# Using Advanced Tri‐Axial Accelerometer Data to Improve Behavioral Time Budgets and Bioenergetic Estimates of Wintering Lesser Scaup

## TL;DR

This study compares traditional and advanced accelerometer-based methods to better understand the behavior and energy use of wintering Lesser Scaup ducks.

## Contribution

The study introduces a new framework using tri-axial accelerometers and machine learning to improve behavioral time budgets and energy expenditure estimates in wintering waterfowl.

## Key findings

- Accelerometer data showed significantly more feeding and less flight behavior compared to traditional scanning methods.
- Feeding occurred 42% more during the day and flying 23% more at night based on accelerometer data.
- Daily energy expenditure estimates from traditional methods were significantly lower than those from accelerometers.

## Abstract

Wildlife behavior studies have provided vital information towards understanding the natural histories of wildlife species and identified crucial components regarding their habitat and metabolic needs. For many species, typical behavioral data are collected using diurnal scans that have limitations in both when and where the data can be collected, ultimately leading to biases in behavioral patterns. With technological and analytical advancements of radiotechnology, behavior data can be collected more often and over larger spatial scales than with traditional methods. This study compares the behavioral time budget estimates between two different observational methods: ground‐truthed diurnal scanning observations and 24‐h tri‐axial accelerometer (ACC) GPS/GSM transmitter data that were classified using machine learning. We used the time budgets produced from the two methodologies and calculated the daily energy expenditure (DEE) for wintering Lesser Scaup (
Aythya affinis
) to explore the implications of biased behavioral data. We found significantly more feeding and less flight behavior of birds in the ACC data than in the visual scanning data. Using the ACC behavior proportions of the two most energetically demanding behaviors (feeding and flying), we found that feeding occurred 42% more during the day and flying occurred 23% more during the night. Lastly, we identified that the DEE estimated using the diurnal scanning observations produced a significantly lower estimate than with the 24‐h ACC data. This advanced way of interpreting wildlife behavior patterns can increase our understanding of wildlife species' natural history and make improved decisions regarding wildlife conservation and management. Incorporating this new technique of wildlife behavioral observations, we provided a new framework to expand our current knowledge of wintering waterfowl behaviors and energetic needs that can be adapted to research the vast intricacies of wildlife behavior.

We compared two observational methods, traditional ground truthed diurnal scanning observations and 24‐h tri‐axial accelerometer GPS/GSM transmitter data, for calculating behavioral time budgets and estimated the daily energy expenditure of wintering Lesser Scaup. While our primary aim of this research was to improve our understanding of wintering behavioral patterns and to apply progressive decision tree analysis when deciphering behavioral observations, it also poses as an example of one of the many potential questions that can be answered when using GPS/GSM transmitters, often used for wildlife movement studies, and to act as a tool for other researchers that are studying high resolution behavioral data. Additionally, this research expands our work by incorporating new technology, techniques, and analysis to calculating behavioral time budgets and daily energetic estimates while simultaneously addressing the incomplete bias of past research and literature regarding this topic.