title: Real time forecasting of near-future evolution.
abstract: A metaphor for adaptation that informs much evolutionary thinking today is that of mountain climbing, where horizontal displacement represents change in genotype, and vertical displacement represents change in fitness. If it were known a priori what the 'fitness landscape' looked like, that is, how the myriad possible genotypes mapped onto fitness, then the possible paths up the fitness mountain could each be assigned a probability, thus providing a dynamical theory with long-term predictive power. Such detailed genotype-fitness data, however, are rarely available and are subject to change with each change in the organism or in the environment. Here, we take a very different approach that depends only on fitness or phenotype-fitness data obtained in real time and requires no a priori information about the fitness landscape. Our general statistical model of adaptive evolution builds on classical theory and gives reasonable predictions of fitness and phenotype evolution many generations into the future.
authors.affiliation: Center for Evolutionary and Theoretical Immunology, Department of Biology, University of New Mexico, Albuquerque, NM 87131, USA. pgerrish@unm.edu
authors.forename: Philip J
authors.initials: PJ
authors.lastname: Gerrish
authors.forename: Paul D
authors.initials: PD
authors.lastname: Sniegowski
mesh_terms: D005075:Biological Evolution; D005544:Forecasting; D008954:Models, Biological