1. Risk Mapping for Forest Pests Kurt W. Gottschalk and Andrew M. Liebhold USDA Forest Service Northeastern Research Station Morgantown, WV USA

2. In the Beginning…  Early 1990’s, Gypsy Moth EIS Team  EIS Team wanted a national susceptibility map or host risk map  They contracted with Sandy and myself to produce it  We spent about 18 months to do so finishing in about 1994-95

3. In the Beginning…  Getting the data was a major task that took about a year and cost a lot of money as the FIA units charged us to create the data files for each state  For the east it was the precursor to the east wide database – we obtained each state independently and created a big data file  For the west, things were much worse, we could get state and private land but not national forest land  We eventually got very old, NF data from the regions and Ft. Collins

4. In the Beginning…  Once we got the data, the bigger problem was how to represent it because:  There was no spatial data associated with the plots other than county or ranger district for some of the western data  So we resorted to using county as our pixel size which meant we also had a problem with representing how much forest land was in a county

6. Most Common Preferred Tree Species

7. White Oak, Quercus alba 0 0 - 0.1 0.1 - 0.5 0.5 - 2.0 2.0 40.0 no data ft 2 / acre

8. Northern Red Oak, Quercus rubra 0 0 - 0.1 0.1 - 0.5 0.5 - 2.0 2.0 40.0 no data ft 2 / acre

9. Total Basal Area of Preferred Species 0 - 0.33 0.33 - 3.6 3.6 - 10.6 10.6 - 18.3 18.3 - 70.1 no data ft 2 / acre

13. Second Effort….  In round two, S&PF was interested in making a national forest pest risk map  Sandy and I were approached about revising our gypsy moth effort  For this effort, we started with the FIA AVHRR forest type map  GM rate of spread was added into this effort as well

14. FIA forest type group map Subset for forest type groups that contain susceptible forest types (Oak-pine, Oak-hickory, Oak-gum-cypress, Elm- ash-cottonwood, and Aspen-birch): Susceptible types

15. FIA Plot Data … excluded any counties where less than 10% of land area was covered by forests that have > 20% BA preferred species… (from: Liebhold et al. J. Forestry 95: 20-24)

16. Susceptible forest types

17. Third effort…  Sandy had been introduced to geostatistics, so the availability of actual GPSed plot locations allowed us to use kriging as a new approach

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10.5 10.5 - 27.6 27.6 - 47.7 47.7 - 70.8 70.8 - 100 1989 Pennsylvania FIA data % basal area: Preferred by gypsy moth

19. 0-15 16-27 28-39 40-52 53-77 % BA Preferred by the Gypsy Moth Kriged from 1989 PA FIA Plot Data

20. Final kriged map for east

21. STDP Proposal for Risk Mapping Technology  Armed with actual plot locations and our new geostatistical tools, we got funded to develop this technology along with rate of spread into a prototype for the National Pest Risk Map

22. METHODS – Forest Susceptibility  The geographical distributions for each pest were mapped by interpolation of host species abundance estimated from 93,611 FIA plots located throughout the eastern U.S  The ordinary kriging procedure was used to interpolate a surface of basal area/ha for the host species of each disturbance agent  We generated maps from the plot data by calculating kriged estimates on a grid of 1- by 1-km cells  Forest susceptibility maps were then adjusted for forest density  The kriged host abundance maps were multiplied by the forest density map to generate forest susceptibility maps adjusted for percent forest cover

23. METHODS – Spread Prediction  A spatial representation of the predicted future range expansion of each of the pests was created by estimating spread from historical records and using these estimates to predict future spread  The rate of spread was estimated as the slope of the linear model of each county’s distance from the initially infested area as a function of the time until establishment in that county  These estimated spread rates were used to generate maps representing the proportion of years of expected presence from 2002 to 2025 in 1- by 1-km grids for each agent  The proportion maps were multiplied by the adjusted forest susceptibility maps to create maps of risk for each pest through 2025  These maps depict risk as an index that includes both forest composition and the expected time the pest is expected to be present

24. RESULTS – Beech bark disease

26. RESULTS – Hemlock Woolly Adelgid

28. RESULTS – Gypsy Moth

30. About this time, Sudden Oak Death hit the scene  I heard a talk by David Rizzo on his tests of northern red oak and black oak  I decided to use this approach to determine the risk to the East from SOD  Our risk map went on to be used as the basis of the national SOD risk map  We then added in the NE shrub data as an additional risk factor

31. Kriged map of the estimated percent forest basal area for the red and live oak groups adjusted for forest density. Estimated Percent Forest Basal Area

32. Kriged probability of overstory hosts of Phytophthora ramorum

33. Kriged probability of understory hosts of Phytophthora ramorum

34. Probability of presence of overstory and understory hosts of Phytophthora ramorum

35. Forest Health Monitoring, Evaluation Monitoring Proposal on Butternut  Butternut canker has been devastating butternut  However, Mike Ostry has some putative resistant genotypes  If true resistance exists, then knowledge on where to restore butternut is needed  We mapped the occurrence of all butternut (live and dead) and also analyzed by ecoregions and found out some interesting things

36. Butternut Presence/Absence Absent Present

37. Estimated probability of occurrence of butternut Estimated Probability

38. 0.007530M231 0.004740M222 0.00500411 0.004610332 0.001580331 0.006150255 0.0012670234 0.01136592232 0.06140648231 0.5824321142212 0.63415626251 0.96291528M212 1.32561474M221 1.39631888221 2.0913862290222 % of Plots w/ Butternut Total # of FIA Plots # of FIA Plots with Butternut Province Butternut occurrence by ecoregion province.

39. A CART analysis of province-level proportion of plots with butternut produced four significantly different groups. All other Provinces Mean = 0.09% Provinces 222, M221, 221, M212 Mean = 1.15% Provinces 222, M221, 221 Mean = 1.2% Province M212 Mean = 0.7% Provinces 212, 251 Mean = 0.4% All other Provinces Mean = 0.008% All provinces Mean = 0.37%

40. 1.68238740M221A 1.7955910M221C 1.9691918M221B 2.0648510221F 2.0748410M212C 2.212265251B 2.31142833212K 2.41133032212F 2.78100628222J 2.8456316222I 2.9251415222H 3.43134246222M 4.3734315221B 6.3921914212E 10.901211132222L % of Plots w/ Butternut Total # of FIA Plots # of FIA Plots with Butternut Section Butternut occurrence by ecoregion section.

41. State & Private wanted an Emerald Ash Borer Risk Map  Based on our success creating the SOD risk map, an EAB risk map was requested  We did two different host layers for this map – an upland ash layer based primarily on FIA plots and a lowland ash layer based on FIA plots and other factors

42. Risk to Emerald Ash Borer based on Forests

43. Riparian ash host risk to EAB

44. Summary  Over time, we have increased our ability to create realistic estimates of species occurrences that allows us to estimate invasive pest risk  Given the host species range, we can produce a host risk map for almost any forest pest  We have used only periodic FIA data so far – the challenge ahead is to figure out how to incorporate annual FIA data into this system