Museum skins enable identification of introgression associated with cytonuclear discordance.

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 datasets and coalescent modelling, 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 hybridisation. We find that described species are well supported as monophyletic taxa for nDNA genes, but not for mtDNA, with cytonuclear discordance involving at least four operational taxonomic units (OTUs) across four species which diverged 183-278 kya. ABC modelling 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.

Where to draw the boundaries? Using landscape genomics to disentangle the scribbly gum species complex.

PREMISE: Species delimitation is an integral part of evolution and ecology and is vital in conservation science. However, in some groups, species delimitation is difficult, especially where ancestral relationships inferred from morphological or genetic characters are discordant, possibly due to a complicated demographic history (e.g., recent divergences between lineages). Modern genetic techniques can take into account complex histories to distinguish species at a reasonable cost and are increasingly used in numerous applications. We focus on the scribbly gums, a group of up to five closely related and morphologically similar "species" within the eucalypts. METHODS: Multiple populations of each recognized scribbly gum species were sampled over a wide region across climates, and genomewide scans were used to resolve species boundaries. RESULTS: None of the taxa were completely divergent, and there were two genetically distinct entities: the inland distributed Eucalyptus rossii and a coastal conglomerate consisting of four species forming three discernible, but highly admixed groups. Divergence among taxa was likely driven by temporal vicariant processes resulting in partial separation across biogeographic barriers. High interspecific gene flow indicated separated taxa reconnected at different points in time, blurring species boundaries. CONCLUSIONS: Our results highlight the need for genetic screening when dealing with closely related taxonomic entities, particularly those with modest morphological differences. We show that high-throughput sequencing can be effective at identifying species groupings and processes driving divergence, even in the most taxonomically complex groups, and be used as a standard practice for disentangling species complexes.

Analyzing trait-climate relationships within and among taxa using machine learning and herbarium specimens.

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.

Capturing Genetic Diversity in Seed Collections: An Empirical Study of Two Congeners with Contrasting Mating Systems.

Plant mating systems shape patterns of genetic diversity and impact the long-term success of populations. As such, they are relevant to the design of seed collections aiming to maximise genetic diversity (e.g., germplasm conservation, ecological restoration). However, for most species, little is known empirically about how variation in mating systems and genetic diversity is distributed. We investigated the relationship between genetic diversity and mating systems in two functionally similar, co-occurring species of Hakea (Proteaceae), and evaluated the extent to which genetic diversity was captured in seeds. We genotyped hundreds of seedlings and mother plants via DArTseq, and developed novel implementations of two approaches to inferring the mating system from SNP data. A striking contrast in patterns of genetic diversity between H. sericea and H. teretifolia was revealed, consistent with a contrast in their mating systems. While both species had mixed mating systems, H. sericea was found to be habitually selfing, while H. teretifolia more evenly employed both selfing and outcrossing. In both species, seed collection schemes maximised genetic diversity by increasing the number of maternal lines and sites sampled, but twice as many sites were needed for the selfing species to capture equivalent levels of genetic variation at a regional scale.

Global genetic diversity status and trends: towards a suite of Essential Biodiversity Variables (EBVs) for genetic composition.

Biodiversity underlies ecosystem resilience, ecosystem function, sustainable economies, and human well-being. Understanding how biodiversity sustains ecosystems under anthropogenic stressors and global environmental change will require new ways of deriving and applying biodiversity data. A major challenge is that biodiversity data and knowledge are scattered, biased, collected with numerous methods, and stored in inconsistent ways. The Group on Earth Observations Biodiversity Observation Network (GEO BON) has developed the Essential Biodiversity Variables (EBVs) as fundamental metrics to help aggregate, harmonize, and interpret biodiversity observation data from diverse sources. Mapping and analyzing EBVs can help to evaluate how aspects of biodiversity are distributed geographically and how they change over time. EBVs are also intended to serve as inputs and validation to forecast the status and trends of biodiversity, and to support policy and decision making. Here, we assess the feasibility of implementing Genetic Composition EBVs (Genetic EBVs), which are metrics of within-species genetic variation. We review and bring together numerous areas of the field of genetics and evaluate how each contributes to global and regional genetic biodiversity monitoring with respect to theory, sampling logistics, metadata, archiving, data aggregation, modeling, and technological advances. We propose four Genetic EBVs: (i) Genetic Diversity; (ii) Genetic Differentiation; (iii) Inbreeding; and (iv) Effective Population Size (Ne ). We rank Genetic EBVs according to their relevance, sensitivity to change, generalizability, scalability, feasibility and data availability. We outline the workflow for generating genetic data underlying the Genetic EBVs, and review advances and needs in archiving genetic composition data and metadata. We discuss how Genetic EBVs can be operationalized by visualizing EBVs in space and time across species and by forecasting Genetic EBVs beyond current observations using various modeling approaches. Our review then explores challenges of aggregation, standardization, and costs of operationalizing the Genetic EBVs, as well as future directions and opportunities to maximize their uptake globally in research and policy. The collection, annotation, and availability of genetic data has made major advances in the past decade, each of which contributes to the practical and standardized framework for large-scale genetic observation reporting. Rapid advances in DNA sequencing technology present new opportunities, but also challenges for operationalizing Genetic EBVs for biodiversity monitoring regionally and globally. With these advances, genetic composition monitoring is starting to be integrated into global conservation policy, which can help support the foundation of all biodiversity and species' long-term persistence in the face of environmental change. We conclude with a summary of concrete steps for researchers and policy makers for advancing operationalization of Genetic EBVs. The technical and analytical foundations of Genetic EBVs are well developed, and conservation practitioners should anticipate their increasing application as efforts emerge to scale up genetic biodiversity monitoring regionally and globally.

Plant collections for conservation and restoration: Can they be adapted and adaptable?

Plant collections are important for the conservation of threatened species, and can provide material for ecological restoration. Typically we want collections to have high genetic diversity so populations founded from it are adaptable to future challenges. Sometimes, we have additional objectives for collections, such as enrichment for desirable traits controlled by adaptive alleles. We used landscape genomic data sets for two plants, Westringia fruticosa and Wilkiea huegeliana, to design collections that are genetically diverse, and that are adapted to warming climates. We characterized temperature adaptation by: (i) using the mean annual temperature of the sites of origin of the plants, and (ii) using the representation of alleles that are associated with warm temperatures. In Westringia fruticosa, there was a negative correlation, or tradeoff, between designing a collection that was both genetically diverse and adapted to warm temperatures. This tradeoff was weaker in Wilkiea huegeliana. We hypothesized this was because neutral genetic variation was strongly correlated with temperature in Westringia fruticosa, and not in Wilkiea huegeliana. Accordingly, when we shuffled the temperature data, breaking up the covariance between Westringia fruticosa genetic variation and temperature, there was a relaxation of the observed tradeoff. In summary, we explore tradeoffs between promoting genetic diversity and selecting for a specific trait in plant collections, and show that the strength of this tradeoff varies between two species. This represents a useful step towards understanding when selection will have a large cost in genetic diversity, and when it will be possible to design a collection that is both adapted and adaptable.

Detecting steps in spatial genetic data: Which diversity measures are best?

Accurately detecting sudden changes, or steps, in genetic diversity across landscapes is important for locating barriers to gene flow, identifying selectively important loci, and defining management units. However, there are many metrics that researchers could use to detect steps and little information on which might be the most robust. Our study aimed to determine the best measure/s for genetic step detection along linear gradients using biallelic single nucleotide polymorphism (SNP) data. We tested the ability to differentiate between linear and step-like gradients in genetic diversity, using a range of diversity measures derived from the q-profile, including allelic richness, Shannon Information, GST, and Jost-D, as well as Bray-Curtis dissimilarity. To determine the properties of each measure, we repeated simulations of different intensities of step and allele proportion ranges, with varying genome sample size, number of loci, and number of localities. We found that alpha diversity (within-locality) based measures were ineffective at detecting steps. Further, allelic richness-based beta (between-locality) measures (e.g., Jaccard and Sørensen dissimilarity) were not reliable for detecting steps, but instead detected departures from fixation. The beta diversity measures best able to detect steps were: Shannon Information based measures, GST based measures, a Jost-D related measure, and Bray-Curtis dissimilarity. No one measure was best overall, with a trade-off between those measures with high step detection sensitivity (GST and Bray-Curtis) and those that minimised false positives (a variant of Shannon Information). Therefore, when detecting steps, we recommend understanding the differences between measures and using a combination of approaches.

Chromosome-level de novo genome assembly of Telopea speciosissima (New South Wales waratah) using long-reads, linked-reads and Hi-C.

Telopea speciosissima, the New South Wales waratah, is an Australian endemic woody shrub in the family Proteaceae. Waratahs have great potential as a model clade to better understand processes of speciation, introgression and adaptation, and are significant from a horticultural perspective. Here, we report the first chromosome-level genome for T. speciosissima. Combining Oxford Nanopore long-reads, 10x Genomics Chromium linked-reads and Hi-C data, the assembly spans 823 Mb (scaffold N50 of 69.0 Mb) with 97.8% of Embryophyta BUSCOs "Complete". We present a new method in Diploidocus (https://github.com/slimsuite/diploidocus) for classifying, curating and QC-filtering scaffolds, which combines read depths, k-mer frequencies and BUSCO predictions. We also present a new tool, DepthSizer (https://github.com/slimsuite/depthsizer), for genome size estimation from the read depth of single-copy orthologues and estimate the genome size to be approximately 900 Mb. The largest 11 scaffolds contained 94.1% of the assembly, conforming to the expected number of chromosomes (2n = 22). Genome annotation predicted 40,158 protein-coding genes, 351 rRNAs and 728 tRNAs. We investigated CYCLOIDEA (CYC) genes, which have a role in determination of floral symmetry, and confirm the presence of two copies in the genome. Read depth analysis of 180 "Duplicated" BUSCO genes using a new tool, DepthKopy (https://github.com/slimsuite/depthkopy), suggests almost all are real duplications, increasing confidence in the annotation and highlighting a possible need to revise the BUSCO set for this lineage. The chromosome-level T. speciosissima reference genome (Tspe_v1) provides an important new genomic resource of Proteaceae to support the conservation of flora in Australia and further afield.

Limited Introgression between Rock-Wallabies with Extensive Chromosomal Rearrangements.

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.

A high-quality pseudo-phased genome for Melaleuca quinquenervia shows allelic diversity of NLR-type resistance genes.

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.

Temperature predicts the rate of molecular evolution in Australian Eugongylinae skinks.

Temperature differences over time and space have been hypothesized to cause variation in the rate of molecular evolution of species, but empirical evidence is mixed. To further test this hypothesis, we utilized a large exon-capture sequence data of Australian Eugongylinae skinks, exemplifying a radiation of temperature-sensitive ectotherms spanning a large latitudinal gradient. The association between temperature (and other species traits) and long-term substitution rate was assessed based on 1268 sequenced exons of 44 species pairs from the Eugongylinae subfamily using regression analyses. Temperature is the strongest, positively correlated predictor of variation in substitution rate across the Australian Eugongylinae. It explains 45% of variation in synonymous substitution rate, and 11% after controlling for all the other factors. Synonymous substitution rate is also negatively associated with body size, with a 6% variation explained by body size after controlling for the effects of temperature. Other factors are not associated with synonymous substitution rate after controlling for temperature. Overall, this study points to temperature as a strong predictor of the molecular evolution rate in the Eugongylinae subfamily, and demonstrates the power of large-scale exonic data to identify correlates of the rate of molecular evolution.

AusTraits, a curated plant trait database for the Australian flora.

We introduce the AusTraits database - a compilation of values of plant traits for taxa in the Australian flora (hereafter AusTraits). AusTraits synthesises data on 448 traits across 28,640 taxa from field campaigns, published literature, taxonomic monographs, and individual taxon descriptions. Traits vary in scope from physiological measures of performance (e.g. photosynthetic gas exchange, water-use efficiency) to morphological attributes (e.g. leaf area, seed mass, plant height) which link to aspects of ecological variation. AusTraits contains curated and harmonised individual- and species-level measurements coupled to, where available, contextual information on site properties and experimental conditions. This article provides information on version 3.0.2 of AusTraits which contains data for 997,808 trait-by-taxon combinations. We envision AusTraits as an ongoing collaborative initiative for easily archiving and sharing trait data, which also provides a template for other national or regional initiatives globally to fill persistent gaps in trait knowledge.

Conserving the genetic diversity of condemned populations: Optimizing collections and translocation.

We consider approaches for conserving genetic diversity from plant populations whose destruction is imminent. We do this using SNP genotype data from two endangered species, Pimelea spicata and Eucalyptus sp. Cattai. For both species, we genotyped plants from a 'condemned' population and designed ex situ collections, characterizing how the size and composition of the collection affected the genetic diversity preserved. Consistent with previous observations, populations where genetic diversity was optimized captured more alleles than populations of equal size chosen at random. This benefit of optimization was larger when the propagation population was small. That is, small numbers of individuals (e.g. 20) needed to be selected carefully to capture a comparable proportion of alleles to optimized populations, but larger random populations (e.g. >48) captured almost as many alleles as optimized populations. We then examined strategies for generating translocation populations based on the horticultural constraints presented by each species. In P. spicata, which is readily grown from cuttings, we designed translocation populations of different sizes, using different numbers of ramets from each member of propagation populations. We then performed simulations to predict the loss of alleles from these populations over 10 generations. Large translocation populations were predicted to maintain a greater proportion of source population alleles than smaller translocation populations, but this effect was saturated beyond 200 individuals. In E. sp. Cattai, we examined strategies to promote the diversity of progeny from a conservation planting scenario with 36 individuals. This included the optimization of the spatial arrangement of the planting and supplementing the diversity of the condemned population with individuals from additional sites. In sum, we studied approaches for designing genetically diverse translocations of condemned populations for two species that require contrasting methods of propagation, illustrating the application of approaches that were useful in different circumstances.

Regarding the F-word: The effects of data filtering on inferred genotype-environment associations.

Genotype-environment association (GEA) methods have become part of the standard landscape genomics toolkit, yet, we know little about how to best filter genotype-by-sequencing data to provide robust inferences for environmental adaptation. In many cases, default filtering thresholds for minor allele frequency and missing data are applied regardless of sample size, having unknown impacts on the results, negatively affecting management strategies. Here, we investigate the effects of filtering on GEA results and the potential implications for assessment of adaptation to environment. We use empirical and simulated data sets derived from two widespread tree species to assess the effects of filtering on GEA outputs. Critically, we find that the level of filtering of missing data and minor allele frequency affect the identification of true positives. Even slight adjustments to these thresholds can change the rate of true positive detection. Using conservative thresholds for missing data and minor allele frequency substantially reduces the size of the data set, lessening the power to detect adaptive variants (i.e., simulated true positives) with strong and weak strengths of selection. Regardless, strength of selection was a good predictor for GEA detection, but even some SNPs under strong selection went undetected. False positive rates varied depending on the species and GEA method, and filtering significantly impacted the predictions of adaptive capacity in downstream analyses. We make several recommendations regarding filtering for GEA methods. Ultimately, there is no filtering panacea, but some choices are better than others, depending on the study system, availability of genomic resources, and desired objectives.

A decision framework for evidence-based climate adaptation interventions.

Climate change is outpacing the ability of species and populations to naturally adapt warranting active interventions to boost climate resilience. In their review paper, Gaitán-Espitia and Hobday discuss how such interventions may, however, counter natural evolutionary processes and adaptive capacity if not underpinned by background knowledge from genes through to ecosystems. They present a robust decision framework to guide implementation of climate adaptation interventions to avoid unintended evolutionary outcomes.

A Mechanistic Model of Macromolecular Allocation, Elemental Stoichiometry, and Growth Rate in Phytoplankton.

We present a model of the growth rate and elemental stoichiometry of phytoplankton as a function of resource allocation between and within broad macromolecular pools under a variety of resource supply conditions. The model is based on four, empirically-supported, cornerstone assumptions: that there is a saturating relationship between light and photosynthesis, a linear relationship between RNA/protein and growth rate, a linear relationship between biosynthetic proteins and growth rate, and a constant macromolecular composition of the light-harvesting machinery. We combine these assumptions with statements of conservation of carbon, nitrogen, phosphorus, and energy. The model can be solved algebraically for steady state conditions and constrained with data on elemental stoichiometry from published laboratory chemostat studies. It interprets the relationships between macromolecular and elemental stoichiometry and also provides quantitative predictions of the maximum growth rate at given light intensity and nutrient supply rates. The model is compatible with data sets from several laboratory studies characterizing both prokaryotic and eukaryotic phytoplankton from marine and freshwater environments. It is conceptually simple, yet mechanistic and quantitative. Here, the model is constrained only by elemental stoichiometry, but makes predictions about allocation to measurable macromolecular pools, which could be tested in the laboratory.

Optimizing the genetic composition of a translocation population: Incorporating constraints and conflicting objectives.

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.

Habitat preference modulates trans-oceanic dispersal in a terrestrial vertebrate.

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.

A quantitative model of nitrogen fixation in the presence of ammonium.

Nitrogen fixation provides bioavailable nitrogen, supporting global ecosystems and influencing global cycles of other elements. It provides an additional source of nitrogen to organisms at a cost of lower growth efficiency, largely due to respiratory control of intra-cellular oxygen. Nitrogen-fixing bacteria can, however, utilize both dinitrogen gas and fixed nitrogen, decreasing energetic costs. Here we present an idealized metabolic model of the heterotrophic nitrogen fixer Azotobacter vinelandii which, constrained by laboratory data, provides quantitative predictions for conditions under which the organism uses either ammonium or nitrogen fixation, or both, as a function of the relative supply rates of carbohydrate, fixed nitrogen as well as the ambient oxygen concentration. The model reveals that the organism respires carbohydrate in excess of energetic requirements even when nitrogen fixation is inhibited and respiratory protection is not essential. The use of multiple nitrogen source expands the potential niche and range for nitrogen fixation. The model provides a quantitative framework which can be employed in ecosystem and biogeochemistry models.

Adaptation and conservation insights from the koala genome.

The koala, the only extant species of the marsupial family Phascolarctidae, is classified as 'vulnerable' due to habitat loss and widespread disease. We sequenced the koala genome, producing a complete and contiguous marsupial reference genome, including centromeres. We reveal that the koala's ability to detoxify eucalypt foliage may be due to expansions within a cytochrome P450 gene family, and its ability to smell, taste and moderate ingestion of plant secondary metabolites may be due to expansions in the vomeronasal and taste receptors. We characterized novel lactation proteins that protect young in the pouch and annotated immune genes important for response to chlamydial disease. Historical demography showed a substantial population crash coincident with the decline of Australian megafauna, while contemporary populations had biogeographic boundaries and increased inbreeding in populations affected by historic translocations. We identified genetically diverse populations that require habitat corridors and instituting of translocation programs to aid the koala's survival in the wild.