Animals

The packs in this study were located in the Okavango Delta region of Northern Botswana and are part of an ongoing study by Botswana Predator Conservation Trust (http://www.bpctrust.org). Every member of a pack of six adult dogs (‘focal pack’) was collared. The pack consisted of a dominant male (‘Kobe’) and dominant female (‘Timbuktu’), two subdominant males (‘MJ’, ‘Scorpion’) and two subdominant females (‘Accra’, ‘Kigali’). Data collection on all pack members started on 13 April 2012 and continued over the following 5–7 months, with one collar failing on 27 May. The collar was replaced, but the failure resulted in a lack of data for one dog (‘Accra’) over a period of 22 days. This time period was removed from our analysis. Collar removal started at the end of August 2012. One dog (‘Kobe’), the dominant male, died on 27 June. The data from the dominant female (‘Timbuktu’) show a period of low activity when she remained at the den with pups. Distance travelled per day was calculated excluding the denning female Timbuktu.

The dogs were immobilized by free darting from a vehicle using xylazine (55 mg), ketamine (50 mg) and atropine (1.1–1.2 mg), and reversed after 45–60 min with yohimbine (4 mg) or atipamezole (5.5 mg). While sedated, anatomic measurements including limb lengths, limb and body girths, and body mass were recorded (Supplementary Table 1). Collar data were retrieved via radio link to a ground vehicle every few weeks.

This work was approved by RVC Ethics & Welfare Committee.

Northern Botswana holds part of one of the largest African wild dog populations. The common kleptoparasites of African wild dogs are spotted hyaenas and lions (Panthera leo).

Comparison with other packs

To demonstrate that our focal pack is representative of all the packs in the area, we used high-resolution GPS collar data from 18 subdominant individuals from 13 different packs in the area. The collars worn by African wild dogs outside the focal pack were either the same as or an earlier version of the collars used on the focal pack. Outside the focal pack data were recorded at 1-h intervals when dogs were resting and at 5- or 10-min intervals when they were moving. Only four collars were allowed to go into ‘run state’ for a limited trial period not exceeding a total of 2 months (see collar below). Data were collected for time slots of different duration (21–409 days) between November 2011 and October 2014.

Distance travelled per day and run parameters (maximum stride speed, centripetal (turning) acceleration and tangential (fore-aft) acceleration and deceleration, duration, distance and mean absolute heading rate) were compared using the mean of the individual medians in the focal group and the group containing all individuals from other packs. We used a two-sample F-test to check for equal variance and subsequently a two-sample t-test or Welch’s test; p values were adjusted using Bonferroni correction.

We found no significant difference (Welch’s test, p=0.09) between the mean of the medians of the distance travelled per day by individual dogs of the focal pack and the mean of medians of the other dogs (Supplementary Table 2).

Equally when we compare run parameters (maximum stride speed, tangential acceleration/deceleration, centripetal acceleration, mean absolute heading rate, duration and distance) of our focal pack to four individuals in different packs, only one, mean absolute heading rate, came out to be significantly different (Supplementary Table 3).

Comparison with cheetahs

We reanalysed previously collected and published GPS/IMU data4 from five wild cheetahs (three female and two male) using the same methods as described here to compare locomotor performance of cheetah and African wild dog. The cheetah data were collected in the same study area (in and around the Moremi game reserve, Okavango Delta, Botswana) between July 2011 and August 2013. Data collection continued after publication4 and we added the new data into our analysis, bringing the total number of runs analysed from 367 to 488, including 468 chases.

Collar design and data recording

Power consumption poses a major challenge in the design of a wildlife tracking collar. To fulfil the demands of sufficient data rate during periods of high animal activity and average low-energy consumption, we used collars designed in-house and previously used successfully on cheetahs4. The collars use in-built solar cells on the top housing and careful management of the GPS sample rate for power conservation. The mass of the mark two collars was ∼340 g. Dropoff units (Sirtrack; 70 g) were used to release two collars at the end of the study. Other collars were removed following immobilization.

The collar was controlled by a low-power MSP430 16-bit microcontroller (Texas Instruments Inc., TX, USA), running custom software written in the ‘C’ programming language. A 2-GB micro-SD flash memory card (Sandisk, CA, USA) was used for on-board data storage.

The collar provides GPS position and instantaneous velocity data as well as three-axis-specific force and rotation rate data. GPS position and velocity were obtained from an LEA-6T GPS module (u-Blox AG). An MMA7331 three-axis accelerometer module (Freescale Semiconductor) provided specific force with a ±12 g range. The roll and pitch rotation rate were measured by a dual-axis gyroscope (ST Microelectronics), and yaw rotation rate by a single-axis gyroscope (ST Microelectronics), both set to the 2,000° s−1 range. Sensor outputs were filtered by simple single-pole analogue filters (100 Hz knee), and then sampled by the microcontroller at 300 (accelerometers) or 100 (gyroscopes) samples per second. Data download from the collar were via a 2.4-GHz chirp-spread-spectrum communication module (Nanotron Technologies Gmbh). Power was provided by two batteries: a 900-mAh lithium–polymer rechargeable battery (Active Robots), charged by a solar cell array consisting of 10 monocrystalline silicon solar cells (Ixys Koria), and a 13-Ah lithium thionyl chloride battery (Saft). The microcontroller measured both battery voltages and the charge current from the solar cell array, and switched the collar electrical load between batteries depending on the battery state.

To manage power consumption effectively, the collar was programmed to switch dynamically between four different operating ‘states’ (Supplementary Fig. 6). The state depended on the time of the day and the animal activity level (measured using the accelerometers). The different states enabled power rationing between average power consumption on the one hand, and quantity and resolution of data on the other. Multiple software updates were installed on the collars (remotely) during the research period to improve performance and capture as many hunts as possible. The default state (‘alert state’) provided GPS positions every hour, and allowed the transition into ‘mooch state’ with 5-min fixes when the animal was deemed active, based on periodic specific force measurements (measurement taken for 10 s at 30 Hz every minute). Initially, the collar was set to ‘ready state’ when the animal was moving between local times of 18:00 and 20:00, since previous work suggested that most hunting occurs around dawn and dusk33. In ‘ready state’, GPS positions and speeds were recorded every 5 s, if the animal was deemed to be active. A transition occurred from ‘ready’ state to ‘run state’ if fore-aft accelerometer data exceeded a threshold equivalent to galloping in three consecutive peaks, and the run was defined as valid and stored if five further peaks were detected. In ‘ready state’, accelerometer data were recorded into a circular buffer at 100 Hz, the buffer storing the latest 3 s of data. This prebuffering allowed open-loop inertial navigation back to the beginning of the run. However, it was later deemed that an extended time allowed for entering ‘run state’ was more beneficial than the prebuffering of data. Prebuffering was abolished on 26 April 2012; this resulted in the loss of the first one or two strides at the beginning of the run. From then on, the collar was allowed to enter ‘run state’ directly from ‘mooch state’ during preselected ‘times of interests’ between 4:00–10:00 and 17:00–22:00 local time. During the ‘times of interest’, GPS data were recorded every 5 min (the same as during normal ‘mooch state’), but sample rates were increased to every 10 s for a 2-h window within the ‘times of interest’ to get a more accurate account of position during times when most hunts were expected to happen based on initial data observations. Initially, this time was chosen to be between 18:00 and 20:00, and later changed to between 06:00 and 08:00 local time.

Signal processing

GPS data with horizontal position accuracy above 8 m were removed for all calculations.

In the ‘run state’, the power management features used gave different sampling rates for accelerometer (300 Hz) and gyro (100 Hz). GPS position (5 Hz) and instantaneous velocity (5 Hz) were usually (but not always) available within 1 s after entering the ‘run state’ but often not accurate until 4–6 s later (Supplementary Fig. 7c–f).

To reduce noise, improve precision and increase temporal resolution in the position and velocity data, GPS and IMU measurements were fused as previously described4 using a 12-state extended Kalman filter55 followed by a Rauch–Tung–Striebel smoother56 written in MATLAB (The Mathworks Inc., MA, USA) (Supplementary Fig. 7a,b).

Definition of locomotion

There is no global definition of the terms hunting, hunt or chases, and in the context of this study, we define the terms as followed: hunting is all locomotion in the pursuit of food and encompasses multiple (mostly unsuccessful) hunts. A hunt is the locomotion in search (slow speed) and pursuit of a prey individual ending in a high-speed run (chase). We realised that some terms used might require a more extensive explanation due to the two-level analysis carried out to look at individual and pack performance. Terms such as ‘hunt’, for example, can be applied to an individual or the pack. At the pack level, it is often defined as the time from the end of one group chase to the end of the next group chase (group hunt). Since not all individuals necessarily participate in a group chase, we defined hunt on an individual basis, encompassing the time and distance from the end of one chase to the end of the next chase by the same individual. A hunt encompasses a slow-speed (search) and a high-speed (chase) phase. Hunters: individuals actively hunting i.e. chasing and killing prey; Followers: individuals accompanying hunters, but not actively pursuing prey (pups and yearlings up to a certain age); Ranging: all non-hunting locomotion such as border patrol or return to the den; Foraging success: Energy gain per day.

Data analysis

The recording at high-sample rate was triggered by the IMU and continued as long as the horizontal acceleration threshold was exceeded within a 5-s window. Overrun times between 5 and 20 s were implemented depending on the software update. Recordings at 5 Hz were restricted to 87 s and runs exceeding this time, while still showing speeds above 3 ms−1 were reconstructed based on 10-s data. We were unable to reconstruct the ending of 5.7% of the runs and assigned an ending randomly chosen out of the pool of reconstructed endings, assuming the distribution is representative for all runs exceeding 87 s. Eighty per cent of the runs lasted <87 s and only a few (2.4%) lasted significantly longer. The difference in median distances covered per run between reconstructed and non-reconstructed data was 2.7%.

Recorded activity lasting <5 s and never exceeding 3 ms−1 (instantaneous GPS velocity) were excluded from the analysis leaving a total of 2,026 runs to be analysed; 69 runs failed to produce converged Kalman-filtered results (speed going towards infinity) and were removed. Sufficient strides (at least three per run) were successfully extracted from 1,641 runs. In 4% of the cases (65 runs), a second run was triggered within 30 s of the first ending, and the two recordings were classified as a single run. The two runs were combined by linear interpolation of position and hence speed to fill the gap between them. A total of 1,551 runs (140,141 strides) contained at least one stride whose average speed exceeded 3 ms−1 (a speed determined to be slow canter). Runs exceeding a 6 ms−1 (galloping) stride speed threshold were classed as chases. We recorded 1,119 valid chases.

Individual kill rate

Individual chases were automatically classified as successful (ending in kill) if the animal remained for at least 5 min within a 50-m radius of the end of the chase. Automatically determined kills were validated by animating chases and observing pack behaviour such as converging to a supposed kill site and remaining there for at least 5 min (examples in ref. 32 and Supplementary Movie 1).

Daily distance travelled

Data collected under the different collar states were combined onto a single timeline to determine distance covered per day. Mean speed of each dog when moving slowly was taken as the straight-line distance/time between 5-min GPS fixes so is an underestimate if a tortuous route was followed.

Calculation of speed and stride frequency

All data analysis was carried out using MATLAB. Fore-aft acceleration was used to determine stride peak times and stride frequency. A band-pass Butterworth filter (4th order) was applied with cutoff frequencies of 1 and 8 Hz, and assuming a maximum stride frequency of 3 Hz, a peak detection function was used to detect peaks with a minimum duration of 0.33 s between peaks and a minimum peak height of 0.5 g. Maximum horizontal stride speed was derived from the Kalman-filtered and smoothed velocity averaged over strides.

Tangential acceleration, change of heading and centripetal acceleration over stride

Mid-stride times were used to calculate tangential (fore-aft) acceleration, centripetal (turning) acceleration and change in heading between strides. The displacement vectors between consecutive strides were then calculated:

and

Where is the two-dimensional position at sample/stride i.

Change of heading (Δθ i ) was calculated from the angle between the two vectors:

Angular velocity (ω i ) was derived by dividing the change of heading by the time between mid-stride positions ΔT:

The tangential or fore-aft acceleration (a t,i ) and centripetal acceleration (a c,i ) were then computed from mid-stride speeds v i :

To reduce outliers, tangential and centripetal acceleration were based on weighted stride speed and weighted heading rate taking the stride before and after into account in an approach described in ref. 4 and the companion cooperation paper.

Negative values for tangential acceleration indicate deceleration. Positive and negative values for centripetal acceleration indicate right (+) and left (−) turns. Positive and negative centripetal acceleration values are presented separately to show if there was a preference for left-hand or right-hand turns.

Run distance

Distances covered within individual runs were calculated by integration of the stride-averaged horizontal speeds over the duration of the run.

Total hunt distance was based on chase distance and the distance covered at lower speeds between the end of one chase to the end of the next chase during a morning and evening hunting session of 5 h each.

Maximum speed reliability

The maximum stride speed of 19 ms−1 was reported for the following reasons: (1) all individuals achieved this speed at least once, (2) 19.4 ms−1 is the 99th percentile from maximum stride speeds from all runs. (3) Using only maximum speeds from runs above 6 ms−1 the 99th percentile is 20.0 ms−1, taking the s.d. of 0.3 ms−1 for Kalman-filtered speeds (Supplementary Fig. 7b) and considering a maximum speed measurement error of three s.d.’s gives a maximum speed of 19 ms−1.

Preferred speeds

African wild dogs travel at distinct preferred speeds while ranging and hunting (outside of chases). To prove this concept, we displayed instantaneous velocity measures by the GPS module for 10-s GPS fixes (walking and trotting) during the morning hunting period. African wild dogs show a preference to move at speeds around 0.35 ms−1 (walking) and 2.5 ms−1 (trotting), with the measured trotting speed close to the 2.3 ms−1 predicted by the equation in ref. 36 of 1.09 × body mass0.222.

Path travelled by follower

As all members of our pack were active hunters, our prediction of the behaviour of followers is partly based on direct observation in other packs. We assume that hunters and followers travel approximately the same distance during the slow phase of the hunt since they travel together. During the chase, we used the instantaneous velocity to predict the length of the path travelled by a dog following versus the one chasing. The dog following would not have gone into 5 Hz GPS update chase mode due to the low speed, making it impossible to determine whether it follows the same path as the chasing dog or takes a more direct route. The difference in sample rate between running and non-running dogs prevented a direct comparison of distance travelled; however, the extent of the paths’ tortuosity can be estimated by comparing instantaneous GPS-derived velocity values with position-differentiated velocity. For this, both high-speed (5 Hz) and low-speed (10 s) data were averaged over 30-s windows. If dogs travelled in a straight line, then the instantaneous and differentiated velocity would be in agreement (on average). If dogs deviated more from a straight path, then instantaneous speed would tend to be higher than that predicted from distance covered over the 30-s period. We selected times when chases occurred and extracted the data from chasing dogs (5 Hz) as well as followers (10 s; Fig. 4b). The data were noisy due to GPS velocity error (Supplementary Fig. 7b), speed changes throughout stride and over 30 s, and variations in paths followed.