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3,583 results for “elevations”
Data from: Biotic interactions help explain variation in elevational range limits of birds among Bornean mountains
Aim <p>Physiological tolerances and biotic interactions along habitat gradients are thought to influence species occurrence. Distributional differences caused by such forces are particularly noticeable on tropical mountains, where high species turnover along elevational gradients occurs over relatively short distances and elevational distributions of particular species can shift among mountains. Such shifts are interpreted as evidence of the importance of spatial variation in interspecific competition and habitat or climatic gradients. To assess the relative importance of competition and compression of habitat and climatic zones in setting range limits, we examined differences in elevational ranges of forest bird species among four Bornean mountains with distinct features.</p> Location <p>Bornean mountains Kinabalu, Mulu, Pueh and Topap Oso.</p> Taxon <p>Rain forest bird communities along elevational gradients.</p> Methods <p>We surveyed the elevational ranges of rain forest birds on four mountains in Borneo to test which environmental variables—habitat zone compression or presence of likely competitors—best predicted differences in elevational ranges of species among mountains. For this purpose, we used two complementary tests: a comparison of elevational range limits between pairs of mountains, and linear mixed models with naïve occupancy as the response variable.</p> Results <p>We found that lowland species occur higher in elevation on two small mountains compared to Mt. Mulu. This result is inconsistent with the expectation that distributions of habitats are elevationally compressed on small mountains, but is consistent with the hypothesis that a reduction in competition (likely diffuse) on short mountains, which largely lack montane specialist species, allows lowland species to occur higher in elevation. The relative influence of competition changes with elevation, and the correlation between lower range limits of montane species and the distribution of their competitors was weaker than in lowland species.</p> Main conclusions <p>These findings provide support for the importance of biotic interactions in setting elevational range limits of tropical bird species, although abiotic gradients explain the majority of distribution patterns. Thus, models predicting range shifts under climate change scenarios must include not only climatic variables, as is currently most common, but also information on potentially resulting changes in species interactions, especially for lowland species.</p>
dh/dt data of the manuscript "Surface Elevation Change of Glaciers Along the Coast of Prudhoe Land, Northwestern Greenland from 1985 to 2018 "
<p>This is a dataset including dt/dt data for periods of T0–T2, T0–T1, and T1–T2, which used in the manuscript "Surface Elevation Change of Glaciers Along the Coast of Prudhoe Land, Northwestern Greenland from 1985 to 2018".</p>
Elevation Models for Reproducible Evaluation of Terrain Representation – Archetypal Landforms – Kočevje Rog
<p>An elevation model of Kočevje Rog, Slovenia</p> <p>Landform features: karstified plateau, karst</p> <p>Resolution: 2 meter, 4,500 x 4,500 height samples</p> <p>File format: Esri ASCII</p> <p>This is one model of a set of elevation models: <a href="http://doi.org/10.5281/zenodo.3938020">https://doi.org/10.5281/zenodo.3938020</a>. Please cite the entire set of models.</p> <p>When using this elevation model in an academic publication, please cite the following article, which describes the process and rationale for compiling elevation models:</p> <p><em>Kennelly, P. J., Patterson, T., Jenny, B., Huffman, D. P., Marston, B. E., Bell, S. and Tait, A. M. (2021). Elevation models for reproducible evaluation of terrain representation. Cartography and Geographic Information Science, 48:1, 63–77. DOI: <a href="http://doi.org/10.1080/15230406.2020.1830856">10.1080/15230406.2020.1830856</a></em></p>
Elevation Models for Reproducible Evaluation of Terrain Representation – Archetypal Landforms – Kočevje Rog GeoTIFF
<p>An elevation model of Kočevje Rog, Slovenia</p> <p>Landform features: karstified plateau, karst</p> <p>Resolution: 2 meter, 4,500 x 4,500 height samples</p> <p>File format: GeoTIFF</p> <p>This is one model of a set of elevation models: <a href="http://doi.org/10.5281/zenodo.3938020">https://doi.org/10.5281/zenodo.3938020</a>. Please cite the entire set of models.</p> <p>When using this elevation model in an academic publication, please cite the following article, which describes the process and rationale for compiling elevation models:</p> <p><em>Kennelly, P. J., Patterson, T., Jenny, B., Huffman, D. P., Marston, B. E., Bell, S. and Tait, A. M. (2021). Elevation models for reproducible evaluation of terrain representation. Cartography and Geographic Information Science, 48:1, 63–77. DOI: <a href="http://doi.org/10.1080/15230406.2020.1830856">10.1080/15230406.2020.1830856</a></em></p>
Elevation Models for Reproducible Evaluation of Terrain Representation – Archetypal Landforms – Bryce Canyon GeoTIFF
<p>An elevation model of Bryce Canyon, USA</p> <p>Landform features: narrow rock formations known as hoodoos</p> <p>Resolution: 1 meter, 4,000 x 3,800 height samples</p> <p>File format: GeoTIFF</p> <p>This is one model of a set of elevation models: <a href="http://doi.org/10.5281/zenodo.3938020">https://doi.org/10.5281/zenodo.3938020</a>. Please cite the entire set of models.</p> <p>When using this elevation model in an academic publication, please cite the following article, which describes the process and rationale for compiling elevation models:</p> <p><em>Kennelly, P. J., Patterson, T., Jenny, B., Huffman, D. P., Marston, B. E., Bell, S. and Tait, A. M. (2021). Elevation models for reproducible evaluation of terrain representation. Cartography and Geographic Information Science, 48:1, 63–77. DOI: <a href="http://doi.org/10.1080/15230406.2020.1830856">10.1080/15230406.2020.1830856</a></em></p>
Elevation Models for Reproducible Evaluation of Terrain Representation – Archetypal Landforms – Bryce Canyon ASCII
<p>An elevation model of Bryce Canyon, USA</p> <p>Landform features: narrow rock formations known as hoodoos</p> <p>Resolution: 1 meter, 4,000 x 3,800 height samples</p> <p>File format: Esri ASCII</p> <p>This is one model of a set of elevation models: <a href="http://doi.org/10.5281/zenodo.3938020">https://doi.org/10.5281/zenodo.3938020</a>. Please cite the entire set of models.</p> <p>When using this elevation model in an academic publication, please cite the following article, which describes the process and rationale for compiling elevation models:</p> <p><em>Kennelly, P. J., Patterson, T., Jenny, B., Huffman, D. P., Marston, B. E., Bell, S. and Tait, A. M. (2021). Elevation models for reproducible evaluation of terrain representation. Cartography and Geographic Information Science, 48:1, 63–77. DOI: <a href="http://doi.org/10.1080/15230406.2020.1830856">10.1080/15230406.2020.1830856</a></em></p>
Elevation Models for Reproducible Evaluation of Terrain Representation – Archetypal Landforms
<p>This is a set of elevation models of archetypal landforms: </p> <ul> <li>volcanic caldera (Crater Lake, Oregon, USA),</li> <li>active sand dunes (Great Sand Dunes, Colorado, USA),</li> <li>a braided riverbed (Jackson Hole, Wyoming, USA),</li> <li>folded ridges (Massanutten Mountain, Virginia, USA),</li> <li>stabilized sand dunes (Sandhills, Nebraska, USA),</li> <li>crater of a shield volcano (Kilauea, Hawaii, USA),</li> <li>karst plateau (Kočevje Rog, Slovenia),</li> <li>narrow rock formations, aka hoodoos (Bryce Canyon, USA)</li> </ul> <p>All elevation models were derived from NED LiDAR sources with cell sizes ranging from 1 to 10 meters. The size of the models varies between approximately 4,000 × 4,000 and 5,500 × 5,500 height samples. The elevation models are available in georeferenced GeoTIFF and Esri ASCII file formats.</p> <p>Version 2 adds models of Kilauea, Hawaii, USA, Kočevje Rog, Slovenia, and Bryce Canyon, USA.</p> <p>When using these elevation models in an academic publication, please cite the following article, which describes the process and rationale for compiling these models:</p> <p><em>Kennelly, P. J., Patterson, T., Jenny, B., Huffman, D. P., Marston, B. E., Bell, S. and Tait, A. M. (2021). Elevation models for reproducible evaluation of terrain representation. Cartography and Geographic Information Science, 48:1, 63–77. DOI: <a href="http://doi.org/10.1080/15230406.2020.1830856">10.1080/15230406.2020.1830856</a></em></p>
Elevation Models for Reproducible Evaluation of Terrain Representation – Archetypal Landforms – Kilauea GeoTIFF
<p>An elevation model of Kīlauea, Hawaii, USA</p> <p>Landform features: shield volcano crater</p> <p>Resolution: 1 meter, 7,200 x 6,800 height samples</p> <p>File format: GeoTIFFI</p> <p>This is one model of a set of elevation models: <a href="http://doi.org/10.5281/zenodo.3938020">https://doi.org/10.5281/zenodo.3938020</a>. Please cite the entire set of models.</p> <p>When using this elevation model in an academic publication, please cite the following article, which describes the process and rationale for compiling elevation models:</p> <p><em>Kennelly, P. J., Patterson, T., Jenny, B., Huffman, D. P., Marston, B. E., Bell, S. and Tait, A. M. (2021). Elevation models for reproducible evaluation of terrain representation. Cartography and Geographic Information Science, 48:1, 63–77. DOI: <a href="http://doi.org/10.1080/15230406.2020.1830856">10.1080/15230406.2020.1830856</a></em></p>
Elevation Models for Reproducible Evaluation of Terrain Representation – Archetypal Landforms – Kilauea ASCII
<p>An elevation model of Kīlauea, Hawaii, USA</p> <p>Landform features: shield volcano crater</p> <p>Resolution: 1 meter, 7,200 x 6,800 height samples</p> <p>File format: Esri ASCII</p> <p>This is one model of a set of elevation models: <a href="http://doi.org/10.5281/zenodo.3938020">https://doi.org/10.5281/zenodo.3938020</a>. Please cite the entire set of models.</p> <p>When using this elevation model in an academic publication, please cite the following article, which describes the process and rationale for compiling elevation models:</p> <p><em>Kennelly, P. J., Patterson, T., Jenny, B., Huffman, D. P., Marston, B. E., Bell, S. and Tait, A. M. (2021). Elevation models for reproducible evaluation of terrain representation. Cartography and Geographic Information Science, 48:1, 63–77. DOI: <a href="http://doi.org/10.1080/15230406.2020.1830856">10.1080/15230406.2020.1830856</a></em></p>
Wildfires and climate change push low-elevation forests across a critical climate threshold for tree regeneration
Climate change is increasing fire activity in the western United States, which has the potential to accelerate climate-induced shifts in vegetation communities. Wildfire can catalyze vegetation change by killing adult trees that could otherwise persist in climate conditions no longer suitable for seedling establishment and survival. Recently documented declines in postfire conifer recruitment in the western United States may be an example of this phenomenon. However, the role of annual climate variation and its interaction with long-term climate trends in driving these changes is poorly resolved. Here we examine the relationship between annual climate and postfire tree regeneration of two dominant, low-elevation conifers (ponderosa pine and Douglas-fir) using annually resolved establishment dates from 2,935 destructively sampled trees from 33 wildfires across four regions in the western United States. We show that regeneration had a nonlinear response to annual climate conditions, with distinct thresholds for recruitment based on vapor pressure deficit, soil moisture, and maximum surface temperature. At dry sites across our study region, seasonal to annual climate conditions over the past 20 years have crossed these thresholds, such that conditions have become increasingly unsuitable for regeneration. High fire severity and low seed availability further reduced the probability of postfire regeneration. Together, our results demonstrate that climate change combined with high severity fire is leading to increasingly fewer opportunities for seedlings to establish after wildfires and may lead to ecosystem transitions in low-elevation ponderosa pine and Douglas-fir forests across the western United States.
Data from Evidence for elevated diversification rate associated with the evolution of active motility in diatoms
<p>These are data files for Evidence for elevated diversification rate associated with the evolution of active motility in diatoms</p>
Supplementary material 1: Elevation data of FSN Dynamics Plot from: Tree Diversity and Dynamics of the Forest of Seu Nico, Viçosa, Minas Gerais, Brazil - Biodiversity Data Journal 3: e5425 (31 July 2015) https://doi.org/10.3897/BDJ.3.e5425
X, Y and elevation values of all vertices from 100 subplots from the one hectar FSN Dynamics Plot relative to starting point forming the northwestern vertex
Paulina Polder Intertidal Elevation
Paulina Polder Intertidal Elevation from Google Earth Engine
Digital Elevation Models of Hunga Volcano, Tonga, from the MAX2201 voyage, July-August 2022
<p>This dataset contains digital elevation models (DEM) of the Hunga Volcano complex, These DEM are from the MAX2201 voyage of the USV <i>Maxlimer</i> which surveyed the volcano July-August 2022.</p><p>Hunga Volcano is a volcanic complex near the island of Tongatapu in the Kingdom of Tonga. The volcano rises from ~2,500 m depth, a caldera at its summit, and two islands, Hunga Tonga and Hunga-Ha'apai, at the on the rim of the caldera. An eruption during December 2014-January 2015 was centered between the islands and combined them into one larger structure named Hunga Tonga – Hunga Ha'apai (HTHH). HTHH erupted violently on 15th January 2022, sending large clouds of ash into the atmosphere, triggering a tsunami, and reducing the size of the islands of Hunga Tonga and Hunga Ha'apai. </p><p>As a result of this event, the NIWA-Nippon Foundation Tonga Eruption Seabed Mapping Project (<strong>TESMaP</strong>) is a multidisciplinary research plan involving geological, oceanographic and biological studies that centered around three objectives: </p><ol><li>To determine the impacts of volcanic ash on ocean productivity, species composition, and biogeochemical cycling in the water column.</li><li>To determine the immediate nature and extent of the impact of ash fall/turbidity flows on deep-sea sediments and benthic ecosystems.</li><li>To determine the recovery potential of the deep-sea ecosystem.</li></ol><p>This project involved two survey voyages of the volcano and its surrounding waters. The first was carried out from <i>RV Tangaroa </i>(TAN2206) in April and May 2022 (Mackay et al., 2022) on the flanks of Hunga volcano and its surrounds; and the second was carried out over the summit of Hunga volcano by the <i>USV Maxlimer</i> (MAX2201) in August 2022.</p><p>TESMaP was funded from a combination of sources including The Nippon Foundation, Japan; the Natural Environmental Research Council, UK, Japan Agency for Marine Earth Science and Technology, the Tangaroa Reference Group (TRG) for ship time and the NIWA Oceans Centre. Support was given by The Nippon Foundation Seabed 2030 project and by GEBCO Alumni.</p>
Elevational and seasonal patterns of plant pollinator networks in two highland tropical ecosystems in Costa Rica
<p>Plant pollinator interaction matrices used to construct plant-pollinator interaction networks in two highland tropical ecosystems in Costa Rica. Numbers in matrices indicates the number of interactions recorded. The names of the files indicates the collect site (MF: Montane Forest; PAR= Paramo) and the season of the year (Dry and Rainy seasons, respectively).</p>
Rates of species turnover across elevation vary with vertical stratum in rainforest ant assemblages
<p>Climatic variation at local scales can influence both exposure and sensitivity of organisms and thereby scale up to influence population persistence and community composition across broader geographic extents. Tropical forest canopies are more climatically dynamic than the understorey. Consequently, the niche space of forest canopies has higher overlap in thermal conditions along elevation gradients, which imposes less of a climatic barrier to arboreal species than their ground-dwelling counterparts. We use ant communities of the Australian Wet Tropics to test the prediction that ground communities should have higher rates of species turnover over elevation compared to arboreal communities. We sampled ground and arboreal ants along elevation gradients at a bioregional scale that includes four mountain sub-regions. We assessed community composition at three spatial resolutions (regional, elevation, vertical) and then calculated beta diversity (species turnover) over elevation for ground and arboreal communities using null modelling procedures to compare different-sized species pools. Vertical niche affinity was a strong contributor to overall biogeographic patterns; indicated by a strong interaction between vertical niche and elevation in beta diversity models. On average, the ground community exhibited a pronounced elevational distance-decay pattern while the arboreal community showed no pattern. Mean species turnover was 36% higher in ground than arboreal communities. Our findings suggest that the vertical niche has a pronounced effect on biogeographic patterns which has important implications for understanding the role of local scale climate conditions in shaping communities and for potential responses to future climate change.</p>
Colour Palettes for Digital Elevation Models
<p>We provide a collection of 18 colour palettes for the efficient visualisation of Digital Elevation Models (DEMs), for example in the form of heatmaps. More precisely, we propose a combination of palettes from our own production and palettes extracted from the GEneral Bathymetric Chart of the Oceans (GEBCO), including their most recent charts as well as first editions of their paper charts from 1910-1930, 1932-1966, 1958-1973 and 1973-1982. These colour palettes have been harmonised in order to span a wide range of elevations, extending from the lowest depth of 11 000 metres, found in the Challenger Deep of the Mariana Trench, to the greatest height of 9 000 metres, reached atop Mount Everest. Moreover, these 18 colour palettes are supplied in the following 5 colour spaces: RGB (Red, Green, Blue), RGB 01, Hexadecimal, HSL (Hue, Saturation, Lightness) and HSV (Hue, Saturation, Value). </p> <p><strong>Related article</strong>: Owein Thuillier, Nicolas Le Josse, Alexandru-Liviu Olteanu, Marc Sevaux, and Hervé Tanguy, Catalogue of coastal-based instances with bathymetric and topographic data. Earth System Science Data Discussions 2024, 1–48. doi:<a href="https://doi.org/10.5194/essd-2024-58" target="_blank" rel="noopener">10.5194/essd-2024-58</a>.</p>
Elevation differently shapes functional diversity patterns in understory forest communities when considering intraspecific and interspecific trait variability
<p>Datasets used for the analysis done for the paper "Elevation differently shapes functional diversity patterns in understory forest communities when considering intraspecific and interspecific trait variability".</p> <p>Files present are:</p> <p>-Species x Plot (vegetation releveés).</p> <p>-Plot x Environment.</p> <p>-Plot x CWM_inter for Plant height, Leaf area, Specific Leaf Area (SLA), Leaf Dry Matter Content (LDMC) using the traits fixed for species, i.e., holding traits constant as the species mean, thus incorporating only turnover.</p> <p>-Plot x CWM_intra for Plant height, Leaf area, Specific Leaf Area (SLA), Leaf Dry Matter Content (LDMC) based on an individual by trait matrix, therefore incorporating both turnover and intraspecific trait variation.</p> <p>-Plot x SES-FD_inter (Standard Effect Size Functional Diversity) for Plant height, Leaf area, Specific Leaf Area (SLA), Leaf Dry Matter Content (LDMC) using the traits fixed for species, i.e., holding traits constant as the species mean, thus incorporating only turnover.</p> <p>-Plot x SES-FD_intra (Standard Effect Size Functional Diversity) for Plant height, Leaf area, Specific Leaf Area (SLA), Leaf Dry Matter Content (LDMC) based on an individual by trait matrix, therefore incorporating both turnover and intraspecific trait variation.</p> <p>All analysis were carried out using the software R version 4.1.2 (R Foundation for Statistical Computing, Vienna, Austria, <a href="http://www.R-project.org">http://www.R-project.org</a>) and can be consulted on GitHub https://github.com/AriannaFerrara/Elevation-and-Intraspecific-trait-variability.git</p>
SLIIDERS: Sea Level Impacts Input Dataset by Elevation, Region, and Scenario
<p>This record includes the Sea Level Impacts Input Dataset by Elevation, Region, and Scenario (SLIIDERS) dataset. It also includes source code to generate this product as well as necessary inputs that are not available for download elsewhere. Both the dataset and the source code are consistent with version 1.2. <strong>Note</strong>: The version associated with <a href="https://gmd.copernicus.org/articles/16/4331/2023/">Depsky et al., 2023</a> is v1.1.</p> <p>The zipped SLIIDERS Zarr store can be downloaded and accessed locally or can be directly accessed via code similar to the following:</p> <pre><code>from fsspec.implementations.zip import ZipFileSystem import xarray as xr xr.open_zarr(ZipFileSystem(url_of_file_in_record}}).get_mapper())</code></pre> <p><strong>File Inventory</strong></p> <p><em>Products</em></p> <ul> <li><strong>sliiders-v1.2.zarr.zip</strong>: SLIIDERS. A global dataset containing 18 socioeconomic variables, reflecting present day socioeconomic and geophysical characteristics of 11,980 coastal regions and projecting capital stock, GDP, and population growth trajectories through 2100 for five SSPs and two economic growth models. These variables are used as inputs to the pyCIAM modeling platform detailed in Depsky et al. 2023.</li> <li><strong>sliiders-v1.2.nc</strong>: Same as the original SLIIDERS dataset, but in netcdf format.</li> </ul> <p><em>Inputs</em></p> <p>All provided inputs are manually created or adjusted points used to create the coastline segments of SLIIDERS:</p> <ul> <li><strong>ciam_segment_pts_manual_adds.parquet</strong>: A list of segment points manually added to those that come from the extreme sea level model CoDEC (<a href="https://doi.org/10.5281/zenodo.3660926">Muis et al. 2020</a>)</li> <li><strong>gtsm_stations_ciam_ne_coastline_snapped.parquet:</strong> Stations from CoDEC snapped to coastlines from <a href="https://www.naturalearthdata.com/downloads/10m-physical-vectors/">Natural Earth</a></li> <li><strong>gtsm_stations_eur_tothin.parquet</strong>: A list of European points in CoDEC to thin. CoDEC provides ~10km resolution in Europe and ~50km elsewhere. For consistency, SLIIDERS uses ~50km spacing for its coastal segments globally.</li> </ul> <p><em>Source Code</em></p> <ul> <li><strong>sliiders-1.2.zip</strong>: The source code used to generate SLIIDERS v1.1. See READMEs within this code for more details. This is consistent with release v1.2 of the code maintained on github at <a href="https://github.com/ClimateImpactLab/SLIIDERS">https://github.com/ClimateImpactLab/SLIIDERS</a></li> </ul>
Data from: Trechus (Coleoptera: Carabidae) of Appalachia: A phylogenetic insight into the history of high elevation leaf litter communities
<p>Elevation gradients provide a wealth of habitats for a wide variety of organisms. The southern Appalachian Mountains in eastern United States are known for their high biodiversity and rates of endemism in arthropods, including in high-elevation leaf-litter taxa that are often found nowhere else on earth. Trechus Clairville (Coleoptera: Carabidae) is a genus of litter inhabitants with a near-global distribution and over 50 Appalachian species. These span two subgenera, Trechus s. str. and Microtrechus Jeannel, largely restricted to north and south of the Asheville basin, respectively. Understanding the diversification of these 3–5 mm flightless beetles through geological time can provide insights into how the litter-arthropod community has responded to historical environments, and how they may react to current and future climate change. We identified beetles morphologically and sequenced six genes to reconstruct a phylogeny of the Appalachian Trechus. We confirmed the Asheville Basin as a biogeographical barrier with a split between the north and south occurring towards the end of the Pliocene. Finer scale biogeography, including mountain-range occupancy, was not a reliable indication of relatedness, with group ranges overlapping and many instances of species-, species group-, and subgeneric sympatry. This may be because of the recent divergence between modern species and species groups. Extensive taxonomic revision of the group is required for Trechus to be useful as a bioindicator, but their high population density and speciose nature make them worth additional time and resources.</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)
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DANDI Archive for NWB datasets
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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.