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Components of plant species diversity in the New Zealand forest Jake Overton Landcare Research Hamilton Acknowledgements NVS data contributors and curators Simon Ferrier and Glenn Manion for development of GDM and collaboration on modelling 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 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 Biotic Data NVS recce (= recon) plots Almost 20000 plots 1220 species Presenceabsence of all vascular plant species in each plot Plots approx 20x20 m (sometimes unbounded) Environmental variables (spatial) Variable abbrev. Description Geographic position Geographic position of plot MAT Mean Annual Temperature Tseas A measure of cold stress, relative to mean annual temperature MAS Mean Annual Solar Radiation Deficit Vapor Pressure deficit VPD Vapor Pressure deficit Calcium Soil Calcium Age Soil age N Total Soil Nitrogen AcidP Available P Drain Soil Drainage Psize Soil Particle Size Indur Soil Induration Slope Topographic slope Discoast Distance to coastline Notho Nothofagus abundance from Leathwick What is Generalised Dissimilarity Modelling? alpha diversity (local richness) beta diversity gamma diversity ‘dissimilarity’ ‘turnover’ ‘complementarity’ Modelling of richness: richness = f (rainfall, temperature, veg type …) can be supplemented by modelling of compositional dissimilarity between locations: dissimilarity = f ( (rainfall, temperature, veg type …), geographical separation) Generalised dissimilarity modelling (GDM) ln 1 dij f k xki f k xkj p k 1 Compositional dissimilarity between pairs of survey sites Environmental & geographical separation Biotic Information Differing units and importance Environmental and Geog Space Ecological Space Same units scaled by importance All species model All species validation Results 1 ln 1 dij f k xki f k xkj p k 1 Unexplained component = 1 – proportion deviance explained Alpha diversity component = Proportion accounted for by local richness = Mean plot richness/ Total species pool Gamma Diversity component = Proportion deviance explained by geography Beta Diversity component = Deviance explained by environment Total species pool 1020 species All species All plant species Snails Group # spp local rich Ferns 147 6.8 Lianes-epiphytesparasites 49 1.9 Trees 107 8.9 Shrubs 255 4.3 Dicot herbs 360 2.4 Monocot herbs 282 1.3 All Species 1020 25.9 Results 1 All Shrubs Ferns Trees Monocot herbs Dicot Herbs All species Ferns Trees Shrubs Monocot herbs Dicot Herbs Predicted distributions of species Constrained environmental classification Biological survey data Generalised dissimilarity modelling Visualisation of spatial pattern in community composition Conservation assessment Environmental predictors Climate-change impact assessment Survey gap analysis Ferrier, S. et al (in press) Using generalised dissimilarity modelling to analyse and predict patterns of beta-diversity in regional biodiversity assessment. Diversity & Distributions Ferrier, S. et al (2004) Mapping more of terrestrial biodiversity for global conservation assessment. BioScience 54: 1101-1109 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. test Sparse sampling relative to grain of compositional turnover - huge number of species, each with very few (or no) records Geographical space (gamma diversity) Geographical space (gamma diversity) Dense sampling relative to grain of compositional turnover - relatively few species, each with many records Environmental space (beta diversity) Environmental space (beta diversity) An example from the arid rangelands of central Australia All species All species 400 600 800 Biological response 1000 1200 1400 Elev ationd Predicted Response 0.4 0.4 0.6 0.6 0.8 0.8 1.0 1.0 0.0 0.0 k 1 0.2 0.2 ln 1 dij f k xki f k xkj p f (Tc10d) Predicted Response Predicted Response Bray-Curtis compositional dissimilarity between all pairs of 248 field survey sites (based on perennial woody plant species) Environmental predictors 262 500 1000 2641500 2000266 2500 268 3000 Eacasp26d 0.0 0.2 0.4 0.6 0.8 Predicted Response 1.0 Tc10d f (Wetness) Predicted Response •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 0 2000 4000 6000 8000 Wetness 10000 12000 What is Generalised Dissimilarity Modelling? 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. All species Ferns Trees Shrubs Monocot herbs Dicot Herbs Ferns Ferns L-E-P test Monocot herbs Monocot herbs Shrubs Shrubs Trees test Dicot Herbs Ferns L-E-P test Monocot herbs Shrubs Trees Trees
