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Chronic Wasting Disease distribution in the United States by state and county (ver. 2.0, April 2024)
Chronic Wasting Disease (CWD) is a fatal, contagious, neuro-degenerative disease affecting multiple members of the Family Cervidae. First detected in 1967, the disease has, as of April 2024, been documented in free-ranging and/or captive cervid populations in 33 states, five Canadian provinces, the Republic of South Korea, Norway, Sweden, and Finland. The data provided here contains information on the known, available, documented distribution of CWD in the United States that is current as of the publication date. The USGS National Wildlife Health Center (NWHC) tracks changes to the known distribution of CWD in the form of a map (available on the NWHC website at https://www.usgs.gov/centers/nwhc/science/expanding-distribution-chronic-wasting-disease....
Categories: Data,
Data Release - Revised;
Types: Citation;
Tags: Alabama,
Alaska,
American Samoa,
Arizona,
Arkansas,
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....
Categories: Data,
Publication;
Types: Citation,
Map Service,
OGC WFS Layer,
OGC WMS Layer,
OGC WMS Service;
Tags: Hawaii,
biota,
gorse,
grass,
herbarium,
Nature’s Network Conservation Design depicts an interconnected network of lands and waters that, if protected, will support a diversity of fish, wildlife, and natural resources that the people of the Northeast and Mid-Atlantic region depend upon. This map serves as the “cover page” for the Nature’s Network (naturesnetwork.org) suite of products: it outlines some of the most important natural areas in the region and provides an entry point to learn more about the information used to identify them. The Conservation Design represents a combination of three Nature’s Network products: 1) the terrestrial core-connector network, 2) aquatic core areas, and 3) core habitat for imperiled species. The Terrestrial Core-connector...
These model objects are the outputs of three Boosted Regression Tree models (for three different time periods) to explore the role of climate change and variability in driving ecological change and transformation. Response variables were the proportion of sites in each ecoregion with peak rates of change at 100-year time steps. Predictor variables included temperature anomaly, temperature trend, temperature variability, precipitation anomaly, precipitation trend, precipitation variability and ecoregion, also at 100-yr time steps. Models focused on the most distant time periods (0-21000 BP and 7500 - 21000 BP) show that rapid vegetation change was initiated across these landscapes once a 2 ℃ temperature increase...
Categories: Publication;
Types: Citation,
Map Service,
OGC WFS Layer,
OGC WMS Layer,
OGC WMS Service;
Tags: The Rockies,
atmospheric and climatic processes,
climate change,
vegetation
The Terrestrial Core-connector Network is one of a suite of products from the Nature’s Network project (naturesnetwork.org). Nature’s Network is a collaborative effort to identify shared priorities for conservation in the Northeast, considering the value of fish and wildlife species and the natural areas they inhabit. This product consists of two components: core areas and connectors. A number of additional datasets that augment or complement the core-connector network, including Road-bounded Natural Blocks that surround the cores areas and a set of Grassland Bird Core Areas, are also available in the Nature’s Network gallery: https://nalcc.databasin.org/galleries/8f4dfe780c444634a45ee4acc930a055. A detailed technical...
These model objects are the outputs of two Bayesian hierarchical models (one for the Middle Rockies and one for the Southern Rockies) to explore the role of landscape characteristics in climate-driven ecological change and transformation. We used the rate of change for each site at 100-yr time steps as the response variable, and included elevation, CHILI, aspect, slope, and TPI as fixed effects in the models, run separately for each ecoregion. We included a random intercept of site to quantify the magnitude of site-level variation in rate-of-change that may be unaccounted for by our covariates.
Categories: Publication;
Types: Citation,
Map Service,
OGC WFS Layer,
OGC WMS Layer,
OGC WMS Service;
Tags: The Rockies,
atmospheric and climatic processes,
climate change,
vegetation
This database integrates a list of vegetation transformations that occurred across the Southern and Middle Rockies since 21,000 years ago, the age of occurrence, the type of vegetation switch that occurred, whether the rates of vegetation change peaked at that time, and when applicable, the duration of peak rates of vegetation change.
Categories: Data,
Publication;
Types: Citation,
Map Service,
OGC WFS Layer,
OGC WMS Layer,
OGC WMS Service;
Tags: The Rockies,
atmospheric and climatic processes,
climate change,
vegetation
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,
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