Unraveling the Mechanisms of Below- And Aboveground Liana-Tree Competition in Tropical Forests

Lianas, or woody vines, are abundant throughout forests worldwide. However, their effect on total forest biomass

biomass

Material that comes from living things, including trees, crops, grasses, and animals and animal waste. Some kinds of biomass, such as wood and biofuels, can be burned to produce energy.

Source: EPA Global Climate Change Source

is puzzling from ecological and biogeochemical perspectives. Lianas are thought to directly contribute much less to total forest biomass than trees because lianas are typically more slender than trees. However, recent experiments have established an indirect effect of lianas on tropical forest biomass. In one case, tropical forest plots were intentionally cleared of lianas; tree growth rates in these cleared plots were monitored for several years, and then compared to tree growth rates in uncleared plots. The results were staggering: tree wood production was 75% greater in plots cleared of lianas than in the uncleared plots. To make this extra wood, the cleared plots absorbed 75% more carbon dioxide

carbon dioxide (CO2)

An important heat-trapping gas, also known as a greenhouse gas, that comes from the extraction and burning of fossil fuels (such as coal, oil, and natural gas), from wildfires, and natural processes like volcanic eruptions.

Source: NASA Source

from the atmosphere than the uncleared plots. The exact reasons for this large indirect effect on biomass are still under debate.
A mechanistic, predictive model can, in principle, trace the essential lines of cause-and-effect and explain why lianas so strongly affect forests. Vexingly, however, lianas are not represented at all in current-day models, and so the modeling approach has not yet been leveraged. Indeed, current models cannot address how lianas affect the strength of the intact tropical forest carbon dioxide sink, which has helped buffer the Earth system against changes in climate, nor can they answer why liana infestation has been observed to have increased in intact tropical forests in recent decades. In order for these gaps to be filled and for accurate liana-predicting models to be developed, better knowledge of liana-specific morphology

morphology

The science of the form and structure of organisms (plants, animals, and other forms of life).

Source: National Cancer Institute Source

and allocation, both above- and belowground, is required. This project has three overarching objectives related to observations, modeling, and synthesis.

synthesis

The process of combining a number of things into a coherent whole to form a theory or system.

Source: National Library of Medicine Source

Observations: Field measurements will be made in tropical dry forests in Guanacaste, Costa Rica. First, excavations of entire trees and lianas will be carried out to enable measurement of belowground (coarse and fine root) and aboveground (woody stem, branch and leaf) biomass of co-occurring trees and lianas, as will coarse and fine root vertical distribution and lateral spread. Second, liana trait measurements will be made including xylem vessel diameter and length, turgor loss point, hydraulic conductivity, vulnerability to embolism, wood density, and specific leaf area. Third, above- and belowground productivity will be measured, and fine root productivity will be assigned to species using high-throughput DNA sequencing. Fourth, a throughfall exclusion experiment (designed to generate drier-than-usual soils) will be used to distinguish the responses of lianas and trees to drought. Fifth, additional measurements will be carried out in the latter part of the project to reduce model uncertainty and improve model quality.
Modeling: Lianas will be incorporated into a mechanistic, individual-based forest dynamics model that includes both trees and lianas. The model will simulate the unique features of lianas, accounting for their structural parasitism and their different (with respect to trees) allocation strategies and morphology. The simulated trees and lianas will compete aboveground for light and belowground for water. Thus, the model will integrate above- and belowground processes and couple the carbon and water cycles. Organism traits measured as part of this project will be used to parameterize the model, and parameter sensitivity will be assessed. Measurements of below- and aboveground productivity and liana colonization and shedding will be used to evaluate the model. Once model biases and trait sensitivities are identified, additional measurements will be planned to further improve model quality.




Synthesis: A working group will be established to plan for the incorporation of lianas into Earth system models

earth system model (ESM)

A model that simulates how chemistry, biology, and physical forces work together. These models are similar to, but much more comprehensive than, global climate models.

Source: U.S. Department of Energy Source

(ESMs). About 25 participants are anticipated, with expertise thoroughly covering ESMs, modeling, lianas, roots, and tropical ecology.

ecology

The interrelationships between living organisms and their environments.

Source: NASA earth observatory Source

In summary, this project includes tightly coupled, synergistic modeling and measurement campaigns. Novel measurements of liana below- and aboveground allocation, productivity, and function will allow the development of an unprecedented liana-simulating forest dynamics model. By tying the coupled model-measurement approach proposed here to the synthesis working groups, this project will achieve better representation of tropical forests in ESMs is essential for simulating global carbon cycle

carbon cycle

The movement and exchange of carbon through living organisms, the ocean, the atmosphere, rocks and minerals, and other parts of the Earth.

Source: a student's guide to Global Climate Change Source

dynamics. Given the recent increases in liana abundance, inclusion of lianas in ESMs will be necessary to achieve a robust, predictive understanding

predictive understanding

Predictive understanding refers to the ability of a system, typically an artificial intelligence or machine learning model, to anticipate future events or outcomes based on historical data and patterns.

Source: Source

of coupled biogeochemical processes

biogeochemical processes

The complex interactions and transformations of chemical elements and compounds that occur throughout Earth's ecosystems, such as nutrient and element cycling between living organisms, the atmosphere, hydrosphere, lithosphere, and pedosphere (soil).

Source: Source

and cycles. This project will also generate publicly-available (via ESS-DIVE) products, including ecological field measurements, DNA barcodes, model source code, and model simulations.