How to Clone a Mammoth: The Science of De-Extinction
Beth Shapiro
An insider's view on bringing extinct species back to life Could extinct species, like mammoths and passenger pigeons, be brought back to life? In How to Clone a Mammoth, Beth Shapiro, an evolutionary biologist and pioneer in ancient DNA research, addresses this intriguing question by walking readers through the astonishing and controversial process of de-extinction. From deciding which species should be restored to anticipating how revived populations might be overseen in the wild, Shapiro vividly explores the extraordinary cutting-edge science that is being used to resurrect the past. Considering de-extinction's practical benefits and ethical challenges, Shapiro argues that the overarching goal should be the revitalization and stabilization of contemporary ecosystems. Looking at the very real and compelling science behind an idea once seen as science fiction, How to Clone a Mammoth demonstrates how de-extinction will redefine conservation's future.
Award History
1 wins · 2 total
| Award | Year | Result | Category / Notes |
|---|---|---|---|
| PROSE Award for Popular Science and Mathematics | 2016 | Winner | Official source |
| Los Angeles Times Book Prize for Science and TechnologyMajor | 2015 | Finalist | Secondary source |
Experimental book profileGenerated by GPT-5.4 nano · may contain inaccuracies
This is an unverified interpretation of the catalog description, offered as an opt-in discovery experiment—not as bibliographic fact.
01 Central figures
None extracted with sufficient confidence.
02 Central places
None extracted with sufficient confidence.
03 Suggested argument
The goal of de-extinction should be the revitalization and stabilization of contemporary ecosystems, balancing practical benefits with ethical challenges in how revived populations are overseen.
Model confidence 72%
04 Reading orientation
62 / 100 academicAcademic
An estimate of intended readership and scholarly apparatus—not quality or importance.
Confidence percentages are the model's own estimates. Profile confidence: 72%.