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142 results for “biomass production”
Litterfall, Biomass and Productivity in CRUI Land Use Project at Harvard Forest 1996-1997
Prior agricultural land use often plays an important role in the present condition of forests. This research was conducted to find any productivity differences among previously plowed, pastured, and woodlot sites. There are six sites, two from each of the land use types. With this research we wanted to estimate the productivity for each of the six stands and test the hypothesis that rates of annual aboveground biomass and annual litter fall in recovering forests would decline in the order woodlot - pastured - plowed. Twenty-five trees, of various diameters and species, were cored at each site. The cores were analyzed and the radial growth was calculated for the most recent 5 and 10 year intervals. Biomass increments were also calculated for the same time span using published allometric equations. Aboveground woody productivity was calculated for diameter classes within species and semi-log relationships of productivity per diameter were used to calculate the productivity of all trees in the stand that were not cored. Site-specific and species-specific equations were used. Litter fall was estimated by collecting litter from ten traps each in the six stands. Litter was sorted by species and reported on a dry matter basis. Red oaks and red maples were the major contributors of biomass and litter fall in all six of the stands, accounting for about seventy percent of the total aboveground biomass. Although woodlots had higher total biomass there was not a significant difference in productivity across the six sites. The range of productivity including both litter fall and aboveground woody production was from 7524-9014 Kg/ha. This fell into the range for temperate forests according to results published by the International Biological Program. Production per unit biomass was greater in the plowed stand and least in the woodlots.
Seasonal aboveground plant biomass estimates at 15 net primary production (NPP) study sites at Jornada Basin LTER from 1989-ongoing
This data package contains aboveground vegetation cover, volume, and calculated biomass values at the 15 Net Primary Production (NPP) study sites on Jornada Experimental Range (JER) and Chihuahuan Desert Rangeland Research Center (CDRRC) lands. Sites were selected to represent the 5 major ecosystem types in the Chihuahuan Desert (upland grasslands, playa grasslands, mesquite-dominated shrublands, creosotebush-dominated shrublands, tarbush-dominated shrublands). For each ecosystem type, three sites were selected to represent the range in variability in production and plant diversity; thus the locations are not replicates. All sites are excluded from domestic grazing. Eleven sites are in non-grazed pastures, and at the other four sites 1 hectare areas around the observational plots were fenced in 1988. At all sites a grid of 49 (48 at one playa location) 1m x 1m replicate quadrats was laid out when sampling began in 1989. For each quadrat, aboveground biomass has been calculated from two data sources: 1) non-destructive horizontal cover and vertical height measurements of individual plants, or plant parts, within each quadrat, and 2) linear regression coefficients for each plant species derived from off-quadrat cover, height, and harvested biomass measurements. Non-destructive measurements (1) are taken during winter, spring, and fall measurement campaigns, then aggregated by species for each quadrat, and resulting dimensions are used to calculate species biomass (grams) by quadrat and season using using the species-specific regression coefficients derived from dataset 2. This is the most detailed biomass dataset available and can be used to derive values of net primary production between seasons or annually. Each dataset record contains calculated biomass (and related variables) by species, quadrat, season, and site. Data collection is ongoing with new observations in spring, fall, and winter of each year, but this data package may be updated less frequently. Attenti
PAB01 Aboveground net primary productivity of tallgrass prairie based on accumulated plant biomass on core LTER watersheds (001d, 004b, 020b)
Data set contains estimates of end-of-season standing crop biomass (grams per square meter) of live graminoids, forbs, woody plants, and previous year's dead vegetation for 2 soil types (shallow and deep) on three core LTER watersheds representing three fire frequency treatments. Twenty quadrats (0.1 square meters) are harvested for each soil/treatment type. NOTE: Early (April) and mid-season (July) biomass was collected from 1983-1988, and these data are available by request.
Production, biomass, and yield estimates for walleye populations in the Ceded Territory of Wisconsin from 1990-2017
Recreational fisheries are valued at $190B globally and constitute the predominant use of wild fish stocks in developed countries, with inland systems contributing the dominant fraction of recreational fisheries. Although inland recreational fisheries are thought to be highly resilient and self-regulating, the rapid pace of environmental change is increasing the vulnerability of these fisheries to overharvest and collapse. We evaluate an approach for detecting hidden overharvest of inland recreational fisheries based on empirical comparisons of harvest and biomass production. Using an extensive 28-year dataset of the walleye fisheries in Northern Wisconsin, USA, we compare empirical biomass harvest (Y) and calculated production (P) and biomass (B) for 390 lake-year combinations. Overharvest occurs when harvest exceeds production in that year. Biomass and biomass turnover (P/B) both declined by about 30% and about 20% over time while biomass harvest did not change, causing overharvest to increase. Our analysis revealed 40% of populations were production-overharvested, a rate about 10x higher than current estimates based on numerical harvest used by fisheries managers. Our study highlights the need for novel approaches to evaluate and conserve inland fisheries in the face of global change.
LTREB: Aboveground biomass, plant density, annual aboveground productivity, plant heights and snail observations in control and fertilized plots in a Spartina alterniflora-dominated salt marsh, North Inlet, Georgetown, SC: 1984-2025
Aboveground biomass and plant density were measured non-destructively as a component of a long-term project seeking to understand how salt marsh primary production and sediment chemistry respond to anthropogenic (e.g. eutrophication) and natural (e.g. sea-level rise) environmental change. Feedbacks between plants, sediments, nutrients and flooding were investigated with particular attention to mechanisms that keep marshes in equilibrium with sea level. Biomass was calculated from plant height measurements using allometric equations. Annual productivity was calculated from approximately-monthly biomass estimates. In addition to plant height measurements, observations of snails in sample plots were recorded. Other data collected as part of the project include marsh surface elevation and porewater nutrient concentrations. These data have been used to develop the Marsh Equilibrium Model, an important tool for coastal resource managers. Sampling occurred at Spartina alterniflora-dominated salt marsh sites in North Inlet, a relatively pristine estuary near Georgetown, SC on the SE coast of the United States. North Inlet is a tidally-dominated, bar-built estuary, with a semi-diurnal mixed tide and a tidal range of 1.4m. The 25-km2 estuary is comprised of about 20.5 km2 of intertidal salt marsh and mudflats, and 4.5 km2 of open water. Sampling began at one location in 1984, and at three additional locations in 1986. Sampling occurred approximately monthly through 2025. The study is on-going. There are four sampling locations at two sites. Two locations are in the low marsh; two locations are in the high marsh. One high marsh location had control sampling plots in addition to plots fertilized with nitrogen and phosphorus.
Root Biomass, Fine Root Production, Soil Mass, and Soil pH in Limed and Control Plots at the Woods Lake Watershed, Adirondack Park, NY, USA, 2021-2022
In 1989, 6.89 Mg/ha of pelletized lime (CaCO3) was applied by helicopter to two subcatchments at the Woods Lake Watershed in Adirondack Park, New York, USA to ameliorate ecosystem acidification. Two unlimed (control) subcatchments were paired with limed subcatchments. In the same year, 99 permanent plots (20 m x 20 m) were established. Between 2008 and 2010, tree inventory and soil physicochemical measurements were made in five plots in each of the four subcatchments (20 plots total). This dataset contains soil physicochemical properties (dry mass, depth, and pH); root biomass (<1 mm, 1-2 mm, and >2 mm diameter); and annual fine root production (<1 mm and 1-2 mm) measurements made between 2021 and 2022 in 19 of these same plots (5 plots per control subcatchment and 4 or 5 plots per limed subcatchment). Data include measurements for all properties for Oe, Oa, and 0-10 cm mineral soil samples collected from 5 locations within each plot.
Data for: Techno-economic analysis of a novel laccase production process utilizing perennial biomass and the aqueous phase of bio-oil, Iowa, USA 2023-2025
This dataset contains the experimental design, measurements, and derived variables used to parameterize a techno‑economic model of laccase production via two‑stage solid‑state fermentation of prairie biomass with bio‑oil aqueous phase induction. It includes nutrient screening data for Pleurotus ostreatus growth on prairie biomass with alternative nitrogen sources and a corn‑steep solids dose series; factorial/response‑surface experiments varying substrate bed depth, substrate‑to‑inoculum (S:I) ratio, and pre‑induction growth time; and time‑resolved induction measurements. For each run and replicate, the data record the full set of spectrophotometric absorbances at 0–210 s, fitted slopes and r-square values, dilution and volume factors, and laccase activities normalized per mL and per gram of biomass, alongside the exact culture timings and environmental conditions used in the ABTS assay at 420 nm. Results tables provide the fitted central‑composite design model terms (coefficients, F‑statistics, and p‑values) used directly as inputs to the minimum laccase selling price (MLSP) calculations, together with the underlying per‑condition raw results.
Patterns of root biomass, productivity, turnover, and decomposition in riverine and scrub mangroves in the Everglades, Florida, USA, immediate-post-Irma, 2018-2019, and post-Irma, 2023-2024
Mangrove root biomass, productivity, and decomposition in the shallow (0-45 cm depth) root zone were estimated at Florida Coastal Everglades Long Term Ecological Research (FCE-LTER) Program Shark River (SRS4, SRS5, SRS6, SRS7) and Taylor River (TS/Ph6b, TS/Ph7b) mangrove sites during 2018-2019 and 2023-2024 following Hurricane Irma’s impacts in September 2017. Root biomass was estimated at all sites during both immediate-post-Irma (March 2018) and post-Irma (February-May 2023) periods with a PVC coring device (10.2 cm diameter x 45 cm length) using the same sampling protocol previously published for the study area (Castañeda-Moya et al. 2011). After collection, root cores were processed individually and initially rinsed with water through a 1-mm screen mesh to remove soil particles. Roots were separated manually based on their buoyancy, turgor, and color into biomass (live) and necromass (dead) components (Castañeda-Moya et al. 2011; Cormier et al. 2015; Medina-Calderon et al. 2021). Live roots were further sorted into three size diameter classes including fine (<2 mm), small (2-5 mm), and coarse (5-20 mm). Roots greater than 20 mm in diameter were not included in this study due to sampling limitations (i.e., core area). All root samples were oven-dried at 60°C to a constant mass and weighed to estimate root biomass and necromass (g m-2). Root productivity was estimated with the ingrowth core technique (Vogt et al., 1998) during both the immediate-post-Irma and post-Irma periods using the same sampling protocol previously published for the study area (Castañeda-Moya et al. 2011). Ingrowth cores (10.2 cm diameter x 45 cm length) were made of synthetic material (3 mm mesh) and filled with root-free commercial sphagnum peat moss. This material has similar soil properties (i.e., bulk density, organic matter content, total C and N) as mangrove peat in our study sites. Ingrowth cores were installed in each of the cored holes formed during sampling of root biomass. At each
Core Research Site Web Seasonal Biomass and Seasonal and Annual NPP Data for the Net Primary Production Study at the Sevilleta National Wildlife Refuge, New Mexico
This long-term study at the Sevilleta LTER measures net primary production (NPP) across four distinct ecosystems: creosote-dominant shrubland (Site C, est. winter 1999), black grama-dominant grassland (Site G, est. winter 1999), blue grama-dominant grassland (Site B, est. winter 2002), and pinon-juniper woodland (Site P, est. winter 2003), which is now in its own dataset, SEV278 (Pinon-Juniper (Core Site) Quadrat Data). Net primary production is a fundamental ecological variable that quantifies rates of carbon consumption and fixation. Estimates of NPP are important in understanding energy flow at a community level as well as spatial and temporal responses to a range of ecological processes. While measures of both below- and above-ground biomass are important in estimating total NPP, this study focuses on above-ground net primary production (ANPP). Above-ground net primary production is the change in plant biomass, including loss to death and decomposition, over a given period of time. Volumetric measurements are made using vegetation data from permanent plots collected in SEV129, "Core Research Site Web Quadrat Data" and regressions correlating biomass and volume constructed using seasonal harvest weights from SEV157, "Net Primary Productivity (NPP) Weight Data."
Burn Study Sites Seasonal Biomass and Seasonal and Annual NPP Data for the Net Primary Production Study at the Sevilleta National Wildlife Refuge, New Mexico
In 2003, the U.S. Fish and Wildlife Service conducted a prescribed burn over a large part of the northeastern corner of the Sevilleta NWR. This study was designed to look at the effect of fire on above-ground net primary productivity (ANPP) within different vegetation types. Net primary production (NPP) is a fundamental ecological variable that measures rates of carbon consumption and fixation. Estimates of NPP are important in understanding energy flow at a community level as well as spatial and temporal responses to a range of ecological processes. While measures of both below- and above-ground biomass are important in estimating total NPP, this study focuses on above-ground net primary production (ANPP). Above-ground net primary production (ANPP) is equal to the change in plant mass, including loss to death and decomposition, over a given period of time. To measure this change, ANPP is sampled twice a year (spring and fall) for all species in each of three vegetation types. In addition, volumetric measurements are obtained from adjacent areas to build regressions correlating biomass and volume. Three vegetation types were chosen for this study: mixed grass (MG), mixed shrub (MS) and black grama (G). Forty permanent 1m x 1m plots were installed in both burned and unburned sections of each habitat type. The core black grama site included in SEV129 was incorporated into this dataset as an unburned control, so an additional unburned G site was not created. The data for this site is noted as site=G and treatment=C (i.e., control). The original mixed-grass unburned plot caught fire unexpectedly in the fall of 2009 and was subsequently moved to the south. Volumetric measurements are made using vegetation data from permanent plots collected in SEV156, "Burn Study Sites Quadrat Data for the Net Primary Production Study" and regressions correlating biomass and volume constructed using seasonal harvest weights from SEV157, "Net Primary Productivity (NPP) Weight Data."
Pinon-Juniper (Core Site) Seasonal Biomass and Seasonal and Annual NPP Data for the Net Primary Production Study at the Sevilleta National Wildlife Refuge, New Mexico
This dataset contains pinon-juniper woodland biomass data and is part of a long-term study at the Sevilleta LTER measuring net primary production (NPP) across four distinct ecosystems: creosote-dominant shrubland (Site C, est. winter 1999), black grama-dominant grassland (Site G, est. winter 1999), blue grama-dominant grassland (Site B, est. winter 2002), and pinon-juniper woodland (Site P, est. winter 2003). Net primary production is a fundamental ecological variable that quantifies rates of carbon consumption and fixation. Estimates of NPP are important in understanding energy flow at a community level as well as spatial and temporal responses to a range of ecological processes. Above-ground net primary production is the change in plant biomass, represented by stems, flowers, fruit and and foliage, over time and incoporates growth as well as loss to death and decomposition. To measure this change the vegetation variables in this dataset, including species composition and the cover and height of individuals, are sampled twice yearly (spring and fall) at permanent 1m x 1m plots within each site. A third sampling at Site C is performed in the winter. Volumetric measurements are made using vegetation data from permanent plots (SEV278, "Pinon-Juniper (Core Site) Quadrat Data for the Net Primary Production Study") and regressions correlating species biomass and volume constructed using seasonal harvest weights from SEV157, "Net Primary Productivity (NPP) Weight Data."
Root biomass, productivity, and turnover of riverine and scrub mangroves in the Everglades, Florida, USA, 2000-2006
Mangrove root biomass and productivity in the shallow (0-45 cm depth) and deeper (45-90 cm) root zones were estimated at Florida Coastal Everglades Long Term Ecological Research (FCE-LTER) Program Shark River (SRS4, SRS5, SRS6) and Taylor River (TS/Ph6b, TS/Ph7b, TS/Ph8) mangrove sites during 2000-2006. Root biomass was estimated at all sites using a PVC coring device (10.2 cm diameter x 45 cm length). After collection, root cores from each zone were processed individually and initially rinsed with water through a 1-mm screen mesh to remove soil particles. Live roots were separated manually based on their buoyancy, turgor, and color. Live roots were further sorted into three size diameter classes including fine (<2 mm), small (2-5 mm), and coarse (5-20 mm). Roots greater than 20 mm in diameter were not included in this study due to sampling limitations (i.e., core area). All root samples were oven-dried at 60°C to a constant mass and weighed to estimate root biomass (g m-2). Root productivity was estimated with the ingrowth core technique (Vogt et al., 1998). Ingrowth cores (10.2 cm diameter x 45 cm length) were made of synthetic material (3 mm mesh) and filled with root-free commercial sphagnum peat moss. This material has similar soil properties (i.e., bulk density, organic matter content, total C and N) as mangrove peat in our study sites. Ingrowth cores were installed in each of the cored holes formed during sampling of root biomass. At each site, ingrowth cores were deployed vertically into the soil and retrieved at one- and three-year intervals, and the subsequent root growth within the ingrowth core was used to estimate annual root production (g m-2 yr-1) in the shallow and deeper root zones across all mangrove sites. After collection, ingrowth cores were processed individually using the same protocol as for root biomass. Root turnover rate in the shallow root zone was calculated as root productivity divided by root biomass of each root size class at all site
PAB04 Aboveground primary productivity of tallgrass prairie based on accumulated plant biomass on miscellaneous LTER watersheds
Data set contains estimates of end-of-season standing crop biomass (grams per square meter) of live graminoids, forbs, woody plants, current year's dead, and previous year's dead vegetation for 2 soil types (shallow and deep) on watersheds of various burning-grazing treatments. Twenty quadrats (0.1 square meters) are harvested for each soil/treatment type. NOTE: Early (April) and mid-season (July) biomass was collected from 1983-1988, and these data are vailable by request.
PBB01 Belowground Plot Experiment: Aboveground net primary productivity of tallgrass prairie based on accumulated plant biomass
To address the potential interactive effects of fire, aboveground biomass removal, and nutrient amendments on above- and belowground responses, a long-term field experiment was initiated in 1986 as part of the Konza Prairie Long-Term Ecological Research (LTER) program. The general goals of this experiment are: 1) to document both short- and long-term responses of plants and soils to fire, aboveground biomass removal (a surrogate for grazing in these small plots), and nutrient amendments (additions of N and/or P); and 2) to provide a better understanding of the mechanisms underlying tallgrass prairie responses to fire, aboveground biomass removal and nutrient enrichment. Peak foliage biomass is measured annually in late fall (September to October) on the 64 belowground plots. Effects of burning, mowing and N + P additions on aboveground NPP are measured. Two 0.1m2 quadrats harvested per plot). 2003 was the last year the mowing treatment was implemented.
PEB01 Aboveground net primary productivity of tallgrass prairie based on accumulated plant biomass in grazing exclsoures on bison-grazed watersheds
Data set contains estimates of end-of-season standing crop biomass (grams per square meter) of live graminoids, forbs, woody plants, and previous year's dead vegetation in grazing exclosures. Date from exclosures is used to determine long-term effects of bison grazing on aboveground net primary productivity.
PAB03 Aboveground primary productivity of tallgrass prairie based on accumulated plant biomass on LTER watersheds burned at different seasons
Data set contains estimates of standing crop biomass (grams per square meter) of live graminoids, forbs, woody plants, and previous year's dead vegetation for 2 soil types (shallow and deep) and seasonal burning treatments (spring, summer, fall, winter).
PAB05 Aboveground net primary productivity of tallgrass prairie based on accumulated plant biomass on the LTER fire reversal experiment watersheds
Data set contains estimates of end-of-season standing crop biomass (grams per square meter) of live graminoids, forbs, woody plants, and previous year's dead vegetation for 2 soil types (shallow and deep) on the four Fire Reversal Experiment watersheds. This experiment is based on reversing fire treatments on four watersheds, two of which had a history of annual spring burning and two of which had a history of long-term fire suppression. The dataset includes both pre- and post-fire treatments.
Data from: Trade-off between standing biomass and productivity in species-rich tropical forest: evidence, explanations and implications
<p>These files are the R code and plot data files used for calculating species population turnover of biomass and abundance in a tropical forest plot.</p> <p>This dataset is a processed subset of the original dataset used in our analysis of biomass turnover across tree populations as demonstrated in <a href="https://doi.org/10.1111/1365-2745.13485">the main paper</a>. Readers interested in using the Pasoh 50-ha plot data for purposes other than reviewing our analysis are advised to contact the <a href="https://www.frim.gov.my/">Forest Research Institute Malaysia (FRIM)</a> and the <a href="https://forestgeo.si.edu/">Center for Tropical Forest Science-Forest Global Earth Observatory (CTFS-Forest GEO)</a>, Smithsonian Tropical Research Institute.</p>
Processing and Data for "Estimating ocean net primary productivity from daily cycles of carbon biomass measured by profiling floats"
<p><strong>Description: </strong></p> <p>These files contain processed BGC-Argo float data, figure data, the radiocarbon productivity subset, bootstrapping results, and the associated Python/Matlab code to calculate net primary productivity from daily cycles of optical backscatter and dissolved oxygen.</p> <p>The raw float data used in this study are available from the Argo Global Data Assembly Centers in Brest, France (ftp://ftp.ifremer.fr/ifremer/argo/dac/coriolis) and Monterey, California (ftp://usgodae.org/pub/outgoing/argo/dac/coriolis). The raw MODIS satellite-based productivity data is available from the Oregon State University Ocean Productivity site (<a href="http://orca.science.oregonstate.edu/npp_products.php">http://orca.science.oregonstate.edu/npp_products.php</a>). The raw MODIS satellite-based euphotic depth estimates are available from the NASA L3 browser (<a href="https://oceancolor.gsfc.nasa.gov/l3/">https://oceancolor.gsfc.nasa.gov/l3/</a>). The original ship-based estimates of net primary productivity are available from the Pangaea (<a href="https://doi.pangaea.de/10.1594/PANGAEA.932417">https://doi.pangaea.de/10.1594/PANGAEA.932417</a>) and the British Oceanography Data Centre (<a href="https://www.bco-dmo.org/dataset/814803">https://www.bco-dmo.org/dataset/814803</a>).</p> <p><strong>Please cite as: </strong></p> <p>Stoer, A., and Fennel, K. 2022. Processing and Data for Estimating ocean net primary productivity from daily cycles of carbon biomass measured by profiling floats. Zenodo. doi: 10.5281/zenodo.6977161.</p> <p><strong>Python/MATLAB Software Description: </strong></p> <p>dielFit_GOPeqCR.m: This code is from Johnson and Bif (2021). We have added outputs for standard errors for linear and PvE models and sunrise/sunset times. To run this code with the associated Python software a MATLAB engine needs to be installed. Please see: <a href="https://www.mathworks.com/help/matlab/matlab-engine-for-python.html">https://www.mathworks.com/help/matlab/matlab-engine-for-python.html</a></p> <p>argo_so_processing_20220815.py: This code is the first of two pieces of software for estimating net primary productivity from floats in the Southern Ocean. The program below obtains the data from the BGC Argo database (Argo, 2021) and processes it. Simple data quality control, interpolation, biogeochemical calculations, and data binning occur. The processed float data is located in the folder 'Processed Argo Transects'.</p> <p>argo_daily_npp_20220815.py: This code using processed Argo float data that contains oxygen and particle backscatter measurements to infer net primary production. The code combines the float that meet the criteria of sampling at all local hours of the day throughout its lifetime. Then, it constructs diel cycles from this data by finding the median value of each hour and uses the code from Johnson and Bif (2021), which is a modified version from Barone et al. (2019). The algorithm used to convert particle backscatter to particulate organic carbon is from Graff et al. (2015). We assume that dissolved primary productivity accounts for 30% of total primary productivity (Moran et al., 2022).</p> <p>argo_daily_npp_bootstrap_20220815.py: This code using processed Argo float data that contains co-located oxygen and particle backscatter measurements to infer net primary production. This code is very similar to argo_daily_npp_20220815.py but randomly samples a subset of the co-located profiles at different sample sizes before calculating net primary productivity. Productivity is calculated at each sample size 1000 times. The results of this analysis is located in the folder 'Bootstrapped Results'. </p> <p>More details can be found in the code itself. </p> <p><strong>Data Descriptions: </strong></p> Data from 'Processed Argo Transects' Folder | Description for each variable <table><tbody><tr> <th>Variable</th> <th>Description</th> <th>Units</th> </tr> </tbody><tbody> <tr> <td>depth</td> <td>Average depth of depth bin</td> <td>m</td> </tr> <tr> <td>mid_depth</td> <td>Center of depth bin</td> <td>m</td> </tr> <tr> <td>pressure</td> <td>Average pressure in depth bin</td> <td>dbar</td> </tr> <tr> <td>profile_index</td> <td>Profile number or index</td> <td> </td> </tr> <tr> <td>profile_longitude</td> <td>Average longitude of profile</td> <td>degE</td> </tr> <tr> <td>profile_latitude</td> <td>Average latitude of profile</td> <td>degN</td> </tr> <tr> <td>profile_time</td> <td>Average UTC time of profile</td> <td>yyyy-mm-dd hh:mm:ss</td> </tr> <tr> <td>profile_local_time</td> <td>Average local time of profile</td> <td>yyyy-mm-dd hh:mm:ss</td> </tr> <tr> <td>profile_local_hour</td> <td>The hour of the local timestamp</td> <td> </td> </tr> <tr> <td>salinity</td> <td>Seawater salinity</td> <td>PSU</td> </tr> <tr> <td>temperature </td> <td>Seawater temperature</td> <td>degC</td> </tr> <tr> <td>oxygen</td> <td>Dissolved oxygen concentration</td> <td>umol kg-1</td> </tr> <tr> <td>oxygen_saturation</td> <td>Saturated dissolved oxygen concentration calculated from the Garcia and Gordon (1992) equation.</td> <td>umol kg-1</td> </tr> <tr> <td>oxygen_anom</td> <td>The difference between observed dissolved oxygen concentration and saturated oxygen </td> <td>umol kg-1</td> </tr> <tr> <td>bbp470</td> <td>Optical backscatter coefficient at 470 nm. Particulate organic carbon is calculated in argo_daily_npp_20220815.py</td> <td>m-1</td> </tr> </tbody> </table> <table> <tbody><tr> <th>Variable</th> <th>Description</th> <th>Units</th> </tr> </tbody><tbody> <tr> <td>wmo</td> <td>WMO number of float</td> <td> </td> </tr> <tr> <td>profile_index</td> <td>Profile index or profile number taken by float</td> <td> </td> </tr> <tr> <td>profile_latitude</td> <td>Average profile latitude</td> <td>degN</td> </tr> <tr> <td>profile_longitude</td> <td>Average profile longitude</td> <td>degE</td> </tr> </tbody> </table> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <table> <tbody><tr> <th>Variable</th> <th>Description</th> <th>Units</th> </tr> </tbody><tbody> <tr> <td>fod</td> <td>Fraction of day</td> <td> </td> </tr> <tr> <td>oxy</td> <td>Sinusoidal curve fit to oxygen</td> <td>mol m-3</td> </tr> <tr> <td>poc</td> <td>Sinusoidal curve fit to particulate organic carbon</td> <td>mol m-3</td> </tr> <tr> <td>oxy_med</td> <td>Hourly median oxygen</td> <td>mol m-3</td> </tr> <tr> <td>oxy_sem</td> <td>Hourly standard error of oxygen</td> <td>mol m-3</td> </tr> <tr> <td>poc_med</td> <td>Hourly median particulate organic carbon</td> <td>mol m-3</td> </tr> <tr> <td>poc_sem</td> <td>Hourly standard error of particulate organic carbon</td> <td>mol m-3</td> </tr> <tr> <td>region</td> <td>Name of data subset (e.g., 30-40 deg N, co-located)</td> <td> </td> </tr> </tbody> </table> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <table> <tbody><tr> <th>Variable</th> <th>Description</th> <th>Units</th> </tr> </tbody><tbody> <tr> <td>region</td> <td>Name of data subset (e.g., 30-40 deg N) </td> <td> </td> </tr> <tr> <td>depth</td> <td>Depth of profile</td> <td>m</td> </tr> <tr> <td>zeu</td> <td>1% euphotic depth from Lee et al. (2013) algorithm from NASA (2022) L3 satellite products. </td> <td>m</td> </tr> <tr> <td>n_profiles_bpp</td> <td>Number of backscatter profiles</td> <td> </td> </tr> <tr> <td>n_profiles_oxy</td> <td>Number of oxygen profiles</td> <td> </td> </tr> <tr> <td>n_floats_bbp</td> <td>Number of floats with backscatter measurements</td> <td> </td> </tr> <tr> <td>n_floats_oxy</td> <td>Number of floats with oxygen measurements</td> <td> </td> </tr> <tr> <td>gop_do</td> <td>Gross oxygen productivity estimated from dissolved oxygen</td> <td>mol m-3 yr-1</td> </tr> <tr> <td>gop_do_serr</td> <td>Standard error of gross oxygen productivity estimated from dissolved oxygen</td> <td>mol m-3 yr-1</td> </tr> <tr> <td>gop_do_p</td> <td>p-value of curve fit to hourly oxygen data</td> <td> </td> </tr> <tr> <td>gop_do_r2</td> <td>r-squared value of curve to hourly oxygen data</td> <td> </td> </tr> <tr> <td>oxy_sr</td> <td>The calculated sunrise time as a fraction of the day</td> <td> </td> </tr> <tr> <td>oxy_ss</td> <td>The calculated sunset time as a fraction of the day</td> <td> </td> </tr> <tr> <td>gpp_bbp</td> <td>Gross carbon productivity estimated from optical backscatter</td> <td>mol m-3 yr-1</td> </tr> <tr> <td>gpp_bbp_serr</td> <td>Standard error of gross carbon productivity estimated from optical backscatter</td> <td>mol m-3 yr-1</td> </tr> <tr> <td>gop_do_p</td> <td>p-value of curve fit to hourly particulate organic carbon data</td> <td> </td> </tr> <tr> <td>gop_do_r2</td> <td>r-squared value of curve to hourly particulate organic carbon data</td> <td> </td> </tr> <tr> <td>gop_bbp</td> <td>Gross oxygen productivity calculated from gross carbon productivity (gpp_bbp)</td> <td>mol m-3 yr-1</td> </tr> <tr> <td>gop_bbp_serr</td> <td>Standard error of gross oxygen productivity calculated from gross carbon productivity (gpp_bbp_serr)</td> <td>mol m-3 yr-1</td> </tr> <tr> <td>npp_bbp</td> <td>Net primary productivity calculated from backscatter-based gross oxygen productivity (gop_bbp)</td> <td>mol m-3 yr-1</td> </tr> <tr> <td>npp_bbp_serr</td> <td>Standard error of net primary productivity calculated from backscatter-based gross oxygen productivity (gop_bbp_serr)</td> <td>mol m-3 yr-1</td> </tr> <tr> <td>npp_do</td> <td>Net primary productivity calculated from oxygen-based gross oxygen productivity (gop_do)</td> <td>mol m-3 yr-1</td> </tr> <tr> <td>npp_do_serr</td> <td>Standard error of net primary productivity calculated from oxygen-based gross oxygen productivity (gop_do_serr)</td> <td>mol m-3 yr-1</td> </tr> </tbody> </table> <table> </table> Data for Fig. S1 | Description for number_of_bbp_profiles_in_each_year.csv and number_of_oxy_profiles_in_each_year.csv <table><tbody><tr> <th>Variable</th> <th>Description</th> <th>Units</th> </tr> </tbody><tbody> <tr> <td>year</td> <td>Year</td> <td> </td> </tr> <tr> <td>bbp470</td> <td>Number of backscatter profiles</td> <td> </td> </tr> <tr> <td>oxygen_anom</td> <td>Number of oxygen profiles</td> <td> </td> </tr> </tbody> </table> <table> </table> <table> <tbody><tr> <th>Variable</th> <th>Description</th> <th>Units</th> </tr> </tbody><tbody> <tr> <td>mid_depth</td> <td>Depth of NPP profile</td> <td>m</td> </tr> <tr> <td>mean</td> <td>Mean volumetric 14C-NPP at depth</td> <td>mmol m-3 yr-1</td> </tr> <tr> <td>median</td> <td>Median volumetric 14C-NPP at depth</td> <td>mmol m-3 yr-1</td> </tr> <tr> <td>min</td> <td>Minimum volumetric 14C-NPP at depth</td> <td>mmol m-3 yr-1</td> </tr> <tr> <td>maximum</td> <td>Maximum volumetric 14C-NPP</td> <td>mmol m-3 yr-1</td> </tr> </tbody> </table> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <table> <tbody><tr> <th><strong>Variable</strong></th> <th>Description</th> <th>Units</th> </tr> </tbody><tbody> <tr> <td>subset</td> <td>Number of profiles randomly sampled from the co-located dataset</td> <td> </td> </tr> <tr> <td>int_npp_do</td> <td>Euphotic-depth-integrated net primary productivity calculated from oxygen-based gross oxygen productivity</td> <td>mol m-2 y-1</td> </tr> <tr> <td>int_npp_bbp</td> <td>Euphotic-depth-integrated net primary productivity calculated from backscatter-based gross oxygen productivity</td> <td>mol m-2 y-1</td> </tr> <tr> <td>gop_do_r2</td> <td>R-squared of the sinusoidal curve to the diel cycle of oxygen anomaly</td> <td> </td> </tr> <tr> <td>gpp_bbp_r2</td> <td>R-squared of sinusoidal curve to the diel cycle of particulate organic carbon</td> <td> </td> </tr> </tbody> </table> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <table> <tbody><tr> <th>Variable</th> <th>Description</th> <th>Units</th> </tr> </tbody><tbody> <tr> <td>ROSE</td> <td>Topographic (negative values are below sea level)</td> <td>m</td> </tr> <tr> <td>ETOPO05_Y</td> <td>Latitude</td> <td>degN</td> </tr> <tr> <td>ETOPO05_X</td> <td>Longitude</td> <td>degE</td> </tr> </tbody> </table> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <table> <tbody><tr> <th>Variable</th> <th>Description</th> <th>Units</th> </tr> </tbody><tbody> <tr> <td>database</td> <td>Database the data was extracted from</td> <td> </td> </tr> <tr> <td>Month</td> <td>Month of NPP measurement</td> <td>month of year</td> </tr> <tr> <td>npp_14c</td> <td>Net primary productivity estimated from the radiocarbon method</td> <td>mmol m-3 y-1</td> </tr> <tr> <td>depth</td> <td>depth of 14C-NPP measurement</td> <td>m</td> </tr> </tbody> </table> <table> </table>
Estimation of biomass combustion carbon emissions data for 2018 in Africa based on GABAM burned area products.
<p>Estimated biomass combustion carbon emissions data for the African region in 2018, based on the GABAM 30m burned area product.The product is geographically (latitude/longitude) projected with a resolution of 0.00025° (approximately 30 meters) using the WGS84 horizontal datum and the EGM96 vertical datum, and consists of 10° x 10° tiles covering the entire African region.</p>
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