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Interfacing Vegetation Databases with ecological theory and practical analysis.

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

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Interfacing Vegetation Databases with ecological theory and practical analysis.

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

  2. 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

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

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

  5. Biograd Database • Grew from minimalist database • Location, plot data, co-occurrence of canopy species, slope, aspect, elevation. • Current size 10027 plots. • Used software packages and GIS to derive environmental variables • Temperature, rainfall, radiation, soil properties. • Predicted potential vegetation from species environmental models

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

  7. 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?

  8. 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 12ºC :- split data.

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

  10. Species density responses to environmental predictors for two models <12 and >12 degrees <12 degrees >=12 degrees topography topography aspect aspect 1=ridge 4=gully

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

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

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

  14. 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?

  15. Predicting Spatial Distribution of Acacia pendula • Acacia pendula occurs onfloodplain 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.

  16. 147 º 148 º 150 º -32.5 º -33 º -33.5 º -34 º Tullamore NSW Condobolin Parkes Forbes Study area 1:100,000 mapsheet boundary Cowra Grenfell The central Lachlan region . . . . . . Selected study area

  17. An integrated approach to vegetation mapping Multivariatepattern analysis Species Prediction Species Prediction Species Prediction Species Prediction Species Predictions Data Collectionand Management Survey Classificationand Mapping Products Relational Database Plot location &environmentaldata Plotvegetationdata Statisticalmodelling ofindividual species Vegetationplot data Soil landscapedata frommanuals Plant speciesdata Survey Spatial allocation tovegetation communities Geographical InformationSystems (GIS) data EnvironmentalStratification DigitalElevationModel (DEM) Climaticattributes PredictedVegetation Soil landscapes DigitalTerrainModels (DTM) Drainage

  18. Species LookupTables Species LookupTables Species LookupTables Species LookupTables Species LookupTables Species Prediction Species Prediction Species Prediction Species Prediction Species Predictions Individual species predictions MeanTemperature Plot Data Species Models TemperatureSeasonality Annual Mean Rainfall S-Plus Grasp RainfallSeasonality ArcView Grasp script Topographic Position Geology Great Soil Group Soil Depth Soil pH Soil Fertility

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

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

  21. Spatial Prediction of Acacia pendula Difference between model predictions

  22. 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

  23. Predicted vegetation map for the central Lachlan region

  24. Current remnant distribution of predicted vegetation communities

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

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

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

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

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

  30. Frequency of species co-occurrences as a function of 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

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