Hunting constrains wintering mallard response to habitat and environmental conditions

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.

Methods
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 >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 >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 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 http://CRAN.R -project.org/package5lme4.