Javascript must be enabled to continue!
A Statistical Growth Property of Plant Root Architectures
View through CrossRef
Numerous types of biological branching networks, with varying shapes and sizes, are used to acquire and distribute resources. Here, we show that plant root and shoot architectures share a fundamental design property. We studied the spatial density function of plant architectures, which specifies the probability of finding a branch at each location in the 3-dimensional volume occupied by the plant. We analyzed 1645 root architectures from four species and discovered that the spatial density functions of all architectures are population-similar. This means that despite their apparent visual diversity, all of the roots studied share the same basic shape, aside from stretching and compression along orthogonal directions. Moreover, the spatial density of all architectures can be described as variations on a single underlying function: a Gaussian density truncated at a boundary of roughly three standard deviations. Thus, the root density of any architecture requires only four parameters to specify: the total mass of the architecture and the standard deviations of the Gaussian in the three x,y,z growth directions. Plant shoot architectures also follow this design form, suggesting that two basic plant transport systems may use similar growth strategies.
American Association for the Advancement of Science (AAAS)
Title: A Statistical Growth Property of Plant Root Architectures
Description:
Numerous types of biological branching networks, with varying shapes and sizes, are used to acquire and distribute resources.
Here, we show that plant root and shoot architectures share a fundamental design property.
We studied the spatial density function of plant architectures, which specifies the probability of finding a branch at each location in the 3-dimensional volume occupied by the plant.
We analyzed 1645 root architectures from four species and discovered that the spatial density functions of all architectures are population-similar.
This means that despite their apparent visual diversity, all of the roots studied share the same basic shape, aside from stretching and compression along orthogonal directions.
Moreover, the spatial density of all architectures can be described as variations on a single underlying function: a Gaussian density truncated at a boundary of roughly three standard deviations.
Thus, the root density of any architecture requires only four parameters to specify: the total mass of the architecture and the standard deviations of the Gaussian in the three x,y,z growth directions.
Plant shoot architectures also follow this design form, suggesting that two basic plant transport systems may use similar growth strategies.
Related Results
Statistical evidence and the criminal verdict asymmetry
Statistical evidence and the criminal verdict asymmetry
AbstractEpistemologists have posed the following puzzle, known as the proof paradox: Why is it intuitively problematic for juries to convict on the basis of statistical evidence an...
KAT4IA: K-Means Assisted Training for Image Analysis of Field-Grown Plant Phenotypes
KAT4IA: K-Means Assisted Training for Image Analysis of Field-Grown Plant Phenotypes
High-throughput phenotyping enables the efficient collection of plant trait data at scale. One example involves using imaging systems over key phases of a crop growing season. Alth...
Plant Phenotyping: Past, Present, and Future
Plant Phenotyping: Past, Present, and Future
A plant develops the dynamic phenotypes from the interaction of the plant with the environment. Understanding these processes that span plant’s lifetime in a permanently changing e...
Convolutional Neural Networks for Image-Based High-Throughput Plant Phenotyping: A Review
Convolutional Neural Networks for Image-Based High-Throughput Plant Phenotyping: A Review
Plant phenotyping has been recognized as a bottleneck for improving the efficiency of breeding programs, understanding plant-environment interactions, and managing agricultural sys...
Introduction: Plant Performance
Introduction: Plant Performance
Plants perform their own interests and purposes. Plants perform in ways that afford and invite specific human experiences. Plants also perform complex biopolitical roles. With thes...
Semisupervised Deep State-Space Model for Plant Growth Modeling
Semisupervised Deep State-Space Model for Plant Growth Modeling
The optimal control of sugar content and its associated technology is important for producing high-quality crops more stably and efficiently. Model-based reinforcement learning (RL...
Assyrian Lexicographical Notes
Assyrian Lexicographical Notes
This Semitic root occurs regularly in Hebrew, Phœnician, and Aramaic in the form r-š-p, although Syriac agrees with Assyrian in having the metathesis š-r-b. The regular Semitic for...
La figura del biostatistico nei Comitati Etici (CE). Linee guida per il ruolo del biostatistico e per l’attività del biostatistico nella revisione dei protocolli degli studi proposti al parere dei CE
La figura del biostatistico nei Comitati Etici (CE). Linee guida per il ruolo del biostatistico e per l’attività del biostatistico nella revisione dei protocolli degli studi proposti al parere dei CE
Gli autori considerano il ruolo del Biostatistico (BS) dei Comitati Etici (CE) per i farmaci in Italia e delineano le competenze che tale figura di professionista dovrebbe posseder...