DOI: 10.5061/dryad.g4f4qrfwb
title: Greater Sage-grouse brood-rearing female habitat data

description: Habitat selection analyses conducted at an individual level may reveal
 patterns in selection not apparent when individuals are pooled in
 population-level approaches. Using GPS transmitters that gather
 high-resolution location data, we explored fine-scale habitat selection
 and space use within home ranges of female greater sage-grouse
 (Centrocercus urophasianus) that raised young (brood-rearing sage-grouse)
 in an oil and gas development area. To evaluate fine-scale habitat
 selection of brood-rearing sage-grouse, we used a two-stage approach.
 First, we developed models for each individual (i.e., individual-level
 modeling) and evaluated individual-level responses to modified habitats
 and infrastructure. Second, we averaged individual-level estimates using a
 bootstrap approach to make population-level inference. The average home
 range size during brood-rearing, from nest hatch to six weeks, in our
 study were 0.85 ± 0.21 km2. Individual- and population-level results
 indicated that brood-rearing females consistently selected for natural
 vegetation and avoided disturbed surfaces at a fine spatial scale. Our
 study area included substantial areas of recent (≤10 years) habitat
 reclamation which females also avoided. Visible power lines consistently
 led to avoidance behavior. In addition to consistent patterns of habitat
 selection, our individual models demonstrated variability and contrasting
 behaviors in how brood-rearing females responded to specific
 infrastructure features and anthropogenic water bodies. At the
 population-level, anthropogenic water bodies were avoided, but at the
 individual-level, the intensity of avoidance was variable among
 individuals. Individual variability was often explained by the age of the
 brood-rearing female (first year or adult). First year females were more
 likely than adults to use habitats close to infrastructure and
 consistently established home ranges in areas with more surface
 disturbance and infrastructure when compared to adults. Our results
 provide new insights into fine scale habitat-selection strategies used by
 female sage-grouse with broods in an area were oil and gas infrastructure
 is widespread and cannot be avoided.
description: Location and movement data came from GPS-transmitted female
 sage-grouse that were with chicks (brood-rearing). Spatial layers were
 created and/or processed using ArcGIS 10.7.0 – 10.7.1 (http://www.esri.com) and
 R statistical software (R Core Team 2020). Spatial varibles were extrated
 for use and pseudo-absence location using R statistical software (R Core
 Team 2020). 
description: **Data\_Kirol\_Fedy
 2023\_Brood\_rearing\_sage\_grouse\_anthropogenic\_landscape** This folder
 contains the data for the article: Kirol and Fedy 2023 Using
 individual-based habitat selection analyses to understand the nuances of
 habitat use in an anthropogenic landscape: A case study using greater
 sage-grouse trying to raise young in an oil and gas field. Comments and
 requests should be addressed to Chris Kirol: chriskirol@gmail.com. Please
 let me know if you intend to use these data. The data for this article is
 a .csv file titled "Data\_wlb3.01111\_Kirol\_Fedy\_2023" We
 followed a use versus availability design. We used weighted Generalized
 Linear Models (GLM) to compare use locations with pseudo-absence locations
 for individual-level models for each brood-rearing sage-grouse. We
 generated available points (i.e., pseudo-absence) at a ratio of 20:1 to
 used points for each individual. We established available points within a
 specific availability domain for each individual. The availability domain
 was based on the maximum linear distance across that individual’s home
 range which corresponds directly to that individual’s movements during the
 brood-rearing period. * "Bird\_name" is the bird id for that
 female's use locations and corresponding available locations. *
 "ID" is the unique identifier for each point, use and available,
 for each female. "step\_id" identifies the use or available
 point sequentially for each female. * "case\_" and
 "type\_" identify the used locations as case\_ =
 "TRUE" or type\_ = "1" and available locations case\_
 = "FALSE" or type\_ = "0". In our GLMM models
 "case\_" is the response variable (y). * "t" is the
 date and time of the location which is NA for available locations. *
 "year" is the year of the location and corresponding available
 locations. * "hr\_c\_dist" was not assessed as a predictor in
 our models but is a descriptive value describing the distance of that
 point to the center of that individual's home range. * The remaining
 columns are all predictor variables (x) assessed in our models. For a
 detailed description of each variable please see Table 1 in the
 manuscript. Environmental variables include: * "VRM" is vector
 roughness measure * "Slope" that is the slope (%) *
 "Sage" that is sagebrush cover (%) * "ShrubHgt" that
 is the shrub height (cm) * "Herb" that is herbaceous cover (%) *
 "Bare" that is bare ground (%) * "NDVI2017" or
 "NDVI2018" or "NDVI2019" is the Normalized Difference
 Vegetation Index for 2017 or 2018 or 2019 which corresponds to the
 "year" for the females' use and available locations. *
 Anthropogenic variables include: * "HabFactor" (Landcover factor
 in the manuscript) is a categorical variable describing undisturbed
 natural vegetation (coded as 0) or disturbed (active + reclamation; coded
 as 1) * "InfraDist" is the linear distance (m) to the nearest
 infrastructure feature * "ResDist" is the distance (m) to the
 closest reservoir (Pond distance in the manuscript) *
 "Structures500Vis" is a count, which can be partial visibility,
 of the visible structures within 500 meters * "Powerline500Vis"
 is a count, which can be partial visibility, of the visible power poles
 within 500 meters