title: Estimating differential reproductive success from nests of related individuals, with application to a study of the mottled sculpin, Cottus bairdi.
abstract: Understanding how variation in reproductive success is related to demography is a critical component in understanding the life history of an organism. Parentage analysis using molecular markers can be used to estimate the reproductive success of different groups of individuals in natural populations. Previous models have been developed for cases where offspring are random samples from the population but these models do not account for the presence of full- and half-sibs commonly found in large clutches of many organisms. Here we develop a model for comparing reproductive success among different groups of individuals that explicitly incorporates within-nest relatedness. Inference for the parameters of the model is done in a Bayesian framework, where we sample from the joint posterior of parental assignments and fertility parameters. We use computer simulations to determine how well our model recovers known parameters and investigate how various data collection scenarios (varying the number of nests or the number of offspring) affect the estimates. We then apply our model to compare reproductive success among different age groups of mottled sculpin, Cottus bairdi, from a natural population. We demonstrate that older adults are more likely to contribute to a nest and that females in the older age groups contribute more eggs to a nest than younger individuals.
authors.affiliation: Department of Molecular Biology and Genetics, Cornell University, Ithaca, NY 14853, USA. m.b.jones@massey.ac.nz
authors.forename: Beatrix
authors.initials: B
authors.lastname: Jones
authors.forename: Gary D
authors.initials: GD
authors.lastname: Grossman
authors.forename: Daniel C I
authors.initials: DC
authors.lastname: Walsh
authors.forename: Brady A
authors.initials: BA
authors.lastname: Porter
authors.forename: John C
authors.initials: JC
authors.lastname: Avise
authors.forename: Anthony C
authors.initials: AC
authors.lastname: Fiumera
mesh_terms: D000465:Algorithms; D000818:Animals; D001499:Bayes Theorem; D003198:Computer Simulation; D030541:Databases, Genetic; D005260:Female; D005399:Fishes / Q000235:genetics* / Q000502:physiology*; D005828:Genetics, Population; D008297:Male; D008957:Models, Genetic; D012098:Reproduction / Q000235:genetics* / Q000502:physiology*