Predicting recruitment from stock size without the mediation of a functional relation

Recruitment of fish is often better treated as a probability distribution than as a deterministic function of stock size. Algorithms that use stock and recruitment data directly, in raw form, may project future recruitments of fish more accurately than algorithms that use estimated parametric summaries. We demonstrate this with examples, and present guidelines for developing raw data algorithms.