Elk Nutrition & Habitat Use
Models for Management
Elk Nutrition & Habitat Use
Models for Management
Blue Mountains Modeling Work
Introduction to
Blue Mountains Elk Models

Nutrition Model

Habitat Use Model
Workshop Objectives

Present modeling methods and results.

Describe key management uses.

Provide case examples of application.

Discuss further steps for model evaluations and
management adoption.

Demonstrate use of the ArcGIS toolbox to run
models (Wednesday).
Elk Modeling Work
For additional information:
•
Visit PNW Research Station website:
http://www.fs.fed.us/pnw/research/elk/
•
Web search “PNW Elk Models”
Partners (Staffing, Funding, or Data)
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American Forest Resource Council
Boise Cascade Corporation
Boone and Crockett Club
Forest Capital LLC
National Council for Air and Stream Improvement
National Fish and Wildlife Foundation
Oregon Department of Fish and Wildlife
Oregon Forest Industries Council
Partners (Staffing, Funding, or Data)
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Oregon State University
Rocky Mountain Elk Foundation
Sporting Conservation Council
USDA Forest Service (WO, R6, PNW)
USDI Bureau of Land Management (WO, OR-WA)
Washington Department of Fish and Wildlife
WEST, Inc
Modeling Objectives
R. Cook
Modeling Work:
• Summer range (elk productivity)
• Female elk
• Focus on late summer
(August data)
• Pre-hunt period
(but populations are
hunted)
Modeling Work:
Importance of late summer period:
• Better nutrition is spatially limited
and below maintenance needs.
• Desire to retain elk on
public lands entering
the hunting seasons.
Modeling Work:
• Large (regional) landscapes,
multiple land ownerships, integrated
management strategies
• Local landscapes,
smaller projects within
large landscapes
Objectives
1. Build a model that predicts nutritional
resources for elk across all landscape
conditions during late summer
(Nutrition Model).
2. Identify and validate the best set of
predictor variables (covariates) that
predict elk use on these same landscapes.
(Habitat Use Model).
Objectives
3. Include nutrition model predictions as a
covariate in the habitat use model.
4. Include additional human disturbance,
vegetation, and physical covariates that
affect or account for elk use of nutritional
resources.
Objectives
5. Use data from
different study
areas & years
(different
environments &
ownerships) to
construct, select,
and validate
habitat use
model.
Modeling Objectives:
• Nutrition Model: Where is the best
available nutrition for elk in late
summer?
• Habitat Use Model: Are elk able to
use areas of best nutrition?
Are desired elk distributions across
ownerships being met?
Work Accomplished
R. Cook
Work Conducted in Past Year

Conducted additional analyses to consider
new covariates to improve models.

Worked with “beta-testers” to begin testing
of management applications of models.

Developed draft protocols and guidelines
(user’s manual & ArcGIS toolbox) for
running the models on example landscapes.
Results from Work Done in Past Year

Nutrition model structure remained the
same but with refined options for covariate
choices to meet objectives.

Added a new covariate to the habitat
model, replacing another covariate, with
improved management application.

Developed draft protocols, guidelines, and
spatial programs for management uses.
Nutrition Model

Set of models that predict elk dietary
digestible energy based on data from
grazing trials conducted with tame elk
across representative environments.
Habitat Use Model

Use available telemetry data for model
development (selection) & validation.

Model selection also referred to as model
“training.”
Elk Nutrition & Habitat Use
Models for Management
Introduction to
Blue Mountains Modeling Work
Starkey vs. Sled Springs Areas

Starkey has more dry forests that typify the
southwestern portions of the Blue
Mountains.

Sled Springs has more wet forests common
to northeastern portions of the Blue
Mountains.

Neither area, however, has high-elevation
subalpine or alpine environments nor lowelevation, driest environments of Blues.
Covariates Considered
Human
Disturbance
Nutrition
Dietary digestible energy
(DDE)
Accepted biomass (AB)
Total forage biomass (FB)
EVI (Enhanced Vegetation
Index)
Distance to:
any road
open road
closed road
class 1 road
class 2 road
class 3 road
class 4 road
class 4 or greater
road
class 1 or 2 road
class 3 or 4 road
Density of:
all roads
open roads and
trails
Vegetation
Physical/
Other
Proportion of vegetation
classes
Percent slope
Overstory canopy cover
(CC)
Percent area in:
flat to gentle slopes
moderate to steep slopes
very steep slopes
Dominant CC class
Cover/forage ratio
Percent area in cover
Distance to:
cover-forage edge
cover patch (3 patch
sizes)
agricultural land
Dominant slope class
Cosine and sine of aspect
Convexity
Solar radiation
Soil depth
Distance to:
water
pond
stream
Dominant landowner
Cattle RSF
Reduced Set of Covariates in Competing Models
Habitat Model (5 covariates)
1.
Dietary Digestible Energy of Forage.
2.
Distance to Class 1 or 2 Open Roads.
3.
Distance to Class 3 or 4 Open Roads.
4.
Slope.
5.
Percent Area in Forest Vegetation Types
(replaces former covariate “Proportion of
Vegetation Types”).
Caveats

Models are designed to evaluate conditions
during late summer (a period of extremely
limited nutrition).

Results from model applications also
represent conditions for elk on fall ranges
outside the hunting seasons.

Models are not designed to evaluate
conditions during spring, winter, or hunting
seasons.
Caveats

Potential effects of livestock grazing on elk
habitat use could not be assessed.

Needed cattle data were not available (except
Starkey) across study areas as well as for
landscapes where models would be applied.

We evaluated surrogates for cattle use (e.g.,
distance to water), but these types of
covariates did not enter into the top-ranked
models.
Caveats
Some additional model evaluations are needed before
formal release of models:

Evaluating variations in some model input data.

Applying the road classification method to additional
test landscapes to refine protocols.

Conducting additional model validations (if
possible).
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Documenting limits of inference space.
Workshop

Feedback from attendees at this workshop
about the models presented here, and how
they are used for management, is essential.
Tuesday Morning Presentations

Sources of Spatial Data: Bridgett Naylor

Nutrition Modeling Methods & Results: Rachel Cook
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Habitat Modeling Data & Methods: Ryan Nielson
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Habitat Modeling Results: Mary Rowland
Tuesday Afternoon Presentations

Summary and Management Uses: Mike Wisdom

McCoy Creek Example: Mark Penninger, Leonard
Erickson
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Mount Emily Example: Mark Penninger, Mark Kirsch

Guided Discussion: Dana Sanchez
Wednesday Session

Running the models with ArcGIS Toolbox:
Bridgett Naylor, Jennifer Hafer

Demonstration data for this session can be
downloaded on your laptop tonight during the
reception, 5-7 pm, Tucannon-Palouse Rooms
Question and Answer/Discussion Session at End of Day