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Evans, T.

Publications and source records attributed to Evans, T..

2 recordsLinked to original sources

Predicting Global Forest Reforestation Potential

Executive SummaryForests are important determinants of the carbon cycle, and they provide countless ecosystem services to support billions of people worldwide. Global-scale forest restoration is one of our most effective weapons in the fight against biodiversity loss, rural poverty and climate change. In this report, we generate a spatial map of tree density within the potential forest restoration areas delineated by the IUCN/WRIs \"Atlas of Forest Landscape Restoration Opportunities\" to estimate the potential number of trees that could be restored at a global scale. We also estimate the number of trees that might be saved by avoiding deforestation in currently forested areas.\n\nWe show that the restoration areas have the capacity to support a total of 1.33 trillion trees. However, given that a considerable proportion of these areas already contain forests, we estimate that 589 billion new trees (larger than 10 cm diameter) could be restored within these areas, which would have the potential to store 65-91 Gigatonnes of carbon after reaching forest maturation. These values will increase marginally over time, as deforestation is responsible for the removal of living trees within the restoration areas. However, if only 50% or 25% of the mosaic areas (the largest of the designated restoration types) are available for reforestation, this total number will fall to approximately 360, or 246 billion trees, respectively, with corresponding decreases in potential carbon storage. Given that anthropogenic carbon emissions are currently in the order of 9-12 Gigatonnes per year, effective global-scale restoration might potentially have a valuable impact on global-scale climate mitigation over the rest of this century.\n\nThis report was produced with funding from WWF-UK as part of the Trillion Trees programme with the Wildlife Conservation Society and BirdLife International

ecology

Virtual Genome Walking: Generating gene models for the salamander Ambystoma mexicanum

Large repeat rich genomes present challenges for assembly and identification of gene models with short read technologies. Here we present a method we call Virtual Genome Walking which uses an iterative assembly approach to first identify exons from de-novo assembled transcripts and assemble whole genome reads against each exon. This process is iterated allowing the extension of exons. These linked assemblies are refined to generate gene models including upstream and downstream genomic sequence as well as intronic sequence. We test this method using a 20X genomic read set for the axolotl, the genome of which is estimated to be 30 Gb in size. These reads were previously reported to be effectively impossible to assemble. Here we provide almost 1 Gb of assembled sequence describing over 19,000 gene models for the axolotl. Gene models stop assembling either due to localised low coverage in the genomic reads, or the presence of repeats. We validate our observations by comparison with previously published axolotl bacterial artificial chromosome (BAC) sequences. In addition we analysed axolotl intron length, intron-exon structure, repeat content and synteny. These gene-models, sequences and annotations are freely available for download from https://tinyurl.com/y8gydc6n. The software pipeline including a docker image is available from https://github.com/LooseLab/iterassemble. These methods will increase the value of low coverage sequencing of understudied model systems.

bioinformatics