1. Impact of snow molds on conifers of the ribbon forest Max Owens EBIO 4100, Spring 2013 Winter Ecology Mountain Research Station University of Colorado-Boulder

2. Introduction Structure of the ribbon forest: snow accumulation has multiple effects on trees (Knight 1994) Snow molds are specialized fungi that attack dormant plants under cover of snow (Pokorny 2009) Many different species of cold-tolerant fungi are present under the snow during winter and disappear quickly after snowmelt (Schmidt et al. 2008)

3. Introduction These psychrophilic (cold-loving) fungi produce mycelia during the severe conditions of winter. (Hoshino et al. 2009) How? Spruce sapling growth is strongly inhibited by presence of snow mold. (Cunningham et al. 2006)

4. Introduction Opportunistic snow molds are insulated from harsh climate of winter by the snowpack, and antagonists are practically absent (Matsumoto 2009) For phytopathogenic fungi adapted to the cold, the subnivean environment is the perfect place to parasitize young trees

5. Question: How does the depth of snow contribute to the maintenance of the structure of the ribbon forest, as related to the presence of phytopathogenic snow mold? Why it matters: Important factor in limiting growth of subalpine trees? Maintenance of ribbon forest, a common structure in subalpine forests near tree line Climate change—precipitation on the rise at higher elevations (Kittel 2013)

6. Hypothesis: The added insulative value of deeper snow creates a better environment for snow molds to parasitize trees. Prediction: We would expect to see a greater incidence of snow mold in the glades of the ribbon forest, where snow is deeper.

7. Site Subalpine ribbon forest above Mountain Research Station at ~3,330m (10,930 ft.) [Google 2013]

8. Methods 2 x 40m transect crossing glade from ribbon to ribbon Snow depth, tree heights, DBHs, and snow mold evidence were measured. Snow was removed along transect to expose small trees, and snow was removed to base of each tree to expose any snow mold damage

9. Methods If present, height of snow mold/snow mold damage extent was measured Trees with a DBH of 8 cm or greater were excluded, since trees this large generally did not have branches beneath the snowpack Only one transect due to time constraints Used Excel and R to create plots, regressions and t- tests

10. Results

11. Physical appearance of infected trees 2 main categories Bundled needles with black mold in between Brown/black needles, not bundled Several trees with healthy needles below snowpack Bundled needles Discolored needles, not bundled

12. Results p-value: 0.268 R 2 : 0.292 Regression

13. Results p-value: 0.0302 Bundled needles Discolored, not bundled Healthy needles 1 Engelmann spruce 4 subalpine firs 2 subalpine firs T-test

14. Results p-value: 0.326 T-test

15. Results Regression of snow depth and height of snow mold was not significant, but trended towards higher extent of snow mold with greater snow depths Significant difference in snow depths between trees with and without snow mold Not enough data to differentiate snow mold presence/appearance between different tree species No significant difference in tree size between trees with and without snow mold

16. Discussion Deeper snow creates favorable environmental conditions (insulation, moisture) for snow mold to infect trees Persistence of snow is critical (Matsumoto 2009) Snow mold infection inhibits spruce growth, and one of the primary drivers of snow mold infection of saplings was snow duration (Cunningham et al. 2006) Snow mold-tree interactions play at least some part in the complex maintenance of ribbon forest structure

17. Discussion Further research Replicates of this study to create an accurate profile of snow mold height extent with snow depth (is there an optimal depth for infection?) A direct correlation between snow mold and snow depth has implications for climate change. Snow depth and the maintenance of the ribbon forest to determine the relative impacts of physical damage, shorter growing seasons, and infection by snow molds on trees How much snow mold damage is too much for a conifer to survive?

18. Conclusion Ribbon forest structure controlled by several factors, including snow molds Fungi that infect plants underneath the snow; limits growth Deeper snow makes snow mold infection more likely Insulation Abundant moisture Not enough data to determine pattern of snow mold damage extent in ribbon forest

19. Acknowledgements Thanks to Tim Kittel for support and analysis help, and to Sebastian Baily, Jake Delfin, and Atty Phleger for data collection help.

20. Literature Cited Cunningham, C., N.E. Zimmermann, V. Stoeckli, and H. Bugmann. 2006. Growth response of Norway spruce saplings in two forest gaps in the Swiss Alps to artificial browsing, infection with black snow mold, and competition by ground vegetation. Canadian Journal of Forest Research. 36(11): 2782-2793. DOI: 10.1139/x06-156 Hoshino, T., N. Xiao, O.B. Tkachenko. 2009. Cold adaptation in the phytopathogenic fungi causing snow molds. Mycoscience. 50: 26–38. DOI: 10.1007/s10267-008-0452-2 Kittel, Timothy. 2013. Historical Climate Change on Niwot Ridge. http://culter.colorado.edu/~kittel/WEcol_GuestLec/ NiwotRidgeClimateChange_TKittel213.pdf. Accessed 21 February 2013.

21. Knight, D.H. 1994. Mountains and Plains: the Ecology of Wyoming Landscapes. Yale University, New Haven. Matsumoto, N. 2009. Snow molds: a group of fungi that prevail under snow. Microbes and Environments. 24(1): 14-20. DOI: 10.1264/jsme2.ME09101 Pokorny, J.D. 2012. Snow Molds of Conifers. http:// www.rngr.net/publications/forest-nursery-pests/ conifer-diseases/snow-molds-of-conifers/? searchterm=snow%20molds%20of%20conifers. Accessed 5 February 2013. Schadt, C.W., A.P. Martin, D.A. Lipson, S.K. Schmidt. 2003. Seasonal dynamics of previously unknown fungal lineages in tundra soils. Science. 301 (5638): 1359-1361. DOI: 10.1126/science.1086940

22. Schmidt, S.K., K.L. Wilson, A.F. Meyer, M.M. Gebauer, A.J. King. 2008. Phylogeny and ecophysiology of opportunistic “snow molds” from a subalpine forest ecosystem. Microbial Ecology. 56 (4): 681-687. DOI: 10.1007/s00248-008-9387-6