Refinement of the Arc-Habcap model to predict habitat effectiveness for elk

Abstract

You may order additional copies of this publication by sending your mailing information in label form through one of the following media. Please specify the publication title and series number. Fort Collins Service Center AbstractWildlife habitat modeling is increasingly important for managers who need to assess the effects of land management activities. We evaluated the performance of a spatially explicit deterministic habitat model (Arc-Habcap) that predicts habitat effectiveness for elk. We used five years of radio-telemetry locations of elk from Custer State Park (CSP), South Dakota, to test predicted habitat effectiveness by the model. Arc-Habcap forage and coverforage proximity components predicted elk distribution in CSP. However, the cover component failed to predict elk distribution in CSP. Habitat effectiveness calculated as the geometric mean of the model components failed to predict elk distribution and resulted in under-utilization of habitats predicted to be good and over-utilization of habitats predicted to be poor. We developed a new formula to calculate habitat effectiveness as an arithmetic average of the model components that weighted forage more than cover or cover-forage proximity. The new formula predicted actual elk distribution across categories of habitat effectiveness. Elk selected cover and forage areas ≤100 m from cover-forage edges. Arc-Habcap predicted that areas adjacent to roads were not usable by elk. Elk used areas adjacent to primary roads, but use was less than the proportional area comprised for primary roads, and about equal to proportional area adjacent to secondary roads and primitive roads. All sapling/pole and mature structural stages of ponderosa pine (Pinus ponderosa) were considered as both forage and cover by Arc-Habcap and consequently considered optimal in the cover-forage model component. We suggested revisions for both the cover-forage proximity component and areas adjacent to roads.

Elk resource selection at parturition sites, Black Hills, South Dakota

“…As pine basal area increases beyond about 18.5 m 2 /ha, or approximately 40% overstory cover, understory production declines considerably in the Black Hills (Bennett et al , Uresk and Severson ). Additionally, predictions of the spatially explicit ArcHSI habitat model for elk in the Black Hills validated our categories and indicated the appropriate foraging and hiding cover opportunities exist for elk in these categories (Benkobi et al , Juntti and Rumble , Rumble et al ). Within the forested landscape, deciduous forests that were predominantly aspen were selected.…”

“…Ponderosa pine sapling or pole stands averaged 19 cm DBH and mature or saw timber stands averaged 23 cm DBH, so we combined them but retained the overstory canopy cover classifications. We reclassified vegetation communities of grass and forb, and ponderosa pine and spruce ≤40% overstory canopy cover as forage, and ponderosa pine and spruce >40% overstory canopy cover as hiding cover (Bennett et al , Benkobi et al , Juntti and Rumble , Rumble et al , Rumble and Gamo ). The resulting vegetation categories for our study included pine seedling or shrub, deciduous forest, forage, and hiding cover.…”

Landscape-Scale Attributes of Elk Centers of Activity in the Central Black Hills of South Dakota

“…The majority of elk habitat effectiveness studies examine each road type individually with results suggesting tertiary roads have less of an effect on elk use of habitat than improved surface roads (Millspaugh 1995, Wisdom 1998, Benkobi et al 2004. However, in the south zone, the only zone in which road density was a significant model variable, cumulative road density rather than density of improved roads influenced elk distribution.…”

Resource selection of a recently translocated elk population in Missouri

“…When the used relocation occurred outside of the predetermined radius, we assumed the radius of availability was the distance between the previous and current used relocation (Durner et al ). We recorded the following habitat variables at each used and available cell: 1) land cover category (Table ), derived from Landsat™ imagery, aerial photography, and ground‐collected GPS points; 2) percent tree canopy cover, derived from the 2011 United States Forest Service National Land Cover Database (http://www.mrlc.gov/nlcd11_data.php, accessed 8 Jan 2015), with canopy cover fixed at 0% within areas classified as a forage opening; 3) years since prescribed fire, with a value of 70 years outside all burn units reflecting the last major wildfire; 4) aspect, derived from a digital elevation model (DEM) provided by the Missouri Spatial Data Information Service (http://www.msdis.missouri.edu/data/dem/index.html, accessed 13 Dec 2013); 5) slope, derived from the DEM; 6) Euclidean distance to the nearest wooded (forest or woodland)‐open area edge, derived from the land cover category map within the Geospatial Modeling Environment (Geospatial Modeling Environment Version 0.7.2.0, http://www.spatialecology.com/gme, accessed 17 May 2013); 7) interspersion and juxtaposition index (IJI), calculated using FRAGSTATS (FRAGSTATS v. 4, http://www.umass.edu/landeco/research/fragstats/fragstats.html, accessed 20 Nov 2015) and a moving window of radius 1.922 km reflecting the mean radius of available habitat across all individuals; 8) Euclidean distance to paved road, derived from TIGER 2010 road data from the United States Census Bureau, Missouri Department of Transportation road data, and logging road data from MDC; 9) Euclidean distance to public gravel road; 10) Euclidean distance to 2‐track road; and 11) road density, in units of km paved and public gravel roads per 95 ha (a circle with radius 550 m), reflecting the distance at which elk respond to human disturbance from roads (Benkobi et al , Rumble et al ).…”