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New algorithm looks at how Amazon vegetation will behave after climate change

Moara Canova photographing a river during her field work. Courtesy of Moara Canova.

  • Brazilian scientists have pioneered a new vegetation model with a broader array of life strategies that is expected to provide a more accurate representation of the Amazon ecosystem’s functioning and the forest’s responses to climate change.
  • The new model suggests that Amazon plants would reorganize, allocating more energy to their roots at the expense of stems and leaves; consequently, they would have a lower capacity to retain and absorb carbon in a scenario with reduced rainfall.
  • Field research and future contributions will add new information to the model, and experts hope it will get better at predicting the future and shaping policies for conservation.

The Amazon Rainforest is widely known for its vital role in mitigating climate change, acting as a carbon sink that absorbs and stores a substantial portion of the CO2 emissions from burning fossil fuels. However, since the 1990s, scientists have noted a concerning trend in the reduction of carbon stocks within the forest. In the face of today’s climate emergency, the uncertainty surrounding the Amazon Rainforest’s resilience and its continued ability to provide this invaluable service to humanity has emerged as a major concern within the scientific community.

To explore potential changes in the forest’s carbon balance, scientists developed vegetation models over the years that simulate projected scenarios of environmental shifts due to climate change. Yet, many of these models oversimplify plant diversity, resulting in less reliable outcomes due to an overestimation of the impacts of environmental changes on the plant community, according to researchers from the State University of Campinas (Unicamp), in the Brazilian state of São Paulo.

In response to this challenge, these researchers pioneered a new vegetation model, incorporating a broader array of life strategies. They say this innovative method provides a more accurate representation of the ecosystem’s functioning and the forest’s responses to climate change in comparison with standard modeling approaches. In an experiment simulating a 50% reduction in precipitation in the Amazon Basin, there was an observed increase in the forest’s functional diversity, coupled with a 57% decrease in carbon stocks.

The diversity referred to by the researchers is not species diversity. To grasp the concept of functional diversity, the authors considered each plant as a life strategy that determines how it will “cope with environmental conditions, nutrient conditions, sunlight, water and CO2 concentration.” These strategies represent a balance of traits that the plant adopts to face the conditions of a particular location.

The new model suggests that in a scenario with reduced rainfall, the plant community would reorganize, resulting in a higher prevalence of plant types that allocate more energy to their roots at the expense of stems and leaves. Consequently, roots have a lower capacity to retain and absorb carbon compared with stems, leading to a reduction in the Amazon Rainforest’s carbon stocks. These findings were published in an article in the journal Ecological Modelling in July.

Javari River in the Brazilian Amazon. Photo by Rhett Butler.

The algorithm, which the researchers named Carbon and Ecosystem Functional-Trait Evaluation (CAETÊ), stands as a milestone as the first exclusively Brazilian model of its kind. Its name also makes reference to the ancient Tupi word for true, virgin forest.

CAETÊ is a computer program composed of “various mathematical equations that describe some eco-physiological processes of the plant, as well as the relationships, for example, between the organs, the number of roots in relation to the number of leaves, and how this energy is distributed,” Bianca Fazio Rius, a Ph.D. candidate in ecology and the lead author of the article, told Mongabay in a video call.

Unlike conventional Dynamic Global Vegetation Models (DGVMs), which often represent vegetation through only three functional types of plants, this new model places a stronger emphasis on diversity. Rius said that in conventional models, “If the climate changes and one of these types of plants can no longer establish and survive, there will be a very drastic change in the Amazon forest, potentially leading to an almost total loss of biomass in the region.”

The main scientific contribution of the innovative CAETÊ model, according to the researchers, lies in its ability to represent more accurately the poorly understood functional diversity of vegetation. Rius said she hopes the inclusion of this diversity can deepen the understanding of ecosystem functioning and projections amid changes.

Unicamp’s research team at LabTerra (Earth System Science Laboratory) chose to simulate 3,000 different types of plants for the study.

The traits that plants adopt as a mechanism of functional diversity describe how the organism will interact with the environment and be able to persist, defining its capacity to grow, reproduce, and leave descendants.

To prevent the emergence of “super-plants” in the model — individuals that maximize all their fitness elements simultaneously — each trait in the model is associated with at least one “trade-off,” meaning it has cost-benefit relationships.

“The plant has a specific amount of energy it can allocate to its organs,” Rius said. “If it puts too much energy into the leaf, there’s little energy left for the root, and then it solves one problem, which is light capture, but creates another, which is water uptake.”

Bárbara Cardeli and Bianca Fazio Rius at LabTerra. Courtesy of Bárbara Cardeli.

Model evaluation

The researchers compared the model with results from prior studies that mapped carbon stocks using satellite imagery to understand whether the new approach contributed to a more accurate representation of carbon stocks in the forest.

It did, according to them. However, the article points out that both approaches still exhibit an overestimation of carbon stocks at the edges of the northwestern and central regions of the Amazon Basin, which may be linked to the lack of representation of human land use, fires and plant hydraulics.

To assess how the forest would fare under an extreme scenario of water stress, researchers applied an experiment that reduced precipitation by 50%. This scenario is projected for some regions of the Amazon, primarily central ones, though not uniformly as represented in the experiment.

The sensitivity test yielded significant insights into how the forest would respond to such a drastic reduction. Both the standard modeling and the new approach indicated a notable loss in the forest’s carbon storage capacity, by 57.8% and 57.5%, respectively.

However, the result that surprised researchers the most was an increase in functional diversity in relation to reduced precipitation. “Commonly, we tend to assume that the harsher and more extreme the environment, the lower the plant diversity in that place. However, we found that in reality, the fact that certain types of plants cease to exist creates space for other types to establish themselves,” Rius said.

Yet, the researcher pointed out: “Is an increase in diversity always positive? Not necessarily.” This increase would also represent a change in the types of plants in the Amazon, resulting in a transformation of the forest and a shift in its carbon balance.

Rius explained that types of plants that invest more energy in root production to cope with drought conditions started to establish themselves. However, to compensate for this, these types of plants invested less energy in building stems. Relatively, stems are more capable of absorbing and retaining carbon than leaves and roots. “This means that a forest that invests more carbon in roots will have a lower capacity for carbon storage,” she said. This process would have serious consequences for climate change and could even aggravate it “because less stored carbon in plants means more CO2 in the atmosphere.”

Flávia Costa, a professor of plant ecology at the National Institute for Amazonian Research, who wasn’t involved in the study, told Mongabay by email that the new model “represents an evident advance in representing the functional variation of trees in the Amazon.”

However, she said, “We must exercise caution in interpreting the results, as the model performs better when compared with a very poor basal model — one with only 3 functional types — and many modeling approaches are capable of achieving better results than this.”

“It’s too early to draw conclusions about what this model suggests because it still needs to improve in correctly representing ecological relationships to maybe well capture the Carbon distribution in the Amazon Basin (and then make predictions for the future),” she wrote to Mongabay.

Moara Canova interviewing a local during her field research in the Amazon. Image courtesy of Moara Canova.

Time machine

Other researchers have already tested the method. Ecologist Moara Almeida Canova Teixeira, a researcher at LabTerra with a Ph.D. in environment and society, used CAETÊ in her doctoral research to develop a socioecological vulnerability index for four cities in the Brazilian Amazon: Manaus, Belém, Silves and Itacoatiara.

By combining socioeconomic indicators, climatic parameters and CAETÊ modeling, Teixeira obtained worrying results for the scenarios from 2040 to 2070.

“The environmental aspect proved to be the most affected, even more so than the socioeconomic conditions,” she told Mongabay in a video call. However, the researcher emphasized that cities in the northern part of the country have comparatively more precarious socioeconomic conditions. Among the four municipalities, Silves, with an economy focused on fishing, would be the most impacted by the effects of this transformation —“extremely vulnerable,” according to Teixeira.

Teixeira also found that some species of cultural and economic importance in the Amazon Rainforest, like timber, may become rarer.

The experts say they believe vegetation models serve as a kind of “time machine,” offering a glimpse into the future. When combined with the study of functional diversity, they can yield even more relevant insights about the forest’s behavior in a changed climate scenario. Such predictions can guide adaptation measures for human communities — 30 million people depend on the forest for their well-being.

“For the population, for the communities, it’s as if we were able to anticipate the problem and provide support for an adaptation that must be made now,” Teixeira said.

Field experiments in the rainforest

The CAETÊ model is being put to use elsewhere as well. It’s one of the components of AmazonFACE, a large-scale experiment in central Amazonia that will expose a portion of the forest to a concentration of CO2 50% higher than the current levels.

“The experiment and the modeling will ultimately clarify to what extent the forest will continue to act as an agent of climate change mitigation,” David Montenegro Lapola, coordinator of the AmazonFACE program and LabTerra, and one of the article’s authors, told Mongabay in a voice message.

Some key questions include: How does the increased atmospheric CO2 affect the forest? What is the magnitude and duration of a potential “CO2 fertilization” effect? And how does the limited availability of phosphorus potentially constrain forest productivity?

“Understanding how it [the forest] will behave, what kind of impact [increased] CO2 will have, and the consequences of climate change, allows us to get ahead of the curve. This grants us valuable time to develop policies and adaptation measures. Assuming these effects are bound to occur, we can work towards lessening their burden on the diverse human populations in the region, as well as on biodiversity,” Lapola said.

AmazonFACE involves the construction of six rings in a mature forest area. Each ring is composed of 16 towers with Free-Air CO2 enrichment technology (FACE). Three of those rings will release CO2-enriched air, while the remaining will release ambient air. This setup allows for a controlled examination of how the ecosystem responds to elevated atmospheric CO2 levels. The experiment is set to begin next year, spanning a decade to provide researchers with sufficient time to monitor how the vegetation reacts to the expected scenarios.

“Models can provide us with hypotheses to be tested in the field experiment,” Lapola said. “After a few years, the field experiment yields results, allowing us to better parameterize the models. This way, it creates a good iteration where one thing improves the other.”

The CAETÊ researchers expect the model will remain in continuous development over the coming years. Bárbara Rocha Cardeli, a Ph.D. researcher at LabTerra, told Mongabay in a video call how she’s enhancing the model to quantify various indicators of ecosystem services, primarily climate and water regulation services.

Looking ahead, CAETÊ will be capable of evaluating the current status of these service indicators as well as projecting their future states. These projections will align with the Intergovernmental Panel on Climate Change (IPCC) scenarios.

Cardeli is also planning a reverse modeling approach, hoping to answer how the forest must be to ensure the well-being of people who rely on it instead of just trying to project how it will be.

This initiative aims to demonstrate which specific traits the forest must maintain to ensure its functionality and preserve its services. “It also examines how protected areas in the Amazon and Indigenous territories can act as a buffer against climate change,” she said.

The Amazon Forest and all life within already face the reality of a human-changed environment, underscoring the need for innovative research and undertakings like this. The clock ticks for all of us.

“The forest can undergo changes and adapt in its own way. Perhaps those who do not adapt in due time are us,” Cardelli said.


Banner image: Moara Canova photographing a river during her field work. Image courtesy of Moara Canova.

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Rius, B. F., Filho, J. P., Fleischer, K., Hofhansl, F., Blanco, C. C., Rammig, A., … Lapola, D. M. (2023). Higher functional diversity improves modeling of Amazon forest carbon storage. Ecological Modelling, 481, 110323. doi:10.1016/j.ecolmodel.2023.110323

Cunha, H. F., Andersen, K. M., Lugli, L. F., Santana, F. D., Aleixo, I. F., Moraes, A. M., … Quesada, C. A. (2022). Direct evidence for phosphorus limitation on Amazon forest productivity. Nature, 608(7923), 558-562. doi:10.1038/s41586-022-05085-2

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