How Constraints Affect the Hunter's Decision to Shoot a Deer

How constraints affect the hunter’s decision to shoot a deer Florian K. Diekerta,b,1, Andries Richtera,c, Inger Maren Rivruda, and Atle Mysteruda aCentre for Ecological and Evolutionary Synthesis, Department of Biosciences, University of Oslo, N-0316 Oslo, Norway; bDepartment of Economics, Heidelberg University, 69115 Heidelberg, Germany; and cEnvironmental Economics and Natural Resources Group, Sub-Department of Economics, Wageningen University, 6700 EW Wageningen, The Netherlands Edited by William C. Clark, Harvard University, Cambridge, MA, and approved October 12, 2016 (received for review May 13, 2016) Hunting is the predominant way of controlling many wildlife pop- team members or factors such as time and quota constraints. ulations devoid of large carnivores. It subjects animals to mortal- The economic literature has looked at hunting mainly from a val- ity rates that far exceed natural rates and that differ markedly uation perspective (e.g., ref. 10), centered around land-use con- in which age, sex, or size classes are removed relative to those flicts (11) or addressing the trade-off between meat and trophy of natural predators. To explain the emerging selection pattern hunting (12). So far, these studies have cast their analysis in the we develop behavioral microfoundations for a hunting model, framework of harvesting models. emphasizing in particular the constraints given by the formal and But hunting is not like harvesting; it is characterized by an informal norms, rules, and regulations that govern the hunter’s individual and nonrecurring encounter between man and animal. choice. We show how a shorter remaining season, competition Thus, we develop a model that focuses on the individual decision among hunters, lower sighting probabilities, and higher costs all to shoot or not shoot a given animal. By framing hunting as an lead to lower reservation values, i.e., an increased likelihood of optimal stopping problem we show how social constraints shape shooting a particular animal. Using a unique dataset on seen the emerging selection pressure: The way the hunt is organized, and shot deer from Norway, we test and confirm the theoreti- the length and timing of a dedicated hunting season, and the cost cal predictions in a recreational and meat-motivated hunting sys- of shooting animals all affect the hunter’s incentives and con- tem. To achieve sustainability, future wildlife management should strain his or her choice set. SCIENCE account for this predictable selection pressure. We exploit a unique dataset on red deer hunting incidents SUSTAINABILITY to confront our theory with data. Our panel covers 256 loca- hunting selection j optimal stopping j hunter behavior j social dilemma tions in Norway between 1999 and 2010 (Fig. 1), where not only the number and type of hunted animal were recorded, but also which animals have been seen, but not shot. These lthough natural large predators are now recolonizing many data allow us to directly construct the conditional probabil- Aindustrialized countries (1), their functional roles are still ity of shooting an animal. Matching these data with meteo- limited and most wildlife populations in Europe are mainly con- rological information on precipitation, temperature, and the trolled by hunting (2). Hunting is the predominant cause of adult moon phase, we can exploit 181,989 choice situations. We show mortality for 80–90% of the individual animals in deer popula- that indeed the likelihood of shooting a given animal is higher tions (3). Despite this high hunting pressure, populations have the shorter the remaining harvest season is, the more hunters grown and expanded their range, often resulting in overabun- that participate in a given trip, and the lower the sighting dance (4). Part of this increase is due to hunter selectivity having probability is. distorted the natural sex and age distribution (5). The reluc- tance to shoot females with offspring facilitates rapid population growth, but skewing sex ratio also affects evolutionary processes. Significance In some ungulate populations with heavy trophy hunting, there is concern that selective removal of specific phenotypic traits Wildlife populations in Europe and North America are regu- leads to evolution even over short timespans of a few tens of lated through hunting, as natural predators are still scarce. generations (6). There is hence an urgent need to inform poli- Therefore, wildlife is a social–ecological system with delicate cies on how to manage wildlife in a way that does not endanger feedbacks between the social and ecological subsystems. Both the health of the supporting ecosystem or the resilience of the for population control and for evolution and because of cul- hunted population. To better understand how overall hunting tural values, it is essential to understand how many and pressure maps to population dynamics and potential for evolu- which animals are removed from the population. However, tion, we need to understand selectivity. the question of how the social context influences the individ- In Norway, the population size, distribution, and resulting ual hunter’s decision to shoot or not to shoot an animal has hunting quota for red deer (Cervus elaphus) have increased dra- not been addressed. We apply insights from economic search matically. The west coast holds the majority of the population, theory to explain how hunter selection is shaped by social with more than 75% of the red deer being harvested in the coun- constraints. We provide convincing evidence, using a unique ties Møre and Romsdal, Sogn and Fjordane, and Hordaland dataset from deer hunting, that selectivity declines with more (Fig. 1). Red deer is the second most valuable hunted species hunters competing and a shorter remaining season. in terms of meat value (after moose, Alces alces), and there is an increasing interest for trophy hunting. Author contributions: F.K.D. designed research; F.K.D., A.R., and I.M.R. performed In the ecological literature, selectivity by hunters has been research; F.K.D., A.R., and I.M.R. analyzed data; and F.K.D., A.R., I.M.R., and A.M. wrote the paper. treated as a static property, focusing mostly on the extreme case of trophy hunting (7, 8). However, only a small proportion of The authors declare no conflict of interest. hunting is for trophies; most hunting is for recreation, meat, This article is a PNAS Direct Submission. or population regulation (9). Most hunters have quotas limit- Freely available online through the PNAS open access option. ing their choices, and they may have limited time to fill their 1To whom correspondence should be addressed. Email: [email protected]. quota (5). There has been no study addressing how the deci- This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10. sion to shoot a sighted animal depends on competition among 1073/pnas.1607685113/-/DCSupplemental. www.pnas.org/cgi/doi/10.1073/pnas.1607685113 PNAS Early Edition j 1 of 6 Downloaded by guest on September 25, 2021 The hunter incurs a fixed cost ci per period (representing, e.g., his or her opportunity cost of labor, access fees to the hunting area, etc., measured in terms of utility), regardless of whether an animal is actually shot or not. Each period, the hunter observes one animal whose value is ex ante a random variable X . Clearly, the hunter goes hunting only if E[X ] > ci . The optimal policy in the basic setup is characterized by a “reservation value” that equals the expected benefit from follow- ing the optimal rule. Eq. 1 describes the “optimal shooting rule,” where ξi is the reservation value: shoot animal if x ≥ ξi [1] do not shoot animal if x < ξi : The reservation value ξi thus defines the selection pressure exerted by this hunter i: All animals whose trait value is at or above the threshold are subject to being hunted, whereas all animals with a lower trait value are safe from being shot. The expected net return from policy Eq. 1 is given by E[maxfξi ; X g] − ci and the reservation value can be expressed R ξi R 1 as ξi = ξi dFi (x) + xdFi (x) − ci , which implies 0 ξi Fig. 1. Map of Western Norway to indicate our study area. The graph shows R 1 ci = (x − ξi )dFi (x): [2] the numbers of shot animals in Norway. ξi Eq. 2 has an intuitive interpretation: The individual thresh- Theoretical Results old value ξi is chosen so that the marginal cost incurred by We develop a behavioral model to predict how constraints on not shooting in the current period and awaiting another period hunter decisions affect selectivity. The centerpieces of a model just equals the expected marginal return from one more obser- that describes the hunting decision are (i) that one shot can vation. The right-hand side of Eq. 2 defines a function Hi kill at most one animal, (ii) that the animal the hunter actually that maps a given trait value x into the utility gain that the observes is essentially an independent and identically distributed hunter i can expect by waiting for the next observation. That is, H (x) ≡ R 1(y − x)dF (y) draw from the population, and (iii) that the hunter is constrained i x i . on how many animals can be shot. The function Hi is convex and strictly decreasing, and it sat- The first feature distinguishes hunting from harvesting, where isfies limx!1Hi (x) = 0 and limx!0Hi (x) = E[X ]. This result many animals are removed from the population in one instant implies that whether a hunter chooses to shoot an animal (there are, for example, thousands of fish in a trawl net). The depends not only on characteristics of the animal (given by the second feature relates to the fact that animals move and that the trait value x), but also on characteristics of the hunter.

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