Tom Quigley3209
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Machine learning helps assign conservation status of over 150,000 plants
https://news.mongabay.com/2019/01/machine-learning-tool-helps-prioritize-plants-for-conservation/
Vertebrates get all the attention, but less than 10% of plants have been assessed for an IUCN Red List conservation status. Now, more than 150,000 plant species have been assessed using machine learning to rapidly ID the most threatened.
"In a first global plant conservation assessment, a multi-institutional research team used the power of open-access databases and machine learning to predict the conservation status of more than 150,000 plants.
They paired geographic, environmental, climatic, and morphological trait information of plant species of known risk of extinction from the IUCN Red List with information on plants of unknown risk in a machine learning model. The model calculated the likelihood that a given unassessed plant species was actually at risk of extinction and identified the variables that best predicted conservation risk.
More than 15,000 of the species–roughly 10 percent of the total assessed by the team—had characteristics similar to those already categorized as at least near-threatened by IUCN and thus at a high likelihood of extinction.
The protocol could provide a first cut in identifying unassessed species likely at risk of extinction and suggest how to allocate scarce conservation resources."
Vertebrates get all the attention, but less than 10% of plants have been assessed for an IUCN Red List conservation status. Now, more than 150,000 plant species have been assessed using machine learning to rapidly ID the most threatened.
"In a first global plant conservation assessment, a multi-institutional research team used the power of open-access databases and machine learning to predict the conservation status of more than 150,000 plants.
They paired geographic, environmental, climatic, and morphological trait information of plant species of known risk of extinction from the IUCN Red List with information on plants of unknown risk in a machine learning model. The model calculated the likelihood that a given unassessed plant species was actually at risk of extinction and identified the variables that best predicted conservation risk.
More than 15,000 of the species–roughly 10 percent of the total assessed by the team—had characteristics similar to those already categorized as at least near-threatened by IUCN and thus at a high likelihood of extinction.
The protocol could provide a first cut in identifying unassessed species likely at risk of extinction and suggest how to allocate scarce conservation resources."