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3,479 results for “Sevilleta LTER”
Sevilleta LTER Vegetation Sample Catalog- Ground Samples for Chemical Analysis
Several long-term studies at the Sevilleta LTER measure net primary production (NPP) across ecosystems and treatments. Net primary production is a fundamental ecological variable that quantifies rates of carbon consumption and fixation. Estimates of NPP are important in understanding energy flow at a community level as well as spatial and temporal responses to a range of ecological processes. The NPP weight data (SEV 157) is obtained by harvesting a series of covers for species observed during plot sampling. These species are always harvested from habitat comparable to the plots in which they were recorded. This data is then used to make volumetric measurements of species and build regressions correlating biomass and volume. From these calculations, seasonal biomass and seasonal and annual NPP are determined. These sampled are then vouchered for use to do analyses of inorganic and organic components such as carbon, nitrogen, and phosphorous as well as and other macro and micro nutrients and organic components such as cellulose and lignin.
Evolutionary Monitoring for the SEV LTER program at the Sevilleta National Wildlife Refuge, New Mexico
This dataset contains collection and specimen archive information for plant and arthropod tissue samples from the Sevilleta National Wildlife Refuge that are currently stored in The Museum of Southwestern Biology’s Division of Genomic Resources at the University of New Mexico. The purpose of the sample collection is to allow researchers in the future to do genetic or genomic work on archived historical samples collected at six-year intervals. Six dominant, foundation plant species are represented. Stored tissues include roots from Bouteloua eriopoda, Bouteloua gracilis, Larrea tridentata, and Machaeranthera pinnatifida, leaves from Juniperus monosperma, Larrea tridentata, and Pinus edulis, and seeds from Larrea tridentata. A few arthropods were collected opportunistically when they adhered to plant material brought back to the lab. Collection dates were from 9 October through 4 November 2019. Samples were collected within four core sites of the Sevilleta National Wildlife Refuge, Socorro, NM: (Plains grassland: core_blue, Desert grassland: core_black, Desert shrubland: core_creosote, and Pinon-juniper woodland: core_PJ).
SEV-LTER Mean - Variance Experiment Seasonal Biomass Data at the Sevilleta National Wildlife Refuge, New Mexico
We designed novel field experimental infrastructure to resolve the relative importance of changes in the climate mean and variance in regulating the structure and function of dryland populations, communities, and ecosystem processes. The Mean - Variance Climate Experiment (MVE) adds three novel elements to prior designs that have manipulated interannual variance in climate in the field (Gherardi & Sala, 2013) by (i) determining interactive effects of mean and variance with a factorial design that crosses reduced mean with increased variance, (ii) studying multiple dryland biomes to compare their susceptibility to transition under interactive climate drivers, and (iii) adding stochasticity to our treatments to permit the antecedent effects that occur under natural climate variability. This new infrastructure enables direct experimental tests of the hypothesis that interactions between the mean and variance of precipitation will have larger ecological impacts than either the mean or variance in precipitation alone. This data package includes species-level plant cover and biomass data from the Mean - Variance Experiment at five sites comprising the major ecosystems of the Sevilleta National Wildlife Refuge: Chihuahuan Desert shrubland, Chihuahuan Desert grassland, Great Plains grassland, Juniper savanna, and pinon-juniper woodland. Species cover and volume in one-meter-squared quadrats are assessed twice-yearly in spring and fall, and regressions correlating biomass and volume constructed using seasonal harvest weights from SEV157, "Net Primary Productivity (NPP) Weight Data."
SEV LTER: Tracking Vegetation Phenology Using PhenoCam Imagery at the Sevilleta National Wildlife Refuge, New Mexico, 2014-2024
As of 03/03/2024, the Sevilleta Long-Term Ecological Research Program is equipped with a total of 65 digital RGB cameras, or PhenoCams, across the Sevilleta National Wildlife Refuge. These cameras are installed on eddy covariance flux towers and at a number of precipitation manipulation experiments to track vegetation phenology and productivity across dryland ecotones. PhenoCams have been paired with eddy covariance flux tower data at the site since 2014, while some Mean-Variance Experiment PhenoCams were installed as recently as June 2023. For information on PhenoCam data processing and formatting, see Richardson et al., 2018, Scientific Data (https://doi.org/10.1038/sdata.2018.28), Seyednasrollah et al., 2019, Scientific Data (https://doi.org/10.1038/s41597-019-0229-9), and the PhenoCam Network web page (https://phenocam.nau.edu/webcam/). The PhenoCam Network uses imagery from digital cameras to track vegetation phenology and seasonal changes in vegetation activity in diverse ecosystems across North America and around the world. Imagery is uploaded to the PhenoCam server hosted at Northern Arizona University, where it is made publicly available in near-real time, every 30 minutes from sunrise to sunset, 365 days a year. The data are processed using simple image analysis tools to yield a measure of canopy greenness, from which phenological metrics are extracted, characterizing the start and end of the growing season. These transition dates have been shown to align well with on-the-ground observations at various research sites. Long-term PhenoCam data can be used to track the impact of climate variability and change on the rhythm of the seasons.
Net primary production (NPP) and climate data from Sevilleta LTER core and control sites in desert grassland and shrubland ecosystems, 1999 - 2017
This dataset and R code were used to create the figures, tables and statistical analyses for the following publication: Rudgers, JA et al. 2018. Climate sensitivity functions and net primary production: A framework for incorporating climate mean and variability. Ecology. Data were collected by the Sevilleta LTER program, which is located in the Sevilleta National Wildlife Refuge (SNWR), New Mexico. These are long-term, continuing data sets. Data collection started in 1999 at the black grama grassland and creosote shrubland, and in 2002 for blue grama grassland. Meteorological stations started recording data as early as 1989. The study abstract from Rudgers et al. 2018 is: Understanding controls on net primary production (NPP) has been a long-standing goal in ecology. Climate is a well-known control on NPP, although the temporal differences among years within a site are often weaker than the spatial pattern of differences across sites. Climate sensitivity functions describe the relationship between an ecological response (e.g., NPP) and both the mean and variance of its climate driver (e.g., aridity index), providing a novel framework for understanding how climate trends in both mean and variance vary with NPP over time. Nonlinearities in these functions predict whether an increase in climate variance will have a positive effect (convex nonlinearity) or negative effect (concave nonlinearity) on NPP. The influence of climate variance may be particularly intense at ecosystem transition zones, if species reach physiological thresholds that create nonlinearities at these ecotones. Long-term data collected at the confluence of three dryland ecosystems in central New Mexico revealed that each ecosystem exhibited a unique climate sensitivity function that was consistent with long-term vegetation change occurring at their ecotones. Our analysis suggests that rising temperatures in drylands could alter the nonlinearities that determine the relative costs and benefits of varia
Lidar digital elevation models and topographic change detection results from burned and unburned plots in the Sevilleta LTER.
These data were generated as part of a research project focused on montiroing sediment flux in dryland ecosystems following wildfire. In six separate small plots, three burned and three unburned, we conducted light detection and ranging (lidar) topographic surveys in 2016, 2017, and 2018 to document elevation changes and the volume of sediment deposition and erosion. At the down-wind edge of each plot, we used sediment catchers to trap sediment exiting the plots and thus estimate erosion volumes using in-situ equipment, which provided a secondary measurement of sediment efflux from all sites in addition to the lidar data. We used the geomorphic change detection software (https://gcd.riverscapes.xyz/) to produce maps of topographic change from the lidar digital elevation models for the 2016-2017 and 2017-2018 periods at all plots, burned and unburned. Results from this project may aid in understanding post-fire transport of sediment and nutrients from drylands following wildfire.
Sevilleta LTER Metstation number 49, precipition, daily raw and gap filled from 1992 - 2015
This file contains daily meteorological data collected from a network of 10 permanent weather stations on the Sevilleta National Wildlife Refuge. Multiple csv files were downloaded from the Sevilleta LTER data-portal (http://tierra.unm.edu/search/climate/search.php) and joined together in excel. Any missing data cells were gap-filled from the nearest meteorological station using Met_gap_fill.r. These data were sub-set for station 49, the station closest to our study site.
LEDAPS corrected Landsat Enhanced Thematic Mapper image data for Sevilleta LTER collected on 1982-11-25
This LTER Remote Sensing spatial raster dataset consists of LEDAPS corrected Landsat Enhanced Thematic Mapper image data for Sevilleta LTER, originally collected on 1982-11-25 (17:01:09.9140060Z) by Landsat 4, row 036, path 032. Cloud cover was 40 percent. The Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) software was originally developed by the National Aeronautics and Space Administration–Goddard Space Flight Center and the University of Maryland to produce top-of-atmosphere reflectance from Landsat Thematic Mapper and Enhanced Thematic Mapper Plus Level 1 digital numbers and to apply atmospheric corrections to generate a surface-reflectance product. The U.S. Geological Survey (USGS) has adopted the LEDAPS algorithm for producing the Landsat Surface Reflectance Climate Data Record. NASA Landsat Program, 2009, Landsat TM LT40320361982329XXX01, LPGS_12.1.3, USGS, Sioux Falls, 2012-12-24T08:34:59Z.
LEDAPS corrected Landsat Enhanced Thematic Mapper image data for Sevilleta LTER collected on 1983-01-28
This LTER Remote Sensing spatial raster dataset consists of LEDAPS corrected Landsat Enhanced Thematic Mapper image data for Sevilleta LTER, originally collected on 1983-01-28 (17:01:56.4520380Z) by Landsat 4, row 036, path 032. Cloud cover was 20 percent. The Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) software was originally developed by the National Aeronautics and Space Administration–Goddard Space Flight Center and the University of Maryland to produce top-of-atmosphere reflectance from Landsat Thematic Mapper and Enhanced Thematic Mapper Plus Level 1 digital numbers and to apply atmospheric corrections to generate a surface-reflectance product. The U.S. Geological Survey (USGS) has adopted the LEDAPS algorithm for producing the Landsat Surface Reflectance Climate Data Record. NASA Landsat Program, 2009, Landsat TM LT40320361983028XXX04, LPGS_12.1.2, USGS, Sioux Falls, 2012-11-07T01:33:34Z.
LEDAPS corrected Landsat Enhanced Thematic Mapper image data for Sevilleta LTER collected on 1987-10-22
This LTER Remote Sensing spatial raster dataset consists of LEDAPS corrected Landsat Enhanced Thematic Mapper image data for Sevilleta LTER, originally collected on 1987-10-22 (16:53:08.4950380Z) by Landsat 4, row 036, path 032. Cloud cover was 0 percent. The Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) software was originally developed by the National Aeronautics and Space Administration–Goddard Space Flight Center and the University of Maryland to produce top-of-atmosphere reflectance from Landsat Thematic Mapper and Enhanced Thematic Mapper Plus Level 1 digital numbers and to apply atmospheric corrections to generate a surface-reflectance product. The U.S. Geological Survey (USGS) has adopted the LEDAPS algorithm for producing the Landsat Surface Reflectance Climate Data Record. NASA Landsat Program, 2009, Landsat TM LT40320361987295XXX03, LPGS_12.1.3, USGS, Sioux Falls, 2012-12-24T08:51:06Z.
LEDAPS corrected Landsat Enhanced Thematic Mapper image data for Sevilleta LTER collected on 1989-06-21
This LTER Remote Sensing spatial raster dataset consists of LEDAPS corrected Landsat Enhanced Thematic Mapper image data for Sevilleta LTER, originally collected on 1989-06-21 (17:06:07.6520880Z) by Landsat 4, row 036, path 032. Cloud cover was 50 percent. The Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) software was originally developed by the National Aeronautics and Space Administration–Goddard Space Flight Center and the University of Maryland to produce top-of-atmosphere reflectance from Landsat Thematic Mapper and Enhanced Thematic Mapper Plus Level 1 digital numbers and to apply atmospheric corrections to generate a surface-reflectance product. The U.S. Geological Survey (USGS) has adopted the LEDAPS algorithm for producing the Landsat Surface Reflectance Climate Data Record. NASA Landsat Program, 2009, Landsat TM LT40320361989172XXX02, LPGS_12.1.3, USGS, Sioux Falls, 2012-11-21T09:19:24Z.
LEDAPS corrected Landsat Enhanced Thematic Mapper image data for Sevilleta LTER collected on 1989-07-07
This LTER Remote Sensing spatial raster dataset consists of LEDAPS corrected Landsat Enhanced Thematic Mapper image data for Sevilleta LTER, originally collected on 1989-07-07 (17:06:11.3220190Z) by Landsat 4, row 036, path 032. Cloud cover was 0 percent. The Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) software was originally developed by the National Aeronautics and Space Administration–Goddard Space Flight Center and the University of Maryland to produce top-of-atmosphere reflectance from Landsat Thematic Mapper and Enhanced Thematic Mapper Plus Level 1 digital numbers and to apply atmospheric corrections to generate a surface-reflectance product. The U.S. Geological Survey (USGS) has adopted the LEDAPS algorithm for producing the Landsat Surface Reflectance Climate Data Record. NASA Landsat Program, 2009, Landsat TM LT40320361989188XXX02, LPGS_12.1.3, USGS, Sioux Falls, 2012-12-24T08:52:52Z.
LEDAPS corrected Landsat Enhanced Thematic Mapper image data for Sevilleta LTER collected on 1987-10-22
This LTER Remote Sensing spatial raster dataset consists of LEDAPS corrected Landsat Enhanced Thematic Mapper image data for Sevilleta LTER, originally collected on 1987-10-22 (16:53:32.3190060Z) by Landsat 4, row 037, path 032. Cloud cover was 0 percent. The Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) software was originally developed by the National Aeronautics and Space Administration–Goddard Space Flight Center and the University of Maryland to produce top-of-atmosphere reflectance from Landsat Thematic Mapper and Enhanced Thematic Mapper Plus Level 1 digital numbers and to apply atmospheric corrections to generate a surface-reflectance product. The U.S. Geological Survey (USGS) has adopted the LEDAPS algorithm for producing the Landsat Surface Reflectance Climate Data Record. NASA Landsat Program, 2009, Landsat TM LT40320371987295XXX03, LPGS_12.1.3, USGS, Sioux Falls, 2012-12-24T08:52:56Z.
LEDAPS corrected Landsat Enhanced Thematic Mapper image data for Sevilleta LTER collected on 1989-06-21
This LTER Remote Sensing spatial raster dataset consists of LEDAPS corrected Landsat Enhanced Thematic Mapper image data for Sevilleta LTER, originally collected on 1989-06-21 (17:06:31.4800060Z) by Landsat 4, row 037, path 032. Cloud cover was 30 percent. The Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) software was originally developed by the National Aeronautics and Space Administration–Goddard Space Flight Center and the University of Maryland to produce top-of-atmosphere reflectance from Landsat Thematic Mapper and Enhanced Thematic Mapper Plus Level 1 digital numbers and to apply atmospheric corrections to generate a surface-reflectance product. The U.S. Geological Survey (USGS) has adopted the LEDAPS algorithm for producing the Landsat Surface Reflectance Climate Data Record. NASA Landsat Program, 2009, Landsat TM LT40320371989172XXX02, LPGS_12.1.3, USGS, Sioux Falls, 2012-12-24T10:42:42Z.
LEDAPS corrected Landsat Enhanced Thematic Mapper image data for Sevilleta LTER collected on 1989-07-07
This LTER Remote Sensing spatial raster dataset consists of LEDAPS corrected Landsat Enhanced Thematic Mapper image data for Sevilleta LTER, originally collected on 1989-07-07 (17:06:35.1490630Z) by Landsat 4, row 037, path 032. Cloud cover was 10 percent. The Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) software was originally developed by the National Aeronautics and Space Administration–Goddard Space Flight Center and the University of Maryland to produce top-of-atmosphere reflectance from Landsat Thematic Mapper and Enhanced Thematic Mapper Plus Level 1 digital numbers and to apply atmospheric corrections to generate a surface-reflectance product. The U.S. Geological Survey (USGS) has adopted the LEDAPS algorithm for producing the Landsat Surface Reflectance Climate Data Record. NASA Landsat Program, 2009, Landsat TM LT40320371989188XXX02, LPGS_12.1.3, USGS, Sioux Falls, 2012-12-24T08:53:47Z.
LEDAPS corrected Landsat Enhanced Thematic Mapper image data for Sevilleta LTER collected on 1982-12-18
This LTER Remote Sensing spatial raster dataset consists of LEDAPS corrected Landsat Enhanced Thematic Mapper image data for Sevilleta LTER, originally collected on 1982-12-18 (17:07:09.6190060Z) by Landsat 4, row 036, path 033. Cloud cover was 10 percent. The Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) software was originally developed by the National Aeronautics and Space Administration–Goddard Space Flight Center and the University of Maryland to produce top-of-atmosphere reflectance from Landsat Thematic Mapper and Enhanced Thematic Mapper Plus Level 1 digital numbers and to apply atmospheric corrections to generate a surface-reflectance product. The U.S. Geological Survey (USGS) has adopted the LEDAPS algorithm for producing the Landsat Surface Reflectance Climate Data Record. NASA Landsat Program, 2009, Landsat TM LT40330361982352XXX01, LPGS_12.1.3, USGS, Sioux Falls, 2012-12-24T11:03:51Z.
LEDAPS corrected Landsat Enhanced Thematic Mapper image data for Sevilleta LTER collected on 1987-11-14
This LTER Remote Sensing spatial raster dataset consists of LEDAPS corrected Landsat Enhanced Thematic Mapper image data for Sevilleta LTER, originally collected on 1987-11-14 (17:00:07.6600810Z) by Landsat 4, row 036, path 033. Cloud cover was 30 percent. The Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) software was originally developed by the National Aeronautics and Space Administration–Goddard Space Flight Center and the University of Maryland to produce top-of-atmosphere reflectance from Landsat Thematic Mapper and Enhanced Thematic Mapper Plus Level 1 digital numbers and to apply atmospheric corrections to generate a surface-reflectance product. The U.S. Geological Survey (USGS) has adopted the LEDAPS algorithm for producing the Landsat Surface Reflectance Climate Data Record. NASA Landsat Program, 2009, Landsat TM LT40330361987318XXX03, LPGS_12.1.3, USGS, Sioux Falls, 2012-12-24T13:19:07Z.
LEDAPS corrected Landsat Enhanced Thematic Mapper image data for Sevilleta LTER collected on 1988-06-25
This LTER Remote Sensing spatial raster dataset consists of LEDAPS corrected Landsat Enhanced Thematic Mapper image data for Sevilleta LTER, originally collected on 1988-06-25 (17:07:25.9500810Z) by Landsat 4, row 036, path 033. Cloud cover was 0 percent. The Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) software was originally developed by the National Aeronautics and Space Administration–Goddard Space Flight Center and the University of Maryland to produce top-of-atmosphere reflectance from Landsat Thematic Mapper and Enhanced Thematic Mapper Plus Level 1 digital numbers and to apply atmospheric corrections to generate a surface-reflectance product. The U.S. Geological Survey (USGS) has adopted the LEDAPS algorithm for producing the Landsat Surface Reflectance Climate Data Record. NASA Landsat Program, 2009, Landsat TM LT40330361988177XXX01, LPGS_12.1.3, USGS, Sioux Falls, 2012-12-24T11:30:34Z.
LEDAPS corrected Landsat Enhanced Thematic Mapper image data for Sevilleta LTER collected on 1988-07-27
This LTER Remote Sensing spatial raster dataset consists of LEDAPS corrected Landsat Enhanced Thematic Mapper image data for Sevilleta LTER, originally collected on 1988-07-27 (17:08:16.6800630Z) by Landsat 4, row 036, path 033. Cloud cover was 40 percent. The Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) software was originally developed by the National Aeronautics and Space Administration–Goddard Space Flight Center and the University of Maryland to produce top-of-atmosphere reflectance from Landsat Thematic Mapper and Enhanced Thematic Mapper Plus Level 1 digital numbers and to apply atmospheric corrections to generate a surface-reflectance product. The U.S. Geological Survey (USGS) has adopted the LEDAPS algorithm for producing the Landsat Surface Reflectance Climate Data Record. NASA Landsat Program, 2009, Landsat TM LT40330361988209XXX03, LPGS_12.1.3, USGS, Sioux Falls, 2012-12-24T11:35:30Z.
LEDAPS corrected Landsat Enhanced Thematic Mapper image data for Sevilleta LTER collected on 1988-11-16
This LTER Remote Sensing spatial raster dataset consists of LEDAPS corrected Landsat Enhanced Thematic Mapper image data for Sevilleta LTER, originally collected on 1988-11-16 (17:10:25.3620690Z) by Landsat 4, row 036, path 033. Cloud cover was 0 percent. The Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) software was originally developed by the National Aeronautics and Space Administration–Goddard Space Flight Center and the University of Maryland to produce top-of-atmosphere reflectance from Landsat Thematic Mapper and Enhanced Thematic Mapper Plus Level 1 digital numbers and to apply atmospheric corrections to generate a surface-reflectance product. The U.S. Geological Survey (USGS) has adopted the LEDAPS algorithm for producing the Landsat Surface Reflectance Climate Data Record. NASA Landsat Program, 2009, Landsat TM LT40330361988321XXX01, LPGS_12.1.3, USGS, Sioux Falls, 2012-12-24T11:34:33Z.
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