Literature DB >> 28865005

A Laplacian characterization of phytoplankton shape.

B B Cael1,2, Courtenay Strong3.   

Abstract

Phytoplankton exhibit pronounced morphological diversity, impacting a range of processes. Because these impacts are challenging to quantify, however, phytoplankton are often approximated as spheres, and when effects of non-sphericity are studied it is usually experimentally or via geometrical approximations. New methods for quantifying phytoplankton size and shape generally, so all phytoplankton are analyzable by the same procedure, can complement advances in microscopic imagery and automated classification to study the influence of shape in phytoplankton. Here we apply to phytoplankton a technique for defining the size of arbitrary shapes based on the Laplacian-the operator that governs processes, such as nutrient uptake and fluid flow, where phytoplankton shape is expected to have the greatest effect. Deviations from values given by spherical approximation are a measure of phytoplankton shape and indicate the fitness increases for phytoplankton conferred by their non-spherical shapes. Comparison with surface-to-volume quotients suggests the Laplacian-based metric is insensitive to small-scale features which can increase surface area without affecting key processes, but is otherwise closely related to surface-area-to-volume, demonstrating this metric is a meaningful measure. While our analysis herein is limited to axisymmetric phytoplankton due to relative sparsity of 3D information about other phytoplankton shapes, the definition and method are directly generalizable to 3D shape data, which will in the near future be more readily available.

Keywords:  Laplace’s equation; Phytoplankton; Shape; Size

Mesh:

Year:  2017        PMID: 28865005     DOI: 10.1007/s00285-017-1176-8

Source DB:  PubMed          Journal:  J Math Biol        ISSN: 0303-6812            Impact factor:   2.259


  7 in total

1.  Three-dimensional mapping of cortical thickness using Laplace's equation.

Authors:  S E Jones; B R Buchbinder; I Aharon
Journal:  Hum Brain Mapp       Date:  2000-09       Impact factor: 5.038

2.  Automatic analysis of aqueous specimens for phytoplankton structure recognition and population estimation.

Authors:  Karsten Rodenacker; Burkhard Hense; Uta Jütting; Peter Gais
Journal:  Microsc Res Tech       Date:  2006-09       Impact factor: 2.769

3.  Sinking velocities of phytoplankton measured on a stable density gradient by laser scanning.

Authors:  Anthony E Walsby; Daryl P Holland
Journal:  J R Soc Interface       Date:  2006-06-22       Impact factor: 4.118

Review 4.  The selective value of bacterial shape.

Authors:  Kevin D Young
Journal:  Microbiol Mol Biol Rev       Date:  2006-09       Impact factor: 11.056

5.  Surface/Volume ratio: implications for phytoplankton morphology.

Authors:  W M Lewis
Journal:  Science       Date:  1976-05-28       Impact factor: 47.728

6.  Primary production of the biosphere: integrating terrestrial and oceanic components

Authors: 
Journal:  Science       Date:  1998-07-10       Impact factor: 47.728

7.  New data-driven method from 3D confocal microscopy for calculating phytoplankton cell biovolume.

Authors:  L Roselli; F Paparella; E Stanca; A Basset
Journal:  J Microsc       Date:  2015-03-18       Impact factor: 1.758

  7 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.