A classic textbook example in evolutionary ecology for phenotypic plasticity—the expression of different phenotypes by a genotype under different environmental conditions—is on Daphnia (Dodson 1989). If various species of this small crustacean are exposed to predation risk or cues thereof, their offspring show induced defense phenotypes including helmets, neck teeth, or head- and tail-spines. These induced morphological changes lower the risk of being eaten by a predator. As in Daphnia, induction can span over generations, while other induced phenotypic plastic changes are almost instantaneous, including many responses in physiology and behaviour (Gabriel et al. 2005). 

Larvae male bulb mites also show plasticity in morphologies throughout development. They can develop into a costly adult fighter morphology or a less costly, but vulnerable, scrambler type. The question Deere and Smallegange (Deere & Smallegange 2023) address is whether male bulb mite larvae can anticipate which type will likely be adaptive once they become adult, or alternatively, whether the resource availability or population density they experience during their larvae phase determines frequencies of adult scrambler and fighter types. They explore this question through experimental evolution, by removing different fractions of developing intermediate larva types. They thereby manipulate the stage structure of populations and alter selective forces on these stages. The potential shift of fixed genetics, imposed by the experimental selection regimes, is evaluated by fitness assays in green garden experiments.

The exciting extension to classical experiments on phenotypic plasticity is that the authors aim at exploring eco-evolutionary feedbacks experimentally in a system that is a little more complex than basic host-parasite or predator-prey systems. The latter involve, for instance, rotifer-algae dynamics (Yoshida et al. 2003; Becks et al. 2012) or similar simple lab systems for which eco-evolutionary feedbacks have been demonstrated. The challenge for the exploration of more complex systems is revealed in the study by Deere and Smallegange. Their findings suggest that the frequencies of adult male morphotype is triggered by the environmental condition (nutrient availability) during the larval phase, i.e. a simple environmental induced plastic response. No fixed genetic shift in determining adult morphotype frequencies occurs. The trigger at the larva phase remains also not perfectly determined in their experiments, as population density and resource (food) availability are partly confounded. Additional complexity and selective aspects come into play in this system, as the targeted developmental stages that develop into fighter male morphs are also dispersal morphs. If selection on dispersal to avoid residing in food-limited environments is strong, triggering genetic shifts in fighter morphs by local population structure might be hard to experimentally achieve. Small sample sizes limit conclusions on complex interactions among duration of the experiment, population size, developmental stage types, and adult fighter frequencies. The presented study (Deere & Smallegange 2023) helps to bridge theoretical predictions and empirical evidence for eco-evolutionary feedbacks that goes beyond simple ecological-driven changes in population dynamics (Govaert et al. 2019).  

References

Becks, L., Ellner, S.P., Jones, L.E. & Hairston, N.G. (2012). The functional genomics of an eco-evolutionary feedback loop: linking gene expression, trait evolution, and community dynamics. Ecol. Lett., 15, 492-501.
https://doi.org/10.1111/j.1461-0248.2012.01763.x
 
Deere, J.A. & Smallegange, I.M. (2023). Individual differences in developmental trajectory leave a male polyphenic signature in bulb mite populations. bioRxiv, 2023.02.06.527265, ver. 3 peer-reviewed and recommended by Peer Community in Evolutionary Biology.
https://doi.org/10.1101/2023.02.06.527265
 
Dodson, S. (1989). Predator-induced Reaction Norms. Bioscience, 39, 447-452.
https://doi.org/10.2307/1311136
 
Gabriel, W., Luttbeg, B., Sih, A. & Tollrian, R. (2005). Environmental tolerance, heterogeneity, and the evolution of reversible plastic responses. Am. Nat., 166, 339-53.
https://doi.org/10.1086/432558
 
Govaert, L., Fronhofer, E.A., Lion, S., Eizaguirre, C., Bonte, D., Egas, M., et al. (2019). Eco-evolutionary feedbacks-Theoretical models and perspectives. Funct. Ecol., 33, 13-30.
https://doi.org/10.1111/1365-2435.13241
 
Yoshida, T., Jones, L.E., Ellner, S.P., Fussmann, G.F. & Hairston, N.G. (2003). Rapid evolution drives ecological dynamics in a predator-prey system. Nat. 2003 4246946, 424, 303-306.
https://doi.org/10.1038/nature01767

Genetic diversity is a key aspect of biodiversity and has important implications for evolutionary potential and thereby the persistence of species. Improving our understanding of the factors that drive genetic structure within and between populations is, therefore, a long-standing goal in evolutionary biology. However, this is a major challenge, because of the complex interplay between genetic drift, migration, and extinction/colonization dynamics on the one hand, and the biology and ecology of species on the other hand (Romiguier et al. 2014, Ellegren and Galtier 2016, Charlesworth 2003). 

Jarne et al. (2021) studied whether environmental and demographic factors affect the population genetic structure of four species of hermaphroditic freshwater snails in a similar way, using comparative analyses of neutral genetic microsatellite markers. 

Specifically, they investigated microsatellite variability of Hygrophila in almost 280 sites in Guadeloupe, Lesser Antilles, as part of a long-term survey experiment (Lamy et al. 2013). They then modelled the influence of the mating system, local environmental characteristics and demographic factors on population genetic diversity.

Consistent with theoretical predictions (Charlesworth 2003), they detected higher genetic variation in two outcrossing species than in two selfing species, emphasizing the importance of the mating system in maintaining genetic diversity. The study further identified an important role of site connectivity, through its influences on effective population size and extinction/colonisation events. Finally, the study detects an influence of interspecific interactions caused by an ongoing invasion by one of the studied species on genetic structure, highlighting the indirect effect of changes in community composition and demography on population genetics.

Jarne et al. (2021) could address the extent to which genetic structure is determined by demographic and environmental factors in multiple species given the remarkable sampling available. Additionally, the study system is extremely suitable to address this hypothesis as species’ habitats are defined and delineated. Whilst the authors did attempt to test for across-species correlations, further investigations on this matter are required. Moreover, the effect of interactions between factors should be appropriately considered in any modelling between genetic structure and local environmental or demographic features.

The findings in this study contribute to improving our understanding of factors influencing population genetic diversity, and highlights the complexity of interacting factors, therefore also emphasizing the challenges of drawing general implications, additionally hampered by the relatively limited number of species studied. Jarne et al. (2021) provide an excellent showcase of an empirical framework to test determinants of genetic structure in natural populations. As such, this study can be an example for further attempts of comparative analysis of genetic diversity.

References

Charlesworth, D. (2003) Effects of inbreeding on the genetic diversity of populations. Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences, 358, 1051-1070. doi: https://doi.org/10.1098/rstb.2003.1296

Ellegren, H. and Galtier, N. (2016) Determinants of genetic diversity. Nature Reviews Genetics, 17, 422-433. doi: https://doi.org/10.1038/nrg.2016.58

Jarne, P., Lozano del Campo, A., Lamy, T., Chapuis, E., Dubart, M., Segard, A., Canard, E., Pointier, J.-P. and David, P. (2021) Connectivity and selfing drives population genetic structure in a patchy landscape: a comparative approach of four co-occurring freshwater snail species. HAL, hal-03295242, ver. 2 peer-reviewed and recommended by Peer Community in Evolutionary Biology. https://hal.archives-ouvertes.fr/hal-03295242

Lamy, T., Gimenez, O., Pointier, J. P., Jarne, P. and David, P. (2013). Metapopulation dynamics of species with cryptic life stages. The American Naturalist, 181, 479-491. doi: https://doi.org/10.1086/669676

Romiguier, J., Gayral, P., Ballenghien, M. et al. (2014) Comparative population genomics in animals uncovers the determinants of genetic diversity. Nature, 515, 261-263. doi: https://doi.org/10.1038/nature13685

Jean Cury and colleagues (2021) have developed a protocol to simulate bacterial evolution in SLiM. In contrast to existing methods that depend on the coalescent, SLiM simulates evolution forward in time. SLiM has, up to now, mostly been used to simulate the evolution of eukaryotes (Haller and Messer 2019), but has been adapted here to simulate evolution in bacteria. Forward-in-time simulations are usually computationally very costly. To circumvent this issue, bacterial population sizes are scaled down. One would now expect results to become inaccurate, however, Cury et al. show that scaled-down forwards simulations provide very accurate results (similar to those provided by coalescent simulators) that are consistent with theoretical expectations. Simulations were analyzed and compared to existing methods in simple and slightly more complex scenarios where recombination affects evolution. In all scenarios, simulation results from coalescent methods (fastSimBac (De Maio and Wilson 2017), ms (Hudson 2002)) and scaled-down forwards simulations were very similar, which is very good news indeed.

A biologist not aware of the complexities of forwards, backwards simulations and the coalescent, might now naïvely ask why another simulation method is needed if existing methods perform just as well. To address this question the manuscript closes with a very neat example of what exactly is possible with forwards simulations that cannot be achieved using existing methods. The situation modeled is the growth and evolution of a set of 50 bacteria that are randomly distributed on a petri dish. One side of the petri dish is covered in an antibiotic the other is antibiotic-free. Over time, the bacteria grow and acquire antibiotic resistance mutations until the entire artificial petri dish is covered with a bacterial lawn. This simulation demonstrates that it is possible to simulate extremely complex (e.g. real world) scenarios to, for example, assess whether certain phenomena are expected with our current understanding of bacterial evolution, or whether there are additional forces that need to be taken into account. Hence, forwards simulators could significantly help us to understand what current models can and cannot explain in evolutionary biology.  

References  

Cury J, Haller BC, Achaz G, Jay F (2021) Simulation of bacterial populations with SLiM. bioRxiv, 2020.09.28.316869, version 5 peer-reviewed and recommended by Peer community in Evolutionary Biology.  https://doi.org/10.1101/2020.09.28.316869

De Maio N, Wilson DJ (2017) The Bacterial Sequential Markov Coalescent. Genetics, 206, 333–343. https://doi.org/10.1534/genetics.116.198796

Haller BC, Messer PW (2019) SLiM 3: Forward Genetic Simulations Beyond the Wright–Fisher Model. Molecular Biology and Evolution, 36, 632–637. https://doi.org/10.1093/molbev/msy228

Hudson RR (2002) Generating samples under a Wright–Fisher neutral model of genetic variation. Bioinformatics, 18, 337–338. https://doi.org/10.1093/bioinformatics/18.2.337

One can only hope that one day, we will be able to evaluate how the ecological complexity surrounding natural populations affects their ability to adapt. This is more like a long term quest than a simple scientific aim. Many steps are heading in the right direction. This paper by Monroe and colleagues (2021) is one of them.
Many ecological and genetic mechanisms shape the evolutionary potential of phenotypic trait variation and many of them involve environmental heterogeneity (Pujol et al 2018). To date, we cannot look into these ecological and genetic mechanisms without oversimplifying their effects. We often look into trait variation one trait at a time albeit the variation of multiple phenotypic traits is often linked at the genetic or environmental level. As a consequence, we put our conclusions at risk by not accounting for the reciprocal impacts of trait changes upon each other (Teplitsky et al 2014). We also usually restrict the study of a continuous gradient of environmental conditions to a few conditions because it would otherwise be impossible to model its environmental effect. As a consequence, we miss the full picture of the continuous often nonlinear phenotypic plastic response. Whether the trait undergo threshold effect changes thereby remains obscured to us. Collectively, these issues impede our ability to understand how selection shapes the ecological strategy of organisms under variable environments.
In this paper, Monroe and colleagues (2021) propose an original approach that raised to these two challenges. They analysed phenotypic plastic changes in response to a continuous environment in a multidimensional trait space, namely the response of Brachypodium plant developmental and physiological traits to a continuous gradient of soil moisture. They used dry down experimental treatments to produce the continuous soil moisture gradient and compared the plant capacity to use water between annual B. distachyon and perennial B. sylvaticum. Their results revealed the best mathematical functions that model the nonlinear curvature of the continuous plastic response of Brachypodium plants. This work reinforces our view that nonlinear plastic responses can result in greater or lesser trait values at any stage of the environmental gradient that were unexpected on the basis of linear predictors (Gienapp and Brommer 2014). Their findings also imply that different threshold responses characterize different genotypes. These could otherwise have been missed by a classical approach. By shedding light on unforeseen interactions between traits that make their correlation vary along the nonlinear response, they were able to describe more accurately Brachypodium ecological strategies and the changes in evolutionary constraints along the soil moisture gradient.
Their empirical approach allows to test what environmental conditions maximises the opportunity for selection to shape trait variation. For example, it revealed unforeseen divergence in potentially adaptive mechanisms or life history strategies – and not just trait values – between annual and perennial species of Brachypodium. Behind every environmental variation of the constraints to the future evolutionary change of multiple traits, we can expect that the evolutionary history of the populations shaped their trait genetic correlations. Investigating the nonlinear signature of adaptive evolution across continuous environments will get us into uncharted territory.
Our ability to predict the adaptive potential of species is limited. With their approach of continuous environmental gradients beyond linearity, Monroe and collaborators (2021) improve our understanding of plant phenotypic responses and open a brand new range of exciting developments. As they mention: "the opportunity for scaling up" their approach is big. To illustrate this prospect, I can easily think of an example: the quantitative genetic random regression model. This model allows to use any degree of genetic relatedness in a wild population to estimate the genetic variation of phenotypic plastic reaction norms (Nussey et al 2007, Pujol and Galaud 2013). However, in this approach, only a few modalities of the environmental gradient are used to model nonlinear phenotypic plastic responses. From there, it is rather intuitive. Combining the best of these two approaches (continuity of genetic relatedness in the wild & continuity of environmental gradient in experiments) could open ground breaking new perspectives in research.

References

Gienapp P. & J.E. Brommer. 2014. Evolutionary dynamics in response to climate change. In: Charmentier A, Garant D, Kruuk LEB, editors. Quantitative genetics in the wild. Oxford: Oxford University Press, Oxford. pp. 254–273. doi: https://doi.org/10.1093/acprof:oso/9780199674237.003.0015
Monroe, J. G., Cai, H., and Des Marais, D. L. (2020). Trait plasticity and covariance along a continuous soil moisture gradient. bioRxiv, 2020.02.17.952853, ver. 5 peer-reviewed and recommended by PCI Evol Biol. doi: https://doi.org/10.1101/2020.02.17.952853
Pujol et al. (2018). The missing response to selection in the wild. Trends in ecology & evolution, 33(5), 337-346. doi: https://doi.org/10.1016/j.tree.2018.02.007
Pujol, B., and Galaud, J. P. (2013). A practical guide to quantifying the effect of genes underlying adaptation in a mixed genomics and evolutionary ecology approach. Botany Letters, 160(3-4), 197-204. doi: https://doi.org/10.1080/12538078.2013.799045
Nussey, D. H., Wilson, A. J., and Brommer, J. E. (2007). The evolutionary ecology of individual phenotypic plasticity in wild populations. Journal of evolutionary biology, 20(3), 831-844. doi: https://doi.org/10.1111/j.1420-9101.2007.01300.x
Teplitsky et al. (2014). Assessing multivariate constraints to evolution across ten long-term avian studies. PLoS One, 9(3), e90444. doi: https://doi.org/10.1371/journal.pone.0090444

Genetic correlations among traits are ubiquitous in nature. However, we still have a limited understanding of the genetic architecture of trait correlations. Some genetic correlations among traits arise because of pleiotropy - single mutations or genotypes that have effects on multiple traits. Other genetic correlations among traits arise because of linkage among mutations that have independent effects on different traits. Teasing apart the differential effects of pleiotropy and linkage on trait correlations is difficult, because they result in very similar genetic patterns. However, understanding these differential effects gives important insights into how ubiquitous pleiotropy may be in nature.
In the preprint "Pleiotropy or linkage? Their relative contributions to the genetic correlation of quantitative traits and detection by multi-trait GWA studies", Chebib and Guillaume [1] explore the conditions under which trait correlations caused by pleiotropy result in similar and different genetic patterns than trait correlations caused by linkage. Their main finding is that pleiotropic architectures result in higher trait correlations than do architectures in which completely linked mutations affect different traits. This results clarifies and goes against a previous theoretical study that predicted that pleiotropic architectures could not be distinguished from completely linked mutations that affect independent traits.
In genome-wide association studies (GWAS), it is difficult to know if a significant signal is a causal variant that truly affects the trait, a false positive neutral variant linked to a causal variant, or a false positive causal variant that affects a different trait but is significant because of trait correlations. In their study, Chebib and Guillaume [1] show that this latter category can be a common source of false positives in GWAS studies when mutations affecting different traits are linked. One of the main limitation of this aspect of their analysis is the lack of simulation of neutral loci, which would likely show even higher rates of false positives than reported in their study.
The main limitation in their study is the restrictive assumptions about the genetic architectures (e.g. all pairs of loci have a fixed recombination rate among them). In reality, new causal mutations that arise near another causal mutation may have higher or lower establishment probabilities depending on the direction of effects on the trait and the parameters for selection and demography. Their study still deserves a recommendation, however, because of the new insights it gives into the genetic architecture of trait correlations.

References

[1] Chebib, J. and Guillaume, F. (2019). Pleiotropy or linkage? Their relative contributions to the genetic correlation of quantitative traits and detection by multi-trait GWA studies. bioRxiv, 656413, v3 peer-reviewed and recommended by PCI Evolutionary Biology. doi: 10.1101/656413