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\begin{document}

\title{Testing the skill of a species distribution model using a 21st Century
virtual ecosystem}



\author[1,1,1]{L R Bardon}%
\author[2,2,2]{B A Ward}%
\author[3,3,3]{S Dutkiewicz}%
\author[4,4,4]{B B Cael}%
\affil[1]{University of Southern California}%
\affil[2]{University of Southampton}%
\affil[3]{Massachusetts Institute of Technology}%
\affil[4]{National Oceanography Centre}%


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  \date{June 18, 2024}


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\begin{abstract}
Plankton play an important role in marine food webs, in biogeochemical
cycling, and in Earth's climate; yet observations are sparse, and
predictions of how they might respond to climate change vary.
Correlative species distribution models (SDM's) have been applied to
predicting biogeography based on relationships to observed environmental
variables. To investigate sources of uncertainty, we use a correlative
SDM to predict the plankton biogeography of a 21st Century marine
ecosystem model (Darwin). Darwin output is sampled to mimic historical
ocean observations, and the SDM is trained using generalised additive
models. We find that predictive skill varies across test cases, and
between functional groups, with errors that are more attributable to
spatiotemporal sampling bias than sample size. End-of-century
predictions are poor, limited by changes in target-predictor
relationships over time. Our findings illustrate the fundamental
challenges faced by empirical models in using limited observational data
to predict complex, dynamic systems.%
\end{abstract}%



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