Today’s Solutions: July 20, 2024

Pretty privilege is not just a bias humans hold within our own species. According to a machine-learning study from the University of Montpellier, reef fish that people perceive as more beautiful are a higher priority for conservation support.

In the study, 13,000 members of the public were asked to rate the attractiveness of 481 photographs of ray-finned reef fishes. The team then plugged this data into a machine learning algorithm that learned how humans perceive beauty in fish and were then able to predict the attractiveness of thousands of other species.

The program found that colorful species with rounded bodies tend to be perceived as more beautiful and interestingly, less evolutionary, and ecologically different. When this was combined with IUCN conservation data, it could be seen that fish with lower aesthetic value was associated with “threatened” or “unknown” status and higher commercial interest from fisheries.

“Our study provides, for the first time, the aesthetic value of 2,417 reef fish species. We found that less beautiful fishes are the most ecologically and evolutionary distinct species and those recognized as threatened. Our study highlights likely important mismatches between potential public support for conservation and the species most in need of this support,” added the lead author of the paper Nicolas Mouqet.

The team hypothesizes that this bias stems from the brain’s processing of color and patterns, however, this innate preference is extremely problematic. Due to the high diversity of ecological and evolutionary traits, unattractive fish are the most important for keeping the whole reef happy and healthy.

This data shines an exposing light on people’s perception of beauty and how that impacts conservation efforts. Being aware of this bias towards more attractive creatures helps break this bias and leads to more support for the ugly, yet vital organisms living in the ocean ecosystem.

Source study: PLOS Biology Ugly’ reef fishes are most in need of conservation support: Machine learning enables largest study to date on aesthetic preferences and fish ecology

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