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GIS Analyst
Bureau of Land Management
Geospatial Ecologist
440 W 200S #500
Salt Lake City
UT
84101
US
blm_ut_so_public_room@blm.gov
20240325
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https://gbp-blm-egis.hub.arcgis.com/
Enterprise Geodatabase Feature Class
GIS Analyst
Bureau of Land Management
Geospatial Ecologist
440 W 200S #500
Salt Lake City
UT
84101
US
blm_ut_so_public_room@blm.gov
Name: Martha Jensen
Organization: Bureau of Land Management
Position: Geospatial Ecologist
Role: Point of Contact
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Utah
BLM-THEME
Wildlife
NONE
Habitat Management
NONE
Greater Sage-Grouse
Habitat
BLM Utah Greater Sage-Grouse Seasonal Habitat Modeled Areas
20240118
20231101
5
U.S. Department of the Interior, Bureau of Land Management (BLM)
Bureau of Land Management
vector digital data
<DIV STYLE="text-align:Left;"><DIV><DIV><P><SPAN>This polygon feature class represents the spatial extent and boundaries for the Utah Greater Sage-Grouse seasonal habitats, as modeled by the Utah State University Kohl Model Version 5. The original habitat model is clipped to a habitat buffer created by the Utah Division of Wildlife Resources. The data contains a clipped polygon for nesting, summer, and winter habitat respectively. Process of creating habitat model, as described by the authors: The process of creating the seasonal habitats layer:. 1. Creation of statewide general habitat model for GRSG (2016). In 2016, researchers at Utah State University (USU) developed a statewide general sage-grouse habitat map using a database of hundreds of lek locations and more than 20,000 GRSG telemetry locations collected statewide from 1998 – 2014. The map depicted habitat suitability on a scale from 0 to 100 at 1 km spatial resolution, based on comparing environmental (vegetation, topography, soils, climate) and anthropogenic (developed land cover, road density, powerline density) conditions at active lek and sage-grouse use locations versus inactive lek and random background locations statewide. Because multiple telemetry locations were often associated with a single brood-rearing or non-breeding bird, the median values of environmental and anthropogenic variables at these telemetry locations were used in the model. A random forest model was used to create the map (Breiman 2001, Cutler et al. 2007). Random forests is a highly accurate non-parametric classification technique that predicts the probability of an outcome (in this case, habitat vs non-habitat) by averaging the results of many classification trees, each of which is trained on a random subset of the available data. The general habitat map was reclassified into ‘habitat’ and ‘non-habitat’ classes such that habitat areas captured 99% of all sage-grouse use locations. These general habitat areas were used as a mask in order to constrain preliminary predictions of seasonal habitats, described below.2. Create preliminary seasonal GRSG habitat models (Jan - May 2017). Telemetry locations in the database were classified into three seasonal habitat types based on time of year and type of GRSG use. Breeding habitat was defined as areas used by GRSG engaged in lekking, nesting, and early brood-rearing, from March 1 – June 14. Summer habitat was defined as areas used by brood-rearing and non-breeding GRSG from June 15 – August 31. The June 15 cutoff date between breeding and summer use locations was selected based on the temporal distribution of nesting and brooding use locations. Winter habitat was defined as areas used by non-breeding GRSG from November 1 – February 29. As in the general habitat modeling approach, environmental conditions at annual brood-rearing or non-breeding locations associated with the same bird were measured as medians over the multiple locations. Seasonal habitats were modeled using the same predictors as the general habitat model, with the addition of distance to leks due to its association with breeding habitat. A random forest model was used to estimate the suitability of general habitat areas statewide (from step 1 above) for breeding, summer, and winter use. For each seasonal use class, a suitability threshold was selected such that 85% of all seasonal use locations were captured in the resulting seasonal habitat map. This resulted in models that were neither overly restrictive nor overly liberal. To reduce the 'salt and pepper’ effect of isolated or scattered habitat pixels, a 3x3 km smoothing window was applied to each of the seasonal habitat layers, assigning the majority value (habitat or non-habitat) to the center pixel. 3. DWR biologists reviewed seasonal models (April - June 2017). An overview of the general and seasonal mapping methodology and preliminary maps was presented to GRSG biologists and managers from the Utah Division of Wildlife Resources (UDWR), Bureau of Land Management (BLM), and Forest Service (FS) at Utah State University in Logan on 4/21/17. Feedback at the meeting led to a few minor changes to the seasonal mapping methods. Because the breeding seasonal use model was not picking up areas around all active leks, distance to leks was dropped as a predictor variable from the seasonal habitat random forest model, and a 3 km buffer around all active leks was manually included in the breeding habitat model. Updated seasonal use models were sent to UDWR on 5/17/17, where they were made available for review by biologists with local area knowledge. An ArcGIS Online webpage was used to share the models with biologists, and allow for them to provide recommended additions / deletions to areas captured by the models. Accompanying the spatial data was an 8 minute webinar communicating the modelling procedure. UDWR returned updated seasonal use models with biologists’ comments, additions, and deletions to USU researchers on 6/6/17. Most but not all areas in the state received substantive feedback and comments from UDWR biologists. 4. Additional review of seasonal use models and final edits performed (July – August 2017). USU researchers reviewed biologist edits on 7/13/17. Most of the areas flagged to be added/removed from the seasonal use models seemed appropriate and helpful. But, there were a few areas UDWR biologists identified to be deleted from the models that USU researchers considered questionable. These areas were identified and shared with UDWR managers on 7/13/17. UDWR managers indicated a desire to have another face to face meeting to make a final determination on areas to include / exclude from the seasonal use models. This meeting occurred on 8/8/17, at which it was determined that it would be preferable to have the final seasonal habitat products reflect both use and potential suitability, as opposed to only areas of known use. This led to rejecting some areas flagged for deletion by biologists, as biologist comments indicated they were conceptualizing the map as primarily a use map only. Numerous small edits were made to the seasonal use layers, including several edits to include seasonal use locations not captured by preliminary models. Finally, all single, isolated habitat pixels were removed from the map. References Breiman, L. (2001), Random Forests, Machine Learning 45(1), 5-32.Cutler, D.R., T.C. Edwards, K.H. Beard, A. Cutler, K.T Hess, J.C. Gibson and J.J. Lawler. 2007. Random forests for classification in ecology. Ecology 88(11):2783-2792.This dataset was used in the preparation of the Greater Sage-grouse (GRSG) 2018 DEIS planning maps and acreages initiated by Secretarial Order (SO) 3353 of June 7, 2017 and the Notice of Intent (NOI) of October 11, 2017. Recipient Principal Investigator/Project Manager: Terry A. Messmer, Jack H. Berryman Institute, Utah State University, 5230 Old Main Hill, Logan, Utah 84322-5230, Phone 435-797-3975, Fax 435-797-3796, E-mail terry.messmer@usu.edu Project Co-PIs and Staff: Dave Dahlgren, Eric Thacker, Kris Hulvey. Michel Kohl (Post-Doc), and Ben Crabb (GIS staff)</SPAN></P></DIV></DIV></DIV>
The polygon feature class represents the boundaries for Greater Sage-Grouse (GRSG) seasonal habitats, as modeled by Utah State University Kohl Model Version 5. The seasonal habitat layers are attributed as nesting, summer or winter habitats. The seasonal habitats have been clipped to a 10km buffer, surrounding the GRSG Habitat Management Area (HMA/SGMA) boundaries.
Bureau of Land Management, BLM
Habitat Model Credits
Recipient Principal Investigator/Project Manager: Terry A. Messmer, Jack H. Berryman Institute, Utah State University, 5230 Old Main Hill, Logan, Utah 84322-5230, Phone 435-797-3975, Fax 435-797-3796, E-mail terry.messmer@usu.edu
Project Co-PIs and Staff: Dave Dahlgren, Eric Thacker, Kris Hulvey. Michel Kohl (Post-Doc), and Ben Crabb (GIS staff)
GIS Analyst
Bureau of Land Management
Geospatial Ecologist
440 W 200 S #500
Salt Lake City
UT
84101
US
blm_ut_so_public_room@blm.gov
boundaries
geoscientificInformation
Greater Sage-Grouse
Habitat
Habitat Management
Utah
Wildlife
<DIV STYLE="text-align:Left;"><DIV><DIV><P><SPAN>These data are provided by Bureau of Land Management (BLM) 'as is' and might 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. The information contained in these data is dynamic and may change over time. The data are not better than the sources from which they were derived, and both scale and accuracy may vary across the data set. These data might not have the accuracy, resolution, completeness, timeliness, or other characteristics appropriate for applications that potential users of the data may contemplate. The User is encouraged to carefully consider the content of the metadata file associated with these data. These data are neither legal documents nor land surveys, and must not be used as such. Official records may be referenced at most BLM offices. Please report any errors in the data to the BLM office from which it was obtained. The BLM should be cited as the data source in any products derived from these data. Any Users wishing to modify the data should describe the types of modifications they have performed. The User should not misrepresent the data, nor imply that changes made were approved or endorsed by BLM. This data may be updated by the BLM without notification.</SPAN></P></DIV></DIV></DIV>
The BLM assumes no responsibility for errors or omissions. No warranty is made by the BLM as to the accuracy, reliability, or completeness of these data for individual use or aggregate use with other data; nor shall the act of distribution to contractors, partners, or beyond, constitute any such warranty for individual or aggregate data use with other data. Although these data have been processed successfully on computers of BLM, no warranty, expressed or implied, is made by BLM regarding the use of these data on any other system, or for general or scientific purposes, nor does the fact of distribution constitute or imply any such warranty. In no event shall the BLM have any liability whatsoever for payment of any consequential, incidental, indirect, special, or tort damages of any kind, including, but not limited to, any loss of profits arising out of the use or reliance on the geographic data or arising out of the delivery, installation, operation, or support by BLM.
None, these data are considered public domain.
true
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28.77
Habitat buffers within the State of Utah
20240118
Esri ArcGIS 13.1.3.41833
1
-114.353619
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42.124307
37.075121
Last metadata review date: 20231215
These data are provided by Bureau of Land Management (BLM) 'as is' and might 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. The information contained in these data is dynamic and may change over time. The data are not better than the sources from which they were derived, and both scale and accuracy may vary across the data set. These data might not have the accuracy, resolution, completeness, timeliness, or other characteristics appropriate for applications that potential users of the data may contemplate. The User is encouraged to carefully consider the content of the metadata file associated with these data. These data are neither legal documents nor land surveys, and must not be used as such. Official records may be referenced at most BLM offices. Please report any errors in the data to the BLM office from which it was obtained. The BLM should be cited as the data source in any products derived from these data. Any Users wishing to modify the data should describe the types of modifications they have performed. The User should not misrepresent the data, nor imply that changes made were approved or endorsed by BLM. This data may be updated by the BLM without notification.
Generally releasable. However, the data must be reviewed prior to release to assure that no protected data is contained within the dataset.
Please contact the distributor for additional information on provenance, history, accuracy and quality of a particular state's data. The positional accuracy across this dataset varies according to the source.
Please contact the distributor for additional information on provenance, history, accuracy and quality of a particular state's data. The accuracy of attributes in this dataset reflects that of the source datasets. Attribute accuracy was tested by validating attribute coded values against accepted domain values, checking that alpha-numeric values followed a valid format, and checking that numeric values were greater than zero.
Please contact the distributor for additional information on provenance, history, accuracy and quality of a particular state's data. The positional accuracy across this dataset varies according to the source.
Please contact the distributor for additional information on provenance, history, accuracy and quality of a particular state's data. The positional accuracy across this dataset varies according to the source.
Please contact the distributor for additional information on provenance, history, accuracy and quality of a particular state's data. The positional accuracy across this dataset varies according to the source.
H-1283-2 Data Administration and Management Handbook
H-1283-2 Data Administration and Management Handbook
2014-12-05
Release 1-1762
This official BLM Data Management handbook contains information about how data is administered, managed, and tracked. This handbook discusses how the BLM operates with regard to data development and design, quality, metadata, and business rules. The information this resource contains also pertains to geospatial data.
http://web.blm.gov/internal/wo-500/directives/dir-hdbk/h1283-2.pdf
The BLM Utah State Office merged the USU Kohl Model Version 5 nesting, summer, and winter habitat polygons into one polygon, attributing each with their seasonal habitat. Once merged, the data was clipped to a 10 kilometer buffer that encompassed all PHMA, GHMA, and SGMA's within the state. The buffer was provided to the BLM by the Utah Division of Wildlife Resources. Original habitat model metadata and process:
The process of creating the seasonal habitats layer:. 1. Creation of statewide general habitat model for GRSG (2016). In 2016, researchers at Utah State University (USU) developed a statewide general sage-grouse habitat map using a database of hundreds of lek locations and more than 20,000 GRSG telemetry locations collected statewide from 1998 – 2014. The map depicted habitat suitability on a scale from 0 to 100 at 1 km spatial resolution, based on comparing environmental (vegetation, topography, soils, climate) and anthropogenic (developed land cover, road density, powerline density) conditions at active lek and sage-grouse use locations versus inactive lek and random background locations statewide. Because multiple telemetry locations were often associated with a single brood-rearing or non-breeding bird, the median values of environmental and anthropogenic variables at these telemetry locations were used in the model. A random forest model was used to create the map (Breiman 2001, Cutler et al. 2007). Random forests is a highly accurate non-parametric classification technique that predicts the probability of an outcome (in this case, habitat vs non-habitat) by averaging the results of many classification trees, each of which is trained on a random subset of the available data. The general habitat map was reclassified into ‘habitat’ and ‘non-habitat’ classes such that habitat areas captured 99% of all sage-grouse use locations. These general habitat areas were used as a mask in order to constrain preliminary predictions of seasonal habitats, described below.2. Create preliminary seasonal GRSG habitat models (Jan - May 2017). Telemetry locations in the database were classified into three seasonal habitat types based on time of year and type of GRSG use. Breeding habitat was defined as areas used by GRSG engaged in lekking, nesting, and early brood-rearing, from March 1 – June 14. Summer habitat was defined as areas used by brood-rearing and non-breeding GRSG from June 15 – August 31. The June 15 cutoff date between breeding and summer use locations was selected based on the temporal distribution of nesting and brooding use locations. Winter habitat was defined as areas used by non-breeding GRSG from November 1 – February 29. As in the general habitat modeling approach, environmental conditions at annual brood-rearing or non-breeding locations associated with the same bird were measured as medians over the multiple locations. Seasonal habitats were modeled using the same predictors as the general habitat model, with the addition of distance to leks due to its association with breeding habitat. A random forest model was used to estimate the suitability of general habitat areas statewide (from step 1 above) for breeding, summer, and winter use. For each seasonal use class, a suitability threshold was selected such that 85% of all seasonal use locations were captured in the resulting seasonal habitat map. This resulted in models that were neither overly restrictive nor overly liberal. To reduce the 'salt and pepper’ effect of isolated or scattered habitat pixels, a 3x3 km smoothing window was applied to each of the seasonal habitat layers, assigning the majority value (habitat or non-habitat) to the center pixel. 3. DWR biologists reviewed seasonal models (April - June 2017). An overview of the general and seasonal mapping methodology and preliminary maps was presented to GRSG biologists and managers from the Utah Division of Wildlife Resources (UDWR), Bureau of Land Management (BLM), and Forest Service (FS) at Utah State University in Logan on 4/21/17. Feedback at the meeting led to a few minor changes to the seasonal mapping methods. Because the breeding seasonal use model was not picking up areas around all active leks, distance to leks was dropped as a predictor variable from the seasonal habitat random forest model, and a 3 km buffer around all active leks was manually included in the breeding habitat model. Updated seasonal use models were sent to UDWR on 5/17/17, where they were made available for review by biologists with local area knowledge. An ArcGIS Online webpage was used to share the models with biologists, and allow for them to provide recommended additions / deletions to areas captured by the models. Accompanying the spatial data was an 8 minute webinar communicating the modelling procedure. UDWR returned updated seasonal use models with biologists’ comments, additions, and deletions to USU researchers on 6/6/17. Most but not all areas in the state received substantive feedback and comments from UDWR biologists. 4. Additional review of seasonal use models and final edits performed (July – August 2017). USU researchers reviewed biologist edits on 7/13/17. Most of the areas flagged to be added/removed from the seasonal use models seemed appropriate and helpful. But, there were a few areas UDWR biologists identified to be deleted from the models that USU researchers considered questionable. These areas were identified and shared with UDWR managers on 7/13/17. UDWR managers indicated a desire to have another face to face meeting to make a final determination on areas to include / exclude from the seasonal use models. This meeting occurred on 8/8/17, at which it was determined that it would be preferable to have the final seasonal habitat products reflect both use and potential suitability, as opposed to only areas of known use. This led to rejecting some areas flagged for deletion by biologists, as biologist comments indicated they were conceptualizing the map as primarily a use map only. Numerous small edits were made to the seasonal use layers, including several edits to include seasonal use locations not captured by preliminary models. Finally, all single, isolated habitat pixels were removed from the map. References Breiman, L. (2001), Random Forests, Machine Learning 45(1), 5-32.Cutler, D.R., T.C. Edwards, K.H. Beard, A. Cutler, K.T Hess, J.C. Gibson and J.J. Lawler. 2007. Random forests for classification in ecology. Ecology 88(11):2783-2792.This dataset was used in the preparation of the Greater Sage-grouse (GRSG) 2018 DEIS planning maps and acreages initiated by Secretarial Order (SO) 3353 of June 7, 2017 and the Notice of Intent (NOI) of October 11, 2017. Recipient Principal Investigator/Project Manager: Terry A. Messmer, Jack H. Berryman Institute, Utah State University, 5230 Old Main Hill, Logan, Utah 84322-5230, Phone 435-797-3975, Fax 435-797-3796, E-mail terry.messmer@usu.edu
Project Co-PIs and Staff: Dave Dahlgren, Eric Thacker, Kris Hulvey. Michel Kohl (Post-Doc), and Ben Crabb (GIS staff)
2023-11-28
GIS Analyst
Bureau of Land Management
Geospatial Ecologist
440W 200S #500
Salt Lake City
UT
84101
US
blm_ut_so_public_room@blm.gov
BLM Utah Greater Sage Grouse Seasonal Habitat Modeled Areas
BLM Utah Greater Sage Grouse Habitat Polygons
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SeasonalHabitat
The Greater Sage Grouse modeled habitat designations.
SeasonalHabitat
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Coordinates defining the features.
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Sequential unique whole numbers that are automatically generated.
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Shape.STLength()
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The feature class was created for mapping and analysis purposes.
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