DOI: 10.5061/dryad.sxksn038q
title: Measuring avian bill size: Comparing and evaluating 3D surface scanning with traditional size estimates in Australian birds

description: Unidimensional measurements for estimating bill size, like length and
 width, are commonly used in ecology and evolution, but can be criticised
 due to issues with repeatability and accuracy. Furthermore, formula-based
 estimates of bill surface area tend to assume uniform bill shapes across
 species, which is rarely the case. 3D surface scanning can potentially
 help overcome some such issues by collecting detailed external morphology
 and direct measurements of surface area, rather than composite estimates
 of size. Here, we evaluate the use of 3D surface scanners on avian museum
 specimens to test the repeatability of 3D-based measurements and compare
 these to traditional formula-based methods of estimating bill size from
 unidimensional measurements. Using 28 Australian bird species, we
 investigate inter-observer repeatability of surface area measurements from
 3D surface scans. We then compare 3D-based size estimates to formula-based
 size estimates to infer the accuracy and precision of formula-based
 measurements of bill surface area. We find that morphometric measurements
 from 3D surface scans are highly repeatable between observers, without the
 need for extensive training, demonstrating an advantage over
 unidimensional measuring methods, like callipers. When comparing 3D-based
 measurements to formula-based estimates of bill surface area, most
 formulae for estimating size consistently underestimate surface area, and
 with considerable variation between species. Where 3D scanning is not
 possible, we find that a commonly used cone formula for estimating bill
 size is most precise across diverse bill shapes, therefore supporting its
 use in interspecific contexts. However, we find that incorporating an
 additional unidimensional measure of bill curvature into formulae improves
 the accuracy of the calculated area. Our results reveal the high potential
 for 3D surface scanners in avian morphometric research, especially for
 studies necessitating large sample sizes collected by multiple observers,
 and gives suggestions for formula-based approaches to estimate bill size.
description: # Measuring avian bill size: comparing and evaluating 3D surface scanning
 with traditional size estimates
 [https://doi.org/10.5061/dryad.sxksn038q](https://doi.org/10.5061/dryad.sxksn038q) We investigated measurement repeatability between observers for bill surface area, as derived from 3D scans. We applied 3D surface scans on a diverse set of birds, and further collected unidimensional measurements. We assess how these unidimensional measurements can be used in different formulae to estimate bill surface area. ## Description of the data and file structure The dataset contains bill surface area measurements, extracted from two observers using 3D surface scans of diverse bird species (columns billSA_measurer1 and 2). It also contains unidimensional measurements (bill length, width, depth, width between the nares, and upper and lower bill profile length; as titled in columns), measured using calipers (or, in the case of total bill width and the profile length, derived from the 3D scans). All measurements are in mm. Finally, it contains important collection information: scientific name, common name, the catalogue number of the individual specimen, any comments on the specimen, its sex (male, female, or unknown), collection latitude and longitude, and collection year and month. The data also contains the R code used to analyse the data (see below in the code section).  ## Sharing/Access information Data was derived from avian specimens from the following institutions: * Australian Wildlife Collection * Melbourne Museum * South Australian Museum * Australian Museum ## Code/Software The code in this submission analyses inter-observer measurement repeatability, a phylogenetic PCA, and calculations of AIC based off of various formula options. More details on statistical methods can be found in the publication, and the code is annotated. For any queries, contact Sara Ryding [s.ryding@deakin.edu.au](mailto:sryding@deakin.edu.au). Code runs in R.