Prepared by Cinthia Silva, Vinícius Londe, André D’Angioli, Bruno Bordron, Carlos Joly & Simone Vieira
Studying the ecosystem’s functioning below the ground surface is not an easy task. As beings that live above the surface of the ground, we cannot learn about it without causing disturbances. However, as actual bridges, plants can access both worlds. Fine roots are the gateway for water and soil nutrients into the plant and for the output of photoassimilate carbon to the soil or root-associated microorganisms.
In our most recent work, we explore the applicability of the temporal prediction method in the study of fine roots. This method assumes that, given a particular volume of soil, if you collect fine roots over different time intervals of the same duration, you will be able to estimate the total mass of roots in that volume of soil. For example, in 10 minutes, you collect 4 g of roots, in 20 minutes 6 g, in 30 minutes 7 g, and in 40 minutes 7.5 g of roots. This estimation is possible because, as sampling takes place, the root mass in the ground decreases as the sampled root mass increases until there comes a time when the gain tends to be close to zero. After all, if you do not stop sampling, there will be a time when you will find no roots anymore!
Our starting point was to find out which mathematical model best fits the fine-root mass accumulation curves that we built from Atlantic Forest data. In this step, we found that the predominant model was Weibull. We then fit the Weibull model to the masses sampled (collected) in the first-time intervals. From this adjustment, we extrapolated the time to check if the biomass estimated by the model was compatible with the biomass collected at that time. We found that it was! We also noted that the estimate was not affected by the duration of a time interval (you can use different time intervals to collect roots from the soil) or the sampled root mass. Additionally, we tested whether, when considering other forests, the estimated relative mass varied, which did not happen.
Thus, we concluded that when the temporal prediction method is used to quantify fine roots, we have estimates independently of the time interval duration, mass, or sampled ecosystem. This finding is of great importance, as it implies the possibility of reducing the time spent removing roots from the soil. Consequently, researchers can increase the number of samples per study site and characterize the environment properly.
This is a plain language summary for the paper of Cinthia Silva, Vinícius Londe, André D’Angioli, Marcos Scaranello, Bruno Bordron, Carlos Joly and Simone Vieira published in Applied Vegetation Science (https://doi.org/10.1111/avsc.12638).