Submitted Successfully!
To reward your contribution, here is a gift for you: A free trial for our video production service.
Thank you for your contribution! You can also upload a video entry or images related to this topic.
Version Summary Created by Modification Content Size Created at Operation
1 + 2583 word(s) 2583 2021-09-24 05:15:01 |
2 The format is correct + 276 word(s) 2859 2021-10-27 10:36:10 |

Video Upload Options

Do you have a full video?


Are you sure to Delete?
If you have any further questions, please contact Encyclopedia Editorial Office.
Li, D. Behavior Monitoring of Crustacean Aquaculture. Encyclopedia. Available online: (accessed on 14 June 2024).
Li D. Behavior Monitoring of Crustacean Aquaculture. Encyclopedia. Available at: Accessed June 14, 2024.
Li, Daoliang. "Behavior Monitoring of Crustacean Aquaculture" Encyclopedia, (accessed June 14, 2024).
Li, D. (2021, October 27). Behavior Monitoring of Crustacean Aquaculture. In Encyclopedia.
Li, Daoliang. "Behavior Monitoring of Crustacean Aquaculture." Encyclopedia. Web. 27 October, 2021.
Behavior Monitoring of Crustacean Aquaculture

Automatic behavior monitoring, also called automated analytics or automated reporting, is the ability of an analytics platform to auto-detect relevant insights—anomalies, trends, patterns—and deliver them to users in real time, without users having to manually explore their data to find the answers they need. An analytics platform with automated behavior monitoring uses algorithms to auto-analyze datasets to search for notable changes in data. 

aquaculture crustacean behavior acoustic technology machine vision movement sensor

1. Introduction

Aquaculture has become one of the largest commercial and economically important industries in recent years [1]. Lobsters, crayfish, crabs, crayfish, prawns, and shrimp are the most valuable crustacean species groups with significant production. Shrimp and prawn catches recorded new highs in 2017 and 2018 at over 336,000 tons [2]. In aquaculture, most of the modern information technologies are applied to production management and reliable monitoring of crustacean behavior is very important for aquaculture industries because it provides a starting point for welfare assessment [3][4]. Traditional crustacean behavior monitoring is mostly based on manual measurement. However, manual monitoring is usually laborious, time-consuming, and ineffective which thus limits its economic benefits [5][6].
Automatic behavior monitoring in aquaculture is defined as the application of process engineering principles and techniques to precision fishery farming to automatically monitor and recognize animal behavior [7][8]. Until now, scholars and researchers have developed various automatic methods to monitor crustacean behaviors in laboratories or ponds, including acoustic technology [9], machine vision [10], and movement sensors [11][12]. Compared with the environmental parameter detection system, automatic behavior monitoring is a posteriori indicated, but it is very meaningful for welfare evaluation [13][14]. In terms of feeding, moving, home range, and activity, rhythms may grasp biological behavior information, monitor animal health in real time, and provide early warning of diseases [15]. Therefore, real-time monitoring of individual behavior is important for improving production in crustaceans, and there is an urgent need for farmers to monitor behavior in real time, which allows fishermen to take actions in the initial stages of welfare or disease problems to meet the intensive aquaculture requirements [16].

2. Important Behaviors in Crustacean Aquaculture

2.1. Feeding Behavior

Feeding is the primary factor for determining the efficiency and cost of aqua feed, which may represent a considerable proportion of the crustacean farming budget [17]. Crustaceans use visual, mechanoreceptor, and chemoreceptor systems to detect the location of food sources, and when food is available, crustaceans change their sound signatures and movements [18]. Feeding behavior can reflect many aspects of an individual organism. The survival rate and molting cycle of red swamp crayfish are associated with different feeding rates [19]. Santos et al. revealed that white shrimp display nocturnal feeding and locomotor rhythms [20].

2.2. Movement Rhythms

In addition to feeding behavior, movement also plays a major role in determining the structure of populations and communities, as well as the evolution and diversity of life [21][22]. Movement rhythms are defined as the recurrence of any event within a biological system at more or less regular intervals. Crustacean movements can be categorized spatially as homing, nomadic, or migratory, and temporally as daily, ontogenetic, or seasonal [23][24]. As a basis for welfare assessment, the rhythms of movement behavior can only be used to help choose the correct location for fishing, and are not the core of assessing performance under aquaculture conditions. Therefore, monitoring of movement rhythms during the fishing season will be of great help for choosing the correct location for fishing.

2.3. Reproductive Behavior

The reproductive behavior of animals is a significant manifestation of their life and mating is a key step in reproduction [25][26]. The mating process includes approach, touch, mount, turn, rolling, and thrust [27], and this process lasts between 28 s and 6.40 min [28][29]. Obvious external action characteristics and behavior duration are the basis for monitoring using automated methods. Monitoring reproductive behavior can accurately determine when mating occurs, and guide fishermen to perform artificial insemination, thereby increasing reproductive yield. In addition, by monitoring whether the reproductive behavior is normal, disease monitoring and prevention can also be effectively carried out. Therefore, analysis of reproductive behavior can effectively improve crustacean production and larval quality.

3. Behavior Monitoring Methods Based on Acoustic Technology

Autonomous acoustic monitoring is a technique using sound waves to remotely measure information. Acoustic technology has been widely used in species identification [30], biomass estimation [31], and behavior monitoring without causing stress to crustaceans [32]. For underwater monitoring, acoustic technology has key advantages over light waves and electromagnetic waves because of the long propagation distances [14]; another advantage of acoustic technology is that its measurement results are less affected by water turbidity and underwater light [33]. According to data acquisition methods, acoustic technology can be divided into passive acoustics and active acoustics. Active acoustics includes sonar, echo, and acoustic telemetry. Sonar and echo technology are more used to measure the density of crustaceans, and acoustic telemetry is more common to monitor crustacean behaviors.

3.1. Passive Acoustics

Many investigations have indicated that when some behaviors occur, crustaceans emit different sound frequencies, including feeding [34], mating [35], carapace vibrations [36], snap [37], and stick and slip friction [38][39][40]. With such variety of sound production mechanisms, the characteristics of the sounds produced by crustaceans are diverse [41][42]. According to the above theoretical basis, experts can identify crustacean behaviors via long-term acoustic monitoring of sounds.

The mechanisms and spectral characteristics of crustacean behaviors are heterogenous. In terms of feeding sounds, the physical production mechanism is that shrimp use mandibles and maxillae to tear feed pellets into pieces before entering the oral cavity [42]. Some scholars have used the sound spectral features of feeding as an indication of pellet consumption [43]. These experimental results show that the correlation between sound and feeding behavior can reach more than 95%. Although passive acoustic technology can provide guidance for measuring the relative intensity of feeding activity, it is unclear how accurate it is at estimating the quantity of consumed pellets from feeding sounds.

3.2. Acoustic Telemetry

Acoustic telemetry is technology to transfer information underwater using sound; it was first used in the early 1970s and has been continuously improved over time [44][45]. Figure 1 is a schematic diagram of acoustic telemetry. An acoustic telemetry system designed specifically for aquaculture includes an acoustic receiver with hydrophones, radio smart transmitters, tags, and base station with antenna and computer [22]. Hydrophones are usually mounted on surface buoys, which listen to the tagged animals [46], and an acoustic transmitter sends out information, e.g., an ID code, as short tone-bursts, which are picked up, decoded, and timestamped by an acoustic receiver [47]. Finally, the radio sends tag information and a time stamp to the base station. Commonly, the base station analyzes the arrival time of different signals to determine the location of the underwater animals; this information consists of presence, movement, and behaviors of the tagged animal [48]. Therefore, this method is effective for estimating daily home ranges, core areas of activity [49], nomadic movements [50][51], activity patterns [52], and distance traveled, as well as behaviors [53] such as feeding, molting, and reproduction. It is worth noting that this technology cannot accurately gauge local movements.
Figure 1. Acoustic technology overview.
In summary, all the above studies show that acoustic telemetry can monitor aquatic animals in a free-living state with the advantage of location. The detailed information concerning crustacean behaviors derived from acoustic technology studies is listed in Table 1. Of course, acoustic telemetry also faces some difficulties and challenges. A common concern is the potentially adverse effects on animal survival and behaviors. The difficulty is that in order to obtain behavior data, the animal to be monitored must be tagged. In addition, telemetry projects are often relatively expensive.
Table 1. Detailed information for behavior monitoring by acoustic technology.
Technology Species Application Results or Accuracy Culture Model Acoustic Features/Principle Reference
Passive acoustics Tiger prawns Feeding R2 = 0.95 and R2 = 0.96 Tank and pond 3 kHz–7.6 kHz [54]
Prawn Confidence intervals:
98.4 ± 0.6
Pond 51.2 kHz [43]
Family Alpheidae Snap R = 0.71–0.92 West Bay Marine Reserve 1.5–20 kHz [55]
Red swamp crayfish Intraspecific interactions and activities 45% and p < 0.0001 Tank and natural environment Peak frequency = 28 kHz
Bandwidth RMS = 20 kHz
  European lobster Seasonal activity p < 0.05 Site Vemco 12 VR2W [57]
  Japanese spiny lobster Movement   Island 0.04–21 kHz [58]
Acoustic telemetry
Active acoustics
European spiny lobster Home range p < 0.001 Protected area Ultrasonic telemetry [52]
American lobsters (523.2 ± 78.1 m/day−1; r2 = 0.62, p = 0.0001) Enclosure VEMCO V8SC-2L [49]
Lobster SE = 0.09, p = 0.02 Coast Vemco V13P–L [6]
European spiny lobster Ranged from 1629.3 to 8641.3 m2 Coast Vemco V9P-1L 69 kHz [50]
Spiny lobster 923 versus 871 m/day, Channel Vemco V16 69 kHz [24]
American lobsters 51% moved <5 km, 19% moved 5–10 km, and 30% moved >10 km Inshore Vemco V13-1L 69 kHz [59]
Lobster The mean daily home range (n = 18) was 1002.0
± 195.7 m2 (mean ± SEM)
Castle VRAP model, VEMCO [60]
American lobsters Home ranges (≈27.4−111.6 m2) Castle VRAP  
Lobster Jasus lalandii Nomadic behavior p = 0.0002 Aquarium Vemco V8-2LR [51]
Lobster Reproductive migrations Three migrations per year by an individual female Western Sambo Ecological Reserve Vemco V16 69 kHz [53]
Lobster Feeding 90% confidence level (p = 0.09, K–S test) Field enclosure Vemco VR2W 69 kHz [32]
Spider crab Migratory patterns 70% recapture rate Coast VEMCO V16 [61]
  Norway lobsters Error < 1 m European waters Vemco [62]
  Lobster Movement patterns r2 = 0.82, DF = 70,
p < 0.0001
2.5 km2 lobster Vemco VRAP [22]
  Spider crab R = 0.353; R = 0.805 Coast VEMCO Ltd. [63]
  Blue crabs R > 0.64; R = 0.71–0.97;
R = 0.25–0.32
Coast Tucson Arizona [64]
  Lobsters; crab   Tank VEMCO Ltd. [65]
  Edible crab p < 0.001–0.042 Coast Vemco VR 60 [66]

4. Behavior Monitoring Based on Machine Vision

Underwater machine vision technology has been used since the 1950s to study the behavior, distribution, and abundance of marine and freshwater organisms [67]. Applications of machine vision have increased considerably in two major aquaculture domains, namely: (1) pre-harvesting and growth of underwater animals and (2) post-harvesting [68]. This technology can provide an effective means for the analysis of individual features [69][70], species classification [71], vocalizations [72], and behavior recognition within complex data sets at scales and resolutions not previously possible [73][74]. Machine vision technology can help us solve some important problems concerning ecology, social structure, collective behavior, communication, and welfare [75]. It can also save initial raw information for potential re-analysis, and record both visible benthic organisms and other biological activity [76]. Machine vision methods can quantitatively analyze behavior and greatly increase the efficiency, repeatability, and accuracy of image review, which is also a prominent advantage compared to acoustic technology. The typical equipment includes an industrial camera, source, acquisition card, and image processor. Based on the different wavelengths utilized by cameras, light can be divided into visible and infrared. The system structure and monitoring flow chart which utilizes visible light as the light source is shown in Figure 2.
Figure 2. System structure and monitoring flow chart.

4.1. Machine Vision Based on Visible Light

Machine vision technology based on visible light is widely used for crustacean behavior monitoring compared to other types of light sources. Extant studies on the monitoring of shrimp behavior can be divided into two categories. Direct methods use the measured videos or images to obtain the feature, trajectory, angle, velocity, and range of crustacean activities, as well as other parameters. With indirect methods, crustacean behavior is monitored from information on uneaten pellets recorded by a camera.

4.1.1. Direct Behavior Monitoring

Studies have shown that crustaceans exhibit particular behaviors in different physiological states [77]. According to the specifics of the experimental environment and the characteristics of action occurrence, image processing systems usually approach this by the applicable algorithms, including image preprocessing, image segmentation, and feature extraction. There are three major branches of image preprocessing, namely image reconstruction, image restoration, and image enhancement [78]. This involves many methods such as linear transformation, histogram equalization, filtering, increasing, and frequency domain enhancement [79]. Especially for aquatic creatures such as crustaceans, which can easily cause water turbidity, image preprocessing is commonly applied to improve the quality of turbid images. Due to the temporal and spatial characteristics of video images, the main idea of the moving target detection method is to extract the changed regions from the background in the video image [80][81]. In recent years, more and more methods have been proposed to provide accurate and consistent segmentation for moving target extraction; commonly used methods include threshold segmentation, region segmentation, and edge detection [82][83]. Analysis and extraction of target features is the final step of behavior identification of moving targets, involving color features, texture features, geometric features, and motion characteristics.

4.1.2. Indirect Behavior Monitoring

In addition, other information can be used to indirectly quantify crustacean behavior. Uneaten pellets and displacement represent important information for analyzing, identifying, and monitoring shrimp behavior. Therefore, such methods can be used to quantify particular behaviors that are difficult to detect [84].
Detection of uneaten pellets is another way of using machine vision to monitor feeding behavior. During this process, the corresponding area and other parameters of the food pellets can be used to indirectly monitor feeding behavior [85]. Those authors also measured organic matter residues in pond sediments to estimate feeding behavior at night time. The remaining pellets can be used as an indicator of the feeding intensity, thereby saving the amount of feed and effectively reducing pollution in culture ponds, but the accuracy of the results cannot be quantified [86]. Although indirect information can be used to monitor behavior, compared with the direct monitoring method, it is less accurate and prone to errors. This information can also be stored in a big data database, and it can help information technology staff build an expert farming system. Long-term underwater imaging and expert systems can also help in terms of smart feeding decisions, smart sewage decisions, and abnormal status warnings.

4.2. Machine Vision Based on Invisible Light

Invisible light technology provides a new method for accurately identifying crustacean behavior and mainly includes infrared imaging technology and X-ray imaging. The advantages of using infrared imaging technology to monitor crustacean behavior, including the fact that crustacean eyes are not sensitive to the infrared light used in the system and the scattering of infrared light in water does not tend to present a problem [87]. However, the major disadvantage of infrared light is that the attenuation coefficient and absorption of light in water increases dramatically as the light wavelength increases into the visible red region and then increases exponentially in the infrared region [88].

5. Electrosensors

5.1. Accelerometer

Accelerometers are electromechanical devices designed to measure acceleration forces caused by gravity and the moving or vibrating activity of a subject. In particular, three-axial accelerometers can measure the motion, vibration, and displacement of underwater animals in X, Y, and Z directions [89]. When crustaceans undergo behavioral changes, they are usually accompanied by changes in movement speed or acceleration. Therefore, the high correlation between accelerometer data and movement of free-living individuals in different behavioral contexts is the key to identifying and monitoring different behavior states [90]. The development of accelerometer data loggers has made it possible to monitor daily patterns of behavior in many crustacean species, mainly lobsters, including slipper, spiny, and clawed.
Accelerometers are very effective in monitoring the activity rhythm of crustaceans, and are currently one of the main application areas of acceleration sensors. The collected accelerometer outputs can be converted into distances moved per unit time and scholars can estimate the distance moved by shrimp in a period of time according to this method to an extent that is statistically significant, that is, p < 0.005 [91][92].

5.2. Electromyography

Electromyography (EMG) is an electrodiagnostic automated technique for evaluating and recording the electrical activity produced by skeletal muscles. An electromyograph detects the electric potential generated by muscle cells when these cells are electrically or neurologically activated. The structure of the system using EMG to monitor shrimp behavior is shown in Figure 3. The signal collected by EMG is converted and transmitted to computers, and the signals can be analyzed to detect physiological abnormalities, activation level, recruitment order, and the biomechanics of crustacean movement [93][94]. Therefore, these electrical signals can be used to design automated growth monitoring systems and develop intelligent decision-making and control systems for aquaculture.
Figure 3. Crustacean behavior monitoring based on EMG.

6. Other Methods

In addition to the methods mentioned above, other technologies have been used to monitor behaviors and may be feasible alternatives, although there is no large-scale application.
The information collected by using a single technology is insufficient. In order to obtain more comprehensive and accurate behavioral information, researchers are trying to simultaneously use different technologies to obtain crustacean behavioral information from multiple angles. The combination of acoustic technology and sensor technology can yield behavior information from multiple angles. The technical fusion of acoustics and sensors can not only be used without obstacles in muddy underwater environments, but there is also an obvious absolute correspondence between the sound frequency of crustacean and the motion acceleration [90]. Therefore, it is feasible to use information fusion technology to make up for the blind spot of a single technology. This also provides a favorable theoretical basis for future large-scale research into information fusion technology in crustacean behavior monitoring.
Radio tag technology can also be used to quantify the behavioral characteristics of crustaceans; it transmits individual information to a receiving station or monitoring center. An RFID tag consists of a tiny radio transponder. When triggered by an electromagnetic interrogation pulse from a nearby RFID reader device, the tag transmits digital data, usually an identifying inventory number, back to the reader [81]. Radio tags are cheaper than acoustic tags and can be used to develop a low-cost real-time tracking system. However, tag loss during molting of the exoskeleton is the main difficulty and challenge of labeling technology to monitor crustacean behavior [95]. Therefore, the invention of internal elastomer tags could provide a new solution for the fixation of the label, and these tags would likely have large-scale applications in commercial fisheries in the future.


  1. FIGIS. FAO Statistics.Global Aquaculture Production 1950–2015. 2015. Available online: (accessed on 7 November 2017).
  2. FAO. The State of World Fisheries and Aquaculture 2020; FAO: Rome, Italy, 2020.
  3. Kubec, J.; Kouba, A.; Buric, M. Communication, behaviour, and decision making in crayfish: A review. Zool. Anz. 2019, 278, 28–37.
  4. Gherardi, F.; Aquiloni, L.; Tricarico, E. Behavioral plasticity, behavioral syndromes and animal personality in crustacean decapods: An imperfect map is better than no map. Curr. Zool. 2012, 58, 567–579.
  5. Briones-Fourzan, P.; Dominguez-Gallegos, R.; Lozano-Alvarez, E. Aggressive behaviour of spotted spiny lobsters (Panulirus guttatus) in different social contexts: The influence of sex, size, and missing limbs. Ices J. Mar. Sci. 2015, 72, 155–163.
  6. Moland, E.; Carlson, S.M.; Villegas-Rios, D.; Wiig, J.R.; Olsen, E.M. Harvest selection on multiple traits in the wild revealed by aquatic animal telemetry. Ecol. Evol. 2019, 9, 6480–6491.
  7. Antonucci, F.; Costa, C. Precision aquaculture: A short review on engineering innovations. Aquac. Int. 2020, 28, 41–57.
  8. Parra, L.; Lloret, G.; Lloret, J.; Rodilla, M. Physical Sensors for Precision Aquaculture: A Review. IEEE Sens. J. 2018, 18, 3915–3923.
  9. Howe, B.M.; Miksis-Olds, J.; Rehm, E.; Sagen, H.; Worcester, P.F.; Haralabus, G. Observing the Oceans Acoustically. Front. Mar. Sci. 2019, 6, 22.
  10. Sbragaglia, V.; Aguzzi, J.; Garcia, J.A.; Sarria, D.; Gomariz, S.; Costa, C.; Menesatti, P.; Vilaro, M.; Manuel, A.; Sarda, F. An automated multi-flume actograph for the study of behavioral rhythms of burrowing organisms. J. Exp. Mar. Biol. Ecol. 2013, 446, 177–185.
  11. Lyons, G.N.; Halsey, L.G.; Pope, E.C.; Eddington, J.D.; Houghton, J.D.R. Energy expenditure during activity in the American lobster Homarus americanus: Correlations with body acceleration. Comp. Biochem. Physiol. A Mol. Integr. Physiol. 2013, 166, 278–284.
  12. Gutzler, B.C.; Butler, M.J. Accelerometry as a tool for studying lobster behavior: Preliminary results from the Florida Keys, FL (USA). Lobster Newsl. 2014, 27, 8–9.
  13. Briffa, M. Contests in crustaceans: Assessments, decisions and their underlying mechanisms. In Animal Contests; Cambridge University Press Location: Cambridge, UK, 2013; pp. 86–112.
  14. Kuklina, I.; Kouba, A.; Kozak, P. Real-time monitoring of water quality using fish and crayfish as bio-indicators: A review. Environ. Monit. Assess. 2013, 185, 5043–5053.
  15. Cuellar-Anjel, J.; Corteel, M.; Galli, L.; Alday-Sanz, V.; Hasson, K.W. Principal Shrimp Infectious Diseases, Diagnosis and Management. In The Shrimp Book; CABI: Wallingford, UK, 2010; pp. 517–621.
  16. Yan, S.; Alfredsen, J.A. Real time lobster posture estimation for behavior research. In Proceedings of the Eighth International Conference on Graphic and Image Processing, Tokyo, Japan, 8 February 2017; Pham, T.D., Vozenilek, V., Zeng, Z., Eds.; SPIE: Bellingham, WA, USA, 2017; 10225.
  17. Bardera, G.; Usman, N.; Owen, M.; Pountney, D.; Sloman, K.A.; Alexander, M.E. The importance of behaviour in improving the production of shrimp in aquaculture. Rev. Aquac. 2019, 11, 1104–1132.
  18. Silva, P.F.; Medeiros, M.d.S.; Alves Silva, H.P.; Arruda, M.d.F. A study of feeding in the shrimp Farfantepenaeus subtilis indicates the value of species level behavioral data for optimizing culture management. Mar. Freshw. Behav. Physiol. 2012, 45, 121–134.
  19. Karadal, O.; Turkmen, G. Effects of feeding frequency on growth performance and molting cycle of two different size classes of red swamp crayfish (Procambarus clarkii). LimnoFish J. Limnol. Freshw. Fish. Res. 2018, 4, 140–145.
  20. Santos, A.d.A.; Lopez-Olmeda, J.F.; Sanchez-Vazquez, F.J.; Fortes-Silva, R. Synchronization to light and mealtime of the circadian rhythms of self-feeding behavior and locomotor activity of white shrimps (Litopenaeus vannamei). Comp. Biochem. Physiology. A Mol. Integr. Physiol. 2016, 199, 54–61.
  21. Nathan, R.; Getz, W.M.; Revilla, E.; Holyoak, M.; Kadmon, R.; Saltz, D.; Smouse, P.E. A movement ecology paradigm for unifying organismal movement research. Proc. Natl. Acad. Sci. USA 2008, 105, 19052–19059.
  22. Morse, B.L.; Comeau, M.; Rochette, R. Ontogenetic changes in movement patterns and activity levels of American lobsters (Homarus americanus) in Anse-Bleue, southern Gulf of St. Lawrence. J. Exp. Mar. Biol. Ecol. 2018, 505, 12–23.
  23. Childress, M.J.J.S.H. Behaviour; Cambridge Dictionary: Cambridge, UK, 2006; p. 112.
  24. Bertelsen, R.D.; Hornbeck, J. Using acoustic tagging to determine adult spiny lobster (Panulirus argus) movement patterns in the Western Sambo Ecological Reserve (Florida, United States). New Zealand J. Mar. Freshw. Res. 2009, 43, 35–46.
  25. Ghanawi, J.; Saoud, I.P. Molting, reproductive biology, and hatchery management of redclaw crayfish Cherax quadricarinatus (von Martens 1868). Aquaculture 2012, 358, 183–195.
  26. Farhadi, A.; Harlioglu, M.M. Photoperiod affects gamete production, and protein and lipid metabolism in male narrow-clawed Crayfish Pontastacus leptodactylus (Eschscholtz, 1823). Anim. Reprod. Sci. 2019, 211, 106204.
  27. Mellan, D.; Warren, A.; Buckholt, M.A.; Mathews, L.M. Sexual History Affects Mating Behavior and Mate Choice in the Crayfish Orconectes limosus. Ethology 2014, 120, 681–692.
  28. Katoh, E. Sex, pheromone and aggression in Norway lobsters (Nephrops norvegicus): For a better future of Scampi. Ph.D. Thesis, University of Hull, Hull, UK, 2011.
  29. Stebbing, P.D.; Bentley, M.G.; Watson, G.J. Mating behaviour and evidence for a female released courtship pheromone in the signal crayfish Pacifastacus leniusculus. J. Chem. Ecol. 2003, 29, 465–475.
  30. Horne, J.K. Acoustic approaches to remote species identification: A review. Fish. Oceanogr. 2000, 9, 356–371.
  31. Tan, C.S.; Lau, P.Y.; Correia, P.L.; Campos, A. Automatic analysis of deep-water remotely operated vehicle footage for estimation of Norway lobster abundance. Front. Inf. Technol. Electron. Eng. 2018, 19, 1042–1055.
  32. McMahan, M.D.; Brady, D.C.; Cowan, D.F.; Grabowski, J.H.; Sherwood, G.D. Using acoustic telemetry to observe the effects of a groundfish predator (Atlantic cod, Gadus morhua) on movement of the American lobster (Homarus americanus). Can. J. Fish. Aquat. Sci. 2013, 70, 1625–1634.
  33. Li, D.; Hao, Y.; Duan, Y. Nonintrusive methods for biomass estimation in aquaculture with emphasis on fish: A review. Rev. Aquac. 2020, 12, 1390–1411.
  34. Jezequel, Y.; Bonnel, J.; Coston-Guarini, J.; Guarini, J.-M.; Chauvaud, L. Sound characterization of the European lobster Homarus gammarus in tanks. Aquatic Biology 2018, 27, 13–23.
  35. Popper, A.N.; Salmon, M.; Horch, K.W. Acoustic detection and communication by decapod crustaceans. J. Comp. Physiol. A-Neuroethol. Sens. Neural Behav. Physiol. 2001, 187, 83–89.
  36. Coquereau, L.; Grall, J.; Clavier, J.; Jolivet, A.; Chauvaud, L. Acoustic behaviours of large crustaceans in NE Atlantic coastal habitats. Aquat. Biol. 2016, 25, 151–163.
  37. Patek, S.N.; Oakley, T.H. Comparative tests of evolutionary trade-offs in a palinurid lobster acoustic system. Evolution 2003, 57, 2082–2100.
  38. Patek, S.N. Spiny lobsters stick and slip to make sound—These crustaceans can scare off predators even when their usual armour turns soft. Nature 2001, 411, 153–154.
  39. Patek, S.N. Squeaking with a sliding joint: Mechanics and motor control of sound production in palinurid lobsters. J. Exp. Biol. 2002, 205, 2375–2385.
  40. Patek, S.N.; Baio, J.E. The acoustic mechanics of stick-slip friction in the California spiny lobster (Panulirus interruptus). J. Exp. Biol. 2007, 210, 3538–3546.
  41. Patek, S.N.; Caldwell, R.L. The stomatopod rumble: Low frequency sound production in Hemisquilla californiensis. Mar. Freshw. Behav. Physiol. 2006, 39, 99–111.
  42. Patek, S.N.; Shipp, L.E.; Staaterman, E.R. The acoustics and acoustic behavior of the California spiny lobster (Panulirus interruptus). J. Acoust. Soc. Am. 2009, 125, 3434–3443.
  43. Smith, D.V.; Shahriar, M.S. A context aware sound classifier applied to prawn feed monitoring and energy disaggregation. Knowl. Based Syst. 2013, 52, 21–31.
  44. Hawkins, A.D.; Maclennan, D.N.; Urquhart, G.G.; Robb, C. Tracking cod gadusmorhua l in a scottish sea loch. J. Fish. Biol. 1974, 6, 225–236.
  45. Donaldson, M.R.; Hinch, S.G.; Suski, C.D.; Fisk, A.T.; Heupel, M.R.; Cooke, S.J. Making connections in aquatic ecosystems with acoustic telemetry monitoring. Front. Ecol. Environ. 2014, 12, 565–573.
  46. Atkinson, L.J.; Mayfield, S.; Cockcroft, A.C. The potential for using acoustic tracking to monitor the movement of the West Coast rock lobster Jasus lalandii. Afr. J. Mar. Sci. 2005, 27, 401–408.
  47. Hellstrom, G.; Klaminder, J.; Jonsson, M.; Fick, J.; Brodin, T. Upscaling behavioural studies to the field using acoustic telemetry. Aquat. Toxicol. 2016, 170, 384–389.
  48. Cooke, S.J.; Hinch, S.G.; Wikelski, M.; Andrews, R.D.; Kuchel, L.J.; Wolcott, T.G.; Butler, P.J. Biotelemetry: A mechanistic approach to ecology. Trends Ecol. Evol. 2004, 19, 334–343.
  49. Scopel, D.A.; Golet, W.J.; Watson, W.H., III. Home range dynamics of the American lobster, Homarus americanus. Mar. Freshw. Behav. Physiol. 2009, 42, 63–80.
  50. Giacalone, V.M.; Barausse, A.; Gristina, M.; Pipitone, C.; Visconti, V.; Badalamenti, F.; D’Anna, G. Diel activity and short-distance movement pattern of the European spiny lobster, Palinurus elephas, acoustically tracked. Mar. Ecol. Evol. Perspect. 2015, 36, 389–399.
  51. Haley, C.N.; Blamey, L.K.; Atkinson, L.J.; Branch, G.M. Dietary change of the rock lobster Jasus lalandii after an ‘invasive’ geographic shift: Effects of size, density and food availability. Estuar. Coast. Shelf Sci. 2011, 93, 160–170.
  52. Giacalone, V.M.; Zenone, A.; Badalamenti, F.; Ciancio, J.; Buffa, G.; Gristina, M.; Pipitone, C.; D’Anna, G. Homing and Home Range Of The European Spiny Lobster, Palinurus Elephas (Decapoda, Palinuridae) Acoustically Tracked. Crustaceana 2019, 92, 463–476.
  53. Bertelsen, R.D. Characterizing daily movements, nomadic movements, and reproductive migrations of Panulirus argus around the Western Sambo Ecological Reserve (Florida, USA) using acoustic telemetry. Fish. Res. 2013, 144, 91–102.
  54. Smith, D.V.; Tabrett, S. The use of passive acoustics to measure feed consumption by Penaeus monodon (giant tiger prawn) in cultured systems. Aquac. Eng. 2013, 57, 38–47.
  55. Bohnenstiehl, D.R.; Lillis, A.; Eggleston, D.B. The Curious Acoustic Behavior of Estuarine Snapping Shrimp: Temporal Patterns of Snapping Shrimp Sound in Sub-Tidal Oyster Reef Habitat. PLoS ONE 2016, 11, e0143691.
  56. Buscaino, G.; Filiciotto, F.; Buffa, G.; Di Stefano, V.; Maccarrone, V.; Buscaino, C.; Mazzola, S.; Alonge, G.; D’Angelo, S.; Maccarrone, V. The underwater acoustic activities of the red swamp crayfish Procambarus clarkii. J. Acoust. Soc. Am. 2012, 132, 1792–1798.
  57. Skerritt, D.J.; Robertson, P.A.; Mill, A.C.; Polunin, N.V.C.; Fitzsimmons, C. Fine-scale movement, activity patterns and home-ranges of European lobster Homarus gammarus. Mar. Ecol. Prog. Ser. 2015, 536, 203–219.
  58. Kikuchi, M.; Akamatsu, T.; Takase, T. Passive acoustic monitoring of Japanese spiny lobster stridulating sounds. Fish. Sci. 2015, 81, 229–234.
  59. Goldstein, J.S.; Watson, W.H., III. Seasonal movements of American lobsters in southern Gulf of Maine coastal waters: Patterns, environmental triggers, and implications for larval release. Mar. Ecol. Prog. Ser. 2015, 524, 197–211.
  60. Watson, W.H., III.; Golet, W.; Scopel, D.; Jury, S. Use of ultrasonic telemetry to determine the area of bait influence and trapping area of American lobster, Homarus americanus, traps. New Zealand J. Mar. Freshw. Res. 2009, 43, 411–418.
  61. Gonzalez-Gurriaran, E.; Freire, J.; Bernardez, C. Migratory patterns of female spider crabs maja squinado detected using electronic tags and telemetry. J. Crustacean Biol. 2002, 22, 91–97.
  62. Masmitja, I.; Navarro, J.; Gomariz, S.; Aguzzi, J.; Kieft, B.; O’Reilly, T.; Katija, K.; Bouvet, P.J.; Fannjiang, C.; Vigo, M.; et al. Mobile robotic platforms for the acoustic tracking of deep-sea demersal fishery resources. Sci. Robot. 2020, 5, eabc3701.
  63. Gonzalezgurriaran, E.; FREIRE, J. Movement patterns and habitat utilization in the spider crab Maja squinado (Herbst) (Decapoda, Maji-dae) measured by ultrasonic telemetry. J. Exp. Mar. Biol. Ecol. 1994, 184, 269–291.
  64. Hines, A.H.; Wolcott, T.G.; González-Gurriarán, E.; González-Escalante, J.L.; Freire, J. Movement patterns and migrations in crabs teleme-try of juvenile and adult behavior in Callinectes sapidus and Maja squinado. J. Mar. Biol. Assoc. U. K. 1995, 75, 27–42.
  65. Rotllant, G.; Aguzzi, J.; Sarria, D.; Gisbert, E.; Sbragaglia, V.; Del Rio, J.; Simeo, C.G.; Manuel, A.; Molino, E.; Costa, C.; et al. Pilot acoustic tracking study on adult spiny lobsters (Palinurus mauritanicus) and spider crabs (Maja squinado) within an artificial reef. Hydrobiologia 2015, 742, 27–38.
  66. Ungfors, A.; Hallback, H.; Nilsson, P.G. Movement of adult edible crab (Cancer pagurus l.) at the swedish west coast by mark-recapture and acoustic tracking. Fish. Res. 2007, 84, 345–357.
  67. Myrberg, A.A. Underwater Television—Tool for Marine Biologist. Bull. Mar. Sci. 1973, 23, 824–836.
  68. Saberioon, M.; Gholizadeh, A.; Cisar, P.; Pautsina, A.; Urban, J. Application of machine vision systems in aquaculture with emphasis on fish: State-of-the-art and key issues. Rev. Aquac. 2017, 9, 369–387.
  69. Trenkel, V.M.; Cotter, J. Choosing survey time series for populations as part of an ecosystem approach to fishery management. Aquat. Living Resour. 2009, 22, 121–126.
  70. Jouffre, D.; Borges, M.d.F.; Bundy, A.; Coll, M.; Diallo, I.; Fulton, E.A.; Guitton, J.; Labrosse, P.; Abdellahi, K.O.M.; Masumbuko, B.; et al. Estimating EAF indicators from scientific trawl surveys: Theoretical and practical concerns. Ices J. Mar. Sci. 2010, 67, 796–806.
  71. Aguzzi, J.; Costa, C.; Fujiwara, Y.; Iwase, R.; Ramirez-Llorda, E.; Menesatti, P. A Novel Morphometry-Based Protocol of Automated Video-Image Analysis for Species Recognition and Activity Rhythms Monitoring in Deep-Sea Fauna. Sensors 2009, 9, 8438–8455.
  72. Brosnan, T.; Sun, D.W. Improving quality inspection of food products by computer vision—A review. J. Food Eng. 2004, 61, 3–16.
  73. Oppedal, F.; Dempster, T.; Stien, L.H. Environmental drivers of Atlantic salmon behaviour in sea-cages: A review. Aquaculture 2011, 311, 1–18.
  74. Salierno, J.D.; Gipson, G.T.; Kane, A.S. Quantitative movement analysis of social behavior in mummichog, Fundulus heteroclitus. J. Ethol. 2008, 26, 35–42.
  75. Valletta, J.J.; Torney, C.; Kings, M.; Thornton, A.; Madden, J. Applications of machine learning in animal behaviour studies. Anim. Behav. 2017, 124, 203–220.
  76. Gage, J.D.; Bett, B.J. Deep-Sea Benthic Sampling. In Methods for the Study of Marine Benthos, 3rd ed.; Blackwell Publishing Ltd.: Hoboken, NJ, USA, 2005; pp. 273–325.
  77. Karplus, I.; Barki, A. Male morphotypes and alternative mating tactics in freshwater prawns of the genus Macrobrachium: A review. Rev. Aquac. 2019, 11, 925–940.
  78. Qiao, X.; Rauschenbach, T.; Li, D. Review of Underwater Machine Vision Technology and Its Applications. Mar. Technol. Soc. J. 2017, 51, 75–97.
  79. Egmont-Petersen, M.; de Ridder, D.; Handels, H. Image processing with neural networks—A review. Pattern Recognit. 2002, 35, 2279–2301.
  80. Yan, F.; Iliyasu, A.M.; Khan, A.R.; Yang, H. Measurements-based Moving Target Detection in Quantum Video. Int. J. Theor. Phys. 2016, 55, 2162–2173.
  81. Aguzzi, J.; Costa, C.; Robert, K.; Matabos, M.; Antonucci, F.; Juniper, S.K.; Menesatti, P. Automated Image Analysis for the Detection of Benthic Crustaceans and Bacterial Mat Coverage Using the VENUS Undersea Cabled Network. Sensors 2011, 11, 10534–10556.
  82. Zhou, C.; Yang, X.; Zhang, B.; Lin, K.; Xu, D.; Guo, Q.; Sun, C. An adaptive image enhancement method for a recirculating aquaculture system. Sci. Rep. 2017, 7, 1–11.
  83. Zhang, H.; Wu, C.; Jiang, D.; Zhao, L.; Gui, F. Monitoring waste cumulating in aquaculture ponds using image processing technology. Oceanol. Et Limnol. Sin. Hai Yang Yu Hu Chao 2016, 47, 374–379.
  84. Aguzzi, J.; Costa, C.; Menesatti, P.; Antonio Garcia, J.; Jose Chiesa, J.; Sarda, F. Monochromatic blue light entrains diel activity cycles in the Norway lobster, Nephrops norvegicus (L.) as measured by automated video-image analysis. Sci. Mar. 2009, 73, 773–783.
  85. Hung, C.-C.; Tsao, S.-C.; Huang, K.-H.; Jang, J.-P.; Chang, H.-K.; Dobbs, F.C. A highly sensitive underwater video system for use in turbid aquaculture ponds. Sci. Rep. 2016, 6.
  86. Huang, I.-J.; Hung, C.-C.; Kuang, S.-R.; Chang, Y.-N.; Huang, K.-Y.; Tsai, C.-R.; Feng, K.-L. The Prototype of a Smart Underwater Surveillance System for Shrimp Farming; IEEE: New York, NY, USA, 2018; pp. 177–180.
  87. Sarria, D.; del Rio, J.; Manuel, A.; Aguzzi, J.; Sarda, F.; Garcia, J.A. Studying the Behaviour of Norway Lobster Using RFID and Infrared Tracking Technologies; IEEE: New York, NY, USA, 2009; p. 4.
  88. Weiss, H.M.; Lozano-Alvarez, E.; Briones-Fourzan, P.; Negrete-Soto, F. Using red light with fixed-site video cameras to study the behavior of the spiny lobster, Panulirus argus, and associated animals at night and inside their shelters. Mar. Technol. Soc. J. 2006, 40, 86–95.
  89. Gleiss, A.C.; Morgan, D.L.; Whitty, J.M.; Keleher, J.J.; Fossette, S.; Hays, G.C. Are vertical migrations driven by circadian behaviour? Decoupling of activity and depth use in a large riverine elasmobranch, the freshwater sawfish (Pristis pristis). Hydrobiologia 2017, 787, 181–191.
  90. Zenone, A.; Ceraulo, M.; Ciancio, J.E.; Buscaino, G.; D’Anna, G.; Grammauta, R.; Mazzola, S.; Giacalone, V.M. The use of 3-axial accelerometers to evaluate sound production in European spiny lobster, Palinurus elephas. Ecol. Indic. 2019, 102, 519–527.
  91. Goldstein, J.S.; Dubofsky, E.A.; Spanier, E. Into a rhythm: Diel activity patterns and behaviour in Mediterranean slipper lobsters, Scyllarides latus. Ices J. Mar. Sci. 2015, 72, 147–154.
  92. Jury, S.H.; Langley, T.; Gutzler, B.C.; Goldstein, J.S.; Watson, W.H. Monitoring the behavior of freely moving lobsters with accelerometers. Bull. Mar. Sci. 2018, 94, 533–553.
  93. Pollak, D.J.; Feller, K.D.; Serbe, E.; Mircic, S.; Gage, G.J. An Electrophysiological Investigation of Power-Amplification in the Ballistic Mantis Shrimp Punch. J. Undergrad. Neurosci. Educ. JUNE A Publ. FUN Fac. Undergrad. Neurosci. 2019, 17, T12–T18.
  94. Chikamoto, K.; Kagaya, K.; Takahata, M. Electromyographic Characterization of Walking Behavior Initiated Spontaneously in Crayfish. Zool. Sci. 2008, 25, 783–792.
  95. Frisch, A.J.; Hobbs, J.-P.A. Long-term retention of internal elastorner tags in a wild population of painted crayfish (Panulirus versicolor Latreille) on the Great Barrier Reef. J. Exp. Mar. Biol. Ecol. 2006, 339, 104–110.
Contributor MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to :
View Times: 502
Revisions: 2 times (View History)
Update Date: 28 Oct 2021
Video Production Service