Skip to main content
Advanced Search

Filters: Tags: {"scheme":"None"} (X) > Date Range: {"choice":"week"} (X) > Categories: Publication (X)

4 results (40ms)   

View Results as: JSON ATOM CSV
thumbnail
The broadly shared information needs for grassland managers in the North Central region to meet conservation goals in a changing climate are presented and ranked as highly relevant, somewhat relevant, or not relevant for federal, state, tribal, and non-governmental grassland-managing entities.
thumbnail
Previous research identified species of invasive plants in Hawai'i which are highly flammable and act as fuels in wildfires across Hawai'i. This work aimed to map the distribution of these species (largely grasses) around the islands of Hawai'i with the goal of using the locations for species distribution modeling. All data represents presence data, no absence data were recorded. Data are largely from within the past 20 years, but some georeferenced herbarium specimens go as far back as 1905. Data were obtained from georeferenced herbarium specimens, vegetation plot data, citizen science data (iNaturalist) reviewed by the authors, and data from roadside surveys conducted as part of this research to map these species....
thumbnail
This file provides a table of all the of Species of Greatest Conservation Need listed in the North Central states' (MT, WY, CO, ND, SD, NE, and KS) State Wildlife Action Plans as of summer 2020. Species are organized by the number of states which listed them as Species of Greatest Conservation Need, and then by scientific name. Federal status is also provided for each species. This table is adapted from an unpublished species list compiled by the North Central Climate Adaptation Science Center.
thumbnail
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...
    map background search result map search result map Species of Greatest Conservation Need in the North Central Region Broadly Shared Information Needs Among Grassland Managers in the North Central Region Fire Risk Scores from Predictive Model Based on Flammability and Fire Ecology of Non-Native Hawaiian Plants from 2020-2021 Locations of Fire Promoting Alien Plants Across the Islands of Hawaii Based on Field Surveys and Museum Collections from 1903-2023 Fire Risk Scores from Predictive Model Based on Flammability and Fire Ecology of Non-Native Hawaiian Plants from 2020-2021 Locations of Fire Promoting Alien Plants Across the Islands of Hawaii Based on Field Surveys and Museum Collections from 1903-2023 Species of Greatest Conservation Need in the North Central Region Broadly Shared Information Needs Among Grassland Managers in the North Central Region