Simulating mangrove light interception

Mangrove forests are the keystone of coastal tropical and subtropical ecosystems. Mangroves can grow where no other tree can and yield huge benefits to coastal ecology. The complex mangrove root systems trap sediment and pollutants and stabilize the coastline. They also provide breeding grounds and habitat for wild-and sea-life and shelter from storms.

Unfortunately, mangroves are facing numerous threats such as sea-level rise, upstream pollution, timber extraction, and urban expansion. Mangrove forests are among the most carbon-rich biomes, and contribute an average of 14% to carbon sequestration in the world’s oceans. But when they are cleared and destroyed, they release massive amounts of carbon dioxide into the atmosphere, contributing to climate change.

Improving our ability to predict the response of mangroves to these threats is urgently needed.

Computational modelling can be used to understand and predict the impact of humans and climate change on this vulnerable ecosystem. First, models must be developed with sufficient detail to represent mangrove’s unique structural and physiological processes.

A plant’s leaf arrangement (phyllotaxy) affects its ability to perform photosynthesis. Optimal positioning of leaves can maximize the surface area available to intercept sunlight. In mangroves species, phyllotaxis is a largely unexplored phenomenon and current mangrove models do not adequately represent the varied morphology of the trees.

Dr. Faustino Chi, Postdoctoral Researcher at Georg-August-Universität Göttingen, and colleagues reconstructed the detailed architecture of red mangrove saplings to create a light interception model.

To collect data on red mangrove, the researchers travelled by boat to the north-east side of Turneffe Atoll, located over 20 miles off the coast of Belize. There, they took high resolution digital photographs, in-situ manual measurements, and conducted 3D digitization using electromagnetic tracking.

Collecting this data was not easy. Chi explains, “some measurements had to be done during low tide. There were also challenges to using the Fastrak digitizing equipment in a remote tropical environment. For example, windy conditions required us to harvest the saplings and use an enclosed scaffold because plants had to be completely still to digitize them. A compact portable generator was needed to power the field equipment. We also needed to have a steady hand during the hours of the digitization process when the mosquitoes and sand flies were out to get you. It was also very important to have water resistant or waterproof containers for keeping the equipment dry from the high humidity and sudden rains while in the field.”

The digitized saplings and manual measurements were used to reconstruct tree architecture. Then, they created an algorithm of phyllotaxis (leaf arrangement on a stem) from the photographs and field notes. This allowed the authors to digitally reconstruct the trees with leaves using the 3D modelling platform GroIMP.

To simulate the interception of light by individual leaves, the authors employed the stochastic raytracing-based radiation model built into GroIMP.

Preliminary results allowed the authors to assess and visualize the proportion of light absorbed by single leaves throughout the canopy and the effect of changing leaf angle on the relative absorbed radiation at the whole sapling level. Measurements of absorbed light are necessary to calculate the photosynthetic contributions of individual leaves in future work.

The next step for the authors is to further develop their 3D sapling model. “In the future, we plan to integrate the simulation of other processes such as xylem and phloem flow and structural mechanical behavior into our mangrove model,” says Dr. Chi.

READ THE ARTICLE:

Chi, F., Streit, K., Tavkhelidze, A. and Kurth, W. (2022) “Reconstruction of phyllotaxis at the example of digitized red mangrove (Rhizophora mangle) and application to light interception simulation,” in silico Plants. https://doi.org/10.1093/insilicoplants/diac002

This manuscript is part of in silico Plant’s Functional Structural Plant Model special issue.