title: Detecting diversity: emerging methods to estimate species diversity.
abstract: Estimates of species richness and diversity are central to community and macroecology and are frequently used in conservation planning. Commonly used diversity metrics account for undetected species primarily by controlling for sampling effort. Yet the probability of detecting an individual can vary among species, observers, survey methods, and sites. We review emerging methods to estimate alpha, beta, gamma, and metacommunity diversity through hierarchical multispecies occupancy models (MSOMs) and multispecies abundance models (MSAMs) that explicitly incorporate observation error in the detection process for species or individuals. We examine advantages, limitations, and assumptions of these detection-based hierarchical models for estimating species diversity. Accounting for imperfect detection using these approaches has influenced conclusions of comparative community studies and creates new opportunities for testing theory.
authors: Department of Environmental Science, Policy and Management, 130 Mulford Hall, University of California, Berkeley, CA 94720-3110, USA.
authors: Kelly J
authors: KJ
authors: Iknayan
authors: Woodrow Wilson School, Princeton University, Princeton, NJ 08544, USA.
authors: Morgan W
authors: MW
authors: Tingley
authors: Department of Environmental Science, Policy and Management, 130 Mulford Hall, University of California, Berkeley, CA 94720-3110, USA; California Department of Fish and Wildlife, 601 Locust Street, Redding, CA 96001, USA.
authors: Brett J
authors: BJ
authors: Furnas
authors: Department of Environmental Science, Policy and Management, 130 Mulford Hall, University of California, Berkeley, CA 94720-3110, USA. Electronic address: beis@berkeley.edu.
authors: Steven R
authors: SR
authors: Beissinger
mesh_terms: D044822:Biodiversity; D003625:Data Collection; D004784:Environmental Monitoring / Q000379:methods* / Q000706:statistics & numerical data; D008954:Models, Biological