Using a more nuanced approach than previous research, a new Yale study has the potential to improve how scientists forecast the growth of different ecological populations.
Researchers in the Department of Ecology and Evolutionary Biology have found that by accounting for changes in an organism’s plasticity — defined as any change in an organism not rooted in its genetic code — they can develop better population predictions for phytoplankton. The study was published in the “Proceedings of The Royal Society B” on Jan. 10.
“The main finding here is that if you want to accurately predict the response of organisms to their environment, you have to take into account current environmental conditions as well as the historical environmental conditions,” said Samuel Fey, one of the study’s lead authors, a former postdoctoral researcher at Yale and current professor of biology at Reed College. “It is analogous to forecasting current pain without accounting for whether or not a person was in a car accident yesterday.”
According to David Vasseur, a professor in the Ecology and Evolutionary Biology Department the study’s senior author, current methods of ecological forecasting are often disappointing. He cited several failures by previous models to accurately account for short-term environmental fluctuations.
“We’re very jealous of meteorology, which has become so successful while ecological forecasting has lagged behind,” he said.
To investigate the problem, the lab studied phytoplankton — single-celled, photosynthetic organisms — that are major players in many critical processes for the earth, such as the carbon cycle. They also play a vital role in aquatic food webs by serving as primary producers for entire ecosystems.
“Phytoplankton cannot regulate their own body temperature and thus depend on the surrounding environmental temperature,” said Colin Kremer, the study’s other lead author. “We wanted to know what happens on a shorter timescale and see right after temperature changes what the phytoplankton were doing.”
To study the predictive effect of an organism’s temperature history on its forecasted response, the researchers devised an experiment in which they allowed five populations of C. reinhardtii, a well-studied phytoplankton, to acclimate over two weeks to temperatures ranging from 14 to 33 degrees Celsius. Then, in a series of trials, they exposed the five differently acclimated populations to high temperatures or alternating high and low temperatures. Using the data they obtained, the scientists predicted population growth patterns using a model that accounted for gradual organismal plasticity. Ultimately, accounting for these oft-ignored variables led to “enhanced predictions,” according to the study.
“If you used the old way, you ended up getting the wrong answer,” Kremer said. “With these new measurements, we have a more nuanced way of looking at the temperature.”
The researchers expressed optimism about the implications of the study. Algal blooms are common environmental phenomena that occur in lakes and reservoirs, and they tend to have detrimental health effects, Kremer said.
“It will be interesting to understand if our process can predict the onset of harmful blooms of algae,” he said.
Another exciting application may be using the findings to increase yield for biofuel operations. Vasseur said higher yields could be obtained by growing algae in environments with fluctuating temperatures rather than by growing the organisms in standard stocks with constant temperatures.
Before the potential applications bear fruit, however, scientists must conduct further research, Fey said. In addition to testing their model on other species, he said, the researchers would like to develop a “more comprehensive framework” of the model that can be used flexibly.
According to the federal Office of Energy Efficiency and Renewable Energy, algae could potentially produce up to 60 times more oil per acre than land-based plants.
Vikram Shaw | email@example.com
Correction, Feb. 7: The article has been updated to reflect Samuel Fey’s latest job title.