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A POISSON–PARETO MODEL OF CHLOROPHYLL-A FLUORESCENCE SIGNALS IN MARINE ENVIRONMENTS

Part of: Mathematical biology in general

Published online by Cambridge University Press:  02 July 2015

S. WOODCOCK ,
B. MANOJLOVIC ,
M. E. BAIRD  and
P. J. RALPH
S. WOODCOCK*
Affiliation:
School of Mathematical Sciences, University of Technology Sydney, Sydney, Australia email [email protected], [email protected]
B. MANOJLOVIC
Affiliation:
School of Mathematical Sciences, University of Technology Sydney, Sydney, Australia email [email protected], [email protected]
M. E. BAIRD
Affiliation:
CSIRO Oceans and Atmosphere Flagship, GPO Box 1538, Hobart 7001, Australia email [email protected]
P. J. RALPH
Affiliation:
Plant Functional Biology and Climate Change Cluster, University of Technology Sydney, Sydney, Australia email [email protected]
*
✉[email protected]
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Abstract

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Because of its central role in the global carbon cycle, quantifying the biomass of photosynthetic microalgae in the oceans is crucial to our ability to estimate the oceans’ carbon drawdown. Many traditional methods of primary production assessment have proven to be extremely time consuming and, consequently, have handled only very small sample sizes. The recent advent of in situ bio-optical sensors, such as the water quality monitor (WQM), is now providing lower cost and higher throughput data on these crucial biological communities. These WQMs, however, only quantify the total fluorescence of all individual cells within their optical sample windows, irrespective of size. In this paper, we further develop an established model, based on Pareto random variables, of the size structure of the microalgae community to understand the effect of the WQMs’ sampling and data pooling on their estimates of algal biomass. Unfortunately, evaluating sums of Pareto variables is a notoriously difficult problem. Here, we utilize an approximation for the right-tail of the resulting distribution to derive parameter estimates for the underlying size structure of the microalgae community.

Keywords

Poisson–Pareto random variablesshifted Hill’s estimators

MSC classification

Primary: 92B05: General biology and biomathematics
Type
Research Article
Information
The ANZIAM Journal , Volume 56 , Issue 4 , April 2015 , pp. 373 - 380
Copyright
© 2015 Australian Mathematical Society 

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