The Mytilobs network datasets

The Mytilobs network, carried out by IFREMER (French Research Institute for Exploitation of the Sea), is a national network dedicated to building long-term physiological variations time series of blue mussels (Mytilus edulis), across a large spatial scale. This observation network, initially designed to survey production yields, also provides valuable data to track environmental variations of coastal ecosystems. Mussels exhibit high phenotypic plasticity in response to environmental variations. Collection of data describing phenotypic variations, over an extended period, reveals small-scale climate and habitat variations. With its broad deployment across time and space, the data produced under Mytilobs will be useful for the establishment of a baseline condition when studying the effect of a perturbation affecting an ecosystem’s functioning. Finally, the monitoring of mussel biometric traits and mortality was coupled with high-frequency measurements of salinity, temperature, and sea level, complementing this multi-layer observational framework.

The Mytilobs network, carried out by IFREMER (French Research Institute for Exploitation of the Sea), is a national network dedicated to building long-term physiological variations time series of blue mussels (Mytilus edulis), across a large spatial scale. This observation network, initially designed to survey production yields, also provides valuable data to track environmental variations of coastal ecosystems. Mussels exhibit high phenotypic plasticity in response to environmental variations. Collection of data describing phenotypic variations, over an extended period, reveals small-scale climate and habitat variations. With its broad deployment across time and space, the data produced under Mytilobs will be useful for the establishment of a baseline condition when studying the effect of a perturbation affecting an ecosystem’s functioning. Finally, the monitoring of mussel biometric traits and mortality was coupled with high-frequency measurements of salinity, temperature, and sea level, complementing this multi-layer observational framework.

Disciplines

Fisheries and aquaculture

Location

51.780013N, 42.842066S, 6.294343E, -5.746672W

Devices

The survey strategy was drafted from the RESCO protocol which is the national benchmark survey strategy for Pacific oysters, implemented since 1993. For mussels, the Mytilobs monitoring program began in 2014 and has been conducted since then on a yearly basis. Each year, one batch of mussels of less than one year old is taken from the same cohort of spat and is distributed simultaneously to different network locations (5 to 9 locations) along the Atlantic and Channel coasts of France. Mussels were taken from a wild spatfall, occuring in spring, in the Breton Sound (inshore part of the Bay of Biscay, between the Isle of Ré and the French coast). Mussels from this area are known to belong to the M. edulis species. For each site, around 14 small plastic meshed bags containing 120 to 200 mussels were deployed in autumn of the year before the annual survey. After a 2-month acclimatization period, annual monitoring began in January. Every month, field operators counted dead and alive individuals of the same single bag and systematically removed empty shells of dead mussels. Another bag was taken back to the laboratory for measurements. Concurrently, field agents immersed a CTD data logger to measure and record salinity, pressure and temperature.

SEANOE deposit contains 3 datasets in csv format. The mortality dataset contains observations related to mortalities obtained from the successive counts of the mussels contained in one meshed bag over the course of the annual survey. For this dataset, one line corresponds to observations taken for one batch of mussels, on one site and on one day. The variable “cumulative mortality” describes the proportion of dead individuals of the initial batch that died from unknown reasons. It has been calculated following Normand et al. 2022, but cannot be directly inferred from variables “alive_nb” and “dead_nb” because “alive_nb” could have fluctuated over the annual survey due to whelk predation or sampling for other research programs.

The columns of the mortality dataset are :

  • year (the id of the annual survey [discrete variable: 9 levels from 2014 to 2022]),
  • location (the id of the location [discrete variable: 9 levels]),
  • latitude (the latitude of the location, in WGS 84 decimal degrees [continuous variable]),
  • longitude (the longitude of the location, in WGS 84 decimal degrees [continuous variable]),
  • day (the day the sample was collected [continuous variable]),
  • alive_nb (the number of live individuals in the bag [continuous variable]),
  • dead_nb (the number of dead individuals in the bag [continuous variable]),
  • cum_morta (cumulative mortality, in percent [continuous variable]).

The growth dataset contains observations related to mussel dimensions obtained by measurements of 30 to 50 individuals in the laboratory. For this dataset, one line corresponds to observations made on one mussel, from one batch, on one site and on one day.

The columns of the growth dataset are :

  • year (the id of the annual survey [discrete variable: 9 levels from 2014 to 2022]),
  • location (the id of the location [discrete variable: 9 levels]),
  • latitude (the latitude of the location, in WGS 84 decimal degrees [continuous variable]),
  • longitude (the longitude of the location, in WGS 84 decimal degrees [continuous variable]),
  • day (the day the sample was collected on [continuous variable]),
  • length (the length of the mussel, in millimeters [continuous variable]),
  • width (the width of the mussel, in millimeters [continuous variable]),
  • thickness (the thickness of the mussel, in millimeters [continuous variable]),
  • totwght (the total weight of the mussel, closed, including the weight of the water retained in the mantel cavity, in grams [continuous variable]),
  • shellwght (the weight of the two valves after desiccation, in grams [continuous variable]),
  • softwght (the weight of the soft tissues after desiccation in grams [continuous variable]).

The environment dataset contains the data collected by the probes when submerged. The frequency is of one record every 15 minutes.

The columns of the environment dataset are :

  • time (year, month, day, hour, minute and second. Every data was recorded, in TU [continuous variable]),
  • location (the id of the location [discrete variable: 9 levels]),
  • latitude (the latitude of the location, in WGS 84 decimal degrees [continuous variable]),
  • longitude (the longitude of the location, in WGS 84 decimal degrees [continuous variable]),
  • temp (seawater temperature in degree Celsius [continuous variable]),
  • wght (seawater height above the probe, in meters [continuous variable]),
  • sal (seawater salinity in grams per liter [continuous variable]).

Observations were collected for the following Locations and Years:

  • Agon : 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022
  • Aiguillon : 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022
  • Boyard : 2015, 2016, 2017, 2018, 2019, 2022
  • Filière : 2014, 2015, 2016, 2017, 2018, 2019, 2020
  • Maison Blanche : 2016, 2017, 2018, 2019, 2020, 2021
  • Pont-Mahé : 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022
  • Roulières : 2015, 2016, 2017, 2018
  • Vivier : 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022
  • Yves : 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021

Reference : Normand Julien, Benabdelmouna Abdellah, Louis Wilfried, Grizon James (2022). MYTILOBS Campagne 2020-2021. Réseau d'observation des moules d'élevage sur la côte Atlantique et dans la Manche. Edition 2022. RST ODE/UL/LERN/22-05. https://archimer.ifremer.fr/doc/00834/94562/

Data

FileSizeFormatProcessingAccess
growth dataset
1 MoTEXTQuality controlled data
mortality dataset
40 KoTEXTQuality controlled data
environment dataset
111 MoTEXTQuality controlled data
How to cite
Normand Julien, Lemesle Stephanie, Grizon James, Louis Wilfried (2023). The Mytilobs network datasets. SEANOE. https://doi.org/10.17882/87816

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