Classification of African ground pangolin behaviour based on accelerometer readouts: validation of bio- logging methods

doi:10.21203/rs.3.rs-4406160/v1

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Research Article

Classification of African ground pangolin behaviour based on accelerometer readouts: validation of bio- logging methods

https://doi.org/10.21203/rs.3.rs-4406160/v1

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published 22 Jul, 2024

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Background

Quantifying animal behaviour is important to many areas of ecology and conservation. Understanding how free ranging animals behave can help in the design of optimal management strategies to both conserve species and enable individuals to express natural behaviours, maximising welfare. This is particularly important for endangered species for which a high level of human intervention is needed to counteract population declines and extinctions. Animal-borne bio-logging devices, such as accelerometers, present the opportunity to passively collect data on the behaviour of free ranging animals. Accelerometers are increasingly used in behavioural research. Validation of accelerometer readouts to enable classification of discrete behaviours, however, has only been carried out for a small number of species. This presents a barrier to the meaningful interpretation of data. Pangolins are a heavily trafficked clade of mammals, all of which are considered vulnerable to extinction. They are also under-researched, with little known about their behaviour in the wild. In this study, we present the first validation of behavioural classification based on accelerometer readouts for a pangolin species; the ground pangolin (Smutsia temminckii).

Results

We present a standardised attachment protocol to minimize the impact of devices on pangolin welfare. We match the readouts from accelerometers to behaviours defined through video observations. Using a Random Forest (RF) classification, we defined five discreet behaviours (walking, digging, feeding, investigating ground, and stationary, accuracy of 85%) and three activity levels (low, medium, and high, accuracy of 94%) from accelerometer readouts. We determine optimal sampling frequency and smoothing window (50 hz and 5 seconds for full behaviour, 10 Hz and 7 seconds for activity level). We then deploy accelerometers and classify the behaviour of three free ranging pangolins for between two and four days.

Conclusion

The methods we present will enable the quantification of ground pangolin behaviour in the wild to improve our understanding of the species’ ecology and help inform conservation efforts. This will also help to improve our fundamental understanding of animal behaviour and ecology.

Accelerometers

Behaviour

Biologging

Conservation

Endangered species

Pangolin

Population monitoring

Rehabilitation

Smutsia temminckii

Trafficking.

Animal behaviour emerges as a result of interactions between the individual and their environment (1). Behavioural plasticity allow an animal to exploit resources and avoid threats, ultimately playing a large role in determining individual fitness and the long-term success of populations and species (2, 3). Quantifying behaviour can give early warning signs of environmental change, both natural and anthropogenic, which may eventually impact population dynamics (4–6). Understanding behaviour is therefore critical both for conservation and for monitoring the health and welfare of animals (7–9).

Behaviour is an aspect of phenotype, tightly linked to genetics and individual experience (10). Studying the behaviour of free ranging animals presents multiple challenges (11, 12). Quantitative assessments of behaviour to determine trends and responses to external factors often require the observation of relatively large numbers of individuals for long periods of time (13). In the wild, species may be difficult to find and track (14, 15). Additionally, the presence of an observer can alter animal behaviour (11, 16, 17). As a result, wild animal behaviour has been understudied relative to more accessible captive animal models (18). Increasingly, remote sensing methods, such as satellites, Unmanned Aerial Vehicles (UAVs/drones), and static camera traps are employed in wildlife monitoring (19–21). While these technologies present new opportunities for studying wild animal behaviour, they are limited to accessible animals and often ill-suited for consistent monitoring over longer time scales, such as detecting seasonal variation in behavioural patterns.

Animal-borne bio-logging devices (bio-loggers) overcome many of the challenges associated with monitoring animal behaviour both in the wild and in captivity (11, 18). These devices are attached to the animal and record data which is either transmitted or collected when the device is retrieved (22). Accelerometers are a promising type of biologger, increasingly used in the study of animal behaviour (11). Accelerometers measure the rate of change in velocity on three axes. Different behaviours result in unique patterns of three-dimensional acceleration, enabling them to be interpreted from accelerometer readouts (22). Accelerometer data has a high degree of temporal resolution making it suitable for automated processing (23–25). Although still limited by the fact they cannot transmit data, accelerometers are increasingly light and affordable, enabling their deployment for extended periods of time to capture biological and ecologically significant events which cannot be monitored through traditional methods (11, 24).

Despite their widespread incorporation into monitoring programmes, there remain several questions which should be answered to ensure effective and ethical data collection using animal-borne accelerometers (11, 26, 27). When conducting animal research, it is the ethical obligation of the researcher to maximise the efficiency of devices and minimize the impact on subjects (25). Many studies using accelerometers have presented little justification of the choice of sampling frequency, smoothing window (the smallest unit of time over which behavioural data is analysed), device placement, or attachment type (11, 26). Selecting an appropriate sampling frequency requires a trade-off between data resolution and internal storage capacity, which in turn determines how often animals are handled (14, 28). Device placement and attachment type should be optimized to ensure there is minimal interference with the animal’s range of motion and welfare (27). For accelerometers, device position also influences how well behaviours can be determined based on readouts (29–31). For each animal model, calibration and standardisation should be conducted (31).

All species of pangolin are currently considered vulnerable to extinction (32). One of the main threats to this clade is poaching and trafficking for bushmeat and use in traditional medicine (33–35). As a result, seizure of live pangolins by authorities is common, and subsequent rehabilitation and release is an important aspect of conservation (36). How well released individuals reintegrate into wild populations is not currently known, with existing monitoring programmes relying on coarse positional data from satellite tags (37, 38). Using accelerometers to quantify the behaviour of free ranging pangolins would present opportunities to improve existing release protocols.

In this study, we present standardised protocols for the use of accelerometers to study the behaviour of African ground pangolins (Smutsia temminckii). We present a standardised position and attachment type to minimize device impact on behaviour and welfare. Through comparison to ethograms, we develop automated classifiers to determine pangolin behaviour and activity level based on accelerometer readouts. We then use accelerometers to quantify the behaviour of free ranging pangolins. Moving forward, this work can be applied immediately for the study of African ground pangolins and developed further to incorporate more pangolin species.

Ground pangolin individuals

Ground pangolins recovered from trafficking in Zimbabwe undergo rehabilitation with the Tikki Hywood Foundation (THF) prior to release (38). Device attachment protocols and validation of interpretation of accelerometer readouts was carried out by attaching devices to five healthy individuals in care (Harare, Zimbabwe) and one free ranging individual post release (Gonarezhou National Park, Zimbabwe). Following validation, devices were attached to three free ranging post release individuals (Gonarezhou National Park, Zimbabwe, Table 1). These individuals were not under observation between device attachment and retrieval.

Table 1

Ground pangolins to which accelerometers were attached. Sex: M = Male, F = Female. Condition: RC = rehabilitation care, FR = free ranging. Location: Harare = close to Harare, Zimbabwe, Gonarezhou = Gonarezhou National Park, Zimbabwe. Purpose: V = validation, C = data collection. Dur. (s) = duration of video recording in seconds.
Ind.	Sex	Condition	Location	Mass (kg)	Purpose	Dur. (s)
1. Chaminuka	M	RC	Harare	12.00	V	2556.54
2. Sindisiwe	F	RC	Harare	4.60	V	1541.34
3. Dakarai	M	RC	Harare	7.10	V	2650.14
4. Impi	M	RC	Harare	8.75	V	2607.15
5. Yakachena	M	RC	Harare	4.50	V	1757.34
6. Shura	M	FR	Gonarezhou	13.99	V & C	1170.05
7. Fikile	F	FR	Gonarezhou	12.35	C	NA
8. Makwande	M	FR	Gonarezhou	14.71	C	NA

Device design, positioning, and attachment.

Ground pangolin scales present many possible attachment sites for biologgers. Previous work has indicated that i) devices should be attached at a single point along the midline so as not to interfere with natural behaviours, ii) devices should be dull and dark in colour, with no exposed lights, iii) devices should break off if they become entangled or during a predator attack, and iv) devices should be equivalent to no more than 3% of the animals body mass (27, 38). In previous work focused on other species, accelerometers have been attached in a manner that left them rigid and fixed in place. This ensures that the dimensions of movements remain constant throughout the deployment (11, 39). Such an attachment would have interfered with the manoeuvrability of pangolins and was deemed inappropriate for the current study.

An attachment site was selected on the first central dorsal scale caudally from the pelvis (Fig. 1A and B). 7 mm holes were drilled through distal portion of scales. AxyTrek accelerometers (TechnoSmart, EU) were mounted on flexible cattle ear tags (length: 9.8cm, width: 4.2cm), enabling removal in the event of predator attack or entanglement (Fig. 1B). Initial validation was carried out using orange tags. These were replaced with black tags for free ranging pangolins (Figure S1). Tags were secured at a single point using a 6 mm bolt with a sleeve threaded through the ventral side and secured with a nut on the dorsal side of the scale (Fig. 1C). The total mass of the device, tag, and bolt was 46g (< 3% the mass of any pangolins used in this study, Table 1).

Data collection.

Accelerometers were configured to record 50 Hz data, with a dynamic range of ± 8 g (G fullscale) and an 8-bit resolution. During validation, pangolin behaviour was recorded using a GoPro Hero 11, with GoPro labs Precision Date and Time (UTC). For pangolins in care, this was done during daily foraging walks. During walks, pangolins were given free range to forage for food while under supervision of a handler. Pangolins explored varied terrain, with the handler intervening only to redirect the pangolin. The one free ranging pangolin included in the validation stage (Shura, Table 1) was opportunistically fit with an accelerometer and filmed while foraging. The start and end times of videos were synchronised to UTC by recording the time on a GPS app (GPS test, Chartcross Limited) (40). Footage of the app was recorded for a minimum of five seconds at the start and end of each filming session.

To include time spent sleeping, which generally occurs in dens in the wild, three pangolins in care (Dakari, Impi, and Yakachena, Table 1) were fit with accelerometers while resting in sleeping boxes (Length = 55cm, Width = 55cm, Height = 60cm). In boxes, pangolins are largely stationary, with a handler remaining in the room and noting any audible movements.

Behavioural classification

Behaviours were grouped into i) seven discrete behaviours and ii) three activity levels (Supplementary materials 1, Table 2). Based on videos, a single observer generated an ethogram (Table 2). The exact time each behaviour started and finished was recorded. Any direct interactions between handlers and pangolins (e.g., redirecting or picking them up) were removed from analysis. A total of 12,282.56 seconds of video footage were annotated. Eight minutes spent in the sleeping box for each of three individuals were labelled as “stationary”. Head tucking (a defensive response to being startled) was excluded due to its short duration (< 2 seconds). Rolling was excluded as it was only recorded in one individual. Based on ethograms, accelerometer readouts were labelled with behaviours and activity levels. To account for the risk of time synchronisation errors, the first and last seconds of each behaviour were omitted (40).

Table 2

Based on video analysis, behaviours were grouped into discrete behaviours and activity levels.
Activity level	Discrete behaviour	Description
High	Digging	Digging in ground
Medium	Walking	Walking. Turning while walking. Collision while walking. Stumble or fall while walking. Returning to standing following a stumble or fall
Medium	Feeding	Actively eating with tongue moving, not digging
Medium	Investigating ground	Sniffing the ground, performing up to three exploratory scratches
Low	Stationary	In the sleeping box. A pause while performing another behaviour. Grooming. Defecating
NA	Rolling	Rolling in mud or faeces. Excluded due to being underrepresented in the data
NA	Head tucking	Rapidly tucks their head assuming a defensive posture. Excluded due to short duration

To optimize smoothing window (the smallest unit of time for which behaviour is analysed), each segment of labelled behaviour in accelerometer readouts was split into equal blocks between one and eight seconds in length. For each of these smoothing windows, optimal sampling frequency was determined by resampling the original 50 Hz data to generate 25,12,10, 8, 4 and 2 Hz datasets (28, 40). Following Collins et al. (2015), 18 summary metrics were calculated for each block (Table 3) using adapted code from (41, 42).

Table 3

The summary metrics calculated from accelerometer readouts. SD = Standard Deviation. Min. = minimum. Max. = maximum.
Summary metric	Description
Overall Dynamic Body Acceleration (ODBA, g)	Summation of absolute values of acceleration on each axis
Vectorial Dynamic Body Acceleration (VeDBA, g)	Vectorial sum of values of acceleration on each axis
Surge (Mean, SD, Min., Max.)	Linear acceleration along the longitudinal axis (tail to head)
Sway (Mean, SD, Min., Max.)	Linear acceleration along the lateral axis (shoulder to shoulder)
Heave (Mean, SD, Min., Max.)	Linear acceleration along the vertical axis (feet to back)
Pitch (Mean and SD)	Rotational acceleration about the lateral axis (shoulder to shoulder)
Roll (Mean and SD)	Rotational acceleration about the longitudinal axis (tail to head)

Random forest classification

Random Forest (RF) classification was used to automatically classify behaviours. Separate RF models were developed to classify each dataset (split based on smoothing window and sampling frequency) into detailed behaviours and activity levels (Table 2). Between the six pangolins, a total of 10,685 seconds of behaviour (“Digging” = 1,140 seconds, “Walking” = 4,338 seconds, “Feeding” = 2,358 seconds, “Investigating ground” = 949 seconds, “Stationary” = 1,900 seconds) were labelled for use in training and testing of the RF model (Table S1).

Each dataset was randomly split into training (70%) and a testing (30%) datasets. RF models were fit to the training data (43). During fitting, minimum node size was set to one, the importance of covariates to classification was calculated based on Gini index, the number of potential covariates to use at each split ranged between one and 18, and a grid search was used to optimise tuning parameters. Due to the unequal behavioural group sizes, up-sampling was conducted within the model. To account for repeated measures (i.e., multiple observations of each behaviour per pangolin), folds were created for individual based cross validation (43, 44). Area Under the receiver operating Curve (AUC), a measure of performance for classification problems, was calculated during a 10-fold cross-validation for model tuning and performance evaluation.

To evaluate performance, RF models were applied to testing datasets (28). A confusion matrix was produced and model accuracies were calculated (45). Optimal sampling frequency and smoothing windows were selected based on highest accuracy. For optimal RF models, the relative importance of summary metrics (Table 3) to accuracy and predictive ability were determined based on the impurity variable importance mode (46). Accelerometer readouts from three free ranging pangolins were classified into discreet behaviours and activity levels using RF models for optimal parameters.

Software.

Video analysis and ethograms generation was carried out in BORIS (Friard and Gamba, 2016). Subsequent data processing and visualisation was conducted in R version 4.3.2 (47) with data organisation assisted by the dplyr, data.table, lubridate, zoo, and tidyverse packages and data visualization using the ggplot2 package (48–53). RF fitting and evaluation was carried out using the caret (54) and ranger (46) packages.

All data and code is available on GitHub https://github.com/jharv3y/Pangolin_Accelerometer_Analysis and Zenodo 10.5281/zenodo.11179372.

Optimal sampling frequency and smoothing window.

The highest overall accuracy of RF classifications for discrete behaviours (accuracy = 85%, upper = 87%, lower = 81%, κ = 0.78) occurred with a smoothing window of five seconds and a sampling frequency of 50 Hz (Fig. 2A, Table S2). The highest overall accuracy for activity levels (94%, upper = 96%, lower = 91%, κ = 0.85) occurred with a smoothing window of 7 seconds and a sampling frequency of 10 Hz (Fig. 2B, Table S3).

For both groupings, maximum heave was the most important predictor for classifying behaviours, closely followed by mean heave for activity levels (Fig. 3).

Changes in sampling frequency had variable effects on the accuracy of predicting individual behaviours. For example, changes in sampling frequency had little effect on the accuracy of predicting the “Stationary”, “Walking”, and “Feeding” behaviours, while the accuracy of “Digging” and “Investigating ground” declined sharply between 50 and 25 Hz (Figure S2A, Table S3). Similarly, the accuracy of predicting the “Low” activity level remained relatively constant across sampling frequencies, while there was a sharp reduction in accuracy between 25 and 12 Hz for “High” activity levels (Figure S2B Table S3).

Classification of behaviour for free ranging pangolins

The behaviour of three free ranging pangolins was classified based on accelerometer readouts into discrete behaviours, using a sampling frequency of 50 Hz and smoothing window of five seconds (Fig. 4), and activity levels, using a sampling frequency of 10 Hz and smoothing window of 7 seconds (Figure S3).

We have presented a validation of the use of accelerometers to quantify the behaviour and activity level of ground pangolins and presented the first detailed record of the behaviour of free ranging pangolins across multiple days. In future studies, we recommend the use of an optimal sampling frequency of 50 Hz and smoothing window of five seconds if quantifying discrete behaviours and a sampling frequency of 10 Hz and smoothing window of seven seconds if quantifying activity levels. For longer deployments when device storage is an issue, reasonable accuracy can also be achieved with sampling frequencies between 6 and 50 Hz. The level of accuracy achieved by Random Forest (RF) classifiers were comparable to other studies of this kind (e.g., an accuracy of 86.96% for adult hawksbill turtle (Eretmochelys imbricata), 79.49% for green turtle (Chelonia mydas) (55), 82% for moose (Alces alces) (56) and 87% for dingo (Canis dingo) (57)). Our validation dataset included a range of pangolin sizes. While we acknowledge a bias towards males, we believe our results are generalizable to individuals of both sexes outside the study.

Animal-borne accelerometers are generally attached in a manner which ensures that devices are fixed in place (e.g., using adhesives or harnesses) (11, 39). Given the small size of pangolin scales relative to devices, it was not possible to attach them in a rigid manner while enabling the animal to carry out a full range of natural behaviours. Instead, single point attachments were used which likely amplified device movement. We have demonstrated that such an attachment can result in classification of fine-scale behaviour with high levels of accuracy. As these protocols were selected while considering the individual welfare of animals, they are suitable for integration into ongoing post release monitoring programmes.

Accelerometers attached to the back of an animal were previously thought to be incapable of accurate classification of fine-scale feeding behaviours (11). We did not find this to be the case for pangolins, with high levels of accuracy (88%) when classifying “Feeding”. This is likely since pangolins are ant and termite eaters which employ specific global body poses and motions during feeding. This may also be true for other species with a similar diet.

We have shown that a smoothing window of five seconds is appropriate to capture all but the most rapid of pangolin behaviours. A five second smoothing widow has been recommended for other species such as reindeer (Rangifer tarandus) and sheep (Ovis aries) (58, 59). Increasing smoothing window size is generally accepted to increase the accuracy of detecting complex behaviours, at a cost of increasing the likelihood of capturing transitions between behaviours, resulting in misclassification (58, 60). If rapid behaviours, such as “Head tucking”, are of interest to future studies, the smoothing window could also be decreased, although this would reduce overall accuracy.

Generally, higher accelerometer sampling frequences result in higher accuracy for behavioural classification, although this depends on the behaviour of interest (28, 40, 60–62). Higher sampling rates consume battery power and memory storage more quickly. Therefore, researchers should carefully consider these trade-offs during device deployment (28, 60, 62). For ground pangolin, we have shown that the highest sampling frequency (50 Hz) gave the highest levels of accuracy for discrete behaviours, however the highest levels of accuracy for activity level were obtained at a lower frequency (10 Hz). In future, considerations should be given to the study question, which may not require full quantification of discrete behaviour.

A common limitation in the classification of accelerometer readouts is distinguishing behaviours which occur at the same time e.g. eating and walking (63). This was not found to be an issue with pangolins as behaviours of interest did not occur concurrently. Although important discrete behaviours could be classified, pangolins in the wild will carry out other behaviours of interest to research, such as mating, responding to predators, and interacting with conspecifics, which could not ethically be simulated for pangolins in care. This is similar to other studies which classify behaviour based on accelerometer readouts (64). Additionally, very rapid behaviours (“Head tucking”) and behaviours which were only observed for one individual (“Rolling”) could not be included in analysis due to the cross-validation design. This could be improved upon in future work by increasing observation time. Nonetheless, this work is the first of its kind for any pangolin species and promises to open new opportunities to study pangolin behaviour in their natural habitat. The methods used could easily be applied to other species of pangolin to validate the use of accelerometers for quantifying behaviour.

Free ranging pangolins were observed to spend a large amount of their time stationary (between 62 and 71%), with foraging related behaviours, such as “Feeding”, “Walking”, and “Digging”, occurring primarily at dawn and in darkness, with sporadic daytime activity, supporting previous observations (65–67).

The definition of a species’ optimal habitat is challenging given the range of complex factors involved as well as the plasticity of both physiology and behaviour within species (68–71). This is a challenge both for deciding where to release rehabilitated animals as well as when designing protected areas intended to benefit a particular species. The quantification of individual behaviour, now possible on a larger scale than ever before thanks to the development of tools such as accelerometers, will inform on key metrics of animal welfare, such as daily activity rates or the time allocated to activities such as foraging. These behaviours can directly inform on habitat usage, allowing the definition of optimal habitats and thus the suitability of certain sites for protection or the release of rehabilitated animals. Daily activity can also be integrated into energy budgets, ultimately building into population level models to inform on population viability (72–75).

Introducing new individuals into ecosystems has the potential to increase interspecific competition (population density dependent effects), for example for food, mates, or suitable denning sites, negatively impacting the welfare of both released and wild animals (76, 77). In population ecology, the concept of carrying capacity is often considered. This concept, however, rarely accounts for the dynamic nature of ecosystems, where the availability of resources as well as the abundance of threats, such as predators, are subject to change. Behavioural changes could give indications as to how increases in population density impact individuals which could be far more inclusive of the dynamics of the natural world than crude evaluation of population level metrics such as carrying capacity. The use of accelerometers to quantify the behaviour of ground pangolins during the transition from care to free ranging could therefore allow guidelines on the number of individuals which can be introduced into a population to be developed.

We have presented a validation and guidelines for the use of accelerometers to quantify the behaviour of ground pangolins, which can be expanded to other pangolin species. This work presents a new tool for the study of pangolin behaviour which promises to provide valuable information for the design of conservation strategies for this and other vulnerable species. It can also be used to improve our fundamental understanding of the interaction between animals and their environment, ultimately gaining new insights into animal ecology, evolution, and how best to protect the natural world.

Ethical statement

Procedures, housing, and monitoring of pangolins were conducted under Zimbabwe Parks and Wildlife Management Authority (ZPWMA), permits: 106(1) (d) & (2) 06/2023, 46 (1) (b) 05/2023, 46 (1) (b) 02/2023, 46 (1) (b) 03/2023, 46 (1) (b) 04/2023. Ethical approval has been granted by the Zimbabwe National Animal Research Ethics Council (NAREC), certificate number: NAREC/018/22.

Author Contribution

JHC conceived the initial idea with insights from DC and EC. JHC, DC, C-MT, and EC collected the data. JHC constructed the ethograms and analysed the data with assistance from DC. JHC and DC led the writing of the manuscript. JHC and DC share first authorship. All authors contributed critically to the drafts and gave final approval for publication.

Acknowledgement

We thank Lisa Hywood and Jordan Bryant for their assistance and insights during validation. We thank the THF field team as well as the staff and management of Gonarezhou National Park for enabling data collection. We thank the TechnoSmart team, particularly Marco Cianchetti, for their support during accelerometer design and deployment along with the BORIS team for support during ethogram generation. This work was supported by the Rufford foundation grant number 37221-1.

Data Availability

All data and code is available on GitHub https://github.com/jharv3y/Pangolin_Accelerometer_Analysis and Zenodo 10.5281/zenodo.11179372.

Shepard ELC, Wilson RP, Quintana F, Laich AG, Liebsch N, Albareda DA, et al. Identification of animal movement patterns using tri-axial accelerometry. Endangered Species Research. 2008;10:47–60.
Snell-Rood EC. An overview of the evolutionary causes and consequences of behavioural plasticity. Animal Behaviour. 2013;85(5):1004–11.
Moiron M, Laskowski KL, Niemelä PT. Individual differences in behaviour explain variation in survival: a meta-analysis. Gurevitch J, editor. Ecology Letters. 2020;23(2):399–408.
Sumpter DJT, Broomhead DS. Relating individual behaviour to population dynamics. Proc R Soc Lond B. 2001;268(1470):925–32.
Morales JM, Moorcroft PR, Matthiopoulos J, Frair JL, Kie JG, Powell RA, et al. Building the bridge between animal movement and population dynamics. Phil Trans R Soc B. 2010;365(1550):2289–301.
Wilson MW, Ridlon AD, Gaynor KM, Gaines SD, Stier AC, Halpern BS. Ecological impacts of human-induced animal behaviour change. Ecology Letters. 2020;23(10):1522–36.
Buchholz R. Behavioural biology: an effective and relevant conservation tool. Trends in Ecology & Evolution. 2007;22(8):401–7.
Roever CL, Beyer HL, Chase MJ, van Aarde RJ. The pitfalls of ignoring behaviour when quantifying habitat selection. Diversity and Distributions. 2014;20(3):322–33.
Matthews SG, Miller AL, Clapp J, Plötz T, Kyriazakis I. Early detection of health and welfare compromises through automated detection of behavioural changes in pigs. The Veterinary Journal. 2016;217:43–51.
Mery F, Burns JG. Behavioural plasticity: an interaction between evolution and experience. Evol Ecol. 2010;24(3):571–83.
Brown DD, Kays R, Wikelski M, Wilson R, Klimley AP. Observing the unwatchable through acceleration logging of animal behavior. Animal Biotelemetry. 2013;1(1).
Hughey LF, Hein AM, Strandburg-Peshkin A, Jensen FH. Challenges and solutions for studying collective animal behaviour in the wild. Phil Trans R Soc B. 2018;373(1746):20170005.
Lush L, Ellwood S, Markham A, Ward AI, Wheeler P. Use of tri-axial accelerometers to assess terrestrial mammal behaviour in the wild. Journal of Zoology. 2016;298(4):257–65.
Hays GC. New insights: animal-borne cameras and accelerometers reveal the secret lives of cryptic species. Journal of Animal Ecology. 2015;84(3):587–9.
Desbiez ALJ, Kluyber D, Massocato GF, Attias N. Methods for the characterization of activity patterns in elusive species: the giant armadillo in the Brazilian Pantanal. Journal of Zoology. 2021;315(4):301–12.
Canine NG. Unrecognized anti-predator behaviour can bias observational data. Animal Behaviour. 1990;39(1):195–7.
Wade MR, Zalucki MP, Franzmann BA. Influence of Observer Presence on Pacific Damsel Bug Behavior: Who is Watching Whom? J Insect Behav. 2005;18(5):651–67.
Wilmers CC, Nickel B, Bryce CM, Smith JA, Wheat RE, Yovovich V. The golden age of bio-logging: how animal-borne sensors are advancing the frontiers of ecology. Ecology. 2015;96(7):1741–53.
O’Connell AF, Nichols JD, Karanth KU. Camera traps in animal ecology: methods and analyses. Vol. 271. Springer; 2011.
Pettorelli N, Laurance WF, O’Brien TG, Wegmann M, Nagendra H, Turner W. Satellite remote sensing for applied ecologists: opportunities and challenges. Milner-Gulland EJ, editor. Journal of Applied Ecology. 2014;51(4):839–48.
Infantes E, Carroll D, Silva WTAF, Härkönen T, Edwards SV, Harding KC. An automated work-flow for pinniped surveys: A new tool for monitoring population dynamics. Frontiers in Ecology and Evolution [Internet]. 2022 [cited 2023 Dec 21];10. Available from: https://www.frontiersin.org/articles/10.3389/fevo.2022.905309
Whitford M, Klimley AP. An overview of behavioral, physiological, and environmental sensors used in animal biotelemetry and biologging studies. Anim Biotelemetry. 2019;7(1):26.
Nathan R, Spiegel O, Fortmann-Roe S, Harel R, Wikelski M, Getz WM. Using tri-axial acceleration data to identify behavioral modes of free-ranging animals: general concepts and tools illustrated for griffon vultures. Journal of Experimental Biology. 2012;215(6):986–96.
Leos-Barajas V, Photopoulou T, Langrock R, Patterson TA, Watanabe YY, Murgatroyd M, et al. Analysis of animal accelerometer data using hidden Markov models. O’Hara RB, editor. Methods in Ecology and Evolution. 2017;8(2):161–73.
Williams HJ, Taylor LA, Benhamou S, Bijleveld AI, Clay TA, de Grissac S, et al. Optimizing the use of biologgers for movement ecology research. Journal of Animal Ecology. 2020;89(1):186–206.
Collins PM, Green JA, Warwick-Evans V, Dodd S, Shaw PJA, Arnould JPY, et al. Interpreting behaviors from accelerometry: a method combining simplicity and objectivity. Ecology and Evolution. 2015;5(20):4642–54.
Soulsbury CD, Gray HE, Smith LM, Braithwaite V, Cotter SC, Elwood RW, et al. The welfare and ethics of research involving wild animals: A primer. Methods in Ecology and Evolution. 2020;11(10):1164–81.
Hounslow JL, Brewster LR, Lear KO, Guttridge TL, Daly R, Whitney NM, et al. Assessing the effects of sampling frequency on behavioural classification of accelerometer data. Journal of Experimental Marine Biology and Ecology. 2019;512:22–30.
Yan RC, Wilson RP. Subjectivity in bio-logging science: do logged data mislead? Memoirs of National Institute of Polar Research Special issue. 2004;(58):23–33.
Kölzsch A, Neefjes M, Barkway J, Müskens GJDM, Van Langevelde F, De Boer WF, et al. Neckband or backpack? Differences in tag design and their effects on GPS/accelerometer tracking results in large waterbirds. Anim Biotelemetry. 2016;4(1):13.
Garde B, Wilson RP, Fell A, Cole N, Tatayah V, Holton MD, et al. Ecological inference using data from accelerometers needs careful protocols. Methods Ecol Evol. 2022;13(4):813–25.
IUCN. The IUCN Red List of Threatened Species. 2023. Report No.: Version 2023-1.
Heinrich S, Wittmann TA, Prowse TAA, Ross JV, Delean S, Shepherd CR, et al. Where did all the pangolins go? International CITES trade in pangolin species. Global Ecology and Conservation. 2016;8:241–53.
Ingram DJ, Coad L, Abernethy KA, Maisels F, Stokes EJ, Bobo KS, et al. Assessing Africa-Wide Pangolin Exploitation by Scaling Local Data. Conservation Letters. 2018;11(2):1–9.
Ingram DJ, Cronin DT, Challender DWS, Venditti DM, Gonder MK. Characterising trafficking and trade of pangolins in the Gulf of Guinea. Global Ecology and Conservation. 2019;17:e00576.
Wright N, Jimerson J. The rescue, rehabilitation and release of pangolins. Pangolins: Science, Society and Conservation. 2020;495–504.
Nash H, Lee PB, Low MR. Rescue, rehabilitation and release of Sunda pangolins in Singapore. Global Reintroduction Perspectives: 2018 Case studies from around the globe. 2018;221.
Carroll D, Harvey-Carroll J, Trivella CM, Connelly E. Non-fatal removal of ground pangolin (Smutsia temminckii Smuts, 1832) tracking devices by predators. African Journal of Ecology [Internet]. 2023 [cited 2023 Oct 30]; Available from: https://onlinelibrary.wiley.com/doi/abs/10.1111/aje.13225
Pavese S, Centeno C, Von Fersen L, Eguizábal GV, Donet L, Asencio CJ, et al. Video Validation of Tri-Axial Accelerometer for Monitoring Zoo-Housed Tamandua tetradactyla Activity Patterns in Response to Changes in Husbandry Conditions. Animals. 2022;12(19):2516.
Auge AC, Blouin-Demers G, Murray DL. Developing a classification system to assign activity states to two species of freshwater turtles. PLOS ONE. 2022;17(11):e0277491.
Clark BL. Northern gannet Morus bassanus foraging ecology: A multidimensional approach. University of Exeter (United Kingdom); 2019.
Clark B, Irigoin-Lovera C, Gonzales-DelCarpio D, Diaz-Santibañez I, Votier S, Zavalaga C. Interactions between anchovy fisheries and Peruvian boobies revealed by bird-borne cameras and movement loggers. Mar Ecol Prog Ser. 2022;701:145–57.
Reisinger RR, Corney S, Raymond B, Lombard AT, Bester MN, Crawford RJM, et al. Habitat model forecasts suggest potential redistribution of marine predators in the southern Indian Ocean. Diversity and Distributions. 2022;28(1):142–59.
Reisinger RR, Raymond B, Hindell MA, Bester MN, Crawford RJM, Davies D, et al. Habitat modelling of tracking data from multiple marine predators identifies important areas in the Southern Indian Ocean. Diversity and Distributions. 2018;24(4):535–50.
Kuhn M. A Short Introduction to the caret Package. R Found Stat Comput. 2015;1:1–10.
Wright MN, Ziegler A. ranger: A Fast Implementation of Random Forests for High Dimensional Data in C + + and R. Journal of Statistical Software. 2017;77:1–17.
R Core Team. R: A language and environment for statistical computing. [Internet]. R Foundation for Statistical Computing, Vienna, Austria.; 2024. Available from: https://www.R-project.org/.
Zeileis A, Grothendieck G. zoo: S3 Infrastructure for Regular and Irregular Time Series. Journal of Statistical Software. 2005;14(6):1–27.
Grolemund G, Wickham H. Dates and Times Made Easy with lubridate. Journal of Statistical Software. 2011;40(3):1–25.
Wickham H. ggplot2: Elegant Graphics for Data Analysis [Internet]. Springer-Verlag New York; 2016. Available from: https://ggplot2.tidyverse.org
Wickham H, Averick M, Bryan J, Chang W, McGowan LD, François R, et al. Welcome to the tidyverse. Journal of Open Source Software. 2019;4(43):1686.
Wickham H, François R, Henry L, Müller K, Vaughan D. dplyr: A Grammar of Data Manipulation [Internet]. 2023. Available from: https://CRAN.R-project.org/package=dplyr
Barrett T, Dowle M, Srinivasan A, Gorecki J, Chirico M, Hocking T. data.table: Extension of `data.frame` [Internet]. 2024. Available from: https://CRAN.R-project.org/package=data.table
Kuhn M. caret: Classification and Regression Training. Astrophysics Source Code Library. 2015;ascl:1505.003.
Jeantet L, Dell’Amico F, Forin-Wiart MA, Coutant M, Bonola M, Etienne D, et al. Combined use of two supervised learning algorithms to model sea turtle behaviours from tri-axial acceleration data. Journal of Experimental Biology. 2018;jeb.177378.
Kirchner TM, Devineau O, Chimienti M, Thompson DP, Crouse J, Evans AL, et al. Predicting moose behaviors from tri-axial accelerometer data using a supervised classification algorithm. Anim Biotelemetry. 2023;11(1):32.
Tatler J, Cassey P, Prowse TAA. High accuracy at low frequency: detailed behavioural classification from accelerometer data. Journal of Experimental Biology. 2018;jeb.184085.
Price E, Langford J, Fawcett TW, Wilson AJ, Croft DP. Classifying the posture and activity of ewes and lambs using accelerometers and machine learning on a commercial flock. Applied Animal Behaviour Science. 2022;251:105630.
Rautiainen H, Alam M, Blackwell PG, Skarin A. Identification of reindeer fine-scale foraging behaviour using tri-axial accelerometer data. Movement Ecology. 2022;10(1):40.
Walton E, Casey C, Mitsch J, Vázquez-Diosdado JA, Yan J, Dottorini T, et al. Evaluation of sampling frequency, window size and sensor position for classification of sheep behaviour. Royal Society Open Science. 2018;5(2):171442.
Broell F, Noda T, Wright S, Domenici P, Steffensen JF, Auclair JP, et al. Accelerometer tags: detecting and identifying activities in fish and the effect of sampling frequency. The Journal of experimental biology. 2013;216(Pt 7):1255–64.
Yu H, Muijres FT, te Lindert JS, Hedenström A, Henningsson P. Accelerometer sampling requirements for animal behaviour classification and estimation of energy expenditure. Animal Biotelemetry. 2023;11(1):28.
Graf PM, Wilson RP, Qasem L, Hackländer K, Rosell F. The Use of Acceleration to Code for Animal Behaviours; A Case Study in Free-Ranging Eurasian Beavers Castor fiber. PLOS ONE. 2015;10(8):e0136751.
Dentinger JE, Börger L, Holton MD, Jafari-Marandi R, Norman DA, Smith BK, et al. A probabilistic framework for behavioral identification from animal-borne accelerometers. Ecological Modelling. 2022;464:109818.
Gaubert P, Wilson D, Mittermeier R. Family manidae. Handbook of the Mammals of the World. 2011;2:82–103.
Kingdon J, Hoffmann M. Carnivores, Pangolins, Equids, Rhinoceroses. The Mammals of Africa, vol. 5. 2013;
Pietersen DW, McKechnie AE, Jansen R. Home range, habitat selection and activity patterns of an arid-zone population of Temminck’s ground pangolins, Smutsia temminckii. African Zoology. 2014;49(2):265–76.
Mihoub J, Le Gouar P, Sarrazin F. Breeding habitat selection behaviors in heterogeneous environments: implications for modeling reintroduction. Oikos. 2009;118(5):663–74.
Scillitani L, Darmon G, Monaco A, Cocca G, Sturaro E, Rossi L, et al. Habitat selection in translocated gregarious ungulate species: An interplay between sociality and ecological requirements. J Wildl Manag. 2013;77(4):761–9.
Richardson KM, Ewen JG. Habitat selection in a reintroduced population: social effects differ between natal and post-release dispersal. Animal Conservation. 2016;19(5):413–21.
Picardi S, Coates P, Kolar J, O’Neil S, Mathews S, Dahlgren D. Behavioural state-dependent habitat selection and implications for animal translocations. Journal of Applied Ecology. 2022;59(2):624–35.
Silva WT, Harding KC, Marques GM, Bäcklin BM, Sonne C, Dietz R, et al. Life cycle bioenergetics of the gray seal (Halichoerus grypus) in the Baltic Sea: population response to environmental stress. Environment International. 2020;145:106145.
Heighton SP, Gaubert P. A timely systematic review on pangolin research, commercialization, and popularization to identify knowledge gaps and produce conservation guidelines. Biological Conservation. 2021;256:109042.
Zanvo S, Djagoun CAMS, Gaubert P, Azihou AF, Jézéquel C, Djossa B, et al. Modeling population extirpation rates of white-bellied and giant pangolins in Benin using validated local ecological knowledge. Conservat Sci and Prac. 2023;5(8):e12986.
Carroll D, Ahola MP, Carlsson AM, Sköld M, Harding KC. 120-years of ecological monitoring data shows that the risk of overhunting is increased by environmental degradation for an isolated marine mammal population: The Baltic grey seal. Journal of Animal Ecology. 2024;1365-2656.14065.
Champagnon J, Elmberg J, Guillemain M, Gauthier-Clerc M, Lebreton JD. Conspecifics can be aliens too: A review of effects of restocking practices in vertebrates. Journal for Nature Conservation. 2012;20(4):231–41.
Harrington LA, Moehrenschlager A, Gelling M, Atkinson RPD, Hughes J, Macdonald DW. Conflicting and Complementary Ethics of Animal Welfare Considerations in Reintroductions. Conservation Biology. 2013;27(3):486–500.

No competing interests reported.

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Classification of African ground pangolin behaviour based on accelerometer readouts: validation of bio- logging methods

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