1. Multiscale Climatic, Topographic, and Biotic Controls of Tree Invasion in a Sub-Alpine Parkland Landscape, Jefferson Park, Oregon Cascades, USA Harold S.J. Zald, Oregon State University MTNCLIM 2010 | 06.09.2010 HJA | Blue River | OR

2. Why Study Tree Invasion in Mountain Ecotones? Globally, treeline positions related to thermal conditions Treeline movement a highly variable response to climate across multiple climate regions, species, & land use histories Treeline movement and meadow encroachment may influence: ecosystem productivity, carbon balance, energy budget, hydrological processes, species distributions, and biodiversity Harsch et al. (2009) Ecology Letters

3. Gentle Elevation Gradient Treeline Denali National Park, Alaska Multiscale Drivers of Tree Invasion Tree invasion fundamentally driven by regeneration processes Not just climate! Biophysical controls: topography, soils, disturbance, seed sources, facilitation, competition, etc. Biophysical factors can control spatial patterns & sensitivity to climate Edaphic “Triple Treeline” Banff National Park, Canada Multiple Gradient Subalpine Parkland Mount Hood, Oregon

4. Spatial Autocorrelation of Biophysical Controls Modified from Brooke et al. 1970 Traditionally, observations of treeline movement & meadow invasion along transects with limited environmental gradients Biophysical variables spatially autocorrelated Difficult to untangle drivers

5. 0 50 100 m Applying New Technologies to Old Questions Light Detection and Ranging (LiDAR) Landscape characterization of fine-scale vegetation structure & topography LiDAR can be used to sample across multiple biophysical gradients at scales compatible regeneration processes

6. Research Questions How have climatic and biophysical factors controlled recent rates & spatial patterns of tree invasion? Background: PNW tree invasion driven by snow depth and persistence, believed to determine growing season length (Franklin et al. 1971, Woodward et al. 1995, Rochefort & Peterson 1996)

7. Mount Jefferson Wilderness Willamette NF Elevation: 1755-1840 m ~130 ha Tree islands of mountain hemlock & Pacific silver fir Geomorphology: glacial & neoglacial debris flows No known fires Unknown grazing history Study Area: Jefferson Park, OR Jefferson Park HJA

8. LiDAR Derived Biophysical Variables Bio Overstory canopy Influences snow depth & persistence, seed sources Physical Topographic position, elevation, radiation Influence snow depth & persistence Landform (glacial v. debris flows) Disturbance, but also influences other biophysical variables

9. LiDAR Driven Sampling Sample along biophysical gradients believed to influence snow depth & persistence Topography (5 Classes) Distance to overstory canopy (5 classes) Combine grids (5 x 5 = 25 classes) 100 x 100 m moving window (20 clusters) Stratified random sample (25 points per cluster) 100 x 100 m cluster

10. Mapping of overstory canopy by species (potential seed sources) Spatial autocorrelation between explanatory variables accounted for Landscape-level estimates of invasion possible LiDAR Driven Sampling Continued

11. Points located with sub-meter GPS 2 m diameter plots 390 on glacial landform 109 on debris flows Snow depth survey July 29- Aug 1, 2008 All trees < 8m tall tallied by species & height (1620 trees) 505 trees aged Field Data

12. Spatial Patterns Snow depth in relation to biophysical controls Tree abundance in relation to biophysical controls Temporal Patterns Tree invasion over time Tree invasion and climate Interactions of Climate & Biophysical Controls Flow of Results

13. More snow with less radiation, lower elevation, distance More linear, reduced interactions, less variance described Mean: 0.2m 95% CI: 0.1-0.3m Debris Flows Glacial Landforms Mean: 0.67m 95%CI: 0.6-0.8m More snow in depressions, lower elevations, distance from overstory Nonlinear interactions between explanatory variables Results: Snow Depth & Biophysical Controls

14. Results: Multi-Scale Controls of Snow Landscape context is important Larger landforms influence both overall snow depth and micro site controls of snow Smoother surface on debris flows Greater wind redistribution of snow on smoother debris flows

15. Results: Tree Abundance & Biophysical Controls Mountain hemlock associated with microtopography and distance to overstory canopy Silver fir strongly associated with distance to potential seed sources, followed by microtopography Relationships between tree invasion and biophysical variables much stronger on glacial landforms Glacial landforms Debris flow

16. Results: Temporal Patterns of Tree Invasion Not just an increase in densities 1950: 7.8% of meadow area with tree invasion 2008: 34.7% of meadow area with tree invasion Invasion dominated by Mountain hemlock, Pacific Silver Fir restricted to under existing trees Invasion rate greater on debris flows (0.75% v. 0.57% Yr -1 )

17. Annual snowfall most important, not temperature On debris flows tree invasion not associated with annual snowfall On debris flows, positive association with spring snowfall! Results: Climate, Landforms, & Invasion Glacial Landforms Adj R 2 = 0.2887 p ≤ 0.01 Debris Flows Adj R 2 = -0.0356 p = 0.5

18. Only For Hemlock on Glacial Landforms Hemlock invasion largely in years with low snow on ridgetops & midslopes During high snow years, less invasion overall & constrained to ridgetops Results: Climate, Microtopography, and Invasion

19. Snow and tree invasion associated with multi-scale landscape controls Species matter Landforms & topography alter both the spatial patterns of tree invasion & response to climate Tree invasion on debris flow landforms Scale & landscape context matter Multiscale and context dependent responses pose problems for modeling future responses to climate at regional and global scales Need for experimentation (future climate now) Conclusions

20. Acknowledgements Funding provided by: USDA Forest Service, Pacific Northwest Research Station USDA Forest Service, Forest Inventory and Analysis Program The Native Plant Society of Oregon Hoener Memorial Fellowship, OSU Waring Travel Grant, OSU Thanks to field assistants: Dan Irvine Alex Gonsiewski

21. Questions?