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Nexus file for phylogenetic analysis. (ZIP 7 kb)

Additional file 3: Table S2. Summary of the Illumina sequencing data.

This dataset contains key characteristics about the data described in the Data Descriptor Geolocation of unpublished archaeological sites in the Peruvian Amazon. Contents: 1. human readable metadata summary table in CSV format 2. machine readable metadata file in JSON format

Complete set of 739 unique cyclodipeptide synthases identified in a global analysis of publicly available prokaryotic genomes. (XLSX 144Â kb)

Recent studies have reported beneficial carryover effects of juvenile development that predict interspecific survival differences at independence. Yet, traits relating to body size (i.e. morphological traits) have proven to be unreliable predictors of juvenile survival within species. Exploring individual variation of growth trajectories and how they covary with physiology could reveal species-specific developmental modes which have implications for our assessments of juvenile quality. Here, we investigated morphological development of European starlings (Sturnus vulgaris) approaching fledging in relation to three components of physiological condition at independence: aerobic capacity, energy state and oxidative status. We found evidence of flexible mass and wing growth which independently covaried with fledgling energy state and aerobic capacity, respectively. By comparison, tarsus and wing length at fledging were unrelated to any physiological trait, while mass was positively associated with principal component scores that comprised aerobic capacity and energy state. Thus, flexible growth trajectories were consistent with ‘developmental plasticity’: adaptive pre-fledging mass recession and compensatory wing growth, which seemingly came at a physiological cost, while fledgling body mass positively reflected overall physiological condition. This highlights how patterns of growth and absolute size may differently reflect fledgling physiology, potentially leading to variable relationships between morphological traits and juvenile fitness.

Additional file 4: Table S3. A. Results for 34 independent ARIC Discovery Meta-Analysis identified mtDNA-CN associated CpGs across all studied cohorts and Validation Meta-Analysis/All Cohort Meta-Analysis. Validation meta-analysis included CHS AA, CHS EA and FHS EA cohorts (P

Targets for goal-directed action can be encoded in allocentric coordinates (relative to another visual landmark), but it is not known how these are converted into egocentric commands for action. Here, we investigated this using a slow event-related fMRI paradigm, based on our previous behavioral finding that the Allocentric to Egocentric (Allo-Ego) conversion for reach is done at the first possible opportunity. Participants were asked to remember (and eventually reach toward) the location of a briefly presented target relative to another visual landmark. After a 1st memory delay, participants were forewarned by a verbal instruction if the landmark would reappear at the same location, (potentially allowing them to plan a reach following the auditory cue before the 2nd delay), or at a different location where they had to wait for the final landmark to be presented before response, and then reach toward the remembered target location. As predicted, participants showed landmark-centered directional selectivity in occipital-temporal cortex during the first memory delay, only developed egocentric directional selectivity in occipital-parietal cortex during the second delay for the “Same cue” task, and during response for the “Different cue” task. We then compared cortical activation between these two tasks at the times when the Allo-Ego conversion occurred, and found common activation in right precuneus, right pre-supplementary area and bilateral dorsal premotor cortex. These results confirm that the brain converts allocentric codes to egocentric plans at the first possible opportunity, and identify the four most likely candidate sites specific to the Allo-Ego transformation for reaches.

Radiodonts, stem-group euarthropods that evolved during the Cambrian explosion, were among the largest and most diversified lower palaeozoic predators. These animals were widespread geographically, occupying a variety of ecological niches, from benthic foragers to nektonic suspension feeders and apex predators. Here, we describe the largest Cambrian hurdiid radiodont known so far, Titanokorys gainesi, gen. et sp. nov., from the Burgess Shale (Marble Canyon, Kootenay National Park, British Columbia). Estimated to reach half-a-meter in length, this new species bears a very large ovoid-shaped central carapace with distinct short posterolateral processes and an anterior spine. Geometric morphometric analyses highlight the high diversity of carapace shapes in hurdiids and show that Titanokorys bridges a morphological gap between forms with long and short carapaces. Carapace shape, however, is prone to homoplasy and shows no consistent relationship with trophic ecology, as demonstrated by new data, including a reappraisal of the poorly known Pahvantia. Despite distinct carapaces, Titanokorys shares similar rake-like appendages for sediment-sifting with Cambroraster, a smaller but much more abundant sympatric hurdiid from the Burgess Shale. The co-occurrence of these two species on the same bedding planes highlights the potential competition for benthic resources and the high diversity of large predators sustained by Cambrian communities.

S1. Systematic review search strategy. Search terms, number of results, and filters used when collecting studies from databases for the systematic review. S2. Abstraction form and dataset. Abstraction form and dataset used for our analysis. (ZIP 148 kb)

Supporting data and code for Wucherpfennig et al. Nature Ecology and Evolution. R codes, count matrices, phenotype files, and genotype files are organized by the figure in which they are used (with the exception of the QTL mapping files assign to Figure 1 that were also used for Extended Data Figures 1 and 2). Shell scripts used for allele-specific RNA-sequencing analysis are numbered in the order in which they were used. Raw sequencing data can be found on SRA and GEO. Other phenotype files are included in the source data of the paper on the NEE website.