A Home Range Model Incorporating Biological Attraction Points

Don, B. A. C.; Rennolls, K.

doi:10.2307/4588

Cited by 63 publications

(37 citation statements)

References 24 publications

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“…Considerable effort has been expended deriving models of home range size (e.g., C alhoun and Casby 1958, Jennrieh and Turner 1969, Koeppl et al 1975, Schoener 1981, An derson 1982, Don and Rennolls 1983. Reliability of home range estimates depends, to varying degrees, on the extent to which assumptions underlying these es timates are valid.…”

Section: Robert K Swihart 2 and Norman A Sladementioning

confidence: 99%

The Importance of Statistical Power When Testing for Independence in Animal Movements

Slade¹

1986

Ecology

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Section: Robert K Swihart 2 and Norman A Sladementioning

confidence: 99%

The Importance of Statistical Power When Testing for Independence in Animal Movements

Slade¹

1986

Ecology

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“…More recently Don and Rennolls ( 1983) used a mixture ofbivariate normal distributions that allowed for the possibility of multimodality. It is possible to think of other, more sophisticated, models that could be used to describe the UD.…”

Section: Introductionmentioning

confidence: 99%

“…Both parametric (Jennrich and Turner 1969, Van Winkle 1975, Don and Rennolls 1983 and nonparametric (Ford and Krumme 1979, Dixon and Chapman 1980, Anderson 1982) methods have been used to estimate the UD. The purpose of this paper is to illustrate how one might proceed with home-range data analysis if simple parametric models are inappropriate for the UD.…”

Section: Introductionmentioning

confidence: 99%

Kernel Methods for Estimating the Utilization Distribution in Home‐Range Studies

Worton¹

1989

Ecology

3,453

2,328

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Abstract. In this paper kernel methods for the non parametric estimation of the utiiz~tion distribution from a random sample oflocational observations made on an animal m Its ho~e range are described. They are of flexible form, thus can be used where simple p~ramet~c models are found to be inappropriate or difficult to specifY. Two examples are given to Illustrate ~he fixed ~nd adaptive kernel ~pproaches in data analysis and to compare t~e method~. Vanous chmces for the smoothmg parameter used in kernel methods are discussed. Smce kernel methods give alternative approaches to the Anderson (1982) Fourier transform method, some comparisons are made.

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“…Independence between successive observations is an implicit assumption in most statistical analyses of an imal movements (Hayne 1949, Calhoun and Casby 1958, Jennrich and Turner 1969, Metzgar 1972, Koeppl et al 1975, 1977, Wierzbowska 1975), yet this as sumption is rarely addressed by ecologists studying the spatial dynamics of organisms (Anderson 1982, Don andRennolls 1983), Stated simply, independence re sults when an animal's position in its home range at time t + k is not a function of its position at time t. In studies characterized by relatively long intervals be tween locational observations, independence of suc cessive observations is likely to be achieved (although temporal rhythmicity in movements may reduce this likelihood). However, frequent monitoring of an in dividual's location via radiotelemetry or direct obser vation severely jeopardizes the validity of the inde pendence assumption (Dunn and Gipson 1977).…”

Section: Introductionmentioning

confidence: 99%

Testing For Independence of Observations in Animal Movements

Swihart¹,

Slade²

1985

Ecology

677

368

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Abstract. Many analyses of animal movements assume that an animal's position at time t + 1 is independent of its position at time t, but no statistical procedure exists to test this assumption with bivariate data. Using empirically derived critical values for the ratio of mean squared distance between successive observations to mean squared distance from the center of activity, we demonstrate a bivariate test of the independence assumption first proposed by Schoener. For cases in which the null hypothesis of independence is rejected, we present a procedure for determining the time interval at which autocorrelation becomes negligible. To illustrate implementation of the test, locational data obtained from a radio-tagged adult female cotton rat (Sigmodon hispidus) were used. The test can be used to design an efficient sampling schedule for movement studies, and it is also useful in revealing behavioral phenomena such as home range shifting and any tendency of animals to follow prescribed routes in their daily activities. Further, the test may provide a means of examining how an animal's use of space is affected by its internal clock.

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A Home Range Model Incorporating Biological Attraction Points

Cited by 63 publications

References 24 publications

The Importance of Statistical Power When Testing for Independence in Animal Movements

The Importance of Statistical Power When Testing for Independence in Animal Movements

Kernel Methods for Estimating the Utilization Distribution in Home‐Range Studies

Testing For Independence of Observations in Animal Movements

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