1. Summary of Presentation and Participation Breakout Group Predictor Variables (climate, Landuse) Layers: Biodiversity PublicAction Motivation Partners

2. Layers: Biodiversity 1. Good species 2. Bad species 3. Richness/texture 4.Some measure of endemism 5.Process/abundance Issues/challenges: Best ways to link display point data Composition Sheer number of variables (species)

3. Predictor Variables (climate, Landuse) Layers: Biodiversity Build the predictor layers now -- stick out your neck and predict where things should be There must be a temporal component! Short-term failure is assumed/expected Engage other operational agencies that have the mandate to do forecasting: NOAA, NMFS, USDA, ….

4. Predictor Variables (climate, Landuse) Layers: Biodiversity

5. Predictor Variables (climate, Landuse) Layers: Biodiversity Public Feedback loops could involve validation/corroboration of ecological forecasts Expectation of error, but emphasis on iterative improvement Considerations of quality sample effort, timestamps, statistical filtering, etc.

6. Predictor Variables (climate, Landuse) Layers: Biodiversity Public Methods: iPhone apps: EOL app (next slide) Weather stations analogy YouTube, Flickr, Facebook Virtual tour?

7. Predictor Variables (climate, Landuse) Layers: Biodiversity Public

8. Predictor Variables (climate, Landuse) Layers: Biodiversity Public Immediate feedback Make people feel successful Hit people where they live More interesting things than just maps Make maps simple, accessible (MaxEnt) Tap into social imperative of humans/ community forming Tap into competitive nature Motivation

9. Predictor Variables (climate, Landuse) Layers: Biodiversity Public Brainstorming: -- on-the-fly IDs -- personalized biodiversity maps show you where you need to go to get more birds… -- relate to time-series at your location -- link with other similar phenomena, areas around the world Motivation

10. Predictor Variables (climate, Landuse) Layers: Biodiversity Public Motivation Partners Partners: Google (google summer of code), Apple, Ecotourist, Lonely Planet, businesses -- groups that know how to interact with the world

11. Predictor Variables (climate, Landuse) Layers: Biodiversity Public Characteristics of methods: Peterson-guide model: focus on what is necessary NPS -interpreter paradigm: hook, bring them in Portable, accessible -- check out the PDA at the hotel like you check out the recorded tour at the museum Possibility of new approaches with digital information theory (vs. typical dichotomous key) “Visual shazam” -- face recognition Motivation

12. Predictor Variables (climate, Landuse) Layers: Biodiversity PublicAction Motivation Partners Potential to influence, change the processes Example: Dave Skole: carbon2market

13. Extras..

14. What’s in it for me?Engage What do to? the web pages Outcome Implement Partners: ecotourist, NPS, industry (apple?), Lonely planet PDA guides? Youtube, flickr Get them where they live -- they’re city dwellers. Audience Competition with other interesting things Relevance Why do I care?

16. Summary of Presentation and Participation Breakout Group What’s in it for me?Engage What do to? Web pages Outcome Implement