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TERN: Data infrastructure that enables fire management

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TERN's Associate Professor Nikki Thurgate's presentation at Bushfire16 on TERN's data infrastructure that enabling fire research and management.

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TERN: Data infrastructure that enables fire management

  1. 1. TERN: Data infrastructure that enables fire management Nikki Thurgate, S. Guru and T. Clancy
  2. 2. TERN: • Infrastructure and networks to support a coordinated, collaborative ecosystem science community • Enabling sustained, long-term collection, storage, synthesis and sharing of ecosystem data • Connecting science with policy and management TERN: Australia’s ecosystem observatory delivering data streams to enable environmental research and management
  3. 3. Instruments + Sensors Policy + Management Analysis + Synthesis Modelling Data Searching Data Sharing Data Curation + Publishing Data Storage Processing + Analysis Collection Methods
  4. 4. TERN FIRE DATA- TASMANIA 2016- COMBINATION OF METHODS ALLOWS IMPROVED BUSHFIRE PREDICTION MODELS Connecting science
  5. 5. TERN Fire data- Repeated fuel reduction burns in temperate forests, like this one in southeast Australia, have little long-term impact on soil greenhouse gas exchange
  6. 6. Fire Management Pre-processed MODIS fire burnt area satellite imagery Vegetation Map and Expert elicitation
  7. 7. Thank you Carl Gosper, Suzanne Prober and Colin Yates t (08) 9333 6442 e carl.gosper@csiro.au CSIRO ECOSYSTEM SCIENCES AND DEPARTMENT OF ENVIRONMENT & CONSERVATION Further information: Parsons & Gosper (2011) International Journal of Wildland Fire 20, 184-194 Prober et al. (2012) Climatic Change 110, 227-248 Gosper et al. (in press) Australian Journal of Botany doi: 10.1071/BT12212
  8. 8. Age-structure in GWW woodlands Age class (years since fire) Percentageofwoodlandarea 0 20 40 60 80 Satellite image analysis 0-60 61+ • Understanding the age structure of GWW woodlands could provide clues as to whether recent levels of woodland fire are unprecedented • To provide a crude assessment of the age structure of woodlands across the GWW, we extrapolated the results from gimlet woodlands and landsat fire mapping to the regional scale, by assuming that the distribution in age classes older than 60 years is proportional to random samples from E. salubris woodlands Area mapped for fire age GWW boundary
  9. 9. Square-root (years since fire) 0 5 10 15 20 25 ShannonDiversity 1.0 1.5 2.0 2.5 3.0 3.5 4.0 y = 3.313 - 0.194x + 0.011x2 Adj. r2 = 0.304, F2,69 = 16.5, P < 0.0001 •globally rare ‘U’-shaped relationship between diversity and time since fire is likely to be driven by dominant trees and shrubs having maximum competitive influence at intermediate times since fire Results (1): Young Mature Intermediate •graph shows linear model for stand age
  10. 10. Management implications • As diversity was highest in mature woodlands, there is no support for gimlet woodlands requiring recurrent fire to maintain plant diversity, at least within 400+ year timeframes • Intense stand-replacing fires at intervals of < 200 yrs would have adverse implications for biodiversity conservation. Species diversity would not increase to the community maximum Multi-century changes in plant diversity
  11. 11. International Partners TERN is supported by the Australian Government through the National Collaborative Research Infrastructure Strategy
  12. 12. www.tern.org.au More information?

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