Skip to main content
Advanced Search

Filters: Tags: Disturbance (X)

3,279 results (62ms)   

Filters
Contacts (Less)
View Results as: JSON ATOM CSV
thumbnail
This release contains Active Layer Thickness (ALT) and Organic Layer Thickness (OLT) measurements measured along transects in Alaska, 2015. Site condition information in terms of wildfire burns is also included.
Woodland caribou (Rangifer tarandus caribou) in Alberta are classified as endangered and apparently have declined. Disturbance from petroleum exploration has been implicated as a possible cause, so we constructed a simple model to estimate the energy costs of multiple encounters with disturbance (i.e., loud noise). Our objective was to estimate if woodland caribou in northeastern Alberta have been exposed to enough disturbance from 1988 to 1993 to cause winter mass loss to exceed either (i) 15% autumn mass or (ii) 20% autumn mass. A single disturbance event costs caribou 3.46-5.81 MJ. Caribou would have to encounter (i) 20-34 (mean = 27) disturbance events to lose >15% mass over winter and (ii) 41-137 (mean = 89)...
1. Soil disturbance by animals affects the availability of water, nutrients, sediment and seeds, which are critical for the maintenance of functional ecosystems. We examined long-lived faunal structures across six vegetation communities in the northern Chihuahuan desert of New Mexico, USA, testing the proposition that disturbances in undesertified grassland differ in magnitude and effect from those in desertified grassland. 2. Vertebrate and invertebrate disturbances totalled 18.9 structures ha−1 across 18 sites. The most common were pits and mounds of American badgers (Taxidea taxus, 32%), nests of the ant Aphaenogaster cockerelli (18.8%) and mounds of kangaroo rats (Dipodomys spectabilis, 31%). 3. Desertification...
thumbnail
This dataset depicts roads built on the Tongass National Forest prior to 1960. This dataset is part of a larger analysis of road building and timber harvest on the Tongass National Forest, compiled for the report Scientific Basis for Roadless Area Conservation (http://www.consbio.org/cbi/projects/show.php?page=roadless/roadless.htm), pp 70-73. Road segments from a US Forest Service roads layer were attributed to the most likely decade in which the road was built, as determined by an analysis of connectivity to clearcuts on the Tongass National Forest from that decade. It was assumed that following 1960, harvests required access to mills or extraction sites, and thus roads connecting to them were most likely built...
thumbnail
This data set includes the relative production scenarios for eight (8) grass species based on linear models from Epstein, et al. (1998). We selected two indicator species for each community: shortgrass prairie: blue grama (Bouteloua gracilis; BOGR) and buffalo grass (Bouteloua dactyloides; BODA); mixedgrass prairie: sideoats grama (Bouteloua curtipendula; BOCU) and little bluestem (Schizachyrium scoparium; SCSC); tallgrass prairie: big bluestem (Andropogon gerardii; ANGE) and Indiangrass (Sorghastrum nutans; SONU); and semiarid grasslands: black grama (Bouteloua eriopoda; BOER) and tobosagrass (Pleuraphis mutica; PLMU). Soil texture (percent by weight) came from the Earth Systems Science Center (2008) which provided...
thumbnail
The Forest and Rangeland Ecosystem Science Center 's mission is to provide scientific understanding and the technology needed to support sound management and conservation of our nation's natural resources, with emphasis on western ecosystems. The scientists from FRESC capitalize on their diverse expertise to answer critically important scientific questions shaped by the equally diverse environments of the western United States. FRESC scientists collaborate with each other and with partners to provide rigorous, objective, and timely information and guidance for the management and conservation of biological systems in the West and worldwide. Research activities are concentrated in Washington, Oregon, Idaho, Nevada,...
Concern over global environmental change and associated uncertainty has given rise to greater emphasis on fostering resilience through forest management. We examined the impact of standard silvicultural systems (including clearcutting, shelterwood, and selection) compared with unharvested controls on tree functional identity and functional diversity in three forest types distributed across the northeastern United States. Sites included the Argonne, Bartlett, and Penobscot Experimental Forests located in Wisconsin, New Hampshire, and Maine, respectively. We quantified functional trait means for leaf mass per area, specific gravity, maximum height, height achieved at 20 years, seed mass, drought tolerance, shade tolerance,...
thumbnail
This data set contains vector lines and polygons representing the shoreline and coastal habitats of Western Alaska classified according to the Environmental Sensitivity Index (ESI) classification system. This data set comprises a portion of the ESI for Western Alaska. ESI data characterize the marine and coastal environments and wildlife by their sensitivity to spilled oil. The ESI data include information for three main components: shoreline habitats, sensitive biological resources, and human-use resources.
thumbnail
The Utah Well & Spring database contains data of 2981 wells, springs, and miscellaneous sites such as collector wells and mines. It is available from the Utah Geological Survey at:http://geology.utah.gov/emp/geothermal/wells_springs_database.htmThe Conservation Biology Institute selected geothermal wells from this database. The geothermal well layer was then used in further analysis and modeling.
thumbnail
This dataset contains values on the potential number of visitors via in year ~2030 who are hikers/cyclists. This assumes a "push" factor of the number of recreationists is equal to 20.9% of population in the CBR/MBR region, which is the average participation rate of off-road recreation usage of both metro and non-metro residents of AZ, CA, NV, and UT from the National survey on recreation and the environment. Cordell et al. 2008. Visitation is assume to decline in half with each 1 hour of travel time.
thumbnail
Some of the NOS rasters intentionally do not align or have the same extent. These rasters were not snapped to a common raster per the authors' discretion. Please review selected rasters prior to use. These varying alignments are a result of the use of differing source data sets and all products derived from them. We recommend that users snap or align rasters as best suits their own projects. - This file includes a downscaled projection of decadal Mean Annual Ground Temperature at 1 Meter Depth (°C) for the decades 2010-2019, 2020-2029, and 2060-2069 at 2km spatial resolution. It represents the A2 emissions scenario and the spatial extent is the NOS REA study area.
thumbnail
Some of the NOS rasters intentionally do not align or have the same extent. These rasters were not snapped to a common raster per the authors' discretion. Please review selected rasters prior to use. These varying alignments are a result of the use of differing source data sets and all products derived from them. We recommend that users snap or align rasters as best suits their own projects. - This file includes a downscaled projection of decadal average January temperature (in °C) for the decades 2010-2019, 2020-2029, and 2060-2069 at 771x771 meter spatial resolution. The file represents a decadal mean calculated from monthly totals, using the A2 emissions scenario. The spatial extent is clipped to the NOS REA...
thumbnail
Some of the NOS rasters intentionally do not align or have the same extent. These rasters were not snapped to a common raster per the authors' discretion. Please review selected rasters prior to use. These varying alignments are a result of the use of differing source data sets and all products derived from them. We recommend that users snap or align rasters as best suits their own projects. - This file includes a downscaled projection of decadal average May temperature (in °C) for the decades 2010-2019, 2020-2029, and 2060-2069 at 771x771 meter spatial resolution. The file represents a decadal mean calculated from monthly totals, using the A2 emissions scenario. The spatial extent is clipped to the NOS REA study...
thumbnail
Some of the NOS rasters intentionally do not align or have the same extent. These rasters were not snapped to a common raster per the authors' discretion. Please review selected rasters prior to use. These varying alignments are a result of the use of differing source data sets and all products derived from them. We recommend that users snap or align rasters as best suits their own projects. - This file includes a downscaled projection of decadal average of spring (March, April, May) total precipitation (in millimeters) for the decades 2010-2019, 2020-2029, and 2060-2069 at 771x771 meter spatial resolution. The file represents a decadal mean of seasonal totals calculated from monthly totals, using the A2 emissions...
thumbnail
For each variable the per pixel change between the recent time slice (1981-2012) or future timslice (2050s) and the baseline (1900-1980) was calculated, identifying climate “deltas” for each pixel. Recent deltas are 800m resolution and use PRISM as the source dataset. Future deltas are 4km resolution and use ClimateWNA as the source dataset. Delta = later timeslice (recent or future) - baseline. Raster values are expressed in climate units either mm for precipitation or degrees c for temperature. delta ratio values are included for precipitation and CMD, which are ratios of change (1 = no change, < 1 = decreasing, > 1 = increasing).
thumbnail
For each variable the per pixel change between the recent time slice (1981-2012) or future timslice (2050s) and the baseline (1900-1980) was calculated, identifying climate “deltas” for each pixel. Recent deltas are 800m resolution and use PRISM as the source dataset. Future deltas are 4km resolution and use ClimateWNA as the source dataset. Delta = later timeslice (recent or future) - baseline. Raster values are expressed in climate units either mm for precipitation or degrees c for temperature. delta ratio values are included for precipitation and CMD, which are ratios of change (1 = no change, < 1 = decreasing, > 1 = increasing).
thumbnail
Average January temperature for 2016-2030 projected by the GFDL2.1 GCM run 1 driven by the A2 emissions scenario at 1/8 degree latitude-longitude (approximately 12km by 12 km) over the Wyoming Basin and surrounding areas. BCSD data downloaded the "Downscaled CMIP3 and CMIP5 Climate and Hydrology Projections," archived at http://gdo-dcp.ucllnl.org/downscaled_cmip_projections/.
thumbnail
Average annual temperature for 2016-2030 projected by the ECHAM5 GCM run 1 driven by the A2 emissions scenario at 1/8 degree latitude-longitude (approximately 12km by 12 km) from a 36-member GCM ensemble (16 GCMs with multiple runs of some GCMs, all the runs available for BCSD) over the Wyoming Basin and surrounding areas. BCSD data downloaded from the "Downscaled CMIP3 and CMIP5 Climate and Hydrology Projections" archived at http://gdo-dcp.ucllnl.org/downscaled_cmip_projections/.
thumbnail
To assess fire frequency and extent, the perimeters of fires overlapping the distribution of pygmy rabbit. Fire occurrences since 1980 were compiled from fire occurrence data sets from U.S. Forest Service, U.S. Geological Survey (GeoMAC), National Park Service, Monitoring Trends in Burn Severity, Western Fires Database, Bureau of Land Management, and National Fire and Aviation Management Web applications.


map background search result map search result map Tongass National Forest Roads - Pre 1960 Permafrost Soil Measurements; Alaska, 2015 Potential productivity and change estimates for eight grassland species to evaluate vulnerability to climate change in the southern Great Plains BLM REA WYB 2011 ECHAM5 projected annual temperature, 2016-2030 BLM REA WYB 2011 GFDL2.1 projected January temperature, 2016-2030 BLM REA WYB 2011 Developed Landcover used in Biome Analysis BLM REA WYB 2011 Pygmy Rabbit Fire BLM REA MAR 2012 Climate "Deltas" 1901_1980_1981_2012_ppt_09 BLM REA MAR 2012 Climate "Deltas" 1901_1980_2040_2069_tmin_07 BLM REA NOS 2012 CL CNL MarchAprilMayPrecipitation LongTerm BLM REA NOS 2012 CL CNL MeanJanuaryTemperature LongTerm BLM REA NOS 2012 CL CNL MeanMayTemperature LongTerm BLM REA NOS 2012 PF CNL MeanAnnualGroundTemperature Current BLM REA MBR 2010 CBR DV Recreation:  Hiker Visitors 2030 BLM REA COP 2014 UT Geothermal Wells BLM REA SNK 2010 NEW Western Alaska ESI: ESI (Environmental Sensitivity Index Shoreline Types - Polygons and Lines) Permafrost Soil Measurements; Alaska, 2015 Tongass National Forest Roads - Pre 1960 BLM REA MAR 2012 Climate "Deltas" 1901_1980_1981_2012_ppt_09 BLM REA MAR 2012 Climate "Deltas" 1901_1980_2040_2069_tmin_07 BLM REA COP 2014 UT Geothermal Wells BLM REA SNK 2010 NEW Western Alaska ESI: ESI (Environmental Sensitivity Index Shoreline Types - Polygons and Lines) BLM REA WYB 2011 Pygmy Rabbit Fire BLM REA WYB 2011 Developed Landcover used in Biome Analysis BLM REA WYB 2011 GFDL2.1 projected January temperature, 2016-2030 BLM REA WYB 2011 ECHAM5 projected annual temperature, 2016-2030 BLM REA MBR 2010 CBR DV Recreation:  Hiker Visitors 2030 BLM REA NOS 2012 CL CNL MarchAprilMayPrecipitation LongTerm BLM REA NOS 2012 CL CNL MeanJanuaryTemperature LongTerm BLM REA NOS 2012 CL CNL MeanMayTemperature LongTerm BLM REA NOS 2012 PF CNL MeanAnnualGroundTemperature Current Potential productivity and change estimates for eight grassland species to evaluate vulnerability to climate change in the southern Great Plains