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

Plastic responses of organisms to their environment may be maladaptive in particular when organisms are exposed to new environments. Phenotypic plasticity may also have opposite effects on the adaptive response of organisms to environmental changes: whether phenotypic plasticity favors or hinders such adaptation depends on a balance between the ability of the population to respond to the change non-genetically in the short term, and the weakened genetic response to environmental change. These topics have received continued attention, particularly in the context of climate change (e.g., Chevin et al. 2013, Duputié et al., 2015, Vinton et al . 2022).

In their work, Ledru et al. focus on the adaptive nature of plastic behavior and on its consequences in a consumer-resource ecosystem. As they emphasize, previous works have found that plastic foraging promotes community stability, but these did not consider plasticity as an evolving trait, so Ledru et al. set out to test whether this conclusion holds when both plastic foraging and niche traits of consumers and resources evolve (though ultimately, their new conclusions may not all depend on plasticity evolving). Along the way, they first seek to clarify when such plasticity will evolve, and how it affects the evolution of the niche diversity of consumers and resources, before turning to the question of consumer persistence. 

The model is rather complex, as three traits are allowed to evolve, and the resource uptake expressed through plastic behavior has its own dynamics affected by some form of social learning. Classically, in models of niche evolution, a consumer's efficiency in exploiting a resource characterized by a trait y (here, the resource's individual niche trait), has been described in terms of location-scale (typically Gaussian) kernels, with mean x (the consumer's individual niche trait) specifying the most efficiently exploited resource, and with variance characterizing individual niche breadth. The evolution of the variance has been considered in some previous models but is assumed to be fixed here.  Rather, the new model considers the evolution of the distribution of resource traits, of the consumer's individual niche trait (which is not plastic), and of a "plastic foraging trait" that controls the relative time spent foraging plastically versus foraging randomly. When foraging plastically, the consumers modify their foraging effort towards the type of resource that maximizes their energy intake. in some previous models, the effect of variation in the extent of plastic foraging was already considered, but the evolution of allocation to a plastic foraging strategy versus random foraging was not considered. The model is formulated through reaction-diffusion equations, and its dynamics is investigated by numerical integration.

Foraging plasticity readily evolves, when resources vary widely enough, competition for resources is strong, and the cost of plasticity is weak. This means in particular that a large individual niche width of consumers selects for increased plastic foraging, as the evolution of plastic foraging leads to reduced niche overlap between consumers. The evolution of plastic foraging itself generally, though not always, favors the diversification of the niche traits of consumers and of resources. There is thus a positive feedback loop between plastic foraging and resource diversity. Ledru et al. conclude that the total niche width of the consumer population should also correlate with the evolution of plastic foraging, an implication which they relate to the so-called niche variation hypothesis and to empirical tests of it. 

The joint evolution of the consumer's individual niche trait and plastic foraging trait generates a striking pattern within populations: consumers whose individual niche trait is at an edge of the resource distribution forage more plastically. The authors observe that this relatively simple prediction has not been subjected to any empirical test. 

Returning to the question of consumer persistence, Ledru et al. evaluate this persistence when consumer mortality increases, and in response to either gradual or sudden environmental changes. These different perturbations all reduce the benefits of plastic foraging. The effect of plastic foraging on stability are complex, being negative or positive effect depending on the type of disturbance, and in particular the ecosystem has a lower sustainable rate of environmental change in the presence of plastic foraging. However, allowing the evolutionary regression of plastic foraging then has a comparatively positive effect on persistence.

Despite the substantial effort devoted to analyzing this complex model, relaxing some of its assumptions would likely reveal further complexities. Notably, the overall effect of plasticity on consumer persistence depends on effects already encountered in models of the adaptive response of single species to environmental change: a fast non-genetic response in the short term versus a weakened genetic response in the longer term. The overall balance between these opposite effects on adaptation may be difficult to predict robustly. In the case of a constant rate of environmental change, the results of the present model depend on a lag load between the trait changes of consumer and resource populations, and the extent of the lag may also depend on many factors, such as the extent of genetic variation (e.g., Bürger & Lynch, 1995) for niche traits in consumers and resources. Here, the same variance of mutational effects was assumed for all three evolving traits. Further, spatial environmental variation, a central issue in studies of adaptive responses to environmental changes (e.g., Parmesan, 2006, Zhu et al., 2012), was not considered. Finally, the rate of adjustment of effort by consumers with given niche trait and plastic foraging trait values was assumed proportional to the density of consumers with such trait values. This was justified as a way of accounting for the use of social cues during foraging, but to the extent that they occur, social effects could manifest themselves through other learning dynamics. 

In conclusion, Ledru et al. have addressed a broad range of questions, suggesting new empirical tests of behavioural patterns on one side, and recovering in the context of community response to environmental changes a complexity that could be expected from earlier works on adaptive responses of organisms but that has been overlooked by previous works on community effects of phenotypic plasticity.

References

Bürger, R. and Lynch, M. (1995), Evolution and extinction in a changing environment: a quantitative-genetic analysis. Evolution, 49: 151-163. https://doi.org/10.1111/j.1558-5646.1995.tb05967.x

Chevin, L.-M., Collins, S. and Lefèvre, F. (2013), Phenotypic plasticity and evolutionary demographic responses to climate change: taking theory out to the field. Funct Ecol, 27: 967-979. https://doi.org/10.1111/j.1365-2435.2012.02043.x

Duputié, A., Rutschmann, A., Ronce, O. and Chuine, I. (2015), Phenological plasticity will not help all species adapt to climate change. Glob Change Biol, 21: 3062-3073. https://doi-org.inee.bib.cnrs.fr/10.1111/gcb.12914

Ledru, L., Garnier, J., Guillot, O., Faou, E., & Ibanez, S. (2023). The evolutionary dynamics of plastic foraging and its ecological consequences: a resource-consumer model. EcoEvoRxiv, ver. 4 peer-reviewed and recommended by Peer Community In Evolutionary Biology. https://doi.org/10.32942/X2QG7M

Parmesan, C. (2006) Ecological and evolutionary responses to recent climate change
Annual Review of Ecology, Evolution, and Systematics 2006 37:1, 637-669. https://doi.org/10.1146/annurev.ecolsys.37.091305.110100

Vinton, A.C., Gascoigne, S.J.L., Sepil, I., Salguero-Gómez, R., (2022) Plasticity’s role in adaptive evolution depends on environmental change components. Trends in Ecology & Evolution, 37: 1067-1078.
https://doi.org/10.1016/j.tree.2022.08.008

Zhu, K., Woodall, C.W. and Clark, J.S. (2012), Failure to migrate: lack of tree range expansion in response to climate change. Glob Change Biol, 18: 1042-1052. https://doi.org/10.1111/j.1365-2486.2011.02571.x

Many viruses are transmitted by biological vectors, i.e. organisms that transfer the virus from one host to another. Dengue virus (DENV) is one of them. Dengue is a mosquito-borne viral disease that has rapidly spread around the world since the 1940s. One recent estimate indicates 390 million dengue infections per year [1]. As many arthropod-borne vertebrate viruses, DENV has to cross several anatomical barriers in the vector, to multiply in its body and to invade its salivary glands before getting transmissible. As a consequence, vectors are not passive carriers but genuine hosts of the viruses that potentially have important effects on the composition of virus populations and, ultimately, on virus epidemiology and virulence. Within infected vectors, virus populations are expected to acquire new mutations and to undergo genetic drift and selection effects. However, the intensity of these evolutionary forces and the way they shape virus genetic diversity are poorly known.

In their study, Lequime et al. [2] finely disentangled the effects of genetic drift and selection on DENV populations during their infectious cycle within mosquito (Aedes aegypti) vectors. They evidenced that the genetic diversity of viruses within their vectors is shaped by genetic drift, selection and vector genotype. The experimental design consisted in artificial acquisition of purified virus by mosquitoes during a blood meal. The authors monitored the diversity of DENV populations in Ae. aegypti individuals at different time points by high-throughput sequencing (HTS). They estimated the intensity of genetic drift and selection effects exerted on virus populations by comparing the DENV diversity at these sampling time points with the diversity in the purified virus stock (inoculum).

Disentangling the effects of genetic drift and selection remains a methodological challenge because both evolutionary forces operate concomitantly and both reduce genetic diversity. However, selection reduces diversity in a reproducible manner among experimental replicates (here, mosquito individuals): the fittest variants are favoured at the expense of the weakest ones. In contrast, genetic drift reduces diversity in a stochastic manner among replicates. Genetic drift acts equally on all variants irrespectively of their fitness. The strength of genetic drift is frequently evaluated with the effective population size Ne: the lower Ne, the stronger the genetic drift [3]. The estimation of the effective population size of DENV populations by Lequime et al. [2] was based on single-nucleotide polymorphisms (SNPs) that were (i) present both in the inoculum and in the virus populations sampled at the different time points and (ii) that were neutral (or nearly-neutral) and therefore subjected to genetic drift only and insensitive to selection. As expected for viruses that possess small and constrained genomes, such neutral SNPs are extremely rare. Starting from a set of >1800 SNPs across the DENV genome, only three SNPs complied with the neutrality criteria and were enough represented in the sequence dataset for a precise Ne estimation. Using the method described by Monsion et al. [4], Lequime et al. [2] estimated Ne values ranging from 5 to 42 viral genomes (95% confidence intervals ranged from 2 to 161 founding viral genomes). Consequently, narrow bottlenecks occurred at the virus acquisition step, since the blood meal had allowed the ingestion of ca. 3000 infectious virus particles, on average. Interestingly, bottleneck sizes did not differ between mosquito genotypes. Monsion et al.’s [4] formula provides only an approximation of Ne. A corrected formula has been recently published [5]. We applied this exact Ne formula to the means and variances of the frequencies of the three neutral markers estimated before and after the bottlenecks (Table 1 in [2]), and nearly identical Ne estimates were obtained with both formulas.

Selection intensity was estimated from the dN/dS ratio between the nonsynonymous and synonymous substitution rates using the HTS data on DENV populations. DENV genetic diversity increased following initial infection but was restricted by strong purifying selection during virus expansion in the midgut. Again, no differences were detected between mosquito genotypes. However and importantly, significant differences in DENV genetic diversity were detected among mosquito genotypes. As they could not be related to differences in initial genetic drift or to selection intensity, the authors raise interesting alternative hypotheses, including varying rates of de novo mutations due to differences in replicase fidelity or differences in the balancing selection regime. Interestingly, they also suggest that this observation could simply result from a methodological issue linked to the detection threshold of low-frequency SNPs.
 

References

[1] Bhatt S, Gething PW, Brady OJ, Messina JP, Farlow AW, Moyes CL, Drake JM, et al. 2013. The global distribution and burden of dengue. Nature 496: 504–7 doi: 10.1038/nature12060

[2] Lequime S, Fontaine A, Gouilh MA, Moltini-Conclois I and Lambrechts L. 2016. Genetic drift, purifying selection and vector genotype shape dengue virus intra-host genetic diversity in mosquitoes. PloS Genetics 12: e1006111 doi: 10.1371/journal.pgen.1006111

[3] Charlesworth B. 2009. Effective population size and patterns of molecular evolution and variation. Nature Reviews Genetics 10: 195-205 doi: 10.1038/nrg2526

[4] Monsion B, Froissart R, Michalakis Y and Blanc S. 2008. Large bottleneck size in cauliflower mosaic virus populations during host plant colonization. PloS Pathogens 4: e1000174 doi: 10.1371/journal.ppat.1000174

[5] Thébaud G and Michalakis Y. 2016. Comment on ‘Large bottleneck size in cauliflower mosaic virus populations during host plant colonization’ by Monsion et al. (2008). PloS Pathogens 12: e1005512 doi: 10.1371/journal.ppat.1005512

Partitioning the effects of vicariance and low dispersal has been a long-standing problem in historical biogeography and phylogeography. While the term “vicariance” refers to divergence in allopatry, caused by some physical (geological, geographical) or climatic barriers (e.g. Rosen 1978), isolation by distance refers to the genetic differentiation of remote populations due to the physical distance separating them, when the latter surpasses the scale of dispersal (Wright 1938, 1940, 1943). 

Vicariance and dispersal have long been considered as separate forces leading to separate scenarii of speciation (e.g. reviewed in Hickerson and Meyer 2008). Nevertheless, these two processes are strongly linked, as, for example, vicariance theory relies on the assumption that ancestral lineages were once linked by dispersal prior to physical or climatic isolation (Rosen 1978). Low dispersal and vicariance are not mutually exclusive, and distinguishing these two processes in heterogeneous landscapes, especially for poor dispersers, remains therefore a severe challenge. For example, low dispersal (and/or small population size) can give rise to geographic patterns consistent with a phylogeographic break and be mistaken for geographic isolation (Irwin 2002, Kuo and Avise 2005).

The study of Rancilliac and colleagues (2023) is at the heart of this issue. It focuses on a nominal lizard species, the red-tailed spiny-footed lizard (Acanthodactylus erythrurus, Squamata: Lacertidae), which has a wide spatial distribution (from the Maghreb to the Iberian Peninsula), is found in a variety of different habitats, and has a wide range of morphological traits that do not always correlate with phylogeny. The main question is the following: have “the morphological and ecological diversification of this group been produced by vicariance and lineage diversification, or by local adaptation in the face of historical gene flow?” To tackle this question, the authors used sequence data from multiple mitochondrial and nuclear markers and a nested analysis workflow integrating phylogeography, multiple correspondence analyses and a relatively novel approach to IBD testing (Hausdorf & Henning, 2020). The latter is based on regression analysis and was shown to be less prone to error than the traditional (partial) Mantel test. 

While this set of methods allowed the partitioning of the effect of isolation by distance and vicariance in shaping contemporary genetic diversity in red-tailed spiny-footed lizards, some of the evolutionary history of this species complex remains blurred by ongoing gene flow and admixture, retention of ancestral polymorphism, or selection. The lack of congruence between mitochondrial and nuclear gene trees once again warns us that proposing evolutionary scenarii based on individual gene trees is a risky business. 

References

Hausdorf B, Hennig C (2020) Species delimitation and geography. Molecular Ecology Resources, 20, 950–960. https://doi.org/10.1111/1755-0998.13184

Hickerson MJ, Meyer CP (2008) Testing comparative phylogeographic models of marine vicariance and dispersal using a hierarchical Bayesian approach. BMC Evolutionary Biology, 8, 322. https://doi.org/10.1186/1471-2148-8-322

Irwin DE (2002) Phylogeographic breaks without geographic barriers to gene flow. Evolution, 56, 2383–2394. https://doi.org/10.1111/j.0014-3820.2002.tb00164.x

Kuo C-H, Avise JC (2005) Phylogeographic breaks in low-dispersal species: the emergence of concordance across gene trees. Genetica, 124, 179–186. https://doi.org/10.1007/s10709-005-2095-y

Rancilhac L, Miralles A, Geniez P, Mendez-Aranda D, Beddek M, Brito JC, Leblois R, Crochet P-A (2023) Phylogeographic breaks and how to find them: An empirical attempt at separating vicariance from isolation by distance in a lizard with restricted dispersal. bioRxiv, 2022.09.30.510256, ver. 4 peer-reviewed and recommended by Peer Community in Evolutionary Biology. https://doi.org/10.1101/2022.09.30.510256

Rosen DE (1978) Vicariant Patterns and Historical Explanation in Biogeography. Systematic Biology, 27, 159–188. https://doi.org/10.2307/2412970

Wright, S (1938) Size of population and breeding structure in relation to evolution. Science 87:430-431.

Wright S (1940) Breeding Structure of Populations in Relation to Speciation. The American Naturalist, 74, 232–248. https://doi.org/10.1086/280891

Wright S (1943) Isolation by distance. Genetics, 28, 114–138. https://doi.org/10.1093/genetics/28.2.114

In mammals, it is well documented that sexual dimorphism and in particular sex differences in behaviour are fine-tuned by gonadal hormonal profiles. For example, in lemurs, where female social dominance is common, the level of testosterone in females is unusually high compared to that of other primate females (Petty and Drea 2015). 

Recent studies however suggest that gonadal hormones might not be the only biological factor involved in establishing sexual dimorphism, sex chromosomes might also play a role. The four core genotype (FCG) model and other similar systems allowing to decouple phenotypic and genotypic sex in mice have provided very convincing evidence of such a role (Gatewood et al. 2006; Arnold and Chen 2009; Arnold 2020a, 2020b). This however is a new field of research and the role of sex chromosomes in establishing sexually dimorphic behaviours has not been studied very much yet. Moreover, the FCG model has some limits. Sry, the male determinant gene on the mammalian Y chromosome might be involved in some sex differences in neuroanatomy, but Sry is always associated with maleness in the FCG model, and this potential role of Sry cannot be studied using this system.

Heitzmann et al. have used a natural system to approach these questions. They worked on the African Pygmy mouse, Mus minutoides, in which a modified X chromosome called X* can feminize X*Y individuals, which offers a great opportunity for elegant experiments on the effects of sex chromosomes versus hormones on behaviour. They focused on maternal care and compared pup retrieval, nest quality, and mother-pup interactions in XX, X*X and X*Y females. They found that X*Y females are significantly better at retrieving pups than other females. They are also much more aggressive towards the fathers than other females, preventing paternal care. They build nests of poorer quality but have similar interactions with pups compared to other females. Importantly, no significant differences were found between XX and X*X females for these traits, which points to an effect of the Y chromosome in explaining the differences between X*Y and other females (XX, X*X). Also, another work from the same group showed similar gonadal hormone levels in all the females (Veyrunes et al. 2022). 

Heitzmann et al. made a number of predictions based on what is known about the neuroanatomy of rodents which might explain such behaviours. Using cytology, they looked for differences in neuron numbers in the hypothalamus involved in the oxytocin, vasopressin and dopaminergic pathways in XX, X*X and X*Y females, but could not find any significant effects. However, this part of their work relied on very small sample sizes and they used virgin females instead of mothers for ethical reasons, which greatly limited the analysis. 

Interestingly, X*Y females have a higher reproductive performance than XX and X*X ones, which compensate for the cost of producing unviable YY embryos and certainly contribute to maintaining a high frequency of X* in many African pygmy mice populations (Saunders et al. 2014, 2022). X*Y females are probably solitary mothers contrary to other females, and Heitzmann et al. have uncovered a divergent female strategy in this species. Their work points out the role of sex chromosomes in establishing sex differences in behaviours. 

References

Arnold AP (2020a) Sexual differentiation of brain and other tissues: Five questions for the next 50 years. Hormones and Behavior, 120, 104691. https://doi.org/10.1016/j.yhbeh.2020.104691

Arnold AP (2020b) Four Core Genotypes and XY* mouse models: Update on impact on SABV research. Neuroscience & Biobehavioral Reviews, 119, 1–8. https://doi.org/10.1016/j.neubiorev.2020.09.021

Arnold AP, Chen X (2009) What does the “four core genotypes” mouse model tell us about sex differences in the brain and other tissues? Frontiers in Neuroendocrinology, 30, 1–9. https://doi.org/10.1016/j.yfrne.2008.11.001

Gatewood JD, Wills A, Shetty S, Xu J, Arnold AP, Burgoyne PS, Rissman EF (2006) Sex Chromosome Complement and Gonadal Sex Influence Aggressive and Parental Behaviors in Mice. Journal of Neuroscience, 26, 2335–2342. https://doi.org/10.1523/JNEUROSCI.3743-05.2006

Heitzmann LD, Challe M, Perez J, Castell L, Galibert E, Martin A, Valjent E, Veyrunes F (2022) Genotypic sex shapes maternal care in the African Pygmy mouse, Mus minutoides. bioRxiv, 2022.04.05.487174, ver. 4 peer-reviewed and recommended by Peer Community in Evolutionary Biology. https://doi.org/10.1101/2022.04.05.487174

Petty JMA, Drea CM (2015) Female rule in lemurs is ancestral and hormonally mediated. Scientific Reports, 5, 9631. https://doi.org/10.1038/srep09631

Saunders PA, Perez J, Rahmoun M, Ronce O, Crochet P-A, Veyrunes F (2014) Xy Females Do Better Than the Xx in the African Pygmy Mouse, Mus Minutoides. Evolution, 68, 2119–2127. https://doi.org/10.1111/evo.12387

Saunders PA, Perez J, Ronce O, Veyrunes F (2022) Multiple sex chromosome drivers in a mammal with three sex chromosomes. Current Biology, 32, 2001-2010.e3. https://doi.org/10.1016/j.cub.2022.03.029

Veyrunes F, Perez J, Heitzmann L, Saunders PA, Givalois L (2022) Separating the effects of sex hormones and sex chromosomes on behavior in the African pygmy mouse Mus minutoides, a species with XY female sex reversal. bioRxiv, 2022.07.11.499546. https://doi.org/10.1101/2022.07.11.499546