Analysis of regional species distribution models based on radio‐telemetry datasets from multiple small‐scale studies1. Issue 4 (4th January 2013)
- Record Type:
- Journal Article
- Title:
- Analysis of regional species distribution models based on radio‐telemetry datasets from multiple small‐scale studies1. Issue 4 (4th January 2013)
- Main Title:
- Analysis of regional species distribution models based on radio‐telemetry datasets from multiple small‐scale studies1
- Authors:
- Rice, Mindy B.
Apa, Anthony D.
Phillips, Michael L.
Gammonley, James H.
Petch, Bradford B.
Eichhoff, Karin - Abstract:
- <abstract abstract-type="main" xml:lang="en"> <title>Abstract</title> <p>The identification of core habitat areas and resulting prediction maps are vital tools for land managers. Often, agencies have large datasets from multiple studies over time that could be combined for a more informed and complete picture of a species. Colorado Parks and Wildlife has a large database for greater sage‐grouse (<italic>Centrocercus urophasianus</italic>) including 11 radio‐telemetry studies completed over 12 years (1997–2008) across northwestern Colorado. We divided the 49, 470‐km<sup>2</sup> study area into 1‐km<sup>2</sup> grids with the number of sage‐grouse locations in each grid cell that contained at least 1 location counted as the response variable. We used a generalized linear mixed model (GLMM) using land cover variables as fixed effects and individual birds and populations as random effects to predict greater sage‐grouse location counts during breeding, summer, and winter seasons. The mixed effects model enabled us to model correlations that may exist in grouped data (e.g., correlations among individuals and populations). We found only individual groupings accounted for variation in the summer and breeding seasons, but not the winter season. The breeding and summer seasonal models predicted sage‐grouse presence in the currently delineated populations for Colorado, but we found little evidence supporting a winter season model. According to our models, about 50% of the study area in<abstract abstract-type="main" xml:lang="en"> <title>Abstract</title> <p>The identification of core habitat areas and resulting prediction maps are vital tools for land managers. Often, agencies have large datasets from multiple studies over time that could be combined for a more informed and complete picture of a species. Colorado Parks and Wildlife has a large database for greater sage‐grouse (<italic>Centrocercus urophasianus</italic>) including 11 radio‐telemetry studies completed over 12 years (1997–2008) across northwestern Colorado. We divided the 49, 470‐km<sup>2</sup> study area into 1‐km<sup>2</sup> grids with the number of sage‐grouse locations in each grid cell that contained at least 1 location counted as the response variable. We used a generalized linear mixed model (GLMM) using land cover variables as fixed effects and individual birds and populations as random effects to predict greater sage‐grouse location counts during breeding, summer, and winter seasons. The mixed effects model enabled us to model correlations that may exist in grouped data (e.g., correlations among individuals and populations). We found only individual groupings accounted for variation in the summer and breeding seasons, but not the winter season. The breeding and summer seasonal models predicted sage‐grouse presence in the currently delineated populations for Colorado, but we found little evidence supporting a winter season model. According to our models, about 50% of the study area in Colorado is considered highly or moderately suitable habitat in both the breeding and summer seasons. As oil and gas development and other landscape changes occur in this portion of Colorado, knowledge of where management actions can be accomplished or possible restoration can occur becomes more critical. These seasonal models provide data‐driven, distribution maps that managers and biologists can use for identification and exploration when investigating greater sage‐grouse issues across the Colorado range. Using historic data for future decisions on species management while accounting for issues found from combining datasets allows land managers the flexibility to use all information available. © 2013 The Wildlife Society.</p> </abstract> … (more)
- Is Part Of:
- Journal of wildlife management. Volume 77:Issue 4(2013)
- Journal:
- Journal of wildlife management
- Issue:
- Volume 77:Issue 4(2013)
- Issue Display:
- Volume 77, Issue 4 (2013)
- Year:
- 2013
- Volume:
- 77
- Issue:
- 4
- Issue Sort Value:
- 2013-0077-0004-0000
- Page Start:
- 821
- Page End:
- 831
- Publication Date:
- 2013-01-04
- Subjects:
- Wildlife management -- Periodicals
Zoology -- Periodicals
333.954 - Journal URLs:
- http://www.bioone.org/bioone/?request=get-archive&issn=0022-5413 ↗
http://www.jstor.org/journals/0022541X.html ↗
http://www.wildlife.org/publications/index.cfm?tname=journal ↗
http://onlinelibrary.wiley.com/ ↗ - DOI:
- 10.1002/jwmg.496 ↗
- Languages:
- English
- ISSNs:
- 0022-541X
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 5072.630000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 3506.xml