1. A dynamic species modeling approach to assessing impacts from climate change on vegetation Lydia P Ries Biogeography Lab Bren School of Environmental Sciences & Management University of California, Santa Barbara ICESS Seminar April 29 2008

2. Post-doctoral Collaborators: Frank Davis, David Stoms Biogeography Lab, Bren Lee Hannah Conservation International, Bren International Collaborators: Guy Midgley Kirstenbosch Research Institute, South African National Biodiversity Institute Ian Davies Australian National University Research School of Biological Sciences Wilfried Thuiller Université J. Fourier, Laboratoire d'Ecologie Alpine Changwan Seo Seoul National University

3. Climate change… …is affecting ecosystems worldwide. species distribution phenology community associations Now what? Euphydryas editha

4. -assess climate change impacts on CA biodiversity, -develop a tool to estimate changes in potential and realized niches for individual species under climate change scenarios. The PIER Ecosystem Modeling Project

5. Outline Species distribution modeling Dynamic species model development: BioMove BioMove applications Case study examples

6. Species distribution modeling Lessons from the past: climate oscillates pollen records-species shift latitude and elevation shifts maintain biodiversity Using same principles, model future response to climate change

7. Species Distribution Modeling ( Thuiller et al. 2003) Building and validating individual species models Reducing uncertainty of models Predicting potential niches

8. Building & validating models Selecting species & environmental data GLM/GAM/GBM/CART/ANN Species Distribution Model Methods Bayesian Approach

9. Climate dataSoil data Max Temp of Warmest Month Min Temp of Coldest Month Annual Temp Range Mean Temp of Wettest Quarter Mean Temp of Driest Quarter Precipitation of Wettest Quarter Precipitation of Driest Quarter Available Water Capacity Soil depth Soil pH Salinity Depth of water table + Species Distribution Model Methods

10. Building & validating models Selecting species & environmental data Projecting models to the future environment Estimating the change of species range GLM/GAM/GBM/CART/ANN Optimizing model outputs Consensus Model Bayesian Approach Species Distribution Model Methods

11. Quercus agrifolia Hadley A2 Scenario Present

12. Quercus agrifolia Hadley A2 Scenario 2080 loss gain

13. Species Distribution Model Output To Date: 314 Species 13 Pinus spp. 15 Quercus spp. 13 Ceanothus spp. 9 Arctostaphylous spp. 89 California Endemic Mojave Mixed conifer Coastal sage scrub

14. Endemic Richness Current 2050 high low

15. Species distribution modeling Lessons from the past: climate oscillates pollen records-species shift On what time scale? Rapid events Reid’s paradox (observed > calculated) Other mechanisms of movement?

16. What about… Dispersal? Short and long distance events Competition? Plant functional type-age and size classes, fecundity, mortality, germination Disturbance? Fire, grazing

17. BioMove; a dynamic modeling approach A spatially explicit model used to predict the movement of individual species Simulates dispersal, competition and disturbance

18. Outline Species distribution modeling Dynamic species model development: BioMove BioMove applications Case study examples

19. Model Overview

20. Demographic model

21. Dispersal Sub-Model: Dispersal

22. Competition Sub-Model: Competition

23. Sub-Model: Climate Change

24. Disturbance Sub-Model: Disturbance

25. Outline Species distribution modeling Dynamic species model development: BioMove BioMove applications Case study examples

26. Model Applications 1. Assess threat to species from climate change 2 % listed at risk from CC based on bioclimatic envelopes extinction risk study assumptions

27. Model Applications 2. Management coordination and decision support: Impact of fire on vegetation Conservation area designation (leading, trailing) Species of concern (threatened, small populations) Land use planning Zaca Fire, 3 August 2007

28. Model Applications 3. Invasive species assessment Studies on conservation areas, agriculture, forestry –Global ( Ficetola et al. 2007, Richardson & Thuiller 2007 ) –Regional ( Parker-Allie et al. 2007 ) Need to incorporate spatial and temporal dynamics Bromus madritensis Calflora.net

29. Model Applications 4. Advanced climate change research The climate is changing fast…and so are the models! –inter-disciplinary model development –GHG stabilization

30. BioMove: a tool for assessing GHG stabilization scenarios GHG Stabilization Targets: EU: 2° C global mean temperature change (~450 ppm, IPCC 2007) U.S.: USCAP 450-550 ppm CO 2 eq. Targets set to avoid ‘dangerous interference’ in the climate system, e.g. avoiding mass extinctions Current levels 420-480 ppm CO 2 eq. (Pew Center on Global Climate Change, 2007)

31. BioMove: a tool for assessing GHG stabilization scenarios Time CO 2 Emissions (ppm) 550 ppm 450 ppm

32. Outline Species distribution modeling Dynamic species model development: BioMove BioMove applications Case study examples

33. BioMove; A Case Study Pinus lambertiana “This is the noblest pine yet discovered, surpassing all others not merely in size but also in kingly beauty and majesty. “ (John Muir, 1894) http://www.lib.berkeley.edu/EART/tour/TahoeNationalForest.gif

34. Species distribution model results: Pinus lambertiana A2B2 C H

35. 2010 BioMove; A Case Study Pinus lambertiana 2010

36. BioMove; A Case Study Pinus lambertiana 2050

37. BioMove; A Case Study Pinus lambertiana 2080

38. Disturbance: Fire CA Dept. of Forestry and Fire Protection, Fire and Resources Assessment Program (FRAP) 54 Year Average Fire Frequency 4 size classes BioMove; A Case Study Pinus lambertiana Zaca Fire, 3 August 2007

39. Quercus douglasii (Blue Oak) Predicted current distributionPredicted change in distribution Mixed blue oak habitat

40. Blue oak under climate change

41. Bromus madritensis (red brome) Lower elevations distribution Fire effects from pine Moving upslope

42. In summary 314 species library for distribution model outputs Coupled outputs with spatially explicit demographic model Refined dynamic species model Predict climate driven shifts CA species Continue incorporating competition and disturbance models Predict differences between GHG stabilization projections Implications for management strategies, conservation allocation

43. Future work Predict differences between GHG stabilization projections Implications for management strategies, conservation allocation Collaborations with: San Diego County 2050 Project CA Air Resources Board IUCN

44. thank you