Attempting to decipher all the factors that influence the behavior of complex ecological communities can be a very difficult task. However, a team of researchers led by the Massachusetts Institute of Technology (MIT) has discovered that the behavior of these ecosystems can be predicted based on solely two pieces of information – the number of species in the community and how strongly they interact with one another.
The dynamics of natural ecosystems are notoriously difficult to study. Scientists can make observations about how species interact with one another, but they generally cannot perform controlled experiments in the wild. This led the MIT researchers to use microbes such as bacteria and yeast to analyze interspecies interactions in a controlled way, hoping to learn more about how natural ecosystems behave.
By creating communities ranging from two to 48 species of bacteria, controlling the number of species by forming different synthetic communities with different sets of species, and strengthening the interactions between the species by increasing the amount of food available, the scientists were able to define three phases of ecological communities and calculate the necessary conditions for them to move from one state to another.
Initially, each ecological community existed in a phase called “stable full existence,” with all species coexisting without interfering with one another. However, as either the number of the species or interactions among them increased, the communities entered a new phase called “stable partial coexistence” in which populations remain stable, but some of the species become extinct. Finally, as the number of species or strength of interactions further increased, the communities entered a third phase, featuring more dramatic fluctuations in population. In this stage, ecosystems became unstable, and populations persistently fluctuated over time.
“While we cannot access all biological mechanisms and parameters in a complex ecosystem, we demonstrate that its diversity and dynamics may be emergent phenomena that can be predicted from just a few aggregate properties of the ecological community: species pool size and statistics of interspecies interactions,” said study lead author Jiliang Hu, an engineer at MIT.
These findings could help ecologists make predictions about what might be happening in complex ecosystems such as forests, even with little information available, since 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’s going on. We don’t even know which species are helping or hurting which other species. These predictions are based purely on the statistical distribution of the interactions within this complex community,” concluded senior author Jeff Gore, an MIT physicist.
The study is published in the journal Science.
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