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Neckband or backpack? Differences in tag design and their effects on GPS/accelerometer tracking results in large waterbirds

Neckband or backpack? Differences in tag design and their effects on GPS/accelerometer tracking... Background: GPS and accelerometer tracking presently revolutionises the fields of ecology and animal behaviour. However, the effects of tag characteristics like weight, attachment and data quality on study outcomes and animal welfare are important to consider. In this study, we compare how different tag attachment types influence the behav- iour of a group of tagged large waterbirds, GPS accuracy and behaviour classification success from accelerometer data. Results: Both neckband and backpack tags had similar effects on the behaviour of six captive Canada geese ( Branta canadensis), increasing the amount of discomfort behaviour in relation to untagged individuals. Both treatment groups also slightly decreased the amount of foraging, but the duration of neither vigilance nor resting was affected. GPS positions that were filtered with classical GPS platform settings (i.e. smoothing) were more accurate than posi- tions improved by satellite-based differential augmentation. Tag attachment, however, did not induce any differ - ences in position accuracy of both data types. Behaviour classification success was generally similar for neckband and backpack tags. But in detail, behaviours mainly performed by the head like foraging and vigilance were better detected from accelerometer data of neckband tags, whereas behaviours like resting and walking were more success- fully detected from backpack tag data. Conclusion: Our findings suggest that the use of neckband or backpack tags for tracking large waterbirds and their behaviour largely depends on which behaviours are most important to detect. However, for wildlife tracking stud- ies, factors like tag retention time are also of great importance, especially for animals like some goose species that are known to quickly destroy backpack tags. For future studies, we advise to carefully evaluate not only tag weight, but also attachment methods and data quality, because the right choice depends on the research question. This will improve the scope of wildlife tracking even more for various scientific, conservation and management applications. Keywords: Animal tracking, Tag attachment, Tag placement, Tag effects, Differential GPS accuracy, SBAS, Behaviour classification, Accelerometer, Canada goose, Branta c. canadensis Background years [1, 2]. It has matured from being used in simple Animal tracking with GPS has become an important home range studies to diverse applications examining and widely used technique for wildlife research in recent habitat selection, animal migration, behaviour and physi- ology [3–6]. This might partly be due to the improved performance of GPS circuits, but also the addition to the *Correspondence: akoelzsch@orn.mpg.de Department of Migration and Immuno-ecology, Max Planck Institute tags of auxiliary sensors, like depth metres, light sensors for Ornithology, Am Obstberg 1, 78315 Radolfzell, Germany or accelerometers [7, 8]. Accelerometers in particular are Full list of author information is available at the end of the article © 2016 Kölzsch et al. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. 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. Kölzsch et al. Anim Biotelemetry (2016) 4:13 Page 3 of 14 revolutionising the field of animal energetics by enabling accelerometer sensors measuring specific body move - researchers to determine animal behaviour remotely [9– ments, other tag placements have been used (e.g. head 12]. Recently, it has even been shown that acceleration [50], scapulae [51]). In this way, the 3D orientation and data are suitable to determine an animal’s internal states, dynamic movement of specific body parts can be quan - like disease status or stress level [13]. tified, allowing conclusions about the use of muscles or Since the first years of animal tracking, researchers other motile body parts for certain behaviours or physi- have been aware that study outcomes might be affected ological processes. by the quality of the tags [14–17] and how the tags Until very recently, due to the large size of traditional affected the animals [18–23]. Fix rate (amount of success - GPS tags, only the largest birds have been studied with fully acquired positions), GPS accuracy (deviation from GPS telemetry, most notably swans, geese and large rap- true location) and precision (measurement reproduc- tors, often focussing on their migration ecology [52–56]. ibility) were considered repeatedly [24, 25], especially for As some goose species are presently of conservation studies in closed or mountainous habitats [26, 27] or for concern, while others are considered pests, the need to those on fine-scale habitat use [28], social interactions for improve our knowledge about these species has increased disease transmission [29] or GPS-based behaviour seg- [57]. Swans and geese have been shown to display a vari- mentation and classification [30–32]. ety of adverse behaviours during being handled and after In order to improve GPS accuracy, which is one focus of tagging, e.g. increased preening or biting the tag/harness this paper, GPS receiver modules apply smoothing algo- [54, 58–61]. To decrease negative effects of tag aversion rithms to the location data before they are output to the [62], it is necessary to shorten handling time and evaluate user. Typically, these algorithms are based on the extended different types of tag placement and attachment meth - Kalman filters [33]. These filters rely on a variety of move - ods for this species group [58, 63–66]. Welfare assess- ment models and are sometimes referred to as platform ments should be made alongside considerations of data settings [34], the choice of which depends on the expected quality in order to balance animal discomfort against the movements of the object being tracked (e.g. station- amount and quality of scientific knowledge gained from ary, pedestrian walking, motor vehicle). The constraints the respective study [67]. Backpack tags attached with imposed by these models can be problematic if different harnesses are the most widely used way of tracking large behaviours are to be extracted from the GPS tracking data waterbirds [59, 60], but with the recent miniaturisation of free-ranging animals. Only one type of filter can be used of GPS tags, it is now possible to also attach or integrate for the whole data set, and a pedestrian walking filter, for them into neckbands. Numbered plastic neckbands have example, might faultily introduce movement at times that been successfully used for many years for individual the animal is resting. Other options for accuracy improve- resighting of wild geese and swans [68–71], and for a few ment are differential GPS systems [35], i.e. ground- and years, these have included radio or GPS tags [54, 72]. satellite-based augmentation systems (GBAS and SBAS), Here, we present a comparison of the performance of which can be applied to the data directly or by post-pro- neckband and backpack GPS tags in captive geese dur- cessing raw GPS data, i.e. satellite pseudo-ranges, protocol ing early habituation. During six successive days after RXM-RAW [34]. The latter system is still under develop - deployment, we have quantified the effect of both tag ment in many countries and presently evaluated [36–38]. types on the birds’ behaviour, namely how much their GPS fix rate and accuracy have also been shown to behaviours deviated from control individuals without depend on the design of the tag, especially on the gain a tag. Then, we have evaluated GPS accuracy (with GPS and orientation of the antenna [39]. However, this is only platform setting ‘pedestrian walking’ and/or SBAS) of one factor to consider during tag development, espe- both tag types, expecting worse performance of the neck- cially with respect to affecting the behaviour, physiology band tags, because antenna orientation deviates more or demography of the animal [20, 21]. Tag weight has often from upwards. Furthermore, the success of classi- received a lot of attention, especially in flying or diving fication of different behaviours from accelerometer data animals [40, 41]. It is generally agreed that tag weight was compared and related to the attachment method. should be <5  % (or <3  % as recently proposed) of the Finally, considering different research questions, a frame - body weight of the animal [42–44]. However, tag shape, work is presented to inform tag design and placement for attachment and placement can be of equal importance future tracking of large waterbirds like swans and geese. and influence maximum suitable tag weight [18, 45–49]. Depending on body structure, tags are mostly placed on Methods the back (close to the centre of gravity), neck or tail of Tests with captive geese terrestrial animals and attached by harnesses, collars or Our experimental animals were six captive, at the time glue. For specific research questions, especially involving flightless Canada geese (Branta c. canadensis) that were Kölzsch et al. Anim Biotelemetry (2016) 4:13 Page 4 of 14 held in a group of ten at the outside waterbird facility of setting different receiver protocols, the tags were acti - the Netherlands Institute of Ecology (NIOO-KNAW). vated to receive SBAS signals (from EGNOS satellites) The geese were sub-adults and had not yet formed sta - for improved position accuracy as well as to collect raw ble couples. The six focal birds (three males and three GPS data for post-processing. females) were selected based on body size, low resistance Every day of the trials, the goose groups were observed to being handled and medium to high social position in a 26 × 26 m fenced field located in a wider open area within the group. They weighed on average 5.2 kg (range at the times that the tags were recording data. Each of 4.2–6.9 kg) so that tags up to a weight of 150 g would be the three experimental animals was observed and its within the more conservative 3  % margin [18] generally behaviour recorded during three periods of 10  min/day; accepted for animal tracking. the timing of those observations was designed in a bal- The tests were performed as three trials, each lasting anced rotational grid to correct for the influence of time 6  days, in January–April 2013. During the trial periods, of day on behaviour. Goose behaviour was recorded live the geese were split into two groups of five individuals, using the Observer XT version 11 software (Noldus IT), each including three experimental birds. The groups were and we discerned six main behaviours: feeding, resting, assembled in a rotational design to account for individ- walking/running, vigilance, shaking and preening. Other ual variability of the geese. On the day before the start behaviours were also scored, but were not included in the of a trial, of each group of experimental birds, one was analyses because of low frequencies. During each 10-min equipped with a neckband tag (70 g, including neck ring, observation period, a second observer recorded the dis- able to freely rotate around the goose’s neck, Fig. 1a), one tance (±2 cm) and angle (±1°) to the lighter breast region with a backpack tag (80 g, including harness, Fig. 1b) and of the focal goose (ca. 10–20 cm from the deployed GPS one was left without a tag as control. The tags were pro - tag) about 5–15 times using a Geosystems Total Station totypes, programmed to continuously collect GPS posi- (TCR 307 version 350.24). The device was at a fixed posi - tions (1 Hz) and tri-axial accelerometer measures (50 Hz) tion that had previously been accurately located (±2 cm) for 2 h/day, one group in the morning (8:00–10:00 GMT) with a DGPS instrument (Ashtech ProMark 800). and the other in the afternoon (11:00–13:00 GMT). They were fitted with helical antennas (Sarantel GeoHelix), Analysis of behavioural observations which were vertical (pointing upward) on the neckbands In total, each of the six experimental geese was observed and horizontal (pointing forward) on the backpacks. The 54 times for 10  min, apart from one bird that had to be omnidirectional reception pattern and physical shape excluded during the last 3  days because of feather wear of a helical antenna enabled more similar performance below a small part of the harness. Thus, our data set between neckband and backpack tags than would be comprised 315 observation periods. For indication of possible with a more conventional patch antenna. By tag-induced behaviour, we extracted the total duration of preening (including pecking the tag) and frequency of shaking (the head or body) per 10-min observation period. To evaluate likely impacts of tag or tag type on other behaviours, we further examined the total dura- tions of feeding, vigilance and resting per observation period. Effects on these behaviours and possible habitu - ation with time of deployment were tested by comparing generalised linear mixed models (GLMMs, R package ‘lme4’) with and without tag type (backpack BP, neckband NB and control C) and day since deployment as fixed fac - tors and date, individual and sex as random factors by a likelihood ratio test (LRT). Processing and evaluation of GPS data Because of several tag failures, we were only able to use 9 days of GPS data from the backpack tags and 7 days of data from the neckband tags, including 32 of the 10-min observation periods. Thus, the data set for the following Fig. 1 Prototype tags used in this study, each mounted on a Canada analyses comprised 18  h (61,873 positions) of backpack goose: a neckband tag, b backpack tag. Note the orientation of the and 14  h (47,178 positions) of neckband GPS data, both tags. The tags were developed by Biotrack Ltd. in the collaborative project E-Track (www.etrack-project.eu). Photography by AK normally processed locations (aka NMEA data; see list of Kölzsch et al. Anim Biotelemetry (2016) 4:13 Page 5 of 14 abbreviations) as well as raw GPS data. The NMEA posi - to classify animal behaviours. All accelerometer data sets tions were improved by the GPS module under the plat- were then divided into static acceleration (as moving form settings of ‘pedestrian walking’ [34]. Of the NMEA averages over a sliding window of 2 s width) and dynamic positions, 29.9 % (BP) and 29.6 % (NB) had incorporated acceleration (difference between raw acceleration and SBAS (EGNOS) signals for improved accuracy. static acceleration). For evaluation of the effect of SBAS improvement inde - Following the workflow of [73], we overlaid accel - pendently of the GPS platform settings, we post-pro- erometer data and behavioural observations and split cessed the raw GPS data with archived SBAS correction the data set into acceleration bursts of the same behav- files (from the EDAS service, ftp://igs.ensg.ign.fr/pub/ iour. Because of observation delays of about 1  s (range igs/products/), using RTKlib version 2.4.2 with single 0.5–2  s), we selected only bursts where the animals point positioning mode, an elevation mask of 15°, IONEX showed the same behaviour for ≥5 s (most used periods TEC Ionosphere Correction, SBAS Satellite Correction were longer than 5 s, and using a longer period of ≥10  s and Precise Satellite Ephemeris. The position data set did not qualitatively change the results). Thus, the main generated was called precise point positions (PPP; see list part of each selected burst showed the targeted behaviour of abbreviations). The settings were selected as to obtain so that influences of delayed scoring were minimised. The independent positions without any inference on move- selection resulted in a data reduction to 38.5 % (BP) and ment type and on the basis of stationary tests with dif- 25.7 % (NB) of all bursts for analysis, leaving us with 848 ferent elevation masks. Because of gaps in the EDAS data (BP) and 537 (NB) behavioural bursts, totalling to 5.63 h set, only 29.3  % (BP) and 25.9  % (NB) of the GPS posi- (BP) and 4.21 h (NB) of behaviour-annotated accelerom- tions could be augmented to PPP data. eter data. Due to this selection, the sample size of shaking To determine GPS accuracy of the different GPS posi - (naturally of short duration) became extremely low and tion types (NMEA and PPP), we compared them with we excluded it from these analyses. the rather exact positions that were calculated from the For each burst, we calculated the proposed acceleration measurements obtained with the Geosystems Total Sta- statistics (see Table  2 of [73], excluding speed) and ana- tion. This resulted in the following data sets that coin - lysed them with a recursive classification tree algorithm cided in time with Total Station positions: 148 (BP) and (R package ‘rpart’), split by tag type. To avoid overfitting, 127 (NB) NMEA positions without SBAS augmentation we pruned both of the resulting classification trees (BP (referred to as NMEA 1; see list of abbreviations), 79 (BP) and NB) to their minimum complexity parameter. Their and 53 (NB) NMEA positions with SBAS augmentation predictive power was finally quantified by prediction (NMEA 2) and 73 (BP) and 50 (NB) PPP positions. After accuracy (proportion correctly classified bursts), overall projection of all positions into the appropriate UTM and for each of the behaviours separately. (Universal Transverse Mercator coordinate system) zone 31, distances between time-overlapping positions were Results calculated, indicating accuracy of the respective GPS Eec ff ts of tags on observed behaviour positions: NMEA 1 (with ‘pedestrian walking’ filter, with - Our analyses showed that both tags had an effect on out SBAS), NMEA 2 (with ‘pedestrian walking’ filter, with the behaviour of the experimental geese (Fig.  2). Espe- SBAS) and PPP (without ‘pedestrian walking’ filter, with cially discomfort behaviours, like preening and shaking, SBAS). We also calculated minimum convex polygon were significantly increased if the geese were carrying a areas of NMEA and PPP tracks for each 10-min period tag. A goose without tag (control C) would be preening and compared how SBAS augmentation and the GPS for about 18  s within a 10-min period, whereas a goose module platform settings (i.e. ‘pedestrian walking’ filter) with a backpack (BP) would preen and peck the tag for influenced the spatial extent of the data sets. Distances c. 83 s and a goose with neckband (NB) for c. 82 s (LRT, and polygon areas were compared between different GPS χ   =  37.0, df  =  2, p  <  0.001). Frequency of shaking (in data sets and tag designs using GLMMs (see above). times per 10-min period) increased somewhat for neck- band tags (C: 0.3 times, BP: 0.4 times, NB: 1.0 times; LRT, Behaviour classification from acceleration data χ   =  26.4, df  =  2, p  <  0.001). This can be explained by Similar to the GPS data, we obtained a data set of 9 days the fact that shaking the head to get rid of the neckband (18  h) of accelerometer measurements from the back- was scored as ‘shaking’. The extra time spent on shaking pack tags and 7 days (14 h) from the neckband tags. The when geese wear the neckband was very small relative acceleration data could not be recorded continuously at to the increased preening time. Thus, both types of tags 50  Hz, because of time required intermittently to write caused extra discomfort to the birds. The geese reacted data to the tag’s memory. Therefore, we down-sampled it to backpacks and neckbands differently, but overall used to a continuous 20  Hz, which is widely used for studies the same amount of time for extra discomfort behaviour. Kölzsch et al. Anim Biotelemetry (2016) 4:13 Page 6 of 14 GPS accuracy There were various effects of tag type and GPS position type on the accuracy in terms of distance to the (exact) positions as obtained by the Geosystems Total Station (Fig.  3). For NMEA 1 positions, modelled inaccuracy (GLMM model estimate of distance to exact position) was smaller for neckband tags (1.8 m) than backpacks (3.5 m; LRT, χ  = 16.3, df = 1, p < 0.001), whereas the inaccura- cies of NMEA 2 were similar for the two tag types (BP: 2.4 m, NB: 2.2 m; LRT, χ  = 0.7, df = 1, p = 0.39). Also for PPP positions, the inaccuracy was smaller for neckbands (BP: 3.7  m, NB: 1.9  m; LRT, χ   =  5.4, df  =  1, p  =  0.02). Consequently, tag type lost its influence on GPS accuracy only if the platform setting (‘pedestrian walking’) and SBAS enhancement were applied simultaneously. If only one or the other was applied, the neckbands were more accurate than the backpack tags. When looking at each tag type separately, only the backpacks revealed an effect of GPS position type on accuracy, with the pedestrian walking filter improving accuracy (NMEA 1: 3.3  m, NMEA 2: 3.2  m, PPP: 3.8  m; LRT, χ  = 7.1, df = 2, p = 0.03). GPS accuracy did not dif- fer by GPS position type, and effect sizes were smaller for neckbands (NMEA 1: 2.5 m, NMEA 2: 2.1 m, PPP: 2.4 m; LRT, χ  = 2.4, df = 2, p = 0.31). Our data did not show significant differences in mini - mum convex polygon area with respect to tag type, 2 2 neither for NMEA data (BP: 464  m , NB: 650  m ; LRT, χ  =  0.9, df =  1, p =  0.33), nor for PPP data tracks (BP: 2 2 2 579  m , NB: 632  m ; LRT, χ   =  0.1, df  =  1, p  =  0.77). However, note that sample size was very low. For back- packs, polygon areas were larger for PPP than for NMEA 2 2 2 positions (NMEA: 547  m , PPP: 663  m ; LRT, χ   =  5.5, df  =  1, p  =  0.02), but there was no difference in neck - Fig. 2 Bar plots (mean ± SD) of discomfort levels of geese carrying 2 2 2 a backpack tag (BP), neckband tag (NB) or no tag (control C). Behav- band tags (NMEA: 566  m , NB: 548  m ; LRT, χ   =  0.01, iours indicating discomfort are a duration of preening (and pecking df = 1, p = 0.94). Thus, for backpacks the PPP positions the tag), b frequency of shaking (the body or the head), c duration of were more spread out. feeding, d duration of being vigilant and e duration of resting behav- iour, each within 10-min periods Behavioural classification from accelerometer data In the behaviour-annotated examples of static and dynamic acceleration for the backpack tag (Fig.  4a, b) and neckband tag (Fig. 4c, d), the variability in alignment Furthermore, a goose with a tag would feed less (static acceleration) was less pronounced in backpack (C: 193  s, BP: 152  s, NB: 148  s; LRT, χ   =  11.1, df  =  2, than in neckband tags and did not as easily match with p = 0.004), but be no less vigilant (LRT, χ  = 0.2, df = 2, behaviours (but see long feeding burst in Fig.  4a). For p  =  0.92) nor rest for shorter times (LRT, χ   =  0.8, the neckband, e.g. feeding events were clearly depicted df  =  2, p  =  0.67). There were no differences between by peaks in the x-axis static acceleration. Note that the the two tag types in terms of duration of feeding, vigi- x-axis pointed towards the head in the neckbands and lance and resting, indicating that they were similar in the was not affected by the regularly occurring, movement degree to which they affected goose comfort. In addi - induced events of the neckband rotation. In the dynamic tion, there was no effect of time since deployment on acceleration patterns, resting was clearly visible for the the extent of any of the behaviours (LRT, χ  < 1, df =  1, backpack as well as the neckband (dynamic acceleration p  >  0.30), showing that the birds were not yet getting of all axes = 0). Feeding could in this example not be well habituated to the tags. Kölzsch et al. Anim Biotelemetry (2016) 4:13 Page 7 of 14 Fig. 3 Example tracks and accuracy statistics for GPS data of backpack (BP) and neckband (NB) tags on geese. a NMEA data (red), PPP data (blue) and distances of time-overlapping NMEA or PPP data with exact measures of the Geosystems Total Station (green) for a 10-min track of one goose with a backpack tag. b Same as a, but for a goose with a neckband tag. c Distances of NMEA 1, NMEA 2 and PPP data to exact positions, split for BP and NB tags. d Minimum convex polygon area of NMEA and PPP tracks for both tag types. Please see the list of abbreviations for explanations of NMEA 1/2 and PPP. Note that measures in the box plots are not model parameters (as reported in the text), but raw data, and do not account for random factors discerned from dynamic acceleration of the backpack: split off by high x-axis frequency at the dominant power similar to walking, it was characterised by a regular wave spectrum (strong wave pattern; fdpsX). The only indica - pattern in the x-axis dynamic acceleration. On the other tion visible for feeding was low x-axis maximum dynamic hand, the dynamic acceleration of the neckband showed body acceleration (mdbaX). rather unique high amplitudes during feeding. How- For the neckband tag, the classification tree looked ever, there are several other peaks that were not easy to very different (Fig.  5b). First, feeding, preening and walk- explain. ing were split off by high odba, indicating a high level When examining the classification trees, the pitch in of general tag movement. Further, splits by mdbaZ and the x-axis for backpack tags (Fig.  5a) was a main statis- rollY indicated that preening contained very strong right/ tic for the first split of resting/feeding (high pitch) from left positional and angular changes. Note that right/ walking/vigilance (low pitch), indicating that body angle left and front/back movements (y- and z-axes) are not (leaning back or forward) was the best initial classifica - easily discernible, because the neckband tag can freely tion criteria. Then, on one part of the tree, resting was rotate around the goose’s neck. Walking was split off by split off by low x-axis overall dynamic body acceleration low mdbaZ and high odbaY, showing the right/left sway- (odbaX), i.e. little front/back movement of the tag. At the ing walk of Canada geese. Feeding showed low angular other part of the tree, vigilance was split off by low odba change in the y-axis (rollY). On the other side of the clas- (very little overall movement) and walking was further sification, tree resting and vigilance were discerned by Kölzsch et al. Anim Biotelemetry (2016) 4:13 Page 8 of 14 Fig. 4 Example data of static and dynamic acceleration data for both tag types, down-sampled to 20 Hz resolution. a Static and b dynamic accel- eration in all three axes (x-blue, y-green, z-red) of a goose with backpack tag with an overlaid bar of observed behaviour inserted (black rest, blue walk, green feed, red preen, pink vigilance). c, d Same as a, b, but for a goose with neckband tag. Note the differences in scale and differentiability of behaviours by tag type. The accelerometer was fitted into the tags so that for the backpacks x is the reverse of surge, y is the sway and z is the heave. For the neckband on a raised goose neck that means x is the reverse of heave and y and z indicate surge and sway depending on how the tag is turned Kölzsch et al. Anim Biotelemetry (2016) 4:13 Page 9 of 14 Fig. 5 Final, pruned classification trees of six main behaviours of observed geese as calculated by accelerometer statistics. a Classification tree for backpack tags with a legend of cross-validation success rates for each behaviour separately and an overall value. b Same as a, but for neckband tag data. The acceleration statistics used in the analyses are: pitchX—body angle along x-axis, pitchZ—body angle along z-axis, rollY—body angle along y-axis, mdbaX/mdbaY/mdbaZ—maximum dynamic body acceleration along the x-/y-/z-axes, odbaX/odbaY/odbaZ—mean dynamic body acceleration along the x-/y-/z-axes, odba—overall dynamic body acceleration (sum of the previous), dpsX/dpsY/dpsZ—maximum power spectral density of dynamic acceleration along x-/y-/z-axes, fdpsX/fdpsY/fdpsZ—frequency at the maximum power spectral density along the x-/y-/z-axes (for more explanation, see [73]) pitchX, indicating that the difference between the two Behaviour-specific classification results differed between rather inert behaviours was in body/head angle. the two types of tags; neckbands showed better results The overall classification success rates of the fitted trees for behaviours involving head movement such as feed- (Fig. 5) were similarly high for both tag types (BP: 72.7 %, ing or vigilance due to their position close to the head, NB: 74.6  %). However, feeding and vigilance behaviours and backpacks were more successful in detecting behav- were better classified for neckbands than for backpacks, iours such as walking or resting, for which it is impor- whereas preening, resting and walking were better tant that the tag is closely fixed to the body (not freely detected in backpack data (Fig.  5, for details see Addi- moving around the neck). Thus, a decision on the use of tional file  1: Table A1). Thus, neckbands were better at neckband or backpack tags for large waterbirds cannot be classifying behaviours that were mainly performed with based on early habituation discomfort of the birds or GPS the head, whereas backpacks seem better able to map position accuracy, but should depend on possibly differ - whole-body behaviours. ential long-term habituation and the research question. Apart from showing that short-term, tag-induced dis- Discussion comfort was similar for both tag types, we have also seen We have compared two of the most widely used types of that time since deployment did not influence the geese’ attachment of GPS/accelerometer tags on large water- behaviours during the first 6 days. This indicates that pre - birds, for the first time in a way that integrated the quan - viously observed habituation to the tags takes longer, up tification of tag-induced adverse behaviour during early to several weeks or months [54], and might then differ habituation, GPS position accuracy and behaviour clas- between different tag attachments. This can be impor - sification success from accelerometer data. In general, tant to consider, because one issue of many tracking both tag types showed a similar short-term discomfort research is the necessary duration of the study, for how effect on the birds, GPS accuracy was only slightly bet - long the animal shall carry a tag and collect data (e.g. for- ter for neckband tags, and overall behavioural classi- aging movement vs. lifetime tracking). Furthermore, it is fication success from accelerometer data was similar. important to understand for which time frame tracking Kölzsch et al. Anim Biotelemetry (2016) 4:13 Page 10 of 14 data are affected by discomfort of the animal and when important to realise that our study was performed with seemingly normal movement data can be observed. captive animals and that the effects of being handled and In the light of animal welfare, a tag should only be carrying the tags on the behaviour of wild birds might mounted on the animal as long as it is working prop- differ. They are usually more constrained in food avail - erly [67]. Therefore, drop-off mechanisms and weak ability, and the need for vigilance for predators is higher. links in harnesses have become more widely used [74]. Therefore, tag-induced discomfort might not be affecting Simple glue-on-feathers has been used for short-term their time budget as much, and our findings of extra time deployments in the past [75], but can damage the feath- spent preening and shaking are conservative measures. ers or skin of animals. However, if it is desirable that On the other hand, they might be initially more stressed the tag stays on the animal for a long time, the material by the tag than captive geese that are somewhat used to and attachment methods should be adapted, taking into being handled. However, we are confident that the gen - account habitat conditions and the destructiveness of eral conclusions of our comparative study can be trans- the animals. Some species of geese are known to destroy ferred to wild waterbirds. harnesses and backpack tags within a short time [61], With awareness that external devices are most likely and there is advice not to use a harness for this species affecting animals (at least short term during habitua - group [58]. However, plastic neckbands are known to tion [54]), it is even more important to ensure the high- have a long retention time [71] and are less accessible for est possible quantity and quality of collected data. For the wearer to inflict damage with its bill. Initial concern long-term studies, the extension of time in functionality regarding neckband icing [76] has been lessened by stud- has successfully been achieved by including solar cells ies showing that icing is exceptionally rare and does not for energy provision, so that tag running times are not have long-term fitness consequences for geese [70, 77]. time-limited by battery power. Regarding data quality, Furthermore, the fact that unique IDs can be inscribed our results suggest that GPS accuracy from the particu- on the outside of a neckband for visual observations lar backpack tags of this study was generally lower and is a large advantage when quantifying survival and tag more strongly improved by filtering and SBAS augmen - functionality. tation than neckband tags. Thus, for data sets that are External tags have been reported to have no significant not continuously of NMEA 2 type, it seems advisable long-term effect on animals [59, 71, 78], but there have to prefer neckband tags to obtain higher GPS accuracy. also been cases showing various negative effects on ani - These findings differ from earlier results on ARGOS mal behaviour and survival [22, 43, 63, 65, 79, 80]. Such reception and lower accuracy of neckbands than back- effects can be intensified if the animal is flying or moving packs [58]. However, in that study the antennas of the through water, depending on tag placement [47, 48]. It is neckbands pointed down the neck of the goose, which possible that neckband tags have a higher aerodynamic has been shown to be problematic [39]. resistance during flight and that their placement away Furthermore, signal frequency, antenna type and orien- from the centre of gravity might affect the bird’s balance, tation will have a profound effect on device performance, leading to higher flight costs. On the other hand, the har - making comparisons difficult. The signal reception pat - ness of a backpack tag is likely to cause abrasions and terns of our tags’ helical antennas in relation to the GPS hamper flapping of the wings, which is especially impor - satellites would have affected the device performance in tant for geese that almost exclusively use flapping flight. ways that are too complex to explore here, but our results It was not possible for us to incorporate flight behaviour are probably influenced most strongly by antenna char - in this study, and there are, as far as we know, no other acteristics. Helical antennas were chosen because their studies that compare the differences of negative effects reception pattern is omnidirectional, and their orienta- of neckbands versus backpacks during flight or diving. tion has less effect on received signal strength than would However, from field experience it seems that the longev - the patch antennas that are normally used with GPS ity of wild geese with neckband tags is higher, possibly receivers because they have higher gain. We did not test due to better manoeuvrability during flight (AK, unpub - the effect of antenna orientation during flight. However, lished data). Furthermore, fat accumulation for migration if the orientation of the neckband tag during flight results can negatively affect body harness fit of backpacks, but in the antenna hanging under the bird’s outstretched is not problematic for neckbands, as neck size does not neck, the ability of the GPS receiver to acquire satellites change. is likely to be reduced. In contrast, the orientation of a Habituation to tags might also depend on the handling back-mounted tag is likely to remain much the same dur- time and procedures during deployment, in which con- ing flight, and indeed the height of the bird and clear line text we consider neckbands more suitable as they are of sight to GPS satellites would probably improve receiver more ‘standardised’ and quickly to attach. However, it is performance. Kölzsch et al. Anim Biotelemetry (2016) 4:13 Page 11 of 14 As more global satellite navigation systems join Amer- unconventional placement of small tags might be most ica’s GPS (e.g. Russia’s GLONASS and Europe’s Galileo) effective and will improve the scope of accelerometer in becoming available for widespread use, and augmenta- data sets even more. tion of GPS accuracy is possible in different ways (differ - We are aware that there might always be limitations ential GPS, SBAS, filtering), improved position data and to animal tracking [1, 20]. Some species simply are too their applicability for ecological research should be evalu- small, do not tolerate handling stress or are hard to catch. ated. For different research questions, GPS fix rate, accu - However, by pushing the technological limits, animal racy or precision is of varying importance. For example, tracking will be refined into a truly revolutionary tool for studies on habitat selection require GPS positions of high wildlife research. Ecologists should use the smallest tags accuracy to allow for correct overlap with e.g. remote with the least effect on the animal’s behaviour giving the sensing data, whereas studies about individual behaviour best quality data for answering the research questions or group movement need high GPS precision. Here, we posed by various disciplines. By extracting natural, objec- take a first step to also raise ecologists’ attention to the tive time budgets and discerning small-scale changes in likelihood that GPS positions from standard devices are movement and other behaviours besides the animal’s augmented by some form of smoothing algorithm (prob- location, we will be able to explore an animal’s true natu- ably based on an extended Kalman filter) before being ral behaviour and apply this knowledge to conservation, output from the device. Rapidly sampled individual loca- management or models of disease spread. tions output from a GPS tag are unlikely to be statisti- cally independent, and the type of filter/platform setting Conclusion applied (e.g. stationary, pedestrian walking, motor vehi- We have shown that captive Canada geese with back- cle) will influence the data. As the smoothing algorithms pack or neckband tags exhibit discomfort behaviours at a depend on fast sampling rates, their influence on infre - similar level during a short habituation period. GPS accu- quent GPS locations is less, probably negligible. However, racy and general behaviour classification success based if a fast sampling rate is used to test the accuracy of a sta- on accelerometer data from both tag types were similar. tionary GPS tag, the results may not be representative of However, some behaviour types were better recognised the performance on the animal [81]. Therefore, the use of by neckbands, others by backpacks. Therefore, we advise raw GPS data might be advisable for high-frequency GPS that the selection of either type of tag attachment method tracking studies. for large waterbirds should depend on the research ques- In most studies, especially working with small species, tion, including focal behaviours and necessary tag reten- tag weight is most important [42, 43, 49] and often lim- tion time. its data quality and quantity options. However, we want Additional file to stress here that tag weight cannot be considered inde- pendent of the tag design and tag attachment method Additional file 1: Table A1. Success rates of behaviour classification [47]. A device mounted on body appendages such as leg, trees for a backpack and b neckband tag accelerometer statistics. tail or head should ideally be somewhat lighter than a tag attached to the back of an animal or implanted [64, 82], which is supported by the whole body mass. For large Abbreviations NMEA: National Marine Electronics Association; : Standard format used for pro- waterbirds, this seems to be less of an issue, the tags used cessed GPS data that are directly obtained from a GPS tracking device. Usually, here are quite heavy in absolute terms, but still <1.5 % of the GPS positions are filtered by platform settings that are often based on the the body weight. extended Kalman filter to improve accuracy [34]. Here, the platform settings were ‘pedestrian walking’; NMEA 1: NMEA data that are filtered as ‘pedestrian The integration of extra sensors into GPS tags is walking’, but where the SBAS correction signal has not been received to improving usability of position data for many applica- additionally improve the positions; NMEA 2: NMEA data that are filtered as tions. Accelerometer measurements are one example ‘pedestrian walking’ and for which the GPS tag had received the SBAS correc- tion signal for improvement of the positions with differential methods; PPP: that is gaining more attention [12, 13], and we show Precise point positions. GPS positions that were extracted by post-processing that for the best use of accelerometer data it is critical to raw GPS data with the use of SBAS correction signals obtained from the EDAS compare different means of attachment and placement service. These positions were not filtered. [51, 83]. An accelerometer records the movements of Authors’ contributions the parts of the body directly underneath the tag, and as AK, HHTP, BHC and BAN designed the study. JB and BHC developed and suc- body parts move differently for the various behaviours, cessively refined the GPS/accelerometer tags. AK, MN, GJDMM devised the different methods of tag attachment, and AK, MN and BAN performed the it is important to have a clear idea which tag placement study. AK, MN and JB analysed the different data sets, and AK, FvL, FdB and can give the most significant acceleration data to detect BAN refined the results. The manuscript was written by AK, and all authors the respective behaviour. Thus, depending on which contributed to refining the text and content. All authors read and approved the final manuscript. type of behaviour is relevant for the attempted study, Kölzsch et al. Anim Biotelemetry (2016) 4:13 Page 12 of 14 Author details 11. Yoda K, Naito Y, Sato K, Takahashi A, Nishikawa J, Ropert-Coudert Y, et al. Department of Animal Ecology, Netherlands Institute of Ecology (NIOO- A new technique for monitoring the behaviour of free-ranging Adelie KNAW ), PO Box 50, 6700 AB Wageningen, The Netherlands. Department penguins. J Exp Biol. 2001;204(4):685–90. of Migration and Immuno-ecology, Max Planck Institute for Ornithology, Am 12. Brown DD, Kays R, Wikelski M, Wilson RP, Klimley AP. Observing the Obstberg 1, 78315 Radolfzell, Germany. Department of Biology, University unwatchable through acceleration logging of animal behaviour. Anim of Konstanz, Universitätsstraße 10, 78464 Constance, Germany. Biotrack Biotelem. 2013;1:20. doi:10.1186/2050-3385-1-20. Ltd., 52 Furzebrook Road, Wareham BH20 5AX, UK. Team Animal Ecology, 13. Wilson RP, Grundy E, Massy R, Soltis J, Tysse B, Holton M, et al. Wild state Ecotoxicology and Wildlife Management, Alterra Wageningen-UR, Droev- secrets: ultra-sensitive measurement of micro-movement can reveal endaalsesteeg 3a, 6708 PB Wageningen, Netherlands. Resource Ecology internal processes in animals. Front Ecol Environ. 2014;12(10):582–7. Group, Wageningen University, Droevendaalsesteeg 3a, 6708 PB Wageningen, doi:10.1890/140068. The Netherlands. Computational Geo-Ecology, Department of Science, 14. Frair JL, Fieberg J, Hebblewhite M, Cagnacci F, DeCesare NJ, Pedrotti L. Institute for Biodiversity and Ecosystem Dynamics, University of Amsterdam, Resolving issues of imprecise and habitat-biased locations in ecologi- PO Box 94248, 1090 GE Amsterdam, The Netherlands. cal analyses using GPS telemetry data. Philos Trans R Soc B Biol Sci. 2010;365(1550):2187–200. doi:10.1098/rstb.2010.0084. Acknowledgements 15. Greenwood RJ, Sargeant AB. Influence of radio packs on cap - This study was performed as part of the E-Track project (full title: ‘EGNOS and tive mallards and blue-winged teal. J Wildl Manag. 1973;37(1):3–9. EDAS enhanced tracking of animal movement and behaviour’) that was car- doi:10.2307/3799732. ried out in the context of the Galileo FP7 R&D programme supervised by the 16. Hebblewhite M, Haydon DT. Distinguishing technology from biol- GSA (nr. 277697-2). 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Eec ff ts of harness acceleration work: on the theory of acceleration as a proxy transmitters on behavior and reprodution of wild mallards. J Wildl Manag. for energy expenditure. Methods Ecol Evol. 2011;2(1):23–33. 1993;57(4):696–703. doi:10.2307/3809068. doi:10.1111/j.2041-210X.2010.00057.x. 81. Wilson AM, Lowe JC, Roskilly K, Hudson PE, Golabek KA, McNutt JW. Locomotion dynamics of hunting in wild cheetahs. Nature. 2013;498(7453):185. doi:10.1038/nature12295. Submit your next manuscript to BioMed Central and we will help you at every step: • We accept pre-submission inquiries • Our selector tool helps you to find the most relevant journal • We provide round the clock customer support • Convenient online submission • Thorough peer review • Inclusion in PubMed and all major indexing services • Maximum visibility for your research Submit your manuscript at www.biomedcentral.com/submit http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Animal Biotelemetry Springer Journals

Neckband or backpack? Differences in tag design and their effects on GPS/accelerometer tracking results in large waterbirds

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Springer Journals
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Copyright © 2016 by Kölzsch et al.
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Life Sciences; Animal Systematics/Taxonomy/Biogeography; Conservation Biology/Ecology; Terrestial Ecology; Bioinformatics; Freshwater & Marine Ecology
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2050-3385
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10.1186/s40317-016-0104-9
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Abstract

Background: GPS and accelerometer tracking presently revolutionises the fields of ecology and animal behaviour. However, the effects of tag characteristics like weight, attachment and data quality on study outcomes and animal welfare are important to consider. In this study, we compare how different tag attachment types influence the behav- iour of a group of tagged large waterbirds, GPS accuracy and behaviour classification success from accelerometer data. Results: Both neckband and backpack tags had similar effects on the behaviour of six captive Canada geese ( Branta canadensis), increasing the amount of discomfort behaviour in relation to untagged individuals. Both treatment groups also slightly decreased the amount of foraging, but the duration of neither vigilance nor resting was affected. GPS positions that were filtered with classical GPS platform settings (i.e. smoothing) were more accurate than posi- tions improved by satellite-based differential augmentation. Tag attachment, however, did not induce any differ - ences in position accuracy of both data types. Behaviour classification success was generally similar for neckband and backpack tags. But in detail, behaviours mainly performed by the head like foraging and vigilance were better detected from accelerometer data of neckband tags, whereas behaviours like resting and walking were more success- fully detected from backpack tag data. Conclusion: Our findings suggest that the use of neckband or backpack tags for tracking large waterbirds and their behaviour largely depends on which behaviours are most important to detect. However, for wildlife tracking stud- ies, factors like tag retention time are also of great importance, especially for animals like some goose species that are known to quickly destroy backpack tags. For future studies, we advise to carefully evaluate not only tag weight, but also attachment methods and data quality, because the right choice depends on the research question. This will improve the scope of wildlife tracking even more for various scientific, conservation and management applications. Keywords: Animal tracking, Tag attachment, Tag placement, Tag effects, Differential GPS accuracy, SBAS, Behaviour classification, Accelerometer, Canada goose, Branta c. canadensis Background years [1, 2]. It has matured from being used in simple Animal tracking with GPS has become an important home range studies to diverse applications examining and widely used technique for wildlife research in recent habitat selection, animal migration, behaviour and physi- ology [3–6]. This might partly be due to the improved performance of GPS circuits, but also the addition to the *Correspondence: akoelzsch@orn.mpg.de Department of Migration and Immuno-ecology, Max Planck Institute tags of auxiliary sensors, like depth metres, light sensors for Ornithology, Am Obstberg 1, 78315 Radolfzell, Germany or accelerometers [7, 8]. Accelerometers in particular are Full list of author information is available at the end of the article © 2016 Kölzsch et al. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. 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. Kölzsch et al. Anim Biotelemetry (2016) 4:13 Page 3 of 14 revolutionising the field of animal energetics by enabling accelerometer sensors measuring specific body move - researchers to determine animal behaviour remotely [9– ments, other tag placements have been used (e.g. head 12]. Recently, it has even been shown that acceleration [50], scapulae [51]). In this way, the 3D orientation and data are suitable to determine an animal’s internal states, dynamic movement of specific body parts can be quan - like disease status or stress level [13]. tified, allowing conclusions about the use of muscles or Since the first years of animal tracking, researchers other motile body parts for certain behaviours or physi- have been aware that study outcomes might be affected ological processes. by the quality of the tags [14–17] and how the tags Until very recently, due to the large size of traditional affected the animals [18–23]. Fix rate (amount of success - GPS tags, only the largest birds have been studied with fully acquired positions), GPS accuracy (deviation from GPS telemetry, most notably swans, geese and large rap- true location) and precision (measurement reproduc- tors, often focussing on their migration ecology [52–56]. ibility) were considered repeatedly [24, 25], especially for As some goose species are presently of conservation studies in closed or mountainous habitats [26, 27] or for concern, while others are considered pests, the need to those on fine-scale habitat use [28], social interactions for improve our knowledge about these species has increased disease transmission [29] or GPS-based behaviour seg- [57]. Swans and geese have been shown to display a vari- mentation and classification [30–32]. ety of adverse behaviours during being handled and after In order to improve GPS accuracy, which is one focus of tagging, e.g. increased preening or biting the tag/harness this paper, GPS receiver modules apply smoothing algo- [54, 58–61]. To decrease negative effects of tag aversion rithms to the location data before they are output to the [62], it is necessary to shorten handling time and evaluate user. Typically, these algorithms are based on the extended different types of tag placement and attachment meth - Kalman filters [33]. These filters rely on a variety of move - ods for this species group [58, 63–66]. Welfare assess- ment models and are sometimes referred to as platform ments should be made alongside considerations of data settings [34], the choice of which depends on the expected quality in order to balance animal discomfort against the movements of the object being tracked (e.g. station- amount and quality of scientific knowledge gained from ary, pedestrian walking, motor vehicle). The constraints the respective study [67]. Backpack tags attached with imposed by these models can be problematic if different harnesses are the most widely used way of tracking large behaviours are to be extracted from the GPS tracking data waterbirds [59, 60], but with the recent miniaturisation of free-ranging animals. Only one type of filter can be used of GPS tags, it is now possible to also attach or integrate for the whole data set, and a pedestrian walking filter, for them into neckbands. Numbered plastic neckbands have example, might faultily introduce movement at times that been successfully used for many years for individual the animal is resting. Other options for accuracy improve- resighting of wild geese and swans [68–71], and for a few ment are differential GPS systems [35], i.e. ground- and years, these have included radio or GPS tags [54, 72]. satellite-based augmentation systems (GBAS and SBAS), Here, we present a comparison of the performance of which can be applied to the data directly or by post-pro- neckband and backpack GPS tags in captive geese dur- cessing raw GPS data, i.e. satellite pseudo-ranges, protocol ing early habituation. During six successive days after RXM-RAW [34]. The latter system is still under develop - deployment, we have quantified the effect of both tag ment in many countries and presently evaluated [36–38]. types on the birds’ behaviour, namely how much their GPS fix rate and accuracy have also been shown to behaviours deviated from control individuals without depend on the design of the tag, especially on the gain a tag. Then, we have evaluated GPS accuracy (with GPS and orientation of the antenna [39]. However, this is only platform setting ‘pedestrian walking’ and/or SBAS) of one factor to consider during tag development, espe- both tag types, expecting worse performance of the neck- cially with respect to affecting the behaviour, physiology band tags, because antenna orientation deviates more or demography of the animal [20, 21]. Tag weight has often from upwards. Furthermore, the success of classi- received a lot of attention, especially in flying or diving fication of different behaviours from accelerometer data animals [40, 41]. It is generally agreed that tag weight was compared and related to the attachment method. should be <5  % (or <3  % as recently proposed) of the Finally, considering different research questions, a frame - body weight of the animal [42–44]. However, tag shape, work is presented to inform tag design and placement for attachment and placement can be of equal importance future tracking of large waterbirds like swans and geese. and influence maximum suitable tag weight [18, 45–49]. Depending on body structure, tags are mostly placed on Methods the back (close to the centre of gravity), neck or tail of Tests with captive geese terrestrial animals and attached by harnesses, collars or Our experimental animals were six captive, at the time glue. For specific research questions, especially involving flightless Canada geese (Branta c. canadensis) that were Kölzsch et al. Anim Biotelemetry (2016) 4:13 Page 4 of 14 held in a group of ten at the outside waterbird facility of setting different receiver protocols, the tags were acti - the Netherlands Institute of Ecology (NIOO-KNAW). vated to receive SBAS signals (from EGNOS satellites) The geese were sub-adults and had not yet formed sta - for improved position accuracy as well as to collect raw ble couples. The six focal birds (three males and three GPS data for post-processing. females) were selected based on body size, low resistance Every day of the trials, the goose groups were observed to being handled and medium to high social position in a 26 × 26 m fenced field located in a wider open area within the group. They weighed on average 5.2 kg (range at the times that the tags were recording data. Each of 4.2–6.9 kg) so that tags up to a weight of 150 g would be the three experimental animals was observed and its within the more conservative 3  % margin [18] generally behaviour recorded during three periods of 10  min/day; accepted for animal tracking. the timing of those observations was designed in a bal- The tests were performed as three trials, each lasting anced rotational grid to correct for the influence of time 6  days, in January–April 2013. During the trial periods, of day on behaviour. Goose behaviour was recorded live the geese were split into two groups of five individuals, using the Observer XT version 11 software (Noldus IT), each including three experimental birds. The groups were and we discerned six main behaviours: feeding, resting, assembled in a rotational design to account for individ- walking/running, vigilance, shaking and preening. Other ual variability of the geese. On the day before the start behaviours were also scored, but were not included in the of a trial, of each group of experimental birds, one was analyses because of low frequencies. During each 10-min equipped with a neckband tag (70 g, including neck ring, observation period, a second observer recorded the dis- able to freely rotate around the goose’s neck, Fig. 1a), one tance (±2 cm) and angle (±1°) to the lighter breast region with a backpack tag (80 g, including harness, Fig. 1b) and of the focal goose (ca. 10–20 cm from the deployed GPS one was left without a tag as control. The tags were pro - tag) about 5–15 times using a Geosystems Total Station totypes, programmed to continuously collect GPS posi- (TCR 307 version 350.24). The device was at a fixed posi - tions (1 Hz) and tri-axial accelerometer measures (50 Hz) tion that had previously been accurately located (±2 cm) for 2 h/day, one group in the morning (8:00–10:00 GMT) with a DGPS instrument (Ashtech ProMark 800). and the other in the afternoon (11:00–13:00 GMT). They were fitted with helical antennas (Sarantel GeoHelix), Analysis of behavioural observations which were vertical (pointing upward) on the neckbands In total, each of the six experimental geese was observed and horizontal (pointing forward) on the backpacks. The 54 times for 10  min, apart from one bird that had to be omnidirectional reception pattern and physical shape excluded during the last 3  days because of feather wear of a helical antenna enabled more similar performance below a small part of the harness. Thus, our data set between neckband and backpack tags than would be comprised 315 observation periods. For indication of possible with a more conventional patch antenna. By tag-induced behaviour, we extracted the total duration of preening (including pecking the tag) and frequency of shaking (the head or body) per 10-min observation period. To evaluate likely impacts of tag or tag type on other behaviours, we further examined the total dura- tions of feeding, vigilance and resting per observation period. Effects on these behaviours and possible habitu - ation with time of deployment were tested by comparing generalised linear mixed models (GLMMs, R package ‘lme4’) with and without tag type (backpack BP, neckband NB and control C) and day since deployment as fixed fac - tors and date, individual and sex as random factors by a likelihood ratio test (LRT). Processing and evaluation of GPS data Because of several tag failures, we were only able to use 9 days of GPS data from the backpack tags and 7 days of data from the neckband tags, including 32 of the 10-min observation periods. Thus, the data set for the following Fig. 1 Prototype tags used in this study, each mounted on a Canada analyses comprised 18  h (61,873 positions) of backpack goose: a neckband tag, b backpack tag. Note the orientation of the and 14  h (47,178 positions) of neckband GPS data, both tags. The tags were developed by Biotrack Ltd. in the collaborative project E-Track (www.etrack-project.eu). Photography by AK normally processed locations (aka NMEA data; see list of Kölzsch et al. Anim Biotelemetry (2016) 4:13 Page 5 of 14 abbreviations) as well as raw GPS data. The NMEA posi - to classify animal behaviours. All accelerometer data sets tions were improved by the GPS module under the plat- were then divided into static acceleration (as moving form settings of ‘pedestrian walking’ [34]. Of the NMEA averages over a sliding window of 2 s width) and dynamic positions, 29.9 % (BP) and 29.6 % (NB) had incorporated acceleration (difference between raw acceleration and SBAS (EGNOS) signals for improved accuracy. static acceleration). For evaluation of the effect of SBAS improvement inde - Following the workflow of [73], we overlaid accel - pendently of the GPS platform settings, we post-pro- erometer data and behavioural observations and split cessed the raw GPS data with archived SBAS correction the data set into acceleration bursts of the same behav- files (from the EDAS service, ftp://igs.ensg.ign.fr/pub/ iour. Because of observation delays of about 1  s (range igs/products/), using RTKlib version 2.4.2 with single 0.5–2  s), we selected only bursts where the animals point positioning mode, an elevation mask of 15°, IONEX showed the same behaviour for ≥5 s (most used periods TEC Ionosphere Correction, SBAS Satellite Correction were longer than 5 s, and using a longer period of ≥10  s and Precise Satellite Ephemeris. The position data set did not qualitatively change the results). Thus, the main generated was called precise point positions (PPP; see list part of each selected burst showed the targeted behaviour of abbreviations). The settings were selected as to obtain so that influences of delayed scoring were minimised. The independent positions without any inference on move- selection resulted in a data reduction to 38.5 % (BP) and ment type and on the basis of stationary tests with dif- 25.7 % (NB) of all bursts for analysis, leaving us with 848 ferent elevation masks. Because of gaps in the EDAS data (BP) and 537 (NB) behavioural bursts, totalling to 5.63 h set, only 29.3  % (BP) and 25.9  % (NB) of the GPS posi- (BP) and 4.21 h (NB) of behaviour-annotated accelerom- tions could be augmented to PPP data. eter data. Due to this selection, the sample size of shaking To determine GPS accuracy of the different GPS posi - (naturally of short duration) became extremely low and tion types (NMEA and PPP), we compared them with we excluded it from these analyses. the rather exact positions that were calculated from the For each burst, we calculated the proposed acceleration measurements obtained with the Geosystems Total Sta- statistics (see Table  2 of [73], excluding speed) and ana- tion. This resulted in the following data sets that coin - lysed them with a recursive classification tree algorithm cided in time with Total Station positions: 148 (BP) and (R package ‘rpart’), split by tag type. To avoid overfitting, 127 (NB) NMEA positions without SBAS augmentation we pruned both of the resulting classification trees (BP (referred to as NMEA 1; see list of abbreviations), 79 (BP) and NB) to their minimum complexity parameter. Their and 53 (NB) NMEA positions with SBAS augmentation predictive power was finally quantified by prediction (NMEA 2) and 73 (BP) and 50 (NB) PPP positions. After accuracy (proportion correctly classified bursts), overall projection of all positions into the appropriate UTM and for each of the behaviours separately. (Universal Transverse Mercator coordinate system) zone 31, distances between time-overlapping positions were Results calculated, indicating accuracy of the respective GPS Eec ff ts of tags on observed behaviour positions: NMEA 1 (with ‘pedestrian walking’ filter, with - Our analyses showed that both tags had an effect on out SBAS), NMEA 2 (with ‘pedestrian walking’ filter, with the behaviour of the experimental geese (Fig.  2). Espe- SBAS) and PPP (without ‘pedestrian walking’ filter, with cially discomfort behaviours, like preening and shaking, SBAS). We also calculated minimum convex polygon were significantly increased if the geese were carrying a areas of NMEA and PPP tracks for each 10-min period tag. A goose without tag (control C) would be preening and compared how SBAS augmentation and the GPS for about 18  s within a 10-min period, whereas a goose module platform settings (i.e. ‘pedestrian walking’ filter) with a backpack (BP) would preen and peck the tag for influenced the spatial extent of the data sets. Distances c. 83 s and a goose with neckband (NB) for c. 82 s (LRT, and polygon areas were compared between different GPS χ   =  37.0, df  =  2, p  <  0.001). Frequency of shaking (in data sets and tag designs using GLMMs (see above). times per 10-min period) increased somewhat for neck- band tags (C: 0.3 times, BP: 0.4 times, NB: 1.0 times; LRT, Behaviour classification from acceleration data χ   =  26.4, df  =  2, p  <  0.001). This can be explained by Similar to the GPS data, we obtained a data set of 9 days the fact that shaking the head to get rid of the neckband (18  h) of accelerometer measurements from the back- was scored as ‘shaking’. The extra time spent on shaking pack tags and 7 days (14 h) from the neckband tags. The when geese wear the neckband was very small relative acceleration data could not be recorded continuously at to the increased preening time. Thus, both types of tags 50  Hz, because of time required intermittently to write caused extra discomfort to the birds. The geese reacted data to the tag’s memory. Therefore, we down-sampled it to backpacks and neckbands differently, but overall used to a continuous 20  Hz, which is widely used for studies the same amount of time for extra discomfort behaviour. Kölzsch et al. Anim Biotelemetry (2016) 4:13 Page 6 of 14 GPS accuracy There were various effects of tag type and GPS position type on the accuracy in terms of distance to the (exact) positions as obtained by the Geosystems Total Station (Fig.  3). For NMEA 1 positions, modelled inaccuracy (GLMM model estimate of distance to exact position) was smaller for neckband tags (1.8 m) than backpacks (3.5 m; LRT, χ  = 16.3, df = 1, p < 0.001), whereas the inaccura- cies of NMEA 2 were similar for the two tag types (BP: 2.4 m, NB: 2.2 m; LRT, χ  = 0.7, df = 1, p = 0.39). Also for PPP positions, the inaccuracy was smaller for neckbands (BP: 3.7  m, NB: 1.9  m; LRT, χ   =  5.4, df  =  1, p  =  0.02). Consequently, tag type lost its influence on GPS accuracy only if the platform setting (‘pedestrian walking’) and SBAS enhancement were applied simultaneously. If only one or the other was applied, the neckbands were more accurate than the backpack tags. When looking at each tag type separately, only the backpacks revealed an effect of GPS position type on accuracy, with the pedestrian walking filter improving accuracy (NMEA 1: 3.3  m, NMEA 2: 3.2  m, PPP: 3.8  m; LRT, χ  = 7.1, df = 2, p = 0.03). GPS accuracy did not dif- fer by GPS position type, and effect sizes were smaller for neckbands (NMEA 1: 2.5 m, NMEA 2: 2.1 m, PPP: 2.4 m; LRT, χ  = 2.4, df = 2, p = 0.31). Our data did not show significant differences in mini - mum convex polygon area with respect to tag type, 2 2 neither for NMEA data (BP: 464  m , NB: 650  m ; LRT, χ  =  0.9, df =  1, p =  0.33), nor for PPP data tracks (BP: 2 2 2 579  m , NB: 632  m ; LRT, χ   =  0.1, df  =  1, p  =  0.77). However, note that sample size was very low. For back- packs, polygon areas were larger for PPP than for NMEA 2 2 2 positions (NMEA: 547  m , PPP: 663  m ; LRT, χ   =  5.5, df  =  1, p  =  0.02), but there was no difference in neck - Fig. 2 Bar plots (mean ± SD) of discomfort levels of geese carrying 2 2 2 a backpack tag (BP), neckband tag (NB) or no tag (control C). Behav- band tags (NMEA: 566  m , NB: 548  m ; LRT, χ   =  0.01, iours indicating discomfort are a duration of preening (and pecking df = 1, p = 0.94). Thus, for backpacks the PPP positions the tag), b frequency of shaking (the body or the head), c duration of were more spread out. feeding, d duration of being vigilant and e duration of resting behav- iour, each within 10-min periods Behavioural classification from accelerometer data In the behaviour-annotated examples of static and dynamic acceleration for the backpack tag (Fig.  4a, b) and neckband tag (Fig. 4c, d), the variability in alignment Furthermore, a goose with a tag would feed less (static acceleration) was less pronounced in backpack (C: 193  s, BP: 152  s, NB: 148  s; LRT, χ   =  11.1, df  =  2, than in neckband tags and did not as easily match with p = 0.004), but be no less vigilant (LRT, χ  = 0.2, df = 2, behaviours (but see long feeding burst in Fig.  4a). For p  =  0.92) nor rest for shorter times (LRT, χ   =  0.8, the neckband, e.g. feeding events were clearly depicted df  =  2, p  =  0.67). There were no differences between by peaks in the x-axis static acceleration. Note that the the two tag types in terms of duration of feeding, vigi- x-axis pointed towards the head in the neckbands and lance and resting, indicating that they were similar in the was not affected by the regularly occurring, movement degree to which they affected goose comfort. In addi - induced events of the neckband rotation. In the dynamic tion, there was no effect of time since deployment on acceleration patterns, resting was clearly visible for the the extent of any of the behaviours (LRT, χ  < 1, df =  1, backpack as well as the neckband (dynamic acceleration p  >  0.30), showing that the birds were not yet getting of all axes = 0). Feeding could in this example not be well habituated to the tags. Kölzsch et al. Anim Biotelemetry (2016) 4:13 Page 7 of 14 Fig. 3 Example tracks and accuracy statistics for GPS data of backpack (BP) and neckband (NB) tags on geese. a NMEA data (red), PPP data (blue) and distances of time-overlapping NMEA or PPP data with exact measures of the Geosystems Total Station (green) for a 10-min track of one goose with a backpack tag. b Same as a, but for a goose with a neckband tag. c Distances of NMEA 1, NMEA 2 and PPP data to exact positions, split for BP and NB tags. d Minimum convex polygon area of NMEA and PPP tracks for both tag types. Please see the list of abbreviations for explanations of NMEA 1/2 and PPP. Note that measures in the box plots are not model parameters (as reported in the text), but raw data, and do not account for random factors discerned from dynamic acceleration of the backpack: split off by high x-axis frequency at the dominant power similar to walking, it was characterised by a regular wave spectrum (strong wave pattern; fdpsX). The only indica - pattern in the x-axis dynamic acceleration. On the other tion visible for feeding was low x-axis maximum dynamic hand, the dynamic acceleration of the neckband showed body acceleration (mdbaX). rather unique high amplitudes during feeding. How- For the neckband tag, the classification tree looked ever, there are several other peaks that were not easy to very different (Fig.  5b). First, feeding, preening and walk- explain. ing were split off by high odba, indicating a high level When examining the classification trees, the pitch in of general tag movement. Further, splits by mdbaZ and the x-axis for backpack tags (Fig.  5a) was a main statis- rollY indicated that preening contained very strong right/ tic for the first split of resting/feeding (high pitch) from left positional and angular changes. Note that right/ walking/vigilance (low pitch), indicating that body angle left and front/back movements (y- and z-axes) are not (leaning back or forward) was the best initial classifica - easily discernible, because the neckband tag can freely tion criteria. Then, on one part of the tree, resting was rotate around the goose’s neck. Walking was split off by split off by low x-axis overall dynamic body acceleration low mdbaZ and high odbaY, showing the right/left sway- (odbaX), i.e. little front/back movement of the tag. At the ing walk of Canada geese. Feeding showed low angular other part of the tree, vigilance was split off by low odba change in the y-axis (rollY). On the other side of the clas- (very little overall movement) and walking was further sification, tree resting and vigilance were discerned by Kölzsch et al. Anim Biotelemetry (2016) 4:13 Page 8 of 14 Fig. 4 Example data of static and dynamic acceleration data for both tag types, down-sampled to 20 Hz resolution. a Static and b dynamic accel- eration in all three axes (x-blue, y-green, z-red) of a goose with backpack tag with an overlaid bar of observed behaviour inserted (black rest, blue walk, green feed, red preen, pink vigilance). c, d Same as a, b, but for a goose with neckband tag. Note the differences in scale and differentiability of behaviours by tag type. The accelerometer was fitted into the tags so that for the backpacks x is the reverse of surge, y is the sway and z is the heave. For the neckband on a raised goose neck that means x is the reverse of heave and y and z indicate surge and sway depending on how the tag is turned Kölzsch et al. Anim Biotelemetry (2016) 4:13 Page 9 of 14 Fig. 5 Final, pruned classification trees of six main behaviours of observed geese as calculated by accelerometer statistics. a Classification tree for backpack tags with a legend of cross-validation success rates for each behaviour separately and an overall value. b Same as a, but for neckband tag data. The acceleration statistics used in the analyses are: pitchX—body angle along x-axis, pitchZ—body angle along z-axis, rollY—body angle along y-axis, mdbaX/mdbaY/mdbaZ—maximum dynamic body acceleration along the x-/y-/z-axes, odbaX/odbaY/odbaZ—mean dynamic body acceleration along the x-/y-/z-axes, odba—overall dynamic body acceleration (sum of the previous), dpsX/dpsY/dpsZ—maximum power spectral density of dynamic acceleration along x-/y-/z-axes, fdpsX/fdpsY/fdpsZ—frequency at the maximum power spectral density along the x-/y-/z-axes (for more explanation, see [73]) pitchX, indicating that the difference between the two Behaviour-specific classification results differed between rather inert behaviours was in body/head angle. the two types of tags; neckbands showed better results The overall classification success rates of the fitted trees for behaviours involving head movement such as feed- (Fig. 5) were similarly high for both tag types (BP: 72.7 %, ing or vigilance due to their position close to the head, NB: 74.6  %). However, feeding and vigilance behaviours and backpacks were more successful in detecting behav- were better classified for neckbands than for backpacks, iours such as walking or resting, for which it is impor- whereas preening, resting and walking were better tant that the tag is closely fixed to the body (not freely detected in backpack data (Fig.  5, for details see Addi- moving around the neck). Thus, a decision on the use of tional file  1: Table A1). Thus, neckbands were better at neckband or backpack tags for large waterbirds cannot be classifying behaviours that were mainly performed with based on early habituation discomfort of the birds or GPS the head, whereas backpacks seem better able to map position accuracy, but should depend on possibly differ - whole-body behaviours. ential long-term habituation and the research question. Apart from showing that short-term, tag-induced dis- Discussion comfort was similar for both tag types, we have also seen We have compared two of the most widely used types of that time since deployment did not influence the geese’ attachment of GPS/accelerometer tags on large water- behaviours during the first 6 days. This indicates that pre - birds, for the first time in a way that integrated the quan - viously observed habituation to the tags takes longer, up tification of tag-induced adverse behaviour during early to several weeks or months [54], and might then differ habituation, GPS position accuracy and behaviour clas- between different tag attachments. This can be impor - sification success from accelerometer data. In general, tant to consider, because one issue of many tracking both tag types showed a similar short-term discomfort research is the necessary duration of the study, for how effect on the birds, GPS accuracy was only slightly bet - long the animal shall carry a tag and collect data (e.g. for- ter for neckband tags, and overall behavioural classi- aging movement vs. lifetime tracking). Furthermore, it is fication success from accelerometer data was similar. important to understand for which time frame tracking Kölzsch et al. Anim Biotelemetry (2016) 4:13 Page 10 of 14 data are affected by discomfort of the animal and when important to realise that our study was performed with seemingly normal movement data can be observed. captive animals and that the effects of being handled and In the light of animal welfare, a tag should only be carrying the tags on the behaviour of wild birds might mounted on the animal as long as it is working prop- differ. They are usually more constrained in food avail - erly [67]. Therefore, drop-off mechanisms and weak ability, and the need for vigilance for predators is higher. links in harnesses have become more widely used [74]. Therefore, tag-induced discomfort might not be affecting Simple glue-on-feathers has been used for short-term their time budget as much, and our findings of extra time deployments in the past [75], but can damage the feath- spent preening and shaking are conservative measures. ers or skin of animals. However, if it is desirable that On the other hand, they might be initially more stressed the tag stays on the animal for a long time, the material by the tag than captive geese that are somewhat used to and attachment methods should be adapted, taking into being handled. However, we are confident that the gen - account habitat conditions and the destructiveness of eral conclusions of our comparative study can be trans- the animals. Some species of geese are known to destroy ferred to wild waterbirds. harnesses and backpack tags within a short time [61], With awareness that external devices are most likely and there is advice not to use a harness for this species affecting animals (at least short term during habitua - group [58]. However, plastic neckbands are known to tion [54]), it is even more important to ensure the high- have a long retention time [71] and are less accessible for est possible quantity and quality of collected data. For the wearer to inflict damage with its bill. Initial concern long-term studies, the extension of time in functionality regarding neckband icing [76] has been lessened by stud- has successfully been achieved by including solar cells ies showing that icing is exceptionally rare and does not for energy provision, so that tag running times are not have long-term fitness consequences for geese [70, 77]. time-limited by battery power. Regarding data quality, Furthermore, the fact that unique IDs can be inscribed our results suggest that GPS accuracy from the particu- on the outside of a neckband for visual observations lar backpack tags of this study was generally lower and is a large advantage when quantifying survival and tag more strongly improved by filtering and SBAS augmen - functionality. tation than neckband tags. Thus, for data sets that are External tags have been reported to have no significant not continuously of NMEA 2 type, it seems advisable long-term effect on animals [59, 71, 78], but there have to prefer neckband tags to obtain higher GPS accuracy. also been cases showing various negative effects on ani - These findings differ from earlier results on ARGOS mal behaviour and survival [22, 43, 63, 65, 79, 80]. Such reception and lower accuracy of neckbands than back- effects can be intensified if the animal is flying or moving packs [58]. However, in that study the antennas of the through water, depending on tag placement [47, 48]. It is neckbands pointed down the neck of the goose, which possible that neckband tags have a higher aerodynamic has been shown to be problematic [39]. resistance during flight and that their placement away Furthermore, signal frequency, antenna type and orien- from the centre of gravity might affect the bird’s balance, tation will have a profound effect on device performance, leading to higher flight costs. On the other hand, the har - making comparisons difficult. The signal reception pat - ness of a backpack tag is likely to cause abrasions and terns of our tags’ helical antennas in relation to the GPS hamper flapping of the wings, which is especially impor - satellites would have affected the device performance in tant for geese that almost exclusively use flapping flight. ways that are too complex to explore here, but our results It was not possible for us to incorporate flight behaviour are probably influenced most strongly by antenna char - in this study, and there are, as far as we know, no other acteristics. Helical antennas were chosen because their studies that compare the differences of negative effects reception pattern is omnidirectional, and their orienta- of neckbands versus backpacks during flight or diving. tion has less effect on received signal strength than would However, from field experience it seems that the longev - the patch antennas that are normally used with GPS ity of wild geese with neckband tags is higher, possibly receivers because they have higher gain. We did not test due to better manoeuvrability during flight (AK, unpub - the effect of antenna orientation during flight. However, lished data). Furthermore, fat accumulation for migration if the orientation of the neckband tag during flight results can negatively affect body harness fit of backpacks, but in the antenna hanging under the bird’s outstretched is not problematic for neckbands, as neck size does not neck, the ability of the GPS receiver to acquire satellites change. is likely to be reduced. In contrast, the orientation of a Habituation to tags might also depend on the handling back-mounted tag is likely to remain much the same dur- time and procedures during deployment, in which con- ing flight, and indeed the height of the bird and clear line text we consider neckbands more suitable as they are of sight to GPS satellites would probably improve receiver more ‘standardised’ and quickly to attach. However, it is performance. Kölzsch et al. Anim Biotelemetry (2016) 4:13 Page 11 of 14 As more global satellite navigation systems join Amer- unconventional placement of small tags might be most ica’s GPS (e.g. Russia’s GLONASS and Europe’s Galileo) effective and will improve the scope of accelerometer in becoming available for widespread use, and augmenta- data sets even more. tion of GPS accuracy is possible in different ways (differ - We are aware that there might always be limitations ential GPS, SBAS, filtering), improved position data and to animal tracking [1, 20]. Some species simply are too their applicability for ecological research should be evalu- small, do not tolerate handling stress or are hard to catch. ated. For different research questions, GPS fix rate, accu - However, by pushing the technological limits, animal racy or precision is of varying importance. For example, tracking will be refined into a truly revolutionary tool for studies on habitat selection require GPS positions of high wildlife research. Ecologists should use the smallest tags accuracy to allow for correct overlap with e.g. remote with the least effect on the animal’s behaviour giving the sensing data, whereas studies about individual behaviour best quality data for answering the research questions or group movement need high GPS precision. Here, we posed by various disciplines. By extracting natural, objec- take a first step to also raise ecologists’ attention to the tive time budgets and discerning small-scale changes in likelihood that GPS positions from standard devices are movement and other behaviours besides the animal’s augmented by some form of smoothing algorithm (prob- location, we will be able to explore an animal’s true natu- ably based on an extended Kalman filter) before being ral behaviour and apply this knowledge to conservation, output from the device. Rapidly sampled individual loca- management or models of disease spread. tions output from a GPS tag are unlikely to be statisti- cally independent, and the type of filter/platform setting Conclusion applied (e.g. stationary, pedestrian walking, motor vehi- We have shown that captive Canada geese with back- cle) will influence the data. As the smoothing algorithms pack or neckband tags exhibit discomfort behaviours at a depend on fast sampling rates, their influence on infre - similar level during a short habituation period. GPS accu- quent GPS locations is less, probably negligible. However, racy and general behaviour classification success based if a fast sampling rate is used to test the accuracy of a sta- on accelerometer data from both tag types were similar. tionary GPS tag, the results may not be representative of However, some behaviour types were better recognised the performance on the animal [81]. Therefore, the use of by neckbands, others by backpacks. Therefore, we advise raw GPS data might be advisable for high-frequency GPS that the selection of either type of tag attachment method tracking studies. for large waterbirds should depend on the research ques- In most studies, especially working with small species, tion, including focal behaviours and necessary tag reten- tag weight is most important [42, 43, 49] and often lim- tion time. its data quality and quantity options. However, we want Additional file to stress here that tag weight cannot be considered inde- pendent of the tag design and tag attachment method Additional file 1: Table A1. Success rates of behaviour classification [47]. A device mounted on body appendages such as leg, trees for a backpack and b neckband tag accelerometer statistics. tail or head should ideally be somewhat lighter than a tag attached to the back of an animal or implanted [64, 82], which is supported by the whole body mass. For large Abbreviations NMEA: National Marine Electronics Association; : Standard format used for pro- waterbirds, this seems to be less of an issue, the tags used cessed GPS data that are directly obtained from a GPS tracking device. Usually, here are quite heavy in absolute terms, but still <1.5 % of the GPS positions are filtered by platform settings that are often based on the the body weight. extended Kalman filter to improve accuracy [34]. Here, the platform settings were ‘pedestrian walking’; NMEA 1: NMEA data that are filtered as ‘pedestrian The integration of extra sensors into GPS tags is walking’, but where the SBAS correction signal has not been received to improving usability of position data for many applica- additionally improve the positions; NMEA 2: NMEA data that are filtered as tions. Accelerometer measurements are one example ‘pedestrian walking’ and for which the GPS tag had received the SBAS correc- tion signal for improvement of the positions with differential methods; PPP: that is gaining more attention [12, 13], and we show Precise point positions. GPS positions that were extracted by post-processing that for the best use of accelerometer data it is critical to raw GPS data with the use of SBAS correction signals obtained from the EDAS compare different means of attachment and placement service. These positions were not filtered. [51, 83]. An accelerometer records the movements of Authors’ contributions the parts of the body directly underneath the tag, and as AK, HHTP, BHC and BAN designed the study. JB and BHC developed and suc- body parts move differently for the various behaviours, cessively refined the GPS/accelerometer tags. AK, MN, GJDMM devised the different methods of tag attachment, and AK, MN and BAN performed the it is important to have a clear idea which tag placement study. AK, MN and JB analysed the different data sets, and AK, FvL, FdB and can give the most significant acceleration data to detect BAN refined the results. The manuscript was written by AK, and all authors the respective behaviour. Thus, depending on which contributed to refining the text and content. All authors read and approved the final manuscript. type of behaviour is relevant for the attempted study, Kölzsch et al. Anim Biotelemetry (2016) 4:13 Page 12 of 14 Author details 11. Yoda K, Naito Y, Sato K, Takahashi A, Nishikawa J, Ropert-Coudert Y, et al. Department of Animal Ecology, Netherlands Institute of Ecology (NIOO- A new technique for monitoring the behaviour of free-ranging Adelie KNAW ), PO Box 50, 6700 AB Wageningen, The Netherlands. Department penguins. 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Journal

Animal BiotelemetrySpringer Journals

Published: May 4, 2016

References