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The percentage which literature models (Table 3) underestimate (negative values) or overestimate (positive values) maximum specific growth rate compared to the combined temperature-photon flux density model presented in this study (Fig. 3). 0% equals no difference between the literature model and the model presented in this study. Contours are at 50% intervals although no data are shown above 300%. Colour scale: specific growth rates (bluered: low-high). Models for Emiliania huxleyi: (A) Findlay et al. (2008), (B) Joassin et al. (2011), (C) Merico et al. (2004, 2006) (D) Oguz & Merico (2006) and (E) Tyrrell & Taylor (1996); and for coccolithophores as a functional group: (F) Gregg et al. (2003) low light model, (G) Gregg et al. (2003) high light model, (H) Gregg & Casey (2007) low light model and (I) Gregg & Casey (2007) high light model 

The percentage which literature models (Table 3) underestimate (negative values) or overestimate (positive values) maximum specific growth rate compared to the combined temperature-photon flux density model presented in this study (Fig. 3). 0% equals no difference between the literature model and the model presented in this study. Contours are at 50% intervals although no data are shown above 300%. Colour scale: specific growth rates (bluered: low-high). Models for Emiliania huxleyi: (A) Findlay et al. (2008), (B) Joassin et al. (2011), (C) Merico et al. (2004, 2006) (D) Oguz & Merico (2006) and (E) Tyrrell & Taylor (1996); and for coccolithophores as a functional group: (F) Gregg et al. (2003) low light model, (G) Gregg et al. (2003) high light model, (H) Gregg & Casey (2007) low light model and (I) Gregg & Casey (2007) high light model 

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Article
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The relationship between the maximum specific growth rate (μ, d-1) of the coccolithophore Emiliania huxleyi and photon flux density (PFD, μmol photons m-2s-1) was quantified using a combination of quantile regression and culture experiment data from the literature (n = 1387). This relationship, used in ecosystem models incorporating E. huxleyi or c...

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... comparison with the combined model presented in this study, all 5 literature models ( Fig. 4A-E) over- estimate E. huxleyi maximum growth rate by > 300% across a wide range of PFDs at low temperatures, while all 5 models overestimate E. huxleyi maximum growth rate to a lesser extent across a wide range of PFDs and temperatures, with the exception of Findlay et al. (2008), where growth rate is underestimated over the majority of ...
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... huxleyi maximum growth rate by > 300% across a wide range of PFDs at low temperatures, while all 5 models overestimate E. huxleyi maximum growth rate to a lesser extent across a wide range of PFDs and temperatures, with the exception of Findlay et al. (2008), where growth rate is underestimated over the majority of the PFD and temperature range (Fig. 4A). The model used by Joassin et al. (2011) overestimated maximum growth rate across the entire PFD and temperature range (Fig. ...
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... maximum growth rate to a lesser extent across a wide range of PFDs and temperatures, with the exception of Findlay et al. (2008), where growth rate is underestimated over the majority of the PFD and temperature range (Fig. 4A). The model used by Joassin et al. (2011) overestimated maximum growth rate across the entire PFD and temperature range (Fig. ...
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... Oguz & Merico (2006) and Joassin et al. (2011), are also higher than that presented in this study. This results in generally shallower PFD− growth rate slopes up to the growth optima in litera- ture models, and therefore in the underestimation of growth rate at low PFD values across all or the major- ity of the temperature range for these studies (Fig. 4A,C,E). Hence, previously used parameters for both PFD and temperature components of growth rate models appear to be inappropriate for E. huxleyi and it is recommended that the combined model parameters presented in this study should be used in ...
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... some studies which model all coccolithophore spe- Table 3. Models used in literature studies to predict maximum specific growth rate (μ) from temperature (T, °C) and photon flux density (PFD, µmol photons m −2 s −1 ). The differences between these models and the combined temperature-photon flux density model calculated in this study are shown in Fig. 4 cies as a combined functional group ( Gregg et al. 2003, Le Quere et al. 2005, Gregg & Casey 2007. When compared to the combined PFD and tempera- ture model presented in this study ( Fig. 4F-I), cocco- lithophore functional group models described by Gregg et al. (2003) and Gregg & Casey (2007) have a lower maximum growth rate over ...
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... µmol photons m −2 s −1 ). The differences between these models and the combined temperature-photon flux density model calculated in this study are shown in Fig. 4 cies as a combined functional group ( Gregg et al. 2003, Le Quere et al. 2005, Gregg & Casey 2007. When compared to the combined PFD and tempera- ture model presented in this study ( Fig. 4F-I), cocco- lithophore functional group models described by Gregg et al. (2003) and Gregg & Casey (2007) have a lower maximum growth rate over almost the entire PFD range due to lower μ opt values. A multi-species coccolithophore population is indeed likely to have a lower combined maximum growth rate than a monospecific E. huxleyi ...

Citations

... The evolutionary effect of temperature on phytoplankton should now be investigated concomitantly to other factors (which has been partially done by [Sauterey et al., 2014]), such as irradiance, nutrient [Irwin et al., 2015] or pH and CO 2 concentrations [Coello-Camba et al., 2014]. The phytoplankton species Emiliana huxleyi is known to be acidsensitive, but its adaptation capability to co-variation of temperature and pH are not clearly understood [Fielding, 2014, Gibbs et al., 2016, Schlüter et al., 2014. ...
Thesis
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Unicellular photosynthetic organisms forming the phytoplankton are the basis of primary production. Because these organisms cannot regulate their inner temperature, the medium temperature strongly constrains their growth. Understanding the impact of this factor is topical in a global change context. In this PhD thesis we have investigated how phytoplankton adapts to temperature. By analyzing the growth rate as a function of temperature for hundreds of species we highlighted the characteristics that can be accurately described by a mathematical model. We have identied the links between the cardinal temperatures as well as their thermodynamical fundament using the mechanistic Hinshelwood model. We then challenged the Eppley hypothesis `hotter is faster' for 5 phylogenetic phytoplankton groups and determined the evolutionary limits for each of them. We have also studied the adaptation mechanisms associated to long term temperature variations by developing an evolutionary model using the adaptive dynamics theory allowing to predict the evolutionary outcome of species adaptation to a simple temperature cycle. Our results have been compared to a selection experiment carried out in a controlled device on Tisochrysis lutea. Our method has been extended to predict the adaptation of a strain to periodic temperature profiles and study phytoplankton adaptation at the global ocean scale. In situ data of sea surface temperature have been used as a forcing variable and have permitted to show that the elevation of temperature will be critical for several species in particular for those living in areas where the annual temperature fluctuation is high such as the Mediterranean sea.