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Keywords:

  • false absence;
  • impact assessment;
  • priors;
  • surveillance;
  • survey effort;
  • trait-based model

Summary

  1. Imperfect detectability is a critical source of variation that limits ecological progress and frustrates effective conservation management. Available modelling methods provide valuable detectability estimates, but these are typically species-specific.
  2. We present a novel application of time-to-detection modelling in which detectability of multiple species is a function of plant traits and observer characteristics. The model is demonstrated for plants in a temperate grassland community in south-eastern Australia.
  3. We demonstrate that detectability can be estimated using observer experience, species population size and likelihood of flowering. The inclusion of flower colour and species distinctiveness improves the capacity of the model to predict detection rates for new species.
  4. We demonstrate the application of the general model to plants in a temperate grassland community, but this modelling method may be extended to other communities or taxa for which time-to-detection models are appropriate.
  5. Detectability is influenced by traits of the species and the observer. General models can be used to derive detectability estimates where repeat survey data, point counts or mark-recapture data are not available. As these data are almost always absent for species of conservation concern, general models such as ours will be useful for informing minimum survey requirements for monitoring and impact assessment, without the delays and costs associated with data collection.