USING RESOURCE UTILIZATION
FUNCTIONS (RUFs) TO ASSESS
WILDLIFE-HABITAT RELATIONSHIPS
Y = β0 + β1x1+ β2x2 + β3x3 + βnxn ……
Brian Kertson
Wildlife Science Group
SFR/WACFWRU
HABITAT IS THE KEY FOR WILDLIFE
• Understanding
relationships is critical
-Food
-Reproduction
-Survivorship
-Predator-prey dynamics
-Behavior and ecology
• Management and
conservation
KEY TERMINOLOGY
• Use: how much, how often – metric matters
• Selection/Avoidance: animal uses resource more
or less than available
• Preference: animal selects between 2 equally
available resources
WILDLIFE-HABITAT METHODS
• Many analytical procedures available
• Common techniques:
-Compositional Analysis
-Resource Selection Functions (RSFs)
-Resource Selection Probability Functions
(RSPFs)
• Varying degrees of rigor, each has advantages
and disadvantages
COMMON PROBLEMS
• Lack of independence of
observations
• Incorrect sampling unit
• Habitat data and scale
-Use of remote sensing
• Unit-sum constraint
• Discrete use
• Failure to connect with
behavior (i.e., fitness)
USED VS. UNUSED LIMIATIONS
• Logistic regression
• Contamination:
-Classified as unused
when it was used
-GPS
-Snow tracking
-Critter cams
PROBLEMS DEFINING AVAILABILITY
You
know
nothing!!
Stupid
hairless
monkeys.
• Can we know how animals
perceive their environment?
• Do we actually know what
is available?
• NO!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
• Arbitrary
• Home range simulations:
-Rigorous: potentially
-Biologically meaningless
ADDITIONAL AVAILABILITY ISSUES
Kertson and Marzluff, in press
Resource Utilization Functions
• Marzluff et al. 2004 (Ecology)
• Continuous:
-High vs. low use (relative comparison)
• Multivariate:
-Multiple regression
• Individual is sampling unit:
-Quantify individual variation
• No measure of availability
HOW THE RUF WORKS
Animal relocations
99% Utilization Distribution
(Use values)
Ruf.fit
Use and habitat covariates
Sampling grid
KEY TOOLS
• ArcMap 9.x
• Hawth’s Tools:
http://www.spatialecology.com/htools/index.php
-Bivariate kernel
• Excel or Notepad
• R statistical computing
-Ruf.fit package
http://csde.washington.edu/~handcock/ruf/
MEASURING USE
Kertson and Marzluff, in press
• Individual = sampling unit
• Sampling design critical
-Individuals
-Monitoring
• Increase monitoring,
more refined UD
• VHF vs. GPS:
-Increased resolution
-Increased accuracy
-Not perfect
UTILIZATION DISTRIBUTION (UD)
• Animal use is not random
-Gradient of use
• Probability Density Function
(pdf)
-Sums to 1
• Use = height (volume) of UD
UD ESTIMATION
• Fixed kernel
• Min. of 30 relocations
-Preferably n ≥ 50
• Resolution (grid size):
-25 or 30 m common
• Bandwidth smoothing (h)
-Most critical component
SELECTION OF h
•
•
•
•
Selection: data
Over vs. under-smoothing
Univariate vs. bivariate
Lots of options:
-Reference (HREF)
-Least-squares cross-validation (LSCV)
-Plug in (PI)
-Solve the equation (STE)
-Biased cross validation (BCV)
• Each has +/-
EFFECTS OF h ON UD
Kertson and Marzluff, in press
Kertson and Marzluff, in press
ESTIMATING h
• Animal Movements Extension (ArcView 3.3)
• ArcMap 9.x:
-Home Range Tools (HRT)
*LSCV, BCV, HREF
• R statistical computing:
-KernSmooth package
-KS package
*PI values from both (bivariate)
UD CHALLENGES
• UD size can push the limits of software:
-Male cougar UDs can exceed 2.0 million points
• Over-smoothing:
-Lakes, rivers, major highways, and other
unsuitable/unusable habitat
• Under-smoothing:
-Donut holes and disconnect cores
• Solutions:
-Clip UD (over-smoothing)
-Adjust h (try different bandwidth method)
-Little bit of black magic here
LANDSCAPE COVARIATES
Percent Conifer Forest
Distance to Water
• Covariate types:
-Categorical
-Continuous
• Resolution:
-As fine as possible
-Landscape configuration
-Remote sensing
• Transformations:
-Can improve model performance
CATEGORICAL COVARIATES
• Common categorical covariates:
-Landcover
-Aspect
• Classes for each variable are not independent
• Must be recoded 0,1
-No. of columns = no. of classes
CATEGORICAL COVARIATES
LC_5
Mixed
Forest
0
0
Riparian
1
4
Conifer
Forest
1
0
Urban
0
0
High
Elevation
0
1
3
5
2
0
0
0
0
0
1
1
0
0
0
0
0
0
1
0
0
0
RUF.FIT
• Developed by Dr. Mark Handcock (UW-CSSS)
• Multiple regression:
Y = β0 + β1x1+ β2x2 + β3x3 + βnxn ……
• Code:
Cat1<- ruf.fit(USE ~ COV1 + COV2 + COV3 + COV4, space= ~ X + Y, data=data_file,
theta=hval, name=“whatever_you_want", standardized=F)
• Corrects for spatial dependence in UD
• Unstandardized and standardized coefficients
MODEL COEFFICIENTS
• Average for sample
• Coefficient signs:
-Increase use (+); decrease use (-)
• Unstandardized:
-Mapping predicted occurrence
• Standardized:
-Statistical significance of individual covariates
-Differences between covariates
-Relative importance
-Proportion of sample +/- influence
RUF.FIT OUTPUT
> summary(CAT1)
Standardized Coefficients for name: Misska
Matern Log-Lik = -9195.395 LS Log-Lik = -9331.533
Change in Log-Lik 136.1377 p-value = < 1e-04
MLE
s.e.
LS estimate
range
149.277454 5.437344
NA
smoothness
1.500000
NA
NA
(Intercept)
30.387746 0.117809 16.851820
PCCREG
7.008595 0.223041 0.265138
PCF
8.226134 0.283357 0.221347
PFOREST
-0.371450 0.191588 -0.018902
DWATER
-2.234107 0.161222 -0.011138
DISTEDGE
3.158826 0.178807 0.053626
DISTROAD
-0.565676 0.120716 -0.004882
DRESD
-6.581544 0.225750 -0.004287
RESDENS1KM 0.854841 0.095044 0.005812
PAR
-1.252951 0.599006 -0.078281
SLOPE
0.272329 0.308374 0.004249
DEM
5.641831 0.406940 0.002025
LS s.e.
NA
NA
0.107699
0.001150
0.000960
0.001464
0.000153
0.000406
0.000147
0.000025
0.000373
0.001941
0.001152
0.000015
β and associated SE
HOW DOES LANDSCAPE INFLUENCE
COUGAR-HUMAN INTERACTION?
• Apex predator with a large home range
• Largest geographic distribution of any
terrestrial mammal in western hemisphere
-Tremendous habitat diversity
• Key landscape resources:
-Ungulate prey
-Cover
• High levels of interaction with people
METHODOLOGY
• Captured 32 cougars in western WA, outfitted
with GPS collars
• Investigated interaction reports
• Focused on landscape metrics I suspect
correlate with prey and cover
• Modeled with RUF
• Quantified individual variation
UNSTANDARDIZED COEFFICIENTS
COUGAR PREDICTED USE
STANDARDIZED COEFFICIENTS
CONSERVATION AND MANAGEMENT
IMPLICATIONS
• Identify key resources to manage and conserve
• Identify high quality habitats
• Develop proactive management strategies
-71.5% of confirmed interactions occurred in
high and med-high use habitats
-Management hotspots
• Space use and interactions with people highly
individualized
-Population approaches may not work
REGIONAL APPLICATIONS
ADDITIONAL APPROACHES
Traveling
Resting/Feeding
Hunting
Nursing
• Sex and age specific RUFS:
-Male vs. female
-Adult vs. subadult
• Behavior specific:
-Movement rates
-Relates habitat use to
different aspects of
fitness
RUF CHALLENGES
• The more RAM the better
• Capable of running full data set, may need to subsample
• Processing time can be significant
• Model comparisons (e.g., model parsimony)
difficult
-RUF outputs log-likelihoods (ΔAIC)
RUF LIMITATIONS
• Models are tools, not absolute truth
• Results are only as good as the data used
-Limitations and accuracy of remotely-sensed data
• Do the results pass the laugh test?
• Subject to same assumptions as normal multiple
regression
• No alternatives for correcting spatial dependence
in UD
NEED DIRECTIONS?