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1,141 results for “primary_productivity”
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.
Underwater Photosynthetically Active Radiation (PAR) from in-situ Lake Primary Production Experiments in the McMurdo Dry Valleys of Antarctica (1995-2024, ongoing)
As part of the Long Term Ecological Research (LTER) project in the McMurdo Dry Valleys of Antarctica, PAR is measured at one depth during primary production experiments in Dry Valley Lakes. This data set quantifies instantaneous underwater PAR at 10-meter depths in Lakes Bonney and Hoare, and at 7-meter depth in Lake Fryxell (depths vary after 2008, check data file for actual depth). Ambient PAR was also measured at the air-ice interface from each of these lakes.
Phytoplankton primary production measurements from discrete water column samples collected from lakes in the McMurdo Dry Valleys, Antarctica (1993-2022, ongoing)
An important part of the McMurdo Long Term Ecological Research (LTER) is monitoring of spatial and temporal patterns, and processes that control phytoplankton production in perennial ice-covered lakes. This dataset addresses this core area of research and quantifies carbon production at specific depths in McMurdo Dry Valley lakes.
MCR LTER: Coral Reef: Water Column: Nearshore Water Profiles, CTD, Primary Production, and Chemistry ongoing since 2005
This data package contains water chemistry measurements taken 2 to 4 times per year at 6 stations on the north shore of Moorea, French Polynesia: Forereef, Lagoon, Fringing Reef, Cooks Bay, Cooks Bay Stream Mouth, and Offshore (5 km due north). Measurements include standard CTD parameters, nutrients, chlorophyll, phaeopigments, particulate organic carbon and nitrogen, dissolved organic carbon, dissolved inorganic carbon, total alkalinity, water column primary production, and abundance of bacteria. Sampling began in August, 2005. All water samples were collected with Niskin Bottles. CTD data were collected with a SBE19-Plus Seacat Profiler outfitted with WetLabs FLNTURT-221 Flurometer/turbidity sensors. CTD and bottle samples were taken on separate casts at each station. Two additional stations on the Moorea east shore were sampled in August 2005 only: LTER 3 Forereef Water Column, LTER 3 Backreef Water Column. This material is based upon work supported by the U.S. National Science Foundation under Grant No. OCE 16-37396 (and earlier awards) as well as a generous gift from the Gordon and Betty Moore Foundation. Research was completed under permits issued by the French Polynesian Government (Délégation à la Recherche) and the Haut-commissariat de la République en Polynésie Francaise (DTRT) (Protocole d'Accueil 2005-2018). This work represents a contribution of the Moorea Coral Reef (MCR) LTER Site.
MCR LTER: Coral Reef: Estimates of component primary production and respiration, 2006-2015
Estimates of primary production and respiration of three representative components of the Moorea coral reef ecosystem were made yearly in a laboratory flume from 2006 through 2015. The components are: algal turf communities, the macroalga Sargassum pacificum, and the common branching coral Pocillopora verrucosa. Metabolism estimates were made using changes in dissolved oxygen over time in a flume in unidirectional flow at saturating irradiances and dark. Rates were normalized to projected (planar) surface area (all components) and biomass (algal turfs, Sargassum). This timeseries completed in 2015.
Water column primary production from inorganic carbon uptake for 24h at simulated in situ light levels in deck incubators, collected at Palmer Station Antarctica during Palmer LTER field seasons, 1994-2025.
Primary Production experiments were led by Vernet from the 1994-1995 season through the 2006-2007 season. Schofield is the current lead, beginning in the 2009-2010 season. Methods have been kept consistent as much as possible over the full time series and different Principal Investigators. Primary production is the uptake of inorganic carbon and assimilation of it into organic matter by phytoplankton. Primary production rates, expressed as mgC per m3 per day were measured by the uptake of radioactive (14C) sodium bicarbonate. Water samples are collected throughout the water column at stations within the Palmer LTER region (primarily B and E, to 50m and 65m respectively). Beginning in the 2020-2021 season, Station B is no longer sampled. Water is put into borosilicate bottles, inoculated with 1 uCi of NaH14CO3 per bottle, and incubated in an outdoor deck incubator. The incubator is plumbed to the Palmer Station sea water system to maintain ambient seawater temperature and bottles are screened to in situ light levels. The uptake of 14C-bicarbonate by the phytoplankton was measured in a scintillation counter after a 24-hour incubation period. Primary production experiments were not conducted during the 2020-2021 nor 2023-2024 field seasons. There was no field season in 2021-2022.
Water column primary production from inorganic carbon uptake for 24h at simulated in situ (SIS) light levels in deck incubators, collected aboard Palmer LTER annual cruises off the coast of the Western Antarctic Peninsula, 1995 – 2023.
Primary Production experiments were led by Vernet from 1995-2008. Schofield is the current lead, beginning in 2009. Methods have been kept consistent as much as possible over the full time series and different Principal Investigators. Primary production is the uptake of inorganic carbon and assimilation of it into organic matter by phytoplankton. Primary production rates, expressed as mgC per m3 per day were measured by the uptake of radioactive (14C) sodium bicarbonate. Water samples are collected throughout the water column at stations along the Western Antarctica Peninsula at regular PAL-LTER grid stations. Water is put into borosilicate bottles, inoculated with 1 uCi of NaH14CO3 per bottle, and incubated in an outdoor deck incubator. The incubator is plumbed to the ship sea water system to maintain ambient seawater temperature and bottles are screened to in situ light levels. The uptake of 14C-bicarbonate by the phytoplankton was measured in a scintillation counter after a 24-hour incubation period. Data is unavailable for the LMG16-01 cruise due to measurement issues. Data is temporarily unavailable for the LMG20-01 cruise. Primary production experiments were not conducted during the 2022 (NBP21-13) nor 2024 (LMG24-01) cruises. There was no cruise in the austral summer of 2021.
Annual primary productivity in control plots at a Spartina alterniflora-dominated salt marsh at Law's Point, Rowley River, Plum Island Ecosystem LTER, MA.
Annual productivity is determined from aboveground biomass data at permanent, high marsh, plots in a Spartina alterniflora-dominated salt marsh on the Rowley River within the Plum Island Ecosystem (PIE) LTER site, MA.
SBC LTER: Cross-shelf Study 2008-2009: Profiles of CTD, biogeochemistry, primary production, abundance of phytoplankton groups, and abundance and production of bacteria
These data were used by the following papers: Goodman, J., M. A. Brzezinski, E. R. Halewood and C. A. Carlson. 2012. Sources of phytoplankton to the inner continental shelf in the Santa Barbara Channel inferred from cross-shelf gradients in biological, physical and chemical parameters. Continental Shelf Research, 48: 27-39. (DOI: 10.1016/j.csr.2012.08.011) Halewood, E. R., C. A. Carlson, M. A. Brzezinski, D. C. Reed and J. Goodman. 2012. Annual cycle of organic matter partitioning and its availability to bacteria across the Santa Barbara Channel continental shelf. Aquatic Microbial Ecology, 67:189-209. (DOI:10.3354/ame01586) These data were collected on monthly day cruises from January 2008 to April 2009 on the RV Kelp Fish at five stations across the shelf starting at Mohawk Reef in the nearshore area of the Santa Barbara Channel, California, USA. Data were collected with a SBE19-Plus and rosette sampler. Measurements include standard CTD parameters in 1 m bins (e.g. salinity, temperature, density). At selected depths (1, 5, 10, 20 m), rosette bottle samples were collected for nutrients, pigments, particulate and dissolved organic carbon and nitrogen, and bacterial abundance, community structure and productivity. Phytoplankton abundances (to genus) were obtained from the 5 m sample only.
Net Primary Productivity (NPP) Weight Data at the Sevilleta National Wildlife Refuge, New Mexico
Several long-term studies at the Sevilleta LTER measure net primary production (NPP) across ecosystems and treatments. Net primary production is a fundamental ecological variable that quantifies rates of carbon consumption and fixation. Estimates of NPP are important in understanding energy flow at a community level as well as spatial and temporal responses to a range of ecological processes. Above-ground net primary production (ANPP) is the change in plant biomass, including loss to death and decomposition, over a given period of time. To measure this change, vegetation variables, including species composition and the cover and height of individuals, are sampled up to three times yearly (winter, spring, and fall) at permanent plots within a study site. The weight data presented here is obtained by harvesting a series of covers for species observed during plot sampling. These species are always harvested from habitat comparable to the plots in which they were recorded. This data is then used to make volumetric measurements of species and build regressions correlating biomass and volume. From these calculations, seasonal biomass and seasonal and annual NPP are determined.
Core Site Grid Quadrat Data for the Net Primary Production Study at the Sevilleta National Wildlife Refuge, New Mexico
Begun in spring 2013, this project is part of a long-term study at the Sevilleta LTER measuring net primary production (NPP) across three distinct ecosystems: creosote-dominant shrubland (Site C), black grama-dominant grassland (Site G), and blue grama-dominant grassland (Site B). 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 foliage, over time and incorporates 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. The data from these plots is used to build regressions correlating biomass and volume via weights of select harvested species obtained in SEV999, "Net Primary Productivity (NPP) Weight Data." This biomass data is included in SEV999, "Seasonal Biomass and Seasonal and Annual NPP for Core Grid Research Sites."
Long-term dynamics of soil organic matter and aboveground net primary production in a Chihuahuan Desert Grassland at the Sevilleta National Wildlife Refuge, New Mexico (1989-2014)
Drylands contain a third of the organic carbon stored in global soils; however, the long-term dynamics of soil organic carbon and soil organic matter (SOM) in drylands remain poorly understood relative to dynamics of the vegetation carbon pool. We examined long-term patterns in SOM against both climate and prescribed fire in a Chihuahuan Desert grassland in central New Mexico, USA. SOM was measured each spring and fall for 25 years (1989–2014) in unburned desert grassland and from 2003 to 2014 following a prescribed fire. SOM concentration from 0-20 cm depth did not show a clear long-term trend but fluctuated seasonally at both burned and unburned sites, ranging from a minimum of 0.9% to a maximum of 3.3%. SOM concentration declined nonlinearly in wet seasons and peaked in dry seasons. These results not only contrast with the positive relationships between aboveground net primary production and precipitation for this region, but also with previous reports of greater SOM in wetter sites across drylands globally, suggesting that space is not a good substitute for time in predicting the dynamics of dryland SOM. We suggest that declines in SOM in wet periods are caused by increased soil respiration, runoff, leaching, and soil erosion. In addition to tracking natural variability in climate, SOM concentration also decreased by 14% following prescribed fire, a response that magnified over time and has persisted for nearly a decade due to the slow recovery of primary production. Our results document the surprisingly dynamic nature of soil organic matter and its high sensitivity to climate and fire in this dryland ecosystem.
Mapping the intertidal microphytobenthos Gross Primary Production
<p>Dataset used in the papers "Mapping the intertidal microphytobenthos Gross Primary Production. PartI & PartII" published in Frontiers Marine Science Research Topic "Advances and Challenges in Microphytobenthos Research: From Cell Biology to Coastal Ecosystem Function", and maps (geotiff) resulting of the use of the GPP-algorithm with Platt or Eilers and Peeters models.</p>
Long-Term Net Primary Productivity Dataset of the Tibetan Plateau from 1982 to 2013
<ul><li>This dataset is generated by the advanced CASA model, encompassing vegetation net primary productivity (NPP) raster data for the Tibetan Plateau from 1982 to 2013. The model's input parameters comprise NDVI time series, monthly average temperature, monthly total precipitation, monthly total solar radiation, and vegetation type. The data is provided at an 8 km × 8 km spatial resolution and is formatted in ENVI format (.dat).</li><li>Remarkably, during cross-validation with the MODIS 500m resolution product (MOD17A3HGF.061), it exhibited significantly strong correlations, with the correlation coefficients (R) ranging from 0.74 to 0.82.</li><li>Please cite this dataset as<br>Tan, Q., Sun, G., & Pang, Y. (2023). Long-Term Net Primary Productivity Dataset of the Tibetan Plateau from 1982 to 2013 (V1.0) [Data set]. Zenodo. https://doi.org/<a href="https://doi.org/10.5281/zenodo.10040818">10.5281/zenodo.10040818</a></li></ul>
Net primary production from the Behrenfeld-CbPM, Westberry-CbPM and Silsbe-CAFE algorithms - HYCOM MLD 0.125 Criterion
<p>Net primary production (mg C m<sup>-2</sup> d<sup>-1</sup>) calculated from the Behrenfeld-CbPM, Westberry-CbPM and Silsbe-CAFE algorithms. MLD data taken from HYCOM using the density criterion of 0.125 kg m<sup>-3</sup>. </p> <p>Data on a regular 25km grid at a 8 day resolution.</p> <p><strong>Version 1.1</strong></p> <p>Fixed minor issues with:</p> <ol> <li>Conversion of bbp(443) to phytoplankton carbon.</li> <li>Missing values at ~180W.</li> </ol> <p> </p> <p><strong>Version 1.2</strong></p> <ol> <li>Updated to include 2023.</li> <li>Westberry-CbPM Nitracline depth updated with World Ocean Atlas 2023 Nitrate Data.</li> <li>Silsbe-CAFE bbwater calculations updated with World Ocean Atlas 2023 Salinity Data.</li> <li>File structure compressed using Zlib.</li> </ol> <p>Please note for Westberry-CbPM and Silsbe-CAFE all years were reprocessed.</p> <p> </p> <p>No further updates are planned for this data product - please email tryankeogh@csir.co.za if you have interest in further updates.</p>
Net primary production from the Behrenfeld-CbPM, Westberry-CbPM and Silsbe-CAFE algorithms - HYCOM MLD 0.030 Criterion
<p>Net primary production (mg C m-2 d-1) calculated from the Behrenfeld-CbPM, Westberry-CbPM and Silsbe-CAFE algorithms. MLD data taken from HYCOM using the density criterion of 0.030 kg m-3. </p> <p>Data on a regular 25km grid at a 8 day resolution.</p> <p><strong>Version 1.1</strong></p> <p>Fixed minor issues with:</p> <ol> <li>Conversion of bbp(443) to phytoplankton carbon.</li> <li>Missing values at ~180W.</li> </ol> <p> </p> <p><strong>Version 1.2</strong></p> <ol> <li>Updated to include 2023.</li> <li>Westberry-CbPM Nitracline depth updated with World Ocean Atlas 2023 Nitrate Data.</li> <li>Silsbe-CAFE bbwater calculations updated with World Ocean Atlas 2023 Salinity Data.</li> <li>File structure compressed using Zlib.</li> </ol> <p>Please note for Westberry-CbPM and Silsbe-CAFE all years were reprocessed.</p> <p> </p> <p>No further updates are planned for this data product - please email tryankeogh@csir.co.za if you have interest in further updates.</p>
Net primary production from the Behrenfeld-CbPM, Westberry-CbPM and Silsbe-CAFE algorithms - HADLEY MLD 0.125 Criterion
<p>Net primary production (mg C m-2 d-1) calculated from the Behrenfeld-CbPM, Westberry-CbPM and Silsbe-CAFE algorithms. MLD data taken from HADLEY EN4.2.2 using the density criterion of 0.125 kg m-3. </p> <p>Data on a regular 25km grid at a 8 day resolution.</p> <p><strong>Version 1.1</strong></p> <p>Fixed minor issues with:</p> <ol> <li>Conversion of bbp(443) to phytoplankton carbon.</li> <li>Missing values at ~180W.</li> </ol> <p> </p> <p><strong>Version 1.2</strong></p> <ol> <li>Updated to include 2023.</li> <li>Westberry-CbPM Nitracline depth updated with World Ocean Atlas 2023 Nitrate Data.</li> <li>Silsbe-CAFE bbwater calculations updated with World Ocean Atlas 2023 Salinity Data.</li> <li>File structure compressed using Zlib.</li> </ol> <p>Please note for Westberry-CbPM and Silsbe-CAFE all years were reprocessed.</p> <p> </p> <p><strong>Version 1.3</strong></p> <ol> <li>Updated to include 2024.</li> </ol>
Net primary production from the Lee-AbPM algorithm
<p>Net primary production (mg C m-2 d-1) calculated from the Lee-AbPM algorithm.</p> <p>Data on a regular 25km grid at a 8 day resolution.</p> <p><strong>Version 1.1</strong></p> <ol> <li>Updated to include 2023.</li> </ol> <p><strong>Version 1.2</strong></p> <ol> <li>Updated to include 2024.</li> </ol>
Dataset from Holding et al. (2019)––Seasonal and spatial patterns of primary production in a high latitude fjord
<p>Unprecedented melting of the Greenland Ice Sheet (GrIS) is impacting the coastal ocean, and its effects on fjord ecology remain understudied. It has been suggested that as glaciers retreat, primary production regimes may be altered, rendering fjords less productive. Here we present data from the paper Holding et al. (2019). Seasonal and spatial patterns of primary production in a high-latitude fjord affected by Greenland Ice Sheet run-off. <em>Biogeosciences</em>, <em>16</em>(19), 3777-3792, /doi.org/10.5194/bg-16-3777-2019. This paper investigates patterns of primary productivity in a northeast Greenland fjord (Young Sound, 74°N), which receives run-off from the GrIS via land-terminating glaciers. This dataset includes measures of size fractioned primary production and chlorophyll <em>a </em>biomass, as well as CTD data and biochemical parameters. Furthermore, primary production was measured using photosynthesis v. irradiance (PI) curves, thus PI curve parameters are also available. The data were taken during the ice-free season along a spatial gradient of meltwater influence. </p> <p>We thank Egon Frandsen, Kunuk Lennert, and Ivali Lennert for excellent assistance during fieldwork. This research has beensupported by the Danish Environmental Protection Agency’s programme for Arctic research (DANCEA) (grant no. MST-112-0023), The Carlsberg Foundation (grant no. 2013_01_0532), the Norwegian Research Council (Mi- croPolar) (grant no. RCN 225956), and the European Commission, H2020 Research Infrastructures (GrIS-Melt (grant no. 752325) and INTAROS (grant no. 727890)). </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>
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