GEOG 566

         Advanced spatial statistics and GIScience

April 6, 2018

The wrong snow for sheep…

Filed under: 2018,My Spatial Problem @ 3:20 pm

Dall Sheep on Jaeger Mesa, Wrangell St Elias National Park, Alaska. Laura Prugh.

Research question.

Dall Sheep are a species of wild sheep whose ranges extend throughout mountainous Alaska and the Yukon Territory. As a large ungulate specialised in grazing sub-Arctic to Arctic alpine regions, they are important maintainers of ecosystem function in habitats considered particularly sensitive to environmental change. They also provide an important service to local, often remote, human populations, traditionally through subsistence hunting but more recently through lucrative trophy hunting and wildlife tourism facilitation.

Since the 1980s range-wide populations of Dall Sheep have decreased by up to 21%, and in some areas emergency harvest closures have been enacted. The common explanation for this population decline has been an increase in extreme winter snow conditions reducing access to forage and increasing energy expenditure. Dall Sheep lamb in April and May and the physical condition of the ewes at the end of winter is a key determinant of the survival of their lambs, and hence longer-term population size.

To date, there has been limited empirical investigation into the relationships between longer term Dall Sheep population health and patterns of seasonal snow cover. This project seeks to address this by answering the following research questions;

  • Has there been an increased frequency of extreme seasonal snow conditions (e.g. snow depth, density/icing, duration) from 1980 to present day in Dall Sheep habitats?
  • Do instances of extreme seasonal snow conditions correlate to reduced recruitment of Dall Sheep?

Dall Sheep ranges in Alaska and the Yukon Territory. Study site is the author’s field site in the Wrangell St Elias.


Seasonal snow condition data for the analyses in this project will be prepared from the outputs of a physically based, spatially explicit snow evolution model, SnowModel (Liston and Elder, 2006). SnowModel has been run at a daily timestep for domains using climate reanalysis forcing data within 6 Alaska National Parks and Preserves (NP) from 1980 to 2017; Wrangell St Elias NP, Lake Clark NP, Denali NP, Gates of the Arctic NP, and Yukon Charley NP. Snow condition data, e.g. depth/density, has been aggregated for sheep habitat (e.g. mean snow depth above shrubline) by month and season (e.g. Winter = December, January and February) for each water year.

For each of these domains we have summer sheep count datasets that have been taken at differing frequencies and methodologies. For the scope of this project we will use a metric derived from these sheep counts as an indication of recruitment success; the lamb:ewe ratio. More lambs per ewe each summer season shows greater recruitment success.


    • Deeper seasonal snow will inhibit Dall Sheep movement and forage access, therefore increasing energy expenditure during winter, leading to decreased spring reproductive success
    • Longer durations of seasonal snow cover will correspondingly cause poorer Spring sheep condition and hence decrease reproductive success


For RQ#1 I would like to explore approaches that detect and describe statistically significant features of trends in snow cover data from 1980 to 2017. This might be the incidence of hazardous events per season, e.g. rain-on-snow; mean snow depth by month/season/water year; or snow cover duration. Potential extension to RQ#1 would be to compare the importance of climate indices, e.g. Pacific Decadal Oscillation, on the spatiotemporal patterns of seasonal snow in each domain.

For RQ#2 varying complexities and flavours of regression analysis will be explored to discover the most important features of the snow season that influence the following summer’s Dall Sheep recruitment. Initial ideas are linear mixed models and random forest.

Preliminary results comparing summer lamb:ewe ratios to simple metrics of seasonal snow and climate

Expected Outcomes

Expected, and hoped for, outcomes are statistical relationships between Dall Sheep recruitment success and seasonal snow conditions, aiding our understanding on the mechanisms driving their population decline. The sheep survey data is patchy in time and space, so identification of broad, range-wide relationships will help identify years that may have been hazardous for sheep but where no surveys were conducted. Statistical relationships describing correlations of seasonal snowfall in relation to the strength of teleconnections, e.g. the Arctic Oscillation, for each domain could help wildlife managers anticipate sheep-hostile years and make informed decisions in regard to their controlled harvest.


This project will help improve our understanding on the drivers of long-term Dall Sheep decline and inter-annual recruitment success. Understanding these drivers will improve evidence-based decision making in regard to their future management, aiding the sustainability of a critical ecosystem service.


I have a reasonable level of proficiency in ArcInfo, less so in Model Builder. I am confident in Python for geospatial analysis, though I most often use Matlab when working SnowModel output. I have zero experience in R.


Liston, G.E. and Elder, K., 2006. A distributed snow-evolution modeling system (SnowModel). Journal of Hydrometeorology7(6), pp.1259-1276.


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1 Comment

  1.   jonesju — April 9, 2018 @ 8:50 am    

    hi Chris,
    Thanks for your explanation of your spatial problem. It appears that the variable of interest (dependent variable) is recruitment success; the lamb:ewe ratio. It seems like you should start by creating a GIS layer showing the locations where this ratio has been determined, with points attributed with information including the numbers of ewes, numbers of lambs, date/time, etc. For the first exercise, then, you would be attempting to determine whether you can see any spatial (or temporal) patterns (without knowing anything about snow). This will require you to identify and evaluate the spatial and temporal resolution and extent of your sheep data, which will ultimately constrain anything you can say.

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