Reverse-engineered computer model provides insights into larval behaviour
New research allows scientists to predict the direction and speed that larvae would have to swim to best match the patterns observed in nature

Ocean flows coloured
with sea surface temperature data (Credit: NASA/Goddard Space Flight Center
Scientific Visualization Studio)
Molly, a BSc (Hons) Marine Biology and Oceanography graduate, said:
“The majority of marine species have a larval development stage, and these larvae are a key component of ocean food webs. Understanding the ecology of larvae is therefore critical, as it will enhance our wider knowledge of the marine environment. Our research does go against previous thinking, but we believe it provides a real step change in how scientists can predict the dispersal of marine species now and in the future.”
Dr Knights, whose research involves using field-based experiments and theoretical models to assess how populations respond to anthropogenic and environmental pressures, added:
“Species dispersal has been a key research focus for many years. Global climate change leads to shifts and expansion in the distribution of terrestrial and marine species altering the structure and functioning of ecosystems, therefore understanding dispersal is imperative to ecosystem management. Our study provides a toolbox for dispersal modellers, providing a realistic insight into behaviour that hasn’t been available before.”
PhD Student Molly James explains how scientists have developed a new approach to describe the behaviours of microscopic marine larvae.
The full study – James et al: Reverse engineering field-derived vertical distribution profiles to infer larval swimming behaviours – is published in Proceedings of the National Academy of Sciences of the United States of America, doi: 10.1073/pnas01900238116
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