title: Non-detection errors in a survey of persistent, highly-detectable vegetation species.
abstract: Rare, small or annual vegetation species are widely known to be imperfectly detected with single site surveys by most conventional vegetation survey methods. However, the detectability of common, persistent vegetation species is assumed to be high, but without supporting research. In this study, we evaluate the extent of false-negative errors of perennial vegetation species in a systematic vegetation survey in arid South Australia. Analysis was limited to the seven most easily detected persistent vegetation species and controlled for observer skill. By comparison of methodologies, we then predict the magnitude of non-detection error rates in a second survey. The analysis revealed that all but one highly detectable perennial vegetation species was imperfectly detected (detection probabilities ranged from 0.22 to 0.83). While focussed in the Australian rangelands, the implications of this study are far reaching. Inferences drawn from systematic vegetation surveys that fail to identify and account for non-detection errors should be considered potentially flawed. The identification of this problem in vegetation surveying is long overdue. By comparison, non-detection has been a widely acknowledged, and dealt with, problem in fauna surveying for decades. We recommend that, where necessary, vegetation survey methodology adopt the methods developed in fauna surveying to cope with non-detection errors.
authors.affiliation: The University of Adelaide, Adelaide, South Australia 5005, Australia. kenneth.clarke@adelaide.edu.au
authors.forename: Kenneth D
authors.initials: KD
authors.lastname: Clarke
authors.forename: Megan
authors.initials: M
authors.lastname: Lewis
authors.forename: Robert
authors.initials: R
authors.lastname: Brandle
authors.forename: Bertram
authors.initials: B
authors.lastname: Ostendorf
mesh_terms: D001315:Australia; D044822:Biodiversity; D003247:Conservation of Natural Resources; D003625:Data Collection / Q000379:methods*; D004784:Environmental Monitoring / Q000379:methods* / Q000706:statistics & numerical data; D063245:Plant Development; D010944:Plants / Q000145:classification