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  • A habitat distribution modelling approach was used to model the spatial distribution of kelp forests within the Bay of Morlaix (France). Lineage: Data represents presence-absence prediction of kelp forest. Biological ground truth data were integrated with high resolution environmental datasets to develop statistical model that accurately predict the structure of Laminaria forests within the Bay of Morlaix. As a direct management output, high-resolution map (25 m2 grid) was produced. Type of occurrence data used: Forest occurrence (presence or absence),representative across the full range of environmental gradients, was sampled through a combination of underwater video surveys and direct diver observations. Environmental covariates/explanatory variables: The probability of kelp forest occurrence and its standard deviation was predicted using an additive multiple regression of water depth, light availability, significant wave height and sediment proximity. Algorithm/modelling approach: Kelp biological response (presence/absence) was estimated using Generalized Additive Models (GAM)

  • A habitat distribution modelling approach was used to model the spatial distribution of the two main species of kelp forests along Molène archipelagos (France). Lineage: Data represents percentage cover of kelp forest as well as biomass of the 2 main species: L. digitata and L. hyperoborea. The used approach consists in firstly establishing surveys and appropriate processing methods in order to provide a detailed underwater topography of the area and to accurately delineate hard substrates (bedrock) potentially colonized by kelp. Secondly, a habitat suitability model is fitted for each species on some carefully selected field stations, measuring kelp presence/absence and biomass. Predictive maps are produced, based on hard substrate areas previously delineated. Type of occurrence data used: In situ data were acquired in the period from late summer to early autumn. Information on the presence/absence of Laminaria species was acquired by towing a high definition video. Species-specific biomass were sampled at low tide for the intertidal areas and by Scuba diving for the sub-tidal areas. Environmental covariates/explanatory variables: Presence–absence of L. digitata distribution was best determined through the combined effects of depth, sediment proximity along current direction, benthic position index (BPI), immersion rate and winter temperature. The sub-model for biomass of L. digitata where present, was predicted using the additional contribution of several variables, with light being the most important (55.62% of deviance explained) and its interactions withwave exposure and spring temperature. Total suspended matter contributed little and only then through the interaction with light. The best model that explained 78.89% of deviance for the presence of L. hyperborea included depth, winter temperature, sediment proximity along current direction and BPI Biomass where L. hyperborea was present was mainly modeled by the same predictors as presence/absence Algorithm/modelling approach: Kelp biological response (presence/absence or biomass) was estimated using Generalized Additive Models (GAM)

  • A habitat distribution modelling approach was used to model the spatial distribution of the two main species of kelp forests along Molène archipelagos (France). Lineage: Data represents percentage cover of kelp forest as well as biomass of the 2 main species: L. digitata and L. hyperoborea. The used approach consists in firstly establishing surveys and appropriate processing methods in order to provide a detailed underwater topography of the area and to accurately delineate hard substrates (bedrock) potentially colonized by kelp. Secondly, a habitat suitability model is fitted for each species on some carefully selected field stations, measuring kelp presence/absence and biomass. Predictive maps are produced, based on hard substrate areas previously delineated. Type of occurrence data used: In situ data were acquired in the period from late summer to early autumn. Information on the presence/absence of Laminaria species was acquired by towing a high definition video. Species-specific biomass were sampled at low tide for the intertidal areas and by Scuba diving for the sub-tidal areas. Environmental covariates/explanatory variables: Presence–absence of L. digitata distribution was best determined through the combined effects of depth, sediment proximity along current direction, benthic position index (BPI), immersion rate and winter temperature. The sub-model for biomass of L. digitata where present, was predicted using the additional contribution of several variables, with light being the most important (55.62% of deviance explained) and its interactions withwave exposure and spring temperature. Total suspended matter contributed little and only then through the interaction with light. The best model that explained 78.89% of deviance for the presence of L. hyperborea included depth, winter temperature, sediment proximity along current direction and BPI Biomass where L. hyperborea was present was mainly modeled by the same predictors as presence/absence Algorithm/modelling approach: Kelp biological response (presence/absence or biomass) was estimated using Generalized Additive Models (GAM)

  • A habitat distribution modelling approach was used to model the spatial distribution of kelp forests along Britanny’s coast (France). Lineage: Data represents percentage cover of kelp forest. The Habitat model was generated from kelp forest presence/absence determined from acoustic surveys of laminarial algae. KdPar, sea surface temperature, depth and current speed were used as predictive variables. Habitat model represents only the areas where kelp forests may occur, not the areas confirmed in field observations. Type of occurrence data used: Acoustic surveys of kelp forest were carried out in spring 2005-2006-2007 in 10 study areas along the coast of Brittany. A specific algorithm was developed for automatic detection of presence/absence of kelp forest from the acoustic data. Environmental covariates/explanatory variables: Covariates used were: - KPAR (proxy of light availability): spatial resolution 1km, averaged values (temporal coverage 7 years -1998-2004-, temporal resolution 1 week) - Sea surface temperature: spatial resolution 1km, averaged values (temporal coverage 20 years, temporal resolution 1 week) - Tidal current velocity: spatial resolution 300m, maximum values (temporal coverage one mean spring tide) Algorithm/modelling approach: A stepwise multiple regression with a backward selection of variables was used to predict values of kelp frequency (%)For EMODNet Seabed habitats portal, the dataset was scaled between 0-1. Original data is 0-100.