reuse information.
Rangeland Analysis Platform - vegetation cover 1988
<p>Rangeland Analysis Platform vegetation cover 1988</p> <p>These data represent rangeland cover estimates determined by Jones et al. (2018)<br> and as accessible on the Rangeland Analysis Platform (https://rangelands.app).<br> Values are percent aerial cover of the following rangeland functional groups:</p> <p>Band 1 - annual forbs and grasses<br> Band 2 - bare ground<br> Band 3 - litter<br> Band 4 - perennial forbs and grasses<br> Band 5 - shrubs<br> Band 6 - trees<br> No Data value = 255</p> <p>Although these data were produced across a broad region, they are primarily<br> intended for rangeland ecosystems. Cover estimates may not be suitable in other<br> ecosystems, e.g., forests, agricultural lands.</p> <p>Data are in WGS84 Geographic Coordinate System (EPSG:4326); spatial resolution<br> is approximately 30m.</p> <p>Please attribute these data to:<br> Jones, M. O., B. W. Allred, D. E. Naugle, J. D. Maestas, P. Donnelly, L. J.<br> Metz, J. Karl, R. Smith, B. Bestelmeyer, C. Boyd, J. D. Kerby, and J. D. McIver.<br> 2018. Innovation in rangeland monitoring: annual, 30 m, plant functional type<br> percent cover maps for U.S. rangelands, 1984-2017. Ecosphere 9:e02430.<br> http://dx.doi.org/10.1002/ecs2.2430</p>
Rangeland Analysis Platform - vegetation cover 1990
<p>Rangeland Analysis Platform vegetation cover 1990</p> <p>These data represent rangeland cover estimates determined by Jones et al. (2018)<br> and as accessible on the Rangeland Analysis Platform (https://rangelands.app).<br> Values are percent aerial cover of the following rangeland functional groups:</p> <p>Band 1 - annual forbs and grasses<br> Band 2 - bare ground<br> Band 3 - litter<br> Band 4 - perennial forbs and grasses<br> Band 5 - shrubs<br> Band 6 - trees<br> No Data value = 255</p> <p>Although these data were produced across a broad region, they are primarily<br> intended for rangeland ecosystems. Cover estimates may not be suitable in other<br> ecosystems, e.g., forests, agricultural lands.</p> <p>Data are in WGS84 Geographic Coordinate System (EPSG:4326); spatial resolution<br> is approximately 30m.</p> <p>Please attribute these data to:<br> Jones, M. O., B. W. Allred, D. E. Naugle, J. D. Maestas, P. Donnelly, L. J.<br> Metz, J. Karl, R. Smith, B. Bestelmeyer, C. Boyd, J. D. Kerby, and J. D. McIver.<br> 2018. Innovation in rangeland monitoring: annual, 30 m, plant functional type<br> percent cover maps for U.S. rangelands, 1984-2017. Ecosphere 9:e02430.<br> http://dx.doi.org/10.1002/ecs2.2430</p>
Rangeland Analysis Platform - vegetation cover 1986
<p>Rangeland Analysis Platform vegetation cover 1986</p> <p>These data represent rangeland cover estimates determined by Jones et al. (2018)<br> and as accessible on the Rangeland Analysis Platform (https://rangelands.app).<br> Values are percent aerial cover of the following rangeland functional groups:</p> <p>Band 1 - annual forbs and grasses<br> Band 2 - bare ground<br> Band 3 - litter<br> Band 4 - perennial forbs and grasses<br> Band 5 - shrubs<br> Band 6 - trees<br> No Data value = 255</p> <p>Although these data were produced across a broad region, they are primarily<br> intended for rangeland ecosystems. Cover estimates may not be suitable in other<br> ecosystems, e.g., forests, agricultural lands.</p> <p>Data are in WGS84 Geographic Coordinate System (EPSG:4326); spatial resolution<br> is approximately 30m.</p> <p>Please attribute these data to:<br> Jones, M. O., B. W. Allred, D. E. Naugle, J. D. Maestas, P. Donnelly, L. J.<br> Metz, J. Karl, R. Smith, B. Bestelmeyer, C. Boyd, J. D. Kerby, and J. D. McIver.<br> 2018. Innovation in rangeland monitoring: annual, 30 m, plant functional type<br> percent cover maps for U.S. rangelands, 1984-2017. Ecosphere 9:e02430.<br> http://dx.doi.org/10.1002/ecs2.2430</p>
Rangeland Analysis Platform - vegetation cover 1991
<p>Rangeland Analysis Platform vegetation cover 1991</p> <p>These data represent rangeland cover estimates determined by Jones et al. (2018)<br> and as accessible on the Rangeland Analysis Platform (https://rangelands.app).<br> Values are percent aerial cover of the following rangeland functional groups:</p> <p>Band 1 - annual forbs and grasses<br> Band 2 - bare ground<br> Band 3 - litter<br> Band 4 - perennial forbs and grasses<br> Band 5 - shrubs<br> Band 6 - trees<br> No Data value = 255</p> <p>Although these data were produced across a broad region, they are primarily<br> intended for rangeland ecosystems. Cover estimates may not be suitable in other<br> ecosystems, e.g., forests, agricultural lands.</p> <p>Data are in WGS84 Geographic Coordinate System (EPSG:4326); spatial resolution<br> is approximately 30m.</p> <p>Please attribute these data to:<br> Jones, M. O., B. W. Allred, D. E. Naugle, J. D. Maestas, P. Donnelly, L. J.<br> Metz, J. Karl, R. Smith, B. Bestelmeyer, C. Boyd, J. D. Kerby, and J. D. McIver.<br> 2018. Innovation in rangeland monitoring: annual, 30 m, plant functional type<br> percent cover maps for U.S. rangelands, 1984-2017. Ecosphere 9:e02430.<br> http://dx.doi.org/10.1002/ecs2.2430</p>
Rangeland Analysis Platform - vegetation cover 1994
<p>Rangeland Analysis Platform vegetation cover 1994</p> <p>These data represent rangeland cover estimates determined by Jones et al. (2018)<br> and as accessible on the Rangeland Analysis Platform (https://rangelands.app).<br> Values are percent aerial cover of the following rangeland functional groups:</p> <p>Band 1 - annual forbs and grasses<br> Band 2 - bare ground<br> Band 3 - litter<br> Band 4 - perennial forbs and grasses<br> Band 5 - shrubs<br> Band 6 - trees<br> No Data value = 255</p> <p>Although these data were produced across a broad region, they are primarily<br> intended for rangeland ecosystems. Cover estimates may not be suitable in other<br> ecosystems, e.g., forests, agricultural lands.</p> <p>Data are in WGS84 Geographic Coordinate System (EPSG:4326); spatial resolution<br> is approximately 30m.</p> <p>Please attribute these data to:<br> Jones, M. O., B. W. Allred, D. E. Naugle, J. D. Maestas, P. Donnelly, L. J.<br> Metz, J. Karl, R. Smith, B. Bestelmeyer, C. Boyd, J. D. Kerby, and J. D. McIver.<br> 2018. Innovation in rangeland monitoring: annual, 30 m, plant functional type<br> percent cover maps for U.S. rangelands, 1984-2017. Ecosphere 9:e02430.<br> http://dx.doi.org/10.1002/ecs2.2430</p>
Rangeland Analysis Platform - vegetation cover 1995
<p>Rangeland Analysis Platform vegetation cover 1995</p> <p>These data represent rangeland cover estimates determined by Jones et al. (2018)<br> and as accessible on the Rangeland Analysis Platform (https://rangelands.app).<br> Values are percent aerial cover of the following rangeland functional groups:</p> <p>Band 1 - annual forbs and grasses<br> Band 2 - bare ground<br> Band 3 - litter<br> Band 4 - perennial forbs and grasses<br> Band 5 - shrubs<br> Band 6 - trees<br> No Data value = 255</p> <p>Although these data were produced across a broad region, they are primarily<br> intended for rangeland ecosystems. Cover estimates may not be suitable in other<br> ecosystems, e.g., forests, agricultural lands.</p> <p>Data are in WGS84 Geographic Coordinate System (EPSG:4326); spatial resolution<br> is approximately 30m.</p> <p>Please attribute these data to:<br> Jones, M. O., B. W. Allred, D. E. Naugle, J. D. Maestas, P. Donnelly, L. J.<br> Metz, J. Karl, R. Smith, B. Bestelmeyer, C. Boyd, J. D. Kerby, and J. D. McIver.<br> 2018. Innovation in rangeland monitoring: annual, 30 m, plant functional type<br> percent cover maps for U.S. rangelands, 1984-2017. Ecosphere 9:e02430.<br> http://dx.doi.org/10.1002/ecs2.2430</p>
Rangeland Analysis Platform - vegetation cover 2000
<p>Rangeland Analysis Platform vegetation cover 2000</p> <p>These data represent rangeland cover estimates determined by Jones et al. (2018)<br> and as accessible on the Rangeland Analysis Platform (https://rangelands.app).<br> Values are percent aerial cover of the following rangeland functional groups:</p> <p>Band 1 - annual forbs and grasses<br> Band 2 - bare ground<br> Band 3 - litter<br> Band 4 - perennial forbs and grasses<br> Band 5 - shrubs<br> Band 6 - trees<br> No Data value = 255</p> <p>Although these data were produced across a broad region, they are primarily<br> intended for rangeland ecosystems. Cover estimates may not be suitable in other<br> ecosystems, e.g., forests, agricultural lands.</p> <p>Data are in WGS84 Geographic Coordinate System (EPSG:4326); spatial resolution<br> is approximately 30m.</p> <p>Please attribute these data to:<br> Jones, M. O., B. W. Allred, D. E. Naugle, J. D. Maestas, P. Donnelly, L. J.<br> Metz, J. Karl, R. Smith, B. Bestelmeyer, C. Boyd, J. D. Kerby, and J. D. McIver.<br> 2018. Innovation in rangeland monitoring: annual, 30 m, plant functional type<br> percent cover maps for U.S. rangelands, 1984-2017. Ecosphere 9:e02430.<br> http://dx.doi.org/10.1002/ecs2.2430</p>
Rangeland Analysis Platform - vegetation cover 1998
<p>Rangeland Analysis Platform vegetation cover 1998</p> <p>These data represent rangeland cover estimates determined by Jones et al. (2018)<br> and as accessible on the Rangeland Analysis Platform (https://rangelands.app).<br> Values are percent aerial cover of the following rangeland functional groups:</p> <p>Band 1 - annual forbs and grasses<br> Band 2 - bare ground<br> Band 3 - litter<br> Band 4 - perennial forbs and grasses<br> Band 5 - shrubs<br> Band 6 - trees<br> No Data value = 255</p> <p>Although these data were produced across a broad region, they are primarily<br> intended for rangeland ecosystems. Cover estimates may not be suitable in other<br> ecosystems, e.g., forests, agricultural lands.</p> <p>Data are in WGS84 Geographic Coordinate System (EPSG:4326); spatial resolution<br> is approximately 30m.</p> <p>Please attribute these data to:<br> Jones, M. O., B. W. Allred, D. E. Naugle, J. D. Maestas, P. Donnelly, L. J.<br> Metz, J. Karl, R. Smith, B. Bestelmeyer, C. Boyd, J. D. Kerby, and J. D. McIver.<br> 2018. Innovation in rangeland monitoring: annual, 30 m, plant functional type<br> percent cover maps for U.S. rangelands, 1984-2017. Ecosphere 9:e02430.<br> http://dx.doi.org/10.1002/ecs2.2430</p>
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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.