Spatial overlap of wildfire and biodiversity in California highlights gap in non‐conifer fire research and management

Centering Amah Mutsun voices in the analysis of a culturally important, fire-managed coastal grassland

Indigenous communities throughout California, USA, are increasingly advocating for and practicing cultural fire stewardship, leading to a host of social, cultural, and ecological benefits. Simultaneously, state agencies are recognizing the importance of controlled burning and cultural fire as a means of reducing the risk of severe wildfire while benefiting fire-adapted ecosystems. However, much of the current research on the impacts of controlled burning ignores the cultural importance of these ecosystems, and risks further marginalizing Indigenous knowledge systems. Our work adds a critical Indigenous perspective to the study of controlled burning in California's unique coastal grasslands, one of the most biodiverse and endangered ecosystems in the country. In this study, we partnered with the Amah Mutsun Tribal Band to investigate how the abundance and occurrence of shrubs, cultural plants, and invasive plants differed among three adjacent coastal grasslands with varying fire histories. These three sites are emblematic of the state's diverging approaches to grassland management: fire suppression, fire suppression followed by wildfire, and an exceedingly rare example of a grassland that has been repeatedly burned approximately every 2 years for more than 30 years. We found that Danthonia californica was significantly more abundant on the burned sites, whereas all included shrub species (Baccharis pilularis, Frangula californica, and Rubus ursinus) were significantly more abundant on the site with no recorded fire, results that have important implications for future cultural revitalization efforts and the loss of coastal grasslands to shrub encroachment. In addition to conducting a culturally relevant vegetation survey, we used Sentinel-2 satellite imagery to compare the relative severities of the two most recent fire events within the study area. Critically, we used interviews with Amah Mutsun tribal members to contextualize the results of our vegetation survey and remote sensing analysis, and to investigate how cultural burning contrasts from typical Western fire management approaches in this region. Our study is a novel example of how interviews, field data, and satellite imagery can be combined to gain a deeper ecological and cultural understanding of fire in California's endangered coastal grasslands.

The importance of habitat type and historical fire regimes in arthropod community response following large-scale wildfires

Novel wildfire regimes are rapidly changing global ecosystems and pose significant challenges for biodiversity conservation and ecosystem management. In this study, we used DNA metabarcoding to assess the response of arthropod pollinator communities to large-scale wildfires across diverse habitat types in California. We sampled six reserves within the University of California Natural Reserve System, each of which was partially burned in the 2020 Lightning Complex wildfires in California. Using yellow pan traps to target pollinators, we collected arthropods from burned and unburned sites across multiple habitat types including oak woodland, redwood, scrub, chamise, grassland, forest, and serpentine habitats. We found no significant difference in alpha diversity values between burned and unburned sites; instead, seasonal variations played a significant role in arthropod community dynamics, with the emergence of plant species in Spring promoting increased pollinator richness at all sites. When comparing all sites, we found that burn status was not a significant grouping factor. Instead, compositional differences were largely explained by geographic differences, with distinct communities within each reserve. Within a geographic area, the response of arthropods to fire was dependent on habitat type. While communities in grasslands and oak woodlands exhibited recovery following burn, scrublands experienced substantial changes in community composition. Our study highlights the importance of examining community responses to wildfires across broad spatial scales and diverse habitat types. By understanding the nuanced dynamics of arthropod communities in response to fire disturbances, we can develop effective conservation strategies that promote resilience and maintain biodiversity in the face of increasing wildfire frequency and severity driven by climate change.

The 2020 to 2021 California megafires and their impacts on wildlife habitat

Fire activity during 2020 to 2021 in California, USA, was unprecedented in the modern record. More than 19,000 km2 of forest vegetation burned (10× more than the historical average), potentially affecting the habitat of 508 vertebrate species. Of the >9,000 km2 that burned at high severity, 89% occurred in large patches that exceeded historical estimates of maximum high-severity patch size. In this 2-y period, 100 vertebrate species experienced fire across >10% of their geographic range, 16 of which were species of conservation concern. These 100 species experienced high-severity fire across 5 to 14% of their ranges, underscoring potentially important changes to habitat structure. Species in this region are not adapted to high-severity megafires. Management actions, such as prescribed fires and mechanical thinning, can curb severe fire behavior and reduce the potential negative impacts of uncharacteristic fires on wildlife.

Explainable machine learning for the prediction and assessment of complex drought impacts

Drought is a common and costly natural disaster with broad social, economic, and environmental impacts. Machine learning (ML) has been widely applied in scientific research because of its outstanding performance on predictive tasks. However, for practical applications like disaster monitoring and assessment, the cost of the models failure, especially false negative predictions, might significantly affect society. Stakeholders are not satisfied with or do not "trust" the predictions from a so-called black box. The explainability of ML models becomes progressively crucial in studying drought and its impacts. In this work, we propose an explainable ML pipeline using the XGBoost model and SHAP model based on a comprehensive database of drought impacts in the U.S. The XGBoost models significantly outperformed the baseline models in predicting the occurrence of multi-dimensional drought impacts derived from the text-based Drought Impact Reporter, attaining an average F2 score of 0.883 at the national level and 0.942 at the state level. The interpretation of the models at the state scale indicates that the Standardized Precipitation Index (SPI) and Standardized Temperature Index (STI) contribute significantly to predicting multi-dimensional drought impacts. The time scalar, importance, and relationships of the SPI and STI vary depending on the types of drought impacts and locations. The patterns between the SPI variables and drought impacts indicated by the SHAP values reveal an expected relationship in which negative SPI values positively contribute to complex drought impacts. The explainability based on the SPI variables improves the trustworthiness of the XGBoost models. Overall, this study reveals promising results in accurately predicting complex drought impacts and rendering the relationships between the impacts and indicators more interpretable. This study also reveals the potential of utilizing explainable ML for the general social good to help stakeholders better understand the multi-dimensional drought impacts at the regional level and motivate appropriate responses.