What question did this study set out to answer?

This research aims to enhance the accuracy of population estimates for North American birds by accounting for detection biases and sampling gaps.

May 30, 2026Avian Conservation and Ecology0 citationsOpen Access

Refining population estimates for North American birds: reducing bias from imperfect detection, spatial sampling, and temporal change

MGMarcel Gahbauer ASAdam B. Smith BEBrandon Edwards

Key Points

This research aims to enhance the accuracy of population estimates for North American birds by accounting for detection biases and sampling gaps.
Developed a Bayesian model integrating detectability estimates, relative abundance surfaces, and spatial data from the North American Breeding Bird Survey (BBS).
Utilized structured observations to calibrate species density estimates on the landscape.
Focused on improving estimates for year-specific population sizes rather than averaging over previous years.
Improved estimates incorporate a formal detectability adjustment, leading to more accurate density and population size assessments.
The model allows for population estimates across custom geographic areas with associated uncertainties.
It demonstrated effectiveness for songbirds but highlights the need for further refinement for other bird species with different nesting behaviors.

Abstract

Partners in Flight (PIF) has been publishing population estimates for landbirds in Canada and the United States since 2004. These estimates have been widely used in support of species status assessments, conservation planning, and communicating the status of bird populations. However, broad uncertainty around many estimates and potential biases due to gaps in geographic and temporal coverage have partly limited their applicability. Determining the absolute size of wild bird populations requires adequate survey data and appropriately complex modeling of detection probability to transform field observations into estimates of density. We have developed an updated approach that integrates data-derived estimates of detectability for hundreds of species, range-wide relative abundance surfaces, and spatial information from the North American Breeding Bird Survey (BBS) into a formal Bayesian model. This improved method combines important elements of the existing PIF population estimates framework with a large database of structured observations that allow formal detectability estimates (NA-POPS), the hierarchical Bayesian count-based trend models applied to the BBS data, and high-resolution spatially explicit estimates of relative abundance from eBird. It calibrates the relative abundance surface of a species to represent its density on the landscape and thereby can generate population estimates, with associated uncertainties, for any custom area. This model also generates population estimates for a single year, improving on the previous estimates that were averaged over 10 years. This new method works well for territorial songbirds and other species effectively monitored using point count field methods and observations during the early morning hours. Other species such as colonially nesting birds, crepuscular or nocturnal species, and species with highly specific habitat requirements, will require further model refinement. PIF will use this revised model to publish estimates of population sizes for North American birds and will continue to improve the model in an open and reproducible way.

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