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    Spatial analysis of roadside Acacia populations on a road network using the network K-function

    Spooner, P. and Lunt, I. and Okabe, A. and Shiode, Shino (2004) Spatial analysis of roadside Acacia populations on a road network using the network K-function. Landscape Ecology 19 (5), pp. 491-499. ISSN 0921-2973.

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    Spatial patterning of plant distributions has long been recognised as being important in understanding underlying ecological processes. Ripley’s K-function is a frequently used method for studying the spatial pattern of mapped point data in ecology. However, application of this method to point patterns on road networks is inappropriate, as the K-function assumes an infinite homogenous environment in calculating Euclidean distances. A new technique for analysing the distribution of points on a network has been developed, called the network K-function for univariate analysis and network cross K-function for bivariate analysis. To investigate its applicability for ecological data-sets, this method was applied to point location data for roadside populations of three Acacia species in a fragmented agricultural landscape of south-eastern Australia. Kernel estimations of the observed density of spatial point patterns for each species showed strong spatial heterogeneity. Combined univariate and bivariate network K-function analyses confirmed significant clustering of populations at various scales, and spatial patterns of Acacia decora suggests that roadworks activities may have a stronger controlling influence than environmental determinants on population dynamics. The network K-function method will become a useful statistical tool for the analyses of ecological data along roads, field margins, streams and other networks.


    Item Type: Article
    Keyword(s) / Subject(s): Acacia, Anthropogenic disturbance, Field margins, Kernel estimation, Road verge, Stream ecology
    School: Birkbeck Faculties and Schools > Faculty of Humanities and Social Sciences > School of Social Sciences
    Depositing User: Shino Shiode
    Date Deposited: 06 Jan 2016 13:22
    Last Modified: 02 Aug 2023 17:20


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