Accelerometers and simple algorithms identify activity budgets and body orientation in African elephants Loxodonta africana

Soltis, Joseph; King, Lucy E.; Vollrath, Fritz; Douglas‐Hamilton, Iain

doi:10.3354/esr00746

Cited by 14 publications

(12 citation statements)

References 28 publications

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“…Such approaches may increase the relative proportion of variation in elephant movement behavior explained by fruit availability. Additionally, coupling GPS collars with accelerometers would help deduce specific behaviors, such as foraging on fruit on the ground or leaves and branches at shoulder height, through movement and body posture (Brown et al, 2013;Soltis et al, 2016).…”

Section: Discussionmentioning

confidence: 99%

Climatic and Resource Determinants of Forest Elephant Movements

et al. 2020

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Section: Discussionmentioning

confidence: 99%

Climatic and Resource Determinants of Forest Elephant Movements

et al. 2020

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“…Of particular note is that the inactive behaviours (within the category 'resting') were rapidly discriminated using two or three decision rules based on static acceleration variables or posture-related parameters (see Fehlmann et al, 2017). This contrasts with other studies that have noted the value of dynamic acceleration (in some ways the polar opposite of static acceleration) in differentiating inactive from active behaviours (Shamoun-Baranes et al, 2012;Soltis et al, 2016). With specific regard to active locomotion behaviours, the direction (or sign) of the 'Diff_Deep' value was used by both algorithms, indicating the direction of movement and demonstrating the value of nonaccelerometer sensors in helping differentiate behaviours, while negative or positive values of StX gave information about the inclination of the body (upward-facing or downward-facing).…”

Section: Discussionmentioning

confidence: 87%

Combined use of two supervised learning algorithms to model sea turtle behaviours from tri-axial acceleration data

Jeantet

Dell’Amico

Forin-Wiart

et al. 2018

Journal of Experimental Biology

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Accelerometers are becoming ever more important sensors in animal-attached technology, providing data that allow determination of body posture and movement and thereby helping to elucidate behaviour in animals that are difficult to observe. We sought to validate the identification of sea turtle behaviours from accelerometer signals by deploying tags on the carapace of a juvenile loggerhead (), an adult hawksbill () and an adult green turtle () at Aquarium La Rochelle, France. We recorded tri-axial acceleration at 50 Hz for each species for a full day while two fixed cameras recorded their behaviours. We identified behaviours from the acceleration data using two different supervised learning algorithms, Random Forest and Classification And Regression Tree (CART), treating the data from the adult animals as separate from the juvenile data. We achieved a global accuracy of 81.30% for the adult hawksbill and green turtle CART model and 71.63% for the juvenile loggerhead, identifying 10 and 12 different behaviours, respectively. Equivalent figures were 86.96% for the adult hawksbill and green turtle Random Forest model and 79.49% for the juvenile loggerhead, for the same behaviours. The use of Random Forest combined with CART algorithms allowed us to understand the decision rules implicated in behaviour discrimination, and thus remove or group together some 'confused' or under--represented behaviours in order to get the most accurate models. This study is the first to validate accelerometer data to identify turtle behaviours and the approach can now be tested on other captive sea turtle species.

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“…We reserved the remainder of the identified behaviors for validation. Static acceleration (i.e., acceleration resulting from the position of the device in relation to the gravitational field) was extracted from the raw data using a 2-second moving window [49–51]. Results were then subtracted from the raw values to determine dynamic acceleration - the acceleration resulting from movement [49, 51].…”

Section: Methodsmentioning

confidence: 99%

“…Static acceleration (i.e., acceleration resulting from the position of the device in relation to the gravitational field) was extracted from the raw data using a 2-second moving window [49–51]. Results were then subtracted from the raw values to determine dynamic acceleration - the acceleration resulting from movement [49, 51]. In addition to static and dynamic acceleration, we calculated the running minimum and maximum of each axis and the vectorial sum of overall body acceleration (VeDBA; [52]), to include as predictor variables in the random forests model.…”

Section: Methodsmentioning

confidence: 99%

Short-term effects of GPS collars on the activity, behavior, and adrenal response of scimitar-horned oryx (Oryx dammah)

Stabach

Cunningham

Connette

et al. 2019

Preprint

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28GPS collars have revolutionized the field of animal ecology, providing detailed information on 29 animal movement and the habitats necessary for species survival. GPS collars also have the 30 potential to cause adverse effects ranging from mild irritation to severe tissue damage, reduced 31 fitness, and death. The impact of GPS collars on the behavior, stress, or activity, however, have 32 rarely been tested on study species prior to release. The objective of our study was to provide a 33 comprehensive assessment of the short-term effects of GPS collars fitted on scimitar-horned oryx 34 (Oryx dammah), an extinct-in-the-wild antelope once widely distributed across Sahelian 35 grasslands in North Africa. We conducted behavioral observations, assessed fecal glucocorticoid 36 metabolites (FGM), and evaluated high-resolution data from tri-axial accelerometers. Using a 37 series of non-standard regression models, we illustrate clear but short-term effects to animals 38 fitted with GPS collars. Behavioral observations highlighted a significant increase in the amount 39 of headshaking from pre-treatment levels, returning below baseline levels during the post-40 treatment period (>3 days post-collaring). Similarly, FGM concentrations (i.e., stress hormones) 41 increased after GPS collars were fitted on animals but returned to pre-collaring levels within 5 42 days of collaring. Lastly, tri-axial accelerometers, collecting data at eight positions per second, 43 indicated a > 480 percent increase in the amount of hourly headshaking immediately after 44 collaring. This post-collaring increase in headshaking was estimated to decline in magnitude 45 within 4 hours after GPS collar fitting. These effects constitute a handling and/or habituation 46 response (model dependent), with animals showing short-term responses in activity, behavior, 47 and stress that dissipated within several hours to several days of being fitted with GPS collars. 48Importantly, none of our analyses indicated any long-term effects that would have more pressing 49 animal welfare concerns. 3 | Stabach et al. 3 50 51 52 Global Positioning System (GPS) devices have revolutionized the field of animal ecology [1-3], 53 providing detailed information about how animals move and utilize space across diverse and 54 often rapidly changing landscapes [e.g., 4-7]. A variety of taxa can now be monitored, ranging 55 in size from pint-sized ovenbirds (Seiurus aurocapilla; [8]) to multi-ton elephants (Loxodonta 56 africana; e.g., [9]). In some instances, animals are now being monitored over their entire 57 lifespans [10], a result of technological innovations (e.g., solar rechargeable batteries) or re-58 tagging efforts, and at temporal resolutions (minutes to seconds) that would have been 59 unimaginable just a few decades ago. 60Since the inception of animal tracking, scientists have recognized the ethical concerns of 61 fitting animals with tracking devices, providing general guidelines (e.g., devices should weigh 62 <5% of total animal body weight) and in most ca...

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Accelerometers and simple algorithms identify activity budgets and body orientation in African elephants Loxodonta africana

Cited by 14 publications

References 28 publications

Climatic and Resource Determinants of Forest Elephant Movements

Climatic and Resource Determinants of Forest Elephant Movements

Combined use of two supervised learning algorithms to model sea turtle behaviours from tri-axial acceleration data

Short-term effects of GPS collars on the activity, behavior, and adrenal response of scimitar-horned oryx (Oryx dammah)

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