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216 results for “land-use”
FIGURE 2 in Land-use changes affect the functional structure of stream fish assemblages in the Brazilian Savanna
FIGURE 2 | Examples of landscape and local-habitat conditions of the streams sampled across a degradation gradient in the Cerrado: A. Streams with a small strip of riparian forests in landscapes dominated by mechanized agriculture; B. Stream with relatively well-preserved local conditions, including forest on both banks; C. Stream surrounded by intermediate riparian cover; and D. Stream running in pasture areas without any forest.
FIGURE 3 in Land-use changes affect the functional structure of stream fish assemblages in the Brazilian Savanna
FIGURE 3 | Predictions tested using structural equation modeling, indicating the expected pathways (arrows) for the effects of catchment degradation (CDI) on stream physical habitat and, consequently, on the functional structure of the fish assemblages. Land use is expected to influence the functional diversity mediated by alterations in habitat heterogeneity and stability (A), and to influence functional identity, mediated by changes in habitat type (B). Arrows indicate expected effects between predictive and response variables, which can be positive (black continuous line), negative (gray continuous line) or non-directional (dashed lines). For physical-habitat codes, calculation and ecological meaning, see Tab. 1.
FIGURE 1 in Land-use changes affect the functional structure of stream fish assemblages in the Brazilian Savanna
FIGURE 1 | Headwater streams (N = 40; black dots) sampled for fish and local physical habitat. All sites drain to Nova Ponte Reservoir in the Araguari River basin, Upper Paraná River, Minas Gerais, Brazil.
Data sets accompanying "Methodological and reporting inconsistencies in land-use requirements misguide future renewable energy planning"
<p>This data sets accompany the publication "Methodological and reporting inconsistencies in land-use requirements misguide future renewable energy planning" in One Earth.</p> <ul> <li>lur-db-output-zenodo.xlsx: contains all land use requirement estimates for renewable energies reviewed in the publication. Meta-data is reported in one sheet, the other sheet contains the original data.</li> <li>authorship-tree-zenodo.xlsx: contains the information necessary to derive the authorship tree shown in the supplementary information. Meta-data is reported in one sheet, the other sheet contains the original data.<br><br><br></li> </ul>
A global land-use data cube 1992-2020 based on the Human Appropriation of Net Primary Production: Dataset 1
<p>This dataset is part of the LUIcube, a global dataset on land-use at 30 arcsecond spatial resolution. The LUIcube includes information on area, the change in NPP due to land conversions (HANPP<sub>luc</sub>), the harvested NPP (including losses, HANPP<sub>harv</sub>), and the NPP remaining in ecosystems after harvest (NPP<sub>eco</sub>) for 32 land-use classes in annual time-steps from 1992 to 2020. A detailed description of the LUIcube is available in the accompanying publication.</p> <p>The layers of land-use areas are provided in square kilometers (km²) per grid cell. All NPP flows are provided in tC/yr per grid cell. Adding HANPP<sub>harv</sub> to NPP<sub>eco</sub> results in the actual NPP available before harvest (NPP<sub>act</sub>=NPP<sub>eco</sub>+HANPP<sub>harv</sub>), and adding HANPP<sub>luc</sub> to NPP<sub>act</sub> results in the potential NPP available in the hypothetical absence of land use (NPP<sub>pot</sub>=NPP<sub>act</sub>+HANPP<sub>luc</sub>) for the given land-use class. Area-intensive values (in gC/m²/yr) can be calculated by dividing the NPP flows by the area of the respective land-use class per grid cell. HANPP in % of NPP<sub>pot</sub> can be calculated by summing up HANPP<sub>harv</sub> and HANPP<sub>luc</sub> and dividing it by NPP<sub>pot</sub>. Areas and NPP flows of land-use classes can be aggregated to calculate their overall HANPP.</p> <p>This Zenodo repository provides data on following land-use classes: unused productive wilderness areas (WILD-core); productive wilderness areas that are sporadically used at very low intensity (WILD-periphery); unused unproductive wilderness areas (WILD-nps); forestry areas, mainly coniferous (FO-con); forestry areas, mainly non-coniferous (FO-ncon); settlements, urban areas and infrastructure (BU-builtup)</p> <p> </p>
A global land-use data cube 1992-2020 based on the Human Appropriation of Net Primary Production: Dataset 6
<p>This dataset is part of the LUIcube, a global dataset on land-use at 30 arcsecond spatial resolution. The LUIcube includes information on area, the change in NPP due to land conversions (HANPP<sub>luc</sub>), the harvested NPP (including losses, HANPP<sub>harv</sub>), and the NPP remaining in ecosystems after harvest (NPP<sub>eco</sub>) for 32 land-use classes in annual time-steps from 1992 to 2020. A detailed description of the LUIcube is available in the accompanying publication.</p> <p>The layers of land-use areas are provided in square kilometers (km²) per grid cell. All NPP flows are provided in tC/yr per grid cell. Adding HANPP<sub>harv</sub> to NPP<sub>eco</sub> results in the actual NPP available before harvest (NPP<sub>act</sub>=NPP<sub>eco</sub>+HANPP<sub>harv</sub>), and adding HANPP<sub>luc</sub> to NPP<sub>act</sub> results in the potential NPP available in the hypothetical absence of land use (NPP<sub>pot</sub>=NPP<sub>act</sub>+HANPP<sub>luc</sub>) for the given land-use class. Area-intensive values (in gC/m²/yr) can be calculated by dividing the NPP flows by the area of the respective land-use class per grid cell. HANPP in % of NPP<sub>pot</sub> can be calculated by summing up HANPP<sub>harv</sub> and HANPP<sub>luc</sub> and dividing it by NPP<sub>pot</sub>. Areas and NPP flows of land-use classes can be aggregated to calculate their overall HANPP.</p> <p>This Zenodo repository provides data on following land-use classes: cropland used for production of potatoes (CL-POTA); sweet potatoes and yams (CL-SWPY); and rest of crops (CL-REST)</p>
A global land-use data cube 1992-2020 based on the Human Appropriation of Net Primary Production: Dataset 4
<p>This dataset is part of the LUIcube, a global dataset on land-use at 30 arcsecond spatial resolution. The LUIcube includes information on area, the change in NPP due to land conversions (HANPP<sub>luc</sub>), the harvested NPP (including losses, HANPP<sub>harv</sub>), and the NPP remaining in ecosystems after harvest (NPP<sub>eco</sub>) for 32 land-use classes in annual time-steps from 1992 to 2020. A detailed description of the LUIcube is available in the accompanying publication.</p> <p>The layers of land-use areas are provided in square kilometers (km²) per grid cell. All NPP flows are provided in tC/yr per grid cell. Adding HANPP<sub>harv</sub> to NPP<sub>eco</sub> results in the actual NPP available before harvest (NPP<sub>act</sub>=NPP<sub>eco</sub>+HANPP<sub>harv</sub>), and adding HANPP<sub>luc</sub> to NPP<sub>act</sub> results in the potential NPP available in the hypothetical absence of land use (NPP<sub>pot</sub>=NPP<sub>act</sub>+HANPP<sub>luc</sub>) for the given land-use class. Area-intensive values (in gC/m²/yr) can be calculated by dividing the NPP flows by the area of the respective land-use class per grid cell. HANPP in % of NPP<sub>pot</sub> can be calculated by summing up HANPP<sub>harv</sub> and HANPP<sub>luc</sub> and dividing it by NPP<sub>pot</sub>. Areas and NPP flows of land-use classes can be aggregated to calculate their overall HANPP. </p> <p>This Zenodo repository provides data on following land-use classes: cropland used for production of wheat (CL-WHEA); maize (CL-MAIZ); soybean (CL-SOYB)</p>
A global land-use data cube 1992-2020 based on the Human Appropriation of Net Primary Production: Dataset 5
<p>This dataset is part of the LUIcube, a global dataset on land-use at 30 arcsecond spatial resolution. The LUIcube includes information on area, the change in NPP due to land conversions (HANPP<sub>luc</sub>), the harvested NPP (including losses, HANPP<sub>harv</sub>), and the NPP remaining in ecosystems after harvest (NPP<sub>eco</sub>) for 32 land-use classes in annual time-steps from 1992 to 2020. A detailed description of the LUIcube is available in the accompanying publication.</p> <p>The layers of land-use areas are provided in square kilometers (km²) per grid cell. All NPP flows are provided in tC/yr per grid cell. Adding HANPP<sub>harv</sub> to NPP<sub>eco</sub> results in the actual NPP available before harvest (NPP<sub>act</sub>=NPP<sub>eco</sub>+HANPP<sub>harv</sub>), and adding HANPP<sub>luc</sub> to NPP<sub>act</sub> results in the potential NPP available in the hypothetical absence of land use (NPP<sub>pot</sub>=NPP<sub>act</sub>+HANPP<sub>luc</sub>) for the given land-use class. Area-intensive values (in gC/m²/yr) can be calculated by dividing the NPP flows by the area of the respective land-use class per grid cell. HANPP in % of NPP<sub>pot</sub> can be calculated by summing up HANPP<sub>harv</sub> and HANPP<sub>luc</sub> and dividing it by NPP<sub>pot</sub>. Areas and NPP flows of land-use classes can be aggregated to calculate their overall HANPP.</p> <p>This Zenodo repository provides data on following land-use classes: cropland used for production of millet (CL-MILL); barley (CL-BARL); sorghum (CL-SORG); rice (CL-RICE)</p>
A global land-use data cube 1992-2020 based on the Human Appropriation of Net Primary Production: Dataset 7
<p>This dataset is part of the LUIcube, a global dataset on land-use at 30 arcsecond spatial resolution. The LUIcube includes information on area, the change in NPP due to land conversions (HANPP<sub>luc</sub>), the harvested NPP (including losses, HANPP<sub>harv</sub>), and the NPP remaining in ecosystems after harvest (NPP<sub>eco</sub>) for 32 land-use classes in annual time-steps from 1992 to 2020. A detailed description of the LUIcube is available in the accompanying publication.</p> <p>The layers of land-use areas are provided in square kilometers (km²) per grid cell. All NPP flows are provided in tC/yr per grid cell. Adding HANPP<sub>harv</sub> to NPP<sub>eco</sub> results in the actual NPP available before harvest (NPP<sub>act</sub>=NPP<sub>eco</sub>+HANPP<sub>harv</sub>), and adding HANPP<sub>luc</sub> to NPP<sub>act</sub> results in the potential NPP available in the hypothetical absence of land use (NPP<sub>pot</sub>=NPP<sub>act</sub>+HANPP<sub>luc</sub>) for the given land-use class. Area-intensive values (in gC/m²/yr) can be calculated by dividing the NPP flows by the area of the respective land-use class per grid cell. HANPP in % of NPP<sub>pot</sub> can be calculated by summing up HANPP<sub>harv</sub> and HANPP<sub>luc</sub> and dividing it by NPP<sub>pot</sub>. Areas and NPP flows of land-use classes can be aggregated to calculate their overall HANPP.</p> <p>This Zenodo repository provides data on following land-use classes: cropland used for production of cassava (CL-CASS); sugarcane (CL-SUGC); sugarbeet (CL-SUGB); cotton (CL-COTT); fruits and vegetables (CL-VEFR)</p>
A global land-use data cube 1992-2020 based on the Human Appropriation of Net Primary Production: Dataset 8
<p>This dataset is part of the LUIcube, a global dataset on land-use at 30 arcsecond spatial resolution. The LUIcube includes information on area, the change in NPP due to land conversions (HANPP<sub>luc</sub>), the harvested NPP (including losses, HANPP<sub>harv</sub>), and the NPP remaining in ecosystems after harvest (NPP<sub>eco</sub>) for 32 land-use classes in annual time-steps from 1992 to 2020. A detailed description of the LUIcube is available in the accompanying publication.</p> <p>The layers of land-use areas are provided in square kilometers (km²) per grid cell. All NPP flows are provided in tC/yr per grid cell. Adding HANPP<sub>harv</sub> to NPP<sub>eco</sub> results in the actual NPP available before harvest (NPP<sub>act</sub>=NPP<sub>eco</sub>+HANPP<sub>harv</sub>), and adding HANPP<sub>luc</sub> to NPP<sub>act</sub> results in the potential NPP available in the hypothetical absence of land use (NPP<sub>pot</sub>=NPP<sub>act</sub>+HANPP<sub>luc</sub>) for the given land-use class. Area-intensive values (in gC/m²/yr) can be calculated by dividing the NPP flows by the area of the respective land-use class per grid cell. HANPP in % of NPP<sub>pot</sub> can be calculated by summing up HANPP<sub>harv</sub> and HANPP<sub>luc</sub> and dividing it by NPP<sub>pot</sub>. Areas and NPP flows of land-use classes can be aggregated to calculate their overall HANPP.</p> <p>This Zenodo repository provides data on following land-use classes: cropland used for production of beans (CL-BEAN); other pulses (CL-OPUL); groundnuts (CL-GROU); bananas and plantains (CL-BANP)</p>
A global land-use data cube 1992-2020 based on the Human Appropriation of Net Primary Production: Dataset 9
<p>This dataset is part of the LUIcube, a global dataset on land-use at 30 arcsecond spatial resolution. The LUIcube includes information on area, the change in NPP due to land conversions (HANPP<sub>luc</sub>), the harvested NPP (including losses, HANPP<sub>harv</sub>), and the NPP remaining in ecosystems after harvest (NPP<sub>eco</sub>) for 32 land-use classes in annual time-steps from 1992 to 2020. A detailed description of the LUIcube is available in the accompanying publication.</p> <p>The layers of land-use areas are provided in square kilometers (km²) per grid cell. All NPP flows are provided in tC/yr per grid cell. Adding HANPP<sub>harv</sub> to NPP<sub>eco</sub> results in the actual NPP available before harvest (NPP<sub>act</sub>=NPP<sub>eco</sub>+HANPP<sub>harv</sub>), and adding HANPP<sub>luc</sub> to NPP<sub>act</sub> results in the potential NPP available in the hypothetical absence of land use (NPP<sub>pot</sub>=NPP<sub>act</sub>+HANPP<sub>luc</sub>) for the given land-use class. Area-intensive values (in gC/m²/yr) can be calculated by dividing the NPP flows by the area of the respective land-use class per grid cell. HANPP in % of NPP<sub>pot</sub> can be calculated by summing up HANPP<sub>harv</sub> and HANPP<sub>luc</sub> and dividing it by NPP<sub>pot</sub>. Areas and NPP flows of land-use classes can be aggregated to calculate their overall HANPP.</p> <p>This Zenodo repository provides data on following land-use classes: cropland used for production of other oilcrops (CL-OOIL); coffee (CL-COFF); fodder crops (CL-FODD)</p>
Vulnerability of estuarine systems in the contiguous United States to water quality change under future climate and land-use
<p>Changes in climate and land-use and land-cover (LULC) are expected to influence surface water runoff and nutrient characteristics of estuarine watersheds, but the extent to which estuaries are vulnerable to altered nutrient loading under future conditions is poorly understood. The present work aims to address this gap through the development of a new vulnerability assessment framework that accounts for (1) estuarine exposure to projected changes in total nitrogen (TN) and total phosphorus (TP) loads as a function of LULC and climate change under several scenarios to altered nutrient loads, (2) sensitivity (i.e., how responsive estuaries are to altered nutrient loads), and (3) adaptive capacity (i.e., how the socio-ecological system can use existing resources to reduce the impacts associated with increased exposure). The framework was applied to 112 estuaries and their contributing watersheds across the contiguous U.S., specifically to look at regional variability in estuarine vulnerability to nutrient loading. Study findings revealed that the largest increases in estuarine nutrient loads are expected in the North and South Atlantic regions and eastern Gulf of Mexico, while the lowest increase is expected in the North and South Pacific regions and the western Gulf of Mexico. However, the North Atlantic and the South Pacific had the highest adaptive capacity, which could potentially counteract the effects of LULC and climate change on nutrient loads. Our findings illustrate the benefits of integrating natural and socio-ecological factors to identify opportunities to develop adaptation plans and policies to mitigate ecological degradation in vitally important estuaries. A<a href="https://lisemontefiore.shinyapps.io/estuarine_vulnerability/"> web-based application</a> has been developed to visualize and download the data.</p>
Concordant and opposing effects of climate and land-use change on avian assemblages in California's most transformed landscapes
<p>Climate and land-use change could exhibit concordant effects that favor or disfavor the same species, which would amplify their impacts, or species may respond to each threat in a divergent manner, causing opposing effects that moderate their impacts in isolation. We used early 20th-century surveys of birds conducted by Joseph Grinnell paired with modern resurveys and land-use change reconstructed from historic maps to examine avian change in Los Angeles and California's Central Valley (and their surrounding foothills). Occupancy and species richness declined greatly in Los Angeles from urbanization, strong warming (+1.8°C) and drying (-77.2 mm), but remained stable in the Central Valley, despite large-scale agricultural development, average warming (+0.9°C), and increased precipitation (+11.2 mm). While climate was the main driver of species distributions a century ago, the combined impacts of land-use and climate change drove temporal changes in occupancy, with similar numbers of species experiencing concordant and opposing effects.</p>
Data for: The effect of land-use change on soil C, N, P, and their stoichiometries: A global synthesis
<p><strong><em>Data description</em></strong></p> <p>This dataset includes detailed information about five different types of land use change reported in “The effect of land-use change on soil C, N, P, and their stoichiometries: A global synthesis (Agriculture, Ecosystems and Environment; <a href="https://doi.org/10.1016/j.agee.2023.108402)">https://doi.org/10.1016/j.agee.2023.108402)</a>”. </p> <p> </p> <p>Lists of five different types of land use change</p> <p>1) conversion of primary forest to cropland</p> <p>2) conversion of primary forest to grassland</p> <p>3) conversion of cropland to forest</p> <p>4) conversion of grassland to forest</p> <p>5) conversion of grassland to cropland</p> <p> </p> <p>Lists of detailed information</p> <ul> <li>Land use change (pre-LUC, post-LUC)</li> <li>Country, Location, Geographic position (Longitude, Latitude) </li> <li>Altitude (m)</li> <li>Climate zone</li> <li>Weather [rainfall (mm yr<sup>-1</sup>) and temperature (°C)]</li> <li>Reported time of change (years)</li> <li>Vegetation type (pre-LUC, post-LUC)</li> <li>Fertilizer (pre-LUC, post-LUC: type, application; change)</li> <li>Soil sampling depth (cm)</li> <li>Soil type [units, pre-LUC, post-LUC, change rate (%)]</li> <li>Soil pH, bulk density, CEC [units, pre-LUC, post-LUC, change rate (%)]</li> <li>Soil organic carbon [units, pre-LUC, post-LUC, change rate (%)]</li> <li>Soil total nitrogen [units, pre-LUC, post-LUC, change rate (%)]</li> <li>Soil total phosphorus [units, pre-LUC, post-LUC, change rate (%)]</li> <li>Soil C:N [units, pre-LUC, post-LUC, change rate (%)]</li> <li>Soil C:P [units, pre-LUC, post-LUC, change rate (%)]</li> <li>Soil N:P [units, pre-LUC, post-LUC, change rate (%)]</li> <li>Reference</li> </ul> <p> </p> <p><em><strong>Data collection method</strong></em></p> <p>We analyzed five different types of LUC: 1) conversion of primary forest to cropland, 2) conversion of primary forest to grassland, 3) conversion of cropland to forest, 4) conversion of grassland to forest, and 5) conversion of grassland to cropland.</p> <p>We classified primary forest as forest that had not previously been cleared and used for other land uses. The conversion of cropland or grassland to forest includes naturally generated and intentionally planted forest. Cropland is land used for growing agricultural crops and may include short pasture phases, and grassland is land used continuously for grazing purposes, but may include occasional and repeated pasture-renewal phases.</p> <p>While we tried to make categorical distinctions between these land-use types, land uses are often more fluid in practice, which may not always have been stated in the publications underlying our data compilation.</p> <p>When a paper reported both contents and stocks, we used the stock-based measure. We used reported stocks if the original work had already been corrected to equivalent soil mass (Ellert and Bettany, 1995) or if corrected stocks had been reported in previous reviews or meta-analyses (Don et al., 2011; Poeplau et al., 2011; Guo and Gifford, 2002). Where bulk-density correction had not been applied, we tried to make those corrections to estimate changes to equivalent soil mass if studies provided sufficient information on soil bulk density and depth, using the method of Zhang et al. (2004). If that was not possible, we used the reported SOC, TN, or TP contents.</p> <p> </p> <p><em><strong>Acknowledgements</strong></em></p> <p>We thank scientists who measured, analyzed, and published the data compiled for this study. We are especially grateful to Drs. Axel Don, Christopher Poeplau, Lex Bouwman, and Gaihe Yang, who provided their global meta-data through personal communication. D.-G.K. acknowledges support from the IAEA CRP D15020. M.U.F.K and L.L.L. were supported by the Strategic Science Investment Fund (SSIF) of New Zealand’s Ministry of Business, Innovation and Employment.</p>
Rapid ant community re-assembly in a Neotropical forest: recovery dynamics and land-use legacy
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Warmer temperatures reinforce negative land-use impacts on bees, but not on higher insect trophic levels
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Vulnerability of estuarine systems in the contiguous United States to water quality change under future climate and land-use
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Concordant and opposing effects of climate and land-use change on avian assemblages in California’s most transformed landscapes
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Agriculture land-use change seasonally rewires stream food webs: A case study from headwater streams in the Lake Erie watershed
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The effects of land-use history and the contemporary landscape on non-native plant invasion at local and regional scales in the French Broad Watersheds, 2007
Determining what factors explain the distribution of non-native invasive plants that can spread in forest-dominated landscapes could advance understanding of the invasion process and identify forest areas most susceptible to invasion. The researchers conducted roadside surveys to determine the presence and abundance of 15 non-native plant species known to invade forests in western North Carolina, USA. Prior to sampling, the researchers identified 15 non-native invasive plant species that were of concern in the study region. Generalized linear models were used to examine how contemporary and historic land use, landscape context, and topography influenced presence and abundance of the species at local and regional scales.
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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.