Adaptation of microalgae to nutrient fertilization

Etienne Low-Décarie
Graham Bell
Gregor Fussmann

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}  In theory, in experiments and possibly in nature
◦  change is usually for the worst
◦  adaptation seen as a rescue from extinction
Bell (2008)
Bell and Collins (2008)
Chevin et al. 2010
period E,
mount pro-
n then has
deteriora-
eles, but at
ally benefi-
upply rate.
fficient to
population
continually
ss depends
ts severity
produce a
n will be
l be more
expressed
vironment
mutational
ws
= w0
–b
mutation
to depend
es, or even
ields simi-
be able to
Evolutionary rescue
The outlook is much less benign, however, if lower mean
0.3
0.4
0.5
0.6
0.7
0.8
0.9
–5 –4 –3 –2 –1 0
ln 1/E
Mean fitness relative to no change
Nu high
Nu low
Nu very low
Figure 2 Effect of environmental change on mean fitness in relation
to mutation supply rate. Based on a mutational landscape model as in
Fig. 1. From Bell (2008).
Bell and Collins

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CO2
past: Tans, P. (2010); predictions: IPCC
(2007)
• Rise in CO2
• Fertilization
• terrestrial
• aqueous (eg. nitrogen eutrophication)
• but for other organisms:
•  rising temperature, reduced predators, change in precipitation…may be ben

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Carbon cycle & Aquatic food-
webs
}  Microalgae are microbes =
experiments
◦  with large populations
◦  over thousands of generations
◦  =perfect model
}  Conditions rapidly changing
for all photosynthetic
organisms = model for
plants
}  Microalgae responsible for
½ of biological carbon
uptake and are omnipresent
}  Base of most aquatic food
webs
NASA

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}  In plants
◦  shifts in species frequency with fertilization and CO2
◦  no conclusive study of adaptation
  some genotype response
}  In microalgae
◦  some studies (nitrification, iron)
◦  shifts in species frequency
◦  focus on adaptation to low concentration
}  Liebig’s Law of the Minimum
rospects for survival and reproduction. Recently, the Fisher and Haldane
are combined (Waxman and Welch 2005): Haldane’s concern is incor-
o Fisher’s model by allowing the intensity of selection to vary between
ontrivial task to measure the fitness functions and action of selection in
now it has been done for many populations and phenotypical traits (King-
Pfennig 2007). Special statistical methods for life-history analysis for in-
fitness and population growth are developed and tested (Shaw et al. 2008).
urther analysis, we do not need exact values of fitness but rather its exis-
ome qualitative features.
all, let us consider an oversimplified situation with identical organ-
n phenotypical treats, fitness W is a function of factor loads: W =
fq). This assumption does not take into account physiological adaptation
as a protection system and modifies the factor loads. This modification is
s of our analysis in the follow-up section, but for now we neglect adapta-
onvention about axes direction means that all the partial derivatives of W
itive ∂W/∂fi
≤ 0.
nition, for a Liebig’s system of factors W is a function of the worst (max-
r intensity: W = W(max{f1,...,fq
}) (Fig. 2a) and for anti-Liebig’s sys-
e function of the best (minimal) factor intensity W = W(min{f1,...,fq
})
Such representations as well as the usual formulation of the Law of the
require special normalization of factor intensities to compare the loads of
ctors.
us types of
of the system of
given state s, the
e is given by the
f1,f2) = W(s).
rates the area with
(“better
rom the line with
(“worse
In Liebig’s (a) and
iebig’s systems (b)
tter conditions is
nti-Liebig’s”
nd the general
stems (d) the area
Ditommaso and Aarssen (1989
Gorban et al. (2010)
tence and some qualitative features.
First of all, let us consider an oversimplified situation with identical organ-
isms. Given phenotypical treats, fitness W is a function of factor loads: W =
W(f1,...,fq). This assumption does not take into account physiological adaptation
that works as a protection system and modifies the factor loads. This modification is
in the focus of our analysis in the follow-up section, but for now we neglect adapta-
tion. The convention about axes direction means that all the partial derivatives of W
are nonpositive ∂W/∂fi
≤ 0.
By definition, for a Liebig’s system of factors W is a function of the worst (max-
imal) factor intensity: W = W(max{f1,...,fq
}) (Fig. 2a) and for anti-Liebig’s sys-
tem it is the function of the best (minimal) factor intensity W = W(min{f1,...,fq
})
(Fig. 2c). Such representations as well as the usual formulation of the Law of the
Minimum require special normalization of factor intensities to compare the loads of
different factors.
Fig. 2 Various types of
organization of the system of
factors. For a given state s, the
bold solid line is given by the
equation W(f1,f2) = W(s).
This line separates the area with
higher fitness (“better
conditions”) from the line with
lower fitness (“worse
conditions”). In Liebig’s (a) and
generalized Liebig’s systems (b)
the area of better conditions is
convex, in “anti-Liebig’s”
systems (c) and the general
synergistic systems (d) the area
of worse conditions is convex.
The dot dash line shows the
border of survival. On the
dashed line, the factors are
equally important (f1 = f2)

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• North of London, England
• Started by Sir John Bennet Lawes and
Sir Joseph Henry Gilbert in 1856
• 9 long-term experiments
• 8 ongoing to this day
• oldest, continuous agronomic
experiments in the world

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• Hay field
• 155 years of ongoing
treatment
• >50 000 generations
of adaptation for soil
microalgae
• Treatments
• addition of N (2), P
(3), K, Na, Mg, Si
• 4 pH levels
controlled by
addition of calcium
carbonate(CaCO3
,
lime/chalk)

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The team, sampling
Experiments
•  Soil water isolated from
16 plots
•  Lab strain of
Chlamydomonas bio-
assay of soil water
•  Micro-algae isolated from
16 plots
•  3 block replication of
complete factorial
reciprocal transplant
Gregor
Graham

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Reciprocal transplant: more than 2 x 2 selection by test
Source of strain
(selection environment)
Plot 1 Plot 2 Plot 3
Source of soil water
(test environment)
16 plots source of
soil water x 16
plots source of
strain =
256 cultures per
bloc x 3 blocks =
768 test cultures

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Accumulation of conditionaly
deleterious mutations
Source of strain
Source of test environment
1
2
3
1 2 3
Cell density
0.0
0.5
1.0
1.5
2.0
2.5
3.0
Specific adaptation with tradeoff
or combination of adaptation
and conditionaly deliterious mutations
Source of strain
1
2
3
1 2 3
Cell density
1
2
3
4
5
Specific adaptation
Source of strain
1
2
3
1 2 3
Cell density
1
2
3
4
5
Conditional deleterious mut. Tradeoff
Specific adaptation
No difference between strains
Source of strain
1
2
3
1 2 3
Cell density
1.0
1.5
2.0
2.5
3.0
Non specific adapatation
Source of strain
1
2
3
1 2 3
Cell density
1
2
3
4
5
fertilization
- +
fertilization
- +
No evolution Non-specific adaptation

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• Davies and Snaydon
(1973-1982)
• single generation
transplants
• differences small
and possibly not
evolutionary/genetic
• single species
° fertilized with
P
• no
fertilization
responded significantly to Ca up to 54 mg 1-' Ca (F
from unlimed plots did not respond significantly t
response to Ca between population types (lim
'population types x Ca' interaction, was significa
the error mean square or the 'populations with
test is more stringent, since it measures differen
relative to differences between populations withi
a~~
4-5 (a) 015
3
E
" 40
30
C
a
5 SD "'
~LSD0 010
I (P =0-05) S
a -
0~~~~1, .2
011 ,/oz *//a 005
-C
~~
~~~~
2-5-
1) 0
0
2 6 18 54 162 t
Calcium concentration (mg 1-') in culture soln
FIG. 1. The effect of Ca concentration
in sand cultu
yield per plant of Anthoxanthum
odoratum popu
Experiment.
(a) The effect of Ca concentration
(3-19
(a) and unlimed (0) plots. Each point is the mean
ship between the Ca response of each population
efficient of loge dry weight yield on loge Ca concen
and the soil pH of its s
Over the range 2-54 mg I- ' Ca, the response
significantly from linearity, when loge dry weig
concentration (Fig. la). It is therefore possible to
lation, over that range, as the linear regressi
population to Ca, as measured by the regressio
0 90, P<0.01) with the soil pH of the source p
regression coefficient of the most responsive pop
of the least responsive population. There are no av
of soils from the Park Grass plots (A. E. Johnst
° fertilized with
Ca
• no fertilization

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Clamydomonas in soil water from each plot
Transformed yield (sqrt of cell/uL)
0
10
20
30
40
50
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12 17 1 3 4/2 15 10 2/24/1 1611/1 7 14/2 8 9/211/2
Plot ID
Nutrient
Ammonium Sulphate
Magnesium Sulphate
Potassium Sulphate
Sodium Nitrate
Sodium Silicate
Sodium Sulphate
Triple Superphosphate
12 17 1 3 4/2 15 10 2/24/1 1611/1 7 14/2 8 9/211/2
Nutrient level
0
1
2
3

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Growth of collected strains in reciprocal transplant
Plot origin of algal strain
Plot origin of soil water
Plot 12
Plot 2−2
Plot 3
Plot 1
Plot 17
Plot 4−1
Plot 4−2
Plot 8
Plot 15
Plot 7
Plot 10
Plot 16
Plot 14−2
Plot 9−2
Plot 11−1
Plot 11−2
Replicate: 1
Plot 12
Plot 2−2
Plot 3
Plot 1
Plot 17
Plot 4−1
Plot 4−2
Plot 8
Plot 15
Plot 7
Plot 10
Plot 16
Plot 14−2
Plot 9−2
Plot 11−1
Plot 11−2
Replicate: 2
Plot 12
Plot 2−2
Plot 3
Plot 1
Plot 17
Plot 4−1
Plot 4−2
Plot 8
Plot 15
Plot 7
Plot 10
Plot 16
Plot 14−2
Plot 9−2
Plot 11−1
Plot 11−2
Replicate: 3
Plot 12
Plot 2−2
Plot 3
Plot 1
Plot 17
Plot 4−1
Plot 4−2
Plot 8
Plot 15
Plot 7
Plot 10
Plot 16
Plot 14−2
Plot 9−2
Plot 11−1
Plot 11−2
Cell densit
standardiz
of strain a
0.5
1.0
1.5
Growth of collected strains in reciprocal transplant
2 3
Cell density
standardized by origin
of strain and of soil water
0.5
1.0
1.5
fertilization
- +
fertilization
- +

View Slide

Triple Superphosphate Magnesium Sulphate
Triple Superphosphate
Nutrient level in
plot origin of algal strain
Nutrient level in
plot origin of soil water
0
1
Replicate: 1
0
1
Replicate: 2
0
1
Replicate: 3
0
1
Log cell density
1
2
3
4
Magnesium Sulphate
Nutrient level in
plot origin of algal strain
Nutrient level in
plot origin of soil water
0
1
Replicate: 1
0
1
Replicate: 2
0
1
Replicate: 3
0
1
Log cell density
1
2
3
4

View Slide

Correlation between Chlamydomonas density and strain density
Mean log cell density in Chlamydomonas bio−assay
(one value per plot source of assay soil water)
Log cell density
in reciprocal transplant
1
2
3
4
Replicate: 1
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2.3 2.4 2.5 2.6 2.7 2.8 2.9
Replicate: 2
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2.3 2.4 2.5 2.6 2.7 2.8 2.9
Replicate: 3
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2.3 2.4 2.5 2.6 2.7 2.8 2.9
Strain.plot
● Plot 1
● Plot 10
● Plot 11−1
● Plot 11−2
● Plot 12
● Plot 14−2
● Plot 15
● Plot 16
● Plot 17
● Plot 2−2
● Plot 3
● Plot 4−1
● Plot 4−2
● Plot 7
● Plot 8
● Plot 9−2

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}  Weak evidence for specific adaptation
}  Accumulation of conditionally deleterious
mutations
}  No evidence for specific adaptation
◦  No evidence of consistent physiological growth
response
◦  Difficulty in detecting evolution other than
specific local adaptation with tradeoff in larger
than 2x2 selection by test environment
matrices
Specific adaptation to high CO2
unlikely?

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}  Predicting
◦  atmospheric CO2
◦  ecological change/species shifts
◦  crop yield
◦  algal blooms
}  Optimizing algal bio fuel production
}  effect on lab strains of benign
environment
}  …

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}  Past and present members of the Bell and Fussmann Labs:
◦  Kathy Tallon, Mark Jewell, Tyler Moulton, Adam Gregory Meyer…
}  Yves Prairie, Neil Price, Michel Loreau, Irene Gregory-Eaves, Mark Romer and
countless others
http://etienne.webhop.org
[email protected]
Lorne Trottier
Philip Carpenter
Mr. Ian Clark
Dr. Penny Hirsch
Dr. Jonathan Storkey,

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Adaptation of microalgae to nutrient fertilization

Adaptation of microalgae to nutrient fertilization

Etienne

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￼￼Adaptation of microalgae to nutrient fertilization

￼￼Adaptation of microalgae to nutrient fertilization

Etienne

More Decks by Etienne

Other Decks in Science

Featured

Transcript

Adaptation of microalgae to nutrient fertilization

Adaptation of microalgae to nutrient fertilization