Filters: Tags: Wildfire (X) > Categories: Publication (X) > Types: Map Service (X)
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Conclusions: Prescribed burns did not supply the stream ecosystem with potentially important nutrient pulses that are often observed after wildfires. Prescribing higher severity burns to more closely mimic wildfires would enhance N cycling in productivity in N-limited headwater watersheds. Thresholds/Learnings: Synopsis: This study aimed to compare the short-term effects of spring prescribed burns and wildfires on Nitrogen cycling dynamics in headwater watersheds of central Idaho. Fire affected N dynamics in both terrestrial and aquatic components of the watershed ecosystem after wildfires but were limited to the terrestrial ecosystem after prescribed burns. Streamwater NO3 concentrations were affected significantly...
Categories: Publication;
Types: Citation,
Map Service,
OGC WFS Layer,
OGC WMS Layer,
OGC WMS Service;
Tags: Ecosystem,
Idaho,
Idaho Batholith,
Land use configuration,
Nitrogen Cycling,
Emerging applications of ecosystem resilience and resistance concepts in sagebrush ecosystems allow managers to better predict and mitigate impacts of wildfire and invasive annual grasses. Soil temperature and moisture strongly influence the kind and amount of vegetation, and consequently, are closely tied to sagebrush ecosystem resilience and resistance (Chambers et al. 2014). Soil taxonomic temperature and moisture regimes can be used as indicators of resilience and resistance at landscape scales to depict environmental gradients in sagebrush ecosystems that range from cold/cool-moist sites to warm-dry sites. We aggregated soil survey spatial and tabular data to facilitate broad-scale analyses of resilience and...
Categories: Data,
Publication;
Types: Citation,
Map Service,
OGC WFS Layer,
OGC WMS Layer,
OGC WMS Service;
Tags: California,
Colorado,
EARTH SCIENCE > LAND SURFACE > LANDSCAPE,
Greater sage-grouse,
Greater sage-grouse,
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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