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233 results for “marine biodiversity”
Biodiversity Index, in 2019 and across RCP 4.5, and 8.5 scenarios in 2050 and 2100 of 1508 European Marine Species based on ensemble Ecological Niche Models developed with Artificial Neural Networks, Maximum Entropy, Support Vector Machines, and AquaMaps at 0.5° Resolution
<p>Biodiversity Index in 2019 and across RCP 4.5, and 8.5 scenarios in 2050 and 2100 of 1508 European marine species based on ensemble Ecological Niche Models developed with Artificial Neural Networks, Maximum Entropy, Support Vector Machines, and AquaMaps at 0.5° Resolution. The Index counts the number of species (among the 1508) potentially present in each 0.5° cell according to the ensemble models. For each ensemble model, a threshold of at least 3 models agreeing on species presence in the cell was used to indicate species presence.</p>
Ensemble Ecological Niche Models and Biodiversity Index for 2019 of 96 European Marine Species based on Ecological Niche Models developed with Artificial Neural Networks, Maximum Entropy, AquaMaps, and Support Vector Machines at 0.1° Resolution
<p>Ensemble Ecological Niche Models for 2019 of 96 European marine species of particular commercial and conservation interest, based on Ecological Niche Models developed with (i) Artificial Neural Networks, (ii) Maximum Entropy, (iii) Support Vector Machines, and (iv) AquaMaps at 0.1° Resolution. The data report, for each 0.1° cell, how many models (from 0 to 4) overcome a model-specific decision threshold to assess species presence in the cell. A Biodiversity Index is also provided as the count of the number of species (among the 96) potentially present in each 0.1° cell according to the ensemble models. For each ensemble model, a threshold of at least 3 models agreeing on species presence in the cell was used to indicate species presence.</p>
Biogeographic data for "The marine biodiversity impact of the Late Miocene Mediterranean salinity crisis"
<p>Lists of species that were present in the Mediterranean Sea both in the pre-evaporitic Messinian and the Zanclean (based on https://doi.org/<a href="../doi/10.5281/zenodo.10782428">10.5281/zenodo.10782428</a>), biogeographic information on their presence outside the Mediterranean, and accordingly their status as either "possible endemic" to the Mediterranean or "non-endemic" if they were also found outside the basin.</p> <p>In this version, we added also the list of species present in the Mediterranean Sea in the pre-evaporitic Messinian that can be considered possible endemics, based on the same rule, and the indication if they survived the MSC.</p>
List and date of establishment of Marine Protected Areas and Key Biodiversity Areas of the Alboran Sea
<p>List of Marine Protected Areas and Key Biodiversity Areas for the Alboran Sea (Abbreviation in Spanish, French and English with lenguage among brackets- Fr: French; S: Spanish), indicating its figure of conservation, year of establishment for each figure of protection and national or regional management body (in brackets). IBA: Importante Bird Area; IMMA: Important Marine Mammals Area; MR: Marine Reserve; MR/FR: Marine and Fishing Reserve; NA: Natural Area; NM: Natural monument; NP: Natural Park; SPAMI: Specially Protected Areas of Mediterranean Importance; RAMSAR: Wetlands of International Importance (Ramsar Sites); SCI: Site of Community Importance of Natura 2000; SAC: Special Area of Conservation of Natura 2000; SPA: Special Protection Area of Natura 2000; ZEPA: Zona de Especial Protección para las Aves; ZEPIM: Zonas Especialmente Protegidas de Importancia para el Mediterráneo; LIC: Lugar de Importancia Comunitaria de Natura 2000; ZEC: Zona de Especial Conservación de Natura 2000; ASPIM: Aire Spécialement Protégée d'Importance Méditerranéenne; SIC: Site d'Importance Communautaire; ZPS: Zones de Protection Spéciale.</p>
Occurrence Record Dataset from "Depth Matters for Marine Biodiversity"
<p>This is the final occurrence record dataset produced for the manuscript "Depth Matters for Marine Biodiversity". Detailed methods for the creation of the dataset, below, have been excerpted from Appendix I: Extended Methods. Detailed citations for the occurrence datasets from which these data were derived can also be foud in Appedix I of the manuscript.</p> <p><span>We first assembled a list of all recognized species of fishes from the orders Scombiformes</span><span> (Betancur-R et al., 2017)</span><span>, Gadiformes, and Beloniformes by accessing FishBase</span><span> (Boettiger et al., 2012; Froese & Pauly, 2017)</span><span> and the Ocean Biodiversity Information System (OBIS; </span><span>OBIS, 2022; Provoost & Bosch, 2019)</span><span> through queries in R</span><span> (R Core Team, 2021)</span><span>. Species were considered Atlantic if their FishBase distribution or occurrence records on OBIS included any area within the Atlantic or Mediterranean major fishing regions as defined by the Food and Agriculture Organization of the United Nations (FAO Regions 21, 27, 31, 34, 37, 41, 47, and 48;</span><span> FAO, 2020)</span><span>. The database query script can be found on the project code repository (</span><a href="https://github.com/hannahlowens/3DFishRichness/blob/main/1_OccurrenceSearch.R"><span>https://github.com/hannahlowens/3DFishRichness/blob/main/1_OccurrenceSearch.R</span></a><span>). We then curated the list of names to resolve discrepancies in taxonomy and known distributions through comparison with the Eschmeyer Catalog of Fishes</span><span> (Eschmeyer & Fricke, 2015)</span><span> , accessed in September of 2020, as our ultimate taxonomic authority. The resulting list of species was then mapped onto the Global Biodiversity Information Facility’s backbone taxonomy</span><span> (Chamberlain et al., 2021; GBIF.org, 2020a)</span><span> to ensure taxonomic concurrence across databases (Supplementary Table 1). The final taxonomic list was used to download occurrence records from OBIS</span><span> (OBIS, 2022)</span><span> and GBIF</span><span> (GBIF.org, 2020b)</span><span> in R through <em>robis</em></span><span> (Provoost & Bosch, 2019)</span><span> and <em>occCite</em></span><span> (Owens et al., 2021)</span><span>. </span></p> <p><span><span> </span>For each species, duplicate points were removed from two- and three-dimensional species occurrence datasets separately, and inaccurate depth records were removed from 3D datasets (all records with and without depth information were retained for the 2D dataset). Depth records were based on the “depth” field in both the GBIF and OBIS datasets, which define the field as “depth below the surface in meters”. We chose this value over incorporating information from “minimumDepthInMeters” and “maximumDepthInMeters” because more records contained information from the “depth” field than either of the two other fields (although when these fields were both supplied, “depth” appears to have been often, but not always, derived by calculated the mean between minimum and maximum depth). We also initially included the “depthAccuracy” field from both datasets but did not ultimately use this field as it was not complete enough to be useful. Instead, we determined depth inaccuracy based on extreme statistical outliers (values greater than 2 or less than -2 when occurrence depths were centered and scaled), depths that exceeded bathymetry at occurrence coordinates, and occurrence depths far outside known depth ranges obtained from FishBase, Eschmeyer’s Catalog of Fishes, and/or congeneric depth ranges in the dataset. Once the resulting data were mapped and curated to remove records with putatively spurious coordinates, under-sampled regions and species were augmented with data from publicly available digital museum collection databases not served through OBIS or GBIF, as well as a literature search. Finally, for datasets with more than 20 points remaining after data curation, occurrence data were downsampled to the resolution of the environmental data; that is, to 1 point per 1 degree grid cell in the 2D dataset, and to one point per depth slice per 1 degree grid cell in the 3D dataset. </span></p> <p> </p> <p>References:</p> <p>Betancur-R, R., Wiley, E. O., Arratia, G., Acero, A., Bailly, N., Miya, M., Lecointre, G., & Ortí, G. (2017). Phylogenetic classification of bony fishes. <em>BMC Evolutionary Biology</em>, <em>17</em>(1), 162. <a href="https://doi.org/10.1186/s12862-017-0958-3">https://doi.org/10.1186/s12862-017-0958-3</a></p> <p>Boettiger, C., Lang, D. T., & Wainwright, P. C. (2012). rfishbase: exploring, manipulating and visualizing FishBase data from R. <em>Journal of Fish Biology</em>, <em>81</em>(6), 2030–2039. <a href="https://doi.org/10.1111/j.1095-8649.2012.03464.x">https://doi.org/10.1111/j.1095-8649.2012.03464.x</a></p> <p>Chamberlain, S., Barve, V., McGlinn, D., Oldoni, D., Desmet, P., Geffert, L., & Ram, K. (2021). <em>rgbif: Interface to the Global Biodiversity Information Facility API</em>. <a href="https://CRAN.R-project.org/package=rgbif">https://CRAN.R-project.org/package=rgbif</a></p> <p>Eschmeyer, & Fricke, W. N. &. (2015). Taxonomic checklist of fish species listed in the CITES Appendices and EC Regulation 338/97 (Elasmobranchii, Actinopteri, Coelacanthi, and Dipneusti, except the genus Hippocampus). <em>Catalog of Fishes, Electronic Version</em>. Accessed September, 2020. <a href="https://www.calacademy.org/scientists/projects/eschmeyers-catalog-of-fishes">https://www.calacademy.org/scientists/projects/eschmeyers-catalog-of-fishes</a></p> <p>FAO. (2020). <em>FAO Major Fishing Areas</em>. United Nations Fisheries and Aquaculture Division. <a href="https://www.fao.org/fishery/en/collection/area">https://www.fao.org/fishery/en/collection/area</a></p> <p>Froese, R., & Pauly, D. (2017). <em>FishBase</em>. Accessed September, 2022. www.fishbase.org</p> <p>GBIF.org. (2020a). <em>GBIF Backbone Taxonomy</em>. Accessed September, 2020. GBIF.org</p> <p>GBIF.org. (2020b). <em>GBIF Occurrence Download</em>. Accessed November, 2020. <a href="https://doi.org/10.15468">https://doi.org/10.15468</a></p> <p>OBIS. (2020). <em>Ocean Biodiversity Information System. Intergovernmental Oceanographic Commission of UNESCO</em>. Accessed November, 2020. www.obis.org</p> <p>Owens, H. L., Merow, C., Maitner, B. S., Kass, J. M., Barve, V., & Guralnick, R. P. (2021). occCite: Tools for querying and managing large biodiversity occurrence datasets. <em>Ecography</em>, <em>44</em>(8), 1228–1235. <a href="https://doi.org/10.1111/ecog.05618">https://doi.org/10.1111/ecog.05618</a></p> <p>Provoost, P., & Bosch, S. (2019). <em>robis: R Client to access data from the OBIS API</em>. <a href="https://cran.r-project.org/package=robis">https://cran.r-project.org/package=robis</a></p> <p>R Core Team. (2021). <em>R: A Language and Environment for Statistical Computing</em>. <a href="https://www.R-project.org/">https://www.R-project.org/</a></p>
Europe-wide public preferences for plankton-based ecosystem services and marine biodiversity from a series of Deliberative Monetary Valuation workshops
<p>The data were collected as part of the Horizon Europe project BIOcean5D – Marine Biodiversity Assessment and Prediction Across Spatial, Temporal and Human Scales. The aim of the study was to elicit public preferences for marine biodiversity and ecosystem services, with a particular focus on plankton. This dataset comprises responses from a series of Deliberative Monetary Valuation workshops held across Europe, along with the corresponding supplementary material. The responses encompass a range of data, including socio-demographic information, personal characteristics, beliefs, prior knowledge, preferences derived from a Discrete Choice Experiment, and the associated motivations of each respondent. In total, 15 workshops were conducted between October 2023 and February 2024 in the following locations: Poland (Poznan and Sopot), Italy (Padova and Chioggia), the Basque Country (Vitoria-Gasteiz and Bilbao), Germany (Bremerhaven and Hannover), and France (Rennes and Brest).</p>
Fig. 8 in Meiofaunal Biodiversity In A Marine Protected Area: A Case Study In The Rocky And Sedimentary Shores Of The Snake Island (North-Western Black Sea)
Fig. 8. Plot of the non-metric multidimensional scaling (nMDS) based on the by Bray–Curtis similarity index for logarithmic values of meiobenthos taxa density in the recognized habitats of the Snake Island MPA (Black Sea).
Fig. 7 in Meiofaunal Biodiversity In A Marine Protected Area: A Case Study In The Rocky And Sedimentary Shores Of The Snake Island (North-Western Black Sea)
Fig. 7. Cluster analysis dendrogram based on meiobenthos density on the different habitats in MPA of the Snake Island (Black Sea).
Fig. 4 in Meiofaunal Biodiversity In A Marine Protected Area: A Case Study In The Rocky And Sedimentary Shores Of The Snake Island (North-Western Black Sea)
Fig. 4. The average density (N, means ± SE ind.·m–2) and biomass (B, means ± SE mg·m–2) of the total meiobenthos with contribution permanent and temporary taxa in the different habitats of the Snake Island MPA (Black Sea).
Fig. 3 in Meiofaunal Biodiversity In A Marine Protected Area: A Case Study In The Rocky And Sedimentary Shores Of The Snake Island (North-Western Black Sea)
Fig. 3. Meiobenthic community structure of different substrate types in the MB143 habitat of the Snake Island MPA (Black Sea).
Fig. 2 in Meiofaunal Biodiversity In A Marine Protected Area: A Case Study In The Rocky And Sedimentary Shores Of The Snake Island (North-Western Black Sea)
Fig. 2. The average density (N, means ± SE ind.·m–2) and biomass (B, means ± SE mg·m–2) of the total meiobenthos of different substrate types in the MB143 habitat of the Snake Island MPA (Black Sea).
Fig. 1 in Meiofaunal Biodiversity In A Marine Protected Area: A Case Study In The Rocky And Sedimentary Shores Of The Snake Island (North-Western Black Sea)
Fig. 1. The map-scheme of the study area near the Snake Island (north-western Ukrainian shelf of the Black Sea).
Fig. 6 in Meiofaunal Biodiversity In A Marine Protected Area: A Case Study In The Rocky And Sedimentary Shores Of The Snake Island (North-Western Black Sea)
Fig. 6. The contribution (%) of each meiobenthic taxon to the average density and biomass in the different habitats of the Snake Island MPA (Black Sea).
Mobilising marine biodiversity data: a new malacological dataset of Italian records (Mollusca)
<p>The location and palaeoceanographic history of the Mediterranean Sea make it a biodiversity hotspot, prompting extensive studies in this region. However, despite the marine biodiversity of this area is apparently widely studied, a large amount of distributional data for Mediterranean taxa is still unpublished or scattered in various sources and formats, causing severe limitations to their potential reuse. This emerges as a particularly thorny issue for highly biodiverse and neglected taxa, such as invertebrates. The mobilisation of these frozen data through a process of standardisation and georeferencing could potentially support biodiversity research and conservation. The aim of this work is to provide a standardised pipeline to integrate these dispersed data, focusing on the Italian waters of the Mediterranean Sea and using molluscs as target taxa. Data were gathered from two main sources: published literature and Natural History Collections. The harmonisation process involved three key steps: 1) terminology and structure standardisation, 2) taxonomy updating and 3) georeferencing. Our efforts yielded over 44000 standardised records of mollusc species from Italian seawaters. These records encompassed primary biodiversity data from newly digitised specimens owned by 11 different institutions and private collectors, as well as secondary biodiversity data extracted from 311 published studies.</p>
Fig. 2 in Axiidea (Crustacea: Callianassidae, Callichiridae and Ctenochelidae) and Gebiidea (Upogebiidae) collected during the Comprehensive Marine Biodiversity Survey of Singapore
Fig. 2. Specimens, habitus (dorsal and ventral views), showing colouration in life: a, Upogebia carinicauda (Stimpson, 1860), male (21/6.1) (NHMW 26035); b, same, male (23/6.6) (NHMW 26034); c, d, U. darwinii (Miers, 1884), ovigerous female (44/12.6) (NHMW 26038); e, U. hexaceras (Ortmann, 1894), male (23/7.0) (ZRC 2018.0557); f, same, ovigerous female (22/6.1) (NHMW 26042); g, U. ancylodactyla de Man, 1905, female (40/11.3) (ZRC 2018.0551); h, same, male (29/8.4) (ZRC 2018.0549). Not to scale. [Photographs by AA].
Fig. 1 in Axiidea (Crustacea: Callianassidae, Callichiridae and Ctenochelidae) and Gebiidea (Upogebiidae) collected during the Comprehensive Marine Biodiversity Survey of Singapore
Fig. 1. Specimens, habitus (dorsal and/or ventral views), showing colouration in life: a, Gourretia sinica Liu & Liu, 2010, female (23/5.3) (ZRC 2017.0947); b, Karumballichirus karumba (Poore & Griffin, 1979), female (32/7.6) (ZRC 2018.0526); c, Aqaballianassa brevirostris Sakai, 2002, ovigerous female (28/5.6) (ZRC 2017.0948); d, Upogebia singaporensis, new species, female holotype (16/5.4) (ZRC 2017.0951); e, f, Neogebicula johorensis, new species, male holotype (18/5.6) (ZRC 2017.0956); g, h, same, ovigerous female paratype (21/6.3) (ZRC 2017.0955); i, j, Wolffogebia phuketensis Sakai, 1982, male (37/10.3) (NHMW 26036). Not to scale. [Photographs by AA].
Fig. 5 in Axiidea (Crustacea: Callianassidae, Callichiridae and Ctenochelidae) and Gebiidea (Upogebiidae) collected during the Comprehensive Marine Biodiversity Survey of Singapore
Fig. 5. Gourretia sinica Liu & Liu, 2010, female (18/7.5) (ZRC 2018.0560); a, left Mxp3, lateral view; major cheliped fingers, lateral (b) and mesial (c) view; d, minor cheliped fingers, lateral view; e, distal articles of pereopod 2, lateral view; f, distal articles of third pereopod, lateral view; g, telson, dorsal view. Scale bar = 1 mm. [Illustrations by PCD].
Figure 1 in Biodiversity of epiphytic marine macroalgae in Mexico: composition and current status
Figure 1: Numbers of algal species in each of the families that are best represented in each biogeographical ecoregion around Mexico, as defined by Spalding et al. (2007).
Biofouling sponges as natural eDNA samplers for marine vertebrate biodiversity monitoring
<p>These are the raw sequencing data and associated analysis codes for the study of "biofouling sponges as natural eDNA samplers for marine <span>vertebrate </span>biodiversity monitoring".</p>
Data from: A sedimentary eDNA record of the Atacama Trench reveals biodiversity changes in the most productive marine ecosystem
<p>The hadopelagic environment remains highly understudied due to the inherent difficulties in sampling at these depths. The use of sediment eDNA can overcome some of these restrictions as settled and preserved DNA represent an archive of the biological communities. We use sediment eDNA to assess changes in the community within one of the world's most productive open ocean ecosystems: the Atacama Trench. The ecosystems around the Atacama Trench have been intensively fished and are affected by climate oscillations, but the understanding of potential impacts on the marine community is limited. We sampled five sites using sediment cores at water depths from 2400 to ~8000m. The chronologies of the sedimentary record were determined using 210Pbex. Environmental DNA was extracted from core slices and metabarcoding was used to identify the eukaryote community using two separate primer pairs for different sections of the 18S rDNA gene (V9 and V7) effectively targeting pelagic taxa. The reconstructed communities were similar among markers and mainly composed of chordates and members of the Chromista kingdom. Alpha-diversity was estimated for all sites in intervals of 15 years (from 1842 to 2018), showing a severe drop in biodiversity from 1970 to 1985 that aligns with one of the strongest known El Niño events. We argue that the harsh adverse ENSO events potentially combined with extensive fishing efforts during this period of time resulted in a distinct reduction of marine biodiversity. Fish and cnidarian read abundance was examined separately to determine if fishing had a direct impact, but no direct relation was found. These results demonstrate that sediment eDNA can be a valuable emerging tool providing insight in historical perspectives on ecosystem developments. This study constitutes one of the first steps toward an improved understanding of the importance of environmental and anthropogenic drivers in affecting open and deep ocean communities.</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
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.