Is there theory that connects longevity, time-scale of environmental disturbance, and adaptation?

I'm thinking here about environmental disturbance or like climate change-driven warming. It seems as if there are two macroevolutionary ways to deal with environmental change:

1) Have short generation times, and evolve fast. For instance, the mosquito Wyeomyia smithii is under selection by the warming climate in North America and it has shown an evolutionary repsonse. The response is "detectable over a time interval as short as 5 years" (Bradshaw and Holzapfel 2001).

2) Be hardy, and 'try' to wait out changes. No real-world example to cite, but imagine a long-lived tree growing in an area that has become to warm for its seeds to effectively produce seedlings.

It seems intuitively like strategy 1 is better in the case of ongoing climate warming. However, we could easily imagine a 5-year hot spell followed by a return to the normal as part of natural weather variation. Perhaps in this situation strategy 2 is better.

Essentially, having a longevity/generation time/hardiness that matches the time-scale of the disturbance would be important - if the disturbance is long (or unidirectional) compared to your lifespan, evolving fast seems best; if the disturbance is short compared to your life, it seems best to wait it out.

So the question is, is there theory that deals with this? I suspect that I just need to hit the population genetics books again or something; or perhaps there's a massive tome by Gould I should be reading?

  • Bradshaw WE, Holzapfel CM. 2001. Genetic shift in photoperiodic response correlated with global warming. PNAS 98: 14509-14511.

If the disturbance is short compared to a species' lifespan, then it's would be difficult for any adaptations to that disturbance to become fixed. Say an individual with a novel mutation is born during the disturbance that would impart some benefit under such conditions; since the species lifespan is much longer than that disturbance, by the time the individual reproduces, the disturbance will no longer be in effect and thus that allele will no longer be beneficial. In fact, this is seen in the thrifty phenotype in humans. When the mother experiences a period of duress during her pregnancy (say, the Dutch Hunger Winter of 1944), the fetus will undergo epigenetic changes that signal, in effect, that food is scarce and resources should be used sparingly. When those children grow up, they find themselves no longer under harsh conditions and this thrifty phenotype becomes a disadvantage, causing diabetes, obesity and so on.

If the disturbance is on the order of the lifespan of the species or slightly larger and especially if it is cyclic, polyphenism would be advantageous. This might also impart some resistance against long-term change, at least up to a certain point. For example, the butterfly Bycyclus anynana shows marked differences when born in the cool, dry season versus the warm, wet season: different body mass, different resting metabolic rate, different wing patterns and so on. This is controlled by hormonal switches in response primarily to temperature.

It seems that the best strategy in the face of long-term change would be gradual evolution due to progressive selection on traits that impart resistance to the disruption, at the cost of being vulnerable to future reversions in the conditions.

But of course, none of this can be "planned for" by the species. In the face of a new change in environmental conditions, be it gradual or sudden, the species can only work from its current genetic background. So whether there is a specific theory that deals with this (aside from the examples given above) is not clear. I would think that what we will see with strong-but-gradual climate change is a progressive loss of biodiversity (due to the inability to adapt in time) followed by rapid expansion to fill newly opened niches. To that end, I guess Gould's "Wonderful Life" would be appropriate to read. Any book on Evo Devo would lend insight into understanding how species might be able to rapidly adapt to a slowly changing environment, albeit in an indirect/abstract manner (i.e. "food for thought"); I like Sean B. Carroll's "Endless Forms Most Beautiful".

Not exactly matching your question, but I think that the idea (from stochastic demography) that life histories should be buffered against environmental variability in influential vital rates (Pfister, 1998, Morris & Doak, 2004) can be related to this issue, even if it is mainly (originally) dealing with stationary environmental fluctuations.

In general, fluctuations in vital rates cause the stochastic population growth rate to decrease. This can be described by Tuljapurkar's approximation (Tuljapurkar, 1982):

$$log lambda_s = log lambda_d - frac {1}{2lambda_d^2} sum_{i,j} V(a_{ij})S^2_{ij}-phi$$

where $lambda_s$ is the stochastic growth rate, $lambda_d$ is the deterministic growth rate based on average conditions, $V(a_{ij})$ is variance in vital rates (here matrix entires) and $S^2_{ij}$ is sensitivity in vital rates. $phi$ represents covariances between rates, which can be important, but can sometimes be ignored for simplicity. This equation shows that the effect of variability in vital rates on stochastic growth rate (which can be interpreted as a measure of fitness) is a product of sensitivity to change and the amount of variability.

Because of the negative consequences on stochastic population growth, selection is expected to minimize variance in population growth rate. Pfister (1998) predicted (based on an evolutionary argument and the equation above) that a species should have smaller temporal variances in the life history traits it is most sensitive to. Therefore, across species, there should be a negative correlation between the sensitivity and the temporal variance of vital rates. This also means that the evolution of life histories and the tolerance of species to variability in environmental conditions are shaped by their life-history patterns.

A consequence of this is that in a long-lived species (as an example), you should find larger variabilty in juvenile survival than in adult survival, since population growth rate is more sensitive to variability in the latter. The lower variability in adult survival can then be seen as an expression of "hardiness" to variability in environmental conditions, and this theory can therefore be used to understand likely evolutionary trajectories of different species, also under a climate change scenario. The ways to minimize the negative effects of variability in population growth in this theory is to either be hardy in important traits (change in environmental conditions doesn't translate much into variability in vital rate) or to decrease the sensitivity in vital rates that vary a lot (which amounts to modifying the life-history pattern of the species). Your question mainly deals with longevity, and this is influenced by both maturation age and adult survival. Therefore, this theory can be useful to think about which species should develop a "hardy" strategy.

However, this answer completely ignores tipping-points in environmental tolerances, nonlinear responses and the genetic variation that selection can act on.

Related articles you might want to check out are Van de Pool et al (2010), Morris et al. (2008) and Doak et al 2005.

  1. Los ambientes acuáticos y terrestres presentan diferencias notables en distintos factores ambientales y en su composición filogenética. Sin embargo, las consecuencias de estas diferencias para la evolución de las estrategias de vida de las especies siguen siendo desconocidas.
  2. En este estudio examinamos si y cómo las estrategias de vida varían entre especies terrestres y acuáticas. Utilizamos datos demográficos de 685 especies de plantas y animales terrestres y 122 acuáticos para estimar rasgos de la historia de vida de dichas especies. Luego, utilizamos regresiones de mínimos cuadrados corregidas filogenéticamente para explorar las posibles diferencias en los ‘trade-offs’ entre los rasgos de la historia de vida de las especies en ambos ambientes. Contrastamos las estrategias de vida de las especies acuáticas frente a las terrestres utilizando un análisis de componentes principales, corrigiendo por la dimensión del cuerpo y las relaciones filogenéticas.
  3. Nuestros resultados muestran que los mismos ‘trade-offs’ estructuran las estrategias de vida terrestres y acuáticas, lo que resulta en dos ejes dominantes de variación que describen el ritmo de vida de las especies y las estrategias reproductivas. Las plantas terrestres muestran una gran diversidad de estrategias, incluyendo las especies más longevas en este estudio. Los animales acuáticos exhiben mayor frecuencia reproductiva que los animales terrestres. Al corregir el tamaño del cuerpo, los organismos terrestres móviles y sésiles muestran ritmos de vida más lentos que los acuáticos.
  4. A pesar de que las especies acuáticas y terrestres se rigen por los mismos ‘trade-offs’ han desarrollado diferentes estrategias en ambos ambientes, probablemente debido a distintas presiones selectivas. Tales diferencias en las estrategias de vida tienen consecuencias importantes para la conservación y la gestión de las especies acuáticas y terrestres.

The rich diversity of life-history strategies world-wide stems from three fundamental demographic building blocks: survival, development and reproduction (Stearns, 1992 ). Importantly, these life histories determine the viability of populations (Paniw, Ozgul, & Salguero-Gómez, 2018 ), rates of speciation (Venditti, Meade, & Pagel, 2010 ), and guide the effectiveness of conservation plans (Carr et al., 2003 Veličković et al., 2016 ). Despite the advanced development of life-history theory (Lande, Engen, & Sæther, 2017 ), few studies have contrasted the validity of life-history principles across terrestrial and aquatic organisms (Webb, 2012 ).

Life-history theory is rooted upon the concept of trade-offs as a unifying principle across the tree of life (Stearns, 1992 ). Given the limitations in available energy and physiological constraints, compromises among survival, development and reproduction are inescapable for any organism, whether aquatic or terrestrial (Stearns, 1992 ). Such constraints should result in a finite set of viable life-history strategies. The evolution of a life-history strategy in a given environment is then determined by two counteracting processes: environmental filtering and evolutionary history (Stearns, 1992 ). Environmental filtering stems from extrinsic factors favouring certain strategies over others. For example, aquatic environments enable the evolution of sessile animals due to the suspended nutrients and organic material in the water column. Such a strategy is not possible for terrestrial animals (Webb, 2012 ). On the other hand, evolutionary history represents the influence of phylogenetic relationships in determining the potential adaptations of a given species (Blomberg & Garland, 2002 Freckleton, 2000 ). Life-history strategies are then expected to be more similar, irrespective of environment, among closely related lineages.

According to life-history theory, the same trade-offs should be experienced by aquatic and terrestrial organisms. Comparative demographic studies have successfully identified and organized trade-offs into a few major axes of trait co-variation (Gaillard et al., 1989 Salguero-Gómez, Jones, Jongejans, et al., 2016 ). A seminal concept in organizing such trait co-variation is the ‘fast–slow continuum’ (Stearns, 1992 ). In it, species are placed along a continuous axis bounded by two extremes: at the fast-living extreme, species develop quickly, are highly reproductive but have short life spans while at the slow extreme, species have high survival rates, develop slowly and live long. However, an explicit comparison of the fast–slow continuum between aquatic and terrestrial species remains, to our knowledge, untested.

If trade-offs are universal, the strong environmental and phylogenetic dissimilarities between aquatic and terrestrial environments should result in different life-history strategies. For example, aquatic and terrestrial habitats impose differing selective pressures on body size. Indeed, aquatic endotherms have larger body sizes than terrestrial ones, due to the strict energetic demands of the aquatic environments (Gearty, McClain, & Payne, 2018 ). Such constrains must have consequences for aquatic life-history strategies, given that a large body size covaries positively with a slow pace of life (Gaillard et al., 1989 Healy et al., 2014 ). On the other hand, aquatic environments allow early life stages to feed and develop during the dispersal phase, promoting external reproduction (Burgess, Baskett, Grosberg, Morgan, & Strathmann, 2016 Bush, Hunt, & Bambach, 2016 Vermeij & Grosberg, 2017 ), while terrestrial species had to evolve reproductive systems independent of environmental water, such as internal fecundity or seeds (Bush et al., 2016 Grosberg, Vermeij, & Wainwright, 2012 Steele, Brink, & Scott, 2019 ). Therefore, aquatic species had to evolve strategies to counteract the uncertainty of recruitment success derived from external reproduction (Charnov & Schaffer, 1973 Tuljapurkar, Gaillard, & Coulson, 2009 ).

The colonization of land likely resulted in the evolution of life-history strategies to deal with higher temporal environmental variation (Dawson & Hamner, 2008 Ruokolainen, Lindén, Kaitala, & Fowler, 2009 ). On land, environmental variation is more stochastic and less temporally auto-correlated than in aquatic environments (Dawson & Hamner, 2008 ). Classical life-history theory predicts the evolution of longevity in constant environments (Lande et al., 2017 ). However, longevity can also be a strategy to deal with environmental variation (McDonald et al., 2017 Morris et al., 2008 ). For example, by spreading their reproductive output across several years, long-lived species are able to exploit favourable conditions in a stochastic environment, compensating for unfavourable years (McDonald et al., 2017 ). Instead, fast life histories are expected to show increasing fluctuations in population sizes with increasing environmental variation. For that reason, some authors have argued that the colonization of land resulted in the evolution of longer life spans to smooth out the large environmental fluctuations in terrestrial environments (sensu Steele et al., 2019 ).

Here, we test the hypotheses that (a) life-history trade-offs are universal across aquatic and terrestrial systems, and that (b) terrestrial species have evolved distinct life-history strategies compared to aquatic ones. We use high-resolution demographic data from 122 aquatic and 685 terrestrial species across the globe from the COMPADRE and COMADRE databases (Salguero-Gómez, Jones, Archer, et al., 2016 Salguero-Gómez et al., 2015 ). We estimate key life-history traits that reflect various moments of population turnover, as well as investments in survival, development and reproduction of each species. To test these hypotheses, we first determine whether correlations between life-history traits differ across environments as a way to examine whether trade-offs diverge between terrestrial versus aquatic species. Second, we explore the main axes of life-history variability shaping aquatic and terrestrial species. The presence of different life-history axes of variation and/or a distinct positioning of aquatic species compared to terrestrial ones within those axes would suggest dissimilar selection pressures occurring in terrestrial and aquatic environments. Given the scarcity of comparative studies and the lack of demographic information for many aquatic species, elucidating these questions is a key step towards understanding the evolution of life histories across environments.


Infectious diseases are an important threat to biodiversity (Daszak et al., 2000 ). This is particularly true for emerging infectious diseases, for which the lack of host-parasite coevolutionary history can lead to extreme levels of parasite virulence and/or host susceptibility, ultimately inducing strong population-level impacts (e.g., Daszak et al., 2000 , 2001 Fisher et al., 2012 Scheele et al., 2019a ). Nonetheless, empirical evidence further reveals that host population collapse is not the only outcome from a novel host-parasite interaction (Tompkins et al., 2011 ). Some populations of susceptible hosts can persist despite initial marked population declines (e.g., fish, Rogowski et al., 2020 amphibians, Briggs et al., 2010 marsupials, Wells et al., 2019 ). Understanding the factors that determine these alternative, sometimes contrasting, population-level impacts of infectious disease has interested disease ecologists for decades and numerous factors about the parasite, the host and the environment have been identified as important in the dynamics of host-parasite systems (Fig. 1 Anderson & May, 1979 Tompkins et al., 2011 ).

We argue that a deeper integration of life-history theory (hereafter LHT) into disease ecology is both timely and necessary to improve our capacity to understand, predict, and mitigate the impact of endemic and emerging infectious diseases in wild populations. A related approach that has provided a fruitful avenue of research is the study of how epidemiological parameters, such as parasite transmission rates (De Leo & Dobson, 1996 ), epidemiological thresholds (Bolzoni et al., 2018 ), and host competence (Downs et al., 2019 ), scale allometrically with host body size. As body size is the main factor shaping interspecific variation in life-history traits (Gaillard et al., 2016 Healy et al., 2019 ), the allometric scaling of epidemiological parameters with host body size is expected to be, at least partially, associated with host life-history characteristics. Yet, body size is not always an accurate proxy of host life-history traits, especially when high-level taxonomic ranks (e.g. class level or higher) are considered. For example, within mammals, humans and bats show a particularly long lifespan and low fecundity for their relatively small body sizes (Gaillard et al., 2016 Healy et al., 2019 ). Indeed, after controlling for allometric constraints, considerable interspecific variation in life-history traits remains and other factors, such as life-history trade-offs, phylogeny, and mode of life, are known to play important roles in shaping the diversity of host life histories (Gaillard et al., 2016 Healy et al., 2019 ). Here, we argue that the position of a host species along the classical slow-fast life-history continuum (see below) can determine their response to parasitic infection (Fig. 1). It is worth noting that other host traits, such as population density and the level of sociality (Han et al., 2015 , 2020 ), as well as parasite life-history traits (Barrett et al., 2008 Silk & Hodgson, 2021 ), also play critical roles in host-parasite dynamics, but those aspects are beyond the scope of this review.

We focus this review on LHT predictions relative to host responses to infectious disease at different levels of organisation, from individual-level susceptibility to host community assembly (Fig. 1). Although these theoretical predictions are broad in scope, with empirical validations in plant and animal species (e.g. plants, Pagán et al., 2008 invertebrates, Agnew et al., 2000 vertebrates, Johnson et al., 2012 ), we emphasise examples in wild vertebrate hosts, a group largely underrepresented in previous syntheses on the intersection of life-history and host responses to parasitism (e.g. Michalakis & Hochberg, 1994 Agnew et al., 2000 ). The review is structured in eight sections. In the first section, we introduce the theory and empirical evidence supporting the existence of a slow-fast continuum of life-history variation in vertebrates. In the second section, which is related to the field of ecoimmunology (see Brock et al., 2014 ), we briefly discuss how the position of hosts along the slow-fast continuum can help predict the type and strength of host immune defences (for more detailed coverage refer to previous reviews, e.g., Lee ( 2006 ), Martin et al. ( 2006 ), Tieleman ( 2018 ), and Albery & Becker ( 2020 )). In the third section, we discuss how life-history constrains the speed of recovery of host populations after short-term disturbances such as disease outbreaks. In the fourth section, we focus on active demographic compensation, a process particularly relevant for the persistence of host populations impacted by emerging infectious diseases. We define the types of active demographic compensation in the context of infectious diseases and discuss how these responses could be modulated by host life histories, introducing a simple theoretical model to illustrate how life-history strategies can be predictive of the magnitude of the negative effects of disease-induced mortality on populations exhibiting density-dependent compensation. In the fifth section, we discuss how host life-history strategies could modulate the rapid evolution of mechanisms of resistance (i.e. the ability of a host to limit or reduce parasite burden) or tolerance (i.e. the ability of a host to limit the negative effects of a given parasite burden). In the sixth section, we briefly review the integration of host life-history, community assembly, and infectious disease. In the seventh section, we discuss how the insights of the previous sections can inform the monitoring and control of infectious diseases in wildlife. In the eighth and concluding section, we provide pointers for future directions for the incorporation of LHT in disease ecology.


Radiocarbon dates and stratigraphy

Radiocarbon dates were obtained for each sediment core (Table 3), providing an age-depth profile and allowing for comparison of disturbance events across these sites (Fig. 3). Basal dates for Deforested Peatland and Peat Swamp Fragment show age inversions and therefore interpretation of pollen data recorded from these cores beyond 200 cm depth, equating to c. 1200 Cal. years BP and 3000 Cal. years BP, respectively, is tentative. Deforested Peatland covers the shortest time period, with the peat swamp starting to develop < 1500 Cal. years BP (Zone D-2, Fig. 2a) on a silty-sandy substrate, suggestive of a riverine environment in proximity to the coast. In Peat Swamp Fragment, the peat swamp was present from c. 3500 Cal. years BP, developing on a clay substrate, probably also associated with a riverine environment. Converted Peatland shows a different pattern of development, with organic-rich deposits originating on what was predominantly a clay substrate c. 5000 Cal. years BP. After this point, a clay-peat soil started to accumulate, interspersed with laminations, which, coupled with the presence of coastal vegetation, indicates the existence of a tidally influenced estuarine mangrove habitat. Peat swamp forest did not start to develop in this site until c. 2800 Cal. years BP (Zone C-2, Fig. 2c). Accumulation rates broadly reflect this transition in depositional environment and associated vegetation through time.

Description of pollen diagrams

The majority of the 179 pollen types identified was from the PSF and PSF+ ecological groups, demonstrating that peat swamp forest has dominated in all three sites, with no major shifts in vegetation communities, since peat accumulation began (Fig. 2). The other two ecological groups that appear most frequently in pollen counts are those comprising degraded peat and coastal vegetation (Fig. 2). Several pollen taxa were common across sites, for example, Dillenia and Poikilospermum, common disturbance indicators associated with degraded peat, and Oncosperma, found in saline–freshwater transition zones. Approximately, 10 pollen types were not identified levels of damaged or obscured grains and spores were greater and varied across samples and sites. The apparently random occurrence of unknown and indeterminate grains across the three sites through time does not have implications for the interpretation of this study's results. There was little concurrence of pollen concentration peaks across sites, except where concentrations broadly increase at the point of peat swamp forest development.

Although there does appear to be a shared pool of species that feature to some extent across all cores, there are unique peat swamp forest pollen assemblages within each site, with varying taxa and abundances through time and space, as exemplified by the different location of vegetation zones in most cases (Fig. 3).

Despite the reported differences, there are three notable similarities observed across the three cores. One is the dominance of PSF vegetation through time, post-initiation of peat swamp development. The next is the strong presence of PSF+ taxa within the peat swamp forest, and frequent fluctuations between pioneer and mature taxa coinciding broadly in each site with changes in fossil charcoal levels. The final similarity is the sharp increase in open vegetation taxa across all sites within the last 1000 years, which, although at varying times, coincides with a change in vegetation zone in each (Fig. 2). From c. 300 Cal. years BP, there is a particularly sharp increase, corresponding with the largest counts of degraded peat taxa in Converted Peatland and Peat Swamp Fragment sites.

Change in disturbance indicators through time

Fire, human impact (inferred from large increases in open vegetation counts) and climatic change are the three disturbance types examined in this study (Fig. 3).

Although to varying degrees, there is evidence for the presence of fire in all sites through time. In addition, there is a general coherence between micro- and macrocharcoal levels, signifying a degree of synchrony between local and regional fire events. One obvious exception, however, is the large quantity of macrocharcoal coinciding with low levels of microcharcoal from c. 7000 to 4000 Cal. years BP in the Converted Peatland site (Fig. 3), indicating intense local burning, albeit in a different ecological context (Fig. 2c). These high macrocharcoal levels are only exceeded here in the last 100 years. The Peat Swamp Forest site experienced greatest levels of local and regional fire between c. 2000 and 3000 Cal. years BP, after which microcharcoal declined significantly until the present day. Charcoal counts from the Converted Peatland site share this trend of heightened burning during this approximately 1000-year period, coincident with an arid episode in the Tropics (Selvaraj et al. 2011 , 2012 ). The record for the Deforested Peatland site does not cover this period in time. Here, the highest levels of burning occur within the last c. 200 years. This pattern of increasing fire in the recent past is also seen in Converted Peatland and Peat Swamp Fragment sites, starting from c. 300 and 500 Cal. years BP, respectively.

The open vegetation count, after maintaining near-zero levels through the majority of the past in all sites, rises significantly after c. 500 Cal. years BP. This trend broadly follows that of charcoal in the latter half of the last millennium, but with a particularly dramatic increase within the last two centuries. Only in the Peat Swamp Fragment site was there a peak c. 2200 Cal. years BP (Fig. 3).

The schematic summarizing variation in ENSO over the last 7000 years (Fig. 3) demonstrates that there were notable changes in regional climate throughout the Holocene. However, comparison of the timings of climatic variability with each vegetation profile suggests that the ENSO phenomenon had little impact on peat swamp forest dynamics across these sites.

Vegetation response to disturbance

Changes in pollen counts coincident with elevated charcoal levels do not show a clearly coherent signal across sites, though there are some notable patterns. During the period of elevated burning between c. 2000 and 3000 Cal. years BP in Peat Swamp Fragment and Converted Peatland (zones P-1 and C-2 respectively, Figs 2 and 3), aligning with the Arid Tropics Events, PSF+ taxa increase. Conversely, in the approximately 1000-year period of greatly reduced fossil charcoal that follows, the count of mature PSF taxa relative to pioneers increases. During the last several hundred years of elevated burning across sites, the notable vegetation change is in the degraded peat (DP) taxa, which contributes a greater proportion to the pollen sum than observed throughout the recorded past in Peat Swamp Fragment and Converted Peatland sites, coincident with a reduced contribution by PSF and PSF+ taxa (zones P-4 and P-5, and C-4, respectively, Figs 2 and 3).

The pattern of elevated DP counts relative to PSF vegetation types in two of the sites also coincides with the notable increases in open vegetation counts (an indicator for human disturbance) within the last several hundred years. A slight increase in DP taxa, and increased fluctuations across ecological groups, is visible in the Converted Peatland site during the last c. 1000 years in parallel with low, yet greater levels of open vegetation in the landscape. A similar response in DP% does not co-occur with the large spike in open vegetation observed in the Peat Swamp Fragment site c. 2200 Cal. years BP, though a notable increase in the relative proportion of PSF+ taxa appears to follow it. Deforested Peatland does not demonstrate the same trend in vegetation change with charcoal or open vegetation counts in the recent past.

In terms of climatic changes, there appear to be no coherent or notable vegetation responses across sites. However, there is a lack of information for each core on the baseline vegetation pre-ENSO intensification, which could be used to assess the impact of this climatic phenomenon on the peat swamp forest community.


De-extinction is a stimulating idea, which has raised, and will continue to raise debates among scientists. Focusing on ethical aspects, Sandler ( 2014 ) recently concluded that de-extinction is not intrinsically problematic, although it is in many respects a luxury. From an evolutionary viewpoint, we agree with Sandler's view and believe that critics from ecologists and evolutionary biologists do not need to focus on de-extinction per se but rather on its potential excesses, such as irrelevant choice of target species, potential of invasive impact on ecosystems, or unreasonable time scales. In particular, one of the most important scientific arguments against de-extinction could be an evolutionary one: extinct species do not evolve, but the rest of the world does. While some recent translocation practices aim at finding genotypes that can match future environments (Aitken & Whitlock 2013 ), de-extinction involves the risk that resurrected species are not adapted to the present, Anthropocene environment.

As the time elapsed since the extinction of the target species becomes longer, (i) the eco-evolutionary experience of the target species to its local environment will become lower and ecological functions provided by the target species will have more chance to have been fulfilled by evolutionary changes having occurred in the community (ii) the technical difficulty will increase due to DNA degradation, in turn increasing the necessity of using phylogenetically closely related extant species for genome reconstruction (Shapiro 2017 ) (iii) our knowledge of the past ecological context and evolutionary history of the target species becomes fragmentary and our responsibility in the initial extinction becomes uncertain.

Both feasibility assessment and selection of species for de-extinction programmes should include these considerations. Candidate species should have gone extinct recently, have high evolutionary distinctiveness and their original environment should be well described. Although species’ traits are likely to influence de-extinction success, determining what life history or ecological traits can mitigate demographic problems associated with small population size, lack of genetic variation and maladaptation is not trivial. As in the case of invasive species, it is likely that barriers and filtering at various stages of de-extinction programmes will shape complex relationships between species traits and success (Capellini et al. 2015 ).

Feasibility assessments and comparisons should rely on thorough interdisciplinary modelling and comparative analysis. Within the last decades, an array of empirical and theoretical modelling techniques have been developed to project past and future environmental, ecological and evolutionary dynamics, such as niche modelling, (no-)analog ecosystem projection, predictive evolutionary modelling and population viability analysis. Embracing these techniques is essential to select best candidate species, optimize release methods and assess the chance of success and potential evolutionary benefits of de-extinction programmes.


A traditional perspective in evolutionary biology is of genes ‘leading’, and phenotypes ‘following’ in the process of adaptive evolution. Evolution is by definition a change in allele frequencies and therefore sufficient heritable genetic variation must exist for evolution by natural selection to occur. Accordingly, the conventional perspective on adaptive evolution focuses almost exclusively on the role of allelic substitution or quantitative genetic variation ( Pigliucci & Murren 2003 Schlichting 2004 ). As a consequence, selection that acts on non-heritable phenotypic variation in a population is often regarded as selection that does not produce an evolutionary response (e.g. Endler 1986 , pp. 12–15), and has been historically dismissed as unimportant in adaptive evolution (e.g. Wright 1931 Simpson 1953 Williams 1966 ). Within this context, environmentally induced variation has been thought to constrain or slow the rate of adaptive evolution by shielding the genotype from the effects of selection (e.g. Grant 1977 Falconer 1981 Levin 1988 ).

An alternative perspective on adaptive evolution argues that phenotypic variation, even when environmentally induced and not under strict genetic control, plays an important role in creating the conditions that result in an adaptive genetic response (i.e. ‘genes as followers’ West-Eberhard 2003 ). This suggests that environmentally induced non-heritable variation such as phenotypic plasticity or learning is initially established in a population, and later becomes genetically ‘assimilated’ such that the environmental stimulus previously required to produce the trait is no longer required ( Baldwin 1896 Waddington 1942 , 1952, 1953 , 1956, 1959 Schmalhausen 1949 ). From this perspective, plasticity may facilitate or even speed up the process of adaptive evolution (reviewed in Robinson & Dukas 1999 Pigliucci & Murren 2003 Price, Qvarnstrom & Irwin 2003 West-Eberhard 2003 Schlichting 2004 Badyaev 2005 ).

In this paper we examine the role of phenotypic plasticity in adaptive evolution by contrasting different types of plasticity (adaptive vs non-adaptive) and how each may facilitate or constrain the process of adaptive evolution in new environments. In recent years the topic of phenotypic plasticity and evolution has been the subject of extensive review in both books ( Schlichting & Pigliucci 1998 Pigliucci 2001 West-Eberhard 2003 DeWitt & Scheiner 2004 ) and review articles (e.g. Thompson 1991 Sultan 1995 DeWitt, Sih & Wilson 1998 Robinson & Dukas 1999 Pigliucci & Murren 2003 Price et al. 2003 Schlichting 2004 Badyaev 2005 de Jong 2005 Grether 2005 van Kleunen & Fischer 2005 ). We primarily focus our efforts on the role of plasticity that is likely to play in the initial stages of a population becoming established in a new environment and the consequence of this plasticity for adaptive evolution. We take this approach because: (i) most cases of contemporary adaptation have occurred within the ecological framework of colonization of new environments (e.g. Reznick & Ghalambor 2001 ), (ii) a body of theoretical and empirical work has focused on this perspective (e.g. Price et al. 2003 Parsons & Robinson 2006 ), and (iii) the lessons learned have direct application to understand the processes that occur in biological invasions (e.g. Sexton, McKay & Sala 2002 Lambrinos 2004 Dybdahl & Kane 2005 Richards et al. 2006 Strauss, Lau & Carroll 2006 ). We begin with a brief overview of plasticity and definitions of important terms. Next, we review the various conceptual arguments as to why plasticity exists and is maintained in populations. Given that plasticity exists, we next explore the various ways by which adaptive and non-adaptive plasticity can facilitate or constrain adaptive genetic differentiation in new environments. We briefly review some relevant theoretical and empirical studies that provide insight into the role of plasticity in evolution and comment on the kinds of data future studies should emphasize. Our paper does not review the role of other types of environmentally induced variation in facilitating adaptive evolution, such as maternal effects (Räsänen & Kruuk 2007) and epigenetic inheritance (e.g. True, Berllin & Lindquist 2004 ).

Background: what is plasticity and why has it evolved?

Phenotypic plasticity is the phenomenon of a genotype producing different phenotypes in response to different environmental conditions and is a ubiquitous aspect of organisms ( Travis 1994 West-Eberhard 2003 ). Phenotypic plasticity is a property of an individual or genotype that may be adaptive, maladaptive or neutral with regard to an individual's fitness. The particular way an individual's (or genotype's) phenotype varies across environments can be described as a reaction norm ( Woltereck 1909 ). Reaction norms for continuously distributed traits, such as many physiological, morphological and life-history traits are typically visualized as a line or curve on a plot of the environmental value vs the phenotypic value (Fig. 1). Alternatively, the reaction norm may be visualized as discrete character states (e.g. Falconer 1990 ), such as in the case of developmental polymorphsisms or polyphenisms. Variation among genotypes in how they respond across environments is referred to as a genotype × environment (G × E) interaction (e.g. Fry 1992 ) and this can be visualized by plotting the reaction norms of multiple genotypes. G × E interactions are thus the property of populations or groups of genotypes ( Falconer 1990 Thompson 1991 Via et al. 1995 ). Evidence suggests that plasticity has evolved and can be visualized as a change in the slope of the reaction norm between the ancestral and derived population or species (e.g. Doughty 1995 Gotthard & Nylin 1995 ), and has been empirically shown to occur in nature between ecotypes and species subject to different selection pressures (e.g. Cook & Johnson 1968 Carroll & Corneli 1999 Pigliucci, Cammell & Schmitt 1999 Morey & Reznick 2000 Haugen & Vøllestad 2000 Ghalambor & Martin 2002 ). The evolution of plasticity may therefore occur independently of, or jointly with, changes in the mean trait value. While this point has been the subject of past debate, from an evolutionary genetics perspective it is most convenient to think of the mean trait value and its reaction norm as separate traits (e.g. Via & Lande 1985 Scheiner 1993 , Via et al. 1995 de Jong 2005 ). For example, selection can change the y-intercept of the reaction norm without changing the slope and vice versa ( de Jong 2005 ).

Example scenarios of adaptive and non-adaptive reaction norms in response to colonization of new environments. (see also van Tienderen 1991, 1997 ) Phenotypic values in the native site are indicated with filled circles. Arrows represent the phenotype that genotype would express if introduced into the new environment. Solid lines depict the reaction norm for this two-state environment. An all-purpose genotype that produces the perfect phenotype in both environments is shown as a dashed line. Panel a – Here two ecotypes (solid lines) have the same degree of plasticity (i.e. similar slope of the reaction norm), but have divergent phenotypes when each is measured in their native habitat. When measured in a common garden (either Low or High), they are still different, but the plastic response reduces the difference between the ecotypes. If the phenotype expressed by each ecotype in its native habitat is optimal, then the plasticity would play a beneficial role in colonizing the new habitat because the plastic response is in the same direction as what is favoured by directional selection. Because the all purpose genotype (dashed line) is capable of producing an optimal phenotype regardless of environment, stabilizing selection should constrain genetic differentiation. Panel b – Here the two ecotypes also have the same degree of plasticity, and if each is measured in their native habitat, they have the same phenotype. However, if measured in a common garden they are clearly diverged. Assuming the native phenotype is optimal, the observed plasticity would likely hinder colonization and subsequent genetic differentiation of the other environment because each ecotype is unable to produce the favoured phenotype. In contrast, the all purpose genotype is canalized and able to produce the same phenotype regardless of the environment (a situation where a lack of plasticity would favour colonization).

Why be plastic? It has long been recognized that adaptive plasticity may be advantageous when it allows a genotype to have a broader tolerance to environmental conditions and hence higher fitness across multiple environments (e.g. Bradshaw 1965 Baker 1974 Sultan 1987 , 1995 Schlichting & Pigliucci 1998 Pigliucci 2001 ). Theoretical models for the evolution of adaptive phenotypic plasticity predict that given genetic variation, selection will favour adaptive plasticity when: (i) populations are exposed to variable environments, (ii) environments produce reliable cues, (iii) selection favours different phenotypes in each environment, and (iv) no single phenotype exhibits superior fitness across all environments (e.g. Bradshaw 1965 Levins 1968 Via & Lande 1985 Lively 1986 Gomulkiewicz & Kirkpatrick 1992 Moran 1992 ). However, the generality of these predictions is sensitive to how fitness costs for the maintenance and/or production of plasticity are expressed (e.g. van Tienderen 1991 , 1997 Moran 1992 DeWitt et al. 1998 Reylea 2002 Ernande & Dieckman 2004 ) and the degree of gene flow between populations distributed among different environments (e.g. Scheiner 1993 de Jong & Behera 2002 Sultan & Spencer 2002 ). Theoretical and empirical studies agree that selection on plasticity can result in phenotypic adaptation to different environmental conditions, yet the question as to whether plasticity acts to facilitate adaptive evolution remains a more contentious issue. Part of the reason for a lack of consensus is that while much attention has been given to case studies of adaptive plasticity, many if not most cases of environmentally induced variation appear to be non-adaptive (e.g. de Jong 2005 van Kleunen & Fisher 2005 ), making it less obvious how such non-heritable variation should facilitate adaptive differentiation of populations. Additionally, few if any empirical examples have convincingly demonstrated that plasticity, whether it was adaptive or not, has played an integral initial role in adaptive differentiation of natural populations ( de Jong 2005 ). Such concerns are particularly relevant when there is a need for a high degree of developmental homeostasis, and genotypes need to be sufficiently buffered against environmental variability in order to produce specific phenotypes (i.e. the degree of canalization, see also Fig. 1b). How selection should act on the developmental tension between the need to be buffered from the environment vs the ability to track and adaptively respond to environmental differences was discussed early on by Waddington (1942 ) and Schmalhausen (1949 ), but progress in understanding the evolutionary outcomes of these processes has been plagued by confusion and a lack of empirical research ( Pigliucci 2001 ).

One reason for a lack of consensus on the role of plasticity in evolution is that plasticity encompasses a variety of different responses. Here, we outline and review the evolutionary implications of different kinds of adaptive and non-adaptive plasticity in new environments. We argue that different kinds of plasticity influence the likelihood and rate of adaptation, depending on how the mean and variance of plastic responses change in the new environment.

The role of plasticity in new environments

The question of whether plasticity is adaptive is dependent upon the environment in which it is expressed. For the purposes of this article, we are concerned with two environments: the current environment and a new one that the population must adapt to. A new environment can be defined by a change in the current environment or by the invasion of a new habitat. By definition any plasticity that allows individuals to have higher fitness in the new environment than it would were it not plastic will be beneficial. However, this does not mean that the reaction norm for a given trait was necessarily shaped by natural selection (i.e. be an adaptation in itself Gotthard & Nylin 1995 ). Thus, it is necessary to specify the impact different kinds of plasticity might have in the face of directional selection. Below, we explore these ideas by considering general scenarios where a newly established population experiences selection for a new adaptive peak (following the framework of Price et al. 2003 ).

Adaptive reaction norms: perfect vs incomplete responses

Extinction is a likely fate for a population that experiences an abrupt or strong episode of directional selection when moving into a new environment ( Haldane 1957 Gomulkiewicz & Holt 1995 Lande 1998 ). Adaptive phenotypic plasticity has long been suspected of playing a critical role in the ability of a species to first tolerate, then adapt to such episodes (e.g. Baldwin 1896 Baker 1974 Robinson & Dukas 1999 Pigliucci 2001 Schlichting 2004 ). Here we define adaptive plasticity simply as a reaction norm that results in the production of a phenotype that is in the same direction as the optimal value favoured by selection in the new environment (Fig. 1a, see also Conover & Schultz 1995 van Tienderen 1997 Trussell & Etter 2001 ). Adaptive plasticity thus satisfies the crucial first step in adaptation to new environments reducing the cost of directional selection (e.g. Haldane 1957 ) and allowing enough time for a population to become established where standing genetic variation in combination with mutation and/or recombination among individuals can provide a range of heritable phenotypes to respond to local selection pressures (reviewed in Pigliucci 2001 ). Such plasticity therefore not only reduces the probability of extinction in new environments, but also allows populations to more easily move from one adaptive peak to another ( Robinson & Dukas 1999 Pigliucci & Murrern 2003 Price et al. 2003 West-Eberhard 2003 Schlichting 2004 Amarillo-Suarez & Fox 2006 ).

A second step of adaptation to new environments via adaptive plasticity can be the conversion of non-heritable environmentally induced variation to heritable variation, a scenario that remains controversial despite theoretical and empirical arguments dating back over a century (e.g. Baldwin 1896 Waddington 1942 , 1952, 1953 , 1956, 1959 Schmalhausen 1949 ). The process by which non-heritable environmentally induced variation leads to adaptive heritable variation is often referred to as the Baldwin Effect or more commonly as genetic assimilation (e.g. Waddington 1942 , 1952, 1953 Simpson 1953 Robinson & Dukas 1999 Pigliucci & Murren 2003 Price et al. 2003 West-Eberhard 2003 Schlichting 2004). Specifically, genetic assimilation is when traits that were originally environmentally induced become (by the process of directional selection) genetically determined and canalized (a loss of plasticity or a flat reaction norm). West-Eberhard (2003 ) advocates a less restrictive term genetic accommodation, which does not necessarily lead to a loss in plasticity. This process can be illustrated with a hypothetical example following West-Eberhard's framework with her general terminology in italics and parentheses as follows: (i) Assume a population of a brightly coloured fish that typically occurs in low predation lakes. (ii) Within the population there is genetic variation for predator-induced phenotypic plasticity in cryptic colouration (an environmentally induced phenotypic variant as opposed to one determined by a mutation – the origin of the trait). (iii) A predator colonizes the lake and many individuals exhibit adaptive plasticity for cryptic colouration and other behaviours that collectively allow for a higher probability of survival compared to individuals that lack plasticity (phenotypic accommodation by individual phenotypes). (iv) Within this selective environment, only those individuals capable of producing the plastic response survive and reproduce (the recurrence of the environmental stimulus leads to a subpopulation of individuals that always express the induced phenotype and facilitates its spread in the population). (v) Directional selection favouring the most cryptic individuals in the population leads to allelic substitutions in the regulatory pathway that controls colour patterns and the loss of individuals capable of expressing bright colour (genetic accommodation). (vi) The establishment of a population that is genetically differentiated from its ancestral state and either constitutively produces cryptic colouration (i.e. canalization) or has become more plastic in response to the presence of the predator (i.e. the slope of the reaction norm has becomes steeper). Waddington's (1953 ) experiments on the genetic assimilation of the loss of cross-veins in Drosophila wings in response to heat shock followed an analogous scenario in the laboratory. West-Eberhard (2003 ) has championed this view as a potentially common or perhaps the predominant way by which adaptive evolution occurs (see also Pigliucci & Murren 2003 ). A key to this argument, and to Waddington's results, is that while the plasticity is environmentally induced, there must still be underlying genetic variation in inducibility and expression (see also de Jong 2005 ). It is this underlying variation that provides the basis for adaptation. Indeed, while a variety of models confirm that adaptive plasticity may facilitate adaptive evolution (e.g. Hinton & Nowlan 1987 Behera & Nanjundiah 2004 Ancel 1999 ), others have shown how plasticity slows the rate of evolution (e.g. Behera & Nanjundiah 1995 Ancel 2000 , Huey, Hertz & Sinervo 2003 ). Below we contrast when adaptive plasticity is likely to slow or speed up the rate of adaptation.

The rate of adaptation to new environments is likely to differ depending on how close the plastic phenotype is to the optimum favoured in the new environment ( Price et al. 2003 ). When adaptive plasticity produces a near perfect match with the optimal phenotype in the new environment (the all purpose genotype in Fig. 1a), the population should experience stabilizing selection with no subsequent genetic differentiation between populations unless there is a substantial fitness cost to plasticity (e.g. Price et al. 2003 ). In other words, adaptive plasticity should slow or constrain adaptive genetic differentiation between populations. The introduced C4 grass Pennisetum setaceum (fountaingrass) in Hawaii may be a case where adaptive plasticity is so good it has prevented adaptive evolution. Fountaingrass is native to North Africa and the Middle East and was introduced as an ornamental into Hawaii over 100 years ago, where it spread rapidly in arid zones ( Wagner, Herbst & Sohmer 1990 ). On the island of Hawaii, fountaingrass colonizes disturbed sites and can become dominant within communities ranging from sea level to almost 3000-m altitude ( Wagner et al. 1990 ). Williams, Mack & Black (1995 ) investigated whether populations from coastal dry grasslands, mid-altitude shrubland and subalpine dry forest sites were genetically differentiated from each other. These sites experience large differences in the seasonal pattern of precipitation and temperature declines markedly with increasing altitude such that coastal sites never experience frost, whereas winter night time frost is common at the subalpine sites ( Williams et al. 1995 ). Individuals from these sites exhibit dramatic differences in morphology, physiology and reproductive strategies that result in locally adaptive, and phenotypically distinct populations ( Williams et al. 1995 ). However, despite very different phenotypes and selective environments, reciprocal transplant experiments reveal little genetic differentiation for most physiological and morphological characters between these populations ( Williams et al. 1995 ). One interpretation of these results is that adaptive plasticity results in such a good match with the environment when there is no opportunity for directional selection to act and hence no evolution. Alternatively, because fountaingrass may have been founded by a small population, there may not be sufficient genetic variation in these populations for selection to act on ( Williams et al. 1995 ). However, a non-significant trend for resident populations at each site to have higher fitness, and for some local adaptation of traits between populations, suggests the potential for genetic differentiation exists ( Williams et al. 1995 ). Other examples of adaptive plasticity producing near perfect responses to different environments and constraining genetic differentiation have been documented in a variety of systems (e.g. Dudley & Schmitt 1996 Mittelbach, Osenberg & Wainwright 1999 Lorenzon, Clobert & Massot 2001 ).

Adaptive plasticity may also result in an incomplete response relative to the new optimum, meaning that the change in the mean trait value is in the same direction favoured by selection in the new environment, but below the new adaptive peak (Fig. 1a). In such cases, the new population will be subjected to directional selection on extreme phenotypes and the potential for adaptation should be facilitated (reviewed in Price et al. 2003 ). Because environments are typically heterogeneous in space and time, incomplete adaptive plasticity is likely to be the most common form of adaptive plasticity. The evolution of offspring size in Trinidadian guppies (Poecilia reticulata) is an example in which incomplete adaptive plasticity may have served as a bridge to evolved adaptation. Guppies are often found in either downstream sites where they co-occur with many predators or in headwater streams where these predators are excluded by waterfalls and rapids ( Endler 1978 ). These low predation headwater streams also tend to have lower light levels and lower primary productivity than high predation streams, which in combination with higher densities of guppies results in greater food limitation ( Reznick, Butler & Rodd 2001 ). Selection is thought to favour larger offspring under such competitive conditions ( Bashey 2006 ). Guppies from low predation environments produce fewer and larger offspring than their counterparts from high predation environments ( Reznick, Rodd & Cardenas 1996 Bashey 2006 ). While these size differences can be shown to have a genetic basis ( Reznick 1982 Reznick & Bryga 1996 ), there is also considerable plasticity in offspring size female guppies that are reared on low food rations or that experience variation in food availability produce offspring that are 15%–20% larger than those that are kept on constant, high food rations ( Reznick & Yang 1993 ). Thus, the plastic response in offspring size is in the same direction favoured by selection in the low predation environments. Larger offspring in response to lower food availability appear adaptive, because larger offspring have a competitive advantage when food availability is low but not when it is high ( Bashey 2006 ). Genetic analyses suggest that low predation populations have independently originated from downstream high predation regions (e.g. Crispo et al. 2006 ). Such a repeated pattern of colonization means that guppies would regularly experience a reduction in food availability as they move upstream and hence experience selection for increased offspring size. Their ability to produce larger offspring in response to low food availability represents an adaptive plastic response that would increase the probability that they could successfully invade these environments, while their genetic capacity to produce larger offspring is likely to represent an adaptation that follows such invasions. Significant genetic changes in offspring size were recorded after 11 years (approximately 16 generations) after transplanting guppies from a high to a low predation environment ( Reznick & Bryga 1987 Reznick, Bryga & Endler 1990 Reznick et al. 1997 ). Thus, plasticity in offspring size does not appear to retard adaptive evolution in guppies, and may even facilitate adaptation to low predation environments, since it will result in females producing larger offspring as soon as they become established in low predation environments, more than a decade before there is detectable evolution in the trait. Other examples of incomplete adaptive plastic responses with respect to the optimum phenotype known to evolve have been documented in various systems (e.g. Day, Pritchard & Schluter 1994 Chapman, Frieston & Shinn 2000 Trussell 2000 Losos et al. 2000 Donohue et al. 2000 Yeh & Price 2004 ).

Non-adaptive reaction norms: environmental heterogeneity and stress

In contrast to new environments that are reasonably similar to native or ancestral ones, new environments that fall outside the range of conditions typically experienced by populations are often studied from the perspective of ‘environmental stress’ (e.g. Bradshaw & Hardwick 1989 Bijlsma & Loeschcke 1997 Hoffman & Parsons 1997 Badyaev 2005 ). Here we define stress as new environments that lie outside the range of preferred conditions and impose a challenge to an organism's ability to maintain homeostasis and proper function. New environments that are stressful will thus pose a twofold challenge to newly established populations: (i) maintaining homeostasis and proper development, and (ii) responding to strong directional selection (e.g. Waddington 1941 Bradshaw & Hardwick 1989 ). The solution to the first challenge lies in the ability of organisms to buffer themselves against these stresses so that proper development and function can still occur (e.g. Waddington 1953 Scharloo 1991 ), whereas the solution to the second challenge is dependent on the relationship between stress-induced phenotypic and genetic variation, and the prevailing selection pressure (e.g. Rutherford & Lindquist 1998 Badyaev 2005 ). In such cases, canalization (i.e. a lack of plasticity) for the most basic physiological and developmental processes to properly function is the best hope for increasing the likelihood of persistence in the new environment. Stressful environments thus illustrate the challenge or trade-off of having a genotype capable of producing the same target (canalized) phenotype under different environments vs a genotype having the ability to produce many potentially adaptive phenotypes in different environments (i.e. plasticity).

Non-adaptive plasticity in response to stress may reflect a fundamental breakdown during development or disruption of physiological function because of changes in temperature, pH or moisture that fall outside of the range historically experienced. By non-adaptive we mean that compared to the ancestral phenotype, the environmentally induced phenotype in the new environment has on average reduced fitness or is further away from the new adaptive peak (Fig. 1B). This type of non-adaptive plasticity represents a fundamentally different kind of environmentally induced effect compared to situations where past selection on the reaction norm allows for adaptive plasticity, and better matching of the phenotype and the environment. It is perhaps the most common form of plasticity to environmental heterogeneity, arising as a ‘passive’ consequence to environmental stress (e.g. Dorn, Pyle & Schmitt 2000 Grether 2005 van Kleunen & Fisher 2005 ).

In such cases, the slope of the reaction norm is such that the optimal phenotype in the new environment is not produced and the plastic response is usually a non-adaptive shift in the mean trait value away from the new optimum (Fig. 1b). Here a lack of plasticity or canalized response, that allows organisms to produce the same phenotype regardless of environment results in the best strategy (Fig. 1b). For example, plants may fail to grow to an optimal height and produce few seeds when occupying a microenvironment that is lacking moisture and/or essential minerals (e.g. van Kleunen & Fisher 2005 ). Grether (2005 ) argues that this kind of non-adaptive plasticity is likely to underlie a form of cryptic evolution because it results in strong directional selection that makes populations in different environments similar to one another as is observed under ‘countergradient variation’ (e.g. Conover & Schultz 1995 Carroll et al. 2001 Trussell & Etter 2001 ). Grether (2005 ) refers to this process as ‘genetic compensation’ to distinguish it from genetic assimilation because selection results in evolutionary changes that serve to re-establish the phenotype because the same optima are favoured in both the new and the ancestral environments.

The anadromous Sockeye salmon and non-anadromous lake-bound Kokanee are genetically distinct forms of Pacific salmon (Oncorhynchus nerka) that provide a good example of how environmental stress acting through a limiting resource results initially in non-adaptive plasticity and ultimately in cryptic adaptive evolution ( Craig & Foote 2001 Craig, Foote & Wood 2005 ). Kokanee populations appear to have evolved repeatedly from anadromous Sockeye individuals that failed to return to the ocean (called ‘residuals’). Kokanee therefore tend to be more closely related to Sockeye inhabiting the same lakes for breeding, than to (phenotypically similar) Kokanee in other lakes (e.g. Foote, Wood & Withler 1989 Taylor, Foote & Wood 1996 Wood & Foote 1996 ). Both Sockeye and Kokanee turn from silver to bright red when they mature and move into streams to spawn, whereas residual Sockeye are distinguished by their olive green skin at maturity ( Craig & Foote 2001 Craig et al. 2005 ). The bright red colouration is produced through the acquisition of dietary carotenoids however, despite Sockeye and Kokanee exhibiting identical red colouration, carotenoid availability is much lower in lakes than it is in the oceans ( Craig & Foote 2001 ). By crossing Sockeye and Kokanee, and measuring their offspring under common environmental conditions, Craig & Foote (2001 ) found that Kokanee are three times more efficient in acquiring and depositing carotenoids in their flesh than Sockeye. In addition, mate choice trials revealed a strong preference in Sockeye for red colouration over green, suggesting that the evolution from green colouration (residuals) to red colouration (Kokanee) is driven by sexual selection ( Craig & Foote 2001 ). These results argue for a compelling case of genetic differentiation via a series of events: (i) ancestral Sockeye colonize freshwater lakes via residuals, (ii) residuals initially fail to produce the desired phenotype due to resource limitation, and (iii) directional selection leads to the evolution of greater efficiency in the use of dietary carotenoids and the return of the ancestral or favoured phenotype. Grether (2005 ) reviews other examples of evolutionary change via a similar process.

Another perspective on non-adaptive plasticity and adaptive evolution considers the role stressful environments play in increasing the expression of genetic and phenotypic variance (e.g. Hoffmann & Parsons 1997 Hoffmann & Merilä 1999 ). In contrast to the previously discussed types of plasticity that act primarily on the mean value of a trait, stressful environments that fall far outside the range historically encountered can break down genetic buffering mechanisms, and in turn increase the variance associated with different traits (e.g. Rutherford 2000 , 2003 ). This type of stress-induced plasticity is thought to reveal cryptic genetic variation which results in an increase in the genotypic and phenotypic variance that is ‘hidden’ or unexpressed under normal environmental conditions (e.g. Rutherford & Lindquist 1998 Rutherford 2000 , 2003 Ruden et al. 2003 see also Schlichting 2004 ). In other words, under typical environmental conditions most individuals in a population will exhibit similar patterns of plasticity (low variance), whereas under stressful environments individuals diverge in their response (high variance). An important aspect of this perspective is that most of the stress induced variants are likely to be quickly eliminated by selection in the new stressful environment because they exhibit deleterious phenotypes. Indeed, studies that have used environmental stress to express cryptic genetic variation produce phenotypes that would be unlikely to survive and reproduce under most natural condition ( Rutherford & Lindquist 1998 Queitsch, Sangster & Lindquist 2002 ). However, if by chance a small number of genotypes exhibit a beneficial plastic response that either allows a subset of individuals to persist long enough to survive and reproduce in the new environment for directional selection to act (see above) or is passed on via a maternal or epigenetic effect, adaptive evolution may occur ( Rutherford 2000 , 2003 ).

An often cited example documenting the interplay between stress, plasticity, and the potential for adaptive evolution via the increased expression of genetic and phenotypic variation are the heat shock proteins (HSPs), specifically Hsp90 (e.g. Rutherford & Lindquist 1998 Rutherford 2000 , 2003 Queitsch et al. 2002 ). HSPs are families of enzymes and chaperones that are mobilized in large numbers by cells under temperature stress to assist in the correct folding of proteins ( Rutherford 2000 , 2003 ). In addition to its increased expression in response to elevated temperatures, Hsp90 also interacts in diverse signalling networks and is intimately involved in several developmental pathways ( Rutherford & Lindquist 1998 Queitsch et al. 2002 ). These diverse functions place Hsp90 in the unique position of not only buffering organisms from external temperature stress, but also preventing the expression of genetic variants which accumulate but are not expressed, such that different genotypes reliably produce the same phenotype across a range of environments ( Rutherford 2003 ). The buffering capacity of Hsp90 has been revealed in complimentary studies in Drosophila ( Rutherford & Lindquist 1998 ) and Arabidopsis ( Queitsch et al. 2002 ) which show that reduced Hsp90 function, whether due to mutation, chemical impairment or changes in temperature, results in significant increases in phenotypic variation due to the expression of previously cryptic genetic differences. While much of this cryptic variation would surely be deleterious under natural conditions, some of the Hsp90 controlled variation could possibly be advantageous under particular environmental conditions and result in an adaptive response ( Queitsch et al. 2002 ). The ‘hopeful monsters’ associated with the release of cryptic genetic variation have been argued to provide a potential mechanism by which stressful environmental change may create the conditions for rapid adaptation through the release of novel variation that selection can act on ( Rutherford & Lindquist 1998 Rutherford 2000 , 2003 Queitsch et al. 2002 ). Badyaev (2005 ) reviews other examples where stress induced variation may have facilitated adaptive evolution.

The mosaic nature of plasticity and evolution in new environments

We have described how different types of plasticity in individual traits can lead to adaptive evolution. However, within any given individual, a new selective environment is likely to induce a variety of responses in different traits (e.g. Williams et al. 1995 Parsons & Robinson 2006 ). Thus, individuals are likely to be made up of both canalized traits that do not respond to novel environmental stimuli as well as traits that differ in the type of plasticity they exhibit (adaptive and non-adaptive), resulting in individuals that represent a mosaic of traits. What is the consequence of this mosaic nature in creating individual variation and its resultant importance to population persistence and adaptive evolution to new environments? To answer this question, we need to know more about: (i) whether suites of plastic and non-plastic traits respond independently or in an integrated manner to environmental change, and (ii) if and how the potential for adaptation is influenced by either of these scenarios. Only a few studies have been designed to consider such questions (e.g. Parsons & Robinson 2006 ), but in the case of the Soapberry bug (Jadera haematoloma) where reaction norms of seven traits were compared between recently ancestral and derived populations, the answer suggests a lack of integration. Floridian Soapberry bugs adopted an introduced plant as a food source in about 1960, a colonization event that caused selection on host-based performance reaction norms over the next tens of generations, and resulted in distinctive ‘host races’ ( Carroll, Dingle & Klassen 1997 Carroll, Klassen & Dingle 1998 ). In the laboratory, bugs of each type were reared on seeds of each host to simultaneously compare reaction norms for beak length, body size, survivorship, development time, fecundity and other traits. Not unexpectedly, the response of certain traits was strongly correlated, such as larger-bodied individuals normally having longer beaks and larger eggs. In contrast however, the direction and magnitude of mean trait responses to being reared on alternate host were not closely correlated. For example, adults from the native host plant were substantially smaller-bodied when reared on the exotic host, but their beak length did not differ and was instead canalized. Most interestingly, in the reciprocal comparison with the derived race, that canalization is lost, and body and beak values responded with similar diminution when rearing was on the native host ( Carroll et al. 1997 ). Thus, even among these recently diverged host races, the patterns of plastic and canalized responses can vary for the same traits.

The mosaic nature of the responses is further illustrated when the reaction norms of different traits are examined. For example, reduced survivorship and development rate of the native-host race reared on the new host reveals the evolutionary path via which countergradient selection has had to overcome stress induced plasticity in order to return the traits in the derived population to their former (ancestral) values (e.g. Carroll et al. 1997 ). In contrast, in other traits, including beak length, the pattern of plasticity on the new host plant is adaptive and in the same direction favoured by selection, suggesting a facilitating role of plasticity in moving the population closer to a new adaptive peak ( Carroll et al. 1997 ). Thus, new maladapted and adaptive reaction norms may simultaneously be generated as a pleiotropic effect, but in other traits (e.g. egg size) the slope and magnitude of environmental effects may also remain the same.

The differentiation between Soapberry bug host races is substantial, and adaptive evolution was likely facilitated by the presence of an abundant new resource and the absence of competitors which permitted unfettered evolutionary ‘experimentation’ in a growing population ( Reznick & Ghalambor 2001 ). Yet the complex mosaic of interacting plastic and non-plastic traits in response to directional selection that has produced the derived race shows that the bugs are altered far beyond what a superficial assessment of current phenotypic differences would suggest, given that some of the original values are now re-established. These results also suggest that adaptive plasticity in at least some traits may play an important role in population persistence to new environments and allowing time for directional selection to act on other traits that exhibit non-adaptive plasticity or are canalized. Such a perspective is consistent with long held ideas that adaptive plasticity in behaviour may help in population persistence to new environments and in turn facilitate evolutionary divergence in morphological or physiological traits (e.g. Losos, Schoener & Spiller 2004 , but see Huey et al. 2003 ). Other currently diversifying populations provide an opportunity to examine the levels of genetic divergence, integrated plastic responses, and the interaction of relative degrees of plasticity and intensities of selection ( Parsons & Robinson 2006 S.P. Carroll & C.W. Fox, unpublished).

Conclusions and perspectives

The concept of energy-limited tolerance to stress provides a useful tool for assessing the effects of multiple stressors under environmentally realistic scenarios in aquatic ectotherms and potentially in other organisms. This approach focuses on the bioenergetic consequences of exposures to stress that arise from the inevitable trade-offs between allocation of energy to survival, stress-tolerance, and other fitness-related functions. It uses energy balance as a common denominator to determine the combined effects of multiple stressors and their consequences for fitness. This makes this approach applicable to a wide variety of environmental variables that have direct or indirect impacts on energy metabolism. Bioenergetic markers proposed in this review can be used to assess ecologically important transitions into the pejus range (where the long-term survival of the population is possible at the cost of reduced production) and the pessimum range (which coincides with the tolerance limits of the population and defines the distributional range of the species in the face of multiple stressors). These markers will certainly be complemented and refined as more studies on the metabolic effects of environmental stressors become available. A potentially fruitful avenue for such studies that has not been fully explored in aquatic ectotherms is the study of cellular signaling cascades involved in metabolic regulation and response to stress. Bioenergetic framework may also be useful for understanding the relationships between aging, longevity, and tolerance to stress, which may be directly linked to aerobic scope in energy-limited natural populations ( Parsons 2003, 2007). Direct effects of bioenergetic shifts on fitness provide a suitable framework for fusing physiology, functional ecology, and evolutionary biology and to improve our understanding of the driving forces and the constraints of environmental adaptations (including but not limited to, scenarios of exposures to multiple stresses common in nature). Focus on the bioenergetic sustainability of populations also can assist in assessment of ecological risk and of conservation practices by identifying the habitats that are capable of supporting viable populations of ecologically or economically important, or otherwise protected, species.


I would like to thank G. Bell for 4 years of invaluable discussion and advice. S. Collins, R. Docking, J. McKenzie, R. Barrett, and S. Renault provided insightful discussion of the microbial literature. Criticisms by R. Kassen, K. Lessells, and an anonymous reviewer improved an earlier draft of this paper. This work was supported by a post-graduate fellowship from the Natural Sciences and Engineering Research Council of Canada and by Natural Environment Research Council funding to the Center for Population Biology.


3.1 Realized gamma diversity

As expected, the realized gamma diversity emerging from the simulations was lower than the theoretical maximum in most diversity scenarios and spatial configurations (Figure 3). In the scenario high+, realized gamma diversity at the end of the 200-year simulation period reached on average 84% (Figure 3a,c) and 92% (Figure 3b,d) of the theoretical maximum in Dischma (under RCP8.5) and Rosalia (under historic climate), respectively. The qualitative differences between the four diversity scenarios were well reflected in the realized gamma diversity. The effective number of species ranged from 1 in the no diversity scenarios to 8.9 in the high+ scenarios of the high-elevation Dischma landscape under climate change (scenario RCP8.5). Climate change strongly increased realized gamma diversity in Dischma (Figure 3a,c), but slightly decreased realized gamma diversity in Rosalia (Figure 3b,d). We found no notable differences in realized gamma diversity between the two spatial configurations (alpha and beta) and the three disturbances scenarios (no disturbance, historic disturbance and future disturbance). Both models were able to maintain high levels of species diversity over the full 200-year simulation period and agreed well on the patterns of realized diversity.

3.2 Effects of tree species diversity on disturbance impacts

Increasing tree species diversity at the landscape scale (gamma diversity) generally reduced disturbance impact for both indicators investigated (biomass, structure Figure 4). A notable exception to this pattern was the Rosalia landscape, where lowest disturbance impacts were simulated for the no diversity scenario (representing pure beech forests over the entire landscape) compared to the scenarios of higher species diversity (Figure 4). Disturbance impacts were generally more pronounced in the conifer-dominated Dischma landscape compared to the broadleaved-dominated Rosalia landscape. Overall, climate change amplified the positive effect of increasing diversity in both landscapes (Figure 4). Furthermore, we found that the effect of spatial configuration was context-dependent, with patterns varying between landscapes and indicators. Biomass impacts were generally lower when species were mixed between stands (beta scenario). Conversely, disturbance impacts on forest structure were lower in the alpha scenario in Dischma, and did not differ between configuration scenarios in Rosalia (Figure 4).

3.3 Effects of tree species diversity on temporal variation

The temporal variation in biomass stocks and forest structure generally increased with increasing intensity of climate change in both landscapes (Figure 5). The role of tree species diversity on temporal variation was strongly context-dependent: For biomass stocks, the low and no diversity scenario were most stable under historic climate while under future climate scenarios of higher tree species diversity were more stable. Forest structure was generally more variable in simulations under the low diversity scenario compared to scenarios with higher gamma diversity. Overall, however, differences between gamma diversity scenarios were small relative to the variation within each scenario. Furthermore, we did not detect differences in the simulated temporal variation between the two spatial configurations (i.e. alpha and beta diversity see Appendix SI5).

3.4 Model effects

iLand and LandClim were mostly consistent in their projections of the effects of gamma diversity and spatial configuration (alpha and beta scenarios, Figure 6). They agreed on biomass impacts generally decreasing with increasing gamma diversity. Furthermore, both models were consistent in simulating lower disturbance impacts on biomass stocks under beta mixtures compared to alpha mixtures.

We did, however, also detect differences between the two models (Appendices SI6 and SI7). iLand generally simulated a denser forest structure (trees >30 cm dbh/ha) and thus higher biomass stocks. Consequently, also disturbance impacts were more pronounced in iLand compared to LandClim for both indicators investigated. Furthermore, model differences were generally greater for forest structure than biomass stocks: While disturbances decreased the number of trees >30 cm dbh/ha in iLand, their numbers even increased slightly under some scenarios in LandClim (Appendix SI6). Temporal variation of biomass stocks and forest structure increased with climate change and decreased with species diversity, consistently across both models. However, buffering effects of species diversity were more pronounced in LandClim compared to iLand.

Trait Multi-Functionality in Plant Stress Response

Plants often experience multiple stresses in a given day or season, and it is self-evident that given functional traits can provide tolerances of multiple stresses. Yet, the multiple functions of individual traits are rarely explicitly considered in ecology and evolution due to a lack of a quantitative framework. We present a theory for considering the combined importance of the several functions that a single trait can contribute to alleviating multiple stresses. We derive five inter-related general predictions: (1) that trait multifunctionality is overall highly beneficial to fitness (2) that species possessing multifunctional traits should increase in abundance and in niche breadth (3) that traits are typically optimized for multiple functions and thus can be far from optimal for individual functions (4) that the relative importance of each function of a multifunctional trait depends on the environment and (5) that traits will be often “co-opted” for additional functions during evolution and community assembly. We demonstrate how the theory can be applied quantitatively by examining the multiple functions of leaf trichomes (hairs) using heuristic model simulations, substantiating the general principles. We identify avenues for further development and applications of the theory of trait multifunctionality in ecology and evolution.