Marine protected areas (MPAs) provide biodiversity conservation benefits in a range of marine habitats. Many protected areas are established and governed through top-down or shared governance arrangements, yet little is known about how these governance strategies compare in terms of the protection benefits they provide to MPAs globally. Using an extensive data set of MPA conditions, we developed a set of Bayesian hierarchical models to understand the role of shared governance versus federal governance on reef fish biomass from 218 global MPAs. We find greater reef fish biomass benefits in MPAs with shared governance than with top-down, or federal arrangements. We also find greater benefits in older MPAs and MPAs farther away from shore. Our results highlight the fundamental importance of multi-stakeholder participation for improving conservation outcomes, representing an important conservation opportunity for new or existing MPAs. 

This pilot project aimed at setting a baseline of eDNA integration with other techniques/end users communities, that can be applied on fixed monitoring points such as cabled observatories. It comprises several aspects: a - monitoring capability aspects on the integration of multiple independent datasets (images, sounds, environmental parameters with molecular genetics approaches), methodological for eDNA (time point sampling using Niskin bottles + filtration vs. autonomous sampler over 24 hours’ period), and use of time-series as a benchmark for comparison. b - methodological aspects to expand the use of eDNA to evaluate the biodiversity monitoring efficiency in a coastal technological hub. The use of different primer combinations would provide feedback on hidden biodiversity components never measured with present sensor assets. c - Validation aspects on the pathway or the creation of in-situ operating eco-genomic sensors: Automated eDNA sampler coupled with time point sampling could be redefined in terms of number (more sampling throughout the day) or filtering larger amounts of water. Comparisons in monitoring capabilities by manual sampling and remotely scheduled filtering to be used as a benchmark to set optimum volumes and frequency of filtering. d - Data processing and statistic elaboration aspects: extract (prior) information from the long time series of OBSEA images, abiotic measurements, and how eDNA can complement the monitoring process, to test hypotheses and cause/effect relationships associated with in situ manipulations.

Rapid changes in ocean circulation and climate have been observed in marine-sediment and ice cores over the last glacial period and deglaciation, highlighting the non-linear character of the climate system and underlining the possibility of rapid climate shifts in response to anthropogenic greenhouse gas forcing. To date, these rapid changes in climate and ocean circulation are still not fully explained. One obstacle hindering progress in our understanding of the interactions between past ocean circulation and climate changes is the difficulty of accurately dating marine cores. Here, we present a set of 92 marine sediment cores from the Atlantic Ocean for which we have established age-depth models that are consistent with the Greenland GICC05 ice core chronology, and computed the associated dating uncertainties, using a new deposition modeling technique. This is the first set of consistently dated marine sediment cores enabling paleoclimate scientists to evaluate leads/lags between circulation and climate changes over vast regions of the Atlantic Ocean. Moreover, this data set is of direct use in paleoclimate modeling studies.

The Pélagiques Gascogne (PELGAS, Doray et al., 2000) integrated survey aims at assessing the biomass of small pelagic fish and monitoring and studying the dynamics and diversity of the Bay of Biscay pelagic ecosystem in springtime. PELGAS has been conducted within the EU Common Fisheries Policy Data Collection Framework and Ifremer’s Fisheries Information System. The PELGAS survey model has allowed for the establishment of a long-term time-series of spatially-explicit data of the Bay of Biscay pelagic ecosystem since the year 2000. Main sampled components of the targeted ecosystem are: hydrology, phytoplankton, mesozooplankton, fish and megafauna (cetacean and seabirds). This dataset presents gridded maps of standard pelagic ecosystem parameters collected in the main sampled components during the PELGAS survey. Ecosystem parameters were mapped on a 15km x 15km grid by applying a block averaging procedure (Petitgas et al., 2009, 2014). The dataset also includes the ecologically meaningful survey dates proposed by Huret et al. (2017), mapped on the same grid. Details on survey protocols and data processing methodologies can be found in Doray et al., (2014, 2017a). This dataset was used in Authier et al., 2017; Doray et al., 2017b, 2017c, 2017a; Huret et al., 2017; Petitgas et al., 2017.

To assess the pressures exerted by scientific research at the Lucky Strike vent field, we compiled a comprehensive inventory of all research activities conducted from 1992 to 2024. These activities included sampling, in situ measurements, mooring deployments, submersible dives, and litter deposition on the seafloor. The type of sampling activities was identified (i.e., biological, rock, sediment, fluid) to evaluate their respective pressures. Litter assessments focused primarily on metallic deadweights, which are currently needed for submersible operations and mooring deployments. These include shuttles —a container deployed to facilitate instrumentation deployment/recovery and sample storage during dives— as well as instrumentation platforms such as the Seamon monitoring nodes, moorings and surface buoy. A census allowed an exhautive list of all available samples, moorings, submersible navigations and litter deposited on the seafloor. In addition, an interactive map allow the visualisation of all these activites, enabling filtration upon the year and type of activity. Rasters for all these activities are also made available (TIF) to be able to import these in GIS projects. In addition, R scripts are joined to analyse these data and compute an Integrated Research Pressure Index (IRPI) and the resulting database is made available in the repository. Temporal and spatial evolution analysis of the IRPI is also available in the scripts. A bibliometric database compiling all publications related to Lucky Strike until 2023 is also available along with a R script to analyse the temporal evolution of publications related to research efforts (IRPI).

Global mean sea level is an integral of changes occurring in the climate system in response to unforced climate variability as well as natural and anthropogenic forcing factors. Its temporal evolution allows detecting changes (e.g., acceleration) in one or more components. Study of the sea level budget provides constraints on missing or poorly known contributions, such as the unsurveyed deep ocean or the still uncertain land water component. In the context of the World Climate Research Programme Grand Challenge entitled “Regional Sea Level and Coastal Impacts”, an international effort involving the sea level community worldwide has been recently initiated with the objective of assessing the various data sets used to estimate components of the sea level budget during the altimetry era (1993 to present). These data sets are based on the combination of a broad range of space-based and in situ observations, model estimates and algorithms. Evaluating their quality, quantifying uncertainties and identifying sources of discrepancies between component estimates is extremely useful for various applications in climate research. This effort involves several tens of scientists from about sixty research teams/institutions worldwide (www.wcrp-climate.org/grand-challenges/gc-sea-level). The results presented in this paper are a synthesis of the first assessment performed during 2017-2018. We present estimates of the altimetry-based global mean sea level (average rate of 3.1 +/- 0.3 mm/yr and acceleration of 0.1 mm/yr2 over 1993-present), as well as of the different components of the sea level budget. We further examine closure of the sea level budget, comparing the observed global mean sea level with the sum of components. Ocean thermal expansion, glaciers, Greenland and Antarctica contribute by 42%, 21%, 15% and 8% to the global mean sea level over the 1993-present. We also study the sea level budget over 2005-present, using GRACE-based ocean mass estimates instead of sum of individual mass components. Results show closure of the sea level budget within 0.3 mm/yr. Substantial uncertainty remains for the land water storage component, as shown in examining individual mass contributions to sea level.

Here, we provide plankton image data that was sorted with the web applications EcoTaxa and MorphoCluster. The data set was used for image classification tasks as described in Schröder et. al (in preparation) and does not include any geospatial or temporal meta-data. Plankton was imaged using the Underwater Vision Profiler 5 (Picheral et al. 2010) in various regions of the world's oceans between 2012-10-24 and 2017-08-08. This data publication consists of an archive containing  "training.csv" (list of 392k training images for classification, validated using EcoTaxa), "validation.csv" (list of 196k validation images for classification, validated using EcoTaxa), "unlabeld.csv" (list of 1M unlabeled images), "morphocluster.csv" (1.2M objects validated using MorphoCluster, a subset of "unlabeled.csv" and "validation.csv") and the image files themselves. The CSV files each contain the columns "object_id" (a unique ID), "image_fn" (the relative filename), and "label" (the assigned name). The training and validation sets were sorted into 65 classes using the web application EcoTaxa (http://ecotaxa.obs-vlfr.fr). This data shows a severe class imbalance; the 10% most populated classes contain more than 80% of the objects and the class sizes span four orders of magnitude. The validation set and a set of additional 1M unlabeled images were sorted during the first trial of MorphoCluster (https://github.com/morphocluster). The images in this data set were sampled during RV Meteor cruises M92, M93, M96, M97, M98, M105, M106, M107, M108, M116, M119, M121, M130, M131, M135, M136, M137 and M138, during RV Maria S Merian cruises MSM22, MSM23, MSM40 and MSM49, during the RV Polarstern cruise PS88b and during the FLUXES1 experiment with RV Sarmiento de Gamboa. The following people have contributed to the sorting of the image data on EcoTaxa: Rainer Kiko, Tristan Biard, Benjamin Blanc, Svenja Christiansen, Justine Courboules, Charlotte Eich, Jannik Faustmann, Christine Gawinski, Augustin Lafond, Aakash Panchal, Marc Picheral, Akanksha Singh and Helena Hauss In Schröder et al. (in preparation), the training set serves as a source for knowledge transfer in the training of the feature extractor. The classification using MorphoCluster was conducted by Rainer Kiko. Used labels are operational and not yet matched to respective EcoTaxa classes.

The upper branch of the Atlantic Meridional Overturning Circulation (AMOC) plays a critical role in ocean circulation and climate change, yet its variability during the last glacial period is poorly documented. Here, we investigate the northward-flowing Glacial Eastern Boundary Current (GEBC) in the North Atlantic, known today as the European Slope Current, and representing the easternmost portion of the upper branch of the AMOC. Based on flow speed (sortable silt, XRF) and radiogenic/stable isotopic records, we show that Dansgaard-Oeschger (D/O) interstadials (stadials) correspond to a faster (weaker) GEBC during the ~50-15 ka period. This, by analogy to present-day conditions, suggests enhanced (reduced) strength of the subpolar gyre and, by extension, of northern-sourced water production and AMOC during D-O interstadials (stadials). Concomitant fluctuations of both the European Ice Sheet and the GEBC between ~30 and 17 ka suggest an active role of the upper branch of AMOC in the poleward transport of heat and freshwater to the northern North Atlantic, with direct impacts on deep water formation and AMOC strength. Our GEBC reconstruction is the first physical (non-chemical) record documenting dynamic upper AMOC variability at high resolution in the eastern basin of the North Atlantic. Together with the deep North Atlantic records of northern-sourced water export, they confirm the central role of the AMOC in the generation of abrupt climate changes.

Current meter Nortek Aquadopp data. Data for 2 current meter deployments of NORTEK Aquadopp instruments at the Rainbow Massif. Instruments where kindly provided by the Parc National d'Instrumentation Océanographique de l'INSU. Data is provided in 2 sets of files, corresponding to deployments AES_C01 and AES_C02. The instruments where installed in a broadband OBS (BBOBS) from the INSU OBS National Park.  Deployment AES_C02 includes two lowerings (see information below, indicated as deployments AES_C02A and AES_C02B). AES_C01 with current meter serial number : A6L6094 (head) AQD11097 (Hardware), deployed on BB03 with deployment AES_B01_BB3. Bottom position: 36.23544°N, 33.91036°W, 2505 m waterdepth. AES_C02_A with current meter serial number : A6L6127 (head) AQD11247 (Hardware), deployed on BB02, deployment AES_B03_BB02. Bottom position: 36.22233°N, 33.87996°W, 1950m waterdepth. AES_C02_B with current meter serial number : A6L6127 (head) AQD11247 (Hardware), deployed on BB02, deployment AES_B06_BB02. Bottom position: 36.2020833°N, 33.8118833°W, 2557m waterdepth. Data are provided in two zipped files that include: AESC0101.aqd: Aquadopp binary file AESC0101.dat: Ascii table of data - full record AESC0101.dia: Aescii data file - partial record AESC0101.hdr: Instrument parameter information and description of columns in AESC0101.dat AESC0101.mat: Matlab file corresponding to AESC0101.dat AESC0101.ssl: Log of instrument (error and info messages) In addition to several instrument logs: AESC01_start.log AES_C01_deploy.dep AESC01_start.dep The ascii file contains 27 columns as follows:  1   Month                            (1-12)  2   Day                              (1-31)  3   Year  4   Hour                             (0-23)  5   Minute                           (0-59)  6   Second                           (0-59)  7   Error code  8   Status code  9   Velocity (Beam1|X|East)          (m/s) 10   Velocity (Beam2|Y|North)         (m/s) 11   Velocity (Beam3|Z|Up)            (m/s) 12   Amplitude (Beam1)                (counts) 13   Amplitude (Beam2)                (counts) 14   Amplitude (Beam3)                (counts) 15   Battery voltage                  (V) 16   Soundspeed                       (m/s) 17   Soundspeed used                  (m/s) 18   Heading                          (degrees) 19   Pitch                            (degrees) 20   Roll                             (degrees) 21   Pressure                         (dbar) 22   Pressure                         (m) 23   Temperature                      (degrees C) 24   Analog input 1 25   Analog input 2 26   Speed                            (m/s) 27   Direction                        (degrees)

This dataset was collected by an Unmanned Aerial Vehicle in Hermitage, Réunion - 2023-12-01. Underwater or aerial images collected by scientists or citizens can have a wide variety of use for science, management, or conservation. These images can be annotated and shared to train IA models which can in turn predict the objects on the images. We provide a set of tools (hardware and software) to collect marine data, predict species or habitat, and provide maps. Survey information - Camera: Hasselblad L1D-20c - Number of images: 119 - Total size : 1.04 Gb - Flight start: 2023:12:01 15:07:31 - Flight end: 2023:12:01 15:13:31 - Flight duration: 0h 6min 0sec - Median height: 79.9 m - Area covered: 3.93 ha Generic folder structure YYYYMMDD_COUNTRYCODE-optionalplace_device_session-number ├── DCIM : folder to store videos and photos depending on the media collected. ├── GPS : folder to store any positioning related file. If any kind of correction is possible on files (e.g. Post-Processed Kinematic thanks to rinex data) then the distinction between device data and base data is made. If, on the other hand, only device position data are present and the files cannot be corrected by post-processing techniques (e.g. gpx files), then the distinction between base and device is not made and the files are placed directly at the root of the GPS folder. │ ├── BASE : files coming from rtk station or any static positioning instrument. │ └── DEVICE : files coming from the device. ├── METADATA : folder with general information files about the session. ├── PROCESSED_DATA : contain all the folders needed to store the results of the data processing of the current session. │ ├── BATHY : output folder for bathymetry raw data extracted from mission logs. │ ├── FRAMES : output folder for georeferenced frames extracted from DCIM videos. │ ├── IA : destination folder for image recognition predictions. │ └── PHOTOGRAMMETRY : destination folder for reconstructed models in photogrammetry. └── SENSORS : folder to store files coming from other sources (bathymetry data from the echosounder, log file from the autopilot, mission plan etc.). Software All the raw data was processed using our worflow. All predictions were generated by our inference pipeline. You can find all the necessary scripts to download this data in this repository. Enjoy your data with SeatizenDOI!