Apr. 10, 2013 —
Spatial measurements of population density could reveal when threatened natural
populations are in danger of crashing.
Many factors
-- including climate change, overfishing or loss of food supply -- can push a
wild animal population to the brink of collapse. Ecologists have long sought
ways to measure the risk of such a collapse, which could help wildlife and
fishery managers take steps to protect endangered populations.
Last year, MIT
physicists demonstrated that they could measure a population's risk of collapse
by monitoring how fast it recovers from small disturbances, such as a food
shortage or overcrowding. However, this strategy would likely require many
years of data collection -- by which time it could be too late to save the
population.
In a paper
appearing in the April 10 online edition of Nature, the same research team
describes a new way to predict the risk of collapse, based on variations in
population density in neighboring regions. Such information is easier to obtain
than data on population fluctuations over time, making it potentially more useful,
according to the researchers.
"Spatial
data are more accessible," says Lei Dai, an MIT graduate student in
physics and lead author of the study. "You can get them by satellite
images, or you could just go out and do a survey."
Led by Jeff
Gore, an assistant professor of physics, Dai and Kirill Korolev, a Pappalardo
Postdoctoral Fellow, grew yeast in test tubes and tracked the populations as
they approached collapse. Yeast cells cooperate with other members of the
population: Each of the organisms secretes an enzyme that breaks down sucrose
in the environment into smaller sugars that it can use as a food source. All of
the yeast benefit from this process, so a population is most successful when it
maintains a certain density -- neither too low nor too high.
No comments:
Post a Comment
You only need to enter your comment once! Comments will appear once they have been moderated. This is so as to stop the would-be comedian who has been spamming the comments here with inane and often offensive remarks. You know who you are!