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.