A Competition Submodel for Parasites and Predators

Griffiths, K. J.; Holling, C. S.

doi:10.4039/ent101785-8

Cited by 90 publications

(52 citation statements)

References 37 publications

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“…In all other respects, including the addition of subroutines CHASE and SUMI~Y, the inclusion of prey reactive distance, and the modified version of G, the two models are identical. Nevertheless the no-prey-learning model gave identical results to that of Griffiths and Holling (1969) when the same parameter values were used (those of Table 4). All simulations were run on an IBM 360/67.…”

Section: The Simulation Modelsupporting

confidence: 55%

“…The maximum effect of avoidance learning was to reduce each predator'~ consumption by about 0.57 prey/day. At least over a certain range, therefore, the expectation of Griffiths and Holling (1969) that "the greater the predator density, the greater the chance that each prey will have acquired an effective way of avoiding attack.., and the lower the attack rate" was verified.…”

Section: Resultsmentioning

confidence: 99%

“…The basis for the present model is the one described in some detail by Griffiths and Holling (1969). Only those changes in the basic model necessitated by the addition of the new component of avoidance learning will be discussed here.…”

Section: The Simulation Modelmentioning

confidence: 99%

“…Components one through six are present in the most advanced version of the model published to date (Griffiths and Holling, 1969); components seven and eight have been conceptualized mathematically and added to a model containing the first four components (Holling, 1965); component number nine has not been examined.…”

Section: Introductionmentioning

confidence: 99%

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An avoidance learning submodel for a general predation model

Dill

1973

Oecologia

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This paper attempts to determine the effect on the number of prey eaten by predators of the addition of the component "avoidance learning by prey" to a computer model of the predation process developed by Holling. Generality was retained by concentrating upon a basic aspect of the prey's behaviour, its distance of reaction to an approaching predator. The zebra danio (Brachydanio rerio), a small freshwater fish, was used as an analogue of a general vertebrate prey. The predator used was the largemouth bass (Micropterus salmoides).Previous work (Dill, 1973b) showed that prey reactive distance increased with increasing experience with the predator. In the present study, this increased prey reactive distance is shown to increase predator pursuit time and hypothesized to decrease predator pursuit success. These relationships were expressed mathematically and built into Holling's (1965, 1966) model of the predation process, along with an equation describing the way in which reactive distance increases following an unsuccessful attack. Other changes necessitated in the model by the addition of the avoidance learning component included: a) Modifications of the calculation of search time to remove a previously implicit time spent unsuccessfully pursuing prey, and to correct the density of prey to account for those whose reactive distances exceed that of the predator and are therefore not susceptible to discovery; b) Addition of a new subroutine (CHASE) to calculate pursuit time, unsuccessful pursuit time, pursuit success, and strike success; c) Changes in subroutine ADCOM to assign prey to different classes (with different reactive distances) according to the number of times they have been unsuccessfully attacked; and d) Addition of a stochastic element via random numbers to determine the class to which an attacked prey belongs, the time to refuge, and the predator's strike success.Simulation was used to explore the consequences of these additions. The capability of learning substantially increased the prey's probability of surviving subsequent attack. Addition of an avoidance learning component caused declines in the predator's functional responses to both prey and predator density. The new component was also suggested to decrease the predator's numerical response to prey density and to increase the probability of stability in a predator-prey interaction.

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Section: The Simulation Modelsupporting

confidence: 55%

Section: Resultsmentioning

confidence: 99%

Section: The Simulation Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An avoidance learning submodel for a general predation model

Dill

1973

Oecologia

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“…So far we have looked at what Holling calls three "basic" components of predation (common to alI predator-prey interactions)-attack rate, handling time, and total time predator and prey are exposed to one anotheramI i'wo"subsidiary components" (which are not universaI)-learning and ~~: Holling has also examined one other subsidiary componentinterference (Griffiths and Holling, 1969). (Dill, 1972, has examined another subsidiary component, prey avoidance learning.)…”

Section: A Analysis Of the Functional Responsementioning

confidence: 99%

Behavioral Aspects of Predation

Krebs

1973

Perspectives in Ethology

207

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In this paper, I discuss four hypotheses concerning mechanisms which might be of general importance in the behavior of predators: hunting by searching image, hunting by expectation, area-restricted search, and "niche" hunting. In addition, I review briefly two approaches which have been taken in studying predator behavior at a more general level: experimental component analysis and optimal foraging theory. I make no attempt to review comprehensively the literature on behavior of predators.The concept of hunting by searching image has been used in a great variety of senses, and to be of value it should be used in a more restricted manner. Searching-image formation in the strict sense of "learning to see" has been demonstrated in several laboratory experiments and perhaps in some seminatural field experiments with birds. The real problem is that it is difficult to distinguish in operational terms between "learning to see" and some other phenomena which result in the predator's preferring one prey type over another.

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A behavioral model of predator-prey functional responses

Holling

Buckingham

1976

Syst. Res.

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A generalized simulation model of the predation process is described in detail. It was developed from an experimental analysis of the action and interaction of the basic and subsidiary components of predation. Eight qualitatively distinct classes of predation can be identified, together with the biological conditions for each. With that classification, it is possible to compress the complexity into a tested and analytically tractable equation of predation that has broad descriptive power. Such an equation can serve as a building block to develop ecosystem simulations whose stability properties can be explored rigorously.

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A Competition Submodel for Parasites and Predators

Cited by 90 publications

References 37 publications

An avoidance learning submodel for a general predation model

An avoidance learning submodel for a general predation model

Behavioral Aspects of Predation

A behavioral model of predator-prey functional responses

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