Modeling Bird Migration in Changing Habitats

1. Modeling Bird Migration in Changing Habitats James A. Smith – NASA GSFC Jill L. Deppe – UMBC GEST

2. Outline • Where we’ve been -- Where we are now – Where we’re going • Some technical aspects of our work • How we’re going about it • Modeling • Calibration • Validation Approach • Phenotypic plasticity scenario • Wrap-up

3. Where We’ve Been Spherical Birds Myiarchus nasaii

4. Where We Are Now

5. Where We’re Going Use primarily a mechanistic approach to understand how migrating organism respond to changes in their environment – What are the impacts of resulting changes in the quality, location, and quantity of stopover habitat? What is the coupling between timing of migration and key environmental processes?

6. Technical challenge • Our animals are completely mobile and free to move anywhere At 10 min resolution, there are over 100,000 candidate stopover locations in NA (Previous research limited to few, fixed stopovers, e.g. ~ 50)

7. Scientific Aspect • We’re attacking the “en-route” migration problem Our organisms move through a changing spatio-temporal environment (Most research deals with spring migration, few address winter – we are model both)

8. Characteristics • Individual based biophysical model • Movement behavior and decision rules • Daily time step • Arbitrary geographic grid Simulate the migration routes, timing and energy budgets of individual birds under dynamic weather and land surface conditions (Driving variables from satellite and numerical weather prediction models)

9. Changing habitat Exploring two avenues • One is data driven using dynamic landscape layers derived from remote sensing and bird location records • Second is a functional “Jarvis” type approach similar to how people model stomatal resistance – again using satellite/climate models (Hybrid)

10. June April May Spring “Effective” Fuel Deposition Rate (FDR) Ecological niche modeling Max Ent

11. Functional approach “FDR” = Fopt * ƒ(NDVI) * f(soil moist) * … Scale between 0 and maximum observed in field

12. Evolutionary learning Initialize a population with random candidate solutions and then repeatedly expose the population to the environment, calculating fitness of survivors, reproducing and selecting individuals for the next population Reproduction Currently – Pseudo Maybe – Natural Later – Group Stopover behavior Endogenous direction

13. Evolutionary learning Fitness (50th percentile) Generations

14. “Consistent and Plausible” Bird banding data Survey data at stopovers Stable isototpe analysis Telemetry data Bird Hydrographs

15. Phenotypic Plasticity • Migrate birds through a non-limiting landscape Then • Fly them over more natural landscapes i) Without “relearning” ii) With ability to adapt their behavior

16. Dynamic Habitat • Monthly Landscape Features i) Maximum Number of Days with Ground Frost (10) ii) Effective “FDR” / Stop overs scaled to NDVI • North American Topographic Barrier (2000 meters --- mainly impact in Rocky Mountains) Average over dry years 1982-1988 Palmer Index

17. Non – Limiting Landscape

18. No Learning Adaptation Shifts in Pattern

19. No Learning Adaptation Change in Stop over Strategy

20. No Learning Adaptation Increase in Fitness Distribution

21. Wrap-Up • Modeling and simulation test bed coming along fine • Developing evidence of “plausibility and consistency” • Have linkages with an AIST GMU project (Liping Di) – pragmatics for linking models to satellite and data systems ( 540 met files = (3 dry years + 3 wet years ) x 90 days for wetland study

22. Publications M. Wikelski, R.W. Kays, N.J. Kasdin, K. Thorup, J.A. Smith and G.W. Swenson, Jr.. 2007. Going wild: what a global small-animal tracking system could do for experimental biologists. Journal of Experimental Biology 210: 181-186. J.A. Smith and J.L. Deppe. 2007. Simulating bird migration using satellites and Biophysics. Proc. Of the IASTED Symposium on Environmental Modeling and Simulation, 509:6-11. J.L. Deppe, K. Wessels, and J.A. Smith. 2007. Alaska at the crossroads of migration: Space-based ornithology. Alaska Park Science, 6:53-58. J.A. Smith and J.L. Deppe. 2008. Simulating the effects of wetland loss and inter-annual Variability of the fitness of migratory bird species. IEEE IGARSS J.A. Smith and J.L. Deppe. 2008. Space-based ornithology—studying bird migration and environmental change in North America. SPIE ERS08, Remote sensing for agriculture, ecosystems, and hydrology.