Testing collection-time reduction in fine-root biomass estimation in Atlantic Forests
2022
Aparecida Silva, Cinthia | Londe, Vinícius | D'Angioli, André Mouro | Scaranello, Marcos A. S. | Bordron, Bruno | Joly, Carlos Alfredo | Aparecida Vieira, Simone
Aims: Fine roots are essential components of the below-ground layer and play an important role in the carbon cycle. Methods for root extraction and biomass estimation have been proposed, including the temporal prediction method. However, there are doubts if the best model to estimate total root mass varies between study sites. Additionally, there are no records regarding the prediction method's efficiency for shorter collection times than 40 min. Here, we aim to clarify these doubts. Location: Brazilian Atlantic Forest. Methods: We extracted 1080 fine-root samples from two contrasting ecosystems at 60 time intervals of 2 min each. We then performed a model selection to identify the best-fit model and used it to find the shortest time suitable for collecting fine-root samples (40, 32, 24, 16, or 8 min). A further 448 root samples were collected from seven ecosystems by employing the shortest time tested (8 min). We calculated the percentage of estimated mass at 120 min and tested for differences between ecosystems. Results: We found that Weibull was the best-fit model, and it performed well for modeling root extraction at shorter collection times. All collection times tested had excellent goodness of fit, and there was strong evidence that the estimated mass did not differ between them. Moreover, collections at 8 min were enough to make reliable estimates of fine-root mass at 120 min in all ecosystems. Conclusions: Weibull is a flexible model and can accurately estimate fine-root mass at 120 min in different ecosystems. The extraction of fine roots can be reduced to four time intervals of 2 min each when using the temporal prediction method. By reducing the time spent removing roots from each soil core, researchers can increase the number of soil cores extracted per study site and characterize the environment properly.
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