DOI: 10.5061/dryad.931zcrjz1
title: From fear to feast: Rattlesnakes navigate the landscape of fear to optimize foraging

description: According to optimal foraging theory, mesopredators should forage in areas
 where their prey is abundant while avoiding high predation risk. Here, we
 investigate how environmental factors influence mesopredators’ abilities
 to minimize spatiotemporal overlap with predators while increasing
 spatiotemporal overlap with prey. We paired 30 western diamond-backed
 rattlesnake (Crotalus atrox) 3D-printed replicas with game cameras in West
 Texas for two years to quantify several spatiotemporal factors affecting
 prey availability and predation risk. Concurrently, 25 Crotalus atrox were
 radiotracked at the same site to gather activity and microhabitat
 selection data regarding free-ranging individuals. Random forest
 algorithms were trained using data obtained from the game camera and
 applied to predict the probability of predation and the probability of
 prey encounter for each radiotracking event. Time of day, month,
 vegetation structure, and concealment percentage all had a significant
 association with the probability of predation and the probability of prey
 encounter. Our results suggest that rattlesnakes choose to be active when
 and where the probability of prey encounter is significantly higher than
 the probability of predator encounter, thus following optimal foraging
 theory. Our results demonstrate that mesopredators increase the chances of
 prey capture while reducing predator detection in natural settings.
description: # From fear to feast: Rattlesnakes navigate the landscape of fear to
 optimize foraging Dataset DOI:
 [10.5061/dryad.931zcrjz1](10.5061/dryad.931zcrjz1) ## Description of the
 data and file structure *Da Cunha O., Dominguez R. P., Horne L. M., Mead
 J. J., Fournier C., Johnson J. D., and Seymoure B. M. From Fear to Feast:
 Rattlesnakes Navigate the Landscape of Fear to Optimize Foraging. In
 Press. Submitted to The American Naturalist on Nov. 15th, 2024.* Authors:
 Da Cunha O., Dominguez R. P., Horne L. M., Mead J. J., Fournier C.,
 Johnson J. D., and Seymoure B. M. University of Texas at El Paso, TX, USA.
 Corresponding authors: Oceane Da Cunha
 ([odacunha2@utep.edu](mailto:odacunha2@utep.edu)). Statistical analyses
 conducted by Oceane Da Cunha. ### Files and variables *Empty cells
 indicate missing or unavailable data.* #### File: RecordingDays_GC.csv
 **Description:** File containing the number of recording days per game
 camera ##### Variables * Camera: Camera ID * Days: Number of recording
 days * Location: Location of the camera #### File:
 Game_Camera_DR_31AUG23.csv **Description:** File containing all game
 camera observations and microhabitats associated with observations. NA
 stands for "Not applicable". ##### Variables * Camera_Number:
 Camera ID * Number: Camera number * Date: Date of observation * Time: Time
 of observation * Time2: Adjusted/alternative time format *
 Common_name: Common species name * Scientific_name: Scientific species
 name * Predation_Y_N: Yes or No * Detection: Yes or No * Notes: Additional
 notes * Rock_per: Percentage of rock * Rock_type: type of rock * DNR:
 Distance to nearest rock (meters) * Soil_per: Percentage of exposed
 substrate * Soil_type: type of soil * DNB: Distance to nearest burrow
 (meters) * DNB_type: Type of burrow * Vegetation_per: Percentage of
 vegetation * Vegetation_type: vegetation species * Vegetation_height: in
 meters * DNV: Distance to nearest vegetation (meters) * DNV_type: species
 of nearest vegetation * DNV_height: height of nearest vegetation (meters)
 * Concealment: Percentage of concealment * Conc_source: Type of
 concealment * Location: Location on the map * Location2: Tank or desert *
 Model_size: small or big * Month: Month of observation * TimeS: hours of
 observation * Predator_Prey_Other: Classification of observed species *
 Rodent: yes or no * Prey: yes or no #### File: DetectionY.csv
 **Description:** Detection observations ##### Variables * Scientific_name:
 * n: number of observations #### File: TrainTest_Pred_noNA2.csv
 **Description:** File containing telemetry (test) and game camera
 observations (train) for predator detection to input in RF models. NA
 stands for "Not applicable". ##### Variables: see previous
 variables in `Game_Camera_DR_31AUG23.csv`, plus: * ID: Snake
 identification or game camera number * Set: training or test set for
 random forest * LocationN: GPS coordinates * LocationE: GPS coordinates
 #### File: PredicSnakePred.csv **Description:** File with predictions of
 detection by predators obtained from the RF model ##### Variables: See
 `Game_Camera_DR_31AUG23.csv and TrainTest_Pred_noNA2.csv` plus: *
 LocationEN: additional GPS coordinate references * N: Probabilities for no
 detection * Y: Probabilities for detection #### File: PreyUsable.csv
 **Description:** File with prey observation to input in RF models. NA
 stands for "Not applicable". ##### Variables: see
 `Game_Camera_DR_31AUG23.csv` plus: * Usable: Yes or No #### File:
 MergedModelPrey.csv **Description:** File containing telemetry (test) and
 game camera observations (train) for prey encounter to input in RF models
 ##### Variables: see previous variables in `Game_Camera_DR_31AUG23.csv,
 TrainTest_Pred_noNA2.csv, and PreyUsable.csv` #### File:
 PredicSnakePrey17JAN.csv **Description:** File with predictions of prey
 encounter obtained from RF models ##### Variables: see previous variables
 in see previous variables in `Game_Camera_DR_31AUG23.csv,
 TrainTest_Pred_noNA2.csv, and PreyUsable.csv` #### Overlap Files
 (`overlap.csv`, `opreysummer.csv`, `overlapFall.csv`, `OverlapPrey.csv`,
 `overlappreyfall.csv`, `overlappreyspring.csv`, `overlapSpring.csv`,
 `overlapSummer.csv`) **Description:** Observations and time stamps to
 calculate seasonal or prey-specific activity overlap. **Variables:** *
 Camera_Number : Camera ID * Date: Date of observation * Time2:  Time of
 observation (adjusted format) * Scientific_name:  Scientific species name
 * type:  Type of animal (e.g., predator, prey, other) #### File:
 Beh_Proba_all.csv **Description:** File with probability of predator
 detection and prey encounter obtained from RF per rattlesnake behavior
 ##### Variables * Individual_ID: snake identification * Beh2: Type of
 behavior * Type: predator or prey  * Proba: probability of detection or
 encounter #### File: OFT_Stats_Feb25.Rmd **Description:** R code for all
 analyses ## Code/software R version 4.2.2 (2022-10-31 ucrt) Platform:
 x86_64-w64-mingw32/x64 (64-bit) Running under: Windows 10 x64 (build
 26100) attached base packages: stats graphics grDevices utils datasets
 methods base loaded via a namespace (and not attached): rstudioapi_0.14
 knitr_1.42 magrittr_2.0.3 tidyselect_1.2.0 munsell_0.5.0 colorspace_2.1-0
 R6_2.5.1 rlang_1.1.0 fastmap_1.1.0 fansi_1.0.4 dplyr_1.1.1 tools_4.2.2
 ggdist_3.3.0 grid_4.2.2 gtable_0.3.3 xfun_0.36 utf8_1.2.3 cli_3.6.0
 htmltools_0.5.4 yaml_2.3.7 digest_0.6.31 tibble_3.2.1 lifecycle_1.0.3
 farver_2.1.1 ggplot2_3.5.1 gghalves_0.1.4 vctrs_0.6.1 evaluate_0.20
 glue_1.6.2 rmarkdown_2.21 compiler_4.2.2 pillar_1.9.0 generics_0.1.3
 scales_1.3.0 distributional_0.3.2 pkgconfig_2.0.3