DOI: 10.5061/dryad.gb5mkkww4
title: Eat or be eaten: Implications of potential exploitative competition between wolves and humans across predator- savvy and -naive deer populations

description: Recolonization of predators to their former ranges is becoming
 increasingly prevalent. Such recolonization places predators amongst their
 prey once again; the latter having lived without predation (from such
 predators) for considerable time. This renewed coexistence creates
 opportunities to explore predation ecology at both fundamental and applied
 levels. We used a paired experimental design to investigate white-tailed
 deer risk allocation in the Upper and Lower Peninsulas (UP and LP) in
 Michigan, USA. Wolves are functionally absent in the LP, while deer in the
 UP coexist with a re-established wolf population. We treated 15 sites each
 in UP and LP with wolf olfactory cues and observed deer vigilance,
 activity, and visitation rates at the interface of habitat covariates
 using remote cameras. Such a paired design across wolf versus no-wolf
 areas allowed us to examine indirect predation effects while accounting
 for confounding parameters such as the presence of other predators and
 human activity. While wolf urine had no effect across most metrics in both
 UP and LP, we observed differences in deer activity in areas with versus
 without wolves. Sites treated with wolf urine in the UP showed a reduction
 in crepuscular deer activity, compared to control/novel-scent treated
 sites. Further, we observed a strong positive effect of vegetation cover
 on deer vigilance in these sites. This indicates that simulated predator
 cues likely affect deer vigilance more acutely in denser habitats, which
 presumably facilitates predation success. Such responses were however
 absent among deer in the LP that are presumably naïve towards wolf
 predation. Where human and non-human predators hunt shared prey, such as
 in Michigan, predators may constrain human hunting success by increasing
 deer vigilance. Hunters may avoid such exploitative competition by
 choosing hunting/bait sites located in open areas. Our results pertaining
 to fundamental predation ecology have strong applied implications that can
 promote human-predator coexistence.
description: <strong>Study
 Area</strong> We
 conducted this study in Michigan’s UP and LP from September 21, 2018 to
 November 10, 2018. The UP study area was entirely within the Hiawatha
 National Forest and was interspersed with unpaved, US Forest Service
 roads. The bait sites in the LP study area were all located in Mackinaw
 State Forest of Michigan, and the area was also interspersed with private
 agriculture, forestland, and paved, secondary roads. The main vegetation
 cover, derived from the National Land Cover Database, at the UP study
 sites was evergreen (5 sites), woody wetland (3 sites), grassland (3
 sites), deciduous (2 sites), and mixed forest (2 sites), while the LP
 study sites were deciduous (6 sites), scrub/shrub (4 sites), grassland (3
 sites), and evergreen (Homer et al., 2015). <strong>Study
 Design</strong> We
 established 30 deer bait sites between the two study areas, 15 in the UP
 and 15 in the LP. To mimic recreational hunters, we selected sites based
 on deer hunting desirability such as habitat openness and/or proximity to
 a deer trail (Peterson, 2015). Sites were at least 1 km away from each
 other to avoid duplication and dependence (Winke, 2012). We baited each site with 3.8 liters of corn
 spread in a 3 by 3-meter- square plot, as per the Michigan deer baiting
 regulations (Michigan Department of Natural Resources, 2018). For the
 duration of the study, all sites were baited each week in order to
 maintain 3.8 liters of corn on the ground to replicate actual hunter
 behavior and to reduce the confounding effect of bait availability on deer
 behavior. We deployed one remote camera (Reconyx Hyperfire series or
 Cuddeback E) on a tree 4 meters from the center of the bait site. Each
 camera was programmed to take 5 burst images with no delay between
 triggers to record the number of deer present, their posture, sex (when
 possible), and presence/absence of fawn(s). We replaced batteries and
 memory cards weekly. <em>BACI Experimental
 Setup</em> We used a
 10 week before-after-control-impact (BACI) design for the experimental
 treatment of both areas. This BACI design was conducted so that all sites
 were paired with themselves with all other factors (e.g., other predators,
 human visitation, environmental effects) remaining comparable. This
 allowed us to tease apart the effects of the treatment. Before
 experimental treatments, we collected data for one week preceding the
 six-week period to establish a baseline condition of deer visitation to
 the bait sites (Stewart-Oaten et al., 1986). For the first 3 weeks
 (September 28-October 20), we baited sites and recorded deer occurrence
 and behavior via remote cameras with no scent treatments applied. For the
 following three weeks, we continued to bait and record deer after treating
 the sites with three scents. In each area, UP and LP, 5 sites were treated
 each with wolf urine (predator treatment), lemon juice (novel scent; not a
 native plant), or distilled water (control). We assigned site treatment
 randomly (Atkins et al., 2016; Wikenros et al., 2015). When we treated
 sites in the field, we visited all ‘control’ sites in an area first
 followed by the ‘novel scent’ sites, and finally ‘experimental/wolf urine’
 sites to avoid cross-site spread of scents. We also used different
 treatment application tools (i.e., pipets, travel containers) for each
 treatment type to avoid cross-contamination of treatments. At each site,
 we applied 10 mL of the given treatment (experimental, novel, or control)
 by dripping it from a pipet onto the bait site. This was done to mimic the
 amount and application of scent marking of a pair of wolves (Peters
 & Mech, 1975). <em>Vegetation
 Cover</em> We also
 recorded predator concealment or hiding potential (horizontal cover) at
 each site to account for site-specific differences in habitat openness -
 an index of perceived predation threat. We used a 2 m cover pole with 20
 sections, each 10 cm long, to measure predator hiding potential at each
 site (Kuijper et al., 2014; Severud et al., 2019). From the center of each
 bait pile, we measured the horizontal cover in each cardinal direction.
 While one person held the cover pole 10-meters away from the center of the
 bait pile, another took a picture of the cover pole with a camera
 stationed on a 1-meter high pole at the center of the bait pile, mimicking
 deer visual height (Severud et al., 2019). We conducted this procedure
 twice during the study, once at the beginning and once when treatment
 started, to account for change in hiding potential with loss of leaves
 later in the year. For each of the 20 (10 cm) sections on the 2 m cover
 pole, we estimated obscurement to the nearest 25% (Severud et al., 2019).
 We calculated a single mean and standard error value for each site and
 each measurement time (beginning and middle of study). <strong>Analysis</strong> <em>Photo Analysis</em> Images were tagged in batches using the DigiKam photo editing software (Niedballa et al., 2016). Each batch was defined by any set of images taken within 5 minutes of the prior image. In each batch, for deer images only, we recorded if fawns were present, labelled all sexes when possible, and tallied a total count. This increased the likelihood that we accounted for all individuals that visited the bait pile and avoided miscounting any that were out of the camera frame. For individual pictures within batches, in addition to generic behaviours (e.g., fighting, nursing, foraging), we recorded the number of individuals with their heads above their shoulders, indicating vigilance/state of alertness (Flagel et al., 2016; Schuttler et al., 2017). We also labelled batches for different species that were captured at site visits including squirrels (<em>Sciurus</em> spp.), raccoons (<em>Procyon lotor</em>), wild turkeys (<em>Meleagris gallopavo</em>), and coyotes. We did not detect additional scent marking by other species. <em>Treatment Impact on Deer Vigilance, Group Size, and Visitation Rate </em> We explored the impact of different treatments in the two areas (UP and LP) on the number of deer at each site, the proportion of the group that was vigilant, and the number of visits made to the site. To account for the paired nature of our treatment design, we first averaged the variable values for each site individually and calculated a difference in values before and during treatment application. We then averaged across all sites within a given treatment. We also calculated a vigilance intensity metric using equation 1. This metric includes both group vigilance and event duration.   (Eq 1)  <em>I</em>=(∑<em>v/g)/e</em> Where <em>I</em> is the vigilance intensity metric for a given event, <em>v</em> is the number of deer vigilant in a single image, <em>g</em> is the group size in a single image, and <em>e</em> is the total time of the event. Hence, vigilance intensity simply standardizes individual vigilance across different group sizes and time spent in front of the camera. Similar to the previous analysis, we calculated a difference in vigilance intensity before and after treatment for each treatment type in each peninsula.  <em>Diel Activity</em> To analyze possible temporal variability in the use of bait sites in the UP and LP before and after treatment, we used nonparametric kernel density estimation (Prugh et al., 2019; Wang et al., 2015). We converted times to radians and used a kernel density estimator to create a probability density distribution for each before or after period (Ridout & Linkie, 2009). We calculated the proportion of temporal overlap between the two treatment periods for each treatment in an area (Wang et al., 2015). We used a Δ̂<sub>4</sub> with a smoothing parameter of 1 because our sample size for all analyses was greater than 50 (Ridout & Linkie, 2009). We conducted this analysis using the overlap package (Meredith & Ridout, 2018; Wang et al., 2015) in R (R Development Core Team 2013). We applied Watson’s U2 statistic with the CircStats package to test for homogeneity between the two samples of interest (i.e., test for/detect a statistically significant shift in the diel pattern before and after treatment) (Lashley et al., 2018; Lund & Agostinelli, 2012). If deer significantly shifted their temporal pattern between the two treatment periods, we expected Watson’s U2 statistic would be greater than the critical value (0.19 for an α value of 0.05) and P &lt; 0.05. In the UP, we predicted that there would be no shift in temporal visitation by deer at the control and lemon treated sites (high Δ̂<sub>4</sub>, U2 ≤ 0.19), while a shift to more nocturnal activity at the wolf urine treated sites would be detected (lower Δ̂<sub>4</sub>, U2 &gt; 0.19; Kohl et al. 2018) to potentially avoid wolves that are known to be typically less active at night (Kohl et al., 2018). In the LP, we expected that we would not see a significant shift in any treatment sites because deer in the LP are ostensibly naive to wolf predation. <em>Effect on Vigilance Behaviour</em> We fitted generalized linear models using pooled data from both the UP and LP to check for the effect on overall vigilance intensity by treatment effect (before/after) and type (predator/novel/control), area (UP or LP) and vegetation cover (i.e., predator hiding potential; S1; Fležar et al., 2019; Prugh et al., 2019) through additive and interactive effects between parameters of interest. Subsequently, we fitted models to examine the relationship between vigilance intensity and vegetation cover at each treatment type within both the UP and LP before and after treatment. Using generalized linear model, we further examined the effects of sex, vegetation cover, and presence of young on vigilance intensity, and included additive and interactive effects with area to detect any difference between the UP and LP. We ranked models based on AICc.
description: ## General information: 1. **Title**: Eat or Be Eaten: Implications of
 potential exploitative competition between wolves and humans across
 predator- savvy and -naive deer populations
 [https://doi.org/10.5061/dryad.gb5mkkww4](https://doi.org/10.5061/dryad.gb5mkkww4) 2. **Author information:** ELLEN M. CANDLER, belle130@umn.edu, *University of Minnesota, 135 Skok Hall, 2003 Upper Buford Circle, St. Paul, MN 55108, USA, ORCID: 0000-0002-5810-1942* STOTRA Chakrabarti, *Macalester College, Olin-Rice Science Center 211, St. Paul, MN 55105, USA, ORCID: 0000-0001-8741-4780* WILLIAM J. SEVERUD, *South Dakota State University, Department of Natural Resource Management, Brookings, South Dakota USA 57007, USA, ORCID: 0000-0003-0150-5986* JOSEPH K. Bump, *University of Minnesota, 135 Skok Hall, 2003 Upper Buford Circle, St. Paul, MN 55108, USA, ORCID: 0000-0002-4369-7990* 3. Date of data collection: September-November 2018 4. Geographic location of data collection: Upper and Northern Lower Peninsula of Michigan, USA 5. Information about funding sources that supported the collection of the data: NSF-GRFP-Grant No. 00039218, NSF ID#1545611, NSF ID#1556676 6. Licenses/restrictions placed on the data: All data was collected by the researchers. For more information, please contact the corresponding author. 7. Links to publications that cite or use the data: NA 8. Links to other publicly accessible locations of the data: None 9. Links/relationships to ancillary data sets: None 10. Was data derived from another source? No 11. Recommended citation for this dataset: Candler, E.M., Chakrabarti, S., Severud, W.J., Bump, J. K., (2023). Data from: Eat or Be Eaten: Implications of potential exploitative competition between wolves and humans across predator-savvy and -naive deer populations. Dryad Digital Repository. [https://doi.org/10.5061/dryad.gb5mkkww4](https://doi.org/10.5061/dryad.gb5mkkww4) ## DATA & FILE OVERVIEW 1. File List: A.) difference\_figure\_three\_explanitory\_var.csv B.) compare\_time\_between\_BACI.R C.) test\_deer\_only\_no\_pre.csv D.) coverpole.csv E.) model\_testing.R 2. Relationship between files, if important: A.) test\_deer\_only\_no\_pre.csv corresponds with compare\_time\_between\_BACI.R B.) coverpole.csv corresponds with model\_testing.R 3. Additional related data collected that was not included in the current data package: None 4. Are there multiple versions of the dataset? No *** #### DATA-SPECIFIC INFORMATION FOR: difference\_figure\_three\_explanitory\_var.csv 1. Description: This file contains the values for the difference between before and after treatment for the different treatment sites and the different response variable (Group vigilance, group size, number of visits, and vigilance intensity. 2. Number of variables: 10 3. Number of cases/rows: 24 4. Variable List: * type: this variable is to connect like variables in the treatment and response columns * area: This variable refers to the location of the site and the measurement (UP-Upper Peninsula, LP-Lower Peninsula, UP and LP\_vig-vigilance intensity in the different areas. * treatment: the type of treatment the site received (wolf-urine, lemon-juice, or control-water) * value: the difference between the pre-treatment and during treatment values for the given response variable. The response variable determines the units. (Number of visits=number of visits made by deer to the bait pile, Group vigilance=the proportion of the group of deer that were vigilant, group size=number of deer in the group, vigilance intensity=value calculated from equation one-see manuscript) * lower: lower 95% Confidence interval * upper: upper 95% Confidence interval * response: the variable being measured (group vigilance, number of visits, group size, or vigilance intensity) * sd: standard deviation * SE: standard error * significance: NA indicates that the difference between the pre-bait and during bait periods are not significantly different. An \* indicates that they are significantly different. 5. Missing data codes: NA 6. Specialized formats or other abbreviations used: \* indicates significance *** #### DATA-SPECIFIC INFORMATION FOR: test\_deer\_only\_no\_pre.csv 1. Description: This file contains data for all images taken during the initial 3 weeks before treatment and three weeks during treatment. The images have been condensed to those that contain deer. Each row represents one image. 2. Number of variables: 29 3. Number of cases/rows: 213264 4. Variable List: * Station: the camera trap and bait pile name * datA.DateTimeOriginal: The date and time as recorded on the image * datetime: The date and time after a correction for any camera malfunctions * Day\_time: The time represented in decimal time. The values go from 0-1 where 0 represents near midnight and 0.99999 represents 11:59. * Species\_1: The first species seen in the image. If there are any deer in the image, it is always listed as species\_1. * Species\_2: If there is something other than a deer in the image, it is listed here. If there are no more species, there will be an "NA" in this column. * deer?: This column indicates whether there are deer (1) or not (0) * Sex\_female: This column indicates whether the deer in the image is female (female) or not (NA) * female\_1\_0: This column is the same as the previous ones. It indicates if there is a female (1) or not (0). * Sex\_male: This column indicates whether the deer in the image is male (male) or not (NA) * male\_1\_0: This column is the same as the previous ones. It indicates if there is a male (1) or not (0). * Sex\_unk: This column indicates whether the deer in the image is an unknown sex (unk) or not (NA) * unk\_1\_0: This column is the same as the previous. It indicates if there is a deer of unknown sex (1) or not (0). * young: This column indicates whether there are young in the image (young) or not (NA) * young\_1\_0: This column is the same as the previous. It indicates if there is a young dee in the image (young) or not (no\_young) * Number\_of\_Deer: number of deer in the image * First\_head\_down: This indicates whether this image in the series of images is the first in the series to have a deer with their head up (First) or not (NA) * number\_vigilent\_in\_picture: number of deer that are vigilant in the image * prop\_vig: proportion of deer in the image that are vigilant. Number\_of\_Deer/number\_vigilent\_in\_picture * at\_least\_one\_vigilent: this column indicates whether there is at least one deer that is vigilant in the image (1) or not (0) * treatment\_type: treatment assigned to that site. Control-water, lemon-lemon juice, wolf-wolf urine * area: The area the site was located in. LP-Lower Peninsula, UP-Upper Peninsula * treatment\_area: A combination of the treatment type and the area * cover\_type: The vegetation cover type determined using the National Land Cover Database classes. * cover\_pole\_mean: mean value of the cover pole measurement (cm) * cover\_pole\_SD: standard deviation of cover pole measurement (cm) * B\_A\_treatment: indicating if the image was taken before or after treatment had been applied * minutes\_spent: total minutes deer spent at the site. values are added to the last image in a series. If the image is not the last image, an "NA" is present. * arrival: When did deer arrive in the image (1). 0 indicates that it is not an arrival image. 5. Missing data codes: NA 6. Specialized formats or other abbreviations used: none *** #### DATA-SPECIFIC INFORMATION FOR: coverpole.csv 1. Description: This file contains data for all images taken during the initial 3 weeks before treatment and three weeks during treatment. The images have been condensed to those that contain deer. Each row represents one image. In addition, this file contains information about the cover pole data that was used to determine vegetation cover/hiding potential. 2. Number of variables: 25 3. Number of cases/rows: 2008 4. Variable List: * Station: the camera trap and bait pile name * datA.DateTimeOriginal: The date and time as recorded on the image * datetime: The date and time after a correction for any camera malfunctions * Day\_time: The time represented in decimal time. The values go from 0-1 where 0 represents near midnight and 0.99999 represents 11:59. * female\_1\_0: This column is the same as the previous ones. It indicates if there is a female (1) or not (0). * male\_1\_0: This column is the same as the previous ones. It indicates if there is a male (1) or not (0). * unk\_1\_0: This column is the same as the previous. It indicates if there is a deer of unknown sex (1) or not (0). * sex\_combined: This variable indicates if the image has males, females, or both * sex\_area: The sex\_combined and area columns combined * young\_1\_0: This column is the same as the previous. It indicates if there is a young dee in the image (young) or not (no\_young) * young\_area: young\_1\_0 and area variables combined * Number\_of\_Deer: number of deer in the image * number\_vigilent\_in\_picture: number of deer that are vigilant in the image * prop\_vig: proportion of deer in the image that are vigilant. Number\_of\_Deer/number\_vigilent\_in\_picture * at\_least\_one\_vigilent: this column indicates whether there is at least one deer that is vigilant in the image (1) or not (0) * treatment\_type: treatment assigned to that site. Control-water, lemon-lemon juice, wolf-wolf urine * area: The area the site was located in. LP-Lower Peninsula, UP-Upper Peninsula * treatment\_area: A combination of the treatment type and the area * cover\_type: The vegetation cover type determined using the National Land Cover Database classes. * cp\_m: mean value of the cover pole measurement (cm) * cover\_pole\_SD: standard deviation of cover pole measurement (cm) * bef\_af: indicating if the image was taken before or after treatment had been applied * minutes\_spent: total minutes of the full events * second\_spent: total seconds of the full event (minutes\_spent\*60) * sum\_prop\_vig: sum of the proportion of deer vigilant in the image (sum of number of deer vigilant in the image/group size) * vig\_int: vigilance intensity = sum\_prop\_vig/total time of the event 5. Missing data codes: NA 6. Specialized formats or other abbreviations used: none ## Description of the data and file structure The two main .csv files (test\_deer\_only\_no\_pre.csv and coverpole.csv) are structured similarly. Each line is an image and summarized the metadata and what was found in the image. The "coverpole.csv" file also contains information about vegetation cover (cp\_m) at each site as well as the vigilance intensity information. The term "area" refers to the Upper Peninsula (UP) or the Lower Peninsula (LP). "B\_A\_treatment" refers to the time before or after a treatment was applied. You will also see that some columns contain combined information (e.g., "treatment\_area"). ## Code/Software We used R and Excel for our analysis. You will need to have R downloaded to view the data. You will also need to rewrite the working directory for the script in order to run it.