Human access constrains optimal foraging and habitat availability in an avian generalist
Data files
Jan 05, 2024 version files 16.09 MB
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EAP23-0437_data.rds
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README.md
Abstract
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.
README: Human access constrains optimal foraging and habitat availability in an avian generalist
https://doi.org/10.5061/dryad.cjsxksnd5
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.
Methods
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. 2014a,b, Palumbo et al. 2019). We fit conditional logit models in the survival package using function clogit (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).