Skip to main content
Download PDF

Dead-reckoning elucidates fine-scale habitat use by European badgers Meles meles

Animal Biotelemetry volume 10, Article number: 10 (2022) Cite this article

Abstract

Background

Recent developments in both hardware and software of animal-borne data loggers now enable large amounts of data to be collected on both animal movement and behaviour. In particular, the combined use of tri-axial accelerometers, tri-axial magnetometers and GPS loggers enables animal tracks to be elucidated using a procedure of ‘dead-reckoning’. Although this approach was first suggested 30 years ago by Wilson et al. (1991), surprisingly few measurements have been made in free-ranging terrestrial animals. The current study examines movements, interactions with habitat features, and home-ranges calculated from just GPS data and also from dead-reckoned data in a model terrestrial mammal, the European badger (Meles meles).

Methods

Research was undertaken in farmland in Northern Ireland. Two badgers (one male, one female) were live-trapped and fitted with a GPS logger, a tri-axial accelerometer, and a tri-axial magnetometer. Thereafter, the badgers’ movement paths over 2 weeks were elucidated using just GPS data and GPS-enabled dead-reckoned data, respectively.

Results

Badgers travelled further using data from dead-reckoned calculations than using the data from only GPS data. Whilst once-hourly GPS data could only be represented by straight-line movements between sequential points, the sub-second resolution dead-reckoned tracks were more tortuous. Although there were no differences in Minimum Convex Polygon determinations between GPS- and dead-reckoned data, Kernel Utilisation Distribution determinations of home-range size were larger using the former method. This was because dead-reckoned data more accurately described the particular parts of landscape constituting most-visited core areas, effectively narrowing the calculation of habitat use. Finally, the dead-reckoned data showed badgers spent more time near to field margins and hedges than simple GPS data would suggest.

Conclusion

Significant differences emerge when analyses of habitat use and movements are compared between calculations made using just GPS data or GPS-enabled dead-reckoned data. In particular, use of dead-reckoned data showed that animals moved 2.2 times farther, had better-defined use of the habitat (revealing clear core areas), and made more use of certain habitats (field margins, hedges). Use of dead-reckoning to provide detailed accounts of animal movement and highlight the minutiae of interactions with the environment should be considered an important technique in the ecologist’s toolkit.

Introduction

Technological advancement including the miniaturisation of animal-borne data loggers now allows intricate data to be collected on animal movement and behaviour [1,2,3] with animal-attached tri-axial accelerometers and tri-axial magnetometers proving to be particularly useful in obtaining information on animals that are elusive or otherwise difficult to study [4]. Importantly, these same sensors are pivotal for the process of ‘dead-reckoning’, which uses movement vectors to reconstruct animal paths in detail (e.g. [5,6,7,8,9]). Such resolution allows inferences to be made about the relationship between the behaviour and location, particularly if the process is GPS-enhanced [6, 10]. However, while this technique has been demonstrated in humans [11] and illustrated in animals [12], there have been relatively few attempts to quantify the movements of free-ranging terrestrial animals. Such analyses should, however, help highlight the limitations of GPS systems used alone, particularly as regards infrequent fixes and linear interpolation between them [10], as well as provide a new standard in how we can define animal interaction with various landscape features, including how animals allocate behaviour to space.

The European Badger (Meles meles) is a semi-fossorial nocturnal social mammal and one of the largest terrestrial carnivore species currently inhabiting the British Isles [13,14,15]. It is also an important animal to study because of its implication in the transmission of bovine tuberculosis (bTB) to cattle [13, 16]. However, despite many decades of research, the exact mode of bTB transmission remains unclear (e.g. [17]). Indeed, knowledge of precise badger movements and behaviours, and interactions with the environment is considered important to further our understanding of potential disease transmission routes [18]. Here, we demonstrate how animal-borne GPS-enhanced dead-reckoning systems deployed on free-ranging badgers can provide information on their intricate movement paths which can then be used to acquire a better understanding of their spatial ecology, and provide particular insight with respect to the transmission of disease.

Methods

Study site and animals

The study was undertaken in an area of mixed arable and grazing farmland within an approximate 100 km2 area in rural Co. Down, Northern Ireland, near the town of Banbridge, using animals that were captured during the ‘Test and Vaccinate or Remove’ (TVR) project [19, 20]. The area is associated with persistent bTB cattle breakdowns [21]. The landscape comprises, predominantly, of improved grassland, with fields enclosed by hedges [22]. Prior to animal trapping, areas were surveyed for signs of badgers, which included badger main setts (burrow systems), outlier setts, latrines, crossing points, paths and areas of soil that had been excavated. Thereafter, traps were pre-baited for approximately seven days, and then set. For this study, two badgers, an adult male (9.67 kg) and an adult female (8.64 kg) were captured and anaesthetised [19]. Individuals were fitted with a neck collar containing a GPS logger (Tellus Light, Followit, Sweden) which was programmed to record positional fixes every 60 min between 21:00 and 04:00, which coincided with their most active periods. Neck collars were also fitted with a ‘daily diary’ (‘DD’) logger (Wildbyte-technologies, Swansea, UK) which contained a temperature sensor, a tri-axial magnetometer and a tri-axial accelerometer, programmed to record continuously at 7, 40 and 40 Hz, respectively, on each channel [23]. Loggers were encapsulated within 3D printed styrene plastic cases, each with a 3.6 V battery (1/2 AA 3.6 V 1200 mAh Lithium Thionyl Chloride, Saft, Levallois-Perret, France) that was secured to the collar that contained the GPS (total weight c. 270 g). After collars were deployed, badgers were returned to the trap at the point of capture until they had sufficiently recovered from the anaesthetic before being released. Both animals were recaptured after one week, by re-baiting the traps at the original locations. Thereafter, the DD loggers were removed, and data were downloaded. GPS, accelerometer, and magnetometer data were used to reconstruct the badger movement paths using dead-reckoning (below in brief, for detailed description of the calculations involved see [9]).

Sett use

Before dead-reckoned paths could be determined, it was necessary to establish when badgers were underground within their sett, and therefore to ensure that reconstructed tracks were representative of the badger’s movements outside the sett. This was achieved using acceleration and temperature measurements. For example, Vectorial Dynamic Body Acceleration, ‘VeDBA’, [24] became dampened immediately when an animal moved underground (e.g. from c. 0.25 g to < 0.1 g), which was presumably indicative of restricted movements within the confines of the sett tunnels, and temperature measurements increased by about 4 °C over the period of 30 min (Fig. 1). Conversely, VeDBA values increased, and temperatures fell when animals emerged from their setts. For example, on one night, within 30 min of entering the sett, temperature measurements rose from 21.6 to 25.6 °C. This approach allowed us to establish when individuals emerged at the beginning of the night (e.g. typically at around 21:00), when they intermittently went below ground during the night, and when they returned to the sett at the end of the night (e.g. typically at 05:00) (Fig. 2).

Fig. 1
figure 1

Activity measured as vectorial dynamic body acceleration (VeDBA, g) and ambient temperature (oC) measured when a badger was outside and inside its sett. The badger entered the sett at approximately 22:05 and exited again at 23:06. The shaded area represents period the badger spent inside the sett. The solid line indicates VeDBA and the dashed line indicates ambient temperature. Note the slight delay in the temperature sensor recording lower values when the animal exited the sett

Full size image
Fig. 2
figure 2

Periods spent outside the sett represented by black bars for a female (top) and male (lower) badger for 7 days after deployment of loggers

Full size image

Dead-reckoning

For each night, between the hours of 21:00 and 04:00 when badgers were out of the sett, GPS and DD data were used to reconstruct dead-reckoned paths [6, 9] (Fig. 3). Vectorial Dynamic Body Acceleration was calculated as a proxy of speed [9, 10, 12] and Framework 4 software ([12], cf. [9]) was used to integrate hourly GPS fixes with the DD data. For this, calculations of an initial dead-reckoned track without reference to the GPS data were produced, using a nominal value for the gradient between VeDBA and speed. Then, using the time reference between the DD and the GPS, points within the GPS tracks were allocated to the time-matched points within the first iteration of the dead-reckoned tracks before the speed ~ VeDBA gradient was altered so that the positions of both systems accorded.

Fig. 3
figure 3

A Example of a GPS-enabled dead-reckoned movement path by a female badger over the course of a nightly outing. B, C Show expanded sections illustrating movements when the badger visited a field and interacted with a hedgerow, respectively. GPS fixes are shown as white dots. Straight-line movements between GPS fixes are shown by dashed lines. The GPS-enabled dead-reckoned path is shown by a black continuous line

Full size image

Home-range and land use

Home-ranges were calculated for both badgers using the package ‘adehabitatHR’ in R (version 3.4.3) [25]. Home-ranges were calculated by two means; (i) using just the hourly GPS data and (ii) using the GPS-corrected dead-reckoned locational fixes (with positions calculated at 40 Hz). Omission of the outermost 5% of locations was undertaken to remove outliers and location errors [26]. Two estimates of home-range were then determined. These were 95% kernel utilisation distribution (KD95), and 95% Minimum Convex Polygons (MCP95) (cf. [27]). The home-ranges of each animal, calculated from GPS and 'GPS-corrected dead-reckoned data' data, for both MCP95 and KD95, were exported as polygon vector shapefiles, transferred into QGIS 3.6.3 [28], and the area of each animal’s home-range calculated in km2 using QGIS.

In addition to home-ranges, for each night that badger activity was recorded, the movement paths of both badgers were determined using both the hourly GPS data and the GPS-enabled dead-reckoned positional fixes. These fixes were overlaid onto 30 cm Digital Globe satellite imagery (Fig. 4) imported into QGIS via the OpenLayers Plugin [29]). We delineated four identifiable landscape features (fields, hedges, buildings, roads) (cf. [30]) from the Digital Globe satellite imagery by hand-drawing polygons representing individual features onto a newly created vector dataset. We then examined the times that both badgers spent in proximity to these landscape features. We considered a badger to be ‘in proximity’ of a feature if it was within 20 m of that particular feature [31]. This allowed for potential inaccuracies of the GPS loggers [32] and the effects of those features on badger behaviour [33]. The number of GPS fixes within 20 m of a particular landscape feature were summed and assumed to be proportional to the length of time spent there (e.g. for GPS locations recorded once per hour, each locational fix was assumed to record 1 h at that point). For the dead-reckoned data, the number of location points within 20 m proximity of various features was also summed and then divided by the sample rate (40 Hz) to provide the time (in seconds) within that landscape feature. We were therefore able to compare the proportion of time each night that badgers spent within 20 m of habitat features according to the GPS-enabled dead-reckoned data and GPS data. All GIS analyses were performed using the UTM Zone 29 N projection.

Fig. 4
figure 4

Movement paths and home-ranges of a female badger, measured using GPS (white circles) and GPS-enabled dead-reckoning (solid black lines), over seven consecutive nights of activity. Panel A shows the home-range calculated using the 95% minimum convex polygon method (MCP95) and panel B shows the home-range calculated using the 95% kernel utilisation distribution method (KD95). Grey shaded area denotes the home-range calculated using just the hourly GPS data. The black cross-hatched area indicates the home-range calculated using the GPS-enabled dead-reckoned data at 40 Hz

Full size image

Track length

To determine the distances travelled each night, by each badger, the straight-line distance between consecutive co-ordinates were calculated for both the GPS (hourly) and dead-reckoned (40 Hz) data. For this, the GPS and GPS-enabled dead-reckoned co-ordinates were uploaded into R (version 3.4.3) and the geodesic distance (the shortest distance between two points on a curved surface [i.e. the Earth]) between consecutive co-ordinates [34] calculated before summing all data.

Statistical analyses

For path length, home-range areas and land use, differences in measurements between GPS and GPS-enabled dead-reckoned data were assessed using paired t-tests. Differences in path length and land use between badgers were assessed using Welch’s t-tests, for both GPS and GPS-enabled dead-reckoned data. Distances are reported ± standard deviations. The proportion of time spent near buildings and roads were linearly transformed by adding a constant of 1.0 to account for values equal to zero, to allow log10-transformation to achieve normality prior to analysis [35]. Differences in landscape use were assessed using linear models for both GPS and GPS-enabled dead-reckoned data. Here, the time that badgers spent near landscape features was included as the dependent variable whilst the landscape feature was included as the independent variable. Post hoc pairwise comparisons of time spent near each landscape feature was carried out using a Tukey honestly significant difference (HSD) test. Statistical analyses were evaluated to a 0.05 p-value level. All data analyses were performed in R (version 3.4.3) [25].

Results

Sett use

Badgers were predominantly nocturnal, emerging from the sett close to sunset (mean first emergence time = 20:12) and returning close to sunrise (mean last sett return time = 06:33) (Fig. 2). The time the badgers spent above ground, outside their sett, varied from 4 h 54 min to 10 h 22 min.

Calculated path lengths using GPS and dead-reckoning data

There was a significant difference in calculated path distances travelled between GPS data and GPS-enabled dead-reckoned data, with badgers measured to travel more than twice as far when using the GPS-enabled dead-reckoned data than with only GPS data (2.53 ± 1.79 and 1.14 ± 0.75 km, respectively, t1,13 = 4.76, p < 0.001, Fig. 4). The path lengths between the two individual badgers did not differ, either when they were calculated using the GPS (0.96 ± 0.59 and 1.32 ± 0.89 km; t1,10.43 = 0.88, p = 0.40) or the GPS-enabled dead-reckoned (2.10 ± 1.33 and 2.96 ± 2.19 km; t1,9.87 = 0.89, p = 0.39) data.

Home-range area

There was no significant difference in home-range size of each badger when calculated using the MCP95 method between GPS and GPS-enabled dead-reckoned data (t1,1 = 1.16, p = 0.45). Home-range areas calculated for both individuals using MCP95 were 0.38 ± 0.08 km2 and 0.59 ± 0.18 km2 for GPS and GPS-enabled dead-reckoned data, respectively. However, home-range sizes using the KD95 method for both individuals together were significantly larger when calculated using the GPS data than the dead-reckoned data (1.15 ± 0.16 km2 and 0.26 ± 0.09 km2, respectively, t1,1 = 17.8, p = 0.04, Fig. 4).

Land use

Differences in land use assessed using both GPS and GPS-enabled dead-reckoned data

Using just the GPS data, significant differences were noted in the times badgers spent near various landscape features (F3, 52 = 105.10, p < 0.001) (Fig. 5). Post hoc analyses revealed that badgers spend most of their time within fields, followed by hedges, and least time near buildings or roads. By comparison, analysis of the GPS-enabled dead-reckoned data produced different results; badgers differed in the time they spent within various landscape features (F3,52 = 122.11, p < 0.001), in this case, a similar amount of time was spent within fields and near hedges, and less time spent near roads and buildings.

Fig. 5
figure 5

Proportion of time spent within various landscape features by two badgers according to whether the positional data were derived from GPS or GPS-enabled dead-reckoned data, labelled “GPS” and “DR”, respectively

Full size image

Comparison of land use between GPS and GPS-enabled dead-reckoned data

Significant differences were noted in the proportions of time badgers spent within fields (t1,13 = 2.43, p = 0.03), and in the proportion of time spent near hedges (t1,13 = 3.05, p = 0.009) when the times within these habitats compared using GPS and GPS-enabled dead-reckoned data. Use of GPS-enabled dead-reckoned data revealed that badgers spent less time in fields and more time close to hedges, than did use of GPS data (Fig. 5). There were no significant differences in the calculated proportions of time spent close to buildings (t1,13 = 0.45, p = 0.66) or close to roads (t1,13 = 0.75, p = 0.47), when these were measured using data from GPS and GPS-enabled dead-reckoning, respectively.

Differences in land use between individual badgers

There were no significant differences in the proportions of time spent within fields (t1,9.62 = 0.50, p = 0.63), near to hedges (t1,9.46 = 0.98, p = 0.35), buildings (t1,11.66 = − 0.39, p = 0.70) or roads (t1,11.66 = 0.20, p = 0.84) between the two badgers when calculated using just the GPS data (Fig. 6). Similarly, there were no significant differences in the proportions of time spent within fields (t1,11.62 = 0.91, p = 0.38), near to hedges (t1,11.95 = 1.27, p = 0.23), buildings (t1,8.86 = 0.22, p = 0.83) or roads (t1,10.15 = 0.40, p = 0.70) between the two badgers when calculated using GPS-enabled dead-reckoned data.

Fig. 6
figure 6

Proportion of time spent within various landscape features by two different badgers (top panel A = female; lower panel B = male) according to whether the positional data were derived from GPS or GPS-enabled dead-reckoned data, labelled “GPS” and “DR”, respectively

Full size image

Discussion

The vectorial process inherent within dead-reckoning can provide detailed information on an animal’s location between sequentially collected GPS points [6] so that, dead-reckoned errors [6, 7, 36] can be corrected to provide information on animal movements with respect to their environment [9, 10]. Although this method was initially employed to deduce the movements of elusive marine species where location measurements are sporadic, and when ground-truthing can generally only occur when the animal surfaces [5, 37], this approach has great potential for the terrestrial environment [6, 38] where periodic ground-truthing using GPS location co-ordinates is less problematic [12, 39]. Nevertheless, current application of this technique to determine movement paths of free-ranging terrestrial species is surprisingly limited ([7, 9, 39] cf. semiaquatic beavers [40]).

The purpose of our study was to demonstrate the extent to which data on the movement paths and habitat use of an important but elusive terrestrial carnivore, the Eurasian badger, depend on whether animal position over time is determined by GPS or GPS-enabled dead-reckoning. Given that our study only elucidates the movements of two free-roaming badgers within a rural mixed arable and pasture farming area of Northern Ireland, we obviously cannot allude to population-level behaviours. Nevertheless, even with our small sample size, a number of important issues emerge. In particular, these issues relate to the extent to which well-defined movements of badgers, including their interactions between various habitat features such as farm buildings and livestock pasture, might elucidate their potential to transmit bovine tuberculosis to cattle [13, 16]. Previous studies have examined badger movements using GPS loggers to measured aspects such as distances travelled, home-ranges and landscape use (e.g. [18, 41, 42]). We note in this, that both our GPS and GPS-enabled dead-reckoning approaches highlighted the general importance of fields, presumed useful for foraging [18, 43], and that both approaches ascribed little time spent near roads or buildings (Fig. 4), which accords with observations made by Mullen et al. [44] and Campbell et al. [16]. However, our work also indicates that such GPS-based estimates may need to be modified for a more comprehensive picture of badger movement ecology. This is manifest by our study that reveals, for instance, that GPS-enabled dead-reckoned data provides greater estimates of distances travelled, in this case c.2.2 times longer, than GPS data alone, an obvious consequence of determining distance in a more tortuous path [6]. At face value, assuming that badgers have potential to transmit disease as a function of the area traversed (simplistically, this being given by the path length multiplied by some nominal ‘infection width’), this means that GPS-enabled dead-reckoning ascribes a probability of disease transmission that is proportionately higher than that deduced from GPS data alone. But, this difference is perhaps less important than space-specific patterns that come from GPS-enabled dead-reckoning compared to just GPS. A good example of this is the finding that GPS-enabled dead-reckoned badgers spent proportionately less time within fields and more near hedgerows (Fig. 4) than the GPS data alone. This is particularly relevant given that hedgerows constitute a much smaller proportion of the total area and so, all things being equal, would be expected to have a correspondingly small time allocation. Hedges and boundaries may, in fact, be areas where badger setts are located, where the animals forage, or where they form latrines [45]. All of this is relevant because badger excreta may be concentrated within such areas [46], again increasing the risk of disease spread within and between such highly utilised sites [47]. In fact, the specifics provided by GPS-enhanced dead-reckoned data should make it possible to provide a detailed probabilistic space–time use approach for both badgers and cattle, if the livestock are similarly tagged (cf. [39]). We envisage a modelling approach that could create a badger area density map incorporating a time element so that both absolute density and the potential for disease transmission decaying over time since the last time the area was visited by the different individuals [48] to provide pivotal information regarding disease transmission. Livestock space and time use of the area could be superimposed on this to help elucidate the probability of cross-species infection [49]. We note that a similar approach has been adopted for the Covid-19 crisis, with a major part of the predictive capacity of transmission being the accurate definition of space use by individuals over time [50]. At a coarser scale, all this makes effective calculation of home-range more than just academic. We noted appreciable differences in apparent habitat utilisation between GPS and GPS-enhanced dead-reckoned data with badger home-ranges calculated using MCP95 from GPS data being 57% smaller. Conversely, home-ranges calculated using KD95 with GPS data were 78% larger than those calculated with the GPS-enhanced dead-reckoned data (Fig. 6). These discrepancies occur because the GPS-enhanced dead-reckoned data produces a larger polygon for the MCP95 because the home-range estimates include internal areas which animals do not necessarily use (e.g. the centre of the field if the badger only walked around the periphery, which is only likely to be shown by GPS-enhanced dead-reckoning). In contrast, KD95 provide home-range estimates based on the clustering of co-ordinates, and the higher frequency provided by GPS-enhanced dead-reckoning provides a tighter clustering of points where the badgers had been, excluding unused areas and producing smaller home-range areas.

Conclusions

Our results, although only derived from two animals over a total of 14 badger days, clearly show the advantages of GPS-enhanced dead-reckoning compared to temporally coarser position-determining systems such as GPS for examining factors important in disease transmission. Although we note that it is possible to derive GPS positions at, e.g. 1 Hz, this necessitates a substantial power source [7] and also does not guarantee the accuracy of the GPS, which is affected by landscape features such as mountains and trees [51, 52]. We also note that such an approach should help elucidate the extent to which culling badgers, in an effort to reduce bTB transmission, may affect the behaviour of conspecifics [53,54,55]. Indeed, the ability to describe in detail what animals do in relation to environmental circumstance must be considered a pivotal point in predicting the spread of any transmittable disease and GPS-enhanced dead-reckoning seems a powerful way of moving closer to this goal.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

References

  1. Ropert-Coudert Y, Wilson RP. Trends and perspectives in animal-attached remote sensing. Front Ecol Environ. 2005;3(8):437–44.

    Google Scholar 

  2. Kays R, Crofoot MC, Jetz W, Wikelski M. Terrestrial animal tracking as an eye on life and planet. Science. 2015;348:6240.

    Google Scholar 

  3. Chung H, Lee J, Lee WJ. A review: marine bio-logging of animal behaviour and ocean environments. Ocean Sci J. 2021;2021(56):117–31.

    Google Scholar 

  4. Brown DD, Kays R, Wikelski M, Wilson R, Klimley AP. Observing the unwatchable through acceleration logging of animal behavior. Anim Biotelem. 2013;1(1):20.

    Google Scholar 

  5. Wilson RP, Wilson MP, Link R, Mempel H, Adams NJ. Determination of movements of African penguins Spheniscus demersus using a compass system: dead-reckoning may be an alternative to telemetry. J Exp Biol. 1991;157(1):557–64.

    Google Scholar 

  6. Bidder OR, Walker JS, Jones MW, Holton MD, Urge P, Scantlebury DM, Marks NJ, Magowan EA, Maguire IE, Wilson RP. Step by step: reconstruction of terrestrial animal movement paths by dead-reckoning. Mov Ecol. 2015;3(1):1–6.

    Google Scholar 

  7. Dewhirst OP, Evans HK, Roskilly K, Harvey RJ, Hubel TY, Wilson AM. Improving the accuracy of estimates of animal path and travel distance using GPS drift-corrected dead-reckoning. Ecol Evol. 2016;6(17):6210–22.

    PubMed  PubMed Central  Google Scholar 

  8. Wright BM, Ford JK, Ellis GM, Deecke VB, Shapiro AD, Battaile BC, Trites AW. Fine-scale foraging movements by fish-eating killer whales (Orcinus orca) relate to the vertical distributions and escape responses of salmonid prey (Oncorhynchus spp.). Mov Ecol. 2017;5(1):3.

    PubMed  PubMed Central  Google Scholar 

  9. Gunner RM, Holton MD, Scantlebury DM, van Schalkwyk OL, English HM, Williams HJ, Hopkins P, Quintana F, Gómez Laich A, Börger L, Redclife J, Yoda K, Yamamoto T, Ferreira S, Govender D, Viljoen P, Bruns A, Bell SH, Marks NJ, Bennett NC, Tonini MH, Duarte CM, van Rooyen MC, Bertelsen MF, Tambling CJ, Wilson RP. Dead-reckoning animal movements in R: a reappraisal using Gundog. Tracks Animal Biotelemetry. 2021;9:23.

    Google Scholar 

  10. Gunner RM, Holton MD, Scantlebury DM, Hopkins P, Shepard ELC, Fell AJ, Garde B, Quintana F, Gómez‑Laich A, Yoda K, Yamamoto T, English H, Ferreira S, Govender D, Viljoen P, Bruns A, Louis van Schalkwyk OL, Cole NC, Tatayah V, Börger L, Redclife J, Bell SH, Marks NJ, Bennett NC, Tonini MH, Williams HJ, Duarte CM, van Rooyen MC, Bertelsen MF, Tambling CJ, Wilson RP. How often should dead-reckoned animal movement paths be corrected for drift? Animal Biotelemetry. 2021; 9:43.

  11. Kang W, Han Y. SmartPDR: smartphone-based pedestrian dead-reckoning for indoor localization. IEEE Sens J. 2014;15(5):2906–16.

    Google Scholar 

  12. Walker JS, Jones MW, Laramee RS, Holton MD, Shepard EL, Williams HJ, Scantlebury DM, Nikki JM, Magowan EA, Maguire IE, Bidder OR. Prying into the intimate secrets of animal lives; software beyond hardware for comprehensive annotation in ‘Daily Diary’tags. Mov Ecol. 2015;3(1):1–6.

    Google Scholar 

  13. Donnelly CA, Woodroffe R, Cox DR, Bourne FJ, Cheeseman CL, Clifton-Hadley RS, Wei G, Gettinby G, Gilks P, Jenkins H, Johnston WT. Positive and negative effects of widespread badger culling on tuberculosis in cattle. Nature. 2006;439(7078):843–6.

    CAS  PubMed  Google Scholar 

  14. Bateman PW, Fleming PA. Big city life: carnivores in urban environments. J Zool. 2012;287(1):1–23.

    Google Scholar 

  15. Byrne AW, Sleeman DP, O'Keeffe J, Davenport J. The ecology of the European badger (Meles meles) in Ireland: a review. Biol Envirn Proc R Ir Acad. 2012; 105–132.

  16. Campbell EL, Byrne AW, Menzies FD, McBride KR, McCormick CM, Scantlebury M, Reid N. Interspecific visitation of cattle and badgers to fomites: a transmission risk for bovine tuberculosis? Ecol Evol. 2019;9(15):8479–89.

    PubMed  PubMed Central  Google Scholar 

  17. Allen AR, Skuce RA, Byrne AW. Bovine tuberculosis in Britain and Ireland–A perfect storm? the confluence of potential ecological and epidemiological impediments to controlling a chronic infectious disease. Front Vet Sci. 2018;5:109.

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Woodroffe R, Donnelly CA, Ham C, Jackson SY, Moyes K, Chapman K, Stratton NG, Cartwright SJ. Badgers prefer cattle pasture but avoid cattle: implications for bovine tuberculosis control. Ecol lett. 2016;19(10):1201–8.

    PubMed  Google Scholar 

  19. Courcier EA, Pascual-Linaza AV, Arnold ME, McCormick CM, Corbett DM, O’Hagan MJ, Collins SF, Trimble NA, McGeown CF, McHugh GE, McBride KR. Evaluating the application of the dual path platform VetTB test for badgers (Meles meles) in the test and vaccinate or remove (TVR) wildlife research intervention project in Northern Ireland. Res Vet Sci. 2020;130:170–8.

    CAS  PubMed  Google Scholar 

  20. Menzies FD, McCormick CM, O'Hagan MJH, Collins SF, McEwan J, McGeown CF, McHugh GE, Hart CD, Stringer LA, Molloy C, Burns G, McBride SJ, Doyle LP, Courcier EA, McBride KR, McNair J, Thompson S, Corbett DM, Harwood RG, Trimble NA. Test and vaccinate or remove: methodology and preliminary results from a badger intervention research project. Vet Rec. 2021:e248

  21. Wright DM, Reid N, Montgomery WI, Allen AR, Skuce RA, Kao RR. Herd-level bovine tuberculosis risk factors: assessing the role of low-level badger population disturbance. Sci Rep. 2015;5:13062.

    CAS  PubMed  PubMed Central  Google Scholar 

  22. Hall VA. Landscape development in northeast Ireland over the last half millennium. Rev Palaeobot Palynol. 1994;82(1–2):75–82.

    Google Scholar 

  23. Wilson PR, Shepard ELC, Liebsch N. Prying into the intimate details of animal lives: use of a daily diary on animals. Endangered Species Res. 2008;4:123–37.

    Google Scholar 

  24. Qasem L, Cardew A, Wilson A, Griffiths I, Halsey LG, Shepard EL, Gleiss AC, Wilson R. Tri-axial dynamic acceleration as a proxy for animal energy expenditure; should we be summing values or calculating the vector? PLoS ONE. 2012;7(2):e31187.

    CAS  PubMed  PubMed Central  Google Scholar 

  25. R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria; 2017. https://www.R-project.org/.

  26. Walter WD, Fischer JW, Baruch-Mordo S, VerCauteren KC. What is the proper method to delineate home-range of an animal using today’s advanced GPS telemetry systems: the initial step. In: Krejcar O (ed) Modern telemetry. IntechOpen, 2011, pp 249–268

  27. Molina-Vacas G, Bonet-Arboli V, Rafart-Plaza E, Rodriguez-Teijeiro JD. Spatial ecology of European badgers (Meles meles L.) in Mediterranean habitats of the north-eastern Iberian Peninsula. I: home-range size, spatial distribution and social organization. Vie Milieu. 2009;59(3):223.

    Google Scholar 

  28. Quantum Geographic Information System Development Team. QGIS Geographic Information System. Open Source Geospatial Foundation Project; 2019. http://qgis.osgeo.org

  29. Kalberer, P. & Walker, M. 2010. OpenLayers QGIS OpenLayers Plugin. https://github.com/sourcepole/qgis-openlayers-plugin. Accessed 14 Jan 2022.

  30. Horn JA, Mateus-Pinilla N, Warner RE, Heske EJ. Home-range, habitat use, and activity patterns of free-roaming domestic cats. J Wildlife Manage. 2011;75(5):1177–85.

    Google Scholar 

  31. McClune DW, Marks NJ, Delahay RJ, Montgomery WI, Scantlebury DM. Behaviour-time budget and functional habitat use of a free-ranging European badger (Meles meles). Anim Biotelem. 2015;3(1):7.

    Google Scholar 

  32. Hast M, Searle KM, Chaponda M, Lupiya J, Lubinda J, Sikalima J, Kobayashi T, Shields T, Mulenga M, Lessler J, Moss WJ. The use of GPS data loggers to describe the impact of spatio-temporal movement patterns on malaria control in a high-transmission area of northern Zambia. Int J Health Geogr. 2019;18(1):19.

    PubMed  PubMed Central  Google Scholar 

  33. Feore S, Montgomery WI. Habitat effects on the spatial ecology of the European badger (Meles meles). J Zool. 1999;247(4):537–49.

    Google Scholar 

  34. Hikmans RJ. Introduction to the ”geosphere” package (Version 1.5–5). June 2016. https://mran.microsoft.com/snapshot/2017-08-13/web/packages/geosphere/vignettes/geosphere.pdf

  35. Wooldridge JM. Introductory econometrics: a modern approach. Nelson Education; 2016.

  36. Wilson RP, Liebsch N, Davies IM, Quintana F, Weimerskirch H, Storch S, Lucke K, Siebert U, Zankl S, Müller G, Zimmer I. All at sea with animal tracks; methodological and analytical solutions for the resolution of movement. Deep Sea Res. Part II Top. Stud. Oceanogr. 2007; 54(3–4):193–210.

  37. Wilson RP, Wilson MP. Dead-reckoning: a new technique for determining penguin movements at sea. Meeresforsch. 1988;32(2):155–8.

    Google Scholar 

  38. Bidder OR, Soresina M, Shepard EL, Halsey LG, Quintana F, Gómez-Laich A, Wilson RP. The need for speed: testing acceleration for estimating animal travel rates in terrestrial dead-reckoning systems. Zoology. 2012;115(1):58–64.

    PubMed  Google Scholar 

  39. di Virgilio A, Morales JM, Lambertucci SA, Shepard EL, Wilson RP. Multi-dimensional Precision Livestock Farming: a potential toolbox for sustainable rangeland management. PeerJ. 2018;6:e4867.

    PubMed  PubMed Central  Google Scholar 

  40. Mortensen RM, Stefanie Reinhardt S, Hjønnevåg ME, Wilson RP, Rosell F. Aquatic habitat use in a semi-aquatic mammal: the Eurasian beaver. Anim Biotelemetry. 2021;9:35. https://doi.org/10.1186/s40317-021-00259-7.

    Article  Google Scholar 

  41. Kauhala K, Holmala K. Landscape features, home-range size and density of northern badgers (Meles meles). Ann Zool Fenn. 2011;48(4):221–33.

    Google Scholar 

  42. Gaughran A, Kelly DJ, MacWhite T, Mullen E, Maher P, Good M, Marples NM. Super-ranging. A new ranging strategy in European badgers. PLoS ONE. 2018;13(2):e0191818.

    PubMed  PubMed Central  Google Scholar 

  43. Mullen EM, MacWhite T, Maher PK, Kelly DJ, Marples NM, Good M. Foraging Eurasian badgers Meles meles and the presence of cattle in pastures. Do badgers avoid cattle? Appl Anim Behav Sci. 2013;144(3–4):130–7.

    Google Scholar 

  44. Mullen EM, MacWhite T, Maher PK, Kelly DJ, Marples NM, Good M. The avoidance of farmyards by European badgers Meles meles in a medium density population. Appl Anim Behav Sci. 2015;171:170–6.

    Google Scholar 

  45. O’Brien J, Elliott S, Hayden TJ. Use of hedgerows as a key element of badger (Meles meles) behaviour in Ireland. Mamm Biol. 2016;81(1):104–10.

    Google Scholar 

  46. Roper TJ, Conradt J, Butler SE, Ostler CJ, Schmid TK. Territorial marking with faeces in badgers (Meles meles): a comparison of boundary and hinterland latrine use. Behaviour. 1993;127:289–307.

    Google Scholar 

  47. Scantlebury M, Hutchings MR, Allcroft DJ, Harris S. Risk of disease from wildlife reservoirs: badgers, cattle, and bovine tuberculosis. J Dairy Sci. 2004;87(2):330–9.

    CAS  PubMed  Google Scholar 

  48. Courtenay O, Reilly LA, Sweeney FP, Hibberd V, Bryan S, Ul-Hassan A, Newman C, Macdonald DW, Delahay RJ, Wilson GJ, Wellington EM. Is Mycobacterium bovis in the environment important for the persistence of bovine tuberculosis? Biol Lett. 2006;2(3):460–2.

    CAS  PubMed  PubMed Central  Google Scholar 

  49. Delahay RJ, Ward AI, Walker N, Long B, Cheeseman CL. Distribution of badger latrines in a high-density population: habitat selection and implications for the transmission of bovine tuberculosis to cattle. J Zool. 2007;272(3):311–20.

    Google Scholar 

  50. Cai J, Sun W, Huang J, Gamber M, Wu J, He G. Indirect virus transmission in cluster of COVID-19 cases, Wenzhou, China, 2020. Emerg Infect Dis. 2020;26(6):1343–5.

    CAS  PubMed  PubMed Central  Google Scholar 

  51. Lewis JS, Rachlow JL, Garton EO, Vierling LA. Effects of habitat on GPS collar performance: using data screening to reduce location error. J Appl Ecol. 2007;44(3):663–71.

    Google Scholar 

  52. Camp MJ, Rachlow JL, Cisneros R, Roon D, Camp RJ. Evaluation of global positioning system telemetry collar performance in the tropical Andes of southern Ecuador. Nat Cons. 2016;14(2):128–31.

    Google Scholar 

  53. Tuyttens FA, Delahay RJ, Macdonald DW, Cheeseman CL, Long B, Donnelly CA. Spatial perturbation caused by a badger (Meles meles) culling operation: implications for the function of territoriality and the control of bovine tuberculosis (Mycobacterium bovis). J Anim Ecol. 2000;69(5):815–28.

    CAS  PubMed  Google Scholar 

  54. Riordan P, Delahay RJ, Cheeseman C, Johnson PJ, Macdonald DW. Culling-induced changes in badger (Meles meles) behaviour, social organisation and the epidemiology of bovine tuberculosis. PLoS ONE. 2011;6(12):e28904.

    CAS  PubMed  PubMed Central  Google Scholar 

  55. Ham C, Donnelly CA, Astley KL, Jackson SY, Woodroffe R. Effect of culling on individual badger Meles meles behaviour: potential implications for bovine tuberculosis transmission. J Appl Ecol. 2019;56(11):2390–9.

    PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We would like to thank Shane Collins, Carl McCormick, Nigel Trimble, and all other DAERA staff involved for their kindness and assistance during the fieldwork. We would also like to thank the landowners for giving permission to conduct the work on their land.

Funding

We are grateful to the Department of Agriculture, Environment and Rural Affairs (DAERA), Northern Ireland, for providing a studentships to EM and SR and to the Department for Economy for providing studentships to IEM and SS.

Author information

Author notes

  1. E. A. Magowan and I. E. Maguire contributed equally to this work

Authors and Affiliations

  1. School of Biological Sciences, Queen’s University Belfast, 19 Chlorine Gardens, Belfast, BT9 5DL, Northern Ireland, UK

    E. A. Magowan, I. E. Maguire, S. Smith, S. Redpath, N. J. Marks & D. M. Scantlebury

  2. Department of Biological Sciences, Institute of Environmental Sustainability, Swansea University, Swansea, UK

    R. P. Wilson

  3. Department of Agriculture, Environment and Rural Affairs, Veterinary Epidemiology Unit, Belfast, UK

    F. Menzies & M. O’Hagan

  4. Randox Laboratories Ltd. Crumlin, Antrim, Northern Ireland, UK

    E. A. Magowan & I. E. Maguire

Authors
  1. E. A. Magowan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. I. E. Maguire
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. S. Smith
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. S. Redpath
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. N. J. Marks
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. R. P. Wilson
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. F. Menzies
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. M. O’Hagan
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. D. M. Scantlebury
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

DMS and NM conceived the initial study. DMS, NM, MO’H and FM oversaw the project and fieldwork. EM, SS, SR and DMS conducted fieldwork and data collection. IM, DMS and NM prepared the initial manuscript. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to N. J. Marks or D. M. Scantlebury.

Ethics declarations

Ethics approval and consent to participate

The TVR project operated under the Animals (Scientific Procedures) Act 1986 (ASPA) licence. ASPA licences were issues to DAERA by the Department of Health, Social Services and Public Safety (DHSSPS) in Northern Ireland. Northern Ireland Environment Agency (NIEA) licences were obtained for the capture, sampling and collaring of badgers with accelerometers.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Magowan, E.A., Maguire, I.E., Smith, S. et al. Dead-reckoning elucidates fine-scale habitat use by European badgers Meles meles. Anim Biotelemetry 10, 10 (2022). https://doi.org/10.1186/s40317-022-00282-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s40317-022-00282-2

Keywords

Animal Biotelemetry

ISSN: 2050-3385