Avian egg collections: museum collection bias driven by shape and size. (1st September 2020)

• Record Type: Journal Article
• Authors: Thompson, Jamie E.; Birkhead, Tim R.
• Is Part Of: Journal of avian biology. Volume 51:Number 8(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-09-01
• Subjects: avian egg shape -- museum egg collections -- pointedness -- elongation -- polar asymmetry -- guillemots; Ornithology -- Periodicals; Ornithology -- Scandinavia -- Periodicals; 598
• DOI: 10.1111/jav.02507
• Languages: English
• ISSNs: 0908-8857

Abstract

Abstract : Avian eggs exhibit substantial intra‐ and interspecific variation in shape, size and colour. Considerable efforts have been made to better understand the evolutionary drivers behind such variation, often using museum egg collections. Usually it is assumed that museum collections accurately represent the variation seen in natural populations, but this may not be the case if there is collection bias. Collection bias may lead to the over‐representation of certain egg traits in collections, due to the aesthetic (or other) preferences of collectors. The aim of this study is to begin to look for the occurrence of potential collection bias in museum egg collections by comparing three shape indices (pointedness/asymmetry, elongation and polar asymmetry) and egg volume between subsets of eggs in museum collections with those of recently sampled eggs in the field for three different bird species: common guillemot Uria aalge, razorbill Alca torda and northern fulmar Fulmarus glacialis . We found no evidence of collection bias in our sampled razorbill and northern fulmar museum collection eggs, but some evidence for a bias in sampled museum collection eggs of common guillemots. Since the guillemot's egg differs from most bird eggs in being pyriform, we suggest that collection bias by historic egg collectors may be more prevalent in species with extreme egg traits. Researchers using museum egg collections to examine questions relating to egg shape should be aware of collection bias risks and consider how to minimise the effect of these possible biases on accumulated datasets.