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

Explaining long periods of evolutionary stasis, the absence of change in trait means over geological times, despite the existence of abundant genetic variation in most traits has challenged evolutionary theory since Darwin's theory of evolution by gradual modification (Estes & Arnold 2007). Stasis observed in contemporary populations is even more daunting since ample genetic variation is usually coupled with the detection of selection differentials (Kruuk et al. 2002, Morrissey et al. 2010). Moreover, rapid adaptation to environmental changes in contemporary populations, fuelled by standing genetic variation provides evidence that populations can quickly respond to an adaptive challenge. Explanations for evolutionary stasis usually invoke stabilizing selection as a main actor, whereby optimal trait values remain roughly constant over long periods of time despite small-scale environmental fluctuations. Genetic correlation among traits may also play a significant role in constraining evolutionary changes over long timescales (Schluter 1996). Yet, genetic constraints are rarely so strong as to completely annihilate genetic changes, and they may evolve. Patterns of genetic correlations among traits, as captured in estimates of the G-matrix of additive genetic co-variation, are subject to changes over generations under the action of drift, migration, or selection, among other causes (Arnold et al. 2008). Therefore, under the assumption of stabilizing selection on a set of traits, phenotypic stasis and genetic divergence in patterns of trait correlations may both be observed when selection on trait correlations is weak relative to its effect on trait means.

Mallard et al. (2023) set out to test whether selection or drift may explain the divergence in genetic correlation among traits in experimental lines of the nematode Caenorhabditis elegans and whether stabilizing selection may be a driver of phenotypic stasis. To do so, they analyzed the evolution of locomotion behavior traits over 100 generations of lab evolution in a constant and homogeneous environment after 140 generations of domestication from a largely differentiated set of founder populations. The locomotion traits were transition rates between movement states and direction (still, forward or backward movement). They could estimate the traits' broad-sense G-matrix in three populations at two generations (50 and 100), and in the ancestral mixed population. Similarly, they estimated the shape of the selection surface by regressing locomotion behavior on fertility. Armed with both G-matrix and surface estimates, they could test whether the G's orientation matched selection's orientation and whether changes in G were constrained by selection. They found stasis in trait mean over 100 generations but divergence in the amount and orientation of the genetic variation of the traits relative to the ancestral population. The selected populations changed orientation of their G-matrices and lost genetic variation during the experiment in agreement with a model of genetic drift on quantitative traits. Their estimates of selection also point to mostly stabilizing selection on trait combinations with weak evidence of disruptive selection, suggesting a saddle-shaped selection surface. The evolutionary responses of the experimental populations were mostly consistent with small differentiation in the shape of G-matrices during the 100 generations of stabilizing selection.

Mallard et al. (2023) conclude that phenotypic stasis was maintained by stabilizing selection and drift in their experiment. They argue that their findings are consistent with a "table-top mountain" model of stabilizing selection, whereby the population is allowed some wiggle room around the trait optimum, leaving space for random fluctuations of trait variation, and especially trait co-variation. The model is an interesting solution that might explain how stasis can be maintained over contemporary times while allowing for random differentiation of trait genetic co-variation. Whether such differentiation can then lead to future evolutionary divergence once replicated populations adapt to a new environment is an interesting idea to follow.

References

Arnold, S. J., Bürger, R., Hohenlohe, P. A., Ajie, B. C. and Jones, A. G. 2008. Understanding the evolution and stability of the G-matrix. Evolution 62(10): 2451-2461. 
https://doi.org/10.1111/j.1558-5646.2008.00472.x
 
Estes, S. and Arnold, S. J. 2007. Resolving the Paradox of Stasis: Models with Stabilizing Selection Explain Evolutionary Divergence on All Timescales.. Am. Nat. 169(2): 227-244.
https://doi.org/10.1086/510633
 
Kruuk, L. E. B., Slate, J., Pemberton, J. M., Brotherstone, S., Guinness, F. and Clutton-Brock, T. 2002. Antler size in red deer: Heritability and selection but no evolution. Evolution 56(8): 1683-1695.
https://doi.org/10.1111/j.0014-3820.2002.tb01480.x
 
Mallard, F., Noble, L., Guzella, T., Afonso, B., Baer, C. F., Teotónio, H. 2023. Phenotypic stasis with genetic divergence. bioRxiv, ver. 3 peer-reviewed and recommended by Peer Community in Evolutionary Biology.
https://doi.org/10.1101/2022.05.28.493856
 
Morrissey, M. B., Kruuk, L. E. B. and Wilson, A. J. 2010. The danger of applying the breeder's equation in observational studies of natural populations. J Evolution Biol 23(11): 2277-2288. 
https://doi.org/10.1111/j.1420-9101.2010.02084.x
 
Schluter, D. 1996. Adaptive radiation along genetic lines of least resistance. Evolution 50(5): 1766-1774. 
https://doi.org/10.1111/j.1558-5646.1996.tb03563.x

The search for physiological markers of health and survival in wild animal populations is attracting a great deal of interest. At present, there is no (and may never be) consensus on such a single, robust marker but of all the proposed physiological markers, telomere length is undoubtedly the most widely studied in the field of evolutionary ecology (Monaghan et al., 2022). 

Broadly speaking, telomeres are non-coding DNA sequences located at the end of chromosomes in eukaryotes, protecting genomic DNA against oxidative stress and various detrimental processes (e.g. DNA end-joining) and thus maintaining genome stability (Blackburn et al., 2015). However, in most somatic cells from the vast majority of the species, telomere sequences are not replicated and telomere length progressively declines with increased age (Remot et al., 2022). This shortening of telomere length upon a critical level is causally linked to cellular senescence and has been invoked as one of the primary causes of the aging process (López-Otín et al., 2023). Studies performed in both captive and wild populations of animals have further demonstrated that short telomeres (or telomere sequences with a fast attrition rate) are to some extent associated with an increased risk of mortality, even if the magnitude of this association largely differs between species and populations (Wilbourn et al., 2018).

The repeated observations of associations between telomere length and mortality risk have called for studies seeking to identify the ecological and biological factors that – beyond chronological age – shape the between-individual variability in telomere length. A wide spectrum of environmental stressors such as the level of exposure to pathogens or the degree of human disturbances has been proposed as possible modulators of telomere dynamics (see Chatelain et al., 2019). However, within species, the relative contribution of various ecological and biological factors on telomere length has been rarely quantified. In that context, the study of Criscuolo and colleagues (2023) constitutes a timely attempt to decipher the relative contribution of environmental and biological factors driving telomere length in nestlings (i.e. when individuals are between 15 and 35 days of age) from a wild population of little owls, Athene noctua.

In addition to chronological age, Criscuolo and colleagues (2023) analysed the effects of two environmental variables (i.e. cohort and habitat quality) as well as three life history traits (i.e. hatching rank, sex and body condition). Among these traits, sex was found to impact nestling’s telomere length with females carrying longer telomeres than males. Traditionally, the among-individuals variability in telomere length during the juvenile period is interpreted as a direct consequence of differences in growth allocation. Fast-growing individuals are typically supposed to undergo more cell divisions and a higher exposure to oxidative stress, which ultimately shortens telomeres (Monaghan & Ozanne, 2018). Whether - despite a slightly female-biased sexual size dimorphism - male little owls display a condensed period of fast growth that could explain their shorter telomere is yet to be determined. Future studies should also explore the consequences of these sex differences in telomere length in terms of mortality risk. In birds, it has been observed that telomere length during early life can predict lifespan (see Heidinger et al., 2012 in zebra finches, Taeniopygia guttata), suggesting that females little owls might live longer than their conspecific males. Yet, adult mortality is generally female-biased in birds (Liker & Székely, 2005) and whether little owls constitute an exception to this rule - possibly mediated by sex-specific telomere dynamics - remains to be explored.   

Quite surprisingly, the present study in little owls did not evidence any clear effect of environmental conditions on nestling’s telomere length, at both temporal and special scales. While a trend for a temporal effect was detected with telomere length being slightly shorter for nestling born the last year of the study (out of 4 years analysed), habitat quality (measured by the proportion of meadow and orchards in the nest environment) had absolutely no impact on nestling telomere length. Recently published studies in wild populations of vertebrates have highlighted the detrimental effects of harsh environmental conditions on telomere length (e.g. Dupoué et al., 2022 in common lizards, Zootoca vivipara), arguing for a key role of telomere dynamics in the emerging field of conservation physiology. While we can recognize the relevance of such an integrative approach, especially in the current context of climate change, the study by Criscuolo and colleagues (2023) reminds us that the relationships between environmental conditions and telomere dynamics are far from straightforward. Depending on the species and its life history, telomere length in early life could indeed capture very different environmental signals.

References

Blackburn, E. H., Epel, E. S., & Lin, J. (2015). Human telomere biology: A contributory and interactive factor in aging, disease risks, and protection. Science, 350(6265), 1193-1198. https://doi.org/10.1126/science.aab3389
 
Chatelain, M., Drobniak, S. M., & Szulkin, M. (2019). The association between stressors and telomeres in non-human vertebrates: A meta-analysis. Ecology Letters, 23, 381-398. https://doi.org/10.1111/ele.13426
 
Criscuolo, F., Fache, I., Scaar, B., Zahn, S. & Bleu, J. (2023). Telomere length vary with sex, hatching rank and year of birth in little owls, Athene noctua. EcoEvoRxiv, ver.4, peer-reviewed and recommended by PCI Evol Biol. https://doi.org/10.32942/X2BS3S
 
Dupoué, A., Blaimont, P., Angelier, F., Ribout, C., Rozen-Rechels, D., Richard, M., & Le Galliard, J. F. (2022). Lizards from warm and declining populations are born with extremely short telomeres. Proceedings of the National Academy of Sciences, 119(33), 2201371119. https://doi.org/10.1073/pnas.2201371119
 
Heidinger, B. J., Blount, J. D., Boner, W., Griffiths, K., Metcalfe, N. B., & Monaghan, P. (2012). Telomere length in early life predicts lifespan. Proceedings of the National Academy of Sciences, 109(5), 1743-1748. https://doi.org/10.1073/pnas.1113306109
 
Liker, A., & Székely, T. (2005). Mortality costs of sexual selection and parental care in natural populations of birds. Evolution, 59(4), 890-897. https://doi.org/10.1111/j.0014-3820.2005.tb01762.x
 
López-Otín, C., Blasco, M. A., Partridge, L., Serrano, M., & Kroemer, G. (2023). Hallmarks of aging: An expanding universe. Cell, 186(2), 243-278. https://doi.org/10.1016/j.cell.2022.11.001
 
Monaghan, P., Olsson, M., Richardson, D. S., Verhulst, S., & Rogers, S. M. (2022). Integrating telomere biology into the ecology and evolution of natural populations: Progress and prospects. Molecular Ecology, 31(23), 5909-5916. https://doi.org/10.1111/mec.16768
 
Monaghan, P., & Ozanne, S. E. (2018). Somatic growth and telomere dynamics in vertebrates: Relationships, mechanisms and consequences. Phil. Trans. R. Soc. B, 373(1741), 20160446. https://doi.org/10.1098/rstb.2016.0446
 
Remot, F., Ronget, V., Froy, H., Rey, B., Gaillard, J., Nussey, D. H., & Lemaitre, J. (2022). Decline in telomere length with increasing age across nonhuman vertebrates: A meta‐analysis. Molecular Ecology, 31(23), 5917-5932. https://doi.org/10.1111/mec.16145
 
Wilbourn, R. V., Moatt, J. P., Froy, H., Walling, C. A., Nussey, D. H., & Boonekamp, J. J. (2018). The relationship between telomere length and mortality risk in non-model vertebrate systems: A meta-analysis. Phil. Trans. R. Soc. B, 373(1741), 20160447. https://doi.org/10.1098/rstb.2016.0447

Accurate information flow is central to living systems. The continuity of genomes through generations as well as the reproducible functioning and survival of the individual organisms require a faithful information transfer during replication, transcription and translation. The differential efficiency of natural selection against “mistakes” results in decreasing fidelity rates for replication, transcription and translation. At each level in the information flow chain (replication, transcription, translation), numerous complex molecular systems have evolved and been selected for preventing, identifying and, when possible, correcting or removing such “mistakes” arising during information transfer.

However, fidelity cannot be improved ad infinitum. First, because of the limits imposed by the physical nature of the processes of copying and recoding information over different molecular supports: all mechanisms ensuring fidelity during biological information transfer ultimately rely on chemical kinetics and thermodynamics. The more accurate a copying process is, the lower the synthesis rate and the higher the energetic cost of correcting errors. Second, because of the limits imposed by random genetic drift: natural selection cannot effectively act on an allele that contributes with a small differential advantage unless effective population size is large. If s <1/Ne (or s <1/(2Ne) in diploids) the allele frequency in the population is de facto subject to neutral drift processes.

In their preprint “Random genetic drift sets an upper limit on mRNA splicing accuracy in metazoans”, Bénitière, Necsulea and Duret explore the validity of this last mentioned “drift barrier” hypothesis for the case study of alternative splicing diversity in eukaryotes (Bénitière et al. 2022). Splicing refers to an ensemble of eukaryotic molecular processes mediated by a large number of proteins and ribonucleoproteins and involving nucleotide sequence recognition, that uses as a molecular substrate a precursor messenger RNA (mRNA), directly transcribed from the DNA, and produces a mature mRNA by removing introns and joining exons (Chow et al. 1977). Alternative splicing refers to the case in which different molecular species of mature mRNAs can be produced, either by cis-splicing processes acting on the same precursor mRNA, e.g. by varying the presence/absence of different exons or by varying the exon-exon boundaries, or by trans-splicing processes, joining exons from different precursor mRNA molecules.

The diversity of mRNA molecular species generated by alternative splicing enlarges the molecular phenotypic space that can be generated from the same genotype. In humans, alternative splicing occurs in around 95% of the ca. 20,000 genes, resulting in ca. 100,000 medium-to-high abundance transcripts (Pan et al. 2008). In multicellular organisms, the frequency of alternatively spliced mRNAs varies between tissues and across ontogeny, often in a switch-like pattern (Wang et al. 2008). In the molecular and cell biology community, it is commonly accepted that splice variants contribute with specific functions (Marasco and Kornblihtt 2023) although there exists a discussion around the functional nature of low-frequency splice variants (see for instance the debate between Tress et al. 2017 and Blencowe 2017). The origin, diversity, regulation and evolutionary advantage of alternative splicing constitutes thus a playground of the selectionist-neutralist debate, with one extreme considering that most splice variants are mere “mistakes” of the splicing process (Pickrell et al. 2010), and the other extreme considering that alternative splicing is at the core of complexity in multicellular organisms, as it increases the genome coding potential and allows for a large repertoire of cell types (Chen et al. 2014).

In their manuscript, Bénitière, Necsulea and Duret set the cursor towards the neutralist end of the gradient and test the hypothesis of whether the high alternative splice rate in “complex” organisms corresponds to a high rate of splicing “mistakes”, arising from the limit imposed by the drift barrier effect on the power of natural selection to increase accuracy (Bush et al. 2017). In their preprint, the authors convincingly show that in metazoans a fraction of the variation of alternative splicing rate is explained by variation in proxies of population size, so that species with smaller Ne display higher alternative splice rates. They communicate further that abundant splice variants tend to preserve the reading frame more often than low-frequency splice variants, and that the nucleotide splice signals in abundant splice variants display stronger evidence of purifying selection than those in low-frequency splice variants. From all the evidence presented in the manuscript, the authors interpret that “variation in alternative splicing rate is entirely driven by variation in the efficacy of selection against splicing errors”.

The authors honestly present some of the limitations of the data used for the analyses, regarding i) the quality of the proxies used for Ne (i.e. body length, longevity and dN/dS ratio); ii) the heterogeneous nature of the RNA sequencing datasets (full organisms, organs or tissues; different life stages, sexes or conditions); and iii) mostly short RNA reads that do not fully span individual introns. Further, data from bacteria do not verify the herein communicated trends, as it has been shown that bacterial species with low population sizes do not display higher transcription error rates (Traverse and Ochman 2016). Finally, it will be extremely interesting to introduce a larger evolutionary perspective on alternative splicing rates encompassing unicellular eukaryotes, in which an intriguing interplay between alternative splicing and gene duplication has been communicated (Hurtig et al. 2020).

The manuscript from Bénitière, Necsulea and Duret makes a significant advance to our understanding of the diversity, the origin and the physiology of post-transcriptional and post-translational mechanisms by emphasising the fundamental role of non-adaptive evolutionary processes and the upper limits to splicing accuracy set by genetic drift.

References

Bénitière F, Necsulea A, Duret L. 2023. Random genetic drift sets an upper limit on mRNA splicing accuracy in metazoans. bioRxiv, ver. 4 peer-reviewed and recommended by Peer Community in Evolutionary Biology. https://doi.org/10.1101/2022.12.09.519597 

Blencowe BJ. 2017. The Relationship between Alternative Splicing and Proteomic Complexity. Trends Biochem Sci 42:407–408. https://doi.org/10.1016/j.tibs.2017.04.001

Bush SJ, Chen L, Tovar-Corona JM, Urrutia AO. 2017. Alternative splicing and the evolution of phenotypic novelty. Philos Trans R Soc Lond B Biol Sci 372:20150474. https://doi.org/10.1098/rstb.2015.0474

Chen L, Bush SJ, Tovar-Corona JM, Castillo-Morales A, Urrutia AO. 2014. Correcting for differential transcript coverage reveals a strong relationship between alternative splicing and organism complexity. Mol Biol Evol 31:1402–1413. https://doi.org/10.1093/molbev/msu083

Chow LT, Gelinas RE, Broker TR, Roberts RJ. 1977. An amazing sequence arrangement at the 5’ ends of adenovirus 2 messenger RNA. Cell 12:1–8. https://doi.org/10.1016/0092-8674(77)90180-5

Hurtig JE, Kim M, Orlando-Coronel LJ, Ewan J, Foreman M, Notice L-A, Steiger MA, van Hoof A. 2020. Origin, conservation, and loss of alternative splicing events that diversify the proteome in Saccharomycotina budding yeasts. RNA 26:1464–1480. https://doi.org/10.1261/rna.075655.120

Marasco LE, Kornblihtt AR. 2023. The physiology of alternative splicing. Nat Rev Mol Cell Biol 24:242–254. https://doi.org/10.1038/s41580-022-00545-z

Pan Q, Shai O, Lee LJ, Frey BJ, Blencowe BJ. 2008. Deep surveying of alternative splicing complexity in the human transcriptome by high-throughput sequencing. Nat Genet 40:1413–1415. https://doi.org/10.1038/ng.259

Pickrell JK, Pai AA, Gilad Y, Pritchard JK. 2010. Noisy splicing drives mRNA isoform diversity in human cells. PLoS Genet 6:e1001236. https://doi.org/10.1371/journal.pgen.1001236

Traverse CC, Ochman H. 2016. Conserved rates and patterns of transcription errors across bacterial growth states and lifestyles. Proc Natl Acad Sci U S A 113:3311–3316. https://doi.org/10.1073/pnas.1525329113

Tress ML, Abascal F, Valencia A. 2017. Alternative Splicing May Not Be the Key to Proteome Complexity. Trends Biochem Sci 42:98–110. https://doi.org/10.1016/j.tibs.2016.08.008

Wang ET, Sandberg R, Luo S, Khrebtukova I, Zhang L, Mayr C, Kingsmore SF, Schroth GP, Burge CB. 2008. Alternative isoform regulation in human tissue transcriptomes. Nature 456:470–476. https://doi.org/10.1038/nature07509

It is easy to define phenotypic plasticity as a mechanism by which traits change in response to a modification of the environment. Many complex mechanisms are nevertheless involved with plastic responses, their strength, and stability (e.g., reliability of cues, type of exposure, genetic expression, epigenetics). It is rather intuitive to think that environmental cues perceived at different stages of development will logically drive different phenotypic responses (Fawcett and Frankenhuis 2015). However, it has proven challenging to try and explain, or model how and why different effects are caused by similar cues experienced at different developmental or life stages (Walasek et al. 2022). The impact of these ‘sensitive windows’ on the stability of plastic responses within or across generations remains unclear. In their paper entitled “Sensitive windows for within- and trans-generational plasticity of anti-predator defences”, Tariel-Adam (2023) address this question.

In this paper, Tariel et al. acknowledge the current state of the art, i.e., that some traits influenced by the environment at early life stages become fixed later in life (Snell-Rood et al. 2015) and that sensitive windows are therefore more likely to be observed during early stages of development. Constructive exchanges with the reviewers illustrated that Tariel et al. presented a clear picture of the knowledge on sensitive windows from a conceptual and a mechanistic perspective, thereby providing their study with a strong and elegant rationale. Tariel et al. outlined that little is known about the significance of this scenario when it comes to transgenerational plasticity. Theory predicts that exposure late in the life of parents should be more likely to drive transgenerational plasticity because the cue perceived by parents is more likely to be reliable if time between parental exposure and offspring expression is short (McNamara et al. 2016). I would argue that although sensible, this scenario is likely oversimplifying the complexity of evolutionary, ecological, and inheritance mechanisms at play (Danchin et al. 2018). Tariel-Adam et al. (2023) point out in their paper how the absence of experimental results limits our understanding of the evolutionary and adaptive significance of transgenerational plasticity and decided to address this broad question.

Tariel-Adam et al. (2023) used the context of predator-prey interactions, which is a powerful framework to evaluate the temporality of predator cues and prey responses within and across generations (Sentis et al. 2018). They conducted a very elegant experiment whereby two generations of freshwater snails Physa acuta were exposed to crayfish predator cues at different developmental windows. They triggered the within-generation phenotypic plastic response of inducible defences (e.g., shell thickness) and identified sensitive windows as to evaluate their role in within-generation phenotypic plasticity versus transgenerational plasticity. They used different linear models, which lead to constructive exchanges with reviewers, and between reviewers, well trained on these approaches, in particular on effect sizes, that improved the paper by pushing the discussion all the way towards a consensus. 

Tariel-Adam et al. (2023) results showed that the phenotypic plastic response of different traits was associated with different sensitive windows. Although early-life development was confirmed to be a sensitive window, it was far from being the only developmental stage driving within-generation plastic responses of defence traits. This finding contributes to change our views on plasticity because where theoretical models predict early- and late-life sensitive windows, empirical results gathered here present a more continuous opportunity for sensitive windows over the lifetime of freshwater snails. This is likely because multifactorial mechanisms drive the reliability and adaptive significance of predator cues. To me, this paper most original contribution lies probably in the empirical investigation of sensitive windows underlying transgenerational plasticity. Their finding implies mechanistic ties between sensitive windows driving within-generation and transgenerational plasticity for some traits, but they also shed light on the possible independence of these processes. Although one may be disheartened by these findings illustrating the ability of nature to combine complex mechanisms in order to produce somewhat unpredictable scenarios, one can only find that this unlimited range of phenotypic plasticity scenarios is a wonder to investigate because much remains to be understood. As mentioned in the conclusion of the paper, the opportunity for sensitive windows to drive such a range of plastic responses may also be an opportunity for organisms to adapt to a wide range of environmental demands. 

References

Danchin E, A Pocheville, O Rey, B Pujol, and S Blanchet (2019). Epigenetically facilitated mutational assimilation: epigenetics as a hub within the inclusive evolutionary synthesis. Biological Reviews, 94: 259-282. https://doi.org/10.1111/brv.12453

Fawcett TW, and WE Frankenhuis (2015). Adaptive Explanations for Sensitive Windows in Development. Frontiers in Zoology 12, S3. https://doi.org/10.1186/1742-9994-12-S1-S3 

McNamara JM, SRX Dall, P Hammerstein, and O Leimar (2016). Detection vs. Selection: Integration of Genetic, Epigenetic and Environmental Cues in Fluctuating Environments. Ecology Letters 19, 1267–1276. https://doi.org/10.1111/ele.12663

Sentis A, R Bertram, N Dardenne, et al. (2018). Evolution without standing genetic variation: change in transgenerational plastic response under persistent predation pressure. Heredity 121, 266–281. https://doi.org/10.1038/s41437-018-0108-8 

Snell-Rood EC, EM Swanson, and RL Young (2015). Life History as a Constraint on Plasticity: Developmental Timing Is Correlated with Phenotypic Variation in Birds. Heredity 115, 379–388. https://doi.org/10.1038/hdy.2015.47

Tariel-Adam J, E Luquet, and S Plénet (2023). Sensitive windows for within- and trans-generational plasticity of anti-predator defences. OSF preprints, ver. 4 peer-reviewed and recommended by Peer Community in Evolutionary Biology. https://doi.org/10.31219/osf.io/mr8hu

Walasek N, WE Frankenhuis, and K Panchanathan (2022). An Evolutionary Model of Sensitive Periods When the Reliability of Cues Varies across Ontogeny. Behavioral Ecology 33, 101–114. https://doi.org/10.1093/beheco/arab113