Estimating detection–effort curves for plants using search experiments

Authors

  • Joslin L. Moore,

    Corresponding author
    1. Applied Environment Decision Analysis CERF, School of Botany, University of Melbourne, Parkville 3010, Victoria, Australia
    2. Australian Research Centre for Urban Ecology, Royal Botanic Gardens, Melbourne, Australia
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  • Cindy E. Hauser,

    1. Applied Environment Decision Analysis CERF, School of Botany, University of Melbourne, Parkville 3010, Victoria, Australia
    2. Australian Centre for Risk Analysis, School of Botany, University of Melbourne, Parkville 3010, Victoria, Australia
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  • Jennifer L. Bear,

    1. Melbourne School of Land and Environment, University of Melbourne, Burnley Campus, 500 Yarra Boulevard, Richmond 3121, Victoria, Australia
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  • Nicholas S. G. Williams,

    1. Melbourne School of Land and Environment, University of Melbourne, Burnley Campus, 500 Yarra Boulevard, Richmond 3121, Victoria, Australia
    2. Australian Research Centre for Urban Ecology, Royal Botanic Gardens, Melbourne, Australia
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  • Michael A. McCarthy

    1. Applied Environment Decision Analysis CERF, School of Botany, University of Melbourne, Parkville 3010, Victoria, Australia
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Abstract

Recent studies suggest that plant detection is not perfect, even for large, highly visible plants. However, this is often not taken into account during plant surveys where failing to detect a plant when present can result in poor management and biodiversity outcomes. Including knowledge of imperfect detectability into survey design and evaluation is hampered by the paucity of empirical data, and in particular, how detectability will change with search effort, plant size and abundance, the surrounding vegetation, or observer experience. We carried out a search experiment to measure the detection–effort curve for the invasive species orange hawkweed (Hieracium aurantiacum) in Victoria, Australia. The probability that hawkweed was detected increased with increasing search effort and the number of plants at the location. While detection probability varied between observers, experience appeared to have little effect. Accounting for imperfect detectability in plant surveys holds much promise for improved survey design and biodiversity outcomes, and we encourage other researchers to undertake similar experiments to further our understanding of plant detectability.

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