A field guide for aging passerine nestlings using growth data and predictive modeling

Abstract

Background: Accurate nestling age is valuable for studies on nesting strategies, productivity, and impacts on reproductive success. Most aging guides consist of descriptions and photographs that are time consuming to read and subjective to interpret. The Western Bluebird (Sialia mexicana) is a secondary cavity-nesting passerine that nests in coniferous and open deciduous forests. Nest box programs for cavity-nesting species have provided suitable nesting locations and opportunities for data collection on nestling growth and development. Methods: We developed models for predicting the age of Western Bluebird nestlings from morphometric measurements using model training and validation. These were developed for mass, tarsus, and two different culmen measurements. Results: Our models were accurate to within less than a day, and each model worked best for a specific age range. The mass and tarsus models can be used to estimate the ages of Western Bluebird nestlings 0–10 days old and were accurate to within 0.5 days for mass and 0.7 days for tarsus. The culmen models can be used to estimate ages of nestlings 0–15 days old and were also accurate to within less than a day. The daily mean, minimum, and maximum values of each morphometric measurement are provided and can be used in the field for accurate nestling age estimations in real time. Conclusions: The model training and validation procedures used here demonstrate that this method can create aging models that are highly accurate. The methods can be applied to any passerine species provided sufficient nestling morphometric data are available.