KARMACTIVE TEAM
A recent study using AI and neural networks estimates that 12.7% of marine teleost fish are near extinction, a sharp rise from the previous 2.5% estimate by the IUCN.
Photo source: Google
The IUCN Red List tracks over 150,000 species with nine threat categories, but the evaluation process is slow and only covers around 163,000 species by 2024.
AI and machine learning offer a cost-effective way to predict extinction risks for species lacking direct evaluations, filling gaps left by traditional assessments.
The new study's AI model identified 38% of marine fish species as data-deficient, resulting in an increase in predicted threatened species from 334 to 1,671.
Key fish families like groupers, rockfishes, and gobies are significantly at risk, highlighting the need for focused conservation efforts in marine ecosystems.
Geographic hotspots for endangered species include the South China Sea, Philippine Seas, Celebes Sea, and western coasts of Australia and North America.
The research underscores the need for more study and protection in the Coral Triangle, a biodiversity hotspot with many data-deficient species.
AI can complement IUCN assessments with a new Status Prediction Index, offering more data to guide conservation efforts and prioritize at-risk species and regions.
The integration of AI in biodiversity research could enhance the accuracy and efficiency of extinction risk assessments, revolutionizing conservation strategies.
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