chemical plumes

Marine snow aggregates

Fecal pelletsMarine snow

Remineralization

How fastWhereTo what extent

Sinking through water column

Recycling of nutrients

5 mm

Photo: Alice Alldredge

Sequestering of carbon

…

What happens to detritus ?

Rich resource

BacteriaCiliatesDinoflagellatesCopepodsLarval fish

Plume of released solutes

colonizers

visitors

gulp

Photo: Alice Alldredge

What mechanisms bring about contact?

Organisms associated with detritus

First demonstration:

The shrimp Segestes acetes following an amino acid trail

generated by a sinkingwad of cotton that was soaked in a solution of

fluorocein and dissolved amino acids.

Hamner & Hamner 1977

Following a chemical trail

Kiørboe 2001

Temora

Copepods detect and track chemical plume

02

CDCut

C

advection diffusion

D

uaPe

Physics of small organisms in a fluid: advection - diffusion

Pe < 1: diffusion dominates

Pe > 1: advection dominates

Heuristic

says nothing about flux

Re = 1 to 10

Pe ≈ 1000

Plume associated with marine snow

Mate tracking

Centropages typicus: pheromone trail

Espen Bagoien17 cm long: 30 sec old

The particle: Sinking rate (w, cm/s)Leakage rate (L, mol/s)

The organism: Detection ability – threshold (C* mol/cm3)Swimming speed (v, cm/s)

w

The medium: Turbulence ( cm2/s3, + ….)Diffusion (D, cm2/s)

*****

What are relevant plume charcteristics ?Approach: analytic and numerical modelling.

Physical parameters for plume encounter

Particle size dependent properties

Sinking rate: bw ar

Leakage rate: dL cr

Stokes' law

Empirical observationsMarine snow:

a = 0.13, b = 0.26Fecal pellets:

a = 2656, b = 2

22

9

gw r

Empirical observations(particle specific leakage rate & size dependent organic matter content)

c = 10-12, d = 1.5

Detection threshold

Typical free amino acid concentration: 3 10-11 mol cm-3

specific amino acid concentrations < than this

Copepod behavioural response (e.g. swarming): 10-11 mol cm-3

Copepod neural response: 10-12 mol cm-3

Species and compound specific

C* from 2 10-12 to 5 10-11 mol cm-3

Zero turbulence

w

*0 *4

LZ

DC

** 0

0 *4

Z LT

w DwC

Jackson & Kiørboe 2004

Length of the plume

Time for which plume element remains detectable

For marine snow r = 0.5 cm and detection threshold C* = 310-11 mol/cm3

Z0* = 100 cm

T0* = 900 sec

V0* = 2.5 cm3 (5particle)

0* = 16 cm2 (20 particle)

Turbulentshear event

Effect of turbulence on plume

Straining and Stretching:

Increases concentration gradients – molecular diffusion faster

Elongates plume lenght

Nonuniform: gaps along plume length

w w + v

Visser & Jackson 2004

Kinematic simulations: analytic expressions that generate turbulence like chaotic stirring

Easily done

Modelling turbulence

Direct numerical simulations: solve the Navier Stokes equations

Hugely expensive

Very accurate

Large eddy simulations: solve the Navier Stokes equations for a limited number of scales

Hugely expensiveRelatively accurate

wave number, k (2/ℓ)

ener

gy d

ensi

ty s

pect

rum

, E(k

) (L

3 /T

2 ) k2/2/L

inertial sub-range

E(k

)

k 5/3

viscous sub-range

k2/2/L

inertial sub-range

E(k

)

k 5/3

viscous sub-range

Governed by 2 parameters

viscosity

dissipation rate

2/3 2/31

3

9

5u C

Remember: Kolmogorov spectra theory

-10

-8

-6

-4

-2

0

2

4

6

8

10

-10

-8

-6

-4

-2

0

2

4

6

8

10

-10

-8

-6

-4

-2

0

2

4

6

8

10

-10

-8

-6

-4

-2

0

2

4

6

8

10

-10

-8-6

-4-2

02

46

810

-10

-8

-6

-4

-2

0

2

4

6

810

Synthetic turbulence simulations

k1 k2 kN

3/50)( kEkE

k2/2/L

inertial sub-range

E(k

)

k 5/3

viscous sub-range

k2/2/L

inertial sub-range

E(k

)

k 5/3

viscous sub-range

t

tt

nnnn

nn

N

nnn

xkkb

xkkaxu

sinˆ

cosˆ ),(1

3/50)( kEkE

3/53/1 kn

2 2 2 ( )n n n na b E k dk

Wave number, k, ranges from kmin to kmax

Assumed energy spectrum:

frequency:

Amplitude of Fouriercoefficients:

nk̂ Random unit vector in 3 D: nn k kk ˆ

nn ba , Random 3 D vectors of magnitude an and bn respectively

Fung, 1996. J Geophys Res

k2/2/L

inertial sub-range

E(k

)

k 5/3

viscous sub-range

k2/2/L

inertial sub-range

E(k

)

k 5/3

viscous sub-range

Synthetic turbulence simulations

Plume

Path of sinking particle

Particle tracking by Runge-Kutta integration

Simulation

Path of a neutrally plume tracer

Particle

C

*

C*

Plume concentration

Plume

Gaussian distribution of solute

ℓ

Plume construct: stretching and diffusing

2

1 2,

expsi i

i i j

Cs

,,

1,

i ji j

i j

s

stretching

i

d i i

i i

C

D D

1

2

2

2

24 4exp

diffusing

Mesopelagic (10-8 cm2/s3) Marine snow: r = 0.1 cm w = 0.07 cm/s (60 m/day)

Themocline (10-6 cm2/s3) Marine snow: r = 0.1 cm w = 0.07 cm/s (60 m/day)

Surface (weak) (10-4 cm2/s3) Marine snow: r = 0.1 cm w = 0.07 cm/s (60 m/day)

Marine snow: r = 0.1 cm w = 0.07 cm/s (60 m/day)

Surface (strong) (10-2 cm2/s3)

10 levels of turbulence

3 particle sizes each for marine snow and fecal pellets

4 replicates for each turbulence – size pairing

3 detection threshold

Model runs

Natural time scales:

turbulence: = ( / )1/2 or 1 / mean rate of strain

plume: T0* time scale for plume element to drop below threshold of

detection.

Metric scale:

nonturbulent values

Metrics of interestLength; cross-sectional area; degree of fragmentation

Marine snow

T0*

10-4 10-3 10-2 10-1 100 101 102 103 104 105 106

V* /

V0*

0.0

0.2

0.4

0.6

0.8

1.0

1.2

Total Volume

** 0

*01 0.25

VV

T

Fit: p < 0.0001

Symbols: different detection thresholdColour: different particle size

Rate of turbulent straining

Rate of diffusion


2/1

Marine snow

T0*

10-4 10-3 10-2 10-1 100 101 102 103 104 105 106

* /

0*

0.0

0.2

0.4

0.6

0.8

1.0

1.2

Total Cross section

** 0

*01 0.1 T

Fit: p < 0.0001


Marine snow

T0*

10-4 10-3 10-2 10-1 100 101 102 103 104 105 106

Z1* /

Z0*

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1st Segment Length (distance following plume)

** * 01 0 *

0

1 0.4

1 0.8

TZ Z

T

Fit: p < 0.0001

What can we use this for

Microsetella(harpacticoida)

Oncaea(cyclopoida)

0.7

mm

Oncaea borealis

Microsetella norvegica

Oncaea similis

5 mm

Oikopleura dioica

Fritillaria borealis

Appendicularia Copepods

Copepod encounter with appendicularian houses

uRCZ 2

Remember: Ballistic model variations

ubCZ )(

b

u

(m2 s-3)

10-10 10-9 10-8 10-7 10-6 10-5 10-4

Cro

ss s

ectio

n (c

m2)

0.01

0.1

1

10

10 m d-1

20 50100200

Maar, Visser, Nielsen, Stips & Saito. accepted

0

0

1.01 T

2/1

*0 4 DwC

LT

2/3

*2/10

24.0

C

L

Dw

26.0)cm(13.0)cm/s( aw

C* = 3 10-8 µM

L = 9 10-14 mol s-1

10-10 10-9 10-8 10-7 10-6 10-5 10-4

Cle

aran

ce r

ate

(cm

3 s

-1)

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

Dissipation rate (m2 s-3)


bv 2

v = 0.1 cm s-1

b = 100 µ

w = 10 m day-1

surface(above 20 m depth) =10-2 cm2/s3

= 1 s-1

below thermocline (below 30m depth) =10-7 cm2/s3

= 10-3 s-1

0.6 per day per copepod2.5 per day per appedicularian house

4.4 per day per copepod18 per day per appedicularian house

Chouse = 244 m-3 below 30 m

Ccopepod = 1000 m-3

Copepod encounter with appendicularian houses

10 m day-1

10% per day

50% per day

Microsetella norwegica

log10 surface dissipation rate (m2 s-3)

-8 -7 -6 -5 -4 -3

Dep

th o

f ce

ntre

of m

ass

(m)

0

20

40

60

80

Skagerrak springSkagerrak summerThe North Sea

r2=0.59p<0.01

r2=0.73p<0.05


Summary remarks

Despite complexity there seem to be global functions relating plume metrics in turbulent and non-turbulent flows.

About 50% of the detectable signal becomes disassociated from the particle in high turbulence.

Significant advantages can be had for chemosensitive organisms searching for detrital material in low turbulent zones (below the thermocline).

Aspects turbulence and its effects on mate finding still to be explored

Sensing ability

Find foodFind matesAvoid predators

Encounter rate is everything to plankton

How to

Relative motion

Turbulence

Encounter processes

Random walks link microscopic (individual) behaviour with macroscopic (population) phenomena

Random walk - diffusion

Ballistic - Diffusive

Scale of interactions

turbulence

Inge

stio

n ra

te

Encounter rate and turbulence: Dome - shape

Simple population models + chaotic stirring → complex spatial patterns

Patchiness

chemical plumes

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