Vegetation models don’t just look good on Instagram. Plants and vegetation and the way they grow play a critical role in supporting life on Earth. And your life too. How will coronavirus affect farms and output next year or the next 10 years? Will climate change wipe out species or promote the growth of latent ones, waiting for the arctic ice to break?
There is still a lot of uncertainty in our understanding of how exactly plants affect the global carbon cycle and ecosystem services. While there has been a big focus on big plants and forests, not much is known on how plants as a whole contribute to global weather patterns. A new study explores the most important organizing principles that control vegetation behavior and how they can be used to improve vegetation models.
“Current models are not able to reliably predict long-term vegetation responses,” explains lead author Oskar Franklin, a researcher at the International Institute for Applied Systems Analysis (IIASA) Ecosystems Services and Management Program.
An international team of researchers endeavored to address this problem by exploring approaches to master this complexity and improve our ability to predict vegetation dynamics. IIASA conducts interdisciplinary scientific studies on environmental, economic, technological and social issues in the context of human dimensions of global change.
They explored key organizing principles that govern these processes – specifically, natural selection; self-organization (controlling collective behavior among individuals); and entropy maximization (controlling the outcome of a large number of random processes).
In general, an organizing principle determines or constrains how components of a system, such as different plants in an ecosystem or different organs of a plant, behave together. Mathematically, such a principle can be seen as an additional equation added to a system of equations, allowing one or more previously unknown variables in the system to be determined and thereby reducing the uncertainty of the solution.
A lot of research has gone into understanding and predicting how plant processes combine to determine the dynamics of vegetation on larger scales. To integrate process understanding from different disciplines, dynamic vegetation models (DVMs) have been developed that combine elements from plant biogeography, biogeochemistry, plant physiology, and forest ecology.
DVMs have been widely used in many fields including the assessment of impacts of environmental change on plants and ecosystems; land management; and feedbacks from vegetation changes to regional and global climates. However, previous attempts to improve vegetation models have mainly focused on improving realism by including more processes and more data. This has not led to the expected success because each additional process comes with uncertain parameters, which has in turn caused an accumulation of uncertainty and therefore unreliable model predictions.
The study found that by representing the principles of evolution, self-organization, and entropy maximization in models, they could better predict complex plant behavior and resulting vegetation as an emerging result of environmental conditions. Consider plants in the desert for instance.
Although each of these principles had previously been used to explain a particular aspect of vegetation dynamics, their combined implications were not fully understood. This approach means that a lot of complex variation and behavior at different scales, from leaves to landscapes, can now be better predicted without additional understanding of underlying details or more measurements.
The authors expect that apart from leading to better tools for understanding and managing the biosphere, the proposed “next-generation approach” may result in different trajectories of projected climate change that both policy and the general public would have to cope with.