Fisher's geometrical model (FGM) has been widely used to depict the fitness effects of mutations. It is a general model with few underlying assumptions that gives a large and comprehensive view of adaptive processes. It is thus attractive in several situations, for example adaptation to antibiotics, but comes with limitations, so that more mechanistic approaches are often preferred to interpret experimental data. It might be possible however to extend FGM assumptions to better account for mutational data. This is theoretically challenging in the context of antibiotic resistance because resistance mutations are assumed to be rare. In this article, we show with Escherichia coli how the fitness effects of resistance mutations screened at different doses of nalidixic acid vary across a dose-gradient. We found experimental patterns qualitatively consistent with the basic FGM (rate of resistance across doses, gamma distributed costs) but also unexpected patterns such as a decreasing mean cost of resistance with increasing screen dose. We show how different extensions involving mutational modules and variations in trait covariance across environments, can be discriminated based on these data. Overall, simple extensions of the FGM accounted well for complex mutational effects of resistance mutations across antibiotic doses.

The concept of “cost of resistance” has been very important for decades, for fundamental reasons (theory of adaptation), with a wide range of applications for the genetics and genomics of resistance: resistance to antibiotics, insecticide, herbicide, fungicides; resistance to chemotherapy in cancer research; coevolution between all kinds of parasites and their hosts. This paper reviews this history, including latest developments, shows the interest of the idea but also challenges the usefulness and limits of this widely used concept, based on the most recent development of adaptation theory.  It explains how the concept can be flawed and how this can impede research efforts in the field of resistance at large, including all applied aspects. In particular, it would be clearer to simply measure the fitness effects of mutations across environments and to better distinguish those effects from ‘pleiotropic effects’ of those mutations. Overall, we show how to correct the concept, and how this correction helps to better understand the wealth of data that has accumulated in recent years. The main points are: 1. The concept of «cost of resistance» needs to be carefully used, to avoid misconceptions, false paradox and flawed applications. The recent developments in adaptation theory are useful to clarify this. 2. “Cost of resistance” and pleiotropy have to be distinguished. More than one trait is required to discuss pleiotropy. Resistance evolution must at least involve the modification of one trait. If there is an irreducible trade-off on that trait between environments with and without drug, it creates a fitness effect that is not due to pleiotropy. Pleiotropic effects can, but need not, occur in addition. 3. “Cost of resistance” must depend on the pair of environments considered with and without drug. Hence, there are as many measures of cost as there are environments without drug. If the focal genotype is not well adapted to one focal environment, it is relatively easy to observe “negative” costs of resistance. There is nothing surprising about this, and it does not indicate an absence of trade-off. 4. Environments with drug can differ according to the dose. It may be more informative to measure the possible trade-offs among all doses than to focus exclusively on the fitness contrast between the presence and the absence of drug.

Antibiotic and pesticide resistance of pathogens are major and pressing worldwide issues. Resistance evolution is often considered in simplified ecological contexts: treated versus nontreated environments. In contrast, antibiotic usually present important dose gradients: from ecosystems to hospitals to polluted soils, in treated patients across tissues. However, we do not know whether adaptation to low or high doses involves different phenotypic traits, and whether these traits trade‐off with each other. In this study, we investigated the occurrence of such fitness trade‐offs along a dose gradient by evolving experimentally resistant lines of Escherichia coli at different antibiotic concentrations for ∼400 generations. Our results reveal fast evolution toward specialization following the first mutational step toward resistance, along with pervasive trade‐offs among different evolution doses. We found clear and regular fitness patterns of specialization, which converged rapidly from different initial starting points. These findings are consistent with a simple fitness peak shift model as described by the classical evolutionary ecology theory of adaptation across environmental gradients. We also found that the fitness costs of resistance tend to be compensated through time at low doses whereas they increase through time at higher doses. This cost evolution follows a linear trend with the log‐dose of antibiotic along the gradient. These results suggest a general explanation for the variability of the fitness costs of resistance and their evolution. Overall, these findings call for more realistic models of resistance management incorporating dose‐specialization.

The cost of resistance, or the fitness effect of resistance mutation in absence of the drug, is a very widepsread concept in evolutionary genetics and beyond. It has represented an important addition to the simplistic view that resistance mutations should solely be considered as beneficial mutations. Yet, this concept also entails a series of serious difficulties in its definition, interpretation and current usage. In many cases, it may be simpler, clearer, and more insightful to study, measure and analyze the fitness effects of mutations across environments and to better distinguish those effects from 'pleiotropic effects' of those mutations.

Background – The role of evolution in biological invasion studies is often overlooked. In order to evaluate the evolutionary mechanisms behind invasiveness, both quantitative and population genetics studies are underway on Robinia pseudoacacia L., one of the worst invasive tree species in Europe.Methods – A controlled experiment was set up using 2000 seeds from ten populations in Southern France and ten populations in Belgium. Seedlings were cultivated in two climatic chambers set at 18°C and 22°C. Early development life history traits (e.g. seedling phenology) and functional traits (e.g. growth rates) were monitored. Genotyping using SNP markers was used to evaluate the genetic differentiation among the populations and a QST – FST comparison was done in order to test for the role of selection.Results – Populations exhibited a strong plasticity to temperature for all measured traits, the warmer environment being generally more suitable, irrespective of their origin. No significant departure from neutral evolution was evidenced by the QST – FST comparisons, although we found a slightly significant differentiation at the molecular level. Conclusion – Plasticity for the functional and life history traits was evidenced but no genetic interaction suggesting no possible evolution of plasticity at those traits. Moreover, no support for genetic differentiation and local adaptation was found among studied populations within invasive range, raising two main questions: first, what is the role of selection on functional and life-history traits; and second, is the elapsed time since first introduction sufficient to allow evolution and local adaptation?

Negative frequency‐dependent selection (NFDS) is an important mechanism for species coexistence and for the maintenance of genetic polymorphism. Long‐term coexistence nevertheless requires NFDS interactions to be resilient to further evolution of the interacting species or genotypes. For closely related genotypes, NFDS interactions have been shown to be preserved through successive rounds of evolution in coexisting lineages. On the contrary, the evolution of NFDS interactions between distantly related species has received less attention. Here, we tracked the co‐evolution of Escherichia coli and Citrobacter freundii that initially differ in their ecological characteristics. We showed that these two bacterial species engaged in an NFDS interaction particularly resilient to further evolution: despite a very strong asymmetric rate of adaptation, their coexistence was maintained owing to an NFDS pattern where fitness increases steeply as the frequency decreases towards zero. Using a model, we showed how and why such NFDS pattern can emerge. These findings provide a robust explanation for the long‐term maintenance of species at very low frequencies.