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5,023 results for “landing”
Unexpected microbial metabolic responses to elevated temperatures and nitrogen addition in subarctic soils under different land-use
<p>This repository contains all necessary raw data as well as the R code used to conduct statistical analysis and create figures of the publication<br> <br><strong>Unexpected microbial metabolic responses to elevated temperatures and nitrogen addition in subarctic soils under different land-use</strong></p><p>Julia Schroeder1, Tino Peplau1, Edward Gregorich2, Christoph C. Tebbe3, Christopher Poeplau1</p><p>1 Thünen Institute of Climate-Smart Agriculture, Bundesallee 68, 38116 Braunschweig, Germany<br>2 Research and Development Centre, Central Experimental Farm, Agriculture and Agri-Food Canada, Ottawa, Canada<br>3 Thünen Institute of Biodiversity, Bundesallee 65, 38116 Braunschweig, Germany</p><p>DOI: https://doi.org/10.1007/s10533-022-00943-7 </p><p>This study investigated how subarctic soils under different land use will respond to warming and increasing N availability to allow for better predictions of C cycling under global change. The short-term temperature sensitivity as well as N-input effects on microbial CUE, respiration, growth and turnover were assessed in a one-day incubation experiment according to the 18O-CUE approach. The warming and N response of SOM decomposition were assessed in a 50-days incubation experiment via measurement of cumulative respiration. Both experiments were conducted with the following three treatments: incubation at 10 °C, incubation at 20 °C, and incubation at 20 °C plus N-fertiliser addition at an amendment rate of 100 kg N ha-1. The response to warming or N addition were expressed as response ratios RRT = 20°C/10°C and RRN = 20°C+N/20°C for warming and N response, respectively.</p><p>The R code was developed under R v3.6.3 and adapted to work under version R v.4.1.2.</p><p>The repository includes the following files:</p><ul><li>general_soil_parameters_per_sample.csv - general soil data for each field sample (n=27)</li><li>general_soil_parameters_per_plot.csv - general soil data assessed on pooled replicated field samples (n=9)</li><li>respiration_over_50d_incubation.csv - respiration rate and cumulative respiration for each time-point and laboratory sample over the 50-days incubation</li><li>sample_data.csv - data measured for each laboratory sample (n=81)</li></ul><p> </p><ul><li>Warming_and_nitrogen_response_of_CUE_in_subarctic_soils.Rproj - Rproject (load project to work on provided scripts and data)</li><li>load_data_script.R - loads required data</li><li>absolute_values_script.R - summary of absolute ranges of parameters per land-use type and site</li><li>absolute_linear_mixed_effects_model_script.R - run statistical analysis</li><li>correlograms_absolute_soil_params_script.R - correlation analysis to identify what drives absolute values</li><li>plot_correlations_absolute_soil_params_script.R - plot drivers of CUE and cumulative respiration</li><li>RRT_RRN_calculation_script.R - calculates response ratios</li><li>plot_RRT_RRN_script.R - plot response ratios</li><li>RRT_RRN_linear_mixed_effects_models_script.R - run statistical analysis</li><li>correlograms_RRT_RRN_soil_param_script.R - correlation analysis to identify drivers of response ratios</li><li>plot_correlations_RRT_RRN_soil_params_script.R - plot drivers of response ratios</li><li>RRT_RRN_resprate_cumulresp_over_time_50d_incubation_script.R - plot response ratios over time course</li></ul>
Number of chamber measurement locations for accurate quantification of landscape-scale greenhouse gas fluxes: Importance of land use, seasonality, and greenhouse gas type
<p>Contains all raw data measured in the Schwingbach Earth Observatory (SEO) from Spring, Summer and Autumn 2020. Data was measured with an on-site LGR laser from the GHG emissions, and with 100cm³ soil cores for the soil characteristics. Details can be found in the corresponding manuscript "Number of chamber measurement locations for accurate quantification of landscape-scale greenhouse gas fluxes: Importance of land use, seasonality, and greenhouse gas type"</p>
A Google Earth Engine code to analyze e visualize land surface temperature and thermal hot-spot patterns: a Rome (Italy) case study
<p>Link to the <strong>Google Earth Engine </strong>(GEE) code: <strong>https://code.earthengine.google.com/cc3ea6593574e321acd7b68c975a9608</strong></p> <p>You can analyze and visualize the following spatial layers by accessing the GEE link: </p> <ol> <li><strong>Daytime summer land surface temperature</strong> (raster data, 30 m horizontal resolution, from Landsat-8 remote sensing data, years 2017-2022)</li> <li><strong>The surface thermal hot-spot pattern </strong>(raster data,30 m horizontal resolution) was obtained by using a statistical-spatial method based on the Getis-Ord Gi* approach through the ArcGIS tool. </li> </ol> <p>Here attached the .txt file from the <strong>GEE code</strong>. </p> <p> </p> <p><em>E-mail</em></p> <p>Giulia Guerri, CNR-IBE, giulia.guerri@ibe.cnr.it</p> <p>Marco Morabito, CNR-IBE, marco.morabito@cnr.it</p> <p>Alfonso Crisci, CNR-IBE, alfonso.crisci@ibe.cnr.it</p>
General causal loop diagram of main land-sea interactions for the Belgian coastal zone.
<p>This is a polished diagram of the main causal land-sea interactions, based on aggregation of the detailed mind maps of land-sea interactions identified by the coastal and rural stakeholders who participated in the Belgian Multi-Actor Lab of the EU-funded H2020 project COASTAL (https://h2020-coastal.eu). The diagrams are used for designing System Dynamics models and evidence-based business road maps. </p>
Generating Annual 4-m Land Cover Products over 28 Metropolises in China during 2000-2020
<p>A 4-m fine-grained land cover products over the 28 provincial capital cities in China (mainly in temperate monsoon climate and subtropical monsoon climate zone, and categorized into 7 classes, i.e., cropland, forest, grass, shrub, water, impervious surface, and bare land) is generated through the proposed model; A 4-m annual land cover products during 2000-2020 in Wuhan, China is generated through the proposed model.</p> <p>To validate the effectiveness of the derived products, three verification approaches are employed. 1) evaluation by the consistency with the contemporary products including 10-m Esri Land Cover, 10-m FROMGLC10, and 30-m GlobeLand30 ; 2) evaluation by the publicly certified 4-m annotation dataset GID; 3) evaluation by the third-party-created annotation samples. The validation results can be viewed in the manuscript, and the reproduction executation details can be viewed at Readme.txt.</p> <p>The products is in .tif format, and validaiton region is in ENVI/hdr format. Users need to install ENVI software or Arcgis to read the product.</p> <p>"open_source_DETNet_code_and_simple_demo.rar" is open source code and the corresponding intruction.</p>
Fig. 5. A in Shell Morphology, Growth Pattern And Population Dynamics Of The Land Snail Xerolenta Obvia (Menke, 1828) In Two Areas Of Different Climatic Conditions Within A Temperate Climate Region
Fig. 5. A) Mean monthly whorl increment of Xerolenta obvia in two growth seasons in SW (solid line) and NE (dashed line) populations; B) mean (black lines), maxi- mum and minimum (grey lines) monthly temperature, and C) total monthly precipitation during the study period in SW (solid lines) and NE (dashed lines) sites. Data from nearest meteorological stations in Wrocław and Suwałki (IMGW-PIB data)
Fig. 2 in Shell Morphology, Growth Pattern And Population Dynamics Of The Land Snail Xerolenta Obvia (Menke, 1828) In Two Areas Of Different Climatic Conditions Within A Temperate Climate Region
Fig. 2. Distribution of shell pattern types of Xero- lenta obvia snails in the two populations studied, SW (n = 781) and NE (n = 1387)
Interactions between land use, taxonomic group and aspects and levels of diversity in a Brazilian savanna: implications for the use of bioindicators
<p>The study was carried out in the Triângulo Mineiro region of Minas Gerais state, covering the municipalities of Uberlândia, Monte Alegre, and Nova Ponte, in south-eastern Brazil. We conducted the study in five habitat types, comprising two natural habitats (savanna and semideciduous forest), and three anthropogenic land-uses: cattle pastures (planted with introduced Urochloa grasses), soy fields (where sampling took place when plants were at the vegetative phase) and plantations of Eucalyptus trees (≥ 6 yrs old). Ants and beetles were sampled at the same 40 sites (8 replicates per land use), and birds at 30 sites (6 replicates per land use), only some of which were the same as for ants and beetles. </p> <p>Ants that forage on ground and dung beetles were sampled using pitfall traps. Sampling took place in November and December (early wet season) 2017. In each site, eight traps were installed with traps located at the corners of a 100×100 m square, and at the mid-points of the sides of the square, keeping a minimum distance of 50 m between any two traps. All traps were at least 75 m distant from the edge of the respective land use. Traps were plastic containers (19 cm diam, 11 cm height) filled with 150 ml of a saline solution and detergent. Each trap had a wire hoop suspended over it to accommodate a small (4 cm diam, 4 cm height) plastic container for holding a dung bait. We used a 20 cm diameter plastic cover supported by three sticks to protect traps from rain. Traps were baited with ~40 g of a mixture of pig dung and human faeces (4:1 proportion) and left in the field for 48-hrs.</p> <p>Birds were surveyed using 20-min point counts in the rainy season (November 2017 to March 2018). At each site, five sampling points were established, 200 m distant from each other. All surveys started at sunrise (about 6 a.m.), and all species seen or heard from each point were recorded. Each sampling site was re-surveyed in the following dry season (April to October to 2018); however, for logistic reasons we were unable to re-survey the plantation sites. </p> <p>Ant and dung beetle species were identified to species or morphospecies by comparison with named species in the Zoological Collection at the Federal University of Uberlândia (UFU) or with specialist assistance from Fernando Vaz de Mello, respectively. Vouchers of all species have been deposited at UFU´s Zoological Collection. Birds were identified directly in the field and species names follow the checklist produced by the Brazilian Ornithological Records Committee.</p> <p>We classified species functionally based on primary diet, foraging location and/or behaviour, and body size, as these traits are known to be sensitive to habitat modifications and of importance for the ecosystem services provided by ants, birds, and dung beetles.</p> <p>Ant species were classified according to their diet as predators, fungivores, nectarivores or omnivores, and according to their main foraging location as arboreal, epigeal (aboveground) or hypogeal (in soil and litter), based on information provided by Brown (2000) and Silvestre et al. (2003). Species were further classified into four body size categories based on our measurements of body length (Weber´s length; Brown, 1953) of 1-5 ant workers per species: 1 (< 0.75 mm), 2 (0.75-1.74 mm), 3 (1.75-3 mm), and 4 (> 3 mm).</p> <p>Dung beetles were classified as coprophagous, necrophagous, frugivore, generalist or predator, according to the type of food resource each species is most often attracted to. This classification was based on over 30 years of field experience throughout Brazil by one of the authors of this study (FVM), who used multiple types of baits (e.g., carcasses, fruits, faeces) to attract and collect dung beetles, and/or on literature information. Although information about the “attractiveness” of different types of baits to dung beetles (used here as a proxy for primary diet) was not obtained directly in the sites of the present study, it is importat to note that we are not aware of any evidence of geographic or habitat variation in bait preference among tropical species of dung beetles. Dung beetles were also classified according to their foraging behaviour as: telecoprid (species that make a dung ball and roll it away for burial), paracoprid (species that store dung in tunnels dug immediately below the dung source), or endocoprid (species living within or immediately below the dung, without moving it). For this, we used the database of the Zoological Collection of the Federal University of Mato Grosso (UFMT). Whenever sample sizes allowed, 30 individuals from each species were weighed for determination of body mass (following Almeida et al., 2011), and species were classified according to the following ordinal scale: 1 (< 10 mg); 2 (10-99 mg); 3 (100-300 mg); and 4 (>300 mg).</p> <p>Each bird species was classified according to its primary diet as frugivores granivore, insectivore, nectarivore, carnivore, detritivore, or omnivore, and according to the main foraging location as ground, understory/shrubby vegetation, or tree canopy, based on the Wilman et al. (2014) database and our own field experience. Using these same sources, we obtained information on mean body weights of each species and assigned them to one of five size categories: 1- (<15 g); 2 (15-39 g); 3 (40-199 g); 4 (200-599 g); and 5 (> 600 g).</p>
Over the hills and through the farms: Land use and topography influence genetic connectivity of northern leopard frog (Rana pipiens) in the Prairie Pothole Region
<p><em>Context</em></p> <p>Agricultural land-use conversion has fragmented prairie wetland habitats in the Prairie Pothole Region (PPR), an area with one of the most wetland-dense regions in the world. This fragmentation can lead to negative consequences for wetland obligate organisms, heightening risk of local extinction and reducing evolutionary potential for populations to adapt to changing environments.</p> <p><em>Objectives</em></p> <p>This study models biotic connectivity of prairie-pothole wetlands using landscape genetic analyses of the northern leopard frog (<em>Rana pipiens</em>) to: (1) identify population structure and (2) determine landscape factors driving genetic differentiation and possibly leading to population fragmentation.</p> <p><em>Methods</em></p> <p>Frogs from 22 sites in the James River and Lake Oahe river basins in North Dakota were genotyped using Best-RAD sequencing at 2868 bi-allelic single nucleotide polymorphisms (SNPs). Population structure was assessed using STRUCTURE, DAPC, and fineSTRUCTURE. Circuitscape was used to model resistance values for ten landscape variables that could affect habitat connectivity.</p> <p><em>Results</em></p> <p>STRUCTURE results suggested a panmictic population, but other more sensitive clustering methods identified six spatially organized clusters. Circuit theory-based landscape resistance analysis suggested land use, including cultivated crop agriculture, and topography were the primary influences on genetic differentiation.</p> <p><em>Conclusions</em></p> <p>While the <em>R. pipiens</em> populations appear to have high gene flow, we found a difference in the patterns of connectivity between the eastern portion of our study area which was dominated by cultivated crop agriculture, versus the western portion where topographic roughness played a greater role. This information can help identify amphibian dispersal corridors and prioritize lands for conservation or restoration.</p>
Land use and land cover changes in the contiguous United States from 1630 to 2020
<p>Through integrating multi-source data including high-resolution remote sensing image-based land use and land cover (LULC) data, model-based land use products, and historical land archives, we reconstructed historical LULC at an annual time scale and 1 km x 1 km resolution in the contiguous United States (CONUS) from 1630 to 2020. Compared to other historical LULC datasets, our data can capture the major characters of LULC as well as provide more accurate information with higher spatial and temporal resolution. The LULC data can be used for regional studies in a wide range of topics including LULC impacts on the ecosystem, biodiversity, water resource, carbon and nitrogen cycles, and greenhouse gas emissions.</p>
Data for: A spatial framework for prioritizing biochar application to arable land: a case study for Sweden
<p>The uploaded data is related to the publication: <em>A spatial framework for prioritizing biochar application to arable land: a case study for Sweden</em>, and contains the following:</p> <p>(I) Raster files for three different biochar prioritization narratives.</p> <p>(II) High-resolution biochar use indication maps (in JPEG) for different prioritization narratives. </p>
Realizing COP26's declaration on deforestation protects forests at the expense of non-forest land
<p>Data and model source code for the manuscript: "Realizing COP26's declaration on deforestation protects forests at the expense of non-forest land"</p> <p>Abhijeet Mishra1,2,*, Florian Humpenöder1, Christopher P.O. Reyer1, Felicitas Beier1,2, Hermann Lotze-Campen1,2, and Alexander Popp1</p> <p>1 Potsdam Institute for Climate Impact Research (PIK), Member of Leibniz Association, P.O.Box 60 12 03, 14412,6<br> Potsdam, Germany<br> 2 Humboldt University of Berlin, Department of Agricultural Economics, Unter den Linden 6, 10099 Berlin,8<br> Germany</p> <p>Abhijeet Mishra<br> *mishra@pik-potsdam.de<br> September 2022</p> <p>See https://github.com/abhimishr/magpie/releases and https://github.com/magpiemodel/magpie/releases/tag/v4.5.0 for further details</p>
Data associated with: Emergence of the physiological effects of elevated CO2 on land-atmosphere exchange of carbon and water
<p>Elevated atmospheric CO<sub>2</sub> (eCO<sub>2</sub>) influences the carbon assimilation rate and stomatal conductance of plants and thereby can affect the global cycles of carbon and water. Yet, the detection of these physiological effects of eCO<sub>2</sub> in observational data remains challenging, because natural variations and confounding factors (e.g., warming) can overshadow the eCO<sub>2</sub> effects in observational data of real-world ecosystems. In this study, we aim at developing a method to detect the emergence of the physiological CO<sub>2</sub> effects on various variables related to carbon and water fluxes. We mimic the observational setting in ecosystems using a comprehensive process-based land surface model QUINCY to simulate the leaf-level effects of increasing atmospheric CO<sub>2</sub> concentrations and their century-long propagation through the terrestrial carbon and water cycles across different climate regimes and biomes. We then develop a statistical method based on the signal-to-noise ratio to detect the emergence of the eCO<sub>2</sub> effects. The signal in gross primary production (GPP) emerges at relatively low CO<sub>2</sub> increase (Δ[CO<span>2</span>] ~ 20 ppm) where the leaf area index is relatively high. Compared to GPP, the eCO<sub>2</sub> effect causing reduced transpiration water flux (normalized to leaf area) emerges only at relatively high CO<sub>2</sub> increase (Δ[CO<sub>2</sub>] >> 40 ppm), due to the high sensitivity to climate variability and thus lower signal-to-noise ratio. In general, the response to eCO<sub>2</sub> is detectable earlier for variables of the carbon cycle than the water cycle, when plant productivity is not limited by climatic constraints, and stronger in forest-dominated rather than in grass-dominated ecosystems. Our results provide a step toward when and where we expect to detect physiological CO<sub>2</sub> effects in in-situ flux measurements, how to detect them and encourage future efforts to improve the understanding and quantification of these effects in observations of terrestrial carbon and water dynamics.</p>
A gridded dataset on population densities, real estate prices, transport and land use inside 192 worldwide urban areas
<p>This dataset provides, on a systematic basis, gridded population densities, rents, real estate prices, and transport times (both in<br> public transport and private car) in 192 cities across the world.</p>
Dataset of pollinator functional traits and interaction networks in neotropical mangroves: effects of patch size and surrounding land use
<p>This is the dataset of the manuscript entitled "Pollinator functional traits and interaction networks in neotropical mangroves: effects of patch size and surrounding land use", which was submitted for publication. The dataset include the functional traits of 162 insect pollinator species and 315 interactions with the mangrove species <em>Avicennia germinans, Conocarpus erectus, Laguncularia racemosa,</em> and <em>Rhizophora</em> <em>mangle</em>. The manuscript evaluates the effects of mangrove patch size and surrounding land use on pollinator functional diversity and plant-pollinator interactions in seven mangrove patches from the Colombian Caribbean region. Data variables are pollinator order, family, species, functional traits (pollinator guilds, body size, feeding preference, sociality, and nesting site) and frequency, interacting mangrove species, mangrove patch name, coordinates and size (ha), surrounding land use areas (urban areas, croplands, conserved dry forest, degraded vegetation areas, beach and water) and landscape diversity (Shannon H').</p>
Fig. 3 in The land snail genus Pincerna Preston, 1907 (Gastropoda: Alycaeidae) from Vietnam and Laos, with description of a new species
Fig. 3. Shells (apertural view, dorsal view, ventral view, and side view) and operculum (outer surface, inner surface, and side view) of Pincerna vanbuensis (Bavay & Dautzenberg, 1900). A, syntype MNHN-IM-2000-31798; B, E, specimen from Thuận Châu, SƠn La, Vietnam, ZMHU; C, specimen from Khoune, Xieng Khouang, Laos, ZMHU; D, specimen from Xiengkho, Houaphanh, Laos, ZMHU.
Fig. 5 in The land snail genus Pincerna Preston, 1907 (Gastropoda: Alycaeidae) from Vietnam and Laos, with description of a new species
Fig. 5. Details of the shell of Pincerna clausa n. sp. (holotype VNMN-IZ 002.304). A–B, protoconch; C, R2 and sutural tube; D, R3 and boundary between R2 and R3; E, aperture; F, umbilicus.
Fig. 2 in The land snail genus Pincerna Preston, 1907 (Gastropoda: Alycaeidae) from Vietnam and Laos, with description of a new species
Fig. 2. Shells (apertural view, dorsal view, ventral view, and side view) of Pincerna species. A, Pincerna anceyi (Mabille, 1887), syntype MNHN-IM-2000-31797; B, C, Pincerna mouhoti (L. Pfeiffer, 1862): B, specimen from MƯờng La, SƠn La, Vietnam, ZMHU; C, syntype NHMUK 20170120; D, E, Pincerna costulosus Bavay & Dautzenberg, 1912: D, syntype MNHN-IM-2000-31786; E, specimen from Phong Thổ, Lai Châu, Vietnam, ZMHU.
Fig. 6 in The land snail genus Pincerna Preston, 1907 (Gastropoda: Alycaeidae) from Vietnam and Laos, with description of a new species
Fig. 6. Synoptic view of Vietnamese and Laos Pincerna species. A, MNHN-IM-2000-31797; B, NHMUK 20170120; C, MNHN- IM-2000-31786; D, VNMN-IZ 002.304; E, MNHN-IM-2000-31798.
Fig. 4 in The land snail genus Pincerna Preston, 1907 (Gastropoda: Alycaeidae) from Vietnam and Laos, with description of a new species
Fig. 4. Shell (apertural view, dorsal view, ventral view, and side view) of Pincerna clausa, new species. A, holotype VNMN-IZ 002.304; B, C, paratypes ZMHU. MOL 044.
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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.