What drives ecosystems to instability?

CAMBRIDGE, MA – Trying to decipher all the factors that influence the behavior of complex ecological communities can be a daunting task. However, MIT researchers have now shown that the behavior of these ecosystems can be predicted based on just two pieces of information: the number of species in the community and the strength with which they interact with each other.

In studies of bacteria grown in the laboratory, researchers have been able to define three states of ecological communities and calculate the conditions necessary for them to change from one state to another. These discoveries allowed the researchers to create a “phase diagram” for ecosystems, similar to the diagrams physicists use to describe the conditions that control the transition of water from solid to liquid to gas.

“The amazing and wonderful thing about a phase diagram is that it summarizes a lot of information in a very simple form,” says MIT physics professor Jeff Gore. “We can draw a frontier that predicts the loss of stability and the onset of fluctuations in a population.”

Gore is the lead author of the study, which appears in Science. Jiliang Hu, an MIT graduate student, is the lead author of the paper. Other authors include Daniel Amor, a former MIT postdoc; Matthieu Barbier, researcher at the Plant Health Institute of the University of Montpellier, France; and Guy Bunin, professor of physics at the Israel Institute of Technology.

Population dynamics

The dynamics of natural ecosystems are difficult to study because while scientists can make observations about how species interact with each other, they usually cannot do controlled experiments in nature. Gore’s lab specializes in using microbes such as bacteria and yeast to analyze interspecific interactions in a controlled way, in hopes of learning more about the behavior of natural ecosystems.

Over the past few years, his lab has demonstrated how competitive and cooperative behaviors affect populations and identified early warning signs of population collapse. During this time, his lab gradually expanded from studying one or two species at a time to larger-scale ecosystems.

As they worked to study larger communities, Gore became interested in trying to test some of the predictions that theoretical physicists have made about the dynamics of large, complex ecosystems. One of these predictions was that ecosystems go through phases of varying stability depending on the number of species in the community and the degree of interaction between species. In this framework, the type of interaction (predatory, competitive or cooperative) does not matter. Only the strength of the interaction counts.

To test this prediction, the researchers created communities ranging from two to 48 species of bacteria. For each community, the researchers controlled the number of species by forming different synthetic communities with different sets of species. They were also able to enhance species interactions by increasing the amount of food available, which leads to greater population growth and can also lead to environmental changes such as increased acidification.

“In order to see phase transitions in the lab, it’s really necessary to have experimental communities where you can turn the knobs yourself and make quantitative measurements of what’s going on,” says Gore.

The results of these experimental manipulations confirmed that the theories had correctly predicted what would happen. Initially, each community existed in a phase called “full and stable existence”, in which all species coexist without interfering with each other.

As the number of species or interactions between them increased, communities entered a second phase, known as “stable partial coexistence”. In this phase, populations remain stable, but some species have disappeared. The overall community has remained in a stable state, which means that the population is returning to a state of equilibrium after the extinction of certain species.

Finally, as the number of species or the strength of interactions increased further, communities entered a third phase, characterized by greater population fluctuations. Ecosystems have become unstable, meaning populations are constantly fluctuating over time. Although some extinctions have occurred, these ecosystems tended to have a larger overall fraction of surviving species.

Predict behavior

Using this data, the researchers were able to draw a phase diagram that depicts how ecosystems change based on just two factors: the number of species and the strength of interactions between them. This is analogous to how physicists are able to describe changes in the behavior of water based on just two conditions: temperature and pressure. Detailed knowledge of the exact speed and position of each water molecule is not necessary.

“Although we cannot access all the biological mechanisms and parameters of a complex ecosystem, we demonstrate that its diversity and dynamics can be emergent phenomena that can be predicted from a few aggregate properties of the ecological community: size of the species pool and statistics of interspecific interactions,” says Hu.

Creating this type of phase diagram could help ecologists make predictions about what might happen in natural ecosystems such as forests, even with very little information, because all they need to know , is the number of species and their degree of interaction.

“We can make predictions or statements about what the community is going to do, even in the absence of detailed knowledge of what is happening,” says Gore. “We don’t even know which species help or hurt which other species. These predictions are based solely on the statistical distribution of interactions within this complex community.

Researchers are now investigating how the flow of new species between otherwise isolated populations (similar to island ecosystems) affects the dynamics of those populations. This could help shed light on how islands are able to maintain species diversity even in the face of extinction.

– This press release was originally published on the Massachusetts Institute of Technology website

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