Cheetah Rangewide Status and Distribution

Marker, Laurie; Cristescu, Bogdan; Morrison, Tess; Flyman, Michael V.; Horgan, Jane; Sogbohossou, Etotépé A.; Bissett, Charlene; Merwe, Vincent van der; Machado, Iracelma B. de Matos; Fabiano, Ezequiel; Meer, Esther van der; Aschenborn, Ortwin; Melzheimer, Joerg; Young-Overton, Kim; Farhadinia, Mohammad S.; Wykstra, Mary; Chege, Monica; Abdoulkarim, Samna; Amir, Osman G.; Mohanun, Ahmed Sh; Paulos, Osman D.; Nhabanga, Abel R.; M’soka, Jassiel; Belbachir, Farid; Ashenafi, Zelealem Tefera; Nghikembua, Matti T.

doi:10.1016/b978-0-12-804088-1.00004-6

Cited by 8 publications

(3 citation statements)

References 22 publications

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“…Cheetahs ( Acinonyx jubatus ) are medium-large felids inhabiting Africa and Iran [ 46 – 48 ]. They are classified as ‘Vulnerable’ by the IUCN [ 46 ] with the most recent population assessment (2014) suggesting just under 7100 adolescent and adult cheetah remain in the wild [ 47 ].…”

Section: Introductionmentioning

confidence: 99%

Categorising cheetah behaviour using tri-axial accelerometer data loggers: a comparison of model resolution and data logger performance

et al. 2022

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Background Extinction is one of the greatest threats to the living world, endangering organisms globally, advancing conservation to the forefront of species research. To maximise the efficacy of conservation efforts, understanding the ecological, physiological, and behavioural requirements of vulnerable species is vital. Technological advances, particularly in remote sensing, enable researchers to continuously monitor movement and behaviours of multiple individuals simultaneously with minimal human intervention. Cheetahs, Acinonyx jubatus, constitute a “vulnerable” species for which only coarse behaviours have been elucidated. The aims of this study were to use animal-attached accelerometers to (1) determine fine-scale behaviours in cheetahs, (2) compare the performances of different devices in behaviour categorisation, and (3) provide a behavioural categorisation framework. Methods Two different accelerometer devices (CEFAS, frequency: 30 Hz, maximum capacity: ~ 2 g; GCDC, frequency: 50 Hz, maximum capacity: ~ 8 g) were mounted onto collars, fitted to five individual captive cheetahs. The cheetahs chased a lure around a track, during which time their behaviours were videoed. Accelerometer data were temporally aligned with corresponding video footage and labelled with one of 17 behaviours. Six separate random forest models were run (three per device type) to determine the categorisation accuracy for behaviours at a fine, medium, and coarse resolution. Results Fine- and medium-scale models had an overall categorisation accuracy of 83–86% and 84–88% respectively. Non-locomotory behaviours were best categorised on both loggers with GCDC outperforming CEFAS devices overall. On a coarse scale, both devices performed well when categorising activity (86.9% (CEFAS) vs. 89.3% (GCDC) accuracy) and inactivity (95.5% (CEFAS) vs. 95.0% (GCDC) accuracy). This study defined cheetah behaviour beyond three categories and accurately determined stalking behaviours by remote sensing. We also show that device specification and configuration may affect categorisation accuracy, so we recommend deploying several different loggers simultaneously on the same individual. Conclusion The results of this study will be useful in determining wild cheetah behaviour. The methods used here allowed broad-scale (active/inactive) as well as fine-scale (e.g. stalking) behaviours to be categorised remotely. These findings and methodological approaches will be useful in monitoring the behaviour of wild cheetahs and other species of conservation interest.

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Section: Introductionmentioning

confidence: 99%

Categorising cheetah behaviour using tri-axial accelerometer data loggers: a comparison of model resolution and data logger performance

et al. 2022

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“…The populations of cheetahs in Eastern Africa are among the largest remnant populations of this vulnerable carnivore (Becker et al 2017;Marker et al 2018). Kenya's cheetah population is estimated at about 1,200 individuals (Durant et al 2017).…”

Section: Introductionmentioning

confidence: 99%

“…Kenya's cheetah population is estimated at about 1,200 individuals (Durant et al 2017). The species occurs in low densities (Marker et al 2018). Most of the individuals occur beyond protected areas (Durant et al 2015(Durant et al , 2017.…”

Section: Introductionmentioning

confidence: 99%

Impact of weather conditions on cheetah monitoring with scat detection dogs

et al. 2021

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Knowledge on cheetah population densities across their current range is limited. Therefore, new and efficient assessment tools are needed to gain more knowledge on species distribution, ecology and behaviour. Scat detection dogs have emerged as an efficient and non-invasive method to monitor elusive and vulnerable animal species, like cheetahs, due to the dog’s superior olfactory system. However, the success of locating scat using detection dogs can be significantly improved under suitable weather conditions. We examined the impact of temperature, humidity and wind speed on detection rates of scat from cheetahs during a scat detection dog survey in Northern Kenya. We found that average wind speed positively influences the scat detection rate of detection dogs working on leash. Humidity showed no significant influence. Temperature showed a strong negative correlation with humidity and thus was excluded from our model analyses. While it is likely that wind speed is especially invalid for dogs working off leash, this study did not demonstrate this. Wind speed could thus influence the success of monitoring cheetahs or other target species. Our findings help to improve the survey and thus maximise the coverage of study area and the collection of target samples of elusive and rare species.

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