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25 results for “delphinids”
Figure 6 in A new delphinid from the lower Pliocene of the North Sea and the early radiations of true dolphins
Figure 6. Schematic dorsal view of the skull in a series of extant small to medium size delphinids. Based on original photos (in large part kindly provided by Giovanni Bianucci) and Arnold and Heinsohn (1996, for Orcaella brevirostris). All crania scaled at same bizygomatic width. Scale bars = 100 mm.
Figure 3 in A new delphinid from the lower Pliocene of the North Sea and the early radiations of true dolphins
Figure 3. Skull of Pliodelphis doelensis gen. nov., sp. nov. IRSNB M.2330 (holotype): (a) right lateral view and (b) interpretive line drawing. Dotted lines correspond to incomplete elements.
Figure 1 in A new delphinid from the lower Pliocene of the North Sea and the early radiations of true dolphins
Figure 1. Locality maps of Antwerp in the north of Belgium and the Deurganck Dock where the specimen studied here (IRSNB M.2330) was discovered, in the Antwerp harbour, as well as other neighbouring docks and a tunnel where geological sections have been studied. Modified from Bisconti et al. (2017).
Figure 5 in A new delphinid from the lower Pliocene of the North Sea and the early radiations of true dolphins
Figure 5. Schematic dorsal view of the skull in a series of extinct delphinids from the late Miocene and Plio–Pleistocene. Based on photos and drawings in Bianucci (1996, 2013); Fordyce et al. (2002); Aguirre-FernAEndez et al. (2009); Murakami et al. (2014), Kimura and Hasegawa (2020), and photos kindly provided by Mark Bosselaers (for Tursiops oligodon) and Giovanni Bianucci (for Lagenorhynchus harmatuki and Stenella rayi). Dotted lines correspond to incomplete elements. All crania scaled at same width of premaxillary sac fossae. Scale bars = 100 mm.
Figure 4 in A new delphinid from the lower Pliocene of the North Sea and the early radiations of true dolphins
Figure 4. Skull of Pliodelphis doelensis gen. nov., sp. nov. IRSNB M.2330 (holotype): (a) ventral view, lacking the basicranium, (b) posterior view, and (c) anterodorsal view.
Figure 2 in Delphinid brain development from neonate to adulthood with comparisons to other cetaceans and artiodactyls
Figure 2. Linear relationship between neonate brain volume and gestation duration (in days). The regression includes only delphinids. Other species were plotted but not included in the regression. The species O. orca is indicated by a black arrow. There is a strong, positive correlation between neonatal delphinid brain volume and gestation duration; gestation duration scales to the 0.23 power of neonatal brain volume.
Figure 1 in Delphinid brain development from neonate to adulthood with comparisons to other cetaceans and artiodactyls
Figure 1. There is a strong, positive correlation between maternal body mass and neonatal brain mass in these four delphinid species; neonatal brain mass scales to the 0.51 power of maternal body mass.
Figure 4 in Delphinid brain development from neonate to adulthood with comparisons to other cetaceans and artiodactyls
Figure 4. Encephalization quotient (EQ) and body lengths. Body lengths are used as a general indicator for maturity of these animals. Three delphinids (Orcinus orca, Tursiops truncatus, and Stenella coeruleoalba) are compared with EQ and body length against two members of Physeteroidea (Kogia breviceps and Physeter macrocephalus) and one member of Phocoenidae (Phocoenoides dalli). In each case, EQ declines as the animal grows toward a mature body length and perhaps beyond. EQ was measured directly from brain masses, except for a few of the larger O. orca for which brain mass was calculated from endocranial volume. Body mass varies considerably in mature animals. As a result, EQ in mature T. truncatus varies from around 3 to 5 and in O. orca from about 1.5 to 3. One outlier EQ value of 2 from a male T. truncatus was from an overweight animal.
Figure 3 in Delphinid brain development from neonate to adulthood with comparisons to other cetaceans and artiodactyls
Figure 3. Brain mass relative to maturity (assessed by body length) in six different species. The horizontal line in each species plot represents the length at maturity. Female killer whales (a) (O. orca) reach sexual maturity at about 460 cm body length and as young as 8 yr of age (Dahlheim and Heyning 1999), while male killer whales (b) reach sexual maturity at about 520 cm length when they are around 15 yr of age (Dahlheim and Heyning 1999). Female Common bottlenose dolphins (c) (T. truncatus) reach sexual maturity at a length of 235 cm and at an average age of 8–9 yr (Wells and Scott 1999), and males (d) reach sexual maturity at a length of about 245 cm and an approximate age of 10 yr (Wells and Scott 1999). Female striped dolphins (e) (S. coeruleoalba) reach sexual maturity at 180 cm and about 7 yr of age (Perrin et al. 1994); males (f) reach sexual maturity at about 185 cm and about 11 yr of age (Perrin et al. 1994). Female pygmy sperm whales (g) (K. breviceps) reach sexual maturity at about 266 cm body length (Caldwell and Caldwell 1989), and males (h) reach sexual maturity at about 270 cm length (Caldwell and Caldwell 1989). Female spinner dolphins (i) (S. longirostris) reach sexual maturity at a length of 165 cm and at an average age of 4–7 yr (Perrin and Gilpatrick 1994) while males of this species (j) attain sexual maturity at a length of about 160 cm and an approximate age of 7–10 yr (Perrin and Gilpatrick 1994). Lastly, female Dall's porpoises (k) (P. dalli) reach sexual maturity at 174 cm and about 5 yr of age (Houck and Jefferson 1999), and males (l) reach sexual maturity at about 175 cm and about 5 yr of age (Houck and Jefferson 1999).
Table 3 in Delphinid brain development from neonate to adulthood with comparisons to other cetaceans and artiodactyls
<p><i>Table 3.</i> Gestation and brain size. The predicted gestation period was derived by applying the Sacher and Staffeldt formula and using our brain mass data. Sheep (<i>O. aries</i>), cows (<i>B. taurus</i>), giraffes (<i>G. camelopardalis</i>), and hippopotamuses (<i>H. amphibius</i>) were included in the table to compare cetaceans to other members of the Cetartiodactyla taxonomic order. Humans (<i>H. sapiens</i>) were also included for comparison. Cetaceans appear to have similar neonatal/adult brain mass ratios compared to other animals of the Cetartiodactlya order. Sources for the published gestation durations and cetacean brain masses can be found in Table S1.</p><table><thead><tr><th></th><th></th><th></th><th></th><th>Published</th><th>Predicted</th></tr></thead><tbody><tr><th>Taxonomic family</th><td>Neonatal</td><td>Adult brain</td><td>Neonate/</td><td>gestation</td><td>gestation</td></tr><tr><th>Genus species</th><td>brain mass (g)</td><td>mass (g)</td><td>adult (%)</td><td>(days)</td><td>(days)</td></tr><tr><th colspan="6">Delphinidae</th></tr><tr><th><i>C. commersonii</i></th><td>370</td><td>783</td><td>47.3</td><td>334</td><td>324</td></tr><tr><th><i>D. delphis</i></th><td>430</td><td>715</td><td>60.2</td><td>363</td><td>359</td></tr><tr><th><i>G. griseus</i></th><td>796</td><td>2,132</td><td>37.3</td><td>410</td><td>386</td></tr><tr><th><i>L. acutus</i></th><td>733</td><td>1,285</td><td>57</td><td>365</td><td>401</td></tr><tr><th><i>L. obliquidens</i></th><td>523</td><td>1,198</td><td>43.6</td><td>356</td><td>352</td></tr><tr><th><i>O. orca S. attenuata S. longirostris</i></th><td>3,006 353 247</td><td>6,642 711 541</td><td>45.3 49.6 45.6</td><td>553 — —</td><td>566 304a 286a</td></tr><tr><th><i>S. bredanensis</i></th><td>706</td><td>1,454</td><td>48.6</td><td>378</td><td>388</td></tr><tr><th><i>T. truncatus</i></th><td>685</td><td>1,550</td><td>44.2</td><td>376</td><td>377</td></tr><tr><th colspan="6">Monodontidae</th></tr><tr><th><i>D. leucas</i></th><td>938</td><td>2,087</td><td>44.9</td><td>456</td><td>414</td></tr><tr><th colspan="6">Phocoenidae</th></tr><tr><th><i>P. phocoena</i></th><td>242</td><td>506</td><td>47.7</td><td>316</td><td>266</td></tr><tr><th><i>P. dalli</i></th><td>270</td><td>803</td><td>33.6</td><td>334</td><td>282</td></tr><tr><th colspan="6">Physeteridae</th></tr><tr><th><i>P. macrocephalus</i></th><td>3,308</td><td>7,693</td><td>43</td><td>547</td><td>582</td></tr><tr><th colspan="6">Pontoporiidae</th></tr><tr><th><i>P. blainvillei</i></th><td>154.9</td><td>223.9</td><td>69.2</td><td>319</td><td>271</td></tr><tr><th colspan="6">Ziphiidae</th></tr><tr><th><i>M. europaeus</i></th><td>971</td><td>1,680</td><td>57.8</td><td>—</td><td>—</td></tr><tr><th colspan="6">Balaenopteridae</th></tr><tr><th><i>B. physalus</i></th><td>2,640</td><td>6,718</td><td>39.3</td><td>342</td><td>537</td></tr><tr><th>Bovidae <i>B. taurus O. aries</i></th><td>199b 69</td><td>456b 130d</td><td>43.6 53</td><td>278c 150e</td><td>270 208</td></tr><tr><th>Giraffidae <i>G. camelopardalis</i></th><td>428f</td><td>537f</td><td>79.7</td><td>459c</td><td>363</td></tr><tr><th>Hippopotamidae <i>H. amphibius</i></th><td>195b</td><td>590b</td><td>33.1</td><td>240e</td><td>258</td></tr><tr><th>Hominidae <i>H. sapiens</i></th><td>380g</td><td>1,400b</td><td>27</td><td>280e</td><td>324</td></tr></tbody></table><p><sup>a</sup> Perrin <i>et al.</i> (1977).</p><p><sup>b</sup> Sacher and Staffeldt (1974).</p><p><sup>c</sup> Kiltie (1982).</p><p><sup>d</sup> Minervini <i>et al.</i> (2016).</p><p><sup>e</sup> Hayssen <i>et al.</i> (1993).</p><p><sup>f</sup> <i>Gra¨ıc et al.</i> (2017).</p><p><sup>g</sup> Blinkov and Glezer (1968).</p>
Table 2 in Delphinid brain development from neonate to adulthood with comparisons to other cetaceans and artiodactyls
<p><i>Table 2.</i> Comparison of seven terrestrial cetartiodactyls (and the African elephant) with eight aquatic cetartiodactyls on brain and body mass for neonates and adults. ABoM = adult body mass; ABrM = adult brain mass; NBoM = neonatal body mass; NBrM = neonatal brain mass. All brain and body mass data for the aquatic species come from Table S1.</p><table><thead><tr><th></th><th></th><th>ABoM</th><th>ABrM</th><th>NboM</th><th>NBrM</th><th>Aquatic</th><th></th><th>AboM</th><th>ABrM</th><th>NboM</th><th>NBrM</th></tr></thead><tbody><tr><th>Terrestrial species</th><td>Common name</td><td>(kg)</td><td>(g)</td><td>(kg)</td><td>(g)</td><td>species</td><td>Common name</td><td>(kg)</td><td>(g)</td><td>(kg)</td><td>(g)</td></tr><tr><th><i>D. dorcas phillipsi S. scrofa</i></th><td>Blesbok antelope Wild boar</td><td>60a 149b</td><td>155a 133b</td><td>— —</td><td>— —</td><td><i>D. delphis L. acutus</i></td><td>Common dolphin Atlantic white-sided</td><td>68 156</td><td>715 1,285</td><td>11 28</td><td>430 733</td></tr><tr><th><i>T. strepsiceros G. camelopardalis C. bactrianus</i></th><td>Greater kudu Giraffe Bactrian camel</td><td>218a 470c 594d</td><td>307a 537c 518d</td><td>— 150c —</td><td>— 428c —</td><td><i>T. truncatus G. griseus G. macrorhynchus</i></td><td>dolphin Bottlenose dolphin Risso’s dolphin Short-finned pilot</td><td>190 301 654</td><td>1,550 2,132 2,679</td><td>18 85 —</td><td>685 796 —</td></tr><tr><th><i>B. taurus H. amphibius</i></th><td>Cow Hippopotamus</td><td>598e 1,351f</td><td>492e 720f</td><td>25g 40g</td><td>199g 195g</td><td><i>D. leucas G. melas</i></td><td>whale Beluga Long-finned pilot</td><td>560 1,369</td><td>2,087 3,499</td><td>50 —</td><td>938 —</td></tr><tr><th><i>L. africana</i></th><td>African elephant</td><td>5,000a</td><td>4,619a</td><td>—</td><td>1,724h</td><td><i>O. orca</i></td><td>whale Killer whale</td><td>3,723</td><td>6,642</td><td>171</td><td>3,006</td></tr></tbody></table><p><sup>a</sup> Herculano-Houzel (2015).</p><p><sup>b</sup> Minervini <i>et al</i>. (2016).</p><p><sup>c</sup> <i>Gra¨ıc et al</i>. (2017).</p><p><sup>d</sup> Xie <i>et al.</i> (2011).</p><p><sup>e</sup> Ballarin <i>et al</i>. (2016).</p><p><sup>f</sup> Silva and Downing (1995).</p><p><sup>g</sup> Sacher and Staffeldt (1974).</p><p><sup>h</sup> Shoshani <i>et al.</i> (2006).</p>
Behavioral response of delphinids to sonar - supporting acoustic data
<p>Oceanic delphinids around naval operations are regularly exposed to intense military sonar broadcast within the frequency range of their hearing. However, empirically measuring the impact of sonar on the behavior of highly social, free-ranging dolphins is challenging. Additionally, baseline variability or the frequency of vocal state-switching among social oceanic dolphins during undisturbed conditions is lacking, making it difficult to attribute changes in vocal behavior to anthropogenic disturbance. Using a network of drifting acoustic buoys in controlled exposure experiments, we investigated the effects of mid-frequency (3-4 kHz) active sonar (MFAS) on whistle production in short-beaked (<em>Delphinus delphis delphis</em>) and long-beaked common dolphins (<em>Delphinus delphis bairdii</em>) in southern California. Given the complexity of acoustic behavior exhibited by these group-living animals, we conducted our response analysis over varying temporal windows (10 min – 5 s) to describe both longer-term and instantaneous changes in sound production. We found that common dolphins exhibited acute and pronounced changes in whistle rate in the 5 s following exposure to simulated Navy MFAS. This response was sustained throughout sequential MFAS exposures within experiments simulating operational conditions, suggesting that dolphins may not habituate to this disturbance. These results indicate that common dolphins exhibit brief yet clearly detectable acoustic responses to MFAS. They also highlight how variable temporal analysis windows – tuned to key aspects of baseline vocal behavior as well as experimental parameters related to MFAS exposure – enable the detection of behavioral responses. We suggest future work with oceanic delphinids explore baseline vocal rates a-priori and use information on the rate of change in vocal behavior to inform the analysis time window over which behavioral responses are measured.</p>
Table 1 in Delphinid brain development from neonate to adulthood with comparisons to other cetaceans and artiodactyls
<p><i>Table 1.</i> Comparison of adult and neonate index of encephalization (EQ) for 15 cetacean (1 mysticete, 14 odontocete) species. EQs were derived from brain and body masses in Table S1.</p><table><thead><tr><th>Family</th><th>Species</th><th>Adult EQ</th><th>Neonate EQ</th></tr></thead><tbody><tr><th>Balaenopteridae</th><td><i>B. physalus</i></td><td>0.495</td><td>2.297</td></tr><tr><th>Delphinidae</th><td><i>C. commersonii</i></td><td>5.149</td><td>7.234</td></tr><tr><th></th><td><i>D. delphis</i></td><td>3.962</td><td>7.801</td></tr><tr><th></th><td><i>G. griseus</i></td><td>4.055</td><td>5.807</td></tr><tr><th></th><td><i>L. acutus</i></td><td>3.805</td><td>6.632</td></tr><tr><th></th><td><i>L. obliquidens</i></td><td>4.635</td><td>8.315</td></tr><tr><th></th><td><i>O. orca</i></td><td>2.425</td><td>8.317</td></tr><tr><th></th><td><i>S. bredanensis</i></td><td>5.633</td><td>8.065</td></tr><tr><th></th><td><i>T. truncatus</i></td><td>3.972</td><td>8.328</td></tr><tr><th>Physeteridae</th><td><i>P. macrocephalus</i></td><td>0.681</td><td>4.402</td></tr><tr><th>Kogiidae</th><td><i>K. breviceps</i></td><td>1.703</td><td>4.767</td></tr><tr><th>Pontoporiidae</th><td><i>P. blainvillei</i></td><td>1.930</td><td>2.571</td></tr><tr><th>Monodontidae</th><td><i>D. leucas</i></td><td>2.643</td><td>5.764</td></tr><tr><th>Phocoenidae</th><td><i>P. phocoena</i></td><td>2.837</td><td>4.406</td></tr><tr><th></th><td><i>P. dalli</i></td><td>2.909</td><td>3.275</td></tr></tbody></table>
Quantifying the age-structure of free-ranging delphinid populations: testing the accuracy of Unoccupied Aerial System-photogrammetry
<p><span>Understanding the population health status of long-lived and slow-reproducing species is critical for their management. However, it can take decades with traditional monitoring techniques to detect population-level changes in demographic parameters. Early detection of the effects of environmental and anthropogenic stressors on vital rates would aid in forecasting changes in population dynamics and therefore inform management efforts. Changes in vital rates strongly correlate with deviations in population growth, highlighting the need for novel approaches that can provide early warning signs of population decline (e.g., changes in age-structure). We tested a novel and frequentist approach, using Unoccupied Aerial System- (UAS) photogrammetry, to assess the population age-structure of small delphinids. First, we measured the precision and accuracy of UAS-photogrammetry in estimating total body length (TL) of trained bottlenose dolphins (<em>Tursiops</em> <em>truncatus</em>). Using a log-transformed linear model, we estimated TL using the blowhole-to-dorsal-fin-distance (BHDF) for surfacing animals. To test the performance of UAS-photogrammetry to age-classify individuals, we then used length measurements from a 35-year dataset from a free-ranging bottlenose dolphin community to simulate UAS-estimates of BHDF and TL. We tested five age-classifiers and determined where young individuals (<10 years) were assigned when misclassified. Finally, we tested whether UAS-simulated BHDF only or the associated TL estimates provided better classifications. TL of surfacing dolphins was overestimated by 3.3% ±3.1% based on UAS-estimated BHDF. Our age-classifiers performed best in predicting age-class when using broader and fewer (two and three) age-class bins with ~80% and ~72% assignment performance, respectively. Overall, 72.5-93% of the individuals were correctly classified within two years of their actual age-class bin. Similar classification performances were obtained using both proxies. UAS-photogrammetry is a non-invasive, inexpensive, and effective method to estimate TL and age-class of free-swimming dolphins. UAS-photogrammetry can facilitate the detection of early signs of population changes, which can provide important insights for timely management decisions.</span></p>
Behavioral response of delphinids to sonar - supporting acoustic data
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Quantifying the age-structure of free-ranging delphinid populations: testing the accuracy of Unoccupied Aerial System-photogrammetry
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Bounding-box detection data for delphinid whistles
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Data from: Limited trophic partitioning among sympatric delphinids off a tropical oceanic atoll
Understanding trophic relationships among marine predators in remote environments is challenging, but it is critical to understand community structure and dynamics. In this study, we used stable isotope analysis of skin biopsies to compare the isotopic, and thus, trophic niches of three sympatric delphinids in the waters surrounding Palmyra Atoll, in the Central Tropical Pacific: the melon-headed whale (Peponocephala electra), Gray's spinner dolphin (Stenella longirostris longirostris), and the common bottlenose dolphin (Tursiops truncatus). δ15N values suggested that T. truncatus occupied a significantly higher trophic position than the other two species. δ13C values did not significantly differ between the three delphinds, potentially indicating no spatial partitioning in depth or distance from shore in foraging among species. The dietary niche area—determined by isotopic variance among individuals—of T. truncatus was also over 30% smaller than those of the other species taken at the same place, indicating higher population specialization or lower interindividual variation. For P. electra only, there was some support for intraspecific variation in foraging ecology across years, highlighting the need for temporal information in studying dietary niche. Cumulatively, isotopic evidence revealed surprisingly little evidence for trophic niche partitioning in the delphinid community of Palmyra Atoll compared to other studies. However, resource partitioning may happen via other behavioral mechanisms, or prey abundance or availability may be adequate to allow these three species to coexist without any such partitioning. It is also possible that isotopic signatures are inadequate to detect trophic partitioning in this environment, possibly because isotopes of prey are highly variable or insufficiently resolved to allow for differentiation.
FIGURE 2 in Molecular Identification of Delphinids and Finless Porpoise (Cetacea) from the Arabian Sea and Bay of Bengal
FIGURE 2. Illustrations of the cetaceans sampled in this study. a—Stenella longirostris: specimens CH02 & CH10 (haplotype code: IndSl2); b—Stenella longirostris: specimen Dol04 (haplotype code: IndSl5); C—Stenella longirostris: specimens Dol05 & Dol06 (haplotype code: IndSl9); d—Tursiops aduncus: specimen CH08 (haplotype code: IndTa2); e—Delphinus capensis (?): specimen Dol03 (haplotype code: IndDc2); f—Sousa chinensis: specimen MNG4 (haplotype code: IndSc1); g— Neophocaena phocaenoides: specimen MNG5 (haplotype code: IndNp1).
Niche variation in sympatric delphinids: Indo-Pacific bottlenose and Indian Ocean humpback dolphins on the south-east coast of South Africa
The ecological interactions and mechanisms that mediate coexistence among species are often poorly understood, despite its relevance to conservation management. A case in point is the Indo-Pacific bottlenose dolphin (Tursiops aduncus) and Indian Ocean humpback dolphin (Sousa plumbea) that occur sympatrically along the south-east coast of South Africa. To improve on our understanding of their coexistence, data on spatial distribution and habitat use were collected using boat-based surveys during 2014-15 along 145 km between Goukamma and Tsitsikamma MPAs. During 235 hours of survey effort, 55 and 42 encounters with T. aduncus and S. plumbea were recorded respectively. We investigated differences in their space use and habitat preferences. There was a strong overlap of the core areas used by both species, located mainly along Goukamma MPA and the eastern section of Plettenberg Bay. For both species, foraging was observed in mornings and travelling in afternoons. T. aduncus showed a preference for rocky areas in the afternoon while S. plumbea preferred estuarine and sandy habitats. We demonstrated that coexistence among dolphins with overlapping distributional patterns can be driven by fine-scale spatial segregation in feeding activities. Spatial conservation management measures such as the protection of sandy and estuarine habitats are recommended.
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Annotated Behaviour and Observability Dataset (ABODe)
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