Page 1 Model interoperations: Community models, models as services, and model webs NASA Biodiversity and Ecological Forecasting Team Meeting New York 8 May 2009 Gary Geller Jet Propulsion Laboratory California Institute of Technology (c) 2009 California Institute of Technology. Government sponsorship acknowledged. National Aeronautics and Space Administration

GEO Biodiversity Observation Network  Global network… Of interoperating biodiversity observation systems  Collect, manage, analyze, share data on status of the world’s biodiversity Scholes et al., Science 321: 22 August 2008, Toward a Global Biodiversity Observing System Page 2

Focus is monitoring  Ecosystems  Species  Genes  Ecosystem services Page 3

Primary tasks 1.Integrate existing observation systems 2.Coordinate sampling efforts 3.Fill in sampling gaps 4.Integrate and add analytical/visualization tools 5.Combine remote sensing and in situ data Page 4

 Concept is all-inclusive  Very ambitious  Organic and opportunistic  Long-term  Community-driven and coordinated Page 5 Self assessment

 Concepts published  Implementation approach articulated  Detailed implementation planning underway Ecosystems Species Genes Terrestrial Marine Aquatic Page 6 Status Sound interesting?

Page 7 Model interoperations: Community models, models as services, and model webs NASA Biodiversity and Ecological Forecasting Team Meeting New York 8 May 2009 Gary Geller Jet Propulsion Laboratory California Institute of Technology (c) 2009 California Institute of Technology. Government sponsorship acknowledged. National Aeronautics and Space Administration

Page 8 Overview  Ecological questions  Greater model integration Community modeling Models as services Model Web

Page 9 General questions 1.What will change? 2.What will be the consequences of those changes?

Page 10 Specific examples  USFWS: Should pika be listed as threatened / endangered?  BCC: What are the ecological and social implications of complete “build-out” in Boulder according to the existing regional zoning plan?  USNPS: Where should we burn? Will we violate AQ standards in the Central Valley?  NGOs: What areas in Asia should be the focus of conservation efforts to save the tiger and its habitat?  USGEO: What are the consequences of alternative land management practices on biological diversity in the context of climate change?  NEON: How do changes in the availability and distribution of the nation’s water affect ecological systems?  DS: How will the boreal forest shift as temperature and precipitation change at high latitudes? What will be the impacts on animal migration patterns and invasive species? How will social and economic factors change forest cover of Peruvian Amazon by 2020? What effect will various management options have on the critical values of my park? What do we need to do to restore the landscape of an overgrazed African ecosystem? How will climate change and socioeconomic factors affect infection rates of vector-borne diseases? How will increased development and forest fragmentation effect the abundance of a forest-dependent bird species? What areas in Burma should be targeted to protect the critically endangered Gurney's Pitta? How much will sea level rise? How will water availability be affected, by annual and seasonal measures? Do we need to build more reservoirs? How will urban temperatures in SF be changed? How will fire risk and intensity be affected? How will snowfall be affected? How will flood risk be affected?

Page 11 How many of these questions can be easily answered, or explored, now? Can a resource manager easily get information on these topics?

Page 12 Limiting factors  Science and observations  Isolated models— limited interoperability  Limited sharing  Limited access Cultural barriers are much more limiting than technical ones

Page 13 “The need for community modeling”  More sharing / improved access  Less reinvention  Facilitates new science  Enhances integrated management and decision support Adapted from: J Famiglietti et al CUAHSI’s efforts towards a Community Hydrologic Modeling Platform (CHyMP) in the USA

Page 14 Should we encourage more community ecological models?

Page 15 Models as services  Service: provision of a product upon request  Service Oriented Architecture Consumer Service Provider Service request Information

Page 16 Models as (web) services  Data provider: Standard method for data sharing Well-known technology  Data consumer: Improved access To people To other models Not for all models

Page 17 Models as services GCMs Physical oceanography Anchovy population Biochemical / primary producers Bluefin tuna population

Page 18 Models as services GCMs Physical oceanography Anchovy population Biochemical / primary producers Bluefin tuna population Model web

Page 19 Benefits  More sharing / improved access  Less reinvention  Facilitates new science  Enhances integrated management and decision support  Greater modeler interaction  More users  more feedback  faster improvement Web 2.0

Page 20 Web 2.0  Web 2.0 Collaboration Communities Interactivity, feedback “Collective intelligence”  Web 3.0? Semantic Web  Model Web ~Community models

Page 21 Summary  Need more model interoperability  Community modeling  Models as services  Room for Web 2.0  Longer term: model web?  Think big, start small, start soon Created with Wordle