1. Warming climate and changes in Alaska’s temperate rainforest: combining predictive modeling with monitoring Tara M. Barrett 1,Greg Latta 2, Paul E. Hennon 3, Bianca N.I. Eskelson 2, Hailemariam Temesgen 2 1 Unaffiliated 2 Oregon State University 3 Pacific Northwest Research Station

3. Distribution of western hemlock trees (Tsuga heterophylla) Western hemlock, along with other temperate rainforest tree species (Sitka spruce, western red cedar, yellow cedar) reaches its northern limit in the Gulf of Alaska region.

4. Hemlock dwarf mistletoe (Arceuthobium tsugense subsp. tsugense) is a small plant that parasitizes western hemlock (Tsuga heterophylla) trees. Brooms (branch deformation) Bole deformation Chlorosis

5. Both western hemlock and western hemlock dwarf mistletoe distributions are at the northern part of their geographic range in Alaska Map by Dustin Wittwer

6. Population size of host (Tsuga heterophylla) and parasite (Arceuthobium tsugense subsp. tsugense) by elevation

9. Projections are for further increases in temperature over the next half century Map from SNAP modeling group, Univ. of Alaska Fairbanks (www.snap.uaf.edu)

10. Climate envelope models  Distribution is limited by a climate envelope outside of which a species cannot survive  For prediction, assumes distribution across climate-space remains the same, but spatial distribution shifts as climate changes  empirical – not process based  habitat only, does not predict actual presence  many different approaches:  GAM, neural nets, customized models logistic modeling Most Similar Neighbors imputation Random Forest imputation

11. Prediction modeling for hemlock dwarf mistletoe and western hemlock Methods 1.Reviewed literature for climate-related mechanisms that might explain mistletoe distributions and abundance. 2.Created climate variables that corresponded to possible explanatory mechanisms. 3.Tested predictive models against field data. 4.Predicted future distribution

12. Western dwarf mistletoe seed and holdfast Reviewed previous research: Extreme minimum winter temperatures reduce seed viability. Snow reduces seed establishment and germination. Rain reduces seed establishment and germination? Spring frosts damage pollen viability Fall frosts damage fruit Life cycle takes longer to complete in Alaska (e.g., 12 yrs) than British Columbia (e.g. 5 yrs) Transplanted to 120 m higher elevation, fruits never matured

13. Predictive Variables Used Growing season variables: GDD (growing degree days above 0 C) RADIANS (modeled solar radiation) Low winter temperatures MINTEMP (min of mean min monthly temp) MINTEMPSD (standard deviation of mean min temps) Precipitation SNOW (modeled precipitation as snow) RAIN (mean annual precipitation - SNOW) Autumn freezes MINFALLTEMP (mean min Sept. temp) Spring freezes MINSPRINGTEMP (mean min April temp) Other SLOPE ET (modeled evapotranspiration) CMI (modeled precip – evapotranspiration)

15. 8 - 12 Mean annual temperature (C) Used 1961-1990 PRISM climate data from Oregon State Univ.: a spatial model of climate normals (monthly temperature and precipitation) to develop climate variables PRISM data was 2 km resolution, so rescaled using 2 dimensional linear interpolation for precipitation and geographically weighted regression for temperature

16. Results – hemlock dwarf mistletoe Sensitivity (correct prediction of presence) MSNRandom ForestsLogistic Development data set293738 Validation data set242037 Specificity (correct prediction of absence) MSNRandom ForestsLogistic Development data set929893 Validation data set909693 Predicted proportions MSNRandom ForestsActual Development data set10.55.610.0 Validation data set11.05.58.9

17. All three methods (Random Forests, Most Similar Neighbors, and Logistic modeling) did fairly well at predicting the current range of hemlock and mistletoe

18. Methods 1.Reviewed literature for climate-related mechanisms that might explain mistletoe distributions and abundance. 2.Created climate variables that corresponded to possible explanatory mechanisms. 3.Tested predictive models against field data. 4.Predict future distribution

19. We used downscaled GCM composites created by the Scenarios Network for Alaska Planning (SNAP 2011). The composite models were made from the MPI ECHAM5, the GFDL CM2.1, the 324 MIROC 3.2 (medres), the UKMO HADCM3, and the CCCma CGCM3.1 models which had been chosen based on relatively good performance in a review of GCMs for Alaska and Greenland by Walsh et al. (2008). PRISM + (GCM_future – GCM_present) = Predicted Scenarios Network for Alaska Planning [SNAP]. 2011. Alaska climate datasets online. Available from www.snap.uaf.edu/downloads/alaska-climate-datasets Walsh, J.E., Chapman, W.L., Romanovsky, V., Christensen, J.H., and Stendel, M. 2008. Global climate model performance over Alaska and Greenland. J. Clim. 21:6156-6171. Future Climate Models

20. ApproachA1BA2B1 MSN137141118 RF112113107 Logistic134136115 Scenario Prediction for 2100 western hemlock habitat as a percent of present (=100), bias adjusted

21. ApproachA1BA2B1 MSN577596416 RF384449374 Logistic724757571 Scenario Prediction for 2100 dwarf mistletoe habitat as a percent of present (=100), bias adjusted

22. BUT … Habitat is not presence Trees migrate slowly eg, Sitka spruce on Kodiak island, roughly 1 mile per century (per Griggs, 1930s) Dwarf mistletoe is dioecious; seeds can only travel (beyond a few dozen meters) with the help of birds or mammals

23. BUT … Western red cedar Yellow cedar

24. Numbers in parenthesis indicate number of forested plots with this species present Forested lands, excludes National Forest wilderness and Glacier Bay NP, only trees >= 5” d.b.h.

25. Summary Plot level imputation is promising for climate/host/parasite mapping as it can be used for (1) range mapping (2) area affected estimates and (3) impact estimates For this case study: Both Most Similar Neighbors and Random Forest do poorly at predicting presence/absence of western hemlock dwarf mistletoe at the plot level. Most Similar Neighbors does moderately well at predicting abundance and distribution. Random Forests does well at predicting distribution, but tends to underestimate abundance

26. Summary For future predictions: Climate Envelope Models may be most useful for discussing why we think they are wrong formulating hypotheses to test Long-term monitoring using stable protocols really needs to accompany predictive modeling

27. For more information: Barrett, T.M.; Latta, G.; Hennon, P.E.; Eskelson, B.N.I.; Temesgen, H. 2012. Host-parasite distributions under changing climate: Tsuga heterophylla and Arceuthobium tsugense in Alaska. Canadian Journal of Forest Research 42:4:642-656 Barrett, T.M. 2011. Change in forests between 1995- 2003 and 2004-2008. In Forest Service Pacific Northwest Research Station General Technical Report PNW-GTR-835.