1. Brody Sandel 1, Leah J. Goldstein 2, Nathan Kraft 1, Jordan Okie 3, Michal Shuldman 1, David D. Ackerly 1, Elsa Cleland 4 and Katharine N. Suding 2 (1)Department of Integrative Biology, UC Berkeley, Berkeley, CA (2)Ecology and Evolutionary Biology, UC Irvine, Irvine, CA (3)Department of Biology, University of New Mexico, Albuquerque, NM (4)Ecology, Behavior & Evolution Section, UC San Diego, La Jolla, CA Contrasting predictions of experimental and observational studies of the response of plant communities to changing precipitation

2. How will the composition of plant assemblages respond to climate change? Precipitation change Weltzin et al. 2003, Bioscience. Plant functional traits Suding et al. 2008, Glob. Change Biol. Experimental/observational Rustad 2006, Plant Ecol. Introduction Plant responses to climate change

3. Wright et al. 2005, Glob. Ecol. Biogeogr. Introduction Traits and climate change N:P Reich and Oleksyn 2004, PNAS Traits vary with climate Can they predict response to changing climate?

4. Advantages of trait-based predictions  Mechanistic interpretations  Allows syntheses  Predictions are generalizable Introduction Traits and climate change

5. Similar predictions?  Direction and magnitude of effect Shifts in functional trait composition are the bases for comparison Introduction Experimental and observational

6. Introduction Similar predictions? Control + Precip Mean trait value Precipitation Mean trait value

7. Direction Control + Precip Mean trait value Precipitation Mean trait value Introduction Similar predictions?

8. Direction Magnitude  ∆T E =? ∆T O Control + Precip Mean trait value Precipitation Mean trait value Equivalent to experiment ∆T E ∆T O Introduction Similar predictions?

9. Combining results  Same direction, different magnitude  (My a priori expectation) Precipitation Mean trait value Experimental studies Observational gradient C T C C T T Introduction Similar predictions?

10. Experimental water additions Natural precipitation gradient Match species lists to trait databases Calculate plot mean trait values  Test for effects of increased water Compare experimental and observational outcomes  Direction  Magnitude Methods Methods overview

11. Four water addition experiments  Konza LTER (1991-2005) Knapp et al. 2001, Ecosystems.  Shortgrass Steppe LTER (2000)  Sevilleta LTER (2004-2006) Baez et al. In prep.  Jasper Ridge Global Change Experiment (1999-2002) Zavaleta et al. 2003, Ecol. Monogr. Between 10% and 190% (mean 50%) precip. increases Plant community composition data Grasslands or mixed grass-shrublands 219 species total Methods Experimental data

12. VegBank (vegbank.org) 21,566 plots from across the country Plant assemblage of all plots 7813 species total Used PRISM climate data to obtain 30-year mean precipitation values Methods Observational data

13. Traits Methods Matched species lists to trait databases  USDA Plants  Kew Gardens Seed Information Database  Glopnet leaf traits Wright et al. 2004. Nature.  More leaf traits Tjoelker et al. 2005, New Phyt.; Reich and Oleksyn 2004, PNAS.  Height Cleland et al. 2008. Ecology.

14. Exp.Nat. Grad. TraitCoverage LL30%21% SLA41%34% N mass 42%43% N area 40%32% A mass 38%23% A area 40%23% Seed94%80% Form100%89% Lifespan98%90% Height100% Traits Methods

15. Abundance-weighted trait means for each plot Percentage cover by a group All analyses performed on these plot-level values Experimental  ANOVA using last year of each study Observational  Aggregated cells at 1 x 1 degree resolution  Linear regression Methods Analyses

16. log(Precip (mm)) log(Seed mass (mg)) Results Seed size example

17. log(Seed mass (mg)) log(Precip (mm)) Results Seed size example

18. log(Precip (mm)) log(Seed mass (mg)) Results Seed size example

19. log(Precip (mm)) log(Seed mass (mg)) Results Seed size example

20. log(Seed mass (mg)) log(Precip (mm)) Results Seed size example

21. Results Seed size example Treatment effect log(Seed mass (mg)) per log(Precip (mm)) Year Slopes of line segments through time

22. Results Summary of all traits Experimental Natural Gradient TraitEffectP r2r2 LL-0.0129+0.154 SLA+0.0297NS0.006 N mass NS †0.1601-0.158 N area +0.0003-0.309 A mass Mixed †0.0189-0.047 A area NS †0.3116-0.101 Seed-0.0071+0.362 GrassNS †0.0717-0.373 Forb+0.0091-0.066 Annual-<.00001-0.122 Short- †<.00001 † indicates a significant site by treatment interaction

23. Results Summary of all traits Experimental Natural Gradient TraitEffectP r2r2 LL-0.0129+0.154 SLA+0.0297NS0.006 N mass NS †0.1601-0.158 N area +0.0003-0.309 A mass Mixed †0.0189-0.047 A area NS †0.3116-0.101 Seed-0.0071+0.362 GrassNS †0.0717-0.373 Forb+0.0091-0.066 Annual-<.00001-0.122 Short- †<.00001 † indicates a significant site by treatment interaction

24. Results Summary of all traits Experimental Natural Gradient TraitEffectP r2r2 LL-0.0129+0.154 SLA+0.0297NS0.006 N mass NS †0.1601-0.158 N area +0.0003-0.309 A mass Mixed †0.0189-0.047 A area NS †0.3116-0.101 Seed-0.0071+0.362 GrassNS †0.0717-0.373 Forb+0.0091-0.066 Annual-<.00001-0.122 Short- †<.00001 † indicates a significant site by treatment interaction

25. Experimental studies  Tall, long-lived forbs with short leaf lifespans, high leaf N concentrations, high specific leaf area, and small seeds Observational analysis  Long-lived woody species with long leaf lifespans, low leaf N concentrations and photosynthetic capacity, and large seeds Results How will communities change?

26. One is right, the other wrong  Experimental artifacts  Unmeasured covariates The different responses may reflect a real, two-phased response to climate change Discussion Why the mismatch?

27. Response to climate change may occur over distinct phases  Why two phases?  Why might the responses in each phase differ?  What determines the time scale of the phases? Discussion A two-phase model

28. Discussion Two phases Premise – Abundance changes happen more quickly than species gain and loss  Phase 1 – Changes in local species abundance  Phase 2 – Changes in species pool Calculating plot trait values not weighted by abundance revealed fewer treatment effects  Abundance shifts were critical in experiments

29. Discussion Two phases Phase 1 – Abundance changes Phase 2 – Species pool changes Increased water Time

30. Discussion Phase differences Why might the trait responses differ in the two phases?  Changing interactions among species  Shifts in the limiting resource The traits of local species that increase are not the same as those of immigrating species

31. Discussion Phase differences Increased water Time Increasing species are able to take advantage of increased resource availability (tall, high leaf N, short-lived leaves, small seeds) Taller stature - light limitation Species must cope with low light environment (woody, low leaf N, long-lived leaves, large seeds)

32. Discussion Time scales Little evidence for phase 2 in the experiments  No convergence through time towards observational results  No treatment affect on species-time relationships JRG KNZ SEV

33. What determines the length of phase 1?  Spatial extent of climate change  Life histories of local species (annual/perennial) At least decades in this case  Lengthened by experimental limitations Discussion Time scales

34. Traits useful predictors Mismatch between experimental and observational results Could be due to different time scales captured by these two types of study Use the appropriate data to predict for a given time scale Discussion Main messages

35. NCEAS, and the coordinators and participants in the distributed graduate seminar William Lauenroth Alan Knapp William Pockman Erika Zavaleta Funding –  NSF grant to NCEAS (EF-0553768)  UC Santa Barbara  LTER network office for cross-site research  NSF LTER program (DEB0218210, BSR 88-11906, DEB9411976, DEB0080529, DEB0217774, DEB0217631)  David and Lucile Packard Foundation  Morgan Family Foundation  Jasper Ridge Biological Preserve The many VegBank contributors Ian Wright and Peter Reich (Glopnet) Acknowledgments

36. Discussion A two-phase model