Elk Nutrition & Habitat Use
Models for Management
Introduction to
Blue Mountains Modeling Work
Blue Mountains Elk Models
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Nutrition Model
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Habitat Use Model
Workshop Objectives

Share methods and draft results of modeling
work and discuss additional work needed.
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Discuss management implications and uses
of models when finalized.
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Propose a “beta-testing” process for starting
model applications with managers.

Obtain feedback about how best to improve
draft models during final modeling work.
Elk Modeling Work
For additional information:
•
Visit PNW Research Station website:
•
Web search “Elk Models PNW Station”
http://www.fs.fed.us/pnw/calendar/workshop/elk/
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
Introduction

Timelines for Work

Background & Need for New Models

Modeling Objectives

Work Accomplished and Some Caveats
Timelines

Westside Modeling: 2009-2011
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Blue Mountains Modeling: 2011-2013
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Workshop held after first year of work to
share draft results with managers and
obtain feedback.
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Final workshop held after second year to
share final results and demonstrate
management applications.
Timelines

April 2012: First Blue Mountains Workshop to share
draft results and obtain feedback.
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Summer 2012: Conduct additional analyses to identify
final model and prepare for “beta-testing.”
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Fall 2012: Start “beta-testing” models with managers.
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Early 2013: Finalize models based on additional
validation tests and results of beta-testing.
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Spring 2013: Final models shared at second Blue
Mountains workshop.
Background and Need
R. Cook
Elk Habitat Models—Who Cares?

Elk compose a multimillion dollar economy
in the western U.S.
(hunting, viewing,
agricultural damage).

Elk distribution across
landscapes, and thus
the species’ economic
impacts across land
ownerships, can be
directly controlled
through management.
Elk Habitat Models—Who Cares?

Elk are a “seral-mosaic”
species whose needs are
compatible with active
silviculture and effective
travel management.

Public land use plans
continue to feature elk
and elk management—
the large majority of
summer range is on
public land.
Background and Need

Models developed in 1970s & 80s with
limited empirical data.

Empirical data restricted to single variable
habitat relations with elk.

Contributions of multiple variables to
predict relative probability of elk use not
empirically-based (e.g., geometric mean);
resource selection modeling not yet
developed.
Why New Elk Habitat Models?

“Old” models still in use
but structure and some
components outdated
(e. g., thermal cover)

Data from many radiotelemetry studies now
available for new
modeling approaches.

Data on elk nutritional
resources now available
for modeling at
landscape scales.
Modeling Objectives
R. Cook
Modeling Work:
• Summer range (elk productivity)
• Large (regional) landscapes,
multiple land ownerships, integrated
management strategies
• Local landscapes,
smaller projects within large
landscapes
Modeling Work:
• Focus on late summer
(August data)
• Non-hunting periods
(but populations
are hunted)
• Female elk
Objectives
1. Build a model that predicts nutritional
resources for elk across all landscape
conditions on summer range
(Nutrition Model).
2. Identify & rank a set of plausible,
competing models that predict elk habitat
use on summer range, & validate “best”
models with independent data
(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.
Objectives
Two predictions of primary interest:
1. Composition (percent area)
of a landscape by nutritional
condition
—Nutrition Model
Objectives
Two predictions of primary interest:
2. Level of elk use
(relative probability of use)
across a landscape, given
all covariates that affect use
—Habitat Use 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
level of elk use on a landscape, given all
factors that affect use (Habitat Use
Model).
Modeling Objectives:
• Habitat Use Model: Designed as a
tool to evaluate and manage elk
distributions within and across land
ownerships on regional landscapes,
and on smaller (local) areas within
the large landscapes.
• Elk management problems often
are related to where elk occur rather
than elk numbers per se.
Work Accomplished
R. Cook
Nutrition Model
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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.”
Two Analytical Steps

Model selection of top-ranked models
among many plausible, competing models.

Validation of top-ranked models
Radio Telemetry Data
GPS or Loran-C technology.
 High location accuracy, relocation
frequency, and no. of collared animals.
 24-hour coverage.

Starkey vs. Sled Springs Areas
Starkey
 Drier
 More rangeland,
less wet forest, few
wet meadows
 Gentle slopes
 Few canyons
 Open canopy from
spruce budworm
Sled Springs
 Wetter
 Less rangeland,
more wet forest, wet
meadows common
 Steep slopes or flat
 Major canyons
 Open canopy from
timber harvest
Starkey vs. Sled Springs Areas

Starkey Area tends to be more
representative of drier forest areas that
typify the southwestern portions of the Blue
Mountains.

Sled Springs Area is more representative of
wetter forest areas common to northeastern
portions of the Blue Mountains.
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Neither area, however, has high-elevation
subalpine forest environments.
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
Intended
Inference Space?
Caveats

Models are designed to evaluate summer
range.

Results from model applications also
represent conditions for elk on fall ranges
outside the hunting seasons.
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Models are not designed to evaluate
conditions on spring or winter ranges.
Caveats

Potential effects of livestock grazing on elk
habitat use could not be fully assessed.

We implemented a cattle model from Starkey
that evaluated cattle habitat use as a potential
covariate affecting elk use.

We also evaluated distance to water sources
(distance to streams, stock ponds, springs, etc.)
as surrogates for cattle use, but these types of
covariates do not directly measure cattle use.
Caveats

Spatial data on cattle presence and absence by
pasture and date, and associated stocking rates
when present in a pasture, were needed but
unavailable for study areas beyond Starkey.

A variety of additional cattle covariates
potentially affecting elk use could not be
considered because of this data void.
Caveats

Continuous coverage spatial data on
livestock grazing systems and stocking
rates, mapped by grazing dates for each
livestock pasture, is an essential and
unmet need for evaluating elk summer
and fall ranges.

Lack of continuous coverage spatial data
on livestock grazing systems and stocking
rates is a common problem throughout
western North America.
Caveats

Today’s presentations contain draft,
preliminary material. Additional analyses
are required before models are completed.

Our “best” nutrition and habitat use models
presented today are likely to be very similar
to the final models that will be completed
and “beta-tested” in late 2012 and early
2013.
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Feedback from attendees at this workshop
about the draft models is essential.
Question and Answer/Discussion Session at End of Day