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1,523 results for “steppe”
Daily river metabolism using oxygen flux at 75 sites in Mongolia or the United States in steppe ecoregions
We obtained GIS data to indicate local geomorphology and watershed-scale values for land use, climate, slope, and elevation for each sampling site. We selected our sites using the GIS-based program RESonate (Williams et al., 2013) to represent replicates in multiple watersheds of different geomorphic patches or Functional Process Zones (FPZs). The FPZs are reoccurring longitudinal geomorphic patches that are hypothesized to control biocomplexity, including community composition and system productivity (Thorp et al., 2006). A detailed description of the FPZ delineation methodology we employed has been provided previously (Maasri et al., 2019a; Erdenee et al., 2021). We classified each study site hierarchically by country, ecoregion, river basin, upper (streams higher in the watershed) or lower (low slope rivers of lower elevations), and relatively constrained valley or wide valley. This approach allowed us to assess reach-scale properties that could directly influence the physiological controls most often collected alongside metabolism data. This provided a framework to evaluate how we may understand the determinants of metabolism at multiple scales. We studied three large-scale temperate steppe ecoregions (Terminal Basin, TB; Montane Steppe, MS; and Grassland Steppe, GS) as characterized by Olson et al. (2001) and updated by Dinerstein et al. (2017) in two countries (Mongolia and the United States, Fig. 2). We aggregated our large-scale ecoregions for the US as follows: TB = Great Basin shrub steppe and Sierra Nevada forest, MS = South Central Rockies forest and Wyoming Basin shrub steppe, GS = Nebraska Sand Hills mixed grasslands and Northern Shortgrass prairie. We aggregated our large-scale ecoregions for Mongolia as follows: TB = Altai mountains forest and forest steppe, Gobi Lakes Valley desert steppe, Great Lakes Basin desert steppe, and Khangai Mountains alpine meadows, MS = Selenge-Orkhon forest steppe and Syan Mountains conifer forests, GS = Daurian Forest s
A long term hourly eddy covariance dataset of consistently processed CO2 and H2O Fluxes from the Tibetan Alpine Steppe at Nam Co (2005 - 2019)
<p>The data set contains nearly 15 years of eddy covariance data from an alpine steppe ecosystem on the central Tibetan Plateau. The data was processed following standardized quality control methods to allow for comparability between the different years of our record and with other data sets. To ensure meaningful estimates of ecosystem atmosphere exchange, careful application of the following correction procedures and analyses was necessary: (1) Due to the remote location, continuous maintenance of the eddy covariance (EC) system was not always possible, so that cleaning and calibration of the sensors was performed irregularly. Furthermore, the high proportion of bare soil and high wind speeds led to accumulation of dirt in the measurement path of the infrared gas analyzer (IRGA). The installation of the sensor in such a challenging environment resulted in a considerable drift in CO2 and H2O gas density measurements. If not accounted for, this concentration bias may distort the estimation of the carbon uptake. We applied a modified drift correction procedure following Fratini et al. (2014) which, instead of a linear interpolation between calibration dates, uses the CO2 concentration measurements from the Mt. Waliguan atmospheric observatory as reference time series. (2) We applied rigorous quality filtering of the calculated fluxes to retain only fluxes which represent actual physical processes. (3) During the long measurement period, there were several buildings constructed in the near vicinity of the EC system. We investigated the influence of these obstacles on the turbulent flow regime to identify fluxes with uncertain land cover contribution and exclude them from subsequent computations. (4) We calculated the de-facto standard correction for instrument surface heating during cold conditions (hereafter called sensor self heating correction) following Burba et al. (2008) and a revision of the original method following Frank and Massman (2020). (5) Subsequently, we applied the traditional and widely used gap filling procedure following Reichstein et al. (2005) to provide a more complete overview of the annual net ecosystem CO2 exchange. (6) We estimated the flux uncertainty by calculating the random flux error (RE) following Finkelstein and Sims (2001) and by using the standard deviation of the fluxes used for gap filling (NEE_fsd) as a measure for spatial and temporal variation.</p> <p>References:</p> <ol> <li>Burba, G. G., McDermitt, D. K., Grelle, A., Anderson, D., and XU, L. (2008). Addressing the influence of instrument surface heat exchange on the measurements of CO2 flux from open-path gas analyzers, Global Change Biology, 14, 1854-1876, <a href="https://doi.org/10.1111/j.1365-2486.2008.01606.x">https://doi.org/10.1111/j.1365-2486.2008.01606.x</a>.</li> <li>Finkelstein, P. L. and Sims, P. F. (2001). Sampling error in eddy correlation flux measurements, J. Geophys. Res. Atmos., 106, 3503–3509, doi:10.1029/2000JD900731.</li> <li>Frank, J. M. and Massman, W. J.: A new perspective on the open-path infrared gas analyzer self-heating correction, Agricultural and Forest Meteorology, 290, 107986, doi:10.1016/j.agrformet.2020.107986, 2020.</li> <li>Fratini, G., McDermitt, D. K., and Papale, D. (2004). Eddy-covariance flux errors due to biases in gas concentration measurements: origins, quantification and correction, Biogeosciences, 11, 1037-1051, <a href="https://doi.org/10.5194/bg-11-1037-2014">https://doi.org/10.5194/bg-11-1037-2014</a>.</li> <li>Reichstein, M., Falge, E., Baldocchi, D., Papale, D., Aubinet, M., Berbigier, P., Bernhofer, C., Buchmann, N., Gilmanov, T., Granier, A., Grunwald, T., Havrankova, K., Ilvesniemi, H., Janous, D., Knohl, A., Laurila, T., Lohila, A., Loustau, D., Matteucci, G., Meyers, T., Miglietta, F., Ourcival, J.-m., Pumpanen, J., Rambal, S., Rotenberg, E., Sanz, M., Tenhunen, J., Seufert, G., Vaccari, F., Vesala, T., Yakir, D., and valentini, R. (20050. On the separation of net ecosystem exchange into assimilation and ecosystem respiration: review and improved algorithm, Global Change Biology, 11, 1424-1439, <a href="https://doi.org/10.1111/j.1365-2486.2005.001002.x">https://doi.org/10.1111/j.1365-2486.2005.001002.x</a>.</li> </ol>
Distribution and habitat suitability maps for Central European steppe plants
<p>This dataset contains distribution maps for Central European steppe plants and coordinates of species occurrence points used by Divíšek et al. (2022) to calibrate habitat suitability models. These models were projected onto past climates and the resulting habitat suitability maps for 10 periods since the Last Glacial Maximum (LGM) are also included. These maps were further used as input data for simulations of species migration from climatically suitable areas in the LGM to identify those that may have served as a source for colonisation of the species' current ranges. For each species, we present maps of climatically suitable areas during the LGM and mid-Holocene (for the latter period, only areas accessible from the LGM are shown), as well as maps of the "source areas" from which the species may have colonised the regions occupied today.</p>
Indicative distribution map for Ecosystem Functional Group T5.1 Semi-desert steppe
<p>This archive contains indicative distribution maps and profiles for <strong>T5.1 Semi-desert steppe</strong>, a ecosystem functional group (EFG, level 3) of the <a href="https://global-ecosystems.org/">IUCN Global Ecosystem Typology</a> (v2.0). Please refer to Keith <em>et al.</em> (2020) for details.</p> <p>The descriptive profiles provide brief summaries of key ecological traits and processes, maps are indicative of global distribution patterns, and are not intended to represent fine-scale patterns. The maps show areas of the world containing major (value of 1, coloured red) or minor occurrences (value of 2, coloured yellow) of each ecosystem functional group. Minor occurrences are areas where an ecosystem functional group is scattered in patches within matrices of other ecosystem functional groups or where they occur in substantial areas, but only within a segment of a larger region. Given bounds of resolution and accuracy of source data, the maps should be used to query which EFG are likely to occur within areas, rather than which occur at particular point locations. Detailed methods and references for the maps are included in the profile (xml format).</p>
Ecological memory effects on plants and soils in early post-fire steppe, Barton Ecological Research Area, Pocatello, Idaho, 2021
In many regions of the world, wildfires are becoming more frequent due to the invasion of exotic grasses that are highly flammable and often replace native plants as burned landscapes regrow. To prevent invasive species from dominating post-burn landscapes, land managers are increasingly applying seeds of native plants to suppress invasive plants and encourage ecosystem recovery. However, there is still much to learn about the ability of seeded species to establish and suppress flammable invaders. It is also unclear how previous human-caused landscape changes, such as nitrogen pollution or the removal of shrubs (a common practice in western USA rangelands), affect the success of native seed additions and plant recovery from fire. This study addresses these issues by building on a long-term experiment investigating the legacy effects of past nitrogen pollution and shrub removal in a highly invaded sagebrush steppe ecosystem at Idaho State University’s Barton Ecological Research Area in Pocatello, ID. This experiment burned in a wildfire in August, 2020, providing a unique opportunity to evaluate how a history of nitrogen pollution and shrub removal influences plant recovery from wildfire. We developed three native seed mixes intended to suppress invasive plants, particularly flammable annual grasses, and in April, 2021, we sowed the experimental mixes into research plots within the original experiment. To measure the initial effects of the experimental seed additions and the legacy effects of previous nitrogen pollution and shrub removal, we collected the data provided here during the summer of 2021, the first growing season following the wildfire. We established 240 monitoring quadrats (1 m²) within the original experiment, dividing the quadrats between areas where shrubs had formerly been (evidenced by stumps) and intershrub areas. At a microhabitat scale, the presence of shrubs alters soil properties and can create legacy effects after shrub death, and we were int
Phlorest phylogeny derived from Chang et al. 2015 'Ancestry-constrained phylogenetic analysis supports the Indo-European steppe hypothesis'
<p>Cite the source of the dataset as:</p> <blockquote> <p>Chang W, Cathcart C, Hall D, & Garrett A. 2015. Ancestry-constrained phylogenetic analysis supports the Indo-European steppe hypothesis. Language, 91(1):194-244.</p> </blockquote>
SNE01 Species richness, community evenness (Evar) and ANPP effects of nitrogen addition across a gradient of 8 levels in a semi-arid shortgrass steppe and a mesic tallgrass prairie, 2014-2018
This dataset contains the first five years (2014-2018) of the effect of nitrogen addition on species richness, species evenness (Evar) and productivity for a long-term nitrogen addition gradient experiment in two North American grasslands: a semi-arid shortgrass steppe and a mesic tallgrass prairie. Fertilization with time-release urea has been on-going since 2014 in a gradient of eight levels: 0, 2.5, 5, 10, 15, 20, 30 g/m-2. The effect of nitrogen on richness, evenness and Aboveground Net Primary Productivity (ANPP g/m-2 yr) is calculated as the absolute change in value from control plots to treatment plots within each block.
Fig. 2 in New record of the steppe longhorn beetle species Phytoecia (Musaria) argus (G. F. Frölich, 1793) (Cerambycidae: Lamiinae) in Bulgaria
Fig. 2. Phytoecia argus (G. F. Frölich, 1793), Chepun Mts., 17.05.2019. A: male; B: female. Scale bar: 1 mm.
Extending Grime's CSR model to predict plant demographic responses across resource availability gradients: evidence from the Patagonian steppes
<p>Sexual reproduction, growth, and survival are crucial demographic strategies for plant population viability. Here, we propose a conceptual model predicting demographic responses of species based on their ecological strategy and the heterogeneity of environmental conditions within a biogeographical unit and then applied it to a case study from a 5-degree latitudinal gradient in the Patagonian steppes. We also aim to disentangle genetic from environmental effects on demographic responses. We performed <em>in-situ </em>and common garden experiments with two species from six local populations of the Occidental Phytogeographical District of the Patagonian steppes. Species differ in key ecological traits, and thus fit into Grime´s model for evolutionary strategies in plants: one as competitive species and the other as stress-tolerant species. We calculated population growth rate (λ) and performed elasticity analyses to compare the contribution of each demographic strategy to population fitness between species and among local populations distributed along 600 km latitudinal gradient with differences in mean annual precipitation (MAP). We highlight four results. First, the competitive species change from sexual reproduction to growth as MAP increases. Second, the stress-tolerant species relied on growth and survival along the MAP gradient. Third, interannual variation in resource availability modulated demographic responses for both strategies. Fourth, based on the comparison of the <em>in-situ</em> and common garden experiments, we submit that demographic responses were genetically driven. Our study shows that demographic responses can be roughly predicted by the ecological strategy across environmental gradients. We show that differences arise not only between species, but also were genetically driven differences within species among local populations. Scaling up plant-level responses to population-level dynamics allows for a process-based understanding of current and future biogeographical species organization. Furthermore, conservation and restoration efforts should be guided by demographic strategies underlying population viability.</p>
Data from: Central Mongolian lake sediments reveal new insights on climate change and equestrian empires in the Eastern Steppes
<p>The data set includes the results of ICP-OES, CNS, biomarker, and stable isotope analyses published in the research paper:</p> <p><strong>Struck, J., Bliedtner, M., Strobel, P., Taylor, W., Biskop, S., Plessen, B., Klaes, B., Bittner, L., Jamsranjav, B., Salazar, G., Szidat, S., Brenning, A., Bazarradnaa, E., Glaser, B., Zech, M., Zech, R.: Central Mongolian lake sediments reveal new insights on climate change and equestrian empires in the Eastern Steppes. Scientific Reports, 12, 2829, (2022). DOI: https://doi.org/10.1038/s41598-022-06659-w</strong></p> <p>For further information, in particular, the analyses and methods applied, we refer the reader/user to the original research paper and the supporting information published in Scientific Reports.</p> <p> </p> <p> </p>
Fig. 5. The marginal response curve for the explanatory variable Bio14 in Modelling The Bioclimatic Niche And Distribution Of The Steppe Mouse, Mus Spicilegus (Rodentia, Muridae), In Ukraine
Fig. 5. The marginal response curve for the explanatory variable Bio14 (Precipitation of driest week). (HS — habitat suitability).
Fig. 1 in Modelling The Bioclimatic Niche And Distribution Of The Steppe Mouse, Mus Spicilegus (Rodentia, Muridae), In Ukraine
Fig. 1. Occurrences of Mus spicilegus in Ukraine and neighbouring areas used for creating the ENM. [Data collected before (triangles) and after (circles) 1990.]
Fig. 4. The marginal response curve for the explanatory variable Bio09 in Modelling The Bioclimatic Niche And Distribution Of The Steppe Mouse, Mus Spicilegus (Rodentia, Muridae), In Ukraine
Fig. 4. The marginal response curve for the explanatory variable Bio09 (Mean temperature of driest quarter). (HS — habitat suitability).
Fig. 7. 0.5 in Modelling The Bioclimatic Niche And Distribution Of The Steppe Mouse, Mus Spicilegus (Rodentia, Muridae), In Ukraine
Fig. 7. 0.5 oC isotherms for Bio09 (Mean temperature of driest quarter) for different time periods: 1 — 1980s; 2 — 2000s; 3 — contemporary; 4 — predicted for 2030.
Fig. 6. A in Modelling The Bioclimatic Niche And Distribution Of The Steppe Mouse, Mus Spicilegus (Rodentia, Muridae), In Ukraine
Fig. 6. A current climate habitat suitability map for the Steppe mouse (Mus spicilegus) in Ukraine. Darker shades of gray denote areas of higher predicted habitat suitability probabilities (≥ 0.5) and lighter shades correspond to lower (≥ 0.311 and <0.5). [Administrative regions in Ukraine: 1 — Chernihiv Region; 2 — Kyiv Region; 3 — Ternopil Region; 4 — Ivano-Frankivsk Region.]
Fig. 1 in The Expansion Of The Blackbird, Turdus Merula (Passeriformes, Muscicapidae), In The Steppe Zone Of Ukraine
Fig. 1. The southern boundary of the Blackbird habitat in the steppe zone of the Right-bank and Left-bank Ukraine and the northern one in the steppe zone of the Crimean peninsula: black circles are artificial tree plantations and light ones are natural forests.
Fig. 2 in Interspecific Agression Of The Passerine Birds (Aves, Passeriformes) On Watering Places In Wood-And-Steppe Zone Of Ukraine
Fig. 2. Grouping of species distribution by demonstration of aggressive behavior at watering places in the State Arboretum "Alexandria".
Fig. 6 in Interspecific Agression Of The Passerine Birds (Aves, Passeriformes) On Watering Places In Wood-And-Steppe Zone Of Ukraine
Fig. 6. Rating of success of attack and defense of birds in biological educational and research institution "Vakalivschyna".
Fig. 3 in Interspecific Agression Of The Passerine Birds (Aves, Passeriformes) On Watering Places In Wood-And-Steppe Zone Of Ukraine
Fig. 3. Grouping of species distribution by demonstration of aggressive behavior at watering places in biological educational and research institution "Vakalivschyna".
Fig. 1 in Interspecific Agression Of The Passerine Birds (Aves, Passeriformes) On Watering Places In Wood-And-Steppe Zone Of Ukraine
Fig. 1. Grouping of species distribution by demonstration of aggressive behavior at watering places in Kaniv Nature Reserve.
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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.