Estimating abundance of unmarked animal populations: accounting for imperfect detection and other sources of zero inflation

摘要

Inference and estimates of abundance are critical for quantifying population dynamics and impacts of environmental change. Yet imperfect detection and other phenomena that cause zero inflation can induce estimation error and obscure ecological patterns. Recent statistical advances provide an increasingly diverse array of analytical approaches for estimating population size to address these phenomena. We examine how detection error and zero inflation in count data inform the choice of analytical method for estimating population size of unmarked individuals that are not uniquely identified. We review two established (GLMs and distance sampling) and nine emerging methods that use N-mixture models (Royle-Nichols model, and basic, zero inflated, temporary emigration, beta-binomial, generalized open-population, spatially explicit, single visit and multispecies) to estimate abundance of unmarked populations, focusing on their requirements and how each method accounts for imperfect detection and zero inflation. Eight of the emerging methods can account for both imperfect detection and additional variation in population size in the forms of non-occupancy, temporary emigration, correlated detection and population dynamics. Methods differ in sampling design requirements (e.g. count vs. detectionon-detection data, single vs. multiple visits, covariate data), and their suitability for a particular study will depend on the characteristics of the study species, scale and objectives of the study, and financial and logistical considerations. Most emerging methods were developed over the past decade, so their efficacy is still under study, and additional statistical advances are likely to occur.