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Reconciling approaches to biogeographical regionalization: a systematic and generic framework examined with a case study of the Australian continent

Authors

  • Brendan G. Mackey,

    Corresponding author
      *Brendan Mackey, The Fenner School of Environment & Society, The Australian National University, Canberra, ACT, 0200, Australia. E-mail: brendan.mackey@anu.edu.au.
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  • Sandra L. Berry,

    1. The Fenner School of Environment & Society, The Australian National University, Canberra, ACT 0200, Australia
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  • Tiffany Brown

    1. The Fenner School of Environment & Society, The Australian National University, Canberra, ACT 0200, Australia
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*Brendan Mackey, The Fenner School of Environment & Society, The Australian National University, Canberra, ACT, 0200, Australia. E-mail: brendan.mackey@anu.edu.au.

Abstract

Aim  To develop a systematic and generic framework for biogeographical regionalizations that can assist in reconciling different approaches and advance their application as a research tool.

Location  The Australian continent is used as a case study.

Methods  A review of approaches to biogeographical regionalization revealed two basic methodologies: the integrated survey method and the parametric approach. To help reconcile these different approaches, we propose a simple, four-step, flexible and generic framework. (1) Identification of the thematic foci from the three main themes (composition and evolutionary legacy; ecosystem drivers; ecosystem responses). (2) Proposal of a theory defining the purpose. (3) Application of a numeric agglomerative classification procedure that requires the user to make explicit assumptions about attributes, the number of classification groups, the spatial unit of analysis, and the metric for measuring the similarity of these units based on their attribute values. (4) Acquisition of spatial estimates of the required input attribute data. For this case study, an agglomerative classification strategy was applied using the functions within patn 3.03, a software package facilitating large-scale, multivariate pattern analysis. The input data to the classifications were continental coverages of 11 environmental variables and three indices of gross primary productivity stored at a grid cell resolution of c. 250 m. The spatial units of analysis were surface hydrological units (SHU), which were derived from a continental digital elevation model based on the contributing areas to stream segments or the area draining into a local sink where there is no organized drainage. The Minkowski series (Euclidean distance) was selected as the association measure to allow weightings to be applied to the variables.

Results  Two new biogeographical regionalizations of the Australian continent were generated. The first was an environmental domain classification, based on 11 climatic, terrain and soil attributes. This regionalization can be used to address hypotheses about the relationship between environmental distance and evolutionary processes. The classification produced 151 environmental groups. The second was a classification of primary productivity regimes based on estimates of the gross primary productivity of the vegetation cover calculated from moderate resolution imaging spectroradiometer (MODIS) normalized difference vegetation index (NDVI) data and estimates of radiation. This classification produced 50 groups, and can be used to examine hypotheses concerning productivity regimes and animal life-history strategies. The productivity classification does not capture all the properties related to biological carrying capacity, process rates and differences in the characteristic biodiversity of ecosystems. Some of these ecologically significant properties are captured by the environmental domain classification.

Main conclusions  Our framework can be applied to all terrestrial regions, and the necessary data for the analyses presented here are now available at global scales. As the spatial predictions generated by the classifications can be tested by comparison with independent data, the approach facilitates exploratory analysis and further hypothesis generation. Integration of the three themes in our framework will contribute to a more comprehensive approach to biogeography.

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