Draft Blue Mountains Elk Model User Guidelines, v. 1.0
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Blue Mountains Elk Nutrition and Habitat Use Models
User Manual
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Table of Contents
Spatial and Temporal Extents for Summarizing Model Results.............................................................5
Defining an Analysis Area for Model Application ..................................................................................... 5
Regional Landscape............................................................................................................................... 5
Local Landscape .................................................................................................................................... 7
Blue Mountains Elk Nutrition and Habitat Use Models ........................................................................9
Model Inputs and Preparation ..........................................................................................................10
Spatial data required to run models ....................................................................................................... 10
Existing Vegetation ............................................................................................................................. 12
Potential Vegetation ........................................................................................................................... 13
Wetlands ............................................................................................................................................. 13
Precipitation ........................................................................................................................................ 14
Roads................................................................................................................................................... 16
Usage Tips for All Tools ........................................................................................................................... 19
Tool Constraints and Notes................................................................................................................. 19
Steps to Run Tools in the Blue Mountains Elk Habitat Models toolbox ................................................. 20
Nutrition Tool ..................................................................................................................................22
Using the Nutrition Tool: Do’s and Don’ts .............................................................................................. 22
The Nutrition Equations ...................................................................................................................... 22
The Nutrition Tool Parameters ........................................................................................................... 23
Tool properties set under the environment tab ................................................................................. 25
Nutrition tool output file definitions .................................................................................................. 25
Elk Habitat Use Tool .........................................................................................................................27
Using the Habitat Use Tool: Do’s and Don’ts .......................................................................................... 28
Habitat Use Tool Parameters .............................................................................................................. 29
Tool properties set under the environment tab ................................................................................. 31
Habitat Use Tool output file definitions.............................................................................................. 32
Update Canopy Cover Tool ...............................................................................................................34
Additional Input Data Needed for the Update Canopy Cover Tool ........................................................ 35
Using the Update Canopy Cover Tool: Do’s and Don’ts.......................................................................... 36
Update Canopy Cover Tool Parameters.............................................................................................. 37
Tool properties set under the environment tab ................................................................................. 38
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Update Canopy Cover Tool output file definitions ............................................................................. 38
Interpretation and Summaries of Model Results ...............................................................................38
Summarizing Results from the Nutrition Model ..................................................................................... 39
Regional Landscape: Existing Condition and any Management Alternatives ..................................... 40
Changes in Regional Landscape: Existing Condition Relative to each Management Alternative ....... 41
Changes in Local Landscape Relative to each Management Alternative ........................................... 42
Summarizing Results from the Elk Habitat Use Model ........................................................................... 42
Regional Landscape: Existing Condition and any Management Alternatives ..................................... 43
Changes in Regional Landscape: Existing Condition Relative to each Management Alternative ....... 46
Local Landscape: Existing Condition and any Management Alternatives .......................................... 50
Changes in Local Landscape: Existing Condition Relative to each Management Alternative ............ 50
ArcGIS SQL, Field Calculator, and Raster Calculator Tips and Tricks ....................................................51
SQL .......................................................................................................................................................... 51
SQL operators (copied from the Arc Help files) .................................................................................. 52
Field Calculator........................................................................................................................................ 55
Raster Calculator ..................................................................................................................................... 56
Literature Cited ................................................................................................................................58
English Equivalents ..........................................................................................................................59
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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
(approx. 25,000 acres) 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
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 compared
to 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
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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.
When predicting elk nutritional conditions with the 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), 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 LANDFIRE, 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,
these products (e.g., LANDFIRE existing vegetation type;
http://www.landfire.gov/NationalProductDescriptions21.php) are suitable for landscape to regionalscale analyses but are insufficiently accurate for local or stand-level applications.
For predicting elk habitat use with the Blue Mountains 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 of habitat use. 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. 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. However, when summarizing results
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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
Figure 1. 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), and within local
landscapes if desired
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
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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.
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). 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
dietary digestible energy (DDE) may increase for 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.
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Blue Mountains Elk Nutrition and Habitat Use Models
We developed two different primary models under the auspices of the Blue Mountains 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 (Blue Mnts Elk Habitat Models
Toolbox), using ESRI ArcGIS 10.0 ModelBuilder. The Blue Mountains Elk Habitat Models Toolbox
contains five tools, including two Nutrition Tools (Ag, no Ag), two Habitat Use Tools (Ag, no Ag), and the
Update Canopy Cover Tool.
Figure 2.
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Model Inputs and Preparation
Spatial data required to run models
(Table 1.)
Data Layer
Source
Attribute
Field to add
n/a
Suggested
Data Type
n/a
Attribute as:
Study Area
Boundary
User defined
Buffered Study
Area Boundary
Created by adding
4km buffer to Study
Area
n/a
n/a
n/a
Existing
Vegetation
Landfire EVT (existing
vegetation type)
layer for 2001 or
2008; pick year
closest to time
period of interest
Model
Text field
Forest
Text field
“H” = habitat
types to
model (land
cover types
potentially
used by elk)
or
“M” = masked
(land cover
types not
used by elk)
or
“AG” = Ag
type
“F” = Forest
or
“NF” =
nonforest
See “Existing
Vegetation”
below for more
details
Canopy Cover
http://www.landfir
e.gov/
Landfire CC (canopy
cover) layer for 2001
or 2008 (not EVC);
pick year closest to
time period of
interest
http://www.landfire.
gov/
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n/a
Run this layer through the Canopy Cover
Update tool to capture recent timber
harvest, fires, or other disturbances in
your study area that may affect CC;
canopy cover update is not an essential
step in order to run the habitat use or
nutrition tools successfully
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Where data
are used
In both
Nutrition and
Habitat Use
tools
In both
Nutrition and
Habitat Use
tools
In the Nutrition
tool as an input
to create DDE;
In the Habitat
Use tool to
create the
mask (i.e.,
designate
where the tool
will be applied)
In the Habitat
Use tool to
create the
percent forest
covariate
In the Nutrition
tool as an input
to create DDE
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Data Layer
Source
Potential
Vegetation
Landfire ESP
(environmental site
potential) layer
See “Potential
Vegetation”
below for more
details
Wetlands
See
“Wetlands”
below for more
details
Precip
See
“Precipitation”
below for more
details
EVI (Enhanced
Vegetation
Index)
DEM
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Attribute
Field to add
Classification
Suggested
Data Type
MUST BE
Short
Integer
USFWS National
Wetlands Inventory
Wetland
Short
Integer
PRISM
http://www.prism.or
egonstate.edu/
Download the data, project to meters,
then convert units to mm.
http://www.fws.go
v/wetlands/
Download the 30-yr
average for the
current month (e.g.,
August) and prior
month (e.g., July);
800-m cell size, units
are mm x 100
MODIS
250-m cell size
Digital Elevation
Model
Must be in meters
“1”=Rangelan
ds
“4”=Dry
Forest
“5”=Wet
Forest
“-9”=Nodata
“1” = Used
or
“0” = not used
Divide raster by 100 to convert units to
mm
Where data
are used
In the Nutrition
tool to define
which DDE
equation is
applied
In the Nutrition
tool as an input
to create DDE
In the Nutrition
tool as an input
to create DDE
Note: if you chose a modeling month
other than August, your Current and Prior
months precip will need to be changed to
match your modeling date
No modifications needed
Note: if you chose a modeling month
other than August, make sure the EVI data
you download is appropriate for the
modeling date you chose. Pick EVI’s two
week span that best covers the modeling
date you chose
No modifications needed
http://ned.usgs.gov/
Download the 1 arcsecond NED layer
(approx. 30 meter
cell size)
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Attribute as:
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In the Nutrition
tool as an input
to create DDE,
but only when
ag lands are
present
In the Habitat
Use tool to
create the
slope covariate
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Data Layer
Source
Roads
GTRN (ground
transportation
network, a statewide
BLM layer) for base
See “Roads”
below for more
details
http://www.blm.gov/
or/gis/datadetails.php?data=ds0
00041
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Attribute
Field to add
Open
Suggested
Data Type
Short
Integer or
text
Class
Short
Integer
Attribute as:
“1” = Open
or
“0” = Closed
1, 2, 3, 4,
etc.
Where data
are used
In the Habitat
Use tool to
create the
distance to
roads
covariates
Existing Vegetation
The EVT (existing vegetation type) grid layer attribute table is used to model existing vegetation types.
Two highlighted attributes were added to this dataset, MODEL and FOREST. The MODEL attribute
defines vegetation types as a) types used by elk and modeled (H = habitat); b) types not used by elk, i.e.,
exlcluded from the modeling process (M = mask); and c) types considered agriculture lands (AG =
agriculture). Types classed as “H” are those that the modeling team deemed as potential habitat for elk
in this region. The MODEL attribute is used in the Nutrition tool to identify agricultural lands, which are
assigned a default DDE value, and also to define the mask (where the model will be applied). The
FOREST attribute defines vegetation types as a) forested (F) or b) non-forested (NF). The FOREST
attribute is used in the Habitat Use Tool to create the percent forest covariate. The attribute name
(MODEL and FOREST) and the attribute codes (H, AG, M or F, NF) are arbitrary and can be altered. The
Blue Mountains Elk toolbox uses the EVT layer in SQL (stuctured query language, a programming
language) expressions which are customizable based on the attribute names and codes you choose. For
example, an SQL expression to select land cover types to model would read “MODEL” In (‘AG’, ‘H’). An
SQL expression to select forest types would read “FOREST” = ‘F’.
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Potential Vegetation
The ESP (environmental site potential) grid layer attribute table is used to model potential vegetation
types. The highlighted attribute, Classification, was added to this dataset. The Classification attribute
defines vegetation types as 1=Rangelands; 4=Dry Forest; 5=Wet Forest; and -9=Nodata. These
classifications were defined by an elk nutrition expert based on the published descriptions of each
potential vegetation type. This attribute is used in the Nutrition tool to define which DDE equations will
be applied to particular portions of your study area. The attribute name (classification) is arbitrary and
can be altered but the attribute codes are SPECIFIC and must not be altered; the tool code is written to
recognize these values (-9, 1, 4, 5) and only these values. If you are modeling outside these boundaries
and create your own classified attribute, remember to use these specific codes (-9, 1, 4, 5), although the
attribute (field) name can be different. See the Landfire website for more information about the ESP
layer http://www.landfire.gov/NationalProductDescriptions19.php.
Wetlands
The wetlands feature layer attribute table. This dataset has one added attribute (Wetlands) that defines
used or not used wetlands features. For our models we consider only two wetland types as elk habitat,
identified in the WETLAND_TY as Freshwater Emergent Wetlands or Freshwater Forested/Shrub
Wetlands. All other wetlands do not resemble wet meadow types, which are the vegetation types for
which the elk grazing trial DDE values were estimated. The two wetlands features identified as habitat
are assigned default DDE values in the Nutrition tool. The attribute name (Wetlands) and the attribute
codes (1, 0) are arbitrary and can be altered. The Blue Mountains Elk tools use the Wetlands layer in
SQL expressions which are customizable based on the attribute name and codes you choose. For
example, an SQL expression to select wetlands used would read “Wetlands” = 1.
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Precipitation
The precipitation data home page from Prism. Download the data (compressed zip file .gz) from the
Prism website http://www.prism.oregonstate.edu/products/matrix.phtml?vartype=ppt&view=maps
(either the 30-arcsec (800 m) Normals or the 2.5-arcmin (4 km) Monthly). Save the zip file to your
computer follow these instructions for extracting data from a compressed zip file (.gz)
http://www.prism.oregonstate.edu/pub/prism/download.html. Be sure to project the data in a meters
projection BEFORE you convert to mm units. The original data is downloaded in decimal degrees, WGS
1984.
Example steps to download and convert precip data, using Aug 30 yr Normal:
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1. Download 30 yr Normal 1981-2010 (us_ppt_1981_2010.08.gz), file size is aprox. 26 mb.
2. Extract the file using winzip (us_ppt_1981_2010.08), file size is now aprox. 117 mb (larger file
size means the file was automatically decompressed when unzipped).
3. Create an ASCII file by adding .asc or .txt, manually change file name from us_ppt_1981_2010.08
to us_ppt_1981_2010_08.txt
4. Convert Ascii to raster, use the ASCII to Raster tool to convert to a grid. This grid will be in
decimal degrees and next needs to be converted to meters.
5. If no spatial reference is recognized in the newly created grid, then set the coordinate system to
WGS 1984.
6. Project raster, use Project Raster tool, you can import a projection that is used in the Landfire
data (Nad 1983 Albers), remember to change the cell size to 800 m if you are converting the 30
yr normal or change to 4000 m if you are converting the monthly. Below is an example of the
Project Raster tool parameters.
7. Convert units to mm, use the Raster Calculator tool and type in the expression “grid name” /
100 and then create the Output Raster name and file path. NOTE: by setting the mask in the
Environment Settings of this tool you can also clip the precip down to a more manageable size if
desired.
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Roads
The roads feature layer attribute table. The two highlighted attributes were added. The Open attribute
defines roads as open (1) or closed (0). The Class attribute defines the Class level of only the open roads.
Both the attribute names (Open, Class) and the attribute codes (0,1 and 0,1,2,3,4) can be altered. The
Blue Mountains Elk tools use the Roads layer in SQL expressions which are customizable based on the
attribute names and codes you choose. For example, an SQL expression to select open roads would read
“Open” = 1; an SQL expression to select class 3 or 4 roads would read “Class” In (3, 4) or “Class”=3 or
“Class”=4.
Road Classification Guidelines
Class I open roads are defined as any federal or state highway.
Class II open roads are county roads, two-digit Forest Service roads, or any other through roads that
branch from a Class I road or that branch from roads within or exiting a town or city. In some special
cases, a dead-end road may be designated as Class II, such as a road leading from a town that ends at a
trailhead of a wilderness area (e.g., the Lostine Road in Wallowa County).
Class III open roads are roads that branch directly from a Class II road, or are short (<1/4-mile) dead-end
roads that branch directly from a Class I road.
Class IV open roads are roads that branch directly from a Class III road, or are short (<1/4-mile) deadend roads that branch directly from a Class II road.
This classification system is an index of the degree of “remoteness” of a given part of an analysis area
from human uses, based on the rate of motorized traffic that is likely to occur along different roads that
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have varying distances from main access points provided by federal and state highways. Class I roads
receive the highest rates of traffic, and subsequent classes should be expected to receive progressively
lower rates of traffic as roads increasingly branch and become farther away from the main access points.
This basic premise of decreasing traffic rates with increasing levels of road branching from main access
points has been studied and well-documented in traffic engineering research. In addition, elk
avoidance of open roads has been shown to increase with increasing rates of traffic, and results of our
modeling work validated this pattern. That is, elk avoidance of Class I or II roads was stronger than for
Class III or IV. Together, these two roads covariates have a strong effect on summer elk habitat use.
Note that only roads open to public motorized uses during summer are included in the classification
system (see details below). Also, open roads that branch from Class IV roads are NOT included, as elk
did not avoid the most remote roads that would be classified as Class V, VI, VII, or higher.
The following steps can be used to map and derive the two roads model covariates as part of running
the Blue Mountains summer elk habitat model:
1. Start with a base roads layer that has the most roads digitized. Attribute all the federal and state
highways as open and class 1.
2. Note that many study areas may not have Class I roads present within the buffered study area
boundaries, but the classification system for the study area will depend on starting the classification
process from the Class I road nearest to the buffered study area boundaries.
3. Clip out the roads layer for the buffered analysis area (the buffer addresses any edge effects that
may influence elk behavior near the edge of your study area) and name it something meaningful.
4. Examine this layer for any missing open roads. Closed roads are not considered in the model. The
definition of “open” for the Blue Mountains Habitat Use Model is any road or trail open to public
motorized use, including open trails used by all-terrain vehicles or dirt bikes. Make sure the layer
contains all roads open to public motorized use during summer for all land ownerships in the
buffered analysis area.
5. Digitize any missing open roads using a current aerial photo background as a guide, e.g. NAIP.
6. The remaining open roads need to be attributed as open. Select all open roads in your new clipped
roads layer (you may want to display roads from other GIS enterprises such as USFS or local counties
to help identify open roads on public and private lands) and calculate Open = 1 (Make sure only the
features you want to update are selected, Open the attribute table and select or highlight the
attribute field called “Open”, Open the Field Calculator, type 1 in calculator box and click OK). When
you are finished, the Open attribute should contain only 0 or 1s. Save and export the selected roads
(all open roads) to a new shapefile called Open_Roads. Exporting the open road layer is optional but
could help eliminate the distractions of the remaining closed roads.
7. To begin classifying open roads, first select through roads (i.e., roads that are not dead ends), such
as county roads or well-travelled forest roads that connect with federal and state highways, or that
connect with a town or city, and assign them BMEMClass = 2. In general, Class II roads are easily
traveled by two-wheel drive passenger cars with low clearance. These roads not only connect
directly with a state or federal highway or with a city or town, but typically are well-maintained as
through roads that receive regular traffic use (e.g., paved county or forest road or a well-maintained
gravel forest road).
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8. After Class II roads are defined, select open roads that branch directly from Class II roads and assign
them BMEMClass = 3. Some through roads that branch from a Class II road also should be assigned
as Class II if such roads can be easily traveled by low-clearance two-wheel drive passenger cars and
also are known to receive frequent traffic in the same manner as the Class II road from which they
branch. The same is true for open roads that branch from a Class III road but that loop back to the
Class III road (all of these road segments would be Class III). That is, a set of two or more roads may
branch and ultimately loop back around to the original branch. These road segments would typically
maintain the same class throughout all road segments (essentially a loop road with multiple
branches).
9. Assign BMEMClass = 4 for roads that branch directly from a Class III road.
10. Continue classifying all open roads with these process steps for the entire analysis area (buffered
study area) until all such roads are assigned as Class I, II, III or IV. Remaining open roads that do not
meet the criteria for Class I – IV are not used in the model and can remain attributed as 0 (the
default value for BMEMClass).
This process of road classification is depicted below. In some cases, designation of Class III or IV roads
may need to be modified to reflect local knowledge of road uses. For example, a road system may
branch twice in a short distance (two road branches within ¼-mile of each other) but the same road
class might be maintained because the road system is known to maintain a consistent level of traffic
before versus after the branches. These special cases need explicit justification based on local,
documented knowledge of road systems and traffic.
Map showing the typical examples of road
classification and some special cases based on the
branching rules described in text. Examples include:
(1) designation of federal and state highways as
Class I; (2) designation of open through roads as
Class II that branch from Class I, in this case a twodigit Forest Service road; (3) designation of open
roads as Class III that branch from Class II; (4)
designation open roads as Class IV that branch from
Class III; (5) maintenance of the same road class for a
set of two or more road segments that branch but
that ultimately loop back around to the original
branch; (6) exclusion of dead-end open roads that
branch from Class IV, which are roads that would be
Class V, VI, VII, or higher; and (7) a short (<1/4-mile)
dead-end spur branching from a Class II and
designated as Class IV .
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Usage Tips for All Tools
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Use ArcGIS 10.0 or newer with an ArcInfo license and Spatial Analyst Extension.
We recommend that input grids 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 existing vegetation grid layer (e.g., EVT) contains vegetation types reclassified into two
attributes.
1. The Model field is where we classified each vegetation type as habitat used by elk,
habitat not used by elk, or agriculture.
2. The Forest field is where we classified each vegetation type as forest or non-forest.
The potential vegetation grid layer (e.g., ESP) contains vegetation zones classified into three
groups that depict which nutrition algorithms are used to calculate DDE.
1. The CROSSWALK field is where we classified each ESP zones occurring in the Blue
Mountains Region of Oregon or Washington as 1=Rangeland, 4=Dry Forest, or5= Wet
Forest.
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 these tools. 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., EVT, 30-m) and will be clipped to the boundary of the Study Area Boundary Plus
Buffer polygon.
The study area code parameter will be the prefix for all output file names.
Some outputs (e.g., mean_dde) have been summarized within a 200-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, table 3, and table 5).
Be sure to check all parameter fields in each tool to make sure the default settings are
appropriate for your data and study area. Default parameter inputs may be incorrect and need
to be reset.
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Tool Constraints and Notes
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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.
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The equations used in the Nutrition tools were derived from plot data collected in eastern
Oregon. Running this tool in other regions or in a study area with portions outside the Blue
Mountains modeling boundary may not predict nutrition accurately.
The tools are intended for application with 30-m resolution raster data, and are not
recommended for use at other resolutions.
Certain existing vegetation types (e.g., Vegetation types in EVT) 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 potential vegetation types could not be cross-walked to one
of the three ESP zones used in the nutrition equations; we also masked out these types as “no
data.”
The Nutrition Tools assigns any land cover types defined as agriculture a default value of 2.40
kcal/g for non-irrigated and 2.65 kcal/g for irrigated lands for DDE; that is, DDE is not calculated
for agricultural lands using the equations in the Nutrition Tools 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.
Steps to Run Tools in the Blue Mountains Elk Habitat Models toolbox
To add the Blue Mnts Elk Habitat Models toolbox 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”.
4. Navigate to the folder where you have saved the “Blue Mnts Elk Habitat Models.tbx,” select the
toolbox and then click Open.
5. Once the toolbox is added to ArcMap, expand the “Blue Mnts Elk Habitat Models” by clicking
the plus sign next to the toolbox. Five separate tools will be listed. Double click on the tool you
wish to run. (You may also right click on the tool and choose Open).
a. A window will open with showing the tool 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 example screen shot shows the nutrition 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 auto populate when the parent parameter 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 Potential Vegetation Classification Lookup parameter correctly. The
default field Value will auto populate after the PNV grid is selected in the parameter box
just prior to the Lookup Column parameter. If this parameter is 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 add the Blue Mnts Elk Habitat Models toolbox to ArcCatalog and run a tool:
1. Open ArcCatalog.
2. Open/Show the ArcToolbox window.
3. Add the Blue Mnts Elk Habitat Models Toolbox as you would in ArcMap.
4. Follow Step 5 from above to run the tools as you would in ArcMap.
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Nutrition Tool
Introduction
The elk habitat use model requires five covariates (Rowland et al., n.d.). 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 200-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 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.
The Nutrition Tool, inputs required and the outputs created, mean DDE, used in the Habitat Use tool.
There are five inputs required to calculate
DDE:
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Overstory canopy cover (%) of all
live trees
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Existing vegetation type (EVT)
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Potential natural vegetation (ESP)
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Cumulative precipitation
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Date
Using the Nutrition Tool: Do’s and Don’ts
The Nutrition Equations
The equations and appropriate tool parameters to predict 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, precipitation, date) 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 this
section (“Using the Nutrition Tool: Do’s and Don’ts”). See Table 2 for a list and description of the outputs
created from the Nutrition Tool.
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The Nutrition Tool Parameters
(Table 2.)
Parameter
Explanation
Data Type
Output Workspace Folder
Select the folder location you want the
tool 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.
Workspace
Study Area Code 4 char max
Choose an arbitrary (but meaningful)
String
four character code (alpha/numeric) that
represents the analysis area the tool 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.
Study Area Boundary Plus Buffer
Select the buffered regional landscape
Feature
study area. A typical buffer extends 4km Layer
beyond the study area boundary.
Including the buffered area allows the
tool to account for edge effects that may
influence elk behavior. Output layers will
be clipped to this boundary.
Choose Month 7-9
Choose the number of the month (7-9),
7=July; 8=August; 9=September) for
which you are determining nutrition.
This tool was developed using late
summer data so for the most accurate
estimates use one of those three
months.
Choose Day 1-31
Choose the number of the day (1- 31) for Double
which you are determining nutrition.
This tool was developed using date as an
input covariate. Date accounts for
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Data Type
seasonal variability.
Existing Vegetation Grid
Select the existing vegetation grid that
contains a forest attribute (classifies as
forest or non-forest) and a vegetation
type attribute (classifies as habitat used,
masked out, or agriculture lands). These
two attributes will often be created by
the user specifically for use in the Blue
Mountains Elk Habitat Models.
Raster
Layer
Expression to Select Existing Vegetation
and Ag Land Types
Create an SQL statement that selects
which existing vegetation types the tool
will model. These are the ‘habitat used’
vegetation types that elk will inhabit,
and agriculture types. Here, non-habitat
vegetation (such as barren lands,
developed, rock/sand, water, etc.)
should be excluded from model analysis.
SQL
Expression
Expression to Select Ag Lands
Create an SQL statement that selects
only the agriculture land types.
SQL
Expression
Current EVI
Select the EVI grid layer that is for the
current modeling month. This should be
data for the 16 day span that covers the
majority of the current modeling month.
Raster
Layer
Canopy Cover Grid
Select the existing canopy cover grid that Raster
contains canopy cover data as attributes Layer
to be used in the tool.
Potential Natural Vegetation
Select the potential vegetation grid that
contains a classified attribute for each
vegetation zone (or series) types. The
classified attribute must be an integer
and include -9 for nodata, 1 for
rangelands, 4 for dry forest, 5 for wet
forest.
Raster
Layer
Potential Vegetation Classification
Lookup Column
Select the attribute field in the potential
vegetation grid layer that contains the
classified data. The classified attribute
must and integer and include -9 for
nodata, 1 for rangelands, 4 for dry
forest, 5 for wet forest. This is a
parameter of the "Lookup" tool.
Field
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Parameter
Explanation
Data Type
Wetlands Feature Layer
Select the wetlands feature layer that
contains a classified attribute field that
defines what wetlands types will be
modeled.
Feature
Layer
Expression to Select Wetlands
Create an SQL statement that selects
which wetlands will be used in the tool.
SQL
Expression
Current Months Precip
Select the precipitation grid layer that is
for the current modeling month. This
should be precipitation data for July,
August, or September; either the
monthly average or the 30 yr average.
Raster
Layer
Previous Months Precip
Select the precipitation grid layer that is Raster
for the previous modeling month. This
Layer
should be precipitation data for June,
July, or August; either the monthly
average or the 30 yr average. Ex: if you
are going to model August nutrition then
use July precipitation for this input
parameter.
Tool properties set under the environment tab
(Table 3.)
Changed (if needed) before the tool is run
Environment Setting
Description
Extent
All new raster layers will be
given this extent.
Mask
All new raster layers will be
limited to this mask
Snap Raster To
Shifts any new raster layers to
match the starting x/y of this
raster.
Cell Size
Makes any new raster have
this cell size.
Default Value
<Study Area Boundary Plus
Buffer>
<Study Area Boundary Plus
Buffer>
Existing Vegetation Grid
Existing Vegetation Grid (30 m)
Nutrition tool output file definitions
(Table 4.)
. All abbreviations will have your study area code prefix (****_)
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Layers in the Output Workspace Folder. These layers are in final format.
Output Abbreviation
****_dde_mn
Description
Mean dietary digestible energy for a 200-m radius circle (kcal/g). This
output is one of the four needed covariates to get the habitat use
index.
Layers in the Temp Folder within Output Workspace Folder. These are intermediate or
temporary layers.
Output Abbreviation
Description
****_aglands
The selected agriculture land cover types that the tool will use when
calculating the ag DDE values.
****_date
The combined Month and Day as a decimal, constant value raster.
****_day
A constant, single value raster created from the Day parameter.
****_dde4cl.img
DDE classified into four nutrition categories; 1=Poor, 2=marginal,
3=good, 4=Excellent.
****_dde6cl.img
DDE classified into six nutrition categories; 1=poor, 2=low-marginal,
3=high-marginal, 4=low-good, 5=high-good, 6=excellent.
****_ddetmp1
DDE values calculated by merging (or stamping) ag lands on the
eq_dde raster. This is the second level of calculating DDE.
****_ddetmp2
DDE values calculated by merging (or stamping) wetlands on the
ddetmp1 raster. This is the third level of calculating DDE.
****_eq_dde
DDE values calculated from the PNV equations. This is the first level of
calculating DDE.
****_evt
The EVT values that are used in the tool. This includes only the “H” and
“Ag” values from the classified attribute field.
****_lkup_pnv
The PNV classified values converted to a value raster. This layer
defines where the DDE equations will be applied.
****_mask
The absolute, overall area where DDE will be calculated. Combines EVT
and PNV but excludes NoData cells.
****_month
A constant, single value raster created from the Month parameter.
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****_pnv_int
The PNV values that are used in the tool. This includes only the 1, 4,
and 5 values from the classification attribute field.
****_wetlands
The raster conversion of the wetlands.shp
****_wetlands.shp
The selected wetlands that the tool will use when calculating the
wetlands DDE values.
Elk Habitat Use Tool
Introduction
The elk habitat use model predicts levels of elk use across the landscape. The model incorporates the
Nutrition Tool described previously to predict elk use.
This tool uses inputs previously explained for the Nutrition Tool, a digital elevation model (DEM), and a
classified open roads layer to create the covariates required to calculate a habitat use index. The
existing vegetation type, used in the Nutrition Tool, is also used in the Habitat Use Tool to create the
forest vegetation type variable.
There are seven inputs required to calculate the predicted level of elk use (five of which are the same
that were described for the Nutrition Tool) (fig. 3):
• mean DDE (dietary digestible energy)
• mean percent slope
• distance (meters) to nearest class 1 or 2 road open to public use (capped at 4000 m)
• distance (meters) to nearest class 3 or 4 road open to public use (capped at 4000 m)
• percent area in forest vegetation type, all created with the Habitat Use Tool
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Figure 4. Elk habitat use model diagram.
By using a single algorithm and these 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 ), the Update Canopy Cover Tool must be run prior to running the
Habitat Use Tools for each alternative of interest.
Using the Habitat Use Tool: Do’s and Don’ts
This 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 and the habitat use
index), 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.
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For example, if distance to class I and II 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 or other covariates, therefore saving time.
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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.
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Habitat Use Tool Parameters
(Table 5)
Parameter
Explanation
Data Type
Output Workspace Folder
Select the folder location you want the tool
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.
Workspace
Study Area Code 4 char max
Choose an arbitrary (but meaningful) four
character code (alpha/numeric) that represents
the analysis area the tool 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.
String
Study Area Boundary
Select the regional landscape study area (not
buffered). Only the habitat use index output layer
will be clipped to this boundary.
Feature Layer
Study Area Boundary Plus
Buffer
Select the buffered regional landscape study area.
A typical buffer extends 4km beyond the study
area boundary. Including the buffered area allows
the tool to account for edge effects that may
influence elk behavior. Output layers will be
clipped to this boundary.
Feature Layer
Choose the Modeling Day 131
Choose the number of the day (1- 31) within the
Double
month of August for which you are determining
nutrition. This tool was developed using date as an
input covariate. Date accounts for seasonal
variability. The month (August) is a preset default
value. By choosing the day you can look at
variability within the month of August. The Habitat
Use tool was designed and tested using August
input data and elk locations, therefore we limit the
habitat use index output to this time period.
Existing Vegetation Grid
Select the existing vegetation grid that contains a
forest attribute (classifies as forest or non-forest)
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Data Type
and a vegetation type attribute (classifies as
habitat used, masked out, or agriculture lands).
These two attributes will often be created by the
user specifically for use in the Blue Mountains Elk
Habitat Models.
Expression to Select Existing
Vegetation and Ag Land
Types
Create an SQL statement that selects which
existing vegetation types the tool will model.
SQL Expression
These are the � habitat used� vegetation types
that elk will inhabit, and the agriculture types.
Here, non-habitat vegetation (such as barren
lands, developed, rock/sand, water, etc.) should be
excluded from model analysis.
Expression to Select Ag Lands
Create an SQL statement that selects the
agriculture land types.
SQL Expression
Expression to Select Existing
Forest Types
Create a SQL statement that selects which existing
vegetation types are forest types. A forest type is
any vegetation type that has trees. This is not a
structural designation (not based on canopy cover
or stand height), but simply, is it a tree species?
SQL Expression
Canopy Cover Grid
Select the existing canopy cover grid that contains
canopy cover data as attributes to be used in the
tool.
Raster Layer
August EVI
Select the August EVI grid layer. EVI data typically
spans a two week period (16 days) so pick the
most appropriate two week span that covers the
beginning or ending period of August, depending
on the modeling date you pick.
Raster Layer
Potential Natural Vegetation
Select the potential vegetation grid that contains a Raster Layer
classified attribute for each vegetation zone (or
series) types. The classified attribute must be an
integer and include -9 for nodata, 1 for rangelands,
4 for dry forest, 5 for wet forest.
Potential Vegetation
Classification Lookup Column
Select the attribute field in the potential
vegetation grid layer that contains the classified
data. The classified attribute must and integer and
include -9 for nodata, 1 for rangelands, 4 for dry
forest, 5 for wet forest. This is a parameter of the
"Lookup" tool.
Field
Wetlands Feature Layer
Select the wetlands feature layer that contains a
Feature Layer
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Explanation
Data Type
classified attribute field that defines what
wetlands types will be modeled.
Expression to Select
Wetlands
Create an SQL statement that selects which
wetlands will be used in the tool.
SQL Expression
August Precip
Select the August precipitation grid layer (the
current modeling month). The Habitat Use tool
was designed and tested using August input data
and elk locations, therefore we limit the habitat
use index output to this time period. The
precipitation data can either the monthly average
or the 30 yr average.
Raster Layer
July Precip
Select the July precipitation grid layer (the
previous modeling month). The Habitat Use tool
was designed and tested using August input data
and elk locations, therefore we limit the habitat
use index output to this time period. The
precipitation data can either the monthly average
or the 30 yr average.
Raster Layer
Roads Feature Layer
Select the roads line feature that contains a
classified attribute field that defines the 4 classes
of roads that are needed for the Blue Mountains
Elk Habitat Models.
Feature Layer
Expression to Select Roads 1
and 2
Create a SQL statement that selects open roads
that are classified as 1 or 2.
SQL Expression
Expression to Select Roads 3
and 4
Create a SQL statement that selects open roads
that are classified as 3 or 4.
SQL Expression
DEM
Select the digital elevation grid (DEM). Units
should be in meters.
Raster Layer
Tool properties set under the environment tab
(Table 6)
Changed (if needed) before the tool is run
Environment Setting
Description
Extent
All new raster layers will be
given this extent.
Snap Raster To
Shifts any new raster layers to
match the starting x/y of this
raster.
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Boundary>
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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 Plus
cell that is outside of this mask. Buffer>
Habitat Use Tool output file definitions
(Table 7)
All abbreviations will have your study area code prefix (**** ).
Layers in the Output Workspace Folder. These layers are in final format.
Output Abbreviation
Description
****_d_cl1_2
Distance grid from the class 1 and 2 roads, truncated at 4km.
****_d_cl1_2
Distance grid from the class 3 and 4 roads, truncated at 4km.
****_dde_mn
Mean dietary digestible energy for a 200-m radius circle (kcal/g). This
output is one of the four needed covariates to get the habitat use
index.
****_mn_slp
The mean slope grid.
****_pct_allf
The percent forest grid, calculated in a 200m circle.
****_use
The habitat use index.
****_use1x6
The habitat use index multiplied by 1 million. Creates a range of values
that are more tangible than the raw values.
Layers in the Temp Folder within Output Workspace Folder. These are intermediate or
temporary layers.
Output Abbreviation
Description
****_aglands
The selected agriculture land cover types that the tool will use when
calculating the ag DDE values.
****_allfor
The EVT vegetation types that were classified as a forest type.
****_Class_1_2_Roads
.shp
The class 1 and class 2 roads there were selected and exported as a
new layer.
****_Class_3_4_Roads
.shp
The class 3 and class 4 roads there were selected and exported as a
new layer.
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****_d_cl1_2
Distance grid from the class 1 and 2 roads, not truncated.
****_d_cl3_4
Distance grid from the class 3 and 4 roads, not truncated.
****_date
The combined Month and Day as a decimal, constant value raster.
****_day
A constant, single value raster created from the Day parameter.
****_dde4cl.img
DDE classified into four nutrition categories; 1=Poor, 2=marginal,
3=good, 4=Excellent.
****_dde6cl.img
DDE classified into six nutrition categories; 1=poor, 2=low-marginal,
3=high-marginal, 4=low-good, 5=high-good, 6=excellent.
****_ddetmp1
DDE values calculated by merging (or stamping) ag lands on the
eq_dde raster. This is the second level of calculating DDE.
****_ddetmp2
DDE values calculated by merging (or stamping) wetlands on the
ddetmp1 raster. This is the third level of calculating DDE.
****_eq_dde
DDE values calculated from the PNV equations. This is the first level of
calculating DDE.
****_evt
The EVT values that are used in the tool. This includes only the “H” and
“Ag” values from the classified attribute field.
****_lkup_pnv
The PNV classified values converted to a value raster. This layer
defines where the DDE equations will be applied.
****_mask
The absolute, overall area where DDE will be calculated. Combines EVT
and PNV but excludes NoData cells.
****_month
A constant, single value raster created from the Month parameter.
****_pnv_int
The PNV values that are used in the tool. This includes only the 1, 4,
and 5 values from the classification attribute field.
****_rclallf
Reclassifies all forest pixels to a value of 1.
****_slp
Slope raster calculated from the DEM input.
****_sum_allf
Sum of the rclallf output in 200 m circles.
****_wetlands
The raster equivalent of the wetlands.shp
****_wetlands.shp
The selected wetlands that the tool will use when calculating the
wetlands DDE values.
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Update Canopy Cover 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., 2008 Landfire) 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 Use Tool (see section D for details) and compare results with the
original, pre-modification conditions. Dietary digestible energy will change with updates in canopy
cover. 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).
The Update Canopy Cover Tool incorporates changes in canopy cover 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
Canopy Cover Tool. If personal knowledge of the area is minimal or harvest plans for canopy cover are
unavailable, calculating the mean or majority value for canopy cover within the Canopy Cover Update
Polygon layer may help estimate those values.
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Figure 4. Complete elk habitat models diagram
If you choose to estimate canopy cover 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 the canopy
cover grid. 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 canopy cover values to edit the attribute table as
described in step 4 below (“Attribute table from existing vegetation grid”).
Additional Input Data Needed for the Update Canopy Cover Tool
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1. Vegetation Update Polygon layer
a. This is a polygon layer that defines the exact areas where vegetation (canopy cover) 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 field to represent the new canopy cover (ex. new_CC), 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.
An example study area in grey (A). Colored polygons are treatment areas to be updated. B shows the
attribute table associated with the layer in A.
A
B
Using the Update Canopy Cover 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.
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Update Canopy Cover Tool Parameters
(Table 8)
Parameter
Explanation
Data Type
Output Workspace Folder
Select the folder location you want the tool
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.
Workspace
Study Area Code 4 char max
Choose an arbitrary (but meaningful) four
character code (alpha/numeric) that represents
the analysis area the tool 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.
String
Study Area Boundary with
Buffer
Select the buffered regional landscape study area.
A typical buffer extends 4km beyond the study
area boundary. Including the buffered area allows
the tool to account for edge effects that may
influence elk behavior. Output layers will be
clipped to this boundary.
Feature Layer
Existing Canopy Cover Grid
Select the existing canopy cover grid that contains
canopy cover data as attributes to be used in the
tool. This is the existing conditions that will be
updated by this tool
Raster Layer
Canopy Cover Change
Polygons
Select the polygon layer that represents the areas
within the regional landscape study area where
canopy cover will be changed. This layer can be a
single or multi-polygon layer.
Feature Layer
Convert Polygon to Raster
Based On This Field
Select the attribute field in the Canopy Cover
Change Polygons layer that contains the new
canopy cover value that you assigned.
Field
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Tool properties set under the environment tab
(Table 9)
Changed, if needed, before the tool is run
Environment Setting
Description
Extent
All new raster layers will be
given this extent.
Snap Raster To
Shifts any new raster layers to
match the starting x/y of this
raster.
Cell Size
Makes any new raster have
this cell size.
Mask
Creates NoData values for any
cell that is outside of this mask.
Default Value
<Study Area Boundary>
<Existing Vegetation Grid>
30
<Study Area Boundary>
Update Canopy Cover Tool output file definitions
(Table 10)
Most abbreviations will have your study area code prefix (****_)
Layer in the Output Workspace Folder. These layers are in final format.
Output Abbreviation
Description
****_cctemp
This is the grid equivalent of the polygon layer for the areas where
vegetation will be updated.
****_updatecc.img
This is the final, updated vegetation grid that will be used in the
subsequent tools such as the Nutrition and Habitat Use Tools.
Interpretation and Summaries of Model Results
Model users can summarize results from the Blue Mountains 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
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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 Blue Mountains regional boundaries
identified in figure 5. 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 Blue Mountains landscapes, the majority
of area will fall into nutritional classes below excellent or
good, given that forage quality is inherently limiting to most
Blue Mountains 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.
Figure 5. The Blue Mountains elk
habitat models encompass four
national forests of northeastern
Oregon.
Table 11 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 Blue Mountains nutrition
model be summarized using these six classes, expressed as the percentage of pixels in the landscape
that fall within each class (e.g., table 12). 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.
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Table 11—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 11) for existing condition and
each management alternative. Note: use the raw DDE grid created by the Nutrition Tool for
nutrition summaries and mapping.
Table 12—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.
3. Map of “ramped” (non-classified) DDE values across the regional landscape.
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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 9).
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 13;
Alternative 2, table 14). 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
13). Alternatively, 1,138 ac originally in Class 1 would improve to Class 6 under Alternative 2
(table 14). 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 13).
Table 13—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:
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
5
0
0
0
0
1293
0
1293
6
275
135
23
7
6
1111
1556
Total
20536
38998
5341
1532
1305
1122
68833
Table 14—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:
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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
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6
1138
1240
117
56
11
1122
3683
Total
20536
38998
5341
1532
1305
1122
68833
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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).
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. 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.
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.
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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).
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
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.
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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 .
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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Summed raw values of predicted level of elk use for the regional landscape and two local landscapes
under existing conditions and three treatment alternatives.
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
0
Regional
Local
Local
Landscape Landscape 1Landscape 2
3. Maps of the input covariate grids for the Habitat Use Tool, i.e., percent area in forested
vegetation, distance to nearest class 1 or 2 or class 3 or 4 open road, 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, 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 .
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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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). 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 15, along with
the percentage of the landscape in each class under the existing conditions and Alternatives 1
and 2.
Table 15—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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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 15); see previous explanation.
7. Map areas where predicted level of use increased, decreased, or showed no change.
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.
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Areas of negative, positive, or no change in predicted level of elk use for three treatments compared to
the existing condition (not shown).
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.
% Change from Existing Conditions
110.2
115
100
% change
85
70
55
25
-5
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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
Local
Landscape 2
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8. Summarize % change in predicted elk use from existing condition to other treatments.
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).
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.
% 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
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 16).
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 14), 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 16—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
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Low
12967
Mediumlow
618
Alternative 1
Medium
125
Mediumhigh
40
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High
17
Total
13767
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Medium
Medium-high
High
Total:
814
372
63
0
14215
11758
552
520
34
13483
50
719
11466
628
535
13473
284
1082
11856
1270
14531
192
295
700
11927
13131
13767
13766
13766
13767
68833
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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ArcGIS SQL, Field Calculator, and Raster Calculator Tips and Tricks
SQL
Structured Query Language (SQL) is a set of defined expressions and syntax used to query and
manipulate data. Syntax refers to correct punctuation using quotes, parentheses, commas, etc. and is
sensitive to type of data (dataset vs. geodatabase)
For the purposes of our models, SQL refers to a selection query that selects specific parts of a feature or
raster layer in order to perform a model process. The following information is specific to this type of
data.
The basic form for SQL has three parts:
1) The field (attribute field that is being selected from)
2) The operator
3) The string (the specific attribute codes being selected)
Ex: “Roads” = “open”, or “Roads” = 1. Both are selecting the same attribute, where:
1) “Roads” is the attribute field name, always enclosed in double quotes;
2) = is the operator;
3) ‘Open’ or 1 is the specific attribute value or code that we want to select for model processing.
Here, text attributes are enclosed in single quotes, but numbers do not require any quotes.
Often there are several different expressions that will result in the same outcome:
When the purpose
is to:
Select a single
attribute value,
e.g., select open
roads or select veg
types designated
as habitat
Expression 1
Expression 2
Expression 3
“Roads”=’open’
(This uses a text data
type, select open
roads)
“Habitat” = ‘H’
“Roads”=1
(This uses a number
data type, select open
roads)
“Habitat” In (‘H’)
Not “Roads” = ‘closed’
(Select all roads not
equal to closed)
(Select attribute ‘H’)
(Select attributes in
the list ‘H’)
Select multiple
attribute codes,
e.g., select veg
types designated
as habitat plus ag
land types
“Habitat” = ‘H’ OR
“Habitat”=’A’
“Habitat” In (‘H’, ‘AG’)
Not "Habitat" In
('AG’,'M’)
(Select all attributes
not including’AG’ or
‘M’)
“Habitat” <> ‘M’
(Select attribute ‘H’
and select attribute
‘A’)
(Select attributes in
the list ‘H’ and ‘AG’)
(Select all attributes
not including ‘M’)
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SQL operators (copied from the Arc Help files)
Operator
Description
Arithmetic operators
You use an arithmetic operator to add, subtract, multiply, and divide numeric values
*
Arithmetic operator for multiplication
/
Arithmetic operator for division
+
Arithmetic operator for addition
-
Arithmetic operator for subtraction
Comparison operators
You use comparison operators to compare one expression to another
<
Less than. It can be used with strings (comparison is based on alphabetical order),
numbers, and dates.
<=
Less than or equal to. It can be used with strings (comparison is based on alphabetical
order), numbers, and dates.
<>
Not equal to. It can be used with strings (comparison is based on alphabetical order),
numbers, and dates.
>
Greater than. It can be used with strings (comparison is based on alphabetical order),
numbers, and dates.
>=
Greater than or equal to. It can be used with strings (comparison is based on
alphabetical order), numbers, and dates. For example, this query selects all the cities
with names starting with the letters M to Z:
"CITY_NAME" >= 'M'
Selects a record if it has a value greater than or equal to x and less than or equal to y.
[NOT] BETWEEN When preceded by NOT, it selects a record if it has a value outside the specified
range. For example, this expression selects all records with a value greater than or
x AND y
equal to 1 and less than or equal to 10:
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"OBJECTID" BETWEEN 1 AND 10
This is the equivalent of the following expression:
"OBJECTID" >= 1 AND OBJECTID <= 10
However, the expression with BETWEEN provides better performance if you're
querying an indexed field.
Returns TRUE if the subquery returns at least one record; otherwise, it returns FALSE.
For example, this expression returns TRUE if the OBJECTID field contains a value of 50:
[NOT] EXISTS
EXISTS (SELECT * FROM parcels WHERE "OBJECTID" = 50)
EXISTS is supported in file, personal, and ArcSDE geodatabases only.
Selects a record if it has one of several strings or values in a field. When preceded by
NOT, it selects a record if it doesn't have one of several strings or values in a field. For
example, this expression searches for four different state names:
[NOT] IN
"STATE_NAME" IN ('Alabama', 'Alaska', 'California', 'Florida')
For file, personal, and ArcSDE geodatabases, this operator can also be applied to a
subquery:
"STATE_NAME" IN (SELECT "STATE_NAME" FROM states WHERE "POP" > 5000000)
IS [NOT] NULL
Selects a record if it has a null value for the specified field. When NULL is preceded by
NOT, it selects a record if it has any value for the specified field. For example, this
expression selects all records with a null value for population:
"POPULATION" IS NULL
Use the LIKE operator (instead of the = operator) with wildcards to build a partial
string search. For example, this expression selects Mississippi and Missouri among
USA state names:
"STATE_NAME" LIKE 'Miss%'
x [NOT] LIKE y
[ESCAPE 'escape- The percent symbol (%) means that anything is acceptable in its place: one character,
character']
a hundred characters, or no character. Alternatively, if you want to search with a
wildcard that represents one character, use and underscore (_). For example, this
expression finds Catherine Smith and Katherine Smith:
"OWNER_NAME" LIKE '_atherine Smith'
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The percent symbol and underscore wildcards work for any file-based data or
multiuser geodatabase data. LIKE works with character data on both sides of the
expression. If you need to access noncharacter data, use the CAST function. For
example, this query returns numbers that begin with 8 from the integer field
SCORE_INT:
CAST ("SCORE_INT" AS VARCHAR) LIKE '8%'
To include the percent symbol or underscore in your search string, use the ESCAPE
keyword to designate another character as the escape character, which in turn
indicates that a real percent sign or underscore immediately follows. For example,
this expression returns any string containing 10%, such as 10% DISCOUNT or A10%:
"AMOUNT" LIKE '%10$%%' ESCAPE '$'
The wildcards you use to query personal geodatabases are asterisk (*) for any number
of characters and question mark (?) for one character. The pound sign (#) is also used
as a wildcard to match a single digit (numeric value). For example, this query returns
parcel numbers A1, A2, and so on, from a personal geodatabase:
[PARCEL_NUMBER] LIKE 'A#'
Logical operators
AND
Combines two conditions together and selects a record if both conditions are true. For
example, the following expression selects any house with more than 1,500 square feet
and a garage for more than two cars:
"AREA" > 1500 AND "GARAGE" > 2
OR
Combines two conditions together and selects a record if at least one condition is
true. For example, the following expression selects any house with more than 1,500
square feet or a garage for more than two cars:
"AREA" > 1500 OR "GARAGE" > 2
NOT
Selects a record if it doesn't match the expression. For example, the following
expression selects all states but California:
NOT "STATE_NAME" = 'California'
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Field Calculator
Used when you are editing specific attributes in an attribute table.
1. Open Attribute Table
2. Select the attribute ROW(S) you want to update or edit. If no rows are selected, the edits will
apply to all of the rows.
3. Select the attribute COLUMN that you want to update or edit. The column does not have to be
selected but you must make sure that you right click on the column you want to edit when you
open the Field Calculator.
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4. Right click on the selected column and chose Field Calculator. A sub window will open:
5. Enter your expression and Click OK when finished
a. Must use double quotes if you are entering text, no quotes if you are enter numbers,
parentheses for field names. Notice the Field name MODEL and the Operator = are
already preset when using the Field Calculator. You only need to finish out the
expression you desire.
b. By entering “H” you will change the current classification of “AG” to “H” in only the two
selected rows for this example.
c. To copy data from another field (ex: a table with the classifications for EVT was joined to
the EVT grid and you want to copy the classification field to your grid) you would then
enter the field name [field] in the field calculator window enclosed by square brackets
(double click the correct field from the Fields: text box).
i. Be aware of any selected rows. Field Calculator will update the entire column
across all rows unless there are selected rows, in which case only those selected
rows will be updated.
Raster Calculator
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Used when you want to edit an entire raster (ex: convert precipitation grid into millimeters) or use the
raster in an expression.
This tool is found in the Spatial Analyst Toolbox, Map Algebra toolset, Raster Calculator tool.
The yellow diamonds on the left are the raster layers available for use (gathered from the current map
project you are working in). Once again, syntax is very important and can foul up a complex expression
very easily if you’re not careful. The Tool Help is a good aid to become familiar with.
Example: Convert precip to millimeters.
To begin,
1. In ArcMap, Open the Raster Calculator tool
2. Double click on the raster layer you wish to edit (yellow diamond). This will begin the expression
for you in the text box. Notice the double quotes put around the raster name.
3. Click on the divide operator
4. Type in 100 (or use the key punch pad on the tool)
5. Be sure to set your Output Raster (the default will be used if you don’t change it)
6. When saving and creating rasters there is some flexibility with the type of raster you can use
(grid, img, tif, etc). We designed the elk models to create standard ESRI grids, meaning they
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don’t have any file extensions. In this example I chose an imagine file (simply type .img at the
end of the file name in the output raster parameter). Imagine files are not subject to name
length restrictions like grids are (grids can only have 13 characters in their name, imagine rasters
are not limited), and thus can be more flexible.
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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English Equivalents
When you know:
Centimeters (cm)
Meters (m)
Hectares (ha)
Kilometers (km)
Grams (g)
Kilograms (kg)
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.394
3.28
2.47
.621
.0352
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To find:
Inches
Feet
Acres
Miles
Ounces
Pounds
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