Effects of imperfect detection on inferences from bird surveys

Abstract

Counts obtained from point count surveys of birds can be treated as an index to bird abundance, but imperfect detectability can complicate inferences about abundance. Detectability-adjusted analysis methods, including double observer, replicated counts, removal, and distance sampling methods, estimate detection as well as abundance but require additional information, with added logistical costs and potentially added sources of error. As a counterpoint to field-based studies, we simulated point counts of birds, modeling birds spatially as moving within territories, modeling song production as an autocorrelated process, and modeling perceptibility as a function of distance to the observer. We simulated counts with parameters reflecting surveys and behavior of Black-throated Blue Warblers (<em>Setophaga caerulescens</em>), analyzed counts using index and detectability-adjusted analysis methods, and then evaluated and compared the performance of analysis methods. Estimates from index methods underestimated true density of birds for all survey types but were highly correlated with true density. Adjusted estimates from distance sampling and removal analysis methods were less biased than index estimates but had reduced correlation with true density. Adjusted estimates from double-observer analysis methods were nearly unchanged from index estimates. Adjusted estimates from replicated-counts analysis methods were susceptible to highly inflated density estimates, resulting in extremely high bias and low correlation with true density. For replicated counts, the maximum count (an index method) produced less biased estimates than N-mixture model estimates. Index methods, while biased, were better correlated with true density than detectability-adjusted methods. If detection is constant and relative abundance is sufficient to meet survey objectives, using an index method is often preferable. For systems with variable detection probability where inference about absolute abundance is necessary or when detection and abundance are both expected to vary across a covariate gradient, practitioners should select detectability-adjusted methods suited to model the source of imperfect detection in their system. Ill-suited detectability-adjusted methods will not improve inference and are no more useful than an index.

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