Multiple growth-correlated life history traits estimated simultaneously in individuals

Accurate estimates of age at maturity from the growth trajectories of fishes and other ectotherms

Age at maturity (AAM) is a key life history trait that provides insight into ecology, evolution, and population dynamics. However, maturity data can be costly to collect or may not be available. Life history theory suggests that growth is biphasic for many organisms, with a change-point in growth occurring at maturity. If so, then it should be possible to use a biphasic growth model to estimate AAM from growth data. To test this prediction, we used the Lester biphasic growth model in a likelihood profiling framework to estimate AAM from length at age data. We fit our model to simulated growth trajectories to determine minimum data requirements (in terms of sample size, precision in length at age, and the cost to somatic growth of maturity) for accurate AAM estimates. We then applied our method to a large walleye Sander vitreus data set and show that our AAM estimates are in close agreement with conventional estimates when our model fits well. Finally, we highlight the potential of our method by applying it to length at age data for a variety of ectotherms. Our method shows promise as a tool for estimating AAM and other life history traits from contemporary and historical samples.