Searching for rare species: A comparison of Floristic Habitat Sampling and Adaptive Cluster Sampling for detecting and estimating abundance

Abstract

Abstract Surveys of rare species are challenging owing to the difficulty of detecting them on a landscape. Survey methods vary, often to achieve different goals. Thus when different survey methods are used in different locations and/or years, it is difficult to compare abundance data between regions or for assessing population trends through time. In many jurisdictions, forest legislation or policy may require managers to carry out surveys to assess presence or confirm absence of rare species. This then can inform forest management decisions that may impact these species, particularly when the rare species is listed for protection, for example under species-at-risk legislation. Because species surveys can be time consuming as managers want to be confident in their ability to detect species (or confirm that observed absences are likely true), survey protocols should be as efficient and effective as possible. Floristic habitat sampling (FHS) is often applied for botanical surveys and focuses on generating a list of species present in a region (sometimes referred to as the releve method) by inventorying an area as thoroughly as possible, including potential microhabitats. The Adaptive Cluster Sampling (ACS) method assumes that rare species are clustered in space and delineates sample plots non-randomly to increase accuracy of abundance estimates. Here, we compare FHS and ACS methods to detect rare lichens in two landscapes on the Avalon Peninsula in Newfoundland, as well as to generate species lists of arboreal lichens in a region. We also carry out a novel field simulation using artificial lichens to test how well ACS estimates known abundance. Finally, we demonstrate the utility of ACS to make new detections of an IUCN red-listed species (Erioderma pedicellatum) in a real-world setting and suggest how survey methods can be chosen to meet different forest management requirements.