A Simulation Model of Animal Movement Patterns

Siniff, Donald B.; Jessen, Christian

doi:10.1016/s0065-2504(08)60259-7

Cited by 123 publications

(78 citation statements)

References 19 publications

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“…When these results are compared with some of the few data available for the relationship between sample size and the reliability of home range estimates, the guideline of 100·200 10-cations is higher. For example, Siniff & Jessen (1969) found that in a simulation of animal movements based on 400-800 locations, 100 points were sufficient for a reliable estimate of movement patterns. For coyotes (Canis latrans), it has been suggested that about 40-50 "independent" locations [assumptions of independence are rarely if ever addressed (Anderson, 1982); see Schoener (1981) for a method for testing independence] were sufficient to estimate reliably home range size (Bowen, 1982;Messier & Barrette, 1982;Smith et al, 1981), whereas for wolves (c. lupus), 35·120 locations were needed (Fritts & Mech, 1981).…”

Section: Discussionmentioning

confidence: 99%

“…As in many other areas of research, data concerning space use and movement patterns often are easy to gather (or appear to be easy to collect) but difficult to interpret. Theoretical approaches are numerous (Anderson, 1982;Cooper, 1978;Ford & Krumme, 1979;Hayne, 1949;Heezen & Tester, 1967;Jennrich& Turner, 1969;Koeppl, Slade, & Hoffmann, 1975Metzgar & Sheldon, 1974;Mohr, 1947;Mohr & Stumpf, 1966;Rasmussen, 1980;Schoener, 1981;Siniff & Jessen, 1969;Stickel, 1954;van Winkle, 1975), but the "correct" method of analysis is closely related to the questions being asked and to the method used to generate the data; there does not appear to be a right or a wrong way to analyze space use or movement patterns.…”

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confidence: 99%

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Simulation analyses of space use: Home range estimates, variability, and sample size

Bekoff

Mech

1984

Behavior Research Methods, Instruments, & Computers

100

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Simulations of space use by animals were run to determine the relationship among home range area estimates, variability, and sample size (number of locations). As sample size increased, home range size increased asymptotically, whereas variability decreased among mean home range area estimates generated by multiple simulations for the same sample size. Our results suggest that field workers should ascertain between 100 and 200 locations in order to estimate reliably home range area. In some cases, this suggested guideline is higher than values found in the few published studies in which the relationship between home range area and number of locations is addressed. Sampling differences for small species occupying relatively small home ranges indicate that fewer locations may be sufficient to allow for a reliable estimate of home range. Intraspecific variability in social status (group member, loner, resident, transient), age, sex, reproductive condition, and food resources also have to be considered, as do season, habitat, and differences in sampling and analytical methods. Comparative data still are needed.The way animals use space is of practical and theoretical interest to a wide variety of scientists, including those studying (1) relationships between behavior, age, sex, and spacing patterns, (2) resource distribution as it affects space use, (3) the relationship between metabolic requirements and home range size, and (4) space requirements for purposes of control or reintroduction of "problem" or endangered species

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Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

Simulation analyses of space use: Home range estimates, variability, and sample size

Bekoff

Mech

1984

Behavior Research Methods, Instruments, & Computers

100

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“…Since most animals have a tendency to move forwards (persistence), CRWs have been frequently used to model animal paths in various contexts (e.g. Siniff & Jessen 1969;Skellam 1973;Kareiva & Shigesada 1983;Bovet & Benhamou 1988;Turchin 1998).…”

Section: Introductionmentioning

confidence: 99%

Random walk models in biology

Codling

Plank

Benhamou

2008

J. R. Soc. Interface.

1,269

1,213

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Mathematical modelling of the movement of animals, micro-organisms and cells is of great relevance in the fields of biology, ecology and medicine. Movement models can take many different forms, but the most widely used are based on the extensions of simple random walk processes. In this review paper, our aim is twofold: to introduce the mathematics behind random walks in a straightforward manner and to explain how such models can be used to aid our understanding of biological processes. We introduce the mathematical theory behind the simple random walk and explain how this relates to Brownian motion and diffusive processes in general. We demonstrate how these simple models can be extended to include drift and waiting times or be used to calculate first passage times. We discuss biased random walks and show how hyperbolic models can be used to generate correlated random walks. We cover two main applications of the random walk model. Firstly, we review models and results relating to the movement, dispersal and population redistribution of animals and micro-organisms. This includes direct calculation of mean squared displacement, mean dispersal distance, tortuosity measures, as well as possible limitations of these model approaches. Secondly, oriented movement and chemotaxis models are reviewed. General hyperbolic models based on the linear transport equation are introduced and we show how a reinforced random walk can be used to model movement where the individual changes its environment. We discuss the applications of these models in the context of cell migration leading to blood vessel growth (angiogenesis). Finally, we discuss how the various random walk models and approaches are related and the connections that underpin many of the key processes involved.

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“…Numerous studies of foraging in fishes have reported patterns of preferences relating to particular habitat types at various scales (Hobson 1975, Cowen 1986, Grossman 1986). Many studies, predominantly theoretical, have attempted to explain foraging-path patterns by relating the distribution and size of preferred habitat patches to patterns of space use during foraging (Siniff & Jessen 1969, Covich 1976. In particular, optimal foraging theory regards the distribution of prey into microhabitat patches and the attendant trade-offs associated with travelling between or remaining within these patches as a major factor affecting the shape of foraging paths (Schoener 1971, Norberg 1977.…”

Section: Introductionmentioning

confidence: 99%

“…In particular, optimal foraging theory regards the distribution of prey into microhabitat patches and the attendant trade-offs associated with travelling between or remaining within these patches as a major factor affecting the shape of foraging paths (Schoener 1971, Norberg 1977. Theoretical predictions based on the distribution of microhabitats indicate that directed (nonoverlapping) paths are best for searching discrete microhabitats in small patches, whereas a convoluted (overlapping) foraging path most efficiently searches locally uniform microhabitats (Siniff & Jessen 1969, Weihs & Webb 1983.…”

Section: Introductionmentioning

confidence: 99%

Patterns of foraging in labrid fishes

Fulton¹,

Bellwood²

2002

Mar. Ecol. Prog. Ser.

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Patterns of foraging behaviour are described for a local assemblage of wrasses (Labridae) at the within-habitat scale of 2 fringing reef sites at Lizard Island, northern Great Barrier Reef, to examine the relationship between locomotor abilities and foraging patterns. Focal individual censuses were used to record the distances travelled by individuals over 30 s and 5 min time periods, and their substratum microhabitat preferences during searching and feeding. Size of short-term foraging ranges, estimated by linear start-to-finish distances, appeared to be driven largely by the shape of the foraging path. Two major foraging modes were identified: directed (widely-foraging) and convoluted (restricted). Within each mode, a strong positive relationship was observed between estimated locomotory ability and foraging distances. Four major groups of foraging-microhabitat preferences were apparent: neutral foraging and positive selection towards aggregates, dead coral heads, or live coral. A weak relationship between foraging mode and estimated patch size of preferred microhabitats was observed, with species having a directed foraging mode most frequently selecting more spatially discrete microhabitats.

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A Simulation Model of Animal Movement Patterns

Cited by 123 publications

References 19 publications

Simulation analyses of space use: Home range estimates, variability, and sample size

Simulation analyses of space use: Home range estimates, variability, and sample size

Random walk models in biology

Patterns of foraging in labrid fishes

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