Ferrier_Combining ALA data with macroecological modelling to inform national-scale conservation assessment

Ferrier_Combining ALA data with macroecological modelling to inform national-scale conservation assessment


Atlas of Living Australia

August 05, 2013


  1. Combining ALA data with macroecological modelling to inform national-scale conservation

    assessment Simon Ferrier, Kristen Williams and Tom Harwood ALA Science Symposium 12 June 2013 CSIRO ECOSYSTEM SCIENCES
  2. Multiple biodiversity policy & planning demands ...

  3. ... requiring multiple scales & modes of assessment addressing multiple

    dimensions & levels of biodiversity
  4. Biodiversity really is diverse, and poorly known MEA (2005) Bork

    et al (2006)
  5. Two major sources of information on the state of biodiversity,

    with complementary strengths • direct detection of structure, function and composition • but sparse, and uneven, spatial coverage In situ (field based) observation Remote sensing • complete spatial coverage • good detection of ecosystem structure & function, but not of biodiversity composition at species/gene level
  6. Therefore a strong emphasis on integrating these through various forms

    of modelling Remotely observed change in ecosystem state / intactness Projected pressures & responses: climate, land-use change etc Remotely mapped base environmental variables Modelling spatial pattern in the distribution of biodiversity Inferring change in the status of biodiversity (past to present) Projecting biodiversity persistence under alternative scenarios Biological collection, observation & survey data In situ (field based) monitoring Evaluating potential policy & management interventions Model evaluation & calibration Assessment of other environmental economic & social values
  7. Spectrum of distributional modelling strategies Ferrier & Guisan (2006) Journal

    of Applied Ecology Individual species distribution (niche) modelling Simultaneous multi-response modelling of multiple species “Assemble first, predict later” techniques Macroecological modelling of collective biodiversity properties (richness, compositional turnover etc) • interested in individual species of particular concern • reasonable number of records per species • interested in biodiversity as a whole • huge number of species, each with few (or no) records “Predict first, assemble later” techniques
  8. Remotely derived environmental variables: climate, terrain, soils, geographic isolation etc

    77,000 records of 2,700 land-snail species Spatial pattern in compositional turnover Funded by Aust. Dept of Sustainability, Environment, Water, Population & Communities Generalised dissimilarity modelling (GDM) Modelling spatial turnover in biodiversity composition using generalised dissimilarity modelling etc ... etc ...
  9. Initial application to National-scale conservation assessment ...

  10. ... then incorporated consideration of potential implications of climate change

    ... Representativeness of reserve system (2070 A1B scenario) Potential change in plant community composition (2030 A1FI scenario)
  11. ... also at State scale

  12. Inferring biodiversity change from remotely-sensed change in habitat condition (BOM-NPEI,

    ALA, CSIRO collaboration)
  13. Extensive use of ALA data in recent analysis of potential

    climate-change refugia for biodiversity > 2.2 million location records of > 35,000 species of plants, vertebrates and invertebrates
  14. Extensive use of ALA data in recent analysis of potential

    climate-change refugia for biodiversity
  15. Reliance on new generation of fine-scaled environmental variables & high-performance

    computing Tmaxadj Tmaxmonth Tminmonth PTmonth 0.5 GCM change grids (1990-Future) ANUCLIM 6.1 TerraFormer Tmaxmonth Tminmonth PTmonth 9s future climate 9s ctiPAWHC 0.05 PAWHC 9s Budyko correction 9s DEM 9s kRs r.Sun GRASS GIS 9s Smonth Tmin RSadj PT EPadj EAadj Minimum Temperature TNM: Annual Mean TNX: Annual Max TNI: Annual Min TNRX: Max monthly change TNRI: Min monthly change Maximum Temperature TXM: Annual Mean TXX: Annual Max TXI: Annual Min TXRX: Max monthly change TXRI: Min monthly change Shortwave radiation RSM: Annual Mean RSX: Annual Max RSI: Annual Min RSRX: Max monthly change RSRI: Min monthly change Precipitation PTS: Annual Total PTX: Annual Max PTI: Annual Min PTRX: Max monthly change PTRI: Min monthly change PTS1: Seasonality summer/winter PTS2: Seasonality spring/autumn Potential Evaporation EPA: Annual Total EPX: Annual Max EPI: Annual Min EPRX: Max monthly change EPRI: Min monthly change Actual Evaporation EAAs: Annual Total (simulated) EAA: Annual Total (topocorrected EAA) WDadj Water Deficit WDA: Annual Total WDX: Annual Max WDI: Annual Min WDRX: Max monthly change WDRI: Min monthly change Temperature Range TRA: Annual Range TRX: Annual Max TRI: Annual Min 9s Terrain Adjusted Climate Projected evapotranspiration GCM GFDL-ESM2 for 2085 RCP8.5
  16. These modelling approaches integrate data & techniques across a wide

    range of scientific disciplines Biodiversity informatics Phylogeography Environmental metagenomics Remote sensing Terrain, climate, soil mapping / modelling Global-change scenario modelling Structured decision making Eco-informatics Assessment of other environmental, social & economic values High-performance computing Mathematical & statistical modelling Population & community ecology Long-term ecological monitoring
  17. Rosauer D, Ferrier S, Williams K, Manion G, Keogh J,

    Laffan S (in press) Ecography Example – incorporating phylogenetic relationships into macroecological modelling
  18. Mokany, K et al (2012) Global Change Biology 18: 3149-3159

    b d Environment Space a b a b d c Example – dynamic macroecological modelling of metacommunity persistence
  19. Example – using paleo-ecological data to test predictions from models

  20. Ferrier et al (2004) BioScience Global application of approaches developed

    and trialled within Australia
  21. Recent global-scale initiatives & activities are opening up new opportunities

  22. A recent proof-of-concept example – based on modelling of all

    GBIF data for ferns (>1.3 million records for >10,000 species) Land-use change (IMAGE) Climate change (IPCC etc) Fern species records (GBIF) Base environment (WorldClim etc) Modelled retention of compositional diversity
  23. Towards a stronger foundation to inform biodiversity policy development, assessment

    and planning
  24. Thank you CSIRO Ecosystem Sciences Simon Ferrier t +61 2

    62464191 e simon.ferrier@csiro.au w www.csiro.au CSIRO ECOSYSTEM SCIENCES