Kernel-based home range method for data with irregular sampling intervals

    Research output: Contribution to journalArticleScientificpeer-review

    Abstract

    Studies of habitat selection and movements often use radio-tracking data for defining animal home ranges. Home ranges (HR) can be approximated by a utilization density distribution (UD) that instead of assuming uniform use of areas within HR boundary provides a probabilistic measure of animal space use. In reality, radio-tracking data contain periods of frequent autocorrelated observations interspersed with temporally more independent observations. Using such temporally irregular data directly may result in biased UD estimates, because areas that have been sampled intensively receive too much weight. The problem of autocorrelation has been tackled by resampling data with an appropriate time interval. However, resampling may cause a large reduction in the data set size along with a loss of information. Evidently, biased UD estimates or reduction in data may prejudice the results on animal habitat selection and movement. We introduce a new method for estimating UDs with temporally irregular data. The proposed method, called the time kernel, accounts for temporal aggregation of observations and gives less weight to temporally autocorrelated observations. A further extension of the method accounts also for spatially aggregated observations with relatively low weights given to observations that are both temporally and spatially aggregated. We test the behaviour of the time kernel method and its spatiotemporal version using simulated data. In addition, the method is applied to a data set of brown bear locations. (c) 2005 Elsevier B.V. All rights reserved.
    Original languageEnglish
    JournalEcological Modelling
    Volume194
    Issue number4
    Pages (from-to)405-413
    Number of pages9
    ISSN0304-3800
    DOIs
    Publication statusPublished - 2006
    MoE publication typeA1 Journal article-refereed

    Fields of Science

    • 1181 Ecology, evolutionary biology
    • 311 Basic medicine
    • 318 Medical biotechnology
    • 411 Agriculture and forestry
    • 219 Environmental biotechnology
    • 519 Social and economic geography

    Cite this

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    title = "Kernel-based home range method for data with irregular sampling intervals",
    abstract = "Studies of habitat selection and movements often use radio-tracking data for defining animal home ranges. Home ranges (HR) can be approximated by a utilization density distribution (UD) that instead of assuming uniform use of areas within HR boundary provides a probabilistic measure of animal space use. In reality, radio-tracking data contain periods of frequent autocorrelated observations interspersed with temporally more independent observations. Using such temporally irregular data directly may result in biased UD estimates, because areas that have been sampled intensively receive too much weight. The problem of autocorrelation has been tackled by resampling data with an appropriate time interval. However, resampling may cause a large reduction in the data set size along with a loss of information. Evidently, biased UD estimates or reduction in data may prejudice the results on animal habitat selection and movement. We introduce a new method for estimating UDs with temporally irregular data. The proposed method, called the time kernel, accounts for temporal aggregation of observations and gives less weight to temporally autocorrelated observations. A further extension of the method accounts also for spatially aggregated observations with relatively low weights given to observations that are both temporally and spatially aggregated. We test the behaviour of the time kernel method and its spatiotemporal version using simulated data. In addition, the method is applied to a data set of brown bear locations. (c) 2005 Elsevier B.V. All rights reserved.",
    keywords = "1181 Ecology, evolutionary biology, 311 Basic medicine, 318 Medical biotechnology, 411 Agriculture and forestry, 219 Environmental biotechnology, 519 Social and economic geography",
    author = "Jonna Katajisto and Atte Moilanen",
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    language = "English",
    volume = "194",
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    }

    Kernel-based home range method for data with irregular sampling intervals. / Katajisto, Jonna; Moilanen, Atte.

    In: Ecological Modelling, Vol. 194, No. 4, 2006, p. 405-413.

    Research output: Contribution to journalArticleScientificpeer-review

    TY - JOUR

    T1 - Kernel-based home range method for data with irregular sampling intervals

    AU - Katajisto, Jonna

    AU - Moilanen, Atte

    PY - 2006

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    N2 - Studies of habitat selection and movements often use radio-tracking data for defining animal home ranges. Home ranges (HR) can be approximated by a utilization density distribution (UD) that instead of assuming uniform use of areas within HR boundary provides a probabilistic measure of animal space use. In reality, radio-tracking data contain periods of frequent autocorrelated observations interspersed with temporally more independent observations. Using such temporally irregular data directly may result in biased UD estimates, because areas that have been sampled intensively receive too much weight. The problem of autocorrelation has been tackled by resampling data with an appropriate time interval. However, resampling may cause a large reduction in the data set size along with a loss of information. Evidently, biased UD estimates or reduction in data may prejudice the results on animal habitat selection and movement. We introduce a new method for estimating UDs with temporally irregular data. The proposed method, called the time kernel, accounts for temporal aggregation of observations and gives less weight to temporally autocorrelated observations. A further extension of the method accounts also for spatially aggregated observations with relatively low weights given to observations that are both temporally and spatially aggregated. We test the behaviour of the time kernel method and its spatiotemporal version using simulated data. In addition, the method is applied to a data set of brown bear locations. (c) 2005 Elsevier B.V. All rights reserved.

    AB - Studies of habitat selection and movements often use radio-tracking data for defining animal home ranges. Home ranges (HR) can be approximated by a utilization density distribution (UD) that instead of assuming uniform use of areas within HR boundary provides a probabilistic measure of animal space use. In reality, radio-tracking data contain periods of frequent autocorrelated observations interspersed with temporally more independent observations. Using such temporally irregular data directly may result in biased UD estimates, because areas that have been sampled intensively receive too much weight. The problem of autocorrelation has been tackled by resampling data with an appropriate time interval. However, resampling may cause a large reduction in the data set size along with a loss of information. Evidently, biased UD estimates or reduction in data may prejudice the results on animal habitat selection and movement. We introduce a new method for estimating UDs with temporally irregular data. The proposed method, called the time kernel, accounts for temporal aggregation of observations and gives less weight to temporally autocorrelated observations. A further extension of the method accounts also for spatially aggregated observations with relatively low weights given to observations that are both temporally and spatially aggregated. We test the behaviour of the time kernel method and its spatiotemporal version using simulated data. In addition, the method is applied to a data set of brown bear locations. (c) 2005 Elsevier B.V. All rights reserved.

    KW - 1181 Ecology, evolutionary biology

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    KW - 318 Medical biotechnology

    KW - 411 Agriculture and forestry

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    KW - 519 Social and economic geography

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