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\begin{document}

\title{The Global Distribution and Drivers of Grazing Dynamics Estimated from
Inverse Modelling~}



\author[1,2]{Tyler Rohr}%
\author[3,4]{Anthony Richardson}%
\author[5]{Andrew Lenton}%
\author[3,5]{Matt Chamberlain}%
\author[1,2,4,5]{Elizabeth Shadwick}%
\affil[1]{Institute for Marine and Antarctic Science, University of Tasmania}%
\affil[2]{Australian Antarctic Partnership Program}%
\affil[3]{School of Mathematics and Physics, The University of Queensland}%
\affil[4]{Commonwealth Scientific and Industrial Research Organisation (CSIRO) Oceans and Atmosphere, 8 BioSciences Precinct (QBP)}%
\affil[5]{Commonwealth Scientific and Industrial Research Organisation (CSIRO) Oceans and Atmosphere}%


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  \date{May 25, 2024}


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\begin{abstract}
We examine how zooplankton influence phytoplankton bloom phenology from
the top-down, then use inverse modelling to infer the distribution and
drivers of mean community zooplankton grazing dynamics based on the
skill with which different simulated grazing formulations are able to
recreate the observed seasonal cycle in phytoplankton biomass. We find
that oligotrophic (eutrophic) biomes require more (less) efficient
grazing dynamics, characteristic of micro- (meso-) zooplankton, leading
to a strong relationship between the observed mean annual phytoplankton
concentration in a region and the optimal grazing parameterization
required to simulate it's observed phenology. Across the globe, we found
that a type III functional response consistently exhibits more skill
than a type II response, suggesting the mean dynamics of a coarse model
grid-cell should offer stability and prey refuge at low biomass
concentrations. These new observationally-based global distributions
will be invaluable to help constrain, validate and develop next
generation of biogeochemical models.%
\end{abstract}%



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