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

The evolution of cells within organisms can be an important determinant of disease. This is especially clear in the emergence of tumors and cancers from the underlying healthy tissue. In the healthy state, homeostasis is maintained through complex regulatory processes that ensure a relatively constant population size of cells, which is required for tissue function. Tumor cells escape this homeostasis, resulting in uncontrolled growth and consequent disease. Disease progression is driven by further evolutionary processes within the tumor, and so is the response of tumors to therapies. Therefore, evolutionary biology is an important component required for a better understanding of carcinogenesis and the treatment of cancers. In particular, evolutionary theory helps define the principles of mutant evolution and thus to obtain a clearer picture of the determinants of tumor emergence and therapy responses.     

The study by Raatz and Traulsen [1] makes an important contribution in this respect. They use mathematical and computational models to investigate trait evolution in the context of evolutionary rescue, motivated by the dynamics of cancer, and also bacterial infections. This study views the establishment of tumors as cell dynamics in harsh environments, where the population is prone to extinction unless mutants emerge that increase evolutionary fitness, allowing them to expand (evolutionary rescue). The core processes of the model include growth, death, and mutations. Random mutations are assumed to give rise to cell lineages with different trait combinations, where the birth and death rates of cells can change.  The resulting evolutionary trajectories are investigated in the models, and interesting new results were obtained. For example, the turnover of the population was identified as an important determinant of trait evolution. Turnover is defined as the balance between birth and death, with large rates corresponding to fast turnover and small rates to slow turnover. It was found that for fast cell turnover, a given adaptive step in the trait space results in a smaller increase in survival probability than for cell populations with slower turnover. In other words, evolutionary rescue is more difficult to achieve for fast compared to slow turnover populations. While more mutants can be produced for faster cell turnover rates, the analysis showed that this is not sufficient to overcome the barrier to the evolutionary rescue. This result implies that aggressive tumors with fast cell birth and death rates are less likely to persist and progress than tumors with lower turnover rates. This work emphasizes the importance of measuring the turnover rate in different tumors to advance our understanding of the determinants of tumor initiation and progression. The authors discuss that the well-documented heterogeneity in tumors likely also applies to cellular turnover. If a tumor consists of sub-populations with faster and slower turnover, it is possible that a slower turnover cell clone (e.g. characterized by a degree of dormancy) would enjoy a selective advantage. Another source of heterogeneity in turnover could be given by the hierarchical organization of tumors. Similar to the underlying healthy tissue, many tumors are thought to be maintained by a population of cancer stem cells, while the tumor bulk is made up of more differentiated cells. Tissue stem cells tend to be characterized by a lower turnover than progenitor or transit-amplifying cells. Depending on the assumptions about the self-renewal capacity of these different cell populations, the potential for evolutionary rescue could be different depending on the cell compartment in which the mutant emerges. This might be interesting to explore in the future.

There are also implications for treatment. Two types of treatment were investigated: density-affecting treatments in which the density of cells is reduced without altering their trait parameters, and trait-affecting treatments in which the birth and/or death rates are altered. Both types of treatment were found to change the trajectories of trait adaptation, which has potentially important practical implications. Interestingly, it was found that competitive release during treatment can result in situations where after treatment cessation, the non-extinct populations recover to reach sizes that were higher than in the absence of treatment. This points towards the potential of adaptive therapy approaches, where sensitive cells are maintained to some extent to suppress resistant clones [2] competitively. In this context, it is interesting that the success of such approaches might also depend on the turnover of the tumor cell population, as shown by a recent mathematical modeling study [3]. In particular, it was found that adaptive therapy is less likely to work for slow compared to fast turnover tumors. Yet, the current study by Raatz and Traulsen [1] suggests that tumors are more likely to evolve in a slow turnover setting.

While there is strong relevance of this analysis for tumor evolution, the results generated in this study have more general relevance. Besides tumors, the paper discusses applications to bacterial disease dynamics in some detail, which is also interesting to compare and contrast to evolutionary processes in cancer. Overall, this study provides insights into the dynamics of evolutionary rescue that represent valuable additions to evolutionary theory.  

References

[1] Raatz M, Traulsen A (2023) Promoting extinction or minimizing growth? The impact of treatment on trait trajectories in evolving populations. bioRxiv, 2022.06.17.496570, ver. 2 peer-reviewed and recommended by Peer Community in Evolutionary Biology. https://doi.org/10.1101/2022.06.17.496570

[2] Gatenby RA, Silva AS, Gillies RJ, Frieden BR (2009) Adaptive Therapy. Cancer Research, 69, 4894–4903. https://doi.org/10.1158/0008-5472.CAN-08-3658

[3] Strobl MAR, West J, Viossat Y, Damaghi M, Robertson-Tessi M, Brown JS, Gatenby RA, Maini PK, Anderson ARA (2021) Turnover Modulates the Need for a Cost of Resistance in Adaptive Therapy. Cancer Research, 81, 1135–1147. https://doi.org/10.1158/0008-5472.CAN-20-0806

Most mutations affecting fitness are deleterious and they have many evolutionary consequences. The dynamics and consequences of deleterious mutations are a long-standing question in evolutionary biology and a strong theoretical background has already been developed, for example, to predict the mutation load, inbreeding depression or background selection. One of the classical results is that inbreeding helps purge partially recessive deleterious mutations by exposing them to selection in homozygotes. However, this mainly results from single-locus considerations. When interactions among several, more or less linked, deleterious mutations are taken into account, peculiar dynamics can emerge. One of them, called pseudo-overdominance (POD), corresponds to the maintenance in a population of two (or more) haplotype blocks composed of several recessive deleterious mutations in repulsion that mimics overdominance. Indeed, homozygote individuals for one of the haplotype blocks expose many deleterious mutations to selection whereas they are reciprocally masked in heterozygotes, leading to higher fitness of heterozygotes compared to both homozygotes. A related process, called associative overdominance (AOD) is the effect of such deleterious alleles in repulsion on the linked neutral variation that can be increased by AOD. Although this possibility has been recognized for a long time (Otha and Kimura 1969), it has been mainly considered an anecdotal process. Recently, both theoretical (Zhao and Charlesworth 2016) and genomic analyses (Gilbert et al. 2020) have renewed interest in such a process, suggesting that it could be important in weakly recombining regions of a genome. Donald Waller (2021) - one of the co-authors of the current work - also recently proposed that POD could be quantitatively important with broad implications, and could resolve some unexplained observations such as the maintenance of inbreeding depression in highly selfing species. Yet, a proper theoretical framework analysing the effect of inbreeding on POD was lacking.

In this theoretical work, Diala Abu Awad and Donald Waller (2022) addressed this question through an elegant combination of analytical predictions and intensive multilocus simulations. They determined the conditions under which POD can be maintained and how long it could resist erosion by recombination, which removes the negative association between deleterious alleles (repulsion) at the core of the mechanism. They showed that under tight linkage, POD regions can persist for a long time and generate substantial segregating load and inbreeding depression, even under inbreeding, so opposing (for a while) to the purging effect. They also showed that background selection can affect the genomic structure of POD regions by rapidly erasing weak POD regions but maintaining strong POD regions (i.e with many tightly linked deleterious alleles).

These results have several implications. They can explain the maintenance of inbreeding depression despite inbreeding (as anticipated by Waller 2021), which has implications for the evolution of mating systems. If POD can hardly emerge under high selfing, it can persist from an outcrossing ancestor long after the transition towards a higher selfing rate and could explain the maintenance of mixed mating systems(which is possible with true overdominance, see Uyenoyama and Waller 1991). The results also have implications for genomic analyses, pointing to regions of low or no recombination where POD could be maintained, generating both higher diversity and heterozygosity than expected and variance in fitness. As structural variations are likely widespread in genomes with possible effects on suppressing recombination (Mérot et al. 2020), POD regions should be checked more carefully in genomic analyses (see also Gilbert et al. 2020).

Overall, this work should stimulate new theoretical and empirical studies, especially to assess how quantitatively strong and widespread POD can be. It also stresses the importance of properly considering genetic linkage genome-wide, and so the role of recombination landscapes in determining patterns of diversity and fitness effects.

Awad DA, Waller D (2022) Conditions for maintaining and eroding pseudo-overdominance and its contribution to inbreeding depression. bioRxiv, 2021.12.16.473022, ver. 3 peer-reviewed and recommended by Peer Community in Evolutionary Biology. https://doi.org/10.1101/2021.12.16.473022

Gilbert KJ, Pouyet F, Excoffier L, Peischl S (2020) Transition from Background Selection to Associative Overdominance Promotes Diversity in Regions of Low Recombination. Current Biology, 30, 101-107.e3. https://doi.org/10.1016/j.cub.2019.11.063

Mérot C, Oomen RA, Tigano A, Wellenreuther M (2020) A Roadmap for Understanding the Evolutionary Significance of Structural Genomic Variation. Trends in Ecology & Evolution, 35, 561–572. https://doi.org/10.1016/j.tree.2020.03.002

Ohta T, Kimura M (1969) Linkage disequilibrium at steady state determined by random genetic drift and recurrent mutation. Genetics, 63, 229–238. https://doi.org/10.1093/genetics/63.1.229

Uyenoyama MK, Waller DM (1991) Coevolution of self-fertilization and inbreeding depression II. Symmetric overdominance in viability. Theoretical Population Biology, 40, 47–77. https://doi.org/10.1016/0040-5809(91)90046-I

Waller DM (2021) Addressing Darwin’s dilemma: Can pseudo-overdominance explain persistent inbreeding depression and load? Evolution, 75, 779–793. https://doi.org/10.1111/evo.14189

Zhao L, Charlesworth B (2016) Resolving the Conflict Between Associative Overdominance and Background Selection. Genetics, 203, 1315–1334. https://doi.org/10.1534/genetics.116.188912

Domesticated organisms are excellent models for understanding ecology and evolution and they are important for our food production and safety. While less studied than plants and animals, micro-organisms have also been domesticated, in particular for food fermentation [1]. The most studied domesticated micro-organism is the yeast used to make wine, beer and bread, Saccharomyces cerevisiae [2, 3, 4].

Filamentous fungi used for cheese-making have recently gained interest, for example Penicillium roqueforti used to make blue cheeses and P. camemberti to make soft cheeses [5, 6, 7, 8]. As for plants and animals, domestication has led to beneficial traits for food production in fermenting fungi, but also to bottlenecks and degeneration [6, 7, 9]; P. camemberti for example does not produce enough spores any more for optimal culture and inoculation and P. roqueforti has lost sexual fertility [9]. The loss of genetic diversity and of species diversity in our food production system is concerning for multiple reasons : i) it jeopardizes future improvement in the face of global changes ; ii) it causes the loss of evolved diversity during centuries under human selection, and therefore of beneficial characteristics and specificities that we may never be able to recover ; iii) it leads to degeneration in the few cultivated strains; iv) it impoverishes the diversity of our food products and local adaptation of production practices. 

The study of domesticated fungi used for food fermentation has focused so far on the evolution of lineages and on their metabolic specificities. Microbiological assemblages and species diversity have been much less studied, while they likely also have a strong impact on the quality and safety of final products. This study by Elisa Michel and colleagues [10] addresses this question, using an interdisciplinary participatory research approach including bakers, psycho-sociologists and microbiologists to analyse bread-making practices and their impact on microbial communities in sourdough. Elisa Michel and colleagues [10] identified two distinct groups of bread-making practices based on interviews and surveys, with farmer-like practices (low bread production, use of ancient wheat populations, manual kneading, working at ambient temperature, long fermentation periods and no use of commercial baker’s yeast) versus more intensive, artisanal-like practices. Metabarcoding and microbial culture-based analyses showed that the well-known baker’s yeast, Saccharomyces cerevisiae, was, surprisingly, not the most common species in French organic sourdoughs. Kazachstania was the most represented yeast genus over all sourdoughs, both in terms of read abundance and of species diversity. Kazachstania species were also often dominant in individual sourdoughs, but Saccharomyces uvarum or Torulaspora delbrueckii could also be the dominant yeast species.

Metabarcoding analyses further revealed that the composition of the fungal communities differed between the farmer-like and more intensive practices, representing the first evidence of the influence of artisanal practices on microbial communities. The fungal communities were impacted by a combination of bread-making variables including the type of wheat varieties, the length of fermentation, the quantity of bread made per week and the use of commercial yeast. Maintaining on farm less intensive bread-making practices, may allow the preservation of typical species and phenotypic diversity in microbial communities in sourdough. Farmer-like practices did not lead to higher diversity within sourdoughs but, overall, the diversity of bread-making practices allow maintaining a larger diversity in sourdoughs. For example, different Kazachstania species were most abundant in sourdoughs from artisanal-like and farmer-like practices. Interviews with the bakers suggested the role of dispersal of Kazachstania species in shaping sourdough microbial communities, dispersal occurring by seed exchanges, sourdough mixing or gifts, bread-making training in common or working in one another’s bakery. Nikolai Vavilov [11] had already highlighted for crops the importance of isolated cultures and selection in different farms for generating and preserving crop diversity, but also the importance of seed exchange for fostering adaptation. 

Furthermore, one of the yeast frequently found in artisanal sourdoughs, Kazachstania humilis, displayed phenotypic differences between sourdough and non-sourdough strains, suggesting domestication. The sourdough strains exhibited significantly higher CO2 production rate and a lower fermentation latency-phase time. 

The study by Elisa Michel and colleagues [10] is thus novel and inspiring in showing the importance of interdisciplinary studies, combining metabarcoding, microbiology and interviews for assessing the composition and diversity of microbial communities in human-made food, and in revealing the impact of artisanal-like bread-making practices in preserving microbial community diversity.

Interdisciplinary studies are still rare but have already shown the importance of combining ethno-ecology, biology and evolution to decipher the role of human practices on genetic diversity in crops, animals and food microorganisms and to help preserving genetic resources [12]. For example, in the case of the bread wheat Triticum aestivum, such interdisciplinary studies have shown that genetic diversity has been shaped by farmers’ seed diffusion and farming practices [13]. We need more of such interdisciplinary studies on the impact of farmer versus industrial agricultural and food-making practices as we urgently need to preserve the diversity of micro-organisms used in food production that we are losing at a rapid pace [6, 7, 14]. 

References

[1] Dupont J, Dequin S, Giraud T, Le Tacon F, Marsit S, Ropars J, Richard F, Selosse M-A (2017) Fungi as a Source of Food. Microbiology Spectrum, 5, 5.3.09. https://doi.org/10.1128/microbiolspec.FUNK-0030-2016

[2] Legras J-L, Galeote V, Bigey F, Camarasa C, Marsit S, Nidelet T, Sanchez I, Couloux A, Guy J, Franco-Duarte R, Marcet-Houben M, Gabaldon T, Schuller D, Sampaio JP, Dequin S (2018) Adaptation of S. cerevisiae to Fermented Food Environments Reveals Remarkable Genome Plasticity and the Footprints of Domestication. Molecular Biology and Evolution, 35, 1712–1727. https://doi.org/10.1093/molbev/msy066

[3] Bai F-Y, Han D-Y, Duan S-F, Wang Q-M (2022) The Ecology and Evolution of the Baker’s Yeast Saccharomyces cerevisiae. Genes, 13, 230. https://doi.org/10.3390/genes13020230

[4] Fay JC, Benavides JA (2005) Evidence for Domesticated and Wild Populations of Saccharomyces cerevisiae. PLOS Genetics, 1, e5. https://doi.org/10.1371/journal.pgen.0010005

[5] Ropars J, Rodríguez de la Vega RC, López-Villavicencio M, Gouzy J, Sallet E, Dumas É, Lacoste S, Debuchy R, Dupont J, Branca A, Giraud T (2015) Adaptive Horizontal Gene Transfers between Multiple Cheese-Associated Fungi. Current Biology, 25, 2562–2569. https://doi.org/10.1016/j.cub.2015.08.025

[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. Molecular Ecology, 29, 2639–2660. https://doi.org/10.1111/mec.15359

[7] 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 Biology, 30, 4441-4453.e4. https://doi.org/10.1016/j.cub.2020.08.082

[8] Caron T, Piver ML, Péron A-C, Lieben P, Lavigne R, Brunel S, Roueyre D, Place M, Bonnarme P, Giraud T, Branca A, Landaud S, Chassard C (2021) Strong effect of Penicillium roqueforti populations on volatile and metabolic compounds responsible for aromas, flavor and texture in blue cheeses. International Journal of Food Microbiology, 354, 109174. https://doi.org/10.1016/j.ijfoodmicro.2021.109174

[9] Ropars J, Lo Y-C, Dumas E, Snirc A, Begerow D, Rollnik T, Lacoste S, Dupont J, Giraud T, López-Villavicencio M (2016) Fertility depression among cheese-making Penicillium roqueforti strains suggests degeneration during domestication. Evolution, 70, 2099–2109. https://doi.org/10.1111/evo.13015

[10] Michel E, Masson E, Bubbendorf S, Lapicque L, Nidelet T, Segond D, Guézenec S, Marlin T, Devillers H, Rué O, Onno B, Legrand J, Sicard D, Bakers TP (2022) Artisanal and farmer bread making practices differently shape fungal species community composition in French sourdoughs. bioRxiv, 679472, ver. 6 peer-reviewed and recommended by Peer Community in Evolutionary Biology. https://doi.org/10.1101/679472

[11] Vavilov NI, Vavylov MI, Dorofeev VF (1992) Origin and Geography of Cultivated Plants. Cambridge University Press.

[12] Saslis-Lagoudakis CH, Clarke AC (2013) Ethnobiology: the missing link in ecology and evolution. Trends in Ecology & Evolution, 28, 67–68. https://doi.org/10.1016/j.tree.2012.10.017

[13] Thomas M, Demeulenaere E, Dawson JC, Khan AR, Galic N, Jouanne-Pin S, Remoue C, Bonneuil C, Goldringer I (2012) On-farm dynamic management of genetic diversity: the impact of seed diffusions and seed saving practices on a population-variety of bread wheat. Evolutionary Applications, 5, 779–795. https://doi.org/10.1111/j.1752-4571.2012.00257.x

[14] Demeulenaere É, Lagrola M (2021) Des indicateurs pour accompagner “ les éleveurs de microbes” : Une communauté épistémique face au problème des laits “ paucimicrobiens ” dans la production fromagère au lait cru (1995-2015). Revue d’anthropologie des connaissances, 15. http://journals.openedition.org/rac/24953