Vitis riparia, a critically important Native American grapevine species, is used globally in rootstock and scion breeding and contributed to the recovery of the French wine industry during the mid-19th century phylloxera epidemic. This species has abiotic and biotic stress tolerance and the largest natural geographic distribution of the North American grapevine species. Here we report an Illumina short-read 369X coverage, draft de novo heterozygous genome sequence of V. riparia Michx. ‘Manitoba 37’ with the size of ~495 Mb for 69,616 scaffolds and a N50 length of 518,740 bp. Using RNAseq data, 40,019 coding sequences were predicted and annotated. Benchmarking with Universal Single-Copy Orthologs (BUSCO) analysis of predicted gene models found 96% of the complete BUSCOs in this assembly. The assembly continuity and completeness were further validated using V. riparia ESTs, BACs, and three de novo transcriptome assemblies of three different V. riparia genotypes resulting in >98% of respective sequences/transcripts mapping with this assembly. Alignment of the V. riparia assembly and predicted CDS with the latest V. vinifera ‘PN40024’ CDS and genome assembly showed 99% CDS alignment and a high degree of synteny. An analysis of plant transcription factors indicates a high degree of homology with the V. vinifera transcription factors. QTL mapping to V. riparia ‘Manitoba 37’ and V. vinifera PN40024 has identified genetic relationships to phenotypic variation between species. This assembly provides reference sequences, gene models for marker development and understanding V. riparia’s genetic contributions in grape breeding and research.

Abstract. Sedimentary charcoal records are widely used to reconstruct regional changes in fire regimes through time in the geological past. Existing global compilations are not geographically comprehensive and do not provide consistent metadata for all sites. Furthermore, the age models provided for these records are not harmonised and many are based on older calibrations of the radiocarbon ages. These issues limit the use of existing compilations for research into past fire regimes. Here, we present an expanded database of charcoal records, accompanied by new age models based on recalibration of radiocarbon ages using IntCal20 and Bayesian age-modelling software. We document the structure and contents of the database, the construction of the age models, and the quality control measures applied. We also record the expansion of geographical coverage relative to previous charcoal compilations and the expansion of metadata that can be used to inform analyses. This first version of the Reading Palaeofire Database contains 1676 records (entities) from 1480 sites worldwide. The database (RPDv1b – Harrison et al., 2021) is available at https://doi.org/10.17864/1947.000345.

No abstract

No abstract

This supplementary contains: 4 SI Table 1. Information of the charcoal records (sites and entities) in the Reading Palaeofire Database version 1. Latitude and longitude are in decimal degrees, and elevation in metres above/below sea level. Fields where information could be available but was never recorded or has subsequently been lost are represented by -999999, fields where we were unable to obtain this information but it could be included in subsequent updates of the database are represented by -777777, fields where specific information is not applicable are represented by -888888. SI Table 2. List of pre-defined valid choices for restricted fields in the Reading Palaeofire Database version 1. SI Table 3: List of charcoal measurement units currently used in the Reading Palaeofire Database version 1 SI Figure 1: Supplementary Figure 1. Summary of the stages used to select the optimum RBacon age models for from ageR. Plots A.-C. show the modelling output from ageR for an example entity from the RPD (Geral core), where the optimum age model selected by ageR A. is a table ranking the age model scenarios by the lowest area between the prior and posterior accumulation rate curves. Note that only the top 5 model scenarios of a total of 25 run for this entity are listed B. Shows the plots for the prior and posterior accumulation rates and the area between curves for the top 5 model scenarios.C. Is the top ranked RBacon age model (Accumulation rate = 15, thickness = 10) which was visually checked to verify that the interpolation through the dates was valid and consistent with the dates. In this example, the top ranked model scenario selected by ageR (Accumulation rate = 15, thickness= 10) was accepted as the chosen model scenario as the interpolation through the dates is valid. SI Figure 2. An example of alternative model scenario selection where the top ranked ageR model scenario is deemed to be inaccurate. In this example, the top ranked model scenario from King Tableland Swamp (accumulation rate = 45, thickness = 5)(A.) with the lowest area between the prior and posterior accumulation rate curves (B.) does not accurately represent the date at 157.5cm. This age was included by the original authors and lies in stratigraphic order with the other dates. Therefore, this model is rejected in favour of the model with the next lowest abc score which accurately reflects the dates included (ageR model ranking 3 in A.). The RBacon plot for this age model scenario is shown in D. (accumulation rate = 90, thickness = 5) and is more accurately and precisely modelled through the dates than the model selected by ageR. Site name Entities (#) Elevation (m) Latitude (°) Longitude (°) Site Type Water depth (m) Basin size (km 2 ) Citation(s)