z-logo
open-access-imgOpen Access
A Statistical Growth Property of Plant Root Architectures
Author(s) -
Samuel Sultan,
Joseph Snider,
Adam Conn,
Mao Li,
Christopher N. Topp,
Saket Navlakha
Publication year - 2020
Publication title -
plant phenomics
Language(s) - English
Resource type - Journals
eISSN - 2097-0374
pISSN - 2643-6515
DOI - 10.34133/2020/2073723
Subject(s) - computer science , gaussian , function (biology) , algorithm , probability density function , population , artificial intelligence , mathematics , statistics , physics , biology , demography , quantum mechanics , evolutionary biology , sociology
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.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.
Having issues? You can contact us here
Accelerating Research

Address

John Eccles House
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom