Estimating resource selection with count data. Issue 7 (7th June 2013)
- Record Type:
- Journal Article
- Title:
- Estimating resource selection with count data. Issue 7 (7th June 2013)
- Main Title:
- Estimating resource selection with count data
- Authors:
- Nielson, Ryan M.
Sawyer, Hall - Abstract:
- <abstract abstract-type="main" id="ece3617-abs-0001"> <title>Abstract</title> <p>Resource selection functions (RSFs) are typically estimated by comparing covariates at a discrete set of "used" locations to those from an "available" set of locations. This RSF approach treats the response as binary and does not account for intensity of use among habitat units where locations were recorded. Advances in global positioning system (GPS) technology allow animal location data to be collected at fine spatiotemporal scales and have increased the size and correlation of data used in RSF analyses. We suggest that a more contemporary approach to analyzing such data is to model intensity of use, which can be estimated for one or more animals by relating the relative frequency of locations in a set of sampling units to the habitat characteristics of those units with count‐based regression and, in particular, negative binomial (NB) regression. We demonstrate this NB RSF approach with location data collected from 10 GPS‐collared Rocky Mountain elk (<italic>Cervus elaphus</italic>) in the Starkey Experimental Forest and Range enclosure. We discuss modeling assumptions and show how RSF estimation with NB regression can easily accommodate contemporary research needs, including: analysis of large GPS data sets, computational ease, accounting for among‐animal variation, and interpretation of model covariates. We recommend the NB approach because of its conceptual and computational simplicity, and<abstract abstract-type="main" id="ece3617-abs-0001"> <title>Abstract</title> <p>Resource selection functions (RSFs) are typically estimated by comparing covariates at a discrete set of "used" locations to those from an "available" set of locations. This RSF approach treats the response as binary and does not account for intensity of use among habitat units where locations were recorded. Advances in global positioning system (GPS) technology allow animal location data to be collected at fine spatiotemporal scales and have increased the size and correlation of data used in RSF analyses. We suggest that a more contemporary approach to analyzing such data is to model intensity of use, which can be estimated for one or more animals by relating the relative frequency of locations in a set of sampling units to the habitat characteristics of those units with count‐based regression and, in particular, negative binomial (NB) regression. We demonstrate this NB RSF approach with location data collected from 10 GPS‐collared Rocky Mountain elk (<italic>Cervus elaphus</italic>) in the Starkey Experimental Forest and Range enclosure. We discuss modeling assumptions and show how RSF estimation with NB regression can easily accommodate contemporary research needs, including: analysis of large GPS data sets, computational ease, accounting for among‐animal variation, and interpretation of model covariates. We recommend the NB approach because of its conceptual and computational simplicity, and the fact that estimates of intensity of use are unbiased in the face of temporally correlated animal location data.</p> </abstract> … (more)
- Is Part Of:
- Ecology and evolution. Volume 3:Issue 7(2013:Jul.)
- Journal:
- Ecology and evolution
- Issue:
- Volume 3:Issue 7(2013:Jul.)
- Issue Display:
- Volume 3, Issue 7 (2013)
- Year:
- 2013
- Volume:
- 3
- Issue:
- 7
- Issue Sort Value:
- 2013-0003-0007-0000
- Page Start:
- 2233
- Page End:
- 2240
- Publication Date:
- 2013-06-07
- Subjects:
- Ecology -- Periodicals
Evolution -- Periodicals
577.05 - Journal URLs:
- http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)2045-7758 ↗
http://onlinelibrary.wiley.com/ ↗ - DOI:
- 10.1002/ece3.617 ↗
- Languages:
- English
- ISSNs:
- 2045-7758
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 3528.xml