3 Importing data

3.1 Load pamlr

library(pamlr)

3.2 Use existing data

pamlr has three integrated datasets for exploring the code. These include:

3.2.1 Hoopoe (Upupa epops)

data("hoopoe")

Figure 3.1: Photograph by Shantanu Kuveskar, (c) creative commons

3.2.2 European Bee-eater (Merops apiaster)

data("bee_eater")

Figure 3.2: Photograph by El Golli Mohamed, (c) creative commons

3.2.3 Alpine Swift (Apus melba)

data("swift")

Figure 3.3: Photograph by Rudraksha Chodankar, (c) creative commons

3.3 Import your own data

Importing data is easy with pamlr. All data files should be found within the same directory which can be accessed through the pathname argument. Currently there are a list of supported file types, which include:

• “.pressure”
• “.glf”
• “.gle”
• “.acceleration”
• “.temperature”
• “.magnetic”

It’s therefore possible to decide which of these to import. By default, all are imported.

PAM_data = create_import(pathname = "C:/Put/your/path/here",
                     measurements = c(".pressure", 
                                      ".glf",
                                      ".acceleration", 
                                      ".temperature", 
                                      ".magnetic")`

3.4 Data details

Once the PAM data are imported, they are stored as a nested list - with each element in the list containing a dataframe of measurements per date. For more details on the format of the data, use:

PAM_data = hoopoe
str(PAM_data)

## List of 6
##  $ id          : chr "16AJ"
##  $ pressure    :'data.frame':    37412 obs. of  2 variables:
##   ..$ date: POSIXct[1:37412], format: "2016-07-15 00:00:00" "2016-07-15 00:15:00" ...
##   ..$ obs : int [1:37412] 969 969 969 969 969 969 969 969 969 969 ...
##  $ light       :'data.frame':    112401 obs. of  2 variables:
##   ..$ date: POSIXct[1:112401], format: "2016-07-15 00:00:00" "2016-07-15 00:05:00" ...
##   ..$ obs : int [1:112401] 0 0 0 0 0 0 0 0 0 0 ...
##  $ acceleration:'data.frame':    111900 obs. of  3 variables:
##   ..$ date: POSIXct[1:111900], format: "2016-07-15 00:00:00" "2016-07-15 00:05:00" ...
##   ..$ pit : int [1:111900] 10 10 10 10 10 10 11 11 11 11 ...
##   ..$ act : int [1:111900] 0 0 0 0 0 0 2 0 0 0 ...
##  $ temperature :'data.frame':    36818 obs. of  2 variables:
##   ..$ date: POSIXct[1:36818], format: "2016-07-15 00:00:00" "2016-07-15 00:15:00" ...
##   ..$ obs : int [1:36818] 33 33 33 33 33 33 33 33 33 33 ...
##  $ magnetic    :'data.frame':    1559 obs. of  7 variables:
##   ..$ date: POSIXct[1:1559], format: "2016-07-15 00:00:00" "2016-07-15 06:00:00" ...
##   ..$ gX  : int [1:1559] 849 -487 211 505 725 -2048 454 -126 919 -886 ...
##   ..$ gY  : int [1:1559] -2035 -2182 -2601 -2581 -2507 -1847 -2582 -2650 -2437 -2574 ...
##   ..$ gZ  : int [1:1559] -1642 -1962 351 -20 118 -1626 41 -76 -152 -1327 ...
##   ..$ mX  : int [1:1559] -1600 -947 -2779 7 -1852 5844 1061 -118 -2196 2493 ...
##   ..$ mY  : int [1:1559] 15645 15610 15259 15549 15561 14545 16631 14548 15195 10924 ...
##   ..$ mZ  : int [1:1559] 5559 4627 6374 6147 5887 10881 5177 9000 6810 13793 ...

• Pressure data are recorded in hectopascals, generally every 15/30 minutes

• Light data are recorded in an arbitrary unit, generally every 2/5 minutes

• Acceleration data are summarised into two variables:

  • act which is short for “activity”“, and is the sum of the difference in acceleration on the z-axis (i.e. ”jiggle"). It is recorded every 5 minutes (summarised from 32 measurements - 10Hz)
  • pit which is short for “pitch”, and is the relative position of the bird’s body relative to the z axis. It is an average over 32 measurements and is summarised every 5 minutes.

• Temperature data are recorded in degrees Celsius, generally every 15/30 minutes

• Magnetic data are in fact the combined recordings from a tri-axial accelerometer and magnetometer.

  • gX, gY and gZ are snapshot tri-axial acceleration data, recorded every 4 hours.
  • mX, mY and mZ are snapshot tri-axial magentic field data, recorded every 4 hours.

3.5 Temporal resolution of data

Note that different sensors and loggers often collect data at different time intervals

Table 3.1: Temporal resolution of difference multisensor loggers

Sensor	Logger	Developper	Resolution	Life	Reference
Pressure	PAM-logger	SOI	15/30 min	1 year	Dhanjal-Adams et al. (2018)
	activity logger	CAMR	1h	1 year	Sjöberg et al.(2018)
	OS barologgers	CLA	1 min	13 days	Shipley et al. (2017)
Temperature	PAM-logger	SOI	15/30 min	1 year	Liechti et al. (2018)
	activity logger	CAMR	1 h	1 year	Sjöberg et al.(2018)
	OS barologgers	CLA	1 min	13 days	Dreelin et al. (2018)
Acceleration
activity	PAM-logger	SOI	5 min	1 year	Meier et al. (2018)
	activity logger	CAMR	1h	1 year	Bäckman et al. (2017)
pitch	PAM-logger	SOI	5 min	1 year	Liechti et al. (2018)
tri-axial	PAM-logger	SOI	4 h	1 year	Liechti et al. (2018)
Magnetic
tri-axial	PAM-logger	SOI	4 h	1 year	Liechti et al. (2018)

SOI = Swiss Ornithological Institute

CAMR = Centre for Animal Movement Research

CLA = Cornell Laboratory of Ornithology

3.6 Cropping the data

Note that very often, a logger continues to record data before and after it is removed from a bird. For example, it might be transported in a rucksack or left in a laboratory until data are downloaded. It’s therefore important to remove these incorrect datapoints. This can be done using cutPAM.

# make sure the cropping period is in the correct date format
start = as.POSIXct("2016-07-01","%Y-%m-%d", tz="UTC")
end = as.POSIXct("2017-06-01","%Y-%m-%d", tz="UTC")

# Crop the data
PAM_data= create_crop(hoopoe,start,end)

3.7 Table references

Bäckman, J., Andersson, A., Alerstam, T., Pedersen, L., Sjöberg, S., Thorup, K., & Tøttrup, A. P. (2017). Activity and migratory flights of individual free-flying songbirds throughout the annual cycle: method and first case study. Journal of Avian Biology, 48(2), 309–319. doi:10.1111/jav.01068

Dhanjal-Adams, K. L., Bauer, S., Emmenegger, T., Hahn, S., Lisovski, S., & Liechti, F. (2018). Spatiotemporal Group Dynamics in a Long-Distance Migratory Bird. Current Biology, 28(17), 2824–2830.e3. doi:10.1016/j.cub.2018.06.054

Dreelin, R. A., Shipley, J. R., & Winkler, D. W. (2018). Flight Behavior of Individual Aerial Insectivores Revealed by Novel Altitudinal Dataloggers. Frontiers in Ecology and Evolution, 6, 182. doi:10.3389/fevo.2018.00182

Hedenström, A., Norevik, G., Warfvinge, K., Andersson, A., Bäckman, J., & Åkesson, S. (2016). Annual 10-Month Aerial Life Phase in the Common Swift Apus apus. Current Biology, 26(22), 3066–3070. doi:10.1016/J.CUB.2016.09.014

Liechti, F., Bauer, S., Dhanjal-Adams, K. L., Emmenegger, T., Zehtindjiev, P., & Hahn, S. (2018). Miniaturized multi-sensor loggers provide new insight into year-round flight behaviour of small trans-Sahara avian migrants. Movement Ecology, 6(1), 19. doi:10.1186/s40462-018-0137-1

Meier, C. M., Karaardıç, H., Aymí, R., Peev, S. G., Bächler, E., Weber, R., … Liechti, F. (2018). What makes Alpine swift ascend at twilight? Novel geolocators reveal year-round flight behaviour. Behavioral Ecology and Sociobiology, 72(3), 45. doi:10.1007/s00265-017-2438-6

Shipley, JR, Kapoor, J, Dreelin, RA, Winkler, DW. (2018) An open‐source sensor‐logger for recording vertical movement in free‐living organisms. Methods in Ecol Evol.; 9: 465– 471. doi:10.1111/2041-210X.12893

Sjöberg, S., Pedersen, L., Malmiga, G., Alerstam, T., Hansson, B., Hasselquist, D., … Bäckman, J. (2018). Barometer logging reveals new dimensions of individual songbird migration. Journal of Avian Biology, 49(9), e01821. doi:10.1111/jav.01821