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

Abstract

Summary1. 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. 2. We address this question based on data from the Swiss Biodiversity Monitoring, 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 Bayesian multispecies site-occupancy model, we estimated occurrence and detection probability of these species and their relation to covariates. 3. 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 LFs. In 60 species, survey season or elevation had significant effects on detection. We illustrated detection probability maps for Switzerland based on the modelled relationships with environmental covariates. 4. 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.

Detecting diversity: emerging methods to estimate species diversity

“…The fundamental unit of all diversity metrics is a count of species, individuals, or both. Yet rarely do circumstances occur when all species or all individuals are detected during a survey, regardless of whether the study organisms are birds [7], mammals [8], insects [9], or plants [10,11]. Imperfect detection has predictable consequences: when species are common, missed individuals result in underestimation of populations; when species are rare, missed individuals result in false absences.…”

Study of biological communities subject to imperfect detection: bias and precision of communityN‐mixture abundance models in small‐sample situations

“…Under this model the community-level hyper-parameters, i.e., the mean and the standard deviation of these normal distributions, are shared by all species in the community; they describe the average of the community and the among-species heterogeneity, respectively. We can also use separate normal distributions for individual species (functional) groups, and examine group-specific responses to covariates (Yamaura et al 2012;Chen et al 2013;Barnagaud et al 2014). Thanks to this sharing of hyperparameters among species, we can obtain better estimates of the parameters of rare species and even those of unobserved species by ''borrowing strength'' (i.e., sharing information) among similar but more common species (Zipkin et al 2009;Ovaskainen and Soininen 2011).…”

“…In these models which estimate abundance of species (herein, community abundance or community N-mixture models), the occurrence (or occupancy) of a species is naturally a function of its local abundance (i.e., a species occurs if its local abundance is greater than zero), and community-level species richness and total abundance is obtained as a derived parameter. We can assume that a studied community is composed of multiple (functional) groups in which species may have similar parameters, which are summarized by group-level hyper-parameters (Sauer and Link 2002;Ruiz-Gutie´rrez et al 2010;Yamaura et al 2012;Chen et al 2013;Barnagaud et al 2014;Pacifici et al 2014).…”

Improving estimates of environmental change using multilevel regression models of Ellenberg indicator values

“…The phenomenon of recorders overlooking species present when performing surveys is a consistent feature of ecological sampling and can lead to bias in estimates of many ecological rate and state variables (Chen et al., 2013; Kéry, 2004; Kéry & Gregg, 2003). While missing species may not be an issue when using weighted averages of EIVs (Ewald, 2003), our analyses on artificially depleted plots for presence/absence data show the utility of hierarchical models to help alleviate inaccuracy in estimates due to imperfect sampling and non‐systematic missing data.…”

“…Incomplete sampling is a common nuisance in ecological studies as some species are more difficult to detect than others, and ease of detection may vary depending on the time of year a particular plot is sampled, and among species (Chen, Kéry, Plattner, Ma, & Gardner, 2013; Kéry, 2004; Kéry & Gregg, 2003). This issue may be further compounded if recorders with differing botanical skills sample different plots, or in resurvey studies where it can be difficult to confirm the completeness of records, and where differing sampling methods may have been used.…”