Assessment of a livestock GPS collar based on an open-source datalogger informs best practices for logging intensity

Abstract

Ecologists have used Global Positioning Systems (GPS) to track animals for 30 years. Issues today include logging frequency and precision in estimating space use and travel distances, as well as battery life and cost. We developed a low-cost (~US$125), open-source GPS datalogger based on Arduino. To test the system, we collected positions at 20-s intervals for several 1-week durations from cattle and sheep on rangeland in North Dakota. We tested two questions of broad interest to ecologists who use GPS collars to track animal movements: (1) How closely do collared animals cluster in their herd? (2) How well do different logging patterns estimate patch occupancy and total daily distance traveled? Tested logging patterns included regular logging (one position every 5 or 10 min), and burst logging (positions recorded at 20-s intervals for 5 or 10 min per hour followed by a sleep period). Collared sheep within the same pasture spent 75% of daytime periods within 51 m of each other (mean = 42 m); collared cattle were within 111 m (mean = 76 m). In our comparison of how well different logging patterns estimate space use versus constant logging, the proportion of positions recorded in 1- and 16-ha patches differed by 2%-3% for burst logging and 1% for regular logging. Although all logging patterns underestimated total daily distance traveled, underestimations were corrected by multiplying estimations by regression coefficients estimated by maximum likelihood. Burst logging can extend battery life by a factor of 7. We conclude that a minimum of two collars programmed with burst logging robustly estimate patch use and spatial distribution of grazing livestock herds. Research questions that require accurately estimating travel of individual animals, however, are probably best addressed with regular logging intervals and will thus have greater battery demands than spatial occupancy questions across all GPS datalogger systems.

Keywords: Arduino; DIY ecology; animal tracking; behavioral ecology; space use patterns.

Figure 1

Top: Illustration of our Global Positioning Systems (GPS) datalogger electronics. Basic components include the Adafruit Feather M0 Adalogger (a) and Adafruit GPS Wing (b). Also shown are the TPL5110 low power timer (c), external JST battery plug (d), and a 47uF capacitor (e) to stabilize the power supply. Bottom: The assembled GPS Wing and Adalogger, with battery, case, clamps, and collar

Figure 2

Top: Attaching a collar with duct tape‐wrapped Global Positioning Systems datalogger on a cow ahead of initial release onto experimental pastures in June. Bottom: Retrieving a collar from a sheep in the field

Figure 3

Mean distance traveled per animal, per hour, across all days within each month. Low activity levels between 22:00 and 04:00 justify removing this nighttime period from subsequent analysis. Data from two cattle and two sheep pastures in Hettinger, North Dakota, with 2–3 collared animals per pasture (actual number within each mean varies with individual collar performance and battery life)

Figure 4

Mean distance between two and three collared animals per pasture between 04:00 and 22:00 during 4‐month‐week‐long Global Positioning Systems datalogger deployments in two cattle and two sheep pastures in Hettinger, North Dakota

Figure 5

Maximum percentage difference among each datalogger deployment between proportion of locations in each 1‐ and 16‐ha patches under four logging patterns (hourly bursts of 5‐ or 10‐min duration, regular 5‐ or 10‐min intervals) compared to the proportion of locations in each patch under continuous logging. Differences expressed as absolute values. Data consist of positions logged between 04:00 and 22:00 during 4–7‐day trials on two cattle and two sheep pastures in Hettinger, North Dakota

Figure 6

An example of four logging patterns (hourly bursts of 5‐ or 10‐min duration, regular 5‐ or 10‐min intervals) compared to constant logging at 20‐s intervals for three sheep fitted with DIY GPS dataloggers at the Hettinger Research Extension Center, Hettinger, North Dakota. Data are from between 04:00 and 22:00 on 7 July 2017. Pasture divisions represent the patches used to compare space use patterns by the four logging settings (Figure 5). These maps also illustrate how different logging patterns might vary in their estimation of total distance traveled, especially over nonlinear/tortuous routes

Figure 7

Differences in total daily distance traveled for collared animals under four logging patterns (hourly bursts of 5‐ or 10‐min duration, regular 5‐ or 10‐min intervals) compared to continuous logging. Data consist of positions logged between 04:00 and 22:00 during 3–7‐day trials on two cattle and two sheep pastures in Hettinger, North Dakota

Figure 8

Slope parameters from linear regression models comparing estimated daily distance traveled for collared animals under four logging patterns (hourly bursts of 5‐ or 10‐min duration, regular 5‐ or 10‐min intervals) to continuous logging. Corrected models multiply the estimated distance in June data by the slope parameter from the uncorrected model, which was determined by maximum likelihood estimation on data from July, August, and September; the closer the corrected parameter is to 1, the better the correction factor performs in increasing the accuracy of distance estimates. Data consist of positions logged between 04:00 and 22:00 during 3–7‐day trials on two cattle and two sheep pastures in Hettinger, North Dakota