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Intensive hunting fundamentally changes human-wildlife relationships

Cite this dataset

Parsons, Arielle et al. (2022). Intensive hunting fundamentally changes human-wildlife relationships [Dataset]. Dryad.


Wildlife alter their behaviors in a trade-off between consuming food and fear of becoming food themselves. The risk allocation hypothesis posits that variation in the scale, intensity, and longevity of predation threats can influence the magnitude of antipredator behavioral responses. Hunting by humans represents a threat to wildlife thought to be perceived as similar to those of a top predator, although hunting intensity and duration vary widely around the world. Here we evaluate the effects of hunting pressure on wildlife by comparing how two communities of mammals under different management schemes differ in their relative abundance and response to humans. Using camera traps to survey wildlife across disturbance levels (yards, farms, forests) in similar landscapes in southern Germany and the southeastern USA, we tested the prediction of the risk allocation hypothesis: that the higher intensity and longevity of hunting in Germany (year-round vs 3 months, 4x higher harvest/km2) would reduce relative abundance of hunted species and result in a more significant fear-based response to humans (i.e., more spatial and temporal avoidance). We further evaluated how changes in animal abundance and behavior would result in potential changes to ecological impacts (i.e., herbivory and predation). We found that hunted species were relatively less abundant in Germany and less associated with humans on the landscape (i.e., yards and urban areas), but did not avoid humans temporally in hunted areas while hunted species in the USA showed the opposite pattern. These results are consistent with the risk allocation hypothesis where we would expect more spatial avoidance in response to threats of longer duration (i.e., year-round hunting in Germany vs. 3-month duration in USA) and less spatial avoidance but more temporal avoidance for threats of shorter duration. The expected ecological impacts of mammals in all three habitats were quite different between countries, most strikingly due to the decreases in the relative abundance of hunted species in Germany, particularly deer, with no proportional increase in unhunted species, resulting in American yards facing the potential for 25x more herbivory than German yards. Our results suggest that the duration and intensity of managed hunting can have strong and predictable effects on animal abundance and behavior, with corresponding changes in the ecological impacts of wildlife. This shows that hunting can be an effective tool for reducing wildlife conflict due to overabundance but may require more intensive harvest than is seen in much of North America.


Study sites: In Germany, we sampled sites around the city of Konstanz (pop 84,911), BW. Our study covered an approximate area of 60,000km2 surrounding the city (Fig. 1) where the landscape was 25.9% forested, 16.8% urban, and 30.7% agricultural land cover with an average population density of 259 people/km2. In the United States, we focused on a similar sized area (50,000km2) from Raleigh, NC (pop 464,485) to the east (Fig. 1), which was 41.4% forested, 9.1% urban and 29% agricultural landcover with an average population density of 103 people/km2. The climates of the two sites were similar (BW=coastal, NC= humid subtropical; Kottek et al. 2006) with similar mean annual precipitation (1195mm BW, 1218mm NC; Fick & Hijmans 2017) but with higher mean annual temperatures in NC (7.5C BW, 15.6C NC; Fick & Hijmans 2017). Both areas had similar levels of gross primary productivity (13083 kg C/square meter BW, 13418 NC in 2015; Hobi et al. 2017) with rolling hills (BW mean elevation = 136m, NC = 146m) of mixed deciduous and coniferous forests fragmented by similar levels of agriculture and urban development. Thus, our two study landscapes were broadly similar with the biggest differences being: 1) the amount of forest cover was higher in NC (41% vs 26%), 2) human population density was higher in BW (259 vs. 102/km2), 3) average temperature was higher in NC (15.6 vs 7.5C) and 4) the German landscape featured small, densely settled villages while the American landscape had one larger city with more dispersed housing across rural areas. As much as possible, our statistical analysis controlled for these differences to strengthen inference related to the different hunting systems.

Our study focused on the big game species which are both largest and most heavily managed (i.e., bag and season limits) and/or heavily hunted in each region, hereafter referred to as “hunted” species (Table 1). In BW these are roe deer and wild boar (Sus scrofa; hereafter “boar”), both having long hunting seasons with no bag limits (Table 1). In NC there are white-tailed deer, American black bear (Ursus americanus; hereafter “bear”), and wild turkey (Meleagris gallopavo; hereafter “turkey”), all of which have short hunting seasons (1-3 months) and strict bag limits (Table 1). Though different in size (roe deer are smaller), roe deer and white-tailed deer are ecologically similar with similar diets (Vangilder et al. 1982; Tixier & Duncan 1996), habitat preferences (Williamson & Hirth 1985; Tufto et al. 1996) and ability to live close to humans (Etter et al. 2002; Wevers et al. 2020). However, deer competitors are absent from NC but present in BW (European fallow deer (Dama dama) and sika deer (Cervus nippon)), though far less common and unlikely to broadly compete with roe deer (Burbaiteė & Csányi 2009). Additionally, large carnivores capable of preying upon deer, especially fawns, are absent from BW but present in NC (coyote (Canis latrans), bobcat (Lynx rufus), and bear; Boone 2019).

Field data collection: We used a consistent camera trapping protocol between sites (BW and NC) to facilitate comparisons. For each site, trained citizen science volunteers (see Parsons et al. 2018 for details) or staff deployed unbaited camera traps across each study region (Fig. 1). Sample size was 233 camera deployment sites in BW and 242 in NC, with camera placement stratified between hunted and unhunted areas as well as residential yards, forest fragments and agricultural fields (> 0.02km2; Appendix 1). Information on whether a site allowed hunting came directly from the property owner. In Germany, all hunted areas were forests with no samples from hunted yards or open areas, while in NC some forests, fields, and rural yards were hunted (Appendix 1). We used Reconyx (RC55, PC800, and PC900, Reconyx, Inc. Holmen, WI, USA) and Bushnell (Trophy Cam HD, Bushnell Outdoor Products, Overland Park, KS, USA) camera traps attached to trees at approximately 40cm above the ground. Trigger sensitivity was set to high for all cameras and we verified that both camera brands had similar trigger speeds (<0.5s). Cameras were left undisturbed for 3-4 weeks and then moved to a new location (at least 200m apart), with sampling taking place over several overlapping seasons and years (2018-2020 Germany, 2013-2019 NC). Cameras recorded multiple photographs per trigger, re-triggering immediately if the animal was still in view. We grouped consecutive photos into one sequence if they were <60 seconds apart (Parsons et al. 2016), and used these sequences as independent records, counting detections by sequence, not individual photos. Initial species identifications were made by volunteers or staff using customized software ( and all were subsequently reviewed for accuracy before being archived at the Smithsonian Digital Repository. Detection rates for each species at each camera site were calculated as the count/days camera ran, considering groups as a single detection.


Deutsche Forschungsgemeinschaft, Award: EXC 2117 – 422037984

National Science Foundation, Award: #1232442 and #1319293

U.S. Forest Service

North Carolina Museum of Natural Sciences