Imputating snag data to forest inventory for wildlife habitat modeling

1. Imputating snag data to forest inventory for wildlife habitat modeling Kevin Ceder College of Forest Resources University of Washington GMUG – 11 February 2008

2. Why impute snag data? • Snags are an important habitat element and needed for habitat assessments. • These data are often not collected in forest inventory • The Large-Landscape Wildlife Assessment models will need these data

3. Why use Nearest-Neighbor? • Non-parametric requiring no assumptions of underlying functional form • Retains the variance/covariance structure of the input data in the output data

4. The Questions • Can snag data be imputed using kNN techniques with stand and site data? • How well do the results fit observed data? • Which distance measure performs best? • What is the effect of increasing neighborhood size? • How do the results compare with random sampling?

5. The Process • The database • FIA integrated database version 2.1 • Data for private forests in western Washington (1510 plots) • Both tree and snag data collected between 1989 - 1991 • Representative of the forest targeted for the LLWA project

6. The Process • The tool - • The yaImpute package for kNN imputation • Raw, Euclidean, Mahalanobis, MSN, MSN2, ICA, and randomForest distance measures • k = 1, 2, 3, 4, 5, 10 • For k>1 imputed data are distance weighted means of neighbors • 9999 permutations of the data for comparisons with random sampling • k = 1, 2, 3, 4, 5, 10 • For k>1 imputed data are distance weighted means of neighbors using Euclidean distance

7. Goodness of fit Comparison with random The Statistics

8. The Input Data – Tree and site data (xData)

9. The Input Data – Snag data (yData) • 695 of 1510 plots did not have snags present

10. Results • Can snag data be imputed using kNN techniques with stand and site data? • Yes!

11. Results • How well do the results fit observed data?

12. Results • How well do the results fit observed data?

13. Results • How well do the results fit observed data?

15. Results • How well do the results fit observed data? • Marginally… • High RMSD and MAD relative to mean snag measures in the data • Observed vs imputed plots show poor patterning

16. Results • Which distance measure performs best? • What is the effect of increasing neighborhood size?

21. Results • Which distance measure performs best? • All are generally similar • randomForest imputations provide lower RMSD and MAD but under-predict more than others • What is the effect of increasing neighborhood size? • Increasing k reduces RMSD and MAD • Little effect on bias • Slightly decreased range in imputed values with k = 10

22. Results • How do the results compare with random sampling?

23. Results • How do the results compare with random sampling?

24. Results • How do the results compare with random sampling? • p-values of 0.001 suggest that there is some underlying very weak relationship between snags and overstory • Imputation is better than just randomly assigning snags to stands

25. Why didn’t it work better? • Very weak correlations between overstory and snags • Snags are from prior stand • Many of the snags in the FIA database have advanced decay classes • Often snags are larger than QMD • Management history • Snags were removed at harvest • Thinning captures mortality

26. Future Direction • Assessing the effects of imputed data on habitat model outputs • If there are big differences then what?