title: A semiparametric Bayesian method for detecting Allee effects.
abstract: The importance of Allee effects has long been recognized both in theoretical studies of population dynamics and in conservation sciences. Although the necessary conditions for Allee effects to occur (e.g., difficulty in finding mates and mortality driven by generalist predators at low density) would seem to apply to many species, evidence for Allee effects in natural populations is equivocal at best. This apparent scarcity might be an artifact driven by poor power to detect them with traditional parametric models. To circumvent this potential problem, we developed a semiparametric Bayesian method based on a Gaussian process prior. We validated the method using simulated data sets and applied it to three herring data sets.
authors.affiliation: School of Marine and Atmospheric Sciences, Stony Brook University, Stony Brook, New York 11794-5000, USA. masatoshi.sugeno@gmail.com
authors.forename: Masatoshi
authors.initials: M
authors.lastname: Sugeno
authors.forename: Stephan B
authors.initials: SB
authors.lastname: Munch
mesh_terms: D000818:Animals; D001499:Bayes Theorem; D018533:Biomass; D003198:Computer Simulation; D005399:Fishes; D008954:Models, Biological; D011157:Population Dynamics