A complete description of telemetry, covariate collecting, and covariate processing can be found in the companion manuscript for this dataset.
Methodology:
Methodology_Type: Lab
Methodology_Description:
This methodology contributed an Argos location filter. Moderating Argos location errors in animal tracking data
Methodology_Citation:
Citation_Information:
Originator: D. C. Douglas
Originator: R. Weinzierl
Originator: S. C. Davidson
Originator: R. Kays
Originator: M. Wikelski
Originator: G. Bohrer
Publication_Date: 2012
Title: Moderating Argos location errors in animal tracking data
Geospatial_Data_Presentation_Form: journal article
Publication_Information:
Publication_Place: Methods in Ecology and Evolution
Publisher: British Ecological Society
Other_Citation_Details: Douglas et al. 2012
Online_Linkage: http://dx.doi.org/10.1111/j.2041-210X.2012.00245.x
Methodology:
Methodology_Type: Lab
Methodology_Description:
This methodology describes how we obtained hourly behavior data from walruses using a transmitter that collected conductivity and a pressure data and relayed it through a satellite data collection system.
Methodology_Citation:
Citation_Information:
Originator: A. S. Fischbach
Originator: C. V. Jay
Publication_Date: 2016
Title:
A strategy for recovering continuous behavioral telemetry data from Pacific walruses
Geospatial_Data_Presentation_Form: journal article
Publication_Information:
Publication_Place: Wildlife Society Bulletin
Publisher: Wildlife Society
Other_Citation_Details: Fischbach and Jay 2016
Online_Linkage: http://dx.doi.org/10.1002/wsb.685
Source_Information:
Source_Citation:
Citation_Information:
Originator: W. S. Beatty
Originator: C. V. Jay
Originator: A. S. Fischbach
Originator: J. M. Grebmeier
Originator: R. L. Taylor
Originator: A. L. Blanchard
Originator: S. C. Jewett
Publication_Date: 2016
Title:
Space use of a dominant Arctic vertebrate: effects of prey, sea ice, and land on Pacific walrus resource selection
Geospatial_Data_Presentation_Form: journal article
Publication_Information:
Publication_Place: Biological Conservation
Publisher: Elsevier
Online_Linkage: http://dx.doi.org/10.1016/j.biocon.2016.08.035
Type_of_Source_Media: paper
Source_Time_Period_of_Content:
Time_Period_Information:
Range_of_Dates/Times:
Beginning_Date: Unknown
Ending_Date: Unknown
Source_Currentness_Reference: ground condition
Source_Citation_Abbreviation: Beatty et al. 2016
Source_Contribution:
The source describes the collection and interpolation of benthic biomass data.
Source_Information:
Source_Citation:
Citation_Information:
Originator: Messinger, F.
Originator: DiMego, G.
Originator: Kalnay, E.
Originator: Mitchell, K.
Publication_Date: 2016
Title: North American Regional Reanalysis
Geospatial_Data_Presentation_Form: journal article
Other_Citation_Details: Bulletin of the American Meteorological Society 87:343-360
Type_of_Source_Media: paper
Source_Time_Period_of_Content:
Time_Period_Information:
Range_of_Dates/Times:
Beginning_Date: 19790101
Ending_Date: 20141020
Source_Currentness_Reference: ground condition
Source_Citation_Abbreviation: Mesinger et al. 2006
Source_Contribution:
The source documents to processing of the weather metric estimates used in the analysis.
Process_Step:
Process_Description:
We collected Argos satellite telemetry locations from a sample of 216 female walruses between 2 June 2008 and 20 October 2014.
Process_Date: Unknown
Process_Step:
Process_Description:
We filtered Argos locations with an algorithm based on spatial redundancy, movement rates, and turning angles (Douglas et al. 2012). We set the algorithm to retain (1) all standard class locations, (2) non-standard class locations within 2 km of the previous or subsequent location, and (3) remaining locations based on a distance-angle-rate filter that accepted a maximum walrus speed of 10 km/h and rejected locations at the apex of highly acute angles (RATECOEF = 25).
Process_Date: 2016
Process_Step:
Process_Description:
For analysis, we selected one Argos location for each walrus tracking day so we could associate daily behavioral intervals with environmental covariates. We selected one walrus location each day by retaining the location record(s) closest in time to local noon. If more than one location were equally close to local noon, we selected the location with the highest Argos location quality (LQ), and if they had the same LQ, we selected the location obtained before local noon. If locations of equal LQ were acquired at the same time before local noon, then we averaged the locations.
Process_Date: 2016
Process_Step:
Process_Description:
We derived chronologies of hourly haulout and foraging states of tagged walruses from data collected by a conductivity sensor and pressure transducer on the radio-tag (Fischbach and Jay 2016). Every one or two seconds (depending on the tag model and deployment year), the conductivity sensor indicated whether the tag was in or out of salt water and the pressure transducer indicated the depth of the tag. If >= 90% of the conductivity measurements within a 1-h interval indicated the tag was out of water, the walrus was considered to be hauled out during that interval. If the majority of the pressure measurements within a 1-h interval indicated the tag was > 10 m deep, the walrus was considered to be foraging during that interval.
Process_Date: 2016
Process_Step:
Process_Description:
We excluded behavioral intervals obtained within the first 24 hours after tag deployment to guard against the possibility that the tagging process altered behavior during this period. We only included data from walruses that provided = > 240 hours of continuous hourly records that were not missing two or more consecutive daily locations.
Process_Date: 2016
Process_Step:
Process_Description:
We assigned each day of walrus behavior as having land available if the daily location was within a day’s reach of land. We defined a day’s reach as the maximum distance an average walrus was likely to travel in a day, and quantified it as the 95th percentile of the distribution of movements from each daily location to the next for each walrus in the data set, averaged over individuals. From this, a day’s reach was found to be 50 km.
Process_Date: 2016
Process_Step:
Process_Description:
We assigned each day of walrus behavior as having sea ice available if the daily location was within a day’s reach of sea ice (i.e. 50 km). We assessed the presence of ice within a day’s reach using the National Ice Center’s daily Marginal Ice Zone (MIZ) product (
http://www.natice.noaa.gov/products/, accessed on 5 December 2014). The Marginal Ice Zone represents regions of pack and marginal sea ice as digitally encoded geospatial polygons based on imagery from satellite-borne sensors acquired near local noon each day. Marginal ice is defined as areas with approximately 10–80% ice concentration, whereas pack ice is defined as areas with > 80% ice concentration.
Process_Step:
Process_Description:
We assigned each day of walrus behavior a covariate in our models to quantify the amount of potential walrus prey within a day’s reach of daily walrus locations. Details of data collection and estimating the distribution of benthic macrofaunal biomass are described in Beatty et al. (2016, Appendix A).
Source_Used_Citation_Abbreviation: Beatty et al. 2016
Process_Date: 2016
Process_Step:
Process_Description:
We characterized air temperature and wind speed coincident with walrus behavior records based on estimates available through the North American Regional Reanalysis (NARR), which provides weather metric estimates at 3 h intervals on an approximately 32.5 km by 32.5 km grid (Mesinger et al. 2006). We acquired NARR estimates of air temperature at 2 m above sea level, and wind speed at 10 m above sea level from the National Oceanic and Atmospheric Administration. (
ftp://ftp.cdc.noaa.gov/Datasets/NARR/monolevel, accessed on 11 March 2015.) We assigned weather covariates to the 1-h behavioral intervals that began at the NARR time points (local hours (UTC - 11 h) of 01:00, 04:00, 07:00, 10:00, 13:00, 16:00, 19:00, 22:00) using an inverse-distance weighted interpolation of the 4 NARR spatial grid points closest to the daily walrus location. We used inverse distance weighting to interpolate weather variables because NARR data represented a continuous grid. We retained only the 1-h behavioral intervals that were assigned weather covariates. Thus, each walrus day had a maximum of 8 behavior records.