Modeling Bird Migration in Changing Habitats
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Transcript of Modeling Bird Migration in Changing Habitats
Modeling Bird Migration in Changing Habitats
James A. Smith – NASA GSFC Jill L. Deppe – UMBC GEST
Outline1. Where we’ve been -- Where we are now
– Where we’re going 2. Some technical aspects of our work3. How we’re going about it
a) Modelingb) Calibration c) Validation Approach
4. Phenotypic plasticity scenario 5. Wrap-up
Where We’ve Been
Myiarchus nasaii
Where We Are Now
Where We’re Going
Use primarily a mechanistic approach to understand how migrating organism respond to changes in their environment –
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 stopoverhabitat?
What is the coupling between timing of migration and key environmental processes?
Technical challenge
1. 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)
Scientific Aspect
2. 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)
Characteristics
• Individual based biophysical model • Movement behavior and decision rules • Daily time step• Arbitrary geographic gridSimulate 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)
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)
“Effective” Fuel Deposition Rate (FDR)
Ecological niche modeling
April
June
MaySpring
Max Ent
Functional approach“FDR” = Fopt * ƒ(NDVI) * f(soil moist) * …
Scale between 0 and maximum observed in field
Evolutionary learning
Currently – Pseudo Maybe – NaturalLater – Group
Initialize a population with random candidate solutions and then repeatedly expose the population to the environment, calculating fitness of survivors, reproducing and selectingindividuals for the next population
Stopover behavior Endogenous direction
Reproduction
Evolutionary learning
Generations
Fitness (50th percentile)
“Consistent and Plausible”
Bird banding dataSurvey data at stopoversStable isototpe analysis Telemetry data
Bird Hydrographs
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
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
Average over dry years
1982-1988
Palmer Index
Non – Limiting Landscape
No Learning Adaptation
Shifts in Pattern
No Learning Adaptation
Change in Stop over Strategy
No Learning Adaptation
Increase in Fitness Distribution
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
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-annualVariability 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.