Using dead-reckoning to track movements and map burrows of fossorial species

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McShea, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4945336/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 04 Apr, 2025 Read the published version in Animal Biotelemetry → Version 1 posted 11 You are reading this latest preprint version Abstract Background Researching the movement patterns of fossorial animals and mapping of burrow systems presents a significant challenge due to the difficulty of direct observation and the limitations of most tracking systems to collect location fixes underground. A potential solution is using archival tags combined with dead-reckoning, a technique employed in nautical navigation to track animal movement underwater and through dense vegetation. However, this method has not yet been applied to the mapping of complex burrow systems in fossorial species. Our study aims to test this approach by using accelerometers and magnetometers attached to collars on prairie dogs (Cynomys spp.) The goal was to determine if dead-reckoning, based on vectors derived from speed and heading data, could accurately track prairie dog movements and, by extension, map the structure of their burrows. To evaluate this method, we deployed 12 tags on wild animals and recoded acceleration and magnetometer data at 40Hz and 16 Hz respectively.. Animals were forced to move through artificial burrows, plastic tubes, whose shape was known prior to tracking of wild movements. The former was used to validate dead reckoning trajectory estimation. We compared the accuracy of five techniques for estimating speed: Vectoral Dynamic Body Acceleration (VeDBA), Vectorial Static Body Acceleration (VeSBA), step count, and constant speed. Results Acceleration signals reliably indicated traveling behavior. Among the methods tested, the Vectorial sum of Dynamic Body Acceleration (VeDBA) proved to be the most accurate proxy for speed, with the smallest mean error. Additionally, the dead-reckoning process demonstrated sufficient precision, with an mean error of 15.38 cm, providing a clear representation of animal movements and the layout of burrows in free-roaming prairie dogs. The animals generally moved at speeds ranging from 0.01 to 1.42 m/s. Conclusions This work highlights the importance of dead-reckoning in studying space use by fossorial animals, essential for understanding how they interact with their environment, including vegetation and topography. Beyond environmental context, analyzing the specifics of animal movement—such as path tortuosity, speed, step lengths, and turn angles—is crucial for insights into species diffusion, foraging strategies, and vigilance. Additionally, research on immovable burrows offers a model for "city" construction, as prairie dog burrow networks, including ventilation, high-speed sections, predator escape routes, gathering spots, storage, and sleeping areas giving us a unique insight into the species societal needs and better predict the spread of disease. Black-tailed prairie dog dead-reckoning fossorial burrows Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Background Animal movement data are key to understanding behavioural ecology [ 1 , 2 ], habitat use [ 3 ], animal energetics [ 4 , 5 ], and disease ecology [ 6 ]. Conventionally, animal movement trajectories are constructed using sequential animal locations obtained using GPS [ 7 ], PTT or VHF telemetry [ 8 ]. However, these methods do not work or perform poorly in areas where the telemetry or GPS signal cannot be transmitted, such as marine environments [ 9 ], and terrestrial habitats under thick vegetation or underground (e.g. [ 8 , 10 ]). To mitigate this, some researchers have used ‘dead-reckoning’, a process that uses information on animal heading (magnetometer), speed (accelerometer), and change in height/depth (barometer) to reconstruct movement paths by vectors [ 11 , 12 ]. Examples of this include diving seabird [ 13 ], marine mammals [ 14 , 15 ] and forest mammals [ 16 ] but there has been few attempts to uncover burrowing species complex underground systems [ 17 ]. Previous studies of fossorial species' behaviour and movement primarily utilized accelerometer data to learn about underground speeds [ 18 ], behaviour [ 19 , 20 ], and energy expenditures [ 18 ]. Dead-reckoning has never been applied to track the fine-scale movements of fossorial species, despite the potential to reveal trajectories of animals inhabiting subterranean spaces where conventional methods are ineffective. Here, we test the application of dead reckoning to reconstruct the trajectories of a fossorial species using a case study of black-tailed prairie dogs ( Cynomys ludovicianus ). Among fossorial species, prairie dogs ( Cynomys spp) represent an ideal study species for evaluating the ability to use dead-reckoning to track subterranean movements. Represented by five species in North America, these relatively large (500-1,500g) ground squirrels and are considered keystone species in grassland ecosystems due to their ability to due to altering grass composition and movement of soil through creating and maintaining complex burrowing systems [ 21 , 22 ]. Prairie dogs are capable of excavating burrows systems comprised of interconnected burrows of 10–30 cm in diameter to depths of up to 5 meters below the surface [ 23 , 24 ]. These colonial animals occur at high densities of 10–35 individuals per hectare and can create aboveground burrow entrances of up to 325 burrows per hectare within colonies that encompass thousands of hectares [ 23 ]. These colonies of prairie dogs provide a source of prey, landscape heterogeneity, and subterranean habitat that supports a host of dependent species, including the endangered black-footed ferret ( Mustela nigripes ; [ 22 , 25 ]. Yet, despite the important ecosystem role prairie dogs serve in grassland communities, very little information has been acquired regarding the subterranean movements and behavior of these rodents, primarily due to the paucity of tracking technology capable of collecting underground data. Here, we tested the use of dead reckoning to reconstruct the movements of black-tailed prairie dogs. Our aims are (i) to examine various speed metrics used to derive distance for the dead-reckoning analysis when prairie dogs move along burrows; (ii) to examine whether tri-axial accelerometer data can define when prairie dogs enter and exit their burrows and (iii) to collect data from numerous individuals within one area to map out the prairie dog burrow systems in two dimensions. Methods Study Site Data collection for this study was conducted from 16th to 25th August 2023 at American Prairie (AP), a privately-owned wildlife conservation area located in Phillips County, Montana, USA. AP is within the North American Great Plains region. The vegetation is dominated by mixed-grass grassland, which consisted of western wheatgrass ( Pascopyrum smithii ), blue grama ( Bouteloua gracilis ), and needle and thread ( Hesperostipa comata ) grasses mixed with silver sagebrush ( Artemisia cana ) and scarlet globemallow ( Sphaeralcea coccinea ), wooly plantain ( Plantago patagonica ), and American vetch ( Vicia americana ; [ 26 ]. The study site consisted of a 200m by 200m plot of a 288-hectare colony of black-tailed prairie dogs ( Cynomys ludovicanus ; Longitude: -107.7520, Latitude: 47.7715). Elevation within the study site ranged from 718-723m. Biologgers To map the movements of prairie dogs using dead-reckoning, we designed and created a collar-based attachment method for affixing a Daily Diary (DD; http://wildbytetechnologies.com/tags.html ) circuit board to each animal (Fig. 1a). The DD was powered by a 50 or 60 milliampere (mAh) rechargeable lithium battery, both of which were contained within a 3D printed Anycubic resin housing attached to the bottom of the collar. The collar was made using a strap of 15mm x 150m biothane synthetic leather (The Strap Warehouse, Millersburg, Ohio, USA). The collar was fastened to the animal using a flat head bolt and nut attached using predrilled holes. Each collar also contained a solar powered GiPSy 6 GPS logger (TechnoSMart, Rome, Italy) and a second 100 mAh rechargeable lithium battery for the GPS. The GPS was not used in this study. The total weight of the collar and all components was ~ 16g making up 1–2% of the species’ body mass. Figure 1 – (A) Photo of the logging system deployed on prairie dogs; (B) with example of constructed tube run with trap connected. Photos were taken at American Praire, Phillips County, Montana, USA between the dates 16th to 25th August 2023. The DD consisted of a multi-sensor biologging unit [ 12 , 27 ], comprising tri-axial accelerometers and tri-axial magnetometers. The unit was programmed to collect both acceleration (at 40 Hz) and magnetic field intensity (16 Hz) in all three orthogonal axes. The logger recorded the data on 128 kilobyte internal memory, allowing up to 8 days of continuous data. On the day of captures, the device was switched on and the DDs were calibrated by engaging them in a defined set of movements, conceived to provide proper 3-dimensional coverage for the G- and M-spheres [ 28 ]. Captures and deployment We captured prairie dogs from 15–25 August 2023 using a matrix of 125 live traps (6x9x24in Tuffy 24; Tru Catch Traps, Belle Fourche, South Dakota, USA) distributed through our study site. We labelled and recorded the location of each trap using a handheld GPS unit. We baited traps with sweet feed grains (MannaPro, St. Louis, Missouri, USA) and set traps open each morning and evening for a period of 4 hours. We visually examined each trap once an hour to ensure captured prairie dogs were not exposed to high temperatures. We transported captured adult prairie dogs weighing > 800g in the traps to nearby shade for processing. Juvenile prairie dogs < 800g were immediately released. We recorded the weight, age, and sex of each animal. We briefly restrained each animal to attach the biologger collar and record neck circumference. We then marked each animal using non-toxic hair dye along the back with a unique pattern for each individual. We returned the animal to the trap and monitored for approximately 15 minutes to ensure the collar remained in position and did not cause undesirable behavioral effects (ie., excessive scratching or lethargy). Before each collared prairie dog was released, we performed a series of trials designed to provide fine-scale movement and location data over a verifiable path to compare the accuracy of the dead-reckoning process used in this study. We constructed “tube runs” by attaching straight and 45 o elbow sections of 120 mm diameter, ventilated, and transparent plastic tubing (Katee Product Inc, Chilton, WI, USA) together to create various shapes and configurations of total lengths between 1–3 m (Fig. 1b). We positioned each tube run such that one end was within 25 cm of the closest burrow to the location of capture of each animal. At the other end, we opened the door to the trap containing each collared prairie dog and allowed the animal to freely exit the trap and into the tube run. We recorded videos of the movement of each individual from the cage, through the tube run, and out into the burrow using smartphones. Camera traps Across the study area, we deployed 87 motion-triggered cameras (Reconyx HyperFire 2, Reconyx, Holmen, WI, USA). We programmed the cameras to take 30-second videos with no delay, anytime a motion was detected throughout the time period when collars were attached to prairie dogs. We positioned camera traps such that the field of view captured in recorded videos included all burrow entrances within 20 m of the location at which the prairie dog was released after the collar was attached. We installed each camera at a height of 50 cm above the ground on a metal rebar stake positioned 2.0-2.5 m from the burrow entrance. Videos were recorded on a 32 gigabyte memory card. We replaced memory cards and camera batteries every 2–4 days to ensure sufficient memory and power remained. To aid in the video review process described below, we recorded the location of each burrow within the field of view of each camera at 20 cm horizontal accuracy using a high-precision GPS receiver (Catalyst DA2, Trimble, Sunnyvale, CA, USA). We identified the position of each burrow in the recorded videos by recording ourselves holding a sign indicating a unique identification number while standing at each burrow. Recaptures After 5 days, we initiated efforts to recapture all collared prairie dogs using the same matrix of traps. We followed the same baiting and trap setting protocol as described above for recapturing all animals. Once a collared prairie dog was recaptured, we performed a second tube run trial before removing the collar. In this case, the tube run was positioned between the trap containing the prairie dog and an empty trap at the other end positioned to safely contain the prairie dog after the animal moved freely through the tube run. We again recorded videos of the movement through the tube runs using smartphones. Once this second tube run was completed, we briefly restrained the prairie dog, removed the collar, and collected data on weight and condition of the animals. The animal was then released at the capture location. Dead-reckoning: comparing speed metrics Dead-reckoning analysis was undertaken to produce paths consisting of 1 location per second for the prairie dogs by taking magnetometry data in tandem with the accelerometers to derive heading [ 11 , 29 ] and assessing using several methods to derive speed, and therefore distance including; (i) Vectoral Dynamic Body Acceleration (VeDBA), (ii) Vectorial Static Body Acceleration (VeSBA), (iii) step count and (iv) constant speed. Each method is explained below: VeDBA VeDBA is the most common metric for speed for the dead-reckoning process [ 30 ] and calculated using; VeDBA = \(\:\sqrt{{\left(DBAX\right)}^{2}+{\left(DBAY\right)}^{2}+\:{\left(DBAZ\right)}^{2}}\) (1) where DBA is the dynamic acceleration for the three axes (X, Y and Z). The dynamic acceleration was calculated by subtracting static acceleration (the raw acceleration smoothed with a running mean over 2 seconds [ 31 ] from the raw acceleration. This removes most of the gravitational influence the tag is undergoing to provide a metric that reflects the dynamism of animal movement [ 32 ]. A VeDBA threshold or window method [ 11 , 32 ] assumes that low-values of VeDBA occur when animals are not travelling, e.g. standing, sitting or lying, or extremely high, short-term (< 5 seconds) VeDBA values when animals shake themselves or roll rapidly. Thus, to identify travelling, we implemented a Boolean rule that highlighted when VeDBA values lay within thresholds. We then implemented dead-reckoning when these conditions were met. These window values are presumed to vary between species and tag attachment [ 33 ] so travelling behaviour should be ground-truthed with observations when possible. In the case of prairie dogs, this threshold was set between 0.1 and 1.5 following observation of the tube runs undertaken by the animals following release. VeSBA VeSBA incorporates all three acceleration axes like VeDBA, but instead removes the dynamism of the animal movement and is particularly valuable when animals ‘pull g ’. VeSBA is derived via; VeSBA = \(\:\sqrt{{\left(SBAX\right)}^{2}+{\left(SBAY\right)}^{2}+\:{\left(SBAZ\right)}^{2}}\) (2) where SBA is the static acceleration in the three axes (X, Y and Z), calculated by running a running mean smoothing window over two seconds across each acceleration axis [ 31 ]. We used a VeSBA window approach in the same way as we did for VeDBA (see above). Step definition One of the most obvious delineators of traveling behaviour and speed is the identification of steps (or strides), assuming they can be defined within the tag data. A particular form of analysis based on a Boolean method, the Lowest Common Denominator (LoCoD) approach, can be used to define individual steps within an animal’s movement [ 34 ]. This approach looks for specific changes and defined patterns in acceleration signals, that occur during movement, that are predictable with each step, and which only occur during traveling behaviour. In the use of the LoCoD approach, we attempted to identify and quantify steps (Fig. 2) and then used a step count to construct a step count vs speed relationship coefficient to quantify distance for dead-reckoning. To implement this, the tube run videos were synchronized with their respective DD data to define the sensor-dependent features of steps. Following this, we produced an algorithm within the Daily Diary Movement Trace (DDMT) software [ 27 ], which implemented the LoCoD method, and searched for steps within any prescribed animal movement data [ 34 ]. For prairie dogs, we calculated the rate of change of acceleration (jerk) across 3 sequential x-axis data points (corresponding to 0.075 s). The quantification of steps had two conditions where x-axis differential (see above) surpassed 0.2 g , and VeDBA smoothed (across half a second) was higher than 0.25 g. To mark individual steps, a blind spot was implemented following identification of a step so that strides were only marked once despite having variable stride lengths [ 34 ]. Our optimal blind spot lasted 5 sequential events (0.125 s). Figure 2. Example of prairie dog movement and stationary behaviour manifest in the three acceleration channels in addition to smoothed rate of change data in the x-axis (heave). The black dots show individual steps marked. No movement definition (e.g. VeDBA threshold) was required as the steps contributed to travel. Acceleration taken from 1 individual during movement/stepping within a tube run, data shown is 12 seconds and collected at American Prairie, Phillips County, Montana, USA between the dates 16th to 25th August 2023. Constant speed The last method tested as a proxy for speed was estimating a constant speed. The metric was taken from the speed value from each tube run. The constant speed in this case would be the distance between the first and last verified point divided by the time taken to complete the tube run. Assessing different speed methods and accuracy using observed tube runs To evaluate if dead-reckoning analysis might be viable for fossorial animals whose movement is constricted by the burrows, we processed the data from the tube run by examining the recorded videos frame by frame to determine ‘true’ location on a second-by-second basis (position determined to the nearest 10 cm). First, we used video editing software (Adobe Premium Pro, Adobe, San Jose, CA, USA) and reviewed the video at a 100 frames per second, from there we could located the position of the prairie dog, and specifically, the collar worn by the prairie dog, at 1-second intervals beginning from the start of each video. We replicated the configuration of each tube run to scale in QGIS version 3.24 by creating a vector shapefile including the dimensions and arrangement of each segment of tube. We then created a point shapefile where points placed along the replicated tube runs in our vector shapefile matched the position of the prairie dog within the tube run at each 1-second interval as observed in the videos. We labelled these ‘true’ locations with the interval number to be used for assessing the accuracy of the dead-reckoning of the movement path of each animal through the tube run. The distance between the tube run location and the dead-reckoned location was calculated using the following equation: $$\:Distance=a\text{cos}\left(\text{sin}{Lat}_{DR}\bullet\:\text{sin}{Lat}_{TR}+\text{cos}{Lat}_{DR}\bullet\:\text{cos}{Lat}_{TR}\bullet\:\text{cos}\left({Lon}_{TR}-{Lon}_{DR}\right)\right)\bullet\:6371$$ 3 This calculation was carried out using the package ‘fossil’ within R [ 35 ]. The same package was used to calculate animal travel speed. Defining entering a burrow, moving underground, and burrow depth To map out the prairie dog burrow system, underground movement needs to be defined. We used the tube runs and camera trap video footage synchronised with the acceleration data to derive a LoCoD-based method (see above) to quantify when animals entered burrows. We used the videos recorded using the array of camera traps we deployed to identify the time, location, and movement (entering a burrow or exiting a burrow) of our collared prairie dogs. We reviewed each video and recorded the time stamp and the location using the burrow identification process described above each time a collared prairie dogs were observed entering or exiting a burrow. We identified individual prairie dogs based on the unique dye-mark given during capture. The rule for entering a burrow used was; when the animal pitch angle (derived from the acceleration x channel [ 31 ]) smoothed (using a running mean across 1 second of data) was less than − 20° and VeDBA smoothed over 0.5s was greater than 1.2 g , then ‘mark as a descent into a burrow’ (Fig. 3). Figure 3 – Tri-axial acceleration data, smoothed VeDBA and smoothed pitch angle of 3 example prairie dogs descending into their burrows. Acceleration traces taken from 3 individual when enter burrow. Behaviour is categorized using video footage after tunnel run or camera trap footage. Data shown is a total of 9 seconds and collected at American Prairie, Phillips County, Montana, USA between the dates 16th to 25th August 2023. The quantification for exiting a burrow utilized a differential channel where the rate of change of pitch angle smoothed (across 1 second) was calculated across a second. The rule had two conditions where the difference in pitch angle smoothed was greater than 30° and VeDBA smoothed (across half a second) was more than 0.4 g (Fig. 4). Another time-based parameter was used where any marked behaviour that last less than half a second was removed to mitigate standing up and some posture changes from causing false positives. Figure 4 – Tri-axial acceleration data, smoothed VeDBA and smoothed pitch angle of 3 example prairie dogs exiting their burrows. Acceleration traces taken from 2 individual when enter burrow. Behaviour is categorized using video footage from camera trap footage. Data shown is a total of 9 seconds and collected at American Prairie, Phillips County, Montana, USA between the dates 16th to 25th August 2023. Applying dead reckoning to ‘free roaming’ data We took horizontal animal movement to map out the burrows defined by the dead-reckoned movements of individuals starting from above ground verified points for a period of time informed by the drift model. We defined verified points as times when the true aboveground location of the prairie dog could be determined because the animal appeared in the camera trap array at a recorded burrow. The dead-reckoned paths were then filtered based on where the prairie dogs had entered the burrow located at each verified point. All spatially relevant underground locations were super-imposed onto one another to estimate the location of underground burrows. A combination of DDMT [ 27 ] and R [ 35 ] with the ‘ggamp’ package was used to visualise and map out the burrows. Revisit and residence time analysis was conducted using the ‘recurse’ package. A 1-meter radius circle was moved along the dead-reckoned underground track, and a 'revisit' was recorded whenever the animal left and then re-entered the circle. Additionally, if the animal remained within the circle, the total time spent at that location was accumulated. Results Dead-reckoning: comparing speed metrics Comparing the different speed methods using DD data obtained from 12 prairie dogs and 23 tube runs of up to 4 m in length, we found the VeDBA metric for speed gave location accuracy within a 20 cm margin of error for 75% of the time across all tube runs. The other methods; Step definition, VeSBA and constant speed had higher levels of error within the desired 20 cm error margins occurring 52%, 42% and 42% of the time across all tube runs, respectively (Fig. 5). Speed coefficients varied between runs (0.009 to 0.042 when VeDBA was used for speed (Supplementary table 1 ) with this variation being the result of individual differences. Figure 5 – Box-whisker plot of the distance errors based on various speed estimates (constant speed, step definition, VeDBA and VeSBA – see text) derived from prairie dogs moving along specified tubes up to 4 m long before entering their burrow. Data taken from 23 tube runs across 12 individuals, data collected at American Prairie, Phillips County, Montana, USA between the dates 16th to 25th August 2023. All prairie dog tube runs were visualised by overlaying the verified points of their positions over the dead-reckoned points with VeDBA being used for speed (Fig. 6). The dead-reckoned estimates of position mostly matched well (15.38 cm mean error), with the largest errors due to quick movements (39.76 cm largest error). Estimation of tube run underperformed when animals speed exceeded 0.38 m/s (see supplmentery Fig. 1). Figure 6 – Comparison of dead-reckoned paths with verified points during prairie dog movement through transparent pipes used to simulate burrow s (see text). Data taken from 23 tube runs across 12 individual, data collected at American Prairie, Phillips County, Montana, USA between the dates 16th to 25th August 2023. Defining entering a burrow Our metric for detecting when a prairie dog entered the burrow system (Fig. 3) successfully identified 22 out of 24 burrow entrances. However, it was less effective at detecting when the animals exited, correctly identifying 4 out of 6 exits (Fig. 4). The tube runs allowed us to validate our method for defining movement under controlled conditions (Fig. 2), achieving 100% accuracy in those scenarios. We only included animals that did not display behaviors that could move the tag during the tube runs, meaning our experimental protocol did not account for non-translocation movement. Applying dead reckoning to ‘free roaming’ data We plotted 31 hours of underground movements for 5 individuals within 2 hours of a verified point (Fig. 7). Across these individuals we successfully mapped out 382m of burrows with further analysis showing a ~ 1% space shared with space use of 4.78 km 2 with max burrow length of 10m (Fig. 7). Revisit analysis indicated the time spent in various location pockets underground and how regularly the burrows were used (Fig. 8). This data from one individual was not sub sampled based on activity showing long periods (hours) spent in one location being inactive or not displacing location based on our definitions (see above). Figure 7 – Location of prairie dog burrows as deduced using dead-reckoning on 5 individuals with (a) Prairie dog 4 and 5 show in blue and purple respectively, tagged on Enrico as one study site, (b) locations of prairie dogs 1, 2 and 3, shown in red, yellow and green respectively, individuals tagged on Box elder, (c) shows how both sites are situated within American Prairie. Data shown is a total of 31 hours across 5 individuals and collected at American Prairie, Phillips County, Montana, USA between the dates 16th to 25th August 2023. Figure 8 – Location of prairie dog burrows as deduced using dead-reckoning on 1 individual (a) shows the total time spent according to location and (b) shows the number of revisits within 1m 2 to particular sites. Data shown is a total of 20 hours for 1 individuals and collected at American Prairie, Phillips County, Montana, USA between the dates 16th to 25th August 2023. Dead-reckoned locations of the same individual, estimated to use the same tunnel with the quantification of ‘entering burrow’ determining the estimate burrow entrance (Fig. 9). Figure 9 – Multiple superimposed tracks of prairie dogs with respect to a verified position (a camera) to illustrate how multiple tracks may function together to provide a more precise estimate of burrow space. Data shown is a total of ~ 4 minutes for 1 individuals and collected at American Prairie, Phillips County, Montana, USA between the dates 16th to 25th August 2023. Discussion While dead-reckoning is well documented for aerial, aquatic and (above ground) terrestrial species [ 10 , 36 , 37 ], there are substantial unknowns in fossorial animals. These unknowns include the extent to which traveling behaviour can be reliably identified and how good metrics for speed, such as VeDBA [ 11 ], apply to animals whose movement is constrained within burrows. This is a first attempt to determine the movement of a fossorial species while underground, and in the process, we derive information on the structure of the burrows. Speed proxies We examined four metrics as proxies for speed and found that VeDBA produced a lower range and mean of error than the other three methods (Fig. 5). However, our defined burrow runs may be overly generous to the VeDBA metric since individuals using our system either moved or were stationary, not engaging in any non-movement activity, such as scratching, that produces an appreciable VeDBA signal. Nonetheless, the value of VeDBA as a speed metric has been stressed in the literature for terrestrial animals [ 11 , 29 ], and it is encouraging that it also seems to work well underground. Our attempts to use VeSBA as a proxy for speed were markedly worse than VeDBA (Fig. 5), most likely because VeSBA tends to be most useful for indicating high speed cornering [ 30 , 38 ]. Whether cornering or not, in any case the movement speed of prairie dogs in the burrows seems to be generally less than 0.15 m/s where any VeSBA signal is likely to be minimal. Constant speed underperformed too, even though, for prairie dogs, the expectation was that, in the confined space of the burrow, the speed would change little. However, within our tube run experiments, the rodents displayed a range of speeds (0.01 m/s to 1.42 m/s) that immediately indicate the expected errors. Finally, step definition was evaluated because many animals have increases in speed accompanied by increases in stride frequency [ 39 , 40 ]. However, the mean error using this metric, although less than VeSBA or constant speed values, was higher than for VeDBA (Fig. 5). One explanation is that prairie dogs change both step frequency and stride length with speed, as do many mammals [ 41 ]. If the conditions for this were precisely defined for prairie dogs, step frequency could still be potentially used to derive speed. More work is needed to elucidate this. It may also be that the ventral mounting of the DD on the collar meant that the unit occasionally touched the ground during travelling, putting in false steps and reducing the accuracy of the approach. Finally, it may be that step resolution would be increased at higher sampling frequencies because the waveform produced by the steps becomes clearer. Again, more work is needed to address this. Burrow use detection and non-travelling movement Our starting point for mapping burrow use was detection of descent into the burrow system using the change in pitch. Although our work with the wild prairie dogs meant that the cameras could identify when the animals descended into their burrow system, it is unrealistic to assume under most field deployments that a camera could monitor every burrow entrance. As such, it is important to be able to identify when the prairie dogs enter and exit their burrows. Our Boolean rule involving pitch performed well for this for the descent, detecting 92% of burrow entries. However, although no misclassifications occurred within camera trap footage, some free roaming prairie dogs were not observed on all occasions of burrow entry so we could not calculate a false positive rate. Exit from the burrows was less reliable. Tags incorporating light sensors and/or barometric pressure sensors would probably make entrance and exit definition more reliable. Any estimate of prairie dog movement using dead-reckoning needs to be able to differentiate between non-translocational body movement, such as shaking or scratching, and genuine travel. Since the animals in our tube runs only engaged in either travel or being stationary, our protocol could not assess this. However, observations of animals in camera trap footage and the feild indicate that such non-translocational movements are very transient, rarely lasting more than 5 seconds (Supplementary. Table 2). Such transient movements can easily be identified and precluded using Boolean rules on the acceleration data [ 34 , 42 ]. Ultimately, unless these behaviours occur frequently, their transient nature means that they would not cause substantial deviations in dead-reckoned paths. Dead-reckoning This work indicates that dead-reckoning has substantial potential for tracking fossorial species, providing information on their speeds and movements, and providing two dimensional location estimates when the animal is beneath the surface. This is highlighted by the detailed manifest by the high-resolution paths derived using sub-second acceleration data, even though verified points may be up to 2 hours apart (Fig. 7). Previous work has shown the importance of verified points, and the frequency with which they should be taken, on dead-reckoned location estimates [ 43 ]. This frequency, which need not take just the form of GPS fixes [ 43 ], varies with animal lifestyle and is critical for minimizing errors. Typically, these verified positions need to occur every 3 h in terrestrial animals [ 36 ]. We find it is more appropriate to talk about verified points that should occur after specific distances rather than times, because it is in the process of moving that errors accumulate. Our data on the general activity patterns from the accelerometer data of the collared prairie dogs used in this study indicate that they spend ~ 27% of their time traveling. If their traveling speed is approximately ~ 0.1 m/s, a two-hour window for determining movements amounts to a distance of approximately 720m. But it is pessimistic to assume that during such movement, the animals continually travel along new trajectories, as do many non-fossorial animals [ 29 , 36 ]. Instead, fossorial animals are constrained to their burrows and so are likely to be back-tracking in a matrix that is more akin to a city street system than it is to over-ground movement of animals which may choose to move in any direction. In this respect, the more localised the burrow system, the more constrained the movements of its inhabitants will be. Therefore, we thus suggest that dead-reckoning the movements of fossorial species can benefit in accuracy from the spatial restriction that the burrows impose. The constraints of the burrows can also be used to refine estimates of the burrow positions in space. A single, or multiple, animals departing from a verified position in one burrow matrix, will have limited choice of movement. Many individuals (or the same individual) will use the same path so that, if tracks are superimposed, the multiple routes can be used to construct a more accurate picture of the underground network (Fig. 8, Fig. 9). In addition to this, we suggest that unusual features within defined burrow sections, such as a point at which the burrow almost doubles back on itself, can be used as verified positions (once the location of this feature has been defined by multiple individual tracks – see above). Thus, there may be circumstances where individuals of fossorial species can have their locations ‘verified’ even if they do not surface to trigger the standard verified position system (camera traps in our case). Although our approach gives some information of the movements of prairie dogs in their burrows, it is naïve to assume that we can map out all the connections in their underground complexes using this method. Notably, we can only apply this approach to the parts of a burrow systems actually used by collared animals as unused burrows or portions of burrows will be excluded during mapping initiatives based purely on animal movements. Including more collared individuals, and for longer periods of time, should increase the likelihood of including greater portions of the burrow network in mapping using this technique. Likewise, independent mapping methods, such as ground penetrating radar, would improve the resolution (as well as helping correct for dead-reckoning errors). Ground penetrating radar has been shown to be a powerful methodology for elucidating spaces underground [ 44 , 45 ] but it does not work under all conditions [ 44 ] and gives no information on the function of burrows. In this respect, it is fortuitous that the dead-reckoning protocol described here uses accelerometers because these sensors are used widely to determine and quantify animal behaviour, including sleeping, feeding, fighting etc [ 28 , 46 , 47 ]. As such, the combination of location via dead-reckoning with behaviour should give important information regarding the extent to which particular behaviours are associated with specific spaces and perhaps even indicate how many individuals occupy the underground spaces simultaneously. This latter element has particular value in consideration of disease transmission such as sylvatic plague [ 48 , 49 ]. Next steps While we used animal pitch to identify when prairie dogs descended into their burrows and hinted at the potential to map burrow systems in 3D, we believe that the method described here may not provide the necessary accuracy. However, further testing is required to draw any definitive conclusions. The combination of pitch with barometric pressure as measured onboard the DD (some sensors can resolve height within 10 cm) could be a very powerful approach for taking the burrow system into three dimensions. Depth of the burrows is of interest as likely plays a crucial role in regulating temperature fluctuations [ 50 ] and influencing oxygen flow, both of which are presumed to impact the overall quality of the underground environment. The system that we tested used cameras to provide verified points, which proved effective. However, cameras cannot be reliably used cover all burrow entrances occurring within a prairie dog colony and camera data is time consuming to assess. Another method that might prove simpler is using rare earth magnets at intervals above the burrows (assuming that the burrow position is known with respect to the surface). Such magnets would produce a spike in the vectorial sum of the magnetometer data as the animals passed them, defining that position [ 17 ]. Indeed, the use of variously strong magnets, which will produce a correspondingly large vectorial sum peak, might also help refine this approach. Otherwise, animals could be fitted with GPS or VHF units on their collars although test would have to be carried out to determine if the systems provide the necessary accuracy. Conclusions Overall, this work underpins the importance of dead-reckoning as a solution for examining space use in fossorial animals. This is relevant for understanding how the animals relate to environmental space (as e.g. determined by vegetation surveys [ 51 , 52 ] and topography, etc [ 53 , 54 ]). However, the specifics of the paths taken by animals themselves (tortuosity [ 55 ], speed [ 38 , 56 ]), step lengths [ 57 ] and turn angles [ 5 , 58 ], etc.) are also important for understanding a suite of animal movement issues, such as species’ diffusion [ 4 ], food location strategies [ 59 , 60 ] and vigilance [ 61 , 62 ]. But the work on immovable burrows has further value in providing a template for ‘city’ construction. As with humans, we expect the burrow network developed by prairie dogs to reflect the needs of their society, incorporating needed attributes such as ventilation, high-speed sections, escape from predator sections, aggregation spots, storage and sleeping spots, providing a comprehensive network that caters for the complex needs of their hidden societies. Abbreviations AP - American Prairie DD - Daily Diary DDMT - Daily Diary Movement Trace GPS - Global Position System LoCoD – Lowest Common Denominator mAh – Milliampere PTT - Platform Transmitting Terminals VHF – Very High Frequency 3D – Three dimensions Declarations Ethics approval and consent to participate This study was conducted in accordance with the [name of the relevant guidelines or ethical standards, e.g., "Ethical Principles of Animal Experimentation"]. Ethical approval for this research was obtained from the Smithsonian Institution’s Animal Care and Use Protocol under approval number SI-23012. All necessary permits were acquired for the described field studies, and the animals involved were handled according to the guidelines established by Smithsonian Institution’s Animal Care and Use Protocol SI-23012 and Montana Fish, Wildlife, and Parks Scientific Collector’s Permit # 2024-022-W . Where applicable, written consent was obtained from all participants or their legal guardians prior to inclusion in the study. Consent for publication All authors consent to the publication of this review. Availability of data and materials The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding This research was supported by the the Paul G. Allen Family Foundation under grant number(s) 505321-. The funding body/bodies had no role in the design of the study, data collection, analysis, interpretation, or in the writing of the manuscript. Authors' contributions JR contributed to the conception, design of the work, acquisition and analysis of data, interpretation of data, drafted the work and substantively revised the work JB contributed to the conception, design of the work, acquisition and analysis of data, interpretation of data and substantively revised the work IN contributed to the design of the work, acquisition and analysis of data, interpretation of data and substantively revised the work RPW contributed to the conception, interpretation of data and substantively revised the work WM contributed by substantively revised the work HS contributed to the conception, acquisition and analysis of data and substantively revised the work Acknowledgements We would like to express our gratitude to Dr Mark Holton for assisting with tag construction and the developments of the logger; Phil Hopkins for invaluable help in designing housings for the devices. 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Additional Declarations No competing interests reported. Supplementary Files UsingdeadreckoningtotrackmovementsandmapburrowsoffossorialspeciesFORAnimalBiotelemetrySUP.docx Cite Share Download PDF Status: Published Journal Publication published 04 Apr, 2025 Read the published version in Animal Biotelemetry → Version 1 posted Editorial decision: Revision requested 23 Dec, 2024 Reviews received at journal 12 Dec, 2024 Reviews received at journal 29 Nov, 2024 Reviewers agreed at journal 11 Nov, 2024 Reviewers agreed at journal 10 Nov, 2024 Reviews received at journal 25 Oct, 2024 Reviewers agreed at journal 19 Sep, 2024 Reviewers invited by journal 27 Aug, 2024 Editor assigned by journal 21 Aug, 2024 Submission checks completed at journal 20 Aug, 2024 First submitted to journal 20 Aug, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Photos were taken at American Praire, Phillips County, Montana, USA between the dates 16\u003c/em\u003e\u003csup\u003e\u003cem\u003eth\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e to 25\u003c/em\u003e\u003csup\u003e\u003cem\u003eth\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e August 2023.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"PDFIGURE1.png","url":"https://assets-eu.researchsquare.com/files/rs-4945336/v1/e2274b3c52f2fb11a11a4be4.png"},{"id":64760869,"identity":"986efad8-1458-4d9e-9051-b8f5e0f804b6","added_by":"auto","created_at":"2024-09-18 13:03:21","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1445165,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eExample of prairie dog movement and stationary behaviour manifest in the three acceleration channels in addition to smoothed rate of change data in the x-axis (heave). The black dots show individual steps marked. No movement definition (e.g. VeDBA threshold) was required as the steps contributed to travel. Acceleration taken from 1 individual during movement/stepping within a tube run, data shown is 12 seconds and collected at American Prairie, Phillips County, Montana, USA between the dates 16\u003c/em\u003e\u003csup\u003e\u003cem\u003eth\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e to 25\u003c/em\u003e\u003csup\u003e\u003cem\u003eth\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e August 2023.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"PDFIGURE2.png","url":"https://assets-eu.researchsquare.com/files/rs-4945336/v1/5a080692557653507f9ec0b4.png"},{"id":64760872,"identity":"3be4b85d-5483-4d9a-8a34-bc8d49311f1a","added_by":"auto","created_at":"2024-09-18 13:03:21","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1434395,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eTri-axial acceleration data, smoothed VeDBA and smoothed pitch angle of 3 example prairie dogs descending into their burrows. Acceleration traces taken from 3 individual when enter burrow. Behaviour is categorized using video footage after tunnel run or camera trap footage. Data shown is a total of 9 seconds and collected at American Prairie, Phillips County, Montana, USA between the dates 16\u003c/em\u003e\u003csup\u003e\u003cem\u003eth\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e to 25\u003c/em\u003e\u003csup\u003e\u003cem\u003eth\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e August 2023.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"PDFIGURE3.png","url":"https://assets-eu.researchsquare.com/files/rs-4945336/v1/4edfd137ef048d642822821b.png"},{"id":64761776,"identity":"dc8f5e4c-75a9-4ffe-b6ec-6fe6e4947644","added_by":"auto","created_at":"2024-09-18 13:11:20","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1755061,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eTri-axial acceleration data, smoothed VeDBA and smoothed pitch angle of 3 example prairie dogs exiting their burrows. Acceleration traces taken from 2 individual when enter burrow. Behaviour is categorized using video footage from camera trap footage. Data shown is a total of 9 seconds and collected at American Prairie, Phillips County, Montana, USA between the dates 16\u003c/em\u003e\u003csup\u003e\u003cem\u003eth\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e to 25\u003c/em\u003e\u003csup\u003e\u003cem\u003eth\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e August 2023.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"PDFIGURE4.png","url":"https://assets-eu.researchsquare.com/files/rs-4945336/v1/a93764309ee9798ea6f59663.png"},{"id":64760870,"identity":"8a060d5b-b9b7-41f0-b0b3-000f4a3044e0","added_by":"auto","created_at":"2024-09-18 13:03:21","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":444252,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eBox-whisker plot of the distance errors based on various speed estimates (constant speed, step definition, VeDBA and VeSBA – see text) derived from prairie dogs moving along specified tubes up to 4 m long before entering their burrow. Data taken from 23 tube runs across 12 individuals, data collected at American Prairie, Phillips County, Montana, USA between the dates 16\u003c/em\u003e\u003csup\u003e\u003cem\u003eth\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e to 25\u003c/em\u003e\u003csup\u003e\u003cem\u003eth\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e August 2023.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"PDFIGURE5.png","url":"https://assets-eu.researchsquare.com/files/rs-4945336/v1/bb540fac9dc688fada665046.png"},{"id":64760868,"identity":"90846854-dfdb-40c9-a3dc-077aa6f36e1e","added_by":"auto","created_at":"2024-09-18 13:03:20","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1931234,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eComparison of dead-reckoned paths with verified points during prairie dog movement through transparent pipes used to simulate \u003c/em\u003eburrow\u003cem\u003es (see text). Data taken from 23 tube runs across 12 individual, data collected at American Prairie, Phillips County, Montana, USA between the dates 16\u003c/em\u003e\u003csup\u003e\u003cem\u003eth\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e to 25\u003c/em\u003e\u003csup\u003e\u003cem\u003eth\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e August 2023.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"PDFIGURE6.png","url":"https://assets-eu.researchsquare.com/files/rs-4945336/v1/aa29da10765fa0b7e5f0e039.png"},{"id":64760873,"identity":"e753a173-a5c8-47bc-a36d-16a897e47e0f","added_by":"auto","created_at":"2024-09-18 13:03:21","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":3201369,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eLocation of prairie dog burrows as deduced using dead-reckoning on 5 individuals with (a) Prairie dog 4 and 5 \u0026nbsp;show in blue and purple respectively, tagged on Enrico as one study site, (b) locations of prairie dogs 1, 2 and 3, shown in red, yellow and green respectively, individuals tagged on Box elder, (c) shows how both sites are situated within American Prairie. Data shown is a total of\u0026nbsp; 31 hours across 5 individuals and collected at American Prairie, Phillips County, Montana, USA between the dates 16\u003c/em\u003e\u003csup\u003e\u003cem\u003eth\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e to 25\u003c/em\u003e\u003csup\u003e\u003cem\u003eth\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e August 2023.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"PDFIGURE7.png","url":"https://assets-eu.researchsquare.com/files/rs-4945336/v1/154633fe531ffc4d5d795ff6.png"},{"id":64760875,"identity":"7fa5b717-9f53-4368-8c9e-d1191fe58086","added_by":"auto","created_at":"2024-09-18 13:03:21","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":3292545,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eLocation of prairie dog burrows as deduced using dead-reckoning on 1 individual (a) shows the total time spent according to location and (b) shows the number of revisits within 1m\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e to particular sites. Data shown is a total of\u0026nbsp; 20 hours for 1 individuals and collected at American Prairie, Phillips County, Montana, USA between the dates 16\u003c/em\u003e\u003csup\u003e\u003cem\u003eth\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e to 25\u003c/em\u003e\u003csup\u003e\u003cem\u003eth\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e August 2023.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"PDFIGURE8.png","url":"https://assets-eu.researchsquare.com/files/rs-4945336/v1/27666ddce6ff7db7555209ee.png"},{"id":64761777,"identity":"d60c3ec9-f442-4fcf-92c6-2772a2d41e29","added_by":"auto","created_at":"2024-09-18 13:11:21","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":1098243,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eMultiple superimposed tracks of prairie dogs with respect to a verified position (a camera) to illustrate how multiple tracks may function together to provide a more precise estimate of burrow space. Data shown is a total of\u0026nbsp; ~4 minutes for 1 individuals and collected at American Prairie, Phillips County, Montana, USA between the dates 16\u003c/em\u003e\u003csup\u003e\u003cem\u003eth\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e to 25\u003c/em\u003e\u003csup\u003e\u003cem\u003eth\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e August 2023.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"PDFIGURE9.png","url":"https://assets-eu.researchsquare.com/files/rs-4945336/v1/a6b77e346d4f37f498df1772.png"},{"id":80082708,"identity":"8b65ea17-e0ea-4d98-b1db-939e34a2761d","added_by":"auto","created_at":"2025-04-07 16:09:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":31464354,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4945336/v1/7100c2d8-8ff4-4ecf-8663-ffde0800900b.pdf"},{"id":64760874,"identity":"d684c707-95ec-4db8-962a-d3f7cb2eec99","added_by":"auto","created_at":"2024-09-18 13:03:21","extension":"docx","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":148196,"visible":true,"origin":"","legend":"","description":"","filename":"UsingdeadreckoningtotrackmovementsandmapburrowsoffossorialspeciesFORAnimalBiotelemetrySUP.docx","url":"https://assets-eu.researchsquare.com/files/rs-4945336/v1/ebac5e60c1d3c056f7b39578.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Using dead-reckoning to track movements and map burrows of fossorial species","fulltext":[{"header":"Background","content":"\u003cp\u003eAnimal movement data are key to understanding behavioural ecology [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], habitat use [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], animal energetics [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], and disease ecology [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Conventionally, animal movement trajectories are constructed using sequential animal locations obtained using GPS [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], PTT or VHF telemetry [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. However, these methods do not work or perform poorly in areas where the telemetry or GPS signal cannot be transmitted, such as marine environments [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], and terrestrial habitats under thick vegetation or underground (e.g. [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]). To mitigate this, some researchers have used \u0026lsquo;dead-reckoning\u0026rsquo;, a process that uses information on animal heading (magnetometer), speed (accelerometer), and change in height/depth (barometer) to reconstruct movement paths by vectors [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Examples of this include diving seabird [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], marine mammals [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] and forest mammals [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] but there has been few attempts to uncover burrowing species complex underground systems [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Previous studies of fossorial species' behaviour and movement primarily utilized accelerometer data to learn about underground speeds [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], behaviour [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], and energy expenditures [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Dead-reckoning has never been applied to track the fine-scale movements of fossorial species, despite the potential to reveal trajectories of animals inhabiting subterranean spaces where conventional methods are ineffective. Here, we test the application of dead reckoning to reconstruct the trajectories of a fossorial species using a case study of black-tailed prairie dogs (\u003cem\u003eCynomys ludovicianus\u003c/em\u003e).\u003c/p\u003e \u003cp\u003eAmong fossorial species, prairie dogs (\u003cem\u003eCynomys\u003c/em\u003e spp) represent an ideal study species for evaluating the ability to use dead-reckoning to track subterranean movements. Represented by five species in North America, these relatively large (500-1,500g) ground squirrels and are considered keystone species in grassland ecosystems due to their ability to due to altering grass composition and movement of soil through creating and maintaining complex burrowing systems [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Prairie dogs are capable of excavating burrows systems comprised of interconnected burrows of 10\u0026ndash;30 cm in diameter to depths of up to 5 meters below the surface [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. These colonial animals occur at high densities of 10\u0026ndash;35 individuals per hectare and can create aboveground burrow entrances of up to 325 burrows per hectare within colonies that encompass thousands of hectares [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. These colonies of prairie dogs provide a source of prey, landscape heterogeneity, and subterranean habitat that supports a host of dependent species, including the endangered black-footed ferret (\u003cem\u003eMustela nigripes\u003c/em\u003e; [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Yet, despite the important ecosystem role prairie dogs serve in grassland communities, very little information has been acquired regarding the subterranean movements and behavior of these rodents, primarily due to the paucity of tracking technology capable of collecting underground data.\u003c/p\u003e \u003cp\u003eHere, we tested the use of dead reckoning to reconstruct the movements of black-tailed prairie dogs. Our aims are (i) to examine various speed metrics used to derive distance for the dead-reckoning analysis when prairie dogs move along burrows; (ii) to examine whether tri-axial accelerometer data can define when prairie dogs enter and exit their burrows and (iii) to collect data from numerous individuals within one area to map out the prairie dog burrow systems in two dimensions.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Site\u003c/h2\u003e \u003cp\u003eData collection for this study was conducted from 16th to 25th August 2023 at American Prairie (AP), a privately-owned wildlife conservation area located in Phillips County, Montana, USA. AP is within the North American Great Plains region. The vegetation is dominated by mixed-grass grassland, which consisted of western wheatgrass (\u003cem\u003ePascopyrum smithii\u003c/em\u003e), blue grama (\u003cem\u003eBouteloua gracilis\u003c/em\u003e), and needle and thread (\u003cem\u003eHesperostipa comata\u003c/em\u003e) grasses mixed with silver sagebrush (\u003cem\u003eArtemisia cana\u003c/em\u003e) and scarlet globemallow (\u003cem\u003eSphaeralcea coccinea\u003c/em\u003e), wooly plantain (\u003cem\u003ePlantago patagonica\u003c/em\u003e), and American vetch (\u003cem\u003eVicia americana\u003c/em\u003e; [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The study site consisted of a 200m by 200m plot of a 288-hectare colony of black-tailed prairie dogs (\u003cem\u003eCynomys ludovicanus\u003c/em\u003e; Longitude: -107.7520, Latitude: 47.7715). Elevation within the study site ranged from 718-723m.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eBiologgers\u003c/h2\u003e \u003cp\u003eTo map the movements of prairie dogs using dead-reckoning, we designed and created a collar-based attachment method for affixing a Daily Diary (DD; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://wildbytetechnologies.com/tags.html\u003c/span\u003e\u003cspan address=\"http://wildbytetechnologies.com/tags.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) circuit board to each animal (Fig.\u0026nbsp;1a). The DD was powered by a 50 or 60 milliampere (mAh) rechargeable lithium battery, both of which were contained within a 3D printed Anycubic resin housing attached to the bottom of the collar. The collar was made using a strap of 15mm x 150m biothane synthetic leather (The Strap Warehouse, Millersburg, Ohio, USA). The collar was fastened to the animal using a flat head bolt and nut attached using predrilled holes. Each collar also contained a solar powered GiPSy 6 GPS logger (TechnoSMart, Rome, Italy) and a second 100 mAh rechargeable lithium battery for the GPS. The GPS was not used in this study. The total weight of the collar and all components was ~\u0026thinsp;16g making up 1\u0026ndash;2% of the species\u0026rsquo; body mass.\u003c/p\u003e \u003cp\u003e \u003cem\u003eFigure 1 \u0026ndash; (A) Photo of the logging system deployed on prairie dogs; (B) with example of constructed tube run with trap connected. Photos were taken at American Praire, Phillips County, Montana, USA between the dates 16th to 25th August 2023.\u003c/em\u003e \u003c/p\u003e \u003cp\u003eThe DD consisted of a multi-sensor biologging unit [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], comprising tri-axial accelerometers and tri-axial magnetometers. The unit was programmed to collect both acceleration (at 40 Hz) and magnetic field intensity (16 Hz) in all three orthogonal axes. The logger recorded the data on 128 kilobyte internal memory, allowing up to 8 days of continuous data. On the day of captures, the device was switched on and the DDs were calibrated by engaging them in a defined set of movements, conceived to provide proper 3-dimensional coverage for the G- and M-spheres [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eCaptures and deployment\u003c/h2\u003e \u003cp\u003eWe captured prairie dogs from 15\u0026ndash;25 August 2023 using a matrix of 125 live traps (6x9x24in Tuffy 24; Tru Catch Traps, Belle Fourche, South Dakota, USA) distributed through our study site. We labelled and recorded the location of each trap using a handheld GPS unit. We baited traps with sweet feed grains (MannaPro, St. Louis, Missouri, USA) and set traps open each morning and evening for a period of 4 hours. We visually examined each trap once an hour to ensure captured prairie dogs were not exposed to high temperatures. We transported captured adult prairie dogs weighing\u0026thinsp;\u0026gt;\u0026thinsp;800g in the traps to nearby shade for processing. Juvenile prairie dogs\u0026thinsp;\u0026lt;\u0026thinsp;800g were immediately released. We recorded the weight, age, and sex of each animal. We briefly restrained each animal to attach the biologger collar and record neck circumference. We then marked each animal using non-toxic hair dye along the back with a unique pattern for each individual. We returned the animal to the trap and monitored for approximately 15 minutes to ensure the collar remained in position and did not cause undesirable behavioral effects (ie., excessive scratching or lethargy).\u003c/p\u003e \u003cp\u003eBefore each collared prairie dog was released, we performed a series of trials designed to provide fine-scale movement and location data over a verifiable path to compare the accuracy of the dead-reckoning process used in this study. We constructed \u0026ldquo;tube runs\u0026rdquo; by attaching straight and 45\u003csup\u003eo\u003c/sup\u003e elbow sections of 120 mm diameter, ventilated, and transparent plastic tubing (Katee Product Inc, Chilton, WI, USA) together to create various shapes and configurations of total lengths between 1\u0026ndash;3 m (Fig.\u0026nbsp;1b). We positioned each tube run such that one end was within 25 cm of the closest burrow to the location of capture of each animal. At the other end, we opened the door to the trap containing each collared prairie dog and allowed the animal to freely exit the trap and into the tube run. We recorded videos of the movement of each individual from the cage, through the tube run, and out into the burrow using smartphones.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eCamera traps\u003c/h2\u003e \u003cp\u003eAcross the study area, we deployed 87 motion-triggered cameras (Reconyx HyperFire 2, Reconyx, Holmen, WI, USA). We programmed the cameras to take 30-second videos with no delay, anytime a motion was detected throughout the time period when collars were attached to prairie dogs. We positioned camera traps such that the field of view captured in recorded videos included all burrow entrances within 20 m of the location at which the prairie dog was released after the collar was attached. We installed each camera at a height of 50 cm above the ground on a metal rebar stake positioned 2.0-2.5 m from the burrow entrance. Videos were recorded on a 32 gigabyte memory card. We replaced memory cards and camera batteries every 2\u0026ndash;4 days to ensure sufficient memory and power remained.\u003c/p\u003e \u003cp\u003eTo aid in the video review process described below, we recorded the location of each burrow within the field of view of each camera at 20 cm horizontal accuracy using a high-precision GPS receiver (Catalyst DA2, Trimble, Sunnyvale, CA, USA). We identified the position of each burrow in the recorded videos by recording ourselves holding a sign indicating a unique identification number while standing at each burrow.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eRecaptures\u003c/h2\u003e \u003cp\u003eAfter 5 days, we initiated efforts to recapture all collared prairie dogs using the same matrix of traps. We followed the same baiting and trap setting protocol as described above for recapturing all animals. Once a collared prairie dog was recaptured, we performed a second tube run trial before removing the collar. In this case, the tube run was positioned between the trap containing the prairie dog and an empty trap at the other end positioned to safely contain the prairie dog after the animal moved freely through the tube run. We again recorded videos of the movement through the tube runs using smartphones. Once this second tube run was completed, we briefly restrained the prairie dog, removed the collar, and collected data on weight and condition of the animals. The animal was then released at the capture location.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eDead-reckoning: comparing speed metrics\u003c/h2\u003e \u003cp\u003eDead-reckoning analysis was undertaken to produce paths consisting of 1 location per second for the prairie dogs by taking magnetometry data in tandem with the accelerometers to derive heading [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] and assessing using several methods to derive speed, and therefore distance including; (i) Vectoral Dynamic Body Acceleration (VeDBA), (ii) Vectorial Static Body Acceleration (VeSBA), (iii) step count and (iv) constant speed. Each method is explained below:\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eVeDBA\u003c/h2\u003e \u003cp\u003eVeDBA is the most common metric for speed for the dead-reckoning process [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] and calculated using;\u003c/p\u003e \u003cp\u003e \u003cem\u003eVeDBA\u003c/em\u003e = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\sqrt{{\\left(DBAX\\right)}^{2}+{\\left(DBAY\\right)}^{2}+\\:{\\left(DBAZ\\right)}^{2}}\\)\u003c/span\u003e\u003c/span\u003e (1)\u003c/p\u003e \u003cp\u003ewhere DBA is the dynamic acceleration for the three axes (X, Y and Z). The dynamic acceleration was calculated by subtracting static acceleration (the raw acceleration smoothed with a running mean over 2 seconds [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] from the raw acceleration. This removes most of the gravitational influence the tag is undergoing to provide a metric that reflects the dynamism of animal movement [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA VeDBA threshold or window method [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] assumes that low-values of VeDBA occur when animals are not travelling, e.g. standing, sitting or lying, or extremely high, short-term (\u0026lt;\u0026thinsp;5 seconds) VeDBA values when animals shake themselves or roll rapidly. Thus, to identify travelling, we implemented a Boolean rule that highlighted when VeDBA values lay within thresholds. We then implemented dead-reckoning when these conditions were met. These window values are presumed to vary between species and tag attachment [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] so travelling behaviour should be ground-truthed with observations when possible. In the case of prairie dogs, this threshold was set between 0.1 and 1.5 following observation of the tube runs undertaken by the animals following release.\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003eVeSBA\u003c/h2\u003e \u003cp\u003eVeSBA incorporates all three acceleration axes like VeDBA, but instead removes the dynamism of the animal movement and is particularly valuable when animals \u0026lsquo;pull \u003cem\u003eg\u003c/em\u003e\u0026rsquo;. VeSBA is derived via;\u003c/p\u003e \u003cp\u003e \u003cem\u003eVeSBA\u003c/em\u003e = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\sqrt{{\\left(SBAX\\right)}^{2}+{\\left(SBAY\\right)}^{2}+\\:{\\left(SBAZ\\right)}^{2}}\\)\u003c/span\u003e\u003c/span\u003e (2)\u003c/p\u003e \u003cp\u003ewhere SBA is the static acceleration in the three axes (X, Y and Z), calculated by running a running mean smoothing window over two seconds across each acceleration axis [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. We used a VeSBA window approach in the same way as we did for VeDBA (see above).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStep definition\u003c/h2\u003e \u003cp\u003eOne of the most obvious delineators of traveling behaviour and speed is the identification of steps (or strides), assuming they can be defined within the tag data. A particular form of analysis based on a Boolean method, the Lowest Common Denominator (LoCoD) approach, can be used to define individual steps within an animal\u0026rsquo;s movement [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. This approach looks for specific changes and defined patterns in acceleration signals, that occur during movement, that are predictable with each step, and which only occur during traveling behaviour. In the use of the LoCoD approach, we attempted to identify and quantify steps (Fig.\u0026nbsp;2) and then used a step count to construct a step count vs speed relationship coefficient to quantify distance for dead-reckoning. To implement this, the tube run videos were synchronized with their respective DD data to define the sensor-dependent features of steps. Following this, we produced an algorithm within the Daily Diary Movement Trace (DDMT) software [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], which implemented the LoCoD method, and searched for steps within any prescribed animal movement data [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. For prairie dogs, we calculated the rate of change of acceleration (jerk) across 3 sequential x-axis data points (corresponding to 0.075 s). The quantification of steps had two conditions where x-axis differential (see above) surpassed 0.2 \u003cem\u003eg\u003c/em\u003e, and VeDBA smoothed (across half a second) was higher than 0.25 \u003cem\u003eg.\u003c/em\u003e To mark individual steps, a blind spot was implemented following identification of a step so that strides were only marked once despite having variable stride lengths [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Our optimal blind spot lasted 5 sequential events (0.125 s).\u003c/p\u003e \u003cp\u003e \u003cem\u003eFigure 2. Example of prairie dog movement and stationary behaviour manifest in the three acceleration channels in addition to smoothed rate of change data in the x-axis (heave). The black dots show individual steps marked. No movement definition (e.g. VeDBA threshold) was required as the steps contributed to travel. Acceleration taken from 1 individual during movement/stepping within a tube run, data shown is 12 seconds and collected at American Prairie, Phillips County, Montana, USA between the dates 16th to 25th August 2023.\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eConstant speed\u003c/h2\u003e \u003cp\u003eThe last method tested as a proxy for speed was estimating a constant speed. The metric was taken from the speed value from each tube run. The constant speed in this case would be the distance between the first and last verified point divided by the time taken to complete the tube run.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eAssessing different speed methods and accuracy using observed tube runs\u003c/h2\u003e \u003cp\u003eTo evaluate if dead-reckoning analysis might be viable for fossorial animals whose movement is constricted by the burrows, we processed the data from the tube run by examining the recorded videos frame by frame to determine \u0026lsquo;true\u0026rsquo; location on a second-by-second basis (position determined to the nearest 10 cm). First, we used video editing software (Adobe Premium Pro, Adobe, San Jose, CA, USA) and reviewed the video at a 100 frames per second, from there we could located the position of the prairie dog, and specifically, the collar worn by the prairie dog, at 1-second intervals beginning from the start of each video. We replicated the configuration of each tube run to scale in QGIS version 3.24 by creating a vector shapefile including the dimensions and arrangement of each segment of tube. We then created a point shapefile where points placed along the replicated tube runs in our vector shapefile matched the position of the prairie dog within the tube run at each 1-second interval as observed in the videos. We labelled these \u0026lsquo;true\u0026rsquo; locations with the interval number to be used for assessing the accuracy of the dead-reckoning of the movement path of each animal through the tube run.\u003c/p\u003e \u003cp\u003eThe distance between the tube run location and the dead-reckoned location was calculated using the following equation:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:Distance=a\\text{cos}\\left(\\text{sin}{Lat}_{DR}\\bullet\\:\\text{sin}{Lat}_{TR}+\\text{cos}{Lat}_{DR}\\bullet\\:\\text{cos}{Lat}_{TR}\\bullet\\:\\text{cos}\\left({Lon}_{TR}-{Lon}_{DR}\\right)\\right)\\bullet\\:6371$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThis calculation was carried out using the package \u0026lsquo;fossil\u0026rsquo; within R [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. The same package was used to calculate animal travel speed.\u003c/p\u003e \u003cp\u003e \u003cem\u003eDefining entering a\u003c/em\u003e burrow, \u003cem\u003emoving underground, and\u003c/em\u003e burrow \u003cem\u003edepth\u003c/em\u003e\u003c/p\u003e \u003cp\u003eTo map out the prairie dog burrow system, underground movement needs to be defined. We used the tube runs and camera trap video footage synchronised with the acceleration data to derive a LoCoD-based method (see above) to quantify when animals entered burrows. We used the videos recorded using the array of camera traps we deployed to identify the time, location, and movement (entering a burrow or exiting a burrow) of our collared prairie dogs. We reviewed each video and recorded the time stamp and the location using the burrow identification process described above each time a collared prairie dogs were observed entering or exiting a burrow. We identified individual prairie dogs based on the unique dye-mark given during capture.\u003c/p\u003e \u003cp\u003eThe rule for entering a burrow used was; when the animal pitch angle (derived from the acceleration x channel [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]) smoothed (using a running mean across 1 second of data) was less than \u0026minus;\u0026thinsp;20\u0026deg; and VeDBA smoothed over 0.5s was greater than 1.2 \u003cem\u003eg\u003c/em\u003e, then \u0026lsquo;mark as a descent into a burrow\u0026rsquo; (Fig.\u0026nbsp;3).\u003c/p\u003e \u003cp\u003e \u003cem\u003eFigure 3 \u0026ndash; Tri-axial acceleration data, smoothed VeDBA and smoothed pitch angle of 3 example prairie dogs descending into their burrows. Acceleration traces taken from 3 individual when enter burrow. Behaviour is categorized using video footage after tunnel run or camera trap footage. Data shown is a total of 9 seconds and collected at American Prairie, Phillips County, Montana, USA between the dates 16th to 25th August 2023.\u003c/em\u003e \u003c/p\u003e \u003cp\u003eThe quantification for exiting a burrow utilized a differential channel where the rate of change of pitch angle smoothed (across 1 second) was calculated across a second. The rule had two conditions where the difference in pitch angle smoothed was greater than 30\u0026deg; and VeDBA smoothed (across half a second) was more than 0.4 \u003cem\u003eg\u003c/em\u003e (Fig.\u0026nbsp;4). Another time-based parameter was used where any marked behaviour that last less than half a second was removed to mitigate standing up and some posture changes from causing false positives.\u003c/p\u003e \u003cp\u003e \u003cem\u003eFigure 4 \u0026ndash; Tri-axial acceleration data, smoothed VeDBA and smoothed pitch angle of 3 example prairie dogs exiting their burrows. Acceleration traces taken from 2 individual when enter burrow. Behaviour is categorized using video footage from camera trap footage. Data shown is a total of 9 seconds and collected at American Prairie, Phillips County, Montana, USA between the dates 16th to 25th August 2023.\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eApplying dead reckoning to \u0026lsquo;free roaming\u0026rsquo; data\u003c/h2\u003e \u003cp\u003eWe took horizontal animal movement to map out the burrows defined by the dead-reckoned movements of individuals starting from above ground verified points for a period of time informed by the drift model. We defined verified points as times when the true aboveground location of the prairie dog could be determined because the animal appeared in the camera trap array at a recorded burrow. The dead-reckoned paths were then filtered based on where the prairie dogs had entered the burrow located at each verified point. All spatially relevant underground locations were super-imposed onto one another to estimate the location of underground burrows. A combination of DDMT [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] and R [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] with the \u0026lsquo;ggamp\u0026rsquo; package was used to visualise and map out the burrows. Revisit and residence time analysis was conducted using the \u0026lsquo;recurse\u0026rsquo; package. A 1-meter radius circle was moved along the dead-reckoned underground track, and a 'revisit' was recorded whenever the animal left and then re-entered the circle. Additionally, if the animal remained within the circle, the total time spent at that location was accumulated.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eDead-reckoning: comparing speed metrics\u003c/h2\u003e \u003cp\u003eComparing the different speed methods using DD data obtained from 12 prairie dogs and 23 tube runs of up to 4 m in length, we found the VeDBA metric for speed gave location accuracy within a 20 cm margin of error for 75% of the time across all tube runs. The other methods; Step definition, VeSBA and constant speed had higher levels of error within the desired 20 cm error margins occurring 52%, 42% and 42% of the time across all tube runs, respectively (Fig.\u0026nbsp;5). Speed coefficients varied between runs (0.009 to 0.042 when VeDBA was used for speed (Supplementary table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) with this variation being the result of individual differences.\u003c/p\u003e \u003cp\u003e \u003cem\u003eFigure 5 \u0026ndash; Box-whisker plot of the distance errors based on various speed estimates (constant speed, step definition, VeDBA and VeSBA \u0026ndash; see text) derived from prairie dogs moving along specified tubes up to 4 m long before entering their burrow. Data taken from 23 tube runs across 12 individuals, data collected at American Prairie, Phillips County, Montana, USA between the dates 16th to 25th August 2023.\u003c/em\u003e \u003c/p\u003e \u003cp\u003eAll prairie dog tube runs were visualised by overlaying the verified points of their positions over the dead-reckoned points with VeDBA being used for speed (Fig.\u0026nbsp;6). The dead-reckoned estimates of position mostly matched well (15.38 cm mean error), with the largest errors due to quick movements (39.76 cm largest error). Estimation of tube run underperformed when animals speed exceeded 0.38 m/s (see supplmentery Fig.\u0026nbsp;1).\u003c/p\u003e \u003cp\u003e \u003cem\u003eFigure 6 \u0026ndash; Comparison of dead-reckoned paths with verified points during prairie dog movement through transparent pipes used to simulate\u003c/em\u003e burrow\u003cem\u003es (see text). Data taken from 23 tube runs across 12 individual, data collected at American Prairie, Phillips County, Montana, USA between the dates 16th to 25th August 2023.\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003cem\u003eDefining entering a\u003c/em\u003e burrow\u003c/p\u003e \u003cp\u003eOur metric for detecting when a prairie dog entered the burrow system (Fig.\u0026nbsp;3) successfully identified 22 out of 24 burrow entrances. However, it was less effective at detecting when the animals exited, correctly identifying 4 out of 6 exits (Fig.\u0026nbsp;4). The tube runs allowed us to validate our method for defining movement under controlled conditions (Fig.\u0026nbsp;2), achieving 100% accuracy in those scenarios. We only included animals that did not display behaviors that could move the tag during the tube runs, meaning our experimental protocol did not account for non-translocation movement.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eApplying dead reckoning to \u0026lsquo;free roaming\u0026rsquo; data\u003c/h2\u003e \u003cp\u003eWe plotted 31 hours of underground movements for 5 individuals within 2 hours of a verified point (Fig.\u0026nbsp;7). Across these individuals we successfully mapped out 382m of burrows with further analysis showing a\u0026thinsp;~\u0026thinsp;1% space shared with space use of 4.78 km\u003csup\u003e2\u003c/sup\u003e with max burrow length of 10m (Fig.\u0026nbsp;7). Revisit analysis indicated the time spent in various location pockets underground and how regularly the burrows were used (Fig.\u0026nbsp;8). This data from one individual was not sub sampled based on activity showing long periods (hours) spent in one location being inactive or not displacing location based on our definitions (see above).\u003c/p\u003e \u003cp\u003e \u003cem\u003eFigure 7 \u0026ndash; Location of prairie dog burrows as deduced using dead-reckoning on 5 individuals with (a) Prairie dog 4 and 5 show in blue and purple respectively, tagged on Enrico as one study site, (b) locations of prairie dogs 1, 2 and 3, shown in red, yellow and green respectively, individuals tagged on Box elder, (c) shows how both sites are situated within American Prairie. Data shown is a total of 31 hours across 5 individuals and collected at American Prairie, Phillips County, Montana, USA between the dates 16th to 25th August 2023.\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eFigure 8 \u0026ndash; Location of prairie dog burrows as deduced using dead-reckoning on 1 individual (a) shows the total time spent according to location and (b) shows the number of revisits within 1m\u003c/em\u003e \u003csup\u003e \u003cem\u003e2\u003c/em\u003e \u003c/sup\u003e \u003cem\u003eto particular sites. Data shown is a total of 20 hours for 1 individuals and collected at American Prairie, Phillips County, Montana, USA between the dates 16th to 25th August 2023.\u003c/em\u003e\u003c/p\u003e \u003cp\u003eDead-reckoned locations of the same individual, estimated to use the same tunnel with the quantification of \u0026lsquo;entering burrow\u0026rsquo; determining the estimate burrow entrance (Fig.\u0026nbsp;9).\u003c/p\u003e \u003cp\u003e \u003cem\u003eFigure 9 \u0026ndash; Multiple superimposed tracks of prairie dogs with respect to a verified position (a camera) to illustrate how multiple tracks may function together to provide a more precise estimate of burrow space. Data shown is a total of ~\u0026thinsp;4 minutes for 1 individuals and collected at American Prairie, Phillips County, Montana, USA between the dates 16th to 25th August 2023.\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eWhile dead-reckoning is well documented for aerial, aquatic and (above ground) terrestrial species [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], there are substantial unknowns in fossorial animals. These unknowns include the extent to which traveling behaviour can be reliably identified and how good metrics for speed, such as VeDBA [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], apply to animals whose movement is constrained within burrows. This is a first attempt to determine the movement of a fossorial species while underground, and in the process, we derive information on the structure of the burrows.\u003c/p\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eSpeed proxies\u003c/h2\u003e \u003cp\u003eWe examined four metrics as proxies for speed and found that VeDBA produced a lower range and mean of error than the other three methods (Fig.\u0026nbsp;5). However, our defined burrow runs may be overly generous to the VeDBA metric since individuals using our system either moved or were stationary, not engaging in any non-movement activity, such as scratching, that produces an appreciable VeDBA signal. Nonetheless, the value of VeDBA as a speed metric has been stressed in the literature for terrestrial animals [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], and it is encouraging that it also seems to work well underground. Our attempts to use VeSBA as a proxy for speed were markedly worse than VeDBA (Fig.\u0026nbsp;5), most likely because VeSBA tends to be most useful for indicating high speed cornering [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Whether cornering or not, in any case the movement speed of prairie dogs in the burrows seems to be generally less than 0.15 m/s where any VeSBA signal is likely to be minimal. Constant speed underperformed too, even though, for prairie dogs, the expectation was that, in the confined space of the burrow, the speed would change little. However, within our tube run experiments, the rodents displayed a range of speeds (0.01 m/s to 1.42 m/s) that immediately indicate the expected errors. Finally, step definition was evaluated because many animals have increases in speed accompanied by increases in stride frequency [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. However, the mean error using this metric, although less than VeSBA or constant speed values, was higher than for VeDBA (Fig.\u0026nbsp;5). One explanation is that prairie dogs change both step frequency and stride length with speed, as do many mammals [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. If the conditions for this were precisely defined for prairie dogs, step frequency could still be potentially used to derive speed. More work is needed to elucidate this. It may also be that the ventral mounting of the DD on the collar meant that the unit occasionally touched the ground during travelling, putting in false steps and reducing the accuracy of the approach. Finally, it may be that step resolution would be increased at higher sampling frequencies because the waveform produced by the steps becomes clearer. Again, more work is needed to address this.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eBurrow use detection and non-travelling movement\u003c/h2\u003e \u003cp\u003eOur starting point for mapping burrow use was detection of descent into the burrow system using the change in pitch. Although our work with the wild prairie dogs meant that the cameras could identify when the animals descended into their burrow system, it is unrealistic to assume under most field deployments that a camera could monitor every burrow entrance. As such, it is important to be able to identify when the prairie dogs enter and exit their burrows. Our Boolean rule involving pitch performed well for this for the descent, detecting 92% of burrow entries. However, although no misclassifications occurred within camera trap footage, some free roaming prairie dogs were not observed on all occasions of burrow entry so we could not calculate a false positive rate. Exit from the burrows was less reliable. Tags incorporating light sensors and/or barometric pressure sensors would probably make entrance and exit definition more reliable.\u003c/p\u003e \u003cp\u003eAny estimate of prairie dog movement using dead-reckoning needs to be able to differentiate between non-translocational body movement, such as shaking or scratching, and genuine travel. Since the animals in our tube runs only engaged in either travel or being stationary, our protocol could not assess this. However, observations of animals in camera trap footage and the feild indicate that such non-translocational movements are very transient, rarely lasting more than 5 seconds (Supplementary. Table\u0026nbsp;2). Such transient movements can easily be identified and precluded using Boolean rules on the acceleration data [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Ultimately, unless these behaviours occur frequently, their transient nature means that they would not cause substantial deviations in dead-reckoned paths.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eDead-reckoning\u003c/h2\u003e \u003cp\u003eThis work indicates that dead-reckoning has substantial potential for tracking fossorial species, providing information on their speeds and movements, and providing two dimensional location estimates when the animal is beneath the surface. This is highlighted by the detailed manifest by the high-resolution paths derived using sub-second acceleration data, even though verified points may be up to 2 hours apart (Fig.\u0026nbsp;7). Previous work has shown the importance of verified points, and the frequency with which they should be taken, on dead-reckoned location estimates [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. This frequency, which need not take just the form of GPS fixes [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], varies with animal lifestyle and is critical for minimizing errors. Typically, these verified positions need to occur every 3 h in terrestrial animals [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWe find it is more appropriate to talk about verified points that should occur after specific distances rather than times, because it is in the process of moving that errors accumulate. Our data on the general activity patterns from the accelerometer data of the collared prairie dogs used in this study indicate that they spend\u0026thinsp;~\u0026thinsp;27% of their time traveling. If their traveling speed is approximately\u0026thinsp;~\u0026thinsp;0.1 m/s, a two-hour window for determining movements amounts to a distance of approximately 720m. But it is pessimistic to assume that during such movement, the animals continually travel along new trajectories, as do many non-fossorial animals [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Instead, fossorial animals are constrained to their burrows and so are likely to be back-tracking in a matrix that is more akin to a city street system than it is to over-ground movement of animals which may choose to move in any direction. In this respect, the more localised the burrow system, the more constrained the movements of its inhabitants will be. Therefore, we thus suggest that dead-reckoning the movements of fossorial species can benefit in accuracy from the spatial restriction that the burrows impose. The constraints of the burrows can also be used to refine estimates of the burrow positions in space. A single, or multiple, animals departing from a verified position in one burrow matrix, will have limited choice of movement. Many individuals (or the same individual) will use the same path so that, if tracks are superimposed, the multiple routes can be used to construct a more accurate picture of the underground network (Fig.\u0026nbsp;8, Fig.\u0026nbsp;9). In addition to this, we suggest that unusual features within defined burrow sections, such as a point at which the burrow almost doubles back on itself, can be used as verified positions (once the location of this feature has been defined by multiple individual tracks \u0026ndash; see above). Thus, there may be circumstances where individuals of fossorial species can have their locations \u0026lsquo;verified\u0026rsquo; even if they do not surface to trigger the standard verified position system (camera traps in our case).\u003c/p\u003e \u003cp\u003eAlthough our approach gives some information of the movements of prairie dogs in their burrows, it is na\u0026iuml;ve to assume that we can map out all the connections in their underground complexes using this method. Notably, we can only apply this approach to the parts of a burrow systems actually used by collared animals as unused burrows or portions of burrows will be excluded during mapping initiatives based purely on animal movements. Including more collared individuals, and for longer periods of time, should increase the likelihood of including greater portions of the burrow network in mapping using this technique. Likewise, independent mapping methods, such as ground penetrating radar, would improve the resolution (as well as helping correct for dead-reckoning errors). Ground penetrating radar has been shown to be a powerful methodology for elucidating spaces underground [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e] but it does not work under all conditions [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e] and gives no information on the function of burrows. In this respect, it is fortuitous that the dead-reckoning protocol described here uses accelerometers because these sensors are used widely to determine and quantify animal behaviour, including sleeping, feeding, fighting etc [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. As such, the combination of location via dead-reckoning with behaviour should give important information regarding the extent to which particular behaviours are associated with specific spaces and perhaps even indicate how many individuals occupy the underground spaces simultaneously. This latter element has particular value in consideration of disease transmission such as sylvatic plague [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eNext steps\u003c/h2\u003e \u003cp\u003eWhile we used animal pitch to identify when prairie dogs descended into their burrows and hinted at the potential to map burrow systems in 3D, we believe that the method described here may not provide the necessary accuracy. However, further testing is required to draw any definitive conclusions. The combination of pitch with barometric pressure as measured onboard the DD (some sensors can resolve height within 10 cm) could be a very powerful approach for taking the burrow system into three dimensions. Depth of the burrows is of interest as likely plays a crucial role in regulating temperature fluctuations [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e] and influencing oxygen flow, both of which are presumed to impact the overall quality of the underground environment.\u003c/p\u003e \u003cp\u003eThe system that we tested used cameras to provide verified points, which proved effective. However, cameras cannot be reliably used cover all burrow entrances occurring within a prairie dog colony and camera data is time consuming to assess. Another method that might prove simpler is using rare earth magnets at intervals above the burrows (assuming that the burrow position is known with respect to the surface). Such magnets would produce a spike in the vectorial sum of the magnetometer data as the animals passed them, defining that position [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Indeed, the use of variously strong magnets, which will produce a correspondingly large vectorial sum peak, might also help refine this approach. Otherwise, animals could be fitted with GPS or VHF units on their collars although test would have to be carried out to determine if the systems provide the necessary accuracy.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOverall, this work underpins the importance of dead-reckoning as a solution for examining space use in fossorial animals. This is relevant for understanding how the animals relate to environmental space (as e.g. determined by vegetation surveys [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e] and topography, etc [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]). However, the specifics of the paths taken by animals themselves (tortuosity [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e], speed [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]), step lengths [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e] and turn angles [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e], etc.) are also important for understanding a suite of animal movement issues, such as species\u0026rsquo; diffusion [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], food location strategies [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e] and vigilance [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. But the work on immovable burrows has further value in providing a template for \u0026lsquo;city\u0026rsquo; construction. As with humans, we expect the burrow network developed by prairie dogs to reflect the needs of their society, incorporating needed attributes such as ventilation, high-speed sections, escape from predator sections, aggregation spots, storage and sleeping spots, providing a comprehensive network that caters for the complex needs of their hidden societies.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003eAP\u0026nbsp;\u003c/strong\u003e- American Prairie\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDD\u0026nbsp;\u003c/strong\u003e- Daily Diary\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDDMT\u0026nbsp;\u003c/strong\u003e- Daily Diary Movement Trace\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGPS\u0026nbsp;\u003c/strong\u003e- Global Position System\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLoCoD\u0026nbsp;\u003c/strong\u003e\u0026ndash; Lowest Common Denominator\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003emAh\u0026nbsp;\u003c/strong\u003e\u0026ndash; Milliampere\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePTT -\u0026nbsp;\u003c/strong\u003ePlatform Transmitting Terminals\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVHF\u0026nbsp;\u003c/strong\u003e\u0026ndash; Very High Frequency\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3D\u0026nbsp;\u003c/strong\u003e\u0026ndash; Three dimensions\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cem\u003eEthics approval and consent to participate\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the [name of the relevant guidelines or ethical standards, e.g., \"Ethical Principles of Animal Experimentation\"]. Ethical approval for this research was obtained from the Smithsonian Institution’s Animal Care and Use Protocol under approval number SI-23012.\u003c/p\u003e\n\u003cp\u003eAll necessary permits were acquired for the described field studies, and the animals involved were handled according to the guidelines established by Smithsonian Institution’s Animal Care and Use Protocol SI-23012 and Montana Fish, Wildlife, and Parks Scientific Collector’s Permit # 2024-022-W . Where applicable, written consent was obtained from all participants or their legal guardians prior to inclusion in the study.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConsent for publication\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAll authors consent to the publication of this review.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAvailability of data and materials\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCompeting interests\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by the the Paul G. Allen Family Foundation under grant number(s) 505321-. The funding body/bodies had no role in the design of the study, data collection, analysis, interpretation, or in the writing of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAuthors' contributions\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eJR contributed to the conception, design of the work, acquisition and analysis of data, \u0026nbsp;interpretation of data, drafted the work and substantively revised the work\u003c/p\u003e\n\u003cp\u003eJB contributed to the\u0026nbsp;conception,\u0026nbsp;design of the work, acquisition and analysis of data, \u0026nbsp;interpretation of data and substantively revised the work\u003c/p\u003e\n\u003cp\u003eIN contributed to the\u0026nbsp;design of the work, acquisition and analysis of data, \u0026nbsp;interpretation of data and substantively revised the work\u003c/p\u003e\n\u003cp\u003eRPW\u0026nbsp;contributed to the conception, \u0026nbsp;interpretation of data and substantively revised the work\u003c/p\u003e\n\u003cp\u003eWM contributed by substantively revised\u0026nbsp;the work\u003c/p\u003e\n\u003cp\u003eHS contributed to the conception, acquisition and analysis of data and substantively revised\u0026nbsp;the work\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAcknowledgements\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to express our gratitude to Dr Mark Holton for assisting with tag construction and the developments of the logger; Phil Hopkins for invaluable help in designing housings for the devices. Thank you to staff at American Prairie, especially Danny Kinka and Dan Stevenson, for their support of this project. We are gratefully for the help of Kirsten Cook, Ashley Rutherford, and Ariana Mandros for their assistance with fieldwork and video review.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eParker KL, Robbins CT, Hanley TA. Energy Expenditures for Locomotion by Mule Deer and Elk. J Wildl Manage. 1984;48:474. \u003c/li\u003e\n\u003cli\u003eLempidakis E, Wilson RP, Luckman A, Metcalfe RS. What can knowledge of the energy landscape tell us about animal movement trajectories and space use? A case study with humans. J Theor Biol [Internet]. 2018;457:101\u0026ndash;11. Available from: https://pubmed.ncbi.nlm.nih.gov/30130547/\u003c/li\u003e\n\u003cli\u003eRoper TJ, Ostler JR, Schmid TK, Christian SF. Sett use in European badgers Meles meles. Behaviour [Internet]. 2001;138:173\u0026ndash;87. 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Behav Ecol [Internet]. 1994;5:28\u0026ndash;34. Available from: https://academic.oup.com/beheco/article-lookup/doi/10.1093/beheco/5.1.28\u003c/li\u003e\n\u003cli\u003eV\u0026aacute;squez RA, Ebensperger LA, Bozinovic F. The influence of habitat on travel speed, intermittent locomotion, and vigilance in a diurnal rodent. Behav Ecol [Internet]. 2002;13:182\u0026ndash;7. Available from: https://academic.oup.com/beheco/article/13/2/182/200699\u003c/li\u003e\n\u003cli\u003eLashley MA, Chitwood MC, Biggerstaff MT, Morina DL, Moorman CE, DePerno CS. White-tailed deer vigilance: The influence of social and environmental factors. PLoS One. 2014;9. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"animal-biotelemetry","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"abit","sideBox":"Learn more about [Animal Biotelemetry](http://animalbiotelemetry.biomedcentral.com)","snPcode":"40317","submissionUrl":"https://submission.nature.com/new-submission/40317/3","title":"Animal Biotelemetry","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Black-tailed prairie dog, dead-reckoning, fossorial, burrows","lastPublishedDoi":"10.21203/rs.3.rs-4945336/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4945336/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eResearching the movement patterns of fossorial animals and mapping of burrow systems presents a significant challenge due to the difficulty of direct observation and the limitations of most tracking systems to collect location fixes underground. A potential solution is using archival tags combined with dead-reckoning, a technique employed in nautical navigation to track animal movement underwater and through dense vegetation. However, this method has not yet been applied to the mapping of complex burrow systems in fossorial species. Our study aims to test this approach by using accelerometers and magnetometers attached to collars on prairie dogs (Cynomys spp.) The goal was to determine if dead-reckoning, based on vectors derived from speed and heading data, could accurately track prairie dog movements and, by extension, map the structure of their burrows. To evaluate this method, we deployed 12 tags on wild animals and recoded acceleration and magnetometer data at 40Hz and 16 Hz respectively.. Animals were forced to move through artificial burrows, plastic tubes, whose shape was known prior to tracking of wild movements. The former was used to validate dead reckoning trajectory estimation. We compared the accuracy of five techniques for estimating speed: Vectoral Dynamic Body Acceleration (VeDBA), Vectorial Static Body Acceleration (VeSBA), step count, and constant speed.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAcceleration signals reliably indicated traveling behavior. Among the methods tested, the Vectorial sum of Dynamic Body Acceleration (VeDBA) proved to be the most accurate proxy for speed, with the smallest mean error. Additionally, the dead-reckoning process demonstrated sufficient precision, with an mean error of 15.38 cm, providing a clear representation of animal movements and the layout of burrows in free-roaming prairie dogs. The animals generally moved at speeds ranging from 0.01 to 1.42 m/s.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThis work highlights the importance of dead-reckoning in studying space use by fossorial animals, essential for understanding how they interact with their environment, including vegetation and topography. Beyond environmental context, analyzing the specifics of animal movement\u0026mdash;such as path tortuosity, speed, step lengths, and turn angles\u0026mdash;is crucial for insights into species diffusion, foraging strategies, and vigilance. Additionally, research on immovable burrows offers a model for \"city\" construction, as prairie dog burrow networks, including ventilation, high-speed sections, predator escape routes, gathering spots, storage, and sleeping areas giving us a unique insight into the species societal needs and better predict the spread of disease.\u003c/p\u003e","manuscriptTitle":"Using dead-reckoning to track movements and map burrows of fossorial species","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-18 13:03:16","doi":"10.21203/rs.3.rs-4945336/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-12-23T12:11:47+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-12-13T03:57:28+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-29T11:25:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"46455320854616378844958669470138704161","date":"2024-11-11T09:45:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"232405603702440421664712841014063940768","date":"2024-11-10T17:14:12+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-25T13:37:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"385679202905394387784984826082691397","date":"2024-09-19T06:48:23+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-08-27T09:22:53+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-08-21T20:23:55+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-08-21T02:55:45+00:00","index":"","fulltext":""},{"type":"submitted","content":"Animal Biotelemetry","date":"2024-08-20T13:25:19+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"animal-biotelemetry","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"abit","sideBox":"Learn more about [Animal Biotelemetry](http://animalbiotelemetry.biomedcentral.com)","snPcode":"40317","submissionUrl":"https://submission.nature.com/new-submission/40317/3","title":"Animal Biotelemetry","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"48318737-f0a3-46d4-9702-34dbbedd4108","owner":[],"postedDate":"September 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-04-07T16:07:33+00:00","versionOfRecord":{"articleIdentity":"rs-4945336","link":"https://doi.org/10.1186/s40317-025-00408-2","journal":{"identity":"animal-biotelemetry","isVorOnly":false,"title":"Animal Biotelemetry"},"publishedOn":"2025-04-04 15:57:13","publishedOnDateReadable":"April 4th, 2025"},"versionCreatedAt":"2024-09-18 13:03:16","video":"","vorDoi":"10.1186/s40317-025-00408-2","vorDoiUrl":"https://doi.org/10.1186/s40317-025-00408-2","workflowStages":[]},"version":"v1","identity":"rs-4945336","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4945336","identity":"rs-4945336","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}