1. USING RESOURCE UTILIZATION FUNCTIONS (RUFs) TO ASSESS WILDLIFE-HABITAT RELATIONSHIPS Y = β 0 + β 1 x 1 + β 2 x 2 + β 3 x 3 + β n x n …… Brian Kertson Wildlife Science Group SFR/WACFWRU

2. HABITAT IS THE KEY FOR WILDLIFE Understanding relationships is critical -Food -Reproduction -Survivorship -Predator-prey dynamics -Behavior and ecology Management and conservation

3. 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

4. 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

5. 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)

6. USED VS. UNUSED LIMIATIONS Logistic regression Contamination: -Classified as unused when it was used -GPS -Snow tracking -Critter cams

7. 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.

8. ADDITIONAL AVAILABILITY ISSUES Kertson and Marzluff, in press

9. 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

10. HOW THE RUF WORKS Animal relocations 99% Utilization Distribution (Use values) Sampling grid Use and habitat covariates Ruf.fit

11. 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/

12. 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

13. UTILIZATION DISTRIBUTION (UD) Animal use is not random -Gradient of use Probability Density Function (pdf) -Sums to 1 Use = height (volume) of UD

14. 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

15. 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 +/-

16. EFFECTS OF h ON UD Kertson and Marzluff, in press

18. 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)

19. 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

20. 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

21. 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

22. CATEGORICAL COVARIATES LC_5Conifer Forest Mixed Forest RiparianHigh Elevation Urban 110000 400010 300100 500001 201000

23. RUF.FIT Developed by Dr. Mark Handcock (UW-CSSS) Multiple regression: Y = β 0 + β 1 x 1 + β 2 x 2 + β 3 x 3 + β n x n …… 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

24. 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

25. 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 NA smoothness 1.500000 NA NA NA (Intercept) 30.387746 0.117809 16.851820 0.107699 PCCREG 7.008595 0.223041 0.265138 0.001150 PCF 8.226134 0.283357 0.221347 0.000960 PFOREST -0.371450 0.191588 -0.018902 0.001464 DWATER -2.234107 0.161222 -0.011138 0.000153 DISTEDGE 3.158826 0.178807 0.053626 0.000406 DISTROAD -0.565676 0.120716 -0.004882 0.000147 DRESD -6.581544 0.225750 -0.004287 0.000025 RESDENS1KM 0.854841 0.095044 0.005812 0.000373 PAR -1.252951 0.599006 -0.078281 0.001941 SLOPE 0.272329 0.308374 0.004249 0.001152 DEM 5.641831 0.406940 0.002025 0.000015 β and associated SE

26. 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

27. 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

28. UNSTANDARDIZED COEFFICIENTS

29. COUGAR PREDICTED USE

30. STANDARDIZED COEFFICIENTS

31. 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

32. REGIONAL APPLICATIONS

33. 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 Hunting Nursing

34. 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)

35. 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

36. NEED DIRECTIONS?