Publications by authors named "Benjamin G Jackson"
Maternal effects (i.e. trans-generational plasticity) and soil legacies generated by drought and plant diversity can affect plant performance and alter nutrient cycling and plant community dynamics.
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- Research on plant traits suggests that above-ground characteristics like leaf nitrogen content are important for predicting ecosystem functions such as productivity and carbon storage.
- The study examined both above- and below-ground plant traits in temperate grassland to see how they relate to each other and to soil properties and ecosystem carbon fluxes.
- Findings indicated that while some relationships between above- and below-ground traits were evident in monocultures, they weakened or disappeared in mixed communities, highlighting the complexity of predicting ecosystem behaviors in diverse plant settings.
Article Synopsis
- - The study explores how different types of fungi in the soil (pathogenic, mycorrhizal, and saprotrophic) affect plant-soil interactions and vegetation dynamics in temperate grasslands.
- - Plants with resource-acquisitive traits (like high nitrogen and thin roots) attract more pathogenic and saprotrophic fungi, leading to reduced growth when grown in their own soil.
- - Soil properties also influence these interactions, with fertile soils fostering negative relationships between fungi and plants, helping to improve our understanding of plant-soil feedbacks and their impact on ecosystem dynamics.
There are numerous ways in which plants can influence the composition of soil communities. However, it remains unclear whether information on plant community attributes, including taxonomic, phylogenetic, or trait-based composition, can be used to predict the structure of soil communities. We tested, in both monocultures and field-grown mixed temperate grassland communities, whether plant attributes predict soil communities including taxonomic groups from across the tree of life (fungi, bacteria, protists, and metazoa).
View Article and Find Full Text PDFBackground: The de novo assembly of genomes and transcriptomes from short sequences is a challenging problem. Because of the high coverage needed to assemble short sequences as well as the overhead of modeling the assembly problem as a graph problem, the methods for short sequence assembly are often validated using data from BACs or small sized prokaryotic genomes.
Results: We present a parallel method for transcriptome assembly from large short sequence data sets.