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34 results for “open oceans”
Physiochemical water column parameters and hydrographic time series from river, lagoon, and open ocean sites along the Alaska Beaufort Sea coast, 2018-ongoing
To understand circulation and seasonality as part of the Beaufort Lagoon Ecosystem Long Term Ecological Research program, temperature, conductivity, salinity, pressure, depth, and current velocity are recorded hourly in situ, starting August 2018 in lagoons across the Beaufort Sea coast (Elson Lagoon, Kaktovik Lagoon, and Jago Lagoon). Moorings include combinations of 1) RBR Concerto CTDs with temperature, conductivity, and pressure sensors; 2) StarOddi CTs with temperature and conductivity sensors; and 3) Lowell TCM-1 Tilt Current meters with MAT-1 Data Loggers for velocity and bearing. In addition, during BLE LTER's annual sampling, water column physiochemical parameters (chlorophyll a, dissolved oxygen, phycoerythrin concentration, pH, temperature, conductivity, salinity) are measured by hand with a YSI data sonde at river, lagoon, and open ocean sites along the Beaufort Sea coast. Here we provide both quality controlled in situ mooring data and all YSI sonde data.
Total suspended solids from river, lagoon, and open ocean sites along the Alaska Beaufort Sea coast, 2022-ongoing
Multiple water types (river, lagoon, ocean) from the North Slope of Alaska and nearshore Beaufort Sea are sampled seasonally by the Beaufort Lagoon Ecosystems LTER (BLE LTER) Core Program to investigate biogeochemical linkages between terrestrial, lagoon, and open ocean ecosystems. Water samples from multiple depths are collected during full ice cover (April), ice break-up (mid-June to early July), and open water (late July and August) periods for quantification of total suspended solids and total organic suspended solids. Concentrations are reported in milligrams per liter of filtered seawater (mg/L).
Carbon and nitrogen content and stable isotope compositions from particulate organic matter samples from lagoon, river, and open ocean sites along the Alaska Beaufort Sea coast, 2018-ongoing
Multiple water types (river, lagoon, ocean) from the North Slope of Alaska and nearshore Beaufort Sea are sampled seasonally by the Beaufort Lagoon Ecosystems LTER (BLE LTER) Core Program to investigate biogeochemical linkages between terrestrial, lagoon, and open ocean ecosystems. Water samples are collected during full ice cover (April), ice break-up (mid-June to early July), and open water (late July and August) periods, and analyzed for particulate organic carbon (POC) and particulate organic nitrogen (PON) content and stable isotopic composition.
Dissolved organic carbon (DOC) and total dissolved nitrogen (TDN) from river, lagoon, and open ocean sites along the Alaska Beaufort Sea coast, 2018-ongoing
Multiple water types (river, lagoon, ocean) from the North Slope of Alaska and nearshore Beaufort Sea are sampled seasonally by the Beaufort Lagoon Ecosystems LTER (BLE LTER) Core Program to investigate biogeochemical linkages between terrestrial, lagoon, and open ocean ecosystems. Water samples are collected during full ice cover (April), ice break-up (mid-June to early July), and open water (late July and August) periods and analyzed for dissolved organic carbon and total dissolved nitrogen content.
Stable oxygen isotope ratios of water (H2O-d18O) from river, lagoon, and open ocean sites along the Alaska Beaufort Sea coast, 2019-ongoing
Multiple water types (river, lagoon, ocean) from the North Slope of Alaska and nearshore Beaufort Sea are sampled seasonally by the Beaufort Lagoon Ecosystems LTER (BLE LTER) Core Program to investigate biogeochemical linkages between terrestrial, lagoon, and open ocean ecosystems. Water samples are collected during full ice cover (April), ice break-up (mid-June to early July), and open water (late July and August) periods, and analyzed for delta 18O (ratio of oxygen-18 to oxygen-16) to use in mixing models for source water contribution.
Ensemble projections (+ uncertainties) of contemporary (2012-2031) and future (2081-2100) mean annual plankton/phytoplankton/zooplankton species diversity (and species turn-over in time) for the global surface open ocean.
<p><em><strong>Gridded spatial fields (raster objects) containing the species distribution models (SDMs) projections of mean annual plankton total plankton, phytoplankton and zooplankton species diversity from Benedetti et al. (2021). </strong></em></p> <p>The present .grd file ('rasterStack' object in R) contain the fields of mean annual surface plankton/phytoplankton/zooplankton species diversity for the contemporary (2012-2031) and future (2081-2100) conditions of the global open ocean (i.e., data underlying those maps in Figure 1 and Figure 3 of Benedetti et al., 2021). Layers quantifying the uncertainty (i.e., the variablity across models projections estimated through the standard deviation) in ensemble projections were also added (i.e., data underlying the maps in Supplementary Figure 4). See the Methods section of Benedetti et al. (2021) for a full description of the methodology and the ensemble SDMs forecasting framework. The raster layers follow the 1°x1° cell grid of the World Ocean Atlas (https://www.ncei.noaa.gov/).</p> <p>In short, we empirically modelled the monthly and mean annual diversity patterns stemming from the distribution of 860 plankton species (336 phytoplankton, 524 zooplankton) spanning 13 phyla, 71 orders and 324 genera through an ensemble approach based on SDMs. The considered species cover a wide range of traits and functions, representing 10 major plankton functional groups (PFGs; three phytoplankton and seven zooplankton groups). We compiled the species occurrence records from various data sources (available here: https://zenodo.org/record/5101349#.YO7Dqm469lM) and aggregated them onto a monthly-resolved 1°x1° grid, excluding observations from regions where the seafloor is shallower than 200 m. We matched these binned open ocean records with observation-based climatologies of environmental predictors (temperature, dissolved oxygen concentration, solar irradiance, macronutrients concentration, chlorophyll a concentration) that reflect the climatic and biogeochemical conditions of the surface open ocean. Four types of SDMs (generalized linear models, generalized additive models, artificial neural networks, and random forests) were fitted to model the species’ current environmental habitat suitability patterns. For each SDMs, we used four alternative pools of predictors. Assuming niche conservatism, we projected each of the 16 resulting species-level habitat suitability models into the future using outputs from five ESMs belonging to the Coupled Model Intercomparison Project 5 (CMIP5) that were forced by the Representative Concentration Pathway 8.5 (RCP8.5) scenario of high greenhouse gas concentrations. To this end, we first computed the modelled monthly climatologies of the selected predictors for the 2012-2031 and 2081-2100 periods, and derive the future monthly anomalies from the differences between these two time periods. These anomalies were added to the observation-based monthly climatologies (i.e., those used to train the SDMs) to estimate the future environmental conditions of the ocean, and projected the SDMs in these future conditions. Finally, we estimated the mean annual present and future alpha diversity (species richness; SR) and beta diversity (species turnover through time) patterns for both trophic levels, for each cell, from the ensemble of SDMs. SR ensembles are estimated as the sum of all species’ habitat suitability patterns averaged across all 80 possible combinations (i.e., "ensemble members") of SDMs (n = 4), ESMs (n = 5) and predictor pools (n = 4). To assess the uncertainties of our diversity projections based on the ensemble members, we compute the interquartile range of the 80 ensemble members SR projections. We calculate species turnover as the change in mean annual species composition between present and future time based on Jaccard’s dissimilarity index and by decomposing this total turnover into the true species turnover (ST, also known as species replacement) and the nestedness (SR change) components. Numerous tests are conducted to ensure the robustness of the results with regard to the spatially and temporally highly uneven sampling effort as well as with regard to the relative role of different predictors.</p> <p><strong>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 862923. This output reflects only the author’s view, and the European Union cannot be held responsible for any use that may be made of the information contained therein.</strong></p>
Concentrations of trace metals (Cu, Cd, Zn) in the ocean at given open and coastal locations
<p>This data compilation contains previously published concentrations of copper, cadmium and zinc in open and coastal oceans of the world. The data was recalculated to nmol/L for consistency, assuming the following molar masses of copper, cadmium and zinc as 63.546, 112.411 and 65.380 g/mol, respectively, and salinity as 1.025 kg/L. The temperature and salinity provided by the authors were also included, in case there is a desire for future users to utilize different conversion methods to recalculate original data. Only data with information on whether the authors measured the trace metal concentrations in open or coastal marine environments were included. The oceans were divided into different geographical regions, namely the Atlantic Ocean, Pacific Ocean, Indian Ocean and Southern Ocean, and subsequently subdivided according to the information authors have given in their publications. In this context, several chemically diverse seas were included in geographical regions in order to limit the number of broad ocean regimes. Chemically diverse sub-regimens were, however, considered within each geographical grouping.</p>
Three reconstructions of the formation of large open ocean polynyas in the Southern Ocean using ice core records
<p>The dataset contains the indices for three reconstructions of open ocean polynya formation in the Southern Ocean over the period 1250-1990 and their uncertainties. See the associated publication</p> <p>Goosse H., Dalaiden Q., Cavitte M.G.P., Zhang L. Can we reconstruct the formation of large open ocean polynyas in the Southern Ocean using ice core records? Climate of the past 2020. <a href="https://doi.org/10.5194/cp-2020-91">https://doi.org/10.5194/cp-2020-91</a></p> <p>The data are included in a text file. The first column is the time (in years). The next six columns are the three reconstructions using data assimilation with SPEAR_AM2 (DA AM2), using data assimilation with SPEAR_LO (DA LO) and a simple average of standardized time series (Stat), each of them directly followed by their uncertainties. The uncertainties are estimated from the standard deviation of the seven reconstructions using different combinations of the available ice core records.</p> <p>Please contact <a href="mailto:hugues.goosse@uclouvain.be">Hugues Goosse</a>(hugues.goosse@uclouvain.be) for more information.</p>
Deep-SDMs in the open oceans - OUTPUTS - World +2°C
<p>This repository contains global distribution maps with a +2°C increase in sea surface temperature, on 4 dates in 2021, as described in the preprint <a href="https://doi.org/10.1101/2023.08.11.551418">Predicting species distributions in the open oceans with convolutional neural networks.</a></p> <p>This deposit contains:</p> <p>1. A <em>00-predictions.csv</em> file containing the raw outputs of the model.</p> <p>2. Distribution maps as png files (named after taxon and date).</p> <p>3. Distribution maps as GeoTIFF rasters (zipped in <em>01-geotiff-rasters.zip</em>).</p> <p>Each of these elements can be downloaded separately by scrolling to the <em>Files</em> section.</p>
Deep-SDMs in the open oceans - OUTPUTS - Western Indian Ocean
<p>This repository contains distribution maps for the Western Indian Ocean on 53 dates in 2021, as described in the preprint <a href="https://doi.org/10.1101/2023.08.11.551418">Predicting species distributions in the open oceans with convolutional neural networks.</a></p> <p>This deposit contains:</p> <p>1. A <em>00-predictions.csv</em> file containing the raw outputs of the model.</p> <p>2. Distribution maps as animated gifs.</p> <p>3. Distribution maps as GeoTIFF rasters (zipped in <em>01-geotiff-rasters.zip</em>).</p> <p>Each of these elements can be downloaded separately by scrolling to the <em>Files</em> section.</p>
Deep-SDMs in the open oceans - OUTPUTS - World
<p>This repository contains global distribution maps on 4 dates in 2021, as described in the preprint <a href="https://doi.org/10.1101/2023.08.11.551418">Predicting species distributions in the open oceans with convolutional neural networks.</a></p> <p>This deposit contains:</p> <p>1. A <em>00-predictions.csv</em> file containing the raw outputs of the model.</p> <p>2. Distribution maps as png files (named after taxon and date).</p> <p>3. Distribution maps as GeoTIFF rasters (zipped in <em>01-geotiff-rasters.zip</em>).</p> <p>Each of these elements can be downloaded separately by scrolling to the <em>Files</em> section.</p>
Deep-SDMs in the open oceans - INPUT DATA
<p>This repository contains input files to train the Deep-SDM model described in the preprint <a href="https://doi.org/10.1101/2023.08.11.551418">Predicting species distributions in the open oceans with convolutional neural networks.</a></p> <p>This deposit contains:</p> <p>1. Training data: CSV dataset + 38 subfolders with data for each species (named after its GBIF id)</p> <p>2. Prediction data:</p> <p>2.1. Global use case (solstices & equinoxes of 2021): CSV dataset + data folder</p> <p>2.2. Western Indian Ocean use case: CSV dataset + data folder</p> <p>3. <em>species.csv </em>contains the taxonomic name of each taxon, as well as its GBIF id.</p> <p>4. <em>stats.npy</em> contains normalization factors for the data files</p> <pre><code class="language-python">meds, perc1, perc99 = np.load("stats.npy") item = np.load(file)[:,:,:25] real_values = (perc99 - perc1) * item + perc1</code></pre> <p> </p> <p>Each of these elements can be downloaded separately by scrolling to the <em>Files</em> section.</p>
Dataset from Lagrangian bio-optical drifters during four experiments in coastal and open ocean waters
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Ocean surface wind estimation from waves based on small GPS buoy observations in a bay and the open ocean
<p>This is dataset of ocean surface wave and wind used in the paper "Ocean surface wind estimation from waves based on small GPS buoy observations in a bay and the open ocean" by Shimura et al. (2022, JGR-Oceans, <a href="https://doi.org/10.1029/2022JC018786">https://doi.org/10.1029/2022JC018786</a> ).</p> <p>"data_bayObservation.nc" contains the observed wind, estimated wind, and observed wave spectral data during the bay observations.</p> <p>"data_openOceanObservation.nc" contains the reanalysis wind, estimated wind, and observed wave spectral data during the open ocean observations.</p> <p> </p>
Synergistic innovations enabled the radiation of anglerfishes in the deep open ocean
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Data from: A phylogenomic framework for pelagiarian fishes (Acanthomorpha: Percomorpha) highlights mosaic radiation in the open ocean
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Data from: Capturing open ocean biodiversity: comparing environmental DNA metabarcoding to the continuous plankton recorder
Environmental DNA (eDNA) metabarcoding is emerging as a novel, objective tool for monitoring marine metazoan biodiversity. Zooplankton biodiversity in the vast and important open ocean is currently monitored through continuous plankton recorder (CPR) surveys, using ship-based bulk plankton sampling and morphological identification. We assessed whether eDNA metabarcoding (2 L filtered seawater) could capture similar Southern Ocean biodiversity as conventional CPR bulk sampling (~1500 L filtered seawater per CPR sample). We directly compared eDNA metabarcoding with (i) conventional morphological CPR sampling and (ii) bulk DNA metabarcoding of CPR collected plankton (two transects for each comparison, 40 and 44 paired samples respectively). A metazoan‐targeted cytochrome c oxidase I (COI) marker was used to characterize species-level diversity. In the 2 L eDNA samples this marker amplified large amounts of non‐metazoan picoplanktonic algae, but eDNA metabarcoding still detected up to 1.6 times more zooplankton species than morphologically analysed bulk CPR samples. COI metabarcoding of bulk DNA samples mostly avoided non-metazoan amplifications and recovered more zooplankton species than eDNA metabarcoding. However, eDNA metabarcoding detected roughly two thirds of metazoan species and identified similar taxa contributing to community differentiation across the subtropical front separating transects. We observed a diurnal pattern in eDNA data for copepods which perform diel vertical migrations, indicating a surprisingly short temporal eDNA signal. Compared to COI, a eukaryote-targeted 18S ribosomal RNA marker detected a higher proportion, but lower diversity, of metazoans in eDNA. With refinement and standardization of methodology, eDNA metabarcoding could become an efficient tool for monitoring open ocean biodiversity.
Data and code for: The evolution of siphonophore tentilla for specialized prey capture in the open ocean
<p>Predator specialization has often been considered an evolutionary 'dead-end' due to the constraints associated with the evolution of morphological and functional optimizations throughout the organism. However, in some predators, these changes are localized in separate structures dedicated to prey capture. One of the most extreme cases of this modularity can be observed in siphonophores, a clade of pelagic colonial cnidarians that use tentilla (tentacle side branches armed with nematocysts) exclusively for prey capture. Here we study how siphonophore specialists and generalists evolve, and what morphological changes are associated with these transitions. To answer these questions, we: (1) measured 29 morphological characters of tentacles from 45 siphonophore species, (2) mapped these data to a phylogenetic tree, and (3) analyzed the evolutionary associations between morphological characters and prey type data from the literature. Instead of a dead-end, we found that siphonophore specialists can evolve into generalists, and that specialists on one prey type have directly evolved into specialists on other prey types. Our results show that siphonophore tentillum morphology has strong evolutionary associations with prey type, and suggest that shifts between prey types are linked to shifts in the morphology, mode of evolution, and genetic correlations of tentilla and their nematocysts. The evolutionary history of siphonophore specialization helps build a broader perspective on predatory niche diversification via morphological innovation and evolution. These findings contribute to understanding how specialization and morphological evolution have shaped present-day food webs.</p>
Data from: Open-ocean fish reveal an omnidirectional solution to camouflage in polarized environments
Despite appearing featureless to our eyes, the open ocean is a highly variable environment for polarization-sensitive viewers. Dynamic visual backgrounds coupled with predator encounters from all possible directions make this habitat one of the most challenging for camouflage. We tested open-ocean crypsis in nature by collecting more than 1500 videopolarimetry measurements from live fish from distinct habitats under a variety of viewing conditions. Open-ocean fish species exhibited camouflage that was superior to that of both nearshore fish and mirrorlike surfaces, with significantly higher crypsis at angles associated with predator detection and pursuit. Histological measurements revealed that specific arrangements of reflective guanine platelets in the fish's skin produce angle-dependent polarization modifications for polarocrypsis in the open ocean, suggesting a mechanism for natural selection to shape reflectance properties in this complex environment.
Seasonal and spatial variation of paraben concentrations in urban coastal waters from freshwater lagoons to the open ocean of Brazil
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.