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During the last ice age, there wasn’t much plant matter to eat on northern steppes, but herbivorous woolly mammoths were abundant. How did they survive?
June 1, 2018|
WIKIMEDIA COMMONS/FLYING PUFFIN
D. Zhu et al., “The large mean body size of mammalian herbivores explains the productivity paradox during the Last Glacial Maximum,” Nat Ecol Evol, 2:640-49, 2018.
A PALEONTOLOGY PARADOX
During the Last Glacial Maximum, global temperatures and atmospheric carbon levels were less than ideal for vegetation to grow in the northern hemisphere, but the fossil record shows that herbivorous woolly mammoths were plentiful in unglaciated regions—a discrepancy termed the “productivity paradox.”
An international team of researchers approached the problem by modeling plant cover based on climate, the water cycle, and other variables, and incorporating the presence of large grazing animals. The scientists tested the model on a variety of modern ecosystems involving grazers, and found that its predictions of grass cover generally matched observations. For the Ice Age scenario, it was the mammoths’ large bodies and relatively efficient metabolisms that allowed them to survive on sparse vegetation, says coauthor Nicolas Viovy, an informatics engineer and biogeochemist at Versailles Saint-Quentin-en-Yvelines University in France.
TOP-DOWN OR BOTTOM-UP?
Love Dalén, a paleogeneticist at the Swedish Museum of Natural History, says the paradox really boils down to whether vegetation limited the abundance of grazers or vice versa. The new study rests on the assumption that mammoths controlled the abundance of vegetation, he says, but it’s still an open question as to whether that was the case.
RUMINATE ON IT
How the mammoths digested plant matter is also important in solving the paradox, says Danielle Fraser, a paleobiologist at the Canadian Museum of Nature. “I would have been interested to see how the model would differ if they included the digestive physiology of mammoths,” which were relatively efficient “hindgut fermenters,” she says. Viovy says the team plans to continue improving the model to account for more variables like this one.