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184 results for “Marine mammals”
Marine Mammal Survey, Sightings and Sampling Event Log at Palmer Station, Antarctica, 2020-2024
Seasonal sea ice-influenced marine ecosystems at both poles are characterized by high productivity concentrated in space and time by local, regional, and remote physical forcing. These polar ecosystems are among the most rapidly changing on Earth. The PALmer (PAL) LTER seeks to build on three decades of long-term research along the western side of the Antarctic Peninsula (WAP) to gain new mechanistic and predictive understanding of ecosystem changes in response to disturbances spanning long-term, subdecadal, and higher-frequency “pulses” driven by a range of processes, including long-term climate warming, natural climate variability, and storms. These disturbances alter food-web composition and ecological interactions across time and space scales that are not well understood. We seek to determine the differential effects of disturbance and resilience on krill predators with different life histories, foraging behaviors, and demographic patterns. Specifically, changes in foraging behavior can affect adult fitness, body condition, and reproductive rates, as well as offspring survival. Preliminary analyses suggest mean chick fledgling mass decreases later in the austral summer as storm disturbances increase. If storms are not a factor influencing chick mass, parental effects or ecosystem phenology may play a larger role. For whales, changes in foraging effort and increases in body condition should correlate with increased pregnancy rates. We will test for linkages between whale foraging efficiency related to storms with female pregnancy rates the following year. Our prediction is that in seasons with more storms and poorer foraging conditions, fewer whales will become pregnant. However, as whales are long-lived, we predict this will not have a major effect on the long-term positive population trend. We will contribute fundamental understanding of how population dynamics and physiological processes are responding within a polar marine ecosystem undergoing profound change
Data supporting Comparison of feeding niches between Arctic and northward moving sub-Arctic marine mammals in Greenland
<p>Data supporting the paper:</p> <blockquote> <p>Land-Miller, H., A. Roos, M. Simon, R. Dietz, C. Sonne, S. Pedro, A. Rosing-Asvid, F. Rigét, and M. McKinney. 2023. Comparison of feeding niches between Arctic and northward moving sub-Arctic marine mammals in Greenland. Marine Ecology Progress Series.</p> </blockquote> <p>This data is in five files:</p> <p>1. <strong>greenland_marmam_metadata.csv</strong> contains metadata for all samples used in this project, including sample identifiers:</p> <ul> <li><em>Sample: </em>unique sample ID per individual animal</li> <li><em>Species</em></li> </ul> <p>and details of collection, including <em>Year, Location </em>(general area), <em>Lat, </em><em>Long, </em>and<em> </em><em>Date. </em>It also includes other data on the animal (<em>Sex, Age, Length</em>), when available, as well as the co-author who provided the sample to the project (<em>Sample sender</em>) and the tissues available/analyzed for each individual (<em>Tissues received</em>).</p> <p>2. <strong>all_sample_locations.csv</strong> includes latitude/longitude of each sample for mapping. Latitude and longitude are consistent with the full metadata file when coordinates were available, and estimated based on general sampling area (<em>Location </em>or <em>Area</em>) when not. The variable <em>estimate</em><strong> </strong>denotes samples for which coordinates were estimated.</p> <p>3. <strong>fatty_acids_greenland_marmams.csv </strong>contains fatty acid data for all samples. Variables <em>8:00</em> to <em>24:1n9</em> represent the proportion of each individual fatty acid, out of total fatty acids in that sample. Data are represented as whole number percents (i.e., 10 = 10% and all fatty acids sum to 100 for each sample). </p> <p>4. <strong>CNS_greenland_McGill.csv</strong> contains bulk stable isotope data for all samples analyzed at McGill. In addition to <em>Sample</em> and <em>Species</em>, this includes:</p> <ul> <li><em>treatment</em>: whether a sample was lipid-extracted (<em>LE</em>) or non-lipid-extracted (<em>nLE</em>) prior to analysis</li> <li><em>d15N</em>: stable isotope ratio δ<sup>15</sup>N</li> <li><em>d13C: </em>stable isotope ratio δ<sup>13</sup>C</li> <li><em>d34S: </em>stable isotope ratio δ<sup>34</sup>S</li> <li><em>perc.C: </em>mass percent of carbon in the sample</li> <li><em>perc.N: </em>mass percent of nitrogen in the sample</li> <li><em>perc.S: </em>mass percent of sulfur in the sample</li> <li><em>C.N.ratio: </em>mass ratio of carbon to nitrogen in the sample </li> </ul> <p>5. <strong>CN_greenland_nLE_copenhagen.csv</strong> contains stable isotope data for non-lipid-extracted samples analyzed at the University of Copenhagen for δ<sup>13</sup>C and δ<sup>15</sup>N. Variables <em>treatment</em>, <em>d13C</em>, and <em>d15N</em> are consistent with CNS_greenland_McGill.csv. </p>
Balancing risks of injury and disturbance to marine mammals when pile driving at offshore windfarms
<p>1. Offshore windfarms require construction procedures that minimise impacts on protected marine mammals. Uncertainty over the efficacy of existing guidelines for mitigating near-field injury when pile-driving recently resulted in the development of alternative measures, which integrated the routine deployment of acoustic deterrent devices (ADD) into engineering installation procedures without prior monitoring by Marine Mammal Observers.</p> <p>2. We conducted research around the installation of jacket foundations at the UK's first deep-water offshore windfarm to address data gaps identified by regulators when consenting this new approach. Specifically, we aimed to a) measure the relationship between noise levels and hammer energy to inform assessments of near-field injury zones, b) assess the efficacy of ADDs to disperse harbour porpoises from these zones.</p> <p>3. Distance from source had the biggest influence on received noise levels but, unexpectedly, received levels at any given distance were highest at low hammer energies. Modelling highlighted that this was because noise from pin pile installations was dominated by the strong negative relationship with pile penetration depth with only a weak positive relationship with hammer energy.</p> <p>4. Acoustic detections of porpoises along a gradient of ADD exposure decreased in the 3-hours following a 15-minute ADD playback, with a 50% probability of response within 21.7 km. The minimum time to the first porpoise detection after playbacks was > 2 hours for sites within 1 km of the playback.</p> <p>5. Our data suggest that the current regulatory focus on maximum hammer energies needs review, and future assessments of noise exposure should also consider foundation type. Despite higher piling noise levels than predicted, responses to ADD playback suggest mitigation was sufficiently conservative. Conversely, strong responses of porpoises to ADDs resulted in far-field disturbance beyond that required to mitigate injury. We recommend that risks to marine mammals can be further minimised by: 1) optimising ADD source signals and/or deployment schedules to minimise broad-scale disturbance; 2) minimising initial hammer energies when received noise levels were highest; 3) extending the initial phase of soft start with minimum hammer energies and low blow rates.Minhyuk Seo</p>
Stranded marine mammals, sea turtles and seabirds in Paraná and Santa Catarina from August 2018 to August 2023
<p><span class="fontstyle0">To assess the potential impacts from oil and gas production in deep waters of Brazil's Santos Basin, the Brazilian environmental agency (IBAMA) required PETROBRAS, the main oil company in the basin, to implement the "Projeto de Monitoramento de Praias da Bacia de Santos" (Santos Basin Beach Monitoring Project - PMP-BS). This project has been operating along the</span> <span class="fontstyle0">states of Santa Catarina, Paraná, São Paulo, and Rio de Janeiro, since August 2015, collecting data from stranded seabirds, turtles, and marine mammals. This dataset includes records from August 2018 to August 2023, along the Paraná and Santa Catarina coastlines. During this period, 58151 animals of at least 93 species were recorded (883 unidentified animals). From this total, 88.6% were dead and 11.4% alive when first observed. This dataset complements previous ones from the same area, and is a high-intensity monitoring effort essential to understand temporal and geographical variation of stranded animals. This data will allow future works aimed at understanding the impacts of human activities on marine ecosystems and environmental changes over time.</span> <br><br></p>
Acoustic features as a tool to visualize and explore marine soundscapes: Applications illustrated using marine mammal Passive Acoustic Monitoring datasets
<p>Passive Acoustic Monitoring (PAM) is emerging as a solution for monitoring species and environmental change over large spatial and temporal scales. However, drawing rigorous conclusions based on acoustic recordings is challenging, as there is no consensus over which approaches, and indices are best suited for characterizing marine and terrestrial acoustic environments.</p> <p>Here, we describe the application of multiple machine-learning techniques to the analysis of a large PAM dataset. We combine pre-trained acoustic classification models (VGGish, NOAA & Google Humpback Whale Detector), dimensionality reduction (UMAP), and balanced random forest algorithms to demonstrate how machine-learned acoustic features capture different aspects of the marine environment.</p> <p>The UMAP dimensions derived from VGGish acoustic features exhibited good performance in separating marine mammal vocalizations according to species and locations. RF models trained on the acoustic features performed well for labelled sounds in the 8 kHz range, however, low and high-frequency sounds could not be classified using this approach.</p> <p>The workflow presented here shows how acoustic feature extraction, visualization, and analysis allow for establishing a link between ecologically relevant information and PAM recordings at multiple scales.</p> <p>The datasets and scripts provided in this repository allow replicating the results presented in the publication. </p>
Figure 3 in Pliocene marine mammals from the Whalers Bluff Formation of Portland, Victoria, Australia
Figure 3. Mysticeti and Physeteridae from the Pliocene Whalers Bluff Formation, Portland. A, Balaenidae gen. et sp. indet., incomplete right periotic, NMV P218269, in ventrolateral view (AC); B, Balaenopteridae gen. et sp. indet., incomplete right periotic, NMV P218268, in ventral view (AC); C, cf. Physeter sp., apical crown of tooth, NMV P218298, in side view (AC). Scale bars equal 10 mm.
Figure 7 in Pliocene marine mammals from the Whalers Bluff Formation of Portland, Victoria, Australia
Figure 7. Delphinus sp. or Stenella sp. (Pliocene Whalers Bluff Formation, Portland, Victoria, Australia), left periotic, NMV P218265 (AC). A, ventral view. B, cranial view. C, medial view. D, lateral view. Scale bar equals 10 mm.
Figure 2 in Pliocene marine mammals from the Whalers Bluff Formation of Portland, Victoria, Australia
Figure 2. Stratigraphic correlation of the Portland fossil marine mammal-bearing formations with selected major late Neogene marine mammalbearing units. Stratigraphy and geochronology are from Barnes (1973, 1977, 1984, 1998), Muizon and DeVries (1985), Muizon and Bellon (1986), Gottfried et al. (1994), Whitmore (1994), Prothero (1998), Fordyce (2002a), Fordyce et al. (2002), Fitzgerald (2004b), Muizon et al. (2004), Barnes et al. (2005) and Gradstein et al. (2004). Abbreviations: AGL, Pisco Formation, Aguada de Lomas level; BL, Batesford Limestone; BRS, Black Rock Sandstone; CLB, Pisco Formation, Cerro la Bruja; ELJ, Pisco Formation, El Jahuay level; GBF, Grange Burn Formation; LAF, Lower Member, Almejas Formation; MTM, Pisco Formation, Montemar level; SAO, Pisco Formation, Sacaco level; SAS, Pisco Formation, Sud-Sacaco level; SDF, San Diego Formation; UAF, Upper Member, Almejas Formation; WBF, Whalers Bluff Formation.
Figure 10 in Pliocene marine mammals from the Whalers Bluff Formation of Portland, Victoria, Australia
Figure 10.?Phocidae gen. et sp. indet. (Pliocene Whalers Bluff Formation, Portland, Victoria, Australia), incomplete left mandible, NMV P218465 (AC). A, dorsal view. B, lateral view. C, medial view. Black arrow in B points to mental foramen. Scale bar equals 10 mm
Figure 1 in Pliocene marine mammals from the Whalers Bluff Formation of Portland, Victoria, Australia
Figure 1. Locality of Portland in Victoria, south-east Australia, and the Portland fossil marine vertebrate localities. Fossils have been collected as float along the beach and from adjacent cliffs between Dutton Way and Portland Harbour. Black shading indicates areas of cliff outcrop of the Whalers Bluff Formation.
Figure 4 in Pliocene marine mammals from the Whalers Bluff Formation of Portland, Victoria, Australia
Figure 4. Miocene to Recent Kogiidae tympanics. A-B, Kogiidae gen. et sp. indet. (Pliocene Whalers Bluff Formation, Portland, Victoria, Australia), incomplete left tympanic, NMV P218407 (AC). C-D, Kogiidae gen. et sp. undet. (Lower Pliocene Yorktown Formation, Lee Creek Mine, North Carolina, U.S.A.), incomplete left tympanic, USNM 251118. E-F, Scaphokogia cochlearis (Upper Miocene Pisco Formation, Aguada de Lomas level, Arequipa Department, Peru), incomplete left tympanic, USNM 452993. G-H, Kogiidae gen. et sp. undet. (Lower Pliocene Yorktown Formation, Lee Creek Mine, North Carolina, U.S.A.), incomplete right tympanic, USNM 183008. I-J, Kogia breviceps (Recent, Shelley Beach, Victoria, Australia), incomplete left tympanic, NMV C24976. A, C, E, G, I, all in dorsal view. B, D, F, H, J, all in ventral view. Scale bars equal 10 mm.
Figure 5 in Pliocene marine mammals from the Whalers Bluff Formation of Portland, Victoria, Australia
Figure 5. Miocene to Pliocene Physeteridae tympanics. A-B, Physeteridae gen. et sp. undet. (Lower Pliocene Yorktown Formation, Lee Creek Mine, North Carolina, U.S.A.), right tympanic, USNM 183007. C-D, Orycterocetus crocodilinus (Middle Miocene Calvert Formation, Zone 14, south of Randle Cliff Beach, Calvert County, Maryland, U.S.A.), right tympanic, USNM 22953. A and C in dorsal view. B and D in ventral view. Scale bars equal 10 mm.
Figure 9 in Pliocene marine mammals from the Whalers Bluff Formation of Portland, Victoria, Australia
Figure 9. Delphinidae gen. et sp. undet. A (Pleistocene-Pliocene Red Crag, Henley, England), right periotic, NMV P218481 (AC). A, ventral view. B, cranial view. C, medial view. D, lateral view. Scale bar equals 10 mm.
Figure 3 in A review of molecular genetic markers and analytical approaches that have been used for delimiting marine mammal subspecies and species
Figure 3. Published values of percent divergence between cetacean subspecies (black bars), species (white bars), and taxa of uncertain taxonomic status (gray bars). Values are based on mtDNA control region sequence data. Not all values represent net sequence divergence. See Table 1 for list of papers corresponding to each value. Since completing this work, Sousa species have been supported ((Mendez et al. 2013) and Inia subspecies changed.
Figure 1 in A review of molecular genetic markers and analytical approaches that have been used for delimiting marine mammal subspecies and species
Figure 1. Sample sizes used in publications of molecular genetic studies of marine mammals at different taxonomic levels. Graphs present the proportion of studies at each taxonomic level that fall into each sample size category. (A) minimum total sample size per focal taxon; (B) maximum sample size per single sampling locality. Papers were categorized as examining taxonomic questions at: species = subspecies/species boundary; subspecies = population/subspecies boundary; uncertain = taxonomic boundary uncertain (see text).
Figure 2 in A review of molecular genetic markers and analytical approaches that have been used for delimiting marine mammal subspecies and species
Figure 2. Types of molecular genetic data used in published studies examining questions at the species-level, subspecies-level, or undefined taxonomic level for marine mammals. Note that studies may have used more than one data type. Mitochondrial DNA sequence data (MtDNASeq), nuclear DNA sequence data (NuSeq), microsatellites (Msats), morphological data (Morph).
Fig. 6. A in Whale temples are unique repositories for understanding marine mammal diversity in Central Vietnam
Fig. 6. A, left dorsolateral view of the skull of DN2019-T4-001, Balaenoptera omurai; B, dorsal view of the vertex of the skull of DN2019-T4-001, Balaenoptera omurai; C, left lateral view of the skull of DN2019-T8-001, Dugong dugon.
Fig. 5. A in Whale temples are unique repositories for understanding marine mammal diversity in Central Vietnam
Fig. 5. A selection of odontocete skulls, highlighting most of the species identified during the survey. All photographs of additional specimens can be viewed in the Supplemental Information. A, dorsal view of the skull of CI2019-T1-002, Neophocaena phocaenoides; B, ventral view of the skull of CI2019-T1-002, Neophocaena phocaenoides; C, dorsal view of the skull of DN2019-T1-005, Tursiops aduncus; D, ventral view of the skull of DN2019-T1-005, Tursiops aduncus; E, dorsal view of the skull of HA2019-T4-006, Sousa chinensis; F, ventral view of the skull of HA2019-T4-006, Sousa chinensis; G, dorsal view of the skull of HA2019-T4-001, Stenella attenuata; H, ventral view of the skull of HA2019-T4-001, Stenella attenuata; I, dorsal view of the skull of DN2019-T2-003, Lagenodelphis hosei; J, ventral view of the skull of DN2019-T2-003, Lagenodelphis hosei; K, dorsal view of the skull of HA2019-T4-002, Pseudorca crassidens; L, ventral view of the skull of HA2019-T4-002, Pseudorca crassidens; M, dorsal view of the skull of CI2019-T1-001, Feresa attenuata; N, ventral view of the skull of CI2019-T1-001, Feresa attenuata; O, dorsal view of the skull of DN2019-T1-022, Globicephala macrorhynchus; P, ventral view of the skull of DN2019-T1-022, Globicephala macrorhynchus; Q, dorsal view of the skull of DN2019-T1-007, Delphinus delphis (long-beaked form); R, ventral view of the skull of DN2019-T1-007, Delphinus delphis (long-beaked form); S, dorsal view of the skull of DN2019-T4-008, Grampus griseus; T, ventral view of the skull of DN2019-T4-008, Grampus griseus.
Fig. 4. A in Whale temples are unique repositories for understanding marine mammal diversity in Central Vietnam
Fig. 4. A, collection of urns with marine mammal skulls from DN2019-T8, Đà Nẵng; B, central altar of HA2019-T4, Hội An; C, central altar of DN2019-T5 (Đà Nẵng) with glass casket of bones in the background; D, large tomb at the temple CI2019-T1 on the Cham Islands.
Fig. 3 in Whale temples are unique repositories for understanding marine mammal diversity in Central Vietnam
Fig. 3. Six examples of central buildings of traditional whale temple complexes. A, HA2019-T2, Hội An; B, DN2019-T6, Đà Nẵng; C, DN2019-T7, Đà Nẵng; D, DN2019-T1, Đà Nẵng; E, HA2019-T1, Hội An; F, DN2019-T1, Đà Nẵng.
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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
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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.