In January, researchers announced that decades of fishing has decimated
the bluefin tuna population by over 90 percent. Just days earlier
, one such Pacific bluefin tuna sold for $1.76 million at an auction
|The bluefin tuna can weigh over 1,000 lbs and swims at speeds over 50 mph. While most remain in the eastern Pacific waters off the coast of Japan, some bluefin species migrate across the Pacific to the coast of California. More than 90 percent of bluefin tuna are caught before they have reproduced, severely damaging the population.|
Image Credit: Aziz Saltik
Climate change, overfishing, and other ecological changes can push wild animal populations towards extinction. For years, scientists have observed changing wildlife populations and seek to measure the risk of population collapse in order to preemptively protect endangered species.
Now, a team of physicists at MIT
have demonstrated that variations in population density may accurately reflect the population's risk of collapse. By studying spatial relationships of neighboring populations, the researchers hoped to catch early signs of population collapse. The research led by Jeff Gore, Lei Dai, and Kirill Korolev at MIT was published online
in the journal Nature
on April 10, 2013.
The researchers used yeast cells as a model population. Unlike larger organisms, a single day can bear witness to 10 generations of yeast. Yeast populations are also cooperative. Each cell produces enzymes that help break down sucrose in the environment into simpler sugar food that benefits rest of the population. As a result, there is an equilibrium density at which a population is strongest.
Scientists have observed that wild populations closer to inhospitable environments are more likely to be affected by the bad neighbor than populations further away from the affected region. Based on this, the team at MIT proposed that a population's susceptibility to collapse could be described by the spatial scale of recovery from a bad neighborhood to one at equilibrium.
To put the theory to the test, the researchers first grew multiple yeast populations into an equilibrium "happy" state. By moving a certain percentage of each population into adjacent regions, the team mimicked populations migrating to new neighborhoods. To test effects of bad habitats like overfishing, environmental changes or invasive species, the researchers then introduced a bad neighborhood by developing a nearby region where only one in 2,500 yeast survived each day.
The researchers found that populations closest to the bad neighborhood had difficulty maintaining the optimal, equilibrium, state. Neighborhoods farther away from the bad habitat maintained their optimal state more easily.
The researchers defined the recovery length
as the distance between the bad neighborhood and the equilibrium neighborhoods. Like a ripple on a pond, the recovery length describes the geographical distance necessary for connected populations to recover from negative effects in a localized region.
The team found
that the recovery length increased dramatically before a population collapsed.
Unlike many methods of studying population dynamics, which require many generations to uncover patterns, the spatial recovery length described by Gore and his colleagues may enable scientists to study population dynamics with survey and satellite data
more readily and speedily available. In the future, the team hopes to expand their research from yeast cells to systems with several microbial populations or other animals like bees and fish.