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Rethinking phenotypic plasticity and its consequences for individuals, populations and species.

ABSTRACT: Much research has been devoted to identify the conditions under which selection favours flexible individuals or genotypes that are able to modify their growth, development and behaviour in response to environmental cues, to unravel the mechanisms of plasticity and to explore its influence on patterns of diversity among individuals, populations and species. The consequences of developmental plasticity and phenotypic flexibility for the performance and ecological success of populations and species have attracted a comparatively limited but currently growing interest. Here, I re-emphasize that an increased understanding of the roles of plasticity in these contexts requires a 'whole organism' (rather than 'single trait') approach, taking into consideration that organisms are integrated complex phenotypes. I further argue that plasticity and genetic polymorphism should be analysed and discussed within a common framework. I summarize predictions from theory on how phenotypic variation stemming from developmental plasticity and phenotypic flexibility may affect different aspects of population-level performance. I argue that it is important to distinguish between effects associated with greater interindividual phenotypic variation resulting from plasticity, and effects mediated by variation among individuals in the capacity to express plasticity and flexibility as such. Finally, I claim that rigorous testing of predictions requires methods that allow for quantifying and comparing whole organism plasticity, as well as the ability to experimentally manipulate the level of and capacity for developmental plasticity and phenotypic flexibility independent of genetic variation.

SUBMITTER: Forsman A

PROVIDER: S-EPMC4815454 | biostudies-other | 2015 Oct

REPOSITORIES: biostudies-other

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Project description:There is a paradigm shift from the traditional focus on the "average" individual towards the definition and analysis of trait variation within individual life-history and among individuals in populations. This is a result of increasing availability of individual phenotypic data. The shift allows the use of genetic and environment-driven variations to assess robustness to challenge, gain greater understanding of organismal biological processes, or deliver individual-targeted treatments or genetic selection. These consequences apply, in particular, to variation in ontogenetic growth. We propose an approach to parameterise mathematical models of individual traits (e.g., reaction norms, growth curves) that address two challenges: 1) Estimation of individual traits while making minimal assumptions about data distribution and correlation, addressed via Approximate Bayesian Computation (a form of nonparametric inference). We are motivated by the fact that available information on distribution of biological data is often less precise than assumed by conventional likelihood functions. 2) Scaling-up to population phenotype distributions while facilitating unbiased use of individual data; this is addressed via a probabilistic framework where population distributions build on separately-inferred individual distributions and individual-trait interpretability is preserved. The approach is tested against Bayesian likelihood-based inference, by fitting weight and energy intake growth models to animal data and normal- and skewed-distributed simulated data. i) Individual inferences were accurate and robust to changes in data distribution and sample size; in particular, median-based predictions were more robust than maximum- likelihood-based curves. These results suggest that the approach gives reliable inferences using few observations and monitoring resources. ii) At the population level, each individual contributed via a specific data distribution, and population phenotype estimates were not disproportionally influenced by outlier individuals. Indices measuring population phenotype variation can be derived for study comparisons. The approach offers an alternative for estimating trait variability in biological systems that may be reliable for various applications, for example, in genetics, health, and individualised nutrition, while using fewer assumptions and fewer empirical observations. In livestock breeding, the potentially greater accuracy of trait estimation (without specification of multitrait variance-covariance parameters) could lead to improved selection and to more decisive estimates of trait heritability.

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Rethinking phenotypic plasticity and its consequences for individuals, populations and species.

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