Movement, resting, and attack behaviors of wild pumas are revealed by tri-axial accelerometer measurements

Abstract

BackgroundAccelerometers are useful tools for biologists seeking to gain a deeper understanding of the daily behavior of cryptic species. We describe how we used GPS and tri-axial accelerometer (sampling at 64 Hz) collars to monitor behaviors of free-ranging pumas (Puma concolor), which are difficult or impossible to observe in the wild. We attached collars to twelve pumas in the Santa Cruz Mountains, CA from 2010-2012. By implementing Random Forest models, we classified behaviors in wild pumas based on training data from observations and measurements of captive puma behavior.ResultsWe applied these models to accelerometer data collected from wild pumas and identified mobile and non-mobile behaviors in captive animals with an accuracy rate greater than 96%. Accuracy remained above 95% even after downsampling our accelerometer data to 16 Hz. We were further able to predict low-acceleration movement behavior (e.g. walking) and high-acceleration movement behavior (e.g. running) with 93.8% and 92% accuracy, respectively. We had difficulty predicting non-movement behaviors such as feeding and grooming due to the small size of our training dataset. Lastly, we used model-predicted and field-verified predation events to quantify acceleration characteristics of puma attacks on large prey.ConclusionThese results demonstrate that accelerometers are useful tools for classifying the behaviors of cryptic medium and large-sized terrestrial mammals in their natural habitats and can help scientists gain deeper insight into their fine-scale behavioral patterns. We also show how accelerometer measurements can provide novel insights on the energetics and predation behavior of wild animals. Lastly we discuss the conservation implications of identifying these behavioral patterns in free-ranging species as natural and anthropogenic landscape features influence animal energy allocation and habitat use.Electronic supplementary materialThe online version of this article (doi:10.1186/s40462-015-0030-0) contains supplementary material, which is available to authorized users.

The golden age of bio‐logging: how animal‐borne sensors are advancing the frontiers of ecology

“…These new devices have given us greater abilities to remotely measure foraging gains as well. Clusters of GPS locations can reveal the kill sites of large terrestrial predators (e.g., Knopff et al 2009), miniature video loggers can identify prey capture, and accelerometers can be used to identify prey capture in a host of species ranging from fish (Viviant et al 2014) to penguins (Watanabe et al 2014) to pumas (Wang et al 2015).…”

Behavioral classification of low‐frequency acceleration and temperature data from a free‐ranging small mammal

“…This results in a potential maximum recording longevity in the order of minutes to days, unless sub-sampling techniques are used (Hammond, Springthorpe, Walsh, & Berg-Kirkpatrick, 2016). However, the few studies that have directly tested this possibility suggested that low recording frequencies have significantly reduced accuracy when using current classification methods (Broell et al, 2013;Pagano et al, 2017;Wang et al, 2015). An alternative method is to extend the sampling duration by reducing the sampling rate.…”

Using accelerometers to remotely and automatically characterize behavior in small animals

“…A variety of methods can determine whether accelerometer data are reliably correlated with behaviors, some of which are being developed for general use Resheff et al, 2014;Sakamoto et al, 2009). In some cases where an independent captive study is not possible, animals may be observed in zoos or in the wild, or surrogate species may be used Grünewälder et al, 2012;Nathan et al, 2012;Wang et al, 2015). Additionally, we assessed the accuracy of our system using a cross-validation method, which generated a system that was robust to individual differences in behavior.…”