Oceanography
Comparing Approaches to the Carbon-Based Productivity Model: Assessing the Sensitivity of Remote Sensing-Derived Phytoplankton Productivity to Mixed Layer Depth.
The purpose of this review is to compare approaches or variations of approaches that are being used to assess the sensitivity of phytoplankton productivity to mixed layer depth.
The challenge to clarifying controls on primary productivity and the related responses and feedbacks is a key objective of research on global change. In order to accomplish this, however, measurements of NPP and the quantification of its variability in space and time must be refined. Carvalho and Eyre (2012), for example, suggest that conventional approaches to OCR and CRR may be misleading. They propose methods for CRR and photosynthetic measurement that can more precisely measure the concentration of dissolved inorganic carbon in water. This paper will review broad variations of the carbon-based productivity model.
References
Behrenfeld, M., E. Boss, D. Siegel, and D. Shea (2005), Carbon-based ocean productivity and phytoplankton physiology from space, Global Biogeochem. Cycles, 19(1), GB1006, doi:10.1029/2004GB002299.
Carvalho, M.C. And Eyre, B.D. (2012). "Measurement of planktonic CO2 respiration in the light." Limnology and Oceanography: Methods 10: 167-178. doi:10.4319/lom.2012.10.167
D. Antoine, K.R. Arrigo, I. Asanuma, O. Aumont, R. Barber, and M. Behrenfeld (2006), A comparison of global estimates of marine primary production from ocean color, Deep Sea Res., Part II, 53(5 -- 7), 741 -- 770, doi:10.1016/j.dsr2.2006.01.028.
Duffy, J.E., and J.J. Stachowicz (2006), Why biodiversity is important to oceanography: Potential roles of genetic, species, and trophic diversity in pelagic ecosystem processes, Mar. Ecol. Prog. Ser., 311, 179 -- 189, doi:10.3354/meps311179.
Friedrichs, M.A.M., et al. (2009), Assessing the uncertainties of model estimates of primary productivity in the tropical Pacific Ocean, J. Mar. Syst., 76(1 -- 2), 113 -- 133, doi:10.1016/j.jmarsys.2008.05.010.
Milutinovic, S. Beherenfeld, M.J., Johannessen, A. And Johannessen, T. (2008). Sensitivity of remote sensing-derived phytoplankton productivity to mixed layer depth: Lessons from the carbon-based productivity model. Global Biogeochemical Cycles, 23 (GB4005). doi: 10.1029.2008GB3431.
Townsend, D.W., L.M. Cammen, P.M. Holligan, D.E. Campbell, and N.R. Pettigrew (1994), Causes and consequences of variability in the timing of spring phytoplankton blooms, Deep Sea Res., Part I, 41(5 -- 6), 747 -- 765, doi:10.1016/0967-0637(94)90075-2.
Westberry, T., M.J. Behrenfeld, D.A. Siegel, and E. Boss (2008), Carbon- based primary productivity modeling with vertically resolved photoacclimation, Global Biogeochem. Cycles, 22, GB2024, doi:10.1029 / 2007GB003078.
" Because of the ability to reproduce in large amounts in a small amount of time, phytoplankton are considered as the first link in the food chain of nearly all marine animals. Phytoplankton provide food for a large variety of organisms, including the microscopic animals (such as the zooplankton), bivalve molluscan shellfish (like mussels, oysters, scallops, and clams), and small fishes (such as anchovies and sardines). To continue the food
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