Non-linear relationships between Topt and μopt

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Table of contents

1 Introduction 
1.1 An ode to phytoplankton
1.2 Phytoplankton and temperature
1.2.1 The direct effect of temperature
1.2.2 Phytoplankton in a changing climate
1.2.3 A decisive parameter in biotechnological applications
1.3 From acclimation to adaptation
1.4 Objectives of the thesis
2 Material and methods 
2.1 Culture device and selection procedure
2.1.1 The selectiostats
2.1.2 Cultivation mode for selection experiments
2.1.3 The TIP device
2.2 Data compilation, parameters identification and models calibration
2.2.1 Thermal growth curves compilation
2.2.2 Species biovolume
2.2.3 CTMI parameters determination
2.2.4 Data sets selection
2.2.5 Hinshelwood model calibration
2.2.6 Data analysis
2.2.6.1 Linear relationships between the cardinal temperatures .
2.2.6.2 Non-linear relationships between Topt and μopt
2.2.6.3 Statistical tools for models comparison
3 Modelling the temperature effect on unicellular organisms from heterotrophic bacteria to autotrophic eukaryotes: a review 
3.1 Introduction
3.2 Modelling the specific growth rate of unicellular organisms as a function of temperature
3.2.1 Methodological clarification
3.2.2 The Arrhenius model from Van’t-Hoff to Eyring:
3.2.3 Empirical approach
3.2.4 Mechanistic approach
3.2.5 The protein thermal stability challenge
3.3 The special case of unicellular photosynthetic organisms
3.3.1 The Eppley point of view for phytoplankton
3.3.2 The link between photosynthesis and temperature in the models .
3.4 The dynamical effect of temperature on unicellular organisms
3.4.1 The metabolic response to temperature acclimation
3.4.2 The thermally-induced death
3.5 Conclusion
4 Correlation between the cardinal temperatures: insight into thermal adaptation 
4.1 Introduction
4.2 Relation between the cardinal temperatures
4.2.1 Linear relationships
4.2.2 Differences among the phylogenetic groups
4.2.3 A closer look at microalgae
4.3 Thermal adaptation and the thermal niche width
4.4 Conclusion
5 Revisiting the Eppley hypothesis 
5.1 Introduction
5.2 Hotter is not always faster
5.2.1 Describing the relationship between Topt and μopt using quantile regression
5.2.2 Group specificities
5.2.3 Scaling law in the thermal growth curve
5.3 The phytoplankton paradigm
5.3.1 The revisited Eppley curve for phytoplankton
5.3.2 A case study: the cyanobacteria Synechococcus sp.
5.4 Conclusion
6 Towards understanding the thermodynamical fundament of the thermal growth curve: a modelling approach 
6.1 Introduction
6.2 The Hinshelwood model as a theoretical framework
6.2.1 Metabolism represented as a set of n autocatalytic reactions
6.2.2 Data normalization and calibration of the Hinshelwood model
6.3 Accounting for the enthalpy-entropy compensation
6.3.1 Theoretical approach
6.3.2 Calibration
6.4 Accounting for the activity-stability trade-off
6.4.1 Theoretical approach
6.4.2 Calibration
6.5 The two parameters Hinshelwood model
6.5.1 Reducing the parameter number of the Hinshelwood model down to 2
6.5.2 Comparison between the reduced Hinshelwood and the reduced CTMI models
6.5.3 Correlation between the cardinal temperatures
6.6 Conclusion
7 Modelling thermal adaptation in microalgae: an adaptive dynamics point of view 
7.1 Introduction
7.2 Simple dynamical model describing the temperature effect on microalgae in chemostat
7.2.1 The Monod model in chemostat
7.2.2 The specific case of the Droop model in chemostat
7.3 Evolutionary Model
7.3.1 General case
7.3.2 Modelling the evolution of the optimal temperature trait
7.3.3 Structural link between adaptive traits
7.4 Fluctuating temperature
7.4.1 Ecological timescale
7.4.2 Evolutionary timescale
7.4.2.1 Case 1: Topt is varying only
7.4.2.2 Case 2: the thermal niche width is kept constant
7.4.3 Evolutionary Branching conditions
7.5 Conclusion
8 Selecting thermal tolerant strains of the Haptophyceae Tisochrysis lutea 
8.1 Introduction
8.2 Selection experiment in controlled systems
8.2.1 Summary of the experiment
8.2.2 Main results
8.3 Modelling selection during the experiment
8.3.1 Re-identification of the final thermal tolerance parameters
8.3.1.1 Identification at the population scale
8.3.1.2 Competition between two thermal phenotypes
8.4 Modelling thermal adaptation during the experiment
8.4.1 Determining the invasion fitness
8.4.1.1 Population dynamics, invasion fitness and selection gradient in a turbidostat growth model
8.4.1.2 Population dynamics, invasion fitness and selection gradient in a fed-batch growth model
8.4.2 Evolutionary dynamics
8.4.3 Evolutionary equilibrium
8.5 Conclusion
9 Modelling the effect of temperature on phytoplankton growth across the global ocean 
9.1 Introduction
9.2 Evolutionary model for thermal adaptation
9.2.1 Slow-fast dynamical system
9.2.2 Evolutionary model using Adaptive Dynamics theory
9.3 Global ocean scale simulations
9.3.1 Evolutionary model with realistic temperature signal
9.3.2 Global scale simulations
9.3.3 Comparison with experimental data
9.3.4 The warming scenario
9.4 Conclusion
10 Conclusion & Perspectives 
10.1 The physiological impacts of temperature on phytoplankton
10.1.1 From empirical models to thermodynamical insights
10.1.2 The submerged iceberg of unknown: future works
10.2 Capturing the evolutionary trajectories
10.2.1 Selection experiments and evolutionary modelling
10.2.2 Evolution in the ocean
10.3 Conclusion
Annexes 
Determining Tmin, Topt, Tmax and muopt in the Hinshelwood model
Autocatalytic view of the Hinshelwood model
Bibliography