Abstract
In many species, individuals that experience harsh conditions during development have poor health and fitness outcomes in adulthood, compared with peers that do not. These early-life contributions to inequality are often attributed to two classes of evolutionary hypotheses: 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 costs individuals incur when they make incorrect predictions about conditions in adulthood. Testing these hypotheses empirically is difficult for conceptual and analytical reasons. Here, we help resolve some of these difficulties by providing mathematical definitions for DC, PAR (particularly focusing on 'external' PAR) and related concepts. We propose a novel, quadratic regression-based statistical test derived from these definitions. Our simulations show that this approach markedly improves the ability to discriminate between DC and PAR hypotheses relative to the status quo approach, which uses interaction effects. Simulated data indicate that the interaction effects approach often conflates PAR with DC, while the quadratic regression approach yields high sensitivity and specificity for detecting PAR. 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. This article is part of the theme issue 'Evolutionary ecology of inequality'.