Cheetah (Acinonyx jubatus)- Data, Pictures & Videos

[Acinonyx sp.]

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Discussion
Recent, ongoing, and imminent species declines have prompted conservation focused research outputs (e.g. [55,56,57,58]). To make worthwhile steps towards successful conservation, it is important to not only know about populations and habitat requirements, but also about species’ behaviour, to ensure all ecological and biological needs can be met. To ensure that natural behaviours are not compromised, monitoring techniques should be minimally invasive [59], for example through the use of remote sensing technologies (such as lightweight GPS and accelerometer devices). Whilst the use of such devices has been gaining momentum for decades, interpretation of their outputs for behavioural categorisation is relatively recent, especially when high resolution and precision are desired (e.g. [31, 37,38,39,40]).
The cheetah (Acinonyx jubatus) is listed as ‘vulnerable’ [46] with wild populations purportedly decreasing [47]. However, descriptions of their movements and behaviour (particularly fine-scale behaviour) remain scarce [7, 30, 41, 49]. Accelerometry has been used to describe coarse behaviours in cheetahs; Grünewälder et al. [41] determined “mobile”, “stationary” and “feeding” with 84–94% accuracy and Shepard et al. [33] provide (without categorisation metrics) acceleration traces of walking, chasing, and trotting behaviours. In the current study, three RF models were constructed for fine-, medium-, and coarse-scale behaviour determination on high (“GCDC”, Maximum acceleration: ~ 8 g; Frequency: 50 Hz) and lower (“CEFAS”, Maximum acceleration: ~ 2 g; Frequency: 30 Hz) capacity devices. Using coarse modelling approaches (behaviours categorised as “Active”, “Inactive”, or “Head movement”.), RF analysis rendered consistent results (93% accuracy) between the two devices. Both devices categorised inactive behaviours well (GCDC = 95.0% accuracy and CEFAS = 95.5% accuracy), with the CEFAS logger performing best (Table 5). However, head movement could be described with just over 50% accuracy using CEFAS loggers (over 10% lower than GCDC devices), often confused with inactivity, which may be due to the core body remaining stationary. This finding suggests that even the use of collars does not guarantee reliable detection of head movement, which may, in fact, be beneficial if coarsely categorising behaviours. Head movement categorisation was better with GCDC devices (GCDC = 61.3% and CEFAS = 50.3% accuracy), which is probably due to their higher frequency recording so slight, short-lived movements were more likely to be detected [60]. In this model, dynamic acceleration (VeDBA and heave) was consistently important across both devices, which is unsurprising given the disparity in dynamic motion between the three categories. However, static acceleration in the heave and surge axes were also important parameters for the CEFAS logger, and several additional measures of static acceleration were important for the GCDC loggers (e.g. VeSBA), suggesting that postural changes may also play a significant role, especially as logger sensitivity increases. Practically, it is important to ensure consistent logger attachment, and device capacity and configuration should be considered when using results from previous studies to underpin novel research. The results of the current study are consistent with the only other study to categorise cheetah behaviour remotely using accelerometers [41]; “stationary” (“inactive”) behaviours were most accurately classified, followed by “mobile” (“active”) behaviours (Table 6). Feeding was specifically measured in this study rather than more generic “head movement” so the two categories may not be directly comparable. Classification of active behaviour was better in the current study and the overall performance was slightly better, which may be due to differences between the loggers used (bi-axial versus tri-axial), logger configuration, or analytical approach (SVM versus RF).




Fine-scale behaviours
One objective of the current study was to determine whether fine-scale cheetah behaviours could be categorised using accelerometers. Such data could provide information on cheetah ecology and assist conservation efforts. For example, if foraging requirements (indicated by chases and stalks), the frequency of abandoned hunts (by identification of stalks with no subsequent pursuit), or changes in behaviour associated with life history such as rearing offspring could be identified accurately, specific ecological needs could be addressed by ensuring prey and habitat requirements were met. In the current study, a fine- and medium-scale behaviour categorisation model was produced for each accelerometer device; the finest-scale model included all behaviours that could be derived from video footage, whilst the medium-scale model collapsed several of these categories together, resulting in marginally coarser classification. Although both performed less well than coarse (active/inactive/head movement) models, there was little difference between the fine- and medium-scale models themselves. As such, it may be prudent to categorise behaviours on the finest or coarsest scales as they are more accurate (coarsest) or the benefit of additional behavioural information outweighs the marginal cost in accuracy (finest). To our knowledge, this study represents the most ambitious attempt to elucidate cheetah behaviour, with the highest resolution, fine-scale models incorporating 16 behaviours, and the coarser, medium-scale models including 11. Across both sets of models and both devices lying, lying stalks, and sitting stalks were always classified with over 90% accuracy; in fact, sitting stalks were classified with 100% accuracy on the GCDC loggers. This is the first time that these behaviours have been classified remotely with such accuracy in cheetahs. Stalks usually occur prior to pursuits of prey in cheetahs [61] so knowledge of the habitats that may facilitate stalks and successful hunts could be of great importance for survival. As such, acceleration data combined with GPS locations could provide vital insights for conservation. Furthermore, sedentary behaviours were categorised with a high degree of accuracy, which, when combined with other approaches, may provide insights into cheetahs’ physiological and habitat requirements.
The lowest (walking) and highest intensity (galloping) locomotory behaviours were categorised best with a higher error rate for intermediate trotting and cantering. Nevertheless, classification accuracy of walking and galloping was always between 68 and 78%. As footfall and rhythmicity of each locomotory gait varies (Fig. 4), incorporating periodicity may beneficial to differentiate them [28] but may be limited by rapid transitions between them and a lack of continuous measurements of any one in isolation. Correct identification of each gait could assist conservation efforts by providing insights into hunting and evasion, potentially facilitating the identification of areas favoured for hunting or resting, or those where cheetahs may be threatened by other species. Incorporation of lab-based techniques, such as indirect calorimetry, would allow us to determine the relative energetic cost of each behaviour and the overall proportion of their daily energy expenditure attributable to each [62]. Such an approach would inform management strategies, potentially reducing conflict with livestock owners [10].



Pouncing represented the worst categorised behaviour, with only 4.8% accuracy on the CEFAS logger (Table 4). This poor performance is likely due to a combination of low recording frequency, the instantaneous nature of the behaviour, and its rarity. However, pouncing is likely to be uncommon in free-ranging adult cheetahs, which primarily implement stalk-and-chase hunting strategies [61]. As such, the low classification accuracy in this context is not concerning but may be problematic when trying to define the behaviour in ambush hunters.
It is worth noting as a caveat that certain behaviours were underrepresented in the datasets e.g. pouncing and stalking, with some others overrepresented (e.g. sedentary behaviours such as lying). This imbalance may have affected how the data were split into training, validation, and test data and, ultimately, the models. However, with the approach taken here ecologically important behaviours such as stalking could be incorporated into the models and was likely to be randomly selected for a split based on its representivity. The result was reasonably reliable models (according to accuracy, MSE, RMSE, and r2) with several under-, over- and well-represented behaviours being predicted accurately,
Application and experimental design
Generally, there was good consistency in model accuracy between CEFAS and GCDC accelerometer data. However, it is important to note that whilst CEFAS loggers categorised more behaviours with > 90% accuracy (n = 8; Table 4), than the GCDC loggers (n = 7), the latter categorised fewer behaviours with < 50% accuracy (GCDC: n = 1; CEFAS: n = 6). It is therefore important to determine a priori, where possible, the scale at which behavioural categorisation is desired and select devices and analytical models accordingly.
Whilst reliable categorisation was established for some cheetah behaviours, the GCDC logger outperformed the CEFAS logger in both fine- and medium-scale models (Table 5). Two potential reasons may explain this better performance: GCDC loggers could record higher accelerations (~ 8 g versus the ~ 2 g capacity of the CEFAS loggers) and were set to record at higher frequencies (50 Hz vs. 30 Hz). During high intensity galloping the CEFAS loggers reached maximum capacity, which may have led to a high frequency of correct categorisation for this behaviour but it may also have contributed to a high false positive rate for other, relatively high intensity behaviours such as trotting. Whilst locomotory behaviours were most often confused with adjacent behaviours for both loggers (e.g. cantering was most likely to be confused with trotting or galloping), trotting, cantering, and trotting stalks were the only locomotory behaviours identified with < 50% accuracy (occurring on the CEFAS loggers). Recording frequency may be important as higher logging rates will generate more data, rendering more information for entrainment of RF models. Recording frequency may be particularly important for rarely occurring and short-lived behaviours such as pouncing. Whilst a multitude of variables were used to entrain RF models, it is possible that others may also assist in categorising behaviours, for example, periodicity (movement rhythmicity) may be useful in discriminating between various locomotory gaits [28]. As there were significant performance differences between the two devices, there is an onus on researchers to select those with an appropriate specification for their study species, or, indeed, to use multiple different loggers in tandem on the same individual.
Behaviours have previously been categorised using accelerometer data loggers without the use of complementary video capture in cheetahs [33] and other species [42]. In such studies, behaviours are usually differentiated via variations in dynamic body accelerations and posture. However, if such an approach were implemented here, fine-scale sedentary behaviours would have been erroneously categorised, resulting in misinterpretation of species behavioural ecology. For example, resting behaviours such as standing or lying down could have been confused with sedentary stalks, where the former would signify true resting but the latter would indicate an attempted hunt. It is therefore recommended that data collected on accelerometer devices are synchronised with an extensive behavioural repertoire for the species.
Conclusions
In this study we found that the ability to categorise behaviours differed significantly between data loggers. The results of the current study can be used to form the basis of remotely monitoring coarse- and fine-scale behaviours of the vulnerable cheetah. Knowledge of their behaviours can inform cheetah biology and ecology, particularly when combined with other loggers such as GPS. Once the basic needs of the cheetah have been firmly established, the efficacy of conservation and management practices can be maximised, and strategies can be implemented to mitigate human-cheetah conflict. The approach taken here may be adopted in remote-sensing studies of other species but careful consideration of logger capacity and recording frequency is recommended.
Availability of data and materials
All data generated or analysed during this study and analysis code are included in supplementary information files. Reply