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\begin{document}

\title{Predicting and prioritizing coexistence: learning outcomes via
experiments}



\author[1]{Benjamin Blonder}%
\author[1]{Michael Lim}%
\author[2]{Oscar Godoy}%
\affil[1]{University of California Berkeley}%
\affil[2]{Estación Biológica de Doñana}%


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  \date{May 22, 2024}


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\begin{abstract}
Community assembly provides the foundation for applications in
biodiversity conservation, climate change, invasion ecology, restoration
ecology, and synthetic ecology. Predicting and prioritizing community
assembly outcomes remains challenging. We address this challenge via a
mechanism-free LOVE (Learning Outcomes Via Experiments) approach
suitable for cases where little data or knowledge exist: we carry out
actions (randomly-sampled combinations of species additions), measure
abundance outcomes, and then train a model to predict arbitrary outcomes
of actions, or prioritize actions that would yield the most desirable
outcomes. When trained on \textless{}100 randomly-selected actions, LOVE
predicts outcomes with 2-5\% error across datasets, and prioritizes
actions for maximizing richness, maximizing abundance, or minimizing
abundances of unwanted species, with 94-99\% true positive rate and
12-83\% true negative rate across tasks. LOVE complements existing
approaches for community ecology by providing a foundation for
additional mechanism-first study, and may help address numerous
ecological applications.%
\end{abstract}%



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