USING RESOURCE UTILIZATION FUNCTIONS (RUFs) TO ASSESS

WILDLIFE-HABITAT RELATIONSHIPS

Y = β0 + β1x1+ β2x2 + β3x3 + βnxn ……

Brian KertsonWildlife Science GroupSFR/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

• Can we know how animals perceive their environment?

• Do we actually know what is available?

• NO!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!• Arbitrary • Home range simulations:

-Rigorous: potentially-Biologically meaningless

You know

nothing!!

Stupid hairless

monkeys.

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 relocations99% Utilization Distribution

(Use values) Sampling grid

Use and habitat covariates

Ruf.fit

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 packagehttp://csde.washington.edu/~handcock/ruf/

MEASURING USE

• Individual = sampling unit• Sampling design critical

-Individuals-Monitoring

• Increase monitoring, more refined UD

• VHF vs. GPS:-Increased resolution-Increased accuracy-Not perfect

Kertson and Marzluff, in press

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

• Covariate types:-Categorical -Continuous

• Resolution:-As fine as possible-Landscape configuration-Remote sensing

• Transformations:-Can improve model performance

Distance to Water

Percent Conifer Forest

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 Conifer Forest

Mixed Forest

Riparian High Elevation

Urban

1 1 0 0 0 04 0 0 0 1 03 0 0 1 0 05 0 0 0 0 12 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 LS s.e.range 149.277454 5.437344 NA NAsmoothness 1.500000 NA NA NA(Intercept) 30.387746 0.117809 16.851820 0.107699PCCREG 7.008595 0.223041 0.265138 0.001150PCF 8.226134 0.283357 0.221347 0.000960PFOREST -0.371450 0.191588 -0.018902 0.001464DWATER -2.234107 0.161222 -0.011138 0.000153DISTEDGE 3.158826 0.178807 0.053626 0.000406DISTROAD -0.565676 0.120716 -0.004882 0.000147DRESD -6.581544 0.225750 -0.004287 0.000025RESDENS1KM 0.854841 0.095044 0.005812 0.000373PAR -1.252951 0.599006 -0.078281 0.001941SLOPE 0.272329 0.308374 0.004249 0.001152DEM 5.641831 0.406940 0.002025 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

• Sex and age specific RUFS:-Male vs. female-Adult vs. subadult

• Behavior specific:-Movement rates-Relates habitat use to different aspects of fitness

Traveling Resting/Feeding

HuntingNursing

RUF CHALLENGES

• The more RAM the better• Capable of running full data set, may need to sub-

sample• 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?