Seeing the future: a better way to model and test for adaptive developmental plasticity
In many species, individuals who experience harsh conditions during development often have poor health and fitness outcomes in adulthood relative to peers who do not. There are two classes of evolutionary hypotheses for the origins of these early life contributors to inequality in adulthood: developmental constraints (DC) models, which focus on the deleterious effects of low-quality early-life environments, and predictive adaptive response (PAR) hypotheses, which emphasize the cost of mismatches between early and adult environments. Distinguishing DC and PAR models empirically is difficult for both conceptual and analytical reasons. Here, we resolve this difficulty by providing explicit mathematical definitions for DC, PARs, and related concepts, and propose a novel, quadratic regression-based statistical test derived from these definitions. Simulations show that this approach improves the ability to discriminate between DC and PAR hypotheses relative to a common alternative based on testing for interaction effects between developmental and adult environments. Simulated data indicate that the interaction effects approach often conflates PARs with DC, while the quadratic regression approach yields high sensitivity and specificity for detecting PARs. Our results highlight the value of linking verbal and visual models to a formal mathematical treatment for understanding the developmental origins of inequitable adult outcomes.