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Can Rates of Ocean Primary Production and Biological Carbon Export Be Related Through Their Probability Distributions?
Author(s) -
Cael B. B.,
Bisson Kelsey,
Follett Christopher L.
Publication year - 2018
Publication title -
global biogeochemical cycles
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.512
H-Index - 187
eISSN - 1944-9224
pISSN - 0886-6236
DOI - 10.1029/2017gb005797
Subject(s) - biogeochemical cycle , biome , log normal distribution , environmental science , scaling , carbon cycle , primary production , phytoplankton , atmospheric sciences , null model , probability distribution , distribution (mathematics) , scale (ratio) , mathematics , statistics , ecosystem , ecology , geology , physics , biology , mathematical analysis , geometry , combinatorics , nutrient , quantum mechanics
We describe the basis of a theory for interpreting measurements of two key biogeochemical fluxes—primary production by phytoplankton ( p , μg C · L −1 · day −1 ) and biological carbon export from the surface ocean by sinking particles ( f , mg C · m −2 · day −1 )—in terms of their probability distributions. Given that p and f are mechanistically linked but variable and effectively measured on different scales, we hypothesize that a quantitative relationship emerges between collections of the two measurements. Motivated by the many subprocesses driving production and export, we take as a null model that large‐scale distributions of p and f are lognormal. We then show that compilations of p and f measurements are consistent with this hypothesis. The compilation of p measurements is extensive enough to subregion by biome, basin, depth, or season; these subsets are also well described by lognormals, whose log‐moments sort predictably. Informed by the lognormality of both p and f we infer a statistical scaling relationship between the two quantities and derive a linear relationship between the log‐moments of their distributions. We find agreement between two independent estimates of the slope and intercept of this line and show that the distribution of f measurements is consistent with predictions made from the moments of the p distribution. These results illustrate the utility of a distributional approach to biogeochemical fluxes. We close by describing potential uses and challenges for the further development of such an approach.