Domestication is a complex process that imprints the demography and the genomes of domesticated populations, enforcing strong selective pressures on traits favourable to humans, e.g. for food production [1]. Domestication has been quite intensely studied in plants and animals, but less so in micro-organisms such as fungi, despite their assets (e.g. their small genomes and tractability in the lab). This elegant study by Bennetot and collaborators [2] on the cheese-making fungus Geotrichum candidum adds to the mounting body of studies in the genomics of fungi, proving they are excellent models in evolutionary biology for studying adaptation and drift in eukaryotes [3].

Bennetot et al. newly showed with whole genome sequences that all G. candidum strains isolated from cheese form a monophyletic clade subdivided into three genetically differentiated populations with several admixed strains, while the wild strains sampled from diverse geographic locations form a sister clade. This suggests the wild progenitor was not sampled in the present study and calls for future exciting work on the domestication history of the G. candidum fungus. The authors scanned the genomes for footprints of adaptation to the cheese environment and identified promising candidates, such as a gene involved in iron uptake (this element is limiting in cheese). Their functional genome analysis also provides evidence for higher contents of transposable elements in cheese-making strains, likely due to relaxed selection during the domestication process.

This paper is particularly impressive in that the authors complemented the population genomic approach with the phenotypic characterization of the strains and tested their ability to outcompete common fungal food spoilers. The authors convincingly showed that cheese-making strains display phenotypic differences relative to wild relatives for multiple traits such as slower growth, lower proteolysis activity and a greater amount of volatiles attractive to consumers, these phenotypes being beneficial for cheese making.

Finally, this work is particularly inspiring because it thoroughly discusses convergent evolution during domestication in different cheese-associated fungi. Indeed, studying populations experiencing similar environmental pressures is fundamental to understanding whether evolution is repeatable [4]. For instance, all three cheese populations of G. candidum exhibit a lower genetic diversity than wild populations. However, only one population displays a stronger domestication syndrome, resembling the Penicillium camemberti situation [5]. Furthermore, different cheese-making practices may have led to varying situations with clonal lineages in non-Roquefort P. roqueforti and P. camemberti [5, 6], while the cheese-making G. candidum populations still harbour some diversity. In a nutshell, Bennetot's study makes an important contribution to evolutionary biology and highlights the value of diversifying our model organisms toward under-represented clades.

REFERENCES

[1] Diamond J (2002) Evolution, consequences and future of plant and animal domestication. Nature 418: 700–707. https://doi.org/10.1038/nature01019

[2] Bennetot B, Vernadet J-P, Perkins V, Hautefeuille S, Rodríguez de la Vega RC, O’Donnell S, Snirc A, Grondin C, Lessard M-H, Peron A-C, Labrie S, Landaud S, Giraud T, Ropars J (2023) Domestication of different varieties in the cheese-making fungus Geotrichum candidum. bioRxiv, 2022.05.17.492043, ver. 4 peer-reviewed and recommended by Peer Community in Evolutionary Biology. https://doi.org/10.1101/2022.05.17.492043 

[3] Gladieux P, Ropars J, Badouin H, Branca A, Aguileta G, de Vienne DM, Rodríguez de la Vega RC, Branco S, Giraud T (2014) Fungal evolutionary genomics provides insight into the mechanisms of adaptive divergence in eukaryotes. Mol. Ecol. 23: 753–773. https://doi.org/10.1111/mec.12631

[4] Bolnick DI, Barrett RD, Oke KB, Rennison DJ, Stuart YE (2018) (Non)Parallel evolution. Ann. Rev. Ecol. Evol. Syst. 49: 303–330. https://doi.org/10.1146/annurev-ecolsys-110617-062240 

[5] Ropars J, Didiot E, Rodríguez de la Vega RC, Bennetot B, Coton M, Poirier E, Coton E, Snirc A, Le Prieur S, Giraud T (2020) Domestication of the Emblematic White Cheese-Making Fungus Penicillium camemberti and Its Diversification into Two Varieties. Current Biol. 30: 4441–4453.e4. https://doi.org/10.1016/j.cub.2020.08.082

[6] Dumas, E, Feurtey, A, Rodríguez de la Vega, RC, Le Prieur S, Snirc A, Coton M, Thierry A, Coton E, Le Piver M, Roueyre D, Ropars J, Branca A, Giraud T (2020) Independent domestication events in the blue-cheese fungus Penicillium roqueforti. Mol Ecol. 29: 2639–2660. https://doi.org/10.1111/mec.15359

Why is there such variation across species and populations in the rate at which individuals show wear and tear as they get older? Several compelling theoretical explanations have been developed on the conditions under which selection allows for or prevents senescence; a notable one being that proposed by George C Williams in 1957 based on the idea of antagonistic pleiotropy (Williams, 1957). One of the testable predictions of this theory is that, in populations where adults experience higher mortality, senescence is expected to be faster. This is one of the most influential predictions of the paper, being intuitive (when individuals are less likely to survive to later age classes, we expect weakened selection on traits that would avoid senescence in these classes), and fitting with ‘live fast, die young’ life history framing. As such, it has been widely incorporated into how we think about the evolution of senescence and has received considerable support in comparative studies across species and populations.  

However, it would be misleading to sit back at this point and think we have ‘solved’ the problem of understanding variation in senescence, and how this is linked with mortality. It turns out that the Williams 1957 paper is hotly contested by theoreticians: for the past 30 years – with increasing focus in the last 4 years – a growing body of models and opinion pieces have proposed flaws in the paper itself and in how it has been interpreted (Abrams, 1993; André and Rousset, 2020; Day and Abrams, 2020; Moorad et al., 2019). Central to several of these critiques is that explicit consideration of density dependence (not considered in Williams’ original paper) changes the conditions under which his predictions hold. A new preprint by de Vries, Gallipaud and Kokko brings further clarity to such critiques of the original paper (Vries et al., 2023). 

Beyond just continuing the tradition of critiquing Williams’ prediction, however, de Vries et al. provide a clear guide that is accessible to non-theoreticians about the issues with William’s prediction, and the way in which density dependence and how it operates can change when we expect senescence to occur. Rather than reiterate their points here, we suggest a close reading of the paper itself, along with an excellent overview of the paper in a recent blog by Daniel Nettle (Nettle, 2022). In brief, the paper starts by synthesizing earlier theoretical and empirical studies on the topic and presenting a very simple model to highlight how – in the absence of density dependence – Williams’ prediction does not hold because of the unregulated population growth, which is necessarily higher when there is low mortality. As a result, for a lineage with low mortality, any fitness advantage of placing offspring into the lineage later (i.e. selection for reduced senescence) is exactly cancelled out by the fact that earlier-produced offspring have higher fitness returns. 

They then present a more complex framework, which incorporates realistic mortality distributions, trade-offs between survival and reproduction, and use a series of 10 scenarios of density dependence (and whether this acts on survival or fecundity, and also whether it depends on a threshold or stochastic, or exerts continuing pressure on the trait) to explore selection on fitness-associated traits with age depending on extrinsic mortality. This then generates a range of results for when the Williams prediction holds, when there is no link between mortality and senescence, and when there is an ‘anti-Williams’ result – i.e., where senescence is slower when there is a high mortality. As has been shown in earlier studies, density dependence and how it operates matters, and Williams’ prediction holds most when density dependence affects juvenile age classes (in their model, when adults are less likely to produce them – i.e. there is density dependence on fecundity; or when there is less recruitment into the adult population due to, for example, competition among juveniles). 

So, perhaps we are now at a point where we can lay to rest the debate on the issues specifically with Williams’ original paper, and instead consider more broadly the key factors to measure when predicting patterns of senescence, and what is tangible for empiricists to quantify in their studies. Here, de Vries et al. provide some helpful insights both into the limitations of their approach and what modelling should be done moving forward, and into how we can link existing studies (for example comparing senescence among populations with varying predation pressure) to the theoretical predictions. At the heart of the latter is understanding the mechanism of density-dependent regulation – does it affect survival or fecundity, which age classes are most sensitive, and how do key traits depend on density? – and this is often difficult to measure in field studies.

And from all this we can learn that even very intuitive and extensively discussed concepts in biology do not always have as firm theoretical underpinnings as assumed, and – as should not be surprising – biology is complex and rather than one clear pattern being predicted, this can depend on a multitude of factors. 

REFERENCES

Abrams PA (1993) Does increased mortality favor the evolution of more rapid senescence? Evolution, 47, 877–887. https://doi.org/10.1111/j.1558-5646.1993.tb01241.x

André J-B, Rousset F (2020) Does extrinsic mortality accelerate the pace of life? A bare-bones approach. Evolution and Human Behavior, 41, 486–492. https://doi.org/10.1016/j.evolhumbehav.2020.03.002

Day T, Abrams PA (2020) Density Dependence, Senescence, and Williams’ Hypothesis. Trends in Ecology & Evolution, 35, 300–302. https://doi.org/10.1016/j.tree.2019.11.005

Moorad J, Promislow D, Silvertown J (2019) Evolutionary Ecology of Senescence and a Reassessment of Williams’ ‘Extrinsic Mortality’ Hypothesis. Trends in Ecology & Evolution, 34, 519–530. https://doi.org/10.1016/j.tree.2019.02.006

Nettle AD (2022) Live fast and die young (maybe). https://www.danielnettle.org.uk/2022/02/18/live-fast-and-die-young-maybe/ (accessed 2.27.23).

de Vries C, Galipaud M, Kokko H (2023) Extrinsic mortality and senescence: a guide for the perplexed. bioRxiv, 2022.01.27.478060, ver. 5 peer-reviewed and recommended by Peer Community in Evolutionary Biology. https://doi.org/10.1101/2022.01.27.478060

Williams GC (1957) Pleiotropy, natural selection, and the evolution of senescence. Evolution, 11, 398–411. https://doi.org/10.1111/j.1558-5646.1957.tb02911.x

Natural populations do not consist of only perfectly adapted individuals. If they did, of course, there would be no fodder for evolution by natural selection. And natural selection is operating all the time, winnowing out less well adapted phenotypes through differential reproduction and survival. Demonstrations of natural selection modifying characters-state distributions to bring phenotypes closer to their optima abound in the evolution literature, with examples of short- and long-term changes in phenotype and allele frequencies.  

However, evolutionary biologists know that populations cannot reach their adaptive peaks. Natural selection is tracking a moving target, always with some generations of lag time. The adaptive landscape is multidimensional, so the optimal combination of multiple character states may be impossible because of constraints and trade-offs. Natural selection does not operate alone or in isolation – new mutations and migrants that were selected under other conditions will inject locally non-adaptive genetic variation and genetic drift can change allele frequencies in random directions. We understand these processes that generate and maintain less advantageous variants on a continuous gradient from an optimal phenotype in a fitness landscape. More puzzling are heritable polymorphisms with distinct morphologies, physiologies or behaviours maintained in populations despite their measurably lower reproductive success. But a complete model of evolution must also be able to accommodate these Darwinian paradoxes.

Raymond et al. (2023) investigate one such Darwinian paradox: In humans, male homosexual mate preference is heritable and is associated with a large reduction in offspring production but nonetheless occurs at relatively high frequencies in most human populations. Furthermore, multiple studies have found that homosexual men come from families that are, on average, larger than those of heterosexual men and that homosexual men have, on average, higher birth rank than do heterosexual men, i.e., having more older siblings and, particularly, more older brothers. Two types of mechanisms consistent with these observations have been proposed: 1) An evolutionary mechanism of sex-antagonistic pleiotropy, whereby highly fecund mothers are more likely to produce homosexual sons, and 2) A mechanistic explanation whereby successive male pregnancies alter the uterine environment by increasing the probability of an immune reaction by the mother to her male fetus, altering development of sexually dimorphic brain structures relevant to sexual orientation.

In this article, the authors explore these two mechanisms of sex-antagonistic effects (AE) and fraternal birth order effects (FBOE) and test how well they account for patterns of male homosexuality in population and family data. Clearly, these two effects are somewhat confounded because high birth ranks can only occur in large families. If, indeed, the probability of male homosexuality increases with increasing numbers of (maternal) older brothers, homosexual males will be more common in larger families. Similarly, if high female fecundity leads to a higher probability of male homosexuality via sex-antagonistic effects, homosexual males will, on average, have more older brothers. To disentangle the actions of these two effects the authors modelled the relationship between birth rank and population fecundity and investigated whether AE or FBOE modified this relationship for homosexual men.  Simulation results were compared with aggregated population data from 13 countries.  Family data on individuals’ sexual preference, birth rank and number of male and female siblings from France, Greece and Indonesia were analysed with generalised linear models and Bayesian approaches to test for a signal of AE or FBOE. 

These analyses revealed a significant older-brother effect (FBOE) explaining patterns of occurrence of homosexuality in population and family data but no significant independent sex-antagonistic effect (AE). Thus larger family sizes of homosexual men appear due to the older-brother effect, with individuals of high birth rank coming necessarily from large sibships. The simulation approach also revealed that modelling a fraternal birth order effect (FBOE), such that individuals with more older brothers are more likely to be homosexual, generates an artefactual older sister effect simply because homosexual men are overrepresented at higher birth ranks. Older-sister effects reported in the literature may, therefore, be statistical artefacts of an underlying older-brother effect.

This paper is interesting for a number of reasons. It does an excellent job of explaining, identifying and dealing with estimation biases and testing for artefactual relationships generated by collinearity. It applies state-of-the art analytical/statistical tools. It breaks down two colinear effects and shows that only one really explains phenotypic variation. This is a great example of how to disentangle correlated variables that may or may not both contribute to trait variation. But most intriguingly, we are left without evidence for an evolutionary mechanism that compensates the large fitness cost associated with male homosexuality in humans. How can we explain high heritability maintained in the face of strong directional selection that should erode heritable genetic variation? The usual suspects include cryptic compensatory mechanisms yet to be discovered or flawed estimates of selection or heritability. For example, data on heritability of male homosexual mate preference in humans come from twin studies and twins share birth rank as well as alleles. Thus it is possible that heritability is over-estimated, including the environmental component associated with birth rank. 

If, as the authors demonstrate here, birth rank is the strongest predictor of male homosexual mate preference, selection may be acting on a non-heritable plastic component of phenotypic variation. This could explain why heritable variation is not exhausted by selection, rendering the paradox less paradoxical, but fails to provide an adaptive explanation for the maintenance of male homosexual mate preference. 

References

Raymond M., Turek D., Durand V., Nila S., Suryobroto B., Vadez J., Barthes J., Apostolou M. and Crochet P.-A. (2023) Increased birth rank of homosexual males: disentangling the older brother effect and sexual antagonism hypothesis. bioRxiv, 2022.02.22.481477, ver. 4 peer-reviewed and recommended by Peer Community in Evolutionary Biology. https://doi.org/10.1101/2022.02.22.481477

Understanding factors explaining both intra and interspecific variation in susceptibility to infection by parasites remains a key question in evolutionary biology. Within a species variation in susceptibility is often explained by differences in behaviour affecting exposure to infection and/or resistance affecting the degree by which parasite growth is controlled (Roy & Kirchner, 2000, Behringer et al., 2000). This can vary between the sexes (Kelly et al., 2018) and may be explained by the ability of a parasite to attack different organs or tissues (Brierley et al., 2019). However, what goes on within one species is not always relevant to another, making it unclear when patterns can be scaled up and generalised across species. This is also important to understand when parasites may jump hosts, or identify species that may be susceptible to a host jump (Longdon et al., 2015). Phylogenetic distance between hosts is often an important factor explaining susceptibility to a particular parasite in plant and animal hosts (Gilbert & Webb, 2007, Faria et al., 2013). 

In two separate experiments, Roberts and Longdon (Roberts & Longdon, 2022) investigated how sex and tissue tropism affected variation in the load of Drosophila C Virus (DCV) across multiple Drosophila species. DCV load has been shown to correlate positively with mortality (Longdon et al., 2015). Overall, they found that load did not vary between the sexes; within a species males and females had similar DCV loads for 31 different species. There was some variation in levels of DCV growth in different tissue types, but these too were consistent across males for 7 species of Drosophila. Instead, in both experiments, host phylogeny or interspecific variation, explained differences in DCV load with some species being more infected than others. 

This study is neat in that it incorporates and explores simultaneously both intra and interspecific variation in infection-related life-history traits which is not often done (but see (Longdon et al., 2015, Imrie et al., 2021, Longdon et al., 2011, Johnson et al., 2012). Indeed, most studies to date explore either inter-specific differences in susceptibility to a parasite (it can or can’t infect a given species) (Davies & Pedersen, 2008, Pfenning-Butterworth et al., 2021) or intra-specific variability in infection-related traits (infectivity, resistance etc.) due to factors such as sex, genotype and environment (Vale et al., 2008, Lambrechts et al., 2006). This work thus advances on previous studies, while at the same time showing that sex differences in parasite load are not necessarily pervasive. 

References

Behringer DC, Butler MJ, Shields JD (2006) Avoidance of disease by social lobsters. Nature, 441, 421–421. https://doi.org/10.1038/441421a

Brierley L, Pedersen AB, Woolhouse MEJ (2019) Tissue tropism and transmission ecology predict virulence of human RNA viruses. PLOS Biology, 17, e3000206. https://doi.org/10.1371/journal.pbio.3000206

Davies TJ, Pedersen AB (2008) Phylogeny and geography predict pathogen community similarity in wild primates and humans. Proceedings of the Royal Society B: Biological Sciences, 275, 1695–1701. https://doi.org/10.1098/rspb.2008.0284

Faria NR, Suchard MA, Rambaut A, Streicker DG, Lemey P (2013) Simultaneously reconstructing viral cross-species transmission history and identifying the underlying constraints. Philosophical Transactions of the Royal Society B: Biological Sciences, 368, 20120196. https://doi.org/10.1098/rstb.2012.0196

Gilbert GS, Webb CO (2007) Phylogenetic signal in plant pathogen–host range. Proceedings of the National Academy of Sciences, 104, 4979–4983. https://doi.org/10.1073/pnas.0607968104

Imrie RM, Roberts KE, Longdon B (2021) Between virus correlations in the outcome of infection across host species: Evidence of virus by host species interactions. Evolution Letters, 5, 472–483. https://doi.org/10.1002/evl3.247

Johnson PTJ, Rohr JR, Hoverman JT, Kellermanns E, Bowerman J, Lunde KB (2012) Living fast and dying of infection: host life history drives interspecific variation in infection and disease risk. Ecology Letters, 15, 235–242. https://doi.org/10.1111/j.1461-0248.2011.01730.x

Kelly CD, Stoehr AM, Nunn C, Smyth KN, Prokop ZM (2018) Sexual dimorphism in immunity across animals: a meta-analysis. Ecology Letters, 21, 1885–1894. https://doi.org/10.1111/ele.13164

Lambrechts L, Chavatte J-M, Snounou G, Koella JC (2006) Environmental influence on the genetic basis of mosquito resistance to malaria parasites. Proceedings of the Royal Society B: Biological Sciences, 273, 1501–1506. https://doi.org/10.1098/rspb.2006.3483

Longdon B, Hadfield JD, Day JP, Smith SCL, McGonigle JE, Cogni R, Cao C, Jiggins FM (2015) The Causes and Consequences of Changes in Virulence following Pathogen Host Shifts. PLOS Pathogens, 11, e1004728. https://doi.org/10.1371/journal.ppat.1004728

Longdon B, Hadfield JD, Webster CL, Obbard DJ, Jiggins FM (2011) Host Phylogeny Determines Viral Persistence and Replication in Novel Hosts. PLOS Pathogens, 7, e1002260. https://doi.org/10.1371/journal.ppat.1002260

Pfenning-Butterworth AC, Davies TJ, Cressler CE (2021) Identifying co-phylogenetic hotspots for zoonotic disease. Philosophical Transactions of the Royal Society B: Biological Sciences, 376, 20200363. https://doi.org/10.1098/rstb.2020.0363

Roberts KE, Longdon B (2023) Heterogeneities in infection outcomes across species: examining sex and tissue differences in virus susceptibility. bioRxiv 2022.11.01.514663, ver. 2 peer-reviewed and recommended by Peer Community in Evolutionary Biology. https://doi.org/10.1101/2022.11.01.514663 

Roy BA, Kirchner JW (2000) Evolutionary Dynamics of Pathogen Resistance and Tolerance. Evolution, 54, 51–63. https://doi.org/10.1111/j.0014-3820.2000.tb00007.x

Vale PF, Stjernman M, Little TJ (2008) Temperature-dependent costs of parasitism and maintenance of polymorphism under genotype-by-environment interactions. Journal of Evolutionary Biology, 21, 1418–1427. https://doi.org/10.1111/j.1420-9101.2008.01555.x

Natural landscapes are often composite, with spatial variation in environmental factors being the norm rather than exception. Adaptation to such variation is a major driver of diversity at all levels of biological organization, from genes to phenotypes, species and ultimately ecosystems. While natural selection favours traits that show a better fit to local conditions, the genomic response to such selection is not necessarily straightforward. This is because many quantitative traits are complex and the product of many loci, each with a small to moderate phenotypic contribution. Adapting to environmental challenges that occur in narrow ranges may thus prove difficult as each individual locus is easily swamped by alleles favoured across the rest of the population range. 

To better understand whether and how evolution overcomes such a hurdle, Laroche and Lenormand [1]  combine quantitative genetics and population genetic modelling to track genomic changes that underpin a trait whose fitness optimum differs between a certain spatial range, referred to as a “pocket”, and the rest of the habitat. As it turns out from their analysis, one critical and probably underappreciated factor in determining the type of genetic architecture that evolves is how fitness declines away from phenotypic optima. One classical and popular model of fitness landscape that relates trait value to reproductive success is Gaussian, whereby small trait variations away from the optimum result in even smaller variations in fitness. This facilitates local adaptation via the invasion of alleles of small effects as carriers inside the pocket show a better fit while those outside the pocket only suffer a weak fitness cost. By contrast, when the fitness landscape is more peaked around the optimum, for instance where the decline is linear, adaptation through weak effect alleles is less likely, requiring larger pockets that are less easily swamped by alleles selected in the rest of the range.  

In addition to mathematically investigating the initial emergence of local adaptation, Laroche and Lenormand use computer simulations to look at its long-term maintenance. In principle, selection should favour a genetic architecture that consolidates the phenotype and increases its heritability, for instance by grouping several alleles of large effects close to one another on a chromosome to avoid being broken down by meiotic recombination. Whether or not this occurs also depends on the fitness landscape. When the landscape is Gaussian, the genetic architecture of the trait eventually consists of tightly linked alleles of large effects. The replacement of small effects by large effects loci is here again promoted by the slow fitness decline around the optimum. This is because any shift in architecture in an adapted population requires initially crossing a fitness valley. With a Gaussian landscape, this valley is shallow enough to be crossed, facilitated by a bit of genetic drift. By contrast, when fitness declines linearly around the optimum, genetic architecture is much less evolutionarily labile as any architecture change initially entails a fitness cost that is too high to bear.     

Overall, Laroche and Lenormand provide a careful and thought-provoking analysis of a classical problem in population genetics. In addition to questioning some longstanding modelling assumptions, their results may help understand why differentiated populations are sometimes characterized by “genomic islands” of divergence, and sometimes not. 

References

[1] Laroche F, Lenormand T (2022) The genetic architecture of local adaptation in a cline. bioRxiv, 2022.06.30.498280, ver. 4 peer-reviewed and recommended by Peer Community in Evolutionary Biology. https://doi.org/10.1101/2022.06.30.498280