Code accompanying the manuscript \"The stability of fatty acid composition in sunflower oil is dependent on environment and affected by structural variation\" (Ingold, Hulke et al.), submitted to Theoretical and Applied Genetics.
The code implements a genome-wide association study of seed fatty acid composition and its stability in the sunflower (Helianthus annuus L.) association mapping (SAM) population of 287 varieties, grown in eight field trials across North America: Vancouver, British Columbia (2010); Moorhead, Minnesota (2015, early and late plantings 2016); Ames, Iowa (2010, 2013, 2014); and Athens, Georgia (2010). Palmitic, stearic, oleic, and linoleic acid were measured by gas chromatography.
Multivariate GWAS was performed on four phenotype sets: mean fatty acid composition within each environment; the same omitting high oleic varieties; within-environment stability quantified by standard errors among replicate samples (alpha stability); and across-environment stability quantified by Eberhart and Russell's beta.
Included are scripts for phenotype preparation, beta stability regression, CHELSA climate data extraction and correlation, genotype and kinship analysis (ADMIXTURE, VanRaden kinship, PCA, LD blocks), multivariate GWAS with GEMMA and univariate GWAS with vcf2gwas, post-GWAS candidate gene identification, and analysis of a chromosome 5 introgression associated with stability under hot, humid conditions.
The code is archived at https://doi.org/10.5281/zenodo.22001495 and developed at https://github.com/BrianSmart/SunflowerFattyAcidStabilityGWAS. SNP genotypes are third-party and available from HelianthOME (http://www.helianthome.org/download/#genotype).
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The code implements a genome-wide association study of seed fatty acid composition and its stability in the sunflower (Helianthus annuus L.) association mapping (SAM) population of 287 varieties, grown in eight field trials across North America: Vancouver, British Columbia (2010); Moorhead, Minnesota (2015, early and late plantings 2016); Ames, Iowa (2010, 2013, 2014); and Athens, Georgia (2010). Palmitic, stearic, oleic, and linoleic acid were measured by gas chromatography.
Multivariate GWAS was performed on four phenotype sets: mean fatty acid composition within each environment; the same omitting high oleic varieties; within-environment stability quantified by standard errors among replicate samples (alpha stability); and across-environment stability quantified by Eberhart and Russell's beta.
Included are scripts for phenotype preparation, beta stability regression, CHELSA climate data extraction and correlation, genotype and kinship analysis (ADMIXTURE, VanRaden kinship, PCA, LD blocks), multivariate GWAS with GEMMA and univariate GWAS with vcf2gwas, post-GWAS candidate gene identification, and analysis of a chromosome 5 introgression associated with stability under hot, humid conditions.
The code is archived at https://doi.org/10.5281/zenodo.22001495 and developed at https://github.com/BrianSmart/SunflowerFattyAcidStabilityGWAS. SNP genotypes are third-party and available from HelianthOME (http://www.helianthome.org/download/#genotype).
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Particulate organic matter (POM) and mineral-associated organic matter (MAOM) fractions were isolated from <2 mm soil and analyzed by diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS). Raw spectra (including bleached NaOCl-oxidized POM and MAOM samples) are stored in SFT_POM_MAOM_raw.rds and processed in Raw_spectra.rmd. Mineral contributions were removed by spectral subtraction (POM \u2013 PX and MAM \u2013 MX), and spectra were transformed using Kubelka\u2013Munk (KM) functions in OMNIC; the resulting spectral subtraction data are stored in SFT_POM_MAOM_sub_KM.rds and SFT_POM_MAOM_sub_KM_ave.rds (replicate spectra are averaged) and processed in Subtracted_Spectra.rmd and Averaged_Spectra.rmd. Soil chemistry and index data (<2 mm soil, POM, MAOM) are in SFT_2023_Metadata.rds and Helix_Index123.rds and analyzed in SFT_Chem.rmd and SFT_stats.rmd. Variables include total carbon (TC), total nitrogen (TN), C:N ratios, SOM, water-holding capacity (WHC), pH, dissolved organic carbon (DOC), dissolved organic nitrogen (DON), microbial biomass carbon (MBC), microbial biomass nitrogen (MBN), and CO2 respiration. These files allow reproduction of DRIFTS spectral analyses (raw, subtraction, and averaged spectra) and statistical analyses of soil chemistry and index values. The READ_ME file describes the main purpose of each data file/analysis, and defines acronyms used.
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Soil was collected in July and August 2023 from four experimental almond orchards that received wood chip amendments by whole orchard recycling (WOR) in 2008 (15 years old), 2017 (6 years old), 2019 (4 years old), and 2020 (3 years old); this serves as a space-for-time (SFT) substitution design, with four replicate blocks sampled for each Treatment \u00d7 Field combination. The orchards are located at the University of California Kearney Agricultural Research and Extension Center (36.5966 \u00b0N, -119.5176 \u00b0W) in Parlier, California (USA), each having 4 replicated WOR and control blocks. WOR treatments received 33 \u2013 85 metric tons of wood chips per acre. Chronologically, the 3-year-old orchard received 60 tons of wood per acre, the 4-year-old orchard received 61 tons of wood per acre, the 6-year-old orchard received 85 tons of wood per acre, and the 15-year-old orchard received 33 tons of wood per acre. Control treatments followed conventional management practices during orchard establishment. For the orchards recycled in 2017, 2019, and 2020, the control soils were unamended and burning served as the control treatment in the 2008 orchard. This yields a chronosequence of soil samples at 15-, 6-, 4-, and 3-years post WOR, as a space for time substitution design. The soil texture primarily consists of fine sandy loam, characterized as coarse-loamy, mixed nonacid thermic Typic Xerorthents of the Hanford series. The National Cooperative Soil Survey characterized the Hanford soil series to typically contain less than 1 % soil organic matter (SOM) content, decreasing with depth (Official Series Description - HANFORD Series, n.d.). The orchard experiences a Mediterranean climate, with dry, hot summers and cool, wet winters and precipitation levels generally falling below almond evapotranspiration requirements for a significant portion of the growing season. On average, the annual rainfall and temperature stand at 285 mm and 17\u00b0C, respectively. Thus, all crop production in the region is irrigated.
Particulate organic matter (POM) and mineral-associated organic matter (MAOM) fractions were isolated from <2 mm soil and analyzed by diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS). Raw spectra (including bleached NaOCl-oxidized POM and MAOM samples) are stored in SFT_POM_MAOM_raw.rds and processed in Raw_spectra.rmd. Mineral contributions were removed by spectral subtraction (POM \u2013 PX and MAM \u2013 MX), and spectra were transformed using Kubelka\u2013Munk (KM) functions in OMNIC; the resulting spectral subtraction data are stored in SFT_POM_MAOM_sub_KM.rds and SFT_POM_MAOM_sub_KM_ave.rds (replicate spectra are averaged) and processed in Subtracted_Spectra.rmd and Averaged_Spectra.rmd. Soil chemistry and index data (<2 mm soil, POM, MAOM) are in SFT_2023_Metadata.rds and Helix_Index123.rds and analyzed in SFT_Chem.rmd and SFT_stats.rmd. Variables include total carbon (TC), total nitrogen (TN), C:N ratios, SOM, water-holding capacity (WHC), pH, dissolved organic carbon (DOC), dissolved organic nitrogen (DON), microbial biomass carbon (MBC), microbial biomass nitrogen (MBN), and CO2 respiration. These files allow reproduction of DRIFTS spectral analyses (raw, subtraction, and averaged spectra) and statistical analyses of soil chemistry and index values. The READ_ME file describes the main purpose of each data file/analysis, and defines acronyms used.
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Trends in records of 50 years or more showed that temperatures have changed from 1900-2020, more for minimum (0.1 - 0.3 \u2103 decade-1) than maximum (-0.1 - 0.2 \u2103 decade-1), more for winter (-0.1 - 0.3 \u2103 decade-1) than summer (-0.1 - 0.1 \u2103 decade-1), and more often in the north than in the south. Except in Mississippi, annual precipitation has increased at rates of 25 mm decade-1 or greater over 1950-2020, but monthly trends were inconsistent. Projected trends suggest continued temperature increases, highlighting the need for research on management systems that are resilient to such increases.
","distribution_titles":["Geodata ISO 19139 metadata","README.txt","README_LOCA2.txt","Station Data.xlsx","NOAA Data and trends.xlsx","LOCA2-LTAR.zip","ESM_Data_trends_AllTrends.xlsx","Climate_Indicators.txt","Coeff_Var_Trender.txt","LOCA_MKTrend.txt","ViolinPlotter.txt"],"harvest_record":"https://catalog.data.gov/harvest_record/73e60f2d-3810-4a41-960d-d3b6150db7c7","harvest_record_raw":"https://catalog.data.gov/harvest_record/73e60f2d-3810-4a41-960d-d3b6150db7c7/raw","has_download":true,"has_spatial":true,"identifier":"10.15482/USDA.ADC/29640977.v1","keyword":["CMIP 6 dataset","LTAR","Trend Analysis Background Data","precipitation","source code","temperature"],"last_harvested_date":"2026-09-30T21:32:32.041052","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"352b4532-793d-4075-a03f-05b778a3c43a","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/usda.png","name":"Department of Agriculture","organization_type":"Federal Government","slug":"usda"},"parent_identifier":null,"popularity":1,"publisher":"Agricultural Research Service","slug":"data-from-are-historical-trends-in-weather-consistent-with-model-predictions-in-the-centra","spatial_centroid":{"lat":39.3375,"lon":-90.45},"spatial_shape":{"coordinates":[[-85.4,42.4],[-93.8,42.3],[-92.1,39.2],[-90.5,33.45]],"type":"MultiPoint"},"theme":["geospatial"],"title":"Data from: Are historical trends in weather consistent with model predictions in the Central United States?","type":"dataset"},{"_score":8.319088,"_sort":[1790803895703,8.319088,4,"1a85cb8e-bfcd-4ee3-b40f-ea15e087a213"],"access_level":"public","dcat":{"@type":"dcat:Dataset","accessLevel":"public","accrualPeriodicity":"irregular","bureauCode":["005:18"],"contactPoint":{"fn":"Liebig, Mark A.","hasEmail":"mailto:mark.liebig@usda.gov"},"description":"Retaining crop residue on the soil surface is important in semiarid cropping systems, where dry conditions and variable weather can render the soil susceptible to degradation and moisture loss. Conversely, removing crop residue can generate an additional income stream for agricultural producers, while broadening the spectrum of uses from harvestable commodities. Understanding potential tradeoffs of crop residue removal across a range of agroecosystems over the long-term is essential to adequately guide management decisions. A study was conducted to quantify crop and soil responses to continuous spring wheat treatments with and without straw removal, each managed under minimum and no-tillage. Treatments were replicated three times and deployed over a 24-yr period. Soil coverage by crop residue was measured annually using two 25 point transects spaced equally along a 7.6 m cable. Spring wheat aboveground biomass was measured prior to combine harvest using 0.33 m2 frames. Biomass samples were threshed to separate grain from straw. Soil samples were collected in 2018 with a hydraulic probe to a 152.4 cm depth in increments of 0-7.6, 7.6-15.2, 15.2-30.5, 30.5-61.0, 61.0-91.4, 91.4-121.9, and 121.9-152.4 cm. Separate samples for aggregate stability analysis were collected with a trowel from the 0-7.6 cm depth. Soil samples were evaluated for soil bulk density, water-stable aggregates (WSA), electrical conductivity, soil pH, nitrate-nitrogen, available phosphorus, sulfate-sulfur, exchangeable cations (Ca, Mg, K, Na), micronutrients (B, Cu, Fe, Mn, Zn), total soil nitrogen, total carbon, inorganic carbon, and particulate organic matter (POM) carbon and nitrogen. Particulate organic matter (POM) was estimated from material retained on a 0.053 mm sieve analyzed for carbon and nitrogen content by dry combustion. Analyses for POM and WSA were conducted for the 0-7.6 cm depth only. Data may be used to better understand crop and soil property responses to residue management and tillage practices under rainfed conditions within a semiarid continental climate. Applicable USDA soil types include Temvik, Wilton, Grassna, Linton, Mandan, and Williams.
The SQM Data Dictionary describes element/value names, data type, etc. for each spreadsheet tab, and the Metadata files describe attributes and units for their respective data files.
Retaining crop residue on the soil surface is important in semiarid cropping systems, where dry conditions and variable weather can render the soil susceptible to degradation and moisture loss. Conversely, removing crop residue can generate an additional income stream for agricultural producers, while broadening the spectrum of uses from harvestable commodities. Understanding potential tradeoffs of crop residue removal across a range of agroecosystems over the long-term is essential to adequately guide management decisions. A study was conducted to quantify crop and soil responses to continuous spring wheat treatments with and without straw removal, each managed under minimum and no-tillage. Treatments were replicated three times and deployed over a 24-yr period. Soil coverage by crop residue was measured annually using two 25 point transects spaced equally along a 7.6 m cable. Spring wheat aboveground biomass was measured prior to combine harvest using 0.33 m2 frames. Biomass samples were threshed to separate grain from straw. Soil samples were collected in 2018 with a hydraulic probe to a 152.4 cm depth in increments of 0-7.6, 7.6-15.2, 15.2-30.5, 30.5-61.0, 61.0-91.4, 91.4-121.9, and 121.9-152.4 cm. Separate samples for aggregate stability analysis were collected with a trowel from the 0-7.6 cm depth. Soil samples were evaluated for soil bulk density, water-stable aggregates (WSA), electrical conductivity, soil pH, nitrate-nitrogen, available phosphorus, sulfate-sulfur, exchangeable cations (Ca, Mg, K, Na), micronutrients (B, Cu, Fe, Mn, Zn), total soil nitrogen, total carbon, inorganic carbon, and particulate organic matter (POM) carbon and nitrogen. Particulate organic matter (POM) was estimated from material retained on a 0.053 mm sieve analyzed for carbon and nitrogen content by dry combustion. Analyses for POM and WSA were conducted for the 0-7.6 cm depth only. Data may be used to better understand crop and soil property responses to residue management and tillage practices under rainfed conditions within a semiarid continental climate. Applicable USDA soil types include Temvik, Wilton, Grassna, Linton, Mandan, and Williams.
The SQM Data Dictionary describes element/value names, data type, etc. for each spreadsheet tab, and the Metadata files describe attributes and units for their respective data files.
Three .csv files. Two years of data collected on critical thermal maxima and minima (CTmax and CTmin) from 14 colonies in Oklahoma from 2017-2018. Additional data files represent thermal traits from 10 colonies in a short term (10 days) acclimation experiment and projections using environmental data for potential activity differences.
Abstract from paper:
How do individuals tolerate both the hot and the cold climate of our planet? One possibility is that organisms have plastic traits like thermal tolerance that allow them to function in highly variable environments. In this study, we tested whether phenotypic plasticity of temperature tolerance (i.e. acclimatization in the field and acclimation in the lab) occurs in the red harvester ant, Pogonomyrmex barbatus, at two temporal scales. We first measured the upper and lower critical thermal limits (CTmax and CTmin) of ants monthly for two years while concurrently measuring environmental conditions. Both CTmax and CTmin co-varied with temperature in a predictable way; values increased in a positive, linear manner. We then experimentally tested whether CTmax and CTmin could shift within a shorter time period by exposing subcolonies of ants to cool (10\u00b0C), moderate (20\u00b0C), and hot (30\u00b0C) temperatures for 10 days. CTmax increased only slightly at the hottest temperature treatment (+1.2\u00b0C), however CTmin increased considerably under both moderate (+2.6\u00b0C) and hot treatments (+3.8\u00b0C). Combined, our results suggest that thermal tolerance of ants may be more plastic than originally hypothesized, potentially aiding an already thermophilic clade.
Methods from paper:
Study site and environmental temperature
We sampled ant workers monthly during their annual active period in 2017 and 2018 (i.e. March-November) from 14 colonies in a 30-ha grazed prairie in the central Great Plains of Oklahoma (34.5478\u00ba N, -98.2311\u00ba W, 330 m elevation). Over two years, ground temperature was recorded every 10 minutes using HOBO U23 Pro v2 External Temperature Data loggers at three equidistant locations within the sampling area. Temperature values were then averaged per month.
Thermal tolerance across months
During each sampling event (n = 18), we collected ~20 workers directly outside the nest of each colony and used 5 workers to measure critical thermal maximum (CTmax) and 5 workers to measure critical thermal minimum (CTmin). We did so using a heating/cooling assay to determine the temperature at which individuals lost muscle control. Thermal assays were conducted by placing individual ants into 1.5ml microcentrifuge tubes and plugging the tops with cotton to remove a potential thermal refuge in the cap. For CTmax, tubes were placed randomly into a Thermal-Lok 2-position dry heat bath that was prewarmed to 36\u00baC. Every 10 minutes, individuals were checked to see if they had reached their critical thermal limit by rotating the tube to check for a righting response. The temperature was then increased by 2\u00baC, with the process repeated until all individuals had reached their critical thermal maximum. CTmin was assayed in a similar manner, but we used a EchoThermTM IC20 chilling/heating dry bath that was precooled to 20\u00baC, following the methods above except with temperature lowered 2\u00b0C every 10 minutes. Additional ants from each colony were kept at ambient conditions as a control during each thermal assay, all of which survived. During each trial, we also confirmed the interior temperature of one unused vial using a thermocouple attached to an Extech MN35 Digital Mini MultiMeter. CTmax and CTmin values were averaged per colony for each month.
Thermal tolerance within a month
In April of 2019, we collected ~200 workers from each of 10 separate colonies to assess if critical thermal limits could change within a single cohort of ants over a short period of time. We split each group of 200 workers into three sub-colonies containing 50 workers and placed these newly created sub-colonies into three environmental chambers set at 10\u00baC, 20\u00baC, and 30\u00baC with a 12:12 L:D cycle and 85% RH. The selected temperatures span the approximate range of average monthly temperatures at our study site during which ants were active. Each sub-colony was provided with water and 20% sucrose solution ad libitum in cotton plugged vials and a small petri dish with Plaster of Paris that was moistened every other day. Critical thermal limits (CTmax and CTmin) were assayed using five individuals from each colony immediately prior to the start of the experiment and for five individuals from each sub-colony after 10 days in the environmental chambers. CTmax and CTmin values were averaged per colony for each temperature treatment.
","distribution":[{"@type":"dcat:Distribution","downloadURL":"https://ndownloader.figshare.com/files/52668251","format":"csv","mediaType":"text/csv","title":"File 2 Across Months Pogo Thermal.csv"},{"@type":"dcat:Distribution","downloadURL":"https://ndownloader.figshare.com/files/52668254","format":"csv","mediaType":"text/csv","title":"File 3 Within Month Pogo Thermal.csv"},{"@type":"dcat:Distribution","downloadURL":"https://ndownloader.figshare.com/files/52668257","format":"csv","mediaType":"text/csv","title":"File 1 METADATA Pogo Thermal.csv"}],"identifier":"10.15482/USDA.ADC/28459058.v1","keyword":["Critical thermal limits","Pogonomyrmex barbatus","temperature","traits"],"license":"https://www.usa.gov/publicdomain/label/1.0/","modified":"2026-08-25","programCode":["005:040"],"publisher":{"@type":"org:Organization","name":"Agricultural Research Service"},"temporal":"2017-03-01/2018-11-30","title":"Data from: Temporal plasticity of thermal tolerance in ants"},"description":"Three .csv files. Two years of data collected on critical thermal maxima and minima (CTmax and CTmin) from 14 colonies in Oklahoma from 2017-2018. Additional data files represent thermal traits from 10 colonies in a short term (10 days) acclimation experiment and projections using environmental data for potential activity differences.
Abstract from paper:
How do individuals tolerate both the hot and the cold climate of our planet? One possibility is that organisms have plastic traits like thermal tolerance that allow them to function in highly variable environments. In this study, we tested whether phenotypic plasticity of temperature tolerance (i.e. acclimatization in the field and acclimation in the lab) occurs in the red harvester ant, Pogonomyrmex barbatus, at two temporal scales. We first measured the upper and lower critical thermal limits (CTmax and CTmin) of ants monthly for two years while concurrently measuring environmental conditions. Both CTmax and CTmin co-varied with temperature in a predictable way; values increased in a positive, linear manner. We then experimentally tested whether CTmax and CTmin could shift within a shorter time period by exposing subcolonies of ants to cool (10\u00b0C), moderate (20\u00b0C), and hot (30\u00b0C) temperatures for 10 days. CTmax increased only slightly at the hottest temperature treatment (+1.2\u00b0C), however CTmin increased considerably under both moderate (+2.6\u00b0C) and hot treatments (+3.8\u00b0C). Combined, our results suggest that thermal tolerance of ants may be more plastic than originally hypothesized, potentially aiding an already thermophilic clade.
Methods from paper:
Study site and environmental temperature
We sampled ant workers monthly during their annual active period in 2017 and 2018 (i.e. March-November) from 14 colonies in a 30-ha grazed prairie in the central Great Plains of Oklahoma (34.5478\u00ba N, -98.2311\u00ba W, 330 m elevation). Over two years, ground temperature was recorded every 10 minutes using HOBO U23 Pro v2 External Temperature Data loggers at three equidistant locations within the sampling area. Temperature values were then averaged per month.
Thermal tolerance across months
During each sampling event (n = 18), we collected ~20 workers directly outside the nest of each colony and used 5 workers to measure critical thermal maximum (CTmax) and 5 workers to measure critical thermal minimum (CTmin). We did so using a heating/cooling assay to determine the temperature at which individuals lost muscle control. Thermal assays were conducted by placing individual ants into 1.5ml microcentrifuge tubes and plugging the tops with cotton to remove a potential thermal refuge in the cap. For CTmax, tubes were placed randomly into a Thermal-Lok 2-position dry heat bath that was prewarmed to 36\u00baC. Every 10 minutes, individuals were checked to see if they had reached their critical thermal limit by rotating the tube to check for a righting response. The temperature was then increased by 2\u00baC, with the process repeated until all individuals had reached their critical thermal maximum. CTmin was assayed in a similar manner, but we used a EchoThermTM IC20 chilling/heating dry bath that was precooled to 20\u00baC, following the methods above except with temperature lowered 2\u00b0C every 10 minutes. Additional ants from each colony were kept at ambient conditions as a control during each thermal assay, all of which survived. During each trial, we also confirmed the interior temperature of one unused vial using a thermocouple attached to an Extech MN35 Digital Mini MultiMeter. CTmax and CTmin values were averaged per colony for each month.
Thermal tolerance within a month
In April of 2019, we collected ~200 workers from each of 10 separate colonies to assess if critical thermal limits could change within a single cohort of ants over a short period of time. We split each group of 200 workers into three sub-colonies containing 50 workers and placed these newly created sub-colonies into three environmental chambers set at 10\u00baC, 20\u00baC, and 30\u00baC with a 12:12 L:D cycle and 85% RH. The selected temperatures span the approximate range of average monthly temperatures at our study site during which ants were active. Each sub-colony was provided with water and 20% sucrose solution ad libitum in cotton plugged vials and a small petri dish with Plaster of Paris that was moistened every other day. Critical thermal limits (CTmax and CTmin) were assayed using five individuals from each colony immediately prior to the start of the experiment and for five individuals from each sub-colony after 10 days in the environmental chambers. CTmax and CTmin values were averaged per colony for each temperature treatment.
","distribution_titles":["File 2 Across Months Pogo Thermal.csv","File 3 Within Month Pogo Thermal.csv","File 1 METADATA Pogo Thermal.csv"],"harvest_record":"https://catalog.data.gov/harvest_record/baca902d-dd40-4535-bead-faa5a68dce8f","harvest_record_raw":"https://catalog.data.gov/harvest_record/baca902d-dd40-4535-bead-faa5a68dce8f/raw","has_download":true,"has_spatial":false,"identifier":"10.15482/USDA.ADC/28459058.v1","keyword":["Critical thermal limits","Pogonomyrmex barbatus","temperature","traits"],"last_harvested_date":"2026-09-30T21:30:45.020435","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"352b4532-793d-4075-a03f-05b778a3c43a","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/usda.png","name":"Department of Agriculture","organization_type":"Federal Government","slug":"usda"},"parent_identifier":null,"popularity":3,"publisher":"Agricultural Research Service","slug":"data-from-temporal-plasticity-of-thermal-tolerance-in-ants","spatial_centroid":null,"spatial_shape":null,"theme":[],"title":"Data from: Temporal plasticity of thermal tolerance in ants","type":"dataset"},{"_score":15.252194,"_sort":[1790803842515,15.252194,1,"a592d3cf-ad2e-4a98-a2bf-e2f7fdd71671"],"access_level":"public","dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["005:18"],"contactPoint":{"fn":"Bandaru, Varaprasad","hasEmail":"mailto:prasad.bandaru@usda.gov"},"description":"The Environmental Policy Integrated Climate (EPIC) model is a comprehensive, field-scale agroecosystem model widely used for both diagnostic and prognostic analyses in agriculture. However, its application at regional scales is limited due to its original design to simulate a limited number of fields. Custom Python or R scripts have attempted to scale EPIC, but they are often inefficient, non-standardized, and not publicly available. To address these issues, a comprehensive Python package, named as GeoEPIC, that streamlines spatial EPIC implementation. GeoEPIC automates input generation from spatial datasets, model calibration, simulation execution, and output post-processing.
","distribution":[{"@type":"dcat:Distribution","downloadURL":"https://smarsgroup.github.io/geo_epic_win/reference/api/core/","mediaType":"text/html","title":"https://smarsgroup.github.io/geo_epic_win/reference/api/core/"}],"identifier":"10779/USDA.ADC.31151371.v1","keyword":["EPIC crop model","Python","Spatial Modeling Approach"],"license":"https://opensource.org/license/BSD-3-Clause","modified":"2026-08-25","programCode":["005:040"],"publisher":{"@type":"org:Organization","name":"Agricultural Research Service"},"temporal":"2024-06-01/2025-11-30","title":"GeoEPIC Python Package"},"description":"The Environmental Policy Integrated Climate (EPIC) model is a comprehensive, field-scale agroecosystem model widely used for both diagnostic and prognostic analyses in agriculture. However, its application at regional scales is limited due to its original design to simulate a limited number of fields. Custom Python or R scripts have attempted to scale EPIC, but they are often inefficient, non-standardized, and not publicly available. To address these issues, a comprehensive Python package, named as GeoEPIC, that streamlines spatial EPIC implementation. GeoEPIC automates input generation from spatial datasets, model calibration, simulation execution, and output post-processing.
","distribution_titles":["https://smarsgroup.github.io/geo_epic_win/reference/api/core/"],"harvest_record":"https://catalog.data.gov/harvest_record/8efe138f-32f1-43cf-ad94-fc669543fae2","harvest_record_raw":"https://catalog.data.gov/harvest_record/8efe138f-32f1-43cf-ad94-fc669543fae2/raw","has_download":true,"has_spatial":false,"identifier":"10779/USDA.ADC.31151371.v1","keyword":["EPIC crop model","Python","Spatial Modeling Approach"],"last_harvested_date":"2026-09-30T21:30:42.515787","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"352b4532-793d-4075-a03f-05b778a3c43a","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/usda.png","name":"Department of Agriculture","organization_type":"Federal Government","slug":"usda"},"parent_identifier":null,"popularity":1,"publisher":"Agricultural Research Service","slug":"geoepic-python-package","spatial_centroid":null,"spatial_shape":null,"theme":[],"title":"GeoEPIC Python Package","type":"dataset"},{"_score":8.30905,"_sort":[1790794424534,8.30905,4,"374f22e6-71ff-41c8-ad59-226d7c041e6e"],"access_level":"public","dcat":{"@type":"dcat:Dataset","accessLevel":"public","contactPoint":{"@type":"vcard:Contact","fn":"OpenData","hasEmail":"mailto:no-reply@data.sf.gov"},"description":"A. SUMMARY\nThe Municipal Natural Gas Equipment Inventory serves to catalog natural gas-fueled equipment used in municipally owned buildings. \nThis inventory, implemented by the SF Environment Department, aims to establish an understanding of the scope of work needed to electrify municipal buildings and inform an effective and collaborative planning process.\nThis effort was identified as an action in Section BO-2.4 of the 2021 Climate Action Plan and is included in the Environment Code Chapter 7 (Municipal Green Building Requirements). \n\nB. HOW THE DATASET IS CREATED\nThe list of buildings required to report data for the Municipal Natural Gas Equipment Inventory was compiled by cross-referencing the City\u2019s Facility Systems of Record and the 2020 municipal benchmarking report to identify all city-owned buildings with non-zero carbon emissions. Numerous municipal buildings are exempt from these reporting requirements, including facilities of the Port of San Francisco and buildings with a primary purpose of providing collection, storage, treatment, delivery, distribution, and/or transmission of water, wastewater, and/or power utilities. \nEach department received an inventory template, provided by the Environment Department, to submit high level building data and detailed information on each piece of natural gas equipment in use in these buildings. Departments were asked to self-report the required building and equipment data over the course of a 6-month data collection period in 2023 and are asked to keep this inventory up to date in the following years as equipment is replaced. \n\nC. UPDATE PROCESS\nThe inventory will be regularly updated by department representatives via the inventory PowerApp. When a gas-powered equipment item is retired or replaced, departments are asked to mark it as no longer in use and provide information on any electric replacement equipment, if applicable. While departments have the flexibility to update the inventory at any time, they are encouraged to do so at 6 month intervals at the minimum. \n\nUpdated inventory data will be automatically reflected in this dataset. \n\nD. HOW TO USE THIS DATASET\nIt is important to note that this dataset does not include facilities of the Port of San Francisco and buildings with a primary purpose of providing collection, storage, treatment, delivery, distribution, and/or transmission of water, wastewater, and/or power utilities, in accordance with Environment Code Chapter 7 exemptions.","distribution":[{"@type":"dcat:Distribution","describedBy":"https://data.sf.gov/api/views/vc6r-v7av/columns.json","describedByType":"application/json","downloadURL":"https://data.sf.gov/api/v3/views/vc6r-v7av/query.json?accessType=DOWNLOAD","mediaType":"application/json"},{"@type":"dcat:Distribution","describedBy":"https://data.sf.gov/api/views/vc6r-v7av/columns.xml","describedByType":"application/xml","downloadURL":"https://data.sf.gov/api/v3/views/vc6r-v7av/query.xml?accessType=DOWNLOAD","mediaType":"application/xml"},{"@type":"dcat:Distribution","downloadURL":"https://data.sf.gov/api/v3/views/vc6r-v7av/export.csv?accessType=DOWNLOAD","mediaType":"text/csv"},{"@type":"dcat:Distribution","downloadURL":"https://data.sf.gov/api/v3/views/vc6r-v7av/export.kml?accessType=DOWNLOAD","mediaType":"application/vnd.google-earth.kml+xml"},{"@type":"dcat:Distribution","downloadURL":"https://data.sf.gov/api/v3/views/vc6r-v7av/export.kmz?accessType=DOWNLOAD","mediaType":"application/vnd.google-earth.kmz"},{"@type":"dcat:Distribution","downloadURL":"https://data.sf.gov/api/v3/views/vc6r-v7av/query.geojson?accessType=DOWNLOAD","mediaType":"application/geo+json"}],"identifier":"https://data.sf.gov/api/views/vc6r-v7av","issued":"2024-03-28","keyword":["environment","environmental health","greenhouse gas emissions","natural gas"],"landingPage":"https://data.sf.gov/d/vc6r-v7av","modified":"2026-09-25","publisher":{"@type":"org:Organization","name":"data.sf.gov"},"theme":["Energy and Environment"],"title":"San Francisco Municipal Natural Gas Equipment Inventory"},"description":"A. 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AMERICA > MARYLAND","CONTINENT > NORTH AMERICA > UNITED STATES OF AMERICA > MASSACHUSETTS","CONTINENT > NORTH AMERICA > UNITED STATES OF AMERICA > MICHIGAN","CONTINENT > NORTH AMERICA > UNITED STATES OF AMERICA > MINNESOTA","CONTINENT > NORTH AMERICA > UNITED STATES OF AMERICA > MISSISSIPPI","CONTINENT > NORTH AMERICA > UNITED STATES OF AMERICA > NEW JERSEY","CONTINENT > NORTH AMERICA > UNITED STATES OF AMERICA > NEW YORK","CONTINENT > NORTH AMERICA > UNITED STATES OF AMERICA > NORTH CAROLINA","CONTINENT > NORTH AMERICA > UNITED STATES OF AMERICA > OHIO","CONTINENT > NORTH AMERICA > UNITED STATES OF AMERICA > OREGON","CONTINENT > NORTH AMERICA > UNITED STATES OF AMERICA > PENNSYLVANIA","CONTINENT > NORTH AMERICA > UNITED STATES OF AMERICA > RHODE ISLAND","CONTINENT > NORTH AMERICA > UNITED STATES OF AMERICA > SOUTH CAROLINA","CONTINENT > NORTH AMERICA > UNITED STATES OF AMERICA > TEXAS","CONTINENT > NORTH AMERICA > UNITED STATES OF AMERICA > VIRGINIA","CONTINENT > NORTH AMERICA > UNITED STATES OF AMERICA > WASHINGTON","CONTINENT > NORTH AMERICA > UNITED STATES OF AMERICA > WISCONSIN","OCEAN > ATLANTIC OCEAN > NORTH ATLANTIC OCEAN > BERMUDA","OCEAN > ATLANTIC OCEAN > NORTH ATLANTIC OCEAN > CARIBBEAN SEA","OCEAN > ATLANTIC OCEAN > NORTH ATLANTIC OCEAN > CARIBBEAN SEA > PUERTO RICO","OCEAN > ATLANTIC OCEAN > NORTH ATLANTIC OCEAN > CARIBBEAN SEA > VIRGIN ISLANDS","OCEAN > ATLANTIC OCEAN > NORTH ATLANTIC OCEAN > GULF OF AMERICA","OCEAN > PACIFIC OCEAN > CENTRAL PACIFIC OCEAN > AMERICAN SAMOA","OCEAN > PACIFIC OCEAN > CENTRAL PACIFIC OCEAN > HAWAIIAN ISLANDS","OCEAN > PACIFIC OCEAN > CENTRAL PACIFIC OCEAN > MIDWAY ATOLL","OCEAN > PACIFIC OCEAN > CENTRAL PACIFIC OCEAN > WAKE ISLAND","OCEAN > PACIFIC OCEAN > NORTH PACIFIC OCEAN > GULF OF ALASKA","OCEAN > PACIFIC OCEAN > SOUTH PACIFIC OCEAN > POLYNESIA > SAMOA","OCEAN > PACIFIC OCEAN > WESTERN PACIFIC OCEAN > MICRONESIA > GUAM","OCEAN > PACIFIC OCEAN > WESTERN PACIFIC OCEAN > MICRONESIA > MARSHALL ISLANDS","ANEMOMETERS > ANEMOMETERS","APS > Air Pressure Sensor","BAROMETERS > BAROMETERS","HUMIDITY SENSORS > HUMIDITY SENSORS","PRESSURE SENSORS > PRESSURE SENSORS","RAIN GAUGES > RAIN GAUGES","TEMPERATURE PROBES > TEMPERATURE PROBES","TEMPERATURE SENSORS > TEMPERATURE SENSORS","THERMISTORS > THERMISTORS","Visibility Sensor > Visibility Sensor","GOES > NOAA Geostationary Operational Environmental Satellites","Models > Coastal Ocean Models","NOCMP > National Operational Coastal Modeling Program","NWLON > National Water Level Observation Network","OFS > Operational Forecast Systems","PORTS > Physical Oceanographic Real-Time System","Coastal U.S. territories","Coastal United States","Gulf of Mexico","U.S. Exclusive Economic Zone","DOC/NOAA/NOS/CO-OPS > Center for Operational Oceanographic Products and Services, National Ocean Service, NOAA, U.S. Department of Commerce","CO-OPS Metadata Library"],"landingPage":"https://www.fisheries.noaa.gov/inport/item/67953","language":[],"license":"https://creativecommons.org/publicdomain/zero/1.0/","modified":"2022-01-01T00:00:00.000+00:00","publisher":{"@type":"org:Organization","name":"Center for Operational Oceanographic Products and Services"},"rights":"otherRestrictions, unclassified","spatial":"-61.8,-14.3,144.6,71.4","temporal":"1990-01-01T00:00:00+00:00/1990-01-01T00:00:00+00:00","theme":["geospatial"],"title":"Meteorological Data (including visibility)"},"description":"The National Ocean Service (NOS) maintains a long-term database containing data from active and historic stations installed all over the United States and U.S. territories. Since the 1990s, NOAA's Center for Operational Oceanographic Products and Services (CO-OPS) has been collecting various meteorological data along the U.S. coastline, around the Great Lakes and connecting channels, as well as in various U.S. territories. Stations are configured for a variety of observation periods, depending upon the location. Some of these sensors are located with water level stations, while others are independent, dedicated meteorological stations. These data are used to support a variety of purposes including but not limited to safe and efficient marine navigation and coastal hazards monitoring. The standard reporting time for collecting data are in 6-minute intervals.","distribution_titles":["CO-OPS web services and GIS access","CO-OPS web services","CO-OPS home page","Tides and Currents Map","GCMD Keyword Forum Page","Full Metadata Record","Entity Attribute Detail URL"],"harvest_record":"https://catalog.data.gov/harvest_record/c7df7228-1e2a-4f13-a374-75499ec42b58","harvest_record_raw":"https://catalog.data.gov/harvest_record/c7df7228-1e2a-4f13-a374-75499ec42b58/raw","harvest_record_transformed":"https://catalog.data.gov/harvest_record/c7df7228-1e2a-4f13-a374-75499ec42b58/transformed","has_download":true,"has_spatial":true,"identifier":"https://data.noaa.gov/waf/NOAA/nos/coops/iso/xml/67953.xml","keyword":["EARTH SCIENCE > ATMOSPHERE > AIR QUALITY > VISIBILITY","EARTH SCIENCE > ATMOSPHERE > ATMOSPHERIC PRESSURE > ATMOSPHERIC PRESSURE MEASUREMENTS","EARTH SCIENCE > ATMOSPHERE > ATMOSPHERIC TEMPERATURE > SURFACE TEMPERATURE > AIR TEMPERATURE","EARTH SCIENCE > ATMOSPHERE > ATMOSPHERIC WINDS > SURFACE WINDS","EARTH SCIENCE > ATMOSPHERE > PRECIPITATION > LIQUID PRECIPITATION > RAIN","EARTH SCIENCE > BIOSPHERE > ECOLOGICAL DYNAMICS > ECOSYSTEM FUNCTIONS","EARTH SCIENCE > CLIMATE INDICATORS > ATMOSPHERIC/OCEAN INDICATORS > PRECIPITATION INDICATORS > SAHEL STANDARDIZED RAINFALL","EARTH SCIENCE > OCEANS > COASTAL PROCESSES > STORM SURGE","EARTH SCIENCE > OCEANS > OCEAN TEMPERATURE > WATER TEMPERATURE","EARTH SCIENCE > OCEANS > OCEAN WINDS > SURFACE WINDS","EARTH SCIENCE > OCEANS > TIDES > STORM SURGE","EARTH SCIENCE > SPECTRAL/ENGINEERING > RADAR > DOPPLER VELOCITY","EARTH SCIENCE > TERRESTRIAL HYDROSPHERE > SURFACE WATER > SURFACE WATER PROCESSES/MEASUREMENTS > INUNDATION","EARTH SCIENCE SERVICES > DATA ANALYSIS AND VISUALIZATION > GEOGRAPHIC INFORMATION SYSTEMS > WEB-BASED GEOGRAPHIC INFORMATION SYSTEMS","EARTH SCIENCE SERVICES > ENVIRONMENTAL ADVISORIES > MARINE ADVISORIES > MARINE WEATHER/FORECAST","CONTINENT > NORTH AMERICA > UNITED STATES OF AMERICA > ALABAMA","CONTINENT > NORTH AMERICA > UNITED STATES OF AMERICA > ALASKA","CONTINENT > NORTH AMERICA > UNITED STATES OF AMERICA > CALIFORNIA","CONTINENT > NORTH AMERICA > UNITED STATES OF AMERICA > CONNECTICUT","CONTINENT > NORTH AMERICA > UNITED STATES OF AMERICA > DELAWARE","CONTINENT > NORTH AMERICA > UNITED STATES OF AMERICA > DISTRICT OF COLUMBIA","CONTINENT > NORTH AMERICA > UNITED STATES OF AMERICA > FLORIDA","CONTINENT > NORTH AMERICA > UNITED STATES OF AMERICA > GEORGIA","CONTINENT > NORTH AMERICA > UNITED STATES OF AMERICA > GREAT LAKES","CONTINENT > NORTH AMERICA > UNITED STATES OF AMERICA > HAWAII","CONTINENT > NORTH AMERICA > UNITED STATES OF AMERICA > ILLINOIS","CONTINENT > NORTH AMERICA > UNITED STATES OF AMERICA > LOUISIANA","CONTINENT > NORTH AMERICA > UNITED STATES OF AMERICA > MAINE","CONTINENT > NORTH AMERICA > UNITED STATES OF AMERICA > MARYLAND","CONTINENT > NORTH AMERICA > UNITED STATES OF AMERICA > MASSACHUSETTS","CONTINENT > NORTH AMERICA > UNITED STATES OF AMERICA > MICHIGAN","CONTINENT > NORTH AMERICA > UNITED STATES OF AMERICA > MINNESOTA","CONTINENT > NORTH AMERICA > UNITED STATES OF AMERICA > MISSISSIPPI","CONTINENT > NORTH AMERICA > UNITED STATES OF AMERICA > NEW JERSEY","CONTINENT > NORTH AMERICA > UNITED STATES OF AMERICA > NEW YORK","CONTINENT > NORTH AMERICA > UNITED STATES OF AMERICA > NORTH CAROLINA","CONTINENT > NORTH AMERICA > UNITED STATES OF AMERICA > OHIO","CONTINENT > NORTH AMERICA > UNITED STATES OF AMERICA > OREGON","CONTINENT > NORTH AMERICA > UNITED STATES OF AMERICA > PENNSYLVANIA","CONTINENT > NORTH AMERICA > UNITED STATES OF AMERICA > RHODE ISLAND","CONTINENT > NORTH AMERICA > UNITED STATES OF AMERICA > SOUTH CAROLINA","CONTINENT > NORTH AMERICA > UNITED STATES OF AMERICA > TEXAS","CONTINENT > NORTH AMERICA > UNITED STATES OF AMERICA > VIRGINIA","CONTINENT > NORTH AMERICA > UNITED STATES OF AMERICA > WASHINGTON","CONTINENT > NORTH AMERICA > UNITED STATES OF AMERICA > WISCONSIN","OCEAN > ATLANTIC OCEAN > NORTH ATLANTIC OCEAN > BERMUDA","OCEAN > ATLANTIC OCEAN > NORTH ATLANTIC OCEAN > CARIBBEAN SEA","OCEAN > ATLANTIC OCEAN > NORTH ATLANTIC OCEAN > CARIBBEAN SEA > PUERTO RICO","OCEAN > ATLANTIC OCEAN > NORTH ATLANTIC OCEAN > CARIBBEAN SEA > VIRGIN ISLANDS","OCEAN > ATLANTIC OCEAN > NORTH ATLANTIC OCEAN > GULF OF AMERICA","OCEAN > PACIFIC OCEAN > CENTRAL PACIFIC OCEAN > AMERICAN SAMOA","OCEAN > PACIFIC OCEAN > CENTRAL PACIFIC OCEAN > HAWAIIAN ISLANDS","OCEAN > PACIFIC OCEAN > CENTRAL PACIFIC OCEAN > MIDWAY ATOLL","OCEAN > PACIFIC OCEAN > CENTRAL PACIFIC OCEAN > WAKE ISLAND","OCEAN > PACIFIC OCEAN > NORTH PACIFIC OCEAN > GULF OF ALASKA","OCEAN > PACIFIC OCEAN > SOUTH PACIFIC OCEAN > POLYNESIA > SAMOA","OCEAN > PACIFIC OCEAN > WESTERN PACIFIC OCEAN > MICRONESIA > GUAM","OCEAN > PACIFIC OCEAN > WESTERN PACIFIC OCEAN > MICRONESIA > MARSHALL ISLANDS","ANEMOMETERS > ANEMOMETERS","APS > Air Pressure Sensor","BAROMETERS > BAROMETERS","HUMIDITY SENSORS > HUMIDITY SENSORS","PRESSURE SENSORS > PRESSURE SENSORS","RAIN GAUGES > RAIN GAUGES","TEMPERATURE PROBES > TEMPERATURE PROBES","TEMPERATURE SENSORS > TEMPERATURE SENSORS","THERMISTORS > THERMISTORS","Visibility Sensor > Visibility Sensor","GOES > NOAA Geostationary Operational Environmental Satellites","Models > Coastal Ocean Models","NOCMP > National Operational Coastal Modeling Program","NWLON > National Water Level Observation Network","OFS > Operational Forecast Systems","PORTS > Physical Oceanographic Real-Time System","Coastal U.S. territories","Coastal United States","Gulf of Mexico","U.S. Exclusive Economic Zone","DOC/NOAA/NOS/CO-OPS > Center for Operational Oceanographic Products and Services, National Ocean Service, NOAA, U.S. Department of Commerce","CO-OPS Metadata Library"],"last_harvested_date":"2026-09-29T22:48:34.415926","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"5f4f1195-e770-4a2a-8f75-195cd98860ce","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/noaa.png","name":"National Oceanic and Atmospheric Administration, Department of Commerce","organization_type":"Federal Government","slug":"noaa"},"parent_identifier":null,"popularity":6,"publisher":"Center for Operational Oceanographic Products and Services","slug":"meteorological-data-including-visibility","spatial_centroid":{"lat":19.98,"lon":28.380000000000003},"spatial_shape":{"coordinates":[[[[-61.8,-14.3],[-61.8,71.4],[-180.0,71.4],[-180.0,-14.3],[-61.8,-14.3]]],[[[180.0,-14.3],[180.0,71.4],[144.6,71.4],[144.6,-14.3],[180.0,-14.3]]]],"type":"MultiPolygon"},"theme":["geospatial"],"title":"Meteorological Data (including visibility)","type":"dataset"},{"_score":2.2021008,"_sort":[1790722008932,2.2021008,0,"6190e5f3-b470-4186-972c-5fb43ca6325c"],"access_level":"non-public","dcat":{"@type":"dcat:Dataset","accessLevel":"non-public","contactPoint":{"@type":"vcard:Contact","fn":"Gajdzik, Laura","hasEmail":"mailto:laura.gajdzik@noaa.gov"},"describedByType":"application/octet-stream","description":"This metadata record describes multiple datasets including traits (reef fish and coral) as well as disturbance exposure (heat and tropical cyclone) of coral reef ecosystems in Guam and the CNMI in 2011 and 2022. Trait values were inferred from online databases, scientific papers, and gaps were filled in by experts. Environmental disturbance data were generated by scientists of the Ecosystem Sciences Division (ESD) of the Pacific Islands Fisheries Science Center (PIFSC). For disturbance exposure, site-level heat stress and cyclone exposure data was generated for 2011 and 2022. These data were derived from NOAA Coral Reef Watch CoralTemp v3.1 daily SST (1985\u2013present) and IBTrACS v4 (1950\u20132022). Both trait and disturbance exposure datasets described in this metadata record were used with the already archived Mariana Archipelago RAMP (MARAMP) and the National Coral Reef Monitoring Program (NCRMP) data, to generate functional trait-based diversity indices, providing another aspect of reef resilience.\n\nThis project was funded by CRCP Project #36143 titled \"Estimating functional diversity and redundancy of reef communities to inform resilience-based management in the Marianas\". \nLink to publication will be provided once published.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://accession.nodc.noaa.gov/#","describedByType":"application/octet-stream","description":"Fish trait values compiled from literature (reports, publication, etc) and online databases. Traits include: taxon name, mobility, schooling, activity, position, and diet.","mediaType":"text/html","title":"ESD_FISHTRAIT_MARIAN"},{"@type":"dcat:Distribution","accessURL":"https://forum.earthdata.nasa.gov/app.php/tag/GCMD%2BKeywords","describedByType":"application/octet-stream","description":"Global Change Master Directory (GCMD). 2026. GCMD Keywords, Version 24. Greenbelt, MD: Earth Science Data and Information System, Earth Science Projects Division, Goddard Space Flight Center (GSFC), National Aeronautics and Space Administration (NASA). URL (GCMD Keyword Forum Page): https://forum.earthdata.nasa.gov/app.php/tag/GCMD+Keywords","mediaType":"text/html","title":"GCMD Keyword Forum Page"},{"@type":"dcat:Distribution","accessURL":"https://www.fisheries.noaa.gov/inport/item/79227","describedByType":"application/octet-stream","description":"View the complete metadata record on InPort for more information about this dataset.","mediaType":"text/html","title":"Full Metadata Record"}],"identifier":"https://data.noaa.gov/waf/NOAA/nmfs/pifsc/iso/xml/79227.xml","issued":"2026-01-01T00:00:00.000+00:00","keyword":["DOC/NOAA/NMFS/PIFSC/ESD > Ecosystem Sciences Division, Pacific Islands Fisheries Science Center, National Marine Fisheries Service, NOAA, U.S. Department of Commerce","EARTH SCIENCE > AGRICULTURE > ANIMAL SCIENCE > ANIMAL ECOLOGY AND BEHAVIOR","EARTH SCIENCE > ATMOSPHERE > ATMOSPHERIC PRESSURE > ANTICYCLONES/CYCLONES","EARTH SCIENCE > BIOSPHERE > ECOSYSTEMS > MARINE ECOSYSTEMS > BENTHIC","EARTH SCIENCE > BIOSPHERE > ECOSYSTEMS > MARINE ECOSYSTEMS > REEF > CORAL REEF","EARTH SCIENCE > CLIMATE INDICATORS > ATMOSPHERIC/OCEAN INDICATORS > SEA SURFACE TEMPERATURE INDICES","36143","Estimating functional diversity and redundancy of reef communities to inform resilience-based management in the Marianas","Numeric Data Sets > Biology","Numeric Data Sets > Oceanography","EARTH SCIENCE > Biosphere > Aquatic Habitat > Reef Habitat","EARTH SCIENCE > Biosphere > Zoology > Corals > Reef Monitoring and Assessment","EARTH SCIENCE > Oceans > Coastal Processes > Coral Reefs","EARTH SCIENCE > Oceans > Marine Biology > Coral","EARTH SCIENCE > Oceans > Marine Biology > Coral Communities","EARTH SCIENCE > Oceans > Marine Biology > Fish","EARTH SCIENCE > Oceans > Ocean Temperature > Sea Surface Temperature","CORAL REEF","CORAL SPECIES","FISH - CORAL REEF","FISH SPECIES","HABITAT - BENTHIC","Tropical Cyclones","WATER TEMPERATURE","derived products","laboratory analysis","CORAL REEF STUDIES","Coral Reef Conservation Program","National Coral Reef Monitoring Program","Pacific Reef and Assessment Monitoring Program","US DOC; NOAA; NMFS; Pacific Islands Fisheries Science Center; Ecosystem Sciences Division","COUNTRY/TERRITORY > Northern Mariana Islands > Rota > Rota Island ( Luta ) (14N145E0007)","COUNTRY/TERRITORY > Northern Mariana Islands > Saipan > Saipan Island (15N145E0002)","COUNTRY/TERRITORY > Northern Mariana Islands > Tinian > Tinian Island (14N145E0005)","COUNTRY/TERRITORY > United States of America > Guam > Guam (13N144E0000)","OCEAN BASIN > Pacific Ocean > Western Pacific Ocean > Guam > Guam (13N144E0000)","OCEAN BASIN > Pacific Ocean > Western Pacific Ocean > Rota Island > Rota Island ( Luta ) (14N145E0007)","OCEAN BASIN > Pacific Ocean > Western Pacific Ocean > Saipan Island > Saipan Island (15N145E0002)","OCEAN BASIN > Pacific Ocean > Western Pacific Ocean > Tinian Island Reefs > Tinian Island (14N145E0005)","OCEAN > PACIFIC OCEAN > WESTERN PACIFIC OCEAN > MICRONESIA > GUAM","NW Pacific (limit-180)","CNMI","CRED","CREP","Commonwealth of the Northern Mariana Islands","Coral Reef Ecosystem Division","Coral Reef Ecosystem Program","ESD","Ecosystem Sciences Division","Guam","Mariana Archipelago","Mariana Islands","Marianas","PIFSC","Pacific Islands Fisheries Science Center","DOC/NOAA/NMFS/PIFSC > Pacific Islands Fisheries Science Center, National Marine Fisheries Service, NOAA, U.S. Department of Commerce","PIFSC Publication"],"landingPage":"https://www.fisheries.noaa.gov/inport/item/79227","language":[],"license":"https://creativecommons.org/publicdomain/zero/1.0/","modified":"2026-01-01T00:00:00.000+00:00","publisher":{"@type":"org:Organization","name":"Pacific Islands Fisheries Science Center"},"rights":"otherRestrictions, unclassified","spatial":"145.85,13.2,144.6,15.35","temporal":"2011-04-06T00:00:00+00:00/2022-06-01T00:00:00+00:00","theme":["geospatial"],"title":"Reef Fish and Coral Traits, as well as Disturbance Exposure (Heat and Cyclone) of Coral Reefs in Guam and the CNMI in 2011 and 2022"},"description":"This metadata record describes multiple datasets including traits (reef fish and coral) as well as disturbance exposure (heat and tropical cyclone) of coral reef ecosystems in Guam and the CNMI in 2011 and 2022. Trait values were inferred from online databases, scientific papers, and gaps were filled in by experts. Environmental disturbance data were generated by scientists of the Ecosystem Sciences Division (ESD) of the Pacific Islands Fisheries Science Center (PIFSC). For disturbance exposure, site-level heat stress and cyclone exposure data was generated for 2011 and 2022. These data were derived from NOAA Coral Reef Watch CoralTemp v3.1 daily SST (1985\u2013present) and IBTrACS v4 (1950\u20132022). Both trait and disturbance exposure datasets described in this metadata record were used with the already archived Mariana Archipelago RAMP (MARAMP) and the National Coral Reef Monitoring Program (NCRMP) data, to generate functional trait-based diversity indices, providing another aspect of reef resilience.\n\nThis project was funded by CRCP Project #36143 titled \"Estimating functional diversity and redundancy of reef communities to inform resilience-based management in the Marianas\". \nLink to publication will be provided once published.","distribution_titles":["ESD_FISHTRAIT_MARIAN","GCMD Keyword Forum Page","Full Metadata Record"],"harvest_record":"https://catalog.data.gov/harvest_record/bbcaa7b7-67e6-4a1a-a7b6-64fb207440d0","harvest_record_raw":"https://catalog.data.gov/harvest_record/bbcaa7b7-67e6-4a1a-a7b6-64fb207440d0/raw","harvest_record_transformed":"https://catalog.data.gov/harvest_record/bbcaa7b7-67e6-4a1a-a7b6-64fb207440d0/transformed","has_download":false,"has_spatial":true,"identifier":"https://data.noaa.gov/waf/NOAA/nmfs/pifsc/iso/xml/79227.xml","keyword":["DOC/NOAA/NMFS/PIFSC/ESD > Ecosystem Sciences Division, Pacific Islands Fisheries Science Center, National Marine Fisheries Service, NOAA, U.S. Department of Commerce","EARTH SCIENCE > AGRICULTURE > ANIMAL SCIENCE > ANIMAL ECOLOGY AND BEHAVIOR","EARTH SCIENCE > ATMOSPHERE > ATMOSPHERIC PRESSURE > ANTICYCLONES/CYCLONES","EARTH SCIENCE > BIOSPHERE > ECOSYSTEMS > MARINE ECOSYSTEMS > BENTHIC","EARTH SCIENCE > BIOSPHERE > ECOSYSTEMS > MARINE ECOSYSTEMS > REEF > CORAL REEF","EARTH SCIENCE > CLIMATE INDICATORS > ATMOSPHERIC/OCEAN INDICATORS > SEA SURFACE TEMPERATURE INDICES","36143","Estimating functional diversity and redundancy of reef communities to inform resilience-based management in the Marianas","Numeric Data Sets > Biology","Numeric Data Sets > Oceanography","EARTH SCIENCE > Biosphere > Aquatic Habitat > Reef Habitat","EARTH SCIENCE > Biosphere > Zoology > Corals > Reef Monitoring and Assessment","EARTH SCIENCE > Oceans > Coastal Processes > Coral Reefs","EARTH SCIENCE > Oceans > Marine Biology > Coral","EARTH SCIENCE > Oceans > Marine Biology > Coral Communities","EARTH SCIENCE > Oceans > Marine Biology > Fish","EARTH SCIENCE > Oceans > Ocean Temperature > Sea Surface Temperature","CORAL REEF","CORAL SPECIES","FISH - CORAL REEF","FISH SPECIES","HABITAT - BENTHIC","Tropical Cyclones","WATER TEMPERATURE","derived products","laboratory analysis","CORAL REEF STUDIES","Coral Reef Conservation Program","National Coral Reef Monitoring Program","Pacific Reef and Assessment Monitoring Program","US DOC; NOAA; NMFS; Pacific Islands Fisheries Science Center; Ecosystem Sciences Division","COUNTRY/TERRITORY > Northern Mariana Islands > Rota > Rota Island ( Luta ) (14N145E0007)","COUNTRY/TERRITORY > Northern Mariana Islands > Saipan > Saipan Island (15N145E0002)","COUNTRY/TERRITORY > Northern Mariana Islands > Tinian > Tinian Island (14N145E0005)","COUNTRY/TERRITORY > United States of America > Guam > Guam (13N144E0000)","OCEAN BASIN > Pacific Ocean > Western Pacific Ocean > Guam > Guam (13N144E0000)","OCEAN BASIN > Pacific Ocean > Western Pacific Ocean > Rota Island > Rota Island ( Luta ) (14N145E0007)","OCEAN BASIN > Pacific Ocean > Western Pacific Ocean > Saipan Island > Saipan Island (15N145E0002)","OCEAN BASIN > Pacific Ocean > Western Pacific Ocean > Tinian Island Reefs > Tinian Island (14N145E0005)","OCEAN > PACIFIC OCEAN > WESTERN PACIFIC OCEAN > MICRONESIA > GUAM","NW Pacific (limit-180)","CNMI","CRED","CREP","Commonwealth of the Northern Mariana Islands","Coral Reef Ecosystem Division","Coral Reef Ecosystem Program","ESD","Ecosystem Sciences Division","Guam","Mariana Archipelago","Mariana Islands","Marianas","PIFSC","Pacific Islands Fisheries Science Center","DOC/NOAA/NMFS/PIFSC > Pacific Islands Fisheries Science Center, National Marine Fisheries Service, NOAA, U.S. Department of Commerce","PIFSC Publication"],"last_harvested_date":"2026-09-29T22:46:48.932365","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"5f4f1195-e770-4a2a-8f75-195cd98860ce","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/noaa.png","name":"National Oceanic and Atmospheric Administration, Department of Commerce","organization_type":"Federal Government","slug":"noaa"},"parent_identifier":null,"popularity":0,"publisher":"Pacific Islands Fisheries Science Center","slug":"reef-fish-and-coral-traits-as-well-as-disturbance-exposure-heat-and-cyclone-of-coral--2022","spatial_centroid":{"lat":14.059999999999999,"lon":145.35},"spatial_shape":{"coordinates":[[[145.85,13.2],[145.85,15.35],[144.6,15.35],[144.6,13.2],[145.85,13.2]]],"type":"Polygon"},"theme":["geospatial"],"title":"Reef Fish and Coral Traits, as well as Disturbance Exposure (Heat and Cyclone) of Coral Reefs in Guam and the CNMI in 2011 and 2022","type":"dataset"},{"_score":9.365789,"_sort":[1790722007003,9.365789,0,"568dfc80-0faf-4183-998b-bf8dc8f7b763"],"access_level":"non-public","dcat":{"@type":"dcat:Dataset","accessLevel":"non-public","contactPoint":{"@type":"vcard:Contact","fn":"Barkley, Hannah C","hasEmail":"mailto:hannah.barkley@noaa.gov"},"describedByType":"application/octet-stream","description":"The calcification rate data described here are derived from Calcification Accretion Units (CAUs) that were deployed and retrieved at long-term climate monitoring sites during NOAA Pacific Islands Fisheries Science Center (PIFSC), Ecosystem Sciences Division (ESD) led National Coral Reef Monitoring Program (NCRMP) missions across the Pacific Remote Island Regions in 2014, 2015, 2017, 2018, and 2023 as well as pre-NCRMP missions in 2010, 2011, and 2012. CAUs are PVC settlement plates that facilitate the recruitment and colonization of crustose coralline algae, hard corals, and other reef calcifiers. Laboratory experiments show that CCA and coral calcification rates are strongly correlated with seawater chemistry, and shifts in carbonate chemistry conditions due to ocean acidification could lead to reduced calcification and accretion rates and ecological phase shifts in coral reef communities.\n\nCoral reef calcium carbonate accretion rates can be estimated by measuring the change in weight of the CAUs between deployment and retrieval. Monitoring net accretion over successive deployments allows for the detection of changes in reef calcification rates over time. Five units were deployed on the seafloor at each CAU site for 3 years in 2015 and 5 years in 2018. The number of processed CAUs for a site may be less than the number deployed, either because the units were lost or damaged at sea and therefore not recovered, or in rare instances, due to errors during laboratory processing.\n\nThis study provides information about spatial and temporal patterns of reef carbonate calcification and accretion rates and serves as a basis for detecting changes associated with changing seawater chemistry due to ocean acidification. These data can also be used in comparative analyses across natural gradients, thereby assisting efforts to determine whether key reef-building taxa can acclimatize to changing oceanographic environments. These data will have immediate, direct impacts on predictions of reef resilience in a higher carbon dioxide (CO2) world and on the design of reef management strategies.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://accession.nodc.noaa.gov/0137093","describedByType":"application/octet-stream","description":"CAU unit recoveries collected across the Pacific Remote Island Areas (Baker, Howland, Jarvis, Johnston, Kingman, Palmyra) by the PIFSC Ecosystem Sciences Division during pre-NCRMP mission in 2012 (HA1201). These CAU units were deployed during pre-NCRMP missions in 2010 (HA1001). Data include calcification rates per CAU unit.","mediaType":"text/html","title":"ESD_NCRMP_CAU_2012_PRIAs.csv"},{"@type":"dcat:Distribution","accessURL":"https://accession.nodc.noaa.gov/0175166","describedByType":"application/octet-stream","description":"CAU unit recoveries collected across the Pacific Remote Island Areas (Wake) by the PIFSC Ecosystem Sciences Division during MARAMP 2017 (HA1701). These CAU units were deployed during MARAMP 2014 (HA1401). Data include calcification rates per CAU unit.","mediaType":"text/html","title":"ESD_NCRMP_CAU_2017_PRIAs.csv"},{"@type":"dcat:Distribution","accessURL":"https://accession.nodc.noaa.gov/0231439","describedByType":"application/octet-stream","description":"CAU unit recoveries collected across the Pacific Remote Island Areas (Baker, Howland, and Jarvis Islands, Kingman Reef, and Palmyra atolls) by the PIFSC Ecosystem Sciences Division during ASRAMP 2018 (HA1801). These CAU units were deployed during ASRAMP 2015 (HA1501), with the exception of one CAU unit from Jarvis Island that was deployed during pre-NCRMP mission in 2012 (HA1201). Data include calcification rates per CAU unit.","mediaType":"text/html","title":"ESD_NCRMP_CAU_2018_PRIAs.csv"},{"@type":"dcat:Distribution","accessURL":"https://accession.nodc.noaa.gov/0159156","describedByType":"application/octet-stream","description":"CAU unit recoveries collected across the Pacific Remote Island Areas (Baker, Howland, Jarvis, Johnston, Kingman, Palmyra) by the PIFSC Ecosystem Sciences Division during ASRAMP 2015 (HA1501). These CAU units were deployed during pre-NCRMP mission in 2012 (HA1201). Data include calcification rates per CAU unit.","mediaType":"text/html","title":"ESD_NCRMP_CAU_2015_PRIAs.csv"},{"@type":"dcat:Distribution","accessURL":"https://accession.nodc.noaa.gov/0159151","describedByType":"application/octet-stream","description":"CAU unit recoveries collected across the Pacific Remote Island Areas (Wake) by the PIFSC Ecosystem Sciences Division during MARAMP 2014 (HA1401). These CAU units were deployed during pre-NCRMP missions in 2011 (HA1101). Data include calcification rates per CAU unit.","mediaType":"text/html","title":"ESD_NCRMP_CAU_2014_PRIAs.csv"},{"@type":"dcat:Distribution","accessURL":"https://accession.nodc.noaa.gov/0318706","describedByType":"application/octet-stream","description":"Quality control report generated for recovered CAU data from sites across the Pacific Remote Island Areas (Baker, Howland) by the PIFSC Ecosystem Sciences Division during ASRAMP 2023 (RA2301).","mediaType":"text/html","title":"ESD_NCRMP_CAU_2023_PRIAs_QC.pdf"},{"@type":"dcat:Distribution","accessURL":"https://forum.earthdata.nasa.gov/app.php/tag/GCMD%2BKeywords","describedByType":"application/octet-stream","description":"Global Change Master Directory (GCMD). 2026. GCMD Keywords, Version 24. Greenbelt, MD: Earth Science Data and Information System, Earth Science Projects Division, Goddard Space Flight Center (GSFC), National Aeronautics and Space Administration (NASA). URL (GCMD Keyword Forum Page): https://forum.earthdata.nasa.gov/app.php/tag/GCMD+Keywords","mediaType":"text/html","title":"GCMD Keyword Forum Page"},{"@type":"dcat:Distribution","accessURL":"https://www.fisheries.noaa.gov/inport/item/36069","describedByType":"application/octet-stream","description":"View the complete metadata record on InPort for more information about this dataset.","mediaType":"text/html","title":"Full Metadata Record"}],"identifier":"https://data.noaa.gov/waf/NOAA/nmfs/pifsc/iso/xml/36069.xml","issued":"2016-01-01T00:00:00.000+00:00","keyword":["DOC/NOAA/NMFS/PIFSC/ESD > Ecosystem Sciences Division, Pacific Islands Fisheries Science Center, National Marine Fisheries Service, NOAA, U.S. Department of Commerce","EARTH SCIENCE > BIOSPHERE > ECOSYSTEMS > MARINE ECOSYSTEMS > BENTHIC","EARTH SCIENCE > BIOSPHERE > ECOSYSTEMS > MARINE ECOSYSTEMS > REEF > CORAL REEF","OCEAN > PACIFIC OCEAN > CENTRAL PACIFIC OCEAN > BAKER ISLAND","OCEAN > PACIFIC OCEAN > CENTRAL PACIFIC OCEAN > HOWLAND ISLAND","OCEAN > PACIFIC OCEAN > CENTRAL PACIFIC OCEAN > JARVIS ISLAND","OCEAN > PACIFIC OCEAN > CENTRAL PACIFIC OCEAN > JOHNSTON ATOLL","OCEAN > PACIFIC OCEAN > CENTRAL PACIFIC OCEAN > KINGMAN REEF","OCEAN > PACIFIC OCEAN > CENTRAL PACIFIC OCEAN > PALMYRA ATOLL","1221","409","587","743","C204 Pacific Reef Assessment and Monitoring Program (Pacific RAMP): Biennial monitoring for the US Pacific Islands and Atolls","National Coral Reef Monitoring Program","Ocean Acidification - Quantification of Calcification and Accretion Rates of Corals and Crustose Coralline Algae across the Pacific Ocean","Pacific Reef Assessment and Monitoring Program: Monitoring coral reef ecosystems of the US Pacific Islands and Atolls","Numeric Data Sets > Calcification Rate","EARTH SCIENCE > Biosphere > Aquatic Habitat > Reef Habitat","EARTH SCIENCE > Biosphere > Vegetation > Algae > Algal Cover","EARTH SCIENCE > Biosphere > Vegetation > Algae > Algal Growth > Calcification Rate","EARTH SCIENCE > Biosphere > Vegetation > Algae > Calcareous Macroalgae","EARTH SCIENCE > Biosphere > Vegetation > Algae > Crustose Coralline Algae","EARTH SCIENCE > Biosphere > Vegetation > Algae > Encrusting Macroalgae","EARTH SCIENCE > Biosphere > Vegetation > Algae > Fleshy Macroalgae","EARTH SCIENCE > Biosphere > Vegetation > Algae > Reef Monitoring and Assessment","EARTH SCIENCE > Biosphere > Vegetation > Algae > Reef Monitoring and Assessment > Calcification Accretion Unit (CAU)","EARTH SCIENCE > Biosphere > Zoology > Corals > Reef Monitoring and Assessment","EARTH SCIENCE > Oceans > Coastal Processes > Coral Reefs","EARTH SCIENCE > Oceans > Ocean Chemistry > Calcification","EARTH SCIENCE > Oceans > Ocean Chemistry > Carbonate Chemistry","EARTH SCIENCE > Oceans > Ocean Chemistry > Ocean Acidification","CALCIFICATION","in situ","laboratory analyses","CORAL REEF STUDIES","Coral Reef Conservation Program","National Coral Reef Monitoring Program","Pacific Reef and Assessment Monitoring Program","US DOC; 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CAUs are PVC settlement plates that facilitate the recruitment and colonization of crustose coralline algae, hard corals, and other reef calcifiers. Laboratory experiments show that CCA and coral calcification rates are strongly correlated with seawater chemistry, and shifts in carbonate chemistry conditions due to ocean acidification could lead to reduced calcification and accretion rates and ecological phase shifts in coral reef communities.\n\nCoral reef calcium carbonate accretion rates can be estimated by measuring the change in weight of the CAUs between deployment and retrieval. Monitoring net accretion over successive deployments allows for the detection of changes in reef calcification rates over time. Five units were deployed on the seafloor at each CAU site for 3 years in 2015 and 5 years in 2018. The number of processed CAUs for a site may be less than the number deployed, either because the units were lost or damaged at sea and therefore not recovered, or in rare instances, due to errors during laboratory processing.\n\nThis study provides information about spatial and temporal patterns of reef carbonate calcification and accretion rates and serves as a basis for detecting changes associated with changing seawater chemistry due to ocean acidification. These data can also be used in comparative analyses across natural gradients, thereby assisting efforts to determine whether key reef-building taxa can acclimatize to changing oceanographic environments. These data will have immediate, direct impacts on predictions of reef resilience in a higher carbon dioxide (CO2) world and on the design of reef management strategies.","distribution_titles":["ESD_NCRMP_CAU_2012_PRIAs.csv","ESD_NCRMP_CAU_2017_PRIAs.csv","ESD_NCRMP_CAU_2018_PRIAs.csv","ESD_NCRMP_CAU_2015_PRIAs.csv","ESD_NCRMP_CAU_2014_PRIAs.csv","ESD_NCRMP_CAU_2023_PRIAs_QC.pdf","GCMD Keyword Forum Page","Full Metadata Record"],"harvest_record":"https://catalog.data.gov/harvest_record/18855a20-4631-4ce5-9df3-6f769695f8f1","harvest_record_raw":"https://catalog.data.gov/harvest_record/18855a20-4631-4ce5-9df3-6f769695f8f1/raw","harvest_record_transformed":"https://catalog.data.gov/harvest_record/18855a20-4631-4ce5-9df3-6f769695f8f1/transformed","has_download":false,"has_spatial":true,"identifier":"https://data.noaa.gov/waf/NOAA/nmfs/pifsc/iso/xml/36069.xml","keyword":["DOC/NOAA/NMFS/PIFSC/ESD > Ecosystem Sciences Division, Pacific Islands Fisheries Science Center, National Marine Fisheries Service, NOAA, U.S. Department of Commerce","EARTH SCIENCE > BIOSPHERE > ECOSYSTEMS > MARINE ECOSYSTEMS > BENTHIC","EARTH SCIENCE > BIOSPHERE > ECOSYSTEMS > MARINE ECOSYSTEMS > REEF > CORAL REEF","OCEAN > PACIFIC OCEAN > CENTRAL PACIFIC OCEAN > BAKER ISLAND","OCEAN > PACIFIC OCEAN > CENTRAL PACIFIC OCEAN > HOWLAND ISLAND","OCEAN > PACIFIC OCEAN > CENTRAL PACIFIC OCEAN > JARVIS ISLAND","OCEAN > PACIFIC OCEAN > CENTRAL PACIFIC OCEAN > JOHNSTON ATOLL","OCEAN > PACIFIC OCEAN > CENTRAL PACIFIC OCEAN > KINGMAN REEF","OCEAN > PACIFIC OCEAN > CENTRAL PACIFIC OCEAN > PALMYRA ATOLL","1221","409","587","743","C204 Pacific Reef Assessment and Monitoring Program (Pacific RAMP): Biennial monitoring for the US Pacific Islands and Atolls","National Coral Reef Monitoring Program","Ocean Acidification - Quantification of Calcification and Accretion Rates of Corals and Crustose Coralline Algae across the Pacific Ocean","Pacific Reef Assessment and Monitoring Program: Monitoring coral reef ecosystems of the US Pacific Islands and Atolls","Numeric Data Sets > Calcification Rate","EARTH SCIENCE > Biosphere > Aquatic Habitat > Reef Habitat","EARTH SCIENCE > Biosphere > Vegetation > Algae > Algal Cover","EARTH SCIENCE > Biosphere > Vegetation > Algae > Algal Growth > Calcification Rate","EARTH SCIENCE > Biosphere > Vegetation > Algae > Calcareous Macroalgae","EARTH SCIENCE > Biosphere > Vegetation > Algae > Crustose Coralline Algae","EARTH SCIENCE > Biosphere > Vegetation > Algae > Encrusting Macroalgae","EARTH SCIENCE > Biosphere > Vegetation > Algae > Fleshy Macroalgae","EARTH SCIENCE > Biosphere > Vegetation > Algae > Reef Monitoring and Assessment","EARTH SCIENCE > Biosphere > Vegetation > Algae > Reef Monitoring and Assessment > Calcification Accretion Unit (CAU)","EARTH SCIENCE > Biosphere > Zoology > Corals > Reef Monitoring and Assessment","EARTH SCIENCE > Oceans > Coastal Processes > Coral Reefs","EARTH SCIENCE > Oceans > Ocean Chemistry > Calcification","EARTH SCIENCE > Oceans > Ocean Chemistry > Carbonate Chemistry","EARTH SCIENCE > Oceans > Ocean Chemistry > Ocean Acidification","CALCIFICATION","in situ","laboratory analyses","CORAL REEF STUDIES","Coral Reef Conservation Program","National Coral Reef Monitoring Program","Pacific Reef and Assessment Monitoring Program","US DOC; NOAA; Office of Oceanic and Atmospheric Research; Ocean Acidification Program","US DOC; NOAA; NMFS; Pacific Islands Fisheries Science Center; Ecosystem Sciences Division","COUNTRY/TERRITORY > United States of America > USA Minor Outlying Islands > Baker Island (00N176W0001)","COUNTRY/TERRITORY > United States of America > USA Minor Outlying Islands > Howland Island (00S176W0001)","COUNTRY/TERRITORY > United States of America > USA Minor Outlying Islands > Jarvis Island (00S160W0001)","COUNTRY/TERRITORY > United States of America > USA Minor Outlying Islands > Johnston Atoll (16N169W0001)","COUNTRY/TERRITORY > United States of America > USA Minor Outlying Islands > Kingman Reef (06N162W0001)","COUNTRY/TERRITORY > United States of America > USA Minor Outlying Islands > Palmyra Atoll (05N162W0001)","COUNTRY/TERRITORY > United States of America > USA Minor Outlying Islands > Wake Atoll (19N167E0001)","OCEAN BASIN > Pacific Ocean > Central Pacific Ocean > Baker Island > Baker Island (00N176W0001)","OCEAN BASIN > Pacific Ocean > Central Pacific Ocean > Howland Island > Howland Island (00S176W0001)","OCEAN BASIN > Pacific Ocean > Central Pacific Ocean > Johnston Atoll > Johnston Atoll (16N169W0001)","OCEAN BASIN > Pacific Ocean > Central Pacific Ocean > Line Islands > Jarvis Island (00S160W0001)","OCEAN BASIN > Pacific Ocean > Central Pacific Ocean > Line Islands > Kingman Reef (06N162W0001)","OCEAN BASIN > Pacific Ocean > Central Pacific Ocean > Line Islands > Palmyra Atoll (05N162W0001)","OCEAN BASIN > Pacific Ocean > Central Pacific Ocean > Wake Atoll > Wake Atoll (19N167E0001)","Central Pacific Ocean","Equatorial Pacific Ocean","Pacific Remote Islands Marine National Monument","HI'IALAKAI","RAINIER","ASRAMP","American Samoa Reef Assessment and Monitoring Program","CRED","CREP","Calcification Plate","Coral Reef Ecosystem Division","Coral Reef Ecosystem Program","ESD","Ecosystem Sciences Division","NCRMP","Ocean Acidification","PIFSC","Pacific Islands Fisheries Science Center","RAMP","Reef Assessment and Monitoring Program","Settlement Plate","calcification accretion unit","triennial","PRIA","PRIMNM","Pacific Remote Island Areas","Calcification Accretion Unit (CAU)","DOC/NOAA/NMFS/PIFSC > Pacific Islands Fisheries Science Center, National Marine Fisheries Service, NOAA, U.S. Department of Commerce","Pacific Remote Island Areas"],"last_harvested_date":"2026-09-29T22:46:47.003597","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"5f4f1195-e770-4a2a-8f75-195cd98860ce","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/noaa.png","name":"National Oceanic and Atmospheric Administration, Department of Commerce","organization_type":"Federal Government","slug":"noaa"},"parent_identifier":null,"popularity":0,"publisher":"Pacific Islands Fisheries Science Center","slug":"national-coral-reef-monitoring-program-calcification-rates-of-crustose-coralline-alga-2023","spatial_centroid":{"lat":6.4759339659999995,"lon":-166.63684},"spatial_shape":{"coordinates":[[[-159.9788,-0.38235459],[-159.9788,16.7633668],[-176.6239,16.7633668],[-176.6239,-0.38235459],[-159.9788,-0.38235459]]],"type":"Polygon"},"theme":["geospatial"],"title":"National Coral Reef Monitoring Program: Calcification Rates of Crustose Coralline Algae Derived from Calcification Accretion Units (CAUs) Deployed across the Pacific Remote Island Areas from 2010 to 2023","type":"dataset"},{"_score":21.686657,"_sort":[1790719430488,21.686657,1,"f7b550e9-2307-4b7d-84fe-355ff778cb91"],"access_level":"public","dcat":{"@type":"dcat:Dataset","accessLevel":"public","contactPoint":{"@type":"vcard:Contact","fn":"Vimal Amin","hasEmail":"mailto:no-reply@opendata.maryland.gov"},"description":"This is a subset of the Maryland 2006-2023 Greenhouse Gas Emissions Inventory dataset. This subset includes only the gross emissions sources and excludes the Forestry & Land Use emissions sinks.\n\nThe Greenhouse Gas Emissions Reduction Act (Maryland Code, Environment Article \u00a72-1203) requires the Maryland Department of the Environment to prepare and publish an updated inventory of statewide greenhouse gas emissions on a three-year cycle. This dataset covers the inventory years of 2006, 2011, 2014, 2017, 2020, and 2023.\n\nMaryland's greenhouse gas emissions inventory tracks emissions of carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), and sulfur hexafluoride (SF6) occurring in the state and from out-of-state electricity generation consumed in the state. These gases have differing lifetimes in the atmosphere and differing warming impacts. The Global Warming Potential (GWP) is a measure of the warming impact of a particular gas over a particular time horizon. GWP values allow for aggregating emissions of the different greenhouse gases into a single metric, known as carbon dioxide equivalent, and reported in the inventory in million metric tons (MMTCO2e). \n\nAs required by the Climate Solutions Now Act of 2022, MD's GHG inventory reports emissions with a GWP considered over a 20-year time horizon. Emissions are also presented using a GWP over a 100-yr time horizon, consistent with conventional national and international inventory protocols. The 20-yr GWP emissions are to be used in evaluating progress towards Maryland's GHG reduction goals.","distribution":[{"@type":"dcat:Distribution","describedBy":"https://opendata.maryland.gov/api/views/xuyh-dmw3/columns.json","describedByType":"application/json","downloadURL":"https://opendata.maryland.gov/api/v3/views/xuyh-dmw3/query.json?accessType=DOWNLOAD","mediaType":"application/json"},{"@type":"dcat:Distribution","describedBy":"https://opendata.maryland.gov/api/views/xuyh-dmw3/columns.xml","describedByType":"application/xml","downloadURL":"https://opendata.maryland.gov/api/v3/views/xuyh-dmw3/query.xml?accessType=DOWNLOAD","mediaType":"application/xml"},{"@type":"dcat:Distribution","downloadURL":"https://opendata.maryland.gov/api/v3/views/xuyh-dmw3/export.csv?accessType=DOWNLOAD","mediaType":"text/csv"}],"identifier":"https://opendata.maryland.gov/api/views/xuyh-dmw3","issued":"2022-09-27","keyword":["air and radiation administration (ara)","climate change","greenhouse gas"],"landingPage":"https://opendata.maryland.gov/d/xuyh-dmw3","modified":"2026-09-23","publisher":{"@type":"org:Organization","name":"opendata.maryland.gov"},"theme":["Energy and Environment"],"title":"Maryland 2006-2023 Greenhouse Gas Emissions Inventory - gross emissions subset"},"description":"This is a subset of the Maryland 2006-2023 Greenhouse Gas Emissions Inventory dataset. This subset includes only the gross emissions sources and excludes the Forestry & Land Use emissions sinks.\n\nThe Greenhouse Gas Emissions Reduction Act (Maryland Code, Environment Article \u00a72-1203) requires the Maryland Department of the Environment to prepare and publish an updated inventory of statewide greenhouse gas emissions on a three-year cycle. This dataset covers the inventory years of 2006, 2011, 2014, 2017, 2020, and 2023.\n\nMaryland's greenhouse gas emissions inventory tracks emissions of carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), and sulfur hexafluoride (SF6) occurring in the state and from out-of-state electricity generation consumed in the state. These gases have differing lifetimes in the atmosphere and differing warming impacts. The Global Warming Potential (GWP) is a measure of the warming impact of a particular gas over a particular time horizon. GWP values allow for aggregating emissions of the different greenhouse gases into a single metric, known as carbon dioxide equivalent, and reported in the inventory in million metric tons (MMTCO2e). \n\nAs required by the Climate Solutions Now Act of 2022, MD's GHG inventory reports emissions with a GWP considered over a 20-year time horizon. Emissions are also presented using a GWP over a 100-yr time horizon, consistent with conventional national and international inventory protocols. The 20-yr GWP emissions are to be used in evaluating progress towards Maryland's GHG reduction goals.","distribution_titles":[],"harvest_record":"https://catalog.data.gov/harvest_record/bec56e56-3db2-428e-b309-3a04fc370f47","harvest_record_raw":"https://catalog.data.gov/harvest_record/bec56e56-3db2-428e-b309-3a04fc370f47/raw","has_download":true,"has_spatial":false,"identifier":"https://opendata.maryland.gov/api/views/xuyh-dmw3","keyword":["air and radiation administration (ara)","climate change","greenhouse gas"],"last_harvested_date":"2026-09-29T22:03:50.488911","organization":{"aliases":["md"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"b98a6f3b-552a-4826-931e-4594420f4596","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/datamaryland_logo_lower_400px_noback.png","name":"State of Maryland","organization_type":"State Government","slug":"maryland"},"parent_identifier":null,"popularity":1,"publisher":"opendata.maryland.gov","slug":"maryland-2006-2020-greenhouse-gas-emissions-inventory-gross-emissions-subset","spatial_centroid":null,"spatial_shape":null,"theme":["Energy and Environment"],"title":"Maryland 2006-2023 Greenhouse Gas Emissions Inventory - gross emissions subset","type":"dataset"},{"_score":22.221407,"_sort":[1790719428300,22.221407,5,"5da319e2-0c01-447c-b624-8d86bb902784"],"access_level":"public","dcat":{"@type":"dcat:Dataset","accessLevel":"public","contactPoint":{"@type":"vcard:Contact","fn":"Vimal Amin","hasEmail":"mailto:no-reply@opendata.maryland.gov"},"description":"The Greenhouse Gas Emissions Reduction Act (Maryland Code, Environment Article \u00a72-1203) requires the Maryland Department of the Environment to prepare and publish an updated inventory of statewide greenhouse gas emissions on a three-year cycle. This dataset covers the inventory years of 2006, 2011, 2014, 2017, 2020, and 2023.\n\nMaryland's greenhouse gas emissions inventory tracks emissions of carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), and sulfur hexafluoride (SF6) occurring in the state and from out-of-state electricity generation consumed in the state. These gases have differing lifetimes in the atmosphere and differing warming impacts. The Global Warming Potential (GWP) is a measure of the warming impact of a particular gas over a particular time horizon. GWP values allow for aggregating emissions of the different greenhouse gases into a single metric, known as carbon dioxide equivalent, and reported in the inventory in million metric tons (MMTCO2e). \n\nAs required by the Climate Solutions Now Act of 2022, MD's GHG inventory reports emissions with a GWP considered over a 20-year time horizon. Emissions are also presented using a GWP over a 100-yr time horizon, consistent with conventional national and international inventory protocols. The 20-yr GWP emissions are to be used in evaluating progress towards Maryland's GHG reduction goals.","distribution":[{"@type":"dcat:Distribution","describedBy":"https://opendata.maryland.gov/api/views/xabh-m3bt/columns.json","describedByType":"application/json","downloadURL":"https://opendata.maryland.gov/api/v3/views/xabh-m3bt/query.json?accessType=DOWNLOAD","mediaType":"application/json"},{"@type":"dcat:Distribution","describedBy":"https://opendata.maryland.gov/api/views/xabh-m3bt/columns.xml","describedByType":"application/xml","downloadURL":"https://opendata.maryland.gov/api/v3/views/xabh-m3bt/query.xml?accessType=DOWNLOAD","mediaType":"application/xml"},{"@type":"dcat:Distribution","downloadURL":"https://opendata.maryland.gov/api/v3/views/xabh-m3bt/export.csv?accessType=DOWNLOAD","mediaType":"text/csv"}],"identifier":"https://opendata.maryland.gov/api/views/xabh-m3bt","issued":"2022-09-27","keyword":["air and radiation administration (ara)","climate change","greenhouse gas"],"landingPage":"https://opendata.maryland.gov/d/xabh-m3bt","modified":"2026-09-23","publisher":{"@type":"org:Organization","name":"opendata.maryland.gov"},"theme":["Energy and Environment"],"title":"Maryland 2006-2023 Greenhouse Gas Emissions Inventory"},"description":"The Greenhouse Gas Emissions Reduction Act (Maryland Code, Environment Article \u00a72-1203) requires the Maryland Department of the Environment to prepare and publish an updated inventory of statewide greenhouse gas emissions on a three-year cycle. This dataset covers the inventory years of 2006, 2011, 2014, 2017, 2020, and 2023.\n\nMaryland's greenhouse gas emissions inventory tracks emissions of carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), and sulfur hexafluoride (SF6) occurring in the state and from out-of-state electricity generation consumed in the state. These gases have differing lifetimes in the atmosphere and differing warming impacts. The Global Warming Potential (GWP) is a measure of the warming impact of a particular gas over a particular time horizon. GWP values allow for aggregating emissions of the different greenhouse gases into a single metric, known as carbon dioxide equivalent, and reported in the inventory in million metric tons (MMTCO2e). \n\nAs required by the Climate Solutions Now Act of 2022, MD's GHG inventory reports emissions with a GWP considered over a 20-year time horizon. Emissions are also presented using a GWP over a 100-yr time horizon, consistent with conventional national and international inventory protocols. The 20-yr GWP emissions are to be used in evaluating progress towards Maryland's GHG reduction goals.","distribution_titles":[],"harvest_record":"https://catalog.data.gov/harvest_record/9ece333f-496a-4346-8b98-ce427ce20373","harvest_record_raw":"https://catalog.data.gov/harvest_record/9ece333f-496a-4346-8b98-ce427ce20373/raw","has_download":true,"has_spatial":false,"identifier":"https://opendata.maryland.gov/api/views/xabh-m3bt","keyword":["air and radiation administration (ara)","climate change","greenhouse gas"],"last_harvested_date":"2026-09-29T22:03:48.300026","organization":{"aliases":["md"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"b98a6f3b-552a-4826-931e-4594420f4596","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/datamaryland_logo_lower_400px_noback.png","name":"State of Maryland","organization_type":"State Government","slug":"maryland"},"parent_identifier":null,"popularity":5,"publisher":"opendata.maryland.gov","slug":"maryland-2006-2020-greenhouse-gas-emissions-inventory","spatial_centroid":null,"spatial_shape":null,"theme":["Energy and Environment"],"title":"Maryland 2006-2023 Greenhouse Gas Emissions Inventory","type":"dataset"},{"_score":10.508976,"_sort":[1790719015222,10.508976,9,"db000b57-a3c5-4244-90ba-1ceb8978ffe5"],"access_level":"public","dcat":{"@type":"dcat:Dataset","accessLevel":"public","contactPoint":{"@type":"vcard:Contact","fn":"DCGISopendata","hasEmail":"mailto:gisgroup@dc.gov"},"description":"The BEPS Program was created by Title III of the?Clean Energy DC Omnibus Act of 2018. The BEPS is a minimum threshold of energy performance that will be no lower than the local median?ENERGY STAR?score by property type (or equivalent metric). The standards were created to drive energy performance in existing buildings to help meet the energy and climate goals of the?Sustainable DC?plan \u2014 to reduce greenhouse gas emissions and energy consumption by 50% by 2032. DOEE established the first set of Standards on January 1, 2021. Standards will then be set every 6 years, creating BEPS Periods (BEPS Period 1, BEPS Period 2, etc.). The 2021 Building Energy Performance Standards and a Guide to the 2021 BEPS are available for viewing on DOEE\u2019s website.
To improve transparency and help building owners understand how their building performs relative to the BEPS, DOEE is publishing this BEPS Disclosure that compares a building\u2019s benchmarking data with the BEPS and provides an estimate of the building\u2019s distance from the standard and estimated performance requirement.
Please note that this dataset is based on information currently available to DOEE using calendar year 2019 benchmarking data provided by the building owner. Some buildings are still being evaluated and therefore have been designated as \u201cUnder Review\u201d in this dataset. Building owners that believe their 2019 calendar year data is incorrect should contact the Benchmarking Help Center (info.benchmark@dc.gov). Additionally, buildings that meet certain criteria may request a variance from the standards by submitting a variance request form on the DOEE website.
The BEPS Program was created by Title III of the?Clean Energy DC Omnibus Act of 2018. The BEPS is a minimum threshold of energy performance that will be no lower than the local median?ENERGY STAR?score by property type (or equivalent metric). The standards were created to drive energy performance in existing buildings to help meet the energy and climate goals of the?Sustainable DC?plan \u2014 to reduce greenhouse gas emissions and energy consumption by 50% by 2032. DOEE established the first set of Standards on January 1, 2021. Standards will then be set every 6 years, creating BEPS Periods (BEPS Period 1, BEPS Period 2, etc.). The 2021 Building Energy Performance Standards and a Guide to the 2021 BEPS are available for viewing on DOEE\u2019s website.
To improve transparency and help building owners understand how their building performs relative to the BEPS, DOEE is publishing this BEPS Disclosure that compares a building\u2019s benchmarking data with the BEPS and provides an estimate of the building\u2019s distance from the standard and estimated performance requirement.
Please note that this dataset is based on information currently available to DOEE using calendar year 2019 benchmarking data provided by the building owner. Some buildings are still being evaluated and therefore have been designated as \u201cUnder Review\u201d in this dataset. Building owners that believe their 2019 calendar year data is incorrect should contact the Benchmarking Help Center (info.benchmark@dc.gov). Additionally, buildings that meet certain criteria may request a variance from the standards by submitting a variance request form on the DOEE website.