http://lod.bco-dmo.org/id/dataset/823008 eng; USA utf8 dataset Highest level of data collection, from a common set of sensors or instrumentation, usually within the same research project Biological and Chemical Oceanography Data Management Office (BCO-DMO) Unavailable 508-289-2009 WHOI MS#36 Woods Hole MA 02543 USA info@bco-dmo.org http://www.bco-dmo.org Monday - Friday 8:00am - 5:00pm For questions regarding this resource, please contact BCO-DMO via the email address provided. pointOfContact 2020-09-01 ISO 19115-2 Geographic Information - Metadata - Part 2: Extensions for Imagery and Gridded Data ISO 19115-2:2009(E) Kelp forest mooring DIC, TA, pCO2, and aragonite saturation state estimations inside the kelp canopy (36° 37.297’ N, 121° 54.102’ W.) at Hopkins Marine Station, recorded between June and October 2018. 2020-09-02 publication 2020-09-02 revision Marine Biological Laboratory/Woods Hole Oceanographic Institution Library (MBLWHOI DLA) 2020-10-16 publication https://doi.org/10.26008/1912/bco-dmo.823008.1 Kerry J. Nickols California State University Northridge principalInvestigator Robert B. Dunbar Stanford University principalInvestigator Heidi Hirsh Stanford University principalInvestigator Stephen G. Monismith Stanford University principalInvestigator David Mucciarone Stanford University principalInvestigator Yuichiro Takeshita Monterey Bay Aquarium Research Institute principalInvestigator Sarah Traiger United States Geological Survey principalInvestigator Biological and Chemical Oceanography Data Management Office (BCO-DMO) Unavailable 508-289-2009 WHOI MS#36 Woods Hole MA 02543 USA info@bco-dmo.org http://www.bco-dmo.org Monday - Friday 8:00am - 5:00pm For questions regarding this resource, please contact BCO-DMO via the email address provided. publisher Cite this dataset as: Hirsh, H., Nickols, K. J., Takeshita, Y., Traiger, S., Monismith, S. G., Mucciarone, D., Dunbar, R. B. (2020) Kelp forest mooring DIC, TA, pCO2, and aragonite saturation state estimations inside the kelp canopy (36° 37.297’ N, 121° 54.102’ W.) at Hopkins Marine Station, recorded between June and October 2018. Biological and Chemical Oceanography Data Management Office (BCO-DMO). (Version 1) Version Date 2020-09-02 [if applicable, indicate subset used]. doi:10.26008/1912/bco-dmo.823008.1 [access date] Dataset Description: &lt;p&gt;These data are published in Hirsh &lt;em&gt;et al., &lt;/em&gt;see related publications section.&lt;/p&gt; Methods and Sampling: &lt;p&gt;In order to estimate other carbonate parameters from sensor pH data, we require an additional carbonate parameter. To accomplish this, we created a local empirical relationship to estimate total alkalinity (TA) based on discrete samples, and combined this with sensor data to calculate DIC, pCO2, and aragonite saturation state (ΩAr).&lt;/p&gt; &lt;p&gt;TA was estimated using a multiple linear regression (MLR) approach, with temperature (T) and salinity (S) as inputs (Alin et al., 2012; Carter et al., 2018). This approach has been demonstrated to be effective in the open ocean (Carter et al., 2018) and along the California Coast below 50 m depth (Alin et al., 2012). Takeshita et al. (2015) demonstrated that this MLR approach can be extended to nearshore environments and showed TA could be estimated to ± 6 μmol kg-1 (RMSE) inside a kelp forest in Southern California by applying an offset to the equations from Alin et al. (2012).&lt;/p&gt; &lt;p&gt;Here, we fit an equation of the form used in Alin et al. (2012) to the discrete samples (n = 271) collected in the kelp forest, and obtained the following equation:&lt;/p&gt; &lt;p&gt;TA &lt;sup&gt;est &lt;/sup&gt;= alpha&lt;sub&gt;0&lt;/sub&gt; + alpha&lt;sub&gt;1&lt;/sub&gt; (T - T&lt;sub&gt;r&lt;/sub&gt;) + alpha&lt;sub&gt;2&lt;/sub&gt; (S - S&lt;sub&gt;r&lt;/sub&gt;) + alpha&lt;sub&gt;3&lt;/sub&gt; (T-T&lt;sub&gt;r&lt;/sub&gt;) x (S - S&lt;sub&gt;r&lt;/sub&gt;)&lt;/p&gt; &lt;p&gt;with a RMSE of ± 7.3 μmol kg-1. T&lt;sub&gt;r&lt;/sub&gt; and S&lt;sub&gt;r&lt;/sub&gt; are the mean temperature and salinity for the deployment. The estimated total alkalinity (TA&lt;sub&gt;est&lt;/sub&gt;) was combined with sensor pH to estimate DIC, pCO2, and ΩAr using equilibrium constants from Lueker et al. (2000) and CO2SYS (van Heuven et al., 2011). For a range of pH from 7.56 to 8.30 the standard uncertainty for estimated carbonate system parameters was 8.98 to 13.43 μmol kg-1 DIC, 54.22 to 9.42 μatm pCO2, and 0.0501 to 0.223 ΩAr. These uncertainties were calculated using the ‘errors’ function in seacarb (Gattuso et al., 2020, version 3.2.13 - Accessed 6 Mar 2020) with inputs of TAest and measured pH, temperature and salinity over depth inside the kelp forest.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt; Funding provided by NSF Division of Ocean Sciences (NSF OCE) Award Number: OCE-1737096 Award URL: https://www.nsf.gov/awardsearch/show-award?AWD_ID=1737096 Funding provided by NSF Division of Ocean Sciences (NSF OCE) Award Number: OCE-1737176 Award URL: https://www.nsf.gov/awardsearch/show-award?AWD_ID=1737176 completed Kerry J. Nickols California State University Northridge (818) 677-6147 18111 Nordhoff St Northridge CA 91330-8303 US kerry.nickols@csun.edu pointOfContact Robert B. Dunbar Stanford University (650) 725-6830 Department of Environmental Earth Systems Science (Braun Hall, bldg 320) Stanford University Stanford CA 94305-2115 USA dunbar@stanford.edu pointOfContact Heidi Hirsh Stanford University 253-514-2804 473 Via Ortega, Rm140 Standford CA 94305 USA pointOfContact Stephen G. Monismith Stanford University 650-723-4764 Y2E2 Rm 183 473 Via Ortega Stanford CA 94305-4020 USA stephen@stanford.edu pointOfContact David Mucciarone Stanford University (650) 723-0817 473 Via Ortega Stanford CA 94305 USA dam1@stanford.edu pointOfContact Yuichiro Takeshita Monterey Bay Aquarium Research Institute 7700 Sandholdt Road Moss Landing CA 95039 USA yui@mbari.org pointOfContact Sarah Traiger United States Geological Survey 1910 Alex Holden Way Juneau AK 99801 USA straiger@usgs.gov pointOfContact asNeeded Dataset Version: 1 Unknown Depth_ID Mooring_ID Latitude Longitude MAB DIC TA Omega pCO2 ISO_DateTime_UTC mFET and SeapHOx theme None, User defined Site_ID buoy_id latitude longitude altitude dissolved inorganic Carbon total alkalinity (TA) Aragonite Saturation State Partial pressure of CO2 ISO_DateTime_UTC featureType BCO-DMO Standard Parameters pH Sensor instrument BCO-DMO Standard Instruments otherRestrictions otherRestrictions Access Constraints: none. Use Constraints: Please follow guidelines at: http://www.bco-dmo.org/terms-use Distribution liability: Under no circumstances shall BCO-DMO be liable for any direct, incidental, special, consequential, indirect, or punitive damages that result from the use of, or the inability to use, the materials in this data submission. If you are dissatisfied with any materials in this data submission your sole and exclusive remedy is to discontinue use. Collaborative Research: RUI: Building a mechanistic understanding of water column chemistry alteration by kelp forests: emerging contributions of foundation species https://osprey.bco-dmo.org/project/748778 Collaborative Research: RUI: Building a mechanistic understanding of water column chemistry alteration by kelp forests: emerging contributions of foundation species <p><em>NSF Award Abstract:</em><br /> Kelp forest ecosystems are of ecological and economic importance globally and provide habitat for a diversity of fish, invertebrates, and other algal species. In addition, they may also modify the chemistry of surrounding waters. Uptake of carbon dioxide (CO2) by giant kelp, Macrocystis pyrifera, may play a role in ameliorating the effects of increasing ocean acidity on nearshore marine communities driven by rising atmospheric CO2. Predicting the capacity for kelp forests to alter seawater chemistry requires understanding of the oceanographic and biological mechanisms that drive variability in seawater chemistry. The project will identify specific conditions that could lead to decreases in seawater CO2 by studying 4 sites within the southern Monterey Bay in Central California. An interdisciplinary team will examine variations in ocean chemistry in the context of the oceanographic and ecological characteristics of kelp forest habitats. This project will support an early career researcher, as well as train and support a postdoctoral researcher, PhD student, thesis master's student, and up to six undergraduate students. The PIs will actively recruit students from underrepresented groups to participate in this project through Stanford University's Summer Research in Geosciences and Engineering (SURGE) program and the Society for Advancement of Hispanics/Chicanos and Native Americans in Science (SACNAS). In addition, the PIs and students will actively engage with the management community (Monterey Bay National Marine Sanctuary and California Department of Fish and Wildlife) to advance products based on project data that will assist the development of management strategies for kelp forest habitats in a changing ocean.</p> <p>This project builds upon an extensive preliminary data set and will link kelp forest community attributes and hydrodynamic properties to kelp forest biogeochemistry (including the carbon system and dissolved oxygen) to understand mechanistically how giant kelp modifies surrounding waters and affects water chemistry using unique high-resolution measurement capabilities that have provided important insights in coral reef biogeochemistry. The project sites are characterized by different oceanographic settings and kelp forest characteristics that will allow examination of relationships between kelp forest inhabitants and water column chemistry. Continuous measurements of water column velocity, temperature, dissolved oxygen, pH, and photosynthetically active radiation will be augmented by twice-weekly measurements of dissolved inorganic carbon, total alkalinity, and nutrients as well as periods of high frequency sampling of all carbonate system parameters. Quantifying vertical gradients in carbonate system chemistry within kelp forests will lead to understanding of its dependence on seawater residence time and water column stratification. Additional biological sampling of kelp, benthic communities, and phytoplankton will be used to 1) determine contributions of understory algae and calcifying species to bottom water chemistry, 2) determine contributions of kelp canopy growth and phytoplankton to surface water chemistry, and 3) quantify the spatial extent of surface chemistry alteration by kelp forests. The physical, biological, and chemical data collected across multiple forests will allow development of a statistical model for predictions of kelp forest carbonate system chemistry alteration in different locations and under future climate scenarios. Threshold values of oceanographic conditions and kelp forest characteristics that lead to alteration of water column chemistry will be identified for use by managers in mitigation strategies such as targeted protection or restoration.</p> Kelp forest biogeochemistry largerWorkCitation project eng; USA oceans -121.9017 -121.9017 36.6216 36.6216 2018-06-07 2018-10-04 Central California 36.6 N 122 W 0 BCO-DMO catalogue of parameters from Kelp forest mooring DIC, TA, pCO2, and aragonite saturation state estimations inside the kelp canopy (36° 37.297’ N, 121° 54.102’ W.) at Hopkins Marine Station, recorded between June and October 2018. Biological and Chemical Oceanography Data Management Office (BCO-DMO) Unavailable 508-289-2009 WHOI MS#36 Woods Hole MA 02543 USA info@bco-dmo.org http://www.bco-dmo.org Monday - Friday 8:00am - 5:00pm For questions regarding this resource, please contact BCO-DMO via the email address provided. pointOfContact http://lod.bco-dmo.org/id/dataset-parameter/823029.rdf Name: Depth_ID Units: unitless Description: <p>ID to distinguish depth</p> http://lod.bco-dmo.org/id/dataset-parameter/823030.rdf Name: Mooring_ID Units: unitless Description: <p>Mooring name</p> http://lod.bco-dmo.org/id/dataset-parameter/823031.rdf Name: Latitude Units: decimal degrees Description: <p>Latitude of mooring location, south is negative</p> http://lod.bco-dmo.org/id/dataset-parameter/823032.rdf Name: Longitude Units: decimal degrees Description: <p>Longitude of mooring location, west is negative</p> http://lod.bco-dmo.org/id/dataset-parameter/823033.rdf Name: MAB Units: meters (m) Description: <p>Meters above bottom</p> http://lod.bco-dmo.org/id/dataset-parameter/823034.rdf Name: DIC Units: micromoles per kilogram (umol/kg) Description: <p>Disolved Inorganic Carbon</p> http://lod.bco-dmo.org/id/dataset-parameter/823035.rdf Name: TA Units: micromoles per kilogram (umol/kg) Description: <p>Total Alkalinity</p> http://lod.bco-dmo.org/id/dataset-parameter/823036.rdf Name: Omega Units: aragonite (ΩAr) Description: <p>Aragonite saturation state</p> http://lod.bco-dmo.org/id/dataset-parameter/823037.rdf Name: pCO2 Units: microatmosphere (uatm) Description: <p>Partial pressureof CO2</p> http://lod.bco-dmo.org/id/dataset-parameter/823038.rdf Name: ISO_DateTime_UTC Units: unitless Description: <p>Timestap (date and time) in ISO format, UTC (yyyy-mm-ddThh:mmZ)</p> GB/NERC/BODC > British Oceanographic Data Centre, Natural Environment Research Council, United Kingdom Biological and Chemical Oceanography Data Management Office (BCO-DMO) Unavailable 508-289-2009 WHOI MS#36 Woods Hole MA 02543 USA info@bco-dmo.org http://www.bco-dmo.org Monday - Friday 8:00am - 5:00pm For questions regarding this resource, please contact BCO-DMO via the email address provided. pointOfContact 23953056 https://darchive.mblwhoilibrary.org/bitstream/1912/26324/1/dataset-823008_kelp-forest-estimated-carbonate-parameters__v1.tsv download https://doi.org/10.26008/1912/bco-dmo.823008.1 download onLine dataset &lt;p&gt;In order to estimate other carbonate parameters from sensor pH data, we require an additional carbonate parameter. To accomplish this, we created a local empirical relationship to estimate total alkalinity (TA) based on discrete samples, and combined this with sensor data to calculate DIC, pCO2, and aragonite saturation state (ΩAr).&lt;/p&gt; &lt;p&gt;TA was estimated using a multiple linear regression (MLR) approach, with temperature (T) and salinity (S) as inputs (Alin et al., 2012; Carter et al., 2018). This approach has been demonstrated to be effective in the open ocean (Carter et al., 2018) and along the California Coast below 50 m depth (Alin et al., 2012). Takeshita et al. (2015) demonstrated that this MLR approach can be extended to nearshore environments and showed TA could be estimated to ± 6 μmol kg-1 (RMSE) inside a kelp forest in Southern California by applying an offset to the equations from Alin et al. (2012).&lt;/p&gt; &lt;p&gt;Here, we fit an equation of the form used in Alin et al. (2012) to the discrete samples (n = 271) collected in the kelp forest, and obtained the following equation:&lt;/p&gt; &lt;p&gt;TA &lt;sup&gt;est &lt;/sup&gt;= alpha&lt;sub&gt;0&lt;/sub&gt; + alpha&lt;sub&gt;1&lt;/sub&gt; (T - T&lt;sub&gt;r&lt;/sub&gt;) + alpha&lt;sub&gt;2&lt;/sub&gt; (S - S&lt;sub&gt;r&lt;/sub&gt;) + alpha&lt;sub&gt;3&lt;/sub&gt; (T-T&lt;sub&gt;r&lt;/sub&gt;) x (S - S&lt;sub&gt;r&lt;/sub&gt;)&lt;/p&gt; &lt;p&gt;with a RMSE of ± 7.3 μmol kg-1. T&lt;sub&gt;r&lt;/sub&gt; and S&lt;sub&gt;r&lt;/sub&gt; are the mean temperature and salinity for the deployment. The estimated total alkalinity (TA&lt;sub&gt;est&lt;/sub&gt;) was combined with sensor pH to estimate DIC, pCO2, and ΩAr using equilibrium constants from Lueker et al. (2000) and CO2SYS (van Heuven et al., 2011). For a range of pH from 7.56 to 8.30 the standard uncertainty for estimated carbonate system parameters was 8.98 to 13.43 μmol kg-1 DIC, 54.22 to 9.42 μatm pCO2, and 0.0501 to 0.223 ΩAr. These uncertainties were calculated using the ‘errors’ function in seacarb (Gattuso et al., 2020, version 3.2.13 - Accessed 6 Mar 2020) with inputs of TAest and measured pH, temperature and salinity over depth inside the kelp forest.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt; Specified by the Principal Investigator(s) &lt;p&gt;CO2SYS (Matlab) was used to estimate DIC, pCO2, and ΩAr from estimated TA and pH. The ‘errors’ function in seacarb (R) was used to estimate uncertainties.&lt;/p&gt; &lt;p&gt;BCO-DMO processing notes:&lt;/p&gt; &lt;ul&gt; &lt;li&gt;Adjusted column headers to comply with database requirements&lt;/li&gt; &lt;li&gt;Converted Timestamp to ISO format&lt;/li&gt; &lt;/ul&gt; Specified by the Principal Investigator(s) asNeeded 7.x-1.1 Biological and Chemical Oceanography Data Management Office (BCO-DMO) Unavailable 508-289-2009 WHOI MS#36 Woods Hole MA 02543 USA info@bco-dmo.org http://www.bco-dmo.org Monday - Friday 8:00am - 5:00pm For questions regarding this resource, please contact BCO-DMO via the email address provided. pointOfContact mFET and SeapHOx mFET and SeapHOx PI Supplied Instrument Name: mFET and SeapHOx PI Supplied Instrument Description:pH was directly measured (via mFET and SeapHOx) on the mooring at 6 depths (and estimated for 2 weeks at the surface based on the near-surface pH-O2 relationship). Please see metadata for kelp mooring data for additional pH details (https://www.bco-dmo.org/dataset/822549). Instrument Name: pH Sensor Instrument Short Name:pH Sensor Instrument Description: An instrument that measures the hydrogen ion activity in solutions. The overall concentration of hydrogen ions is inversely related to its pH.  The pH scale ranges from 0 to 14 and indicates whether acidic (more H+) or basic (less H+).