Developmental plasticity

Early life constraints affecting phenotype in later life is a trend consistently observed across different species. Of particular relevance to evolutionary medicine are the effects of intra-uterine growth leading to diabetes, hypertension, cardiovascular disease and other age-related disorders. The broader question of conditions under which such developmental plasticity or predictive adaptive response (PAR) can evolve and further whether individual variability in plasticity will evolve have been theoretically addressed only recently [1–3]. Lea et al. [4], in this issue, expose some of the gaps and ambiguities in the concept, suggest experimental approaches and explore possible proximate mechanisms of plasticity. Being able to effectively bridge the proximate and ultimate would be the ultimate goal of understanding developmental plasticity. But currently there are many fundamental questions haunting the field. One is that of clearly classifying early life effects and being able to resolve between them. A variety of attempts to define and classify are suggested including developmental constraints (DCs) versus PAR, internal PAR versus external PAR [2] and developmental versus activational plasticity [5]. Although there are attempts to segregate DC from PAR, the two are not mutually exclusive and can also mask each other’s effects making empirical testing difficult. Lea et al. [6] argued that since baboons born in a famine year are not better adapted to subsequent famine, the PAR hypothesis is not supported. However interpreting such observations is not simple [7]. If the adaptive component of the phenotype or the proximate mechanism is well-defined, a more rigorous experimental design [2, 7] is possible and desirable. A comparison of the fitness of individuals born with a constraint and developing the adaptive phenotype with individuals born with the constraint but without the phenotype would reflect on PAR reliably [2]. In the absence of this dimension, the match-mismatch experiment is inadequate to test PAR. Further, since there are many alternative paths from early life input to a resultant phenotype and more than one path may be operational in a given example, I would suggest a flow chart representation (Fig. 1) rather than a dichotomous classification. For multiple pathways, a factorial design may be inadequate and a path analysis approach or a multiple predictions approach to test many alternative hypotheses [8] might prove better.