An integrated model decomposing the components of detection probability and abundance in unmarked populations. Issue 3 (6th March 2019)

• Record Type: Journal Article
• Authors: Hostetter, Nathan J.; Gardner, Beth; Sillett, T. Scott; Pollock, Kenneth H.; Simons, Theodore R.
• Is Part Of: Ecosphere. Volume 10:Issue 3(2019)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-03-06
• Subjects: detection probability -- distance sampling -- integrated model -- N‐mixture model -- temporary emigration -- time‐of‐detection -- unmarked populations; Ecology -- Periodicals; Ecology; Periodicals; 577.05
• DOI: 10.1002/ecs2.2586
• Languages: English
• ISSNs: 2150-8925

Abstract

Abstract: Accurate estimates of population abundance are essential to both theoretical and applied ecology. Rarely are all individuals detected during a survey and abundance models often incorporate some form of imperfect detection. Detection probability, however, consists of three components: probability of presence during a survey, probability of availability given presence, and probability of detection given availability and presence. We develop an integrated model to separate these three detection components and provide abundance estimates for the available, present, and superpopulation of individuals. Our framework integrates several common survey methods for unmarked populations: spatially and temporally replicated counts, distance sampling data, and time‐of‐detection data. Simulations indicated relatively unbiased estimates for detection and availability probabilities. Negative bias in estimated superpopulation abundance was present with three temporally replicated surveys, but greatly reduced with six surveys. In a case study of Island Scrub‐Jays ( Aphelocoma insularis ), posterior modes for presence, availability, and detection probabilities were 0.78, 0.96, and 0.26, respectively, from 10‐min point counts repeated at 97 sites on three occasions, with noticeable differences among available, present, and superpopulation abundance estimates. This generalizable framework integrates common sampling protocols and provides joint inferences on the components of detection probability, spatial and non‐spatial temporary emigration, and abundance in unmarked populations.