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Earth’s oceans are warming and becoming more acidic as the climate changes. For much of the flora and fauna of the sea, that could mean extinction, unless species can adapt to new conditions and food sources—or migrate to more hospitable waters.
However, endangered species may be able to receive assistance from humans. Katie Lotterhos, Associate professor of marine- and environmental sciences at Northeastern. As long as scientists can accurately determine which species will require help.
That’s where Lotterhos and her colleagues come in.
There is often genetic variation within species. Some genetic strains are more able to adapt to new conditions than others. Researchers can pinpoint which genetic strains are most likely to survive in these new conditions so that restoration and protection efforts can be focused on them. Or, Lotterhos says, scientists could help species adapt to climate change by moving them to places that are likely to be more hospitable down the road in a concept called “assisted migration.” Scientists and industry leaders are already Consider this approach Agriculture and Trees.
“There is an urgent societal need to better match genetic strains with environments for restoration efforts in the face of climate change,” Lotterhos says. To do that, scientists have been developing methods for “genomic forecasting,” she says, which can use genetic data to “predict how a genetic strain will perform in different environments.”
But right now, scientists aren’t quite sure if those predictions are accurate. Lotterhos and his associates put a machine-learning algorithm to the test. Their results were published in a recent PaperPublished in the journal Evolutionary Applications.
The machine-learning algorithm combines genetic and environmental information to predict how poorly adapted a given genetic strain of a species would be to a certain set of environmental conditions in a measure called “genomic offset,” Lotterhos says. To test how accurately the algorithm predicts genomic offset, she explains, the team created computer simulations of what they call “virtual species” that don’t exist in the real world but undergo birth, death, dispersal, evolutionary selection, and mutation in the same ways that real species do in nature.
“Our study shows that genomic forecasting methods hold promise, but that we still don’t have a full understanding of their strengths and limitations,” Lotterhos says. When researchers kept the inputs simple and only considered genetic information or environmental information, the machine-learning method performed better than other methods for predicting genomic offset. Lotterhos admits that the results may be misleading when taken as a whole.
To test the machine-learning approach further, Lotterhos’s team is developing more simulations. The scientists will also conduct field experiments and take this experiment offline.
Lotterhos was recently ReceivedTwo prestigious awards have been presented to you: A Fulbright scholarship, and a National Science Foundation CAREER Award. Lotterhos and her colleagues are carrying out tests of the genomic forecasting techniques in oysters with the support of the CAREER award. She was awarded a Fulbright scholarship and has been to Sweden to test the methods on sea life, including marine snails, eelgrass and isopods (an order of crustaceans that includes woodlice).
“The Baltic Sea is an interesting study system because many species have genetically adapted to a steep environmental gradient from benign ocean conditions to a more acidic freshwater environment,” Lotterhos says. “The goal is to determine how well these methods work, and under what conditions they perform well.”
For media inquiriesPlease contact Ed Gavaghan at [email protected]617-373-5718
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