| Synthesizing information for the environmental sciences |
| Example: the climate change puzzle |
| Models of current climate |
| Models of future climate |
| Models of future climate |
| Models of response |
| Models of response |
| Source of immigrants? |
| Source of immigrants? |
| Biodiversity responds to spatial heterogeneity |
| Seed has limited production and dispersal |
| What do ÔprocessÕ data say? |
| Residents vs potential invaders |
| Seedling germination |
| Seedling growth rates |
| Seedling survival |
| Process data climate correlations |
| Challenges |
| Outline |
| Outline |
| Prediction framework |
| Outline |
| Our approach |
| Long term, heterogeneous data |
| Heterogeneous data |
| Simulator for analysis/prediction |
| Missing the connections |
| Hierarchical Bayes model |
| Light availability: ground to remote data |
| Ground measurements |
| Remote sensing |
| Solar geometry models |
| Estimates combine three data types |
| What makes this possible? |
| Inference |
| Inference |
| Inference |
| Inference |
| Estimates of hidden states |
| Sources of variability/uncertainty in fecundity |
| Inference |
| Mortality risk with size, growth rate |
| Allocation: among individuals |
| Many estimates |
| Slide 45 |
| Slide 46 |
| Prediction framework |
| The implications: Scalable Landscape Inference & Prediction (SLIP) |
| SLIP Light Model |
| Detail where it matters |
| SLIP Dispersal Algorithm |
| Forward simulation: individuals respond to landscape influences |
| Competitive exclusion |
| with random effects |
| The advantages of multilevel modeling |
| The challenges |
| Collaborators |