Distribution of Scopoli's shearwater (Calonectris diomedea) in the Mediterranean Sea during the summer season Distance sampling surveys are extensively used to estimate the abundance of wide-ranging species but are prone to detection biases. This may be particularly acute for strip-transect protocols, which assume perfect detection. We examined this assumption by quantifying the detection probability of a declining seabird (Scopoli’s shearwater, Calonectris diomedea), with particular attention to time-of-day and observation conditions at sea. We found detection probability was negatively affected by sun glare but positively by cloud cover and considerably dropped during mid-day hours due to circadian changes in behaviour (reduced detectability while resting). This result urges for systematically assessing and correcting detection bias when using strip-transect data to derive abundance information. Here, we did so by building a detection-corrected presence-absence ensemble model and combining it with a compilation of colony sizes and locations. A Monte-Carlo simulation ensured uncertainty propagation within and across data sources. The corrected abundance map showed shearwaters were largely prevalent in the central Mediterranean, Tunisia hosting most of the population both at sea and at colonies (45% of the global population; 79% of breeding pairs). This first accurate map is an essential conservation tool, emphasizing the importance of transnational actions for such species, that know no political boundaries.

These datasets contain 4D (x, y, z, t) weekly temperature and marine heatwaves (MHW) categories estimated from the surface up to 300-m depth, at a 0.25°x0.25° horizontal grid resolution and for 4 areas of interest that are: • Area 1 (around the Madeira Islands): 30°N-35°N, 15°W-20°W • Area 2 (Tropical Pacific Ocean): 30°S-30°N, 120°E-130°W • Area 3 (Mediterranean Sea): 40°N-45°N, 15°W-20°W • Area 4 (Global): 82.875°S-89.875°N, 0.125°E-359.875°E The weekly MHW are centered on the date of the file (±3days). For the temperature reconstruction, 2 approaches have been used: - for the regional areas, the temperature has been computed with a 2 steps method: a first estimate of the vertical temperature profiles by using a machine learning approach (Multi-Layer Perceptron (MLP)) and then, a combination of this field with in situ temperature profiles observations through an optimal interpolation algorithm. The Copernicus Marine Service ARMOR3D dataset was used as the targeted temperature field for the MLP. The input data used are: • First step: ◦ SST data are from daily OSTIA analyses [from Copernicus Marine Service: SST_GLO_SST_L4_REP_OBSERVATIONS_010_011 product] interpolated over the 0.25°x0.25° targeted grid resolution; ◦ SLA data are from the Copernicus Marine Service product SEALEVEL_GLO_PHY_L4_REP_OBSERVATIONS_008_047/dataset-duacs-rep-global-merged-allsat-phy-l4 • Second step: ◦ The in situ data are from the Copernicus Marine Service In Situ TAC and contains several observations type: CTD, Argo floats, drifting buoys, moorings, marine mammals). - For the global area, the temperature comes from the Copernicus Marine Service product ARMOR3D (MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012 (https://doi.org/10.48670/moi-00052). The MHW categories are derived from the Hobday’s method [Hobday et al.,2018] for the 4 areas. Each MHW event is classified among four categories (moderate to extreme), identified in terms of multiples of the local difference between the 90th percentile and climatological values, and defined as moderate (1-2×, Category I), strong (2-3×, Category II), severe (3-4×, Category III), and extreme (>4×, Category IV). When the category is zero, this means that there is no MHW. The period 1993-2021 is used as a baseline for defining the climatology to be as close as possible to the 30-year period suggested by Hobday. This choice is motivated by the need of altimetry data to constrain the vertical temperature reconstruction, which is required for most ocean reanalyses as well, therefore the baseline period slightly differs from the one used for the 2D atlas.

Raw single-beam echosounder data archived at SISMER, acquired: - by oceanographic vessels and national equipment managed by the French Oceanographic Fleet (FOF) - by foreign oceanographic vessels in collaboration with Ifremer - by Ifremer's historic vessels (Jean Charcot, Nadir, Suroit) operated before the FOF was set up

In order to manage coastal monitoring data, Ifremer has developed the Quadrige information system which connects a database to a wide array of tools for interpreting and designing information products. Quadrige is just one element of the Water Information System (SIE) www.eaufrance.fr and, as such, contributes toward the work of the French National Adminitrative Service for Water-Related Data (SANDRE) www.sandre.eaufrance.fr. The main aim of the Quadrige thematic databank is to manage and enhance data from coastal observation and monitoring networks. On a national level, Quadrige is today designated by the French Environment Ministry as the definitive information system for coastal waters, and the tool is therefore common to all of those working in the marine environment sector. The Quadrige databank is composed of data from the Quadrige database and products described or made available on the Envlit website. The Quadrige database contains results about most physical, chemical and biological environmental description parameters. The first data for example dates back to 1974 for the parameters relating to general water quality and contaminants, 1987 for phytoplankton and phycotoxins, 1989 for microbiology, from the early 2000s for the benthic zone. The data is permanently being updated. In Quadrige, an observation location is a geographical location where observations, measurements and/or samples will be taken. These locations can be located in a unique way thanks to their appearance on a map (polygon, line or point). A measurement location can be used by multiple programmes.