Spatiotemporal risk factors predict landscape‐scale survivorship for a northern ungulate

Abstract

Effective wildlife conservation requires decomposing the drivers of population dynamics for species affected by anthropogenic habitat alterations. Ungulates are often the focus of management actions to restore habitat and maintain connectivity as they are sensitive to landscape disturbances. We used Bayesian proportional hazards models to assess anthropogenic risk factors that could potentially predict landscape-scale survivorship for pronghorn (Antilocapra americana) in the Northern Sagebrush Steppe ecosystem, where extensive habitat alterations occurred from the conversion of native sagebrush grasslands to agricultural lands. Using 170 adult female pronghorn monitored from 2003 to 2011, we tested the importance of linear features (road and fence densities) and forage productivity (maximum decadal normalized difference vegetation index [NDVI]) for spatiotemporal pronghorn mortality risk, while accounting for a seasonally varying proxy of snow depth. We found moderate support for the effects of linear features on mortality risk as coefficient estimates translated to predicted declines in survivorship of 27.1% over the observed range of road densities (0-1.4 km/km 2 ) and 11.8% over the range of fence densities (0-6.1 km/km 2 ) encountered by pronghorn. Our results also suggested that agricultural areas could act as ecological traps for pronghorn, based on mortality risk increasing by a factor of 14.3% with every 0.1 increase in maximum decadal NDVI in summer (range = 0.38-0.73). Like our previous findings, we found considerable support for the effects of average depth (in centimeters) of snow water equivalent (SWE; SWE depth = snow depth  snow density/water density) within pronghorn seasonal ranges, with mortality risk increasing by 45.7% with every 1 cm increase in SWE depth (range = 0-5.37 cm). We then developed the first broadscale, spatially explicit map of predicted annual pronghorn survivorship based on anthropogenic features and environmental gradients to identify areas for conservation and habitat restoration efforts.These efforts to highlight anthropogenic risk factors on the landscape will hopefully support conservation and habitat restoration for pronghorn populations at the northern periphery of their range.

Grassland intactness outcompetes species as a more efficient surrogate in conservation design

“…Terrestrial species, encumbered to make every step of migration, are particularly vulnerable to catastrophic population consequences from severed pathways (Bolger et al, 2008). Even beyond migration and dispersal, daily movements of terrestrial species, exemplified by pronghorn, can be affected by habitat fragmentation and barriers to movement such as roads and fences (Eacker et al, 2023; Jones, Jakes, et al, 2019). Therefore, it may be worthwhile to address movement requirements by focusing on terrestrial species such as ungulates (Gaston & Fuller, 2008).…”

Spatially and temporally explicit environmental drivers of fawn recruitment in a native ungulate

“…While environmental conditions both pre‐ and post‐parturition have been shown to influence pronghorn recruitment in Idaho (Panting et al, 2021; Smyser, 2005), other population dynamic measures such as pronghorn density, snow depth, linear features, doe and buck survival, disease, predation, and hunting success likely influenced overall pronghorn population levels (Barnowe‐Meyer et al, 2011; Bodie, 1979; Danvir, 2000; Eacker et al, 2023; Hoffman et al, 2010; Keller et al, 2013; O'Gara, 2004b; Rattenbury et al, 2018). These measures were not included in our analysis because these additional metrics were not available at the temporal and spatial scales required for our analysis.…”

“…We included forbs, grasses, and shrubs as these cover types were most important at providing forage for pronghorn. Recent studies have reported how variation in climate, environmental conditions, and plant phenology influenced recruitment and adult survival in ungulates (e.g., DeMars et al, 2021; Eacker et al, 2023; Jesmer et al, 2021; Reinking et al, 2018). Therefore, we used the SD of each variable within each time frame in our analysis which allowed an examination of the predictability or variability of climatic and environmental variables influencing fawn recruitment.…”

“…Density‐dependent and density‐independent factors both should be considered when investigating population dynamics through time in large ungulate populations. Ungulate populations can be regulated by anthropogenic factors (e.g., Christie et al, 2015; Eacker et al, 2023), and biotic and abiotic factors, which can be influenced by density‐dependent and density‐independent processes (Peláez et al, 2022; Williams et al, 2002). Biotic factors, such as competition from other species, disease, or vegetation quality and type, often influence ungulate populations throughout the year, while abiotic factors, such as precipitation and temperature, can influence survival and recruitment on monthly and seasonal bases.…”