Extended moult phenology models improve inferences about moult duration and timing

Robin, by Edmund Fellowes

Author(s): Boersch-Supan, P.H., Hanmer, H.J. & Robinson, R.A.

Published: January 2024  

Journal: Ornithology

Digital Identifier No. (DOI): 10.1093/ornithology/ukae003

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Moult is an essential but understudied life-history event in birds, and one that is difficult to study with current statistical approaches. This paper presents an extended modelling framework that should remove barriers to modelling the phenology of moult.


Moult is an essential life-history event in birds and many mammals, as maintenance of feathers and fur is critical for survival. Despite this moult remains an understudied life-history event. Non-standard statistical techniques are required to estimate the phenology of moult from observations of plumage or pelage state, and existing moult phenology models have strict sampling requirements which can be difficult to meet under real-world conditions.

We present an extended modelling framework that can accommodate features of real-world moult datasets, such re-encounters of individuals, misclassified moult states, and/or moult state-dependent sampling bias. We demonstrate that such features can lead to biased inferences when using existing moult phenology models, and show that our model extensions can improve inferences about moult  phenology under a wide range of sampling conditions. 

We hope that our novel modelling framework removes barriers for modelling moult phenology data from real-world datasets and thereby further facilitates the uptake of appropriate statistical methods for such data. Although we focus on moult, the modelling framework is applicable to other phenological processes which can be recorded using either ordered categories or approximately linear progress scores.


We thank the thousands of fieldworkers and volunteers who contribute records to the British and Irish Ringing Scheme and other schemes collecting molt data worldwide, and in particular Hugh Insley for providing the Siskin dataset. Simulation studies were conducted on JASMIN, the UK’s collaborative data analysis environment.
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