Draft Westside Elk Model User Guidelines, v. 2.0
1
User Guidelines for Application, Summary,
and Interpretation of Westside
Elk Nutrition and Habitat Use Models
Draft Version 2.0
January 2013
Authors:
Mary M. Rowland1
Jennifer M. Hafer1
Bridgett J. Naylor1
Priscilla K. Coe2
Michael J. Wisdom1
John G. Cook3
Rachel C. Cook3
Ryan M. Nielson4
Bruce K. Johnson2
1
USDA Forest Service, Pacific Northwest Research Station; 2 Oregon Department of Fish and
Wildlife; 3 National Council for Air and Stream Improvement; 4 Western EcoSystems Technology
(WEST), Inc.
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Table of Contents
Abstract .............................................................................................................................................4
A. Introduction to Westside Elk Models .............................................................................................4
B. Spatial and Temporal Extents for Summarizing Model Results ........................................................6
Defining an Analysis Area for Model Application ..................................................................................... 7
Regional Landscape............................................................................................................................... 7
Local Landscape .................................................................................................................................... 9
C. Westside Elk Nutrition Model ......................................................................................................10
Nutrition Tool .......................................................................................................................................... 11
Input Data Needed and Preparation................................................................................................... 14
Using the Nutrition Tool: Do’s and Don’ts .......................................................................................... 15
Steps to Run the Nutrition Tool .......................................................................................................... 19
Tables .................................................................................................................................................. 21
Update Vegetation Tool .......................................................................................................................... 22
Input Data Needed and Preparations ................................................................................................. 23
Using the Update Vegetation Tool: Do’s and Don’ts .......................................................................... 26
Steps to run the Update Vegetation Tool ........................................................................................... 28
Tables .................................................................................................................................................. 29
D. Westside Elk Habitat Use Model ..................................................................................................30
Habitat Covariates Tool........................................................................................................................... 30
Input Data Needed and Preparations ................................................................................................. 31
Using the Habitat Covariates Tool: Do’s and Don’ts ........................................................................... 32
Steps to Run the Habitat Covariates Tool ........................................................................................... 38
Tables .................................................................................................................................................. 39
Habitat Use Tool...................................................................................................................................... 41
Input Data Needed and Preparations ................................................................................................. 41
Using the Habitat Use Tool: Do’s and Don’ts ...................................................................................... 42
Steps to Run the Habitat Use Tool ...................................................................................................... 43
Tables .................................................................................................................................................. 44
E. Interpretation and Summaries of Model Results...........................................................................45
Summarizing Results from the Nutrition Model ..................................................................................... 45
Regional Landscape: Existing Condition and any Management Alternatives ..................................... 46
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Changes in Regional Landscape: Existing Condition Relative to each Management Alternative ....... 47
Changes in Local Landscape Relative to each Management Alternative ........................................... 48
Summarizing Results from the Elk Habitat Use Model ........................................................................... 49
Regional Landscape: Existing Condition and any Management Alternatives ..................................... 49
Changes in Regional Landscape: Existing Condition Relative to each Management Alternative ....... 52
Local Landscape: Existing Condition and any Management Alternatives .......................................... 56
Changes in Local Landscape: Existing Condition Relative to each Management Alternative ............ 56
Appendix 1. Literature Cited ............................................................................................................57
Appendix 2. Beta Testers for Westside Elk Modeling ........................................................................58
Appendix 3. Commonly Used Data Sources for Elk Model Application...............................................60
Appendix 4. Selectivity of Elk Forage Species In Western Oregon and Washington ............................61
Glossary of Terms and Abbreviations................................................................................................66
English Equivalents ..........................................................................................................................67
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Abstract
Scientists from state, federal, private, and tribal entities have developed two new models, elk (Cervus
elaphus) nutrition and elk habitat use, for management application in western Oregon and western
Washington. The models provide managers with contemporary tools to evaluate nutritional and habitat
conditions for elk at landscape scales commensurate with all-lands management of the species among
diverse partners. The models focus on summer range, because this period is considered most
important to elk productivity in this region. Model users can not only evaluate current nutritional
conditions and predicted use by elk but also evaluate effects of land management, such as thinning
projects or road closures, on elk. The nutrition and habitat use models are combined in a single ArcGIS
toolbox. These guidelines provide information about (1) the history of the elk nutrition and habitat use
models, (2) selecting regional and local analysis areas, (3) incorporating the models in a spatial
framework within ArcGIS, (4) preparing input data and caveats for usage, and (5) interpreting and
summarizing model outputs. The guidelines describe each of the six tools created for the modeling
project and the tool parameters and processes required to apply them. The guidelines conclude with
detailed examples of data summaries for outputs from the nutrition and habitat use models.
Suggested citation: Rowland, Mary M.; Hafer, Jennifer M.; Naylor, Bridgett J.; Coe, Priscilla K.;
Wisdom, Michael J.; Cook, John G.; Cook, Rachel C.; Nielson, Ryan M.; Johnson, Bruce K. 2013.
User guidelines for application, summary, and interpretation of Westside elk nutrition and
habitat use models. Gen. Tech. Rep. PNW-GTR. Portland, OR: U.S. Department of Agriculture,
Forest Service, Pacific Northwest Research Station.
A. Introduction to Westside Elk Models
Scientists from state, federal, private, and tribal entities have developed and validated two new models elk nutrition and elk habitat use - for management application in western Oregon and western
Washington (Westside region, fig. 1). The objective of this work was to provide managers with
contemporary tools to evaluate nutrition and habitat conditions for elk at landscape scales
commensurate with all-lands management of the species among state, federal, tribal, and private
partners.
The models focus on summer range, because this period is considered most important to elk
productivity in the Westside region (Harper 1987, Hutchins 2006, Cook et al., in press). The nutrition
model predicts dietary digestible energy (DDE; glossary terms are bolded at first use and defined in the
Glossary at the end of this document) that elk can acquire from each plant community during summer.
The predictions are based on diet data collected during elk grazing trials across the Westside region
from 2000 -2002 (Cook et al., n.d. a). Digestible energy levels in elk diets in summer are affected by
nutritional adequacy of the various vegetation communities used by elk while foraging and are related
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to reproduction and survival in summer and subsequent seasons (Cook et al., n.d. a). The nutrition
model predicts DDE for landscapes across the Westside region.
Figure 1—Westside region of Oregon and Washington
(shaded areas), where elk nutrition and habitat
modeling work has been completed. Development of a
nutrition model for elk in SW Oregon is underway.
The summer nutrition model was integrated as one of
several predictor variables (covariates) in the elk habitat
use model, which predicts level of elk use based on the
environmental conditions that most affect or account
for landscape-level use by elk. The habitat modeling
process consisted of two parts: model selection and
model validation. The model selection process initially
considered over 50 environmental covariates related to
nutritional, human disturbance, and biophysical
conditions. We then considered subsets of these
covariates in various combinations in more than 10
unique models to identify which model was best
supported by patterns of observed elk use, based on
radio-telemetry locations of elk across Oregon and
Washington.
A combination of four covariates consistently
explained patterns of elk habitat use based on
radio-telemetry data collected over 5 years from
three primary study areas (fig.2):
(1) elk dietary digestible energy (higher DDE,
higher predicted elk use);
(2) distance to roads open to public access
(farther from roads, higher use);
(3) percent slope (flatter slopes, higher use); and
(4) distance to cover/forage edge (closer to edge,
higher use).
Figure 2—Study areas used for model selection
vs. model validation for elk habitat modeling in
western Oregon and Washington.
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We used a model validation process to evaluate how well predictions of elk use based on this topranked, regional elk habitat use model matched actual elk use, as applied to five additional radiotelemetry data sets (fig. 2). The telemetry data sets used for validation were independent of data used
in model selection. Validation tests produced correlation coefficients for predicted and observed use
ranging from about 0.32 to 0.99 - with most values greater than 0.80 – across the five independent data
sets.
We then used a process of “beta testing” the models with biologists and GIS (Geographic Information
System) staff from Region 6 of the U.S. Forest Service, the Bureau of Land Management
(Oregon/Washington State Office), and Washington Department of Fish and Wildlife (app. 2). The team
used feedback from the beta testers to refine the modeling algorithms and guidelines presented in this
document.
These guidelines instruct model users in application, summary, and interpretation of the nutrition and
habitat use models. The basic steps are:
•
•
•
•
•
•
•
•
•
Define analysis area(s) for model application, including the regional and local landscape(s).
Obtain necessary spatial layers for model inputs, e.g., vegetation, DEM (digital elevation model),
and roads. See appendix 3 for suggested data sources or visit the Westside Elk Nutrition and
Habitat Modeling Project website to download example layers
(http://www.fs.fed.us/pnw/research/elk/).
Update existing vegetation layer if necessary to correct for changes in vegetation between the
year of the vegetation layer and current conditions (Update Vegetation Tool).
Run the Nutrition Tool.
Run the Habitat Covariates Tool.
Run the Habitat Use Tool.
Update roads and/or vegetation layers if necessary to simulate changes under various
management alternatives.
Re-run the Nutrition, Habitat Covariates, and Habitat Use Tools as needed to produce outputs
for each alternative of interest.
Summarize results for existing condition and management alternatives.
B. Spatial and Temporal Extents for Summarizing Model Results
The habitat use model is applied across regional landscapes. However, summaries of model outputs can
be completed at two spatial extents: (1) the regional landscape, which should be an area >10,000 ha on
which the Nutrition and Habitat Use Tools are applied (analyzed); and (2) the local landscape, areas of
800 ha or larger and nested within the regional landscape, for which a subset of the results from the
regional landscape analysis can be summarized.
Users can summarize results for one time period (existing condition) as well as for different
management alternatives (future conditions), under which potential effects of management may be
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evaluated and results contrasted with the existing condition. These summaries can be completed for
both the regional and local landscapes (see “Interpretation and Summaries of Model Results”).
Defining an Analysis Area for Model Application
Selection of an analysis area for running the nutrition model, elk habitat use model, or both will depend
on the specific objectives of the user. However, general guidelines should be followed to ensure that
the models are applied in a way that is consistent with their development and intended usage. We
provide basic guidance below for defining analysis areas at both regional and local extents.
Regional Landscape
Regional analyses with the nutrition and elk habitat use models typically address management of elk
distribution across large areas (e.g., 10,000 ha or more) and across multiple land ownerships. Model
outputs can be used not only to inform management of elk distribution, but also to estimate some
measures of animal performance (Cook et al., n.d. b). Elk will generally use areas that maximize animal
performance; thus, effective management of elk distributions across large landscapes and multiple land
ownerships may also benefit elk performance.
We recommend using ecological boundaries that are based on collaboration and agreement with state
wildlife agencies to select the analysis area over which the models will be applied. These boundaries, as
defined in partnership with the Oregon Department of Fish and Wildlife and Washington Department of
Fish and Wildlife, reflect landscape-scale patterns of elk distribution and use across multiple land
ownerships on summer range. For example, a population of elk in a particular state wildlife
management unit may be located disproportionally on private lands, where use is not desired, and less
often on adjacent public lands, where use is desired. Identifying regional boundaries that encompass
the entire landscape of potential summer range across these land ownerships is essential to
comprehensively evaluate nutrition and habitat use with the models. Analysis within a well-selected
study boundary allows different management options to be considered in relation to desired
distributions of elk in time and space.
By using an ecologically defined boundary the user will avoid imposing biologically meaningless
boundaries, such as ownership or other land allocation, on the study area. However, elk movements are
often impeded by features such as interstate highways, residential and commercial developments, and
large river corridors; these barriers should be considered when defining an analysis area. In some
instances, the user will be most interested in modeling elk use in administratively defined areas such as
a game management unit, Forest Service District, or Bureau of Land Management (BLM) resource area.
In these cases, the model can still be applied across an ecologically defined area, but results summarized
by administrative unit. Regardless of the primary motivation in selecting a study area, model users
should carefully consider knowledge of local elk distribution and movements in the evaluation area.
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When predicting elk nutritional conditions with the Westside nutrition model, the minimum-sized area
for analysis will depend on the origins of the input data. If biologists have field data available (e.g.,
canopy cover, or abundance of elk forage species; app. 4), the analysis area can be a stand or site. The
empirical data used to predict nutrition were collected in macroplots of at least 1.0 ha in size; thus,
applying the nutrition model to smaller areas should be avoided. If using model inputs derived from
remote sensing, such as canopy cover from the Gradient Nearest Neighbor (GNN) layer, users should
select a larger area such as a subwatershed (e.g., 4,000 ha). If you are using remotely-sensed data but
also have a more local data set for comparison, we suggest comparing the two products to better
understand the quality of the remotely-sensed layer. Generally, GNN and similar products (e.g.,
LANDFIRE existing vegetation type; http://www.landfire.gov/NationalProductDescriptions21.php) are
suitable for landscape to regional-scale analyses but are insufficiently accurate for local or stand-level
applications.
For predicting elk habitat use with the Westside elk habitat use model, we recommend that the
minimum regional analysis area be ~10,000 ha or larger. This scale corresponds to elk landscape use for
a regional (e.g., multiple local herds) population of elk. The elk habitat use model was developed and
validated in a variety of study areas ranging from 2,400 to 53,000 ha. Even when evaluating the
potential effects of management actions (e.g., road closures or silviculture treatments) in smaller, local
areas (~2,000 ac or larger), it is important to retain the original regional scale used to develop the
models when making predictions. One can always “zoom in” to isolate an area and summarize results
within smaller local landscapes after predictions are made for the larger study area (see Local Landscape
below).
Before the elk habitat use model can be applied, a 4-km buffer should be drawn around the regional
landscape (fig. 3). The model is run inside this larger area (i.e., regional landscape plus buffer) to
capture potential effects of roads and vegetation conditions adjacent to the analysis area. This distance
was based on modeling results demonstrating that elk respond to roads open to the public as far as 4
km away. Thus, an open road in the buffer area may affect predicted use by elk within the analysis area
and must be accounted for when the elk habitat use model is applied. Likewise, the nearest
cover/forage edge for pixels in the analysis area but close to the buffer could be in the buffer, rather
than the study area, thus affecting predictions of elk use. However, when summarizing results of the
elk habitat use model, only use the area within the regional landscape boundary; the buffer area
should not be included in data summaries.
An additional step before model application is to mask or set null values for any land cover types for
which predictions of elk use are illogical (e.g., urban/developed landscapes, rivers, lakes, reservoirs and
other water bodies, scree and talus slopes). The habitat use model should be applied across all lands
within the analysis area (other than the masked areas mentioned above), regardless of ownership or
other status
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Figure 3—A regional analysis area, its 4-km buffer, and two local landscapes with treatment units
displayed for each. The elk nutrition and habitat use models are applied within the regional landscape
and its buffered boundary; however, data are summarized only within the regional landscape boundary
(that is, without the area of the 4-km buffer included).
Local Landscape
Analysis of large landscapes is focused on comprehensive management of elk distributions across large
areas of summer range that encompass multiple land ownerships and associated landscape
management issues. By contrast, analysis of local areas is more site specific and often relates to
particular management activities on a given area and land ownership. Examples of local effects might
be a set of commercial thinning units, paired with changes in access management, and evaluating how
these local activities might affect an individual herd of elk on summer range in a small area typically
used by 10-20 elk (e.g., areas as small as 800 ha).
These local, smaller-scale summaries complement the regional-scale application of the nutrition and elk
habitat use models, and model results can be compared among projects or to the regional landscape
results (see “Interpretation and Summaries of Model Results” for guidance on specific data summaries
of model outputs). Local landscapes within the larger regional area (including the buffer around the
actual treatment units; see explanation below) should be >800 ha, due to the spatial resolution and
accuracy of the vegetation layers used in developing the elk habitat use and nutrition models. Values
for model covariates in areas smaller than 800 ha will likely be below the precision required by the
models.
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In most cases, local landscape summaries will involve a collection of several small treatments, all of
which should be summarized within one boundary. For example, if the proposed management involves
20 small thinning units, a polygon should be drawn around these units and model outputs summarized
within this boundary (local landscape summary boundary; fig. 3). As described for the regional level, a
buffer should also be applied around the project area. This buffer is required because benefits will
extend to areas near but outside the actual treatment sites. For example, with decreased cover from
thinning and the creation of additional cover/forage edges, distance to the nearest edge is decreased for
many pixels near the treated sites. If a basic minimum convex polygon were drawn around the units
(i.e., essentially “connecting the dots” of the outermost points of each unit), these benefits would not be
completely accounted for in local landscape summaries of nutrition or predicted elk use.
There is no hard and fast rule to guide the establishment of local landscape boundaries, within which
the results of the prior regional landscape modeling results are summarized. However, the way in which
the boundary is drawn around the local analysis area will affect subsequent comparisons of model
results (e.g., percentage of area by nutritional class) among potential management alternatives. If the
actual acreage treated is a very small percentage (e.g., <5%) of the local landscape, improvements in
nutrition or predictions of elk habitat use will be difficult to detect. Again, there is no hard and fast
guidance in this regard. The exact spatial arrangement and size of the treatment units within the
boundary of the local analysis area will affect all summaries at this scale, so careful consideration is
required in delineating this boundary to ensure that real benefits of management treatments will be
detected.
To reiterate, all model runs (analyses) occur at the regional landscape extent; that is, this is the spatial
extent at which the models are applied. Once results are obtained for the regional landscape, we
recommend that you summarize outputs from your regional landscape first, and then further summarize
results for any local landscapes of interest. You can “zoom in” to these local boundaries for your
calculations and compare summaries across local landscapes or to the larger, regional landscape.
An exception is application of the nutrition model. If desired, the nutrition model can be applied in a
local landscape independently of the elk habitat use model. That is, a local landscape can be input as
the study area boundary for application of the nutrition model by itself, to estimate existing nutritional
conditions, or to project nutritional conditions under several alternatives in which vegetation is altered
across the local landscape. However, as stated above, the elk habitat use model should only be applied
across the regional landscape, although model outputs can be summarized for the regional and local
landscapes.
C. Westside Elk Nutrition Model
We developed two different primary models under the auspices of the Westside elk modeling project –
an elk nutrition model and an elk habitat use model. In order to run these models in a GIS framework,
these models were incorporated in an ESRI ArcGIS toolbox (Westside Elk Habitat Models Toolbox; fig. 4),
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using ESRI ArcGIS 10.0 ModelBuilder. Figure 4 displays the relationship between the toolbox and the six
supporting tools.
Figure 4—The Westside Elk Habitat Models Toolbox contains six tools, including three Nutrition Tools
(Nooksack, Springfield, Willapa Hills), the Habitat Covariates Tool, the Habitat Use Tool, and the Update
Vegetation Tool.
Nutrition Tool
Introduction
The elk habitat use model requires four covariates (Rowland et al., n.d.; fig. 4). One of these is mean
dietary digestible energy (DDE). The following text and tables describe processes used to derive DDE
and how to run the Nutrition Tool to obtain predictions of DDE in an example study area. The nutrition
model is a stand-alone application that can be applied independently to assess nutritional conditions for
elk. However, mean DDE within a 350-m radius circle (one of the model’s outputs) is also used as one of
the four inputs to run the full Habitat Use Tool. Note: The Nutrition Tool produces both “raw” DDE
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values for each pixel in the output grid and mean DDE. The raw values are used in mapping and for
summaries of nutrition across regional or local landscapes (see “Summarizing Results from the Nutrition
Model”), whereas the mean DDE output grid is the input used in the Habitat Use Tool, but can also be
used in summaries.
There are four inputs required to calculate DDE:
•
Potential natural vegetation (PNV) zone.
•
Modeling region (one of 3).
•
Overstory canopy cover (%) of all live trees.
•
Proportion of total live trees (>2.5 cm dbh) that are hardwoods (note that in the nutrition
equations this is a proportion, from 0 to 1, whereas canopy cover is a percentage, ranging from 0
to 100).
Predicting Elk Nutrition
Elk nutrition in our model is represented by dietary digestible energy, the level of DE that elk can be
expected to acquire in their diets in kilocalories per gram (kcal/g) of forage consumed. The final
equations that predict DDE use abundance of “neutral” plant species (NB) and “selected” species (SB) in
kilograms (kg) of forage per hectare (ha) as inputs (neutral and selected are two categories of plants
reflecting elk preferences; see app. 4 for a list of elk forage species categorized by selectivity). Levels of
DDE and elk forage preferences were determined using tame, trained elk in field studies in western
Washington and Oregon, 2000-2002 (Cook et al., n.d., a). Values for plant abundance are in turn
generated by another series of equations for three potential natural vegetation (PNV) series, or zones,
in western Oregon and Washington: western hemlock series (Tsuga heterophylla), mountain hemlock
series (Tsuga mertensiana), and Pacific silver fir (Abies amabilis) series, as described by Franklin and
Dyrness (1988). The mountain hemlock and Pacific silver fir series were combined for modeling elk
nutrition. All additional PNV zones that occur in the Westside modeling region were cross-walked to
one of these three zones for application of the appropriate set of prediction equations (see app. 3 for
data sources used). For zones that could not be cross-walked, such as tanoak (Lithocarpus densiflorus)
in southern Oregon, the nutrition model cannot be applied; see Constraints and Notes, below.
For higher elevation vegetation zones (i.e., the mountain hemlock/silver fir), there is only one equation
for each type of forage and for DDE. That is, regardless of the locale within the Westside region for
which you are predicting forage abundance or DDE, only one equation is used to predict abundance and
DDE within the mountain hemlock/silver fir zone. However, for the western hemlock zone, there are
separate equations for forage abundance in each of the three modeling regions in which the elk field
work was conducted: Nooksack, Springfield, and Willapa Hills. Likewise, the DDE prediction equations
in this zone differ for each of these three regions (fig. 5).
For mapping, DDE can be displayed as a continuous variable or categorical. If categorical, six classes are
recommended, which are generated when using the Nutrition Tool (see “Summarizing Results from the
Nutrition Model”). These DDE levels relate to specific measures of elk productivity (Cook et al. 2004:55).
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Depending on the size and location of a particular study area, some Westside sites may have no or very
little area in the highest nutrition classes.
Figure 5—Three modeling regions used for assigning the correct
Nutrition Tool to a study area.
Using the Nutrition Equations
The equations and appropriate tool parameters to predict forage
abundance and, ultimately, DDE are embedded within the Nutrition
Tool. For users who intend to predict elk nutritional conditions
outside the modeling framework we created in ArcGIS (i.e., the
Nutrition Tool created in ModelBuilder), the equations and explicit
methods to quantify forage abundance and DDE are available upon
request. The equations, however, can also be found by opening the
Nutrition Tool in “Edit” mode. For model users interested in simply
exploring the relation between different values of the input variables (e.g., tree canopy cover and
hardwood proportion) and resulting DDE values in a non-spatial environment, the equations can be
copied to a spreadsheet and used with multiple, plausible combinations of the input variables. We
describe the Nutrition Tool in the following section of this document (“Using the Elk Nutrition Tool: Do’s
and Don’ts”). In that section we also provide guidelines for data preparation if you are predicting
nutrition with other tools in ArcGIS or other GIS software.
Tool Constraints and Notes
•
•
•
•
•
•
Some tool inputs are vector format and others are raster format. During tool execution all
inputs are converted to raster format and the outputs from the Nutrition and Habitat Use Tools
are raster format. Summary instructions are given for raster format outputs.
The equations used in this tool were derived from plot data collected in western Oregon and
Washington. Running this tool in other regions or in a study area with portions outside the
Westside modeling boundary may not predict nutrition accurately.
The tool is intended for application with 30-m resolution raster data, and is not recommended
for use at other resolutions.
The CC and HW100 variables (see “Input Data Needed and Preparation,” below) must be integer
data types for the Lookup tool to work properly. Using decimal data will skew the values
towards zero.
Certain existing vegetation types (e.g., Ecological Systems in GNN) are unsuitable as potential elk
habitat (e.g., water, ice, talus); we assigned these types a value of “M” (mask) and modeled
them as “no data.” Similarly, some PNV types could not be cross-walked to one of the two PNV
zones used in the nutrition equations; we also masked out these types as “no data.”
The Nutrition Tool assigns any land cover types defined as agriculture a default values of 2.825
kcal/g for DDE; that is, DDE is not calculated for agricultural lands using the equations in the
Nutrition Tool in ArcGIS. If agricultural lands are in your analysis area and you know the specific
crops grown, adjust DDE values upward or downward as appropriate, based on standard crop
quality information.
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Input Data Needed and Preparation
1. Existing Vegetation grid (e.g., GNN; app. 3)
a. This layer must include canopy cover, stand height, and hardwood proportion attributes
b. This layer is modified by adding new fields in the attribute table that are specific for all
the tools. These fields are described below (Note: Stand height is included below,
although it is not required for the Nutrition Tool, but is required for the Habitat
Covariates Tool described later in this document):
Field Name
Elk_Hab_Ma
CANCOV_I
HW100_I
STNDHGT_I
Definition
A text field that defines
which vegetation values
are elk habitat (modeled)
and non-habitat (not
modeled)
Percent canopy cover as
defined by the existing
vegetation layer, then
converted to an integer
data type
Hardwood proportion as
defined by the existing
vegetation layer, then
multiplied by 100 and
converted to an integer
data type
Stand height (meters)
converted to an integer
data type
Values
H=habitat (modeled)
M=non-habitat (masked)
A=agriculture land cover
types
0-100
0-100
>=1
2. Potential Natural Vegetation grid (PNV; app. 3)
a. This layer defines the potential vegetation zones
b. This layer is modified by adding a new field to the attribute table that is specific to the
tool. This field is described below:
Field Name
CROSSWALK_I
Definition
Values
An integer field that
1=WHZ
classifies each vegetation
or
zone type into either WHZ 2=MHSFZ
or MHSFZ
c. These two zone classifications are used in the tool to define which algorithms are used
to calculate plant abundance and DDE.
3. Study Area Boundary feature layer
a. This is a polygon layer that defines your study area plus a buffered distance around it
(fig. 3).
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Using the Nutrition Tool: Do’s and Don’ts
NOTE: There are three modeling regions in western Oregon and Washington: Nooksack (NK), Willapa
Hills (WH), and Springfield (SP; fig. 5), and three corresponding nutrition tools. The nutrition equations
vary by region specifically for the western hemlock zone. Be sure to use the correct regional Nutrition
Tool for the area in which you are applying the model. To determine which regional model is
appropriate, overlay your study area boundary with the “Model Region.shp” file and choose the
appropriate region.
The Nutrition Tool predicts plant abundance (biomass in kg/ha) as an intermediate step in the prediction
of DDE. Calculating plant abundance requires three input variables: (1) canopy cover (percentage
canopy cover of all live trees), (2) hardwood proportion (proportion of all live trees of dbh >2.5 cm that
are hardwood species, such as alder (Alnus spp.), bigleaf maple (Acer macrophyllum), and paper birch
(Betula papyrifera), and (3) potential natural vegetation series: mountain hemlock/silver fir
(TSME/ABAM) and western hemlock (TSHE), or no data. You also need to know which modeling region
your study area falls within to select the proper set of nutrition equations, addressed previously (fig. 5).
Usage Tips
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•
Use ArcGIS 10.0 or newer with an ArcInfo license and Spatial Analyst Extension.
Input grids should be 30-m resolution.
We recommend that all input grid and feature layers have the same projection and that they
match the *.mxd data frame coordinate system and projection to prevent any shifts or errors in
calculations.
The vegetation grid layer (e.g., GNN) contains the Canopy Cover variable (CANCOV_I field),
Hardwood Proportion variable (HW100_I field), and a variable used to classify the GNN values as
modeled and not modeled, the ELK_HAB_MA field.
1. The CANCOV_I and HW100_I fields must be integer values for the Lookup tool to work
properly. If you are using your own existing vegetation layer, ensure that the canopy
cover and hardwood proportion attribute values are formatted as integer type and
range from 0-100.
a. HW100_I is a proportion and then multiplied by 100 to maintain precision and
convert this value to a percentage and an integer; final values range from 0-100.
b. The plant abundance and digestible energy equations were adjusted
accordingly. Any reference to HW is now HW100/100 in the equations in
ModelBuilder.
2. The tool is not designed to use separate input layers for CC or HW. These variables are
attributes of the GNN or other vegetation layer, and the tool uses the “Lookup” tool to
create single value grids for each variable. If you are using your own existing vegetation
layer ensure that it is one raster layer with both these attribute fields added.
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The potential vegetation grid layer (e.g., PNV) contains vegetation zones re-classified into either
the Western Hemlock (WHZ) or Silver Fir/Mountain Hemlock (SFMHZ) vegetation series (or
zones).
1. The CROSSWALK_I field is where we re-classified each individual PNV zone occurring in
western Oregon or Washington to one of these two classifications – 1=WHZ or
2=SFMHZ.
2. Any vegetation zone that is not cross-walked is given a No Data (-9) assignment and is
not suitable for modeling with these equations.
In-line variable substitution is used in this tool. Ex: %Study Area Code%, as seen in the file path
name of many processes, will be replaced with the alpha/numeric code you chose. See the
ArcGIS Desktop Help for further assistance.
Output grids will have the same projection and resolution as the input Vegetation Grid Layer
(e.g., GNN, 30-m) and will be clipped to the boundary of the Study Area Boundary polygon.
The study area code parameter will be the prefix for all output file names.
Some outputs (e.g., mean_dde) produced by this tool have been summarized within a 350-m
radius circle surrounding the center pixel. This is the scale at which the models were developed
and validated, and should not be changed.
Certain settings are pre-set, located under the Environment Tab. These settings may need to be
edited if errors occur when running the tool (table 1).
Be sure to check all parameter fields in the tool to make sure the default settings are
appropriate for your data and study area. Default parameter inputs or SQL expressions may be
incorrect and need to be reset.
Table 2 contains a list of outputs and their definition.
The Elk Nutrition Tool Parameters
Expression
Explanation
Study Area Code (4 char max) (Required)
Choose up to a four character abbreviation (alpha/numeric)
that represents the analysis area in which the tool is applied.
The abbreviation cannot exceed four characters nor can it
begin with a number, or the tool will not run. This code will
precede the name of each output layer. Grids that are created
cannot exceed 13 character naming conventions; hence this
study area code cannot exceed four characters.
Output Workspace Folder (Required)
Select the folder location in which you want the outputs to be
saved. Within this folder, a temporary "temp" folder is
automatically created. Each output will be saved in the Output
Workspace Folder if it's a permanent file or the temporary
folder if it's an intermediate file. You should have overwrite
processes activated (found in the Tools menu, Options,
Geoprocessing Tab); if not, you will need to create a new folder
each time the tool is run.
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Expression
Explanation
Study Area Boundary (Required)
Select the polygon layer that represents the regional landscape
study area. This layer should include a buffered area around
the actual study area as described earlier.
Existing Vegetation Grid (Required)
Select the existing vegetation grid that contains canopy cover
and hardwood proportion data as attributes to be used in the
tool.
Existing Vegetation Where Clause
(Required)
Create a SQL statement that defines which values in the
existing vegetation grid layer are to be used for modeling
(which is considered elk habitat, natural and agricultural lands).
Here, non-habitat vegetation (such as barren lands, developed,
rock/sand, water, etc.) should be excluded from analysis.
Existing Vegetation Canopy Cover
Lookup Column (Required)
Select the field in the existing vegetation grid layer that
contains the canopy cover data as an integer data type. Values
will range from 0-100. This is a parameter of the "Lookup" tool.
Existing Vegetation Hardwood
Proportion Lookup Column (Required)
Select the field in the existing vegetation grid layer that
contains the hardwood proportion data as an integer data
type. This is a parameter of the "Lookup" tool.
Potential Vegetation Zone Grid
(Required)
Select the potential vegetation grid layer that contains the
vegetation zone (or series) types.
Potential Vegetation Crosswalk
Classification Lookup Column (Required)
Select the field in the potential vegetation grid layer that
contains the crosswalk classification data as an integer data
type. Values will include 1 and 2. This is a parameter of the
"Lookup" tool.
The Elk Nutrition Tool Processes
Name
Explanation
Create Temp Folder
This creates a temporary folder where intermediate data will
be saved. Uses the "Create Folder" tool.
Extract Ag Lands
This creates a layer that contains only ag vegetation types from
the existing vegetation grid. Uses the “Extract by Attributes”
tool.
Reclass Ag DDE
This creates the DDE value for all agricultural (ag) types. That
value is 2.825 kcal/g. Uses the “Raster Calculator” tool with
expression: Con("%Ag Lands%" < 0, 2.825).
Reclass Ag Biomass
This creates the Biomass value for all ag types. That value is
640.7 kg/ha. Uses the “Raster Calculator” tool with expression:
Con("%Ag Lands%" < 0, 640.7).
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Name
Explanation
Lookup PNV Crosswalk
This creates a value grid of the classified vegetation zone types
from the potential vegetation zone grid layer. Uses the
"Lookup" tool.
Remove NoData from PNV Grid
This creates a grid layer that contains only the potential
vegetation zones that are used in the tool by removing any PNV
type that is not relevant, not modeled. Uses the “Raster
Calculator” tool with expression: SetNull("%PNV Crosswalk
Grid%" == -9, "%PNV Crosswalk Grid%").
Extract Existing Vegetation Mask
This extracts the herbaceous habitat values from the existing
vegetation grid layer. Uses the "Extract by Attributes" tool.
Combine Veg Masks
This combines the existing vegetation mask and potential
vegetation zone mask grids. This creates the final mask for the
tool, ensuring that only legitimate existing vegetation and
potential zone types will be used. Uses the "Raster Calculator"
tool with expression: "%Veg Mask%" & "%PNV_INT%".
Lookup CC
This creates a value grid for the Canopy Cover variable. Uses
the "Lookup" tool.
Lookup HW100
This creates a value grid for the Hardwood Proportion variable.
Uses the "Lookup" tool.
Calc eq_ab
This calculates the accepted biomass (kg/ha). Uses the " Raster
Calculator " tool with expression: Con("%PNV_INT%" == 1,
707.3 - (13.93 * "%CC%") + (0.0731 * "%CC%" * "%CC%") +
(383.17 *"%HW100%" / 100), Con("%PNV_INT%" == 2, 657.6 (11.28 * "%CC%") + (0.0458 * "%CC%" * "%CC%") + (553.06 *
"%HW100%" / 100))).
Stamp Ag Lands on AB
This merges the ag lands with the accepted biomass grid. Uses
the “Raster Calculator” tool with expression: Con(IsNull("%Ag
Biomass%") == 0, "%Ag Biomass%", "%eq_ab%").
Truncate AB
This adjusts the accepted biomass (kg/ha) in the event negative
values were attained; changing any negative value to 0. Uses
the "Raster Calculator" tool with expression: Con("%ab1%" <0,
0, "%ab1%").
Calc eq_nb
This calculates the neutral biomass (kg/ha). Uses the "Raster
Calculator" tool with expression: Con("%PNV_INT%" == 1,
671.8- (16.91 * "%CC%") + (0.1092 * "%CC%" * "%CC%") +
(268.13 *"%HW100%" / 100), Con("%PNV_INT%" == 2, 527.8 (6.09 * "%CC%") + (590.49 * "%HW100%" / 100))).
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Name
Explanation
Truncate NB
This adjusts the neutral biomass (kg/ha) in the event negative
values were attained; changing any negative value to 0. Uses
the "Raster Calculator" tool with expression: Con("%eq_nb%"
<0, 0, "%eq_nb%").
Calc eq_sb
This calculates the selected biomass (kg/ha). Uses the "Raster
Calculator" tool with expression: Con("%PNV_INT%" == 1, 80.1
- (0.66 *"%CC%") + (99.83 * "%HW100%" / 100),
Con("%PNV_INT%" == 2, 1 / (0.00833 + (0.00062 * "%CC%")))).
Truncate SB
This adjusts the selected biomass (kg/ha) in the event negative
values were attained; changing any negative value to 0. Uses
the "Raster Calculator" tool with expression: Con("%eq_sb%"
<0, 0, "%eq_sb%").
Calculate DE
This calculates the digestible energy (kcal/g). Uses the "Raster
Calculator" tool with expression: Con("%PNV_INT%" == 1,
2.362 + (0.00108 * "%NB%") + (0.000504 * "%SB%") (0.00000361 * "%SB%" * "%NB%"), Con("%PNV_INT%" == 2,
2.44 + (0.000889 * "%NB%") + (0.00308 * "%SB%") (0.00000546 * "%NB%" * "%SB%"))).
Stamp Ag Lands on DE
This step creates the final DDE (digestible energy) (kcal/g) layer
by correcting DE values in ag lands. Uses the "Raster Calculator
" tool with expression: Con(IsNull("%Ag DDE%") == 0, "%Ag
DDE%", "%de%").
Classify DDE to Nutrition Classes
This classifies the DDE into six categories; 1=Poor, 2=Lowmarginal, 3=High-marginal, 4=Low-good, 5=High-good,
6=Excellent. Uses the "Reclassify" tool.
Create Integer DDE * 1000
This creates an integer grid that has an attribute table by
multiplying the DDE grid by 1000. Uses the "Raster Calculator"
tool with expression: Int(1000 * "%DDE%").
Focal Mean DDE
This creates the mean dietary digestible energy using 350m
radius circles. Creates a continuous grid of mean DDE values.
Uses the "Focal Statistics" tool.
Apply Mask to Mean DDE
This re-applies a mask to the final mean DDE grid. Uses the
“Raster Calculator” too with expression: "%temp mean_dde%".
Steps to Run the Nutrition Tool
To add the Nutrition Tool to an ArcMap project file and run from ArcMap:
1. Open ArcMap.
2. Open/Show the ArcToolbox window if not already open.
3. Right click on ArcToolbox and choose “Add Toolbox”.
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4. Navigate to the folder where you have saved the “Westside Elk Habitat Models.tbx,” select the
toolbox and then click Open.
5. Once the toolbox is added to ArcMap, expand the “Westside Elk Habitat Models” by clicking the
plus sign next to the toolbox. Six separate tools will be listed. Double click on the regional
nutrition tool appropriate for your study area. (You may also right click on the tool and choose
Open).
a. A window will open with several blank fields that need to be populated with the
appropriate input data before running the tool; once you have selected these input
layers, click OK.
b. This screen shot shows the tool ready for inputs. Populate each box with your data. You
can browse to the data by clicking the yellow browse button or if the data is already
added to the ArcMap document, you can click on the drop down arrow and select the
appropriate layer.
c. Be sure to check any parameters that fill in by default when data is added. The default
value may or may not be correct for your data. If not correct, replace with an
appropriate SQL expression or attribute field for your data.
d. Be sure to set the three Lookup Column parameters correctly. (Canopy Cover Lookup
Column to CANCOV_I, Hardwood Proportion Lookup Column to HW100_I, and Potential
Vegetation Crosswalk to CROSSWALK_I.) If these parameters are left at the default
setting (Value), the tool will not run correctly.
e. Once all parameters are filled in with the correct data layers and SQL statements, click
“OK” to run the tool.
To run the Nutrition Tool from ArcCatalog:
1. Open ArcCatalog.
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2. Show the ArcToolbox window.
3. Add the Westside Elk Habitat Models Toolbox as you would in ArcMap.
4. Follow Step 5 from above to run the tools as you would in ArcMap.
Tables
Table 1—Tool Properties set under the Environment Tab (to be noted and changed (if needed)
before the tool is run)
Environment Setting
Description
Default Value
Extent
All new raster layers will be
<Study Area Boundary>
given this extent.
Snap Raster To
Shifts any new raster layers to Existing Vegetation Grid
match the starting x/y of this
raster.
Cell Size
Makes any new raster have
Existing Vegetation Grid (30 m)
this cell size.
Table 2— Nutrition Tool Output File Definitions. All abbreviations will have your study area code
prefix (****_)
Layers in the Output Workspace Folder. These layers are in final format.
Output Abbreviation
Description
****_6class
DDE classified into six nutrition categories; 1=poor, 2=low-marginal,
3=high-marginal, 4=low-good, 5=high-good, 6=excellent.
****_cc
Canopy Cover; an integer value grid.
****_dde
Dietary Digestible Energy (kcal/g).
****_dde1000
Dietary Digestible Energy multiplied by 1000, an integer value grid that
can be used for analysis.
****_hw100
Hardwood proportion; an integer value grid.
****_mask
All pixels used for modeling. Includes valid Vegetation types and
Vegetation Zones only.
****_mean_dde
Mean dietary digestible energy for a 350-m radius circle (kcal/g).
Layers in the Temp Folder within Output Workspace Folder. These are intermediate or
temporary layers.
Output Abbreviation
Description
****_ab
Accepted biomass (kg/ha).
****_ab1
Accepted biomass layer prior to adjusting for Ag land (kg/ha).
****_agbiom
Biomass value for ag lands as an integer grid.
****_agdde
DDE value for ag lands as an integer grid.
****_aglands
Agriculture land cover types as classified by the Vegetation Raster
Layer.
****_de
Dietary digestible energy layer before adjusting for Ag lands (kcal/g).
****_eq_ab
Accepted biomass from raw equations (kg/ha).
****_eq_nb
Neutral biomass from raw equations (kg/ha).
****_eq_sb
Selected biomass from raw equations (kg/ha).
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****_lkup_pnv
****_mean_dde
****_nb
****_pnv_int
****_sb
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All Existing Vegetation grid values that are classified as habitat (types
likely to be used by elk).
Potential Vegetation Zone grid converted to a value grid.
Mean dietary digestible energy for a 350-m radius circle (kcal/g)
before the mask is re-applied.
Neutral biomass after adjusting for negative values (kg/ha).
All Potential Vegetation Zone grid values that are classified as WHZ or
MHSFZ.
Selected biomass after adjusting for negative values (kg/ha).
Update Vegetation Tool
Introduction
The tool to update an existing vegetation grid can be used to meet two primary objectives: (1) to update
a vegetation grid to account for canopy cover removal or growth between the date of the base
vegetation layer (e.g., 2006 GNN) and current conditions, or (2) to simulate the removal or growth of
canopy cover in designated polygons (e.g., harvest areas) within a planning unit for comparison of
management alternatives with current conditions. Outputs from this tool can be used as inputs to rerun the Nutrition Tool or Habitat Covariates Tool (see section D for details) and compare results with the
original, pre-modification conditions. (Both dietary digestible energy and distance to nearest
cover/forage edge will change with updates in canopy cover and stand height values). To meet
objective 1 we suggest using aerial photography (e.g., NAIP imagery) from the same (or similar) year for
which you want to estimate nutritional conditions or habitat use and then digitize the polygons in which
vegetation in the imagery differs significantly from vegetation in the grid. (We describe methods to
meet the second objective in the “Habitat Use Tool” section). This tool is not designed to update a
vegetation layer pixel-by-pixel. Rather, it is designed to update a grid at the stand level or larger,
through the use of polygons. It also assumes that all treatments (i.e., areas where values will be
updated) will be applied consistently throughout each defined polygon (i.e., in every pixel).
Before running the Update Vegetation Tool, you must calculate three covariates: canopy cover
(percentage total cover), hardwood proportion, and stand height (meters). When using GNN data, the
fields used to calculate the three covariates are:
Covariate
name used in
the models
CANCOV_I
HW100_I
Original GNN
field name of
covariate
CANCOV
TPH_GE_3 and
TPHH_GE_3
STNDHGT_I
STNDHGT
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Location of original GNN
field
Process used to create model
covariate
GNN grid vat
GNN attribute table located
in the
GNN_2006_Attributes.mdb
GNN grid vat
Round value to nearest integer
[TPHH_GE_3]/[TPH_GE_3]*100
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The Update Vegetation Tool incorporates changes in values of these three covariates from existing
conditions to values that reflect harvest treatments, or to update a vegetation grid from a prior or later
year to match the time period desired for modeling. In order to fulfill either objective, values from
these thee covariates in the areas where the vegetation layer will be updated may need to be estimated
before using the Update Vegetation Tool. If personal knowledge of the area is minimal or harvest plans
for CC/HW/STNDHGT are unavailable, calculating the mean or majority value for CC/HW/STNDHGT
within the Vegetation Update Polygon layer may help estimate those values.
Some questions to ask before getting started:
1. Is the harvest a complete clear-cut?
• If NO, proceed to questions 2 and 3.
• If YES, then change CC/HW/STNDHGT values to 0.
2. If harvesting occurs, will hardwood removal be included in the treatment?
• If NO, calculate mean or majority HW of current vegetation conditions. Use this value in
the new layer. (Note: a hardwood value grid is an output of the nutrition tool and can be
used to calculate the mean or majority value for current conditions).
• If YES, calculate mean or majority HW value of current vegetation conditions and adjust
the value appropriately for the estimated amount of hardwood removal. (Note: use the
hardwood value grid mentioned previously to calculate the mean or majority value for
current conditions).
3. If harvesting occurs, will the overall stand height change? (e.g., thinning an 80% CC stand down
to a 60% CC may not change the stand height, whereas a thinning change from 50% CC down to
25% CC could likely change the stand height).
• IF NO, calculate mean/majority STNDHGT of current vegetation conditions. Use this
value in the new layer.
• If YES, calculate mean/majority STNDGHT of current vegetation conditions and adjust
the value appropriately for the estimated change in height.
If you choose to estimate CC/HW/STNDHGT values by using the mean or majority of the values, use the
“Zonal Statistic as Table” tool in ArcGIS Spatial Analyst to calculate an array of basic statistics (min, max,
range, mean, majority, minority, standard dev, sum, etc.) Be sure that your input grid is not the GNN
grid itself but rather the value grids created from the Nutrition Tool (output grids ****_cc and
****_hw100) or from the Habitat Covariates Tool (output grid ****_stndhgt) when those tools are
applied using the existing vegetation conditions. This tool creates a .dbf file that contains the mean and
majority value for each polygon. Those values can be used to help estimate appropriate
CC/HW/STNDHGT values to edit the attribute table as described in step 4 below (“Attribute table from
existing vegetation grid”).
Input Data Needed and Preparations
1. Study Area Boundary layer
a. This is a polygon that defines the entire study area you plan to analyze plus any buffered
area.
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b. The new vegetation layer (e.g., output grid %SA%_veg) will be clipped to this boundary.
2. Existing Vegetation Grid
a. This layer must include canopy cover, hardwood proportion, and stand height attributes
in addition to a mask field.
i. The mask field (Elk_Hab_Ma) is a field that defines each vegetation type as
modeled or not modeled.
b. This is the layer that will be updated.
3. Vegetation Update Polygon layer
a. This is a polygon layer that defines the exact areas where vegetation (CC, HW, and
STNDHGT) will be changed.
i. This change can result from harvesting or can reflect planned improvements or
changes in existing vegetation grid.
b. This is a layer you create. It can be a single polygon or contain multiple polygons.
c. You must add 1 required field, “Value,” as a long integer data type in the attribute table
for this layer.
i. This Value field is what the tool will look for when creating a grid from your
polygon layer.
ii. Each Value is unique to a particular CC, HW, and STNDHGT combination. Thus,
polygons can be assigned the same value when their input values for CC, HW,
and STNDHGT are exactly the same (e.g., two polygons can be assigned a Value
of -400 when their CC values are 20, HWs are 10, and STNDHGT values are 8).
iii. You decide what values (any whole number) to assign to each polygon in the
Value field. Do not use a value already used in the Value field of the Base
Vegetation Grid attribute table.
iv. If using GNN as your base vegetation layer, we recommend using negative
values less than -400 for the Value field. These values go beyond the range of
the GNN values for this field and are easy to recognize as vegetation-adjusted
areas.
d. You can add three other fields to the attribute table of your polygon boundary layer “CC,” “HW,” and “STNDHGT,” all three as short integer data types. These are optional
fields because the CC, HW, and STNDHGT values assigned here are not used to update
the new vegetation grid. These fields will help you keep track of the changes if you
choose to assign polygons to different CC/HW/STNDHGT values in the same run.
e. See figure 6 for an example attribute table from the polygon layer.
4. Attribute table from existing vegetation grid (e.g., GNN)
a. This table is provided for the GNN data in the GNN_2006_Attributes.mdb.
b. It can be edited in ArcGIS or Microsoft Access.
c. You will need to edit it (add new rows) to match your Vegetation Update Polygon layer
attribute table. See figure 7 for an example.
i. Fields from the attribute table to be edited:
Field Name
Value
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Description
The value assigned to each polygon in the Vegetation Update
Polygon layer. This value is unique to each CC/HW/STNDHGT
combination. Several polygons can be assigned the same value.
NOTE: Do not use any VALUE that is already in use by the existing
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ESLF_NAME (see Glossary)
CANCOV_I
HW100_I
STNDHGT_I
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vegetation grid (e.g. GNN). To avoid potential errors we
recommend assigning a VALUE less than -400 (see figs. 6 and 7).
Must be “H”; “H” = habitat type to be modeled.
A short description of the changes that occurred (e.g., Clear cut
area).
The new CC value, 0-100.
The new HW value, 0-100.
The new STNDHGT value, >=1.
d. This attribute table will be joined to the new vegetation grid that is created by the tool.
This is how the CC, HW, and STNDHGT attributes are re-assigned to the grid so that it
can be used in the Nutrition and Habitat Covariates Tools.
Figure 6— Example study area in grey (A). Colored polygons are treatment areas to be updated. Figure
6B shows the attribute table associated with the layer in A. The highlighted column is the only required
field that the user MUST ADD. The CC, HW100, and STNDHGT fields are optional here. The brown
shaded polygons from figure 6A are assigned the same “Value” (-400) in the table from figure 6B. Those
four polygons all have the same attributes. The remaining three polygons are each assigned a different
“Value” because their combinations of attributes (e.g. , CC, HW100, and STNDHGT) are unique.
A
B
Figure 7—Attribute table for the GNN data (used here as the existing vegetation grid).
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The first four rows have been added to this table via an edit session in ArcMap or Micro Soft Access.
They are the new data that originated from the Vegetation Update Polygon layer. The required fields
(columns) are already created in this table and the blue columns are the fields outlined in the list from
bullet 3ci. These are the fields that MUST be filled in. Once in an edit session in ArcMap (or when open
in Access), scroll to the bottom the table and begin adding new ROWS by filling in these five highlighted
fields. The ESLF_NAME (see Glossary) field is an optional (but recommended) text field that allows the
user to include a description of each value. This table contains numerous more fields that originate from
the GNN data. They must remain included if the user would like that attribute information to remain
associated with the GNN (existing vegetation).
Using the Update Vegetation Tool: Do’s and Don’ts
What GIS skills are needed?
1. Able to create and modify shapefiles and attribute tables in an edit session in ArcMap.
2. Able to re-project input data.
Usage Tips
•
•
•
•
•
•
•
•
•
•
Use ArcGIS 10.0 or newer with an ArcInfo license and Spatial Analyst extension.
Input grids should be 30-m resolution.
We recommended that all input grid and feature layers have the same projection and that they
match the .mxd data frame coordinate system and projection to prevent any shifts or wrong
calculations.
The existing vegetation grid layer (e.g. GNN) contains the Canopy Cover variable (CANCOV_I field),
Hardwood Proportion variable (HW100_I field), Stand Height variable (STNDHGT_I field), and a
variable used to classify the GNN values as modeled and not modeled, ELK_HAB_MA field.
1. The CANCOV_I , HW100_I, and STNDHGT_I fields need to be integer values for the
Lookup tool to work properly.
2. The tool is not designed to use separate input layers for CC or STNDHGT. These variables
are attributes of the GNN layer, and the tool uses the Lookup tool to create single value
grids for each variable.
In-line variable substitution is used in this tool. Ex: %Study Area Code% as seen in the file path name
of many processes will be replaced with the alpha/numeric code you chose. See the ArcGIS Desktop
Help for further assistance.
Output grids will have the same projection and resolution as the input Existing Vegetation Grid layer
(e.g. GNN, 30-m) and will be clipped to the boundary of the Study Area Boundary polygon.
The study area code parameter will be the prefix for all output layer names.
Certain tool settings are pre-set, located under the Environment Tab. These settings may need to be
edited if errors occur when running the tool. See table 3 for these settings.
Be sure to check all parameter fields in the tool to make sure the default settings are appropriate to
your data and study area. Default parameter inputs or SQL expressions may be incorrect and need
to be reset.
In the Vegetation Change Polygons shapefile, when assigning a VALUE to each polygon we suggest
using numbers less than -400 if you are using GNN for your vegetation data. This range will not
conflict with prior values already used in the GNN attribute table.
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In the GNN_2006_Attribute.mdb\gnn2006att table, make sure the VALUE, ELK_HAB_MA,
CANCOV_I, HW100_I, and STNDHGT_I fields are populated for all new rows you add that
correspond to your Vegetation Change Polygons shapefile. These fields are needed in the Elk
Nutrition Tool and Habitat Covariates Tool, but first need to be populated here in the Update
Vegetation Tool.
Only add each new VALUE once to the GNN_2006_Attribute.mdb\gnn2006att table; in other words,
do not duplicate the data. The join process will accommodate adding the appropriate
CC/HW/STNDHGT data to multiple polygons that have the same VALUE in your shapefile.
Remember that the VALUE you assign in both the shapefile and the
GNN_2006_Attribute.mdb\gnn2006att table is unique to every CC/HW/STNDHGT combination, not
unique to each individual polygon. Polygons can have the same VALUE.
Table 4 contains a list of Update Vegetation Tool outputs and their description.
Update Vegetation Tool Par
ameters
Expression
Explanation
Study Area Code (4 char
max) (Required)
Select up to four characters (alpha/numeric) that represent the analysis
area (or study area). It cannot exceed four characters nor can it begin
with a number. This code will precede the name of each output layer.
Output Workspace Folder
(Required)
Select the folder location in which you want the outputs to be saved.
Study Area Boundary
(Required)
Select the polygon layer that represents the regional landscape study
area. This layer should include a buffered area around the actual study
area as described earlier.
Vegetation Change Polygons
(Required)
Select the polygon layer that represents the areas within the regional
landscape study area where vegetation will be changed; canopy cover,
hardwood proportion, and stand height. This layer can be a single
polygon or a multi-polygon layer.
Convert Polygon to Raster
Based On This Field
(Required)
Select the field in the Vegetation Change Polygons layer that contains
the value ID that you assigned. Typically this is the Value field. This is the
field that the attribute table will be joined to. This is an environment
parameter of the "Polygon to Raster" tool.
Existing Vegetation Grid
(Required)
Select the vegetation grid that represents your starting point (e.g.
existing vegetation conditions). This grid should contain the canopy
cover (CC), hardwood proportion (HW), and stand height (STNDHGT)
attributes as integer values. This is the vegetation grid layer that will be
updated with the new CC, HW, and STNDHGT values.
Attribute Table With New
Vegetation Values
(Required)
Select the attribute table that includes all attributes from the Existing
Vegetation Grid along with additional attributes from the Vegetation
Change Polygons layer. The "Value" column from this table is the joining
field for the new vegetation grid created from this tool.
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Update Vegetation Tool Processes
Name
Explanation
Polygon to Raster
This converts the polygons to a raster grid. The conversion is based on
the "Value" column in the attribute table. Uses the "Polygon to Raster"
tool.
Merge Veg Change Areas
with Existing Veg Grid
This merges the Existing Vegetation grid with the new Vegetation
Updated Areas grid. Uses the "Raster Calculator" tool with expression:
Con(IsNull("%Vegetation Updated Areas%") == 0, "%Vegetation Updated
Areas%", "%Existing Vegetation Grid%").
Build Raster Attribute Table
This creates an attribute table for the Vegetation Merged grid. Uses the
"Build Raster Attribute Table" tool.
Join Field
This joins the full attribute table to the vegetation grid. Uses the "Add
Join" tool.
Delete Field
This deletes the extra or unwanted columns after the join is performed.
Uses the "Delete Field" tool.
Steps to run the Update Vegetation Tool
To add the Update Vegetation Tool to an ArcMap document and run from ArcMap:
1. Open ArcMap.
2. Open/Show the ArcToolbox window if not already open.
3. Right click on ArcToolbox and choose Add Toolbox.
4. Navigate to the folder where you have saved the “Westside Elk Habitat Models.tbx,” select the
toolbox, and then click Open.
5. Once the toolbox has been added to ArcMap, expand the "Westside Elk Habitat Models” by
clicking the plus sign next to the toolbox. Double click on the “Update Vegetation” tool to open
it. (You may also right click on the tool and choose Open).
a. A window will open with several blank fields that need to be populated with the
appropriate input data before running the tool; once you have selected these input
layers, click OK.
b. This is a screen shot shows the tool ready for inputs. Populate each box with your data.
You can browse to the data by clicking the yellow browse button or if the data is already
added to the ArcMap document, you can click on the drop down arrow and select the
appropriate layer.
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c. Be sure to check any parameters that fill in by default when data is added. The default
value may or may not be correct for your data. If not correct, select the correct attribute
field for your data.
d. Once all parameters are filled in with the correct data layers, click OK to run the tool.
To run the Habitat Covariates Tool from ArcCatalog:
1. Open ArcCatalog.
2. Show the ArcToolbox window.
3. Add the “Westside Elk Habitat Models” Toolbox as you would in ArcMap.
4. Follow Step 5 from above to run the tool as you would in ArcMap.
Tables
Table 3—Tool properties set under the Environment Tab of the Update Vegetation Tool (to be
noted and changed, if needed, before the tool is run)
Environment Setting
Description
Default Value
Extent
All new raster layers will be
<Study Area Boundary>
given this extent.
Snap Raster To
Shifts any new raster layers to <Existing Vegetation Grid>
match the starting x/y of this
raster.
Cell Size
Makes any new raster have
30
this cell size.
Mask
Creates NoData values for any <Study Area Boundary>
cell that is outside of this mask.
Table 4—Update Vegetation Tool output file definitions. Most abbreviations will have your
study area code prefix (****_)
Layer in the Output Workspace Folder. These layers are in final format.
Output Abbreviation
Description
****_change
This is the grid equivalent of the polygon layer for the areas where
vegetation will be updated.
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This is the final, updated vegetation grid that will be used in the
subsequent tools such as the Nutrition and Habitat Covariates Tools.
D. Westside Elk Habitat Use Model
The elk habitat use model predicts levels of elk use across the landscape. The model incorporates
outputs from two tools – the Nutrition Tool described previously, and the Habitat Covariates Tool
described in this section – into a third tool, the Habitat Use Tool, to predict elk use (fig. 8). In addition, if
the existing vegetation layer requires updating to reflect either changes in conditions from the year
(vintage) of the layer to the year of interest, or to reflect effects of simulated management alternatives
on elk nutritional conditions and subsequent predicted use, a third tool is used – the Update Vegetation
Tool (described previously; see fig. 4).
Figure 8—A minimum of three tools in ArcGIS is required to predict elk use across landscapes: the
Nutrition Tool, the Habitat Covariates Tool, and the Habitat Use Tool.
Habitat Covariates Tool
Introduction
This tool creates the three remaining habitat covariate inputs needed to run the complete, fourcovariate Habitat Use Tool. (Previously we provided instructions for creating mean dietary digestible
energy (DDE), which is the 4th covariate, using the Nutrition Tool.) These covariates are:
1. Distance to cover/forage edge
2. Distance to nearest road open to public use
3. Mean slope.
Distance to cover/forage edge is calculated with a set of geoprocesses that first classify the vegetation
grid into cover/forage/nodata cells, and then nibble away the isolated (less than 2x2 cells) nodata cells
into either cover or forage based on their surrounding cells. After that, the tool calculates cover that is
part of a 3x3 cell minimum-sized block and converts the remaining cover (i.e., smaller patches) to
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"forage." (In our modeling, cover is initially defined as canopy cover >40% and stand height >2 m.)
Next, the tool calculates forage that is at least part of a 3x3 cell block and converts the remaining cells to
"not classified." Finally, the tool delineates the edges between the blocked cover and forage cells only
and calculates the distance (in meters) of each pixel from these lines.
Distance to nearest road open to public use is simpler. All study area roads are classified into two
groups: open to public use or not. The “open” roads are identified, and the tool calculates the distance
(in meters) of each pixel to the nearest open road segment. When attributing roads care should be
taken to identify as open only those roads where unregulated public use is permitted during the
modeling period (that is, June through August).
Mean slope is calculated for a 350-m radius circle around each pixel from a slope grid derived from a
digital elevation layer (DEM).
Input Data Needed and Preparations
The inputs for this tool are similar to the Elk Nutrition Tool, but there are additional layers required. You
will need the following layers to run the complete elk habitat use model:
1. Existing Vegetation grid (e.g., GNN)
a. This layer must include canopy cover and stand height attributes
b. This layer is edited by adding fields in the attribute table that are specific to the tool,
described below:
Field Name
Definition
Values
Elk_Hab_Ma
A text field that defines
H=habitat types to model
which existing vegetation
M=non-habitat types
values are elk habitat
excluded from modeling
(modeled) and nonA = agriculture land cover
habitat (not modeled).
types
CANCOV_I
Percent canopy cover
0-100
converted to an integer
data type.
STNDHGT_I
Stand height (meters)
>=1
converted to an integer
data type.
2. Potential Natural Vegetation grid (PNV)
a. This layer defines the potential vegetation zones
b. This layer is edited by adding a new field in the attribute table that is specific to the tool.
That field is described below:
Field Name
Definition
Values
CROSSWALK_I
An integer field that re1=WHZ
classifies each potential
or
vegetation zone type into
2=MHSFZ
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either WHZ or MHSFZ.
c. These two zone classifications are used in this tool to define a mask that tells the tool
where to proceed, i.e., what areas can be modeled versus those that need to be
masked.
3. Study Area Boundary feature layer
a. This is a polygon layer that defines your study area plus a buffered distance around it.
b. See fig. 3 in “Defining an area for model application.”
4. Roads feature layer
a. This layer should contain a field in the attribute table that describes the open/closed
status of each road segment. This field can contain alpha or numeric data. You will need
to create an SQL statement when you run the tool that uses the attribute that defines
the open roads.
• Ex: “<Attribute Column Name>” = “open” or “<Attribute Column Name>” = 1, (if
1=open).
5. DEM grid
a. The DEM is a standard elevation grid in meters.
b. It is suggested that this grid be 30-m resolution.
The tool has a linked set of geoprocesses for each of the three covariates. You can run the tool with all
geoprocesses at once from the Open option (which will create all three covariates), or you can run each
part independently using the Edit option (which creates any single covariate or all three covariates). The
Edit option thus will allow you to focus on any single covariate of interest in this tool.
•
For example, if distance to open public roads is the only covariate of interest, you can choose to
run only this process. You can then edit your roads feature layer (such as closing roads) and run
the process again to compare outputs. You can do this without having to run the additional
processes for the mean slope and distance to cover/forage edge covariates, therefore saving
time.
•
Running this tool in “Edit” mode requires advanced understanding of ModelBuilder, i.e., being
able to single out the parameters needed for the covariate of interest and then running only
those processes.
Using the Habitat Covariates Tool: Do’s and Don’ts
Usage Tips
•
•
•
•
Use ArcGIS 10.0 or newer with an ArcInfo license and Spatial Analyst Extension.
Input grids should be 30-m resolution.
We recommended that all input grid and feature layers have the same projection and that they
match the .mxd data frame coordinate system and projection to prevent any shifts or wrong
calculations.
The existing vegetation grid layer (e.g., GNN) contains the Canopy Cover variable (CANCOV_I
field), Stand Height variable (STNDHGT_I field), and a variable used to classify the GNN values as
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modeled and not modeled, ELK_HAB_MA field. If you are using your own existing vegetation
grid layer ensure that it contains both fields in integer format.
1. The CANCOV_I and STNDHGT_I fields need to be integer values for the Lookup tool to
work properly.
2. The tool is not designed to use separate input layers for CC or STNDHGT. These variables
should be attributes of the GNN or other existing vegetation layers. The tool uses the
Lookup tool to create single value grids for each variable.
The potential vegetation grid layer (e.g. PNV) contains vegetation zones re-classified into either
the Western Hemlock (WHZ) or Silver Fir/Mountain Hemlock (SFMHZ) vegetation series (or
zones).
1. The CROSSWALK_I field is where we re-classified each individual PNV zone occurring in
western Oregon or Washington to one of these two classifications, 1=WHZ or 2=SFMHZ,
for which the nutrition equations were developed.
2. Any vegetation zone that is not cross-walked is given a No Data assignment (-9) and is
not suitable for modeling with these equations.
In-line variable substitution is used in this tool. Ex: %Study Area Code% as seen in the file path
name of many processes will be replaced with the alpha/numeric code you chose. See the
ArcGIS Desktop Help for further assistance.
Output grids will have the same projection and resolution as the input Vegetation Grid Layer
(e.g. GNN, 30-m) and will be clipped to the boundary of the Study Area Boundary polygon.
The study area code parameter will be the prefix for all output file names.
Some outputs (e.g., mean_slope) are summarized within a 350-m radius circle surrounding the
center pixel. This is the scale at which the models were developed and validated, and should
not be changed.
Certain tool settings are pre-set, located under the Environment Tab. These settings may need
to be edited if errors occur when running the tool. See table 5 for these settings.
Be sure to check all parameter fields in the tool to make sure the default settings are
appropriate for your data and study area. Default parameter inputs or SQL expressions may be
incorrect and need to be reset.
Table 6 contains a list of all Habitat Covariates Tool outputs and their description.
Habitat Covariates Tool Parameters
Expression
Explanation
Study Area Code (4 char max) (Required)
Choose up to a four character abbreviation (alpha/numeric)
that represents the analysis area for running the model. The
abbreviation cannot exceed four characters nor can it begin
with a number, or the tool will not run. This code will precede
the name of each output layer. Grids that are created cannot
exceed 13 character naming conventions; hence this study area
code cannot exceed four characters.
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Expression
Explanation
Output Workspace Folder (Required)
Select the folder location you want the outputs to be saved in.
Within this folder, a temporary "temp" folder is automatically
created. Each output will be saved in the Output Workspace
Folder if it's a permanent file or the temporary folder if it's an
intermediate file. If you do not have overwrite processes
activated (found in the Tools menu, Options, Geoprocessing
Tab) you will need to create a new folder each time the tool is
run.
Study Area Boundary (Required)
Select the polygon layer that represents the regional landscape
study area. This layer should include a buffered area around
the actual study area as described earlier.
Existing Vegetation Grid (Required)
Select the vegetation grid that contains canopy cover data and
stand height data to be used in the tool.
Existing Vegetation Where Clause
(Required)
Create a SQL statement that defines which values in the
Vegetation Raster Layer are to be used (which is habitat). Here,
non-habitat vegetation (such as barren lands, developed,
rock/sand, water, etc.) should be excluded from model
analysis.
Existing Vegetation Canopy Cover
Lookup Column (Required)
Select the field in the Vegetation Raster Layer that contains the
canopy cover data as an integer data type. Values will range
from 0-100. This is a parameter of the "Lookup" tool.
Existing Vegetation Stand Height Lookup
Column (Required)
Select the field in the Vegetation Raster Layer that contains the
stand height data as an integer data type. Values will range
from 0-100. This is a parameter of the "Lookup" tool.
Potential Vegetation Zone Grid
(Required)
Select the vegetation raster that contains the vegetation zone
(or series) types.
Potential Vegetation Zone Where Clause
(Required)
Create a SQL statement that defines which values in the
Vegetation Zone Raster Layer are to be used. Here, vegetation
zones (or series) not classified to Western Hemlock or
Mountain Hemlock/Silver Fir zones (such as ponderosa pine,
water, etc.) should be excluded from model analysis. For
clarification on which vegetation zones (or series) are
applicable please see other documentation.
Road Feature Layer (Required)
Select the line feature layer that represents the roads for your
study area. The road segments should be attributed in a field as
open and closed. These attributes can be alpha (O vs C; Open
vs Closed; etc.) or numeric (1 vs 0, etc.)
Select Open Roads Expression (Optional)
Create a SQL statement that defines which values in the Road
Feature Layer are to be used. This statement should define only
the open to public roads. Ex: "Attribute Field Name" = "open"
or "Attribute Field Name" = 1 (when 1=open).
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Expression
Explanation
DEM Grid (Required)
Select the DEM grid (elevation raster) that will be used to
create the mean slope covariate.
Habitat Covariates Tool Processes
Name
Explanation
Create Temp Folder
This creates a temporary folder where intermediate data will
be saved. Uses the "Create Folder" tool.
Extract Potential Vegetation Zone Mask
This extracts the classified vegetation zone types from the
Vegetation Zone Raster Layer that will be used in the tool. Uses
the "Extract by Attributes" tool.
Extract Existing Vegetation Mask
This extracts the habitat types from the Existing Vegetation
Raster Layer that will be used in the tool. Uses the "Extract by
Attributes" tool.
Combine Potential & Existing Veg Mask
This combines the Existing Vegetation Mask and Potential
Vegetation Zone Mask rasters. This creates the final mask for
the tool, ensuring that only legitimate vegetation and zone
types will be used. Uses the "Raster Calculator" tool with
expression: "%Veg Mask%" & "%PNV_INT%"
Lookup STNDHGT
This creates a value grid for the Stand Height variable. Uses the
"Lookup" tool.
Lookup CANCOV
This creates a value grid for the Canopy Cover variable. Uses
the "Lookup" tool.
Calc Cover Areas
This calculates cover areas only. Uses the "Raster Calculator"
tool with expression: ("%Stand Height%" > 2) & ("%Canopy
Cover%" >= 40).
Extract No Data Cells
This calculates the mask inverse; all pixels not in the mask are
now called No Data. Some of these no data cells will be
grouped into small patches that will either become cover or
forage areas. Uses the "Raster Calculator" tool with expression:
SetNull(IsNull("%Final Mask%") ==0,1).
Create No Data Region Groups
This rejoins all the no data cells into connected region groups.
Uses the "Region Group" tool.
Lookup No Data Group Cell Count
This creates a value grid based on the count field of the No
Data Region Groups raster. Uses the "Lookup" tool.
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Name
Explanation
Extract Small No Data Patches
This creates a patch raster of no data. Uses the "Raster
Calculator" tool with expression: Con("%No Data Cell Count%"
<= 3,1, SetNull("%No Data Cell Count%" > 3,"%No Data Cell
Count%")). Data from this process will be added back into the
cover and forage patch rasters.
Merge Cover Areas & Small No Data
Patches
This creates a merged raster of all cover areas and small no
data patches. Uses the "Raster Calculator" tool with
expression: Con(IsNull("%Small No Data Patches%") ==
1,"%Cover Areas%",9).
Calc Focal Majority 3x3 For Small No
Data
This calculates the focal majority value for a 3x3 patch raster.
Uses the "Focal Statistics" tool.
Calc Focal Majority 5x5 For Small No
Data
This calculates the focal majority value for a 5x5 patch raster.
Uses the "Focal Statistics" tool.
Calc Focal Majority 7x7 For Small No
Data
This calculates the focal majority value for a 7x7 patch raster.
Uses the "Focal Statistics" tool.
Calc Focal Majority 9x9 For Small No
Data
This calculates the focal majority value for a 9x9 patch raster.
Uses the "Focal Statistics" tool.
Create Updated Cov-For-ND Layer
This fills in the small no data cells with either a forage or cover
value based on the previous focal majority girds. If the focal
majority value for a small no data cell is either 0 or 1, it uses
that value in the Cov-For-ND output and if not, it goes to the
larger filter (i.e. 5x5) and uses that value but only if it is 0 or 1,
and so on. Uses the "Single Output Map Algebra" tool with
expression: merge(setnull(([Small No Data Patches]==1 AND
[Small No Data Focal Majority 3x3] >1), [Small No Data Focal
Majority 3x3]), setnull(([Small No Data Patches]==1 AND [Small
No Data Focal Majority 5x5] >1), [Small No Data Focal Majority
5x5]), setnull(([Small No Data Patches]==1 AND [Small No Data
Focal Majority 7x7] >1), [Small No Data Focal Majority 7x7]),
setnull(([Small No Data Patches]==1 AND [Small No Data Focal
Majority 9x9] >1), [Small No Data Focal Majority 9x9]),
con(isnull([Cover Areas])==1, 99, [Cover Areas]),999).
Create Cover Only
This extracts only the cover areas from the updated
cover/forage/no data raster (Cov-For-ND). Uses the "Raster
Calculator" tool with expression: SetNull("%Cov-For-ND%" <>
1,1).
Calc Focal Sum For Cov3
This calculates the focal sum of the Cover Only raster for a 3x3
rectangle. Uses the "Focal Statistic" tool.
Create Cov3-Raw Patch
This creates a patch raster of cover only areas that are at least
3x3 pixels. Uses the "Raster Calculator" tool with expression:
Expand(SetNull("%Focal Sum Cov3-Raw%" < 9,1), 1, 1).
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Name
Explanation
Create Cov3-Raw-For-ND
This merges the patch cover raster with the raw cover/raw
forage/no data raster but also converts all the raw cover that
was not in a 3x3 cell block to raw forage. Uses the "Raster
Calculator" tool with expression: Over(IsNull("%Patch for Cov3Raw%") ==0, Con("%Cov-For-ND%" == 99, 99, 0)).
Create Variety Grid
This calculates the variety in values for a 3x3 pixel area. Uses
the "Create Variety Grid" tool.
Convert Isolated For to Cover
This converts all 'isolated' forage (i.e. not part of a 3x3 pixel
area of forage) to cover. Uses the "Raster Calculator" tool with
expression: Con(("%Cov3-Raw-For Var%" > 1) & ("%Cov3-RawFor-ND%" == 0),1,"%Cov3-Raw-For-ND%").
Expand Forage
This expands the forage by one cell to get it back to a 3x3 pixel
area (was reduced by the "Create Variety Grid" process). Uses
the "Expand" tool.
Create Cov3-Raw Only
This creates a grid with only 3x3 pixel cover and sets all other
cells to no data. Uses the "Raster Calculator" tool with
expression: SetNull("%Raw-Cov3-For3%" <> 1,1).
Re-Calc Focal Sum for Cov3
This calculates the focal sum for all the cover (value= 1) within
a 3x3 pixel area. Uses "Focal Statistics" tool.
Create Cov3-For3 Final
This creates the final cover/forage gird with no data values.
Uses the "Single Output Map Algebra" tool with expression:
merge(expand(setnull([Focal Sum for Cov3-Raw] < 9, 1), 1, list,
1), con([Raw-Cov3-For3]==1, 999, [Raw-Cov3-For3])).
Create Cov3 & For3 Only
This creates the final cover/forage map by excluding all no data
pixels. Uses the "Raster Calculator" tool with expression:
SetNull("%Cov3-For3%" > 1,"%Cov3-For3%").
Create Cov3-For3 Polygons
This converts the cover/forage raster map to a polygon layer
based on the Value field of the raster layer. Uses the "Raster to
Polygon" tool.
Convert Polygons to Lines
This converts the polygon cover/forage layer into a line feature
layer. Uses the "Polygon to Line" tool.
Select Cov3-For3 Edge
This selects specific line segments from the cover/forage line
feature layer. Using the expression: "LEFT_FID" >=0 AND
"RIGHT_FID" >=0, only line segments that border cover and
forage areas are selected. Uses the "Select" tool.
Shift Cov3-For3 15 m
This process shifts the cover/forage edge by 15 m in both the X
and Y directions. This will allow the distance to edge raster to
be centered over the true cover/forage edge. Uses the "Shift"
tool.
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Name
Explanation
Calc Distance to Cov3-For3 Edge
This calculates a distance grid from the selected line segments
that represent only cover/forage edges. Uses the "Euclidean
Distance" tool. Also uses the output raster "Shifted Cov3-For3"
as a snap to parameter so the distance grid begins in the exact
center of the cover/forage edge.
Select Open Roads
This uses a SQL expression to select only the open to public use
roads from the Road Feature Layer. Uses the "Select" tool.
Calculate Distance to Open Roads
This creates a 30-m distance raster from the Open Roads
Feature Layer. Uses the "Euclidean Distance" tool.
Calculate Slope
This calculates slope in percent rise from a DEM input raster.
Uses the "Slope" tool.
Calc mean slope
This calculates the mean slope from the slope derived input
raster using 350m radius circles. Uses the "Focal Statistics" tool.
Cap Distance to 4000m
This caps all distances >4000 to a max of 4000. This cap was
created because beyond 4000 m, effects of roads on elk
dissipated or were confounded with other covariates.
Steps to Run the Habitat Covariates Tool
To add the Habitat Covariates Tool to an ArcMap document and run from ArcMap:
1. Open ArcMap.
2. Open/Show the ArcToolbox window if not already open.
3. Right click on ArcToolbox and choose Add Toolbox.
4. Navigate to the folder where you have saved the “Westside Elk Habitat Models.tbx,” select the
toolbox and then click Open.
5. Once the toolbox is added to ArcMap, expand the “Westside Elk Habitat Models” by clicking the
plus sign next to the toolbox. Double click on the toolbox to open it. (You may also right click on
the toolbox and choose Open).
a. A window will open with several blank fields that need to be populated with the
appropriate input data before running the tool; once you have selected these input
layers, click OK.
b. This screen shot shows the tool ready for inputs. Populate each box with your data. You
can browse to the data by clicking the yellow browse button or if the data is already
added to the ArcMap document, you can click on the drop down arrow and select the
appropriate layer.
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c. Be sure to check any parameters that fill in by default when data is added. The default
value may or may not be correct for your data. If not correct, replace with an
appropriate SQL expression or attribute field for your data.
d. Be sure to set the Canopy Cover Lookup Column to CANCOV_I and the Stand Height
Lookup Column to STNDHGT_I. If these parameters are set to Value, the tool will not
run correctly.
e. Once all parameters are filled in with the correct data layers or SQL statements, click OK
to run the tool.
To run the Habitat Covariates Tool from ArcCatalog:
1. Open ArcCatalog.
2. Show the ArcToolbox window.
3. Add the “Westside Elk Habitat Models” toolbox as you would in ArcMap.
4. Follow Step 5 from above to run the tool as you would in ArcMap.
Tables
Table 5—Tool properties set under the Environment Tab of the Habitat Covariates Tool (to be
noted and changed (if needed) before the tool is run)
Environment Setting
Description
Default Value
Extent
All new raster layers will be
<Study Area Boundary>
given this extent.
Snap Raster To
Shifts any new raster layers to Existing Vegetation Grid
match the starting x/y of this
raster.
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Mask
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Makes any new raster have
Existing Vegetation Grid (30 m)
this cell size.
Creates NoData values for any <Study Area Boundary>
cell that is outside of this mask.
Table 6—Habitat Covariates Tool Output File Definitions. All abbreviations will have your study area
code prefix (****_).
Layers Within the Output Workspace Folder. These layers are in final format.
Output Abbreviation
Description
****_d_edge
Distance to the nearest cover/forage edge
****_d_roads
Distance to the nearest open to public road
****_mean_slp
Mean Slope
Layers in the Temp Folder within Output Workspace Folder. These are intermediate or temporary
layers. They should not be used for analysis.
Output Abbreviation
Description
****_c3f3
Only final cover and forage patches.
****_c3rf_v23
All forage less than a 3x3 cell area are converted to cover and combined
with cover patches and no data patches.
****_ca
Raw (un-smoothed) cover areas = 1 and all other veg. = 0.
****_ca_temp
Raw cover areas (****_ca; = 0 and 1)merged with small no data
patches (****_nd_iso; = 9).
****_cancov
Canopy cover values extracted from the vegetation grid.
****_cov
Only cover extracted from (****_covfor).
****_cov3for3
Cover/Forage Grid; 0=Forage, 1=Cover, 99 and 999 = nodata.
****_Cov3For3_edge.shp The edges created from the cov3for3 grid.
****_Cov3For3_lines.shp Polylines of all cover and forage patches.
****_cov3for3_poly.shp Polygons of all cover and forage patches.
****_cov3r
Raw cover patches.
****_cov3rf_v
Variety of values within 3x3 cell area from ****_cov3rfor.
****_cov3rfor
Cover patches combined with raw forage and no data; all other cover
that was less than 3x3 cells was converted to forage.
****_covfor
Cover = 1, forage = 0, and no data (greater than 3 cell region) = 99; all
other no data have been assigned a cover or forage value from focal
majority grids.
****_evtmask
Only the values from the Existing Vegetation Grid that are used in the
tool.
****_fs_c3r2
Second focal sum for all cover patches after isolated forage has been
added.
****_fs_cov3r
Focal sum for all cover within 3x3 cell neighborhood (ie. 9 = all nine
neighboring cells were cover or 0 = no neighboring cells were cover).
****_mask
All pixels used for modeling. Includes valid Existing Vegetation types
and Potential Vegetation Zones only.
****_nd_cnt
Grid with number of total cells in each region group.
****_nd_iso
Only the no data cells that were part of a region group of three cells or
less, essentially the isolated no data cells.
****_nd_maj3
Focal majority value for 3x3 cell area of temp cover area
(****_ca_temp).
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****_nd_maj7
****_nd_maj9
****_nd_rgp
****_nodata
****_open_roads.shp
****_p_cov3r
****_pnvmask
****_rawc3f3
****_shift
****_slope
****_stndhgt
****_tmp_d_rd
41
Focal majority value for 5x5 cell area of temp cover area
(****_ca_temp).
Focal majority value for 7x7 cell area of temp cover area
(****_ca_temp).
Focal majority value for 9x9 cell area of temp cover area
(****_ca_temp).
Numbered region groups of no data cells (a region group is defined as
all the cells that connect by touching in any of the eight directions
(including diagonals) from the cell).
All no data cells from final mask.
Only the open to public use roads.
All patches of cover 3x3 cells or greater.
Only the values from the Potential Vegetation Zone Grid that are used
in the tool.
Raw cover and forage patches (areas at least 3x3 cells).
Cover and forage patch grid, shifted 15 m to match distance to edge
grid.
True slope grid before the mean is calculated.
Stand height values extracted from the existing vegetation grid
Distance to roads before the 4km max is set.
Habitat Use Tool
Introduction
This tool uses a collection of four inputs created from the Nutrition Tool and Habitat Covariates Tool.
The inputs are: mean DDE (dietary digestible energy; created with the Nutrition Tool), and mean percent
slope, distance (meters) to nearest road open to public use (capped at 4000 m), and distance (meters) to
nearest cover/forage edge, all created with the Habitat Covariates Tool (fig. 4). By using a single
algorithm and these four inputs, an output grid is created that represents the predicted level of use by
elk - that is, the likelihood that an elk will occur in a given area. This output is an index, and the greater
the value, the higher the predicted level of use. If comparisons are desired of predicted use from
current conditions with predicted use under various alternatives that involve altering canopy cover (e.g.,
to enhance DDE levels or alter distance to cover/forage edge), the Update Vegetation Tool must be run
prior to running the Nutrition and Habitat Covariates Tools for each alternative of interest.
Input Data Needed and Preparations
The inputs for this tool are outputs from prior tools (figs. 4, 8). Having run the prior tools, you should
not have any data preparation needs. With that in mind, you will need the following layers to run the
complete tool:
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2.
3.
4.
5.
42
Mean DDE grid (****_mean_dde output from the Nutrition Tool)
Distance to cover/forage edge grid (****_d_edge output from the Habitat Covariates Tool)
Distance to open roads grid (****_d_roads output from the Habitat Covariates Tool)
Mean slope grid (****_mean_slp output from the Habitat Covariates Tool)
Study Area Boundary polygon feature layer
a. This layer is the boundary of your landscape level study area, not including the buffered
area.
b. For analysis purposes, data within the actual study area boundary is what should be
analyzed, not data within the buffered area.
Using the Habitat Use Tool: Do’s and Don’ts
Usage Tips
•
•
•
•
•
•
•
•
•
•
Use ArcGIS 10.0 or newer with an ArcInfo license and Spatial Analyst Extension.
Input grids should be 30-m resolution.
We recommended that all input grid and feature layers have the same projection and that they
match the *.mxd data frame coordinate system and projection to prevent any shifts or wrong
calculations.
In-line variable substitution is used in this tool. Ex: %Study Area Code% as seen in the file path name
of many processes will be replaced with the alpha/numeric code you chose. See the ArcGIS Desktop
Help for further assistance.
Output grids will have the same projection and resolution as the input Existing Vegetation Grid Layer
(e.g. GNN, 30-m) and will be clipped to the boundary of the Study Area Boundary polygon.
The study area code parameter will be the prefix for all output layer names.
Certain tool settings are pre-set, located under the Environment Tab. These settings may need to be
edited if errors occur when running the tool. See table 7 for these settings.
Be sure to check all parameter fields in the tool to make sure the default settings are appropriate to
your data and study area. Default parameter inputs or SQL expressions may be incorrect and need
to be reset.
Be sure that this tool is applied to the regional analysis area excluding the buffer.
Table 8 contains a list of all outputs and their description.
Habitat Use Tool Parameters
Expression
Explanation
Study Area Code (4 char max)
(Required)
Choose up to a four character abbreviation (alpha/numeric) that
represents the analysis area the model is being calculated for. It cannot
exceed four characters nor can it begin with a number, or the tool will not
run. This code will precede the name of each output layer. Grids that are
created cannot exceed 13 character naming conventions; hence this
study area code cannot exceed four characters.
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Output Workspace Folder
(Required)
Select the folder location in which you want to save the outputs. Within
this folder, a temporary "temp" folder is automatically created. Each
output will be saved in the Output Workspace Folder if it's a permanent
file or the temporary folder if it's an intermediate file. If you do not have
overwrite processes activated (found in the Tools menu, Options,
Geoprocessing Tab) you will need to create a new folder each time the
tool is run.
Study Area Boundary
(Required)
Select the polygon layer that represents the regional landscape study
area. This layer should only be the regional landscape analysis area (no
buffer included).
Mean DDE (Required)
Select the grid layer that is the mean DDE (dietary digestible energy). This
grid was created using the Elk Nutrition Tool.
Distance to Cover/Forage Edge
(Required)
Select the grid layer that is the distance to cover/forage edge. This grid
was created using the Habitat Covariates Tool.
Distance to Open Public Roads
(Required)
Select the grid layer that is the distance to open roads. This grid was
created using the Habitat Covariates Tool. This grid should have a max
value of <=4000.
Mean Slope (Required)
Select the grid layer that is the mean slope. This grid was created using
the Habitat Covariates Tool.
Habitat Use Tool Processes
Name
Explanation
Predicted Level of Use
Equation
This uses a single algorithm equation using four input grids to create the
raw grid for predicted level of use. Uses the "Raster Calculator" tool with
expression: Exp(-27.75650 + (8.63746 * "%Mean DDE%") + (0.24788 /
1000 * "%Distance to Open Public Roads%") - (0.67417 / 1000 *
"%Distance to Cover/Forage Edge%") - (0.04372 * "%Mean Slope%"))
Mult Raw grid by million
This creates a tangible raw grid by multiplying the raw grid by one million.
Uses the "Raster Calculator" tool with expression: Float("%Raw grid%" *
1000000)
Steps to Run the Habitat Use Tool
To add the Habitat Use Tool to an ArcMap project and run from ArcMap:
1. Open ArcMap.
2. Open/Show the ArcToolbox window if not already open.
3. Right click on ArcToolbox and choose “Add Toolbox”.
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4. Navigate to the folder where you have saved the “Westside Elk Habitat Models.tbx,” select the
toolbox and then click Open.
5. Once the toolbox is added to ArcMap, expand “Westside Elk Habitat Models” by clicking the plus
sign next to the toolbox. Double click on the “Habitat Use” tool to open it. (You may also right
click on the tool and choose Open).
a. A window will open with several blank fields that need to be populated with the
appropriate input data before running the tool; once you have selected these input
layers, click OK.
b. This screen shot shows the tool ready for inputs. Populate each box with your data. You
can browse to the data by clicking the yellow browse button or if the data is already
added to the ArcMap document, you can click on the drop down arrow and select the
appropriate layer.
c. Once all parameters are filled in with the correct data layers, click OK to run the tool.
To run the Habitat Use Tool from ArcCatalog:
1. Open ArcCatalog.
2. Show the ArcToolbox window.
3. Add the “Westside Elk Habitat Models” toolbox as you would in ArcMap.
4. Follow Step 5 from above to run the tool as you would in ArcMap.
Tables
Table 7—Tool Properties set under the Environment Tab of the Habitat Use Tool (to be noted
and changed (if needed) before the tool is run)
Environment Setting
Description
Default Value
Extent
All new raster layers will be
<Study Area Boundary>
given this extent.
Snap Raster To
Shifts any new raster layers to Mean DDE Layer
match the starting x/y of this
raster.
Cell Size
Makes any new raster have
Mean DDE Layer (30 m)
this cell size.
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Creates NoData values for any
cell that is outside this mask.
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<Study Area Boundary>
Table 8— Habitat Use Tool Output File Definitions. All abbreviations will have your study area code
prefix (****_).
Output Abbreviation
Description
****_use
These are the raw values created from the predicted level of use algorithm.
The higher the number, the greater the predicted level of use.
****_use1x6
These are the raw values multiplied by 1,000,000. This creates a tangible
output to use for analysis.
E. Interpretation and Summaries of Model Results
Model users can summarize results from the Westside elk nutrition and habitat use models in a variety
of ways. Below we describe potential methods of summarizing results from the outputs of the Nutrition
and Habitat Use Tools.
As described previously (Section B, “Spatial and Temporal Extents for Summarizing Model Results”),
summaries of model outputs can be made at two spatial extents: (1) the regional landscape, across
which the models are applied; and (2) the local landscape. Summaries can also be completed for one
time period (existing condition) as well as for different management alternatives under which potential
effects of management are evaluated, and results contrasted with the existing condition. These
summaries can be completed for both the regional and local landscapes.
Summarizing Results from the Nutrition Model
The Nutrition Tool predicts the dietary digestible energy of forage available to elk for any specified area
within the Westside regional boundaries identified in figure 1. The predictions can be made for any
landscape condition (e.g., existing condition or any proposed management alternatives). Results from
the nutrition predictions have a direct relation to animal performance, based on the work by Cook et al.
(2004). The authors grouped DDE values into four classes and described estimated levels of performance
as a function of DDE for a variety of metrics, including calf mass, yearling cow mass, lactating adult cow
fat percentage, pregnancy rates of yearling and adult cows, and breeding date for adult cows.
For nearly all Westside landscapes, the majority of area will fall into nutritional classes below excellent
or good, given that forage quality is inherently limiting to most Westside environments (Harper 1987;
Cook et al., n.d. a). Accordingly, any increase in area within the good or excellent nutrition classes can
potentially result in substantial benefits to elk nutrition and associated animal performance.
Table 9 below is an expansion to six classes of the four original DDE classes described in Cook et al.
(2004): poor, marginal, good, and excellent. We suggest that outputs from the Westside nutrition
model be summarized using these six classes, expressed as the percentage of pixels in the landscape
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that fall within each class (e.g., table 10). This more finely-divided set of classes allows the user to
better quantify potential or real improvements in nutritional conditions in landscapes dominated by
marginal or good classes. Users should not report the mean DDE value (that is, the sum of all DDE
values divided by the total pixel count) for any analysis area, whether local or regional landscape,
because of the high spatial variability in nutritional values. If summaries of DDE using the original four
classes are desired, simply add the counts for the low-marginal and high-marginal for the “marginal
class” and low-good and high-good for the “good” class.
Table 9—Dietary digestible energy (DDE) for elk, assigned to six classes for better discrimination of
intermediate quality habitats. Adapted from Cook et al. (2004).
Class
1
2
3
4
5
6
Description
Poor
Low-marginal
High-marginal
Low-good
High-good
Excellent
DDE
<2.40
>2.40 to <2.575
>2.575 to <2.75
>2.75 to <2.825
>2.825 to <2.90
>2.90
Regional Landscape: Existing Condition and any Management Alternatives
1. Percent of the landscape (and acres, if desired) by DDE class (table 9) for existing condition and
each management alternative. Note: use the raw DDE grid created by the Nutrition Tool for
nutrition summaries and mapping.
Table 10—Percentage area in each DDE class for the existing landscape (Existing) and based on
predictions using two different management alternatives.
DDE Class
Existing Alternative 1 Alternative 2
1
29.8%
29.5%
28.2%
2
56.7%
54.9%
54.9%
3
7.8%
9.3%
7.6%
4
2.2%
2.1%
2.1%
5
1.9%
1.9%
1.9%
6
1.6%
2.3%
5.4%
2. Map of DDE classes across landscape (fig. 9).
3. Map of “ramped” (non-classified) DDE values across the regional landscape.
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Figure 9—Maps of
predicted dietary
digestible energy
(DDE) classes under
alternative 1 (left) and
alternative 2 (right).
Classes are based on
values in table 9.
Changes in Regional Landscape: Existing Condition Relative to each Management
Alternative
4. Change in DDE classes from existing condition to various management alternatives, represented
by the acres that have moved from each DDE class under the existing condition to other classes
under alternatives; table 11).
In addition to looking at DDE maps and the percentage of the landscape in each DDE class, we
can calculate the total acreage in each class and how much of the landscape in Class 1 will be
improved and moved to Class 2, 3, 4, 5 or 6 under various alternatives (Alternative 1, table 11;
Alternative 2, table 12). For example, under the existing condition there is a total of 20,536 ac in
DDE Class 1 (poor), but under Alternative 1 275 ac would be improved to Class 6 (excellent; table
11). Alternatively, 1,138 ac originally in Class 1 would improve to Class 6 under Alternative 2
(table 12). This type of comparison shows that DDE values actually decrease for some locations
under Alternative 1 (e.g., 63 ac originally in Class 2 pre-treatment get downgraded to Class 1
under Alternative 1; table 11).
Table 11—Number of existing acres (Existing) in each DDE class (1 – 6) and the number of acres
that move into other DDE classes under Alternative 1.
Existing
1
2
3
4
5
6
Total:
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1
20261
63
1
0
1
6
20333
2
0
37758
0
0
0
0
37758
Alternative 1
3
4
0
0
1042
0
5316
0
48
1476
5
0
5
0
6416
1476
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5
0
0
0
0
1293
0
1293
6
275
135
23
7
6
1111
1556
Total
20536
38998
5341
1532
1305
1122
68833
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Table 12—Number of existing acres (Existing) in each DDE class (1 – 6) and the number of acres
that move into other DDE classes under Alternative 2.
Existing
1
2
3
4
5
6
Total:
1
19398
0
0
0
0
0
19398
2
0
37758
0
0
0
0
37758
Alternative 2
3
4
0
0
0
0
5224
0
0
1476
0
0
0
0
5224
1476
5
0
0
0
0
1293
0
1293
6
1138
1240
117
56
11
1122
3683
Total
20536
38998
5341
1532
1305
1122
68833
5. Map of change in DDE classes from existing
condition to classes under various
management alternatives (change map; can
use integer values to quantify class
differences or designate as +, -, or no
change; fig. 10).
Figure 10—Changes in DDE class from existing
conditions to Alternative 2. Numbers represent
difference in class values between the 2
alternatives. Classes are based on values in table 9.
You can also quantify changes in DDE class for
individual pixels and map these differences (e.g.,
DDE class under existing conditions vs. Alternative
1). If using six DDE classes, the best possible change would be 5 (i.e., moving from DDE Class 1 to Class
6) (fig. 10). For DDE, you would not expect much negative change to occur with management
treatments; however, in some situations, such as using dynamic vegetation simulation models to predict
future conditions, negative changes in DDE class could occur.
Local Landscape: Existing Condition and any Management Alternatives
Summaries and maps of nutrition for a local landscape will be the same as those at the regional level,
but with data summarized and displayed within the boundary defined as the local analysis area (see
“Defining an Analysis Area” above). No new analysis is required at this level; however, percentages and
other summary statistics will be different when confined to the local landscape boundary.
Changes in Local Landscape Relative to each Management Alternative
Summaries and maps of changes in nutrition for a local landscape will be the same as those for the
region, but with data summarized and displayed within the boundary defined as the local analysis area.
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Summarizing Results from the Elk Habitat Use Model
Predictions from the Habitat Use Tool should be referred to as "Predicted Level of Use.” The higher the
values, the higher the elk use predicted for a given set of pixels within a given landscape. These
predictions of use are made for each pixel within a given landscape, but are not standardized. Summing
these values across a landscape can result in numbers that can be quite large, and that can vary widely,
depending on the size of the landscape and the associated values of the model covariates. Appropriate
interpretation of these predictions requires standard methods of summary, allowing for easy
interpretation at both regional and local extents, and across time.
The Habitat Use Tool predicts the distribution of elk within a given area. Thus, predictions can be used
to manage elk distributions across different land ownerships and across different areas, within a given
analysis area in relation to management objectives. For example, actively managing open road systems
will alter elk distributions because of the strong influence of this covariate on predicted elk use
(Rowland et al., n.d.) Likewise, because elk distributions are predicted to be strongly influenced by DDE
levels, areas of higher use by elk reflect landscape conditions that convey positive benefits to animal
performance (Cook et al. 2004). That is, the areas where animals spend more time (higher levels of
predicted use) reflect portions of a given area that provide greater benefits to animal performance.
In the following text we present suggestions for summarizing predictions of elk habitat use for
management and interpretation. To complete the following summaries, a prerequisite is to create an XY
grid for the region analyzed, with every cell (pixel) attributed for each analysis. The grid should have a
format resembling the table below, with each row representing a pixel in the regional landscape:
X
XXXXX
XXXXX
XXXXX
Y
YYYYY
YYYYY
YYYYY
Area
1
2
3
Pred. Use 1
xx
xx
xx
Pred. Use 2
xx
xx
xx
Pred. Use 3
Xx
Xx
Xx
Landowner
A
B
C
In addition to the XY coordinates for each pixel, you may want to designate the area in which the pixel is
located (e.g., if you are conducting a regional as well as local assessment, or have different treatment
units), the output value from the Habitat Use Tool under different management alternatives, as well as
landowner or other attributes by which you may want to summarize your data. Remember that no data
from the buffer should be included in summaries of predicted use by elk.
Regional Landscape: Existing Condition and any Management Alternatives
1. Map of predicted levels of use, using raw use values placed into four to ten equal-area classes
(“quantiles” in ArcMap) (fig. 11).
Run the tool across the landscape for the existing condition and other management alternatives
of interest, which yields “raw” values of predicted use for all pixels in the landscape. These
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maps will demonstrate distribution of elk across the landscape. The number of prediction
classes chosen is somewhat subjective and depends on the data at hand. With too few classes,
changes in predicted use between alternatives can be difficult to depict in a map; however, with
too many classes, it can be difficult to distinguish the different values on the map. If changes
are not obvious when comparing between existing conditions and an alternative, try increasing
the number of classes displayed.
Figure 11—Predicted levels
of use by elk for the
existing regional landscape
(Existing), and under three
management alternatives.
Predictions of levels of elk
use are separated into five
equal-area classes for each
map.
2. Charts of predicted use by elk (fig. 12).
Run the tool as above across the region (no. 1). Sum the raw predicted levels of use for the
landscape under existing conditions as well as any other management alternatives of interest to
compare differences across management alternatives.
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Sum of Predicted Level of Use
180
160
Sum of Use
140
120
Existing
100
80
Alternative 1
60
Alternative 2
40
Alternative 3
20
Figure 12—Summed raw values
of predicted level of elk use for
the regional landscape and two
local landscapes under existing
conditions and three treatment
alternatives.
0
Regional
Local
Local
Landscape Landscape 1Landscape 2
3. Maps of the input covariate grids for the Habitat Use Tool, i.e., distance to nearest edge,
distance to nearest road open to public, mean slope, and mean DDE.
Examination of the input grids can help in understanding predicted patterns of elk use. For
example, if predicted use is relatively low in one portion of the landscape, but predicted DDE
levels are high, looking at the mean slope, distance to edge, and roads grids can help explain the
patterns of predicted use seen on the maps generated in process no. 1 above.
4. Summarize predicted level of elk use within the regional landscape by landowner or other
categories of interest (fig. 13).
Sum the raw predicted levels of elk use for the entire regional landscape and for other areas of
interest in the landscape, for existing condition or other management alternatives of interest.
For example, dividing the summed values for a particular local treatment area by the summed
values for the entire regional landscape and multiplying by 100 reveals what percentage of total
predicted use occurs within the local landscape. Similarly, one could partition the region by land
ownership and sum the values for each landowner, divide these sums by the summed value for
the entire landscape, and multiply by 100. This will demonstrate relative elk use among
ownerships. You can also compare these percentages to the actual percentage of an area
occupied by each landowner to see if elk use is occurring in proportion to the “available” land
managed by each landowner.
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52
Figure 13—Percentage of predicted levels of use by landowner within an example regional landscape.
Changes in Regional Landscape: Existing Condition Relative to each Management
Alternative
5. Map of predicted levels of use, using raw use values placed in four to 10 equal-area classes for
Alternative 2 or multiple alternatives (as in no. 1 above), but making comparisons based on the
classes for the existing condition.
In some situations it can be difficult to see differences among maps displaying equal-area
classes. In addition, habitat enhancement may result in a much higher level of use in some
areas, which may not be evident when each map is broken down into five equal area classes
(e.g., Existing vs. Alternative 2, fig. 11). A clearer picture may be obtained by creating maps and
tables describing the landscape using the same class breakpoints as developed for the Existing
map. For example, the breakpoints for the Existing map in figure 11 are presented in table 13,
along with the percentage of the landscape in each class under the existing conditions and
Alternatives 1 and 2. The associated maps are displayed in figure 14.
Table 13—Five equal-area prediction classes for elk habitat use based on the existing regional
landscape (Existing), and the percentage of the landscape in each class for the existing regional
landscape and under three management alternatives.
Prediction
Predicted Level
Percentage of Study Area
Alternative Alternative Alternative
Class
of Use
Existing
1
2
3
Low
<0.0094
20.0%
20.7%
19.3%
18.7%
Medium-low
0.0094 to 0.0165
20.0%
19.6%
18.5%
19.2%
Medium
0.0166 to 0.0270
20.0%
19.6%
18.9%
19.3%
Medium-high
0.0271 to 0.0501
20.0%
21.1%
20.7%
21.1%
High
>0.0501
20.0%
19.1%
22.6%
21.8%
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53
Figure 14—Predicted levels of use by elk for the existing regional landscape (Existing), and under three
management alternatives. In this map, the breakpoints created for the Existing condition were used to
classify each of the subsequent management alternative maps. Predictions of levels of elk use are
separated into five equal-area classes for each map.
6. Summarize the percentage of the landscape in each of the classes based on original breakpoints
developed for existing condition (table 13); see previous explanation.
7. Map areas where predicted level of use increased, decreased, or showed no change (fig. 15).
Similar to process no. 5 for DDE above, changes in predicted use from an existing condition to
other management alternatives can be calculated on a pixel-by-pixel basis and mapped in simple
categories of negative, positive, or no change. This is an intensive GIS process, requiring these
steps: (1) Predicted Use Alternative 1 – Predicted Use Existing Condition; (2) reclass differences
into three groups (<0, 0, >0); (3) extract values, e.g., Sample tool, by XY points into Access; and
(4) summarize in Access and report back to Excel for bar charts (fig. 16).
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Figure 15—Areas of negative, positive, or no change in predicted level of elk use for three treatments
compared to the existing condition (not shown).
% Change from Existing Conditions
110.2
115
100
85
% change
Figure 16—Example of the
percentage of an analysis
area categorized by
negative, no, or positive
change in relation to
existing conditions for a
regional landscape and two
local landscapes for
Management Alternative 2.
70
55
25
-5
1/30/13
Alternative 2
31.9
26.0
26.0
40
10
Alternative 1
43.9
0.0
8.2
8.8
Regional
Landscape
Alternative 3
-1.5
Local
Landscape 1
***DRAFT***
Local
Landscape 2
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55
8. Summarize % change in predicted elk use from existing condition to other treatments (fig. 17).
For this analysis, divide the sum of the raw predicted level of use for the regional landscape
under a certain management alternative by the sum of the raw predictions for the existing
condition and multiply by 100. This analysis quantifies the benefits of various management
alternatives for a landscape (or local area).
% Area
% Change due to Alternative 2
80
70
60
50
40
30
20
10
0
67
62
40
38
Negative Change
24
9
Region
45
16
0
No Change
Figure 17—Percentage of
change in predicted level of elk
use under three management
alternatives, compared to the
existing conditions, for the
regional landscape and two local
landscapes.
Positive Change
Local
Local
Landscape 1 Landscape 2
Change from Existing Conditions
9. Table of acres moved, by class of predicted elk use, from existing conditions to other
management alternatives (table 14).
Similar to our calculations of DDE class and how many acres from each class experienced an
improvement or decline in DDE predictions under Alternative 2 (table 12), we can calculate the
total acres in Predicted Level of Use Class 1 (Low) (as an example) in the Existing map that
improved to the Medium-low, Medium, Medium-high and High prediction classes under various
alternatives.
Table 14—Number of existing acres in each class of Predicted Elk Use and the number of acres that
move into other prediction classes under Alternative 1.
Existing
Low
Medium-low
Medium
Medium-high
High
Total:
1/30/13
Low
12967
814
372
63
0
14215
Mediumlow
618
11758
552
520
34
13483
Alternative 1
Medium
125
719
11466
628
535
13473
***DRAFT***
Mediumhigh
40
284
1082
11856
1270
14531
High
17
192
295
700
11927
13131
Total
13767
13767
13766
13766
13767
68833
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56
Local Landscape: Existing Condition and any Management Alternatives
Summaries of predicted level of use by elk for the local landscape can be created by clipping the outputs
from the tool run for the region and displaying results in the local area, per instructions for the regional
analyses. However, the Habitat Use Tool is not to be re-applied and run within this smaller boundary.
Changes in Local Landscape: Existing Condition Relative to each Management Alternative
As above, changes in predicted use by elk in the local landscape can be summarized within the local area
boundary using methods described for regional analyses.
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57
Appendix 1. Literature Cited
Chesson, J. 1978. Measuring preference in selective predation. Ecology. 59: 211-215.
Cook, J.G.; Cook, R.C.; Davis, R.W.; Irwin, L.L. [N.d.]. a. Relations among habitat characteristics, plant
succession, and nutrition of foraging elk during summer and autumn in temperate forests of the Pacific
Northwest. [La Grande, OR]: [National Council for Air and Stream Improvement]; final report.
Cook, J.G.; Cook, R.C.; Davis, R. [et al.]. [N.d.]. b. Development and description of nutrition mapping
algorithm. In: Rowland, M. M.; Wisdom, M.J.; Cook, J.G. [et al.]. Nutrition and habitat use models for
landscape-level research and management of elk in Western Oregon and Washington. Wildlife
Monographs. Chapter 2.
Cook, J.G.; Johnson, B.K.; Cook, R.C. [et al.]. 2004. Effects of summer-autumn nutrition and parturition
date on reproduction and survival of elk. Wildlife Monographs. No. 155.
Cook, R.C.; Cook, J.G.; Vales, D.J. [et al.]. [In press]. Regional and seasonal patterns of nutritional
condition and reproduction in elk. Wildlife Monographs.
Franklin, J.F.; Dyrness, T.C. 1988. Natural vegetation of Oregon and Washington. Revised ed. Corvallis,
OR: Oregon State University Press. 452 p.
Harper, J.A. 1987. Ecology and management of Roosevelt elk in Oregon. Report. Portland, OR: Oregon
Department of Fish and Wildlife.
Hutchins, N.R. 2006. Diet, nutrition, and reproductive success of Roosevelt elk in managed forests of the
Olympic Peninsula, Washington. Arcata, CA: Humbolt State University. 104 p. M.S. thesis.
Ivlev, V.S. 1961. Experimental ecology of the feeding of fishes. New Haven , CT: Yale University Press.
302 p.
Manly, B.; McDonald, L.; Thomas, D. 1993. Resource selection by animals. New York: Chapman and Hall.
192 p.
Rowland, M. M.; Wisdom, M.J.; Cook, J.G. [et al.]. [N.d.]. Nutrition and habitat use models for landscapelevel research and management of elk in Western Oregon and Washington. Wildlife Monographs.
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58
Appendix 2. Beta Testers for Westside Elk Modeling
Name
Agency
Position
Locale
Kurt Aluzas
USFSa
wildlife biologist
Olympic NF, Quilcene, WA
Robert Alvarado
USFS
Regional wildlife biologist
R6 Regional Office,
Portland, OR
Jon Belcher
USFS
GIS/Data Services
Specialist
Willamette NF,
Springfield, OR
Carol Chandler
USFS
wildlife program manager
Gifford Pinchot NF,
Vancouver, WA
Josh Chapman
USFS
Forest wildlife biologist
Umpqua NF, Roseburg,
OR
Ray Davis
USFS
wildlife biologist
Umpqua NF, Roseburg,
OR
Joe Doerr
USFS
wildlife biologist
Willamette NF,
Springfield, OR
Andy Duff
WDFW
Westside GIS and Data
Support Analyst
Wildlife Science Division,
Olympia, WA
Justin Hadwen
USFS
wildlife technician
Umpqua NF, Roseburg,
OR
Jeremy Hobson
USFS
GIS
Willamette NF,
Springfield, OR
Cindy Lou McDonald
BLM
GIS specialist
BLM Oregon/Washington
State Office, Portland, OR
Bryant Mecklem
BLM
GIS specialist
BLM Oregon/Washington
State Office, Portland, OR
Arthur Miller
BLM
GIS specialist
BLM Oregon/Washington
State Office, Portland, OR
Lisa Renan
BLM
wildlife biologist
Roseburg District,
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59
Roseburg, OR
Todd Thompson
USFS
wildlife biologist
BLM Oregon/Washington
State Office, Portland, OR
Sonja Weber
USFS
wildlife biologist
Willamette NF SO;
Springfield, OR
a
USFS = U.S. Department of Agriculture, Forest Service; WDFW = Washington Department of Fish and
Wildlife; BLM = U.S. Department of the Interior, Bureau of Land Management.
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60
Appendix 3. Commonly Used Data Sources for Elk Model
Application
Data
Description
source
Download source
GNN
http://www.fsl.orst.edu/lem
ma/splash.php
DEM
GTRN
NAIP
PNV
Digital Elevation Model; a spatial grid of elevation used to derive the
mean slope covariate in the regional Habitat Use Model. We used
the 1/3 second (~30-m) National Elevation Dataset from USGS.
“Gradient Nearest Neighbor;” a modeling method that incorporates
multivariate statistics and imputation to produce a variety of
vegetation maps, based on field data and mapped (explanatory)
data. For elk nutrition and habitat use modeling, we used key fields
from the March 2010 release of the GNN species-size model,
developed for Northwest Forest Plan Effectiveness Monitoring.
Roads and highways compiled by BLM for Oregon and Washington;
includes both BLM inventoried and non-inventoried roads. Updated
weekly. Spatial accuracy is undefined in the metadata, but we have
found the layer to be very accurate when overlaid with aerial
imagery. However, attributes are not well-populated for noninventoried roads. The user is advised to first merge the highways
and roads layers, add missing roads using aerial imagery, and then
create a new field. Populate this field with a code of 0 for roads
closed to public access and 1 for roads open to public access.
U.S. Department of Agriculture's National Agriculture Imagery
Program is a collection of aerial photographs. USDA web site for
more information:
http://www.fsa.usda.gov/FSA/apfoapp?area=home&subject=prog&t
opic=nai
Forest Service and BLM employees can access NAIP through the
Image Server function in ArcGIS 9.3 and newer versions. NAIP is also
available through the USDA Geospatial Data Gateway.
Potential Natural Vegetation; potential vegetation zones are an input
required to run the Elk Nutrition Model. We used the state-wide
PNV grids developed by Region 6 of the Forest Service and
crosswalked each zone to one of the two zones used in development
of the nutrition equations (see text in “Predicting Elk Nutrition” for
more detail). The PNV zone layer was produced through a modeling
process incorporating field data and spatial data layers.
1/30/13
***DRAFT***
http://ned.usgs.gov/
http://www.blm.gov/or/gis/
data.php
http://datagateway.nrcs.usd
a.gov/
http://ecoshare.info/categor
y/gis-data-vegzones/
Version 2.0
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61
Appendix 4. Selectivity of Elk Forage Species In Western Oregon
and Washington
Selectivity indices (Ivlev [1961] and Chesson [1978], as reported in Manly et al. 1993), percentage of
diets, and selectivity category for plant species in western Oregon and Washington. Plants are arranged
alphabetically within categories of decreasing order of selectivity. Data were collected using captive elk
during summer and autumn, 2000-2002 (see Cook et al., n.d. a for details).
Scientific Name
Acer circinatum
Acer macrophyllum
Alnus incana
Alnus rubra
Anaphalis margaritacea
c
Bryoria spp.
Buddleja davidii
Calamagrostis canadensis
Carex spp.
Cinna latifolia
Clintonia uniflora
Corylus cornuta
Galepsis tetrahit
Hypericum perforatum
Hypochaeris radicata
Iris tenax
Ligusticum apiifolium
Linnaea borealis
Lolium perenne
Lonicera ciliosa
Luzula spp.
Maianthemum dilatatum
c
Mushroom
Oplopanax horridum
Oxalis oregana
Pedicularis spp.
Petasites fridigus
Phalaris arundinacea
Phleum pratense
Pyrus communis
Pyrus fusca
Rhamnus purshiana
Ribes sanguineum
Rubus parviflorus
Salix spp.
Smilicina spp.
Sorbus sitchensis
Spiraea douglasii
Symphoricarpos spp.
1/30/13
Plant
a
Group
DS
DS
DS
DS
Fb
NV
DS
Gr
Gr
Gr
Fb
DS
Fb
Fb
Fb
Fb
Fb
ES
Gr
DS
Gr
Fb
NV
DS
Fb
Fb
Fb
Gr
Gr
DS
DS
DS
DS
DS
DS
DS
DS
DS
DS
Plant
Code
ACCI
ACMA
ALIN
ALRU
ANMA
BRYO
BUDA
CACA
CARE
CILA
CLUN
COCO
GATE
HYPE
HYRA
IRTE
LIAP
LIBO
LOPE
LOCI
LUZU
MADI
MUSH
OPHO
OXOR
PEDI
PEFR
PHAR
PHPR
PYCO
PYFU
RHPU
RISA
RUPA
SALI
SMIL
SOSI
SPDO
SYMP
b
n
Ivlev
Chesson
Diet (%)
Selectivity
62
28
1
36
48
10
11
1
70
1
13
24
2
18
25
32
1
13
1
4
43
16
14
12
13
1
6
4
1
1
1
24
21
45
16
10
11
8
38
0.626
0.474
0.875
0.232
0.272
0.091
0.102
0.0757
0.033
0.025
0.435
0.966
0.237
0.936
0.800
0.571
0.550
0.222
0.340
0.540
0.974
0.506
0.954
0.565
0.192
0.499
0.079
0.2305
0.043
0.0527
0.109
0.074
0.008
0.015
0.057
0.038
0.1625
0.030
0.136
0.028
0.033
0.104
0.526
0.671
0.868
0.524
0.903
0.846
0.984
0.857
0.329
0.272
0.191
0.647
0.368
0.871
0.666
0.487
0.080
0.109
0.0212
0.059
0.104
0.0242
0.2476
0.0341
0.046
0.038
0.030
0.086
0.038
0.109
0.142
0.053
11.39
4.86
1.50
1.69
1.93
3.75
1.72
5.80
2.05
3.00
14.52
4.63
0.35
1.09
5.50
5.50
7.69
2.88
4.20
1.23
4.21
6.81
0.02
4.73
9.15
8.50
1.53
4.75
1.20
12.30
1.30
1.73
2.98
8.38
4.17
1.44
10.65
4.11
6.51
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
***DRAFT***
Version 2.0
Draft Westside Elk Model User Guidelines, v. 2.0
Scientific Name
Tolfieldia glutinosa
Trifolium spp.
Vaccinium parvifolium
Valeriana sitchensis
Achlys triflora
Adenocaulon bicolor
Adiantum pedatum
Agrostis exarata
Agrostis sp
Alnus sinuata
Amelanchier alnifolia
Anemone deltoidea
Angelica genuflexa
Antennaria spp.
Arctostaphylos columbiana
Arenaria macrophylla
Artemisia suksdorfii
Aruncus sylvester
Asarum caudatum
Astragalus spp.
c
Attached leaf lichens
Bromus vulgaris
Campanula scouleri
Castanopsis chrysophylla
Chrysanthemum leucanthemum
Cirsium spp.
Cornus canadensis
Cornus nuttallii
Cornus stolonifera
Crepis spp.
Cytisus scoparius
Dactylis glomerata
Daucus carota
Dianthus armeria
Dicentra formosa
Disporum spp.
Dryopteris austriaca
Elymus glaucus
Elymus spp.
Epilobium angustifolium
Equisetum spp.
Festuca occidentalis
Festuca sublulata
Fragaria vesca
c
Fungi
Galium oreganum
Galium triflorum
Geum macrophyllum
Glyceria elata
Glyceria spp.
Gymnocarpium dryopteris
1/30/13
Plant
a
Group
Fb
Fb
DS
Fb
Fb
Fb
Fn
Gr
Gr
DS
DS
Fb
Fb
Fb
ES
Fb
Fb
Fb
Fb
Fb
NV
Gr
Fb
ES
Fb
Fb
DS
DS
Fb
Fb
DS
Gr
Fb
Fb
Fb
Fb
Fb
Gr
Gr
Fb
Gr
Gr
Gr
Fb
NV
Fb
Fb
Fb
Gr
Gr
Fn
62
Plant
Code
TOGL
TRIF
VAPA
VASI
ACTR
ADBI
ADPE
AGEX
AGRO
ALSI
AMAL
ANDE
ANGE
ANTE
ARCO
ARMA
ARSU
ARSY
ASCA
ASTR
ALLI
BRVU
CASC
CACH
CHLE
CIRS
COCA
CONU
COST
CREP
CYSC
DAGL
DACA
DIAR
DIFO
DISP
DRAU
ELGL
ELYM
EPAN
EQUI
FEOC
FESU
FRVE
FUNG
GAOR
GATR
GEMA
GLEL
GLYC
GYDR
b
n
Ivlev
Chesson
Diet (%)
Selectivity
1
4
72
4
8
14
7
11
32
5
4
5
1
1
5
2
1
2
6
3
13
41
17
8
20
36
13
2
1
11
3
5
3
2
14
19
18
16
40
46
10
13
3
5
13
4
40
4
7
1
3
0.778
0.730
0.559
0.534
0.275
0.189
-0.197
-0.029
0.199
0.265
0.174
-0.190
0.000
0.000
-0.267
0.741
0.000
-0.200
-0.221
-0.444
0.016
0.152
0.138
0.064
0.014
0.013
0.038
0.008
0.060
0.021
0.021
0.005
0.0062
0.0038
0.006
0.132
0.0016
0.003
0.004
0.008
0.047
-0.175
0.024
-0.181
0.071
-0.018
0.778
0.333
-0.232
-0.333
0.541
0.489
-0.500
0.170
-0.008
-0.113
-0.179
-0.046
0.117
-0.106
-0.295
-0.115
-0.666
0.042
0.006
0.022
0.004
0.016
0.016
0.087
0.0109
0.004
0.005
0.125
0.047
0.001
0.027
0.010
0.014
0.017
0.047
0.028
0.013
0.004
0.004
0.002
0.257
0.048
-0.600
0.187
0.333
0.133
0.018
0.015
0.002
0.036
0.0041
0.006
0.80
8.95
10.35
2.25
1.18
0.86
0.70
1.49
3.55
5.34
1.13
0.18
0.30
0.10
0.14
1.00
0.20
0.20
0.30
0.67
0.40
2.32
0.20
0.79
0.56
4.00
2.02
2.40
0.20
0.33
0.07
6.34
0.63
0.05
0.74
0.38
0.38
1.06
2.68
9.34
2.04
0.14
0.83
1.02
0.64
0.55
2.21
0.10
3.49
0.20
0.27
S
S
S
S
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
***DRAFT***
Version 2.0
Draft Westside Elk Model User Guidelines, v. 2.0
Scientific Name
Hieracium albiflorum
Holcus lanatus
Holodiscus discolor
Hydrophyllum fendleri
Juncus spp.
Lactuca muralis
Lathyrus spp.
Lilium columbianum
Lupinus spp.
Lysichitum americanum
Melica bulbosa
Mimulus moschatus
Monotropa uniflora
Montia sibirica
Oemleria cerasiformis
Penstemon spp.
Phacelia hastata
Plantago spp.
Poa compressa
Populus trichocarpa
Prunus emarginata
Ranunculus spp.
Ribes bracteosum
Ribes lacustre
Ribes spp.
Rosa gynmocarpa
Rubus nivalis
Rubus pedatus
Rubus spectabilis
Rumex occidentalis
Rumex spp.
Sambucus spp.
Satureja douglasii
Scutellaria galericulata
Scutellaria lateriflora
Senecio sylvaticus
Senecio triangularis
Solidago canadensis
Sonchus asper
Sorbus aucuparia
Stachys cooleyae
Stellaria spp.
Streptopus roseus
SyNthyris reniformis
Taraxacum spp.
Thalictrum occidentale
Tiarella trifoliata
Tolmiea menziesii
Trisetum cernuum
Typha latifolia
Vaccinium alaskaense/ovalifolium
1/30/13
Plant
a
Group
Fb
Gr
DS
Fb
Gf
Fb
Fb
Fb
Fb
Fb
Gr
Fb
Fb
Fb
DS
Fb
Fb
Fb
Gr
DS
DS
Fb
DS
DS
DS
DS
DS
DS
DS
Fb
Fb
DS
Fb
Fb
Fb
Fb
Fb
Fb
Fb
DS
Fb
Fb
Fb
Fb
Fb
Fb
Fb
Fb
Gr
Gr
DS
63
Plant
Code
HIAL
HOLA
HODI
HYFE
JUNC
LAMU
LATH
LICO
LUPI
LYAM
MEBU
MIMO
MOUN
MOSI
OECE
PENS
PHHA
PLAN
POCO
POTR
PREM
RANU
RIBR
RILA
RIBE
ROGY
RUNI
RUPE
RUSP
RUOC
RUME
SAMB
SADO
SCGA
SCLA
SESY
SETR
SOCA
SOAS
SOAU
STCO
STEL
STRO
SYRE
TARA
THOC
TITR
TOME
TRCE
TYLA
VALOV
b
n
Ivlev
Chesson
Diet (%)
Selectivity
16
34
23
2
9
36
9
3
5
5
1
1
3
15
7
7
1
4
3
3
27
2
2
1
1
34
3
9
44
3
13
38
1
5
2
6
4
6
5
4
2
30
4
2
3
1
18
10
5
1
23
0.000
0.120
0.226
0.000
-0.369
0.029
0.254
0.167
-0.115
0.339
-0.739
0.000
0.367
-0.176
0.160
-0.105
-1.000
-0.017
0.059
-0.012
0.045
-0.030
-0.063
0.000
-1.000
0.044
-0.615
-0.445
-0.047
-0.556
-0.013
0.076
0.000
-0.033
-0.073
0.037
0.337
-0.056
0.110
0.054
0.614
-0.008
0.248
-0.333
-0.333
0.000
-0.168
-0.354
-0.250
0.857
0.290
0.042
0.026
0.035
0.004
0.007
0.032
0.015
0.038
0.004
0.010
0.0028
0.0021
0.010
0.012
0.022
0.014
0
0.007
0.009
0.080
0.013
0.032
0.003
0.0055
0
0.025
0.001
0.005
0.017
0.001
0.012
0.061
0.0051
0.059
0.002
0.011
0.021
0.004
0.010
0.012
0.033
0.010
0.021
0.005
0.003
0.0013
0.035
0.007
0.058
0.106
0.055
2.11
5.36
2.20
0.10
0.21
4.67
0.46
0.77
0.34
4.90
0.30
0.10
0.27
0.39
0.50
1.44
0.00
0.18
0.40
1.90
1.43
1.60
0.40
0.10
0.00
0.79
0.07
0.41
9.03
0.07
0.41
2.68
0.10
2.70
0.75
0.37
0.43
0.15
0.24
0.03
1.10
0.39
0.65
0.10
0.07
0.10
1.24
1.81
0.98
1.30
21.23
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
***DRAFT***
Version 2.0
Draft Westside Elk Model User Guidelines, v. 2.0
Scientific Name
Vaccinium membranaceum
Vancouveria hexandra
Veratrum viride
Veronica officinalis
Vicia spp.
Viola spp.
Xerophyllum tenax
Abies amabilis
Abies procera
Arbutus menziesii
Athyrium filix-femina
Berberis nervosa
Blechnum spicant
Ceanothus velutinus
Chimaphila menziesii
Circaea alpine
c
Clubmoss, liverwort, moss
Deschampsia elongata
Digitalis purpurea
Epilobium paniculatum
Epilobium watsonii
Erechtites minima
Gaultheria ovatifolia
Gaultheria shallon
Ilex sp.
Mentha arvensis
Menziesia ferruginea
Nothochelone nemorosa
Osmorhiza chilensis
Phododendron albiflorum
Phyllodoce empetriformis
Pinus monticola
Poa pratensis
Poa sp.
Polystichum munitum
Prunella vulgaris
Pseudotsuga menziesii
Pteridium aquilinium
Pyrola spp.
Rhododendron macrophyllum
Ribes divaricatum
Rubus discolor
Rubus laciniatus
Rubus leucodermis
Rubus ursinus
Solanum dulcamara
Tanacetum vulgare
Taxus brevifolia
Tellima grandiflora
Thuja plicata
Trientalis latifolia
1/30/13
Plant
a
Group
DS
Fb
Fb
Fb
Fb
Fb
Fb
Cn
Cn
ES
Fn
ES
Fn
ES
ES
Fb
NV
Gr
Fb
Fb
Fb
Fb
ES
ES
ES
Fb
DS
Fb
Fb
ES
ES
Cn
Gr
Gr
Fn
Fb
Cn
Fn
Fb
ES
DS
DS
ES
DS
DS
Fb
Fb
ES
Fb
Cn
Fb
64
Plant
Code
VAME
VAHE
VEVI
VEOF
VICI
VIOL
XETE
ABAM
ABPR
ARME
ATFI
BENE
BLSP
CEVE
CHME
CIAL
MOSS
DEEL
DIPU
EPPA
EPWA
ERMI
GAOV
GASH
ILEX
MIAR
MEFE
NONE
OSCH
RHAL
PHEM
PIMO
POPR
POAS
POMU
PRVU
PSME
PTAQ
PYRO
RHMA
RIDI
RUDI
RULA
RULE
RUUR
SODU
TAVU
TABR
TEGR
THPL
TRLA
b
n
Ivlev
Chesson
Diet (%)
Selectivity
6
21
7
12
3
44
2
11
4
1
25
64
17
8
2
5
7
15
18
8
22
6
7
54
3
5
12
1
6
3
2
1
15
8
76
7
57
71
1
4
8
24
24
24
80
4
1
5
5
9
29
-0.963
0.059
0.035
0.011
-0.394
-0.165
-0.727
-1.000
-1.000
-0.412
-0.255
-0.678
-0.920
-0.500
-1.000
-0.800
0.000
0.017
0.007
0.023
0.015
0.010
0.001
0.000
0.000
0.0039
0.008
0.005
0.000
0.005
0.000
0.000
-0.448
-0.952
-0.521
-0.322
-0.448
-0.952
-0.247
-0.667
-0.600
-0.887
-0.909
-0.460
-0.867
-1.000
-1.000
-0.699
-0.640
-0.874
-0.643
-0.342
-0.122
-1.000
-0.883
-0.377
-0.305
-0.505
-0.563
-0.587
-1.000
-0.778
-1.000
-0.700
-1.000
-0.412
0.005
0.000
0.003
0.005
0.002
0.000
0.035
0.002
0.002
0.000
0.0002
0.001
0.000
0.000
0
0.003
0.006
0.004
0.001
0.031
0.037
0
0.000
0.008
0.011
0.005
0.002
0.003
0.000
0.0002
0.000
0.002
0.000
0.003
0.03
0.76
0.31
0.76
0.33
0.55
1.80
0.00
0.00
0.50
1.72
1.34
0.04
1.86
0.00
0.02
3.37
0.35
0.06
0.08
0.58
0.17
0.07
9.25
0.03
0.04
0.04
0.20
0.08
0.03
0.00
0.00
0.10
0.98
0.82
0.06
3.05
6.80
0.00
0.55
0.20
2.46
0.48
0.40
1.94
0.00
0.10
0.00
0.06
0.00
0.13
N
N
N
N
N
N
N
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
***DRAFT***
Version 2.0
Draft Westside Elk Model User Guidelines, v. 2.0
Scientific Name
Trillium spp.
Tsuga heterophylla
Tsuga mertensiana
Urtica dioica
Veronica americana
Whipplea modesta
Plant
a
Group
Fb
Cn
Cn
Fb
Fb
Fb
65
Plant
Code
TRIL
TSHE
TSME
URDI
VEAM
WHMO
b
n
Ivlev
Chesson
Diet (%)
Selectivity
6
31
5
2
2
3
-0.417
-0.800
-1.000
-1.000
-1.000
-0.897
0.003
0.002
0.000
0.000
0.000
0.000
0.07
0.07
0.00
0.00
0.00
0.07
A
A
A
A
A
A
a
Plant codes are: Cn = conifer; DS = deciduous shrub; ES = evergreen shrub; Fb = forb; Fn = fern; Gr =
graminoid; NV = nonvascular plant.
b
S = significantly selected, A = significantly avoided, and N = neither selected nor avoided.
C
Biomass availability was not measured and thus no estimates of selection could be calculated. Dietary
percentages are based only on those elk pens where the species was present in elk diets. The selection
category was based on the authors’ general observations of elk response to these species.
1/30/13
***DRAFT***
Version 2.0
Draft Westside Elk Model User Guidelines, v. 2.0
66
Glossary of Terms and Abbreviations
Term
AB
ABAM
CANCOV (CC)
DDE
ESLF
GNN
GTRN
HW
HW100
MHSFZ
Tool Parameters
NB
Nooksack (NK)
PNV
SB
Springfield (Sp)
STNDHGT
TSHE
TSME
WHZ
Willapa Hills (WH)
1/30/13
Definition
Accepted biomass (kg/ha), or abundance of selected and neutral plant
species combined
Abies amabilis (Pacific silver fir)
Canopy cover (%) of all live trees; an attribute of GNN
Dietary digestible energy (kcal/g)
Ecological System Life Form; a method of classifying land cover types used
in GNN
Gradient nearest neighbor (method used to produce a suite of vegetation
layers in OR and WA);
http://www.fsl.orst.edu/lemma/main.php?project=nwfp&id=home)
Ground transportation network (http://www.blm.gov/or/gis/data.php);
roads layer used in modeling elk habitat use
Proportion of stems in dominant canopy layer that are hardwood tree
species (e.g., red and other alders, big leaf maple, and paper birch).
“HW” multiplied by 100
Mountain Hemlock/Silver Fir Zone; See “PNV,” “TSME,” and ”ABAM”
Input data needed for each tool
Neutral biomass (kg/ha); biomass of elk forage species that elk neither
significantly avoided or selected
Region defined as northern Cascades near Mount Baker
Potential natural vegetation; http://ecoshare.info/products/gis-data/
Selected biomass (kg/ha); biomass of those forage species that elk
significantly selected
Region defined as central Cascades west of Springfield, OR
Stand height (meters); attribute of GNN
Tsuga heterophylla (western hemlock)
Tsuga mertensiana (Mountain Hemlock)
Western Hemlock Zone; See “PNV” and “TSHE”
Region defined as coastal foothills of OR and WA west of Centralia, WA;
see fig. 5
***DRAFT***
Version 2.0
Draft Westside Elk Model User Guidelines, v. 2.0
English Equivalents
When you know:
Centimeters (cm)
Meters (m)
Hectares (ha)
Kilometers (km)
Grams (g)
Kilograms (kg)
1/30/13
67
Multiply by:
.394
3.28
2.47
.621
.0352
2.205
***DRAFT***
To find:
Inches
Feet
Acres
Miles
Ounces
Pounds
Version 2.0