Researchers develop a method for predicting unprecedented events

Date:
July 23, 2020
Source:
Stanford University
Summary:
Researchers combined avalanche physics with ecosystem data to create
a computational method for predicting extreme ecological events. The
method may also have applications in economics and politics.



FULL STORY ==========================================================================
A black swan event is a highly unlikely but massively consequential
incident, such as the 2008 global recession and the loss of one-third of
the world's saiga antelope in a matter of days in 2015. Challenging the quintessentially unpredictable nature of black swan events, bioengineers
at Stanford University are suggesting a method for forecasting these
supposedly unforeseeable fluctuations.


==========================================================================
"By analyzing long-term data from three ecosystems, we were able to
show that fluctuations that happen in different biological species are statistically the same across different ecosystems," said Samuel Bray,
a research assistant in the lab of Bo Wang, assistant professor of bioengineering at Stanford. "That suggests there are certain underlying universal processes that we can take advantage of in order to forecast
this kind of extreme behavior." The forecasting method the researchers
have developed, which was detailed recently in PLOS Computational
Biology, is based on natural systems and could find use in health
care and environmental research. It also has potential applications
in disciplines outside ecology that have their own black swan events,
such as economics and politics.

"This work is exciting because it's a chance to take the knowledge and
the computational tools that we're building in the lab and use those
to better understand -- even predict or forecast -- what happens in the
world surrounding us," said Wang, who is senior author of the paper. "It connects us to the bigger world." From microbes to avalanches Over years
of studying microbial communities, Bray noticed several instances where
one species would undergo an unanticipated population boom, overtaking
its neighbors. Discussing these events with Wang, they wondered whether
this phenomenon occurred outside the lab as well and, if so, whether it
could be predicted.



==========================================================================
In order to address this question, the researchers had to find other
biological systems that experience black swan events. The researchers
needed details, not only about the black swan events themselves but
also the context in which they occurred. So, they specifically sought ecosystems that scientists have been closely monitoring for many years.

"These data have to capture long periods of time and that's hard to
collect," said Bray, who is lead author of the paper. "It's much more
than a PhD-worth of information. But that's the only way you can see the spectra of these fluctuations at large scales." Bray settled on three
eclectic datasets: an eight-year study of plankton from the Baltic Sea
with species levels measured twice weekly; net carbon measurements from
a deciduous broadleaf forest at Harvard University, gathered every 30
minutes since 1991; and measurements of barnacles, algae and mussels on
the coast of New Zealand, taken monthly for over 20 years.

The researchers then analyzed these three datasets using theory about avalanches -- physical fluctuations that, like black swan events,
exhibit short-term, sudden, extreme behavior. At its core, this theory
attempts to explain the physics of systems like avalanches, earthquakes,
fire embers, or even crumpling candy wrappers, which all respond to
external forces with discrete events of various magnitudes or sizes --
a phenomenon scientists call "crackling noise." Built on the analysis,
the researchers developed a method for predicting black swan events,
one that is designed to be flexible across species and timespans, and
able to work with data that are far less detailed and more complex than
those used to develop it.



========================================================================== "Existing methods rely on what we have seen to predict what might happen
in the future, and that's why they tend to miss black swan events,"
said Wang. "But Sam's method is different in that it assumes we are
only seeing part of the world. It extrapolates a little about what we're missing, and it turns out that helps tremendously in terms of prediction." Forecasting in the real world The researchers tested their method using
the three ecosystem datasets on which it was built. Using only fragments
of each dataset -- specifically fragments which contained the smallest fluctuations in the variable of interest -- they were able to accurately predict extreme events that occurred in those systems.

They would like to expand the application of their method to other systems
in which black swan events are also present, such as in economics, epidemiology, politics and physics. At present, the researchers are
hoping to collaborate with field scientists and ecologists to apply
their method to real-world situations where they could make a positive difference in the lives of other people and the planet.

This research was funded by the Volkswagen Foundation and Arnold and
Mabel Beckman Foundation. Wang is also a member of Stanford Bio-X and
the Wu Tsai Neurosciences Institute.


========================================================================== Story Source: Materials provided by Stanford_University. Original written
by Taylor Kubota.

Note: Content may be edited for style and length.


========================================================================== Journal Reference:
1. Samuel R. Bray, Bo Wang. Forecasting unprecedented ecological
fluctuations. PLOS Computational Biology, 2020; 16 (6): e1008021
DOI: 10.1371/journal.pcbi.1008021 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2020/07/200723143755.htm

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