Area-restricted search by the plains pocket gopher (<i>Geomys bursarius</i>) in tallgrass prairie habitat

Benedix, J. H.

doi:10.1093/beheco/4.4.318

Cited by 72 publications

(55 citation statements)

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“…ARS behavior is observed in a wide variety of animals (Bell, 1991), including house flies (White et al, 1984), leeches (O'Gara et al, 1991), moths (Vickers, 2000), populations of ladybug beetles (Kareiva and Odell, 1987), and rodents (Benedix, 1993). Specific molecular mechanisms have not been reported for the control of these behaviors.…”

Section: Discussionmentioning

confidence: 99%

“…One type of foraging strategy that is found in virtually all organisms ranging from insects to vertebrates is the arearestricted search (ARS) (Kareiva and Odell, 1987;Bell, 1991;Benedix, 1993;Keaser et al, 1996;Grunbaum, 1998). Animals using ARS will turn more frequently after an encounter with food, restricting their search around the area where food was last found.…”

Section: Introductionmentioning

confidence: 99%

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Dopamine and Glutamate Control Area-Restricted Search Behavior inCaenorhabditis elegans

Hills

Brockie

Maricq³

2004

J. Neurosci.

323

354

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Area-restricted search (ARS) is a foraging strategy used by many animals to locate resources. The behavior is characterized by a timedependent reduction in turning frequency after the last resource encounter. This maximizes the time spent in areas in which resources are abundant and extends the search to a larger area when resources become scarce. We demonstrate that dopaminergic and glutamatergic signaling contribute to the neural circuit controlling ARS in the nematode Caenorhabditis elegans. Ablation of dopaminergic neurons eliminated ARS behavior, as did application of the dopamine receptor antagonist raclopride. Furthermore, ARS was affected by mutations in the glutamate receptor subunits GLR-1 and GLR-2 and the EAT-4 glutamate vesicular transporter. Interestingly, preincubation on dopamine restored the behavior in worms with defective dopaminergic signaling, but not in glr-1, glr-2, or eat-4 mutants. This suggests that dopaminergic and glutamatergic signaling function in the same pathway to regulate turn frequency. Both GLR-1 and GLR-2 are expressed in the locomotory control circuit that modulates the direction of locomotion in response to sensory stimuli and the duration of forward movement during foraging. We propose a mechanism for ARS in C. elegans in which dopamine, released in response to food, modulates glutamatergic signaling in the locomotory control circuit, thus resulting in an increased turn frequency.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Dopamine and Glutamate Control Area-Restricted Search Behavior inCaenorhabditis elegans

Hills

Brockie

Maricq³

2004

J. Neurosci.

323

354

View full text Add to dashboard Cite

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“…This pattern is the temporal analog of the widely recognized area-concentrated search strategy (Benhamou, 1992), first described as area-restricted search by Tinbergen (1967) and shown to occur in a wide variety of species (e.g., Benedix, 1993;Dixon, 1959;HaskeU, 1997;Smith, 1974). AnimaIs using this strategy temporarily intensifY or concentrate their search effort in space upon discovery of a prey item.…”

Section: IXmentioning

confidence: 89%

Time-concentrated sampling: a simple strategy for information gain at a novel, depleted patch

2006

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When an animal has found and consumed food at a new location, information about whether and when food will be present again could improve future foraging efficiency. A series of rapid returns followed by less frequent visits and finally abandonment of the patch could provide such information. By analogy with area-concentrated (area-restricted) search, we call this hypothesized pattern “time-concentrated sampling”. We tested whether eastern chipmunks ( Tamias striatus (L., 1758)) would show time-concentrated sampling in the field and whether the pattern of visits would be affected by patch value. We used peanuts to induce animals to discover a small patch of sunflower seeds. After depleting the patch, returning to find it empty, and leaving without food, 36 of 40 animals returned on sampling visits. Sampling rate was initially high and declined over 4 h. The number of peanuts and number of visits where seeds were obtained positively predicted sampling rate, but the volume of sunflower seeds presented and the distance to the burrow did not. We conclude that chipmunks exhibit flexible time-concentrated sampling.

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“…The model used for this study was a FORTRAN77 version of a MATLAB simulation model called SEARCH (Simulator for Exploring AreaRestricted search in Complex Habitats) (Leising 2000) In general, the copepods move around in a 2 dimensional grid that has reflecting boundaries at the top and bottom, and wrap-around boundaries on the sides. The 'area-restricted search' behavior, defined in the 'Introduction', was chosen because it has been seen to result in aggregation into areas of higher food resources in other animals, including insects (Karieva 1982, Karieva & Odell 1987, Ferran & Dixon 1993, birds (Tinbergen et al 1967, Smith 1974, Zach & Falls 1977, and mammals (Benedix 1993, Cassini & Föger 1995, Haskell 1997. Some copepods have been observed to decrease their swimming speed or decrease their frequency of 'hop' or 'jump' behavior when encountering higher food concentrations, and may also turn at greater angles, a characteristic of arearestricted search behavior (Pseudocalanus minutus, Buskey 1984; Oithona davise females, Uchima & Hiramo 1988; Temora longicornus and Pseudocalanus elongans, Tiselius & Jonsson 1990; Acartia tonsa females, Tiselius 1992).…”

Section: Methodsmentioning

confidence: 99%

Copepod foraging in patchy habitats and thin layers using a 2-D individual-based model

Leising¹

2001

Mar. Ecol. Prog. Ser.

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Evidence has shown that thin, horizontally extending phytoplankton layers may be comprised of smaller high-concentration aggregations of phytoplankton, rather than a homogeneous high-concentration sheet. A 2-D (horizontal and vertical dimensions) individual-based model of copepod foraging was developed, in order to examine whether the foraging success of a copepod would be significantly affected by phytoplankton patchiness. The foraging rules for the simulated copepods were to decrease speed and increase turning angle when high food concentrations are encountered. The underlying distributions of phytoplankton used in the model were, for the patchy layer scenario, representations of raw 2-D field fluorescence obtained using the Optical Serial Section Tomography (OSST) device, and for the homogeneous layer scenario, distributions created by simulated vertical sampling of the OSST distributions with a CTD/Fluorometer. In both the patchy and homogeneous layer scenarios, the copepods always had higher net foraging efficiency than randomly behaving controls, suggesting that the simple behavioral rules adopted are advantageous for copepod-like organisms. Foraging efficiency was significantly greater for the patchy layer scenarios than for the homogeneous layer scenarios when patches were small (i.e. one step length in width) and intense (i.e. near ingestion-saturating concentrations). Ingestion was up to 30% higher in the most patchy case versus its paired homogeneous case, suggesting that the existence of patchiness is critical to copepod survival, and that sampling scales should not exceed the step length of a copepod.KEY WORDS: Acartia · Foraging · Area-restricted search · Thin layers · Copepods · Individual-based model Resale or republication not permitted without written consent of the publisher

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Area-restricted search by the plains pocket gopher (Geomys bursarius) in tallgrass prairie habitat

Cited by 72 publications

References 0 publications

Dopamine and Glutamate Control Area-Restricted Search Behavior inCaenorhabditis elegans

Dopamine and Glutamate Control Area-Restricted Search Behavior inCaenorhabditis elegans

Time-concentrated sampling: a simple strategy for information gain at a novel, depleted patch

Copepod foraging in patchy habitats and thin layers using a 2-D individual-based model

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