Resource Selection Modeling
Approach to Westside
Elk Habitat Models
by Ryan Nielson (WEST, Inc.)
Resource/Habitat Selection Analysis
Westside Modeling
Objectives
Covariate Reduction
Model Selection
Model Validation
Photo by Rachel Cook (NCASI)
Resource/Habitat Selection Analysis
Objective:
Identify resources / habitat characteristics
necessary to sustain a population
– Sometimes for basic biological information, ideas
for conservation or mitigation
– Can also be used help forecast changes in habitat
use based on potential changes in land
management
Resource/Habitat Selection Analysis
Resource Selection Function:
A model with one or more covariates describing
habitat/landscape characteristics, and coefficients
estimated from spatially explicit location data that
describe the relationships between use and habitat
Advantages:
Coefficients allow for interpretation of relationships
and making predictions
We can compare several different models (variable
importance) and evaluate goodness-of-fit
Westside Modeling Objectives
1. Model the probability of elk use
2. Develop a regional model of elk use for
western OR and WA using a meta-analysis
approach
3. Treat the collared elk as the experimental
unit within each study area
A Model for Probability of Use
Definition of Probability:
“Long-run Relative Frequency of Occurrence”
A Model for Probability of Use
Basic Sampling Situation…
3
7
5
7
8
4
9
0
N=60
1
2
3
3
A Model for Probability of Use
Relate counts to habitat characteristics
RSF
Poisson or Negative Binomial Model:
log(counti) = β0 + β1X1i + …+ βpXpi + εi
We now have a model for counts of use, but
NOT Probability of Use…
A Model for Probability of Use
3 /60
7/60
5/60
7/60
8/60
4/60
0/60
9/60
N=60
1/60
2/60
3 /60
3/60
Divide count by total = relative frequency = Probability of Use!
A Model for Probability of Use
Can we still use Negative Binomial model to relate
relative frequencies to habitat characteristics?
YES, If we include an offset term = log(total):
log(counti) = log(total) + β0 + β1X1i + … + βpXpi + εi
= log(counti/total) = β0 + β1X1i + … + βpXpi + εi
= log(Relative Frequencyi) = β0 + β1X1i + … + βpXpi + εi
We now have a model for Probability of Use
Westside Modeling Objectives
1. Model the probability of elk use
2. Develop a regional model of elk use for
western OR and WA using a meta-analysis
approach
3. Treat the collared elk as the experimental
unit
Regional Model Using Meta-analysis
• Develop a model for each year/study area
• Combine the estimated models to produce one
regional model representing use by the average
animal
• Quantifies use by individual elk within each study
area, and is a valid method of assessing regionallevel use by averaging coefficients among study areas
Westside Modeling Objectives
1. Model the probability of elk use
2. Develop a regional model of elk use for
western OR and WA using a meta-analysis
approach
3. Treat the collared elk as the experimental
unit
Treat Collared Elk as Experimental Units
• Important to recognize that within each study
area, the elk were sampled first
• Bootstrapping individual elk hundreds of times to
produce hundreds of estimates of model
coefficients for each study area and the regional
model
90% Confidence Intervals using ‘percentile
method’ from the hundreds of estimates for each
model coefficient
Westside Modeling Objectives
1. Model the probability of elk use
2. Develop a regional model of elk use for
western OR and WA using a meta-analysis
approach
3. Treat the collared elk as the experimental
unit
Covariate Reduction
The dozens of covariates calculated for each
circular buffer/cell were examined for:
•
•
•
•
•
Consistency in distributions
Ease of calculation
Relevance to elk use
Relevance to management
Multicolinearity
This led to a reduction of covariates within each
model set (e.g., nutrition, human disturbance)
Nutrition
DDE (continuous)
DDE (categorical)
Accepted Biomass (AB)
Distance to:
High DDE
Mod DDE
Percent area in M to H DDE
Quadratic, Cubic forms
Human
Disturbance
Vegetation
Physical/
Other
Density of & Distance to:
Open Roads
Closed Road
High Traffic Roads
Low Traffic Roads
Public Use Roads
Administrative Use Only
Motorized Use Trails
Overstory CC
Slope (continuous)
Dominant CC
Slope (categorical)
Cover-Forage Ratio
Percent Area in:
Flat to Gentle Slope
Mod to Steep Slope
Very Steep Slope
Quadratic, Cubic forms
Distance to:
Forage
Cover
Cover-Forage Edge
Optimal Cover
Thermal Cover
Hiding Cover
Habitat Effectiveness of
size/spacing of Cov & For
Cover Quality
Aspect
Convexity
Curvature
Soil Depth
Solar Radiation
Distance to Water
Land Ownership
Reduced Sets of
Nutrition, Human Disturbance, Vegetation, & Physical
Covariates in Competing Models
Model Selection
Model Sets Started with:
1) Nutrition
1)
2)
3)
Dietary Digestible Energy (DDE)
Accepted Biomass
DDE in MGE + % Area in MGE + Interaction
2) Human Disturbance
1)
2)
Dist. to Public Roads
Dist. To Public Roads + (Dist. to Public Roads)2
Model Selection
Process:
1. Fit each model in Set 1 (Nutrition Models) to each study
area
2. Calculated AIC values and AIC weights for each model in
each study area
3. Ranked each model within each study area according to
AIC weights (lower rank better – think golf not baseball)
4. Summed the ranks for each model among the study
areas
The Nutrition Model with the lowest sum of
ranks was brought forward
Model Selection
Example…
Data
Elwha 2009
White River 2004
White River 2005
White River 2007
Green/Cedar 2008
Model #
1
2
3
3
1
2
1
2
3
2
3
1
1
2
3
AIC AIC weight Rank
1081.37
0.39
1
1081.61
0.35
2
1082.13
0.27
3
5142.44
1.00
1
5153.92
0.00
2
5182.87
0.00
3
2207.98
0.53
1
2208.69
0.37
2
2211.22
0.10
3
5474.73
0.56
1
5475.26
0.43
2
5483.61
0.01
3
5132.19
0.45
1
5132.94
0.31
2
5133.43
0.24
3
Sum of Ranks
Model 1: 8
Model 2: 10
Model 3: 12
Model 1
Brought Forward
Model Selection
Process (cont’d):
5.
6.
Went through same procedure for Human Disturbance Models
Best Nutrition Model + physical/vegetation covariates:
Model 1
Best Nutrition Model +
% Slope
7.
Model 2
Best Nutrition Model +
Dist. to Edge
Model 3
Best Nutrition Model +
% Slope
Dist. to Edge
Best Human Disturbance Model + physical/vegetation covariates:
Model 1
Best Human Disturbance +
% Slope
Model 2
Best Human Disturbance +
Dist. to Edge
Model 3
Best Human Disturbance +
% Slope
Dist. to Edge
Model Selection
Process (cont’d):
8. Best Nutrition Model + Best Human Disturbance Model +
physical/vegetation covariates:
Model 1
Best Nutrition Model +
Best Human Disturbance +
% Slope
Model 2
Best Nutrition Model +
Best Human Disturbance +
Dist. to Edge
Model 3
Best Nutrition Model +
Best Human Disturbance +
% Slope +
Dist. to Edge
9. New Versions of 1986 Westside Model
Model 1
Size and Spacing +
Density of Public Roads +
Optimal Cover +
Accepted Biomass
Model 2
Size and Spacing +
Density of Public Roads +
Optimal Cover
Model 3
Size and Spacing +
Density of Public Roads
Accepted Biomass
Model Selection
Process (cont’d):
10. Best from each of the Model Sets:
1.
2.
3.
4.
5.
6.
Best Nutrition Model
Best Human Disturbance Model
Best Nutrition + Human Disturbance Model
Best Nutrition + Physical/Vegetation Model
Best Human Disturbance + Physical/Vegetation Model
Best Nutrition + Human Disturbance +
Physical/Vegetation Model
7. Best 1986 Westside Model
Validation
• Predictions of use
based on regional
model for
independent data
• Divided into 20 classes
– each with equal area
• Count the total
number of elk
locations within each
class
• Correlation Analysis
Summary
• Modeled Probability of Use
– Negative Binomial Regression RSF has passed many peer
reviews and has been previously published
• Covariate selection was logical and based on the study
objectives
• a priori group of model sets
• Logical, a priori model selection protocol was followed
• Created a regional model using a meta-analysis
approach
• Validation run on the regional model
Thank You
Photo by Rachel Cook (NCASI)