RT Journal Article
SR Electronic
T1 Why scaling up uncertain predictions to higher levels of organisation will underestimate change
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.05.26.117200
DO 10.1101/2020.05.26.117200
A1 Orr, James
A1 Piggott, Jeremy
A1 Jackson, Andrew
A1 Arnoldi, Jean-François
YR 2020
UL http://biorxiv.org/content/early/2020/05/30/2020.05.26.117200.abstract
AB Uncertainty is an irreducible part of predictive science, causing us to over- or underestimate the magnitude of change that a system of interest will face. In a reductionist approach, we may use predictions at the level of individual system components (e.g. species biomass), and combine them to generate predictions for system-level properties (e.g. ecosystem function). Here we show that this process of scaling up uncertain predictions to higher levels of organization has a surprising consequence: it will systematically underestimate the magnitude of system-level change, an effect whose significance grows with the system’s dimensionality. This stems from a geometrical observation: in high dimensions there are more ways to be more different, than ways to be more similar. This general remark applies to any complex system. Here we will focus on ecosystems thus, on ecosystem-level predictions generated from the combination of predictions at the species-level. In this setting, we show that higher ecosystem dimension does not necessarily mean more constituent species, but more diversity. Furthermore, while dimensional effects can be obscured when predicting change of a single linear aggregate property (e.g. total biomass), they are revealed when predicting change of non-linear aggregate properties (e.g. absolute biomass change, stability or diversity), and when several properties are considered at once to describe the ecosystem, as in multi-functional ecology. Our findings highlight the dimensional effects that inevitably play out when uncertain predictions are scaled up, and are therefore relevant to any field of science where a reductionist approach is used to generate predictions.Competing Interest StatementThe authors have declared no competing interest.