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Moose (Alces americanus) are large herbivores that inhabit forests, wetlands, and riparian areas. The species is an important subsistence resource in the YKL study area. This dataset provides the most up-to-date spatial distribution of Moose (Alces americanus) calving concentrations within the YKL study area for the analysis of the Terrestrial Fine-Filter Conservation Element and Management Question #6. We heads-up digitized seasonal Moose concentrations; including calving ranges, from scanned distribution maps from the Alaska habitat management guide. During calving, rutting, and winter, moose are generally found concentrated around riparian areas. According to ADFG management reports, the majority of radio-collared...
We analyzed 12 indicators of ecological integrity and 2 Key Ecological Attributes (KEA) to assess the current status of the Aquatic Course Filter Conservation Elements. The indicators used were: 1) Riparian Corridor Fragmentation, 2) Landscape Condition Model Index, 3) Perennial Flow Network Fragmentation by Dams, 4) Surface Water Use/Discharge Ratio, 5) Ground Water Use/Discharge Ratio, 6a) Perennial Flow Modification by Diversion Structures, 6b) Flow Modification by Dams, 7) Condition of Groundwater Recharge Zone, 8a) Atmospheric Deposition-Nitrate Loading , 8b) Atmospheric Deposition--Toxic Mercury Loading, 9) State-Listed Water Quality Impairments, 10) Sediment Loading Index, 11) Presence of Invasive Plant Species,...
This feature class describes areas used for subsistence harvesting of beaver in 2009 by surveyed households in Upper Kalskag, Alaska. This is a partial representation of areas used for resource harvesting in 2009.
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 Snow Day Fraction (%) for the decades 2010-2019, 2020-2029, and 2060-2069, and months January, February, March, April, May, September, October, November, and December at 771x771 meter spatial resolution. The file represents a decadal mean calculated from monthly means, using the...
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 Snow Day Fraction (%) for the decades 2010-2019, 2020-2029, and 2060-2069, and months January, February, March, April, May, September, October, November, and December at 771x771 meter spatial resolution. The file represents a decadal mean calculated from monthly means, using the...
This dataset is a raster of predicted suitable bioclimate using statistical correlations between known habitat and current climate (1950-1999 average) , and then projecting that niche into the future. The future timeslices used are 2020's, which is an average of 2020-2029, and 2050's which is 2050-2059. The Values 1-6 show the degree of model agreement (For example: areas with a value of 1 is where only 1 GCM predicted suitability; pixels with a value of 6 are where 6 GCMs predicted suitability, ect). *see Maxent output pdfs for more details about model inputs and settings.
This dataset represents the probability of occurrence for the azonal carbonate rock crevices species assemblage within the Central Great Basin and Mohave Basin Ecoregion. This model represents the composite of multiple cross-validated inductive (Maximum Entropy) models of species distributions using non-spectral landscape variables. Input Variables: NatureServe's Ecological systems map, soil pH, distance to calcium carbonate soils, elevation, slope, geology, distance to hydric soils, distance to perennial streams, distance to intermittent streams, average rock fragments in soil, aspect, and available water holding capacity. Classification Model: 1 - High Habitat Potential NoData - Very Low Habitat Potential or "Non-Habitat"
This data set represents the GIS Version of the Public Land Survey System including both rectangular and non-rectangular surveys. The primary source for the data is cadastral survey records housed by the BLM supplemented with local records and geographic control coordinates from states, counties as well as other federal agencies such as the USGS and USFS. The data has been converted from source documents to digital form and transferred into a GIS format that is compliant with FGDC Cadastral Data Content Standards and Guidelines for publication. This data is optimized for data publication and sharing rather than for specific "production" or operation and maintenance. This data set includes the following: PLSS Fully...
Mapping of terrestrial vertebrates focuses on linking a spatial representation of species-habitat matrices to geographic distribution. Each model is a combination of distribution from regional and state references in association with contiguous appropriate habitats. Ranges for all species were based on 8-digit HUCs. Habitats were based on a raster SWReGAP 1 acre MMU land cover data set, with hydrology habitats added in from USGS NHD dataset directly or through modeling. Habitat association information was obtained from various state, regional, and national references with updates from scientific literature. This portion of the Southwest Regional Gap Analysis Project produced predicted habitat distribution maps for...
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 Snow Day Fraction (%) for the decades 2010-2019, 2020-2029, and 2060-2069, and months January, February, March, April, May, September, October, November, and December at 771x771 meter spatial resolution. The file represents a decadal mean calculated from monthly means, using the...
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. - The fine-scale invasion vulnerability model, combining higher probability sites for non-native plant importation and establishment, suggests that the region currently and into the near term is likely to have a non-native plant species restricted to a very small area. By 2060 however, all villages and the human footprint associated...
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. - Assessing the impact of oil and gas development on vegetation and hydrology on the North Slope study area involves identifying the accumulation of effects and assessing the relative magnitude of each. Impacts on vegetation include the direct effects associated with the construction of pipelines, roads, gravel pads, and seismic...
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. - Growing season length and change in growing season length were extracted to the distribution of sand sheet moist tundra and wetland. Increases in July temperature both in the near and long term are not expected to be significant across most of the coastal plain ecoregion, including the arctic sandy lowland region. By the 2060s...
This layer represents the near-future scorecard of one indicator of ecosytem integrity. This ecosystem assessment is for projected 2025 landscape condtion (LCM). NatureServe’s ecological integrity framework provides a practical approach to organize criteria and indicators for this purpose (Faber-Langendoen et al. 2006, Unnasch et al. 2008). This framework provides a scorecard for reporting on the ecological status of a given CE within a given location, and if needed, facilitates the aggregation and synthesis of the component results for broader measures of ecological integrity at broader scales. Please see "cmbrCE and indicators.xlsx" for a complete list of which measures were applied to individual CEs for current...
Mapping of terrestrial vertebrates focuses on linking a spatial representation of species-habitat matrices to geographic distribution. Each model is a combination of distribution from regional and state references in association with contiguous appropriate habitats. Ranges for all species were based on 8-digit HUCs. Habitats were based on a raster SWReGAP 1 acre MMU land cover data set, with hydrology habitats added in from USGS NHD dataset directly or through modeling. Habitat association information was obtained from various state, regional, and national references with updates from scientific literature. This portion of the Southwest Regional Gap Analysis Project produced predicted habitat distribution maps for...
This dataset shows an estimate of the probability of human-caused fire occurrence, based on 30 years of occurrence data using a MaxEnt model based on several factors including distance to roads, urban areas, vegetation type, and climate. This near-term estimate is based on projecting the Maxent model developed on current climate conditions onto downscaled climate projections from RegCM3 based on ECHAM5 boundary conditions. The model performed reasonably well, with an AUC of 0.704 Significant predictive factors include distance to highways, distance to major rivers, distance to urban areas, distance to roads, and winter precipitation. Caution should be exercised in interpreting this dataset, as it is based on an...
Some of the CYR 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 summer (June, July, August) 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 seasonal averages, which in turn were calculated from monthly means, using...
Trends measure the magnitude and statistical significance within the recent 32-year timeslice (1981-2012) using a combination of two tests. Theil-Sen slope was used to calculate magnitude of change within the recent timeframe; a Theil-Sen linear regression line is fit to the 32-year time series. The change in the value of this line across the 32-year period indicates magnitude of climate change. The Mann-Kendall test was used to calculate p-values to measure the statistical significance of the magnitude of the 32-year trend. Change was only deemed statistically significant in places where the Mann-Kendall p-value was less than 0.05.
Fifth-level watersheds (HUC10) with high or very high conservation potential for at least one aquatic species evaluated as a Conservation Element by land ownership and protection from PADUS.
Wild and Scenic Rivers.
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