DOI: 10.5061/dryad.cjsxksnd5
title: Human access constrains optimal foraging and habitat availability in an avian generalist

description: Animals balance costs of anti-predator behaviors with resource acquisition
 to minimize hunting and other mortality risks and maximize their
 physiological condition. This inherent trade-off between forage abundance
 and quality, and mortality risk is intensified in human-dominated
 landscapes because fragmentation, habitat loss, and degradation of natural
 vegetation communities is often coupled with artificially-enhanced
 vegetation (i.e., food plots) creating high-risk high-reward resource
 selection decisions. Our goal was to evaluate autumn–winter resource
 selection trade-offs for an intensively hunted avian generalist. We
 hypothesized human access was a reliable cue for hunting predation risk
 and thus predicted resource selection patterns would be spatiotemporally
 dependent upon levels of access and their perceived risk. Specifically, we
 evaluated resource selection of local-scale flights between diel periods
 of 426 mallards (Anas platyrhynchos) relative to wetland type, forage
 quality, and differing levels of human access across hunting and
 non-hunting seasons. Mallards selected areas that prohibited human access
 and generally avoided areas that allowed access diurnally, especially
 during hunting season. Mallards compensated by selecting for high-energy
 and greater quality foraging patches on allowable human access areas
 nocturnally when they were devoid of hunters. Post-season selection across
 human access gradients did not return to pre-hunting levels immediately,
 perhaps suggesting a delayed response to reacclimate to non-hunted
 activities and thus agreeing with the assessment mismatch hypothesis.
 Last, wetland availability and human access constrained selection for
 optimal natural forage quality (i.e., seed biomass and forage
 productivity) diurnally during pre-season and hunting season,
 respectively; however, mallards were freed from these constraints
 nocturnally during hunting season and during post-season. Our results
 suggest risk-avoidance of human accessible (i.e., hunted) areas is a
 primary driver of resource selection behaviors by mallards and could be a
 local to landscape-level process influencing distributions, instead of
 forage abundance and quality, which has long-been assumed by waterfowl
 conservation planners in North America. Broadly, even an avian generalist,
 well-adapted to anthropogenic landscapes, avoids areas where hunting and
 human access is allowed. Future conservation planning and implementation
 must consider management for recreational access (i.e., people) equally
 important as foraging habitat management for wintering waterfowl.
description: We captured male and female mallards in Tennessee from
 October–February 2019 through 2022. We banded ducks with U.S. Geological
 Survey aluminum tarsal bands and determined sex and age based on cloacal
 inversion, wing plumage and bill color (Carney 1992). We attached 20 g
 solar rechargeable and remotely programmable, OrniTrack Global Positioning
 System-Global System for Mobile transmitters (GPS-GSM; Ornitela, UAB
 Švitrigailos, Vilnius, Lithuania) to birds weighing ≥1,000 g to ensure
 deployment packages remained below 3 of an individuals’ body weight (Frair
 et al. 2010). We programmed GPS-GSM transmitters to record hourly
 locations throughout the duration of the study. We
 filtered “used” locations to only those that were recorded within our
 spatial wetland extent data layer. Next, we generated 20-km circular
 buffers around used locations which corresponded to the maximum distance
 associated with local-scale flights (Appendix S1:Figure A1). We
 intersected 20-km buffers by our wetland data layer to ensure random
 locations were generated on available foraging habitat (i.e., on water) at
 each step. We then simulated 19 random locations within each buffer so
 each strata comprised 1 used location and 19 available locations for a 5%
 to 95% used to available ratio (de la Torre 2022). We
 modeled resource selection using conditional logit models (i.e., discrete
 choice; Beatty et al. 2014<em>a</em>,<em>b</em>,
 Palumbo et al. 2019). We fit conditional logit models in the
 <em>survival </em>package using function
 <em>clogit</em> (Therneau 2020). We fitted separate candidate
 models for each season (i.e., pre-, hunting, and post-season) and diel
 period combination (i.e., diurnal and nocturnal) to account for and
 interpret variation in food depletion (Hagy and Kaminski 2015, Highway
 2022), life-history events (e.g., pairing chronology; Heitmeyer
 1985:268–269), and hunting mortality exposure (Palumbo et al. 2019; 6
 candidate model sets × 2 diel periods × 3 seasons).
description: # Human access constrains optimal foraging and habitat availability in an
 avian generalist The
 dataset was collected in western Tennessee where GPS-marked mallards were
 monitored across three winters (November through March 2019-2022). We used
 conditional logistic regression to fit step selection functions to model
 resource selection by male and female mallards during winter across three
 periods (pre-season, hunting season, and post-hunting season) and night
 vs. day. We found that mallards selected areas free of human hunting and
 other disturbance (i.e., waterfowl sanctuary), which limited available
 habitat diurnally. However, mallards were freed from this constraint and
 selected "riskier" areas that were associated with hunting and
 other human disturbance nocturnally. Mallards selected sanctuary during
 pre-season, suggesting limited water availability and mallards did not
 immediately perceive human-accessible areas as safe during post-season,
 suggesting a lag-effect to reacclimate to non-hunting conditions. In
 general, mallards showed a functional response for high-energy (managed)
 habitat resources throughout seasons but especially nocturnally when the
 landscape was devoid of hunting and other human disturbance. ##
 Description of the data and file structure Due to size limitations, data
 is archived in a tabular format as an RData or ".rds" file. This
 file includes (1) mallard hourly telemetry locations for 426 individuals
 and nearly 43,000 "local-scale" flights and associated covariate
 vectors including (2) the surface water inundation; Allen 2016; (2)
 landcover layers for 2019, 2020, 2021 from publicly sourced datasets; (3)
 the managed habitat layers for each year that were field-sampled and
 computer digitized; and (4) the "human access" categorical
 variable depicting a gradient of hunting and other anthropogenic
 disturbance. All covariates were aligned to the same extent and resolution
 by resampling and stacking each .tif file and related to the locational
 data by extracting covariate values at each GPS location using the raster
 and terra package in R. To read the file into R, users need only to
 specify the path of the saved .rds file and use the function readRDS from
 base R. ## Sharing/Access information The raw GPS data is stored in the
 Movebank data repository and can be shared upon reasonable request.
 Geospatial data was derived from: The PAD-US public repository and U.S.
 Department of Agriculture's "Cropscape" Cropland Data
 Layer. Cropscape includes cropland land cover classes and non-agricultural
 land cover classes; the latter are derived from the National Landcover
 Database (Dewitz et al. 2021). These datasets are publicly available. ##
 Code/Software No new software or specialized functions were created. The
 authors used freely available R statistical software and packages
 including raster, terra to align .tif covariates and extract values to
 individual GPS locations. The authors used the clogit and amt packages to
 fit candidate models with robust standard errors (Therneau 2021, Signer et
 al. 2021). Last, the authors used the predict function in R to generate
 bootstrapped predictions for focal variables of interest, while holding
 non-focal variables at their mean values.