DOI: 10.5061/dryad.qrfj6q5jh
title: Heterogeneity in risk-sensitive allocation of somatic reserves in a long-lived mammal

description: Patterns of food quality and availability, when combined with energetic
 demands in seasonal environments, shape resource acquisition and
 allocation by animals and hold consequences for life-history strategies.
 In long-lived species with extensive maternal care, regulation of somatic
 reserves of energy and protein can occur in a risk-sensitive manner,
 wherein resources are preferentially allocated to support survival at the
 cost of investment in reproduction. We investigated how Rocky Mountain
 bighorn sheep (Ovis canadensis), an alpine mammal in a highly seasonal
 environment, allocate somatic reserves across seasons. In accordance with
 the hypothesis of risk-sensitive resource allocation, we expected
 accretion and catabolism of somatic reserves to be regulated relative to
 pre-season nutritional state, reproductive state, and vary among
 populations in accordance with local environmental conditions. To test
 that hypothesis, we monitored seasonal changes of percent ingesta-free
 body fat (IFBFat) and ingesta-free, fat-free body mass (IFFFBMass) in
 three populations of bighorn sheep in northwest Wyoming between 2015 and
 2019 through repeated captures of female sheep in December and March of
 each year in a longitudinal study design. Regulation of somatic reserves
 was risk sensitive and varied relative to the amount of somatic reserves
 an animal had at the beginning of the season. Regulation of fat reserves
 was sensitive to reproductive state and differed by population,
 particularly over the summer. In one population with low rates of
 recruitment of young, sheep that recruited offspring lost fat over the
 summer in contrast to the other two populations where sheep that recruited
 gained fat. And yet, all populations exhibited similar changes in fat
 catabolism and risk sensitivity over winter. Magnitude of body fat and
 mass change across seasons may be indicative of sufficiency of seasonal
 ranges to meet energetic demands of survival and reproduction.
 Risk-sensitive allocation of resources was pervasive suggesting
 nutritional underpinnings are foundational to behavior, vital rates, and
 ultimately, population dynamics. For species living in alpine
 environments, risk-sensitive resource allocation may be essential to
 balance investment in reproduction with ensuring survival.
description: Beginning in December 2015, we captured
 adult females using helicopter net-gunning (Whiskey Mountain and Jackson
 populations; Krausman et al. 1985; Wagler et al. 2022) or ground-darting
 and chemical immobilization (Upper Shoshone population) and fitted each
 with an individually identifiable GPS collar (various models, Advanced
 Telemetry Systems, Isanti, MN, USA; Vectronics Aerospace, Berlin,
 Germany). Every animal collared was assigned a unique identifier (animal
 ID). Each subsequent March and December 2015–2019, we
 recaptured collared females whenever possible and captured uncollared
 females to maintain sufficient sample sizes (25 in Whiskey Mountain, 20 in
 Jackson, 15 in Upper Shoshone populations). We programmed GPS collars to
 record locations 2–24 times per day. Upon first capture, we estimated age using a
 combination of horn annuli and tooth eruption and wear (Geist 1966; Rubin
 et al. 2000; Dekelaita et al. 2020). Each March we determined pregnancy
 status using ultrasonography (Stephenson et al. 1995). Each December we
 determined lactation status (a proxy for recruitment) by palpating the
 udder and attempting to express milk (Monteith et al. 2014). At each
 capture event, we measured nutritional condition using ultrasonography to
 measure subcutaneous rump fat (5‐MHz transducer; Ibex Pro, E.I. Medical
 Imaging, Loveland, CO, USA) according to methods described in Stephenson
 et al. (2020). If there was no measurable subcutaneous rump fat, we used
 manual palpation to assign a body condition score (BCS; Stephenson et al.
 2020).We used the ultrasonography measurements and BCS to estimate percent
 ingesta-free body fat (% IFBFat) using equations developed and validated
 for bighorn sheep (Stephenson et al. 2020). To measure body mass, we
 weighed each sheep using a platform or hanging scale to the nearest 0.1
 kg. We subtracted 2 kg from pregnant sheep in March to account for fetal
 mass and products of conceptus (Cook et al. 2010, Monteith et al. 2013).
 We calculated ingesta-free body mass using the following equation
 (Stephenson et al. 2020): Ingesta-free body mass (kg) = 0.668(body
 mass; kg) + 6.418 (Eq. 1) We calculated ingesta-free, fat free body
 mass (IFFFBMass, kg) using the following equation for animals with no
 measurable subcutaneous rump fat (MAXFAT; Stephenson et al. 2020):
 IFFFBMass (kg) = IFBMass - 2.11(BCS) - 1.46
 (Eq. 2A) We calculated IFFFBMass using the following
 equation for animals with measurable subcutaneous rump fat (MAXFAT;
 Stephenson et al. 2020): IFFFBMass (kg) = IFBMass - 6.85(MAXFAT) +
 3.28 (Eq. 2B) <em>Spatial data and statistical
 analysis</em> We completed all data analyses in Program R
 (R Core Team 2020). We defined summer as June through August because it is
 the primary growing season in the alpine zone and thus the primary period
 for nutrient acquisition and potential accumulation of somatic reserves.
 We defined winter as December through March because most sheep were on
 their winter ranges and most snowpack accumulated during these months. We
 randomly selected  two locations per day for each sheep to create a
 balanced sample size across individuals. We excluded sheep with fewer than
 30 GPS locations during a season (Street et al. 2021). We calculated
 seasonal home ranges as 75% kernel density utilization distributions using
 a 250 m resolution for each sheep using the adehabitatHR package (Calenge
 2006). We chose a 75% utilization distribution to include the core home
 range and the primary area that sheep are using while excluding areas that
 sheep likely are not using (Downs & Horner 2008, Clapp &
 Beck 2015) that may bias spatial variable estimates (e.g., heavily
 forested areas on the edge of escape terrain that may inflate biomass
 estimates). We extracted spatially explicit data for mean snow depth (m)
 at a 1-km resolution (National Operational Hydrologic Remote Sensing
 Center 2021) of each winter home range and herbaceous biomass estimates
 (Rangeland Analysis Platform; kg/ha; annual temporal resolution) for
 summer and winter home ranges at a 30-m resolution as an index of forage
 availability on seasonal ranges (Robinson et al. 2019, Jones et al. 2020).
 We extracted the spatial data for December through March for winter home
 ranges and June through August for summer home ranges. We used these
 spatial data on snow depth and biomass to assess the effects of weather
 and forage conditions on seasonal changes in fat and lean body mass of
 sheep. Annual estimates of biomass on winter range was used as an estimate
 of relative forage availability that was available following the primary
 growing season.