DOI: 10.5061/dryad.2547d7x3h
title: Wintering mallard survival is unaffected by brief anthropogenic disturbance on protected areas

description: Human activities in natural areas can impose both lethal and non-lethal
 impacts on animals. Furthermore, anthropogenic disturbance is analogous to
 predation risk and can cause animals to adjust their behaviors to avoid
 humans. Quantifying if disturbance-induced behavioral shifts affect
 individual fitness or population dynamics is needed to guide science-based
 conservation and management decisions. We experimentally disturbed
 GPS-marked mallards (Anas platyrhynchos) on sanctuaries weekly to evaluate
 the effects of brief pulses (1 hr) of non-lethal anthropogenic disturbance
 on individual survival. We used Cox proportional hazard models to examine
 how single and cumulative disturbance affected survival and tested whether
 body mass or hunting season mediated the effects of disturbance. One
 hundred and eighty-eight mallards were disturbed ≥1 time resulting in 629
 disturbance encounters. Only 3 individuals died immediately following
 disturbance, representing &lt;0.5% of encounters. Collectively, we
 found no effect of disturbance on daily survival, and our cumulative
 disturbance model showed undisturbed mallards had lower survival than
 disturbed mallards. Standardized body mass or hunting season did not
 mediate the effect of disturbance on survival. Together, we concluded no
 effect of our brief experimental disturbance treatments on mallard
 survival. Instead, diurnal sanctuary use and individual characteristics,
 including age, sex, and standardized body mass, affected survival. Diurnal
 sanctuary use was positively related to survival, and for every 20%
 increase in diurnal sanctuary use, the risk of mortality decreased by 15%.
 Additionally, female mallards were 2.7 times more likely to die compared
 to males, and juveniles had 53% greater risk of mortality than adults.
 Lastly, for every 100g heavier than average mallards were, we found a 23%
 lower risk of mortality during our study. If a primary goal of waterfowl
 sanctuary is including non-consumptive recreational use, our results
 suggest controlled access (e.g., ~1 hr/week) may have minimal effects on
 survival and be consistent with multi-use objectives on public lands with
 waterfowl sanctuaries. If additional recreational access to support
 multiple public uses is a goal on public lands managed as sanctuaries, we
 recommend future work identify disturbance thresholds at which point
 survival or other fitness metrics are impacted by disturbance related to
 public uses of protected areas.
description: <strong>Study area</strong> Our
 study took place in northwestern Tennessee, USA (~6,000
 km<sup>2</sup>) which encompassed 3 federally owned and 7
 state-owned sanctuaries (Figure 1). Agriculture was the dominant land use
 in the region, and the majority of agricultural fields were harvested in
 late summer and autumn; however, intensive wetland management designed to
 attract waterfowl for recreational or conservation purposes were
 commonplace on both federally and state-owned sanctuaries, public hunted
 areas, and private property throughout our study area (Gray et al. 2021,
 Masto et al. 2024a, Highway et al. 2025). Managed wetlands were impounded
 and often had water control structures that allowed managers and
 landowners to produce and then flood annual seed-producing moist-soil
 vegetation and/ or planted crops (e.g., corn [<em>Zea
 mays</em>], millet [<em>Echinochloa</em> spp.;
 <em>Urochloa</em> spp.], and rice [<em>Oryza
 sativa</em>]; Gray et al. 2021). Our study area contained abundant
 food resources for wintering waterfowl across public and private
 properties. In fact, during a concurrent study, Masto (2023) estimated
 managed moist-soil vegetation within our study area contributed 6.7
 million duck energy days and unharvested flooded corn contributed 85.6 mil
 duck energy days. All sanctuaries, besides one
 state-managed sanctuary, contained both managed moist-soil and planted
 crops (i.e., predominantly corn) during our study: on average, sanctuaries
 contained 56.1 ha (range = 0–214.2 ha; median = 18.0 ha) of managed
 moist-soil and 25.9 ha (range = 0–58.0 ha; median = 25.6 ha) of flooded
 unharvested corn. State sanctuaries tended to contain more flooded
 unharvested corn than managed moist-soil (x̄ = 52.9% in corn; range =
 16.1–66.7 %), and the federal sanctuaries tended to contain more managed
 moist-soil than corn (x̄ = 65% in moist-soil; range = 32–85 %). The
 remaining cover on sanctuaries were predominantly woody wetlands and
 harvested agriculture. Hunting and other public access (i.e., vehicular
 and foot traffic) was prohibited on portions of state and federal lands
 managed as sanctuaries on or before 15 November and until 1 March each
 year. During our study, the waterfowl hunting season
 lasted 60 days over two segments with two days in mid- to late-November
 and the remaining 58 days in December through the end of January, with
 opening weekends in mid-November for Reelfoot Lake and late-November for
 the remainder of the study area (Figure 1). Following opening weekends,
 the waterfowl hunting season then closed until early December when it
 reopened and remained open until the end of January each year. Weekly
 aerial surveys were used to estimate hunting activity across the study
 area. Hunting activity was consistent throughout the 60-day waterfowl
 season and did not differ by week of year or between weekends and weekdays
 (Masto et al. 2024c). Additionally, hunting intensity was highest within
 river channels, near waterfowl sanctuaries (Blake-Bradshaw et al. 2023),
 and on public and privately hunted areas that typically had no specified
 rest-days (Masto et al. 2024a, c). <strong>Animal
 capture and telemetry</strong> We captured
 mallards using rocket-nets and swim-in traps on waterfowl sanctuaries in
 western Tennessee, USA during winter (1 November–28 February 2019 –2022).
 We aged mallards as juveniles or adults using cloacal inversion and wing
 plumage characteristics (Carney 1992). All mallards were weighed to index
 body condition (± 1 g) and banded with USGS standard aluminum tarsal
 bands. We equipped mallards with 20-g solar-rechargeable and remotely
 programmable OrniTrack Global Positioning System-Global System for Mobile
 transmitters (GPS-GSM; Ornitela, UAB Švitrigailos, Vilnius, Lithuania). We
 attached transmitters via dorsally-mounted body harnesses made of
 automotive moisture-wicking elastic ribbon (Masto et al. 2022, Highway et
 al. 2024). We programed transmitters to record GPS locations at 1-hr and
 2-hr intervals. We reduced GPS-frequency to once every 36 hr when
 transmitter battery was &lt;25%. We monitored individuals until
 transmitter failure, an individual was reported legally harvested, or we
 identified natural mortality using tri-axial accelerometer
 graphs. <strong>Explanatory
 variables</strong> To evaluate the influence of
 sanctuary disturbance on wintering mallard survival, we conducted three
 distinct disturbance treatments of increasing intensity on sanctuaries:
 accessing sanctuaries by covered vehicle (i.e., truck), pedestrian, or
 uncovered vehicle (i.e., all-terrain vehicle [ATV] or motor boat; Pease et
 al. 2005, Guay et al. 2019; Figure S1). For the truck disturbance,
 observers drove predetermined routes along roads or levees estimating
 waterfowl abundance from pre-specified vantage points. Route length
 differed among sanctuaries based on sanctuary size and vegetative cover
 (e.g., forested vs. open water). For the intermediate intensity pedestrian
 disturbance, two observers walked separate routes along levees (i.e., 4.8
 km/hr) during a given visit and altered routes when normal routes were
 inaccessible (e.g., flooding events; Bregnballe et al. 2009, Guay et al.
 2019; Figure S1). For the highest intensity disturbance treatment, we
 drove an ATV or a surface drive motorboat into wetlands (Havera et al.
 1992, Knapton et al. 2000; Figure S1). We maintained speed of motorized
 vehicles to approximately 16 km/hr for 10 minutes, stopped for 5 minutes,
 and repeated until 1 hour elapsed. We conducted a
 disturbance treatment approximately once per week, separating treatments
 on each sanctuary by ≥5 days to isolate disturbance events and allow
 GPS-marked ducks to resume normal activities (Dooley et al. 2010b). We
 disturbed sanctuaries for one hour between 0700 and 1100 hours. We
 determined an individual received a disturbance treatment when they had ≥1
 GPS location on the disturbed sanctuary between sunrise and the end of the
 disturbance. This determination ensured that a GPS-marked mallard was
 exposed to a disturbance event. For instance, if we compared hourly
 GPS-marked mallard locations/timestamps to just the duration of the
 disturbance treatment (i.e., 1 hour), we could incorrectly determine
 whether a mallard was disturbed, especially if it left the sanctuary
 immediately in response to disturbance. Rather, our data and previous
 studies with high-frequency GPS-GSM transmitters on waterfowl show that
 ducks move to a single wetland or sanctuary area approximately at dawn and
 remain in the same location throughout the day (McDuie et al. 2019,
 Blake-Bradshaw et al. 2024). For instance, if a mallard was located on a
 sanctuary within an hour of nautical dawn (i.e., morning nautical twilight
 starts; ~1 hour prior to sunrise), it was highly likely the individual
 would remain on that same sanctuary throughout that day (Figure S2). Thus,
 we are confident this approach accurately represented whether a GPS-marked
 individual was disturbed. We collapsed disturbance
 treatments into a single binary variable for model convergence (i.e.,
 yes/no; Table 1). Additionally, we calculated cumulative disturbance as
 the number of disturbances experienced during winter. Few mallards had
 &gt;3 disturbance encounters so we binned the upper end of the
 cumulative disturbance covariate to 3 (Table 1). We included age and sex
 as independent variables (Table 1). Support for body condition indices
 (i.e., function of mass and structural components) is mixed and may not
 accurately represent of energetic stores in waterfowl (Klimas et al.
 2020); thus, we standardized individual body mass by averaging mass for
 the four age-sex cohorts (i.e., adult and juvenile males, adult and
 juvenile females) and subtracted this value from each individual’s weight
 depending on their age-sex cohort, uncorrected for a structural component
 (Veon et al. 2024; Table 1). Veon et al. (2024) found standardized body
 mass and structurally corrected body condition were highly correlated
 (|<em>r</em>| ≥ 0.9), so we use standardized body mass as a
 proxy for body condition. To evaluate if mortality risk (i.e., hunting
 season) mediated the effects of disturbance on mallard survival, we
 created a binary variable representing hunting and non-hunting periods.
 Hunting periods occurred during December and January and non-hunt periods
 during November and February (Table 1). Last, we calculated a 3-day
 rolling average of proportion of diurnal locations on a sanctuary for each
 individual to represent short-term propensity of an individual mallard to
 use sanctuary (Table 1). This time-dependent variable was represented by a
 proportion (i.e., 0–1) and updated daily with individual’s mean diurnal
 sanctuary use over the three preceding days, increasing towards 1 over
 time as diurnal sanctuary use increased and towards 0 with decreased
 diurnal sanctuary use. We defined the diurnal period as 30 minutes prior
 to sunrise until sunset, mirroring legal hunting hours.
 <strong>Survival analyses</strong>
 We estimated daily survival during winter 1 November–28 February
 2019–2022 via known-fate Cox proportional hazard models using the
 <em>survival </em>package (Therneau and Grambsch 2000,
 Therneau 2022). We estimated robust standard errors around parameter
 estimates to account for repeated sampling of the same individual
 (Therneau and Grambsch 2000). We generated daily encounter histories for
 each individual using a staggered entry design due to differences in
 capture dates (Pollock et al. 1989). If a transmitter did not report GPS
 data during a given day, we right-censored the individual for that date
 and re-entered them into the daily encounter histories on the next date
 with GPS locations. We left-censored each mallard for 4 days after they
 were equipped with a GPS-transmitter to allow acclimation to the
 transmitter (Cox and Afton 1998) and right-censored individuals that were
 still alive at the end of the study or stopped providing data. If a
 transmitter stopped reporting data, we assumed transmitter failure and
 right-censored the individual on the last day data were received. We
 assumed unbiased censoring (Benson et al. 2014). To model daily and
 120-day winter survival rates, we generated daily survival estimates from
 a Kaplan-Meier survival curve using ‘survfit’ in the
 <em>survival</em> package (Karp and Gehr 2020, Therneau
 2022). <strong>Statistical
 analyses</strong> Prior to model formulation, we
 checked for multicollinearity and removed one of a pair of correlated
 variables (|<em>r</em>| ≥ 0.6; Dormann et al. 2013). We tested
 whether covariates met the proportional hazards assumption by examining
 Schoenfeld residuals (Therneau 2022). We standardized continuous variables
 by z-scoring. To estimate daily survival, we fitted separate models during
 the hunt and non-hunt periods, both included the binary disturbance
 variable. We used the log-rank χ<sup>2</sup> test with an
 <em>a priori</em> α = 0.05 to test whether disturbance
 influenced daily survival during hunt and non-hunt periods. We fit a model
 to evaluate whether cumulative disturbance(s) affected daily survival and
 another model to evaluate whether survival differed more generally between
 mallards that received ≥1 experimental disturbance and mallards that did
 not receive any experimental disturbances (i.e., undisturbed mallards). We
 fit the remaining <em>a priori</em> alternative hypotheses
 (Table 1) and implemented model selection using Akaike’s Information
 Criterion corrected for small sample sizes (AIC<sub>c</sub>;
 Burnham and Anderson 2002). We present predictions for variables which 85%
 confidence intervals did not overlap zero and associated hazard ratios
 (Therneau and Grambsch 2000). We evaluated competing models for parsimony
 to avoid uninformative parameters and performed model averaging across
 models ≤2 ΔAIC<sub>c</sub> if there was model selection
 uncertainty (Burnham and Anderson 2002: 151–153, Arnold 2010). We
 evaluated goodness-of-fit using Harrell’s Concordance index (Harrell et
 al. 1982, Thernau 2022). All analyses were conducted in program R v.4.2.2
 (R Core Team 2022).
description: # Wintering mallard survival is unaffected by brief anthropogenic
 disturbance on protected areas Dataset DOI:
 [10.5061/dryad.2547d7x3h](10.5061/dryad.2547d7x3h) ## Description of the
 data and file structure We experimentally disturbed GPS-marked mallards
 (*Anas platyrhynchos*) on sanctuaries weekly to evaluate the effects of
 brief pulses (1 hr) of non-lethal anthropogenic disturbance on individual
 survival. We used Cox proportional hazard models to examine how single and
 cumulative disturbance affected survival and tested whether body mass or
 hunting season mediated the effects of disturbance. ### Files and
 variables #### File: surv\_datum.csv **Description:** Dataset with all
 variables needed for analyses testing the effects of disturbance,
 sanctuary use, and individual characteristics on winter survival. #####
 Variables * trackId: individual mallard identification information *
 time1: time (in seconds) of start of day * time2: time (in seconds) of end
 of day * date: date of observation * mortality: binary variable
 representing 1 if the individual died that day and 0 if the individual did
 not die that day * mass_diff_age_sex: continuous variable representing age
 and sex "corrected" mass following recommendations by Veon et
 al. (2024) where we calculated the average mass by age and sex cohort and
 subtracted each individual's mass. Then, this variable either
 represented a positive number if the individual was heavier than average
 for their age-sex cohort or negative if the individual was lighter than
 average for their age-sex cohort. * age_adjust: binary variable
 representing adult or juvenile * sex: binary variable representing male or
 female * hunt_seas: binary variable representing hunting or non-hunting
 period * pref_avg: 3-day rolling average of diurnal sanctuary use
 calculated for each individual spanning 0 - 1. No units. * disturbed_day:
 binary variable (1 vs 0) whether an individual mallard was disturbed that
 day * cumulative_dist3: factor variable representing the number of times
 an individual mallard was disturbed at a given time * ever_dist2: Binary
 variable representing whether an individual mallard was disturbed &gt;=1
 time (1) or not (0) during the study * season: season of data collection
 (one, two, and three) #### File: survival\_protected\_areas\_code.txt
 **Description:** Contains R code for all analyses