An analysis of locomotor behaviour of goldfish (Carassius auratus)

Kleerekoper, H.; Timms, A. M.; Westlake, G. F.; Davy, F. Brian; Malar, T.; Anderson, V.

doi:10.1016/s0003-3472(70)80044-6

Cited by 79 publications

(23 citation statements)

References 66 publications

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“…The locomotor behavior of the shark was monitored by the technique described earlier (Kleerekoper, 1969, Kleerekoper, et al, 1970Matis, et al, 1973). For purposes of the present paper the method will be described briefly below.…”

Section: Methodsmentioning

confidence: 99%

“…Over the past several years, various studies have enquired into the characteristics and organization of the locomotor behavior of fishes with the ultimate aim of gaining insight into locomotor control and orientation mechanisms in these animals (Kleerekoper, 1967a(Kleerekoper, , b, 1969Kleerekoper and Mogensen, 1963;Kleerekoper, et al, 1970Kleerekoper, et al, , 1975Matis, et al, 1973Matis, et al, , 1974aMatis, et al, , b, c, 1975.…”

Section: Introductionmentioning

confidence: 99%

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A stochastic locomotor control model for the nurse shark, ginglymostoma cirratum

1978

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The locomotor behavior of the nurse shark (Ginglymostoma cirratum) is characterized by 17 variables (frequency and ratios of left, right, and total turns; their radians; straight paths (steps); distance travelled; and velocity) Within each of these variables there is an internal time dependency the structure of which was elaborated together with an improved statistical model predicting their behavior within 90% confidence limits. The model allows for the sensitive detection of subtle locomotor response to sensory stimulation as values of variables may exceed the established confidence limits within minutes after onset of the stimulus. The locomotor activity is well described by an autoregression time series model and can be predicted by only seven variables. Six of these form two independently operating clusters. The first one consists of: the number of right turns, the distance travelled and the mean velocity; the second one of: the mean size of right turns, of left turns, and of all turns. The same clustering is obtained independently by a cluster analysis of cross-sections of the seven time series. It is apparent that, among a total of 17 locomotor variables, seven behave as individually independent agents, presumably controlled by seven separate and independent centers. The output of each center can only be predicted by its own behavior. In spite of the individual of the seven variables, their internal structure is similar in important aspects which may result from control by a common command center. The shark locomotor model differs in important aspects from the previously constructed for the goldfish. The interdependence of the locomotor variables in both species may be related to the control mechanisms postulated by von Holst for the coordination of rhythmic fin movements in fishes. A locomotor control model for the nurse shark is proposed.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A stochastic locomotor control model for the nurse shark, ginglymostoma cirratum

1978

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“…Movements to the right or left were presumed equal and were ignored. The directionality index ranges from zero, when movement is random, to 1, where it is in a straight line (Kleerekoper et al 1970;Pyke 1978). For this calculation the number of grid points is scaled to territory size, such that there are~20 grid points for each territory (see Pyke 1978).…”

Section: Foraging Behaviour and Activity Patternsmentioning

confidence: 99%

Investigator Group Expedition 2006: Habitat-Dependent Foraging Behaviour and Diet of the Scalyfin, Parma Victoriae, in South Australia

Shepherd

Clark

Ferguson

2008

Transactions of the Royal Society of South Australia

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The foraging behaviour, territory size and diet of the scalyfin, Parma victoriae, were studied at three sites in South Australia. Two sites were in Ecklonia habitat on an exposed coast, one of them, Site 1, in a marine reserve at West I., and the other, Site 2, in an intensely fished area at The Bluff, Victor Harbor. The third, Site 3, was sheltered, in a fucoid community in Groper Bay, Flinders I., in the eastern Great Australian Bight. The algal food supply was highest at the reserve site (1), and lowest at the sheltered site (3). The scalyfin spent a greater proportion of time foraging, and a lower proportion of time sheltering, at the reserve site (1), than at the Bluff site (2). At Site 3, territories were about seven times larger than at the other two sites, and fish spent a higher proportion of time in defence and aggressive interactions than at the other sites. The diet at all sites was predominantly browsed rhodophytes, but at Site 3 the rhodophytes eaten were almost entirely epiphytic on fucoid algae and in low abundance. At the exposed sites (1, 2), where food algae were patchy, scalyfin removed Ecklonia sporophytes experimentally placed in their territories, but not at Site 3, dominated by fucoids. At the exposed sites they employed a saltatory foraging mode, whereas at Site 3 they adopted a cruise search foraging behaviour over their larger territories. Both modes seem optimal in their respective habitats.

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“…Rather abstract simulation models without using much empirical data were developed by Raup & Seilacher (1969), Rohlf & Davenport (1969), and Papentin (1973). Kleerekoper et al (1970) analyzed random movements of goldfish. Land & Collett (1974) quantified chasing flight of houseflies, and Siniff& Jessen (1970) quantified and simulated two-dimensional movements of mammals in their home range using observed frequency distributions of distance and angles.…”

Section: Description Models and Behaviourmentioning

confidence: 99%

Quantitative description and simulation of stochastic behaviour in dragonflies (Aeschna cyanea, odonata)

Kaiser

1976

Acta Biotheor

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1. A dynamic description model of the flight path a dragonfly takes is required in order to derive the encounter frequency of the dragonfly males visiting a pond. It is considered as the first part of a realistic description of the regulation of dragonfly density at the pond by behavioural interaction. 2. Since the dragonfly males fly strictly along the shoreline of the pond, their displacement may be perceived as a quasi linear movement. The flight paths of dragonflies were recorded in field by registering at regular time intervals (1.5 seconds) the number of the shoreline section (1 m length) in which the dragonfly was staying at that moment. 3. The recorded flight paths show a wide variety from circling around the pond or flying widely to and fro to remaining in a narrow sector of the shoreline (figs. 2--4). 4. The records were classified into 9 flight style groups. The flight style is temperature dependent, but there are also individual differences between the dragonflies. 5. The strategy adopted for model building is to start with the most simple assumptions and parameters. The dynamic performance of each model is assessed by simulations. The model has to be extended until the simulated flight path patterns resemble the observed patterns. 6. In a model with one parameter the displacement for every time interval is given by a stochastic decision using the probabilities evaluated from the observed frequencies of step length ( fig. 5). 7. This model was run with different sets of data. The simulated patterns using the data from all records together slightly resemble some observed patterns. They represent, however, only a small range of the variability observed. With data from the different style groups or the single records the simulated patterns are much more variable, but they are too irregular and contain too many turns (figs. 7-9). 8. Therefore the description model was extended by a second parameter determining the direction of each step. The probability of turning is dependent on the number of steps already taken in the same direction ( fig. 10). The probabilities are evaluated from the observed frequencies of step series. i I am indebted to Mrs. I. Gronenborn for her continued technical assistence, to R. J6dicke for his help with the field work, to him and Mrs. R. Ratte, Dipl. Biol. H.-T. Ratte, Dipl. Biol. V. Kriegeskorte for their help in evaluating the observation data. Prof. Dr. D. Neumann read the manuscript critically, Mrs. E. Fiedler B. S. and Miss Dr. A. C. Hobson chocked the English. Dr. H. and G. K6rner offered generous hospitality during the field work. The staff of the Rechenzvntrum der Univvrsitat zu K61n gave advice and support. The Deutsche Forschungsgemeinschaft granted financial support. I express my thanks to them all. 164 HEINRICH KAISER 9.With the data from all records together, this model yields patterns more similar to the observed flight paths but they still represent only a restricted range of the observed variability. The patterns obtained using the data from the different style ...

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An analysis of locomotor behaviour of goldfish (Carassius auratus)

Cited by 79 publications

References 66 publications

A stochastic locomotor control model for the nurse shark, ginglymostoma cirratum

A stochastic locomotor control model for the nurse shark, ginglymostoma cirratum

Investigator Group Expedition 2006: Habitat-Dependent Foraging Behaviour and Diet of the Scalyfin, Parma Victoriae, in South Australia

Quantitative description and simulation of stochastic behaviour in dragonflies (Aeschna cyanea, odonata)

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