DOI: 10.5061/dryad.k6djh9wdg
title: Hunting constrains wintering mallard response to habitat and environmental conditions

description: The spatiotemporal allocation of activity is fundamental to how organisms
 balance energetic intake and predation risk. Activity patterns fluctuate
 daily and seasonally, and they are proximately affected by exogenous and
 endogenous conditions. For birds, flight activity is often necessary for
 relocating between foraging patches but is energetically expensive and can
 increase mortality risk. Hunted species may have to adjust their behavior
 and activity patterns to minimize anthropogenic mortality risk. We used
 hourly locations from 336 GPS-marked mallards (Anas platyrhynchos) to
 examine how hunting pressure affected flight activity in response to
 weather conditions and habitat availability during winter in western
 Tennessee, USA. Mallards were more likely to fly during crepuscular times,
 particularly dusk, across winter months. Mallards conducted more flights
 after shooting hours when habitat availability increased during open
 hunting season; conversely, mallard flights decreased with increasing
 habitat availability when hunters were present on the landscape. Mallards
 were least active during periods open to hunting. However, indicators of
 approaching inclement weather (i.e., increased wind speed, precipitation,
 and decreasing barometric pressure) increased flights during periods open
 to hunting. Mallard flights decreased at lower temperatures except when
 hunting season was closed, wherein mallards increased nighttime flights.
 Flight activity was directly influenced by hunting disturbance which
 constrained when and how mallards reacted to environmental and habitat
 conditions. An understanding of the temporal shifts in waterfowl flight
 patterns can be used by natural resource managers to better manage
 stakeholder satisfaction and expectations.
description: We captured male and female mallards using swim-in traps,
 confusion traps, and rocket nets from November–January 2019–2022. We
 placed individually marked United States Geological Survey aluminum leg
 bands on all captured waterfowl. We also affixed solar rechargeable and
 remotely programmable, OrniTrack-20 Global Positioning System-Global
 System for Mobile (GPS-GSM) transmitters (Ornitela, UAB Švitrigailos,
 Vilnius, Lithuania) on mallards of all age and sex classes. We glued a
 1.6-mm thick Cross-Linked Polyethylene Skin (Foam Factory, Macomb,
 Michigan, USA) to the GPS-GSM transmitters to help shed moisture from the
 area between the mallards’ skin and the bottom of the transmitter. We
 attached transmitters to mallards with dorsally-mounted body harnesses
 made of automotive moisture-wicking elastic ribbon (Conrad Jarvis,
 Pawtucket, Rhode Island, USA). Completed harnesses had two body loops
 knotted and sealed with cyanoacrylic glue, one above the bird’s keel and
 one across the abdomen. We took GPS locations at 1- to 24-hour intervals
 depending on transmitter battery level. We censored the first 4 days of
 GPS fixes to allow ducks to recover from capture and handling. We
 monitored ducks from first capture until transmitters failed to report GPS
 fixes (i.e., battery malfunction), ducks were harvested by hunters, or GPS
 fixes and tri-axial accelerometry sensors indicated mortality.
 We quantified mallard behaviors by first calculating step-lengths
 (m) between successive GPS fixes and removed successive fixes taken
 &gt;1 hr. apart. We fit a Gaussian kernel density estimator to the
 natural log transformed distribution of step-lengths and partitioned
 spatial scales of movement by visually identifying breaks in the
 distribution of the smoothed data. We interpreted step-lengths 0–0.4 km as
 micro-scale movements in which mallards were likely moving within resource
 patches, 0.4–20 km as local-scale flights in which mallards were flying
 between resource patches, and &gt;20 km as relocation events in which
 mallards were migrating or shifting to a different wintering location.
 Micro-scale movements and relocation events were removed from all analyses
 because we were interested in mallard flights corresponding to
 3<sup>rd</sup> order selection, i.e., how mallards use habitat
 components within their home range. We measured how
 deviations from historic daily minimum temperature, precipitation, wind
 speed, month, river level, change in barometric pressure, time of day, and
 hunting season affected mallard movements. We first
 counted the number of local-scale flights made by each individual, for
 each day of winter (1 November– 28 February) and examined the proportion
 of days in which mallards made a certain number of flights. To examine how
 hunting pressure affected temporal allocation of flights, we used
 generalized linear mixed models (GLMM) in <em>lme4</em> (Bates
 et al. 2011) in R version 4.1.2 (R Development Core Team 2022) with a
 binomial distribution which modeled flight probability as a function of
 diel period (dawn, diurnal, dusk, nocturnal), hunting season (open vs.
 closed), and their interaction. We also used a GLMM
 with a Poisson error distribution to explain variation in mallard flight
 activity relative to hunting pressure, habitat, and environmental
 conditions. We specified month, deviation in minimum temperature from the
 historic mean, daily precipitation, change in daily barometric pressure
 from the previous day, average daily wind speed, and river level as
 independent variables and the number of daily flights an individual made
 as the response variable. Because we were interested in how mallard
 responses to habitat and environmental conditions were affected by hunter
 disturbance, we partitioned our data into four periods: 1) open hunting
 season and during hunting hours, 2) open hunting season and during
 non-hunting hours, 3) closed hunting season and during hunting hours, and
 4) closed hunting season and during non-hunting hours. Hunting hours were
 the period one half-hour before sunrise to sunset, regardless of hunting
 season status. Bates D., M. Maechler, and B. M. Bolker.
 2011. lme4: Linear mixed-effects models using S4 classes version
 0.999375-39. Available at http://CRAN.R
 -project.org/package5lme4.
description: # Data for: Hunting constrains wintering mallard response to habitat and
 environmental conditions. 
 GENERAL INFORMATION 1. Title of Dataset: Data for: Hunting constrains
 wintering mallard response to habitat and environmental conditions. 2.
 Author Information: Cory J. Highway, Department of Biology, Tennessee Tech
 University, Cookeville, TN 38505, USA Abigail G. Blake-Bradshaw, School of
 Environmental Studies, Tennessee Tech University, Cookeville, TN 38505,
 USA Nicholas M. Masto, School of Environmental Studies, Tennessee Tech
 University, Cookeville, TN 38505, USA Allison C. Keever, Department of
 Biology, Tennessee Tech University, Cookeville, TN 38505, USA Jamie C.
 Feddersen, Tennessee Wildlife Resources Agency, Nashville, TN 37211, USA
 Heath M. Hagy, United States Fish and Wildlife Service, Bismarck, ND
 58501, USA Daniel L. Combs, Department of Biology, Tennessee Tech
 University, Cookeville, TN, 38505 USA Bradley S. Cohen, Department of
 Biology, Tennessee Tech University, Cookeville, TN 38505, USA 3. Date of
 Collection 2019-2022 4. Geographic Location of data collection: Tennessee,
 USA 5. Information about funding sources that supported the collection of
 the data: This project was funded by Wildlife Restoration Grants
 administered by the U.S. Fish and Wildlife Service, Wildlife and Sport
 Fish Restoration Program: Partnering to fund conservation and connect
 people with nature and the U.S. Fish and Wildlife Service National
 Wildlife Refuge System, Southeast Region. DATA & FILE OVERVIEW 1.
 Description of dataset: These data were collected from GPS marked mallards
 to identify activity patterns and the influence of environmental and
 anthropogenic variables on mallard movements. 2. File List:
 mallardactivity.csv: Hourly movements of individual GPS marked mallards
 with variables used in analysis associated with each hourly data point.
 METHODOLOGICAL INFORMATION We captured male and female mallards from
 November through January 2019–2022 at state and federal waterfowl refuges.
 We placed individually marked United States Geological Survey aluminum leg
 bands on all captured waterfowl. We also affixed solar rechargeable and
 remotely programmable, OrniTrack-20 Global Positioning System-Global
 System for Mobile (GPS-GSM) transmitters (Ornitela, UAB Švitrigailos,
 Vilnius, Lithuania) on mallards of all age and sex classes. We took GPS
 locations at 1- to 24-hour intervals depending on transmitter battery
 level. We censored the first 4 days of GPS fixes to allow ducks to recover
 from capture and handling. We monitored ducks from first capture until
 transmitters failed to report GPS fixes (i.e., battery malfunction), ducks
 were harvested by hunters, or GPS fixes and tri-axial accelerometry
 sensors indicated mortality. We quantified mallard behaviors by first
 calculating step-lengths (m) between successive GPS fixes and removed
 successive fixes taken &gt;1 hr. apart. We fit a Gaussian kernel density
 estimator to the natural log transformed distribution of step-lengths and
 partitioned spatial scales of movement by visually identifying breaks in
 the distribution of the smoothed data. We interpreted step-lengths 0–0.4
 km as micro-scale movements in which mallards were likely moving within
 resource patches, 0.4–20 km as local-scale flights in which mallards were
 flying between resource patches, and &gt;20 km as relocation events in
 which mallards were migrating or shifting to a different wintering
 location (Figure 2). Micro-scale movements and relocation events were
 removed from all analyses because we were interested in mallard flights
 corresponding to 3rd order selection, i.e., how mallards use habitat
 components within their home range. We measured how deviations from
 historic daily minimum temperature, precipitation, wind speed, month,
 river level, change in barometric pressure, time of day, and hunting
 season affected mallard movements. We first counted the number of
 local-scale flights made by each individual, for each day of winter (1
 November– 28 February) and examined the proportion of days in which
 mallards made a certain number of flights. To examine how hunting pressure
 affected temporal allocation of flights, we used generalized linear mixed
 models (GLMM) in lme4 (Bates et al. 2011) in R version 4.1.2 (R
 Development Core Team 2022) with a binomial distribution which modeled
 flight probability as a function of diel period (dawn, diurnal, dusk,
 nocturnal), hunting season (open vs. closed), and their interaction. We
 also used a GLMM with a Poisson error distribution to explain variation in
 mallard flight activity relative to hunting pressure, habitat, and
 environmental conditions. We specified month, deviation in minimum
 temperature from the historic mean, daily precipitation, change in daily
 barometric pressure from the previous day, average daily wind speed, and
 river level as independent variables and the number of daily flights an
 individual made as the response variable. Because we were interested in
 how mallard responses to habitat and environmental conditions were
 affected by hunter disturbance, we partitioned our data into four periods:
 1) open hunting season and during hunting hours, 2) open hunting season
 and during non-hunting hours, 3) closed hunting season and during hunting
 hours, and 4) closed hunting season and during non-hunting hours. Hunting
 hours were the period one half-hour before sunrise to sunset, regardless
 of hunting season status. Bates D., M. Maechler, and B. M. Bolker. 2011.
 lme4: Linear mixed-effects models using S4 classes version 0.999375-39.
 Available at -project.org/package5lme4.
 DATA-SPECIFIC INFORMATION FOR: mallardactivity.csv 1. Number of Variables:
 11 2. Number of cases/rows: 383699 3. Variable List: 1. timestamp: The
 date and time when the GPS location was taken; character 2. TagID: The
 distinct name provided to each GPS marked individual; character 3. Sex:
 Sex of the mallard, either male (m) or female (f); character 4. pressdiff:
 The difference in barometric pressure from one 24 hour period to the next;
 number; hPa 5. PRCP: Cumulative precipitation in a 24 hour period; number;
 cm 6. mean_wind: Average wind speed in a 24 hour period; number; m/s 7.
 h_season: whether or not hunting season is open, "hunt" if open,
 "non_hunt" if not; character 8. local: binomial variable showing
 if an individual made a local scale movement from one GPS fix to the
 other, "1" if yes, "0" if no; integer 9. habitat: The
 river height in meters over 3 meters. If river height was less than 3
 meters the variable will be "0"; number; m 10. hunttime: A
 variable indicating whether GPS fixes were taken during shooting hours,
 "open" during shooting hours "closed if not";
 character 11. mindiff: the difference in observed daily average minimum
 temperature from the daily historic average minimum temperature; number; C