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193 results for “marine ecology”
Data for Marine Ecological Niche Models, for 2019 and across RCP 2.6, 4.5, and 8.5 scenarios in 2050 and 2100: Global-scale Environmental parameters at 0.1° and 0.5° resolutions, Presence and Absence Records of 1508 European-seas Species
<p>Data for Ecological Niche Models: Global-scale Environmental parameters at 0.1° and 0.5° resolutions, Presence and Absence Records of 1508 European-seas Species.</p>
Ecological Niche Models, in 2019 and across RCP 2.6, 4.5, and 8.5 scenarios in 2050 and 2100, of 1508 European Marine Species, developed with Artificial Neural Networks, Maximum Entropy, Support Vector Machines, and AquaMaps at 0.5° Resolution
<p>Native ecological niche models of 1508 European species (894 fish and 614 non fish) developed with AquaMaps, Artificial Neural Networks, Maximum Entropy, and Support Vector Machines, for 2019 and under RCP 2.6, 4.5, and 8.5 scenarios in 2050 and 2100, at 0.5° spatial 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
<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>
Ecological Niche Models of 96 European Marine Species, for 2019, developed with AquaMaps, Artificial Neural Networks, Maximum Entropy, and Support Vector Machines at 0.1° Resolution
<p>Native ecological niche models of 96 European marine species of particular commercial and conservation interest developed with AquaMaps, Artificial Neural Networks, Maximum Entropy, and Support Vector Machines, for 2019 at 0.1° spatial resolution.</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>
Coastal and Marine Ecological Classification Standard (CMECS) Catalog
<p>The <strong>Coastal and Marine Ecological Classification Standard (CMECS) Catalog</strong> is the authoritative collection of ecological units (terms + definitions) and unit relationships (the CMECS classification framework).</p> <p>The CMECS Catalog is the complete representation of the CMECS classification. It contains all units that are or have been members of the CMECS classification throughout its lifecycle, as well as various annotations that provide metadata for each unit that enable Findability, Accessibility, Interoperability, and Reuse (FAIR, <a href="https://www.go-fair.org/fair-principles/" rel="nofollow">https://www.go-fair.org/fair-principles/</a>). The CMECS Catalog (cmecs.owl) file is stored and managed in a Git repository; authoritative versions are publicly released via <a href="https://github.com/NOAA-OCM/cmecs" target="_blank" rel="noopener">the NOAA-OCM/cmecs GitHub</a> as changes are made. Version releases also include the CMECS Catalog in CSV and XLSX formats. A browsable text output of the CMECS Catalog ecological units and implementation guidance, the <a href="https://github.com/NOAA-OCM/cmecs/wiki/CMECS-Thesaurus-Quick-Link"><strong>CMECS Thesaurus</strong></a>, is also available in PDF and MD formats.</p> <p>This release includes changes to the Substrate Component Unit Codes and fixes to Biotic Component typographical errors. Details are available on the <a href="https://github.com/NOAA-OCM/cmecs/releases/tag/v1.1.1" target="_blank" rel="noopener">CMECS GitHub v1.1.1 Release Page.</a></p> <p><strong>Questions? Please contact the CMECS Implementation Group at ocm.cmecs-ig@noaa.gov</strong></p> <p>For more information about the CMECS Catalog, see the <a href="https://github.com/NOAA-OCM/cmecs/wiki">https://github.com/NOAA-OCM/cmecs/wiki.</a></p> <p>For more information about CMECS, including technical guidance and classification examples, visit the <a href="https://iocm.noaa.gov/standards/cmecs-home.html" rel="nofollow">NOAA Integrated Ocean and Coastal Mapping (IOCM) team's CMECS webpage</a>.</p> <p>CMECS follows a Dynamic Standard Process to review and adopt changes that are proposed by the CMECS user community when necessary. More information about CMECS maintenance can be found on the <a href="https://www.ncei.noaa.gov/products/coastal-marine-ecological-classification-standard" rel="nofollow">NOAA National Centers for Environmental Information (NCEI) CMECS webpage</a> under the <strong>Vocabulary Maintenance</strong> section, along with instructions for proposing revisions to CMECS and a form for submitting proposals.</p>
Ecological forecasts for marine resource management during climate extremes
<p><span>Forecasting weather has become commonplace, but as society faces novel and uncertain environmental conditions there is a critical need to forecast ecology. Forewarning of ecosystem conditions during climate extremes can support proactive decision-making, yet applications of ecological forecasts are still limited. We showcase the capacity for existing marine management tools to transition to a forecasting configuration and provide skilful ecological forecasts up to 12 months in advance. The management tools use ocean temperature anomalies to help mitigate whale entanglements and sea turtle bycatch, and we show that forecasts can forewarn of human-wildlife interactions caused by unprecedented climate extremes. <span>We further show that regionally downscaled forecasts are not a necessity for ecological forecasting and can be less skilful than global forecasts if they have fewer ensemble members.</span> Our results highlight capacity for ecological forecasts to be explored for regions without the infrastructure or capacity to regionally downscale, ultimately helping to improve marine resource management and climate adaptation globally.</span></p>
Fig. 6 in Marine insects of the Maldives (Heteroptera: Gerridae, Hermatobatidae and Veliidae; Diptera: Chironomidae) with notes on taxonomy, Indo-Pacific distribution, and ecology
Fig. 6. Hermatobates djiboutensis. Specimens from Meerufenfushi Island, Kaafu (North Malé) Atoll, 14 November 2006. Scale bar = 0.5 mm. A, male, dorsal view; B, male ventral view (length ca. 3.8 mm); C, male front leg showing teeth and tubercles on tibia (length of femur ca. 0.8 mm); D, female, dorsal view (length ca. 3.6 mm); E, female, ventral view.
Fig. 5 in Marine insects of the Maldives (Heteroptera: Gerridae, Hermatobatidae and Veliidae; Diptera: Chironomidae) with notes on taxonomy, Indo-Pacific distribution, and ecology
Fig. 5. Halobates germanus. Specimens collected inside Haa Alifu Atoll at night, January 2007. Scale bar = 0.5 mm. A, adult female, dorsal view (body length = 3.7 mm); B, male genitalia, dorsal view (genital segment length = 1.9 mm); C, male genitalia, ventral view (genital length = 1.5 mm).
Fig. 4 in Marine insects of the Maldives (Heteroptera: Gerridae, Hermatobatidae and Veliidae; Diptera: Chironomidae) with notes on taxonomy, Indo-Pacific distribution, and ecology
Fig. 4. Halobates formidabilis. Specimens from Maalhendhoo Island, Noonu Atoll, 5 May 2013. Scale bars = 0.5 mm. A, 5th instar nymph, dorsal view, showing colour pattern (length = 4.2 mm); B, adult male, dorsal view (length = 5.6 mm); C, male genitalia, dorsal view (genital segment length = 2.0 mm); D, male genitalia, ventral view (genital length = 1.4 mm).
Fig. 3. Halobates formidabilis. 5 in Marine insects of the Maldives (Heteroptera: Gerridae, Hermatobatidae and Veliidae; Diptera: Chironomidae) with notes on taxonomy, Indo-Pacific distribution, and ecology
Fig. 3. Halobates formidabilis. 5th instar nymph, live individual, Dhidhdhoofinolhu Island, Alifu Dhaalu Atoll, 13 May 2006; not measured, approximate length 5 mm. A, dorsal view; B, ventral view, note uniform pale colouration.
Fig. 3 in The nematode assemblage as a tool for the assessment of marine ecological quality status: a case-study in the Central Adriatic Sea
Fig. 3: Composition of colonizers-persisters (c-p) at each station and in each period. The c-p1 and c-p5 were excluded because they were not found in the study area.
Fig. 2 in The nematode assemblage as a tool for the assessment of marine ecological quality status: a case-study in the Central Adriatic Sea
Fig. 2: a) Shannon Index; b) Pielou Index; c) Maturity Index; d) Index of Trophic Dominance calculated for the nematode assemblage at each station in the study area.
Data from: Ecological forensic testing: Using multiple primers for eDNA detection of marine vertebrates in an estuarine lagoon subject to anthropogenic influences
<p>Many critical aquatic habitats are in close proximity to human activity (i.e., adjacent to residences, docks, marinas, etc.), and it is vital to monitor biodiversity in these and similar areas that are subject to ongoing urbanization, pollution, and other environmental disruptions. Environmental DNA (eDNA) metabarcoding is an accessible, non-invasive genetic technique used to detect and monitor species diversity and is a particularly useful approach in areas where traditional biodiversity monitoring methods (e.g., visual surveys or video surveillance) are challenging to conduct. In this study, we implemented an eDNA approach that used a combination of three distinct PCR primer sets to detect marine vertebrates within a canal system of Biscayne Bay, Florida, an ecosystem representative of challenging sampling conditions and a myriad of impacts from urbanization. We detected fish species from aquarium, commercial, and recreational fisheries, as well as invasive, cryptobenthic, and endangered vertebrate species, including charismatic marine mammals such as the protected West Indian manatee, <em>Trichechus manatus</em>. Our results support the potential for eDNA analyses to supplement traditional biodiversity monitoring methods and ultimately serve as an important tool for ecosystem management. This approach minimizes stress or disturbance to organisms and removes the intrinsic risk and logical limitations of SCUBA diving, snorkeling, or deploying sensitive equipment in areas that are subject to high vessel traffic and/or low visibility. Overall, this work sets the framework to understand how biodiversity may change over different spatial and temporal scales in an aquatic ecosystem heavily influenced by urbanization and validates the use of eDNA as a complementary approach to traditional ecological monitoring methods.</p>
FIGURE 18 in Feeding in marine mammals: An integration of evolution and ecology through time
FIGURE 18. Ancestral state reconstruction of prey capture strategies and prey type preference in stem and crown Mysticeti. Topology follows Gatesy et al. (2013) and Fordyce and Marx (2018). Details on the tree can be found in Appendix 1. All data matrices and complete trees with branch lengths can be found in the Supplementary Information (https://doi.org/10.6086/d14671).
FIGURE 3 in Feeding in marine mammals: An integration of evolution and ecology through time
FIGURE 3. Anatomical features associated with suction feeding in walrus (Odobenus rosmarus skull, from Jefferson et al., 2015) and North Sea beaked whale (Mesoplodon bidens skull, authors' work).
FIGURE 11 in Feeding in marine mammals: An integration of evolution and ecology through time
FIGURE 11. Sirenia stem and familial level diversity through time. "Protosirenidae / Prorastomidae" includes all taxa that are not included in the two extant groups. Dashed vertical lines: black, epoch boundaries; gray, age boundaries.
FIGURE 7 in Feeding in marine mammals: An integration of evolution and ecology through time
FIGURE 7. Pinnipedimorpha stem taxa and familial level diversity through time. "Stem-Pinnipedimorpha" includes all stem taxa that are not included in Desmatophocidae and the three extant groups. Dashed vertical lines: black, epoch boundaries; gray, age boundaries.
FIGURE 5 in Feeding in marine mammals: An integration of evolution and ecology through time
FIGURE 5. Anatomical features associated with grazing in African manatee (Trichechus senegalensis skull, from Werth, 2000), Desmostylia (Paleoparadoxia skull, public domain), and aquatic sloth (Thalassocnus sp. skull, modified from de Muizon et al., 2004).
FIGURE 8 in Feeding in marine mammals: An integration of evolution and ecology through time
FIGURE 8. Cetacean stem and Neoceti taxa diversity through time. "Archaeoceti" includes all stem taxa that are not included in the two extant groups. Dashed vertical lines: black, epoch boundaries; gray, age boundaries.
ScienceDex guides
Understand access before you commit
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.