Transcript Cullen_Phyto_Communities_Agouron_2007 - C-MORE

Describing and Predicting Environmental
Influences on Phytoplankton Communities and
Food Web Structure
John J. Cullen
Department of Oceanography, Dalhousie University
Halifax, Nova Scotia, Canada B3H 4J1
Microbial Oceanography Summer Course:
Genomes to Biomes
University of Hawai‘i
June 27, 2007
Supported by NSERC, NOPP & ONR
International Coalition of
Ocean Observation Laboratories
John Cullen: Ocean Sciences 2006
A principal goal of microbial oceanography
Describing and explaining the
distributions and activities of marine microbes
marine.rutgers.edu/opp/
John Cullen: Agouron Institute 2007
…and using this information to describe the causes and
consequences of variations in key biogeochemical processes
marine.rutgers.edu/opp/
John Cullen: Agouron Institute 2007
Key biogeochemical processes
•
•
•
•
•
Primary production
Nitrogen fixation
Denitrification
Trace gas production
…and the many other processes that
make those processes possible
John Cullen: Agouron Institute 2007
This can be achieved
only through an
integrated approach
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.
Rothstein et al., Oceanography Mag.
John Cullen: Agouron Institute 2007
Arguably, this represents the state of the art
PARADIGM Global Biogeochemistry - Ecology - Circulation model (Doney and colleagues)
Rothstein et al. 2006 – Oceanography Magazine
John Cullen: Agouron Institute 2007
Ultimate Target
Ocean Observing
Systems
Genomics &
the other
‘omics
Hydrological &
Atmospheric
Optics
[+Acoustics]
Physical
Chemical
& Biological
Oceanography
Process Studies
Theory / Models
Physiological
Ecology Including
Gene Expression
John Cullen: Agouron Institute 2007
Biological oceanography and phytoplankton ecology
Describe the causes and consequences of
variations in primary productivity
(and food web structure)
marine.rutgers.edu/opp/
John Cullen: Agouron Institute 2007
An overview of established approaches to
marine prediction
Gordon Riley
John Cullen: Agouron Institute 2007
Approach #1: Observation, analysis, inference
(empirical, diagnostic models)
Behrenfeld and Falkowski 1997b L&O
Modeling the pattern in the measurements — not
necessarily primary productivity!
John Cullen: Agouron Institute 2007
Result: Quantitative predictions that are as good as
the data & assumptions that go into them
Inputs/Assumptions of the
Productivity Model(s)
Validity of the Statistical Analysis
John Cullen: Agouron Institute 2007
Approach #2: Observation, analysis, inference
(qualitative, mechanistic, predictive models)
John Cullen:
Agouron
Institute (2005)
2007
Arrigo,
Nature
The testing of qualitative, mechanistic, predictive
models may be messy
John Cullen:
AgouronNature
Institute 2007
Arrigo,
(2005)
Approach #3: Prognostic models
(quantitative, mechanistic, predictive models)
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.
John Cullen: Agouron Institute 2007
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
John Cullen: Agouron Institute 2007
ocean’s new frontier. Frontiers in Ecology and the Environment 2: 457-466.
Conventionally tested by the “LPG” criterion —
but that is changing
Looks Pretty Good!
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.
John Cullen: Agouron Institute 2007
Approaches to Ecosystem Modeling
Maud Guarrracino - Lunenburg Bay
John Cullen: Ocean Sciences 2006
Fundamentally, ecosystem models
should predict population dynamics
Growth
Loss
Accumulate (Bloom)
Daughter
Cell
Be eaten
Blow up (viral lysis)
Single Cell
Sink
Daughter
Cell
Die (e.g., apoptosis)
A bit weird, because “population” refers
to species, and many species are often lumped
John Cullen: Agouron Institute 2007
Biogeochemical models must include
functional groups
John Cullen: Agouron Institute 2007
Essential Knowledge:
Environmental Influences on the Growth and
Chemical Composition of Phytoplankton
Cell Division
LIGHT
Daughter
Cell
Photosynthesis
Single Cell
Doubled Biomass
NUTRIENTS
Nutrient Uptake
Daughter
Cell
Growth rate
Chemical composition
Biogeochemical transformations
John Cullen: Agouron Institute 2007
Critical to
know the
Environmental
Influences on
the Growth of
Phytoplankton:
Alexandrium
ostenfeldii
Growth
vs Temperature
Growth rate (d-1)
0.25
0.20
0.15
0.10
0.05
0.00
5
10
15
20
25
30
Growth Rate (d-1)
Nutrient
Uptake Kinetics
Temmperature
)
0.25
-1
Specific Uptake Rate (d
Temperature
Light
Daylength
Nutrients
0.20
0.15
0.10
Effe ct of Salinity
0.28
Jensen and Moestrup
A. ostenfeldii
0.24
0.20
0.16
0.12
0.08
0.05
0.04
0.00
0.00
0
2
4
6
8
10
Nitrate Concentration (µM)
12
5
10
15
20
25
30
35
40
45
Salinity
John Cullen: Agouron Institute 2007
Challenge:
Figuring out what to do with exploding knowledge of
biological complexity in the ocean
Venter et al. Science 2004
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Growth vs Temperature
John Cullen: Agouron Institute 2007
Many other things define the growthniche of marine phytoplankton
From Margalef et al. 1979 in Cullen et al. Oceanography Mag. 2007
John Cullen: Agouron Institute 2007
And cell division is only
half of the battle!
http://jaffeweb.ucsd.edu/pages/celeste/copepods.html
John Cullen: Agouron Institute 2007
Reduction of loss can be
as good as an increase
of growth rate
John Cullen: Agouron Institute 2007
It’s the gene, stupid!
So rapid growth is not
the only strategy for
survival / selection
www.andyslocum.com/images/tortoise&hare.jpg
John Cullen: Agouron Institute 2007
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)
John Cullen: Agouron Institute 2007
Top-down control
John Cullen: Agouron Institute 2007
Global test of the top-down hypothesis?
Decline of fish stocks since 1960
Myers and Worm Nature 2003
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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 Lo nghurst’s section of Cullen et al., 2002, The Sea)
John Cullen: Agouron Institute 2007
A Tool for Making Sense of Physically Forced
Ecosystem Dynamics:
Margalef’s Mandala
John Cullen: Agouron Institute 2007
Event-Scale Forcing
—>
Not simply temperature,
irradiance,
nutrients
<— Succession
LARGER CELLS
HIGHER BIOMASS
SLOWER TURNOVER
SELECTIVE PRESSURE
TO SEQUESTER NUTRIENTS
AND MINIMIZE LOSSES
(e.g., NOXIOUS/TOXIC BLOOMS)
SMALLER CELLS
HIGH TURNOVER
COMPETITION FOR NUTRIENTS
RETENTION BY RECYCLING
(MICROBIAL LOOP)
LARGER CELLS
HIGHER BIOMASS
TRANSIENT & SELF-LIMITING
SELECTION FOR
RAPID GROWTH
(DIATOMS)
LOW BIOMASS
SLOW TURNOVER
ADAPTATIONS FOR
EFFICIENT USE OF
LIGHT&NUTRIENTS
(HIGH-LATITUDE
“HNLC”)
High Turbulence and Low Nutrients
High Turbulence and High Nutrients
Low Turbulence and Low Nutrients
Low Turbulence and High Nutrients
Cullen et
al. 2002,
The Sea
Potential for Production and Export —> John Cullen: Agouron Institute 2007
The challenge: quantitative description of the
niches of functional groups
John Cullen: Agouron Institute 2007
Critical to
know the
Environmental
Influences on
the Growth of
Phytoplankton:
Alexandrium
ostenfeldii
Growth
vs Temperature
Growth rate (d-1)
0.25
0.20
0.15
0.10
0.05
0.00
5
10
15
20
25
30
Growth Rate (d-1)
Nutrient
Uptake Kinetics
Temmperature
)
0.25
-1
Specific Uptake Rate (d
Temperature
Light
Daylength
Nutrients
0.20
0.15
0.10
Effe ct of Salinity
0.28
Jensen and Moestrup
A. ostenfeldii
0.24
0.20
0.16
0.12
0.08
0.05
0.04
0.00
0.00
0
2
4
6
8
10
Nitrate Concentration (µM)
12
5
10
15
20
25
30
35
40
45
Salinity
John Cullen: Agouron Institute 2007
Keep in the back of your mind:
Surely, all
this means
something!
John Cullen: Agouron Institute 2007
Ecosystem modeling ground zero:
Acclimated growth rate: genotypic
Cullen et al. in prep
0.60
CCMP 1978
-1
Specific Growth Rate (d )
0.50
0.40
0.30
0.20
0.10
0.00
0
100
200
300
400
500
-2
600
-1
PAR Irradiance (µmol m s )
 (E)  ( max )(1  e
-(E  K C )
(K E  K C )
)
if E  K C
 (E)  0
if E  K C
Species evolve different functions
through adaptation and selection
John Cullen: Agouron Institute 2007
µ as a function of T and E may not be
relevant for describing growth rate in
dynamic environments
Strain CB501
47 µmol m-2 s-1
y = 0.3585x - 2.3346
R2 = 0.9991
10
9
Growth determined using the
method of Brand and
Guillard
8
7
ln(Fl)
Alexandrium fundyense
6
5
4
3
2
1
0
0
20
Time (d)
40
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.
John Cullen: Agouron Institute 2007
But it is an excellent start
for identifying niches
John Cullen: Agouron Institute 2007
Photosynthesis vs Irradiance: phenotypic
P (gC gChl
-1 -1
h )
12.0
10.0
8.0
6.0
4.0
2.0
0.0
Oscillatoria agardhii
"metalimnion"
6.0
P (gC gChl-1 h-1)
912
410
200
50
9
PEg
4.0
2.0
Diatom grown at
50 µmol m -2 s-1
0.0
0
500
1000
1500
-2
Irradiance (µmol m
2000
-1
s )
0
500
1000
1500
2000
Irradiance (µmol m -2 s-1)
Species develop different functions through acclimation
John Cullen: Agouron Institute 2007
Photosynthesis vs Irradiance: phenotypic
P (gC gChl-1 h-1)
Layer former
Oscillatoria agardhii
"metalimnion"
6.0
4.0
2.0
Diatom grown at
50 µmol m -2 s-1
Mixer
0.0
0
500
1000
1500
2000
Irradiance (µmol m -2 s-1)
Strategies to respond to environmental variability are adaptations
John Cullen: Agouron Institute 2007
Adaptations to oceanic vs coastal conditions
John Cullen: Agouron Institute 2007
Oceanic species has much reduced
capability for regulation
Lean but valiant oceanic survivor
Rough and ready coastal mixer
John Cullen: Agouron Institute 2007
Niches abound!
John Cullen: Agouron Institute 2007
But what about quantitative prediction?
Rothstein et al. Oceanography Mag. 2006
John Cullen: Agouron Institute 2007
Growth must be described as a function of
environmental conditions
Alexandrium ostenfeldii
Growth rate (d-1)
0.25
0.20
0.15
0.10
0.05
0.00
10
15
20
25
30
Growth Rate (d-1)
Nutrie
nt Upta ke Kine tics
Temmperature
)
0.25
-1
Specific Uptake Rate (d
5
0.20
0.15
0.10
Effe ct of Salinity
0.28
Jensen and Moestrup
A. ostenfeldii
0.24
0.20
0.16
0.12
0.08
0.05
0.04
0.00
0.00
0
2
4
6
8
10
Nitrate Concentration (µM)
12
5
10
15
20
25
30
35
40
45
Salinity
John Cullen: Agouron Institute 2007
Functions can be Developed
  f (D, E,N,T)
McGillicuddy,
Stock, Anderson,
and Signell
WHOI
(Daylength, Irradiance, Temperature, Nutrients)
John Cullen: Agouron Institute 2007
…but
• 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
John Cullen: Agouron Institute 2007
Good for identifying
environmental
ranges and optima
Growth Rate (d-1)
Summary: µ as a function of
environmental conditions
Effe ct of Salinity
0.28
Jensen and Moestrup
A. ostenfeldii
0.24
0.20
0.16
0.12
0.08
0.04
0.00
5
10
15
20
25
30
35
40
45
Salinity
John Cullen: Agouron Institute 2007
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.
Even more ancient:
Eppley, R. W. 1972. Temperature and
phytoplankton growth in the sea. Fish.
Bull. 70: 1063-1085.
John Cullen: Agouron Institute 2007
Can we assume that adaptation provides all the raw
genetic material for aggregate super-bugs?
Figure from C.B. Miller, “Biological Oceanography” after Smayda, 1976
see Eppley, R. W. 1972. Temperature and phytoplankton growth in the sea. Fish. Bull. 70: 1063-1085.
John Cullen: Agouron Institute 2007
A novel approach
John Cullen: Agouron Institute 2007
Everything (well, a lot of things) is
everywhere and the environment selects
Natural selection in silico
John Cullen: Agouron Institute 2007
Rothstein et al. 2006
The
microbial
assemblage
structures
the chemical
environment
Only some of
the ecotypes
survive – the
number
corresponds
to
environmental
complexity
John Cullen: Agouron Institute 2007
A test of our understanding of what structures marine
ecosystems (LPG)
John Cullen: Agouron Institute 2007
Future steps:
Assembly and natural selection of microbial
communities guided by metagenomics
John Cullen: Agouron Institute 2007
Dave Karl, Nature 2002
Future tests:
How much biological
complexity is needed
to describe and predict
ecological and
biogeochemical
variability in the sea?
John Cullen: Agouron Institute 2007
Conclusions
Relationships 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!
(tomorrow’s lecture)
Mahalo!
John Cullen: Agouron Institute 2007