NASA Technical Reports Server (NTRS) 20100020816: Bird Migration Under Climate Change - A Mechanistic Approach Using Remote Sensing

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BIRD MIGRATION UNDER CLIMATE CHANGE—A MECHANISTIC
APPROACH USING REMOTE SENSING
James A. Smith
NASA Goddard Space Flight Center
James.A Sniith4 nasa4ov
Tim Blattner
University of Maryland Baltimore County
tbl att I ((^, urnbc edu
Peter Messmer
Tech-X Corporation
messioer((k! JXc_orp^c0111
I. INTRODUCTION
The broad-scale reductions and shifts that may be expected under climate change in the availability and quality of
stopover habitat for long-distance migrants is an area of increasing concern for conservation biologists [5].
Researchers generally have taken two broad approaches to the modeling of migration behaviour to understand the
impact of these changes on migratory bird populations. These include models based on causal processes and their
response to environmental stimulation, "mechanistic models", or models that primarily are based on observed
animal distribution patterns and the correlation of these patterns with environmental variables, i.e. "data driven"
models [1][6][7]. Investigators have applied the latter technique to forecast changes in migration patterns with
changes in the environment, for example, as might be expected under climate change, by forecasting bow the
underlying environmental data layers upon which the relationships are built will change over time. The learned
geostatstical correlations are then applied to the modified data layers.. However, this is problematic. Even if the
projections of how the underlying data layers will change are correct, it is not evident that the statistical
relationships will remain the same, i.e. that the animal organism may not adapt its' behaviour to the changing
conditions. Mechanistic models that explicitly take into account the physical, biological, and behaviour responses
of an organism as well as the underlying changes in the landscape offer an alternative to address these
shortcomings.
The availability of satellite remote sensing observations at multiple spatial and temporal scales [3], coupled with
advances in climate modeling and information technologies enable the application of the mechanistic models to
predict how continental bird migration patterns may change in response m environmental ohau9 e[8].|n
earlier work, we simulated the impact of effects of wetland kmx and inter-annual vodu6i6(y on the fimcxx of
migratory shorebirds io the central fly ways ofNorth America [7]. Y90 demonstrated the phenotypic plasticity of
u migratory population of Pectoral sandpipers consisting ofooensemble of 10,000 individual birds in response io
changes in stopover locations using an individual based migration model driven by remotely sensed land surface
duta, dimu|o data and biological field data.
With the advent o[ new computing capabilities enabled hy recent OPU-GY computing paradigms and commodity
6uu}won: [4], it now is possible tn simulate both larger ensemble populations and to incorporate more realistic
mechanistic factors into migration models. Here, we take our first steps use these tools N study the impact of
long-term drought variability on shorebird survival [9].
2. APPROACH
The structure of our modeling framework is shown in the attached figure. We simulate the migration trajectories
of individual 6iu] "particles", including their positions, velocities, and fuel om/ua using u apu6u/|y explicit,
biophysical flight model. We fly birds over auser-specified Lagrangian grid and run the model atu daily time
step to simulate spring or fall migration. The model b objec1'wrieoted, allowing the extension ur incorporation of
multiple species or moobuniszua. We use cvu|odouu/y programming techniques to train the nnndc/ [2]. In the
6uuro, we hope to include io{crucbog species (o capture potential phcno}ngioo| changes in movement patterns
between predator-prey species under environmental or climate change.
We have applied the zoodo| to population sizes of 10,000 birds, u land-use grid consisting of 100,00 grid cells
covering North America, and o daily time step. 8ovvcrcr, typical shorebird populations mu8u in the millions and
finer spatial zomoluinu would provide further insight in areas of bi&b variability under c|i/uwo or land use
rbxngcm. The evolutionary pn4paroruing technique also calls for multiple simulations of hiui ensemble
movements in order /o reach stability.
Several aspects of our individual base (agent modeling) approach lend themselves to GPU-GP computing [4]; for
example, the vector addition of bird particle and wind ,c|ooi6ca at each time step in the spatial grid. We are
exploring the appropriate olg,rduam to distribute the computational tasks hctvecu the CPU and 0yU
ozobitnctuzou. However, preliminary experiments suggests uuincrease iu the number of bird -pouic|cu'`simulated
bya factor of]O8o, more and increase b/ grid cell resolution 6y a factor oF|0vz more will 6cfeasible.
Simulate the migration routes, timi
and energy budgets of individual bilprgs
under dynamic weather and land
surface conditions
{ Iterate over: Population, daily time step
3.
To illustrate our approach we present several simulations of the migration patterns of shorebirds through the
central North American flywayS under varying periods of drought variability as experienced during the past 2UO0
years [9]. Analysis of the drought pm1zrum from the historical and geological record suggest that the droughts of
the i*cu|ie/h century were eclipsed movcoU times in the pue1 by periods of longer duration and greater mpo|iuJ
extent. These events of extreme variability likely will occur in the future and may be enhanced by anthropogenic-
inducedimcremaedugemofc|iuuuhcxbuuge.
4.
The roacxn6 described in this paper was supported under uNASA Interdisciplinary Science research proposal
entitled, "Forecasting the effects nf wetland loss and inter-annual variability oo the fitness of migratory bird
species" uud_ in part, by a NASA Applied Information Science Technology ympomJ um6Ucd ''A general
framework and system prototypes for eu}f-aduptive Earth predictive uyo|oms-Jynmnicu\ly coupling sensor with
Earth System mod6u.^ Experiments in the application of GPULib were supported, in part, by a SBIR Phase III
award k`Tech-X from NASA entitled, "Exploration of GPU Computing for Advanced Bird Migration Modeling."
Mr. Tim Blatt is a. graduate student in the Computer Science and Electrical Engineering Department at UMBC.
5. REFERENCES
[qA.n.Farmer and John*eins, "Optimal migration schedules depend on the landscape and the physical environment: «dynamic
modeling view,"z Avian xwlom, vol. 29, pp, 40o-4o.|em.
[z] David a. Fogel, "What im evolutionary mmpooaioa?,^/mEE Spectrum, vol, pp. za'sz,zwno.
[»] Christopher o.x. moimh. Compton J. Tucker, and John R.G. Trwnobcod. ^^mvrt ` American vegetation dynamics observed with multi-
resolution satellite data,~ Remote Sensing vy Environment, vol. //2,mp^/74o-/772,2vox.
[4] John o. Owens, uui.''m survey ormcoon/ -purpose computation mo graphics uumwmr`~ Computer Graphics h»rum. vol. z^.pp^uv'
)/s.zunr.
[5]s.u. Skagen, 'xmigomkmstopov*rsooucuom^murmiomormzoo-ureeuuucuouamu*a^ouo/rero.'`AmKvv|. 123, pp. 313-322, 2006.
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,^y~voxmm 2vov. vol. a.pp. /n/s'|o|4,zu-z4 Sept. zun4
[7] James A. Smith and /u| L. oomm. ^simoluxvu the effects of wetland loss and inter-annual "uriu^oxr on the fitness of migratory bird
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in Remote Sensing for Agriculture, Ecosystems, and Hydrology z. edited ur Christopher ax.o. Neale, Manfred Owe, Guido o'orm,
avcv/SrIE, vol. ?/ow.7/o4vz-ccc code: oz77-7u*zmu/$7o doi: 10. 1117/12.801243
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