scholarly journals The Influence of Temperature and Community Structure on Light Absorption by Phytoplankton in the North Atlantic

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4182 ◽  
Author(s):  
Robert J.W. Brewin ◽  
Stefano Ciavatta ◽  
Shubha Sathyendranath ◽  
Jozef Skákala ◽  
Jorn Bruggeman ◽  
...  

We present a model that estimates the spectral phytoplankton absorption coefficient ( a p h ( λ ) ) of four phytoplankton groups (picophytoplankton, nanophytoplankton, dinoflagellates, and diatoms) as a function of the total chlorophyll-a concentration (C) and sea surface temperature (SST). Concurrent data on a p h ( λ ) (at 12 visible wavelengths), C and SST, from the surface layer (<20 m depth) of the North Atlantic Ocean, were partitioned into training and independent validation data, the validation data being matched with satellite ocean-colour observations. Model parameters (the chlorophyll-specific phytoplankton absorption coefficients of the four groups) were tuned using the training data and found to compare favourably (in magnitude and shape) with results of earlier studies. Using the independent validation data, the new model was found to retrieve total a p h ( λ ) with a similar performance to two earlier models, using either in situ or satellite data as input. Although more complex, the new model has the advantage of being able to determine a p h ( λ ) for four phytoplankton groups and of incorporating the influence of SST on the composition of the four groups. We integrate the new four-population absorption model into a simple model of ocean colour, to illustrate the influence of changes in SST on phytoplankton community structure, and consequently, the blue-to-green ratio of remote-sensing reflectance. We also present a method of propagating error through the model and illustrate the technique by mapping errors in group-specific a p h ( λ ) using a satellite image. We envisage the model will be useful for ecosystem model validation and assimilation exercises and for investigating the influence of temperature change on ocean colour.

2006 ◽  
Vol 3 (3) ◽  
pp. 607-663 ◽  
Author(s):  
E. Litchman ◽  
C. A. Klausmeier ◽  
J. R. Miller ◽  
O. M. Schofield ◽  
P. G. Falkowski

Abstract. Phytoplankton community composition profoundly influences patterns of nutrient cycling and the structure of marine food webs; therefore predicting present and future phytoplankton community structure is of fundamental importance to understanding how ocean ecosystems are influenced by physical forcing and nutrient limitations. In this paper, we develop a mechanistic model of phytoplankton communities that includes multiple taxonomic groups, test the model at two contrasting sites in the modern ocean, and then use the model to predict community reorganization under different global change scenarios. The model includes three phytoplankton functional groups (diatoms, coccolithophores, and prasinophytes), five nutrients (nitrate, ammonium, phosphate, silicate and iron), light, and a generalist zooplankton grazer. Each taxonomic group was parameterized based on an extensive literature survey. The model successfully predicts the general patterns of community structure and succession in contrasting parts of the world ocean, the North Atlantic (North Atlantic Bloom Experiment, NABE) and subarctic North Pacific (ocean station Papa, OSP). In the North Atlantic, the model predicts a spring diatom bloom, followed by coccolithophore and prasinophyte blooms later in the season. The diatom bloom becomes silica-limited and the coccolithophore and prasinophyte blooms are controlled by nitrogen, grazers and by deep mixing and decreasing light availability later in the season. In the North Pacific, the model reproduces the low chlorophyll community dominated by prasinophytes and coccolithophores, with low total biomass variability and high nutrient concentrations throughout the year. Sensitivity analysis revealed that the identity of the most sensitive parameters and the range of acceptable parameters differed between the two sites. Five global change scenarios are used to drive the model and examine how community dynamics might change in the future. To estimate uncertainty in our predictions, we used a Monte Carlo sampling of the parameter space where future scenarios were run using parameter combinations that produced adequate modern day outcomes. The first scenario is based on a global climate model that indicates that increased greenhouse gas concentrations will cause a later onset and extended duration of stratification and shallower mixed layer depths. Under this scenario, the North Atlantic spring diatom bloom occurs later and is of a smaller magnitude, but the average biomass of diatoms, coccolithophores and prasinophytes will likely increase. In the subarctic North Pacific, diatoms and prasinophytes will likely increase along with total chlorophyll concentration and zooplankton. In contrast, coccolithophore densities do not change at this site. Under the second scenario of decreased deep-water phosphorus concentration, coccolithophores, total chlorophyll and zooplankton decline, as well as the magnitude of the spring diatom bloom, while the average diatom and prasinophyte abundance does not change in the North Atlantic. In contrast, a decrease in phosphorus in the North Pacific is not likely to change community composition. Similarly, doubling of nitrate in deep water does not significantly affect ecosystems at either site. Under decreased iron deposition, coccolithophores are likely to increase and other phytoplankton groups and zooplankton to decrease at both sites. An increase in iron deposition is likely to increase prasinophyte and diatom abundance and decrease coccolithophore abundance at both sites, although more dramatically at the North Pacific site. Total chlorophyll and zooplankton are also likely to increase under this scenario at both sites. Based on these scenarios, our model suggests that global environmental change will inevitably alter phytoplankton community structure and potentially impact global biogeochemical cycles.


1995 ◽  
Vol 348 (1324) ◽  
pp. 191-202 ◽  

Remote sensing of ocean colour affords us our only window into the synoptic state of the pelagic ecosystem, and is likely to remain the only such option into the foreseeable future. Estimation of primary production from remotely sensed data on ocean colour is a research problem in two parts: (i) the construction of a local algorithm; and (ii) the development of a protocol for extrapolation. Good local algorithms exist but their proper implementation requires that certain parameters be specified. Protocols for extrapolation have to include procedures for the assignment of these parameters. One suitable approach is based on partition of the ocean into a suite of domains and provinces within which physical forcing, and the algal response to it, are distinct. This approach is still in its infancy, but is best developed for the North Atlantic. Using this method, and using the accumulated data from oceanographic expeditions, leads to an estimate for the annual primary production of the North Atlantic at the basin scale. Direct validation of the result is not possible in the absence of an independent calculation, but the potential errors involved may be assessed.


Author(s):  
C Fuentes-Yaco ◽  
C Caverhill ◽  
H Maass ◽  
C Porter ◽  
GN White

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