Construction, Validation, and
Management Applications
of Elk Habitat Models in
Western OR and WA:
New Landscape Approaches
with Multiple Data Sources
Modeling Team:
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Jennifer Boyd
Priscilla Coe
John Cook
Rachel Cook
Greg Davidson
Ray Davis
Bill Gaines
Justin Hadwen
Bruce Johnson
John Lehmkuhl
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Bridgett Naylor
Ryan Nielson
Peter Singleton
Scott McCorquodale
Rob McCoy
Mary Rowland
David Vales
Marty Vavra
Mike Wisdom
Additional thanks to:
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Scott Harris, Washington Dept. of Fish & Wildlife
Chris Danilson, Sauk-Suiattle Indian Tribe
Frank Geyer, Quileute Tribe
Eric Cole, National Elk Refuge
Dewaine Jackson & Mike Pope, Oregon Dept. of
Fish & Wildlife
Bob Anthony, Oregon St. Univ.
Dana Sanchez, Oregon St. Univ.
Partners (Staffing, Funding, or Data)
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Boone and Crockett Club
Lower Elwha Klallam Tribe
Makah Nation
Muckleshoot Indian Tribe
National Council for Air and Stream Improvement
National Fish and Wildlife Foundation
Oregon Department of Fish and Wildlife
Oregon State University
Partners (Staffing, Funding, or Data)
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Quileute Tribe
Rocky Mountain Elk Foundation
Sauk-Suiattle Indian Tribe
Sporting Conservation Council
USDA Forest Service
USDI Bureau of Land Management
Washington Department of Fish and Wildlife
WEST, Inc.
Presentation
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Background & Need for Elk Habitat Models
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Modeling Objectives
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Topics Addressed in Presentations Today
Background and Need
R. Cook
Elk Habitat Models—Who Cares?
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Elk compose a multimillion dollar economy
in the western U.S.
(hunting, viewing,
damage).
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Elk distribution and
abundance across
landscapes, and thus
the species’ economic
impacts across land
ownerships, can be
directly controlled
through management.
Elk Habitat Models—Who Cares?
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Elk are an “early-seral”
species whose needs
are compatible with
active silviculture.
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Other “early-seral”
species may benefit
from habitat
management for elk.
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Public land use plans
continue to feature elk
and elk management.
Background and Need
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Models developed in 1970s & 80s with
limited empirical data.
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Empirical data restricted to single variable
habitat relations with elk.
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Contributions of multiple variables to
predict relative probability of elk use not
empirically-based (e.g., geometric mean);
resource selection modeling not yet
developed.
1986 Westside Model
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Size & Spacing of
Cover & Forage Areas
Density of Open Roads
Cover Quality
Forage Quality
Habitat effectiveness
= geometric mean of
the 4 covariates
Why New Elk Habitat Models?
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“Old” models still in use
but structure and some
components outdated
(e. g., thermal cover)
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Data from many radiotelemetry studies now
available for new
modeling approaches.
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Data on elk nutritional
resources now available
for modeling at
landscape scales.
Modeling Work:
• Summer range (June 1-Aug. 31)
• Landscape extents (>50,000 ac)
• Regional Inference Space
• Non-hunting periods
(but populations
are hunted)
• Female elk
Objectives
1. Build a nutrition model that predicts
nutritional resource values (e.g., dietary
digestible energy) for elk across all
landscape conditions on summer range.
2. Build and validate a set of plausible,
competing resource selection models that
predict the probability of elk use at
landscape extents on summer range.
Objectives
3. Include nutrition model predictions as a
covariate in resource selection models.
4. Include additional human disturbance and
physical covariates that potentially affect
or account for the probability of elk use of
nutritional resources.
Objectives
5. Use data from
multiple study
areas (diverse
environments &
land ownerships)
to construct,
select, and
validate models.
Objectives
Two predictions of primary interest:
1. Composition (percent area)
of a landscape by nutritional
condition (e.g., excellent,
good, marginal, poor)
—Nutrition Model
Objectives
Two predictions of primary interest:
2. Probability of elk use of a
landscape, given all
covariates that affect use
—Resource Selection Model.
Objectives
Two management options of primary interest:
1. Degree to which management can change
landscape area by nutritional condition
(Nutrition Model).
2. Degree to which management can change
the probability of elk use, given all major
factors that affect use (Resource
Selection Model).
Presentations
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Nutrition Modeling: John Cook
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Telemetry Data: Priscilla Coe
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Spatial Data: Bridgett Naylor
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Resource Selection Methods: Ryan Nielson
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Resource Selection Results: Mary Rowland
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Management Applications: Mike Wisdom
Nutrition Model
• % Canopy Cover
• % Hardwoods
• Potential Natural
Vegetation Zone
Dietary Digestible Energy (DDE)
Resource Selection Model
• DDE
• Dist. to Public Roads
• % Slope
• Dist. to Edge
Probability of Elk Use
Nutrition Model
Resource Selection Model
• % Canopy Cover
• DDE
• % Hardwoods
• Dist. to Public Roads
• Potential Natural
• % Slope
Vegetation Zone
Dietary Digestible Energy (DDE)
• Dist. to Edge
Probability of Elk Use
Nutrition Modeling: John Cook
Spatial Data: Bridgett Naylor
Nutrition Model
• % Canopy Cover
• % Hardwoods
• Potential Natural
Vegetation Zone
Dietary Digestible Energy (DDE)
• Telemetry Data: Priscilla Coe
• Spatial Data: Bridgett Naylor
• Methods: Ryan Nielson
• Results: Mary Rowland
Resource Selection Model
• DDE
• Dist. to Public Roads
• % Slope
• Dist. to Edge
Probability of Elk Use
Management Applications: Mike Wisdom
Nutrition Model
Resource Selection Model
• %Canopy Cover
• DDE
• % Hardwoods
• Dist. to Public Roads
• Potential Natural
• % Slope
Vegetation Zone
Dietary Digestible Energy (DDE)
• Dist. to Edge
Probability of Elk Use
Caveats
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Results are preliminary.
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Purpose of workshop is to share current
results and obtain feedback about the
scientific rigor and management utility of
“best” model(s).
Finalizing Model(s)
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Help needed from biologists to test and
evaluate management utility of models.
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Westside modeling process will be finished
in the coming months and an additional
workshop held by early next year.
Discussion Session
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Tuesday, April 20, 8 am-noon, same
location.
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Obtain feedback about scientific and
management utility of “best” model(s).
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Break-out groups will address specific
questions about management uses.