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Figure 8 in Phylogeny, species delimitation and population structure of the steppe-inhabiting land snail genus Helicopsis in Eastern Europe
Figure 8. Distribution of Helicopsis species in Central and Eastern Europe and localities from which 16S rDNA sequences are available (symbols). The geographical boundaries of H. striata, H. hungarica and H. lunulata are not yet clear because of the lack of reliable morphological characteristics.
Figure 6 in Phylogeny, species delimitation and population structure of the steppe-inhabiting land snail genus Helicopsis in Eastern Europe
Figure 6. Distribution of clusters obtained from the admixture analysis of AFLP data of Helicopsis from Ukraine with STRUCTURE. A, solution for K = 2. B, solution for K = 5. C, solution for K = 7. Population numbers are given next to the bar plots (Supporting Information, Table S1). The bar plots show the proportional assignment of an individual to a cluster (see also Fig. 5).
Figure 7 in Phylogeny, species delimitation and population structure of the steppe-inhabiting land snail genus Helicopsis in Eastern Europe
Figure 7. Relationships between Jaccard distances between individuals of Helicopsis filimargo and H. lunulata based on AFLP data and logarithmized geographical distances. Black circles and red triangles: distances between individuals belonging to H. filimargo and H. lunulata, respectively; green crosses: distances between individuals belonging to different species; black and red broken lines: regression lines fitted within species; green broken line: regression line fitted on the within-group distances only (i.e. the black circles and red triangles taken together); green solid line: regression line fitted on all distances together; blue lines: centres of the betweengroups geographical distances.
Figure 4 in Phylogeny, species delimitation and population structure of the steppe-inhabiting land snail genus Helicopsis in Eastern Europe
Figure 4. Neighbour-net network of Helicopsis from Ukraine based on Jaccard distances obtained from AFLP data. Coloration of clusters corresponds to the STRUCTURE solution for K = 2. Red corresponds to H. filimargo, yellow to H. lunulata. DNA voucher numbers for specimens are given at the tips of the network. Coloured dots at the tips of the network correspond to cluster assignments (on majority-rule basis) of specimens in the STRUCTURE solutions for K = 5 (inner) and K = 7 (outer). For locality data and the distribution of clusters, see also Supporting Information, Table S1 and Figure 6, respectively.
Figure 1 in Phylogeny, species delimitation and population structure of the steppe-inhabiting land snail genus Helicopsis in Eastern Europe
Figure 1. Sampled Helicopsis populations in Ukraine, the adjacent Central Russian Upland and Romania and distribution of mitochondrial haplotype clades: H. lunulata (yellow dots), H. hungarica (magenta triangles) and H. filimargo (clade A, red diamonds, clade B, green diamonds and mixed population of clades Aand B, blue diamonds).
Datasets of "The Onset of a Globally Ice-covered State for a Land Planet"
<p>This dataset contains results to create figures in the paper "The Onset of a Globally Ice-covered State for a Land Planet" by T. Kodama et al.</p> <p>It contains data from our GCM calculations to create figures. We used Gtool3-dcl5 which is developed by the GFD-DENNOU Club for analysis (<a href="https://www.gfd-dennou.org/">https://www.gfd-dennou.org</a>).</p>
Land use and land cover in communities along the Bons Sinais estuary, Mozambique
<p><span>The Bons Sinais estuary (BSE) is one of the most important estuaries in the central region of the Mozambican coast. The BSE plays an important role as main source of food and income for many people living along the estuary. The present data contain information on the land use and cover of eight communities located along the Bons Sinais estuary, Mozambique, over the time period 2019-2020. The dataset was created by drawing shape files over high-resolution satellite imagens obtained from Bing and Google Satellites. All images were analysed using QGIS software. A total of 101 shapefiles were created. These files will help local managers and researchers to better understand the human use of the natural resources and landscape, as well as to predict impacts of the community's growth on natural resources in the BSE.</span></p>
Biophysical effects of bioenergy crop cultivation scenarios on global land air temperature
<p>Biophysical effects of different bioenergy crop cultivation scenarios on global land air temperature simulated by coupled IPSL-CM model, with ORCHIDEE-MICT-BIOENERGY as the land component and LMDz as the atmosphere component. The spatial resolution of the coupled model was 1.26° latitude × 2.5° longitude (i.e., 143*144 grid cells globally).</p> <p>Datasets includes:</p> <p>1. Source data for global air temperature change in each grid from different scenarios based on the idealized cultivation map and the land cover type for the simulations.</p> <p>2. Source data for global temperature change and its different contributors.</p> <p>3. Source data for global temperature change in different regions.</p> <p>4. Source data for global temperature change and different contributors in scenarios based on different cultivation maps.</p> <p> </p>
Figure 6. Branch length comparisons for 147 taxa. All common bipartition branch lengths plotted against the 147t 3p in Beyond the prolegomenon: a molecular phylogeny of the Australian camaenid land snail radiation
Figure 6. Branch length comparisons for 147 taxa. All common bipartition branch lengths plotted against the 147t 3p analysis bipartition branch lengths. Confidence intervals (CI) as for Figure 9.
Figure 4 in Beyond the prolegomenon: a molecular phylogeny of the Australian camaenid land snail radiation
Figure 4. Consensus trees of the 147 taxa that have at least two genes, using the 3p model. A,147t Markov chain Monte Carlo (MCMC) all-compatible consensus with branch lengths, posterior probabilities (PP; decimal format to the right of nodes) and RAxML bootstrap values (5000 replicates; percentages to left of nodes); B, strict consensus of three models by two taxa sets (six analyses).
Figure 5. Branch length comparisons across models and taxa sets for 62 in Beyond the prolegomenon: a molecular phylogeny of the Australian camaenid land snail radiation
Figure 5. Branch length comparisons across models and taxa sets for 62 taxa. All common bipartition branch lengths from each of the three taxa set analyses (pruned to the 62 taxa) plotted against the 62t analysis 3p model bipartition branch lengths. Confidence intervals (CI) based on MCMC variation in the most data complete analysis, are approximated by dashed lines.
Figure 3 in Beyond the prolegomenon: a molecular phylogeny of the Australian camaenid land snail radiation
Figure 3. Consensus trees of the 62 taxa that have all three genes, using the 3p model. A, 62t Markov chain Monte Carlo (MCMC) all-compatible consensus tree with branch lengths, posterior probabilities (PP; decimal format to the right of nodes) and RAxML bootstrap values (5000 replicates; percentages to left of nodes); B, strict consensus amongst all-compatible consensus trees for three models by three taxa sets (nine analyses).
Figure 5 in Geographic isolation and human-assisted dispersal in land snails: a Mediterranean story of Helix borealis and its relatives (Gastropoda: Stylommatophora: Helicidae)
Figure 5. Results of biogeographic reconstruction using the DEC model in RASP using 1000 time-trees estimated with BEAST from the full dataset (see text). Maximum of three areas was allowed for ancestral ranges and unlikely disjunctive ranges were prohibited. The areas (A–F, in parentheses) of which the ranges of the terminals consist are shown in Figure 2.
Figure 2 in Geographic isolation and human-assisted dispersal in land snails: a Mediterranean story of Helix borealis and its relatives (Gastropoda: Stylommatophora: Helicidae)
Figure 2. Distribution of sampling sites and assignment of locally sampled individuals to major mitochondrial clades of the Mediterranean clade of Helix. Different symbols and colours correspond to different groups as in Figure 4. Areas used for biogeographic reconstructions are shown and labelled with letters corresponding to those in Figure 5. A, northern Adriatic basin; B, northern Africa; C, Apennine Peninsula and Corsica; D, Greece, western Aegean and Crete; E, eastern Aegean and western Anatolia; F, northern Levant.
Figure 1 in Geographic isolation and human-assisted dispersal in land snails: a Mediterranean story of Helix borealis and its relatives (Gastropoda: Stylommatophora: Helicidae)
Figure 1. Live individuals of the Mediterranean clade Helix species: H. cincta (A), H. borealis (B–E) and H. melanostoma (F). A, Cyprus, Kato Drys; B, Greece, Evvia, between Vassilika and Ellinika; C, Greece, Peloponnese, by Rogi near Kalavryta; D, Greece, Kefalonia: Asos; E, Greece, Peloponnese, near Sparti; F, north-western Tunisia, Kef Governorate, near Sers.
Figure 4 in Geographic isolation and human-assisted dispersal in land snails: a Mediterranean story of Helix borealis and its relatives (Gastropoda: Stylommatophora: Helicidae)
Figure 4. Detailed unrooted phylogeny of the Mediterranean clade inferred from a concatenated alignment of partial mitochondrial COI and 16S gene sequences. The tree is a 50% majority rule consensus of the posterior of a Bayesian analysis with MrBayes. Branch labels indicate SH-aLRT support values, standard bootstrap proportions from 1000 pseudoreplicates, and posterior probabilities from a Bayesian analysis, respectively. Only posterior probabilities are shown for some branches near the tips due to space constraints. Tips are labelled by isolate codes (our data) or GenBank accession numbers of 16S sequences (data from other authors) and two-letter ISO country codes; where needed for interpretation of the results, geographic origin of the sample is given, usually by the closest significant town. Red arrowheads point to samples presumed here to be taken outside of the native range of the respective species. Clade symbols and colours correspond to Figure 2. Abbreviations: anct, anctostoma; thiess, thiesseana; undesc, undescribed; valent, valentini.
Clear-sky profile database for the development of Land Surface Temperature algorithms
<p>This dataset includes clear sky atmospheric profiles from the European Centre for Medium Range Forecast (ECMWF) version-5 reanalysis (ERA5), specially selected to support the development of algorithms of Land Surface Temperature (LST) retrieval from Earth observation (EO) data. The profiles were re-sampled from an ERA5 dataset covering the 2009-2019 period, with a 1x1 degree spatial resolution, hourly sampling and using the full vertical resolution (137 model levels). The re-sampling technique is based on a dissimilarity criterion applied to profiles of temperature and specific humidity, in order to obtain regular distributions of atmospheric variables of relevance for LST retrieval in the Thermal Infrared (TIR) spectral range. The database is limited to clear-sky conditions over land, being therefore suitable for the development of satellite land products relying on optical and thermal infrared imagery in general, despite targeting especially LST.</p> <p><strong>Dataset description:</strong></p> <p>The dataset is divided in multiple netCDF4 files based on the range of skin temperature (Tskin; Kelvin) and the range of total column water vapour (TCWV; mm). Each file includes the following variables:</p> <ul> <li>Time</li> <li>Longitude</li> <li>Latitude</li> <li>2-m temperature (t2m)</li> <li>Surface pressure (sp)</li> <li>Total cloud cover (tcc)</li> <li>Total column water vapour (tcwv)</li> <li>Skin temperature (skt)</li> <li>Surface emissivity (emis)</li> <li>Land cover classification (lcc)</li> <li>Temperature profile (t)</li> <li>Specific humidity profile (q)</li> <li>Ozone profile (o3)</li> <li>Pressure profile (p)</li> </ul> <p>All profiles are provided on model levels. For each profile, 6 values of skin temperature and 25 values of emissivity are provided (see publication for details). Emissivity values correspond to the wavelengths of ~11 and ~12 µm.</p> <p><strong>Credit:</strong></p> <p>To use this data please cite this dataset and the respective journal publication:</p> <p>Ermida, S.L.; Trigo, I.F. (2022) A Comprehensive Clear-Sky Database for the Development of Land Surface Temperature Algorithms. <em>Remote Sens.</em>, <em>14</em>, 2329. <a href="https://doi.org/10.3390/rs14102329">https://doi.org/10.3390/rs14102329</a> </p> <p> </p> <p><strong>Access: </strong></p> <p>Currently, Zenodo does not provide a simple way to download datasets with a large number of files. We recomend trying the <a href="https://zenodo.org/record/3676567#.YnJAxtPMJhE">Zenodo_get</a> to simplify the download.</p>
Land use induced emissions of land acquisitions in Laos
<p>Title Emission effects of land acquisitions in Laos</p> <p>Data support the findings of the study "Emission effects of land acquisitions in Laos", Ecology and Society Project Hidden Emissions Of Forest Transitions, ERC-2017-StG 757995 HEFT, 2018-2023 PI: Simone Gingrich</p> <p>Authors Sonja Bauernschuster Melanie Pichler Vong Nanhthavong Rasso Bernhard Michael Epprecht Simone Gingrich</p> <p> </p> <p>Index</p> <p> </p> <p>Land Conversion Matrizes</p> <p>C Stock Change Matrizes</p> <p>Indicators</p> <p>Sensitivity Analysis</p> <p> </p> <p>Contact</p> <p>Sonja Bauernschuster sonja.bauernschuster@boku.ac.at</p> <p> </p> <p>Large-scale land acquisitions repeatedly fall short of their acclaimed socio-economic benefits and are associated with unintended social, economic and ecological costs. In Laos, the government starts to question its own “Turning Land Into Capital” policy and reviews land acquisitions or concessions with regard to their socio-economic impacts. Empirical investigations of environmental impacts of land concessions, however, remain underrepresented. This article links the nation-wide concession development between 2001 and 2017 with associated land-use changes and quantifies related land-use change induced emissions. Results show that land acquisitions for agriculture, forestry and mining mainly affect forests and land previously used for shifting cultivation and permanent agriculture, e.g. rice paddies. Consequently, land conversions caused by concessions resulted in net carbon emissions of 4.9 Mt CO<sub>2</sub>e yr<sup>-1</sup> on average in 2001-2017, amounting to 34% of total emissions from land conversions. Even tree plantations that are meant to serve as net C sinks, caused net emissions, but data here are least robust. The relatively low C emission intensity of shifting cultivation compared to the high C emission intensity of concessions challenges the dominant narrative of shifting cultivation as a causal factor for forest degradation. Political implications to foster sustainable development include the reduction of land acquisitions because of their emissions intensity, and minimization of emissions and social conflict induced by granted concessions, e.g. by allocating land with low C densities and obtaining consent of local land users.</p>
Stronger effect of litter quality than microorganisms on leaf and root litter C and N loss at different decomposition stages following a subtropical land use change
<p>Litter decomposition contributes largely to global carbon (C) and nitrogen (N) cycling, and it is strongly determined by litter quality and microbial community composition in ways that are poorly understood. Here, we conducted a 2-year field litter decomposition experiment by collecting leaf and root litter of crops (from cropland), shrubs (from shrubland), and wood (from woodland) and placing samples for decomposition in woodland soil in central China to investigate the effects of litter quality and microbial community composition on C and N loss of leaf and root litter of three species under different decomposition stages. Our results showed that the leaf litter C and N losses of shrubs were significantly higher than those of crops and wood, whereas the root litter C and N losses of crops were significantly higher than those of shrubs and wood. Generally, the leaf litter C and N losses of the three species were higher on average than those of fine root litter under the whole decomposition period. For the C loss of the three species, litter lignin and phosphorus as well as initial litter quality were predominant drivers of root litter decomposition, while litter lignin, cellulose, and hemicellulose concentrations were dominant for leaf litter decomposition. For N loss, litter stoichiometry and litter quality directly governed leaf and root litter N loss, and the initial litter quality largely regulated N loss at the late decomposition stage. Unexpectedly, the effect of microbial community composition on litter C and N loss was relatively weak and only exhibited an effect on litter C and N loss during the early stage of decomposition. Thus, our results revealed the huge disparity in C and N loss of plant species and litter types at different decomposition stages, which should be considered jointly when evaluating their roles in plant-soil feedbacks under global land use change.</p>
Changes in plant species richness due to land use and nitrogen deposition across the globe
<p>Data and scripts corresponding to the article "Combined effects of land use and nitrogen deposition on plant species richness worldwide". In the article, we quantified the combined effects of land use and nitrogen deposition on terrestrial plant species richness at a 0.25º spatial resolution across the globe. We first determined the proportional changes in plant species richness for different land-use types and N deposition values through meta-analyses of local monitoring data obtained from the literature. We then combined the site-level responses with global land use and N deposition maps in a new multi-pressure species-area relationship (mp-SAR) model to provide estimates of changes in plant species richness at a resolution of 0.25º (about 25 km at the equator) worldwide.</p> <p>In this repository you will find all the data necessary to get the final results and the code in R to guide you through out the analysis.</p>
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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.