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Adaptation of microalgae to nutrient fertilization

Etienne
February 22, 2013

Adaptation of microalgae to nutrient fertilization

Experimental evolution has predominantly focused on survival and adaptation of organisms subject to stressful changes in their environment. However, in many cases, environmental change shifts conditions towards the optimum of a species. Current theory may not be well suited to predicting the response of organisms to this type of changes. We investigate the adaptation of soil micro-algae to the addition of a variety of fertilizers. We isolated and tested soil micro-algae from plots of the Park Grass Experiment at Rothamsted Research, which have been subject to fertilization treatment continuously for over 150 years. This research will test evolutionary theory in new contexts and will help predict the response of organisms benefiting from global changes, including rising CO2, eutrophication, increased nitrogen availability and rising temperatures.

Etienne

February 22, 2013
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  1. Etienne Low-Décarie
    Graham Bell
    Gregor Fussmann

    View Slide

  2. }  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

    View Slide

  3. 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

    View Slide

  4. 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

    View Slide

  5. }  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)

    View Slide

  6. • 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

    View Slide

  7. • 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)

    View Slide

  8. 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

    View Slide

  9. 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

    View Slide

  10. 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
    Source of test environment
    1
    2
    3
    1 2 3
    Cell density
    1
    2
    3
    4
    5
    Specific adaptation
    Source of strain
    Source of test environment
    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
    Source of test environment
    1
    2
    3
    1 2 3
    Cell density
    1.0
    1.5
    2.0
    2.5
    3.0
    Non specific adapatation
    Source of strain
    Source of test environment
    1
    2
    3
    1 2 3
    Cell density
    1
    2
    3
    4
    5
    fertilization
    - +
    fertilization
    - +
    No evolution Non-specific adaptation

    View Slide

  11. • 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

    View Slide

  12. Clamydomonas in soil water from each plot
    Transformed yield (sqrt of cell/uL)
    0
    10
    20
    30
    40
    50











    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

    View Slide

  13. 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

  14. 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

  15. 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

    View Slide

  16. }  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?

    View Slide

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

    View Slide

  18. }  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,

    View Slide