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25 results for “diving behavior”
Dataset for "Large scale patterns and drivers of the diving behavior of gill-breathing large pelagic predators"
<p>This dataset includes all supporting data and scritps to generate figure panels in the paper "Large scale patterns and drivers of the diving behavior of gill-breathing large pelagic predators" (A. Nuno, J. Guiet, B. Baranek and D. Bianchi)</p>
Diving behaviors of juvenile northern and southern elephant seals
<p><span></span>Understanding the ontogeny of diving behavior in marine megafauna is crucial due to its influence on foraging success, energy budgets, and mortality. We compared the ontogeny of diving behavior in two closely related species – northern elephant seals (<em>Mirounga angustirostris</em>, n = 4) and southern elephant seals (<em>Mirounga leonina</em>, n = 9) – to shed light on the ecological and evolutionary processes underlying migration. Although both species have similar sizes and behaviors as adults, we discovered that juvenile northern elephant seals have superior diving development, reaching 260 meters diving depth in just 30 days, while southern elephant seals require 160 days. Similarly, northern elephant seals achieve dive durations of ~11 minutes on their first day of migration, while southern elephant seals take 125 days. The faster physiological maturation of northern elephant seals could be related to longer offspring dependency and post-weaning fast durations, allowing them to develop their endogenous oxygen stores. Comparison across both species suggests that weaned seal pups face a trade-off between leaving early with higher energy stores but poorer physiological abilities or leaving later with improved physiology but reduced fat stores. This trade-off might be influenced by their evolutionary history, which shapes their migration behaviors in changing environments over time.</p>
Figure 6. 24 h in Summer diving and haul-out behavior of leopard seals (Hydrurga leptonyx) near mesopredator breeding colonies at Livingston Island, Antarctic Peninsula
Figure 6. 24 h rose plots of leopard seal dive activity by hour of day from the parametric data set. Red arrows represent the mean vector of dive activity. (A) all dives pooled from the 2010 season (n = 6,017) from three seals (4OR, 9OR, and 390G). (B) Activity for leopard seal 4OR (n = 2,292 dives) was significantly different from the 2010 mean and the other two seals; (Watson's two sample tests, P <0.05). (C) Activity for leopard seal 9OR (n = 2,283 dives) was significantly different from the 2010 mean and the other two seals (Watson's two sample tests, P <0.001). (D) Activity for leopard seal 390G (n = 1,442 dives) was significantly different from the 2010 mean and the other two seals (Watson's two sample tests, P <0.001).
Figure 5. 24 h in Summer diving and haul-out behavior of leopard seals (Hydrurga leptonyx) near mesopredator breeding colonies at Livingston Island, Antarctic Peninsula
Figure 5. 24 h rose plots of dive activity by hour of day. The red arrows represents the mean vector (direction = time of day, length = mean number of dives) of dive activity (dives/h) for: (A) all dives (n = 40,308). Gray shaded areas represent the crepuscular periods (+1 h from sunset and sunrise) across the study; (B) all dives pooled from the 2010 season (n = 13,373); (C) all dives pooled from the 2011 season (n = 6,545); (D) all dives pooled from the 2014 season (n = 8,723). The null hypothesis that patterns of diel dive activity were equivalent between seasons could not be rejected (Watson's two-sample tests, P> 0.05).
Figure 1 in Summer diving and haul-out behavior of leopard seals (Hydrurga leptonyx) near mesopredator breeding colonies at Livingston Island, Antarctic Peninsula
Figure 1. Cape Shirreff, Livingston Island, Antarctica. The black star in the right pane indicates the location of Cape Shirreff in the western Antarctic Peninsula region.
Figure 2 in Use of time-at-temperature data to describe dive behavior in five species of sympatric deep-diving toothed whales
Figure 2. Mean locations of time-at-temperature (TAT) histograms and time-at-depth (TAD) histograms from transmitter tags deployed on each of five species in the Great Bahama Canyon: (a) melon-headed whale (Peponocephala electra, NSPOT = 9, NSPLASH = 4), (b) shortfinned pilot whale (Globicephala macrorhynchus, NSPOT = 12, NSPLASH = 3), (c) sperm whales (Physeter macrocephalus, NSPOT=21, NSPLASH = 6), (d) Blainville's beaked whale (Mesoplodon densirostris, NSPOT = 3, NSPLASH = 9), and (e) Cuvier's beaked whale (Ziphius cavirostris, NSPOT = 1, NSPLASH = 6). The mean locations were derived by fitting a movement model (Johnson et al. 2008) to smooth and filter irregularly spaced Argos telemetry estimates from SPOT and SPLASH tags, respectively. The study area boundary and U.S. Navy's Atlantic Test and Evaluation Center (AUTEC) are also shown.
Figure 4 in Summer diving and haul-out behavior of leopard seals (Hydrurga leptonyx) near mesopredator breeding colonies at Livingston Island, Antarctic Peninsula
Figure 4. Comparison by dive types between (A) behavior predicted from the k-means cluster analysis of time-depth dive records (n = 38,338) and (B) behavior manually scored from animal-borne video dive data (n = 309).
Figure 3 in Summer diving and haul-out behavior of leopard seals (Hydrurga leptonyx) near mesopredator breeding colonies at Livingston Island, Antarctic Peninsula
Figure 3. The mean proportion (with SD whiskers) of dives that were classified into each dive type (1–4) for all dives in the cluster data set (n = 38,338).
Figure 2 in Summer diving and haul-out behavior of leopard seals (Hydrurga leptonyx) near mesopredator breeding colonies at Livingston Island, Antarctic Peninsula
Figure 2. (A) Empirical haul-out probability distributions for leopard seals at Cape Shirreff based on 209 haul outs from 18 animals in January and February from 2008 to 2014. (B) A polynomial linear regression (solid line) which predicts haul-out probability based on time (h) from local apparent noon; 95% confidence intervals (dashed lines).
Figure 5 in Use of time-at-temperature data to describe dive behavior in five species of sympatric deep-diving toothed whales
Figure 5. Boxplots comparing approximate dive depth distributions derived using time-attemperature (TAT) data from SPOT satellite tags, to time-at-depth (TAD) summaries, generated from directly observed dive depth time series from SPLASH satellite tag deployments on (a) melon-headed whales (Peponocephala electra), (b) short-finned pilot whales (Globicephala macrorhynchus), (c) sperm whales (Physeter macrocephalus), (d) Blainville's beaked whales (Mesoplodon densirostris), and (e) Cuvier's beaked whales (Ziphius cavirostris). Mean of bottom depths (MBD) at the continuous time correlated random walk (CTCRW) maximum likelihood estimated locations of TAT histograms are shown on each plot.
Figure 4 in Use of time-at-temperature data to describe dive behavior in five species of sympatric deep-diving toothed whales
Figure 4. Illustrating three representations of 8.5 d time series of melon-headed whale (Peponocephala electra, (a–c), and sperm whale (Physeter macrocephalus, (d–f) time-at-temperature (TAT) histograms. Column 1 shows the median and variability in the proportion of time spent in 12 depth/temperature strata in a box-plot representation. Column 2 shows a time series representation with a fixed depth scale and variable box dimensions representing the local estimated depths of TAT strata. Column 3 shows the same data in an analogous representation, but with a depth scale that indicates the study-area-wide central tendency of isotherm depths and internal box dimensions that remain fixed.
Figure 3 in Use of time-at-temperature data to describe dive behavior in five species of sympatric deep-diving toothed whales
Figure 3. Prediction surfaces of the (a) linearly approximated depth observations and estimated mean depth field of three example isotherms (8°C, 14°C, and 20°C), that were predicted using five interpolation methods: (b) 0.5° grid cell mean, (c) HYCOM reanalysis, (d) quadratic linear model, (e) objective analysis based on the quadratic linear model, and (f) generalized additive model. The color scale in each panel represents a 250 m range centered on median observed depth of each displayed isotherm, thus the relatively muted color contrast in the 20°C series of plots reflects the lower total variability in isotherm depth at this temperature level when compared with the 8°C and 14°C series of plots.
Fig. 3 in Decompression syndrome and diving behavior in Odontochelys, the first turtle
Fig. 3. Enface view of right proximal humeral articular surface of Odontochelys semitestacea Li, Wu, Rieppel, Wang, and Zhao, 2008 from the Lower Carnian (Upper Triassic) Wayao Member of the Falang Formation; Guanling, Guizhou Province, southwest China (IVPP V 13240). Irregular defect (arrow) is characteristic for the avascular necrosis found with decompression syndrome.
Fig. 2 in Decompression syndrome and diving behavior in Odontochelys, the first turtle
Fig. 2. Enface view of left proximal humeral articular surface of Odontochelys semitestacea Li, Wu, Rieppel, Wang, and Zhao, 2008 from the Lower Carnian (Upper Triassic) Wayao Member of the Falang Formation; Guanling, Guizhou Province, southwest China (IVPP V 13240). Irregular defect (arrow) is characteristic for the avascular necrosis found with decompression syndrome.
Fig. 1 in Decompression syndrome and diving behavior in Odontochelys, the first turtle
Fig. 1. Ventral view of anterior body of Odontochelys semitestacea Li, Wu, Rieppel, Wang, and Zhao, 2008 from the Lower Carnian (Upper Triassic) Wayao Member of the Falang Formation; Guanling, Guizhou Province, southwest China (IVPP V 13240). Defects are present on proximal humeral articular surfaces.
Dataset for "Large scale patterns and drivers of the diving behavior of large pelagic predators"
<p>This dataset comprises 694 independent diving depth estimates of large pelagic predators extracted from 101 tagging studies. For both daytime and nighttime observations, two unambiguous quantities were reported: 1) the preferred diving depth (D<sub>pref</sub>), representative of the approximate depth at which individuals spend most of the time, or in other words a mean representative depth; and (2) the preferred diving depth range (ΔD<sub>pref</sub>), representative of the portion of the water column where individuals are most commonly recorded by the tags, or, in other words, the vertical range over which they are most commonly observed. Alongside the diving depth data, three additional types of information were extracted to co-locate diving depth observations with environmental variables, and to account for potential ontogenetic behavioral effects. These additional pieces of information include the location and period of the observations, and a representative size of the tagged individual or group of individuals. The extraction process was replicated by two separate analysts to ensure accuracy and reliability. For further details on the dataset and extraction procedure, refer to the manuscript "<em>Large scale patterns and drivers of the diving behavior of large pelagic predators"</em>.</p>
Data from: Genomic signatures of convergent shifts to plunge-diving behavior in birds
<p>Understanding the genetic basis of convergence at broad phylogenetic scales remains a key challenge in biology. Kingfishers (Aves: Alcedinidae) are a cosmopolitan avian radiation with diverse colors, diets, and feeding behaviors—including the archetypal plunge-dive into water. Transitioning from air to water poses major sensory and locomotor challenges that might affect the evolution of both sensory genes and morphological structures involved in these functions. Kingfishers therefore offer a powerful opportunity to explore the effects of convergent behaviors on the evolution of genomes and phenotypes, as well as direct comparisons between continental and island lineages. Here, we use whole-genome sequencing of 31 diverse kingfisher species to identify the genomic signatures associated with convergent feeding behaviors. We show that species with smaller ranges (i.e., on islands) have experienced stronger demographic fluctuations than those on continents, and that these differences have influenced the dynamics of molecular evolution. Comparative genomic analyses reveal positive selection and genomic convergence in brain and dietary genes in plunge-divers. These findings enhance our understanding of the connections between genotype and phenotype in a diverse avian radiation.</p>
Data from: Genomic signatures of convergent shifts to plunge-diving behavior in birds
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Diving behaviors of juvenile northern and southern elephant seals
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Data from: The nightscape of the Arctic winter shapes the diving behavior of a pelagic predator
<p><span>Predator-prey interactions in marine ecosystems are dynamically structured by light, which is exemplified by diel vertical migrations of low-trophic level organisms. At high latitudes, the long winter nights provide foraging opportunities for marine predators targeting vertically migrating prey closer to the surface at night while minimizing energy expenditure, but there is limited documentation of such diel patterns under extreme light regimes. To address this knowledge gap, we recorded the diving behavior of 17 harbour porpoises just south of the Arctic circle in West Greenland, from summer to winter. Unlike classical diel vertical migration, the porpoises dove three times deeper at night and the frequency of deep dives (>100 m) increased tenfold as they entered the darkest months. The daily mean depth was negatively correlated with daylength, confirming this reverse diel migration and suggesting an increased activity—presumably to target prey at greater depths—when approaching the polar night. Our findings illustrate a light-mediated strategy in which harbour porpoises would maximize energy gain during long periods of darkness while minimizing energy expenditure by accessing vertically migrating prey, which are otherwise inaccessible in deep waters. Extreme light regimes observed at high latitudes are therefore critical in structuring pelagic communities and food webs.</span></p>
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