Predicting recruitment density dependence and intrinsic growth rate for all fishes worldwide using a data‐integrated life‐history model Fish and Fisheries

Fisheries scientists use biological models to determine sustainable fishing rates and forecast future dynamics. These models require both life-history parameters (mortal - ity, maturity, growth) and stock-recruit parameters (juvenile production). However, there has been little research to simultaneously predict life-history and stock-recruit parameters. I develop the first data-integrated life-history model, which extends a simple model of evolutionary dynamics to field measurements of life-history pa - rameters as well as historical records of spawning output and subsequent recruit - ment. This evolutionary model predicts recruitment productivity (steepness) and variability (variance and autocorrelation in recruitment deviations) as well as mortal -