Survival and cause‐specific mortality of female Rocky Mountain elk exposed to human activity

Abstract

Animal populations are becoming increasingly exposed to human activity as human populations expand and demand for energy resources (e.g., coal, oil and natural gas) increases. We initiated this study to document survival and cause-specific mortality patterns of female Rocky Mountain elk (Cervus elaphus) exposed to increasing levels of human activity. We fitted 184 females with VHF or GPS collars over 4 years and used the Kaplan-Meier survival estimator to calculate annual survival rates. We used multinomial logistic regression to assess differences in cause-specific mortality and generalized linear mixed models to determine how probability of survival was structured during hunting season; both analyses examined a suite of 5 covariates (i.e., age, year, extent of space use, cover, and human footprint) as potentially influencing cause-specific mortality and survival probability. Annual probability of survival averaged 0.8 (±0.02 SE) over 4 years but averaged 0.91 (±0.03 SE) when harvest mortality was excluded, which was the most significant source of mortality in most years (" x ¼ 0:13 AE 0:02 SE). We found no difference between cause-specific mortality sources relative to elk that survived during the hunting season (v 10 2 = 5.79, P = 0.832). The probability of a female surviving during hunting season was negatively influenced by age, year, extent of space use, cover, and human footprint. We found evidence that human activity may have influenced annual rates of natural survival (i.e., exclusive of hunting mortality) and probability of survival during the hunting season. We note that this study occurred largely on privately owned and managed residential and ranch land and focused on female elk; we acknowledge that survival rate and cause-specific patterns of mortality may vary as a function of land ownership (private vs. public), demographic status, and management and harvest practices. While temporal and spatial scales of 1 week may be sufficient to describe patterns of direct mortality during hunting season, broad temporal or spatial scale analyses may be needed to address natural mortality during other seasons.

An adaptive behavioural response to hunting: surviving male red deer shift habitat at the onset of the hunting season

“…Previous studies have found that higher hunting pressure and hunter accessibility negatively affect wapiti survival at the scale of seasonal home ranges, but that there are no significant associations between cover and survival at this scale (McCorquodale, Wiseman, & Marcum, 2003;Unsworth, Kuck, Scott, & Garton, 1993). Nor are there significant associations between wapiti survival and the amount of cover at the scale of weekly home ranges (Webb et al, 2011). In contrast, a finer-scale analysis has revealed that bold wapiti individuals, with higher rates of movement, weaker response to human activity and greater use of open terrain, are more likely to be harvested than shy individuals (Ciuti et al, 2012).…”

Impacts of human hunting on spatial behavior of white-tailed deer (Odocoileus virginianus)

“…It appears that male deer respond spatially to temporal risk from hunters in general (mean response and trends), but also appear to respond in situation-specific manners (large variation around the mean). In elk, modified behavior and resource selection have been found to reduce the probability of mortality (Webb et al 2011c;Dzialak et al 2011). It appears that adult male deer are able to adopt strategies that lend themselves to avoiding detection by hunters (Little et al 2016), so these strategies likely could increase fitness through increased survival when deer are not at risk of harvest.…”

Landscape features and weather influence nest survival of a ground-nesting bird of conservation concern, the greater sage-grouse, in human-altered environments

“…We used Spatial Analyst in ArcGIS ® 10.0 to calculate raster values and to extract values from raster data to location data for all covariates. See Visual Learning Systems, Inc. (2008) and Webb et al (2011) for details on using Feature Analyst, and Dzialak et al (2011a) for a more complete description of covariates, data sources, and methods.…”