Find research datasets worth reusing

openCC0Mar 2024View details →

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.

openCC0Aug 2024View details →

Comparative Bird Community Assessments in Grassland, Shrubland, and Woodland Habitats at the Sevilleta National Wildlife Refuge, New Mexico (1991-1997 and 2022-2023)

Across North America, avifauna abundance has declined by 30% since 1970 (Rosenberg, K.V. et al. 2019). Direct mortality from anthropogenic sources (pets, cars, collisions with building, power lines, wind turbines, etc.) and indirect mortality (habitat loss, disturbance, climate change, etc.) have both been major contributors to these declines (Loss, S.R. et al. 2015 and Calvert, A.M. et al. 2013). Variables such as migration patterns, family, breeding and non-breeding biomes show differing rates of decline (Rosenberg, K.V. et al. 2019). In New Mexico, there are three breeding biomes all classified with declining avian abundance. Avian abundance in grasslands has declined by 53.3% since 1970, western forests by 29.5% and arid lands by 17.0% (Rosenberg, K.V. et al. 2019). All three of these biomes also occur at the Sevilleta National Wildlife Refuge thus temporal declines in species richness and abundance are expected. This project was originally designed to sample the species richness and abundance of birds on the Sevilleta National Wildlife Refuge in three types of habitat: grassland, creosote shrubland and pinyon-juniper woodland. Surveys were conducted between January 1991 and May 1997 (Parmenter, R. 2016). Surveys were re-established in 2022 to document current species richness and abundance and to capture any temporal changes from the 90s data. Avian point count survey stations in grassland, creosote and pinyon-juniper habitats run through existing study sites which have all been subjected to intense research activity. Literature Cited A. M. Calvert, C. A. Bishop, R. D. Elliot, E. A. Krebs, T. M. Kydd, C. S. Machtans, G. J. Robertson, A synthesis of human-related avian mortality in Canada. Avian Conserv. Ecol. 8, art11 (2013). https://www.ace-eco.org/vol8/iss2/art11/ Loss, S. R., Will, T., Marra, P. P. 2015. Direct Mortality of Birds from Anthropogenic causes. Annu. Rev. Ecol. Evol. Syst. 46, 99–120. https://www.annualreviews.org/doi/10.1146/annurev-ecolsys-1124

Small Mammal Mark-Recapture Population Dynamics at Core Research Sites at the Sevilleta National Wildlife Refuge, New Mexico (1989-present)

This file contains mark/recapture trapping data collected from 1989-present on permanently established web trapping arrays at sites on the Sevilleta National Wildlife Refuge in central New Mexico.. The trapping sites are representative of Chihuahuan Desert Grassland, Chihuahuan Desert Shrubland, Pinyon-Juniper Woodland, Juniper Savanna, Plains-Mesa Sand Scrub and Blue Grama Grassland. Not all sites have been trapped for the entire period: goatdraw (1992-2008), blue grama (2002-2004) rsgrass (1989-1998), rslarrea (1989-2009), two2 (1989-1998), savanna (1999-2002). Only 2 sites have been continuously been sampled since 1989 (5pgrass and 5plarrea). At each site 3 trapping webs are sampled for 3 consecutive nights in spring and fall. Each trapping web consists of 145 rebar stakes numbered from 1-145. There are 148 traps deployed on each web: 12 along each of 12 spokes radiating out from a central point (stake #145) plus 4 traps placed at the center of each web. The wide format facilitates community composition and species diversity analyses. Wide format has been reshaped so that the count data for each species are presented in a unique column. Data are summarized for each trapping web X trapping bout to present the mean number of animals per trap per night of the trapping bout. Wide format fills zeros for species that were not captured on a web during a given trapping bout. Long format facilitates filtering the dataset to a particular small mammal species of interest, but this format requires the addition of zeros to be functional for accurate data analysis requiring counts of animals.

openCC0Jan 2021View details →

Small Mammal Exclosure Study (SMES) Surface Soil Disturbance in the Chihuahuan Desert Grassland and Shrubland at the Sevilleta National Wildlife Refuge, New Mexico (1995-2005)

The purpose of this study is to determine whether or not the activities of small mammals regulate plant community structure, plant species diversity, and spatial vegetation patterns in Chihuahuan Desert shrublands and grasslands. What role if any do indigenous small mammal consumers have in maintaining desertified landscapes in the Chihuahuan Desert? Additionally, how do the effects of small mammals interact with changing climate to affect vegetation patterns over time? This is data for animal created soil surface disturbance measured from each of the SMES study plots. Soil surface disturbance was measured from each of the 36 one-meter2 quadrats twice each year when vegetation was measured.

opencc-by-4.0Nov 2023View details →

Deep learning to extract the meteorological by-catch of wildlife cameras: Supporting data, models and code

This repository contains the data, models and code to train and deploy deep learning models related to the paper "Deep learning to extract the meteorological by-catch of wildlife cameras" published in the journal Global Change Biology (<a href="https://doi.org/10.1111/gcb.17078">https://doi.org/10.1111/gcb.17078</a>).

opencc-by-4.0Sep 2022View details →

Data from: Seasonal variation in wildlife roadkills in plantations and tropical rainforest in the Anamalai Hills, Western Ghats, India

This dataset contains animal roadkill data (2011-13) from the Valparai Plateau and Anamalai Tiger Reserve, Western Ghats, India. Occurrence records were gathered in the field by researchers of the <a href="https://www.ncf-india.org">Nature Conservation Foundation, India</a>. The dataset corresponds to the following publication: Jeganathan, P., Mudappa, D., Kumar, M. A., and Raman, T. R. S. 2018. <a href="https://doi.org/10.18520/cs/v114/i03/619-626">Seasonal variation in wildlife roadkills in plantations and tropical rainforest in the Anamalai Hills, Western Ghats, India</a>. Current Science 114(3): 619-626. DOI: 10.18520/cs/v114/i03/619-626 CONTACT #1 1. Name: P. Jeganathan 2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India 3. Work Phone: +91 821 2515601 4. Email address: jegan@ncf-india.org 5. ORCID: https://orcid.org/0000-0002-0238-0655 CONTACT #2 1. Name: Divya Mudappa 2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India 3. Work Phone: +91 821 2515601 4. Email address: divya@ncf-india.org 5. ORCID: https://orcid.org/0000-0001-9708-4826 CONTACT #3 1. Name: M. Ananda Kumar 2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India 3. Work Phone: +91 821 2515601 4. Email address: anand@ncf-india.org 5. ORCID: https://orcid.org/0000-0001-7094-1314 CONTACT #4 1. Name: T. R. Shankar Raman 2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India 3. Work Phone: +91 821 2515601 4. Email address: trsr@ncf-india.org 5. ORCID: https://orcid.org/0000-0002-1347-3953 Keywords: tropical rainforest, plantations, Anamalai Hills, animal roadkill, linear infrastructure intrusions, highways, road ecology, animal-vehicle collisions   Geographic Coverage: 1. Location/Study Area: Valparai Plateau, Tamil Nadu, India; Anamalai Tiger Reserve, Tamil Nadu, India 2. GPS coordinates: Valparai Plateau (10°15'- 10°22'N, 76°52' - 76°59'E); Anamalai Tiger Reserve (10°12' - 10°35'N, 76°49' - 77°24'E) Temporal Coverage: 1. Begins: 2011-06-01 (Year, Month, Day) 2. Ends: 2013-05-31 (Year, Month, Day) Methods: Methods involved repeated surveys along the road routes searching for roadkills and habitat sampling as described in <a href="https://doi.org/10.18520/cs/v114/i03/619-626">Jeganathan et al. (2018), Current Science 114(3): 619-626</a>, DOI: 10.18520/cs/v114/i03/619-626 Files included: Besides this 00_README.txt file, the dataset includes the following six files as explained below: 1) 01_habitat_length.csv -- details of road routes surveyed as line transects 2) 02_sampling_events.csv -- details of individual line transect sample surveys along road routes 3) 03_roadkill_data_final.csv  -- roadkill occurrence data from sample surveys along road routes 4) 04_canopy_and_habitat.csv -- canopy and habitat readings along road routes (transects) surveyed 5) 05_roadkill_transects_all.kml -- KML file containing geographic tracks of 11 road routes surveyed as roadkill transects 6) 06_road_transects_map.jpg -- Map of surveyed routes corresponding to Figure 1 in Jeganathan et al. (2018) 01_habitat_length.csv transect: name of road route surveyed as a line transect route_description: description of road route tlength_km: transect length along road in kilometres (km) tlength_m: transect length along road in metres (m) forest: extent of the road in metres (m) with forest on both sides forest_tea: extent of the road in metres (m) with forest on one side, tea on the other coffee_forest: extent of the road in metres (m) with forest on one side, coffee plantation on the other tea: extent of the road in metres (m) with tea plantation on both sides coffee: extent of the road in metres (m) with coffee plantation on both sides eucalyptus: extent of the road in metres (m) with eucalyptus plantation on both sides eucalyptus_tea: extent of the road in metres (m) with eucalyptus on one side, tea plantation on the other 02_sampling_events.csv season: monsoon (June to December 2011) or summer (March to June 2012 prior to the onset of 2012 monsoon) transect: name of road route surveyed as a line transect transect: name of road route surveyed as a line transect tcode: unique code for each individual survey of a road route (transect) coevered on a specific date eventDate: date of road survey tlength: transect length along road in kilometres (km) 03_roadkilldata_final.csv sno: serial number of observation season: monsoon (June to December 2011) or summer (March to June 2012 prior to the onset of 2012 monsoon) transect: name of road route surveyed as a line transect tcode: unique code for each individual survey of a road route (transect) coevered on a specific date eventDate: date of road survey fielddate: date of road survey as initially noted (for two surveys completed over two successive days, the initial date was recorded as eventDate for 2011-06-17 = 2011-06-16 and eventDate for 2011-07-06 = 2011-07-05 tlength: transect length along road in kilometres (km) verbatimIdentification: original identification of roadkilled taxon vernacularName: common name of taxon scientificName: scientific name of taxon for corresponding taxonomic level of identification taxonRank: rank of taxon indicating for corresponding taxonomic level of identification taxonRemarks: category of taxon as noted for analysis verbatimCoordinateSystem: coordinate system used for initial data collection verbatimSRS: SRS of the location data collected (EPSG:32643/WGS84) georeferenceRemarks: note indicating locations were converted from UTM (zone 43 N) to latitude longitude using QGIS software verbatimLongitude: UTM longitude (Easting) as originally recorded verbatimLatitude: UTM latitude (Northing) as originally recorded decimalLongitude: longitude in decimal degree East decimalLatitude: latitude in decimal degrees North habitat: habitat on either side of the road (forest - forest on both sides; forest_tea - forest on one side, tea on the other; human - human settlements; coffee - coffee plantation on both sides; coffee_forest - coffee on one side, forest on the other; eucalyptus - eucalyptus plantation on both sides; eucalyptus_tea - eucalyptus on one side, tea on the other; tea - tea plantation) individualCount: number of individuals recorded as roadkill (0 if no roadkills in that survey) occurrenceStatus: indicated as 'present' for roadkills, or 'absent' if no roadkills recorded occurrenceRemarks: notes and remarks if any 04_canopy_and_habitat.csv transect: name of road route surveyed as a line transect verbatimroute: route name as originally noted sno: serial number canopycover: 0 if tree canopy absent, 1 if tree canopy present above point of observation canopyoverlap: horizontal overlap of tree canopy above point of observation ranked as 0 - no canopy above; 1 canopy present but barely touching or overrlapping; 2 - canopy overlapping with sky still visible through leaves; 3 - canopy overlaps overhead densely with sky scarcely visible verticaloverlap: vertical gap between canopy or branches of trees above point of observation ranked as 0 - very wide; 1 - barely touching, 2 - significant vertical overlap, 3 - substantial and dense vertical overlap habcode: two letter alphabetical code with each letter indicating habitat on one side of the road at the point of observation with f - forest, t - tea, c - coffee, e - eucalyptus, v - village or human habitation, m - dam or reservoir habno: numeric category for habitat on either side coded as 1 for monocultures (ee, tt); 2 for mixed forest and plantation (ef, ft, etc.); 3 for forest (ff), and 4 for coffee plantation (cc) longitude: longitude in decimal degrees east latitude: latitude in decimal degrees north 05_roadkill_transects_all.kml This KML file contains all 11 road routes surveyed as roadkill transects. 06_road_transects_map.jpg This map illustrating the surveyed road routes corresponds to Figure 1 in <a href="https://doi.org/10.18520/cs/v114/i03/619-626">Jeganathan et al. (2018), Current Science 114(3): 619-626</a>, DOI: 10.18520/cs/v114/i03/619-626

opencc-by-4.0May 2018View details →

Data and code from: Evaluating spatially explicit density estimates of unmarked wildlife detected by remote cameras.

Detection data from American black bears and code used in "Evaluating spatially explicit density estimates of unmarked wildlife detected by remote cameras" published in the Journal of Applied Ecology (Evans & Rittenhouse 2018).  Unmarked detection data was collected using remote cameras in northwest Connecticut in 2014, and individual detection data was determined from unique genotypes obtained from non-invasive hair snares constructed at camera sampling locations. EN14.rds contains detection data as an R list: $y (num): J (sites) x K (occasions) matrix containing detection counts $X (int): 2 x J matrix of site coordinates $xlims (num): bounding x-coordinates $ylims (num): bounding y-coordinates $M (int): upper bound for super population of individuals $nTraps (int): number of sampling sites (J) $nReps (int): number of MCMC interations $forest (num): vector of site-specific covariates $mark (int): K x I matrix storing site numbers at which individual (i) was detected on occasion k FullModel.R provides functions used to fit constant density models to unmarked detections incorporating covariates of detection probability. partialID.R provides functions and code used to estimate density from mixtures of marked and unmarked detection data VariableDensity.R provides functions and code used to fit variable density models to unmarked detection data incorporating spatial covariates of density.

opencc-by-4.0Aug 2024View details →

World Wildlife Fund, 2006

World Wildlife Fund. 2006. WildFinder: Online database of species distributions, ver. Jan-06. www.worldwildlife.org/WildFinderWorld Wildlife Fund. 2006. WildFinder: Online database of species distributions, ver. Jan-06. www.worldwildlife.org/WildFinder

opencc-by-4.0Aug 2024View details →

World Wildlife Fund, 2006: world_wildlife_fund.tar.gz (DwCA)

opencc-by-4.0Jan 2023View details →

Wildlife–vehicle collisions (WVC) on interurban roads in Spain (2016-2021)

CSV that contains 1.000 records of wildlife–vehicle collisions (WVC) on interurban roads in Spain between 2016 and 2021. If you are interested in the whole country dataset, please do not hesitate to contact me and I will forward it to you.  Data source of each WVC record is the Spanish General Directorate of Traffic (DGT), but the dataset has been enhanced by the integration of other sources: OpenStreetMap (OSM), Global Biodiversity Information Facility (GBIF), the National Geographic Institute of Spain (IGN), State Meteorological Agency (AEMET).Therefore, each record describes an accident by the following fields: •        id_num (int8): the unique identifier for an accident. •        ind_accda (int8): a binary variable for property damages involved or not (encoded). •        nombre_ind_accd (str): a statement for property damages involved or not (decoded). •        ind_acciv (int8): a binary variable for personal damages involved or not (encoded). •        nombre_ind_acciv (str): a statement for personal damages involved or not (decoded). •        total_mu30df (int8): the total number of deaths from the accident. •        total_hg30df (int8): the total number of injured with hospitalization from the accident. •        total_hl30df (int8): the total number of injured without hospitalization from the accident. •        fecha_accidente (date): the reported date of the collision, following ISO 8601 date-time standard.  •        hora_accidente (str): the reported hour of the collision in 24-hour notation.  •        mes_1f (int8): the month as integer of the event date (encoded). •        nombre_mes (str): the month name of the event date (decoded). •        anyo (int8): the four-digit year of the event date. •        ccaa_1f (int8): the autonomous region code from INE where accident is registered (encoded). •        nombre_ccaa (str): the name of the autonomous region where accident is registered (decoded). •        provincia_1f (int8): the province code from INE where the accident is registered (encoded). •        nombre_provincia (str): the province name where the accident is registered (decoded). •        cod_municipio (int8): the municipality code from INE where the accident is registered (encoded). •        nombre_municipio (str): the municipality name where the accident is registered (decoded). •        carretera (str): the road attending to the national road numbering system in Spain where the accident is located. •        km (float): the kilometre point of the road where the accident is located. •        sentido_1f (int8): the vehicle’s direction of traffic reported as integer when the accident occurred (encoded). •        nombre_sentido (str): the vehicle’s direction of traffic reported when the accident occurred (decoded). •        tipo_via_3f (int8): the type of road as integer attending to the project road classification (encoded). •        nombre_tipo_via (str): the type of road description attending to the project road classification (decoded). •        titularidad_via_2f (int8): the road ownership type as integer (encoded). •        nombre_titularidad_via (str): the road ownership type description (decoded). •        tipo_animal_1f (int8): the animal species involved in the accident as integer (encoded). •        nombre_tipo_animal_1f (str): the animal species name involved in the accident (decoded). •        tipo_animal_2f (int8): the reported animal type of breeding as integer (encoded). •        nombre_tipo_animal_2f (str): the type of animal breeding description (decoded). •        longitud (float): the length of the accident location coordinate in decimal degrees. •        latitud (float): the latitude of the accident location coordinate in decimal degrees. •        geom (geometry): geometry from latitude and longitude position. Developed for this project. •        dia_semana (int8): the integer day of the week when the accident occurred (encoded). •        nombre_dia_semana (str): the name of the day when the accident occurred (decoded). •        tipo_dia (str): the category name of the day type to separate weekday from weekend (decoded). •        parte_dia (str): the part name of the day when the accident is registered including day, night and the transitions. •        luna (int8): the portion of illuminated moon surface represented as an integer value from 0 to 100. •        prec (float): the daily rainfall measurement of the event day based on pluviometric days. •        tmin (float): the minimum temperature in Celsius of the event day. •        tmed (float): the average temperature in Celsius of the event day. •        tmax (float): the maximum temperature in Celsius of the event day. •        sol (float): the accumulated sun hours of the event day. •        uso_suelo (str): the main land usage of the accident area. •        altitud (float): the altitude in meters above sea level. •        pendiente (float): the slope median value of a 30 meters buffer around the accident location. •        taxonkey (str): a taxon key from the GBIF backbone. •        imd_total (float): the average daily traffic intensity of the accident year. •        maxspeed (int): the maximum speed of the road section where the reported collision. The context is the Final Master's Degree Project 'Analysis and Predictive Modelling of Wildlife–Vehicle Collision on Interurban Roads in Spain' (Data Science Master’s Degree of Universitat Oberta de Catalunya - UOC). This dataset is the output of the wildlife–vehicle collision analysis and the <a href="https://github.com/alba620/analisis-prediccion-accidentes-trafico-animales">code repository</a> is available on GitHub.

opencc-by-4.0Sep 2022View details →

A data directory to facilitate investigations on worldwide wildlife trafficking

We describe a novel, open-access data directory  on wildlife trafficking and a corresponding visualization tool that can be used to identify data for multiple purposes, such as exploring wildlife trafficking hotspots and convergence points with other crime, discovering key drivers or deterrents of wildlife trafficking, and uncovering structural patterns. Keyword searches, expert elicitation, and peer-reviewed publications were used to search for extant sources used by industry and non-profit organizations, as well as those leveraged to publish academic research articles.  The open-access data directory is designed to be a living document and searchable according to multiple measures. The directory can be instrumental in the data-driven analysis of unsustainable illegal wildlife trade, supply chain structure via link prediction models, the value of demand and supply reduction initiatives via multi-item knapsack problems, or trafficking behavior and transportation choices via network interdiction problems.

openCC (other)Jun 2023View details →

Decomposition of Microstegium vimineum litter, plants grew through the Big Oaks National Wildlife Refuge in 2019. Litter used in this experiment naturally senesced in the fall 2019, decomposition data collected through 2020. Plants were infected or not-infected with the foliar fungal pathogen Bipolaris gigantea during the 2019 growing season.

Decomposition of plant litter, facilitated primarily by microbial decomposers, plays a critical role in biogeochemical cycling and ecosystem function. Emerging pathogens have the potential to impact litter decomposition by altering the chemical composition and associated microbial community of host tissue. Here, we compared litter decomposition of the invasive grass Microstegium vimineum collected from sites with Bipolaris leaf spot symptoms and sites with no apparent disease symptoms in a common garden experiment. Our results revealed that leaf tissue from litter from non-infected sites decomposed more rapidly through the spring than litter from infected sites. Differences in fungal composition between infected and non-infected litter at the start of the experiment largely persisted through the summer. Our work demonstrates that pathogen colonization may facilitate the persistence of infected host litter, potentially slowing the return of nutrients to the environmental pool while also promoting the survival and dispersal of primary inoculum the following season.

openCC (other)Nov 2023View details →

California Department of Fish and Wildlife Enhanced Large Fish Study, San Francisco Estuary, California, 2023 Gillnet Survey

The Enhanced Large Fish Study (ELFS) was established and included in the Interagency Ecological Program (IEP) work plan in 2023 to fill some of the monitoring gap of fishes in the San Francisco Estuary (SFE), California. The fish monitoring of the IEP prior to ELFS primarily consisted of trawl- and seine-based surveys, which generally capture small and/or juvenile fishes due to survey gear and methodologies. In order to more effectively sample the large/adult fish portion of SFE fish communities, the ELFS uses American Fisheries Society experimental gillnets, plus the inclusion of the optional "large fish panel," to sample the waters of the SFE. The experimental gillnets measure 24.4m in length, 1.8m in depth, and includes eight 3m length panels with stretch mesh measurements of 76.2mm, 114.3mm, 50.8mm, 88.9mm, 38.1mm, 127.0mm, 63.5mm, and 101.6mm. The optional large fish panel measures 9.1m by 1.8m in depth and includes three 3m length panels with stretch mesh measurements of 152.4, 177.8, and 203.2mm. The ELFS conducted its first year of sampling in the North Delta, California, in 2023, and will be expanded to the greater Delta and Suisun Bay and Marsh in future sampling years. The ELFS is currently funded as a special study to inform future monitoring, with funding contracted through fiscal year 2026-2027.

Extreme Drought in Grasslands Experiment (EDGE): High frequency measurements from the northern Chihuahuan Desert site, Sevilleta National Wildlife Refuge, NM, USA (2013-2023)

The Extreme Drought in Grasslands Experiment (EDGE) is distributed across six representative grassland ecosystems of the central United States. EDGE serves as an important research platform for understanding the resistance and resilience of these grassland ecosystems to extreme prolonged drought as well as to changes in precipitation seasonality. This data package contains high-frequency environmental sensor measurements from the northern Chihuahuan Desert site, dominated by black grama (Bouteloua eriopoda), located in the Sevilleta National Wildlife Refuge in central New Mexico.

Extreme Drought in Grasslands Experiment (EDGE): High frequency measurements from the southern Great Plains site, Sevilleta National Wildlife Refuge, NM, USA (2013-2023)

The Extreme Drought in Grasslands Experiment (EDGE) is distributed across six representative grassland ecosystems of the central United States. EDGE serves as an important research platform for understanding the resistance and resilience of these grassland ecosystems to extreme prolonged drought as well as to changes in precipitation seasonality. This data package contains high-frequency environmental sensor measurements from the southern Great Plains site, dominated by blue grama (Bouteloua gracilis), located in the Sevilleta National Wildlife Refuge in central New Mexico.

Sevilleta Field Station Meteorological Network (SevMET): High frequency measurements from the Black Butte Meteorological Station (BLBT), Sevilleta National Wildlife Refuge, NM, USA, 2024-ongoing.

The Sevilleta Field Station Meteorological Network (SevMET) is a spatially distributed, long-term climate monitoring network established to enhance and expand climate monitoring across a variety of dryland ecosystems (e.g., grasslands, shrublands, woodlands) within the Sevilleta National Wildlife Refuge in central New Mexico. Ecosystem processes in drylands are strongly regulated by climatic drivers that are highly variable in space and time, both within and among years. Therefore, accurate measurement of environmental variables at high spatial and temporal resolution is fundamental to understanding biophysical processes in these ecosystems. SevMET consists of fifteen standardized research-grade weather stations located across multiple dryland ecosystem types (e.g., grasslands, shrublands, woodlands) representative of the southwestern US. Stations continuously measure a standard suite of meteorological variables at five-minute intervals, including air temperature, relative humidity, precipitation, photosynthetically active radiation, incoming shortwave radiation, wind speed and direction, dew point, vapor pressure, and, at a subset of stations, barometric pressure. Stations also measure a suite of soil parameters (bulk electrical conductivity, dielectric permittivity, temperature, and volumetric water content) at six depths (5, 10, 20, 30, 40, and 50 cm) below the ground surface using 1-2 integrated soil profilers. Additionally, phenocams at each station capture images at thirty-minute intervals during daylight hours. This data package contains high-frequency meteorological measurements from the Black Butte Meteorological Station (BLBT). Phenocam images can be accessed through the PhenoCam Network at: https://phenocam.nau.edu/webcam/sites/sevmetblbt/. These data complement and extend meteorological data recorded by an adjacent station (Met54), accessible at: https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-sev&identifier=1.

Sevilleta Field Station Meteorological Network (SevMET): High frequency measurements from the Bronco Well Meteorological Station (BRWL), Sevilleta National Wildlife Refuge, NM, USA, 2024-ongoing.

The Sevilleta Field Station Meteorological Network (SevMET) is a spatially distributed, long-term climate monitoring network established to enhance and expand climate monitoring across a variety of dryland ecosystems (e.g., grasslands, shrublands, woodlands) within the Sevilleta National Wildlife Refuge in central New Mexico. Ecosystem processes in drylands are strongly regulated by climatic drivers that are highly variable in space and time, both within and among years. Therefore, accurate measurement of environmental variables at high spatial and temporal resolution is fundamental to understanding biophysical processes in these ecosystems. SevMET consists of fifteen standardized research-grade weather stations located across multiple dryland ecosystem types (e.g., grasslands, shrublands, woodlands) representative of the southwestern US. Stations continuously measure a standard suite of meteorological variables at five-minute intervals, including air temperature, relative humidity, precipitation, photosynthetically active radiation, incoming shortwave radiation, wind speed and direction, dew point, vapor pressure, and, at a subset of stations, barometric pressure. Stations also measure a suite of soil parameters (bulk electrical conductivity, dielectric permittivity, temperature, and volumetric water content) at six depths (5, 10, 20, 30, 40, and 50 cm) below the ground surface using 1-2 integrated soil profilers. Additionally, phenocams at each station capture images at thirty-minute intervals during daylight hours. This data package contains high-frequency meteorological measurements from the Bronco Well Meteorological Station (BRWL). Phenocam images can be accessed through the PhenoCam Network at: https://phenocam.nau.edu/webcam/sites/sevmetbrwl/. These data complement and extend meteorological data recorded by an adjacent station (Met45), accessible at: https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-sev&identifier=1.

Sevilleta Field Station Meteorological Network (SevMET): High frequency measurements from the Burris Well Meteorological Station (BUWL), Sevilleta National Wildlife Refuge, NM, USA, 2024-ongoing.

The Sevilleta Field Station Meteorological Network (SevMET) is a spatially distributed, long-term climate monitoring network established to enhance and expand climate monitoring across a variety of dryland ecosystems (e.g., grasslands, shrublands, woodlands) within the Sevilleta National Wildlife Refuge in central New Mexico. Ecosystem processes in drylands are strongly regulated by climatic drivers that are highly variable in space and time, both within and among years. Therefore, accurate measurement of environmental variables at high spatial and temporal resolution is fundamental to understanding biophysical processes in these ecosystems. SevMET consists of fifteen standardized research-grade weather stations located across multiple dryland ecosystem types (e.g., grasslands, shrublands, woodlands) representative of the southwestern US. Stations continuously measure a standard suite of meteorological variables at five-minute intervals, including air temperature, relative humidity, precipitation, photosynthetically active radiation, incoming shortwave radiation, wind speed and direction, dew point, vapor pressure, and, at a subset of stations, barometric pressure. Stations also measure a suite of soil parameters (bulk electrical conductivity, dielectric permittivity, temperature, and volumetric water content) at six depths (5, 10, 20, 30, 40, and 50 cm) below the ground surface using 1-2 integrated soil profilers. Additionally, phenocams at each station capture images at thirty-minute intervals during daylight hours. This data package contains high-frequency meteorological measurements from the Burris Well Meteorological Station (BUWL). Phenocam images can be accessed through the PhenoCam Network at: https://phenocam.nau.edu/webcam/sites/sevmetbuwl/. These data complement and extend meteorological data recorded by an adjacent station (Met50), accessible at: https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-sev&identifier=1.

Sevilleta Field Station Meteorological Network (SevMET): High frequency measurements from the Contreras Meteorological Station (CONT), Sevilleta National Wildlife Refuge, NM, USA, 2024-ongoing.

The Sevilleta Field Station Meteorological Network (SevMET) is a spatially distributed, long-term climate monitoring network established to enhance and expand climate monitoring across a variety of dryland ecosystems (e.g., grasslands, shrublands, woodlands) within the Sevilleta National Wildlife Refuge in central New Mexico. Ecosystem processes in drylands are strongly regulated by climatic drivers that are highly variable in space and time, both within and among years. Therefore, accurate measurement of environmental variables at high spatial and temporal resolution is fundamental to understanding biophysical processes in these ecosystems. SevMET consists of fifteen standardized research-grade weather stations located across multiple dryland ecosystem types (e.g., grasslands, shrublands, woodlands) representative of the southwestern US. Stations continuously measure a standard suite of meteorological variables at five-minute intervals, including air temperature, relative humidity, precipitation, photosynthetically active radiation, incoming shortwave radiation, wind speed and direction, dew point, vapor pressure, and, at a subset of stations, barometric pressure. Stations also measure a suite of soil parameters (bulk electrical conductivity, dielectric permittivity, temperature, and volumetric water content) at six depths (5, 10, 20, 30, 40, and 50 cm) below the ground surface using 1-2 integrated soil profilers. Additionally, phenocams at each station capture images at thirty-minute intervals during daylight hours. This data package contains high-frequency meteorological measurements from the Burris Well Meteorological Station (BUWL). Phenocam images can be accessed through the PhenoCam Network at: https://phenocam.nau.edu/webcam/sites/sevmetcont/.