*
+
Temperature Influence on Phytoplankton Community Growth Rates
Elliot Sherman and Prof. Keith Moore
University of California, Irvine
Email: [email protected]
Abstract
To predict how marine phytoplankton will respond to warming oceans, we
need to understand the relation between temperature and growth rate. The Q10
factor describes how metabolic/growth rates change with a 10 oC temperature
change. The standard Q10 of ~2.0 often used in ecosystem models may not be
an accurate predictor of phytoplankton community growth rates. A global
database of phytoplankton growth and grazing estimates from the dilution
method of Landry and Hasset (1982) was compiled and then analyzed to
examine the temperature-growth relationship. Our analysis shows that a Q10 of
~1.5 best represents community phytoplankton growth rates in the ocean. This
Q10 is significantly different than the standard Q10 value of ~2.0. We compare
these observations with output from an ocean biogeochemical model that
includes explicit phytoplankton functional groups.
Figure 2 Spatial plot showing the
observational growth rates on the
ocean model grid.
Figure 3 BEC Model
temperature as a function of
observed temperature.
Figure 7 Observed growth
rates plotted as a function of
nitrate concentrations.
Model Description
Figure 8 Observed growth
rates plotted as a function of
latitude.
Discussion
The Biogeochemical Elemental Cycling (BEC) model is a coupled ocean
biogeochemical/ecosystem model, which runs within the NCAR CCSM3 (Moore et al.,
2004). Ocean circulation is simulated with CCSM Parallel Ocean Program circulation
module in coarse resolution (3.6° longitude and 0.9°-2.0° latitude) (Collins et al.,
2006; Yeager et al., 2006). A new, more realistic sedimentary source of dissolved iron
is also included in these simulations (Moore and Braucher, 2008). For the results
shown here phytoplankton growth rates are controlled by a temperature function with
Q10 = 2.0. The maximum growth rate is reduced according to this temperature
function multiplied by functions accounting for light and nutrient limitation of growth.
Small Phytoplankton
Coccolithophores
Diazotrophs
Diatoms
Phaeocystis
C, N, Fe, P, Chl
C, N, Fe, P, Si, Chl
C, N, Fe, P, Chl
C, N, Fe, P, CaCO3, Chl
Zooplankton
Nitrate
C, N, Fe, P
Ammonium
Small Detritus
DOM
Phosphate
Figure 4 Observed growth rates
plotted as a function of temperature.
Lower light blue line shows best fit
trend, which gives a Q10 factor of 1.5.
Purple line shows best fit with a
prescribed Q10 of 2.0.
Figure 5 BEC Model predicted
growth rates plotted as a function of
temperature. Lines as in Figure 4.
Results
Our evaluation of the Q10 effect has brought new insight on community
phytoplankton growth rates. Our research suggests that a Q10 much lower
than 2.0 is being exhibited in ocean waters. Although our sampling sites were
not as uniformly distributed as we would have liked, it did include sites all over
the oceans (Figure 2). An accurate representation of the Q10 affect on
phytoplankton growth rates is important for ecosystem models that predict
how global warming will influence marine ecosystems and biogeochemical
cycling. As global warming continues to take effect on our planet, it is vital that
we have the most accurate information possible.
The Q10 of 1.5 approximates the maximum growth rates across all
temperatures in the observations (Figure 4). However, the model predicted
rates are lower than expected at high temperatures, given the assumed Q10
of 2.0 in the model (Figure 5). This is due to strong nutrient limitation of
growth in warm waters in the model. The fact that such a decrease at high
temperatures is not seen in the observational data suggests that across all
temperatures, the ambient phytoplankton community is growing at relatively
high growth rates (not strongly nutrient limited).
C, N, Fe, P
Sinking Particulates
C, N, Fe, P, Si, CaCO3
Silicate
Figure 1. Structure of the BEC marine ecosystem model.
Evaluating the Q10 Effect on Phytoplankton Community Growth
An extensive database of over 800 phytoplankton community growth and grazing
rates, estimated from dilution experiments, was compiled to provide a reference for
evaluating the Q10 factor in the oceans (Figure 2). This work builds on an earlier
compilation by Calbet and Landry (2004). This observational data is plotted as a
function of environmental factors such as temperature. Six variables were
examined: chlorophyll, grazing/growth, grazing, growth, nitrate and temperature.
Some field studies did not report the ambient sea surface temperature (SST). For
the studies that did report temperature, it was highly correlated with the SST
predicted by the BEC model (Figure 3). Thus, we substitute the model predicted
monthly temperature for studies that did not report temperature.
The graphs of observed and BEC Model estimated growth rates versus observed
temperature can be used to better interpret and refine the appropriate Q10 for use in
the BEC Model. We fit a growth vs. temperature line to the observations that
includes a temperature function multiplied by a reference growth rate at 30 oC. We
optimized both the reference growth rate and the Q10 factor by minimizing the rms
difference. We also fit a line with an assumed Q10 factor of 2.0 to both the
observational data and to output from the BEC model extracted at the same
locations, depth, and month as the field data.
Our findings show that there is a strong positive correlation between growth rates and
ambient temperature (Figure 4 and 6) as theorized previously (Eppley, 1972; Brown
et al., 2004). However, our findings show that a Q10 factor of ~1.5 is a better fit to the
observations than a value of ~2.0. The Q10 of 1.5 was the best fit to all the data and
also approximated the upper bound of growth rates (light blue lines in Figure 4)
compared with a Q10 of 2.0 (purple line). Somewhat surprisingly, our results suggest
no correlation between ambient nitrate concentration and growth rates (Figure 7). A
negative relationship was found between growth rate and latitude. Growth rates
tended to decrease as latitude increased (Figure 8). This can be explained with the
positive relationship exhibited by temperature and growth rate.
Acknowledgements
This work was supported by NSF grants OCE-0928204 and
ARC-0902045 to J.K. Moore. We would like to thank all the researchers
who performed the field experiments measuring growth and grazing
rates, and provided this data, either directly to us or through
publications.
References
Growth v.s. Temperature
1.2
1
Growth (/day)
Iron
0.8
0.6
0.4
0.2
0
-3-0
0-3
3-6
6-9
9-12
12-15
15-18
18-21
21-24
Temperature (C)
Figure 6 Observed phytoplankton community
growth rates binned by ocean temperature.
24-27
27-30
Brown, James, James Gillooly, Andrew Allen, Van Savage, and Geoffrey West. "Toward a
Metabolic Theory of Ecology." Ecology 85.7 (2004): 1771-789.
Calbet, A, and M.R. Landry, 2004. Phytoplankton growth, microzooplankton grazing, and carbon
cycling in marine systems. Limnol. Oceanogr., 49: 51-57.
Collins W. D., Blackmon M. and Bitz C. M., et al., J. Climate, 19(2006), pp. 2122–2143.
Eppley, Richard W. "Temperature and Phytoplankton Growth in the Sea." Fishery Bulletin 70
(1972): 1063-085.
Landry, M. R., and R. P. Hassett. "Estimating the Grazing Impact of Marine Micro-zooplankton."
Marine Biology 67 (1982): 283-88.
Moore J. K. and Braucher O., Biogeosciences, 5(2008), pp. 631-656.
Moore J. K., Doney S. C., and Lindsay K., Glob. Biogeochem. Cycles, 18(2004), GB4028,
doi:10.1029/2004GB002220.
Yeager S.G., Shields C.A., Large W.G., et al. J. Climate, 19(2006), pp. 2545-2566.