behavior, physiology and the niche of marine...

Behavior, physiology and the niche of marine phytoplankton

Ocean Tracking Network

John J. Cullen !

Department of Oceanography, Dalhousie University Halifax, Nova Scotia, Canada B3H 4R2

!2014 C-MORE Summer Training Course

“Microbial Oceanography: Genomes to Biomes”

John Cullen: C-MORE 2014

A principal goal of microbial oceanography Describing and explaining the

distributions and activities of marine microbes

marine.rutgers.edu/opp/

John Cullen: C-MORE 2014marine.rutgers.edu/opp/

…and using this information to describe the causes and consequences of variations in key biogeochemical processes


Key biogeochemical processes

• Primary production!• Nitrogen fixation!• Denitrification!• Trace gas production!• …and the many other processes that

make those processes possible


This can be achieved only through an integrated approach

Rothstein et al., Oceanography Mag.

The role of the oceans in Earth systems ecology, and the effects of climate variability on the ocean and its ecosystems, can be understood only by observing, describing, and ultimately predicting the state of the ocean as a physically forced ecological and biogeochemical system.


Models integrate information

Mick Follows


So do you


Physiological Ecology Including Gene Expression

Ocean Observing Systems

Genomics & the other ‘omics

Theory / ModelsProcess Studies

Hydrological & Atmospheric

Optics [+Acoustics]

Physical Chemical

& Biological Oceanography

Assimilative

biogeochemical m

odelsInte

grat

ed in

terd

isci

plin

ary

sens

or s

yste

ms

One Ultimate Target


Biological oceanography and phytoplankton ecology

marine.rutgers.edu/opp/

Describe the causes and consequences of variations in primary productivity

(and food web structure)


An overview of established approaches to marine modelling

Prognostic Model

Diagnostic Model

Surface Chl!mg m-2

Fennell et al. 2006


Approach #1: Observation, analysis, inference (empirical, diagnostic models)

Behrenfeld and Falkowski 1997b L&O

Modeling the pattern in the measurements — not necessarily primary productivity!


Result: Quantitative predictions that are as good as the data & assumptions that go into them


Approach #2: Observation, analysis, inference (qualitative, mechanistic, predictive models)

Arrigo, Nature (2005)


The testing of qualitative, mechanistic, predictive models may be messy

Arrigo, Nature (2005)


But predictions can be tested with appropriate observations

Arrigo, Nature (2005)Riser and Johnson Nature 2008


Diagnostic models can be used to test hypotheses rather than to consolidate and interpret observations


Approach #3: Prognostic models(quantitative, mechanistic, predictive models) -- e.g., BOCGMs

Doney, S. C., M. R. Abbott, J. J. Cullen, D. M. Karl, and L. Rothstein. 2004. From genes to ecosystems: the ocean’s new frontier. Frontiers in Ecology and the Environment 2: 457-466.


Complexity is added to increase realism and to test hypotheses

Doney, S. C., M. R. Abbott, J. J. Cullen, D. M. Karl, and L. Rothstein. 2004. From genes to ecosystems: the ocean’s new frontier. Frontiers in Ecology and the Environment 2: 457-466.


Conventionally tested by the “LPG” criterion — but that is changing

Follows, M. J., S. Dutkiewicz, S. Grant, and S. W. Chisholm. 2007. Emergent biogeography of microbial communities in a model ocean. Science 315: 1843-1846.

Looks Pretty Good!


e.g., Taylor diagrams


Fundamentally, ecosystem models should predict population dynamics

Daughter Cell

DaughterCell

!Accumulate (Bloom) !Be eaten !Blow up (viral lysis) !Sink !Die (e.g., apoptosis)

Single Cell

Growth Loss

A bit weird, because “population” refers!to species, and many species are often lumped


Biogeochemical models must include functional groups


Critical to know the Environmental Influences on the Growth of Phytoplankton: TemperatureLightDaylength Nutrients

Growth vs Temperature

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5 10 15 20 25 30

Alexandrium ostenfeldii

Gro

wth

rate

(d-1

)

Temmperature

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0.04

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0.16

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0.24

0.28

0 500 1000 1500

Growth vs Irradiance

Gro

wth

Rat

e (d

-1)

Irradiance (µmol m- 2 s- 1)

0.00

0.05

0.10

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0.20

0.25

0 2 4 6 8 10 12

Nutrient Uptake Kinetics

Spec

ific U

ptak

e Ra

te (d

-1)

Nitrate Concentration (µM)

0.00

0.04

0.08

0.12

0.16

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0.24

0.28

5 1 0 1 5 2 0 2 5 3 0 3 5 4 0 4 5

Effect of Salinity

Gro

wth

Rat

e (d

-1)

Salinity

Jensen and MoestrupA. ostenfeldii


Figuring out what to do with exploding knowledge of biological complexity in the ocean

Challenge:

Venter et al. Science 2004


A big part of the answer is here


Many other things define the growth-niche of marine phytoplankton

From Margalef et al. 1979 in Cullen et al. Oceanography Mag. 2007

John Cullen: C-MORE 2014http://jaffeweb.ucsd.edu/pages/celeste/copepods.html

And cell division is only half of the battle!


Reduction of loss can be as good as an increase

of growth rate


So rapid growth is not the only strategy for survival / selection

0.0

0.5

1.0

1.5

2.0

2.5

0 2 4 6 8 10 12

Nutrient Uptake

Spec

ific

Rat

e of

Upt

ake

(d-1)

Nutrient Concentration (µM)

I

IIV

max = 2.25 d-1

Ks = 2.0 µM

Vmax

= 1.5 d-1

Ks = 0.5 µM

www.andyslocum.com/images/tortoise&hare.jpg

It’s the gene, stupid!


Same framework

From Margalef et al. 1979 in Cullen et al. The Sea 2002


Top-down or bottom-up control?

Sverdrup’s (1955) map of productivity based on vertical

convection, upwelling and turbulent diffusion

Global productivity estimated from remote sensing

(Falkowski et al. 1998)

As presented by John McGowan (Oceanography Mag., 2004)


Top-down control


Global test of the top-down hypothesis?

Myers and Worm Nature 2003Decline of fish stocks since 1960


Top-down effects?


Links to useful references


Many scientific controversies get sorted out in time


Bottom-up processes

It can be argued that a similarly parsimonious set of factors

determines the distribution of pelagic biomes, each with its

characteristic flora and fauna... Copepods and whales do not

determine which groups of plants will flourish; like the

phytoplankton, they are themselves expressions of the regional

physical oceanographic regime.

(Alan Longhurstʼs section of Cullen et al., 2002, The Sea )


40


A Tool for Making Sense of Physically ForcedEcosystem Dynamics:

Margalef’s Mandala


Not simply temperature, irradiance, nutrients

Cullen et al. 2002, The Sea

LARGER CELLS HIGHER BIOMASS

TRANSIENT & SELF-LIMITING SELECTION FOR RAPID GROWTH

(DIATOMS)

SMALLER CELLS HIGH TURNOVER

COMPETITION FOR NUTRIENTS RETENTION BY RECYCLING

(MICROBIAL LOOP)

LARGER CELLS HIGHER BIOMASS

SLOWER TURNOVER SELECTIVE PRESSURE

TO SEQUESTER NUTRIENTS AND MINIMIZE LOSSES

(e.g., NOXIOUS/TOXIC BLOOMS)Nutrien

ts —

>

LOW BIOMASS SLOW TURNOVER ADAPTATIONS FOR EFFICIENT USE OF LIGHT&NUTRIENTS

(HIGH-LATITUDE “HNLC”)

Turbulence —>

Potential for Production and Export —>

Event-Scale Forcing —> <— Succession

High Turbulence and Low Nutrients

High Turbulence and H

igh NutrientsLo

w T

urbu

lenc

e an

d Lo

w N

utrie

nts

Low Turbulence and High Nutrients


The challenge: quantitative description of the niches of functional groups


Critical to know the Environmental Influences on the Growth of Phytoplankton: TemperatureLightDaylength Nutrients


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0.05

0.10

0.15

0.20

0.25

5 10 15 20 25 30


Gro

wth

rate

(d-1

)

Temmperature

0.00

0.04

0.08

0.12

0.16

0.20

0.24

0.28

0 500 1000 1500


Gro

wth

Rat

e (d

-1)


0.00

0.05

0.10

0.15

0.20

0.25

0 2 4 6 8 10 12


Spec

ific U

ptak

e Ra

te (d

-1)


0.00

0.04

0.08

0.12

0.16

0.20

0.24

0.28

5 1 0 1 5 2 0 2 5 3 0 3 5 4 0 4 5

Effect of Salinity

Gro

wth

Rat

e (d

-1)

Salinity



Acclimated growth rate: genotypic

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CCMP 1978

Spec

ific

Gro

wth

Rat

e (d

-1)

PAR Irradiance (µmol m-2 s-1)

Species evolve different functions through adaptation and selection

µ(E) = (µmax )(1− e-(E−KC )

(KE −KC ) ) if E > KC

µ(E) = 0 if E ≤ KC

Cullen et al. in prep

Ecosystem modeling ground zero:


µ as a function of T and E may not be relevant for describing growth rate in

dynamic environmentsStrain CB501

47 µmol m-2 s-1

y = 0.3585x - 2.3346

R2 = 0.9991

0

1

2

3

4

5

6

7

8

9

10

0 20 40

Time (d)

ln(F

l)

Alexandrium fundyense !Growth determined using the method of Brand and Guillard !!Brand, L. E. and Guillard, R. R. L. (1981). A method for the rapid and precise determination of acclimated phytoplankton reproduction rates. J. Plankton Res. 3: 191-201.


But it is an excellent startfor identifying niches


Photosynthesis vs Irradiance: phenotypic

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4.0

6.0

8.0

10.0

12.0

0 500 1000 1500 2000

912410200509PEg

P (g

C g

Chl

-1 h

-1)


Species develop different functions through acclimation

0.0

2.0

4.0

6.0

0 500 1000 1500 2000

P (g

C g

Chl

-1 h

-1)

Irradiance (µmol m-2 s-1)

Diatom grown at50 µmol m-2 s-1

Oscillatoria agardhii"metalimnion"


Photosynthesis vs Irradiance: phenotypic

Strategies to respond to environmental variability are adaptations

0.0

2.0

4.0

6.0

0 500 1000 1500 2000

P (g

C g

Chl

-1 h

-1)


Diatom grown at50 µmol m-2 s-1

Oscillatoria agardhii"metalimnion"

Layer former

Mixer


Adaptations to oceanic vs coastal conditions


Oceanic species has much reduced capability for regulation

Rough and ready coastal mixer

Lean but valiant oceanic survivor


And what about vertical migrators?

Nutrient concentration? !Temperature?

0 2 4 6 8 100

50

100

150

200

Chlorophyll a (mg m -3 )

Dep

th (c

m) Daytime

distribution

Nighttimedistribution

Heat trap

Acrylic cover

PVC column

Water bath

Sampling ends

Thermistor

Light source

Flow

Fluorometer

Pump

Discrete analyses

“Cheap Tank” — A useful tool for studying the

behavior and physiology of phytoplankton

Other mesocosms have been used in a few labs (e.g., M. Estrada)


Migration studies

Excellent tool for describing behavior in natural gradients of environmental factors !Difficult to specify growth rate in nature !Good for examining interspecific and intraspecific variability in ecologically important traits


Alexandrium fundyense Results for 6 strains: Growth Rate vs Irradiance

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CB 301

D

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CB 307

Spec

ific

Gro

wth

Rat

e (d

-1)


E

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CCMP 1979

B

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CCMP 1980

Spec

ific

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wth

Rat

e (d

-1)

C

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CCMP 1978

Spec

ific

Gro

wth

Rat

e (d

-1)

A

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CB501


F

John Cullen: C-MORE 2014Top Layer < 5 µM Nitrate RestoredBottom Layer < 5 µM

All hours 301

0.001

0.01

0.1

1

1 0

1 0 0

1000

0 1 0 2 0 3 0

Day

Flu

ore

scen

ce in

Top o

r B

ott

om

Fif

th/

Rem

ain

der

Surface Deep

Behavior showed significant variability:!! Aggregation at top vs bottom all hours

All hours 307

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Day

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ore

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ce in

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om

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th/

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All hours 501

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ore

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ce in

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r B

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om

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der

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All hours 1978

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ore

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ce in

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All hours 1979

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ore

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ce in

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r B

ott

om

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th/

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ain

der

Surface Deep

All hours 1980

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ore

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ce in

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p o

r B

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om

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th/

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ain

der

Surface Deep


Bouncing around Redfield


http://www.whoi.edu/oceanus/viewImage.do?id=4950&aid=2387

Azam, F. (1998), Microbial control of oceanic carbon flux: The plot thickens, Science, 280, 694-696.

Chemotaxis!


Niches abound!


But what about quantitative prediction?

Rothstein et al. Oceanography Mag. 2006


Growth must be described as a function of environmental conditions

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5 10 15 20 25 30


Grow

th ra

te (d

-1)

Temmperature

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0 500 1000 1500


Grow

th R

ate

(d-1)


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0 2 4 6 8 10 12


Spec

ific U

ptake

Rate

(d-1

)


0.00

0.04

0.08

0.12

0.16

0.20

0.24

0.28

5 1 0 1 5 2 0 2 5 3 0 3 5 4 0 4 5

Effect of Salinity

Grow

th Ra

te (d

-1)

Salinity



Functions can be Developed

(Daylength, Irradiance, Temperature, Nutrients)

€

µ = f (D,E,N ,T )

McGillicuddy, Stock, Anderson, and Signell WHOI


• Requires a huge amount of work with cultures!• Algae should be acclimated to each set of conditions!

– This can require several weeks

• Conditions in nature are almost never so stable!– Phytoplankton are subject to vertical mixing – Vertical migration

• All combinations of Daylength, Irradiance, Nutrients and Temperature are essentially impossible to test

…but


Summary: µ as a function of environmental conditions

Good for identifying environmental ranges and optima

0.00

0.04

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0.28

5 1 0 1 5 2 0 2 5 3 0 3 5 4 0 4 5

Effect of Salinity

Gro

wth

Rat

e (d

-1)

Salinity



Summary: µ as a function of environmental conditions

Excellent for describing differences between species!!(and variations among strains of the same species)!!Ancient example:!

!Gallagher, J. C. (1982). Physiological variation and electrophoretic banding patterns of genetically different seasonal populations of Skeletonema costatum (Bacillariophyceae). J. Phycol. 18: 148-162. Eppley, R. W. 1972. Temperature and

phytoplankton growth in the sea. Fish. Bull. 70: 1063-1085.

Even more ancient:


Can we assume that adaptation provides all the raw genetic material for aggregate super-bugs?

see Eppley, R. W. 1972. Temperature and phytoplankton growth in the sea. Fish. Bull. 70: 1063-1085.

Figure from C.B. Miller, “Biological Oceanography” after Smayda, 1976


A novel approach


Everything (well, a lot of things) is everywhere and the environment selects

Natural selection in silico


The microbialassemblage structures the chemical environment

Only some of the ecotypes survive – the number corresponds to environmental complexity

Rothstein et al. 2006


A test of our understanding of what structures marine ecosystems (LPG)


Future steps:Assembly and natural selection of microbial communities guided by metagenomics


A big part of the answer is here


Future tests: How much biological complexity is needed to describe and predict ecological and biogeochemical variability in the sea?

Dave Karl, Nature 2002


ConclusionsRelationships between environmental variability and microbial diversity must be described and ultimately predicted to understand the ecology and biogeochemistry of the sea!

Niches abound: wide range of environmental tolerances specialized life-styles (nutrient requirements, alternate modes of photosynthesis ! physiological plasticity vs specialization for stable environments Complexity will never be fully described with numerical models !The degree of model complexity can and should be related to the ecological/biogeochemical question and its scale.

!This can be done — but models must be verified by measurements! Mahalo!

behavior, physiology and the niche of marine...

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