Effects of life-state on detectability in a demographic study of the terrestrial orchid Cleistes bifaria


  • Marc Kéry,

    1. Patuxent Wildlife Research Center, US Geological Survey, 11510 American Holly Drive, Laurel, MD 20708, and
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  • Katharine B. Gregg

    Corresponding author
    1. Patuxent Wildlife Research Center, US Geological Survey, 11510 American Holly Drive, Laurel, MD 20708, and
    2. West Virginia Wesleyan College, 59 College Avenue, Buckhannon, WV 26201
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Katharine B. Gregg, Department of Biology, West Virginia Wesleyan College, Buckhannon, WV 26201, USA (tel. 304-473-8124; fax 304-472-2571; e-mail gregg@wvwc.edu).


  • 1Most plant demographic studies follow marked individuals in permanent plots. Plots tend to be small, so detectability is assumed to be one for every individual. However, detectability could be affected by factors such as plant traits, time, space, observer, previous detection, biotic interactions, and especially by life-state.
  • 2We used a double-observer survey and closed population capture–recapture modelling to estimate state-specific detectability of the orchid Cleistes bifaria in a long-term study plot of 41.2 m2. Based on AICc model selection, detectability was different for each life-state and for tagged vs. previously untagged plants. There were no differences in detectability between the two observers.
  • 3Detectability estimates (SE) for one-leaf vegetative, two-leaf vegetative, and flowering/fruiting states correlated with mean size of these states and were 0.76 (0.05), 0.92 (0.06), and 1 (0.00), respectively, for previously tagged plants, and 0.84 (0.08), 0.75 (0.22), and 0 (0.00), respectively, for previously untagged plants. (We had insufficient data to obtain a satisfactory estimate of previously untagged flowering plants).
  • 4Our estimates are for a medium-sized plant in a small and intensively surveyed plot. It is possible that detectability is even lower for larger plots and smaller plants or smaller life-states (e.g. seedlings) and that detectabilities < 1 are widespread in plant demographic studies.
  • 5State-dependent detectabilities are especially worrying since they will lead to a size- or state-biased sample from the study plot. Failure to incorporate detectability into demographic estimation methods introduces a bias into most estimates of population parameters such as fecundity, recruitment, mortality, and transition rates between life-states. We illustrate this by a simple example using a matrix model, where a hypothetical population was stable but, due to imperfect detection, wrongly projected to be declining at a rate of 8% per year.
  • 6Almost all plant demographic studies are based on models for discrete states. State and size are important predictors both for demographic rates and detectability. We suggest that even in studies based on small plots, state- or size-specific detectability should be estimated at least at some point to avoid biased inference about the dynamics of the population sampled.