The functions and services provided by ecosystems emerge from myriad interactions between organisms and their environment. The difficulty of incorporating this complexity into quantitative models has hindered our ability to predictively link species-level composition with ecosystem function. This represents a major obstacle towards engineering ecological systems for environmental and biotechnological purposes. Inspired by similar findings in evolutionary genetics, here we show that the function of ecological communities often follows simple equations that allow us to accurately predict and optimize ecological function. This predictability is facilitated by emergent "species-by-ecosystem" interactions that mirror the patterns of global epistasis observed in many genetic systems. Our results illuminate an unexplored path to quantitatively linking the composition and function of ecological communities, bringing the tasks of predicting biological function at the genetic, organismal, and ecological scales under the same quantitative formalism.
Epistatic interactions between mutations add substantial complexity to adaptive landscapes and are often thought of as detrimental to our ability to predict evolution. Yet, patterns of global epistasis, in which the fitness effect of a mutation is well-predicted by the fitness of its genetic background, may actually be of help in our efforts to reconstruct fitness landscapes and infer adaptive trajectories. Microscopic interactions between mutations, or inherent nonlinearities in the fitness landscape, may cause global epistasis patterns to emerge. In this brief review, we provide a succinct overview of recent work about global epistasis, with an emphasis on building intuition about why it is often observed. To this end, we reconcile simple geometric reasoning with recent mathematical analyses, using these to explain why different mutations in an empirical landscape may exhibit different global epistasis patterns—ranging from diminishing to increasing returns. Finally, we highlight open questions and research directions.
This article is part of the theme issue ‘Interdisciplinary approaches to predicting evolutionary biology’.
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