Skip to main content Accessibility help



  • Access

  • Field and Laboratory Methods in Animal Cognition
  • A Comparative Guide
  • Online publication date: July 2018
  • pp 354-380



      • Send chapter to Kindle

        To send this chapter to your Kindle, first ensure is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about sending to your Kindle.

        Note you can select to send to either the or variations. ‘’ emails are free but can only be sent to your device when it is connected to wi-fi. ‘’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

        Find out more about the Kindle Personal Document Service.

        Available formats

        Send chapter to Dropbox

        To send content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about sending content to Dropbox.

        Available formats

        Send chapter to Google Drive

        To send content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about sending content to Google Drive.

        Available formats
Export citation

16 Sharks – Elasmobranch Cognition

Tristan L. Guttridge , Kara E. Yopak and Vera Schluessel

Species Description


Cartilaginous fishes (class: Chondrichthyes) hold an important basal position at the onset of gnathostomes (jawed vertebrates), with evidence in the fossil record dating back approximately 400 million years ago (see Grogan et al., 2012). They share several distinctive features, including an internal skeleton comprised entirely of cartilage, paired fins, placoid dermal scales, external copulatory organs and internal fertilization, 5–7 paired gill slits, and a battery of highly developed senses (see Collin et al., 2015). There are approximately 1200 extant species, which are divided into the Elasmobranchii (sharks, skates and rays), representing 96 per cent of described species, and the Holocephali (e.g. chimaeras and ratfishes). Taxonomic relationships within these groups remain partially unresolved (see Naylor et al., 2012).

Furthermore, sharks have a remarkable diversity in terms of their morphological and physiological attributes, including body form, swimming mode, ventilation strategy and reproductive mode. Indeed, members of this group range from the smallest documented species, the dwarf lantern shark, Etmopterus perryi (180–212 mm), up to the largest fish in the sea, the whale shark, Rhincodon typus (up to 12 m). Although all swim with a form of lateral undulation, this can range from anguilliform (e.g. Orectolobiformes) to thunniform (e.g. Lamniformes) swimming. Many of the large-bodied carangiform and thunniform swimmers are also obligate ram ventilators, whereby they must swim continuously, thus expending the greatest amount of metabolic energy, while many less-active, benthic species ventilate via active buccal pumping (see Wegner, 2015).

Ecological Characteristics

Chondricthyans are one of the most speciose lineages of predators on earth, playing important functional roles in the top-down control of coastal and oceanic ecosystem structure and function (Ferretti et al., 2010). They occupy nearly every aquatic niche, from coastal habitats, to coral reefs, to the open ocean, to deep water and freshwater. Within these habitats, feeding strategy can range from planktivores (e.g. Rhincodon, Megachasma) to apex predators (e.g. Carcharodon), from specialists (e.g. Sphyrna tiburo) to generalist feeders (e.g. Galeocerdo), with shifts in diet that occur ontogenetically (Wetherbee and Cortes, 2004). Further, many species are known to undertake long-range movements and therefore serve as important mobile link species between ecosystems, playing an important part in the structure and stability of these systems (Ferretti et al., 2010).

Perception – Peripheral and Central Nervous System

As previously stated, sharks occupy nearly every aquatic niche and each species is adapted to a complex set of environmental conditions. Although all species possess the same sensory modalities, which include chemoreception (olfaction and gustation), vision, mechanoreception (both lateral line and cutaneous touch), audition and electroreception, the relative importance of different sensory systems likely varies both inter- and intraspecifically, in terms of size and morphology of the peripheral sense organs, detection thresholds and sensitivity (Gardiner et al., 2012; Collin et al., 2015).

Brain Anatomy

Gnathostomes possess a common brain ‘bauplan’, consisting of the forebrain (olfactory bulbs, telencephalon and diencephalon), midbrain (mesencephalon) and hindbrain (cerebellum and medulla; Striedter, 2005; Figure 16.1). The most rostral component of the brain is the paired olfactory bulbs (OBs), which are associated with processing olfactory information (Meredith and Kajiura, 2010). Olfactory information is sent from the OBs on to the telencephalon, which can comprise up to as much as 67 per cent of the brain in the great, hammerhead, Sphryna mokarran (Yopak et al., 2007). This structure is implicated in a variety of functions (Hofmann and Northcutt, 2012), including multimodal sensory integration, complex behavioural control and higher cognitive functions, such as learning and memory, which likely occur in different subregions (Schluessel, 2015). The telencephalon is heavily interconnected with the diencephalon, which acts as an interface between the brain and the endocrine system (Smeets et al., 1983). The midbrain is most readily associated with vision and visual processing (Graeber and Ebbesson, 1972), and is also involved in the processing of multisensory information (see Yopak, 2012a).

Figure 16.1. Photomicrographs of the brain of (A) the great white shark, Carcharodon carcharias (adapted from Yopak, 2012a, illustration reprinted with kind permission of John Wiley and Sons), (B) the smooth hammerhead, Sphyrna zygaena (adapted from Yopak et al., 2007, © 2007 Karger Publishers, Basel, Switzerland), and (C) the blackbelly lanternshark, Etmopterus lucifer (adapted from Yopak and Montgomery, 2008, Copyright © 2008 Karger Publishers, Basel, Switzerland), in dorsal view, representing the dramatic differences in brain size and morphology across species. Scale bars correspond to 1 cm.

The function of the cerebellum has been an area of considerable speculation, although it is generally agreed that it is an effective regulator of motor control and adaptive motor learning (Montgomery et al., 2012). Variation in size, convolution and symmetry of this structure across species (e.g. Puzdrowski and Gruber, 2009; Ari, 2011; Yopak et al., 2016) likely reflects variation in cerebellar-dependent function and behavioural complexity (Yopak et al., 2017). The medulla oblongata is the most caudal component of the hindbrain; it receives primary projections from the octavolateralis senses, which include the acoustic and vestibular system, electroreceptors and mechanoreceptive lateral line, and processes motor and sensory information (Smeets et al., 1983). It also functions in sensory adaptive filtering, which effectively cancels out expected electroreceptive input (Bell et al., 1997; Bodznick et al., 1999), with implications for animals housed in captivity, who may habituate to continuous sensory signals.

Life History

Chondrichthyans tend to be long-lived, slow-growing species and possess the most diverse array of reproductive strategies of any vertebrate group, ranging from egg-laying to live-bearing with placental matrotrophy (e.g. Dulvy and Reynolds, 1997; Reynolds et al., 2002). In many species, the developmental period is long (e.g. 6–10 months) and results in the production of small numbers of highly developed young. Chondrichthyans also exhibit direct development, where newborns resemble miniature versions of the adults (Helfman et al., 1997). Most captive studies involve juveniles for practical purposes, due to size limitations of holding tanks and maintenance, although numerous characters can vary from juvenile to adult stages.

Ontogenetic Shifts in the Brain and Sensory Systems

Cartilaginous fishes experience indeterminate growth (Sebens, 1987), with brains (and other organs) that continue to grow throughout their lifespan (Gage, 2002), resulting in a highly plastic nervous system. The life histories of most sharks are characterized by ontogenetic shifts in habitat, movement patterns, diet and behaviour (e.g. Wetherbee and Cortés, 2004; Grubbs, 2010). Similarly, ontogenetic shifts in sensory systems have been documented, including response properties in the visual system (e.g. Litherland et al., 2009a, b; Lisney et al., 2012; Harahush et al., 2014) and in the electroreceptive system (Sisneros and Tricas, 2002). These shifts in sensory organization may confer a functional shift in sensitivity and/or specialization, and suggest the peripheral nervous system may be highly susceptible to environmental conditions in a captive setting. In addition to post-parturition changes in peripheral sense organs, there is also evidence for varying sensory sensitivity in embryonic stages (Peters and Evers, 1985; Kempster et al., 2013), with repeated exposure to sensory stimuli during the embryonic phase possibly impacting learning post-hatching, although the severity of the effects may vary across study species.

Although data are sparse, these ontogenetic shifts in habitat and sensory systems may have corresponding ontogenetic shifts in brain organization. For example, Lisney and colleagues (2007) demonstrated that, across seven species, the optic tectum is relatively enlarged in juveniles, while the olfactory bulbs were relatively enlarged in adults, suggestive of an ontogenetic shift in the importance of vision versus olfaction. Studies on the whale shark, Rhincodon typus (Yopak and Frank, 2009), and the blue spotted stingray, Neotrygon kuhlii (Lisney et al., 2017), both showed an increase in the degree of folding of the cerebellum throughout life, which may reflect improved locomotor performance or the expansion of activity space. N. kuhlii also shows an increase in the size of the olfactory bulbs in mature individuals, although less than other species (Lisney et al., 2007), which suggests that the degree of plasticity may be more pronounced in some species over others.

Given their functional implications, consideration of these ontogenetic shifts in peripheral and central organization is critical when undertaking behavioural studies. While juveniles are often easier to house in a laboratory setting, they may be more susceptible to plastic changes in the brain in relation to their rearing environment than adults. Although the brain continues to grow through adulthood, the steepest period of brain growth is often during the early juvenile stages (e.g. Lisney et al., 2017), suggestive of a sensitive period before the onset of sexual maturity, where structural (and potentially functional) changes may be induced more rapidly.

Captive Rearing and Its Effects on the Brain

Having a brain that grows forever can have substantial implications for animals reared in captivity (see Gonda et al., 2013). Although not studied extensively in cartilaginous fishes, work on bony fishes has shown that brain size and brain organization can vary between wild-caught and captive-reared populations (e.g. Marchetti and Nevitt, 2003; Lema et al., 2005). Artificial selection studies in guppies have shown that large-brained individuals outperform small-brained individuals in a cognitive learning task, which suggests that an increase in brain size confers a cognitive advantage (e.g. Kotrschal et al., 2013). Given this evidence in other species with indeterminate growth, captivity is likely to similarly induce plastic changes in the brain in cartilaginous fishes. Thus, consideration (and consistency) of rearing environment, learning opportunities and spatial structures across specimens, in addition to age class, are vital when selecting study species and designing cognitive studies in this group.


Although the social characteristics of sharks and rays are not well understood, some species, such as the white shark (Carcharodon carcharias), are known to be solitary, while others, such as sphyrnid sharks and myliobatid rays, are considered social animals (Jacoby et al., 2012a), which aggregate or form true schools, ranging in size from less than ten to thousands of individuals. These groupings are often segregated by sex or size, which may partly be the result of ontogenetic shifts in habitat and availability of resources (see Grubbs, 2010). There is also evidence of relatively complex social and reproductive behaviours, such as dominance hierarchies and courtship behaviour (see Jacoby et al., 2012b). More recent studies have shown that some shark species organize into structured social networks, have preferred associations, recognize familiars and are able to learn from conspecifics (Jacoby et al., 2012b; Mourier et al., 2012; Guttridge et al., 2013).

State of the Art

Information on cognitive skills in elasmobranchs is still limited (Guttridge et al., 2009a; Yopak et al., 2012a; Schluessel, 2015). Most behavioural studies conducted thus far have been on a few species that are easy to access, handle and keep, such as the lemon shark, Negaprion brevirostris, the grey or brown-banded bamboo shark, Chiloscyllium punctautum, the nurse shark, Ginglymostoma cirratum, or several Potamotryon species, as well as several marine stingrays from the Dasyatidae or Urotrygonidae families (e.g. Urobatis jamaicensis). There have also been studies on a few temperate-water species, such as the small-spotted catshark, Scyliorhinus canicula and the Port Jackson shark, Heterodontus portusjacksoni. Generally, warm-water species are easier to maintain in a laboratory setting than cold-water species, as the technical equipment is far more accessible and less expensive. Further, owing to their more sedentary lifestyle, benthic species are easier to keep than benthopelagic species. Due to the associated drawbacks in maintenance and handling, most studies are performed with small numbers of individuals (< 10), often limiting the power of statistical analyses.

Learning in elasmobranchs has usually been tested with reinforced operant conditioning, in which an animal is rewarded (e.g. with food or the company of conspecifics; see below) for performing a particular task. If punishment instead of reinforcement is used, sharks, like other animals, often become stressed and anxious, and usually terminate participation (e.g. Schwarze et al., 2013).


Most sharks move constantly in search for food, mates and shelter; many perform also daily, seasonal or annual migrations. To accomplish this, sharks use different orientation strategies, form spatial memories and maintain knowledge of spatial tasks for at least 12 weeks (Schluessel and Bleckmann 2005, 2012; Fuss et al., 2014ac). Ocellate freshwater stingrays, Potamotrygon motoro (Schluessel and Bleckmann, 2005) and bamboo sharks, Chiloscyllium griseum (Fuss et al., 2014a,b), learn to locate a fixed food source by using either body-centred turns, a variety of landmarks or a combination of the two, possibly even by constructing cognitive spatial maps. Ocellate river stingrays generally place more importance on the overall environmental or geometric arrangement of an experimental arena than on the landmarks within it when memorizing the location of a food source (Schluessel et al., 2015).

Tool Use

Fish manipulate objects to increase feeding efficiency, move objects or clean nests (Brown, 2012). In the only study on tool use in elasmobranchs, Kuba and colleagues (2010) investigated the ability of five subadult freshwater stingrays, Potamotrygon castexi, to use water as a tool to extract food from a tube. All rays accomplished the task, but applied different methods in the process, such as undulating fin movements, suction and/or a combination of both.

Social Cognition

While many shark species are solitary, juvenile lemon sharks prefer size-matched conspecifics over unmatched ones, and favour conspecifics over heterospecifics (Guttridge et al., 2009b). Active partner preferences and differing leadership roles were also observed in lemon sharks (Guttridge et al., 2011), in benthic wobbegong sharks, Orectolobus maculatus (Armansin et al., 2016) and blacktip reef sharks, Carcharhinus melanopterus (Mourier et al., 2012). The latter showed long-term associations and evidence for community structure driven by individual social preferences. Familiarity may be an important factor driving these preferences in small-spotted catsharks and lemon sharks (Jacoby et al., 2012b; Keller et al., 2017), with neonates hatching or being born in close temporal and spatial proximity developing familiarity and hence aggregating together. Results of a food-finding task, which tested for social learning abilities, indicated that test animals that had been allowed to watch conspecifics with previous experience completed more task-related behaviours and were faster than test animals that had been paired with naive conspecifics (Guttridge et al., 2013; Thonhauser et al., 2013).

Personality and Laterality

To date, only a handful of studies have investigated shark personality (see Finger et al., 2017). In Port Jackson sharks, individuals vary greatly and reliably in terms of boldness and stress reactivity (Byrnes and Brown, 2016), with differences between males and females (Byrnes et al., 2016b). They also show individual and sex-biased variation in laterality (i.e. the tendency for some neural functions of cognitive processes to be more dominant in one brain hemisphere than the other), which may be linked to stress reactivity (Byrnes et al., 2016a). Moreover, Jacoby and colleagues (2014), working with juvenile small-spotted catsharks, found that individuals displayed consistent social interactions across varying habitat types. Finally, Finger and colleagues (2018) found that juvenile lemon sharks showed consistent individual differences in their social behaviours (e.g. following and paralleling), across short (4–18 days) and long (4 months) time scales. Habituation over consecutive trials largely varies across individuals, and habituation rate is negatively related to individual’s movement behaviour in the very first open field trial (Finger et al., 2016).

Object Recognition

Recognition and discrimination abilities are essential for a wide range of behaviours, such as the selection of food sources, identification of prey and predators, recognition of conspecifics, heterospecifics and potential partners, as well as recognition of territories and home ranges. Sharks show visual target discrimination (Tester and Kato, 1963; Aronson, 1967; Graeber et al., 1972; Graeber and Ebbesson, 1973) and discriminate between symmetrical and asymmetrical shapes (Schluessel et al., 2015), contrasts but not colours (Schluessel et al., 2014a), stationary 2D objects (Schluessel and Duengen, 2015) and between moving objects, ranging from moving circles to differently moving organisms (Fuss et al., 2017). In addition, sharks recognize some organisms based on their unique movement, but have difficulty recognizing familiar organisms shown from a new perspective (Fuss et al., 2017). Bamboo sharks also successfully discriminate between various two-dimensional geometric stimuli and succeed in reversal tasks, likely retaining some information about previously learned stimuli, when progressing to new ones (Fuss et al., 2014c). Finally, sharks show remarkable categorization abilities, i.e. using abstract stimuli (Schluessel, 2015) as well as 2D images of organisms (Schluessel and Duengen, 2015). Grey bamboo sharks were also tested for their ability to perceive a range of optical illusions (Fuss et al., 2014d; Fuss and Schluessel, 2017), and were found to fall for Kanizsa figures (Figure 16.2) and subjective contours, but not Müller-Lyer or Ebbinghaus illusions.

Figure 16.2. Shown are the stimuli that were presented to each group, during regular training and transfer test trials in experiments 1a and 1b. The positive, rewarded stimulus is indicated by a check mark. (A) In group 1, an empty square was the positive, rewarded stimulus; in group 2 it was an empty triangle. During the T1 transfer tests of experiment 1a, sharks were ‘expected’ to choose the correct Kanizsa figure. (B) During experiment 1b, group 1 was trained to recognize an empty square over an empty triangle, whereas group 2 was trained vice versa. During the T2 transfer tests, sharks were expected to choose the Kanizsa figure resembling the stimulus they had been trained on.

Adapted from Fuss et al., 2014d, illustration under Creative Commons Attribution License.

Further, several studies have investigated electroreceptive discrimination abilities (Kimber et al., 2011, 2014; Siciliano et al., 2013). Small-spotted catsharks discriminate between prey-type electric fields; they prefer the stronger of two artificial fields with direct current (DC) and an alternative current (AC) over a DC current of the same magnitude, but not a natural DC current associated with a food reward over an artificial DC current of similar magnitude. Yellow stingrays, Urobatis jamaicensis, were successfully trained to bite either the anode or cathode, showing significant polarity discrimination (Siciliano et al., 2013), and to associate a magnetic stimulus with a food reward, showing for the first time object discrimination based on magnetoreceptive cues (Newton and Kajiura, 2017).

Memory Retention

Memory windows vary greatly between species and tasks (Brown, 2001). In some cases, it may be advantageous for an animal to forget particular behaviours quickly to retain flexibility (Warburton, 2003), while others should be retained for longer periods of time or even indefinitely. Grey bamboo sharks were trained in two different spatial tasks and tested for their ability to solve them after an absence of reinforcement (Schluessel and Bleckmann, 2012). All sharks successfully remembered what they had been trained in, up to the maximally tested break period of 6 weeks. Sharks were also successful at remembering a two-alternative choice discrimination task, even after an absence of reinforcement of 50 weeks (Fuss and Schluessel, 2015). Similarly, Port Jackson sharks trained to pair an LED light or a stream of air bubbles with a food reward retained the learned associations for a period of at least 24 hours, and possibly up to 40 days (Guttridge and Brown, 2014). In addition, yellow stingrays solved a different task successfully after intervals of 90–180 days post-learning (Newton and Kajiura, 2017). Small-spotted catshark trained to associate a food reward with an artificial electric field, however, failed to retain the association after a 3-week interval, possibly because forgetting may be advantageous to species living in more variable environments (Kimber et al., 2014). While feeding-related information may sometimes only be retained over short periods of time, life-threatening behaviour should preferentially be retained indefinitely. Mourier and colleagues (2017) found that blacktip reef sharks learned to avoid rod and line capture, which ultimately increased network robustness to experimental catch-and-release in a shark social network.

Brain and Cognition

While the link between a larger than expected brain for a given body size (encephalization) and enhanced cognitive capabilities continues to be highly contentious (Mitchell, 2016), there are documented patterns of encephalization across chondrichthyans, with cognitive implications. Indeed, the most encephalized species are chiefly found in reef-associated or oceanic habitats and/or often pursue more active predation strategies, such as Sphyrna, Carcharhinus and Dasyatis (see Yopak, 2012a), which may reflect higher cognitive demands. In particular, the cognitive requirements for learning the complex spatial organization of a coral reef, in addition to the complex social behaviours and intra- and interspecific interactions that are often prevalent in reef habitats, termed ‘social intelligence’ (Kotrschal et al., 1998), might have influenced the evolution of brain size in these chondrichthyans.

Similarly, brain organization is often used as a neuroanatomical proxy for functional specialization. Reef-associated species have the largest telencephalons (which are associated to higher cognitive functions: Yopak et al., 2007; Lisney et al., 2007; Ari, 2011), large, highly foliated cerebellums (Yopak et al., 2007), relatively small olfactory bulbs (Yopak et al., 2015) and relatively large optic tectums (Yopak and Lisney, 2012), possibly reflecting the visual demands of complex reef habitats. In contrast, deep-sea chondrichthyan brains may reflect lower activity levels and a specialization of non-visual senses in bathyal environments, with small telencephalons (Yopak and Montgomery, 2008) and optic tectums (Yopak and Lisney, 2012), small, smooth cerebellums (Montgomery et al., 2012), relatively enlarged olfactory bulbs (Yopak et al., 2015) and a clear relative hypertrophy of the central termination sites for primary projections from the octavolateralis senses (Yopak and Montgomery, 2008). Therefore, the relative importance of different sensory systems likely varies across species (Collin et al., 2015) and should always be considered when designing experiments. Finally, links between cognition and the relevant neural substrates are limited (Graeber et al., 1978; Graeber 1978, 1980; Schwarze et al., 2013; Fuss et al., 2014a,b).

All for One and One for All

Stewart Nicol
Monotreme Cognition

Just as sharks are not ‘typical fish’, monotremes are not typical mammals. Like sharks, the platypus (Ornithorhynchus antinus), the short-beaked echidna (Tachyglossus aculeatus) and the long-beaked echidnas (Zaglossus spp.) are often dismissed as being primitive, and this has influenced approaches to monotreme cognition. While egg-laying (a plesiomorphic feature) is a defining characteristic of the group, it is often not appreciated that monotremes have large brains. The echidnas have particularly large gyrified brains, equivalent in size to those of carnivores of the same mass, and because of their low metabolic rates, echidnas have brain size to basal metabolic rate relationships similar to those of primates (Nicol, 2017). This suggests that there must be very considerable fitness benefits for the echidnas to maintain such large brains, i.e. the cognitive benefits must outweigh the metabolic costs. But how should this be tested?

There have been four laboratory studies of echidna cognition. Saunders and colleagues (1971) tested the ability of echidnas to remember the position of a food reward in a T-maze. Gates (1978) used two-choice doors with a food reward to test the ability of echidnas to discriminate between symbols of different brightness, orientation and shape. Buchmann and Rhodes (1978) employed operant techniques: echidnas were trained to press a treadle for a food reward in response to visual, tactile and positional cues. Burke and colleagues (2002) attempted to study spatial memory performance of echidnas in terms of their foraging ecology, by testing their learning response to the positioning of food in two- and four-way mazes. All studies demonstrated cognitive performance at least equivalent to that by rats and cats.

Most of these studies preceded any systematic studies of behaviour in the field. Laboratory tests carry the risk of posing problems in an ‘unfair’ manner, particularly when done with very limited knowledge of normal behaviour. This must be particularly so for monotremes, whose sensory modalities, like those of sharks, include electroreception and a strong reliance on olfaction. While the semi-aquatic platypus has 40,000 mucous gland electroreceptors in the bill skin, short-beaked echidnas have only 400 (Pettigrew, 1999), and their predominant sensory input is olfactory: the echidna is the only mammal known to have a gyrified olfactory bulb, probably to expand the number of synaptic glomeruli available for the analysis of the odorant repertoire (Ashwell, 2013). We have identified a total of 186 compounds which are potentially used in olfactory communication by echidnas (Harris et al., 2012), but testing how echidnas respond to subtle, probably unmeasurable changes in a complex suite of chemical signals is not practicable in the laboratory. By contrast, Manger and Pettigrew (1995) investigated how electroreception facilitates prey capture in platypuses by using an electrically shielded tank in which they could combine varying electrical stimuli with video recordings of feeding behaviour. Such a study is only practicable under laboratory conditions. In a very simple study, Augee and Gooden (1992) demonstrated that echidnas could detect buried 9-volt batteries in their normal environment, but the ecological significance of this is not clear.

Ashwell, K. W. S. (2013). Reflections: monotreme neurobiology in context. Neurobiology of monotremes: brain evolution in our distant mammalian cousins (pp. 285298). Collingwood: CSIRO Publishing.
Augee, M. L., and Gooden, B. A. (1992). Evidence for electroreception from field studies of the echidna, Tachyglossus aculeatus. In Platypus and echidnas (pp. 211215). Mosman: Royal Zoological Society of New South Wales.
Buchmann, O. L. K., and Rhodes, J. (1978). Instrumental learning in the echidna. The Australian Zoologist, 20, 131145.
Burke, D., Cieplucha, C., Cass, J., Russell, F., and Fry, G. (2002). Win-shift and win-stay learning in the short-beaked echidna (Tachyglossus aculeatus). Animal Cognition, 5, 7984.
Gates, G. R. (1978). Vision in the monotreme anteater. In: Monotreme biology (pp. 147169). Mosman: Royal Zoological Society of New South Wales.
Harris, R. L., Davies, N. W., and Nicol, S. C. (2012). Chemical composition of odorous secretions in the Tasmanian short-beaked echidna (Tachyglossus aculeatus setosus). Chemical Senses, 37, 819836.
Manger, P. R., and Pettigrew, J. D. (1995). Electroreception and the feeding behaviour of platypus (Ornithorhynchus anatinus: Monotremata: Mammalia). Philosophical Transactions of the Royal Society B: Biological Sciences, 347, 359381.
Nicol, S. C. (2017). Energy homeostasis in monotremes. Frontiers in Neuroscience, 11, 117.
Pettigrew, J. D. (1999). Electroreception in monotremes. The Journal of Experimental Biology, 202, 14471454.
Saunders, J. C., Teague, J., Slonim, D., and Pridmore, P. A. (1971). A position habit in the monotreme Tachyglossus aculeatus (the spiny ant eater). Australian Journal of Psychology, 23, 4751.

Field Guide

Species Selection

How do you design cognition experiments for one of the earth’s most revered predators? Firstly, not all sharks are large, apex predators; there is great diversity among this group (see above), ranging from small-bodied, tropical species (such as the bamboo shark), to giant, sluggish deep-ocean dwellers (such as the Greenland shark, Somniosus microcephalus, the longest-living vertebrate currently described (Nielsen et al., 2016). Selecting the latter for cognition experiments would be rather challenging. However, the former is a useful model, with an egg-laying reproductive mode that allows for captive rearing, a preference for warm water (resulting in greater feeding motivation) and a small body size (facilitating capture, processing and replication). The decision to select a species will also depend on your research question, on their behaviour and biology, the availability of subjects, cost of capture and maintenance, accessibility, and ease of obtaining animal ethics approval and collection permits.

Sharks in Captivity

You cannot test an animal that you cannot catch, keep or maintain. An important first question is whether there is evidence of previous husbandry success. Small (< 1 m total length) benthic species like cat, bamboo and horn sharks are successfully kept in captivity. However, grey nurse, Carcharias taurus, sandbar, Carcharhinus plumbeus, sevengill, Notorynchus cepedianus, and scalloped hammerhead, Sphyrna lewini, sharks have also been successfully maintained in lab-based aquaria throughout the world, even though some of these species are almost 3 m in total length. Generally, large, pelagic species do very poorly in captivity (e.g. white shark, Carcharodon carcharias: Ezcurra et al., 2012). Furthermore, the process to transition a large pelagic species from the wild to captivity is costly and time-consuming.

An alternative to laboratory testing is a semi-captive set-up, where sharks are kept in anchored ocean or shallow-water pens, or flow-through arena (Figure 16.3), with exposure to ambient conditions (e.g. water currents, temperature and tidal fluctuations). Such set-ups have been used effectively on juvenile nurse and lemon sharks in Bimini, Bahamas, with experiments exploring social preferences, learning (Guttridge et al., 2009b, 2013) and personality (Finger et al., 2017). Similarly, bonnethead, S. tiburo, scalloped hammerhead and sandbar sharks in Hawaii, USA, and small and large-spotted catsharks, S. stellaris, in Plymouth, UK, were housed successfully in comparable set-ups: a concrete tank (3–7 m diameter) with flow-through system.

Figure 16.3. Semi-captive set-up of social learning experiments for juvenile lemon sharks in Bimini, Bahamas. (A) Positions and measurements of zones, target and reward. (B) Schematic representation of the target mechanism, showing covered and exposed position as well as side and front views. (C) Steps of the food task or trial.

Copyright Springer-Verlag 2012, adapted with permission of Springer. This was original copyright in the article from Animal Cognition, Social learning in juvenile lemon sharks, Negaprion brevirostris, 16 (2013), 55–64, Tristan L. Guttridge, Sander van Dijk, Eize J. Stamhuis, Jens Krause, Samuel H. Gruber, Culum Brown (Guttridge et al., 2013).

Where are Sharks Sourced From?

Studies on shark cognition often use either captive-bred subjects or those that researchers capture on their own. Schluessel and colleagues have a long-term collaboration with Haus des Meeres, a public aquarium in Vienna, Austria, who breed and rear bamboo sharks (Schluessel, 2015). Researchers in Plymouth, UK, used 392 captive-reared small-spotted catsharks in experiments investigating social cognition (Jacoby et al., 2012b) and trawl-captured adult small-spotted catsharks in learning and memory foraging tasks (Kimber et al., 2014). Juvenile lemon and nurse sharks in Bimini, Bahamas, were captured via gillnetting and free-dive techniques, whereas scientists in Hawaii, USA, used simple rod and reel to obtain juvenile scalloped hammerhead or sandbar sharks. Sharks for all these experiments were transported by a small research vessel to nearby semi-captive pens, via aerated large tub or live-well (Kajiura, 2003; Guttridge et al., 2009b, 2013). More recently, Guttridge and Brown (2014) working in Merimbula, Australia, obtained juvenile Port Jackson sharks from a commercial fisher who regularly caught the species as bycatch. Most studies on shark cognition suffer from a low sample size (Guttridge et al., 2009a; Schluessel et al., 2015), but often enough test subjects can be obtained or reared to make stronger conclusions about shark cognitive abilities.

How are Sharks Maintained in Captivity?

Successful maintenance begins with careful consideration of ecological, physiological and behavioural requirements of the species held. Tank size (horizontal and vertical) or shape will vary depending on the species. Circular tanks or pens are generally used, or rectangles with buffered corners; tanks with right-angle corners often cause stress, as sharks struggle to navigate out of them (Gruber et al., 2001). For researchers choosing a semi-captive set-up, it is important to consider environmental factors, such as tidal changes, wave action or weather conditions, as these could impact space availability, water flow, visibility and temperature. We recommend the use of non-metal materials in tank and pen construction, specifically in behavioural experiments, as sharks can detect electric fields.

Adequate food as well as motivation to feed is essential. A diet similar to what the species naturally feeds on is optimal; as this may not be practical, a mixed diet of pre-frozen (to eliminate parasites) squid or fish, with mineral and vitamin supplements, is recommended (Kimber et al., 2014; Schluessel, 2015). The amount and frequency of feeds depends on many factors (e.g. experiment, metabolism, age class, food type, tank size, water temperature). Daily rations for sharks have been estimated in the lab and captivity (Wetherbee and Cortes, 2004) and can range from 0.2 per cent body weight per day for the adult sevengill, to 4.34 per cent body weight per day for the bonnethead shark. During cognition experiments, test sharks are usually fed on a percentage just below the recommended daily ration (Kimber et al., 2014). If test species’ daily rations have not been calculated, we suggest trialling individuals before experimentation (Guttridge and Brown, 2014).

Prior to starting experiments, we recommend a captive acclimation period of 3–7 days. Knowledge of natural behaviours, sociobiology and physiology is important, as these norms can be compared to observations of reactions in captive situations. Some common parameters that could be used as indicators of stress for sharks include inappetence and anorexia, excessive resting, evasive or avoidance behaviour, and changes to any of the following: skin coloration (often blotches), ventilation rate or swimming behaviour (disorientation, obstacle contact, wall leaning or thigmotaxis). Having a detailed ethogram of behaviours for your test species is also useful when quantifying conditioned responses during learning trials, as often subtle movements can be important indicators (Guttridge and Brown, 2014).

Table 16.1. Considerations for elasmobranch cognitive experiments.

Species selection
Sharks in captivityUsually select small-bodied, benthic specialized species or juveniles, with history of successful husbandry and motivation to feed. Take into account cost of capture, transport and maintenance
Sharks in the wildSelect those that can be resighted or recaptured, resident to a habitat or area, or tagged with monitoring equipment (e.g. biotelemetry)
Sample sizeFor your study take enough sharks to be housed, tested or tracked for meaningful inference (power analysis). Take into account the possibility of using conspecifics, mixed sex but size-matched individuals
ProvenanceWild-caught (e.g. use of fishers, either recreational or commercial) or captive-bred; consider logistics and practicality of transport from capture site or breeding facility to experimental site
HousingLab-based housing (Figure 16.4); swimming pool or pen mesh fencing (Figure 16.3); flow-through system on the coast or similar penned area
MaintenanceConsider food type (squid, fish pieces) and amount (literature search, typically < 2 per cent daily ration); water flow, conductance, social and general tank environment; shark health (parasite load, weight loss, fin rot, blotches); euthanasia protocols (e.g. MS222)
IdentificationExternal marker tags for ID (e.g. colour code, rototag), pattern discrimination (e.g. bamboo shark), biotelemetry or bio-logging techniques to track individuals for extensive periods
PermitsImplications of conservation status (use animals with low concern status in the IUCN red list); country of testing (Bahamas vs. Germany/UK – countries have very different requirements), Animal Ethics (IACUC)
Experimental design
Lab methodsTraditional methods comprise: T-maze, force-two choice, classical or operant conditioning, shuttle-box
MaterialsUse synthetic materials such as plastics (Perspex, PVC), because they are good for building compartments, arenas or targets for training; opaque barriers can be used to block off areas
AcclimationAllow 2–3 days in captivity to ensure sharks exhibit normal behaviours, monitor stress signals (e.g. body blotching, not feeding, weight loss, wall leaning/thigmotaxis)
Stimulus selectionPrior to testing, control for detection and sensitivity to cues (e.g. visual, electrical, mechanical); during testing, ensure consistency of stimulus presentation (time of day, illumination)
Training paradigmControl the number of feeding trials per day (linked to food quantity), intertrial time, ceiling time for trials, unintentional cueing, and mixing of water within set-up to avoid spatial biases; include further controls as needed
Feeding apparatusManually operated by experimenter, automated feeder
Moving between experimental compartmentsManually operated guillotine doors, ushering with net or mesh
Pre-trainingConsisting of exposure to the experimental set-up and feeding protocol, use of food cues to attract sharks to areas within the set-up, set criterion for obtaining food rewards and once reached training commences
EthogramDevelop an ethogram via observation of subjects prior to testing; some typical behaviours might include head shake, turn, swimming, bite or head contact with apparatus; if social, following, circling, or tactile resting
Data analysis
Information to be recordedLatency to feed, side preference, search patterns, percentage of correct choices
Software and toolsFor tracking individuals (image J, Mtrack J); overhead cameras (gopro, CCTV)
StatisticsOften non-parametric for traditional experiments (due to low sample size); exciting new tools available to analyse tracking data and bio-logging (network analysis, machine learning)

Identification and Wild Observation

For some sharks, it is possible to identify individuals via pattern discrimination, fin markings or notches, as in captive bamboo sharks (Schluessel, 2015). However, tags are often needed to discriminate between individuals, attached either through the dorsal fin, and anchored or injected in the adjacent muscle via dart (plastic or stainless steel) or hypodermic needle. Tags can be colour-coded or numbered, microchip (PIT; Destron Fearing Inc.), fluorescent visible elastomer implant, T-bar, streamer, or plaque-shaped and of differing sizes, depending on the species (see below). Their use has been most effective for mark–recapture studies exploring life-history traits and movement (Casey and Natansen, 1992); however, studies on shark social cognition have also benefitted from using such tags (Guttridge et al., 2013; Jacoby et al., 2014; Keller et al., 2017). For sharks, recent advances in remote monitoring devices, such as biotelemetry (satellite, radio and acoustic telemetry) and bio-logging (archival loggers), have revolutionized our capabilities for observation (Hussey et al., 2015). Using these tools, it is now possible to reliably observe and relocate sharks, as well as record detailed information about their behaviour – an important pre-requisite for wild cognition experiments (Pritchard et al., 2015).


Consideration should always be given to the conservation status and the requisite permits for collection and experimentation of a given species. The former can be found on the International Union for Conservation of Nature (IUCN) Red List of Threatened Species website (, while the latter are dependent on the jurisdiction and institute through which the research will be conducted. It is important to note that international journals will not consider reviewing a manuscript without evidence of collection and ethics permits.

Experimental Design

Designing an experiment investigating shark cognition should really be no different from that for any other animal, and includes determining how many animals to test, identifying which behaviours to quantify, which stimuli and set-up to use, and selecting a training protocol (Lieberman, 1990). Some classical approaches have been modified successfully to investigate cognitive processes in sharks. For example, the two-choice T-maze design was used effectively to examine spatial memory and orientation (Schluessel and Bleckmann, 2012), and classical or operant conditioning paradigms for exploring associative and social learning (Gruber and Schneiderman, 1975; Guttridge et al., 2013; Schwarze et al., 2013), as well as object categorization and symmetry perception (Fuss et al., 2014c; Schluessel et al., 2014b; Schluessel and Duengen, 2015; Figure 16.4). Below, we present tips and ideas gleaned from our personal experiences and discussed in the literature, to help overcome the challenges of designing an experiment investigating cognition in sharks.

Figure 16.4. The experimental set-up within the experimental basin inside a white pavilion. The keyhole-shaped set-up consists of a starting compartment, a decision area and a frosted screen for projection, with a divider to allow the projection of two 2D objects at a time and to provoke a clear, unambiguous choice (left or right). For projection, an LED beamer is used. Sharks are placed within the SC at the start of each trial. OHL, overhead light.

Adapted with permission of Springer. This was original copyright in the article from Zoology, Visual discrimination abilities in the gray bamboo shark (Chiloscyllium griseum), 117 (2014), 104–111, Theodora Fuss, Horst Bleckmann, Vera Schluessel (Fuss et al., 2014c).

Stimulus Selection and Presentation

Sharks exhibit great variation in their sensory systems, which suggests that the relative importance of sensory systems varies between species (see above). From experimental studies, Gardiner and colleagues (2014) found that, by blocking olfactory cues, nurse sharks were unable to detect food and subsequently feed, whereas blacktip sharks, Carcharhinus limbatus, and bonnethead sharks undergoing the same treatment detected prey at 1–2 m distance, using visual cues. Working with Port Jackson sharks, Guttridge and Brown (2014) found that individuals trained on a conditioning regime with air bubbles displayed significantly more anticipatory behaviours (e.g. turning towards the air bubbles and biting) than those trained on an underwater LED, possibly because the air bubble stimulus is more biologically relevant in this species. These examples emphasize how the perceptual ability of test subjects can impact task performance and thus should be an important factor to consider. Moreover, the spatial and/or temporal relationship between conditioning and food reward can influence the speed at which learning takes place, and the nature and intensity of the conditioned response (Lieberman, 1990). For example, Schluessel (2015) presented stimuli 3 cm from the tank base, due to the bottom-dwelling nature of their test subjects.

Most studies on shark cognition used positive reinforcement for conditioning, due to limited success of aversive stimuli (Tester and Kato, 1963; but see Gruber and Schneiderman, 1975). For example, Schwarze and colleagues (2013), using an electric shock paired with a green light, documented individual variation in avoidance responses, including backwards swimming, side-to-side head and/or tail movements, making conclusions difficult to interpret. Furthermore, there was evidence that bamboo sharks habituated to an electric shock (Kimber et al., 2014). Interestingly, during categorization learning, bamboo sharks learnt positive ‘fish’ images, but did not learn the unrewarded ‘snail’, unlike cichlids that learnt both negative and positive associations (Schluessel et al., 2014b; Schluessel and Duengen, 2015). Finally, when presenting stimuli to sharks, it is essential to prevent any unintentional cueing, both through the experimenter and set-up environment (e.g. Schluessel, 2015).

Set-up Familiarity and Pre-training

Pre-exposure to the experimental set-up is important to avoid confounding effects of novelty and to eliminate unexpected problems with handling or feeding prior to testing. Previous studies have allowed test subjects to swim freely through the set-up, start box and doors, across a 1–3-day period (Guttridge and Brown, 2014; Kimber et al., 2014; Schluessel, 2015). Some studies fed subjects in a pre-training phase to ensure that food rewards could be retrieved and to identify potential side-preferences (Schluessel et al., 2012). Food should be delivered in a smooth and consistent manner, and arrive without delay in the appropriate location. For benthic species, rewards delivered close to the substrate (Fuss et al., 2014c; Guttridge and Brown, 2014; Kimber et al., 2014) were most effective, while feeding trials with lemon sharks had greater success at the surface (Guttridge et al., 2013). Most studies used automated feeders or manually operated devices, varying from a syringe with plastic tubing to feeding rods to electronic or manually operated compartment feeders (Guttridge and Brown, 2014; Kimber et al., 2014; Schluessel, 2015). The amount of food that test subjects receive daily should be enough to sustain them, but maintain motivation. The number of trials conducted per day, and the time between them (intertrial time), is important for any experiments using food rewards. During feeding, sharks tend to become ‘switched-on’ and participate eagerly; thus, we recommend short intertrial times and short daily experiment duration. For example, Schluessel and colleagues tested bamboo sharks once per day, with 10 trials per session. Guttridge and Brown (2014) conducted six trials per day with Port Jackson sharks, whereas Kimber and colleagues (2014) trained small-spotted catsharks in two trials per day. Intertrial time was similar (e.g. 60 s for Port Jackson sharks, 30–90 s for bamboo sharks and 300 s for small-spotted catsharks).

Recording Behaviours and Data Analyses

Probably, the best advice for any researcher is to first get to know their test subject, as many animals display subtle behavioural and/or physical changes in response to captivity, novelty, conspecifics, prey or predators. We suggest developing an ethogram of these behaviours, as well as recording their context, duration and whether one usually precedes another. This is particularly informative when deciding which behaviours to record during experiments. Most studies record the following information: latency to feed and/or respond with bite, left or right choice, percentage of correct choices in two-choice trials, trial duration, and behavioural information such as search patterns, body movements and type of swimming. These can be recorded by an observer and should be recorded by a camera for validation and reference, as subtle behaviours can be missed by an observer. Indeed, for some lab experiments, image tracking programs (e.g. image J, Mtrack J, Ctrax) have been used effectively to monitor interactions of schooling fish (Herbert-Read et al., 2011), and these could be modified for sharks, given the right background and illumination. In a lab environment, conditions such as conductivity, pH and water temperature need to be maintained, but for semi-captive and/or wild studies it may be easier to record such environmental information and account for it in the analyses.

Wild vs. Lab

David M. P. Jacoby
Elasmobranch Cognition in the Wild

Elasmobranchs are wonderfully diverse and extremely well-adapted, over a long evolutionary history, to the specific environments they occupy. As demonstrated throughout this chapter, elasmobranch cognitive abilities are intrinsically related to their lifestyle and, in particular, the relationship between their habitat and hunting strategies (e.g. ambush/pursuit predators, visual/olfactory). Thus, considerable variation in cognition exists between species (Yopak, 2012).

Advances in our understanding of cognition in sharks and rays has almost exclusively relied on controlled captive/semi-captive experiments (e.g. Guttridge and Brown, 2014). While such studies will undoubtedly continue to prove crucial for guiding progress in this field, translating some of this work to wild elasmobranchs is hugely challenging. However, technological innovations, e.g. animal-borne tags that are becoming increasingly smaller, cheaper and more sophisticated, continue to push the boundaries of animal ecology (Cooke et al., 2013; Hussey et al., 2015; Kays et al., 2015), as do the techniques advancing analyses of large telemetry data sets (Krause et al., 2013; Jacoby et al., 2016). In fact, biotelemetry is already being employed to record fine-scale measurements on the activity of sharks, alongside visual observations in captive experiments on shark social behaviour (Jacoby et al., 2010; Wilson et al., 2015), hinting that the transition to fully wild data on cognitive function in elasmobranchs is imminent. Further, proximity logging two-way, acoustic transmitters have been shown to record social behaviour in wild sharks at very fine spatial scales (Guttridge et al., 2010; Holland et al., 2010). Using conventional tags (satellite/acoustic), the accuracy of determining the exact locations of individual sharks is still relatively low, unless the animal is at the surface. Animal-borne data loggers, such as accelerometers or depth-temperature recorders, however, can log at very high resolution, and this is facilitating exciting progress within the machine learning community in developing algorithms for pattern recognition and the detection of behavioural switching from raw telemetry data (Olden et al., 2008; Krause et al., 2013).

The use of electronic tags is, of course, costly and not always feasible. Therefore, observations and ethograms of wild shark behaviour at provisioning sites, where sharks are attracted to an area to feed for the benefit of paying tourists, actually offer an interesting compromise between captive and wild studies. Although careful interpretation is required to tease apart ‘natural’ and ‘induced’ behaviours, some studies have shown increased residency and aggression in sharks acclimatised to provisioning areas (e.g. Clua et al., 2010).

Measuring and quantifying decision-making processes and repeatable behaviours in elasmobranchs may take many forms, including estimating consistency in navigation routes, feeding specializations, social network position or dominance. However, all rely on the ability to monitor individuals over time and space and still retrieve the data. Moving forward, small benthic/demersal sharks will unquestionably continue to play an important role as model species; adult cat, lemon, bamboo and Port Jackson sharks (to name a few) are small enough to be housed and observed in captivity, yet large enough to carry logging or acoustic tags in the wild, allowing for repeated testing of individuals in both laboratory and wild contexts.

Clua, E., Buray, N., Legendre, P., Mourier, J., and Planes, S. (2010). Behavioural response of sicklefin lemon sharks Negaprion acutidens to underwater feeding for ecotourism purposes. Marine Ecology Progress Series, 414, 257266.
Cooke, S. J., Midwood, J. D., Thiem, J. D., et al. (2013). Tracking animals in freshwater with electronic tags: past, present and future. Animal Biotelemetry, 1, 119.
Guttridge, T. L., and Brown, C. (2014). Learning and memory in the Port Jackson shark, Heterodontus portusjacksoni. Animal Cognition, 17, 415425.
Guttridge, T. L., Gruber, S. H., Krause, J., and Sims, D. W. (2010). Novel acoustic technology for studying free-ranging shark social behaviour by recording individuals’ interactions. PLoS ONE, 5, 18.
Holland, K. N., Meyer, C. G., and Dagorn, L. C. (2010). Inter-animal telemetry: results from first deployment of acoustic ‘business card’ tags. Endangered Species Research, 10, 287293.
Hussey, N. E., Kessel, S. T., Aarestrup, K., et al. (2015). Aquatic animal telemetry: a panoramic window into the underwater world. Science, 348, 1255642.
Jacoby, D. M. P., Busawon, D. S., and Sims, D. W. (2010). Sex and social networking: the influence of male presence on social structure of female shark groups. Behavioral Ecology, 21, 808818.
Jacoby, D. M. P., Papastamatiou, Y. P., and Freeman, R. (2016). Inferring animal social networks and leadership: applications for passive monitoring arrays. Journal of The Royal Society Interface, 13, 20160676.
Kays, R., Crofoot, M. C., Jetz, W., and Wikelski, M. (2015). Terrestrial animal tracking as an eye on life and planet. Science, 348, aaa2478.
Krause, J., Krause, S., Arlinghaus, R., Psorakis, I., Roberts, S., and Rutz, C. (2013). Reality mining of animal social systems. Trends in Ecology and Evolution, 28, 541551.
Olden, J. D., Lawler, J. J., and Poff, N. L. (2008). Machine learning methods without tears: a primer for ecologists. The Quarterly Review of Biology, 83, 171193.
Wilson, A. D. M., Brownscombe, J. W., Krause, J., et al. (2015). Integrating network analysis, sensor tags, and observation to understand shark ecology and behavior. Behavioral Ecology, 26, 15771586.
Yopak, K. E. (2012). Neuroecology of cartilaginous fishes: the functional implications of brain scaling. Journal of Fish Biology, 80, 19682023.



  • Smith, M., Warmolts, D., Thoney, D., and Hueter, R. (2017). Elasmobranch husbandry manual II: captive care of sharks, rays and their relatives. Ohio Biological Survey.

  • Carrier, J. C., Musick, J. A., and Heithaus, M. R. (2012). Biology of sharks and their relatives. Boca Raton, FL: CRC Press.

  • Brown, C., Laland, K., and Krause, J. (2011). Fish cognition and behaviour. Oxford: Wiley-Blackwell.



  • Assistant Professor Christine Bedore; Georgia Southern University, USA; Elasmobranch Sensory Biology.

  • Associate Professor Culum Brown; Macquarie University, Australia; Behavioral Ecology and Evolution of Fishes.

  • Professor Shaun Collin; University of Western Australia, Australia; Neuroecology.

  • Professor Samuel Gruber and Dr Tristan Guttridge; Bimini Biological Field Station Foundation, Bimini, Bahamas; Bimini Sharklab.

  • Professor Kim Holland; Hawaii Institute of Marine Biology, USA; Shark and Reef Fish Research.

  • Dr Robert Hueter; Mote Marine Lab, Sarasota, USA; Sharks and Rays Conservation Program.

  • Dr Stephen Kajiura; Florida Atlantic University, USA; Elasmobranch Research Laboratory.

  • Associate Professor Vera Schluessel; University of Bonn, Germany; Cognition in Elasmobranchs and Teleost Fishes.

  • Professor David Sims; Marine Biological Association, Plymouth, UK; Movement Ecology and Conservation of Marine Predators.

  • Assistant Professor Kara Yopak; University North Carolina Wilmington, USA; Evolutionary Neuroecology.

Tagging information:


Tristan is mesmerized by sharks. Being in the water with them is where he feels most inspired. His PhD at the Bimini Sharklab investigated the social organization of lemon sharks. In Australia he studied Port Jackson sharks. In 2012, Tristan returned to Bimini Sharklab as the Director to study sharks’ personality.

Vera has been fascinated by sharks since a book she read at six. Her first hands-on experience came at the Bimini Sharklab. Then she studied stingray cognition and biology of white spotted eagle rays. Currently Vera works on cognition in sharks and stingrays (behaviour and anatomy) at Bonn University.

Kara has always been compelled to understand elasmobranchs’ behaviour and sensory biology. Because her questions kept coming back to the brain, where all behaviours begin and end, her lab at the University of North Carolina Wilmington explores how the development of major brain areas (and behaviour) varies between species.


Ari, C. (2011). Encephalization and brain organization of mobulid rays (Myliobatiformes, Elasmobranchii) with ecological perspectives. The Open Anatomy Journal, 3, 113.
Armansin, N. C., Lee, K. A., Huveneers, C., and Harcourt, R. G. (2016). Integrating social network analysis and fine-scale positioning to characterize the associations of a benthic shark. Animal Behaviour, 115, 245258.
Aronson, L. R., Aronson, F. R., and Clark, E. (1967). Instrumental conditioning and light-dark discrimination in young nurse sharks. Bulletin of Marine Science, 17, 249256.
Bell, C., Bodznick, D., Montgomery, J., and Bastian, J. (1997). The generation and subtraction of sensory experiments within cerebellar-like structures. Brain, Behaviour and Evolution, 50, 1731.
Bodznick, D., Montgomery, J., and Carey, M. (1999). Adaptive mechanisms in the elasmobranch hindbrain. Journal of Experimental Biology, 22, 13571364.
Brown, C. (2001). Familiarity with the test environment improves the escape responses in the crimson spotted rainbowfish, Melanotaenia duboulayi. Animal Cognition, 4, 109113.
Brown, C. (2012). Tool use in fishes. Fish Fisheries, 13, 105115.
Byrnes, E. E., and Brown, C. (2016). Individual personality differences in Port Jackson sharks Heterodontus portusjacksoni. Journal of Fish Biology, 89, 11421157.
Byrnes, E. E., Vila-Pouca, C., and Brown, C. (2016a). Laterality strength is linked to stress reactivity in Port Jackson sharks (Heterodontus portusjacksoni). Behavioural Brain Research, 305, 239246.
Byrnes, E. E., Pouca, C. V., Chambers, S. L., and Brown, C. (2016b). Into the wild: developing field tests to examine the link between elasmobranch personality and laterality. Behaviour, 153, 17771793.
Casey, J. G., and Natanson, L. J. (1992). Revised estimates of age and growth of the sandbar shark (Carcharhinus plumbeus) from the western North Atlantic. Canadian Journal of Fisheries and Aquatic Sciences, 49, 14741477.
Collin, S., Kempster, R., and Yopak, K. (2015). Sensing the environment. In Physiology of elasmobranch fishes (pp. 1999). New York, NY: Elsevier.
Dulvy, N. K., and Reynolds, J. D. (1997). Evolutionary transitions among egg-laying, live-bearing, and maternal inputs in sharks and rays. Proceedings of the Royal Society of London B: Biological Sciences, 264, 13091315.
Ezcurra, J. M., Lowe, C. G., Mollet, J. F., Ferry, L. A., and O’Sullivan, J. B. (2012). Captive feeding and growth of young-of-the-year white sharks, Carcharodon carcharias, at the Monterey Bay Aquarium. In Global perspectives on the biology and life history of the great white shark research (Carcharodon carcharias) (pp. 316). Boca Raton, FL: Taylor & Francis.
Ferretti, F., Worm, B., Britten, G. L., Heithaus, M. R., and Lotze, H. K. (2010). Patterns and ecosystem consequences of shark declines in the ocean. Ecology Letters, 13, 10551071.
Finger, J. S., Dhellemmes, F., Guttridge, T. L., Kurvers, R. H. J. M., Gruber, S. H., and Krause, J. (2016). Rate of movements of juvenile lemon sharks in a novel open field, are we measuring activity or reaction to novelty? Animal Behaviour, 116, 7582.
Finger, J. S., Dhellemmes, F., and Guttridge, T. L. (2017). Personality in elasmobranchs with a focus on sharks: early evidence, challenges, and future directions. In Personality in non-human animals (pp. 129152). Cham: Springer.
Finger, J. S., Guttridge, T. L., Wilson, A. D. M., Gruber, S. H., and Krause, J. (2018). Are some sharks more social than others? Short and long-term consistency in the social behaviour of juvenile lemon sharks. Behavioural Ecology and Sociobiology, 72, 17.
Fuss, T., and Schluessel, V. (2015). Something worth remembering: Visual discrimination in sharks. Animal Cognition, 18, 463471.
Fuss, T., and Schluessel, V. (2017). The Ebbinghaus illusion in the gray bamboo shark (Chiloscyllium griseum) in comparison to the teleost damselfish (Chromis chromis). Zoology, 123, 1629.
Fuss, T., Bleckmann, H., and Schluessel, V. (2014a). Place learning prior to and after telencephalon ablation in bamboo and coral cat sharks (Chiloscyllium griseum and Atelomycterus marmoratus). Journal of Comparative Physiology, 200, 3752.
Fuss, T., Bleckmann, H., and Schluessel, V. (2014b). The shark Chiloscyllium griseum can orient using turn responses before and after partial telencephalon ablation. Journal of Comparative Physiology, 200, 1935.
Fuss, T., Bleckmann, H., and Schluessel, V. (2014c). Visual discrimination abilities in the gray bamboo shark (Chiloscyllium griseum). Zoology, 117, 104111.
Fuss, T., Bleckmann, H., and Schluessel, V. (2014d). The brain creates illusions not just for us: sharks (Chiloscyllium griseum) can “see the magic” as well. Frontiers of Neural Circuits, 8, 24.
Fuss, T., Russnak, V., Stehr, K., and Schluessel, V. (2017). World in motion: perception and discrimination of movement in grey bamboo sharks (Chiloscyllium griseum). Animal Behavior and Cognition, 4, 223241.
Gage, F. (2002). Neurogenesis in the adult brain. Journal of Neuroscience, 22, 612613.
Gardiner, J. M., Hueter, R. E., Maruska, K. P., et al. (2012). Sensory physiology and behavior of elasmobranchs. In Biology of sharks and their relatives (pp. 349402). New York, NY: CRC Press.
Gardiner, J. M., Atema, J., Hueter, R. E., and Motta, P. J. (2014). Multisensory integration and behavioral plasticity in sharks from different ecological niches. PLoS ONE, 9(4), e93036.
Gonda, A. I., Herczeg, G. B., and Merila, J. (2013). Evolutionary ecology of intraspecific brain size variation: a review. Ecology and Evolution, 3, 27512764.
Graeber, R. C. (1978). Behavioral studies correlated with central nervous system integration of vision in sharks. In Sensory biology of sharks, skates, and rays (pp. 195225). Washington, DC: Government Printing Office.
Graeber, R. C. (1980). Telencephalic function in elasmobranchs. In: Comparative neurology of the telencephalon (pp. 1739). Boston, MA: Springer.
Graeber, R. C., and Ebbesson, S. O. E. (1972). Visual discrimination learning in normal and tectal-ablated nurse sharks (Ginglymostoma cirratum). Comparative Biochemistry and Physiology, 42, 131139.
Graeber, R. C., Ebbesson, S. O. E., and Jane, J. A. (1973). Visual discrimination in sharks without optic tectum. Science, 180, 413415.
Graeber, R. C., Schroeder, D. M., Jane, J. A., and Ebbesson, S. O. E. (1978). Visual discrimination following partial telencephalic ablations in nurse sharks (Ginglymostoma cirratum). Journal of Comparative Neurology, 180, 325344.
Grogan, E. D., Lund, R., and Greenfest-Allen, E. (2012). The origin and relationships of early Chondrichthyans. In Biology of sharks and their relatives (pp. 329). Boca Raton, FL: CRC Press.
Grubbs, R. (2010). Ontogenetic shifts in movements and habitat use. In Sharks and their relatives II: biodiversity, adaptive physiology, and conservation (pp. 319350). Boca Raton, FL: CRC Press.
Gruber, S. H., and Schneiderman, N. (1975). Classical conditioning of the nictitating membrane response of the lemon shark (Negaprion brevirostris). Behavior Research Methods, 7, 430434.
Gruber, S. H., De Marignac, J. R., and Hoenig, J. M. (2001). Survival of juvenile lemon sharks at Bimini, Bahamas, estimated by mark–depletion experiments. Transactions of the American Fisheries Society, 130, 376384.
Guttridge, T. L., and Brown, C. (2014). Learning and memory in the Port Jackson shark, Heterodontus portusjacksoni. Animal Cognition, 17, 415425.
Guttridge, T. L., Myrberg, A. A., Porcher, I. F., Sims, D. W., and Krause, J. (2009a). The role of learning in shark behavior. Fish Fisheries, 10, 450469.
Guttridge, T. L., Gruber, S. H., Gledhill, K. S., Croft, D. P., Sims, D. W., and Krause, J. (2009b). Social preferences of juvenile lemon sharks Negaprion brevirostris. Animal Behaviour, 78, 543548.
Guttridge, T. L., Gruber, S. H., DiBattista, J. D., et al. (2011). Assortative interactions and leadership in a free-ranging population of juvenile lemon shark Negaprion brevirostris. Marine Ecology Progress Series, 423, 235245.
Guttridge, T. L., van Dijk, S., Stamhuis, E. J., Krause, J., Gruber, S. H., and Brown, C. (2013). Social learning in juvenile lemon sharks Negaprion brevirostris. Animal Cognition, 16, 5564.
Harahush, B., Hart, N., and Collin, S. (2014). Ontogenetic changes in retinal ganglion cell distribution and spatial resolving power in the brown-banded bamboo shark Chiloscyllium punctatum (Elasmobranchii). Brain Behavior and Evolution, 83, 286300.
Helfman, G., Collette, B., and Facey, D. (1997). The diversity of fishes. Oxford: Blackwell Science.
Herbert-Read, J. E., Perna, A., Mann, R. P., Schaerf, T. M., Sumpter, D. J. T., and Ward, A. J. W. (2011). Inferring the rules of interaction of shoaling fish. Proceedings of the National Academy of Sciences, 108, 1872618731.
Hofmann, M. H., and Northcutt, R. G. (2012). Forebrain organization in elasmobranchs. Brain, Behaviour and Evolution, 80, 142151.
Hussey, N. E., Kessel, S. T., Aarestrup, K., et al. (2015). Aquatic animal telemetry: a panoramic window into the underwater world. Science, 348, 1255642.
Jacoby, D. M., Croft, D. P., and Sims, D. W. (2012a). Social behaviour in sharks and rays: analysis, patterns and implications for conservation. Fish and Fisheries, 13, 399417.
Jacoby, D. M. P., Sims, D. W., and Croft, D. P. (2012b). The effect of familiarity on aggregation and social behaviour in juvenile small spotted catsharks Scyliorhinus canicula. Journal of Fish Biology, 81, 15961610.
Jacoby, D. M. P., Fear, L. N., Sims, D. W., and Croft, D. P. (2014). Shark personalities? Repeatability of social network traits in a widely distributed predatory fish. Behavioral Ecology and Sociobiology, 68, 19952003.
Kajiura, S. M. (2003). Electroreception in neonatal bonnethead sharks, Sphyrna tiburo. Marine Biology, 143, 603611.
Keller, B., Finger, J.-S., Gruber, S. H., Abel, D. C., and Guttridge, T. L. (2017). The effects of familiarity on the social interactions of juvenile lemon sharks, Negaprion brevirostris. Journal of Experimental Marine Biology, 489, 2431.
Kempster, R., Hart, N., and Collin, S. (2013). Survival of the stillest: predator avoidance in shark embryos. PLoS ONE, 8, e52551.
Kimber, J. A., Sims, D. W., Bellamy, P. H., and Gill, A. B. (2011). The ability of a benthic elasmobranch to discriminate between biological and artificial electric fields. Marine Biology, 158, 18.
Kimber, J. A., Sims, D. W., Bellamy, P. H., and Gill, A. B. (2014). Elasmobranch cognitive ability: using electroreceptive foraging behaviour to demonstrate learning, habituation and memory in a benthic shark. Animal Cognition, 17, 5565.
Kotrschal, A., van Staaden, M. J., and Huber, R. (1998). Fish brains: evolution and environmental relationships. Reviews in Fish Biology and Fisheries, 8, 373408.
Kotrschal, A., Rogell, B., Bundsen, A., et al. (2013). Artificial selection on relative brain size in the guppy reveals costs and benefits of evolving a larger brain. Current Biology, 23, 168171.
Kuba, M. J., Byrne, R. A., and Burghardt, G. M. (2010). A new method for studying problem solving and tool use in stingrays (Potamotrygon castexi). Animal Cognition, 13, 507513.
Lema, S. C., Hodges, M. J., Marchetti, M. P., and Nevitt, G. A. (2005). Proliferation zones in the salmon telencephalon and evidence for environmental influence on proliferation rate. Comparative Biochemistry and Physiology A, 141, 327335.
Lieberman, D. A. (1990). Learning: behaviour and cognition. Belmont, CA: Wadsworth.
Lisney, T. J., Bennett, M. B., and Collin, S. P. (2007). Volumetric analysis of sensory brain areas indicates ontogenetic shifts in the relative importance of sensory systems in elasmobranchs. Raffles Bulletin of Zoology, 14, 715.
Lisney, T. J., Theiss, S. M., Collin, S. P., and Hart, N. S. (2012). Vision in elasmobranchs: 21st century advances. Journal of Fish Biology, 80, 20242054.
Lisney, T. J., Yopak, E. E., Camilieri-Asch, V., and Collin, S. P. (2017). Ontogenetic shifts in brain organization in the bluespotted stingray Neotrygon kuhlii (Chondrichthyes: Dasyatidae). Brain, Behaviour and Evolution, 89, 6883.
Litherland, L., Collin, S., and Fritsches, K. (2009a). Eye growth in sharks: ecological implications for changes in retinal topography and visual resolution. Visual Neuroscience, 26, 397409.
Litherland, L., Collin, S., and Fritsches, K. (2009b). Visual optics and ecomorphology of the growing shark eye: a comparison between deep and shallow water species. Journal of Experimental Biology, 212, 35833594.
Marchetti, M. P., and Nevitt, G. A. (2003). Effects of hatchery rearing on brain structures of rainbow trout, Oncorhynchus mykiss. Environmental Biology of Fishes, 66, 914.
Meredith, T. L., and Kajiura, S. M. (2010). Olfactory morphology and physiology of elasmobranchs. Journal of Experimental Biology, 213, 34493456.
Mitchell, C. (2016). The evolution of brains and cognitive abilities. In: Evolutionary biology (pp. 7387). Cham: Springer.
Montgomery, J. C., Bodznick, D., and Yopak, K. E. (2012). The cerebellum and cerebellar-like structures of cartilaginous fishes. Brain, Behaviour and Evolution, 80, 152165.
Mourier, J., Vercelloni, J., and Planes, S. (2012). Evidence of social communities in a spatially structured network of a free-ranging shark species. Animal Behavior, 83, 389401.
Mourier, J., Brown, C., and Planes, S. (2017). Learning and robustness to catch-and-release fishing in a shark social network. Biology Letters, 13, 20160824.
Naylor, G. J. P., Caira, J. N., Jensen, K., Rosana, K. A. M., Straube, N., and Lakner, C. (2012). Elasmobranch phylogeny: a mitochondrial estimate based on 595 species. In Biology of sharks and their relatives (pp. 3156). New York, NY: CRC Press.
Newton, K. C., and Kajiura, S. M. (2017). Magnetic field discrimination, learning and memory in the yellow stingray (Urobatis jamaicensis). Animal Cognition, 20, 603614.
Nielsen, J., Hedeholm, R. B., Heinemeier, J., et al. (2016). Eye lens radiocarbon reveals centuries of longevity in the Greenland shark (Somniosus microcephalus). Science, 353(6300), 702704.
Peters, R., and Evers, H. (1985). Frequency selectivity in the ampullary system of an elasmobranch fish Scyliorhinus canicula. Journal of Experimental Biology, 118, 99109.
Pritchard, D. J., Hurly, T. A., Tello-Ramos, M. C., and Healy, S. D. (2016). Why study cognition in the wild (and how to test it)? Journal of the Experimental Analysis of Behvior, 105, 4155.
Puzdrowski, R. L., and Gruber, S. (2009). Morphologic features of the cerebellum of the Atlantic stingray, and their possible evolutionary significance. Integrative Zoology, 4, 110122.
Reynolds, J. D., Goodwin, N. B., and Freckleton, R. P. (2002). Evolutionary transitions in parental care and live bearing in vertebrates. Philosophical Transactions of the Royal Society B: Biological, 357, 269281.
Schluessel, V. (2015). Who would have thought that ‘Jaws’ also has brains? Cognitive functions in elasmobranchs. Animal Cognition, 18, 1937.
Schluessel, V., and Bleckmann, H. (2005). Spatial memory and orientation strategies in the elasmobranch Potamotrygon motoro. Journal of Comparative Physiology A, 191, 695706.
Schluessel, V., and Bleckmann, H. (2012). Spatial learning and memory retention in the grey bamboo shark (Chiloscyllium griseum). Zoology, 115, 346353.
Schluessel, V., and Duengen, D. (2015). Irrespective of size, scales, color or body shape, all fish are just fish: object categorization in the gray bamboo shark Chiloscyllium griseum. Animal Cognition, 18, 497507.
Schluessel, V., Beil, O., Weber, T., and Bleckmann, H. (2014a). Symmetry perception in bamboo sharks (Chiloscyllium griseum) and Malawi cichlids (Pseudotropheus sp.). Animal Cognition, 17, 11871205.
Schluessel, V., Rick, I. P., and Plischke, K. (2014b). No rainbow for grey bamboo sharks: evidence for the absence of colour vision in sharks from behavioral discrimination experiments. Journal of Comparative Physiology A, 200, 939947.
Schluessel, V., Herzog, H., and Scherpenstein, M. (2015). Seeing the forest before the trees – spatial orientation in freshwater stingrays (Potamotrygon motoro) in a hole-board task. Behavioural Processes, 119, 105115.
Schwarze, S., Bleckmann, H., and Schluessel, V. (2013). Avoidance conditioning in bamboo sharks (Chiloscyllium punctatum and C. griseum): behavioural and neuroanatomical aspects. Journal of Comparative Physiology A, 199, 843856.
Sebens, K. P. (1987). The ecology of indeterminate growth in animals. Annual Review of Ecology and Systematics, 18, 371407.
Siciliano, A. M., Kajiura, S. M., Long Jr, J. H., and Porter, M. H. (2013). Are you positive? Electric dipole polarity discrimination in the yellow stingray Urobatis jamaicensis. Biology Bulletin, 225, 8589.
Sisneros, J. A., and Tricas, T. C. (2002). Neuroethology and life history adaptations of the elasmobranch electric sense. Journal of Physiology, 96, 379389.
Smeets, W. J. A. J., Nieuwenhuys, R., and Roberts, B. L. (1983). The central nervous system of cartilaginous fishes: structural and functional correlations. New York, NY: Springer.
Smith, M., Warmolts, D., Thoney, D., and Hueter, R. (2017). Elasmobranch husbandry manual II: captive care of sharks, rays and their relatives. Columbus, OH: Ohio Biological Survey.
Striedter, G. F. (2005). Principles of brain evolution. Sunderland, MA: Sinauer Associates.
Tester, A., and Kato, S. (1963). Visual target discrimination in blacktip sharks (Carcharhinus melanopterus) and grey sharks (C. menisorrah). Pacific Science, 20, 461471.
Thonhauser, K. E., Gutnick, T., Byrne, R. A., Kral, K., Burghardt, G. M., and Kuba, M. J. (2013). Social learning in cartilaginous fish (stingrays Potamotrygon falkneri). Animal Cognition, 16, 927932.
Warburton, K. (2003). Learning of foraging skills by fish. Fish and Fisheries, 4, 203215.
Wegner, N. (2015). Elasmobranch gill structure. In Fish physiology. Physiology of Elasmobranch fishes. (Vol. 34A, pp. 1999). New York, NY: Elsevier.
Wetherbee, B., and Cortés, E. (2004). Food consumption and feeding habits. In Biology of sharks and their relatives (pp. 239264). Boca Raton, FL: CRC Press.
Yopak, K. E. (2012a). Neuroecology in cartilaginous fishes: the functional implications of brain scaling. Journal of Fish Biology, 80, 19682023.
Yopak, K. E. (2012b). The nervous system of cartilaginous fishes. Brain, Behavior, and Evolution, 80, 7779.
Yopak, K. E., and Frank, L. R. (2009). Brain size and brain organization of the whale shark, Rhincodon typus, using Magnetic Resonance Imaging. Brain, Behavior, and Evolution, 74, 121142.
Yopak, K. E., and Lisney, T. J. (2012). Allometric scaling of the optic tectum in cartilaginous fishes. Brain, Behavior, and Evolution, 80, 108126.
Yopak, K. E., and Montgomery, J. C. (2008). Brain organization and specialization in deep-sea chondrichthyans. Brain, Behavior, and Evolution, 71, 287304.
Yopak, K. E., Lisney, T. J., Collin, S. P., and Montgomery, J. C. (2007). Variation in brain organization and cerebellar foliation in chondrichthyans: sharks and holocephalans. Brain, Behaviour and Research, 69, 280300.
Yopak, K. E., Lisney, T. J., and Collin, S. P. (2015). Not all sharks are swimming noses: variation in olfactory bulb size in cartilaginous fishes. Brain Structure and Function, 220, 11271143.
Yopak, K., Galinsky, V. L., Berquist, R. M., and Frank, L. R. (2016). Quantitative classification of cerebellar foliation in cartilaginous fishes (class: Chondrichthyes) using 3D shape analysis and its implications for evolutionary biology. Brain, Behavior, and Evolution, 87, 252264.
Yopak, K. E., Pakan, J., and Wylie, D. (2017). The cerebellum of non-mammalian vertebrates. In Evolution of nervous systems (Vol. 1, pp. 373385). Kidlington, UK: Elsevier.