Interfacing Vegetation Databases with ecological theory and practical analysis. Mike Austin, Margaret Cawsey and Andre Zerger CSIRO Sustainable Ecosystems Canberra Australia

Examples of Current Vegetation Databases Purpose:Vegetation classification –TurboVeg: Phytosociological relevees –Vegbank: General vegetation classification Purpose: Vegetation Analysis –Minimalist: minimum data set –Biograd: Regional prediction and mapping

Purpose and Product Statistical methods model Ecological theory model Data Measurement model Relational Database Geographic Information System (GIS)

Topics Interface between vegetation databases theory and analysis Interface between data and practical applications for conservation evaluation

Biograd Database Grew from minimalist database –Location, plot data, co-occurrence of canopy species, slope, aspect, elevation. –Current size plots. Used software packages and GIS to derive environmental variables –Temperature, rainfall, radiation, soil properties. Predicted potential vegetation from species environmental models

Application to Theory Pattern of Species Density in relation to climate.

Questions What is a suitable statistical method for species/environment modelling What environmental variables predict species density? What is their relative importance? Does their importance vary with mean annual temperature? What does this say about models of species density determinants? What are the Database requirements for this type of analysis?

Some Suggested Answers Statistical modelling using Generalized Additive Modelling (GAM) Predictors: use both climatic and local variables ( 7 variables used) Importance: GAM gives relative measure Hypothesis: Behaviour of tree species density differs above and below 12 º C :- split data.

Mean annual temperature Mean annual rainfall slope Mean annual temperature Mean annual rainfall slope >=12 degrees<12 degrees Species density responses to environmental predictors for two models 12 degrees

topography aspect >=12 degrees<12 degrees Species density responses to environmental predictors for two models 12 degrees topography aspect 4=gully1=ridge

lithology relative heat load >=12 degrees<12 degrees Species density responses to environmental predictors for two models 12 degrees relative heat load and lithology are not included in this model

>=12 degrees model<12 degrees model Relative contribution of environmental predictors

Purpose and Product Statistical methods model Ecological theory model Data Measurement model Relational Database Geographic Information System (GIS)

Application to conservation evaluation Problem of aggregating data into classes for inclusion in a data base How many soil types should be recognised? What are the implications for predicting species distribution?

Predicting Spatial Distribution of Acacia pendula Acacia pendula occurs on floodplain soils under low rainfall conditions (<600mm mean annual rainfall) in the Central Lachlan region of New South Wales, Australia. GAM models of 135 tree and shrub species including A. pendula were used to predict potential vegetation on cleared areas in the region.

Condobolin Tullamore Parkes Forbes Grenfell Cowra 147 º 148 º 150 º º -33 º º -34 º Selected study area The central Lachlan region Study area 1:100,000 mapsheet boundary NSW......

Relational Database Geographical Information Systems (GIS) data Digital Elevation Model (DEM) Data Collection and Management Survey Classification and Mapping Products Soil landscape data from manuals Environmental Stratification Survey Digital Terrain Models (DTM) Climatic attributes Soil landscapes Multivariate pattern analysis Statistical modelling of individual species Species Prediction Species Prediction Species Prediction Species Prediction Species Predictions Spatial allocation to vegetation communities Predicted Vegetation Vegetation plot data Plant species data Plot location & environmental data Plot vegetation data An integrated approach to vegetation mapping Drainage

Individual species predictions Mean Temperature Geology Topographic Position ArcView Grasp script Great Soil Group Soil Depth Soil pH Soil Fertility Temperature Seasonality Rainfall Seasonality Annual Mean Rainfall S-Plus Grasp Species Lookup Tables Plot Data Species Models Species Prediction Species Prediction Species Prediction Species Prediction Species Predictions

Spatial Prediction of Acacia pendula using original Great Soil Groups Masked mean annual rainfall > 568mm

Spatial Prediction of Acacia pendula using reaggregated Great Soil Groups Masked mean annual rainfall >568mm

Spatial Prediction of Acacia pendula Difference between model predictions

Conclusions Small changes in attribute classification can have a marked impact on outcomes Attributes in a database should be kept at as disaggregated a level as possible How cost-effective are databases where numerous attributes are kept which may not be used? Is this best done with “in-house” or commercial software

Predicted vegetation map for the central Lachlan region

Current remnant distribution of predicted vegetation communities

Remaining area for different communities (based on M305 mapping of woody vegetation) Red < 10 % remainingGreen > 30 % remaining

Purpose and Product Statistical methods model Ecological theory model Data Measurement model Relational Database Geographic Information System (GIS) Final

Vegetation plots in “good” condition (Good condition is defined as greater than 50% native plant cover in the lower vegetation layer)

Area and condition estimates for communities Red < 10 % in “modest” condition

COMMUNITY AS AN AREAL CONCEPT RECOGNITION OF COMMUNITIES DEPENDS ON THE FREQUENCY OF ENVIRONMENTAL COMBINATIONS IN THE LANDSCAPE

Topographic distribution of “communities” as indicated in previous slide Altered topographic distribution of “communities” with the lowest bench at 170m and the highest bench at 430m Frequency of species co-occurrences as a function of landscape