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Some of the SNK 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 dataset consists of raster distribution maps for terrestrial vertebrate species in Alaska. Individual species distribution maps were developed using the best available known occurrence points for each species and modeled using MaxEnt software and a series of environmental predictor variables. Output maps were clipped...
Observations (reduced to detected/not detected) of 45 vertebrate species (seven mammals, seven amphibians, and 31 reptiles) across Southern California pitfall sampling projects conducted between 1995 through 2015. Habitat patch locations of every pitfall sampling project presented in a shapefile. Habitat patches were measured based on the size when pitfall sampling began within each. Sampling projects within the same geographic area may have different sized patches based on date of project sampling and if patch erosion occurred. A matrix of whether each species was expected within each habitat patch's species pool based on range maps and published records is also included. These data support the following publication:...
This report summarizes the results of a three-year investigation of terrestrial habitat connectivity priorities for the South Atlantic Landscape Conservation Cooperative (South Atlantic LCC). Our primary objective was to generate results that could be used to drive fine‐scaled conservation planning to enhance habitat connectivity across the South Atlantic LCC. The project focused on seven target species, including large mammals (black bear, red wolf, Florida panther/eastern cougar) and a group of terrestrial reptiles (eastern diamondback rattlesnake, timber rattlesnake, pine snake, and box turtle). We used two different modeling approaches to identify areas with either high predicted flow of a given species (Circuitscape)...
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Geoinformación es un portal de información geográfica donde se puede consultar, visualizar y descargar cartografía temática de diferentes escalas generada y recopilada por CONABIO. Para desarrollar este portal se utilizó software de código abierto (open source). Las características principales del portal, son, desarrollar mecanismos de acceso a los acervos de datos geográficos de la CONABIO a través de servicios de información especializados, como: Vista gráfica de la información por temas generales. Información detallada sobre la cartografía disponible a través de búsquedas en los metadatos. Descarga de información en un formato compatible (shapefile) La información cartográfica es administrada por la Subdirección...
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At the direction of the ACC, the 99th Civil Engineering Squadron, Natural and Cultural Resources Flight (99 CES/CEVN) has prepared an Integrated Natural Resource Management Plan (INRMP) to serve as a practical management guideline for the day-to-day operations and management of the natural resources on NAFB and NTTR. The INRMP incorporates natural resource management policies, available regulatory guidance documents, and current natural resource data for NAFB and NTTR to produce a practical guidance document that recognizes and respects the goals and objectives of the Nellis mission while conserving and sustaining the natural resources of both areas. To meet that end, the INRMP provides simple natural resource management...
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The Southwest Regional Gap Analysis Project (SWReGAP) is an update of the Gap Analysis Program’s mapping and assessment of biodiversity for the five-state region encompassing Arizona, Colorado, Nevada, New Mexico, and Utah. It is a multi-institutional cooperative effort coordinated by the U.S. Geological Survey Gap Analysis Program. The primary objective of the update is to use a coordinated mapping approach to create detailed, seamless GIS maps of land cover, all native terrestrial vertebrate species, land stewardship, and management status, and to analyze this information to identify those biotic elements that are underrepresented on lands managed for their long term conservation or are “gaps.”
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Pima County makes extensive use of Geographical Information System (GIS) technology for making maps. For the Sonoran Desert Conservation Plan, this was an important technology for assembling the extensive existing data, both digital and non-digital, identifying critical gaps in the data and potential remedies, and providing a means for analyzing the information on biological and physical resources over the six million acre study area. SDCP Mapguide was created to display many of the natural resource GIS data layers, but Mapguide is being replaced with PimaMaps. You can use either to make your own overlays on aerial photos, line maps, or USGS topography. Customize your online map while panning and zooming on the...
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La deforestación, sobreexplotación del agua superficial y subterránea, así como de los recursos naturales en general, la contaminación, la introducción de especies invasoras y el cambio climático son causales directas de pérdida de biodiversidad que responden a factores indirectos como los demográficos, las políticas públicas inadecuadas y los desarrollos tecnológicos. El presente Plan de Acción responde a la necesidad de contar con un marco de referencia para el manejo adecuado de las especies prioritarias presentes en el estado de Chihuahua, que permita la toma de decisiones, así como el desarrollo y ejecución de políticas públicas adecuadas, para proteger, conservar y aprovechar la biodiversidad estatal de manera...
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Predicted probability of fisher winter occurrence created with Maxent (Phillips et al. 2006) using fisher detections (N = 33, December – April, spanning 1995 – 2011) and eight predictor variables: mean winter (January – March) precipitation, mean winter (January – March) minimum temperature, mean fraction of vegetation carbon burned, mean understory index (fraction of grass vegetation carbon in forest), mean fraction of total forest carbon in coarse wood carbon, mean forest carbon (g C m2), mean fraction of vegetation carbon in forest, and modal vegetation class. Predictor variables had a grid cell size of 4 km by 4 km, vegetation variables were simulated by the MC1 dynamic global vegetation model (Bachelet et al....
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Predicted probability of marten year-round occurrence derived from future (2076-2095) climate projections and vegetation simulations. Projected marten distribution was created with Maxent (Phillips et al. 2006) using marten detections (N = 302, spanning 1990 – 2011) and nine predictor variables: mean winter (January – March) precipitation, mean amount of snow on the ground in March, mean understory index (fraction of grass vegetation carbon in forest), mean fraction of total forest carbon in coarse wood carbon, average maximum tree LAI, mean fraction of vegetation carbon burned, mean forest carbon (g C m2), mean fraction of vegetation carbon in forest, and modal vegetation class. Future climate drivers were...
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Agreement in predicted marten year-round distribution derived from future (2046-2065) climate projections and vegetation simulations using 2 GCMs (Hadley CM3 (Johns et al. 2003) and MIROC (Hasumi and Emori 2004)) under the A2 emissions scenario (Naki?enovi? et al. 2000). Projected marten distribution was created with Maxent (Phillips et al. 2006) using marten detections (N = 302, spanning 1990 – 2011) and nine predictor variables: mean winter (January – March) precipitation, mean amount of snow on the ground in March, mean understory index (fraction of grass vegetation carbon in forest), mean fraction of total forest carbon in coarse wood carbon, average maximum tree LAI, mean fraction of vegetation carbon burned,...
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Predicted probability of marten year-round occurrence derived from future (2046-2065) climate projections and vegetation simulations. Projected marten distribution was created with Maxent (Phillips et al. 2006) using marten detections (N = 302, spanning 1990 – 2011) and nine predictor variables: mean winter (January – March) precipitation, mean amount of snow on the ground in March, mean understory index (fraction of grass vegetation carbon in forest), mean fraction of total forest carbon in coarse wood carbon, average maximum tree LAI, mean fraction of vegetation carbon burned, mean forest carbon (g C m2), mean fraction of vegetation carbon in forest, and modal vegetation class. Future climate drivers were...
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The Sevilleta LTER supports a long-term, integrated, interdisciplinary research program addressing key hypotheses on pattern and process in aridland ecosystems. Sevilleta LTER research includes studies in desert grassland and shrubland communities, and riparian and mountain forests emphasizing pulse driven processes in space and time. Key drivers (e.g., climate, fire, water, resource availability) govern dynamics in each landscape component. Our focus on how biotic and abiotic drivers affect spatial and temporal dynamics of aridland ecosystems allows us to conduct long-term research that addresses important basic theories and yet has significant relevance to regional, national and international priorities. The...
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Grasslands of the Sky Islands region once covered over 13 million acres in southeastern Arizona and adjacent portions of New Mexico, Sonora, and Chihuahua. Attempts to evaluate current ecological conditions suggest that approximately two thirds of these remain as intact or restorable grassland habitat. These grasslands provide watershed services such as flood control and aquifer recharge across the region, and continue to support dozens of species of concern. Prioritizing conservation interventions for these remaining grassland blocks has been challenging. Reliable data on condition and conservation value of grasslands in the region have not been systematically summarized. State and national boundaries further complicate...
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Our mission is to develop and maintain a cost-effective, central information source and inventory of the locations, biology, and status of all threatened, endangered, rare, and at-risk plants and animals in Nevada. We use the best available biological data to continually evaluate conservation priorities for over 700 native animals, plants, and vegetation types, focusing on those that are at greatest risk of extinction or serious decline. As a non-regulatory, independent resource for scientifically objective data, environmental review, and technical assistance and expertise, we support the needs of diverse planning, conservation management, research, education, and economic development activities in Nevada. The...
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Predicted probability of fisher year-round occurrence derived from future (2046-2065) climate projections and vegetation simulations. Projected fisher distribution was created with Maxent (Phillips et al. 2006) using fisher detections (N = 302, spanning 1990 – 2011) and five predictor variables: mean annual precipitation, mean summer (July – September) precipitation, mean understory index (fraction of grass vegetation carbon in forest), mean forest carbon (g C m2), and mean fraction of vegetation carbon in forest. Future climate drivers were generated using statistical downscaling (simple delta method) of general circulation model projections, in this case CSIRO Mk3 (Gordon 2002) under the A2 emission scenario...
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Predicted probability of marten year-round occurrence derived from future (2046-2065) climate projections and vegetation simulations. Projected marten distribution was created with Maxent (Phillips et al. 2006) using marten detections (N = 302, spanning 1990 – 2011) and nine predictor variables: mean winter (January – March) precipitation, mean amount of snow on the ground in March, mean understory index (fraction of grass vegetation carbon in forest), mean fraction of total forest carbon in coarse wood carbon, average maximum tree LAI, mean fraction of vegetation carbon burned, mean forest carbon (g C m2), mean fraction of vegetation carbon in forest, and modal vegetation class. Future climate drivers were...
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Predicted probability of marten year-round occurrence created with Maxent (Phillips et al. 2006) using marten detections (N = 302, spanning 1990 – 2011) and nine predictor variables: mean winter (January – March) precipitation, mean amount of snow on the ground in March, mean understory index (fraction of grass vegetation carbon in forest), mean fraction of total forest carbon in coarse wood carbon, average maximum tree LAI, mean fraction of vegetation carbon burned, mean forest carbon (g C m2), mean fraction of vegetation carbon in forest, and modal vegetation class. Predictor variables had a grid cell size of 4 km by 4 km, vegetation variables were simulated with MC1 dynamic global vegetation model (Bachelet...
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Predicted probability of marten year-round occurrence created with Maxent (Phillips et al. 2006) using marten detections (N = 302, spanning 1990 – 2011) and eight predictor variables: mean annual precipitation, mean summer (July – September) precipitation, mean summer temperature amplitude, mean annual temperature maximum, mean fraction of vegetation carbon burned, mean understory index, mean vegetation carbon (g C m-2), and modal vegetation class. Predictor variables had a grid cell size of 10 km, vegetation variables were simulated with MC1 (Hayhoe et al. 2004), historical climate variables were provided by the PRISM GROUP (Daly et al. 1994), and future climate projections were obtained from the Hadley Center...
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In addition to current distribution of each mammal species, this map shows their current and near-term status within the ecoregion. Current, long-term, and summary bioclimate data is also include for several of these mammal species. The input datasets used in the distribution model are also included. These data are provided by Bureau of Land Management (BLM) "as is" and may contain errors or omissions. The User assumes the entire risk associated with its use of these data and bears all responsibility in determining whether these data are fit for the User's intended use. These data may not have the accuracy, resolution, completeness, timeliness, or other characteristics appropriate for applications that potential...


map background search result map search result map Overlay of projected marten distributions, 2046-2065, 4 km resolution Predicted probability of fisher year-round occurrence, 2046-2065, CSIRO Mk3 A2, 800 m resolution Predicted probability of marten year-round occurrence, 2076-2095, MIROC A2, 4 km resolution Predicted probability of marten year-round occurrence, 2046-2065, MIROC A2, 4 km resolution Predicted probability of marten year-round occurrence, 2046-2065, Hadley CM3 A2, 4 km resolution Predicted probability of marten year-round occurrence, 1986-2005, 4 km resolution Predicted probability of fisher occurrence in winter (December – April), 1986-2005, 4 km resolution Predicted probability of marten year-round occurrence, 1986-2005, Hadley CM3 A1fi, 10 km resolution Southwest Regional Gap Analysis Project Sonoran Desert Conservation Plan MapGuide Map Sevilleta LTER Sustaining the Grassland Sea Nevada Natural Heritage Program Portal de Geoinformación Plan de Acción para la Conservación y Recuperación de Especies de Fauna Silvestre Prioritaria en el Estado de Chihuahua Nellis Air Force Base Plan 126-4 BLM REA SNK 2010 Alaska Gap Analysis Project: Breeding Season Distribution Map for Somateria fischeri BLM REA CBR 2010 Terrestrial Species Mammals Status - Kit Fox Species Observations from Pitfall Trap Arrays, Species Pool Matrices, and Patch Locations in Southern California from 1995-2015 Sevilleta LTER Sonoran Desert Conservation Plan MapGuide Map Nellis Air Force Base Plan 126-4 Species Observations from Pitfall Trap Arrays, Species Pool Matrices, and Patch Locations in Southern California from 1995-2015 Sustaining the Grassland Sea Plan de Acción para la Conservación y Recuperación de Especies de Fauna Silvestre Prioritaria en el Estado de Chihuahua Nevada Natural Heritage Program BLM REA SNK 2010 Alaska Gap Analysis Project: Breeding Season Distribution Map for Somateria fischeri Predicted probability of fisher year-round occurrence, 2046-2065, CSIRO Mk3 A2, 800 m resolution Overlay of projected marten distributions, 2046-2065, 4 km resolution Predicted probability of marten year-round occurrence, 2076-2095, MIROC A2, 4 km resolution Predicted probability of marten year-round occurrence, 2046-2065, MIROC A2, 4 km resolution Predicted probability of marten year-round occurrence, 2046-2065, Hadley CM3 A2, 4 km resolution Predicted probability of marten year-round occurrence, 1986-2005, 4 km resolution Predicted probability of fisher occurrence in winter (December – April), 1986-2005, 4 km resolution Southwest Regional Gap Analysis Project Predicted probability of marten year-round occurrence, 1986-2005, Hadley CM3 A1fi, 10 km resolution BLM REA CBR 2010 Terrestrial Species Mammals Status - Kit Fox Portal de Geoinformación