Data for: Hunting mode and habitat selection mediate the success of human hunters
Data files
Dec 08, 2023 version files 46.35 MB
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all-available-point-cov.csv
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hmm_data_with_model_predictions_annotated.csv
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hunter_cluster_success_long.csv
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igotu_data_3min_covariates.csv
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igotu_metadata.csv
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README.md
Abstract
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.
README
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: https://cran.r-project.org/web/packages/moveHMM/vignettes/moveHMM-starting-values.pdf 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.
Methods
Methods
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.
Study area
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.
Data collection
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.
Spatial data
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.
Behavioral state classification with Hidden Markov Models
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 (<10 meters from road) and off road (>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).
Identification of distinct hunting modes
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.
Evaluating habitat selection
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.