Seasonal influence of snow conditions on Dall’s sheep productivity in Wrangell-St Elias National Park and Preserve

Abstract

Dall’s sheep (Ovis dalli dalli) are endemic to alpine areas of sub-Arctic and Arctic northwest America and are an ungulate species of high economic and cultural importance. Populations have historically experienced large fluctuations in size, and studies have linked population declines to decreased productivity as a consequence of late-spring snow cover. However, it is not known how the seasonality of snow accumulation and characteristics such as depth and density may affect Dall’s sheep productivity. We examined relationships between snow and climate conditions and summer lamb production in Wrangell-St Elias National Park and Preserve, Alaska over a 37-year study period. To produce covariates pertaining to the quality of the snowpack, a spatially-explicit snow evolution model was forced with meteorological data from a gridded climate re-analysis from 1980 to 2017 and calibrated with ground-based snow surveys and validated by snow depth data from remote cameras. The best calibrated model produced an RMSE of 0.08 m (bias 0.06 m) for snow depth compared to the remote camera data. Observed lamb-to-ewe ratios from 19 summers of survey data were regressed against seasonally aggregated modelled snow and climate properties from the preceding snow season. We found that a multiple regression model of fall snow depth and fall air temperature explained 41% of the variance in lamb-to-ewe ratios (R2 = .41, F(2,38) = 14.89, p<0.001), with decreased lamb production following deep snow conditions and colder fall temperatures. Our results suggest the early establishment and persistence of challenging snow conditions is more important than snow conditions immediately prior to and during lambing. These findings may help wildlife managers to better anticipate Dall’s sheep recruitment dynamics.

Habitat selection by Dall’s sheep is influenced by multiple factors including direct and indirect climate effects

“…In addition, we documented that climate variables influenced habitat selection through both direct and potentially indirect pathways. During spring and winter, sheep selected habitat with warmer air temperatures and lower snow depths, conditions that likely reduce energy expenditure via moderation of the thermal environment and reduction of energetic costs of movement [35,39,66]. Indirectly, climate is linked to the expansion of shrubs in alpine regions of southern Alaska [12], and shrub/scrub vegetation was significantly avoided by sheep during all seasons.…”

“…Severe weather events, such as icing and snow cover persisting into late spring, have reduced the amount of available habitat during critical periods potentially leading to declines in sheep populations across Alaska, including in Gates of the Arctic, Denali, and Lake Clark National Parks and Preserves [28,32,36]. Although previous studies have characterized seasonal patterns of habitat selection by Dall's sheep [20,25,37,38], only Mahoney et al [35] and Cosgrove et al [39] incorporated climate variables on a daily basis. Consequently, an…”

Collaborative wildlife–snow science: Integrating wildlife and snow expertise to improve research and management

“…Remotely sensed snow data may enable researchers to better balance the goal of assessing general, study area snowscape conditions (the second example GYE research question) with ensuring personnel safety and logistical efficiency. To this end, the research team may wish to identify other snow data collection methods that would allow remote monitoring for a longer time, such as a network of trail cameras and snow stakes to assess changes in depth throughout the fall, winter, and spring (Cosgrove et al, 2021;Sirén et al, 2018). This solution could eliminate human travel in avalanche-prone areas and allow for additional data collection.…”

“…For wildlife, snow properties can impact individuals by affecting movements and behaviors (Balkenhol et al, 2020; Berman et al, 2019; Boelman et al, 2017; Chimienti et al, 2020; Coady, 1974; Droghini & Boutin, 2018; Mahoney et al, 2018; Oliver et al, 2018; Oliver et al, 2020; Pedersen et al, 2021); predator–prey interactions (Horne et al, 2019; Nelson & Mech, 1986; Peers et al, 2020; Sirén et al, 2021); energetics related to foraging (Dumont et al, 2005; Fancy & White, 1985), locomotion (Fancy & White, 1987; Gurarie et al, 2019; Lundmark & Ball, 2008; Parker et al, 1984), and thermoregulation (Karniski, 2014; Pruitt Jr., 1957; Thompson III & Fritzell, 1988); forage accessibility (Hupp & Braun, 1989; Takatsuki et al, 1995; Visscher et al, 2006; White et al, 2009); as well as ground (Boelman et al, 2016) and subnivean habitat use (Bilodeau et al, 2013; Glass et al, 2021; Petty et al, 2015). Additionally, the effects of snow on individual survival (Hurley et al, 2017; Reinking et al, 2018; Shipley et al, 2020) and reproduction (Apollonio et al, 2013; Barnowe‐Meyer et al, 2011; Liston et al, 2016; Schmidt et al, 2019) can ultimately alter population‐level demographics (Apollonio et al, 2013; Berteaux et al, 2017; Boelman et al, 2019; Cosgrove et al, 2021; Desforges et al, 2021; Van de Kerk et al, 2018; Van de Kerk et al, 2020). Effective evaluation of such wildlife–snow relationships requires identification, acquisition, and incorporation of appropriate and relevant snow property information.…”

Assimilation of citizen science data in snowpack modeling using a new snow data set: Community Snow Observations

“…Both ways are functionally equivalent because they apply a simple, scalarbased correction surface to the precipitation fluxes. In our calibration process, we chose to use SnowAssim to address the precipitation deficiencies in the reanalysis product, following the approach of other recent studies in mountainous regions of Alaska and following the original purpose of the SnowAssim model (Cosgrove et al, 2021, and their Calibration of SnowModel section; Liston and Heimstra, 2008;Young et al, 2020, and their Sect. 3.4).…”