Integrative Framework for Long-Term Activity Monitoring of Small and Secretive Animals: Validation with a Cryptic Pitviper

The use of miniature accelerometer (ACT) data-loggers for remote and continuous recording of animal movement behavior is becoming increasingly common. Until recently, size constraints limited most animal-borne ACT applications to large-bodied species. We capitalized on the ongoing miniaturization and advancement of these technologies and associated computational techniques to develop a framework for long-term, low-frequency ACT monitoring of activity in small and secretive terrestrial species. We achieved this by internally implanting coupled radio transmitters and tri-axial ACTs in rattlesnakes (Crotalus atrox). Periodic field-validation observations of behavior were used to train and test supervised learning models (Random Forest, RF; Generalized Linear Elastic Net, GLMNET) for activity classification. The best performing RF model classified periods of movement and immobility in rattlesnakes with high accuracy (movement = 96%, immobile = 99%), and was applied to extensive ACT field datasets (median = 35 days, range = 6-289 days;N= 12) to produce activity budgets at multiple temporal scales. In general, these cryptic ambush predators were found to be highly sedentary, with activity budgets characterized by long periods of immobility interrupted by punctuated bouts of movement. The same temporal daily activity pattern was conserved across all active seasons (spring, summer non-mating, summer mating, fall), as most movement always occurred during the evening or nocturnal diel periods. Contrary to movement timing, daily movement duration was seasonally variable, as movement increased during the summer-mating season-possibly reflecting a combination of more favorable weather conditions (onset of rainy season) and mate-searching efforts by male rattlesnakes. This radio telemetry-accelerometry (RT-ACT) framework provides an objective and flexible set of data collection and processing procedures for long-term ACT field datasets. Expanding our coarse-scale behavioral classification scheme could provide a foundation for future investigations using the RT-ACT framework to explore relationships between individual behavioral decisions and performance in snakes and other small and secretive terrestrial vertebrates.