Adaptive radiation plays a fundamental role in our understanding of the evolutionary process. However, the concept has provoked strong and differing opinions concerning its definition and nature among researchers studying a wide diversity of systems. Here, we take a broad view of what constitutes an adaptive radiation, and seek to find commonalities among disparate examples, ranging from plants to invertebrate and vertebrate animals, and remote islands to lakes and continents, to better understand processes shared across adaptive radiations. We surveyed many groups to evaluate factors considered important in a large variety of species radiations. In each of these studies, ecological opportunity of some form is identified as a prerequisite for adaptive radiation. However, evolvability, which can be enhanced by hybridization between distantly related species, may play a role in seeding entire radiations. Within radiations, the processes that lead to speciation depend largely on (1) whether the primary drivers of ecological shifts are (a) external to the membership of the radiation itself (mostly divergent or disruptive ecological selection) or (b) due to competition within the radiation membership (interactions among members) subsequent to reproductive isolation in similar environments, and (2) the extent and timing of admixture. These differences translate into different patterns of species accumulation and subsequent patterns of diversity across an adaptive radiation. Adaptive radiations occur in an extraordinary diversity of different ways, and continue to provide rich data for a better understanding of the diversification of life.

Reconstructing species’ demographic histories is a central focus of molecular ecology and evolution. Recently, an expanding suite of methods leveraging either the sequentially Markovian coalescent (SMC) or the site-frequency spectrum has been developed to reconstruct population size histories from genomic sequence data. However, few studies have investigated the robustness of these methods to genome assemblies of varying quality. In this study, we first present an improved genome assembly for the Tasmanian devil using the Chicago library method. Compared with the original reference genome, our new assembly reduces the number of scaffolds (from 35,975 to 10,010) and increases the scaffold N90 (from 0.101 to 2.164 Mb). Second, we assess the performance of four contemporary genomic methods for inferring population size history (PSMC, MSMC, SMC++, Stairway Plot), using the two devil genome assemblies as well as simulated, artificially fragmented genomes that approximate the hypothesized demographic history of Tasmanian devils. We demonstrate that each method is robust to assembly quality, producing similar estimates of Ne when simulated genomes were fragmented into up to 5,000 scaffolds. Overall, methods reliant on the SMC are most reliable between ∼300 generations before present (gbp) and 100 kgbp, whereas methods exclusively reliant on the site-frequency spectrum are most reliable between the present and 30 gbp. Our results suggest that when used in concert, genomic methods for reconstructing species’ effective population size histories 1) can be applied to nonmodel organisms without highly contiguous reference genomes, and 2) are capable of detecting independently documented effects of historical geological events.

Emerging infectious diseases pose one of the greatest threats to human health and biodiversity. Phylodynamics is often used to infer epidemiological parameters essential for guiding intervention strategies for human viruses such as severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2). Here, we applied phylodynamics to elucidate the epidemiological dynamics of Tasmanian devil facial tumor disease (DFTD), a fatal, transmissible cancer with a genome thousands of times larger than that of any virus. Despite prior predictions of devil extinction, transmission rates have declined precipitously from ~3.5 secondary infections per infected individual to ~1 at present. Thus, DFTD appears to be transitioning from emergence to endemism, lending hope for the continued survival of the endangered Tasmanian devil. More generally, our study demonstrates a new phylodynamic analytical framework that can be applied to virtually any pathogen.

As next-generation sequencing data become increasingly available for non-model organisms, a shift has occurred in the focus of studies of the geographic distribution of genetic variation. Whereas landscape genetics studies primarily focus on testing the effects of landscape variables on gene flow and genetic population structure, landscape genomics studies focus on detecting candidate genes under selection that indicate possible local adaptation. Navigating the transition between landscape genomics and landscape genetics can be challenging. The number of molecular markers analyzed has shifted from what used to be a few dozen loci to thousands of loci and even full genomes. Although genome scale data can be separated into sets of neutral loci for analyses of gene flow and population structure and putative loci under selection for inference of local adaptation, there are inherent differences in the questions that are addressed in the two study frameworks. We discuss these differences and their implications for study design, marker choice and downstream analysis methods. Similar to the rapid proliferation of analysis methods in the early development of landscape genetics, new analytical methods for detection of selection in landscape genomics studies are burgeoning. We focus on genome scan methods for detection of selection, and in particular, outlier differentiation methods and genetic-environment association tests because they are the most widely used. Use of genome scan methods requires an understanding of the potential mismatches between the biology of a species and assumptions inherent in analytical methods used, which can lead to high false positive rates of detected loci under selection. Key to choosing appropriate genome scan methods is an understanding of the underlying demographic structure of study populations, and such data can be obtained using neutral loci from the generated genome-wide data or prior knowledge of a species' phylogeographic history. To this end, we summarize recent simulation studies that test the power and accuracy of genome scan methods under a variety of demographic scenarios and sampling designs. We conclude with a discussion of additional considerations for future method development, and a summary of methods that show promise for landscape genomics studies but are not yet widely used.

Understanding the genetic basis of disease-related phenotypes, such as cancer susceptibility, is crucial for the advancement of personalized medicine. Although most cancers are somatic in origin, a small number of transmissible cancers have been documented. Two such cancers have emerged in the Tasmanian devil (Sarcophilus harrisii) and now threaten the species with extinction. Recently, cases of natural tumor regression in Tasmanian devils infected with the clonally contagious cancer have been detected. We used whole-genome sequencing and FST-based approaches to identify the genetic basis of tumor regression by comparing the genomes of seven individuals that underwent tumor regression with those of three infected individuals that did not. We found three highly differentiated candidate genomic regions containing several genes related to immune response and/or cancer risk, indicating that the genomic basis of tumor regression was polygenic. Within these genomic regions, we identified putative regulatory variation in candidate genes but no nonsynonymous variation, suggesting that natural tumor regression may be driven, at least in part, by differential host expression of key loci. Comparative oncology can provide insight into the genetic basis of cancer risk, tumor development, and the pathogenicity of cancer, particularly due to our limited ability to monitor natural, untreated tumor progression in human patients. Our results support the hypothesis that host immune response is necessary for triggering tumor regression, providing candidate genes that may translate to novel treatments in human and nonhuman cancers.

Spontaneous tumor regression has been documented in a small proportion of human cancer patients, but the specific mechanisms underlying tumor regression without treatment are not well-understood. Tasmanian devils are threatened with extinction from a transmissible cancer due to universal susceptibility and a near 100% case fatality rate. In over 10,000 cases, fewer than 20 instances of natural tumor regression have been detected. Previous work in this system has focused on Tasmanian devil genetic variation associated with the regression phenotype. Here, we used comparative and functional genomics to identify tumor genetic variation associated with tumor regression. We show that a single point mutation in the 5' untranslated region of the putative tumor suppressor RASL11A significantly contributes to tumor regression. RASL11A was expressed in regressed tumors but silenced in wild-type, non-regressed tumors, consistent with RASL11A down-regulation in human cancers. Induced RASL11A expression significantly reduced tumor cell proliferation in vitro. The RAS pathway is frequently altered in human cancers, and RASL11A activation may provide a therapeutic treatment option for Tasmanian devils as well as a general mechanism for tumor inhibition.

Whether hybridization generates or erodes species diversity has long been debated, but to date most studies have been conducted at small taxonomic scales. Salamanders (order Caudata) represent a taxonomic order in which hybridization plays a prevalent ecological and evolutionary role. We employed a recently developed model of trait-dependent diversification to test the hypothesis that hybridization impacts the diversification dynamics of species that are currently hybridizing. We find strong evidence supporting this hypothesis, showing that hybridizing salamander lineages have significantly greater net-diversification rates than non-hybridizing lineages. This pattern is driven by concurrently increased speciation rates and decreased extinction rates in hybridizing lineages. Our results support the hypothesis that hybridization can act as a generative force in macroevolutionary diversification.

The geographic distribution of biodiversity is central to understanding evolutionary biology. Paleogeographic and paleoclimatic histories often help to explain how biogeographic patterns unfold through time. However, such patterns are also influenced by a variety of other factors, such as lineage diversification, that may affect the probability of certain types of biogeographic events. The complex and well-known geologic and climatic history of Afro-Arabia, together with the extensive research on reptile systematics in the region, makes Afro-Arabian squamate communities an ideal system to investigate biogeographic patterns and their drivers. Here we reconstruct the phylogenetic relationships and the ancestral geographic distributions of several Afro-Arabian reptile clades (totaling 430 species) to estimate the number of dispersal, vicariance and range contraction events. We then compare the observed biogeographic history to a distribution of simulated biogeographic events based on the empirical phylogeny and the best-fit model. This allows us to identify periods in the past where the observed biogeographic history was likely shaped by forces beyond the ones included in the model. We find an increase in vicariance following the Oligocene, most likely caused by the fragmentation of the Afro-Arabian plate. In contrast, we did not find differences between observed and expected dispersal and range contraction levels. This is consistent with diversification enhanced by environmental processes and with the establishment of a dispersal corridor connecting Africa, Arabia and Eurasia since the middle Miocene. Finally, here we show that our novel approach is useful to pinpoint events in the evolutionary history of lineages that might reflect external forces not predicted by the underlying biogeographic model.