Patterns of local adaptation are expected to emerge when selection is spatially heterogeneous and sufficiently strong relative to the action of other evolutionary forces. The observation of local adaptation thus provides important insight into evolutionary processes and the adaptive divergence of populations. The detection of local adaptation, however, suffers from several conceptual, statistical and methodological issues. Here, we provide practical recommendations regarding (1) the definition of local adaptation, (2) the analysis of transplant experiments and (3) the optimisation of the experimental design of local adaptation studies. Together, these recommendations provide a unified approach for measuring local adaptation and understanding the adaptive divergence of populations in a wide range of biological systems.

Understanding how changes in antibiotic consumption affect the prevalence of antibiotic resistance in bacterial pathogens is important for public health. In a number of bacterial species, including Streptococcus pneumoniae, the prevalence of resistance has remained relatively stable despite prolonged selection pressure from antibiotics. The evolutionary processes allowing the robust coexistence of antibiotic sensitive and resistant strains are not fully understood. While allelic diversity can be maintained at a locus by direct balancing selection, there is no evidence for such selection acting in the case of resistance. In this work, we propose a mechanism for maintaining coexistence at the resistance locus: linkage to a second locus that is under balancing selection and that modulates the fitness effect of resistance. We show that duration of carriage plays such a role, with long duration of carriage increasing the fitness advantage gained from resistance. We therefore predict that resistance will be more common in strains with a long duration of carriage and that mechanisms maintaining diversity in duration of carriage will also maintain diversity in antibiotic resistance. We test these predictions in S. pneumoniae and find that the duration of carriage of a serotype is indeed positively correlated with the prevalence of resistance in that serotype. These findings suggest heterogeneity in duration of carriage is a partial explanation for the coexistence of sensitive and resistant strains and that factors determining bacterial duration of carriage will also affect the prevalence of resistance.A ntibacterial resistance is a serious threat to public health, with resistant strains emerging in numerous pathogens. Although estimates of resistance levels vary by region, pathogen, and antibiotic type, a common feature is that fixation of resistance is rarely observed: sensitive and resistant strains tend to coexist robustly. For example, according to the European Antimicrobial Resistance Surveillance Network (EARS-Net; available at ecdc.europa.eu) (1), the prevalence of penicillin and macrolide nonsensitivity in Streptococcus pneumoniae has been stable at around 10 and 15% respectively for the past 15 years in Europe. Similarly, EARS-Net estimates of multidrug resistance in Klebsiella pneumoniae have varied around 20% since 2009 and those for methicillin resistance in Staphylococcus aureus have ranged between 15 and 25% from 1999 onwards, with no persisting directional trend.The stable coexistence of sensitive and resistant strains is unexpected: because these strains compete for the same hosts, simple ecological models predict that the fitter strain would dominate and the weaker strain become extinct ("competitive exclusion"). Understanding how coexistence is maintained is therefore important for predicting the prevalence of resistant strains and for explaining the approximately linear relationship between regional antibiotic consumption and resistance (2). Predicting the prevalence of resistance is, in turn, crucial...

Local adaptation experiments are widely used to quantify the levels of adaptation within a heterogeneous environment. However, theoretical studies generally focus on the probability of fixation of alleles or the mean fitness of populations, rather than local adaptation as it is commonly measured experimentally or in field studies. Here, we develop mathematical models and use them to generate analytical predictions for the level of local adaptation as a function of selection, migration and genetic drift. First, we contrast mean fitness and local adaptation measures and show that the latter can be expressed in a simple and general way as a function of the spatial covariance between population mean phenotype and local environmental conditions. Second, we develop several approximations of a population genetics model to show that the system exhibits different behaviours depending on the rate of migration. The main insights are the following: with intermediate migration, both genetic drift and migration decrease local adaptation; with low migration, drift decreases local adaptation but migration speeds up adaptation; with high migration, genetic drift has no effect on local adaptation. Third, we extend this analysis to cases where the trait under selection is continuous using classical quantitative genetics theory. Finally, we discuss these results in the light of recent experimental work on local adaptation.

Sewall Wright (1932) introduced the metaphor of "fitness landscapes" to think about evolutionary processes. A fitness landscape is defined by a set of genotypes, the mutational distance between them, and their associated fitness. Populations are abstracted into groups of particles that navigate on this landscape (Orr 2005). In this regard, the process of adaptation by natural selection depends on the structure of the fitness landscape. Many fundamental features of adaptation depend on whether the landscape is smooth or rugged, and on the level of epistasis between genotypes on the landscape (note that these two properties are related, Weinreich et al. 2005;Poelwijk et al. 2011). For examples, levels and type of epistasis determine the probability of speciation (Gavrilets 2004;Chevin et al. 2014) and the benefits of sexual reproduction (Kondrashov and Kondrashov 2001;de Visser et al. 2009;Otto 2009;Watson et al. 2011). The ruggedness of the landscape determines the repeatability and predictability of adaptation (Kauffman 1993;Colegrave and Buckling 2005;Chevin et al. 2010;Salverda et al. 2011).It is now possible to explore the fitness landscapes of microbial species using several experimental methods. A common type of experiment consists in isolating a number of mutants and measuring the fitness of genotypes with either a single mutation or various combinations of mutations. The most fascinating of these experiments are perhaps those considering a small number (L) of mutations and reconstructing all possible genotypes (2 L genotypes) from the wild type to the evolved (reviewed in de Visser et al. 1997;Lee et al. 1997;Whitlock and Bourguet 2000;Lunzer et al. 2005;Weinreich et al. 2006;O'Maille et al. 2008;Lozovsky et al. 2009;da Silva et al. 2010;Chou et al. 2011;Khan et al. 2011;Weinreich et al. 2013). The properties of these reconstructed fitness landscapes determine whether adaptation was constrained to follow the particular sequence of mutations that indeed evolved in the experiment, or whether mutations could have evolved in any order with similar probabilities.3The experimental data can be interpreted in the light of various theoretical fitness landscape models. Many models directly define the mapping between individual genotypes and fitness ("discrete" fitness landscape models). The simplest is the additive model, whereby the log-fitness is the sum of additive contributions by individual loci. This model results in no epistasis and a very smooth landscape. At the opposite extreme, the "House of Cards" model (Kingman 1978) assumes that the fitness of each genotype is drawn independently of other genotypes in some distribution. This model results in a highly epistatic and rugged landscape. In between these two extremes, two models where the roughness is a tunable parameter have been designed. The "Rough Mount Fuji" model assumes that log-fitness of a genotype is the sum of additive contributions from mutations and a House of Cards random component (Franke et al. 2011;Szendro et al. 2013). Kauffman's NK model ...

The relationship between SARS-CoV-2 viral load and infectiousness is poorly known. Using data from a cohort of cases and high-risk contacts, we reconstructed viral load at the time of contact and inferred the probability of infection. The effect of viral load was larger in household contacts than in non-household contacts, with a transmission probability as large as 48% when the viral load was greater than 1010 copies per mL. The transmission probability peaked at symptom onset, with a mean probability of transmission of 29%, with large individual variations. The model also projects the effects of variants on disease transmission. Based on the current knowledge that viral load is increased by two- to eightfold with variants of concern and assuming no changes in the pattern of contacts across variants, the model predicts that larger viral load levels could lead to a relative increase in the probability of transmission of 24% to 58% in household contacts, and of 15% to 39% in non-household contacts.

The evolution of resistance to antibiotics is a major public health problem and an example of rapid adaptation under natural selection by antibiotics. The dynamics of antibiotic resistance within and between hosts can be understood in the light of mathematical models that describe the epidemiology and evolution of the bacterial population. “Between‐host” models describe the spread of resistance in the host community, and in more specific settings such as hospitalized hosts (treated by antibiotics at a high rate), or farm animals. These models make predictions on the best strategies to limit the spread of resistance, such as reducing transmission or adapting the prescription of several antibiotics. Models can be fitted to epidemiological data in the context of intensive care units or hospitals to predict the impact of interventions on resistance. It has proven harder to explain the dynamics of resistance in the community at large, in particular because models often do not reproduce the observed coexistence of drug‐sensitive and drug‐resistant strains. “Within‐host” models describe the evolution of resistance within the treated host. They show that the risk of resistance emergence is maximal at an intermediate antibiotic dose, and some models successfully explain experimental data. New models that include the complex host population structure, the interaction between resistance‐determining loci and other loci, or integrating the within‐ and between‐host levels will allow better interpretation of epidemiological and genomic data from common pathogens and better prediction of the evolution of resistance.

The evolution of antibiotic resistance in opportunistic pathogens such as Streptococcus pneumoniae, Escherichia coli or Staphylococcus aureus is a major public health problem, as infection with resistant strains leads to prolonged hospital stay and increased risk of death. Here, we develop a new model of the evolution of antibiotic resistance in a commensal bacterial population adapting to a heterogeneous host population composed of untreated and treated hosts, and structured in different host classes with different antibiotic use. Examples of host classes include age groups and geographic locations. Explicitly modelling the antibiotic treatment reveals that the emergence of a resistant strain is favoured by more frequent but shorter antibiotic courses, and by higher transmission rates. In addition, in a structured host population, localized transmission in host classes promotes both local adaptation of the bacterial population and the global maintenance of coexistence between sensitive and resistant strains. When transmission rates are heterogeneous across host classes, resistant strains evolve more readily in core groups of transmission. These findings have implications for the better management of antibiotic resistance: reducing the rate at which individuals receive antibiotics is more effective to reduce resistance than reducing the duration of treatment. Reducing the rate of treatment in a targeted class of the host population allows greater reduction in resistance, but determining which class to target is difficult in practice.

Studying the evolution of viruses and their molecular epidemiology relies on accurate viral sequence data, so that small differences between similar viruses can be meaningfully interpreted. Despite its higher throughput and more detailed minority variant data, next-generation sequencing has yet to be widely adopted for HIV. The difficulty of accurately reconstructing the consensus sequence of a quasispecies from reads (short fragments of DNA) in the presence of large between- and within-host diversity, including frequent indels, may have presented a barrier. In particular, mapping (aligning) reads to a reference sequence leads to biased loss of information; this bias can distort epidemiological and evolutionary conclusions. De novo assembly avoids this bias by aligning the reads to themselves, producing a set of sequences called contigs. However contigs provide only a partial summary of the reads, misassembly may result in their having an incorrect structure, and no information is available at parts of the genome where contigs could not be assembled. To address these problems we developed the tool to pre-process reads for quality and contamination, then map them to a reference tailored to the sample using corrected contigs supplemented with the user’s choice of existing reference sequences. Run with two commands per sample, it can easily be used for large heterogeneous data sets. We used to reconstruct the consensus sequence and minority variant information from paired-end short-read whole-genome data produced with the Illumina platform, for sixty-five existing publicly available samples and fifty new samples. We show the systematic superiority of mapping to s constructed reference compared with mapping the same reads to the closest of 3,249 real references: median values of 13 bases called differently and more accurately, 0 bases called differently and less accurately, and 205 bases of missing sequence recovered. We also successfully applied to whole-genome samples of Hepatitis C Virus and Respiratory Syncytial Virus. is publicly available from https://github.com/ChrisHIV/shiver.