ReadMe for “An application of upscaled optimal foraging theory using hidden Markov modelling: year-round behavioural variation in a large arctic herbivore” 
By LT Beumer, J Pohle, NMS Schmidt, M Chimienti, JP Desforges, LH Hansen, R Langrock, SH Pedersen, M Stelvig, FM van Beest

2020-04-17

This readme file describes the data files (2) accompanying the above publication. For further queries please contact nms@bios.au.dk 

The following files are included:

1) "muskox_tracking_data_summer_20200417.txt”  
2) "muskox_tracking_data_winter_20200417.txt" 

Combined, these two datasets represent the dataset collected by GPS collars fitted on 19 female muskoxen in northeast Greenland (28 muskox-years with 153-1062 observation days/animal) and associated extracted covariates, divided into a summer and winter season dataset as modelled in the article. Data here are given as included in the models (for a description of cleaning procedures, see article). 

All continuous, non-cyclical covariates were standardised to have zero mean and unit standard deviation to improve numerical stability of parameter estimation. This is indicated by “_scaled” in the column name. 

The datasets contain the following columns:

⁃	"burst_id" provides the id for each individual track burst included in the model. For a description of how tracks were split into burst, see the article. Naming (e.g. burst_id “4022_03_1_1_snow_201314”) follows the following convention: collarID_animalID_[unique trackID for cases where animal has been collared multiple times with the same collarID]_burstID_season_year, with season being denoted “snow” for winter and “snow-free” for summer.  
⁃	"step" is step length, i.e. Euclidian distance (m) between GPS locations
⁃	"angle" is the turning angle (radians) between GPS locations        
⁃	columns “x” and “y” are the coordinates of the GPS location in the projection WGS 84 / UTM zone 27N (CRS(“+proj=utm +zone=27 +datum=WGS84 +units=m +no_defs")           
⁃	"datetime" gives the date and time of the GPS location           
⁃	"tday" is the hour of the day at which the GPS location was recorded
⁃	"month" is the month in which the GPS location was recorded
⁃	"julian" is the Julian day (I.e. day of year) on which the GPS location was recorded
⁃	"light" is a dummy variable for whether it was light (daylight) or dark at the time of the GPS observation (light = coded as 1, dark = 0)              
⁃	the columns “year_201314”/“year_2014, “year_201415”/“year_2015”, “year_201516”/“year_2016”, “year_201617”/“year_2017” (as denoted in winter/summer dataset) give the seasonyear in which the GPS location was recorded (e.g.”year_201314” = winter 2013-2014, “year_2014” = summer 2014) 
⁃	"elevation_scaled" gives elevation (m above sea level) of the recorded GPS location
⁃	"dist_sea_scaled" gives Euclidian distance between recoded GPS location and coastline (m)    
⁃	"hillshade_scaled"  gives amount of incoming radiation (unitless) at recoded GPS location
⁃	"ruggedness_scaled" gives an index of terrain ruggedness at recoded GPS location
⁃	columns “ocean”, “water”, “glacier”, “bareground”, “sparseveg”, “denseveg" are dummy variables for different landcover classes (coded 1-0)
⁃	"snowdepth_scaled" gives snow depth (m) at recoded GPS location 
⁃	"airtemp_scaled" gives ambient temperature (°C) at recoded GPS location
⁃	"windspeed_scaled" gives windspeed (m/s) at recoded GPS location
⁃	"winddir_interpol" gives wind direction (in degrees from North) at recoded GPS location    
⁃	"total_precip_scaled" gives amount of precipitation (mm) at recoded GPS location and time of observation
⁃	“ndvi_scaled” gives NDVI (Normalised differential vegetation index) (not included in winter data set)
⁃	"viterbi" gives the latent state of each observation based on the final seasonal models, decoded using the Viterbi algorithm

For a more detailed description of environmental covariates and their resolution/data sources, see Table 2 in the article.