Researchers, using a kind of machine learning to assess the successes and failures of wildlife conservation over time, indicate that we’re getting better at reintroducing species to the wild. 

In the study, appearing March 19 in a data science journal, Patterns, researchers assessed abstracts of more than 4,000 studies of species reintroduction across four decades.

“We wanted to learn some lessons from the vast body of conservation biology literature on reintroduction programmes that we could use here in California as we try to put sea otters back into places they haven’t roamed for decades.

But what sat in front of us was millions of words and thousands of manuscripts.

We wondered how we could extract data from them that we could actually analyse, and so we turned to natural language processing,” says senior author Kyle Van Houtan, chief scientist at Monterey Bay Aquarium in California.

Natural language processing is a kind of machine learning that analyses strings of human language to extract useable information, essentially allowing a computer to read documents like a human.

Sentiment Analysis, used in this paper, looks more specifically at a trained set of words assigned a positive or negative emotional value to assess the positivity or negativity of the text overall.

The trends suggested greater conservation success. “Over time, there’s a lot less uncertainty in the assessment of sentiment in the studies, and we see reintroduction projects become more successful — and that’s a big takeaway,” he says.

“Looking at thousands of studies, it seems like we’re getting better at it, and that’s encouraging.”

“If we are going to maximise our conservation dollars, then we need to be able to quickly assess what works and what doesn’t,” says study co-author Lucas Joppa, Chief Environmental Officer at Microsoft. -Science Daily