DOI: 10.5061/dryad.44j0zpcmc
title: Data from: Disparate home range dynamics reflect nutritional inadequacies on summer range for a large herbivore

description: The spatial distribution of animals has consequences for nutrition,
 predator-prey dynamics, spread of diseases, and population dynamics in
 general. Animals must establish a home range to secure adequate resources
 to fuel their energetic needs. Home ranges, therefore, are temporally and
 spatially dynamic given the changing requirements of an animal and the
 availability of resources on the landscape. We used data from two
 populations of bighorn sheep with contrasting population dynamics
 following pneumonia epizootics and different habitat quality on their
 summer range to test the hypothesis that the distribution and size of home
 ranges are influenced by environmental conditions and reproductive status.
 We used a combination of data from 768 vegetation transects and remotely
 sensed metrics to index forage quality of consecutive biweekly home ranges
 for 27 bighorn sheep, June–August 2019–2021. There were population
 differences in space use that were consistent with resource limitations in
 the population declining in abundance. Animals in both populations
 increased the area of their space use through the summer in association
 with declining forage quality indexed by plant phenology. Furthermore,
 animals in the Whiskey Mountain population without live offspring used
 areas more than twice the size of animals with offspring, whereas there
 were no differences in the area of space use between animals with and
 without offspring in Jackson. We demonstrated that limitations young
 offspring impose on space use of a mother may have consequences for
 animals living where larger home ranges are needed to secure adequate
 resources—sheep in Whiskey Mountain had to travel 1,000 m from escape
 terrain to access the same amount of biomass that the Jackson sheep could
 access directly adjacent to escape terrain. Forage quality and
 availability influence movement and home range dynamics. In the presence
 of disease, movement and home range dynamics may influence pathogen
 transmission and persistence. Thus, forage availability may play an
 indirect role in population dynamics in the presence of disease, which is
 another line of evidence for how environmental and nutritional conditions
 may influence population dynamics of populations coping with disease.
description: We used data from two populations of Rocky Mountain bighorn sheep
 within the Greater Yellowstone Ecosystem in northwest Wyoming, USA,
 2019–2021 (Figure 1). The Whiskey Mountain population experienced a
 pneumonia epizootic in 1991 (Ryder et al. 1992) and has since exhibited
 population decline via low juvenile recruitment (22 juveniles per 100
 adult females in winter on average 2019–2021; Wyoming Game and Fish
 Department 2021a), leaving the population at ~20% of its former population
 size (upwards of 1,500 animals; Wyoming Game and Fish Department,
 unpublished data). The Jackson population experienced pneumonia epizootics
 in 2001 and 2012 but has been able to recover to previous population size
 (~ 400 animals) and maintain higher juvenile recruitment (38.6 juveniles
 per 100 adult females; Wyoming Game and Fish Department 2021b).
 Study animals were seasonal elevational migrants. Summer ranges
 were high-elevation (~3,000m) alpine habitats with alpine meadows, talus
 fields, and rocky outcrops. Winter ranges were mixed conifer, mountain
 shrub, and grasslands. The climate of both study areas was cool, short
 summers and long winters with high snowfall, especially at high
 elevations. The summer range of the Jackson population had higher biomass,
 species diversity, and available macro- and micronutrients than that of
 the Whiskey Mountain population (Wagler et al. 2023; Figure 1). See Wagler
 et al. (2023) for more detail on the study area. Animal
 capture and handling We captured adult female bighorn
 sheep via helicopter net-gunning (Krausman et al. 1985, Wagler et al.
 2022) in the Jackson and Whiskey Mountain populations in late winter
 2019–2021. We fit sheep with a GPS collar with hourly fix rates (Vertex
 Plus, VECTRONIC Aerospace Gmbh, Berlin, Germany). We censored GPS
 locations that had dilution of precision &gt; 10, or speed &gt;
 3.6 km per hour, which were arbitrary thresholds that removed problem
 locations for our data (D'Eon et al. 2002). We used ultrasonography
 (5-MHz transducer, Ibex Pro, E.I. Medical Imaging, Loveland, CO, USA) to
 determine pregnancy status (Stephenson et al. 1995) and fit pregnant
 animals with a vaginal implant transmitter (VIT, VECTRONIC Aerospace Gmbh,
 Berlin, Germany) programmed to notify field personnel of a birth event.
 Once a birth event was detected we captured neonates by hand (Smith et al.
 2014) and fit them with an expandable GPS collar (Vertex Mini, VECTRONIC
 Aerospace Gmbh, Berlin, Germany) with 4-hour fix rates and set to enter
 mortality mode after no movement for 8 hours. Field personnel investigated
 the site to confirm mortality when a collar entered mortality mode,
 allowing us to determine the date of death. All animal handling was
 approved by the Wyoming Game and Fish Department (Chapter 33-1278),
 Institutional Animal Care and Use Committee (IACUC; 20180305KM00296), and
 was in accordance with the guidelines of the American Society of
 Mammologists (Sikes 2016). Home range
 dynamics To quantify home range size, we created 95%
 utilization distributions using dynamic Brownian bridge movement models
 (Horne et al. 2007) with the BBMM package in R (Nielson et al. 2013).
 Dynamic BBMMs provide an occurrence distribution and were able to capture
 the directional movement of our study animals without inflating the area
 of space use. We calculated occurrence distributions for 6 consecutive
 2-week periods throughout the summer (1 June–31 Aug) for each animal year.
 Home range size was the area (km2) of the 95% contour. We used two-week
 intervals because this was short enough to capture behavioral changes
 associated with the loss of a juvenile but long enough to accurately
 describe the space use of each animal. To further assess home range
 dynamics, we calculated the degree of spatial overlap in home ranges
 between each consecutive 2-week period for each animal. We used
 utilization distribution overlap index (Fieberg and Kochanny 2005) for
 this metric—higher values indicated an animal did not move its home range
 at all from one period to the next and a 0 indicated that an animal
 completely relocated its home range. Assessment of
 forage quality We used a combination of vegetation
 transects and remotely sensed data to assess forage quality within
 biweekly home ranges. Data from vegetation transects represented core
 foraging areas and remotely sensed metrics represent the entire home
 range. For each transect, we calculated six metrics of forage quality
 (biomass, species diversity, dry matter digestibility [DMD], crude protein
 [CP], and DMD and CP relative to plant cover). Biomass was indexed with
 the spatially explicit above-ground biomass estimate for the starting
 point of each transect (Rangeland Analysis Platform [RAP];
 kilograms/hectare; 30-m spatial resolution; yearly temporal resolution)
 (Robinson et al. 2019). We used the Shannon-Weiner species diversity index
 (Whittaker 1972) to calculate species diversity of each transect. We
 calculated two metrics for macronutrients (DMD and CP) to index forage
 quality. First, we calculated the mean of each macronutrient at each
 transect weighted by the number of hits per genera, ignoring inorganic
 hits. This metric reflects the quality of forage at the transect,
 regardless of how much was available at the transect. To account for how
 plant cover affects the availability of macronutrients, we calculated a
 second weighted mean at each transect including a 0 for each inorganic
 hit. The second metric is an index of macronutrient availability at a
 transect. See Wagler et al. (2023) for more detail on vegetation transects
 and quality metrics. Quality of forage for ungulates is
 directly linked to phenological stage, which can be indexed with remotely
 sensed metrics of vegetation biomass, specifically the Normalized
 Difference Vegetation Index (NDVI) (Hamel et al. 2009). We used the mean
 and coefficient of variation of the number of days to or from peak
 instantaneous rate of green up (IRG) to index forage quality at a larger
 scale (95% 2-week home range) than the transect level metrics (core
 foraging areas). We calculated IRG (scaled between 0 and 1) with the first
 derivative of the NDVI curve, which results in a curve that peaks when
 vegetation growth is most rapid and thus, when forage quality peaks for
 ungulates (Fryxell 1991, Hebblewhite et al. 2008) (Appendix S1).
 To index forage quality for each biweekly home range, we
 calculated the mean of each forage quality metric (DMD, CP, DMD and CP
 relative to plant cover, species diversity, and biomass) of each transect
 that was within the corresponding 2-week home range contour (i.e. any
 transect contained within a biweekly home range was included regardless of
 the animal the transect corresponded to). A transect represented all the
 biweekly home ranges that spatially overlapped it. Because we expect
 quality of forage for ungulates to be highest during maximum IRG and to
 decline in quality as it senesces throughout the summer (Bischof et al.
 2012, Geremia et al. 2019), we adjusted macronutrient values for each
 biweekly period (i.e. macronutrient values were different for the same
 transect at the beginning of June than at the end of August) using
 equations described in Wagler et al. (2023) and reported in Appendix S1
 Tables S1 and S2. This phenology adjustment allowed us to use
 macronutrient values from every transect for each biweekly home range,
 regardless of the date the forage samples were collected. Additionally, we
 calculated the mean and coefficient of variation of the number of days to
 or from peak IRG for each home range. To compare
 landscape structure between the two summer ranges, we fit a linear
 mixed-effects model with herbaceous biomass (Rangeland Analysis Platform
 [RAP]; kilograms/hectare; 30-m spatial resolution; yearly temporal
 resolution) as the response and an interaction between distance to escape
 terrain (m) and population as the predictor. The biomass and escape
 terrain values were extracted from each GPS location June–August. We
 defined escape terrain as areas that had slope &gt; 30 degrees and
 terrain roughness (the difference between the minimum and maximum value of
 a cell and its 8 surrounding cells; Wilson et al. 2007) &gt; 70. The
 model included a random intercept for animal year.
description: # Disparate home range dynamics reflect nutritional inadequacies on summer
 range for a large herbivore
 [https://doi.org/10.5061/dryad.44j0zpcmc](https://doi.org/10.5061/dryad.44j0zpcmc) ## Description of the data and file structure **PCA\_data** This file contains all the meterics that went into the PCA with varimax reduction. For more detail on exactly how these metrics were quantified see Wagler et al. 2023. Implications of forage quality for population recovery of bighorn sheep following a pneumonia epizootic. Journal of Wildlife Management 87:e22452. Each metric reflects the mean of that variable for the line point intercept transect. DMD\_rx = mean dry matter digestibility (%) for each plant along the transect. this metric accounts for for inorganic hits (inorganic hits accounted for in the mean with a 0) CP\_rx = mean crude protien (%) for each plant along the transect. this metric accounts for inorganic hits (inorganic hits accounted for in the mean with a 0) Biomass\_kgha\_tran = biomass estimate for the starting point of the transect (Rangeland Analysis Platform; kg / ha) Sp\_div = Shannon-Weiner species diversity index for the transect DMD\_norx = mean dry matter digestibility (%) for each plant along the transect. this metric ignores inorganic hits CP\_norx = mean crude protien (%) for each plant along the transect. this metric ignores inorganic hits **BiomassEscpTrr\_data** This file contains biomass estimates from the Rangeland Analysis Platform and the distance to escape terrain (m) for each GPS location June - August 2019-2021. AnimalID = unique animal identifier. This has been randomized to protect sensitive information bio\_kgha = biomass estimate for the GPS location (Rangeland Analysis Platform; kg / ha) dist2escp\_m = distance to the closest pixel containing escape terrain (m) Herd = population (Jackson or Whiskey Mountain (Dubois)) year = year of the GPS location season = biweekly season (consecutive 2-week intervals from the beginning of June to the end of August) **HR\_size\_data** This file contains the variables associated with the home rage size analysis. pc1 = mean score of principal component 1 for each transect included in the corrosponding biweekly homerange pc2 = mean score of principal component 2 for each transect included in the corrosponding biweekly homerange pc3 = mean score of principal component 3 for each transect included in the corrosponding biweekly homerange N\_tran = number of transects contained within the biweekly homerange dynBB95 = area (km2) from the 95% contour of the brownian bridge occurance distribution AnimalID = unique animal identifier. This has been randomized to protect sensitive information Herd = population (Jackson or Whiskey Mountain (Dubois)) year = year of the GPS location season = biweekly season (consecutive 2-week intervals from the beginning of June to the end of August) Pregnant = 0 inidcates animal was not pregnant in March of the corrosponding year and 1 indicates she was pregnant LambAlive = 1 indicates the animal had a live juvenile during the time the biweekly home range was calculated days2IRG = mean number of days to or from peak instantaneous rate of green-up CVdays2IRG = coefficient of variation for the number of days to or from peak IRG snow\_m = mean snow depth (m; National Operational Hydrologic Remote Sensing Center) precip\_m = mean daily precipitation (cm; Daymet) **HR\_ovlp\_data** This file contains the variables associated with the home range overlap analysis. We used metrics for the first home range of the comparison to represent forage quality. pc1 = mean score of principal component 1 for each transect included in the corrosponding biweekly homerange pc2 = mean score of principal component 2 for each transect included in the corrosponding biweekly homerange pc3 = mean score of principal component 3 for each transect included in the corrosponding biweekly homerange N\_tran = number of transects contained within the biweekly homerange AnimalID = unique animal identifier. This has been randomized to protect sensitive information year = year of the GPS location season = biweekly season (consecutive 2-week intervals from the beginning of June to the end of August) Herd = population (Jackson or Whiskey Mountain (Dubois)) days2IRG = mean number of days to or from peak instantaneous rate of green-up CVdays2IRG = coefficient of variation for the number of days to or from peak IRG snow\_m = mean snow depth (m; National Operational Hydrologic Remote Sensing Center) precip\_m = mean daily precipitation (cm; Daymet) ovlp = overlap metric for within animal home range overlap of consecutive biweekly home ranges died = 1 indicates the offspring died during the biweekly transition, 0 indicates no change in offspring status suring the transition