Advantages of nonlinear mixed models for fitting avian growth curves. (25th June 2013)

• Record Type: Journal Article
• Authors: Sofaer, Helen R.; Chapman, Phillip L.; Sillett, T. Scott; Ghalambor, Cameron K.
• Is Part Of: Journal of avian biology. Volume 44:Number 5(2013:Sep.)
• Page Start: 469
• Page End: 478
• Publication Date: 2013-06-25
• Subjects: Ornithology -- Periodicals; Ornithology -- Scandinavia -- Periodicals; 598
• DOI: 10.1111/j.1600-048X.2013.05719.x
• Languages: English
• ISSNs: 0908-8857

Abstract

<abstract abstract-type="main" xml:lang="en"> <title> <x xml:space="preserve">Abstract</x> </title> <p>Our understanding of avian growth rates can benefit from the use of two statistical approaches that explicitly model the sources of intraspecific variation. First, random effects can evaluate whether there are consistent differences between individuals and groups of siblings within a population, and also account for any lack of statistical independence among data points. Second, nonlinear fixed‐effect functions can be extended to test specific biological hypotheses of interest, such as for differences between groups or populations. We illustrate the advantages of these methods by using nonlinear mixed models to study variation in the growth trajectories of nestling orange‐crowned warblers <italic>Oreothylpis celata</italic>. Specifically, we quantify the sources of variation within populations, analyze the effects of asynchronous hatching, and test for a difference in the growth rates of populations in Alaska and California, which are at the northern and southern limits of the species' breeding distribution. We found that growth rates did not consistently vary between nests and individuals within populations and were not affected by asynchronous hatching, but were higher in Alaska than in California. Our extensions of traditional methods allowed us to accurately quantify this difference between populations, which is consistent with life history theory but has rarely been demonstrated in previous comparisons of intraspecific passerine populations. The methods we present can be applied to any taxonomic group and adjusted to fit any nonlinear function, and we provide code and implementation advice to facilitate the use of this analytical framework in future studies.</p> </abstract>