Ecological opportunity – through entry into a new environment, the origin of a key innovation or extinction of antagonists – is widely thought to link ecological population dynamics to evolutionary diversification. The population‐level processes arising from ecological opportunity are well documented under the concept of ecological release. However, there is little consensus as to how these processes promote phenotypic diversification, rapid speciation and adaptive radiation. We propose that ecological opportunity could promote adaptive radiation by generating specific changes to the selective regimes acting on natural populations, both by relaxing effective stabilizing selection and by creating conditions that ultimately generate diversifying selection. We assess theoretical and empirical evidence for these effects of ecological opportunity and review emerging phylogenetic approaches that attempt to detect the signature of ecological opportunity across geological time. Finally, we evaluate the evidence for the evolutionary effects of ecological opportunity in the diversification of Caribbean Anolis lizards. Some of the processes that could link ecological opportunity to adaptive radiation are well documented, but others remain unsupported. We suggest that more study is required to characterize the form of natural selection acting on natural populations and to better describe the relationship between ecological opportunity and speciation rates.

Coevolutionary interactions between species are thought to be an important cause of evolutionary diversification. Despite this general belief, little theoretical basis exists for distinguishing between the types of interactions that promote diversification and those types that have no effect or that even restrict it. Using analytical models and simulations of phenotypic evolution across a metapopulation, we show that coevolutionary interactions promote diversification when they impose a cost of phenotype matching, as is the case for competition or host-parasite antagonism. In contrast, classical coevolutionary arms races have no tendency to promote or inhibit diversification, and mutualistic interactions actually restrict diversification. Together with the results of recent phylogenetic and ecological studies, these results suggest that the causes of diversification in many coevolutionary systems may require reassessment.

Genome-wide association study (GWAS) has revolutionized the search for the genetic basis of complex traits. To date, GWAS have generally relied on relatively sparse sampling of nucleotide diversity, which is likely to bias results by preferentially sampling high-frequency SNPs not in complete linkage disequilibrium (LD) with causative SNPs. To avoid these limitations we conducted GWAS with >6 million SNPs identified by sequencing the genomes of 226 accessions of the model legume Medicago truncatula. We used these data to identify candidate genes and the genetic architecture underlying phenotypic variation in plant height, trichome density, flowering time, and nodulation. The characteristics of candidate SNPs differed among traits, with candidates for flowering time and trichome density in distinct clusters of high linkage disequilibrium (LD) and the minor allele frequencies (MAF) of candidates underlying variation in flowering time and height significantly greater than MAF of candidates underlying variation in other traits. Candidate SNPs tagged several characterized genes including nodulation related genes SERK2, MtnodGRP3, MtMMPL1, NFP, CaML3, MtnodGRP3A and flowering time gene MtFD as well as uncharacterized genes that become candidates for further molecular characterization. By comparing sequence-based candidates to candidates identified by in silico 250K SNP arrays, we provide an empirical example of how reliance on even high-density reduced representation genomic makers can bias GWAS results. Depending on the trait, only 30–70% of the top 20 in silico array candidates were within 1 kb of sequence-based candidates. Moreover, the sequence-based candidates tagged by array candidates were heavily biased towards common variants; these comparisons underscore the need for caution when interpreting results from GWAS conducted with sparsely covered genomes.

Local adaptation and adaptive clines are pervasive in natural plant populations, yet the effects of these types of adaptation on genomic diversity are not well understood. With a data set of 202 accessions of Medicago truncatula genotyped at almost 2 million single nucleotide polymorphisms, we used mixed linear models to identify candidate loci responsible for adaptation to three climatic gradientsannual mean temperature (AMT), precipitation in the wettest month (PWM), and isothermality (ITH)-representing the major axes of climate variation across the species' range. Loci with the strongest association to these climate gradients tagged genome regions with high sequence similarity to genes with functional roles in thermal tolerance, drought tolerance, or resistance to herbivores of pathogens. Genotypes at these candidate loci also predicted the performance of an independent sample of plant accessions grown in climatecontrolled conditions. Compared to a genome-wide sample of randomly drawn reference SNPs, candidates for two climate gradients, AMT and PWM, were significantly enriched for genic regions, and genome segments flanking genic AMT and PWM candidates harbored less nucleotide diversity, elevated differentiation between haplotypes carrying alternate alleles, and an overrepresentation of the most common haplotypes. These patterns of diversity are consistent with a history of soft selective sweeps acting on loci underlying adaptation to climate, but not with a history of long-term balancing selection. L OCAL and clinal adaptation is widespread in natural populations (Clausen et al. 1941;Leimu and Fischer 2008), which, by definition, results from selection that varies across a species' range. Most methods to search for the targets of adaptation are designed to identify gene regions that have experienced "hard" selective sweeps, in which selection acts on new mutations that confer a selective advantage across the entire range of a sample (Maynard Smith and Haigh 1974;Nielsen 2005;Pritchard and Di Rienzo 2010;Kelly et al. 2013). These methods are not designed to identify targets of adaptation to selective environments that vary across the range of sampled populations. The targets of locally variable selection either may be maintained as stable polymorphisms or experience partial, or "soft," sweeps either because local adaptation involves fixation of different alleles in different portions of a species' range or because selection acts on standing variation (Hermisson and Pennings 2005;Pavlidis et al. 2012;Messer and Petrov 2013).Identifying the molecular targets of clinal adaptation offers an opportunity not only to identify functionally important genes, but also to further our understanding of adaptation itself. If the selective environment that drives clinal adaptation is stable, and alleles responsible for adaptation are at stable equilibria, then the loci responsible for adaptation may bear population genetic signatures of balancing selection: elevated differentiation between haplotypes linked to the ...

The geographic mosaic theory of coevolution is stimulating much new research on interspecific interactions. We provide a guide to the fundamental components of the theory, its processes and main predictions. Our primary objectives are to clarify misconceptions regarding the geographic mosaic theory of coevolution and to describe how empiricists can test the theory rigorously. In particular, we explain why confirming the three main predicted empirical patterns (spatial variation in traits mediating interactions among species, trait mismatching among interacting species and few species-level coevolved traits) does not provide unequivocal support for the theory. We suggest that strong empirical tests of the geographic mosaic theory of coevolution should focus on its underlying processes: coevolutionary hot and cold spots, selection mosaics and trait remixing. We describe these processes and discuss potential ways each can be tested.