roostR: An R package to examine diel activity patterns from Motus radio telemetry data
Williams, K. A.; Morgan, J. E.; Lyons, S. M.
Show abstract
1. Monitoring the diel activity patterns of free-living animals is a methodological challenge. Signal strength fluctuations from Very High Frequency (VHF) radio transmitters deployed within the Motus Wildlife Tracking System can be used as a proxy for activity. However, analytical tools to extract behavioral metrics that quantify activity patterns from these data are needed. 2. We developed roostR, an open-source R package that converts Motus detection data into quantitative behavioral metrics, including roost initiation and departure, roost duration, observation time, and restlessness. The package uses a sequential pipeline built around signal volatility and rolling medians to detect transitions between active and inactive states. Default parameter values were tuned using data from 55 dark-eyed juncos (Junco hyemalis) overwintering in southeastern Ohio. 3. We provide an example from a dark-eyed junco over a 58-day period. roostR estimated roost onset on 48 nights and departure on 56 mornings, with higher rolling median signal differences during the day than at night, consistent with a diurnal animal, and variable restlessness periods each night. We also used the package to estimate roost behavior of an American tree sparrow (Spizelloides arborea) over 43-nights. 4. roostR enables researchers to extract individual activity data from Motus datasets. Because all thresholds are user-adjustable, the pipeline is adaptable across species, tag specifications, and ecological contexts, enabling researchers to test hypotheses about how environmental factors influence diel activity patterns.
Matching journals
The top 1 journal accounts for 50% of the predicted probability mass.
Similar papers in this journal
- A Guide to Pre-Processing High-Throughput Animal Tracking Data 94%
- A model-based hypothesis framework to define and estimate the diel niche via the 'Diel.Niche' R package 93%
- Accounting for individual-specific variation in habitat-selection studies: Efficient estimation of mixed-effects models using Bayesian or frequentist computation 92%
Similar papers in this journal
Similar papers in this journal
- Machine learning goes wild: Using data from captive individuals to infer wildlife behaviour 95%
- easyFulcrum: An R package to process and analyze ecological sampling data generated using the Fulcrum mobile application 92%
- BirdNET can be as good as experts for acoustic bird monitoring in a European city 91%
Similar papers in this journal
- A fresh look at an old concept: Home-range estimation in a tidy world 93%
- Multi-Object Tracking in Heterogeneous environments (MOTHe) for animal video recordings 91%
- Longer study length, standardized sampling techniques, and broader geographic scope leads to higher likelihood of detecting stable abundance patterns in long term black-legged tick studies 90%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.