Developing a classification system to assign activity states to two species of freshwater turtles

• Published in: PloS one 2022-11, Vol.17 (11), p.e0277491-e0277491
• Main Authors: Auge, Anne-Christine, Blouin-Demers, Gabriel, Murray, Dennis L
• Format: Article
• Language: English
• Subjects: Accelerometers; Animal behavior; Animal research; Animals; Behavior; Behavior, Animal; Biology and Life Sciences; Classification; Decision trees; Earth Sciences; Ecology; Ecology and Environmental Sciences; Engineering and Technology; Environment; Environmental aspects; Fresh Water; Freshwater ecology; Global positioning systems; GPS; Habitats; Identification and classification; Logging; Model accuracy; Modelling; Natural environment; Reptiles & amphibians; Research and Analysis Methods; Robustness; Sampling; Sensors; Social Sciences; Species; Species classification; Turtles; Varieties; Water
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0277491

Research in ecology often requires robust assessment of animal behaviour, but classifying behavioural patterns in free-ranging animals and in natural environments can be especially challenging. New miniaturised bio-logging devices such as accelerometers are increasingly available to record animal behaviour remotely, and thereby address the gap in knowledge related to behaviour of free-ranging animals. However, validation of these data is rarely conducted and classification model transferability across closely-related species is often not tested. Here, we validated accelerometer and water sensor data to classify activity states in two free-ranging freshwater turtle species (Blanding's turtle, Emydoidea blandingii, and Painted turtle, Chrysemys picta). First, using only accelerometer data, we developed a decision tree to separate motion from motionless states, and second, we included water sensor data to classify the animal as being motionless or in-motion on land or in water. We found that accelerometers separated in-motion from motionless behaviour with > 83% accuracy, whereas models also including water sensor data predicted states in terrestrial and aquatic locations with > 77% accuracy. Despite differences in values separating activity states between the two species, we found high model transferability allowing cross-species application of classification models. Note that reducing sampling frequency did not affect predictive accuracy of our models up to a sampling frequency of 0.0625 Hz. We conclude that the use of accelerometers in animal research is promising, but requires prior data validation and development of robust classification models, and whenever possible cross-species assessment should be conducted to establish model generalisability.