DOI: 10.5061/dryad.bnzs7h4jm
title: Heterogeneity of locked-pasture snow conditions modulate habitat and movement choices of a facultative migrant, data archive

description: Habitat selection and movement are key mechanisms by which animals can
 respond to and potentially cope with highly variable and rapidly changing
 environmental conditions. Optimal responses likely vary, however,
 depending on the severity and scope of limiting conditions. We tested this
 hypothesis using a facultative migrant species, the Great Gray Owl (Strix
 nebulosa), which exhibits high inter- and intra-individual variation in
 the timing, direction, and distance of winter movements. Specifically, we
 evaluated whether espisodic, spatiotemporally variable “locked-pasture”
 snow conditions, which restrict access to subnivean food, prompted shifts
 in habitat selection or long-distance movements by owls. We GPS-tracked 42
 owls across the annual cycle within the Greater Yellowstone Ecosystem, USA
 during 2017–2022. We used a novel ecological application of SnowModel, a
 snow evolution modeling system, to estimate fine-scale, physical snow
 properties likely to influence prey access. Variables included snow depth,
 snow crusts produced by wind, and ice crusts produced by melt-freeze and
 rain-on-snow events. Owls proximately avoided deeper snow and more severe,
 heterogeneously distributed wind crusts via local shifts in habitat
 selection. However, more widely distributed and persistent ice crusts
 elicited long-distance movements away from affected home ranges.
 Ultimately, the specific behavioral tactics employed varied with the
 severity, spatial extent, and duration of limiting environmental
 conditions. Our results provide a clear demonstration of behavioral
 flexibility in response to extremely limiting, highly variable
 environmental conditions. Such behavioral responses determine species
 distribution, with implications for community dynamics in spatiotemporally
 variable systems. Such understanding of responses to environmental
 variability is increasingly important given the scope of on-going global
 change.
description: Our research objective was to evaluate snow conditions in
 relation to Great Gray Owl movement behavior.  We estimated snow depth and
 snow crust conditions across the study area (Greater Yellowstone
 Ecosystem) and period (1 September 2017 - 31 August 2022) using SnowModel.
 SnowModel is a spatiotemporally distributed snow-evolution modeling system
 that utilizes land cover, topography, meteorology,
 and <em>in-situ</em> observations of snow characteristics to
 simulate snowpack evolution across space and time. The modeling system is
 versatile in terms of spatiotemporal domain and resolution and can create
 nuanced, project-specific snow data products required to understand
 mechanistic snow-wildlife relationships.  We modeled
 the evolution of snow conditions at two spatiotemporal resolutions to
 address our study objectives. First, we simulated snow characteristics at
 a three-hour time step and 30 m × 30 m spatial resolution to assess
 fine-scale habitat selection in response to local snow conditions. Second,
 we modeled snow evolution at a three-hour time step and 500 m × 500 m
 spatial resolution to assess probability of long-distance movements
 relative to broad-scale snow conditions. We used
 knowledge of the physical processes underlying snow crust formation to
 develop a comprehensive snow crust evolution submodel. Specifically, we
 implemented a physics-based model that simulated the spatiotemporal
 evolution of snow crusts associated with the three main phenomena that
 produce them: melt-freeze, rain-on-snow, and blowing and drifting snow
 associated with strong wind events. Melt-freeze crusts form via snowmelt
 occurring on the snow surface or within the snowpack, and then percolating
 down into sub-freezing snowpack layers where the snowmelt water refreezes;
 we call this a melt-freeze process or cycle. If the snowmelt water runs
 out of the bottom of the snowpack, a melt-freeze layer does not form. We
 identified snow crusts that formed during a melt-freeze cycle by
 calculating when snowmelt occurred within a cold snowpack (i.e., snowpack
 layer temperature &lt; −1.0 °C) and accounted for subsequent freeze
 events after melting by identifying when snowmelt occurred followed by a
 snowpack layer temperature &lt; −1.0 °C within the following 12 hr
 period.  Rain-on-snow crusts form when rain precipitation falls on a cold
 (below freezing) snow surface or on a snowpack with cold internal snow
 layers, and the liquid freezes into an ice layer. Using the SnowModel
 output variables, we identified events during which rain (≥ 1.0 mm) fell
 on a cold snowpack (i.e., a snowpack layer with a depth ≥ 5.0 cm (based on
 minimum depth required to create subnivean habitat for small mammals) and
 temperature &lt; −1.0 °C), and the liquid water froze in the surface
 layer, or layers below, and did not produce runoff. We
 indexed the severity of both melt-freeze and rain-on-snow crusts based on
 the amount of liquid water (rainfall or snowmelt, in millimeters) that
 reached a &lt; −1.0 °C snowpack layer. Although ice crust severity was
 calculated simply as an index, the calculation is based on amount of
 liquid water (in millimeters) incorporated into the crust and, therefore,
 ice crust severity had units of 1.0 mm of liquid water frozen within the
 snowpack (e.g., an ice crust severity index of 10.0 meant 10.0 mm of
 liquid water had frozen). The reason we refer to this as an index is
 because the model is not performing a complete physics-driven energy and
 moisture balance; we have assumed that all that is required to freeze all
 the available liquid is the presence of cold snow (there could be
 instances where the snow was not cold enough to freeze all of the liquid
 that reached the cold layer). Melt-freeze and rain-on-snow crust indices
 were comparable in scale and formation process, so we combined melt-freeze
 and rain-on-snow events into a single variable: ice crust events. Our
 additional reasoning here was that only ice crust presence and magnitude
 was relevant to the focal species, not the precise processes that formed
 them. Wind crust formation occurs when high-speed winds
 (e.g., winds generally greater than 5 to 7 m s<sup>-1</sup>)
 interact with falling or blowing snow. Such events result in the
 mechanical breakage or tumbling of snow into small, sometimes sharp-edged
 crystals that can form strong, grain-to-grain bonds, through a process
 called sintering, once they stop moving. To model wind crust events, we
 calculated the change in snow density due to blowing snow based on the
 wind speed 2 m above the land surface. To index the severity of a wind
 crust event, we calculated the relative increase in snow density (again,
 for snowpacks with depth ≥ 5.0 cm based on required depth for subnivean
 habitat for small mammals) due to wind speed and amount of blowing snow.
 The severity indices for ice versus wind crusts are not directly
 comparable due to the inherent differences in how these crusts form in the
 natural system. For this reason, we did not combine the ice and wind
 crusts into a single, all-encompassing crust index. In
 our modeling system, once formed, both wind and ice crusts can persist,
 even as new snow falls and buries them below the surface. We calculated
 the formation and initial severity index of each crust (a crust event),
 and their cumulative severity index (a summed event severity index over
 time) and persistence (consecutive number of days that the crust persisted
 within the snowpack). In our ice and wind crust model, crusts disappear
 from a snowpack when it becomes isothermal <em>at</em> 0.0 °C
 and liquid snowmelt water reaches the ground (typically in the spring, or
 in low elevations, periodically throughout the winter). Once runoff
 occurred, we reset the cumulative variables (cumulative severity index,
 persistence) to zero, to represent this
 process. <br><em><br></em>Additionally, we
 collected GPS locations for tagged, adult <em>Strix
 nebulosa</em> (Great Gray Owls) (n = 42) in northwestern Wyoming
 between 2017-2022. We used a used-available framework and integrated
 step-selection analysis to evaluate step selection by owls in relation to
 snow conditions. A step consists of both a starting and ending location
 between a set time interval. This analysis assumes that, for each observed
 step, alternate available steps exist that the animal could have selected.
 We subsampled Great Gray Owl GPS location data to include the core snow
 period defined as the average dates of the longest period during which
 snow cover was consistent (15 September–15 April) and one location per
 day, such that successive locations were 24 hr apart. For each observed
 step, we genearted generated five corresponding random steps for each
 observed step, drawn from theoretical distributions of average step
 lengths and turning angles for that specific individual (Weibull
 distribution and Von Mises distribution for step-length and turning angle,
 respectively). At the endpoint of each step, we extracted values of snow
 depth; wind crust conditions (severity, cumulative severity, and
 persistence); and ice crust conditions (severity, cumulative severity, and
 persistence). To evaluate the snow conditions that
 precipitated migratory movements by owls, we identified long-distance
 movement events by <em>S. nebulosa</em> during the core snow
 period (15 September – 15 April). We used Net Squared Displacement
 analysis, which characterizes movement trajectories by calculating the
 squared Euclidean distance between a starting location and subsequent
 locations. We also visually inspected GPS location data to confirm that
 long-distance movement events met specific criteria. We identified a
 migratory movement event as a significant movement away from a discrete
 breeding or winter range. A significant movement was defined as a movement
 of 8 km or farther (based on twice the diameter of the mean overall winter
 range area (19.5 km<sup>2</sup>, based on 99% Kernel Density
 Estimates (KDE)). We defined a breeding home range based on a 95% KDE
 using locations between April–September. A discrete winter home range was
 defined as an area &lt; 3.7 km in diameter (based on mean winter home
 range size of 13.4 km<sup>2</sup> 95% KDE) in which an owl
 settled for a minimum of 1 week (between 1 October–30 March). We excluded
 movements away from discrete winter ranges in which owls returned to their
 breeding range, because we could not ascertain whether factors other than
 snow conditions (e.g., acquisition and defense of a territory and mate)
 influenced decisions. We subsampled all GPS locations to one location per
 day per individual and excluded movement sequences with &gt; 5 days of
 missing location data and/or &lt; 6 months of GPS location data total.
 For each long-distance movement event, we identified the onset of risk as
 the point at which the owl first settled in either a discrete breeding or
 winter range (settlement decision), and included all locations in which
 the owl remained settled (settlement) up until the point at which the owl
 departed the settled range to undertake a long-distance movement
 (departure event). For all locations beginning with the settlement
 decision through the departure event, we extracted values of snow depth
 and crust severity, cumulative crust severity, and crust persistence for
 both ice and wind crusts. We used Cox proportional hazards analysis to
 evaluate whether snow conditions influenced probability of departure by
 Great Gray Owls.
description: # Heterogeneity of locked-pasture snow conditions modulate habitat and
 movement choices of a facultative migrant Authors = Katherine Gura,
 Colorado State University,
 [Katherine.Gura@ColoState.Edu](mailto:Katherine.Gura@ColoState.Edu) Glen
 E. Liston, Colorado State University,
 [Glen.Liston@ColoState.Edu](mailto:Glen.Liston@ColoState.Edu) \[Access
 this dataset on Dryad]
 ([https://doi.org/10.5061/dryad.bnzs7h4jm)](https://doi.org/10.5061/dryad.bnzs7h4jm) # Description of the data and file structure The data include two datasets: 1) a dataset of used and available step locations for GPS-tagged adult owls during winter and associated snow condition data (GGOW_SSF_Snow_Data.csv), and 2) a dataset of locations by GPS-tagged owls that includes locations on home ranges (breeding and winter) until the initiation of long-distance movements away from that range, and associated snow condition data for each location (GGOW_TimetoMigration_Snow_Data.csv). Updates to the datasets: the original snow data were modeled using a 1 mm minimum snow depth for snow crusts.  In the revised datasets, we modeled snow crusts that occurred with a minimum of 5 cm of snow.  The updated data files contain the updated snow data. ## 1) GGOW\_SSF\_Snow\_Data.csv This file contains associated snow data for actual observed locations used by individual Strix nebulosa (Great Gray Owls) and randomly-selected available adjacent locations that were available to each individual at that point in time.  Thus, the dataset allows for step-selection analysis of the actual locations chosen by each individual versus potential, alternate areas it could have used.  The dataset consists of a series of locations, and information associated with each location. It includes an identification distinguishing each individual owl by year ("Individual ID/Year"), date/time information ("Date/Time") for the location, strata (which are values that refer to each grouping of a used location and it's associated, randomly-selected five available steps), whether the location was used or available ("Used/Available"; 1 = Used, 0 = Available), the distance from the starting point to the endpoint of the step ("Distance from Step Start to End Point"), the absolute step angle (the angle of the step end point relative to true north ("Absolute Step Angle")), and the relative step angle (the angle of the step end point relative to the starting location of the step ("Relative Step Angle"). For each location, corresponding (in space and time) snow data were extracted based on a 30m, 3-hourly resolution SnowModel simulation that estimated snow depth and snow crust conditions. Snow data include snow depth (in centimeters), wind crust event severity, cumulative wind crust severity, wind crust persistence (in days), melt crust event severity (due to melt-freeze events), cumulative melt crust severity, melt crust persistence (in days), rain-on-snow crust event severity, cumulative rain-on-snow crust severity, rain-on-snow crust persistence (in days), ice crust event severity (which is a combination of melt-freeze events and rain-on-snow events), cumulative ice crust severity, and ice crust persistence (in days). Wind crust severity is an index based on relative change in snow density to blowing snow. Rain-on-snow, melt-freeze, and ice crust (combined rain-on-snow and melt-freeze) severity are each an index based on the amount of liquid water (rainfall or snowmelt, in millimeters) that reached a snowpack layer cold enough to freeze it as a crust layer. ## 2) GGOW\_TimetoMigration\_Snow\_Data.csv This file contains associated snow data for actual locations used by *Strix nebulosa* (Great Gray Owls) while on known breeding and winter ranges up until they initiated a long-distance movement away from that range. Thus, the dataset allows for time-to-event analysis of whether a change in snow conditions triggered a migratory movement.  The dataset consists of a series of locations, and information associated with each location. It includes an identification of the individual owl by year ("Individual ID/Year"), date/time information ("Date/Time"), the year, the Julian day (note: because the analysis focused on winter migratory events, an each analysis was based on a given breeding-winter season, the Julian day value for days after 1 December continue to count up past 365 until the individual returned to and remained settled on its breeding ranges during the subsequent breeding season; for example, a location on 10 January was assigned a Julian day value of 375), information on whether the owl was settled on a home range or initiating a migratory event ("Migrating;" a value of 0 = settled, a value of 1 = initiating a long-distance movement), and two time step values (which simply are a sequence of numbers to track and link subsequent locations used by individual owls from settlement on a home range through the initiation of a long-distance movement; "TimeStep" and "TimeStep2" (the subsequent time step)).  For each observation (of the owl's location in space and time), corresponding snow data were extracted based on a 30m, 3-hourly resolution SnowModel simulation that estimated snow depth and snow crust conditions. Snow data include snow depth (in centimeters), wind crust event severity, cumulative wind crust severity, wind crust persistence (in days), melt crust event severity (due to melt-freeze events), cumulative melt crust severity, melt crust persistence (in days), rain-on-snow crust event severity, cumulative rain-on-snow crust every, rain-on-snow crust persistence (in days), ice crust event severity (which is a combination of melt-freeze events and rain-on-snow events), cumulative ice crust severity, and ice crust persistence (in days). Wind crust severity is an index based on relative change in snow density to blowing snow. Rain-on-snow, melt-freeze, and ice crust (combined rain-on-snow and melt-freeze) severity are each an index based on the amount of liquid water (rainfall or snowmelt, in millimeters) that reached a snowpack layer cold enough to freeze it as a crust layer.