1. Components of plant species diversity in the New Zealand forest Jake Overton Landcare Research Hamilton

2. Acknowledgements NVS data contributors and curators Simon Ferrier and Glenn Manion for development of GDM and collaboration on modelling

3. General Question Investigate components of richness Alpha diversity Beta diversity Gamma diversity How do these compare between groups? Approach: Use a new modelling technique, Generalised Dissimilarity Modelling (GDM) to estimate components of diversity

4. Components of diversity (sensu Cody 1986) Alpha diversity = local richness Beta diversity = turnover in species due to habitat or environment Gamma diversity = turnover in species due to geographic distance or barriers All three components contribute to regional richness

5. Presence- absence of all vascular plant species in each plot Plots approx 20x20 m (sometimes unbounded) Almost 20000 plots 1220 species NVS recce (= recon) plots Biotic Data

8. Mocoto herbs Variable abbrev.Description Geographic positionGeographic position of plot MATMean Annual Temperature TseasA measure of cold stress, relative to mean annual temperature MASMean Annual Solar Radiation DeficitVapor Pressure deficit VPDVapor Pressure deficit CalciumSoil Calcium AgeSoil age NTotal Soil Nitrogen AcidPAvailable P DrainSoil Drainage PsizeSoil Particle Size IndurSoil Induration SlopeTopographic slope DiscoastDistance to coastline NothoNothofagus abundance from Leathwick Environmental variables (spatial)

9. alpha diversity (local richness) beta diversity gamma diversity ‘dissimilarity’ ‘turnover’ ‘complementarity’ richness = f (rainfall, temperature, veg type …) dissimilarity = f ( (rainfall, temperature, veg type …), geographical separation) Modelling of richness: can be supplemented by modelling of compositional dissimilarity between locations: What is Generalised Dissimilarity Modelling?

10. Compositional dissimilarity between pairs of survey sites Environmental & geographical separation Generalised dissimilarity modelling (GDM) Biotic Information Environmental and Geog Space Ecological Space Same units scaled by importance Differing units and importance

11. All species model

12. Results 1 All species validation

13. Mocoto herbs All species Total species pool 1020 species Alpha diversity component = Proportion accounted for by local richness = Mean plot richness/ Total species pool Unexplained component = 1 – proportion deviance explained Gamma Diversity component = Proportion deviance explained by geography Beta Diversity component = Deviance explained by environment

14. Mocoto herbs All plant species Snails

15. Group# spplocal rich Ferns1476.8 Lianes-epiphytes- parasites 491.9 Trees1078.9 Shrubs2554.3 Dicot herbs3602.4 Monocot herbs2821.3 All Species102025.9

16. Results 1

17. Mocoto herbs All Ferns Shrubs Trees Monocot herbs Dicot Herbs

18. Mocoto herbs All species Dicot Herbs Ferns Monocot herbs Shrubs Trees

19. Biological survey data Environmental predictors Visualisation of spatial pattern in community composition Predicted distributions of species Constrained environmental classification Survey gap analysis Conservation assessment Climate-change impact assessment Generalised dissimilarity modelling Ferrier, S. et al (in press) Using generalised dissimilarity modelling to analyse and predict patterns of beta-diversity in regional biodiversity assessment. Diversity & Distributions

20. Ferrier, S. et al (2004) Mapping more of terrestrial biodiversity for global conservation assessment. BioScience 54: 1101-1109

21. Conclusions GDM is an exciting new tool for biodiversity analyses Its main application is for biodiversity modelling and planning, but it has promise for untangling components of diversity Plant species show relatively strong environmental influence and some geographic influence on turnover Groups differ in the explained turnover, and in relative importance of different variables.

22. test

23. Dense sampling relative to grain of compositional turnover - relatively few species, each with many records Sparse sampling relative to grain of compositional turnover - huge number of species, each with very few (or no) records Environmental space (beta diversity) Geographical space (gamma diversity) Environmental space (beta diversity)

24. An example from the arid rangelands of central Australia

25. Mocoto herbs All species

26. Mocoto herbs

27. All species

28. Environmental predictors Radiometrics – Total Count Landsat TM – Band 2 Radiation of Warmest Quarter Topographic Wetness Index Precipitation of Driest Period Isothermality Minimum Temperature of Coldest Period Elevation Diversity for 300m radius Landsat TM – PD54 vegetation index Mean Temperature of Wettest Quarter Radiometrics – Uranium f (Tc10d) f (Wetness) Biological response Bray-Curtis compositional dissimilarity between all pairs of 248 field survey sites (based on perennial woody plant species)

29. Models species turnover (dissimilarity) between locations as a function of geography and environment Uses matrix regression, using GLMs. Developed by Simon Ferrier, (Department of Environment and Conservation, Armidale New South Wales, Australia) Programmed by Glenn Manion, DEC, Armidale. What is Generalised Dissimilarity Modelling?

30. Mocoto herbs All species Dicot Herbs Ferns Monocot herbs Shrubs Trees

31. Ferns

32. test L-E-P

33. Monocot herbs

34. Shrubs

35. Trees

36. test Dicot Herbs

37. Ferns

38. test L-E-P

39. Monocot herbs

40. Shrubs

41. Trees