Statistical design and analysis for plant cover studies with multiple sources of observation errors

Summary 1.Effective wildlife habitat management and conservation requires understanding the factors influencing distribution and abundance of plant species. Field studies, however, have documented observation errors in visually estimated plant cover including measurements which differ from the true value (measurement error) and not observing a species that is present within a plot (detection error). Unlike the rapid expansion of occupancy and N-mixture models for analyzing wildlife surveys, development of statistical models accounting for observation error in plants has not progressed quickly. Our work informs development of a monitoring protocol for managed wetlands within the National Wildlife Refuge System. 2.Zero-augmented beta regression is the most suitable method for analyzing areal plant cover recorded as a continuous proportion but assumes no observation errors. We present a model extension that explicitly includes the observation process thereby accounting for both measurement and detection errors. Using simulations, we compare our approach to a zero-augmented beta regression that ignores observation errors (naive model) and an ‘ad hoc’ approach using a composite of multiple observations per plot within the naive model. We explore how sample size and within-season revisit design effect the ability to detect a change in mean plant cover between two years using our model. 3.Explicitly modeling the observation process within our framework produced unbiased estimates and nominal coverage of model parameters. The naive and ‘ad hoc’ approaches resulted in underestimation of occurrence and overestimation of mean cover. The degree of bias was primarily driven by imperfect detection and its relationship with cover within a plot. Conversely, measurement error had minimal impacts on inferences. We found > 30 plots with at least three within-season revisits achieved reasonable posterior probabilities for assessing change in mean plant cover. 4.For rapid adoption and application, code for Bayesian estimation of our single-species zero-augmented beta with errors model is included. Practitioners utilizing our R-based simulation code can explore trade-offs among different survey efforts and parameter values, as we did, but tuned to their own investigation. Less abundant plant species of high ecological interest may warrant the additional cost of gathering multiple independent observations in order to guard against erroneous conclusions. This article is protected by copyright. All rights reserved.