How useful is expert opinion for predicting the distribution of a species within and beyond the region of expertise? A case study using brush-tailed rock-wallabies Petrogale penicillata

Authors

  • Justine V. Murray,

    Corresponding author
    1. The Ecology Centre, School of Biological Sciences, The University of Queensland, Brisbane, Qld 4072, Australia
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  • Anne W. Goldizen,

    1. The Ecology Centre, School of Biological Sciences, The University of Queensland, Brisbane, Qld 4072, Australia
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  • Rebecca A. O’Leary,

    1. School of Mathematical Sciences, Queensland University of Technology, Brisbane, Qld 4000, Australia
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  • Clive A. McAlpine,

    1. The Ecology Centre, School of Biological Sciences, The University of Queensland, Brisbane, Qld 4072, Australia
    2. School of Geographical Sciences, Planning and Environmental Management, The University of Queensland, Brisbane, Qld 4072, Australia
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  • Hugh P. Possingham,

    1. The Ecology Centre, School of Biological Sciences, The University of Queensland, Brisbane, Qld 4072, Australia
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  • Samantha Low Choy

    1. School of Mathematical Sciences, Queensland University of Technology, Brisbane, Qld 4000, Australia
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*Correspondence author. E-mail: justine.murray@uqconnect.edu.au

Summary

1. Species’ distribution modelling relies on adequate data sets to build reliable statistical models with high predictive ability. However, the money spent collecting empirical data might be better spent on management. A less expensive source of species’ distribution information is expert opinion. This study evaluates expert knowledge and its source. In particular, we determine whether models built on expert knowledge apply over multiple regions or only within the region where the knowledge was derived.

2. The case study focuses on the distribution of the brush-tailed rock-wallaby Petrogale penicillata in eastern Australia. We brought together from two biogeographically different regions substantial and well-designed field data and knowledge from nine experts. We used a novel elicitation tool within a geographical information system to systematically collect expert opinions. The tool utilized an indirect approach to elicitation, asking experts simpler questions about observable rather than abstract quantities, with measures in place to identify uncertainty and offer feedback. Bayesian analysis was used to combine field data and expert knowledge in each region to determine: (i) how expert opinion affected models based on field data and (ii) how similar expert-informed models were within regions and across regions.

3. The elicitation tool effectively captured the experts’ opinions and their uncertainties. Experts were comfortable with the map-based elicitation approach used, especially with graphical feedback. Experts tended to predict lower values of species occurrence compared with field data.

4. Across experts, consensus on effect sizes occurred for several habitat variables. Expert opinion generally influenced predictions from field data. However, south-east Queensland and north-east New South Wales experts had different opinions on the influence of elevation and geology, with these differences attributable to geological differences between these regions.

5.Synthesis and applications. When formulated as priors in Bayesian analysis, expert opinion is useful for modifying or strengthening patterns exhibited by empirical data sets that are limited in size or scope. Nevertheless, the ability of an expert to extrapolate beyond their region of knowledge may be poor. Hence there is significant merit in obtaining information from local experts when compiling species’ distribution models across several regions.

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