Iversen, C. | Search bioRxiv

bioRxiv2024

Divide and conquer: Using RhizoVision Explorer to aggregate data from multiple root scans using image concatenation and statistical methods

Roots are important in agricultural and natural systems for determining plant productivity and soil carbon inputs. The collection of root samples from the field and their subsequent cleaning and scanning in a water-filled tray ranging in size from 5 to 20 cm, followed by digital image analysis has been commonly used since the 1990s for measuring root length, volume, area, and diameter. However, one common issue has been neglected. Sometimes, the amount of roots for a sample is too much to fit into a single scanned image, so the sample is divided among several scans. There is no standard method to aggregate the root measurements across the scans of the same sample. Here, we describe and validate two methods for standardizing measurements across multiple scans: image concatenation and statistical aggregation. Both methods rely on standardizing file naming conventions to identify scans that belong to the same sample. Image concatenation refers to combining digital images into a single larger image while maintaining the original resolution. We developed a Python script that identifies which images belong to the same sample and returns a single, larger concatenated image for every set of images in a directory. These concatenated images (combining up to 10 scans) and the original images were processed with RhizoVision Explorer, a free and open-source software developed for estimating root traits from images, with the same settings. An R script was developed that can identify the rows of data belonging to the same sample in RhizoVision Explorer data files and apply correct statistical methods such as summation, weighted average by length, and average to the appropriate measurement types to return a single data row for each sample. These two methods were compared using example images from switchgrass, poplar, and various tree and ericaceous shrub species from a northern peatland and the Arctic. Overall, the new methods accomplished the goal of standardizing measurement aggregation. Most root measurements were nearly identical except median diameter, which can not be accurately computed by statistical aggregation. We believe the availability of these methods will be useful to the root biology community.

plant biology↗

bioRxiv2022

Embracing fine-root system complexity to improve the predictive understanding of ecosystem functioning

Projecting the functioning of the biosphere requires a holistic consideration of whole-ecosystem processes. Although improving leaf and canopy processes has been the focus of ecosystem model development since the 1970s, the arbitrary homogenization of fine-root systems into a single pool is at odds with observations. This discrepancy has increased in the last two decades as accelerated conceptual and empirical advances have revealed functional differentiation and cooperation conferred by the hierarchical structure of fine-root orders and associations with mycorrhizal fungi in fine-root systems. To close this model-data gap, we propose a 3-pool structure comprising Transport and Absorptive fine roots with Mycorrhizal fungi (TAM) to model vertically resolved fine-root systems across organizational and spatial-temporal scales. A comparison of TAM to the single fine-root structure in a state-of-the-art Earth System Model using the big-leaf approach demonstrates robust impacts on carbon cycling in temperate forests, lending further quantitative support to the empirical and theoretical basis for TAM. Strong support in both theory and practice therefore suggests a move beyond the useful but incorrect paradigm of single-pool homogenization, echoing a broad trend of embracing ecological complexities in terrestrial ecosystem modelling. Although challenges lay ahead towards realizing TAM in ecologically realistic demography models simulating emergent functioning from pattern and diversity, adoption of TAM by both modelers and empiricists holds promise to build a better predictive understanding of ecosystem functioning in the context of global change.

ecology↗