DOI: 10.5061/dryad.000000083
title: Data for: Hunting mode and habitat selection mediate the success of human hunters

description: As a globally widespread apex predator, humans have unprecedented lethal
 and non-lethal effects on prey populations and ecosystems. Yet compared to
 non-human predators, little is known about the drivers and consequences of
 human hunting behavior. Here, we characterized the hunting modes, habitat
 selection, and harvest success of 483 rifle hunters in California using
 high-resolution GPS data. We used Hidden Markov Models to characterize
 fine-scale behavior, and k-means clustering to group hunters by hunting
 mode, on the basis of their time spent in each behavioral state. Hunters
 exhibited three distinct and successful hunting modes (“coursing”,
 “stalking”, and “sit-and-wait”), with stalking as the most successful
 strategy. Across hunting modes, there was variation in patterns of
 selection for roads, topography, and habitat cover, with important
 differences in habitat use of successful and unsuccessful hunters across
 modes. Our study indicates that hunters can successfully employ a
 diversity of harvest strategies, and that hunting success is mediated by
 the interacting effects of hunting mode and landscape features. Such
 results highlight the breadth of human hunting modes, even within a single
 hunting technique, and lend insight into the varied ways that humans exert
 predation pressure on wildlife.
description: <strong>Methods</strong> To
 understand patterns of hunter movement behavior, we collected GPS tracks
 of all hunters in the study area (2015-2022). We first classified the
 behavioral state of each location for each hunter, using Hidden Markov
 Models (HMMs; location-level classification of behavior). Next, we used
 k-means clustering to group hunters into distinct hunting modes based on
 the relative time that they spent in each behavioral state (hunter-level
 classification of behavioral strategy). Finally, we used Resource
 Selection Functions (RSFs) to evaluate patterns of habitat selection for
 each hunting mode, comparing habitat selection between successful and
 unsuccessful hunters. <strong><em>Study
 area</em></strong> We conducted primary
 data collection at the 2,168-hectare Hopland Research and Extension Center
 (HREC) in Mendocino County, California (Latitude: 39.002, Longitude:
 -123.084; Figure 1). The site features habitat types including grassland,
 oak woodland, and chaparral, with a network of dirt roads and fences. The
 site hosts an annual public hunt, in which twenty hunters per day are
 selected by lottery from a pool of applicants, for 4-6 days each year. In
 2020, a restricted multi-day hunt was introduced for a small number of
 hunters. <strong><em>Data
 collection</em></strong> Our study took
 place each August-September from 2015-2022, excluding 2018 due to
 wildfire. We invited all hunters at the study site to participate in our
 study. We had a 100% rate of participation (n = 483 hunters representing
 648 hunter-days). We provided each hunter with a GPS
 unit (i-gotU GT-600) that was programmed to take a GPS fix every 5 seconds
 from 5am to 10pm to encompass legal hunting hours at the study site. We
 asked hunters to keep the GPS unit in a pocket that would remain on their
 person, even when they were moving on foot. All harvested deer were
 brought back to headquarters, and we confirmed with the hunters whether
 each logger was associated with a successful or unsuccessful hunt. Upon
 data retrieval, we resampled all tracks to a fix rate of 3 minutes to
 accommodate GPS error and computational limitations. We followed data
 cleaning procedures described in detail in the supplementary
 methods. <strong><em>Spatial
 data</em></strong> We identified
 environmental features that we a priori hypothesized to influence hunter
 behavior and habitat use: distance to nearest road, ruggedness, viewshed,
 and density of each of the three habitat types (woodland, grassland, and
 chaparral). These hypotheses were drawn from existing literature on human
 hunter movement and behavior. We generated raster layers for each feature
 in the study area. Additional details on the development of spatial
 variables are provided in the supplementary methods section. We extracted
 spatial covariates at each point, and we calculated the elapsed time since
 sunrise for each point using the suncalc package. We standardized all
 covariates prior to modeling.
 <strong><em>Behavioral state classification with
 Hidden Markov Models</em></strong> To
 identify fine-scale behaviors of hunters, we used the moveHMM package to
 fit a hidden Markov model to the hunter movement data. We ran a global
 model with all predictors (distance to road, viewshed, ruggedness,
 woodland density, chaparral density, and time since sunrise). We assigned
 movement points to one of three behavioral states, as initial modeling
 indicated that three-state models performed better than two-state models
 (based on AIC), and best corresponded to self-described hunter behavior.
 We interpreted State 1 as corresponding to a stationary state (searching,
 resting, or processing deer), State 2 to walking on foot, and State 3 to
 driving in a vehicle. We followed best practices when
 choosing initial parameter values. We included a zero-mass parameter for
 step length given the high proportion of step lengths equal to 0 (17% of
 all steps). To determine whether our models were sensitive to initial
 parameter choice, we ran 100 iterations of the model with randomly-chosen
 starting parameters for step length mean, step length standard deviation,
 step length zero mass, and turning angle concentration. Our model
 converged on the same parameters for 82 of 100 of the iterations, and this
 model had the maximum likelihood, indicating numerical stability. We then
 used the parameter values from the best model as our starting values for
 all subsequent modeling. Based on the global model, we determined the most
 probable behavioral state at each step for each hunter, and determined the
 percentage of time that each hunter spent in each of the behavioral
 states. Following identification of the three states from movement
 parameters, we further distinguished between resting behavior on road
 (&lt;10 meters from road) and off road (&gt;10 meters from road),
 as these behaviors are associated with different hunting strategies
 (resting on the road to visually scan for deer on the landscape vs.
 resting off the road in a sit-and-wait hunting strategy).
 <strong><em>Identification of distinct hunting
 modes</em></strong> To identify the
 dominant hunting mode of each hunter, we used k-means clustering to group
 hunters on the basis of their time spent in each fine-scale behavioral
 state. We determined the optimal value of k using the elbow method
 heuristic. Specifically, we plotted the total within-cluster sum of
 squares as a function of k, and determined the value of k at which this
 sum of squares began declining linearly. We then ran
 logistic regressions to evaluate the effect of hunting mode on harvest
 success. Additional model covariates included year (as we were interested
 in whether hunting success changed over time) and whether the track came
 from a single-day or multi-day hunter. We tested all possible covariate
 combinations and we also explored interactions among hunting mode and the
 other covariates, to examine whether the effectiveness of different
 hunting modes changed over time, or varied between single- and multi-day
 hunts. We compared models using AIC. We also determined relative variable
 importance (RVI), as calculated by summing the Akaike weights of all
 models in which the variables appeared. We also
 evaluated whether the time of day at which deer were harvested varied
 across hunting modes, for hunters for which we had known harvest times (n
 = 37 of 39 successful tracked hunters). We compared harvest time (elapsed
 time since sunrise) for each of the three clusters using an
 Anderson-Darling test, a non-parametric rank test of whether samples from
 different groups came from the same distribution.
 <strong><em>Evaluating habitat
 selection</em></strong> To evaluate
 patterns of habitat selection by hunters using different hunting modes,
 and to evaluate connections between habitat selection and harvest success,
 we used Resource Selection Functions (RSFs). RSFs compare environmental
 features of used versus available locations in a logistic regression. We
 compared locations recorded by hunter GPS trackers (used locations) to
 locations that we systematically sampled throughout the huntable area at a
 30 x 30 m resolution. To evaluate potential links between habitat
 selection and hunting success, we ran separate models for successful and
 unsuccessful hunters in each of the hunting modes, and we used the same
 predictors in all models to facilitate comparison of model coefficients.
 Model covariates included the same spatial covariates used in the HMM:
 ruggedness, viewshed, chaparral density, and woodland density. We assigned
 a weight of 5,000 to the available points, and 1 to the used points. We
 first ran mixed models with a random intercept for track ID, but
 among-individual variance was 0 for all models, resulting in a singular
 fit. We therefore removed the random intercept to ensure estimate
 stability. In addition, because RSFs assume spatiotemporal independence
 between points, we checked the effect of fix interval. We thinned the
 “used” points to a 30 min interval to reduce spatial autocorrelation
 between points, while retaining sufficient data for each individual hunter
 (mean of 20.3 used points per hunter). Conclusions remained unchanged
 despite the 10-fold reduction in fix rate, and the results of this model
 are presented in the supplementary material. To rule
 out any potential issues of circularity when using some of the same
 spatial covariates to classify behavior (which was then used to identify
 hunting mode) and to compare differences in habitat selection across
 hunting modes, we also re-ran the HMMs without any spatial covariates,
 classifying behavior based only on step length and turn angle. We then
 re-ran the k-means clustering analysis and RSFs with the updated behavior,
 and conclusions again remained unchanged. We have chosen to retain the
 spatial covariates in the HMM for all analyses presented here, to improve
 the accuracy of behavioral classification (particularly with regard to
 driving). The HMM uses information about the spatial covariates to improve
 predictions of behavioral states at a given location, while the RSF
 compares all used locations (regardless of behavioral state) to a random
 set of available points within the study area to evaluate patterns of
 habitat selection.  
description: ## Data for "Hunting mode and habitat selection mediate the success
 of human hunters" As a globally widespread apex predator, humans have
 unprecedented lethal and non-lethal effects on prey populations and
 ecosystems. Yet compared to non-human predators, little is known about the
 drivers and consequences of human hunting behavior. Here, we characterized
 the hunting modes, habitat selection, and harvest success of 483 rifle
 hunters in California using high-resolution GPS data. We used Hidden
 Markov Models to characterize fine-scale behavior, and k-means clustering
 to group hunters by hunting mode, on the basis of their time spent in each
 behavioral state. Hunters exhibited three distinct and successful hunting
 modes (coursing, stalking, and sit-and-wait), with stalking as the most
 successful strategy. Across hunting modes, there was variation in patterns
 of selection for roads, topography, and habitat cover, with important
 differences in habitat use of successful and unsuccessful hunters across
 modes. Our study indicates that hunters can successfully employ a
 diversity of harvest strategies, and that hunting success is mediated by
 the interacting effects of hunting mode and landscape features. Such
 results highlight the breadth of human hunting modes, even within a single
 hunting technique, and lend insight into the varied ways that humans exert
 predation pressure on wildlife. ## Description of the data scripts ###
 Data files #### Raw input data ##### **igotu\_data\_3min\_covariates.csv**
 Movement trajectories of hunters, subsampled to a 3 minute fix interval.
 Used as input in 01-identify-starting-parameters.R and 02-run-hmm-model.R.
 Each row corresponds to a unique GPS location, with associated metadata.
 Columns include: * ID: unique identifier of individual hunter movement
 trajectory * Longitude * Latitude * DateTime: local date and time of the
 GPS fix, in Pacific Daylight Time * Ruggedness: calculated with Vector
 Ruggedness Measure in ArcGIS; measure of ruggednes in neighborhood of 900
 meters squared * Viewshed: calculated with Viewshed 2 tool in ArcGIS;
 measure of relative amount of visible area in 400m buffer from point *
 Road_Distance: distance to nearest road, in meters * Chaparral_120m:
 density of chaparral habitat around point, in 120 meter window *
 Elapsed_Time_Sunrise: time (in hours) from sunrise on the day of data
 collection * Harvest: Y = yes, N = no ##### **igotu\_metadata.csv**
 Metadata associated with iGotU movement trajectories. Each row corresponds
 to a unique hunter GPS trajectory for which data were successfully
 recovered. Used as input in 04-success-models.R.,
 06-explore-harvest-times.R. Columns include: * ID: unique identifier of
 individual hunter movement trajectory * Date: YYYY-MM-DD * Year * Harvest:
 Y = yes, N = no * Harvest_time: time of deer harvest, HH:MM (empty if
 hunter did not harvest deer) * Hunt_type: Single_day or Multi_day #####
 **all-available-point-cov.csv** Covariate values for all available points
 in the study area, at a 30x30 meter resolution. Each row corresponds to a
 unique location (grid cell). Used in 05-rsf-by-behavior.R as input for
 Resource Selection Functions (available points sampled randomly for each
 individual hunter). Columns include: * Ruggedness: calculated with Vector
 Ruggedness Measure in ArcGIS; measure of ruggednes in neighborhood of 900
 meters squared * Viewshed: calculated with Viewshed 2 tool in ArcGIS;
 measure of relative amount of visible area in 400m buffer from point *
 Road_Distance: distance to nearest road, in meters * Chaparral_120m:
 density of chaparral habitat around point, in 120 meter window *
 Woodland_120m: density of woodland habitat around point, in 120 meter
 window #### Derived data #####
 **hmm\_data\_with\_model\_predictions\_annotated.csv** Movement
 trajectories of hunters, subsampled to a 3 minute fix interval, with
 annotated model predictions and scaled covariates. Each row corresponds to
 a unique GPS location, with associated metadata. Used as input for
 03-cluster-analysis.R. Created by 02-run-hmm-model.R. Columns include: *
 ID: unique identifier of individual hunter movement trajectory * step:
 step length * angle: turn angle * x: longitude * y: latitude * DateTime:
 local date and time of the GPS fix, in Pacific Daylight Time * Ruggedness:
 calculated with Vector Ruggedness Measure in ArcGIS; measure of ruggednes
 in neighborhood of 900 meters squared * Viewshed: calculated with Viewshed
 2 tool in ArcGIS; measure of relative amount of visible area in 400m
 buffer from point * Road_Distance: distance to nearest road, in meters *
 Chaparral_120m: density of chaparral habitat around point, in 120 meter
 window * Woodland_120m: density of woodland habitat around point, in 120
 meter window * Elapsed_Time_Sunrise: elapsed time from sunrise, in hours *
 Harvest: whether or not the trajectory was associated with a successful
 hunt, Y = Yes, N = No * Ruggedness_scale, Viewshed_scale,
 Road_Distance_scale, Chaparral_120m_scale, Woodland_120m_scale,
 Sunrise_Scale: normalised covariates * State: predicted most likely
 behavioral state from HMM: Driving, Stationary, Walking * Stationary_Prob:
 probability that location is in stationary state * Walking_Prob:
 probability that location is in walking state * Driving_Prob: probability
 that location is in driving state * state_2stationary: predicted most
 likey behavioral state from HMM, splitting stationary locations into
 Stationary_onroad and Stationary_offroad #####
 **hunter\_cluster\_success\_long.csv** Information about behavioral states
 and hunting modes of hunters. There is one row for each behavioral state
 for each trajectory (long format). Used as input for 04-success-models.R,
 05-rsf-by-behavior.R, and 06-explore-harvest-times.R. Created by
 03-cluster-analysis.R. Columns include: * ID: unique identifier of
 individual hunter movement trajectory * State: predicted most likely
 behavioral state from HMM: Driving, Walking, Stationary_onroad and
 Stationary_offroad * Percentage: percentage of points in the trajectory
 that are predicted to be in that state * Cluster * Harvest: whether or not
 the trajectory was associated with a successful hunt, Y = Yes, N = No ###
 Scripts ##### **01-identify-starting-parameters.R** Identifies the optimal
 starting parameters for the Hidden Markov Model (for 02-run-hmm-model.R),
 following the procedure outlined in this vignette:
 Takes igotu_data_3min_covariates.csv as input. ##### **02-run-hmm-model.R** Runs Hidden Markov Model to identify behavioral states associated with hunter GPS trajectories. Input file is igotu_data_3min_covariates.csv. Output file is data frame annotated with probable behavioral states: hmm_data_with_model_predictions_annotated.csv. ##### **03-cluster-analysis.R** Conducts k-means clustering of individual hunter trajectories, based on the modeled time spent in each behavioral state (determined by HMM in 02-run-hmm-model.R). Input file is hmm-data-with-model-predictions-annotated.csv. Output file is hunter_cluster_success_long.csv. ##### **04-success-models.R** Models explaining success of hunters, based on hunting mode, year, and single vs multi-day hunt. Input files are hunter_cluster_success_long.csv and igotu_metadata.csv. ##### **05-rsf-by-behavior.R** Runs Resource Selection Functions to assess habitat selection for successful and unsuccessful hunters in each of the three hunting modes (Coursing, Stalking, Sit-and-Wait). Input files are all-available-point-cov.csv, igotu_data_3min_covariates.csv, and hunter_cluster_success_long.csv. ##### **06-explore-harvest-times.R** Explores diel patterns of deer harvest by hunters across hunting modes. Scales times to radians based on solar time, and compares distributions using Anderson-Darling test. Input files are hunter_cluster_success_long.csv and igotu_metadata.csv. ## Sharing/access Information The data was not derived from another source.