9228291d-fd7b-4a16-a7c3-b6b80d341257 Vaz Sandrine IFREMER Station de Sète - Avenue Jean Monnet - CS 30171 - 34203 Sète Cedex Station de Sète - Avenue Jean Monnet - CS 30171 - 34203 Sète Cedex Station de Sète - Avenue Jean Monnet - CS 30171 - 34203 Sète Cedex Sete France France France Sandrine.Vaz@ifremer.fr pointOfContact 2025-05-15T23:20:25.31883Z ISO 19115-3 - SEXTANT ISO 19115-3 - SEXTANT 1.0 WGS 84 (EPSG:4326) EPSG 8.6 Mediterranean process-driven seafloor habitat sensitivity Mediterranean process-driven seafloor habitat sensitivity Mediterranean process-driven seafloor habitat sensitivity 2016-09-20T00:00:00 publication DOI:10.12770/9228291d-fd7b-4a16-a7c3-b6b80d341257 Process-driven seafloor habitat sensitivity (PDS) has been defined from the method developed by Kostylev and Hannah (2007), which takes into account physical disturbances and food availability as structuring factors for benthic communities. It is a conceptual model, relating species’ life history traits to environmental properties. Physical environment maps have been converted into a map of benthic habitat types, each supporting species communities with specific sensitivity to human pressures. It is based on two axes of selected environmental forces. The "Disturbance" (Dist) axis reflects the magnitude of change (destruction) of habitats (i.e. the stability through time of habitats), only due to natural processes influencing the seabed and which are responsible for the selection of life history traits. The "Scope for Growth" (SfG) axis takes into account environmental stresses inducing a physiological cost to organisms and limiting their growth and reproduction potential. This axis estimates the remaining energy available for growth and reproduction of a species (the energy spent on adapting itself to the environment being already taken into account). It can be related to the metabolic theory of the ecology. The process-driven sensitivity (PDS) can be seen as a risk map that combines the two previous axes and reflects the main ecological characteristics of the benthic habitats regarding natural processes. Areas with low disturbance are areas with a naturally low reworking of the sediment, allowing the establishment of a rich sessile epifauna community, with K-strategy species. Areas with low SfG means that the environmental factors, even though there are not limiting, are in lower values, i.e. that it imposes a cost for species to live. In areas combining low disturbance and low SfG, big suspension-feeder species with long life and slow growth can often be found: these species are more vulnerable in case of added disturbance. Ifremer IFREMER Station de Sète - Avenue Jean Monnet - CS 30171 - 34203 Sète Cedex Station de Sète - Avenue Jean Monnet - CS 30171 - 34203 Sète Cedex Station de Sète - Avenue Jean Monnet - CS 30171 - 34203 Sète Cedex Sete France France France publisher Vaz Sandrine IFREMER Station de Sète - Avenue Jean Monnet - CS 30171 - 34203 Sète Cedex Station de Sète - Avenue Jean Monnet - CS 30171 - 34203 Sète Cedex Station de Sète - Avenue Jean Monnet - CS 30171 - 34203 Sète Cedex Sete France France France Sandrine.Vaz@ifremer.fr author Llapasset Margaux AMOP author https://sextant.ifremer.fr/geonetwork/srv/api/records/9228291d-fd7b-4a16-a7c3-b6b80d341257/attachments/PDSrisk_med.jpg /Biological Environment/Habitats /Biological Environment/Habitats /Milieu biologique/Habitats Thèmes Sextant Thèmes Sextant 2024-09-27 publication geonetwork.thesaurus.local.theme.sextant-theme Habitats and biotopes Habitats and biotopes Habitats et biotopes GEMET - INSPIRE themes, version 1.0 GEMET - INSPIRE themes, version 1.0 2018-07-27T14:39:48 publication geonetwork.thesaurus.external.theme.httpinspireeceuropaeutheme-theme /Metropolitan France/Western Mediterranean /Metropolitan France/Western Mediterranean /Métropole/Méditerranée occidentale Sous-regions marines Sous-regions marines 2024-07-26 publication geonetwork.thesaurus.local.place.dcsmm.area /Environmental Status/Geology /Environmental Status/Geology /Etat du Milieu/Géologie Research Research Recherche /Environmental Status/Habitats /Environmental Status/Habitats /Etat du Milieu/Habitats /Environmental Status/Hydrodynamics /Environmental Status/Hydrodynamics /Etat du Milieu/Hydrodynamique Thématiques - SIMM Thématiques - SIMM 2024-07-26 publication geonetwork.thesaurus.local.theme.simm.thematiques CC-BY-NC-SA (Creative Commons - Attribution, No commercial usage, Sharing under the same conditions) CC-BY-NC-SA (Creative Commons - Attribution, No commercial usage, Sharing under the same conditions) CC-BY-NC-SA (Creative Commons - Attribution, Pas d’utilisation commerciale, Partage dans les mêmes conditions) grid utf8 environment -5.1826 35.9502 30.4941 45.1263 1998-01-01T00:00:00 2014-12-31T00:00:00 http://marine.copernicus.eu/ http://marine.copernicus.eu/ WWW:LINK MyOcean https://doi.org/10.12770/18ff0d48-b203-4a65-94a9-5fd8b0ec35f6 https://doi.org/10.12770/18ff0d48-b203-4a65-94a9-5fd8b0ec35f6 WWW:LINK EMODnet Bathymetry Consortium (2018): EMODnet Digital Bathymetry (DTM). https://www.niot.res.in/COAT/coat_pdf/CHAP%20III%20-%20Equation%20of%20State.pdf https://www.niot.res.in/COAT/coat_pdf/CHAP%20III%20-%20Equation%20of%20State.pdf WWW:LINK Density of sea water at high pressure from temperature and salinity http://ocean.jfe-advantech.co.jp/english/sensor/img/do.pdf http://ocean.jfe-advantech.co.jp/english/sensor/img/do.pdf WWW:LINK The equation of dissolved oxygen concentration in the water https://doi.org/10.1016/0011-7471(70)90037-9 https://doi.org/10.1016/0011-7471(70)90037-9 WWW:LINK R.F. Weiss, 1970. The solubility of nitrogen, oxygen and argon in water and seawater, Deep Sea Research and Oceanographic Abstracts, Volume 17, Issue 4, 1970, Pages 721-735, ISSN 0011-7471, https://sextant.ifremer.fr/services/wms/galion https://sextant.ifremer.fr/services/wms/galion OGC:WMS Habitat_sensitivity_sfg Scope for Growth Scope for Growth https://sextant.ifremer.fr/services/wms/galion https://sextant.ifremer.fr/services/wms/galion OGC:WMS Habitat_sensitivity_risk Risk Risk https://sextant.ifremer.fr/services/wms/galion https://sextant.ifremer.fr/services/wms/galion OGC:WMS Habitat_sensitivity_dist Disturbance Disturbance COPYFILE Scope for Growth COPYFILE Risk COPYFILE Disturbance https://doi.org/10.12770/9228291d-fd7b-4a16-a7c3-b6b80d341257 https://doi.org/10.12770/9228291d-fd7b-4a16-a7c3-b6b80d341257 DOI DOI of the dataset Disturbance was computed by combining: - Seabed shear stress (in N.m-2) based on Mediterranean modelled seabed shear stress (90th percentile) - Sediment average grain size based on Modelled seabed sediment average grain size in the Mediterranean Scope for Growth factors involved are: - Surface Chlorophyll a (Chl.a ; mg.m-3) Computed as maximum concentration of surface chlorophyll obtained from monthly satellite observations from 1998 to 2014 (MyOcean (http://marine.copernicus.eu/): "OCEANCOLOUR_MED_CHL_L4_REP_OBSERVATIONS_009_078"). - Bathymetry EMODnet Bathymetry Consortium (2018): EMODnet Digital Bathymetry (DTM). https://doi.org/10.12770/18ff0d48-b203-4a65-94a9-5fd8b0ec35f6 - Stratification Here, stratification was considered as the average absolute difference between surface and 30 (± 5) m depth density over 20 years. Salinity and temperature data used to compute density cover the 1994-2014 period and were from monthly model predictions (MyOcean (http://marine.copernicus.eu/): "MEDSEA_REANA-LYSIS_PHYS_006_004"). High Pressure International Equation of State of Seawater (1980) was used to compute Sea water Density. The description of the computation of density of sea water at high pressure from temperature and salinity is available at https://www.niot.res.in/COAT/coat_pdf/CHAP%20III%20-%20Equation%20of%20State.pdf. - Bottom temperatures from 1994 à 2014 from monthly model predictions (MyOcean (http://marine.copernicus.eu/): "MEDSEA_REANA-LYSIS_PHYS_006_004") were used to compute average bottom temperature (BT), inter- and intra-annual bottom temperature standard deviations (Ti and Ta respectively). Ti, is the standard deviation between yearly averages and Ta, is the standard deviation between months averages. - Average bottom disolved oxygen concentration (O ; mmol.m-3) from1999 to 2014, from monthly model predictions (MyOcean (http://marine.copernicus.eu/): "MEDSEA_REANALYSIS_BIO_006_008"). This was further transformed in % disolved oxygen using the equation of dissolved oxygen concentration in the water (after Weiss, 1970) found in JFE Advantech Co.,Ltd., Ocean and River Instruments Division, 2015 (http://ocean.jfe-advantech.co.jp/english/sensor/img/do.pdf) R.F. Weiss, 1970. The solubility of nitrogen, oxygen and argon in water and seawater, Deep Sea Research and Oceanographic Abstracts, Volume 17, Issue 4, 1970, Pages 721-735, ISSN 0011-7471, https://doi.org/10.1016/0011-7471(70)90037-9. The critical shear stress is the the minimal critical current needed to initiate sedimentary movement and was computed as a function of the log grain size (X, gain size in mm) and derived from the Hjulström diagram empirically approximated by the equation: Y= -0.0272 × X^4- 0.0905 ×X^3+ 0.2411 × X^2+ 0.4691×X+1.8761 and critical shear stress = (10^y)/100 Seabed shear stress (in N.m-2) was used to describe friction intensity. All layers were limited to the same extent and rasterised at the same resolution (by bilinear interpolation). The disturbance was then defined as Dist = log(friction intensity/ critical shear stress) followed by centering, standardisation and rescaling between 0-1. The critical shear stress is the the minimal critical current needed to initiate sedimentary movement and was computed as a function of the log grain size (X, gain size in mm) and derived from the Hjulström diagram empirically approximated by the equation: Y= -0.0272 × X^4- 0.0905 ×X^3+ 0.2411 × X^2+ 0.4691×X+1.8761 and critical shear stress = (10^y)/100 Seabed shear stress (in N.m-2) was used to describe friction intensity. All layers were limited to the same extent and rasterised at the same resolution (by bilinear interpolation). The disturbance was then defined as Dist = log(friction intensity/ critical shear stress) followed by centering, standardisation and rescaling between 0-1. Disturbance (Dist) computation Disturbance (Dist) computation Before compiling the different environmental layers to define the scope for growth, data were recoded between 0 and 1. Since this reclassification is sensible to the geographical extent of the studied area, all layers were limited to the same extent and rasterised at the same resolution (by bilinear interpolation). The equation to calculate scope for growth is defined as: SfG= (Fa+Temp+O)/3 with Fa, food availability, Temp, temperature impact, O, %dissolved oxygen. SfG was then centered, standardised and rescaled between 0-1 Food availability was computed as: Fa = F - stratification F=log(Chl.a/bathymetry), followed by centering, standardisation and rescaling between 0-1 and stratification was also centered, standardised and rescaled between 0-1 before computing Fa. Once computed Fa was also centered, standardised and rescaled between 0-1. Temperature impact was computed as Temp= BT-Ta-Ti, followed by centering, standardisation and rescaling between 0-1 %dissolved oxygen was expressed as ratio instead of %. Before compiling the different environmental layers to define the scope for growth, data were recoded between 0 and 1. Since this reclassification is sensible to the geographical extent of the studied area, all layers were limited to the same extent and rasterised at the same resolution (by bilinear interpolation). The equation to calculate scope for growth is defined as: SfG= (Fa+Temp+O)/3 with Fa, food availability, Temp, temperature impact, O, %dissolved oxygen. SfG was then centered, standardised and rescaled between 0-1 Food availability was computed as: Fa = F - stratification F=log(Chl.a/bathymetry), followed by centering, standardisation and rescaling between 0-1 and stratification was also centered, standardised and rescaled between 0-1 before computing Fa. Once computed Fa was also centered, standardised and rescaled between 0-1. Temperature impact was computed as Temp= BT-Ta-Ti, followed by centering, standardisation and rescaling between 0-1 %dissolved oxygen was expressed as ratio instead of %. Scope for Growth (SfG) computation Scope for Growth (SfG) computation The process-driven sensitivity (PDS) can be seen as a risk map that combines the two previous axes, according the formula : risk =sqrt[(1-SfG)²+(1-Dist)²] followed by centering, standardisation and rescaling between 0-1 The process-driven sensitivity (PDS) can be seen as a risk map that combines the two previous axes, according the formula : risk =sqrt[(1-SfG)²+(1-Dist)²] followed by centering, standardisation and rescaling between 0-1 Process-driven sensitivity (PDS) computation Process-driven sensitivity (PDS) computation