Together, these studies provide just a few striking examples of the impact early life environments can have on later life traits. Because the impact of early conditions can be so dramatic, with potent effects on reproduction and survival, developmental plasticity is of central interest to multiple disciplines.
Researchers in medicine, public health, psychology, economics, and sociology seek to understand the link between early conditions and adult health because of its relevance to disease treatment and prevention reviewed in [ 9—14 ].
Evolutionary Adaptation and Positive Selection in Humans | Learn Science at Scitable
Concurrently, evolutionary biologists seek to understand the impact of early environments on traits related to Darwinian fitness, because this knowledge is important for understanding the evolution of complex traits and the selection pressures that shape them reviewed in references [ 15—19 ]. The parallel importance of early environmental effects in both health and evolutionary research presents an opportunity to forge a more complete, interdisciplinary understanding of developmental plasticity.
We argue that realizing this opportunity should be a priority, but doing so is challenging because conflicting theories and terminology have arisen in the different fields in the absence of the cross-talk necessary to resolve them. Our goal here is thus to initiate and encourage increased connectivity, specifically by outlining key questions shared by health and evolutionary researchers, as well as approaches from diverse fields that could be used to address them.
In doing so, we do not comprehensively review developmental plasticity research we refer interested readers to many excellent, recent reviews [ 9—19 ].
Instead, we aim to spark excitement about work in three distinct areas where the potential for cross-disciplinary gain is high. In the first section, we discuss evolutionary explanations for developmental plasticity and highlight critical tests that distinguish between potential explanations. In each area, we discuss current knowledge gaps, promising approaches for filling these gaps and the gains in human health and evolutionary biology that would follow.
Because of our focus on human health, we primarily draw on studies of humans and other mammals, but where relevant, we include important work from birds, insects, and other taxa. Table 1. Key terms in developmental plasticity research. Under natural selection, genetic variants that contribute to superior phenotypes increase in frequency.
It occurs when some individuals or groups are more likely than others to be sampled, or when some individuals or groups are more likely than others to experience a given set of conditions. When these types of selection occur, the effects of early life conditions are observed in a non-random sample of individuals.
Used to describe a trait for which a measurable proportion of total phenotypic variance is explained by genetic differences among individuals. Sometimes used in the non-technical sense to mean that phenotypes of offspring and parents are correlated, without demonstration that this phenotypic similarity is due to genetic similarity.
Used to describe a trait that increases the average fitness of individuals that express it, relative to individuals that do not express the trait. Used to describe short-term physiological adjustment to a current environment e. Used to describe a trait that appears to be beneficial i. Used to describe a trait that decreases the net average fitness of individuals that express it, relative to individuals that do not express the trait. Traits are not maladaptations simply because they impose costs; immediate costs of a trait may be offset by benefits that trait-bearers accrue at other stages of the life history, or may result from tradeoffs that allow survival at the cost of suboptimal phenotypes.
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True maladaptations i. Used to describe a trait that appears to be detrimental to health or well-being in a particular environment. When this use is employed, net costs and benefits over the course of the lifetime, and potential tradeoffs between traits, are usually not considered.
Box 1. A brief guide to evolutionary explanations for developmental plasticity. This hypothesis was later updated to posit that metabolic disorders in later life would be milder if adult nutritional conditions closely matched those in childhood [ ], reviewed in [ 15 ].
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The accumulation of such challenges results in increased susceptibility to disease [ ]. This hypothesis is often overlooked in the developmental plasticity literature, but we include it here because, by explicitly invoking stressors during development as contributors to poor health in later life, it has the hallmarks of a development constraints model. Contrary to some interpretations, developmental constraints models are models of an adaptive process: a developmentally plastic organism will generally have higher fitness than one that is unable to alter any development processes in the face of environmental limitations [ 18 ].
Another problem for predictive models is that adaptive adjustments can only occur if a cue during development accurately predicts the adult environment [ 18 , 33 , 34 ], a condition that is rare for long-lived organisms.
The iPAR converges with developmental constraints models in invoking developmental tradeoffs as a primary driver of poor adult outcomes. However, it differs from constraints models by proposing that the developing organism will respond to these tradeoffs by maturing early to maximize reproduction under a shorter life expectancy [ 26 ]; see [ , ] for recent empirical tests of iPAR. The ACM also invokes later life plasticity, making the difficult-to-test prediction that individuals may re-calibrate their physiological responses to adversity later in life in response to environmental conditions.
The result has been a plethora of explanations for how natural selection has produced a trait, namely early environmental sensitivity, that can sometimes lead to detrimental health or fitness outcomes. These explanations fall into two broad categories, both of which assume a role for adaptive evolution Box 1. However, the two categories of models differ in whether they view plastic responses as determined by immediate constraints or as anticipatory.
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In other words, natural selection may attempt to optimize overall fitness relative to individuals that exhibit no plasticity by accepting tradeoffs that carry long-term costs relative to individuals who did not experience resource limitation. If these early cues induce an incorrect predictive response e. Despite the conceptual impact of both developmental constraints and predictive models, few studies have attempted to empirically distinguish between them, especially in humans or other long-lived mammals but see [ 27—31 ].
In addition, the last few years have brought new hypotheses that merge some aspects of constraints and predictive frameworks [ 23 , 26 , 32 ] Box 1. Only recently have researchers attempted to identify the contexts in which predictive versus constraints-induced plasticity evolves [ 18 , 33—38 ]. Expanding on these two areas of research—testing predictions from theory and identifying contexts that promote plasticity evolution—is critical for understanding the evolution and maintenance of early life effects, as well as the health costs incurred by early adversity.
Below we discuss approaches for doing so, as well as the specific gains that would follow. A critical test of predictive versus constraints models requires comparing the fitness of individuals born in high-quality environments with those born in low-quality environments, when both sets of individuals experience both high- and low-quality conditions as adults [ 15 , 27 , 29 , 30 , 39 ]. Under a predictive model, fitness will be maximized when individuals encounter matched early life and adult environments, whereas under a constraints model, individuals born in high-quality environments will consistently outperform individuals born in low-quality environments.
However, in a few striking cases, experimental manipulations of early life auditory cues that signal environmental quality have produced strong evidence for predictive plasticity e. In humans, inferences about the evolution of developmental plasticity have been largely based on cross-population or between-cohort comparisons [ 42—45 ], as well as longitudinal studies that do not satisfy the fully factorial design. This limitation exists because identifying human populations that are appropriate for fully factorial, within-individual tests is challenging, as is obtaining individual-based, longitudinal data.
Consequently, in spite of enthusiasm for the idea that mismatches between early and later life environments produce pathology in humans, evidence in support of this idea is very limited. Indeed, the only critical tests of predictive models in humans that we know of, in pre-industrial Finnish populations, find no support for predictive adaptive responses PARs.
Instead, these studies find that pre-industrial Finns born in poor environments exhibit fertility and survival detriments, rather than enhancements, when they re-encounter challenging environments in adulthood [ 20 , 23 ]. Inspite of the challenges of studying developmental plasticity in long-lived species, several longitudinal studies of human populations exposed to varying levels of early adversity are underway [ 46—48 ]. With time, these studies have the potential to identify the environments and traits that are best explained by constraints versus predictive models.
Meanwhile, long-term studies of long-lived mammals offer great potential for performing fully factorial tests, especially in cases where longitudinal data exist or are being collected. Already, three groups have leveraged data from well-studied populations of roe deer, bighorn sheep and yellow baboons to examine fitness outcomes when individuals naturally encounter environments that both match and mismatch their early life conditions [ 27 , 30 , 31 ].
All three studies found stronger support for developmental constraints than predictive plasticity, with no or limited support for predictive plasticity overall see Box 1. Other long-term studies of wild mammals [ 49 ] are poised to contribute similar tests. In addition to circumventing limitations faced by human studies, such work will be important for interpreting the evolutionary history of developmental plasticity in a comparative framework, including the degree to which aspects of early life effects in humans are unique.
Recently, several groups have used simulations and mathematical models to identify the contexts in which predictive plasticity should evolve [ 18 , 33 , 34 , 50 ]. This work has highlighted two predictions relevant to human disease, as well as the evolution of plasticity more generally.
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This situation is commonly encountered by long-lived animals, who often experience unpredictable heterogeneity in rainfall, food availability, and other environmental variables over the course of their multi-year lives [ 27—30 ]. Thus, we should expect predictive plasticity to be rare in these contexts. Second, organisms can only evolve plastic responses to environments they are repeatedly and consistently exposed to over evolutionary time, and responses to novel or atypical environments may generally be disadvantageous [ 52 , 53 ] Box 1 [ 33 , 54 ].
If true, this prediction implies that for experimental studies focusing on extreme or novel early life challenges e. Testing these two predictions— i that predictive responses are unlikely to evolve in stochastic environments and ii that highly novel early environments trigger maladaptive responses—is fundamental for assessing and refining current frameworks.
Such tests have already begun in systems amenable to experimental evolution e.
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For example, Dey et al. Though compelling, evolutionary experiments of this type are infeasible for long-lived organisms, and alternative approaches are needed. Cross-taxon analysis can be used to test whether predictive plasticity evolves when future environments are predictable: once empirical tests have been performed in a sufficiently diverse set of species, researchers can examine the relationship between life history variation, ecological variation and predictive plasticity evolution.
Amassing the required set of empirical tests is clearly a tall order, but points to an important goal for the research community. For instance, studies that examine behavioral responses to human-induced environmental change are generating considerable data on the extent and nature of maladaptive responses to highly novel environments [ 60 , 61 ]. This framework could be expanded to incorporate early life effects.
Importantly, the prediction that novel environments trigger maladaptive responses would hold under both a constraints and predictive plasticity framework: both models assume plasticity has evolved through natural selection, a process that usually requires many generations of environmental exposure. Under a predictive model, health is maximized when early and adult environments are concordant, suggesting, for instance, that manipulating adult diet or lifestyle could mitigate the effects of undernourishment in early life.
Sound theory and a robust body of empirical work are essential for understanding whether predictive plasticity is expected to be common or rare in long-lived species such as humans, and for which traits. For evolutionary biologists, understanding the distribution of predictive versus constraints-induced plasticity in nature will help reveal the tradeoffs organisms make under resource-limited conditions, as well as the selective pressures that determine variation in tradeoffs across species.
In addition, understanding the drivers of plasticity evolution is important for predicting how species will cope with environmental change [ 62 ], including through the evolution of plastic responses [ 33 , 63 , 64 ]. Identifying genes and genetic variants that contribute to plasticity is critical for understanding the evolutionary history of plastic traits, as well as sources of inter-individual variation in the capacity for plasticity. We focus on two questions related to this topic. First, what genes are involved in generating developmental plasticity within a given species? To date, work on this question has largely focused on polyphenisms, which occur when two or more discontinuous phenotypes are produced from the same genotype.
Classic examples include caste differentiation in eusocial insects and seasonal morphs in butterflies, which have been well-studied at the molecular level [ 65—69 ]. A second, related question is: to what degree does genetic variation for plasticity itself exist among individuals of a given species?
Polyphenisms the appearance of discrete phenotypes in response to environmental variation can arise in two ways: A when environmental variation is discontinuous so that only two regions of the reaction norm are ever expressed, or B when the organism exhibits a switch point or threshold value at which an alternate morph is produced. Modified from [ ]; colored backgrounds indicate the nature of environmental variation, while dots indicate the environments in which organisms are sampled.