title: Estimating evolutionary parameters when viability selection is operating.
abstract: Some individuals die before a trait is measured or expressed (the invisible fraction), and some relevant traits are not measured in any individual (missing traits). This paper discusses how these concepts can be cast in terms of missing data problems from statistics. Using missing data theory, I show formally the conditions under which a valid evolutionary inference is possible when the invisible fraction and/or missing traits are ignored. These conditions are restrictive and unlikely to be met in even the most comprehensive long-term studies. When these conditions are not met, many selection and quantitative genetic parameters cannot be estimated accurately unless the missing data process is explicitly modelled. Surprisingly, this does not seem to have been attempted in evolutionary biology. In the case of the invisible fraction, viability selection and the missing data process are often intimately linked. In such cases, models used in survival analysis can be extended to provide a flexible and justified model of the missing data mechanism. Although missing traits pose a more difficult problem, important biological parameters can still be estimated without bias when appropriate techniques are used. This is in contrast to current methods which have large biases and poor precision. Generally, the quantitative genetic approach is shown to be superior to phenotypic studies of selection when invisible fractions or missing traits exist because part of the missing information can be recovered from relatives.
authors.affiliation: University of Edinburgh, Edinburgh EH8 9YL, UK. j.hadfield@ed.ac.uk
authors.forename: Jarrod D
authors.initials: JD
authors.lastname: Hadfield
mesh_terms: D000818:Animals; D015982:Bias; D005075:Biological Evolution; D049628:Body Size; D003627:Data Interpretation, Statistical; D005828:Genetics, Population / Q000379:methods*; D008136:Longevity; D008957:Models, Genetic; D012641:Selection, Genetic; D013997:Time Factors