Behavioral changes and nutritional consequences to elk (Cervus canadensis) avoiding perceived risk from human hunters

Abstract

The life‐and‐death stakes of predator–prey encounters justify the high price of many anti‐predator behaviors. In adopting these behaviors, prey incur substantial non‐consumptive costs that can have population‐level consequences. Because prey knowledge of risk is imperfect, individuals may even adopt these costly behaviors in the absence of a real threat. For example, rather than only avoid hunters, many species categorically avoid all anthropogenic activity. Although hunting seasons only increase risk for specific individuals (e.g., males), non‐target individuals may still perceive human hunters as a source of risk and respond accordingly. Here, we used a large‐scale experiment including 89 animal‐years of location data from 62 unique individuals over 6 yr to quantify the duration, magnitude, and energetic consequences of changes to movement and resource selection behavior adopted by non‐target female elk (Cervus canadensis) in response to human hunters during three separate experimental 5‐d hunts (elk archery, deer rifle (Odocoileus hemionus or Odocoileus virginianus), and elk rifle). We predicted that elk response to hunters would be brief, but that strong behavioral responses to hunters (e.g., strengthened avoidance of roads and trails) would carry nutritional costs. We measured the duration of hunt‐related changes in elk speed using quantile regression, further quantified the strength of elk behavioral responses to hunters using population‐level resource selection functions, and evaluated whether anti‐predator resource selection behavior translated to measurable metabolic costs in the form of reduced body fat heading into winter. Elk responses to human hunters were stronger in the day than at night and were generally more pronounced during the elk hunts than during deer hunts. During hunts, elk shifted their diurnal behavior to avoid forage and intensified their avoidance of roads and trails. The combination of these changes in behavior led to a predicted pattern of distribution during the hunt that differed substantially from the distribution prior to the hunt. Lactating females that more strongly avoided roads entered winter in poorer nutritional condition, suggesting that the changes in resource selection we describe carry corresponding nutritional costs that have the potential to impact subsequent population performance.

Sex‐Specific Behaviors of Hunted Mule Deer During Rifle Season

“…This is consistent with behavior during summer in previous studies on mule deer in arid climates (Hayes and Krausman 1993, Tull et al 2001, Ager et al 2003, Morano et al 2019), where deer are more active in foraging areas at night and select resting areas with moderate canopy cover during the day (other ungulates, Giotto et al 2013, Long et al 2014). During the rifle season, habitat use by males was not related to density of canopy, but males increased distance from motorized routes, suggesting that avoidance of human activity was a priority behavior, as is true during hunting season in other ungulates (white‐tailed deer [ Odocoileus virginianus ], Kilgo et al 1998; roe deer [ Capreolus capreolus ], Bonnot et al 2013; and elk, Proffitt et al 2013, Paton et al 2017, Spitz et al 2019, Lamont et al 2020).…”

“…In general, however, studies in western North America (Conner et al 2001;Vieira et al 2003;Proffitt et al 2009Proffitt et al , 2010Proffitt et al , 2013Ranglack et al 2017;DeVoe et al 2019) have reported habitat shifts toward refugia or security areas during hunting season (Mikle et al 2019, Lamont et al 2020. Specifically, disturbance from hunting can cause animals to move to areas farther from roads (Bonnot et al 2013, Paton et al 2017, DeVoe et al 2019, Spitz et al 2019, with higher canopy cover (Ranglack et al 2017), or restricted public access (Proffitt et al 2009(Proffitt et al , 2010Mikle et al 2019). Consequently, access to quality forage important to autumn nutrition (Cook et al 2004, Tollefson et al 2010, Hurley et al 2014 may be compromised (Benhaiem et al 2008, Spitz et al 2019.…”

“…Specifically, disturbance from hunting can cause animals to move to areas farther from roads (Bonnot et al 2013, Paton et al 2017, DeVoe et al 2019, Spitz et al 2019, with higher canopy cover (Ranglack et al 2017), or restricted public access (Proffitt et al 2009(Proffitt et al , 2010Mikle et al 2019). Consequently, access to quality forage important to autumn nutrition (Cook et al 2004, Tollefson et al 2010, Hurley et al 2014 may be compromised (Benhaiem et al 2008, Spitz et al 2019. The notion that mule deer select for areas of high-quality forage in autumn (Cook et al 2004, Tollefson et al 2010, Hurley et al 2014, even during hunting season, has been supported by recent work on mule deer in the Starkey Research Unit in Oregon, USA (Brown et al 2020).…”

Incorporating reef fish avoidance behavior improves accuracy of species distribution models

“…Predator avoidance behavior diverts time and energy away from health and fitness enhancing activities such as foraging, parental care, and mating (Lima, 1998;Frid & Dill, 2002) and can also have consequences for ecosystem functions (Madin et al, 2010;Rizzari et al, 2014). The application of a predation risk framework may help to interpret the extent to which underwater survey counts reflect actual abundances vs a behavioral bias (Frid, McGreer & Frid, 2019), the costs of avoidance behavior on body condition and reproduction (Spitz et al, 2019), and how these factors (together with fishery removals) may influence the results of SDMs.…”

Peer Review #1 of "Incorporating reef fish avoidance behavior improves accuracy of species distribution models (v0.2)"

“…Predator avoidance behavior diverts time and energy away from health and fitness enhancing activities such as foraging, parental care, and mating (Lima 1998, Frid andDill 2002) and can also have consequences for ecosystem functions (Madin et al 2010, Rizzari et al 2014. The application of a predation risk framework may help to interpret the extent to which underwater survey counts reflect actual abundances vs a behavioral bias (Frid et al 2019), the costs of avoidance behavior on body condition and reproduction (Spitz et al 2019), and how these factors (together with fishery removals) may influence the results of SDMs.…”