Diagnostics for a troubled backbone:testing topological hypotheses of trapelioid lichenized fungi in a large-scale phylogeny of Ostropomycetidae(Lecanoromycetes)

Trapelioid fungi constitute a widespread group of mostly crust-forming lichen mycobionts that are key to understanding the early evolutionary splits in the Ostropomycetidae,the second-most species-rich subclass of lichenized Ascomycota.The uncertain phylogenetic resolution of the approximately 170 species referred to this group contributes to a poorly resolved backbone for the entire subclass.Based on a data set including 657 newly generated sequences from four ribosomal and four protein-coding gene loci,we tested a series of a priori and new evolutionary hypotheses regarding the relationships of trapelioid clades within Ostropomycetidae.We found strong support for a monophyletic group of nine core trapelioid genera but no statistical support to reject the long-standing hypothesis that trapelioid genera are sister to Baeomycetaceae or Hymeneliaceae.However,we can reject a sister group relationship to Ostropales with high confidence.Our data also shed light on several longstanding questions,recovering Anamylopsoraceae nested within Baeomycetaceae,elucidating two major monophyletic groups within trapelioids(recognized here as Trapeliaceae and Xylographaceae),and rejecting the monophyly of the genus Rimularia.We transfer eleven species of the latter genus to Lambiella and describe the genus Parainoa to accommodate a previously misunderstood species of Trapeliopsis.Past phylogenetic studies in Ostropomycetidae have invoked Bdivergence order^for drawing taxonomic conclusions on higher level taxa.Our data show that if backbone support is lacking,contrasting solutions may be recovered with different or added data.We accordingly urge caution in concluding evolutionary relationships from unresolved phylogenies.

Rapid Evaluation and Response to Impacts on Critical End-Use Loads Following Natural Hazard-Driven Power Outages:A Modular and Responsive Geospatial Technology

The disparate nature of data for electric power utilities complicates the emergency recovery and response process.The reduced efficiency of response to natural hazards and disasters can extend the time that electrical service is not available for critical end-use loads,and in extreme events,leave the public without power for extended periods.This article presents a methodology for the development of a semantic data model for power systems and the integration of electrical grid topology,population,and electric distribution line reliability indices into a unified,cloud-based,serverless framework that supports power system operations in response to extreme events.An iterative and pragmatic approach to working with large and disparate datasets of different formats and types resulted in improved application runtime and efficiency,which is important to consider in real time decision-making processes during hurricanes and similar catastrophic events.This technology was developed initially for Puerto Rico,following extreme hurricane and earthquake events in 2017 and 2020,but is applicable to utilities around the world.Given the highly abstract and modular design approach,this technology is equally applicable to any geographic region and similar natural hazard events.In addition to a review of the requirements,development,and deployment of this framework,technical aspects related to application performance and response time are highlighted.