joint with amir bozorgmagham, phanindra tallapragada , binbin...

Atmospheric coherent structures and aerobiological invasions!

!

Shane Ross!!

Joint with Amir BozorgMagham, Phanindra Tallapragada, !Binbin Lin, A.J. Prussin, David Schmale!

!Virginia Tech, Department of Engineering Science and Mechanics!

Coastal flow

Invasive species riding the atmosphere!

Hurricane Ivan (2004) brought new crop disease (soybean rust) to U.S.

Red=infected US regions From Rio Cauca region of Colombia

2004

Coastal flow

Invasive species riding the atmosphere!

Hurricane Ivan (2004) brought new crop disease (soybean rust) to U.S.

Cost of invasive organisms is!>$100 billion per year in U.S.!

! !

From Rio Cauca region of Colombia

2004

Airborne pathogen

Plant pathogens linked to water cycle

What you grow affects local microclimate (Cindy Morris, INRA)

•  Spore producNon, release, escape from surface •  Long-‐range transport (Nme-‐scale hours to days) •  DeposiNon, infecNon efficiency, host suscepNbility

Atmospheric transport of microorganisms

e.g., Fusarium fungal spores

Schmale & Bergstrom [2003]!




Schmale & Bergstrom [2003], Trail et al. [2005]!




Schmale & Bergstrom [2003], Trail et al. [2005]!

spore ~100 µm



Aylor [1999]!



Prussin et al. [2014a,b] Plant Disease!

Plume follows changing wind direcNon Temporally varying source strength

Experimentally derived dispersal kernels



Aylor [1999]; Prussin et al [2014]!



Aylor [1999]!

DeposiNon paXerns can be patchy

Aerial sampling drones:!40 m – 400 m altitude!

(David Schmale’s group)!

Samples collected over 10-30 minute intervals at constant elevation (e.g., 400m) above ground level!

Collect spores, idenNfy species

Fluctuations in fungal spore concentration

0 10 20 30 40 50 60 70 80 90 1000

4

8

12

16

Sample number

Spore

conce

ntr

atio

n (

spore

s/m

3)

Concentration of Fusarium spores (number/m3) for samples from 100 flights conducted

between August 2006 and March 2010.

iii

A Nme series at a single sampling locaNon. Total spore concentraNon (all species).

Sources are unknown

If sources were known, could model plume

Sources are unknown We are sampling from many sources

Sources are unknown

We are sampling a superposiNon of plumes from various distant sources (e.g., diseased fields) Spores are airborne long enough to be mixed a bit by atmospheric transport structures

We are sampling from many sources

Sources are unknown



e.g., can imagine ‘invisible’ smoke plumes

Sources are unknown



A B

Sample here

C D


Sources are unknown




A

A

B B

Sample here

C C

D

D

Pop. structure

Time


0 10 20 30 40 50 60 70 80 90 1000

4

8

12

16

Sample number

Spore

conce

ntr

atio

n (

spore

s/m

3)



iii


0 10 20 30 40 50 60 70 80 90 1000

4

8

12

16

Sample number

Spore

conce

ntr

atio

n (

spore

s/m

3)



iii

1 2 3 4 5 6 7 8 9 10 110

5

10

15

20

25

30

35

40

Flights F147−F157

num

ber o

f spo

res

Fusarium spores collected, broken down by species (normalized to 15 minute sample flights)

F. lateritiumF. equiseti−likeF. oxysporumF. graminearumF. sporotrichioidesF. fujikuroiF. equisetiF. babinda−like

Considering all fungal spores: can break down by species later -‐>


0 10 20 30 40 50 60 70 80 90 1000

4

8

12

16

Sample number

Spore

conce

ntr

atio

n (

spore

s/m

3)



iii

Spore impact on Petri dishes is an inhomogenous Poisson process with slowly varying intensity

Lin et al. [2012] Aerobiologia!


0 10 20 30 40 50 60 70 80 90 1000

4

8

12

16

Sample number

Spore

conce

ntr

atio

n (

spore

s/m

3)



iii

Spore impact on Petri dishes is an inhomogenous Poisson process with slowly varying intensity

SomeNmes significant variability over short Nmescales, unexplained by Poisson staNsNcs

Lin et al. [2012] Aerobiologia!

Punctuated changes in fungal spore concentration

0 10 20 30 40 50 60 70 80 90 1000

4

8

12

16

Sample number

Sp

ore

co

nce

ntr

atio

n (

spo

res/

m3)

17:00 18:00 19:00 20:00 21:000

4

8

12

16

16 Sep 2006

00:00 12:00 00:00 12:00 00:00 12:000

4

8

12

30 Apr 2007 1 May 2007 2 May 2007



vi

A classic punctuated change

Time

Spore

conce

ntr

atio

n (

spore

s/m

3)

00:00 12:00 00:00 12:00 00:00 12:000

4

8

12

30 Apr 2007 1 May 2007 2 May 2007

Time series of concentration {(t0, C0), . . . , (tN�1, CN�1)}

vii

A classic punctuated change

Time

Spore

conce

ntr

atio

n (

spore

s/m

3)

00:00 12:00 00:00 12:00 00:00 12:000

4

8

12

30 Apr 2007 1 May 2007 2 May 2007

Time series of concentration {(t0, C0), . . . , (tN�1, CN�1)}

vii

Define punctuated change as low probability events (assuming Poisson process), e.g., changes which have probability < 1%

Coastal flow

Detected concentrationof Fusarium at sampling location

time

Punctuated changes: How to understand cloud edges?

Coastal flow

Detected concentrationof Fusarium at sampling location

time

Punctuated changes: How to understand cloud edges?

Sampling location

Coastal flow

LCS, repelling (orange) and attracting (blue)!

!Atmospheric Superhighway,!

a skeleton of large-scale horizontal transport!

!!

Relevant for large-scale spatiotemporal patterns!

of pollution but also !biological agents!

!!

orange = repelling LCSs, blue = attracting LCSs!

Atmospheric transport network!

Mesoscale to synopNc scale moNon •  Consider first 2D moNon, then fully 3D •  Quasi-‐2D moNon (isobaric) over Nmescales of interest, < 12-‐24 hrs, given by fungal spore viability

700 mb

800 mb

900 mb

Using wind fields from NOAA

Curtain-like partitions moving over landscape!

IdenNfy coherent structures •  Coherent atmospheric filaments or vorNces mix liXle with surroundings, analogous to ocean eddies

•  Temporarily isolated sub-‐systems

Volumes of differing spore composition !partitioned by LCS!

Our unmanned aerial vehicles (UAVs) are usually sampling one side or the other

Example: Filament bounded by repelling LCSB

(d)

(a) (b) (c)

100 km 100 km 100 km

Time

Spore

conce

ntr

atio

n (

spore

s/m

3)

00:00 12:00 00:00 12:00 00:00 12:000

4

8

12

30 Apr 2007 1 May 2007 2 May 2007

12:00 UTC 1 May 2007 15:00 UTC 1 May 2007 18:00 UTC 1 May 2007xxv

Filament with high pathogen values ‘sandwiched’ by LCS!


(d)

(a) (b) (c)

100 km 100 km 100 km

Time

Spore

conce

ntr

atio

n (

spore

s/m

3)

00:00 12:00 00:00 12:00 00:00 12:000

4

8

12

30 Apr 2007 1 May 2007 2 May 2007

12:00 UTC 1 May 2007 15:00 UTC 1 May 2007 18:00 UTC 1 May 2007xxv

(a)

(b)

(c)



Sampling

location

(d) (e) (f)

(a) (b) (c)

100 km 100 km 100 km

12:00 UTC 1 May 2007 15:00 UTC 1 May 2007 18:00 UTC 1 May 2007xxiv


•  Punctuated change was associated with a LCS passage >70% of the Nme

•  Airborne biological agent concentraNons can provide a proxy for measuring Lagrangian transport structure

Sampling on either side of a LCS

•  If seeking geographically diverse samples, sample on other side of a passing LCS


Back trajectories with aXracNng LCS

Red: sample :me: 1315 UTC Blue: sample :me: 1415 UTC

Red: sample :me: 1315 UTC Blue: sample :me: 1415 UTC Green: sample :me 1515 UTC

Back-‐trajectories shown Movie is showing Nme

backwards

ProbabilisNc source regions (considering sub-‐grid scale turbulence)

Sampling :mes: 1) 9:15 am (leG cloud), 2) 10:15 am (right cloud), Sept 29, 2010


•  Sampling point: Virginia Tech campus •  Sampling :mes: 8AM – 8AM, Sep 29 & 30, 2010 •  Integra:on :me: -‐ 24 h.

Backward trajectory of par:cles, :me delay = 1h

Sampling biological agents at a fixed locaNon

Sourceline of sampled points

Strain along this curve related to FTLE

made of iniNal points from sequenNally sampled pathlines

SequenNal release (or capture) from a fixed locaNon

Distance between the ini:al posi:on of the sampled par:cles

Sourceline of sampled points FTLE for T = -‐24 hr

Distance between adjacent points along sourceline

FTLE Nme series at one fixed locaNon

SequenNal release (or capture) from a fixed locaNon

ForecasNng atmospheric LCS Wind field errors are not small or localized in Nme

BozorgMagham, Ross, Schmale [2013] Physica D!

ForecasNng atmospheric LCS

Ensemble average Standard deviaNon

Using an ensemble forecasNng approach -‐ Global scale (low res.) model -‐ ensemble of 15 members

BozorgMagham, Ross [2013]!

ForecasNng atmospheric LCS

Can correctly forecast within 2 hours 60% of the Nme

ForecasNng an LCS passage Nme

BozorgMagham, Ross [2013]!

Turbulence and source regions PracNcal applicaNon: early warning systems LCS or other transport analysis methods could help inform farmers regarding possible zones of disease spread

Turbulence and source regions Likely dispersal pathways or persistent barriers

Kellogg, Griffin [2006]!

Lagrangian ‘bridges’ connecNng distant ecosystems

Aerobiology and the global transport of desert dust

NOAA simulation!

Final thoughts: coherence/transport in aerobiology •  Could provide insight to spaNotemporal data and models

•  Future: •  3D •  Can LCS inform beXer management pracNces (e.g., fungicide spraying?)

•  Map of global connecNvity •  Effect of climate change

transport layers

ground layer

The End

For papers, movies, etc., visit:www.shaneross.com

Main Papers:• BozorgMagham, Ross, Schmale [2013] Real-time prediction of atmospheric Lagrangian

coherent structures based on forecast data: An application and error analysis. PhysicaD 258, 47-60.

• Lin, BozorgMagham, Ross, Schmale [2013] Small fluctuations in the recovery of fusariaacross consecutive sampling intervals with unmanned aircraft 100 m above groundlevel. Aerobiologia 29(1), 45-54.

• BozorgMagham, Ross [2013] Atmospheric Lagrangian coherent structures consideringunresolved turbulence and forecast uncertainty, submitted.

• Prussin, Marr, Schmale, Ross [2013] Experimental validation of a long-distance trans-port model for plant pathogens: Application to Fusarium graminearum. Agriculturaland Forest Meteorology, accepted.

• Tallapragada, Ross, Schmale [2011] Lagrangian coherent structures are associated withfluctuations in airborne microbial populations. Chaos 21, 033122.

• Lekien & Ross [2010] The computation of finite-time Lyapunov exponents on unstruc-tured meshes and for non-Euclidean manifolds. Chaos 20, 017505.

xlv

Sponsors: NSF DEB-0919088!!NSF CMMI-1100263!! ! !!Thank You

Extra Slides

5 acres of winter wheat

Inoculum preparation

Field inoculation

Field experiments: tracking movement of plant pathogen from a controlled source

Dozens of samplers

Pathogen deposited decreases with distance

12 Jul 2003 13:57 AR AR192-PY41-08.tex AR192-PY41-08.sgm LaTeX2e(2002/01/18) P1: GJB/GCE10.1146/annurev.phyto.41.121902.102839

Annu. Rev. Phytopathol. 2003. 41:155–76doi: 10.1146/annurev.phyto.41.121902.102839

Copyright c� 2003 by Annual Reviews. All rights reservedFirst published online as a Review in Advance on April 16, 2003

THE THREAT OF PLANT PATHOGENS ASWEAPONS AGAINST U.S. CROPS

L.V.Madden1 andM. Wheelis21Department of Plant Pathology, Ohio State University, Wooster, Ohio 44691;email: [email protected] of Microbiology, University of California, Davis, California 95616;email: [email protected]

Key Words agricultural vulnerability, biological weapons, bioterrorism, cropbiosecurity, plant disease invasion, plant disease persistence and spread, risk analysis

■ Abstract The U.S. National Research Council (NRC) concluded in 2002 thatU.S. agriculture is vulnerable to attack and that the country has inadequate plans fordealing with agricultural bioterrorism. This article addresses the vulnerability of U.S.crops to attack from biological weapons by reviewing the costs and impact of plantdiseases on crops, pointing out the difficulty in preventing deliberate introduction ofpathogens and discovering new disease outbreaks quickly, and discussing why a plantpathogen might be chosen as a biological weapon. To put the threat into context, a briefhistorical review of anti-crop biological weapons programs is given. The argument ismade that the country can become much better prepared to counter bioterrorism bydeveloping a list of likely anti-crop threat agents, or categories of agents, that is basedon a formal risk analysis; making structural changes to the plant protection system,such as expanding diagnostic laboratories, networking the laboratories in a nationalsystem, and educating first responders; and by increasing our understanding of themolecular biology and epidemiology of threat agents, which could lead to improveddisease control, faster andmore sensitive diagnosticmethods, andpredictions of diseaseinvasion, persistence, and spread following pathogen introduction.

INTRODUCTION

Using [biological weapons] to attack livestock, crops, or ecosystems offersan adversary the means to wage a potentially subtle yet devastating form ofwarfare, one which would impact the political, social, and economic sectorsof a society. . .

Kadlec (42)

TheUnited States is vulnerable to bioterrorismdirected against agriculture . . .The nation has inadequate plans to deal with agricultural bioterrorism.

NRC report on Countering Agricultural Bioterrorism (60)

0066-4286/03/0901-0155$14.00 155

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Food supply concerns, bioterrorism!

Microbes ride in clouds, catalyze rain

Coastal flow

Yields autocorrelaNon Nmescale of about 6 – 9 hours >> 10 min sample intervals

Auto-‐correlaNon vs. Nme-‐lag

Can be interpreted as microbial ‘clouds’ with coherence scales of 20 -‐ 100 km

Future and ongoing work •  Airborne collecNons may encode recent atmospheric mixing events

1 2 3 4 5 6 7 8 9 10 110

5

10

15

20

25

30

35

40

Flights F147−F157

num

ber o

f spo

res

Fusarium spores collected, broken down by species (normalized to 15 minute sample flights)

F. lateritiumF. equiseti−likeF. oxysporumF. graminearumF. sporotrichioidesF. fujikuroiF. equisetiF. babinda−like

•  Consider geodesic theory to find KAM-‐like boundaries which can transport microbes

joint with amir bozorgmagham, phanindra tallapragada , binbin...

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