The evolution of selfing results from a balance between multiple evolutionary forces. Selfing provides an "automatic advantage" due to the higher efficiency of selfers to transmit their genes via selfed and outcrossed offspring. Selfed offspring, however, may suffer from inbreeding depression. In principle the ultimate evolutionary outcome is easy to predict from the relative magnitude of these two evolutionary forces [1,2]. Yet, several studies explicitly taking into account the genetic architecture of inbreeding depression noted that these predictions are often too restrictive because selfing can evolve in a broader range of conditions [3,4].
The present work by Abu Awad and Roze [5] provides an analytic understanding of these results. Abu Awad and Roze analyse the evolution of selfing in a multilocus model where some loci are coding for selfing while others are under direct selection. The evolution of selfing depends on (i) the classical benefit of selfing (automatic advantage), (ii) the cost of selfing due to inbreeding depression, (iii) the association between the loci coding for selfing and the loci under direct selection (likely to be positive because selfing is expected to be found in better purged genetic backgrounds) and (iv) the association between the loci coding for selfing and the linkage between loci under selection (this final term depends on the magnitude and the type of epistasis). Because these last two terms depend on genetic associations they are expected to play in when selection is strong and recombination is small. These last two terms explain why selfing is evolving under a range of conditions which is broader than predicted by earlier theoretical models. The match between the approximations for the different terms acting on the evolution of selfing and individual based simulations (for different fitness landscapes) is very convincing. In particular, this analysis also yields new results on the effect of different types of epistasis on inbreeding depression.
Another remarkable and important feature of this work is its readability. The analysis of multilocus models rely on several steps and approximations that often result in overwhelmingly complex papers. Abu Awad and Roze’s paper [5] is dense but it provides a very clear and comprehensive presentation of the interplay between multiple evolutionary forces acting on the evolution of selfing.

References

[1] Holsinger, K. E., Feldman, M. W., and Christiansen, F. B. (1984). The evolution of self-fertilization in plants: a population genetic model. The American Naturalist, 124(3), 446-453. doi: 10.1086/284287
[2] Lande, R., and Schemske, D. W. (1985). The evolution of self‐fertilization and inbreeding depression in plants. I. Genetic models. Evolution, 39(1), 24-40. doi: 10.1111/j.1558-5646.1985.tb04077.x
[3] Charlesworth, D., Morgan, M. T., and Charlesworth, B. (1990). Inbreeding depression, genetic load, and the evolution of outcrossing rates in a multilocus system with no linkage. Evolution, 44(6), 1469-1489. doi: 10.1111/j.1558-5646.1990.tb03839.x
[4] Uyenoyama, M. K., and Waller, D. M. (1991). Coevolution of self-fertilization and inbreeding depression I. Mutation-selection balance at one and two loci. Theoretical population biology, 40(1), 14-46. doi: 10.1016/0040-5809(91)90045-H
[5] Abu Awad, D. and Roze, D. (2020). Epistasis, inbreeding depression and the evolution of self-fertilization. bioRxiv, 809814, ver. 4 peer-reviewed and recommended by PCI Evol Biol. doi: 10.1101/809814

Many parasites can manipulate their hosts, thus increasing their transmission to new hosts [1]. This is particularly the case for vector-borne parasites, which can alter the feeding behaviour of their hosts. However, predicting the optimal strategy is not straightforward because three actors are involved and the interests of the parasite may conflict with that of the vector. There are few models that consider the evolution of host manipulation by parasites [but see 2-4], but there are virtually none that investigated how parasites can manipulate the host choice of vectors. Even on the empirical side, many aspects of this choice remain unknown. Gandon [5] develops a simple evolutionary epidemiology model that allows him to formulate clear and testable predictions. These depend on which actor controls the trait (the vector or the parasite) and, when there is manipulation, whether it is realised via infected hosts (to attract vectors) or infected vectors (to change host choice). In addition to clarifying the big picture, Gandon [5] identifies some nice properties of the model, for instance an independence of the density/frequency-dependent transmission assumption or a backward bifurcation at R0=1, which suggests that parasites could persist even if their R0 is driven below unity. Overall, this study calls for further investigation of the different scenarios with more detailed models and experimental validation of general predictions.

References

[1] Hughes D, Brodeur J, Thomas F. 2012. Host manipulation by parasites. Oxford University Press.

[2] Brown SP. 1999. Cooperation and conflict in host-manipulating parasites. Proceedings of the Royal Society of London B: Biological Sciences 266: 1899–1904. doi: 10.1098/rspb.1999.0864

[3] Lion S, van Baalen M, Wilson WG. 2006. The evolution of parasite manipulation of host dispersal. Proceedings of the Royal Society of London B: Biological Sciences. 273: 1063–1071. doi: 10.1098/rspb.2005.3412

[4] Vickery WL, Poulin R. 2010. The evolution of host manipulation by parasites: a game theory analysis. Evolutionary Ecology 24: 773–788. doi: 10.1007/s10682-009-9334-0

[5] Gandon S. 2017. Evolution and manipulation of vector host choice. bioRxiv 110577, ver. 3 of 7th June 2017. doi: 10.1101/110577

Understanding how species respond to environmental changes is becoming increasingly important in order to predict the future of biodiversity and species distributions under current global warming conditions (Rezende 2020; Bennett et al 2021). Two key factors to take into account in these predictions are the tolerance of organisms to heat stress and subsequently how they adapt to increasingly warmer temperatures. Coupled with this, one important factor that is often overlooked when addressing the evolution of thermal tolerance, is the correlated responses in traits that are important to fitness, such as life histories, behavior and the underlying metabolic processes.

The rate and intensity of the thermal stress are expected to be major factors in shaping the evolution of heat tolerance and correlated responses in other traits. For instance, lower rates of thermal stress are predicted to select for individuals with a slower metabolism (Santos et al 2012), whereas low metabolism is expected to lead to a lower reproductive rate (Dammhahn et al 2018). To quantify the importance of the rate and intensity of thermal stress on the evolutionary response of heat tolerance and correlated response in behavior, Mesas et al (2021) performed experimental evolution in Drosophila subobscura using selective regimes with slow or fast ramping protocols. Whereas both regimes showed increased heat tolerance with similar evolutionary rates, the correlated responses in thermal performance curves for locomotor behavior differed between selection regimes. These findings suggest that thermal rate and intensity may shape the evolution of correlated responses in other traits, urging the need to understand possible correlated responses at relevant levels such as life history and metabolism. 

In the present contribution, Mesas and Castañeda (2022) investigate whether the disparity in thermal performance curves observed in the previous experiment (Mesas et al 2021) could be explained by differences in metabolic energy production and consumption, and how this correlated with the reproductive output (fecundity and viability). Overall, the authors show some evidence for lowered enzyme activity and increased performance in life-history traits, particularly for the slow-ramping selected flies. Specifically, the authors observe a reduction in glucose metabolism and increased viability when evolving under slow ramping stress. Interestingly, both regimes show a general increase in fecundity, suggesting that adaptation to these higher temperatures is not costly (for reproduction) in the ancestral environment. The evidence for a somewhat lower metabolism in the slow-ramping lines suggests the evolution of a slow “pace of life”. The “pace of life” concept tries to bridge variation across several levels namely metabolism, physiology, behavior and life history, with low “pace of life” organisms presenting lower metabolic rates, later reproduction and higher longevity than fast “pace of life” organisms (Dammhahn et al 2018, Tuzun & Stocks 2022).  As the authors state there is not a clear-cut association with the expectations of the pace of life hypothesis since there was evidence for increased reproductive output under both selection intensity regimes. This suggests that, given sufficient trait genetic variance, positively correlated responses may emerge during some stages of thermal evolution. As fecundity estimates in this study were focussed on early life, the possibility of a decrease in the cumulative reproductive output of the selected flies, even under benign conditions, cannot be excluded. This would help explain the apparent paradox of increased fecundity in selected lines. In this context, it would also be interesting to explore the variation in reproductive output at different temperatures, i.e to obtain thermal performance curves for life histories. 

Mesas and Castañeda (2022) raise important questions to pursue in the future and contribute to the growing evidence that, in order to predict the distribution of ectothermic species under current global warming conditions, we need to expand beyond determining the physiological thermal limits of each organism (Parratt et al 2021). Ultimately, integrating metabolic, life-history and behavioral changes during evolution under different thermal stresses within a coherent framework is key to developing better predictions of temperature effects on natural populations.  

References

Bennett, J.M., Sunday, J., Calosi, P. et al. The evolution of critical thermal limits of life on Earth. Nat Commun 12, 1198 (2021). https://doi.org/10.1038/s41467-021-21263-8

Dammhahn, M., Dingemanse, N.J., Niemelä, P.T. et al. Pace-of-life syndromes: a framework for the adaptive integration of behaviour, physiology and life history. Behav Ecol Sociobiol 72, 62 (2018). https://doi.org/10.1007/s00265-018-2473-y

Mesas, A,  Jaramillo, A,  Castañeda, LE.  Experimental evolution on heat tolerance and thermal performance curves under contrasting thermal selection in Drosophila subobscura. J Evol Biol  34, 767– 778 (2021). https://doi.org/10.1111/jeb.13777

Mesas, A, Castañeda, LE Evolutionary responses of energy metabolism, development, and reproduction to artificial selection for increasing heat tolerance in Drosophila subobscura. bioRxiv, 2022.02.03.479001, ver. 4 peer-reviewed and recommended by Peer Community in Evolutionary Biology. https://doi.org/10.1101/2022.02.03.479001

Parratt, S.R., Walsh, B.S., Metelmann, S. et al. Temperatures that sterilize males better match global species distributions than lethal temperatures. Nat. Clim. Chang. 11, 481–484 (2021). https://doi.org/10.1038/s41558-021-01047-0

Santos, M, Castañeda, LE, Rezende, EL Keeping pace with climate change: what is wrong with the evolutionary potential of upper thermal limits? Ecology and evolution, 2(11), 2866-2880 (2012). https://doi.org/10.1002/ece3.385

Tüzün, N, Stoks, R. A fast pace-of-life is traded off against a high thermal performance. Proceedings of the Royal Society B, 289(1972), 20212414 (2022). https://doi.org/10.1098/rspb.2021.2414

Rezende, EL, Bozinovic, F, Szilágyi, A, Santos, M. Predicting temperature mortality and selection in natural Drosophila populations. Science, 369(6508), 1242-1245  (2020). https://doi.org/10.1126/science.aba9287

Sex chromosomes are special in the genome because they are often highly differentiated over much of their lengths and marked by degenerative evolution of their gene content. Understanding why sex chromosomes differentiate requires deciphering the forces driving their recombination patterns. Suppression of recombination may be subject to selection, notably because of functional effects of locking together variation at different traits, as well as longer-term consequences of the inefficient purge of deleterious mutations, both of which may contribute to patterns of differentiation [1]. As an example, male and female functions may reveal intrinsic antagonisms over the optimal genotypes at certain genes or certain combinations of interacting genes. As a result, selection may favour the recruitment of rearrangements blocking recombination and maintaining the association of sex-antagonistic allele combinations with the sex-determining locus.
The hypothesis that sexually antagonistic selection might drive recombination suppression along the sex chromosomes is not new, but there are surprisingly few studies examining this empirically [1]. Support mainly comes from the study of guppy populations Poecilia reticulata in which the level of sexual dimorphism (notably due to male ornaments, subject to sexual selection) varies among populations, and was found to correlate with the length of the non-recombining region on the sex chromosome [2]. But the link is not always that clear. For instance in the fungus Microbotryum violaceum, the mating type loci is characterized by adjacent segments with recombination suppression, despite the near absence of functional differentiation between mating types [3].
In this study, Shearn and colleagues [4] explore the patterns of recombination suppression on the sex chromosomes of primates. X and Y chromosomes are strongly differentiated, except in a small region where they recombine with each other, the pseudoautosomal region (PAR). In the clade of apes and monkeys, including humans, large rearrangements have extended the non recombining region stepwise, eroding the PAR. Could this be driven by sexually antagonistic selection in a clade showing strong sexual differentiation?
To evaluate this idea, Shearn et al. have compared the structure of recombination in apes and monkeys to their sister clade with lower levels of sexual dimorphism, the lemurs and the lorises. If sexual antagonism was important in shaping recombination suppression, and assuming lower measures of sexual dimorphism reflect lower sexual antagonism [5], then lemurs and lorises would be predicted to show a shorter non-recombining region than apes and monkeys.
Lemurs and lorises were terra incognita in terms of genomic research on the sex chromosomes, so Shearn et al. have sequenced the genomes of males and females of different species. To assess whether sequences came from a recombining or non-recombining segment, they used coverage information in males vs females to identify sequences on the X whose copy on the Y is absent or too divergent to map, indicating long-term differentiation (absence of recombination). This approach reveals that the two lineages have undergone different recombination dynamics since they split from their common ancestor: regions which have undergone further structural rearrangements extending the non-recombining region in apes and monkeys, have continued to recombine normally in lemurs and lorises. Consistent with the prediction, macroevolutionary variation in the differentiation of males and females is indeed accompanied by variation in the size of the non-recombining region on the sex chromosome.
Sex chromosomes are excellent examples of how genomes are shaped by selection. By directly exploring recombination patterns on the sex chromosome across all extant primate groups, this study comes as a nice addition to the short series of empirical studies evaluating whether sexual antagonism may drive certain aspects of genome structure. The sexual selection causing sometimes spectacular morphological or behavioural differences between sexes in many animals may be the visible tip of the iceberg of all the antagonisms that characterise male vs. female functions generally [5]. Further research should bring insight into how different flavours or intensities of antagonistic selection can contribute to shape genome variation.

References

[1] Charlesworth D (2017) Evolution of recombination rates between sex chromosomes. Philosophical Transactions of the Royal Society B: Biological Sciences, 372, 20160456. https://doi.org/10.1098/rstb.2016.0456
[2] Wright AE, Darolti I, Bloch NI, Oostra V, Sandkam B, Buechel SD, Kolm N, Breden F, Vicoso B, Mank JE (2017) Convergent recombination suppression suggests role of sexual selection in guppy sex chromosome formation. Nature Communications, 8, 14251. https://doi.org/10.1038/ncomms14251
[3] Branco S, Badouin H, Vega RCR de la, Gouzy J, Carpentier F, Aguileta G, Siguenza S, Brandenburg J-T, Coelho MA, Hood ME, Giraud T (2017) Evolutionary strata on young mating-type chromosomes despite the lack of sexual antagonism. Proceedings of the National Academy of Sciences, 114, 7067–7072. https://doi.org/10.1073/pnas.1701658114
[4] Shearn R, Wright AE, Mousset S, Régis C, Penel S, Lemaitre J-F, Douay G, Crouau-Roy B, Lecompte E, Marais GAB (2020) Evolutionary stasis of the pseudoautosomal boundary in strepsirrhine primates. bioRxiv, 445072. https://doi.org/10.1101/445072
[5] Connallon T, Clark AG (2014) Evolutionary inevitability of sexual antagonism. Proceedings of the Royal Society B: Biological Sciences, 281, 20132123. https://doi.org/10.1098/rspb.2013.2123

In recent years many studies have documented shifts in phenology in response to climate change, be it in arrival times in migrating birds, budset in trees, adult emergence in butterflies, or flowering time in annual plants (Coen et al. 2018; Piao et al. 2019). While these changes are, in part, explained by phenotypic plasticity, more and more studies find that they involve also genetic changes, that is, they involve evolutionary change (e.g., Metz et al. 2020). Yet, evolutionary change may occur through genetic drift as well as selection. Therefore, in order to demonstrate adaptive evolutionary change in response to climate change, drift has to be excluded as an alternative explanation (Hansen et al. 2012). A new study by Gay et al. (2021) shows just how difficult this can be. 

The authors investigated a recent evolutionary shift in flowering time by in a population an annual plant that reproduces predominantly by self-fertilization. The population has recently been subjected to increased temperatures and reduced rainfalls both of which are believed to select for earlier flowering times. They used a “resurrection” approach (Orsini et al. 2013; Weider et al. 2018): Genotypes from the past (resurrected from seeds) were compared alongside more recent genotypes (from more recently collected seeds) under identical conditions in the greenhouse. Using an experimental design that replicated genotypes, eliminated maternal effects, and controlled for microenvironmental variation, they found said genetic change in flowering times: Genotypes obtained from recently collected seeds flowered significantly (about 2 days) earlier than those obtained 22 generations before. However, neutral markers (microsatellites) also showed strong changes in allele frequencies across the 22 generations, suggesting that effective population size, Ne, was low (i.e., genetic drift was strong), which is typical for highly self-fertilizing populations. In addition, several multilocus genotypes were present at high frequencies and persisted over the 22 generations, almost as in clonal populations (e.g., Schaffner et al. 2019). The challenge was thus to evaluate whether the observed evolutionary change was the result of an adaptive response to selection or may be explained by drift alone. 

Here, Gay et al. (2021) took a particularly careful and thorough approach. First, they carried out a selection gradient analysis, finding that earlier-flowering plants produced more seeds than later-flowering plants. This suggests that, under greenhouse conditions, there was indeed selection for earlier flowering times. Second, investigating other populations from the same region (all populations are located on the Mediterranean island of Corsica, France), they found that a concurrent shift to earlier flowering times occurred also in these populations. Under the hypothesis that the populations can be regarded as independent replicates of the evolutionary process, the observation of concurrent shifts rules out genetic drift (under drift, the direction of change is expected to be random). 

The study may well have stopped here, concluding that there is good evidence for an adaptive response to selection for earlier flowering times in these self-fertilizing plants, at least under the hypothesis that selection gradients estimated in the greenhouse are relevant to field conditions. However, the authors went one step further. They used the change in the frequencies of the multilocus genotypes across the 22 generations as an estimate of realized fitness in the field and compared them to the phenotypic assays from the greenhouse. The results showed a tendency for high-fitness genotypes (positive frequency changes) to flower earlier and to produce more seeds than low-fitness genotypes. However, a simulation model showed that the observed correlations could be explained by drift alone, as long as Ne is lower than ca. 150 individuals. The findings were thus consistent with an adaptive evolutionary change in response to selection, but drift could only be excluded as the sole explanation if the effective population size was large enough. 

The study did provide two estimates of Ne (19 and 136 individuals, based on individual microsatellite loci or multilocus genotypes, respectively), but both are problematic. First, frequency changes over time may be influenced by the presence of a seed bank or by immigration from a genetically dissimilar population, which may lead to an underestimation of Ne (Wang and Whitlock 2003). Indeed, the low effective size inferred from the allele frequency changes at microsatellite loci appears to be inconsistent with levels of genetic diversity found in the population. Moreover, high self-fertilization reduces effective recombination and therefore leads to non-independence among loci. This lowers the precision of the Ne estimates (due to a higher sampling variance) and may also violate the assumption of neutrality due to the possibility of selection (e.g., due to inbreeding depression) at linked loci, which may be anywhere in the genome in case of high degrees of self-fertilization. 

There is thus no definite answer to the question of whether or not the observed changes in flowering time in this population were driven by selection. The study sets high standards for other, similar ones, in terms of thoroughness of the analyses and care in interpreting the findings. It also serves as a very instructive reminder to carefully check the assumptions when estimating neutral expectations, especially when working on species with complicated demographies or non-standard life cycles. Indeed the issues encountered here, in particular the difficulty of establishing neutral expectations in species with low effective recombination, may apply to many other species, including partially or fully asexual ones (Hartfield 2016). Furthermore, they may not be limited to estimating Ne but may also apply, for instance, to the establishment of neutral baselines for outlier analyses in genome scans (see e.g, Orsini et al. 2012). 

References

Cohen JM, Lajeunesse MJ, Rohr JR (2018) A global synthesis of animal phenological responses to climate change. Nature Climate Change, 8, 224–228. https://doi.org/10.1038/s41558-018-0067-3

Gay L, Dhinaut J, Jullien M, Vitalis R, Navascués M, Ranwez V, Ronfort J (2021) Evolution of flowering time in a selfing annual plant: Roles of adaptation and genetic drift. bioRxiv, 2020.08.21.261230, ver. 4 recommended and peer-reviewed by Peer Community in Evolutionary Biology. https://doi.org/10.1101/2020.08.21.261230

Hansen MM, Olivieri I, Waller DM, Nielsen EE (2012) Monitoring adaptive genetic responses to environmental change. Molecular Ecology, 21, 1311–1329. https://doi.org/10.1111/j.1365-294X.2011.05463.x

Hartfield M (2016) Evolutionary genetic consequences of facultative sex and outcrossing. Journal of Evolutionary Biology, 29, 5–22. https://doi.org/10.1111/jeb.12770

Metz J, Lampei C, Bäumler L, Bocherens H, Dittberner H, Henneberg L, Meaux J de, Tielbörger K (2020) Rapid adaptive evolution to drought in a subset of plant traits in a large-scale climate change experiment. Ecology Letters, 23, 1643–1653. https://doi.org/10.1111/ele.13596

Orsini L, Schwenk K, De Meester L, Colbourne JK, Pfrender ME, Weider LJ (2013) The evolutionary time machine: using dormant propagules to forecast how populations can adapt to changing environments. Trends in Ecology & Evolution, 28, 274–282. https://doi.org/10.1016/j.tree.2013.01.009

Orsini L, Spanier KI, Meester LD (2012) Genomic signature of natural and anthropogenic stress in wild populations of the waterflea Daphnia magna: validation in space, time and experimental evolution. Molecular Ecology, 21, 2160–2175. https://doi.org/10.1111/j.1365-294X.2011.05429.x

Piao S, Liu Q, Chen A, Janssens IA, Fu Y, Dai J, Liu L, Lian X, Shen M, Zhu X (2019) Plant phenology and global climate change: Current progresses and challenges. Global Change Biology, 25, 1922–1940. https://doi.org/10.1111/gcb.14619

Schaffner LR, Govaert L, De Meester L, Ellner SP, Fairchild E, Miner BE, Rudstam LG, Spaak P, Hairston NG (2019) Consumer-resource dynamics is an eco-evolutionary process in a natural plankton community. Nature Ecology & Evolution, 3, 1351–1358. https://doi.org/10.1038/s41559-019-0960-9

Wang J, Whitlock MC (2003) Estimating Effective Population Size and Migration Rates From Genetic Samples Over Space and Time. Genetics, 163, 429–446. PMID: 12586728

Weider LJ, Jeyasingh PD, Frisch D (2018) Evolutionary aspects of resurrection ecology: Progress, scope, and applications—An overview. Evolutionary Applications, 11, 3–10. https://doi.org/10.1111/eva.12563