Imperfect detection is the rule rather than the exception in plant distribution studies

Summary Imperfect detection can seriously bias conventional estimators of species distributions and species richness. Plant traits, survey‐specific conditions and site‐specific characteristics may influence plant detection probability. However, the generality of the problems induced by imperfect detection in plants and the magnitude of this challenge for plant distribution studies are currently unknown. We address this question based on data from the S wiss B iodiversity M onitoring, in which vascular plants are surveyed twice in the same year along a 2.5‐km transect in 451 1‐km 2 quadrats. Overall, 1700 species were recorded. We chose a random sample of 100 species from the 1700 species to determine general detection levels. To examine the relationship of covariates on detection, we chose a stratified random sample of 100 species from 886 species that were detected in at least 18 locations, with 25 each from four life‐forms ( LF ): grass, forb, shrub and tree. Using a B ayesian multispecies site‐occupancy model, we estimated occurrence and detection probability of these species and their relation to covariates. Based on the random sample of 100 species, detection probability during the first survey ranged 0.03–0.99 (median 0.74) and during the second survey, 0.03–0.99 (median 0.82). Based on the stratified random sample of 100 species, detection probability during the first survey ranged 0.02–0.99 (median 0.87) and during the second survey, 0.01–1 (median 0.89). Detection probability differed slightly among the four LF s. In 60 species, survey season or elevation had significant effects on detection. We illustrated detection probability maps for S witzerland based on the modelled relationships with environmental covariates. Synthesis . Our findings suggest that even in a standardized monitoring program, imperfect detection of plants may be common. With the absence of a correction for detection errors, maps in plant distribution studies will be confounded with spatial patterns in detection probability. We presume that these problems will be much more widespread in the data sets that are used for conventional plant species distribution modelling. Imperfect detection should be estimated, even in distribution studies of plants and other sessile organisms, to better control detection errors that may compromise the results of species distribution studies.