chemical plumes
DESCRIPTION
Physics of small organisms in fluids. Chemical plumes. What happens to detritus ?. Fecal pellets Marine snow. Sinking through water column. Remineralization. Marine snow aggregates. How fast Where To what extent. Recycling of nutrients. Sequestering of carbon. …. 5 mm. - PowerPoint PPT PresentationTRANSCRIPT
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
Visser & Jackson 2004
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
Visser & Jackson 2004
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)
Maar, Visser, Nielsen, Stips & Saito. accepted
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
Maar, Visser, Nielsen, Stips & Saito. accepted
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