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

In this preprint, Rimbaud et al. (2023) examine whether Adult Plant Resistance (APR), where plants delay their response to pathogens, is a viable alternative when the solution to evolve complete resistance from the seedling stage exists. At first glance, delaying resistance seems like a counter-intuitive strategy, unless it can result in a weaker selection of the pathogen, and therefore slow down its adaption to plant resistance.

The approach of Rimbaud et al. is to incorporate as much of the mechanisms as possible into a model. By accounting for explicit spatio-temporal dynamics, stochasticity, and the coupling between demography and population genetics, to simulate an agricultural landscape, they reach a nuanced conclusion.

Weaker and delayed activation of genes that confer APR does indeed reduce the selection pressure acting on the pathogen, at the cost of overall less effective protection. The alternative strategy of rapid or complete activation of these genes, although it results in better results in defending against the pathogen, is at risk of being overcome because it introduces a stronger selection pressure.

One important feature of this work is that it accounts for agricultural practices. The landscape that is simulated can account for monoculture, mosaic cultures, mixed cultures, and rotations of crops (with different strategies for resistance). This introduces an interesting element to the conclusion: that human practices will have an impact on the selection pressures acting within the system.

Perhaps the most striking result is that, for the plants, it might be more beneficial to bear the cost of a wild-type pathogen that can benefit from delayed activation of resistance, and therefore exclude the more virulent strains by simply being there first, and essentially buying the plant some time before it activates its resistance more completely. When the landscape is aggregated, even wild-type pathogens can cause severe epidemics; increasing fragmentation, because it enables connectivity between patches of plants with different strategies, allows pathogens to move across cultivars, and reduces the epidemic risk on susceptible plants.

These results should encourage scaling up the perspective on APR, and indeed Rimbaud et al. adopt a landscape-scale perspective, to show that APR genes and genes conferring more complete resistance early on can have synergistic effects. This is, again, both an interesting result for evolutionary biologists, but also a useful way to prioritize different crop management strategies over large spatial scales.

References

Rimbaud, Loup, et al. Durable Resistance or Efficient Disease Control? Adult Plant Resistance (APR) at the Heart of the Dilemma. 2023. bioRxiv, ver. 2 peer-reviewed and recommended by Peer Community in Evolutionary Biology. https://doi.org/10.1101/2022.08.30.505787

Models explaining the evolutionary processes operating in living beings are often impossible to test in the real world. This is mainly because of the long time (i.e., the number of generations) which is necessary for evolution to unfold. In addition, any such experiment would require a large number of individuals and, more importantly, many replicates to account for the inherent variance of the evolutionary processes under investigation. Only organisms with fast generation times and favourable rearing conditions can be used to explicitly test for specific evolutionary hypotheses.

Computer simulations have filled this gap, revolutionising experimental testing in evolutionary biology by integrating genetic models into complex population dynamics, which can be run for (potentially) any length of time. Without going into an extensive description of the many available approaches for population genetics simulations (an exhaustive review can be found in Hoban et al 2012), three main aspects are, in my opinion, important for categorising and choosing one simulation approach over another. The first concerns the basic distinction between coalescent-based and individual-based simulators: the former being an efficient approach, which simulates back in time the coalescence events of a sample of homologous DNA fragments, while the latter is a more computationally intensive approach where all of the individuals (and their underlying genetic/genomic features) in the population are simulated forward-in-time, generation after generation. The second aspect concerns the simulation of natural selection. Although natural selection can be integrated into backward-in-time simulations, it is more realistically implemented as individual-based fitness in forward-in-time simulators. The third point, which has been often overlooked in evolutionary simulations, is about the possibility to design a simulation scenario where individuals and populations can exploit a physical (geographical) space.

Amongst the coalescent-based simulators, SPLATCHE (Currat et al 2004), and its derivatives, is one of the few simulation tools deploying the coalescence process in sub-demes which are all connected by migration, thus getting as close as possible to a spatially-explicit population. On the other hand, individual-based simulators, whose development followed the increasing power of computational machines, offer a great opportunity to include spatio-temporal dynamics within a genomic simulation model. One of the most realistic and efficient individual-based forward-in-time simulators available is SLiM (Haller and Messer 2017), which allows users to implement simulations in arbitrarily complex spaces. Here, the more challenging part is encoding the spatially-explicit scenarios using the SLiM-specific EIDOS language. 

The new R package slendr (Petr et al 2022) offers a practical solution to this issue. By wrapping different tools into a well-known scripting language, slendr allows the design of spatiotemporal simulation scenarios which can be directly executed in the individual-based SLiM simulator, and the output stored with modern tree-sequence analysis tools (tskit; Kellerer et al 2018). Alternatively, simulations of non-spatial models can be run using a coalescent-based algorithm (msprime; Baumdicker et al 2022). The main advantage of slendr is that the whole simulative experiment can be performed entirely in the R environment, taking advantage of the many libraries available for geospatial and genomic data analysis, statistics, and visualisation. The open-source nature of this package, whose main aim is to make complex population genomics modelling more accessible, and the vibrant community of SLiM and tskit users will very likely make slendr widely used amongst the molecular ecology and evolutionary biology communities. 

Slendr handles real Earth cartographic data where users can design realistic demographic processes which characterise natural populations (i.e., expansions, displacement of large populations, interactions among populations, migrations, population splits, etc.) by changing spatial population boundaries across time and space. All in all, slendr is a very flexible and scalable framework to test the accuracy of spatial models, hypotheses about demography and selection, and interactions between organisms across space and time. 

REFERENCES

Baumdicker, F., Bisschop, G., Goldstein, D., Gower, G., Ragsdale, A. P., Tsambos, G., ... & Kelleher, J. (2022). Efficient ancestry and mutation simulation with msprime 1.0. Genetics, 220(3), iyab229. https://doi.org/10.1093/genetics/iyab229

Currat, M., Ray, N., & Excoffier, L. (2004). SPLATCHE: a program to simulate genetic diversity taking into account environmental heterogeneity. Molecular Ecology Notes, 4(1), 139-142. https://doi.org/10.1046/j.1471-8286.2003.00582.x

Haller, B. C., & Messer, P. W. (2017). SLiM 2: flexible, interactive forward genetic simulations. Molecular biology and evolution, 34(1), 230-240. https://doi.org/10.1093/molbev/msw211

Hoban, S., Bertorelle, G., & Gaggiotti, O. E. (2012). Computer simulations: tools for population and evolutionary genetics. Nature Reviews Genetics, 13(2), 110-122. https://doi.org/10.1038/nrg3130

Kelleher, J., Thornton, K. R., Ashander, J., & Ralph, P. L. (2018). Efficient pedigree recording for fast population genetics simulation. PLoS computational biology, 14(11), e1006581. https://doi.org/10.1371/journal.pcbi.1006581

Petr, M., Haller, B. C., Ralph, P. L., & Racimo, F. (2023). slendr: a framework for spatio-temporal population genomic simulations on geographic landscapes. bioRxiv, 2022.03.20.485041, ver. 5 peer-reviewed and recommended by Peer Community in Evolutionary Biology. https://doi.org/10.1101/2022.03.20.485041

The causes and consequences of the evolution of sexual reproduction are a major topic in evolutionary biology. With advances in sequencing technology, it becomes possible to compare sexual chromosomes across species and infer the neutral and selective processes shaping polymorphism at these chromosomes. Most sex and mating-type chromosomes exhibit an absence of recombination in large genomic regions around the animal, plant or fungal sex-determining genes. This suppression of recombination likely occurred in several time steps generating stepwise increasing genomic regions starting around the sex-determining genes. This mechanism generates so-called evolutionary strata of differentiation between sex chromosomes (Nicolas et al., 2004, Bergero and Charlesworth, 2009, Hartmann et al. 2021). The evolution of extended regions of recombination suppression is also documented on mating-type chromosomes in fungi (Hartmann et al., 2021) and around supergenes (Yan et al., 2020, Jay et al., 2021). The exact reason and evolutionary mechanisms for this phenomenon are still, however, debated.

Two hypotheses are proposed: 1) sexual antagonism (Charlesworth et al., 2005), which, nevertheless, does explain the observed occurrence of the evolutionary strata, and 2) the sheltering of deleterious alleles by inversions carrying a lower load than average in the population (Charlesworth and Wall, 1999, Antonovics and Abrams, 2004). In the latter, the mechanism is as follows. A genetic inversion or a suppressor of recombination in cis may exhibit some overdominance behaviour. The inversion exhibiting less recessive deleterious mutations (compared to others at the same locus) may increase in frequency, before at higher frequency occurring at the homozygous state, expressing its genetic load. However, the inversion may never be at the homozygous state if it is genetically linked to a gene in a permanently heterozygous state. The inversion can then be advantageous and may reach fixation at the sex chromosome (Charlesworth and Wall, 1999, Antonovics and Abrams, 2004, Jay et al., 2022). These selective mechanisms promote thus the suppression of recombination around the sex-determining gene, and recessive deleterious mutations are permanently sheltered. This hypothesis is corroborated by the sheltering of deleterious mutations observed around loci under balancing selection (Llaurens et al. 2009, Lenz et al. 2016) and around mating-type genes in fungi and supergenes (Jay et al. 2021, Jay et al., 2022).

In this present theoretical study, Tezenas et al. (2022) analyse how linkage to a necessarily heterozygous fungal mating type locus influences the persistence/extinction time of a new mutation at a second selected locus. This mutation can either be deleterious and recessive, or overdominant. There is arbitrary linkage between the two loci, and sexual reproduction occurs either between 1) gametes of different individuals (outcrossing), or 2) by selfing with gametes originating from the same (intra-tetrad) or different (inter-tetrad) tetrads produced by that individual. Note, here, that the mating-type gene does not prevent selfing. The authors study the initial stochastic dynamics of the mutation using a multi-type branching process (and simulations when analytical results cannot be obtained) to compute the extinction time of the deleterious mutation. The main result is that the presence of a mating-type locus always decreases the purging probability and increases the purging time of the mutations under selfing. Ultimately, deleterious mutations can indeed accumulate near the mating-type locus over evolutionary time scales. In a nutshell, high selfing or high intra-tetrad mating do increase the sheltering effect of the mating-type locus. In effect, the outcome of sheltering of deleterious mutations depends on two opposing mechanisms: 1) a higher selfing rate induces a greater production of homozygotes and an increased effect of the purging of deleterious mutations, while 2) a higher intra-tetrad selfing rate (or linkage with the mating-type locus) generates heterozygotes which have a small genetic load (and are favoured). The authors also show that rare events of extremely long maintenance of deleterious mutations can occur.

The authors conclude by highlighting the manifold effect of selfing which reduces the effective population size and thus impairs the efficiency of selection and increases the mutational load, while also favouring the purge of deleterious homozygous mutations. Furthermore, this study emphasizes the importance of studying the maintenance and accumulation of deleterious mutations in the vicinity of heterozygous loci (e.g. under balancing selection) in selfing species.

References

Antonovics J, Abrams JY (2004) Intratetrad Mating and the Evolution of Linkage Relationships. Evolution, 58, 702–709. https://doi.org/10.1111/j.0014-3820.2004.tb00403.x

Bergero R, Charlesworth D (2009) The evolution of restricted recombination in sex chromosomes. Trends in Ecology & Evolution, 24, 94–102. https://doi.org/10.1016/j.tree.2008.09.010

Charlesworth D, Morgan MT, Charlesworth B (1990) Inbreeding Depression, Genetic Load, and the Evolution of Outcrossing Rates in a Multilocus System with No Linkage. Evolution, 44, 1469–1489. https://doi.org/10.1111/j.1558-5646.1990.tb03839.x

Charlesworth D, Charlesworth B, Marais G (2005) Steps in the evolution of heteromorphic sex chromosomes. Heredity, 95, 118–128. https://doi.org/10.1038/sj.hdy.6800697

Charlesworth B, Wall JD (1999) Inbreeding, heterozygote advantage and the evolution of neo–X and neo–Y sex chromosomes. Proceedings of the Royal Society of London. Series B: Biological Sciences, 266, 51–56. https://doi.org/10.1098/rspb.1999.0603

Hartmann FE, Duhamel M, Carpentier F, Hood ME, Foulongne-Oriol M, Silar P, Malagnac F, Grognet P, Giraud T (2021) Recombination suppression and evolutionary strata around mating-type loci in fungi: documenting patterns and understanding evolutionary and mechanistic causes. New Phytologist, 229, 2470–2491. https://doi.org/10.1111/nph.17039

Jay P, Chouteau M, Whibley A, Bastide H, Parrinello H, Llaurens V, Joron M (2021) Mutation load at a mimicry supergene sheds new light on the evolution of inversion polymorphisms. Nature Genetics, 53, 288–293. https://doi.org/10.1038/s41588-020-00771-1

Jay P, Tezenas E, Véber A, Giraud T (2022) Sheltering of deleterious mutations explains the stepwise extension of recombination suppression on sex chromosomes and other supergenes. PLOS Biology, 20, e3001698. https://doi.org/10.1371/journal.pbio.3001698

Lenz TL, Spirin V, Jordan DM, Sunyaev SR (2016) Excess of Deleterious Mutations around HLA Genes Reveals Evolutionary Cost of Balancing Selection. Molecular Biology and Evolution, 33, 2555–2564. https://doi.org/10.1093/molbev/msw127

Llaurens V, Gonthier L, Billiard S (2009) The Sheltered Genetic Load Linked to the S Locus in Plants: New Insights From Theoretical and Empirical Approaches in Sporophytic Self-Incompatibility. Genetics, 183, 1105–1118. https://doi.org/10.1534/genetics.109.102707

Nicolas M, Marais G, Hykelova V, Janousek B, Laporte V, Vyskot B, Mouchiroud D, Negrutiu I, Charlesworth D, Monéger F (2004) A Gradual Process of Recombination Restriction in the Evolutionary History of the Sex Chromosomes in Dioecious Plants. PLOS Biology, 3, e4. https://doi.org/10.1371/journal.pbio.0030004

Tezenas E, Giraud T, Véber A, Billiard S (2022) The fate of recessive deleterious or overdominant mutations near mating-type loci under partial selfing. bioRxiv, 2022.10.07.511119, ver. 2 peer-reviewed and recommended by Peer Community in Evolutionary Biology. https://doi.org/10.1101/2022.10.07.511119

Yan Z, Martin SH, Gotzek D, Arsenault SV, Duchen P, Helleu Q, Riba-Grognuz O, Hunt BG, Salamin N, Shoemaker D, Ross KG, Keller L (2020) Evolution of a supergene that regulates a trans-species social polymorphism. Nature Ecology & Evolution, 4, 240–249. https://doi.org/10.1038/s41559-019-1081-1

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