Filters: Tags: Wildfire (X) > Date Range: {"choice":"month"} (X) > partyWithName: U.S. Geological Survey (X)

Filters Date Types (for Date Range) Extensions Citation (1) Types Citation (1) Map Service (1) OGC WFS Layer (1) OGC WMS Layer (1) OGC WMS Service (1) Contacts Kevin Faccenda Kimberley Davis University of Hawaii Climate Adaptation Science Centers Curt Daehler More... Categories Data (2) Publication (1) Tag Types Theme (2) Place (1) Tag Schemes None (2) ISO 19115 Topic Category (2) View Results as: JSON ATOM CSV

Maps of post-fire conifer recruitment from: Fire-catalyzed vegetation shifts in ponderosa pine and Douglas-fir forests of the western United States This dataset includes spatial projections of the post-fire recruitment index for ponderosa pine (Pinus ponderosa) and Douglas-fir (Pseudotsuga menziesii) using climate data from different time periods (1980-1989, 1990-1999, 2000-2009, 2010-2014) and a future climate scenario of a global mean increase in temperature of two degrees Celsius. The post-fire recruitment index varies from 0 to 1 and represents the proportion of the first five years following wildfire that had climate suitable for regeneration of the given species. We chose a five-year window because the majority (69%) of recruitment across all sites in the dataset used to build our recruitment models occurred within the first five post-fire years. In the... Categories: Data; Tags: Pinus ponderosa, Pseudotsuga menziesii, US Intermountain West, biota, environment, All tags... post-fire regeneration, tree regeneration, wildfire, Fewer tags Fire Risk Scores from Predictive Model Based on Flammability and Fire Ecology of Non-Native Hawaiian Plants from 2020-2021 We developed a screening system to identify introduced plant species that are likely to increase wildfire risk, using the Hawaiian Islands to test the system and illustrate how the system can be applied to inform management decisions. Expert-based fire risk scores derived from field experiences with 49 invasive species in Hawai′i were used to train a machine learning model that predicts expert fire risk scores from among 21 plant traits obtained from literature and databases. The model revealed that just four variables can identify species categorized as higher fire risk by experts with 90% accuracy, while low risk species were identified with 79% accuracy. We then used the predictive model to screen 365 naturalized... Categories: Data, Publication; Types: Citation, Map Service, OGC WFS Layer, OGC WMS Layer, OGC WMS Service; Tags: Hawaii, farming, fire, fire risk, flammability, All tags... invasive plants, post-fire regeneration, species profile, weeds, wildfire, Fewer tags