Parasitoid wasps have developed different mechanisms to increase their parasitic success, usually at the expense of host survival (Fellowes and Godfray, 2000). Eggs of these insects are deposited inside the juvenile stages of their hosts, which in turn deploy several immune response strategies to eliminate or disable them (Yang et al., 2020). Drosophila melanogaster protects itself against parasitoid attacks through the production of specific elongated haemocytes called lamellocytes which form a capsule around the invading parasite (Lavine and Strand, 2002; Rizki and Rizki, 1992) and the subsequent activation of the phenol-oxidase cascade leading to the release of toxic radicals (Nappi et al., 1995). On the parasitoid side, robust responses have evolved to evade host immune defenses as for example the Drosophila-specific endoparasite Leptopilina boulardi, which releases venom during oviposition that modifies host behaviour (Varaldi et al., 2006) and inhibits encapsulation (Gueguen et al., 2011; Martinez et al., 2012).
Studies have shown that the wasp parasitic capacity is correlated to venom presence and its content (Colinet et al., 2009; Poirié et al., 2014), including that evolution of venom protein composition is driven by different levels of host susceptibility to infection (Cavigliasso et al., 2019). However, it had not been determined to this day, if and how parasitic range can affect venom protein composition and to which extent host specialization requires broad-spectrum factors or a plethora of specialized components.
These outstanding questions are now approached in a study by Cavigliasso and colleagues (Cavigliasso et al., 2021), where they perform experimental evolution of L. boulardi for 9 generations exposing it to different Drosophila host species and genetic backgrounds (two strains of D. melanogaster, D. simulans and D. yakuba). The authors tested whether the parasitic success of each selection regime was host-specific and how they influenced venom composition in parasitoids. For the first part, infection outcomes were assayed for each selection regime when cross-infecting different hosts. To get a finer measurement of the mechanisms under selection, the authors differentiated three phenotypes: overall parasitic success, encapsulation inhibition and escape from capsule. Throughout the course of experimental evolution, only encapsulation inhibition did not show an improved response upon selection on any host. Importantly, the cross-infection scenario revealed a clear specificity to the selected host for each evolved resistance.
As for venom composition, a trend of differential evolution was detected between host species, although a significant part of that was due to a larger differentiation in the D. yakuba regime, which showed a completely different directionality. Importantly, the authors could identify some of the specific proteins targeted by the several selection regimes, whether selected or counter-selected for. Interestingly, the D. yakuba regime is the only case where the key parasitoid protein LbSPNy (Colinet et al., 2009) was not counter-selected and the only regime in which the overall venom composition did not evolve towards the Ism strain, one of the two ancestral strains of L. boulardi used in the study. It is possible that these two results are correlated, since LbSPNy has been described to inhibit activation of the phenoloxidase cascade in D. yakuba and is one of the most abundant proteins in the ISy venom, making it a good target for selection (Colinet et al., 2013). The authors also discuss the possibility that this difference is related to the geographical distribution of the strains of L. boulardi, since each coincide with either D. melanogaster or D. yakuba.
This methodologically broad work by Cavigliasso and colleagues constitutes an important experimental contribution towards the understanding of how parasitoid adaptation to specific hosts is achieved at different phenotypic and mechanistic levels. It provides compelling evidence that venom composition evolves differently in response to specific parasitic ranges, particularly considering the evolutionary difference between the selective hosts. In line with this result, it is also concluded that the majority of venom proteins selected are lineage-specific, although a few broad-spectrum factors could also be detected. 
The question of whether parasitic range can affect venom composition and parasitic success is still open to more contributions. A potentially interesting long-term direction will be to use a similar setup of experimental evolution on the generalist L. heterotoma (Schlenke et al., 2007) . On a more immediate horizon, comparing the venom evolution of both L. heterotoma and L. boulardi under selection with different hosts and under cross-infection scenarios could reveal interesting patterns. The recent sequencing of the L. boulardi genome together with the vast number of studies addressing mechanisms of Drosophila resistance to parasitoid infection, will enable the thorough characterization of the genetic basis of host-parasitoid interactions and the deeper understanding of these ubiquitous and economically-relevant relationships.
 
*This recommendation text has been co-written with Tânia F. Paulo who is not a recommender of PCI Evol Biol

References

Cavigliasso, F., Mathé-Hubert, H., Gatti, J.-L., Colinet, D. and Poirié, M. (2021) Parasitic success and venom composition evolve upon specialization of parasitoid wasps to different host species. bioRxiv, 2020.10.24.353417, ver. 3 peer-reviewed and recommended by Peer Community in Evolutionary Biology. https://doi.org/10.1101/2020.10.24.353417

Cavigliasso, F., Mathé-Hubert, H., Kremmer, L., Rebuf, C., Gatti, J.-L., Malausa, T., Colinet, D., Poiré, M. and  Léne. (2019). Rapid and Differential Evolution of the Venom Composition of a Parasitoid Wasp Depending on the Host Strain. Toxins, 11(629). https://doi.org/10.3390/toxins11110629

Colinet, D., Deleury, E., Anselme, C., Cazes, D., Poulain, J., Azema-Dossat, C., Belghazi, M., Gatti, J. L. and  Poirié, M. (2013). Extensive inter- and intraspecific venom variation in closely related parasites targeting the same host: The case of Leptopilina parasitoids of Drosophila. Insect Biochemistry and Molecular Biology, 43(7), 601–611. https://doi.org/10.1016/j.ibmb.2013.03.010

Colinet, D., Dubuffet, A., Cazes, D., Moreau, S., Drezen, J. M. and  Poirié, M. (2009). A serpin from the parasitoid wasp Leptopilina boulardi targets the Drosophila phenoloxidase cascade. Developmental and Comparative Immunology, 33(5), 681–689. https://doi.org/10.1016/j.dci.2008.11.013

Fellowes, M. D. E. and  Godfray, H. C. J. (2000). The evolutionary ecology of resistance to parasitoids by Drosophila. Heredity, 84(1), 1–8. https://doi.org/10.1046/j.1365-2540.2000.00685.x

Gueguen, G., Rajwani, R., Paddibhatla, I., Morales, J. and  Govind, S. (2011). VLPs of Leptopilina boulardi share biogenesis and overall stellate morphology with VLPs of the heterotoma clade. Virus Research, 160(1–2), 159–165. https://doi.org/10.1016/j.virusres.2011.06.005

Lavine, M. D. and  Strand, M. R. (2002). Insect hemocytes and their role in immunity. Insect Biochemistry and Molecular Biology, 32(10), 1295–1309. https://doi.org/10.1016/S0965-1748(02)00092-9

Martinez, J., Duplouy, A., Woolfit, M., Vavre, F., O’Neill, S. L. and  Varaldi, J. (2012). Influence of the virus LbFV and of Wolbachia in a host-parasitoid interaction. PloS One, 7(4), e35081. https://doi.org/10.1371/journal.pone.0035081

Nappi, A. J., Vass, E., Frey, F. and  Carton, Y. (1995). Superoxide anion generation in Drosophila during melanotic encapsulation of parasites. European Journal of Cell Biology, 68(4), 450–456.

Poirié, M., Colinet, D. and  Gatti, J. L. (2014). Insights into function and evolution of parasitoid wasp venoms. Current Opinion in Insect Science, 6, 52–60. https://doi.org/10.1016/j.cois.2014.10.004

Rizki, T. M. and  Rizki, R. M. (1992). Lamellocyte differentiation in Drosophila larvae parasitized by Leptopilina. Developmental and Comparative Immunology, 16(2–3), 103–110. https://doi.org/10.1016/0145-305X(92)90011-Z

Schlenke, T. A., Morales, J., Govind, S. and  Clark, A. G. (2007). Contrasting infection strategies in generalist and specialist wasp parasitoids of Drosophila melanogaster. PLoS Pathogens, 3(10), 1486–1501. https://doi.org/10.1371/journal.ppat.0030158

Varaldi, J., Petit, S., Boulétreau, M. and  Fleury, F. (2006). The virus infecting the parasitoid Leptopilina boulardi exerts a specific action on superparasitism behaviour. Parasitology, 132(Pt 6), 747–756. https://doi.org/10.1017/S0031182006009930

Yang, L., Qiu, L., Fang, Q., Stanley, D. W. and  Gong‐Yin, Y. (2020). Cellular and humoral immune interactions between Drosophila and its parasitoids. Insect Science. https://doi.org/10.1111/1744-7917.12863

In this paper, Geoffroy et al. [1] deal with partner choice as a mechanism of maintaining cooperation, and argues that rather than being unequivocally a force towards improved payoffs to everyone through cooperation, partner choice can lead to “over-cooperation” where individuals can evolve to invest so much in cooperation that the costs of cooperating partially or fully negate the benefits from it. This happens when partner choice is consequential and effective, i.e., when interactions are long (so each decision to accept or reject a partner is a bigger stake) and when meeting new partners is frequent when unpaired (so that when one leaves an interaction one can find a new partner quickly). Geoffroy et al. [1] show that this tendency to select for overcooperation under such regimes can be counteracted if individuals base their acceptance-rejection of partners not just on the partner cooperativeness, but also on their own. By using tools from matching theory in economics, they show that plastic partner choice generates positive assortment between cooperativeness of the partners, and in the extreme case of perfectly assortative pairings, makes the pair the unit of selection, which selects for maximum total payoff.
This study is a nice contribution to the literature that illustrates potential complexities with partner choice as a mechanism for cooperation, including how the proximate mechanisms of partner choice can significantly alter the evolutionary trajectory of cooperation. Modeling choice as a reaction norm that depends on one’s own traits also adds a layer of realism to partner choice theory.
The authors are also to be commended for the revisions they made through the review process. Earlier versions of the model somewhat overstated the tendency for fixed partner choice strategies to lead to over cooperation, missing some of the important features in previous models, notably McNamara et al. [2] that can counter this tendency. In this version, the authors acknowledge these factors, mainly, mortality during partner choice (which increases the opportunity cost of forgoing a current partner) and also the fact that endogenous distribution of alternative partners (which will tend to be worse than the overall population distribution, because more cooperative types spend more time attached and less cooperative types more time unattached). These two factors can constrain cooperation from “running away” as the authors put it, but the main point of Geoffroy et al. [1] that plastic choice can create selection against inefficient cooperation stands.
I think the paper will be very stimulating to theoretical and empirical researchers working on partner choice and social behaviors, and happy to recommend it.

References

[1] Geoffroy, F., Baumard, N., & Andre, J.-B. (2019). Why cooperation is not running away. bioRxiv, ver. 5 peer-reviewed and recommended by PCI Evol Biol. doi: 10.1101/316117
[2] McNamara, J. M., Barta, Z., Fromhage, L., & Houston, A. I. (2008). The coevolution of choosiness and cooperation. Nature, 451, 189–192. doi: 10.1038/nature06455

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

The social amoeba Dictyostelium discoideum is an important model system for the study of cooperation and multicellularity as is has both unicellular and aggregative life phases. In the aggregative phase, which typically occurs when nutrients are limiting, individual cells eventually gather together to form a fruiting bodies whose spores may be dispersed to another, better, location and whose stalk cells, which support the spores, die. This extreme form of cooperation has been the focus of numerous studies that have revealed the importance genetic relatedness and kin selection (Hamilton 1964; Lehmann and Rousset 2014) in explaining the maintenance of this cooperative collective behavior (Strassmann et al. 2000; Kuzdzal-Fick et al. 2011; Strassmann and Queller 2011). However, much remains unknown with respect to how the interactions between individual cells, their neighbors, and their environment produce cooperative behavior at the scale of whole groups or collectives. In this preprint, Forget et al. (2021) describe how the D. discoideum system is crucial in this respect because it allows these cellular-level interactions to be studied in a systematic and tractable manner.
Spore bias, which is the tendency of a particular genotype or strain to disproportionately migrate to the spore instead of the stalk, is often used to define which strains are "cheaters" (positive spore bias) and which are "cooperative" (negative spore bias). Forget et al. (2021) note that spore bias depends on a number of stochastic factors including external drivers such as variation in environmental (or nutrient) quality and internal drivers like cell-cycle phase at the time of starvation. Spore bias is also affected by the social environment where the fraction of cheater strains in a spore may be limited by the ability of the remaining stalk cells to support the spore. The social environment can also affect cells through their differential responsiveness to the chemical factors that induce differentiation into stalk cells; responsiveness is partly a function of nutrient quality (Thompson and Kay 2000), which in turn can be a function of cell density. Thus, Forget et al. (2021) highlight a number of mechanisms that could generate frequency-dependent selection that would lead to the stable maintenance of multiple strains with different spore biases; in other words, both cheater and cooperative strains might stably coexist due to these cellular-level interactions.
The cellular-level interactions that Forget et al. (2021) highlight are particularly important because they pose a challenge evolutionary theory: some evolutionary models of social and collective behavior neglect or simplify these interactions. For example, Forget et al. (2021) note that the developmental, behavior, and environmental timescales relevant for Dictyostelium fruiting body formation all overlap. Evolutionary analyses often assume some of these timescales, for example developmental and behavior, are separate in order to simplify the analysis of any interactions. Thus, new theoretical work that allows these timescales to overlap may shed light on how cellular-level interactions can produce environmental, physiological, and behavioral feedbacks that drive the evolution of cooperation and other collective behaviors.

References

Forget, M., Adiba, S. and De Monte, S.(2021) Social conflicts in *Dictyostelium discoideum *: a matter of scales. HAL, hal-03088868, ver. 2 peer-reviewed and recommended by PCI Evolutionary Biology. https://hal.archives-ouvertes.fr/hal-03088868/

Hamilton, W. D. (1964). The genetical evolution of social behaviour. II. Journal of theoretical biology, 7(1), 17-52. doi: https://doi.org/10.1016/0022-5193(64)90039-6

Kuzdzal-Fick, J. J., Fox, S. A., Strassmann, J. E., and Queller, D. C. (2011). High relatedness is necessary and sufficient to maintain multicellularity in Dictyostelium. Science, 334(6062), 1548-1551. doi: https://doi.org/10.1126/science.1213272

Lehmann, L., and Rousset, F. (2014). The genetical theory of social behaviour. Philosophical Transactions of the Royal Society B: Biological Sciences, 369(1642), 20130357. doi: https://doi.org/10.1098/rstb.2013.0357

Strassmann, J. E., and Queller, D. C. (2011). Evolution of cooperation and control of cheating in a social microbe. Proceedings of the National Academy of Sciences, 108(Supplement 2), 10855-10862. doi: https://doi.org/10.1073/pnas.1102451108

Strassmann, J. E., Zhu, Y., & Queller, D. C. (2000). Altruism and social cheating in the social amoeba Dictyostelium discoideum. Nature, 408(6815), 965-967. doi: https://doi.org/10.1038/35050087

Thompson, C. R., & Kay, R. R. (2000). Cell-fate choice in Dictyostelium: intrinsic biases modulate sensitivity to DIF signaling. Developmental biology, 227(1), 56-64. doi: https://doi.org/10.1006/dbio.2000.9877

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