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Figure 5 in Effect of three different land use types on the temporal dynamics of microarthropod abundance in the high Guinean savanna of Ngaoundéré (Adamawa, Cameroon)
Figure 5. Temporal variation of Collembola, Oribatida and Gamasina abundance (tsd. ind./m2) in savanna and experimental field in (A) 2017 and (B) 2018. Abundance being significantly different from each other in time per treatment are marked by different letters for each microarthropod group (Wilcoxon signed-rank test [p ˂ 0.05]). Months not sampled are marked by asterisks. Error bars = standard error. sav ctrl: savanna, mb: maize bare, mbnpk: maize with chemical NPK fertilizer, mmdom: maize with dry organic matter (DOM).
Figure 2 in Effect of three different land use types on the temporal dynamics of microarthropod abundance in the high Guinean savanna of Ngaoundéré (Adamawa, Cameroon)
Figure 2. Completely randomized block design of the experimental field; experimental field before sowing (A: 08/05/17) and with maize crop (B: 28/06/17); sav ctrl: savanna untreated (outside). mb(1): maize bare = maize plot without mulch or NPK fertilizer; mbnpk(2): maize bare with NPK = Maize plot treated with NPK fertilizer only and mmdom(3): maize mulch = maize plot treated with mulch only.
Figure 1 in Effect of three different land use types on the temporal dynamics of microarthropod abundance in the high Guinean savanna of Ngaoundéré (Adamawa, Cameroon)
Figure 1. Weather data for the Ngaoundéré area, Jan. to Dec. 2017 and 2018, provided by Ngaoundéré airport meteorological station; t: temperature, p: precipitation, h: relative humidity; h: averages per month; p: sum per month.
Figure 3 in Effect of three different land use types on the temporal dynamics of microarthropod abundance in the high Guinean savanna of Ngaoundéré (Adamawa, Cameroon)
Figure 3. Abundances (mean ± S.E.) of Acari and Collembola for the rainy seasons of 2017 (n = 4) and 2018 (n = 4) as well as for the dry season of 2017/18 (n = 2). n sampling campaigns with 9 samples per plot [sav ctrl: savanna; mb: maize bare; mbnpk: maize with chemical NPK fertilizer; mmdom: maize with dead organic matter (DOM)].
Figure 4 in Effect of three different land use types on the temporal dynamics of microarthropod abundance in the high Guinean savanna of Ngaoundéré (Adamawa, Cameroon)
Figure 4. Abundances (mean ± S.E.) of Oribatida and Gamasina for the rainy seasons of 2017 (n = 4) and 2018 (n = 4) as well as for the dry season of 2017/18 (n = 2). n sampling campaigns with 9 samples per plot [sav ctrl: savanna; mb: maize bare; mbnpk: maize with chemical NPK fertilizer; mmdom: maize with dead organic matter (DOM)].
ERA5-Land selected indicators daily aggregates for the Latin America region, 2023
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land eight selected indicators, covering the Latin America region, for 2023.</p> <p>Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates.</p> <p>For 2m dewpoint pressure, 10m u component of wind, 10m v component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean and minimum were used for aggregation.</p> <p>Those files were created using the <a href="https://github.com/ErikKusch/KrigR">KrigR</a> package.</p>
A SSP1-Low emission land use scenario based on LCM2019 for Scotland - Land Use Change only - baseline 2019 and scenario 2050 (nov22)
<p>This set of datasets contains a land use change scenario (2050) for Scotland within the scope of a SSP1 - Low emissions scenario (Shared Socio-Economic Pathways). For achieving a low-emission scenario, simulated land use change targeted woodland expansion (including silvo-arable and silvo-pastoral) and decreased grazing intensity, both land use changes also aimed at benefitting four aspects of ecosystem services: carbon storage through tree planting, emission reduction through deintensification, biodiversity enhancement through tree planting, and pollination to support food production.</p> <p>The baseline dataset is based on the Land Cover Map 2019 (Morton et al, 2020) aggregated at 100m resolution. Grazing intensity was added to it by using estimations of stocking rates from IACS (Wardell-Johnson, 2022), and grazing conservation thresholds (Chapman, 2007; FAS, 2021). From the baseline dataset, the land use scenario map was created using the SLM-OptionsTool, a land use change tool for Ecosystem Services based on the LandSFACTS model (Castellazzi et al, 2010). The attached land use scenario map for 2050 is not an optimised result, but it is only one possibility that meets all the constraints stipulated for the scenario.</p> <p><strong>For a detailed description of the scenario refer to the following web storymap : <a href="https://storymaps.arcgis.com/stories/c3d3feff85f14460b6c973127089d6f9">https://storymaps.arcgis.com/stories/c3d3feff85f14460b6c973127089d6f9</a></strong></p> <p>This analysis was conducted as part of the Land use Transformations (<a href="https://landusetransformations.hutton.ac.uk/">https://landusetransformations.hutton.ac.uk/</a>) project (JHI-C3-1) in the Scottish Government funded Strategic Research Programme 2022-27.</p> <p> </p> <p><strong>This version of the datasets only includes 100m cells with land use change (14% of Scotland). The full dataset has a non-commercial version of the licence (<a href="https://doi.org/10.5281/zenodo.10927157">https://doi.org/10.5281/zenodo.10927157</a>).</strong></p> <p> </p> <p><strong>-------------------------</strong></p> <p><strong>Datasets accessible here : <a href="https://openscience.hutton.ac.uk/dataset/low-emission-land-use-scenarios-land-use-change">SSP1-Low Emission Land Use Scenarios - land use change - Dataset - Natural Asset Register Data Portal (hutton.ac.uk)</a></strong></p> <p><strong>License</strong>: CC-BY-4.0 namely “Creative Commons Attribution 4.0 International“ <a name="_Hlk161153952"></a>(https://creativecommons.org/licenses/by/4.0/)</p> <p><strong>Copyright to display of the datasets</strong>: <br>“Contains Data owned by UK Centre for Ecology & Hydrology © Database Right/Copyright UKCEH. Based on Data from LPIS and JAC (Scottish Government, 2019).”</p> <p><strong>2 Main files :</strong></p> <ul> <li><strong>SSP1LEonLCM19_LUC_2019.tif</strong> : original land uses (2019) on which the scenario is based on. This land use map, of a resolution of 100m, is based on the Land Cover Map 2019 (Morton et al, 2020), estimations of stocking rates from IACS (Wardell-Johnson, 2022), and grazing conservation thresholds (Chapman, 2007; FAS, 2021). This version of the datasets only includes 100m cells with land use change (14% of Scotland).<br><br><strong>Contributions </strong>to the baseline dataset (SSP1LEonLCM19_LUC_2019.tif) : <ul> <li>100% of 100m cells: Land Cover Map 2019 (Morton et al, 2020)</li> <li>93.88% of 100m cells: the LCM 2019 was subdivided by grazing intensity using estimations of stocking rates from IACS (Wardell-Johnson, 2022), and grazing conservation thresholds (Chapman, 2007; FAS, 2021). This impacts the grasslands, heathers, bogs and arable classes.</li> <li>Estimated overall contributions: 65% UKCEH, 35% JHI</li> </ul> </li> </ul> <ul> <li><strong>SSP1LEonLCM19_LUC_2050.tif :</strong> land use scenario (2050), which is within the scope of a SSP1 - Low emissions scenario (Shared Scocio-Economic Pathways). The scenario was created using the SLM-OptionsTool, a land use change tool for Ecosystem Services based on the LandSFACTS model (Castellazzi et al, 2010). For a detailed description of the scenario refer to the following web storymap : <a href="https://storymaps.arcgis.com/stories/c3d3feff85f14460b6c973127089d6f9">https://storymaps.arcgis.com/stories/c3d3feff85f14460b6c973127089d6f9</a><u>. </u>This version of the datasets only includes 100m cells with land use change (14% of Scotland).<br><br><strong>Contributions</strong> to the scenario dataset (SSP1LEonLCM19_LUC_2050.tif) : <ul> <li>cf. contribution to the baseline (above)</li> <li>100% of 100m cells: modelled land use change</li> <li>Estimated overall contributions: 50% UKCEH, 50% JHI</li> </ul> </li> </ul> <p> </p> <p><strong>Main references:</strong></p> <p>Morton, R. D., Marston, C. G., O’Neil, A. W., & Rowland, C. S. (2020). Land Cover Map 2019 (25m rasterised land parcels, GB) [Data set]. NERC Environmental Information Data Centre. <a href="https://doi.org/10.5285/F15289DA-6424-4A5E-BD92-48C4D9C830CC">https://doi.org/10.5285/F15289DA-6424-4A5E-BD92-48C4D9C830CC</a></p> <p>Wardell-Johnson, D. (2022) Stocking rates derived from IACS 2019 version 4. <br>Based on data from Land Parcel Information System (2019) courtesy of Rural Payments and Inspections Division, Scottish Government.<br>Based on data from the June Agricultural Census (2019) courtesy of Rural and Environment Science and Analytical Services, Agricultural Statistics team, Scottish Government.</p> <p>Chapman, P. (2007) Conservation Grazing of Semi-natural Habitats. Technical note TN586. SAC tn586-conservation.pdf (sruc.ac.uk)</p> <p>FAS (2021) Practical Guide: Managing Peatlands and Upland Habitats. <a href="https://www.fas.scot/environment/biodiversity/protecting-scotlands-peatlands/practical-guide-managing-peatlands-and-upland-habitats/">https://www.fas.scot/environment/biodiversity/protecting-scotlands-peatlands/practical-guide-managing-peatlands-and-upland-habitats/ </a>(author: Paul Chapman)</p> <p>Castellazzi, M.S.; Gimona, A. (2021) SLM-OptionsTool, a land use change tool for Ecosystem Services (arcgis toolbox and user manual included, part of RESAS Deliverable-O1.4.2ciiD27).</p> <p>Castellazzi, M.S., Matthews, J., Angevin, F., Sausse, C., Wood, G.A., Burgess, P.J., Brown I., Conrad, K.F., Perry J.N. (2010). Simulation scenarios of spatio-temporal arrangement of crops at the landscape scale . Environmental Modelling and Software 25, 1881-1889. <a href="https://doi.org/10.1016/j.envsoft.2010.04.006">https://doi.org/10.1016/j.envsoft.2010.04.006</a> </p> <p><a href="https://www.hutton.ac.uk/research/departments/information-and-computational-sciences/tools/landsfacts">https://www.hutton.ac.uk/research/departments/information-and-computational-sciences/tools/landsfacts</a></p> <p> </p> <p> </p>
FIG. 5. Correlation between the spatial distribution index and population density. A in Demographic and spatial structure at the stage of expansion in the populations of some alien land snails in Belgorod city (Central Russian Upland)
FIG. 5. Correlation between the spatial distribution index and population density. A. For Brephulopsis cylindrica and Xeropicta derbentina at 160 plots for three years. B. For Harmozica ravergiensis in nine sites×20 plots for two years. РИС. 5. Корреляция меЖду индексом пространственного распределения и плотностью популяции. А. Для Brephulopsis cylindrica и Xeropicta derbentina на 160 плоЩадках За три года. В. Для Harmozica ravergiensis на девяти участках по 20 плоЩадок За два года.
FIG. 4 in Demographic and spatial structure at the stage of expansion in the populations of some alien land snails in Belgorod city (Central Russian Upland)
FIG. 4. Boxplots for estimating the density of different age classes in the Xeropicta derbentina population in warm months of 2017, 2019, and 2020 for 160 test plots. Adult snails are represented by red boxes; juvenile snails are represented by blue boxes. РИС. 4. Боксплоты для оценок плотности раЗличных воЗрастных классов в популяции Xeropicta derbentina в раЗные теплые месяцы 2017, 2019 и 2020 гг. для 160 пробных плоЩадок. ВЗрослые особи покаЗаны красным цветом, ювенильные особи покаЗаны голубым цветом.
FIG. 3 in Demographic and spatial structure at the stage of expansion in the populations of some alien land snails in Belgorod city (Central Russian Upland)
FIG. 3. Boxplots for estimating the density of different age classes in the Brephulopsis cylindrica population in warm months of 2017, 2019, and 2020 for 160 test plots. Adult snails are represented by red boxes; juvenile snails are represented by blue boxes. РИС. 3. Боксплоты для оценок плотности раЗличных воЗрастных классов в популяции Brephulopsis cylindrica в раЗные теплые месяцы 2017, 2019 и 2020 гг. для 160 пробных плоЩадок. ВЗрослые особи покаЗаны красным цветом, ювенильные особи покаЗаны голубым цветом.
FIG. 2 in Demographic and spatial structure at the stage of expansion in the populations of some alien land snails in Belgorod city (Central Russian Upland)
FIG. 2. Scheme of plots in a regular grid. A. Study site. B. Brephulopsis cylindrica and Xeropicta derbentina in the field. C. Scheme of plots in a regular grid. РИС. 2. Регулярная сетка плоЩадок. A. РасполоЖение исследуемого участка. B. Brephulopsis cylindrica и Xeropicta derbentina в месте обитания. C. Схема регулярной сетки плоЩадок.
FIG. 5 in Harmozica zangezurica (Gastropoda, Pulmonata, Hygromiidae) - a cryptic species of land molluscs from southern Armenia
FIG. 5. Habitat characteristic of H. zangezurica (А) and adult individual found during egg-laying (B). Both photos were taken at locus typicus.
РИС. 3. Вскрытые особи H. zangezurica c раЗных участков. A. Типовое местонахождение (участок № 1). B. Между СраШеном и Цавом (участок № 5). С. Цав (участок № 6). D. Ваганаванк (участок № 7). Стрелками покаЗаны вагинальные придатки. МасШтаб 1 мм. in Harmozica zangezurica (Gastropoda, Pulmonata, Hygromiidae) - a cryptic species of land molluscs from southern Armenia
РИС. 3. Вскрытые особи H. zangezurica c раЗных участков. A. Типовое местонахождение (участок № 1). B. Между СраШеном и Цавом (участок № 5). С. Цав (участок № 6). D. Ваганаванк (участок № 7). Стрелками покаЗаны вагинальные придатки. МасШтаб 1 мм.
FIG. 2 in Harmozica zangezurica (Gastropoda, Pulmonata, Hygromiidae) - a cryptic species of land molluscs from southern Armenia
FIG. 2. The reproductive system of two conchologically similar species. A. Harmozica pisiformis after Schileyko (1978). B. Holotype of H. zangezurica after Gural-Sverlova et al. (2017b). Arrows indicate vaginal appendages.
РИС. 1. Расположение участков, на которых были собраны анатомически исследованные половоЗрелые особи Harmozica zangezurica. in Harmozica zangezurica (Gastropoda, Pulmonata, Hygromiidae) - a cryptic species of land molluscs from southern Armenia
РИС. 1. Расположение участков, на которых были собраны анатомически исследованные половоЗрелые особи Harmozica zangezurica.
РИС. 4. Раковины анатомически проверенных особей H. zangezurica с раЗных участков. A. Типовое местонахождение (участок № 1). B. Шикахох (участок № 2). С. СраШен (участок № 4). D. Ваганаванк (участок № 7). МасШтаб 5 мм. in Harmozica zangezurica (Gastropoda, Pulmonata, Hygromiidae) - a cryptic species of land molluscs from southern Armenia
РИС. 4. Раковины анатомически проверенных особей H. zangezurica с раЗных участков. A. Типовое местонахождение (участок № 1). B. Шикахох (участок № 2). С. СраШен (участок № 4). D. Ваганаванк (участок № 7). МасШтаб 5 мм.
A global land-use data cube 1992-2020 based on the Human Appropriation of Net Primary Production: Dataset 2
<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: grazing land characterized by open wooded lands (GL-owl)</p>
GlobalHighCO: Global Daily Seamless 1 km Ground-Level CO Dataset over Land (2018–Present)
<p>GlobalHighCO is part of a series of long-term, seamless, global, high-resolution, and high-quality datasets of air pollutants over land (i.e., GlobalHighAirPollutants, GHAP). It is generated from big data sources (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence, taking into account the spatiotemporal heterogeneity of air pollution.</p> <p>Here is the big data-derived gapless (spatial coverage = 100%) daily, monthly, and yearly 1 km (i.e., D1K, M1K, and Y1K) global ground-level CO dataset over land <strong>from 2019 to the present</strong>. This dataset exhibits high quality, with a cross-validation coefficient of determination (CV-R<sup>2</sup>) of 0.93 and a root-mean-square error (RMSE) of 0.21 mg m<sup>-3</sup> on a daily basis.</p> <p><strong>More GHAP datasets for different air pollutants are available at: <a href="https://weijing-rs.github.io/product.html">https://weijing-rs.github.io/product.html</a></strong></p>
GlobalHighO₃: Global Daily Seamless 10 km Ground-Level O₃ Dataset over Land (2000–Present)
<p>GlobalHighO<sub>3</sub> is part of a series of long-term, seamless, global, high-resolution, and high-quality datasets of air pollutants over land (i.e., GlobalHighAirPollutants, GHAP). It is generated from big data sources (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence, taking into account the spatiotemporal heterogeneity of air pollution.</p> <p>Here is the first big data-derived gapless (spatial coverage = 100%) daily, monthly, and yearly 10 km (i.e., D10K, M10K, and Y10K) global ground-level maximum daily 8-hour average (MDA8) O<sub>3</sub> dataset over land <strong>from 2000 to the present</strong>. This dataset exhibits high quality, with a cross-validation coefficient of determination (CV-R<sup>2</sup>) of 0.86 and a root-mean-square error (RMSE) of 6.25 ppb on a daily basis.</p> <p><strong>More GHAP datasets for different air pollutants are available at: <a href="https://weijing-rs.github.io/product.html">https://weijing-rs.github.io/product.html</a></strong></p>
Preliminary land use scenarios for Nature Future Framework
<p>This deliverable is the product of tasks 5.2, we run high-resolution (1km) spatial land-use models that quantify potential land-use change and land management changes consistent with developed NFF storylines (T5.1). The European spatial land-use model (CLUMondo) was parametrized using economic modelling results and European SSP scenarios as input.</p>
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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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.