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382 results for “high elevation”
High-mountain Asia glacier elevation change trend (dh/dt) map for the period spanning 2000 to 2018
<p>See manuscript for methodology and dataset description:</p> <p>Shean DE, Bhushan S, Montesano P, Rounce DR, Arendt A and Osmanoglu B (2020) A Systematic, Regional Assessment of High-Mountain Asia Glacier Mass Balance. Front. Earth Sci. 7:363. DOI: 10.3389/feart.2019.00363</p> <p>https://www.frontiersin.org/articles/10.3389/feart.2019.00363/full</p> <p>GeoTiff header contains relevant metadata and georeferencing information (30 m pixel size, Albers Equal Area projection). Proj string is '+proj=aea +lat_1=25 +lat_2=47 +lat_0=36 +lon_0=85 +x_0=0 +y_0=0 +ellps=WGS84 +datum=WGS84 +units=m +no_defs'</p> <p>External overview file (.ovr) contains pyramidal overviews for improved visualization performance at different zoom levels.</p>
An hourly ground temperature dataset for 16 high-elevation sites (3493–4377 m a.s.l.) in the Bale Mountains, Ethiopia (2017–2020)
<p>This is a multiannual ground temperature dataset covering sixteen high elevation sites (3493-4377 m a.s.l.) in the Bale Mountains, southern Ethiopian Highlands</p> <p>The dataset is described in detail in the corresponding data paper by Groos et al. 2021 (https://doi.org/10.5194/essd-2021-268)</p> <p>The repository contains a readme file ("readme.txt"), a GeoPackage ("Data_Logger_Location.gpkg"), a thermal infrared time-lapse video ("thermal_infrared_time-lapse_video.mp4"), a metadata file for the video ("video_metadata.txt"), and two sub-folders: "raw_data" and "processed_data"</p> <p>The GeoPackage provides information on the location and environmental setting of each logger and can be easily opened and displayed in a Geographic Information System. The coordinate reference system is WGS84 / Geographic (EPSG code: 4326).</p> <p>The thermal infrared time-lapse video (<a href="https://vimeo.com/676294827">https://vimeo.com/676294827</a>) visualises the phenomenon of nocturnal cold air drainage and ponding in the Bale Mountains (for more information see the metadata file and Appendix C in the corresponding data paper).</p> <p>The folder "raw_data" contains the original logfiles of all GT and TM data loggers (see Table 1) in a tab-delimited text format with the logger ID and download date encoded in the file name. The date format of the GT data loggers is YYYY.MM.DD hh:mm:ss East Africa Time (EAT). The date format of the TM data loggers is DD.MM.YYYY hh:mm:ss EAT.</p> <p>The folder "processed_data" contains the followings two files:</p> <p>"Information_Sheet_Data_Gap-Filling.ods": An overview table with relevant information regarding the filling of (longer) data gaps in the ground temperature time series. The gap-filling procedure based on simple linear regression models is described individually for each logger.</p> <p>"Hourly_Ground_Temperatures.csv": Compilation of hourly ground temperature data from all GT and TM data loggers installed in the Bale Mountains (see Table 1 in the data paper). The dataset covers the period from 1 January 2017 to 31 January 2020, but individual time series may be shorter or contain data gaps (see Fig. 3 in the data paper). We use the international date format (ISO 8601): YYYY-MM-DD hh:mm:ss EAT. The following numerical indices (or a combination of them) in the columns starting with "Flag_*" are used to provide additional information on the post-processing of each hourly measurement of each time series:</p> <p>0 no data available<br> 1 original data (no post-processing)<br> 2 data interpolated to full hour<br> 3 erroneous data corrected<br> 4 erroneous data removed<br> 5 data gap-filled</p> <p>The meteorological data from the ten automatic weather stations in the Bale Mountains, which are operated since 2017, are currently post-processed and analysed in the framework of the DFG Research Unit 2358 "The Mountain Exile Hypothesis". The data will be made publicly available at some point in the future. However, individual access to the weather station data may be granted before on request to the coordination board of the research unit (bale@staff.uni-marburg.de).</p>
Luquillo Experimental Forest atmospheric and high and mid elevation weather data.
The data archive is here:https://doi.org/10.2737/RDS-2022-0050 please use this DOI when citing this dataset. This data publication contains daily means from laser ceilometer data from Sabana, ozone data from Bisley, and meteorological data collected from Bisley and the mountain top station of East Peak, all located on the Luquillo Experimental Forest (El Yunque National Forest) in Puerto Rico. Atmospheric data include: mean cloud observed frequency, mean lowest height cloud (cloud base), mean daytime mixing layer height observed frequency, mean lowest mixing layer height from hours 7am and 7pm only, and mean daytime mixing layer height collected from February 2013 through April 2021. Also included is mean ozone amount collected from April 2008 through early April 2021. The cloud and mixing layer frequency and low values were calculated using the Automated Surface Observing System (ASOS) method employed at airports in the area. Weather data include high elevation East Peak mean northeast wind speed (wind rose quartiles 45° to 90°), mean southeast wind speed (wind rose quartiles 90° to 135°), and mean total wind speed measured from October 2009 through 2020 (and a few months in 2021). Additional weather data collected from January 2008 through mid October 2020 include mid elevation Bisley mean precipitation, mean relative humidity, and mean temperature. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
Electron shuttling capacity and greenhouse gas production of soils for three high-elevation wetlands at Niwot Ridge, 2024.
High-elevation wetlands are important indicators of how mountain ecosystems may respond to global climate change. These wetlands also act as locations of disproportionate biogeochemical processing on the landscape, but they remain relatively understudied compared to lowland wetlands. This study aimed to characterize redox-active organic matter (RAOM) reduction, a known key control on carbon cycling in high-latitude peatland ecosystems, to better understand biogeochemical cycling in high elevation wetlands and carbon greenhouse gas production at Niwot Ridge LTER. Soils were collected from three different types of wetlands, a subalpine wetland, a periglacial solifluction lobe, and an alpine wet meadow. Samples were incubated at a common temperature in the laboratory to measure RAOM reduction, carbon dioxide production, and methane production over 63-d. This dataset reports the electron shuttling values, a measure of RAOM reduction, and the greenhouse gas production over the incubation period.
Measurements of aquatic production and respiration in tidal creeks draining high and low elevation marshes.
Production and respiration measurements of aquatic systems can help to inform calculations of whole system metabolism. These measurements are focused on creeks draining high and low elevation marsh systems to develop a better understanding of connectivity between aquatic production and consumption and coastal salt marshes. Production and respiration were determined by measuring oxygen changes in creek water incubated in light and dark bottles.
Factors determining distributions of rainforest Drosophila shift from interspecific competition to high temperature with decreasing elevation (original datasets)
<p>This repository provides the data for the manuscript "Factors determining distributions of rainforest Drosophila shift from interspecific competition to high temperature with decreasing elevation"</p> <p>We investigated thermal tolerances and interspecific competition as causes of species turnover in the nine most abundant species of <em>Drosophila</em> along elevational gradients in the Australian Wet Tropics. Specifically, we 1) analyzed the distribution patterns of the studies <em>Drosophila</em> species; 2) fitted thermal performance curves; 3) tested the correlation between multiple thermal traits and distribution patterns; 4) fitted the Beverton-Holt model to describe the single-generation intra- and inter-specific competition effect; 5) examined the long-term effect of competition and temperature on the population size of a pair of Drosophila species.</p> <p>More details are provided in the README file.</p>
Supplementary material for "High turn-over rates at the upper range limit and elevational source-sink dynamics in a widespread songbird"
<p><strong>Abstract</strong></p> <p>The formation of an upper distributional range limit for species breeding along mountain slopes is often based on environmental gradients resulting in changing demographic rates towards high elevations. However, we still lack an empirical understanding of how the interplay of demographic parameters forms the upper range limit in highly mobile species. Here, we study apparent survival and within-study area dispersal over a 700 m elevational gradient in barn swallows (<em>Hirundo rustica</em>) by using 15 years of capture-mark-recapture data. Annual apparent survival of adult breeding birds decreased while breeding dispersal probability of adult females, but not males increased towards the upper range limit. Individuals at high elevations dispersed to farms situated at elevations lower than would be expected by random dispersal. These results suggest higher turn-over rates of breeding individuals at high elevations, an elevational increase in immigration and thus, within-population source-sink dynamics between low and high elevations. The formation of the upper range limit therefore is based on preference for low-elevation breeding sites and immigration to high elevations. Thus, shifts of the upper range limit are not only affected by changes in the quality of high-elevation habitats but also by factors affecting the number of immigrants produced at low elevations.</p>
Data from: Nest orientation and proximity to snow patches are important for nest site selection of a cavity breeder at high elevation
<p><strong>Abstract</strong></p> <p>Reproductive timing and location are central to breeding success across taxa. Many species have evolved specific strategies to cope with environmental variability including shifts in timing of reproduction tracking resource availability or selecting favourable nest location. In mountain ecosystems, complex topography and pronounced seasonality result in particularly high spatiotemporal variability of environmental conditions, and the risk of climate-induced resource mismatches is particularly acute given that temperature is increasing more rapidly than in the lowlands.<br>We investigated how a high-elevation passerine, the white-winged snowfinch <em>Montifringilla nivalis</em>, selects its nest site in relation to nest cavity characteristics, habitat composition and snow condition. We used a combination of field habitat mapping and satellite remote sensing to compare occupied nest sites with randomly selected pseudo-absence sites. In the first half of the breeding season, snowfinches preferred nest cavities oriented towards the morning sun while they used cavities proportional to their availability later on. This preference might relate to the nest microclimate offering eco-physiological advantages, namely thermoregulatory benefits for incubating adult and nestlings under the harsh conditions typically encountered in the alpine environment. Nest sites were consistently located in areas with greater-than-average snow cover at hatching date, likely mirroring the foraging preferences for tipulid larvae developing in meltwater along snowfields. Due to the particularly rapid climate shifts typical of mountain ecosystems, spatiotemporal mismatches between foraging grounds and nest sites are expected in the future, which may negatively influence demographic trajectories of the species concerned. The installation of well-designed nest boxes in optimal habitat configurations could to some extent help mitigate this risk.</p> <p> </p>
A new inventory of High Mountain Asia surging glaciers derived from multiple elevation datasets since the 1970s
<p>Glacier surging is an unusual undulation instability of ice flow and complete surging glacier inventories are important for regional mass balance studies and assessing glacier-related hazards. Glacier surge events in High Mountain Asia (HMA) are widely reported. Through the estimated elevation changes from multiple DEMs sources that acquired from 1970s to 2020, and morphologic changes from 1986 to 2021, here we present a new surging glacier inventory across HMA. The inventory has incorporated 890 surging and 336 surge-like glaciers, each glacier is assigned with indicators of surging feature and surge possibility. Compared to previous surging glacier inventory in HMA, our inventory is theoretically more complete because of the much longer observation period. This data repository contains the surging glacier inventory and glacier elevation change maps. The inventory is stored in the format of GeoPackage (.gpkg) and ESRI Shapefile format (.shp), which is represented by glacier polygon (from GAMDAM2) or surface point with geometric attributes. The multi-temporal elevation change maps of identified surging glaciers were divided into 1×1° tiles, storing in the format of GeoTiff(*.tif). Detailed description of the dataset including the file contents and attributes information can be found in the metadata file (README.txt).</p>
Temperature datasets for stock tanks and natural sites at High, Medium, and Low elevations, as part of the LTREB Swordtail project in Hidalgo, Mexico, 2015 - 2025
The core of this project focuses on monitoring the phenotypic and genotypic evolution of experimental and natural hybrid populations of swordtails for ten generations. To get a clear understanding of how evolution shapes genome wide ancestry and the distribution of species-specific alleles at functional loci during early generations of hybridization, it's important to monitor these populations using experimental crosses. Eight replicate 2000 L mesocosm stock tanks were built at high (1514 m), intermediate (980 m), and low (186 m) elevations near the CICHAZ field site were seeded with Xiphophorus birchmanni – X. malinche F1 hybrids. The F1s were generated by crossing X. malinche females with X. birchmanni malesin stock tanks at CICHAZ, a research station in Calnali, Mexico. Tanks at higher elevations experience cooler water temperatures. Our experimental design thereby allows us to characterize how ecological selection shapes genotypic and phenotypic differences in thermal tolerance across hybrid populations exposed to different temperature regimes. The data contained in these files include recorded water temperature (in Celsius), taken every six hours from three stock tanks at low (186 m: STL), medium (980 m: STM), and high (1514 m: STH) elevations, along with three natural river sites. File Natural.csv contains the natural sites and the file Stock Tanks.csv contains the corresponding natural sites. Acuapa (ACUA) is paired with STL, Aguazarca (AGZC) is paired with STM, and Tlatemaco (TLMC) is paired with STH.
High-frequency observations of overwash water and bed elevations from five MeOw (Measuring Overwash) stations: Smith Island, VA, August-November 2019
Water and bed surface elevation measurements from five MeOw (Measuring Overwash) stations installed on Smith Island, VA, from 21 August 2019 to 22 November 2019. The five MeOw stations consist of one ultrasonic distance sensor inside a stilling well to measure water levels, and a second attached to an arm extending outwards from the well to measure the elevation of the sediment surface (when dry) or the water surface (when inundated). Note: Station MeOw2 was damaged and produced no data, and so is not included in this dataset.
Data and code for: High light alongside elevated pCO2 alleviates thermal depression of photosynthesis in a hard coral (Pocillopora acuta)
<p>Data and R scripts of analyses performed for the manuscript "<strong>High light alongside elevated pCO<sub>2</sub> alleviates thermal depression of photosynthesis in a hard coral (<em>Pocillopora acuta</em>)</strong>"</p>
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>
Fig. 15. – Panicum spergulifolium A. Camus, high elevation morphotype. A in Revision of the group previously known as Panicum L. (Poaceae: Panicoideae) in Madagascar
Fig. 15. – Panicum spergulifolium A. Camus, high elevation morphotype. A. Habit; B. Ligule; C. Panicle branch; D. Lower glume, dorsal view; E. Lower glume, ventral view; F. Upper glume, dorsal view; G. Upper glume, ventral view; H. Lower lemma, dorsal view; I. Lower lemma, ventral view; J. Lower palea; K. Upper lemma, dorsal view; L. Upper lemma, ventral view; M. Upper palea, dorsal view; N. Upper floret, the lemma removed. [Vorontsova et al. 1218, K] [Drawing: Lucy T. Smith]
Fig. 5 in A New Species of Nannoscincus Günther (Squamata: Scincidae) from High Elevation Forest in Southern New Caledonia
Fig. 5. Isolated closed forest patch on Mont Çidoa, typical of habitat in the area from which the types of Nannoscincus garrulus n.sp. were collected.
Fig. 2 in A New Species of Nannoscincus Günther (Squamata: Scincidae) from High Elevation Forest in Southern New Caledonia
Fig. 2. Dorsal (upper), lateral (middle), and ventral (lower) views of the head of holotype of Nannoscincus garrulus
Fig. 3 in A New Species of Nannoscincus Günther (Squamata: Scincidae) from High Elevation Forest in Southern New Caledonia
Fig. 3. Left manus of holotype of Nannoscincus garrulus n.sp. (MNHN 2003.1002) showing extensive underside "webbing" between digits of the forelimbs (right) and enlarged dorsal scales at the base of the third and fourth digits (left).
High elevation forest age structure across an elevational gradient in the Greater Yellowstone Ecosystem
<p>Dataset for Blomdahl et al. 2022. Drivers of forest change in the Greater Yellowstone Ecosystem. Journal of Vegetation Science. </p> <p>See publication for site description and methods. </p> <p>Descriptions for variables in “trees_seedlings.csv”:</p> <p><strong>Plot_ID: </strong>Plot identifier. Nomeclature follows transect name and plot number. ECO="Ecotone" transect, SBM="South Bird Mountain" transect.</p> <p><strong>Year_Sampled: </strong>Samples collected 2017-2019.</p> <p><strong>Tree_ID: </strong>Identifier for unique trees and seedlings. </p> <p><strong>Core: </strong>Tree core sample identifier. Applies only to trees (cores not taken from seedlings). Generally, 2 cores were taken per Tree >5 cm DCH, though sometimes up to 4 were collected if a sample was rotten.</p> <p><strong>Sample_ID: </strong>Identifier for unique samples, some of which come from the same tree (for unique individuals: "Tree_ID"). Applies to trees and seedlings.</p> <p><strong>Form: </strong>Stems >5 cm diameter at coring height (DCH), coring height=30 cm; Seedlings >30: Stems <5 cm DCH and >30 cm in height (sometimes referred to as "saplings"); Seedlings <30: Stems <30 cm in height</p> <p><strong>Species: </strong>ABLA=<em>Abies</em> <em>lasiocarpa</em>, PIAL=Pinus <em>albicaulis</em>, PICO=<em>Pinus</em> <em>contorta</em>, PIEN=<em>Picea</em> <em>engelmannii</em>, PSME=<em>Pseudotsuga</em> <em>menziesii</em></p> <p><strong>Diam_30_cm: </strong>Diameter (cm) at 30 cm sample height.</p> <p><strong>Diam_0_cm: </strong>Diameter (cm) at 0 cm sample height (i.e., the base). Only seedlings were measured at base, not trees.</p> <p><strong>Seedling_Ht_cm: </strong>Length of seedling stem (cm).</p> <p><strong>Bark_Thick_cm: </strong> Bark thickness (cm). Not recorded in 2018. Bark thickness assumed to be <0.1 cm for seedlings.</p> <p><strong>Live_Dead: </strong>Live/Dead status when sampled. L=Live, D=Dead.</p> <p><strong>Canopy: </strong>Canopy position. D=Dominant, C=Codominant. S=Suppressed. Not recorded in 2017. All seedlings assumed suppressed.</p> <p><strong>Outer_Ring: </strong>Last complete year of growth, generally one year prior to Year_Sampled for live trees. Mortality year for dead trees.</p> <p><strong>Inner_Ring:</strong> Year of innermost ring measured in tree core sample measured at 30 cm sample height. Does not apply to seedlings, which were sampled as cross sections, and therefore the pith was always measureable.</p> <p><strong>Pith_30: </strong>Year of the first ring of the tree or sapling, measured at 30 cm sampling height. </p> <p><strong>Pith_0: </strong>Year of the first ring of the seedling, measuring at 0 cm sampling height (i.e., the base). Applies only to seedlings, which were destructively sampled at the base.</p> <p><strong>Estab_Year: </strong>Estimated year of establishment for trees and saplings, same as Pith_0 for seedlings. See methods of Blomdahl et al., 2022, for how establishment year was estimated.</p> <p><strong>Age:</strong> Estimated age of the tree.</p>
FIG. 2 in A new terrestrial species of Colura (Marchantiophyta: Lejeuneaceae) at tropical high elevations (Boyacá, Colombia)
FIG. 2. — Colura stotleri sp. nov. A, ventral view of plant, showing an underleaf; B, stem leaf; C, stem leaf, showing inflated lobule; D, Habitat, Valle de los cojines, PNN sierra Nevada del Cocuy. Scale bars: A, 190 µm; B, C, 240 µm.
FIG. 1 in A new terrestrial species of Colura (Marchantiophyta: Lejeuneaceae) at tropical high elevations (Boyacá, Colombia)
FIG. 1. — Colura stotleri sp.nov. A, sporophyte; B, stem leaf; C, underleaf;D, sporophyte, apical part; E, valve with hinge cells; F, hinge cells and hyaline papilla. Drawn from holotype. Scale bars: A, 1600 µm; B, 200 µm; C, 150 µm; D, 900 µm; E, F, 50 µm.
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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.