Mediterranean seagrass metabolic rates

[Geographic location of data collection] Mediterranean basin, seagrass meadows of Posidonia oceanica and Cymodocea nodosa in coastal regions, max. depth 17m.

[File List] datacompilation_med_seagrass_metabolic_rates_hendriks.csv, readme.txt.

[Relationship between files, if important] readme provides background information for csv datafile.

[Additional related data collected that was not included in the current data package] dissolved nutrients for author data (available upon request).

[Description of methods used for collection/generation of data] Data on metabolic rates was extracted from the literature, through a literature search (March 2020) on SCOPUS and the Web of Science using the keywords “Posidonia”, OR “Cymodocea”, OR “Seagrass”, AND “Productivity”, OR “Metabolism” and manually screened for data on metabolism in the Mediterranean basin. This database was extended with unpublished data from the authors and data from dedicated sampling campaigns in 2016 in Mallorca (Western Mediterranean) and 2017 in the Eastern basin (Crete and Cyprus). We compiled data from multiparametric sensors, and data using the benthic chambers methodology with a temporal cover from 1982 to 2019.

[Methods for processing the data] For benthic chambers, reported metabolic rates were extracted from the literature. For measurements with multiparametric sensors we used time series of dissolved oxygen (DO, in mg/L), salinity and temperature (C) measured in P. oceanica and/or C. nodosa meadows. With the time series of dissolved oxygen (DO), temperature (°C) and salinity we calculated the metabolic rates of the seagrass habitats using a modification of the model of Coloso et al., (2008) implemented in MATLAB (version 7.5. the Mathworks Inc.) explained in detail in Vaquer-Sunyer et al., (2012). Wind speed was estimated at each station for the same interval as oxygen measurements to predict k660 (air-sea gas transfer velocity for oxygen at 20º C and salinity 35) based on Kihm et al., (2010) and Cole et al., (1998). Schmidt number equations for seawater according to Wanninkhof (1992) were used for the k calculation from k660. As the cubic model equals the model proposed by Wanninkhof et al., (1999) for short-term winds this parameterization by Kihm et al., (2010) is used. Meteorological data (windspeed) for the deployment period was obtained from the Agencia Estatal de Meteorología (AEMET) for the stations in Mallorca, from the Cyprus Department of Meteorology for Cyprus sampling sites and from the Hellenic National Meteorological Service for the locations in Crete.--

[Standards and calibration information] Sensors were calibrated before each deployment; oxygen sensors (Hach LDOTM) were calibrated using the water saturated air method calibration. For validation of salinity, specific conductance calibrations were performed with 50.000uS/cm buffers. For depth measurements, pressure readings were corrected for specific conductance.

[Environmental/experimental conditions] Coastal seagrass meadows with max. 17m depth.

[Describe any quality-assurance procedures performed on the data] Negative respiration rates (oxygen production) at night for sensor deployments, were discarded as this was interpreted as an indication for the influence of lateral advection and passing of different water masses. Therefore, we trimmed the dataset to contain only measurements where this influence was not detected. Respiration rates were notated as oxygen consumption (positive values, literature reports differ in notation).

[People involved with sample collection, processing, analysis and/or submission, please specify using CREDIT roles https://casrai.org/credit/: Conceptual idea IEH and NM. Data collection in the field MW, SF, RVS, IEH, NM. Literature compilation IEH and AEM. Data curation AEM and IEH.

[Data-specific information] 1. Number of variables: 21. 2. Number of cases/rows: 151. 3. Variable List: Reference, Journal, Methodology, Year, Month, Season, Site, Region, Latitude, Longitude, Species, Temperature_C, Salinity, Depth, NCP, NCP_SD, CR, CR_SD, GPP, GPP_SD, Wind_m_s. 4. Missing data codes: Empty cell. 5. Specialized formats or other abbreviations used: C (degree Celcius), SD (Standard Deviation), m_s (Meter per second). Depth in meter. Latitude and Longitude in Decimal Degrees (DD).

The data is a compilation of information on metabolic rates of Mediterranean seagrasses obtained by two different methodologies (benthic incubations and multiparametric sensors) from published literature and data from the authors.

The Spanish Ministry of Economy and Competitiveness (Project MEDSHIFT, CGL2015-71809-P). Project RTI2018-095441-B-C21 (SUMAECO) from the Spanish Ministry of Science, Universities and Innovation. SF was supported by a “Margalida Comas” postdoctoral scholarship, funded by the Balearic Islands Government. Also funding was received from “projectes de recerca La Caixa en àrees estratègiques” (2018) through a grant to IEH at the University of the Balearic islands.

datacompilation_med_seagrass_metabolic_rates_hendriks.csv, readme.txt

Peer reviewed