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ATP synthase evolution on a cross-braced dated tree of life
<p><strong>Abstract</strong></p><p>The timing of early cellular evolution, from the divergence of Archaea and Bacteria to the origin of eukaryotes, is poorly constrained. The ATP synthase complex is thought to have originated prior to the Last Universal Common Ancestor (LUCA) and analyses of ATP synthase genes, together with ribosomes, have played a key role in inferring and rooting the tree of life. We reconstruct the evolutionary history of ATP synthases using an expanded taxon sampling set and develop a phylogenetic cross-bracing approach, constraining equivalent speciation nodes to be contemporaneous, based on the phylogenetic imprint of endosymbioses and ancient gene duplications. This approach results in a highly resolved, dated species tree and establishes an absolute timeline for ATP synthase evolution. Our analyses show that the divergence of ATP synthase into F- and A/V-type lineages was a very early event in cellular evolution dating back to more than 4Ga, potentially predating the diversification of Archaea and Bacteria. Our cross-braced, dated tree of life also provides insight into more recent evolutionary transitions including eukaryogenesis, showing that the eukaryotic nuclear and mitochondrial lineages diverged from their closest archaeal (2.67-2.19Ga) and bacterial (2.58-2.12Ga) relatives at approximately the same time, with a slightly longer nuclear stem-lineage.</p><p><strong>Repository Contents</strong></p><p><strong>1_100Eukaryote_genomes.tar.gz</strong>: includes all protein sequence files for the 100 Eukaryotes sampled in this study. </p><p><strong>2_Phylogenies.tar.gz</strong>: includes all files used for phylogenetic analyses. Folders are organized as follows: </p><ul><li><strong>1_ATPsynthase_gene_trees</strong>: this folder contains all sequence, alignment, and tree files for the ATP synthase gene trees. Files are organized as follows and are associated with the corresponding parts of the manuscript: Figure 3, Figure 5B, Supplementary Figures 5-10, Supplementary Figures 18-19<ul><li>Folder '1_sequences' includes all unaligned fasta sequence files for each ATP synthase gene tree (see Methods)</li><li>Folder '2_alignments' includes all alignments generated using MAFFT L-INS-i (subdirectory: 1_untrimmed) and trimmed with BMGE (subdirectory: 2_trimmed)</li><li>Folder '3_treefiles' includes all IQ-TREE2 output files for all ATP synthase gene phylogenies. Any files with suffix *taxa.treefile contain the full taxonomic string for each accession. </li><li>Folder '4_pdfs' includes PDF files for each ATP synthase gene tree</li></ul></li><li><strong>2_Eukaryotic_subsets</strong>: this folder contains all sequence, alignment, and tree files for ATP synthase Eukaryotic subset gene trees. Files are organized as follows and are associated with the corresponding parts of the manuscript: Supplementary Figure 11 <ul><li>Folder '1_sequences' includes all unaligned fasta sequence files for the eukaryotic subsets.</li><li>Folder '2_alignments' includes all alignments generated using MAFFT L-INS-i (subdirectory: 1_untrimmed) and trimmed with BMGE (subdirectory: 2_trimmed).</li><li>Folder '3_treefiles' includes all Bayesian trees inferred for eukaryotic subsets.</li><li>Folder '4_pdfs' includes PDF files for each eukaryotic subset tree </li></ul></li><li><strong>3_21eLife_concatenated_species_tree</strong>: this folder contains all sequence, alignment, and tree files for the single gene tree and concatenated phylogeny analyses (inferred using 21 single-copy marker genes, see Methods). Files are organized as follows and are associated with the following parts of the manuscript: Figure 1, Supplementary Figure 20 <ul><li>Folder '1_inspection_start' corresponds to the initial manual inspection of the single gene trees and includes the following subdirectories:<ul><li>Folder '1_sequences' includes all protein sequence fasta files corresponding to the 27 original single-copy marker genes</li><li>Folder '2_alignments' includes all alignment files generated using MAFFT L-INS-i (subdirectory: 1_untrimmed) and trimmed with BMGE (subdirectory: 2_untrimmed)</li><li>Folder '3_treefiles' includes all IQ-TREE2 output files for all phylogenies (27 single-copy marker genes)</li><li>Folder '4_pdfs' includes PDF files for each single gene tree</li></ul></li><li>Folder '2_inspection_final' corresponds to the final manual inspection of the single gene trees and includes the following subdirectories:<ul><li>Folder '1_sequences' includes all protein sequence fasta files corresponding to the final 21 single-copy marker genes</li><li>Folder '2_alignments' includes all alignment files generated using MAFFT L-INS-i (subdirectory: 1_untrimmed) and trimmed with BMGE (subdirectory: 2_untrimmed)</li><li>Folder '3_treefiles' includes all IQ-TREE2 output files for all phylogenies (21 single-copy marker genes)</li><li>Folder '4_pdfs' includes PDF files for each single gene tree</li></ul></li><li>Folder '3_concatenated_phylogeny' contains concatenated alignment generated from the final 21 single-copy marker gene alignments<ul><li>Folder '1_alignment' includes the concatenated alignment generated from the 21 trimmed alignments from the final inspection</li><li>Folder '2_treefiles' includes all IQ-TREE2 output files for trees inferred using the two different models (subdirectories: LG+C20+R+F and LG+C60+R+F)</li></ul></li><li>Folder '4_Eukaryote_only_phylogeny' contains sequence, alignment, and tree files for 21 single-copy marker genes used to infer a Eukaryote-only phylogeny. Folder is organized as follows and files correspond to Supplementary Figure 3: <ul><li>Folder '1_sequences' includes all protein sequence fasta files corresponding to the 21 single-copy marker genes with only Eukaryotes</li><li>Folder '2_alignments' includes all alignment files generated using MAFFT L-INS-i (subdirectory: 1_untrimmed) and trimmed with BMGE (subdirectory: 2_untrimmed)</li><li>Folder '3_concatenated_phylogeny' includes concatenated alignment generated from 21 single-copy markers with only Eukaryotes (subdirectory: 1_alignment) and all IQ-TREE2 output files for the concatenated phylogeny (subdirectory: 2_treefiles)</li><li>Folder '4_pdfs' includes PDF files for the concatenated Eukaryote tree</li></ul></li></ul></li><li><strong>4_Ribosomal_species_tree</strong>: this folder contains all sequence, alignment, and tree files for the single gene tree and concatenated phylogeny analyses (inferred using 12 ribosomal marker genes, see Methods). Files are organized as follows and are associated with the corresponding parts of the manuscript: Figure 5A, Figure 5C, Supplementary Figures 12-16, Supplementary Figure 21<ul><li>Folder '1_sequences' includes all protein sequence fasta files for the original 15 ribosomal proteins. Sequence sets include the best-hit Archaea and Bacteria, and nuclear, mitochondrial, and plastid eukaryotic homologs</li><li>Folder '2_alignments' includes all alignment files generated using MAFFT L-INS-i (subdirectory: 1_untrimmed) and trimmed with TRIMAL (gappy-out) (subdirectory: 2_trimmed)</li><li>Folder '3_treefiles' includes all original FastTree tree files, tree files with highlighted sequences to remove (*blue-to-rem = eukaryotic nuclear homolog only; *colored-to-rem = eukaryotic nuclear, mitochondrial, and plastid homologs). PDFs of each marker gene tree are also included that depict highlighting of sequences to keep and/or remove. </li><li>Folder '4_concatenated_phylogeny' contains concatenated alignment generated from the final 12 ribosomal marker genes<ul><li>Folder '1_alignment' includes the concatenated alignment generated with 12 ribosomal marker proteins in MAFFT L-INS-i and trimmed with TRIMAL (gappy-out)</li><li>Folder '2_phylogeny' includes all IQ-TREE2 output files for the species tree inferred using the LG+C60+R+F model</li></ul></li></ul></li><li><strong>5_Dating_analysis</strong>: includes all Mcmcdate output files for the dating analyses (species tree and ATP synthase gene tree, see Methods). <ul><li>Folder '0_Starting_species_phylogenies' includes the treefiles (with and without taxonomic string) for the Edited1 and Edited2 topologies that were used in the dating analyses (see Methods). </li><li>Folder '1_Edited1_dating' includes all dated tree files and monitor files for braced and unbraced analyses of the Edited1 species tree topology. Data corresponds to Supplementary Figure 12, Supplementary Figure 14-15 </li><li>Folder '2_Edited2_dating' includes all dated tree files and monitor files for braced and unbraced analyses of the Edited2 (focal) species tree topology. Data corresponds to Figure 5A, Figure 5C, Supplementary Figure 13, Supplementary Figure 16.</li><li>Folder '3_ATP_synthase_dating' includes all dated tree files and monitor files for braced and unbraced analyses of the ATP synthase gene tree. Data corresponds to Figure 5B, Supplementary Figures 18-19.</li></ul></li></ul><p><strong>3_Scripts.tar.gz</strong>: includes all workflows and scripts used for phylogenetic analyses. </p><ul><li><strong>1_workflows</strong>: includes bash workflows for phylogenetic analyses (details on software versions are included in each workflow summary): <ul><li>Workflow_ATPsynthase_gene_trees.sh: generation of the ATP synthase phylogenies</li><li>Workflow_21eLife_marker_phylogeny.sh: inferring the 21 marker-gene species tree </li><li>Workflow_Ribosomal_species_tree.sh: inferring the 12 ribosomal marker-gene species tree </li><li>Workflow_Database_annotations.sh: workflow for gene annotation for 800 sampled Archaea, Bacteria, and Eukaryota</li></ul></li><li><strong>2_R_scripts</strong>: includes R scripts used for the Eukaryote sequence contamination screening (Figure 1, Figure 2, Supplementary Figure 2, Supplementary Figures 4, 5, 8-10), presence-absence analyses (Figure 1, Figure 2, Supplementary Figure 2), and plotting tree figures (Supplementary Figures 4-10). Input mapping files and R output files are included.<ul><li>Folder '1_Euk_contamination_screen' contains workflow 'Eukaryote_contamination_screen.Rmd' used to inspect Eukaryotic ATP synthase sequences for bacterial contamination</li><li>Folder '2_Presence_absence' includes sub-directories:<ul><li>Folder '1_Species_tree' includes the treefile(s) used for ordering the plots in Figure 1 and Supplementary Figure 2 ('1_tree'), the taxonomic and COG mapping files and the list of putative contamination to remove ('2_input_files'), the raw count table for all 800 taxa ('3_Output_files'), R output plot(s) ('4_Plotting'), and the script to generate presence-absence plots 'Presence-absence.R'. </li><li>Folder '2_Eukaryotes_only' includes organelle information, protein mapping files, taxonomic mapping files, and list of putative contamination to remove ('1_Input_files'); raw count table of ATP synthase subunits ('2_Output_files'); and R output plots ('3_Output_files').<br><i>Please see 'Eukaryote_contamination_screen.Rmd' in parent directory '2_R_scripts' for more information on how Eukaryotic sequences were screened, how the list of contaminating sequences was curated, and how the plot for Figure 2 was generated. </i></li></ul></li><li>Folder '3_Plotting_trees' includes the rectangular and radial trees generated for each ATP synthase trees (see Supplementary Figures 5-10). Trees were generated from the treefiles for the ATP synthase gene trees (see above), and script 'Plotting_trees.Rmd'</li><li>'Marker_gene_counts.R' script used to count marker genes per genome (see Methods)</li></ul></li><li><strong>3_TimeTree</strong>: includes python scripts used to generate the time-trees (Figure 5C, Supplementary Figures 15 and 19)</li><li><strong>4_ALE_workflow</strong>:<strong> </strong>example bash workflow used to run ALE. For details see Methods. </li></ul>
Dating the bacterial tree of life based on ancient symbiosis
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Truly ubiquitous CRESS DNA viruses scattered across the eukaryotic tree of life
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Data from: Why do phylogenomic data sets yield conflicting trees? Data type influences the avian tree of life more than taxon sampling
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Data from: An R package and online resource for macroevolutionary studies using the ray-finned fish tree of life
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Data from: hiding in plain sight: phylogenomics reveals a new branch on the Noctuoidea tree of life
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Data from: Phylogenomics reveals ancient gene tree discordance in the amphibian Tree of Life
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Data from: The interplay of past diversification and evolutionary isolation with present imperilment across the amphibian tree of life
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Data from: Remote homolog detection places insect chemoreceptors in a cryptic protein superfamily spanning the tree of life
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Microbial communities of wild bees and comparative phylogenetics of key bacterial taxa across the bee tree of life
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Closing the life cycle of forest trees: The difficult dynamics of seedling-to-sapling transitions in a subtropical rain forest
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Phylogenomic analyses of 2,786 genes in 158 lineages support a root of the eukaryotic tree of life between opisthokonts and all other lineages
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An unbiased molecular approach using 3'UTRs resolves the avian family-level tree of life
<p>Presumably, due to a rapid early diversification, major parts of the higher-level phylogeny of birds are still resolved controversially in different analyses or are considered unresolvable. To address this problem, we produced an avian tree of life, which includes molecular sequences of one or several species of ∼ 90% of the currently recognized family-level taxa (429 species, 379 genera) including all 106 for the non-passerines and 115 for the passerines (Passeriformes). The unconstrained analyses of noncoding 3-prime untranslated region (3'UTR) sequences and those of coding sequences yielded different trees. In contrast to the coding sequences, the 3'UTR sequences resulted in a well-resolved and stable tree topology. The 3'UTR contained, unexpectedly, transcription factor binding motifs that were specific for different higher-level taxa. In this tree, grebes and flamingos are the sister clade of all other Neoaves, which are subdivided into five major clades. All non-passerine taxa were placed with robust statistical support including the long-time enigmatic hoatzin (Opisthocomiformes), which was found being the sister taxon of the Caprimulgiformes. The comparatively late radiation of family-level clades of the songbirds (oscine Passeriformes) contrasts with the attenuated diversification of non-passeriform taxa since the early Miocene. This correlates with the evolution of vocal production learning, an important speciation factor, which is ancestral for songbirds and evolved convergent only in hummingbirds and parrots. Since 3'UTR-based phylotranscriptomics resolved the avian family-level tree of life, we suggest that this procedure will also resolve the all-species avian tree of life</p> <p> </p>
Data from: Life-history dimensions indicate non-random assembly processes in tropical island tree communities
<p>Community assembly processes on islands are often non-random. The mechanisms behind non-random assembly, however, are generally difficult to disentangle. Functional diversity in combination with a null model approach that accounts for differences in species richness among islands can be used to test for non-random assembly processes, but has been applied rarely to island communities. By linking functional diversity of trees on islands with a null model approach, we bridge this gap and test for the role of stochastic vs. non-random trait-mediated assembly processes in shaping communities by studying functional diversity-area relationships. We measured 11 plant functional traits linked to species dispersal and resource acquisition strategies of 57 tree species on 40 tropical islands. We grouped traits into four life-history dimensions representing (i) dispersal ability, (ii) growth strategy, (iii) light acquisition, and (iv) nutrient acquisition. To test for non-random assembly processes, we used null models that account for differences in species richness among the islands. Our results reveal contrasting responses of the four life-history dimensions to island area. The dispersal and the growth strategy dimensions were underdispersed on smaller islands, whereas the light acquisition dimension was overdispersed. The nutrient acquisition dimension did not deviate from null expectations. With increasing island area, shifts in the strength of non-random assembly processes increased the diversity of dispersal and acquisition strategies in island communities. Our results suggest that smaller islands may be more difficult to colonize and provide more limited niche space compared to larger islands, whose tree communities are likely determined by stochastic processes and higher niche diversity. Our null model approach highlights that analysing the functional diversity of different life-history dimensions provides a powerful framework to unravel community assembly processes on islands. These complex, non-random assembly processes are masked by measures of functional diversity that do not account for differences in species richness between islands.</p>
Data from: Effects of growth rate, size, and light availability on tree survival across life stages: a demographic analysis accounting for missing values and small sample sizes
Background: Plant survival is a key factor in forest dynamics and survival probabilities often vary across life stages. Studies specifically aimed at assessing tree survival are unusual and so data initially designed for other purposes often need to be used; such data are more likely to contain errors than data collected for this specific purpose. Results: We investigate the survival rates of ten tree species in a dataset designed to monitor growth rates. As some individuals were not included in the census at some time points we use capture-mark-recapture methods both to allow us to account for missing individuals, and to estimate relocation probabilities. Growth rates, size, and light availability were included as covariates in the model predicting survival rates. The study demonstrates that tree mortality is best described as constant between years and size-dependent at early life stages and size independent at later life stages for most species of UK hardwood. We have demonstrated that even with a twenty-year dataset it is possible to discern variability both between individuals and between species. Conclusions: Our work illustrates the potential utility of the method applied here for calculating plant population dynamics parameters in time replicated datasets with small sample sizes and missing individuals without any loss of sample size, and including explanatory covariates.
Data from: Loss of animal seed dispersal increases extinction risk in a tropical tree species due to pervasive negative density dependence across life stages
Overhunting in tropical forests reduces populations of vertebrate seed dispersers. If reduced seed dispersal has a negative impact on tree population viability, overhunting could lead to altered forest structure and dynamics, including decreased biodiversity. However, empirical data showing decreased animal-dispersed tree abundance in overhunted forests contradict demographic models which predict minimal sensitivity of tree population growth rate to early life stages. One resolution to this discrepancy is that seed dispersal determines spatial aggregation, which could have demographic consequences for all life stages. We tested the impact of dispersal loss on population viability of a tropical tree species, Miliusa horsfieldii, currently dispersed by an intact community of large mammals in a Thai forest. We evaluated the effect of spatial aggregation for all tree life stages, from seeds to adult trees, and constructed simulation models to compare population viability with and without animal-mediated seed dispersal. In simulated populations, disperser loss increased spatial aggregation by fourfold, leading to increased negative density dependence across the life cycle and a 10-fold increase in the probability of extinction. Given that the majority of tree species in tropical forests are animal-dispersed, overhunting will potentially result in forests that are fundamentally different from those existing now.
Data from: Tropical tree height and crown allometries for the Barro Colorado Nature Monument, Panama: a comparison of alternative hierarchical models incorporating interspecific variation in relation to life history traits
Tree allometric relationships are widely employed for estimating forest biomass and production and are basic building blocks of dynamic vegetation models. In tropical forests, allometric relationships are often modeled by fitting scale-invariant power functions to pooled data from multiple species, an approach that fails to capture changes in scaling during ontogeny and physical limits to maximum tree size and that ignores interspecific differences in allometry. Here, we analyzed allometric relationships of tree height (9884 individuals) and crown area (2425) with trunk diameter for 162 species from the Barro Colorado Nature Monument, Panama. We fit nonlinear, hierarchical models informed by species traits – wood density, mean sapling growth, or sapling mortality – and assessed the performance of three alternative functional forms: the scale-invariant power function and the saturating Weibull and generalized Michaelis–Menten (gMM) functions. The relationship of tree height with trunk diameter was best fit by a saturating gMM model in which variation in allometric parameters was related to interspecific differences in sapling growth rates, a measure of regeneration light demand. Light-demanding species attained taller heights at comparatively smaller diameters as juveniles and had shorter asymptotic heights at larger diameters as adults. The relationship of crown area with trunk diameter was best fit by a power function model incorporating a weak positive relationship between crown area and species-specific wood density. The use of saturating functional forms and the incorporation of functional traits in tree allometric models is a promising approach for improving estimates of forest biomass and productivity. Our results provide an improved basis for parameterizing tropical plant functional types in vegetation models.
Data from: Ecosystem biomonitoring with eDNA: metabarcoding across the tree of life in a tropical marine environment
Effective marine management requires comprehensive data on the status of marine biodiversity. However, efficient methods that can document biodiversity in our oceans are currently lacking. Environmental DNA (eDNA) sourced from seawater offers a new avenue for investigating the biota in marine ecosystems. Here, we investigated the potential of eDNA to inform on the breadth of biodiversity present in a tropical marine environment. Directly sequencing eDNA from seawater using a shotgun approach resulted in only 0.34% of 22.3 million reads assigning to eukaryotes, highlighting the inefficiency of this method for assessing eukaryotic diversity. In contrast, using 'tree of life' (ToL) metabarcoding and 20-fold fewer sequencing reads, we could detect 287 families across the major divisions of eukaryotes. Our data also show that the best performing 'universal' PCR assay recovered only 44% of the eukaryotes identified across all assays, highlighting the need for multiple metabarcoding assays to catalogue biodiversity. Lastly, focusing on the fish genus Lethrinus, we recovered intra- and inter-specific haplotypes from seawater samples, illustrating that eDNA can be used to explore diversity beyond taxon identifications. Given the sensitivity and low cost of eDNA metabarcoding we advocate this approach be rapidly integrated into biomonitoring programs.
Data from: Functional traits as predictors of vital rates across the life cycle of tropical trees
The 'functional traits' of species have been heralded as promising predictors for species' demographic rates and life history. Multiple studies have linked plant species' demographic rates to commonly measured traits. However, predictive power is usually low – raising questions about the practical usefulness of traits – and analyses have been limited to size-independent univariate approaches restricted to a particular life stage. Here we directly evaluated the predictive power of multiple traits simultaneously across the entire life cycle of 136 tropical tree species from central Panama. Using a model-averaging approach, we related wood density, seed mass, leaf mass per area and adult stature (maximum diameter) to onset of reproduction, seed production, seedling establishment, and growth and survival at seedling, sapling and adult stages. Three of the four traits analysed here (wood density, seed mass and adult stature) typically explained 20–60% of interspecific variation at a given vital rate and life stage. There were strong shifts in the importance of different traits throughout the life cycle of trees, with seed mass and adult stature being most important early in life, and wood density becoming most important after establishment. Every trait had opposing effects on different vital rates or at different life stages; for example, seed mass was associated with higher seedling establishment and lower initial survival, wood density with higher survival and lower growth, and adult stature with decreased juvenile but increased adult growth and survival. Forest dynamics are driven by the combined effects of all demographic processes across the full life cycle. Application of a multitrait and full-life cycle approach revealed the full role of key traits, and illuminated how trait effects on demography change through the life cycle. The effects of traits on one life stage or vital rate were sometimes offset by opposing effects at another stage, revealing the danger of drawing broad conclusions about functional trait–demography relationships from analysis of a single life stage or vital rate. Robust ecological and evolutionary conclusions about the roles of functional traits rely on an understanding of the relationships of traits to vital rates across all life stages.
Data from: Phylogenetically informed spatial planning is required to conserve the mammalian tree of life
In the face of the current extinction crisis and severely limited conservation resources, safeguarding the tree of life is increasingly recognized as a high priority. We conducted a first systematic global assessment of the conservation of phylogenetic diversity (PD) that uses realistic area targets and highlights the key areas for conservation of the mammalian tree of life. Our approach offers a substantially more effective conservation solution than one focused on species. In many locations, priorities for PD differ substantially from those of a species-based approach that ignores evolutionary relationships. This discrepancy increases rapidly as the amount of land available for conservation declines, as does the relative benefit for mammal conservation (for the same area protected). This benefit is equivalent to an additional 5900 Myr of distinct mammalian evolution captured simply through a better informed choice of priority areas. Our study uses area targets for PD to generate more realistic conservation scenarios, and tests the impact of phylogenetic uncertainty when selecting areas to represent diversity across a phylogeny. It demonstrates the opportunity of using rapidly growing phylogenetic information in conservation planning and the readiness for a new generation of conservation planning applications that explicitly consider the heritage of the tree of life's biodiversity.
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