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The importance of long-distance dispersal (LDD) in shaping geographical distributions has been debated since the nineteenth century. In terrestrial vertebrates, LDD events across large water bodies are considered highly improbable, but organismal traits affecting dispersal capacity are generally not taken into account. Here, we focus on a recent lizard radiation and combine a summary-coalescent species tree based on 1225 exons with a probabilistic model that links dispersal capacity to an evolving trait, to investigate whether ecological specialization has influenced the probability of trans-oceanic dispersal. Cryptoblepharus species that occur in coastal habitats have on average dispersed 13 to 14 times more frequently than non-coastal species and coastal specialization has, therefore, led to an extraordinarily widespread distribution that includes multiple continents and distant island archipelagoes. Furthermore, their presence across the Pacific substantially predates the age of human colonization and we can explicitly reject the possibility that these patterns are solely shaped by human-mediated dispersal. Overall, by combining new analytical methods with a comprehensive phylogenomic dataset, we use a quantitative framework to show how coastal specialization can influence dispersal capacity and eventually shape geographical distributions at a macroevolutionary scale.

Leaf area varies within and between species, and previous work has linked this variation to environment and evolutionary history. However, many previous studies fail to examine both these factors and often are data‐limited.To address this, our study developed a new workflow using machine learning to automate the extraction of leaf area from herbarium collections of Australian eucalypts (Eucalyptus, Angophora and Corymbia). This dataset included 136,599 measurements, expanding existing data on this taxon's leaf area by roughly 50‐fold. Our methods were validated using field standard metrics of accuracy, and comparisons to manual measurements both from the present study and existing datasets.With this dataset for the eucalypt clade, we observed positive relationships between leaf area and mean annual temperature and precipitation similar to those reported for the global flora. However, these relationships were not consistently observed within species, potentially due to gene flow suppressing local adaptation. When we examined these relationships at different phylogenetic levels, the slope of trait–climate associations within lineages converged towards the overall eucalypt slope at shallow phylogenetic scales (5–12 MY), suggesting that effects of gene flow relax just above the species level.The strengthening of trait–climate correlations at evolutionary scales just beyond the intraspecific level may represent a widespread phenomenon across various traits and taxa. Future studies can unveil these relationships with the larger sample sizes of new trait datasets generated through machine learning.Synthesis. Using machine learning, researchers are able to confirm current positive global relationships between leaf area and mean annual temperature and precipitation. Additionally, they were able to take this a step further and examine how it changes across time. Here they saw that at roughly 5–12 million years ago in the phylogenetic tree, the trait–climate slope begins to show significantly less variation. Overall, the study shows the potential of machine learning in ecology, with exciting new potential findings with its use. [ABSTRACT FROM AUTHOR]

Increased sampling of genomes and populations across closely related species has revealed that levels of genetic exchange during and after speciation are higher than previously thought. One obvious manifestation of such exchange is strong cytonuclear discordance, where the divergence in mitochondrial DNA (mtDNA) differs from that for nuclear genes more (or less) than expected from differences between mtDNA and nuclear DNA (nDNA) in population size and mutation rate. Given genome-scale data sets and coalescent modeling, we can now confidently identify cases of strong discordance and test specifically for historical or recent introgression as the cause. Using population sampling, combining exon capture data from historical museum specimens and recently collected tissues we showcase how genomic tools can resolve complex evolutionary histories in the brachyotis group of rock-wallabies (Petrogale). In particular, applying population and phylogenomic approaches we can assess the role of demographic processes in driving complex evolutionary patterns and assess a role of ancient introgression and hybridization. We find that described species are well supported as monophyletic taxa for nDNA genes, but not for mtDNA, with cytonuclear discordance involving at least 4 operational taxonomic units across 4 species which diverged 183–278 kya. ABC modeling of nDNA gene trees supports introgression during or after speciation for some taxon pairs with cytonuclear discordance. Given substantial differences in body size between the species involved, this evidence for gene flow is surprising. Heterogenous patterns of introgression were identified but do not appear to be associated with chromosome differences between species. These and previous results suggest that dynamic past climates across the monsoonal tropics could have promoted reticulation among related species. [ABSTRACT FROM AUTHOR]

Premise: Continental‐scale leaf trait studies can help explain how plants survive in different environments, but large data sets are costly to assemble at this scale. Automating the measurement of digitized herbarium collections could rapidly expand the data available to such studies. We used machine learning to identify and measure leaves from existing, digitized herbarium specimens. The process was developed, validated, and applied to analyses of relationships between leaf size and climate within and among species for two genera: Syzygium (Myrtaceae) and Ficus (Moraceae). Methods: Convolutional neural network (CNN) models were used to detect and measure complete leaves in images. Predictions of a model trained with a set of 35 randomly selected images and a second model trained with 35 user‐selected images were compared using a set of 50 labeled validation images. The validated models were then applied to 1227 Syzygium and 2595 Ficus specimens digitized by the National Herbarium of New South Wales, Australia. Leaf area measurements were made for each genus and used to examine links between leaf size and climate. Results: The user‐selected training method for Syzygium found more leaves (9347 vs. 8423) using fewer training masks (218 vs. 225), and found leaves with a greater range of sizes than the random image training method. Within each genus, leaf size was positively associated with temperature and rainfall, consistent with previous observations. However, within species, the associations between leaf size and environmental variables were weaker. Conclusions: CNNs detected and measured leaves with levels of accuracy useful for trait extraction and analysis and illustrate the potential for machine learning of herbarium specimens to massively increase global leaf trait data sets. Within‐species relationships were weak, suggesting that population history and gene flow have a strong effect at this level. Herbarium specimens and machine learning could expand sampling of trait data within many species, offering new insights into trait evolution. [ABSTRACT FROM AUTHOR]

Background Melaleuca quinquenervia (broad-leaved paperbark) is a coastal wetland tree species that serves as a foundation species in eastern Australia, Indonesia, Papua New Guinea, and New Caledonia. While extensively cultivated for its ornamental value, it has also become invasive in regions like Florida, USA. Long-lived trees face diverse pest and pathogen pressures, and plant stress responses rely on immune receptors encoded by the nucleotide-binding leucine-rich repeat (NLR) gene family. However, the comprehensive annotation of NLR encoding genes has been challenging due to their clustering arrangement on chromosomes and highly repetitive domain structure; expansion of the NLR gene family is driven largely by tandem duplication. Additionally, the allelic diversity of the NLR gene family remains largely unexplored in outcrossing tree species, as many genomes are presented in their haploid, collapsed state. Results We assembled a chromosome-level pseudo-phased genome for M. quinquenervia and described the allelic diversity of plant NLRs using the novel FindPlantNLRs pipeline. Analysis reveals variation in the number of NLR genes on each haplotype, distinct clustering patterns, and differences in the types and numbers of novel integrated domains. Conclusions The high-quality M. quinquenervia genome assembly establishes a new framework for functional and evolutionary studies of this significant tree species. Our findings suggest that maintaining allelic diversity within the NLR gene family is crucial for enabling responses to environmental stress, particularly in long-lived plants. [ABSTRACT FROM AUTHOR]

Chromosome rearrangements can result in the rapid evolution of hybrid incompatibilities. Robertsonian fusions, particularly those with monobrachial homology, can drive reproductive isolation amongst recently diverged taxa. The recent radiation of rock-wallabies (genus Petrogale) is an important model to explore the role of Robertsonian fusions in speciation. Here, we pursue that goal using an extensive sampling of populations and genomes of Petrogale from north-eastern Australia. In contrast to previous assessments using mitochondrial DNA or nuclear microsatellite loci, genomic data are able to separate the most closely related species and to resolve their divergence histories. Both phylogenetic and population genetic analyses indicate introgression between two species that differ by a single Robertsonian fusion. Based on the available data, there is also evidence for introgression between two species which share complex chromosomal rearrangements. However, the remaining results show no consistent signature of introgression amongst species pairs and where evident, indicate generally low introgression overall. X-linked loci have elevated divergence compared with autosomal loci indicating a potential role for genic evolution to produce reproductive isolation in concert with chromosome change. Our results highlight the value of genome scale data in evaluating the role of Robertsonian fusions and structural variation in divergence, speciation, and patterns of molecular evolution. [ABSTRACT FROM AUTHOR]

Translocations of threatened species can reduce the risk of extinction from a catastrophic event. For plants, translocation consists of moving individuals, seeds, or cuttings from a native (source) population to a new site. Ideally a translocation population would be genetically diverse and consist of fit founding individuals. In practice, there are challenges to designing such a population, including constraints on the availability of material, and tradeoffs between different goals. Here, we present an approach for designing a translocation population that identifies sets of founders that are optimized according to multiple criteria (e.g., genetic diversity), while also conforming to constraints on the representation of different founders (e.g., propagation success). It uses flexible inputs, including SNP genotypes, matrices of similarity between individuals, and vectors of phenotype data. We apply the approach to a critically endangered plant, Hibbertia puberula subsp. glabrescens (Dilleniaceae), which was genotyped at thousands of SNP loci. The goals of minimizing genetic similarity among the founding individuals and maximizing genetic diversity were largely complementary: populations optimized for one of these criteria were near‐optimal for the other. We also performed analyses in which we minimized genetic similarity among founding individuals while imposing selection (against hypothetical deleterious alleles, and against undesirable phenotypes, respectively), and here characterized sharp tradeoffs. This was useful in allowing the benefits of selection to be weighed against costs in terms of genetic similarity. In summary, we present an approach for designing a translocation population that allows flexible inputs, the imposition of realistic constraints, and examination of conflicting goals. [ABSTRACT FROM AUTHOR]