Shifting social-ecological fire regimes explain increasing structure loss from Western wildfires.

Structure loss is an acute, costly impact of the wildfire crisis in the western conterminous United States ("West"), motivating the need to understand recent trends and causes. We document a 246% rise in West-wide structure loss from wildfires between 1999-2009 and 2010-2020, driven strongly by events in 2017, 2018, and 2020. Increased structure loss was not due to increased area burned alone. Wildfires became significantly more destructive, with a 160% higher structure-loss rate (loss/kha burned) over the past decade. Structure loss was driven primarily by wildfires from unplanned human-related ignitions (e.g. backyard burning, power lines, etc.), which accounted for 76% of all structure loss and resulted in 10 times more structures destroyed per unit area burned compared with lightning-ignited fires. Annual structure loss was well explained by area burned from human-related ignitions, while decadal structure loss was explained by state-level structure abundance in flammable vegetation. Both predictors increased over recent decades and likely interacted with increased fuel aridity to drive structure-loss trends. While states are diverse in patterns and trends, nearly all experienced more burning from human-related ignitions and/or higher structure-loss rates, particularly California, Washington, and Oregon. Our findings highlight how fire regimes-characteristics of fire over space and time-are fundamentally social-ecological phenomena. By resolving the diversity of Western fire regimes, our work informs regionally appropriate mitigation and adaptation strategies. With millions of structures with high fire risk, reducing human-related ignitions and rethinking how we build are critical for preventing future wildfire disasters.

All-hazards dataset mined from the US National Incident Management System 1999-2020.

This paper describes a dataset mined from the public archive (1999-2020) of the US National Incident Management System Incident Status Summary (ICS-209) forms (a total of 187,160 reports for 35,170 incidents, including 34,478 wildland fires). This system captures detailed daily/regular information on incident development and response, including social and economic impacts. Most (98.4%) reports are wildland fire-related, with other incident types including hurricane, hazardous materials, flood, tornado, search and rescue, civil unrest, and winter storms. The archive, although publicly available, has been difficult to use for research due to multiple record formats, inconsistent data entry, and no clean pathway from individual reports to high-level incident analysis. Here, we describe the open-source, reproducible methods used to produce a science-grade version of the data, including formal connections made to other published wildland fire data products. Among other applications, this integrated and spatially augmented dataset enables exploration of the daily progression of the most costly, damaging, and deadly environmental-hazard events in recent US history.

Country-level fire perimeter datasets (2001-2021).

Fire activity is changing across many areas of the globe. Understanding how social and ecological systems respond to fire is an important topic for the coming century. But many countries do not have accessible fire history data. There are several satellite-based products available as gridded data, but these can be difficult to access and use, and require significant computational resources and time to convert into a usable product for a specific area of interest. We developed an open source software package called Fire Event Delineation for python (FIREDpy) which automatically downloads and processes all of the source files for an area of interest from the MODIS burned area product, and runs a spatiotemporal flooding algorithm that converts those hundreds of grids into a single fire perimeter shapefile. Here we present a collection of fire event perimeter datasets for every country on the globe that we created using the FIREDpy software. We will continue to improve the efficiency and flexibility of the underlying algorithm, and intend to update these datasets annually.

BAY 11-7082 inhibits the secretion of interleukin-6 by senescent human microglia.

The accumulation of senescent cells in aged tissues has been implicated in a variety of age-related diseases, including cancer and neurodegenerative disorders. Recent studies have demonstrated a link between age-associated increase of senescent glial cells in the brain and the pathogenesis of Alzheimer's disease (AD). However, there is a lack of in vitro cellular models of senescent human microglia, which significantly limits our approaches to study AD pathogenesis. Here, we show for the first time that ionizing radiation (IR) dose-dependently induces premature senescence in HMC3 human microglial cells. Senescence-associated ß-galactosidase activity, a well-characterized marker of cellular senescence, was substantially increased in irradiated HMC3 cells compared with control cells. Furthermore, we found that phosphorylated p53 levels and p21 expression levels were markedly higher in IR-induced senescent microglia than in control cells. Senescent human microglia exhibited the senescence-associated secretory phenotype (SASP), as evidenced by the increased secretion of pro-inflammatory cytokine interleukin-6 (IL-6). Treatment with an NF-κB inhibitor, BAY 11-7082, inhibits the secretion of IL-6 by senescent HMC3 cells. Collectively, our studies have established an in vitro cellular model of human microglial senescence and suggest that the NF-κB pathway may play a critical role in regulating the SASP of senescent HMC3 cells.

MODIS Imagery Improves Pest Risk Assessment: A Case Study of Wheat Stem Sawfly (Cephus cinctus, Hymenoptera: Cephidae) in Colorado, USA.

Wheat stem sawfly (Cephus cinctus Norton, Hymenoptera: Cephidae) has long been a significant insect pest of spring, and more recently, winter wheat in the northern Great Plains. Wheat stem sawfly was first observed infesting winter wheat in Colorado in 2010 and, subsequently, has spread rapidly throughout wheat production regions of the state. Here, we used maximum entropy modeling (MaxEnt) to generate habitat suitability maps in order to predict the risk of crop damage as this species spreads throughout the winter wheat-growing regions of Colorado. We identified environmental variables that influence the current distribution of wheat stem sawfly in the state and evaluated whether remotely sensed variables improved model performance. We used presence localities of C. cinctus and climatic, topographic, soils, and normalized difference vegetation index and enhanced vegetation index data derived from Moderate Resolution Imaging Spectroradiometer (MODIS) imagery as environmental variables. All models had high performance in that they were successful in predicting suitable habitat for C. cinctus in its current distribution in eastern Colorado. The enhanced vegetation index for the month of April improved model performance and was identified as a top contributor to MaxEnt model. Soil clay percent at 0-5 cm, temperature seasonality, and precipitation seasonality were also associated with C. cinctus distribution in Colorado. The improved model performance resulting from integrating vegetation indices in our study demonstrates the ability of remote sensing technologies to enhance species distribution modeling. These risk maps generated can assist managers in planning control measures for current infestations and assess the future risk of C. cinctus establishment in currently uninfested regions.