REVIEW: Optimality models in the age of experimental evolution and genomics

Optimality models have been used to predict evolution of many properties of organisms. They typically neglect genetic details, whether by necessity or design. This omission is a common source of criticism, and although this limitation of optimality is widely acknowledged, it has mostly been defended rather than evaluated for its impact. Experimental adaptation of model organisms provides a new arena for testing optimality models and for simultaneously integrating genetics. First, an experimental context with a well‐researched organism allows dissection of the evolutionary process to identify causes of model failure – whether the model is wrong about genetics or selection. Second, optimality models provide a meaningful context for the process and mechanics of evolution, and thus may be used to elicit realistic genetic bases of adaptation – an especially useful augmentation to well‐researched genetic systems. A few studies of microbes have begun to pioneer this new direction. Incompatibility between the assumed and actual genetics has been demonstrated to be the cause of model failure in some cases. More interestingly, evolution at the phenotypic level has sometimes matched prediction even though the adaptive mutations defy mechanisms established by decades of classic genetic studies. Integration of experimental evolutionary tests with genetics heralds a new wave for optimality models and their extensions that does not merely emphasize the forces driving evolution.     

Mature male parr contribution to the effective size of an anadromous Atlantic salmon (Salmo salar) population over 30 years

We describe temporal changes in the genetic composition of a small anadromous Atlantic salmon (Salmo salar) population from South Newfoundland, an area where salmon populations are considered threatened (COSEWIC 2010). We examined the genetic variability (13 microsatellite loci) in 869 out‐migrating smolt and post‐spawning kelt samples, collected from 1985 to 2011 for a total of 22 annual collections and a 30 year span of assigned cohorts. We estimated the annual effective number of breeders (N_b) and the generational effective population size (N_e) through genetic methods and demographically using the adult sex ratio. Comparisons between genetic and demographic estimates show that the adult spawners inadequately explain the observed N_e estimates, suggesting that mature male parr are significantly increasing N_b and N_e over the study period. Spawning as parr appears to be a viable and important strategy in the near absence of adult males.     

Molecular tools and bumble bees: revealing hidden details of ecology and evolution in a model system

Bumble bees are a longstanding model system for studies on behaviour, ecology and evolution, due to their well‐studied social lifestyle, invaluable role as wild and managed pollinators, and ubiquity and diversity across temperate ecosystems. Yet despite their importance, many aspects of bumble bee biology have remained enigmatic until the rise of the genetic and, more recently, genomic eras. Here, we review and synthesize new insights into the ecology, evolution and behaviour of bumble bees that have been gained using modern genetic and genomic techniques. Special emphasis is placed on four areas of bumble bee biology: the evolution of eusociality in this group, population‐level processes, large‐scale evolutionary relationships and patterns, and immunity and resistance to pesticides. We close with a prospective on the future of bumble bee genomics research, as this rapidly advancing field has the potential to further revolutionize our understanding of bumble bees, particularly in regard to adaptation and resilience. Worldwide, many bumble bee populations are in decline. As such, throughout the review, connections are drawn between new molecular insights into bumble bees and our understanding of the causal factors involved in their decline. Ongoing and potential applications to bumble bee management and conservation are also included to demonstrate how genetics‐ and genomics‐enabled research aids in the preservation of this threatened group.     

Population genomics and the causes of local differentiation

Exactly 50 years ago, a revolution in empirical population genetics began with the introduction of methods for detecting allelic variation using protein electrophoresis (Throckmorton 1962; Hubby 1963; Lewontin & Hubby 1966). These pioneering scientists showed that populations are chock‐full of genetic variation. This variation was a surprise that required a re‐thinking of evolutionary genetic heuristics. Understanding the causes for the maintenance of this variation became and remains a major area of research. In the process of addressing the causes, this same group of scientists documented geographical genetic structure (Prakash et al. 1969), spawning the continued accumulation of what is now a huge case study catalogue of geographical differentiation (e.g. Loveless & Hamrick 1984; Linhart & Grant 1996). Geographical differentiation is clearly quite common. Yet, a truly general understanding of the patterns in and causes of spatial genetic structure across the genome remains elusive. To what extent is spatial structure driven by drift and phylogeography vs. geographical differences in environmental sources of selection? What proportion of the genome participates? A general understanding requires range‐wide data on spatial patterning of variation across the entire genome. In this issue of Molecular Ecology, Lasky et al. (2012) make important strides towards addressing these issues, taking advantage of three contemporary revolutions in evolutionary biology. Two are technological: high‐throughput sequencing and burgeoning computational power. One is cultural: open access to data from the community of scientists and especially data sets that result from large collaborative efforts. Together, these developments may at last put answers within reach.     

Evolution of aging: individual life history trade‐offs and population heterogeneity account for mortality patterns across species

A broad range of mortality patterns has been documented across species, some even including decreasing mortality over age. Whether there exist a common denominator to explain both similarities and differences in these mortality patterns remains an open question. The disposable soma theory, an evolutionary theory of aging, proposes that universal intracellular trade‐offs between maintenance/lifespan and reproduction would drive aging across species. The disposable soma theory has provided numerous insights concerning aging processes in single individuals. Yet, which specific population mortality patterns it can lead to is still largely unexplored. In this article, we propose a model exploring the mortality patterns which emerge from an evolutionary process including only the disposable soma theory core principles. We adapt a well‐known model of genomic evolution to show that mortality curves producing a kink or mid‐life plateaus derive from a common minimal evolutionary framework. These mortality shapes qualitatively correspond to those of Drosophila melanogaster, Caenorhabditis elegans, medflies, yeasts and humans. Species evolved in silico especially differ in their population diversity of maintenance strategies, which itself emerges as an adaptation to the environment over generations. Based on this integrative framework, we also derive predictions and interpretations concerning the effects of diet changes and heat‐shock treatments on mortality patterns.     

Genomic dissection of variation in clutch size and egg mass in a wild great tit (Parus major) population

Clutch size and egg mass are life history traits that have been extensively studied in wild bird populations, as life history theory predicts a negative trade‐off between them, either at the phenotypic or at the genetic level. Here, we analyse the genomic architecture of these heritable traits in a wild great tit (Parus major) population, using three marker‐based approaches – chromosome partitioning, quantitative trait locus (QTL) mapping and a genome‐wide association study (GWAS). The variance explained by each great tit chromosome scales with predicted chromosome size, no location in the genome contains genome‐wide significant QTL, and no individual SNPs are associated with a large proportion of phenotypic variation, all of which may suggest that variation in both traits is due to many loci of small effect, located across the genome. There is no evidence that any regions of the genome contribute significantly to both traits, which combined with a small, nonsignificant negative genetic covariance between the traits, suggests the absence of genetic constraints on the independent evolution of these traits. Our findings support the hypothesis that variation in life history traits in natural populations is likely to be determined by many loci of small effect spread throughout the genome, which are subject to continued input of variation by mutation and migration, although we cannot exclude the possibility of an additional input of major effect genes influencing either trait.     

"Reverse ecology" and the power of population genomics

Rapid and inexpensive sequencing technologies are making it possible to collect whole genome sequence data on multiple individuals from a population. This type of data can be used to quickly identify genes that control important ecological and evolutionary phenotypes by finding the targets of adaptive natural selection, and we therefore refer to such approaches as "reverse ecology." To quantify the power gained in detecting positive selection using population genomic data, we compare three statistical methods for identifying targets of selection: the McDonald-Kreitman test, the mkprf method, and a likelihood implementation for detecting d(N)/d(S) > 1. Because the first two methods use polymorphism data we expect them to have more power to detect selection. However, when applied to population genomic datasets from human, fly, and yeast, the tests using polymorphism data were actually weaker in two of the three datasets. We explore reasons why the simpler comparative method has identified more genes under selection, and suggest that the different methods may really be detecting different signals from the same sequence data. Finally, we find several statistical anomalies associated with the mkprf method, including an almost linear dependence between the number of positively selected genes identified and the prior distributions used. We conclude that interpreting the results produced by this method should be done with some caution.     

Genome size in arthropods; different roles of phylogeny,habitat and life history in insects and crustaceans

Despite the major role of genome size for physiology, ecology, and evolution, there is still mixed evidence with regard to proximate and ultimate drivers. The main causes of large genome size are proliferation of noncoding elements and/or duplication events. The relative role and interplay between these proximate causes and the evolutionary patterns shaped by phylogeny, life history traits or environment are largely unknown for the arthropods. Genome size shows a tremendous variability in this group, and it has a major impact on a range of fitness‐related parameters such as growth, metabolism, life history traits, and for many species also body size. In this study, we compared genome size in two major arthropod groups, insects and crustaceans, and related this to phylogenetic patterns and parameters affecting ambient temperature (latitude, depth, or altitude), insect developmental mode, as well as crustacean body size and habitat, for species where data were available. For the insects, the genome size is clearly phylogeny‐dependent, reflecting primarily their life history and mode of development, while for crustaceans there was a weaker association between genome size and phylogeny, suggesting life cycle strategies and habitat as more important determinants. Maximum observed latitude and depth, and their combined effect, showed positive, and possibly phylogenetic independent, correlations with genome size for crustaceans. This study illustrate the striking difference in genome sizes both between and within these two major groups of arthropods, and that while living in the cold with low developmental rates may promote large genomes in marine crustaceans, there is a multitude of proximate and ultimate drivers of genome size.     

Fine‐scale temporal adaptation within a salmonid population: mechanism and consequences

We demonstrate a clear example of local adaptation of seasonal timing of spawning and embryo development. The consequence is a population of pink salmon that is segmented into spawning groups that use the same limited habitat. We synthesize published observations with results of new analyses to demonstrate that genetic variation of these traits results in survival differentials related to that variation, and that density‐dependent embryo mortality and seasonally variable juvenile mortality are a mechanism of selection. Most examples of local adaptation in natural systems depend on observed correlations between environments and fitness traits, but do not fully demonstrate local adaptation: that the trait is genetically determined, exhibits different fitness in common environments or across different environments, and its variation is mechanistically connected to fitness differences. The geographic or temporal scales of local adaptation often remain obscure. Here, we show that heritable, fine‐scale differences of timing of reproductive migration in a pink salmon (Oncorhynchus gorbuscha) resulted in temporal structure that persisted several generations; the differences enable a density‐dependent population to pack more spawners into limited spawning habitat, that is, enhance its fitness. A balanced trade‐off of survivals results because embryos from early‐migrating fish have a lower freshwater survival (harsh early physical conditions and disturbance by late spawners), but emigrant fry from late‐migrating fish have lower marine survivals (timing of their vernal emergence into the estuarine environment). Such fine‐scale local adaptations increase the genetic portfolio of the populations and may provide a buffer against the impacts of climate change.     

Sex‐allocation conflict and sexual selection throughout the lifespan of eusocial colonies

Models of sex‐allocation conflict are central to evolutionary biology but have mostly assumed static decisions, where resource allocation strategies are constant over colony lifespan. Here, we develop a model to study how the evolution of dynamic resource allocation strategies is affected by the queen‐worker conflict in annual eusocial insects. We demonstrate that the time of dispersal of sexuals affects the sex‐allocation ratio through sexual selection on males. Furthermore, our model provides three predictions that depart from established results of classic static allocation models. First, we find that the queen wins the sex‐allocation conflict, while the workers determine the maximum colony size and colony productivity. Second, male‐biased sex allocation and protandry evolve if sexuals disperse directly after eclosion. Third, when workers are more related to new queens, then the proportional investment into queens is expected to be lower, which results from the interacting effect of sexual selection (selecting for protandry) and sex‐allocation conflict (selecting for earlier switch to producing sexuals). Overall, we find that colony ontogeny crucially affects the outcome of sex‐allocation conflict because of the evolution of distinct colony growth phases, which decouples how queens and workers affect allocation decisions and can result in asymmetric control.     

In defence of model‐based inference in phylogeography

Recent papers have promoted the view that model‐based methods in general, and those based on Approximate Bayesian Computation (ABC) in particular, are flawed in a number of ways, and are therefore inappropriate for the analysis of phylogeographic data. These papers further argue that Nested Clade Phylogeographic Analysis (NCPA) offers the best approach in statistical phylogeography. In order to remove the confusion and misconceptions introduced by these papers, we justify and explain the reasoning behind model‐based inference. We argue that ABC is a statistically valid approach, alongside other computational statistical techniques that have been successfully used to infer parameters and compare models in population genetics. We also examine the NCPA method and highlight numerous deficiencies, either when used with single or multiple loci. We further show that the ages of clades are carelessly used to infer ages of demographic events, that these ages are estimated under a simple model of panmixia and population stationarity but are then used under different and unspecified models to test hypotheses, a usage the invalidates these testing procedures. We conclude by encouraging researchers to study and use model‐based inference in population genetics.     

Fun in facets: A flexible new tool for population genomics in R

The landscape of analytical tools for population genomics continues to evolve. However, these tools are scattered across programming languages, making them largely inaccessible for many biologists. In this issue of Molecular Ecology Resources, Hemstrom and Jones, 2022 (Mol Ecol Resour; 962) introduce a new R package, snpR. This package combines a large number of existing analyses, to provide a one-stop shop for population genomics. F-statistics, admixture analyses, effective population size inferences, genome-wide association studies (GWAS), and parentage analyses are all implemented natively within the package. A variety of third-party software can also be run without leaving the R environment. The authors pay particular attention to data structure – avoiding redundancy – and allowing analyses to be run across multiple sample or single-nucleotide polymorphism (SNP) groupings. Because of its great accessibility and wide range of analyses, snpR has the potential to become a favourite within the Molecular Ecology community.     

The costs of being dark: the genetic basis of melanism and its association with fitness‐related traits in the sand cricket

Melanism is an important component of insect cuticle and serves numerous functions that enhance fitness. Despite its importance, there is little information on its genetic basis or its phenotypic and genetic correlation with fitness‐related traits. Here, we examine the heritability of melanism in the wing dimorphic sand cricket and determine its phenotypic and genetic correlation with wing morphology, gonad mass and size of the dorso‐longitudinal muscles (the principle flight muscles). Previously demonstrated trade‐offs among these traits are significant factors in the evolution of life history variation. Using path analysis, we show that melanization is causally related to gonad mass, but not flight muscle mass. Averaged over the sexes, the heritability of melanism was 0.61, the genetic correlation with gonad mass was ?0.36 and with wing morph was 0.51. The path model correctly predicted the ranking of melanization score in lines selected for increased ovary mass, increased flight muscle mass, an index that increased both traits and an unselected control. Our results support the general hypothesis that melanization is costly for insects and negatively impacts investment in early reproduction.     

snpR: User friendly population genomics for SNP data sets with categorical metadata

The analysis of genomic data can be an intimidating process, particularly for researchers who are not experienced programmers. Commonly used analyses are spread across many programs, each requiring their own specific input formats, and so data must often be repeatedly reorganized and transformed into new formats. Analyses often require splitting data according to metadata variables such as population or family, which can be challenging to manage in large data sets. Here, we introduce snpR, a user-friendly data analysis package in R for processing SNP genomic data. snpR is designed to automate data subsetting and analyses across categorical metadata while also streamlining repeated analyses by integrating approaches contained in many different packages in a single ecosystem. snpR facilitates iterative and efficient analyses centred on a single R object for an entire analysis pipeline.     

Experimentally induced host‐shift changes life‐history strategy in a seed beetle

Expansion of the host range in phytophagous insects depends on their ability to form an association with a novel plant through changes in host‐related traits. Phenotypic plasticity has important effects on initial survival of individuals faced with a new plant, as well as on the courses of evolutionary change during long‐term adaptation to novel conditions. Using experimental populations of the seed beetle that evolved on ancestral (common bean) or novel (chickpea) host and applying reciprocal transplant at both larval and adult stage on the alternative host plant, we studied the relationship between the initial (plastic) phases of host‐shift and the subsequent stages of evolutionary divergence in life‐history strategies between populations exposed to the host‐shift process. After 48 generations, populations became well adapted to chickpea by evolving the life‐history strategy with prolonged larval development, increased body mass, earlier reproduction, shorter lifespan and decreased plasticity of all traits compared with ancestral conditions. In chickpea‐adapted beetles, negative fitness consequences of low plasticity of pre‐adult development (revealed as severe decrease in egg‐to‐adult viability on beans) exhibited mismatch with positive effects of low plasticity (i.e. low host sensitivity) in oviposition and fecundity. In contrast, beetles adapted to the ancestral host showed high plasticity of developmental process, which enabled high larval survival on chickpea, whereas elevated plasticity in adult behaviour (i.e. high host sensitivity) resulted in delayed reproduction and decreased fecundity on chickpea. The analysis of population growth parameters revealed significant fluctuation during successive phases of the host‐shift process in A. obtectus.     

Life‐history strategy varies with the strength of competition in a food‐limited ungulate population

Fluctuating population density in stochastic environments can contribute to maintain life‐history variation within populations via density‐dependent selection. We used individual‐based data from a population of Soay sheep to examine variation in life‐history strategies at high and low population density. We incorporated life‐history trade‐offs among survival, reproduction and body mass growth into structured population models and found support for the prediction that different life‐history strategies are optimal at low and high population densities. Shorter generation times and lower asymptotic body mass were selected for in high‐density environments even though heavier individuals had higher probabilities to survive and reproduce. In contrast, greater asymptotic body mass and longer generation times were optimal at low population density. If populations fluctuate between high density when resources are scarce, and low densities when they are abundant, the variation in density will generate fluctuating selection for different life‐history strategies, that could act to maintain life‐history variation.     

Boechera, a model system for ecological genomics

The selection and development of a study system for evolutionary and ecological functional genomics (EEFG) depend on a variety of factors. Here, we present the genus Boechera as an exemplary system with which to address ecological and evolutionary questions. Our focus on Boechera is based on several characteristics as follows: (i) native populations in undisturbed habitats where current environments reflect historical conditions over several thousand years; (ii) functional genomics benefitting from its close relationship to Arabidopsis thaliana; (iii) inbreeding tolerance enabling development of recombinant inbred lines, near-isogenic lines and positional cloning; (iv) interspecific crosses permitting mapping for genetic analysis of speciation; (v) apomixis (asexual reproduction by seeds) in a genetically tractable diploid; and (vi) broad geographic distribution in North America, permitting ecological genetics for a large research community. These characteristics, along with the current sequencing of three Boechera species by the Joint Genome Institute, position Boechera as a rapidly advancing system for EEFG studies.     

Molecular evolutionary analysis of seminal receptacle sperm storage organ genes of Drosophila melanogaster

Sperm storage organs are common and broadly distributed among animal taxa. However, little is known about how these organs function at the molecular level. Additionally, there is a paucity of knowledge about the evolution of genes expressed in these organs. This investigation is an evolutionary expressed sequence tag (EST) study of genes expressed in the seminal receptacle, one of the sperm storage organs in Drosophila. The incidence of positive selection is higher for the seminal receptacle genes than Drosophila reproductive genes as a whole, but lower than genes associated with the spermatheca, a second type of Drosophila sperm storage organ. By identifying overrepresented classes of proteins and classes for which sperm storage function is suggested by the nature of the proteins, candidate genes were discovered. These candidates belong to protein classes such as muscle contraction, odorant binding and odorant receptor, protease inhibitor and immunity.