Are old-growth forests special? Or just old?

1. Are old-growth forests special? Or just old? How would you know?

2. Hemlock-northern hardwood forests Shade-tolerant canopy species: Acer saccharum (sugar maple) Fagus grandifolia (American beech) Tsuga canadensis (eastern hemlock) Less tolerant canopy species: Betula alleghaniensis (yellow birch) Acer rubrum (red maple) Shade-tolerant sub-canopy species: Abies balsamea (balsam fir) Ostrya virginiana (hop-hornbeam) Shade-tolerant canopy, swamp forest Thuja occidentalis Picea glauca Less tolerant canopy, swamp forest Fraxinus nigra

3. Textbook: Stands at equilibrium composition - frequent, gap-phase -> statistical self-replacement of shade-tolerant dominants - some ‘gap specialists’ persist (Betula alleghaniensis)

4. Mesic forests in Upper Great Lakes good candidates for equilibrium: - fire extremely rare - stand-initiating wind-disturbance return time est >2000 yr - large areas with no anthropogenic disturbance

5. HOWEVER, model rarely tested by direct evidence. Indirect approaches -- palynology, simulation models, chronosequences -- all have severe limitations in regard to assumptions or scale.

6. But direct, observational data are difficult to get because SLOW systems. Dominants can live over 400 years, remain suppressed in understory many decades.

7. This can be discouraging -- UNLESS...

8. Lake Superior N Huron Mt. Club Dukes Experimental Forest Lake Michigan

9. DUKES Research Natural Area (Hiawatha National Forest) 100 ha, free of logging or other intensive use 246 circular plots, established 1935 - 0.2 acre (~800 m2) = ~20% of stand - grid 2 chain x 5 chain (40 x 100 m) 200 m

10. Peat, conifers Hardpan, mixed High Ca, Acer Peat, conifers podzol, Tsuga podzol, Tsuga Hardpan, mixed High Ca, Acer Habitat is spatially patchy 200 m

11. 20 m Data set is spatially complicated

12. And temporally even more complicated 1935 1948 1974-1980 1989-2004 n = 238 (8 ‘transitional’ plots unsampled) Stems > 5 in (12.7 cm) dbh tallied by 2.5 cm category n = 123 (alternating along N-S lines) Stems > 5 in (12.7 cm) dbh tallied by 2.5 cm category n = 242 Stems > 0.5 in (1.3 cm) dbh tallied by 0.1 in category n = 197 Upland: stems > 5 cm mapped full plot, >1 cm 8 m radius subplot, remeasured 5-yr cycle. Swamp: stems >5 cm full plot, >1 cm 8 m sub- plot tallied only

13. I. Is it necessary to know stand history to understand stand properties, or does ‘uniformitarian’ assumption apply? II. Are old-growth forests compositionally steady-state? III. Are dynamics ‘predictable’? From initial composition? From other community or site properties? IV. How do answers depend on scale in time and space? (For example, ‘equilibrial’ communities may or may not be ‘predictable’, depending on scale and criteria.)

14. Total basal area dynamics could be consistent with steady state at stand scale.

15. But population trends say otherwise Shade-tolerant species are increasing.

16. Less tolerant species and subcanopy species are decreasing.

17. Complex spatial structure; strong patterning related to substrate for shade-tolerant species

18. But not for others; implies more likely role of historical factors?

19. Further differences among species with higher temporal resolution; trends more or less uniform for shade-tolerant species

20. But Betula dynamics pronouncedly non-stable over time

21. Many other results consistent to produce first stage results: - forest compositionally dynamic - strong population trends (= predictable?) at stand scale - consistent with successional hypothesis, though no direct evidence of initiating event - implies long-lasting importance of historical events - What kind of events? ‘Intermediate’ disturbance? => Requires long-term, observational study Fagus: all size classes show large increases Betula: small size classes collapsing

22. 21 July 2002: max winds 150 km/hr, return time ca. 250 yr Dukes RNA

24. Initial assessment: consistent with inferences from initial results - Acer and Fagus, low base-line mortality, high storm mortality; - Reverse pattern for Betula - base-line mortality hyper-dispersed, storm mortality strongly clustered, producing gaps of ~30 m).

26. By 2004, abundant Betula (and other) seedlings on mounds. The next cohort?

27. SO, some ‘predictability’ at population level. But evidence of coherent pattern at community level? Tsuga Fagus UPLAND PLOTS ONLY - In full species-space, Mantel test suggests significant predictability, but - Apparent coherence low in ordination space Acer

28. Higher temporal resolution: - Locally coherent trends and convergence - Mantel test results vary - Patterns shift with time intervals.

29. Acer-dominated portion of ordination field: differences in pattern and magnitude of vectors.

30. But little evidence of coherent trajectories in swamp forests. (Mantel test, p=.07, .03) Thuja

31. Are different areas or types behaving differently? MRPP comparisons of change vectors 1935-1970s 1970s-2000

32. Are plot trajectories convergent? Convergence should change distribution of inter-plot distances in species-space.

33. But suggestion of convergence is evident only in upland stands All upland plots combined Conifer swamp stands

34. Storm effects may act to counter successional convergence

35. Summary of ecological results: - Ancient forests, with no recent disturbance, can be dynamic. - Dynamics can be influenced by site-conditions, current composition, and infrequent historical events, and all of these factors interact. - Changes may be predictable at stand scale as results of competitive relationships and environmental change. BUT Current composition relatively weak predictor of trajectories at plot scale.

36. Methodological and conceptual needs and challenges: - spatial and temporal scale and resolution are important. - Historical data sets are of critical value-- but inherited sampling design may constrain how questions can be addressed SO - Need data management and analysis tools capable of dealing with variable sampling protocols and intervals and other ‘inherited’ problems with historical data. - Need focus on development and evaluation of analytical tools for assessing relationships in multi-dimensional data sets over variable scales.

37. Thanks to: • National Science Foundation, Andrew W. Mellon Foundation, US Forest Service, Huron Mt. Wildlife Foundation for support • Fred Metzger, Eric Bourdo, Jan Schultz for sharing data

38. AND MANY Bennington College undergraduates for doing the work.

40. Peat Peat

42. Monitoring of individuals allows further demographic insights: growth rates and mortality related.

43. Spatially explicit individual monitoring permits analysis of neighbor interactions: competition affects growth rates and mortality in species-specific patterns.

44. Initial analyses: observed stand properties consistent with effects of such events Differences between ‘base-line’ mortality and storm effects are species- and size-specific - Shade-tolerant species favored by ‘base-line’ small-gap regime - Less-tolerant species (Betula) by larger disturbance

45. Across stand at plot scale, spatial pattern complex, but difficult to interpret.

47. Spatial Pattern: Mapped Stand Mortality Ripley’s K Analyses • Trees random to hyper-dispersed • Baseline mortality hyper-dispersed • Storm mortality clumped • Pattern at this scale not detectable from plot data

48. BUT NEW OBSERVATION: Acer seedlings show extensive stem-layering.

49. Can layered seedlings be released to become viable saplings? YES In fact, most saplings in ‘regeneration patches’ appear to have been layered.

50. Even where deer destroy regeneration in small gaps, large gaps may offer episodic, local escape from browse. SO, - fitness payoff for long- persisting seedlings even greater? - episodic, patchy dynamics even more fundamental?