Plants adapt to their local conditions. So what prevents plants spread over a wide geographic range from specialising? A study by Keep and colleagues takes a close look at the genome of Lolium perenne. The scientists examined sections of the genome connected to adaptive traits. They found that variability of climate across time, as well as space, kept the gene pool stirred to preserve genetic diversity. The results suggest ways to direct adaptation to local environments further as climates change.
You’re likely to have seen Lolium perenne, perennial ryegrass, even if you haven’t heard of it. This summer, you can certainly see it as it’s the grass used at Wimbledon Tennis club for the courts. It’s also a popular choice for feeding grazing animals. But grass isn’t just grass. Developing a better ryegrass can improve animal growth, so there’s value in studying it. L. perenne has proven so popular that it’s regularly grown outside its home range of southern Europe, North Africa and Central Asia. What allows L. perenne to adapt to so many environments?
In all plants, the DNA holds the instructions for making these adaptations as genes. There can be different varieties of the same gene. For example, the ‘blade length gene’ could come in ‘long’ and ‘short’ varieties. The same could be true for root depth and so on. In reality, combining a few factors might determine blade length and not just one gene. Rather than identifying every gene, looking for QTLs makes it possible to track differences in the genome directly.
A QTL is a quantitative trait locus. This is a patch of the genome that’s known to vary, with some variations producing one effect and some producing another. Examining the QTLs allows botanists to focus in on the parts of the genome that matter for their research. They can ignore the regions of the genome that direct features of the plant that they’re not interested in.
Keep and colleagues settled on seven adaptive traits related to reproductive phenology and vegetative potential growth seasonality. They looked at the QTLs for these traits from populations in Spain and Ireland in the west to Turkey and Estonia in the east. However, they weren’t just looking for differences in QTLs between sites. They also looked at the diversity of QTLs within sites.
“The most significant predictor of within-population trait-associated diversity (HeA) was a mean climate indicator for three traits (aftermath heading, summer canopy height, yearly cumulated canopy height) whereas it was a climate variability indicator for the four remaining traits (heading date, heading in first year, spring canopy height, winter growth score),” write the authors.
At any site, the climate will not be constant. It will vary with the seasons and also in a random fashion from one year to the next. As climate changes, there will also be a long-term drift in temperatures. So at any site, there will not be one ideal condition that the plants can aim at. “This can explain why a climate variability indicator was the most significant predictor of within-population trait-associated diversity for some of the studied putatively adaptive traits (heading date, heading in first year, spring canopy height, winter growth score) and was also one of its significant predictors for the three other traits (aftermath heading, summer canopy height, yearly cumulated canopy height),” write Keep and colleagues.
The team found relatively high genetic diversity in southern Europe, L. perenne‘s home range. The botanists argue that this is the region where the grass will have been exposed to the greatest fluctuations in climate because it’s lived here the longest. As a result, it’s here that the grass has found the most use for having diverse QTLs to deal with whatever the weather throws at it.
The authors argue that this ability to draw on diversity in the gene pool will improve L. perenne‘s future adaptation. “Local natural populations of grassland species are threatened by climate change and will likely need to evolve quickly so as to remain adapted to their environment,” they write. “The capacity of a grassland species population to adapt to the changing climate may notably depend on the level of past inter- annual stochastic local climatic variability.”