Supplement 1. Example data and R code.

<h2>File List</h2><blockquote> <p><a href="available_habitat.txt">available_habitat.txt</a> - data file representing the available habitat</p> <p><a href="gps_locations.txt">gps_locations.txt</a> - data file representing the GPS location data</p> <p><a href="Rcode.R">Rcode.R </a>- R code to analyze the example data using the RSF likelihood for GPS fix success</p> <p><a href="all_files.zip">all_files.zip</a> - all files at once</p> </blockquote><h2>Description</h2><blockquote> <p>Rcode.R analyzes the example data, which is one of the simulated data sets with 90% GPS fix success contained in the Nielson et al. paper. There are two data files (available_habitat.txt and gps_locations.txt) representing the available habitat and the GPS location data, respectively. The example data can be analyzed by saving both data files to a working directory, opening an R session and copying and pasting all text below at the R command prompt. Select quantities are output to the terminal. The description of columns in the data files is provided below.</p><p><b>In available_habitat.txt:</b></p> <p>(1) utmX = utm easting coordinate of habitat unit</p> <p>(2) utmY = utm northing coordinate of habitat unit</p> <p>(3) unit.id = habitat unit ID</p> <p>(4) prcnt.sage = % Wyoming Big Sage</p> <p>(5) elevation = elevation (km)</p> <p><b>In gps_locations.txt:</b></p> <p>(1) unit.id = habitat unit of GPS location (missing = NA)</p> <p>(2) fix.attempt = sequential fix attempt number</p> <p><b>Column sums for 'available_habitat.txt" (in order):</b></p> <p> utmX = 4.985109e+7 = 0.0000004985109<br> utmY = 8.483487e+8 = 0.00000008483487<br> unit.id = 1.7205e+4 = 0.00017205<br> prcnt.sage = 1.06875e+4 = 0.000106875<br> elevation = 3.795941e+2 = 0.03795941</p> <p><b>Column sums for "gps_locations.txt" (in order):</b></p> <p> unit.id = 42761 (note some "NA" values, which indicate missing)<br> fix.attempt = 98790</p> </blockquote

Pacific Walrus (<i>Odobenus rosmarus divergens</i>) Resource Selection in the Northern Bering Sea

<div><p>The Pacific walrus is a large benthivore with an annual range extending across the continental shelves of the Bering and Chukchi Seas. We used a discrete choice model to estimate site selection by adult radio-tagged walruses relative to the availability of the caloric biomass of benthic infauna and sea ice concentration in a prominent walrus wintering area in the northern Bering Sea (St. Lawrence Island polynya) in 2006, 2008, and 2009. At least 60% of the total caloric biomass of dominant macroinfauna in the study area was composed of members of the bivalve families Nuculidae, Tellinidae, and Nuculanidae. Model estimates indicated walrus site selection was related most strongly to tellinid bivalve caloric biomass distribution and that walruses selected lower ice concentrations from the mostly high ice concentrations that were available to them (quartiles: 76%, 93%, and 99%). Areas with high average predicted walrus site selection generally coincided with areas of high organic carbon input identified in other studies. Projected decreases in sea ice in the St. Lawrence Island polynya and the potential for a concomitant decline of bivalves in the region could result in a northward shift in the wintering grounds of walruses in the northern Bering Sea.</p></div

Radio-tags attached to Pacific walruses to derive choice sets of available macroinfauna and sea ice concentrations to model resource selection in 2006, 2008, and 2009 in the northern Bering Sea.

<p>Radio-tags attached to Pacific walruses to derive choice sets of available macroinfauna and sea ice concentrations to model resource selection in 2006, 2008, and 2009 in the northern Bering Sea.</p

Correlations among covariates used in models fitted to estimate Pacific walrus resource selection in the northern Bering Sea (correlations >0.60 in bold).

<p>B = Bivalvia, P = Polychaeta.</p

Pacific walrus radio-tracks within benthic sampling areas in 2006 and 2008–2009.

<p>Also indicated are radio-tag deployment locations (pink circles) and subsequent daily mean locations (black dots). Movements from the deployment location with durations greater than one day or outside of the benthic sampling area are represented by lighter-colored radio-tracks to provide a larger context of walrus movements.</p

Average probability of walrus resource selection within benthic sampling areas in 2006 and 2008–2009.

<p>Average probability of walrus resource selection within benthic sampling areas in 2006 and 2008–2009.</p

Relative variable importance and direction of variable coefficients among 319 models fitted to estimate Pacific walrus resource selection in the northern Bering Sea.

a<p>Estimated from model average coefficients <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0093035#pone.0093035-Burnham1" target="_blank">[50]</a>.</p

Percent cumulative caloric biomass within benthic sampling areas in 2006 and 2008–2009.

<p>Dominant taxa from each sampling period were identified by the minimum number of top-ranked taxa comprising at least 80% of the cumulative caloric biomass (only the first 15 ranked taxa are shown).</p

Mean (top row) and sample standard deviation (bottom row) daily sea ice concentration (%).

<p>Means and standard deviations were calculated for each 2×2 km cell within the benthic sampling areas in 2006 and 2008–2009 during walrus tracking periods (tracking periods indicated in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0093035#pone-0093035-t001" target="_blank">Table 1</a>).</p

Change in AIC among AIC-ranked models considered in multimodel inferences of walrus resource selection.

<p>Change in AIC among AIC-ranked models considered in multimodel inferences of walrus resource selection.</p