Data for: Hunting mode and habitat selection mediate the success of human hunters

AbstractAs 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.
MethodsMethods 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...