Predictive modeling of vegetation distributions

1. Predictive modeling of vegetation distributions Symposium on Bioinformatics: Temporal and Spatial Syntheses of Vegetation Data International Association of Vegetation Science 49th Annual Meeting, Palmerston North, New Zealand 12-16 Feb 2007 Janet Franklin Vegetation Science & Landscape Ecology Laboratory Department of Biology San Diego State University

2. Acknowledgements • US National Science Foundation (0452389) Geography & Regional Science Program • Jennifer Miller, West Virginia University • Robert Taylor, US National Park Service, VTM data champion • Tom Edwards, Mike Austin, Kim van Neil and many others…

3. Outline • Introduction • What is Species Distribution Modeling (SDM)? • What is special about vegetation data? • Framework for SDM • The Data Model and Vegetation Data • Sample design • Response variable • Explanatory environmental variables • Scale

4. What are species distribution models? • Quantitative models of species-environment relationships… • …used to predict the occurrence of a species for locations where survey data are lacking (interpolate biological data in space) • Species abundance or presence • Habitat suitability • Realized niche

5. What do you need? • data on species occurrence in geographical space • maps of environmental variables • A model linking habitat requirements to environmental variables • A way to produce a map of predicted species occurrence -- GIS • Data to validate the predictions

6. Elevation, Quercus pacificaPresence (n=131),Absence (n=797) The Data

7. Potential Solar Radiation (winter solstice)

8. Probability of Species Presence Channelislandsrestoration.com

9. Whymake spatial predictions of species distributions? • Conservation planning • Reserve design • Impact assessment • Land and resource management • Climate change • Invasive species • Ecological restoration • Population viability analysis • Modeling community dynamics

10. What is Special About Vegetation Databases and Databanks? + Lots of it + Multiple species (community) + Presence and absence, abundance + Plants not (usually) (very) cryptic or mobile - May come from multiple surveys - Time periods may vary - Protocols may vary - May lack locational precision

11. Wieslander California Vegetation Type Mapping Survey -1930s 18,000 plots state-wide 1481 Southern California shrubland plots 400-m2, 233 species(http://vtm.berkeley.edu/) Los Angeles San Diego

12. Framework for Modeling Species Distributions “Any mechanistic process model of ecosystem dynamics should be consistent with a static, quantitative and rigorous description of the same ecosystem” (Austin 2002, p. 112) Ecological Model Data Model Empirical Model

13. The Data Model • “Theory and decisions about how the data are sampled and measured” • Sampling in space and time • Response variable • Predictor variables • Spatial scale • Resolution • Extent

14. Sampling in Vegetation Surveys - Not always probability-based But… + dense data can be sampled + can supplement with random sample Yucca brevifoliaAlliancePr/Abs

15. Response Variable in Vegetation Surveys • Presence or abundance of all plant species makes it possible to • Model species • Model communities • Predict (species) first, then classify • Classify or ordinate (community) first, then predict (review of modeling communities by Ferrier and Guisan 2006 J. Appl Ecol 43:393-404)

16. SDM is direct gradient analysis Fundamental vs. realized niche Resource utilization function Date from John T. Curtis. Figure from Gurevitch et al. The Ecology of Plants

17. Model species first, then classify community • Vegetation continuum, composition varies continuously, individual species responses to gradients(Austin 1998 AMOB 85:2) Ferrier et al. 2002, Biodiv. & Conserv 11:2309

18. Classify first, then model • “Predictive Vegetation Modelling” (Franklin 1995 Progr Phy Geogr) Yucca brevifoliaAlliancePr/Abs

19. Ordinate and model together (CCA) • Oregon coastal ranges, forest (800 plots, multiple surveys and agencies) (Ohmann and Gregory 2002 Can J For Res)

20. Classify or ordinate first, then model(or classify and model together) • Classify first, then model starts with indirect gradient analysis of communities • Classify/ordinate and model environment together is direct gradient analysis of communities

21. Summary – Vegetation Surveys and Databanks… • Are large datasets, often geographically comprehensive + Can overcome some sampling problems + New modeling methods robust to data quality

22. Summary – Vegetation Surveys and Databanks… • Usually include P/A or abundance of all plant species + P/A data yield powerful species models ? Community composition data may be underutilized in vegetation modelling

23. Thank you!Questions?

24. What do we really want?

25. Plant Distributions: Primary Environmental Regimes Guisan & Zimmerman (2000)

26. Predictor Variables for Vegetation Modelling Slope Curvature Solar Radiation

27. Scale in Species Distribution Modeling • Biogeographical scale • Point observations • Lots of them • Not from designed surveys • Presence only, atlases, collections • Resolution of analysis 10x10-50x50 km • Many to one • Ecological scale • Scale of data collection 102-103 m2 • Probability sample designs • Resolution of analysis 10x10 to 1000x1000 m • One to one McPherson et al. (2006)

28. Biogeographical Scale Assessment of Potential Future Vegetation Changes in the Southwestern United StatesRobert S. Thompson, Katherine H. Anderson,, Patrick J. Bartlein http://geochange.er.usgs.gov/sw/impacts/biology/veg_chg_model/

29. Scale in Species Distribution Modeling • Biogeographical scale • Point observations • Lots of them • Not from designed surveys • Presence only, atlases, collections • Resolution of analysis 10x10-50x50 km • Many to one • Ecological scale • Scale of data collection 102-103 m2 • Probability sample designs • Resolution of analysis 10x10 to 1000x1000 m • One to one

30. Ecological Scale Channelislandsrestoration.com

31. Biogeographical scale Ecological scale

32. Summary – Vegetation Surveys and Databanks… • Plant distributions primarily controlled by light, heat sum, water and nutrients + Tools and data exist for mapping environmental gradients related to these primary regimes

33. Summary – Vegetation Surveys and Databanks… • Modeling and spatial prediction at biogeographical or ecological spatial scale + Coarse-scale modeling can overcome locational errors in historical surveys - But limited to coarse-scale predictors (climate, not terrain)

34. Conceptual model of geographical data(Goodchild 1994) • Field: geographical space is a multivariate vector field where variables can be defined and measured at any location • Elevation • Vegetation type • Entity: empty geographical space contains objects • Tree • Species occurrence • Fire perimeter

35. The Species Data Model • In species distribution modeling we start with entities… • observations of species occurrence • and end with fields • Maps of probability of occurrence

36. What do we really want? San Diego County is 11,721 km2 San Diego Bird Atlas: http://www.sdnhm.org/research/birdatlas/yellowwarbler.html