We estimate the abundance of minke whales (Balaenoptera acutorostrata) from the Icelandic coastal shelf aerial surveys carried out as part of the 1987 and 2001 North Atlantic Sightings Sur- veys (NASS). In the case of the 1987 survey, the probability of detecting animals at distance zero (g(0)) is very close to 1 but there is substantial random measurement error in estimating distanc- es. To estimate abundance from these data, we use methods which assume g(0)=1 but which in- clude a distance measurement error model. In the case of the 2001 survey, measurement errors were sufficiently small to be negligible, and we use double platform methods which estimate g(0) and assume no measurement error to estimate abundance. From the 1987 survey, we esti- mate abundance to be 24,532 animals, with 95% CI (13,399; 44,916). From the 2001 NASS survey data, minke whale abundance is estimated to be 43,633 animals, with 95% CI (30,148; 63,149). Borchers, D.L., Pike, D.G., Gunnlaugsson, Th. and Víkingsson, G.A. 2009. Minke whale abundance estimation from the NASS 1987 and 2001 aerial cue–counting surveys taking appropriate account of distance estimation errors. NAMMCO Sci. Publ. 7:95-110.
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An alternative is to use ˆ N obs only to estimate mean school size, and to use conventional line transect methods on schools to estimate school abundance. If schools are readily identiﬁable (using some pre-deﬁned criteria) and detection probability does not depend on school size, this seems OK to me - although it is hardly elegant. Details to be worked out.
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sampled in relative abundance estimates–or a series of absolute abundance estimates. Here we show that generating absolute abund- ance estimates, i.e., that representing the entire population size, would have to include surveying the sea ice zone using icebreaker- supported helicopters or using fixed-wing aircraft operating from airfields on the continent (with provision to estimate both perception and availability bias). Our results imply that detecting trends in total minke whale abundance and predicting species-level responses to changes in sea ice or other environmental drivers will be far more challenging and expensive than merely conducting circumpolar ship-board surveys in open water.
A spatial model was developed to model humpback, fin, and minke whale distribution from the CCAMLR-IWC data (Hedley et al. 2001). In fact, the CCAMLR-IWC estimate of 6991 animals was approximately 3.8 times our estimate of 1829. The CCAMLR-IWC estimate of 7395 minke whales was 4.8 times as high as our estimate of 1544. Our fin whale estimate of 4427 whales was approximately three times as high as the CCAMLR-IWC estimate of 1492. These calculations represent very simplistic comparisons of very complex systems: indeed, not all whales return to the feeding grounds annually, so neither study’s estimates should be interpreted as “truth.” However, in all three cases, the differences were in the direction of the trend predicted. And the differences are on the order of magnitude that one would expect given estimated differences in study area size, and observed inter- specific differences in animal density. Thus, we conclude that our methods provided a rough but unbiased estimate of the average whale abundance in the region during the two austral summers of the study. Including both years in the study was necessary because of sample size constraints, but may have introduced additional variability. Ongoing work using these platforms will allow us to assess in future inter-annual variability in whale density in this region, as well as factors that influence inter-annual variability in animal distribution.
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Although the differences between the WSA humpback whale abundance estimates in 2008 from the aerial and the ship surveys (difference = 7,110, CV = 0.641) can be justified in many ways, their confidence intervals overlap. However, numbers are undoubtedly different, with the point estimate of the former representing nearly half of the latter. This should be confirmed by a statistical test (z-test), which would require the degrees of freedom of both estimates. While both are subject to potential sources of bias, we consider the ship survey reported here to be robust and more representative of the size of the population wintering in coastal waters off Brazil. This is justified by the more accurate estimates of group sizes and because the probabil- ity of missing groups on the trackline is substantially reduced when surveys of marine mam- mals are conducted from a slow surface platform. In fact, studies conducted to estimate correction factors for whales that are missed on the trackline during shipboard surveys sug- gested that detection of species with visible bodies and conspicuous blows, such as humpback whales, are close to 100% [57,58].
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To derive abundance estimates for deep diving species, we used data from three different surveys, which used the same data collection methodology to ensure consistency across the entire surveyed region. Although the SCANS-II survey occurred two years prior to the CODA and T-NASS surveys, 90% of the sightings of deep diving species were made during the 2007 offshore surveys. In addition, there is no reason to believe that any directional shift occurred in distribution and abundance during the period between surveys. The effect of any random changes in distribution does not cause bias but may increase the variance of the abundance estimates, so-called additional variance or “process error” - see, for example, the Norwegian six year cycle of “mosaic” surveys to estimate minke whale abundance in the North-East Atlantic (Solvang et al., 2015). Thus, our estimates of abundance should be unbiased but the variance could be underestimated.
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Abstract: Amikacin, an aminoglycoside antimicrobial, was administered to a killer whale (Orcinus orca) and a beluga whale (Delphinapterus leucas) for the treatment of clinical signs consistent with gram-negative aerobic bacterial infections. Dosage regimens were designed to target a maximal plasma concentration 8–10 times the minimum inhib- itory concentrations of the pathogen and to reduce the risk of aminoglycoside toxicity. Allometric analysis of published pharmacokinetic parameters in mature animals yielded a relationship for amikacin’s volume of distribution, in milliliters, given by the equation Vd 5 151.058(BW) 1.043 . An initial dose for amikacin was estimated by calculating the volume
It is concerning that narrow-ridged finless porpoises and common dolphins comprised a significant proportion of whale meat sales in South Korea. These species might be disguised as minke whale meat in the markets because of the supply and demand of these species and because of the difference in the market value of the different meats. In South Korea, whale meat consumers favor minke whale meat over other cetacean species because of its flavor. Han (2012) reported that 400 individuals of minke whale are necessary to meet the annual domestic demand. However, the official, mean annual number of minke whales available from bycatch has been far less, at ~80 per year, for the last 10 years (Table 2). Conversely, a large number of narrow-ridged finless porpoises, which are not as popular with consumers, are caught as bycatch every year. The imbalance between supply and demand of minke whale meat and narrow-ridged finless porpoise meat has also resulted in a large difference in market value between these two species. The price of minke whale meat is ~60 times that of narrow-ridged finless porpoise meat in South Korean markets. This difference in market value is presumably a strong incentive for merchants to disguise a species that has a high rate of bycatch, such as narrow-ridged finless porpoise and common dolphin, as the species that has high consumer demand, i.e., minke whale.
Distance_X_rand=abs(C*X_rand (j)-P (i, j)); equation9 P (i, j) =X_rand (j)-A*Distance_X_rand; equation10 Seyedali Mirjalili and Andrew Lewis tested 29 mathematical benchmark fuctions (unimodel[F1 to F7] ,multimodel [F8 to F13],fix dimension multimodel[F14 to F23] and composite [F24 to F29]) using Whale optimization AlgorithmWOA, Differential Evolution(DE) (Stron.R,PriceK,1997), Fas t Evolutionary Algorithm(FEP)(Yao X et al,1999) , Particle Swarm Optimization(PSO) (KennedyJ,Eberhart R,1995) ,Gravitational Search Algorithm(GSA) (Rashedi E et al,2009) and find best optimal value of each benchmark objective function for every algorithm.
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the sound source. As we have illustrated with simulations, the same error structure will result in localization errors quite distinct for good geometries, e.g., sources half way between units, vs. bad geometries, e.g., sources approximately along the line going through the sensors. Note in particular that for a source exactly along that line even in the absence of measurement error no position estimate would be possible, since we would get two 3D parallel bearing angles. That ambiguity fades away given that in general the whale will not be on the same plane, i.e. at the same depth, as the units, but in practice that might not be enough for a reliable position estimate.
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Beaked whale FM clicks resemble the FM signals used by bats, which are believed to rely on a matched filter receiver model where the animal innately compares the received echo with the outgoing click to obtain ranging information. Why and whether beaked whales would rely on a different technique from other odontocetes remains unknown and might be related to their unique life history. Johnson et al. hypothesized that the use of FM signals during the search phase might improve the detection and discrimination of specific prey in a scattered environment, thus “maximizing the net energy return of foraging during long breath-hold dives” [p. 5047 in Johnson et al. (Johnson et al., 2006)]. If M. densirostris relies on a different echolocation strategy to locate and identify their prey and use “prey- specific signatures in the returning echoes” [p. 5047 in Johnson et al. (Johnson et al., 2006)] (Madsen et al., 2005), extremely sensitive hearing in the frequency range of the FM clicks would represent a definite advantage to cross correlate the returning echo to the emitted signal. Interestingly, the audiogram range of best hearing does not overlap as well with the frequency range of the terminal buzz clicks, which indicates that the animal might not fully hear these broadband clicks (–10dB bandwidth from 25 to 80kHz) (Johnson et al., 2006). Although acoustic tagging research has provided a more comprehensive picture of the ecology and behavior of beaked whales, these species remain amongst the most cryptic marine mammals. Some species have been only identified only within the last 10years and have never been observed alive (Reyes et al., 1991; Dalebout et al., 2002). Most of the knowledge about beaked whales has been obtained through strandings. In recent years, special interest has arisen after multiple unusual mass strandings have been linked to military exercises (reviewed in Cox et al., 2006; Rommel et al., 2006; Nowacek et al., 2007; D’Amico et al., 2009; Filadelfo et al., 2009a; Filadelfo et al., 2009b). MFAS uses frequencies between 1 and 10kHz (D’Amico and Pittenger, 2009). The Blainville’s beaked
African countries, in the vote about whether to resume the ivory trade at the 10 th CITES Meeting in Zimbabwe in 1997. Danaher (2003: 164) writes that this tactic was ‘clear at both the annual IWC Meetings in 2000 and 2001, where Japan “encouraged” a bloc of six Caribbean countries to support its successful stance against establishing a whale sanctuary in the South Pacific’. To counter these accusations, officials from Japan’s Fisheries Agency claim that their ODA-induced incentives are no different from countries using military threats to get what they want (Komatsu 2001). At IWC meetings, the majority of Caribbean member states are vociferously supportive of Japan’s sustainable-use of non-endangered cetaceans attitude, mainly because, as anti-whaling NGOs would say, the Japanese Government helps fund various public institutions in the Caribbean states such as schools, hospitals and fish processing plants and vessels. Danaher (2002) suggests that this is why these countries support the lifting of the whaling moratorium despite development aid pressure or the threat of trade sanctions from the United States aimed at winning anti-whaling votes. In addition, Japanese officials frequently cite the threat of the anti-whaling position to the food security of these Caribbean nations. In an interview in 2007, Joji Morishita (Japan’s IWC Commissioner) stated 39
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Hb. Horse Mb was converted from the ferric (met) to the ferrous (oxy) form by standard procedures after adding solid dithionite and desalting on a PD10 column (GE Healthcare) equilibrated with the gel filtration buffer described above. Mb purity was assessed by sodium dodecyl sulfate (SDS) and isoelectric focusing (IEF) on polyacrylamide gels (Phast System, GE Healthcare). Isoelectric points (pIs) of the native purified Mbs were obtained by IEF in the pH3–9 range, using a Broad pI Kit (pH3–10, GE Healthcare). The heme oxygenation/oxidation state was assessed by ultraviolet–visible (UV-vis) absorption spectroscopy in the range 400–700nm by using absorption peaks known for Mb (Antonini and Brunori, 1971). All whale Mbs were purified as oxy derivatives, without detectable met heme. This shows that all whale Mbs were in good condition despite muscle samples and purified Mbs being of varying age.
I reproduce here the brief summary from that paper of the plot and main characters of the novel. In Moby-Dick, the narrator (‘Call me Ishmael’) is employed as a deck-hand on a whaling-ship, the Pequod, with its captain, Ahab, first mate Starbuck (latterly of coffee-shop fame) and second mate Stubb. It becomes gradually clear that for Ahab the purpose of this voyage is revenge: he is hell-bent on hunting and killing Moby Dick, the great white whale who was the cause of his lost leg. This fixation gradually takes over the whole mission; Ahab becomes increasingly obsessive and irrational, ultimately sealing his own fate and that of his crew with him. Ishmael alone lives to tell the tale. Right from the start it is clear that this is no ordinary seafaring yarn. The first chapters consist of ‘excerpts’ describing mentions of whales and whaling from a vast array of sources from the Bible, to
(Physeter macrocephalus) showing placement of the tag. B, brain; Bl, blow hole; Di, distal air sac; Fr, frontal air sac; Ju, junk; Ln, left naris; Ma, mandible; Mo, monkey lips/museau de singe; MT, muscle/tendon layer; Ro, rostrum; Rn, right naris; So, spermaceti organ; T, tag. Spermaceti oil is contained in the spermaceti organ and in the spermaceti bodies of the junk. The muscle/tendon layer covers the entire dorso-lateral part of the spermaceti organ and inserts into the connective tissue around and in front of the monkey lips. Arrows indicate the sound path according to the modified (by Møhl, 2001) theory of Norris and Harvey (1972): it is proposed that air forced from the right naris through the monkey lips generates the sound pulse. The majority of the sound energy is due to the geometry of the lips and the reflective properties of the distal air sac, directed backwards into the spermaceti organ. When it reaches the frontal air sac, the sound pulse is reflected into the junk complex and directed into the water in front of the whale. The
Evidence from a number of studies at aggregation sites such as Ningaloo Reef, Western Australia (Meekan et al. 2006), Belize (Heyman et al. 2001) and Christmas Island (Meekan et al. 2009) suggest that whale sharks congregate in coastal habitats to target local pulses of prey availability (Colman, 1997, Compagno, 2001). A wide range of planktonic and nektonic organisms including copepods, gelatinous zooplankton (such as salps, siphonophores and jellyfish), chaetognaths, krill, mysids, amphipods, sergestids, fish eggs, small fish, shrimp and crab larvae have been identified as whale shark prey (see review Rowat and Brooks, 2012). Most reports of diet are based either on anecdotal observations of whale sharks feeding in coastal surface waters during the day, plankton tows (Clark and Nelson, 1997, Heyman et al. 2001, Nelson and Eckert, 2007, Taylor 2007, Motta et al. 2010, Ketchum et al. 2013, Robinson et al. 2013, Rohner et al. 2015) or stomach (e.g. Silas and Rajagopalan, 1963) and faecal (Jarman and Wilson, 2004, Meekan et al. 2009) analyses. However, these methods have some well-recognized drawbacks as they can overestimate the importance of some prey and only represent ‘snapshots’ of recent feeding events (Iverson 2009). For these reasons, a more holistic approach to examine temporal and spatial patterns in the feeding habits of these sharks is required (Iverson 2009).
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These legal requirements have been based on current understanding of whale behaviour, in particular humpback whales, and the whales’ responses to contact with boats and people. As we learn more about dwarf minke whales and their responses to interaction with humans, additional specific provisions may be developed that complement existing legal requirements.
The Whale Shark is known as ‘Baghua Timi’ or ‘Timiri Magar’ in Odisha. Previous records of Whale Shark (both dead and live) along the Odisha coast are given in Table 1. Except one, all the Whale Shark sightings were reported either from the coastal waters off Rushikulya River mouth or Gopalpur during February–March (Bar
Plastic associated pollutants, including phthalates and brominated flame retardants [e.g., polybrominated diphenyl ethers (PBDEs)] and POPs (DDTs, PCBs, and other organochlorine compounds) were detected in biopsy sampled skin from whale sharks feeding in La Paz Bay and blubber from fin whales feeding in La Paz Bay and the Mediterranean Sea (Fossi et al., 2016, 2017). Microplastic abundance estimates in PB and the theoretical estimates of plastic ingestion by whale sharks at this location are markedly greater than those for La Paz Bay (Table 5). However, whether whale sharks, manta rays and other large filter feeders are exposed to comparable or higher levels of POPs as those reported for La Paz Bay and the Mediterranean is less certain. Based on a 2009 global assessment of POPs in coastal waters via the analysis of PE pellets collected from beaches (Ogata et al., 2009), pollution levels of PCBs and DDTs were markedly lower in Jakarta Bay, Indonesia than those in the American West Coast and the Mediterranean Sea. However, more recent pellet analyses indicate that Indonesian pollution levels have increased (International Pellet Watch, 2019). Heavy metal contamination of mobulids, including manta rays, has been confirmed (Essumang, 2010; Ooi et al., 2015); however, this contamination is yet to be linked to heavy metal exposure via plastic ingestion.
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Here is a summary of the solutions that achieved the best results on this dataset. Deep learning have been very successful in recent years with visual recognition problems. However, it was very di ffi cult to make it work for this type of data. This is mainly because the size of the data is very small. Anil Thomas from Nervana Systems  suggested locating the bonnet and the blow hole on the whale. The solution which is based on a deep learning library called Neon  used these two points to extract a patch that contains the whale’s head. This approach proved to be very e ff ective. First, it made the training process much faster because the training is being done on smaller images. Second, these head patches are better than the original images for training a deep learning model. This is mainly because training on these head patches made the model focus only on the most discriminative features (the head callosities) and ignore unimportant features such as features from the surrounding water.
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