DOI: 10.5061/dryad.tx95x6b8v
title: Data from: Using advanced tri-axial accelerometer data to improve behavioral time budgets and bioenergetic estimates of wintering Lesser Scaup

description: 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 is collected using diurnal scan sampling
 techniques 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-hr 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-hr 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.
description: # Calculating and comparing daily energy expenditure of Lesser Scaup using
 observational scanning samples and tri-axial accelerometer readings from
 GPS/GSM transmitters Author List Hannah L. Schley, Department of
 Entomology and Wildlife Ecology, University of Delaware, Newark, Delaware,
 USA Christopher K. Williams, Entomology and Wildlife Ecology, University
 of Delaware, Newark, Delaware, USA Josh Homyack, Maryland Department of
 Natural Resources, Cambridge, Maryland, USA William F. Harvey (ret),
 Maryland Department of Natural Resources, Cambridge, Maryland, USA Glenn
 H. Olsen, U.S. Geological Survey, Eastern Ecological Science Center,
 Laurel, Maryland, USA Sharon Johnson, U.S. Geological Survey, Eastern
 Ecological Science Center, Laurel, Maryland, USA Funding for this research
 was provided by the Maryland Department of Natural Resources and Ducks
 Unlimited. Additional funding was provided by the U.S. Department of
 Agriculture Hatch (DEL00774) and the University of Delaware Waterfowl and
 Upland Gamebird Center. Lesser Scaup trapping occurred between December
 2021 - March 2022 and December 2022 - March 2023 at Eastern Neck Wildlife
 Refuge, Rock Hall, Maryland, USA, and all transmitter implant surgeries
 occurred at the U.S. Geological Survey, Eastern Ecological Science Center,
 Laurel, Maryland, USA. Dataset DOI: 10.5061/dryad.wpzgmsc1m ## DATA &
 FILE OVERVIEW: Description of the data: These data were collected to
 calculate and compare daily energy expenditure of wintering Lesser Scaup
 in the Chesapeake Bay using two different methods for assigning behaviors
 during the wintering period of 2022-2023. The first method used to collect
 wintering behavior was performing diurnal instantaneous scan observations
 while the second analyzed 24-h tri-axial accelerometer (ACC) data
 collected using GPS/GSM transmitters. Data collected using transmitters
 were additionally used to analyze a separate tracking study. Methods and
 Materials We used OrniTrak-I30 3 G Ornitela 30-g GPS/GSM internal
 transmitters that were set to take a GPS location once every hour and an
 ACC x-y-z directional degree reading every 10 min for 5 sec bursts at 10
 hz. For every 1-hr scan sample, we performed six separate 10-min scans
 using a tripod spotting scope and binoculars on lookouts, beaches and
 piers. ### Files and variables #### Folder 1: ACC_behaviors.zip Files in
 ACC_behaviors.zip contain ACC data from captive (n = 2) and wild caught (n
 = 29) Lesser Scaup that were used to verify behaviors from captive scaup
 based on their x-y-z ACC reading and predict those behaviors on data that
 were unassigned from wild caught scaup.  **File 1:
 lesc_unassigned_ACC.csv** Number of Variables: 95 Variable List: *
 device_id: individual Lesser Scaup identification * timestamp: date
 (YYYY-MM-DD) and time (HH:MM:SS) in Coordinated Universal Time * Latitude:
 Latitude given from transmitter * Longitude: Longitude given from
 transmitter Column numbers 5-94: * Pattern displays x1, y1, z1, x2, y2, z2
 ... x30, y30, z30 * x(1-30): x coordinate from tri-axial accelerometer
 reading * y(1-30): y coordinate from tri-axial accelerometer reading *
 z(1-30): z coordinate from tri-axial accelerometer reading * behaviors:
 default "Rest" behavior needed for model to assign predicted
 behaviors. **File 2: verified_trial_3s_4beh.csv** Number of Variables: 91
 Variable List: Column numbers 1-90: * Pattern displays x1, y1, z1, x2, y2,
 z2 ... x30, y30, z30 * x(1-30): x coordinate from tri-axial accelerometer
 reading from two recorded lesser scaup individuals in dive tank simulation
 verified with camera footage * y(1-30): y coordinate from tri-axial
 accelerometer reading two recorded lesser scaup individuals in dive tank
 simulation verified with camera footage * z(1-30): z coordinate from
 tri-axial accelerometer reading two recorded lesser scaup individuals in
 dive tank simulation verified with camera footage * behaviors: assigned
 behaviors of individual Lesser Scaup in dive tank simulation verified with
 camera footage #### Folder 2: Changepoint_censoring.zip Files in
 Changepoint_censoring.zip contain 1-hr GPS location data of Lesser Scaup
 (n = 29) that survived &gt; 14 day post transmitter implant to analyze
 changes in daily movement and when best to censor GPS data **File 1:
 lesc_14days_postop.csv** Number of Variables: 6 Variable List: *
 device_id: individual Lesser Scaup identification * UTC_datetime: date
 (YYYY-MM-DD) and time (HH:MM:SS) in Coordinated Universal Time *
 UTC_date: date (YYYY-MM-DD) in Coordinated Universal Time * UTC_time: time
 (HH:MM:SS) in Coordinated Universal Time * Latitude: Latitude given from
 transmitter * Longitude: Longitude given from transmitter **File 2:
 Data_censor_changepoint.R** RStudio script calculating the daily distance
 traveled for 20 days of 29 Lesser Scaup and identifying when the greatest
 distance traveled per day were made in order to censor GPS data
 accordingly #### Folder 3: Energetics.zip Files in Energetics.zip contain
 behavior data from instantaneous scan sampling and predicted behaviors
 from the ACC data (described in Folder 1: ACC_behaviors.zip) that were
 used to calculate the estimated daily and hourly energy expenditure of
 Lesser Scaup in the Chesapeake Bay **File 1:
 full_predictedbeh_4day_10day.xgb.csv** Number of Variables: 5 Variable
 List: * device_id: individual Lesser Scaup identification *
 timestamp: date (YYYY-MM-DD) and time (HH:MM:SS) in Eastern Standard Time
 * Latitude: Latitude given from transmitter * Longitude: Longitude given
 from transmitter * predictions: behavior as predicted based on model from
 ACC data (see ACC_behaviors.zip) **File 2:
 scanning_observations_2022to2023.csv** Number of Variables: 28 Variable
 List: * date: date of scan (MM/DD/YYYY) * Site: individual scanning site
 identification name * lat: Latitude of observation point at scanning site
 * lon: Longitude of observation point at scanning site * Sunrise: time at
 which sunrise began at each site (H:MM) in Eastern Standard Time * Sunset:
 time at which sunset ended at each site (H:MM) in Eastern Standard Time *
 TempC: Temperature (C) at the beginning of each individual 10-min scan *
 WindSpM/S: Wind speed (m/s) at the beginning of each scan * Wind
 Direction: Direction where wind is coming from * N = North * E = East * W
 = West * S = South * NE = North east * NW = North west * SE = South east *
 SW = South west * Tide Phase: Tidal phase at the beginning of each scan *
 Rising = tide is rising * Falling = tide is falling * Low = tide is at
 lowest point * High = tide is at highest point * Moon Phase: Phase the
 moon is in for the day the scan occurred * LQ = last quarter * FQ = first
 quarter * NM = new moon * FM = full moon * WNC = waning crescent * WXC =
 waxing crescent * WNG = waning gibbous * WXG = waxing gibbous  * Ice Over:
 Percent of water covered in ice at the beginning of scan * Cloud Cover:
 Amount of cloud cover at beginning of scan * 1 = no cloud * 2 = partly
 cloudy * 3 = half cloud cover * 4 = full cloud cover * Precipitation:
 Precipitation type at the beginning of scan * 0 = snow * 1 = no
 precipitation * 2 = rain * Scan Number: Individual 10-min scan number
 within the full hour with values ranging from the first can being 1 and
 last scan being 6 * Scan Start: Start of individual 10-min scan (H:MM) in
 Eastern Standard Time * Scan End: End of individual 10-min scan (H:MM) in
 Eastern Standard Time * Scan Direction: Direction each 10-min scan
 occurred either being Right to Left (R-L) or Left to Right (L-R) *
 Species: Waterfowl Species identified in scan * Swimming: Number of
 individuals swimming during 10-min scan * Sleeping: Number of individuals
 sleeping during 10-min scan * Flying: Number of individuals flying during
 10-min scan * Feeding_n: Number of individuals diving and eating during
 10-min scan * Loafing: Number of individuals loafing during 10-min scan *
 Preening: Number of individuals preening during 10-min scan * Agnostic:
 Number of individuals displaying aggressive behavior during 10-min scan *
 Mating: Number of individuals showing mating displays during 10-min scan *
 Walking: Number of individuals walking during 10-min scan ## CODE &
 SOFTWARE OVERVIEW All code was produced and ran in RStudio version 4.3.1. 
 Folder 1: ACC_behaviors.zip Featured libraries: tidyverse version 2.0.0
 rabc version 0.1.0 Predicting_ACC_behaviors.R RStudio script outlining
 analysis on creating an algorithm from the verified ACC trial data to
 identify behaviors based on x-y-z tri-ACC readings and create a model used
 to predict behaviors on unassigned x-y-z ACC readings using XGBoost
 decision tree analysis Folder 2: Changepoint_censoring.zip Featured
 libraries: tidyverse version 2.0.0 amt version 0.2.1.0 changepoint version
 2.2.4 Data_censor_changepoint.R RStudio script calculating the daily
 distance traveled for 20 days of 29 Lesser Scaup and identifying when the
 greatest distance traveled per day were made in order to censor GPS data
 accordingly