Measurements of Canada goose morphology - Sources of error and effects on classification of subspecies

Subspecific classification of Canada geese (Branta canadensis) based on mor phological measurements serves many management and research functions, such as determining harvest pressure on subspecies or estimating the population composition of wintering flocks. Despite this widespread use, the magnitud e of error involved in such measurements. the effect of observer experience on measurement error, and the effect of measurement error on classificatio n are not known. To investigate these issues, we carried out a study on Can ada geese harvested in Wisconsin involving replicated measurements by obser vers of different experience levels. Measurement error for experienced obse rvers was half as large as that for inexperienced observers (6-10% vs. 13-2 1% of all variability for all structures except the tarsus). Experienced ob servers measured the skull and culmen most precisely, the tarsus, least pre cisely. Consistent differences among observers (observer bias) that could b ias classification were smaller for experienced observers. We used referenc e data and distributional assumptions to estimate that without observer bia s or other forms of measurement error. 8-9% of geese measured would be misc lassified because of actual size overlap between subspecies. Without observ er bias, remaining measurement error among experienced and inexperienced ob servers increased misclassification by 1% and 2%, respectively. Observer bi as can increase misclassification substantially beyond these levels, depend ing on the magnitude and direction of observer bias and the prevalence of t he subspecies. Misclassification of geese resulted in overestimating the pr evalence of the less common subspecies in mixed populations. which may be i mportant in developing management strategies. We recommend training observe rs and standardizing measurement procedures primarily to reduce observer bi as that leads to biased classification of geese, and secondarily to reduce other components of measurement error.