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Title: How “hot” are hotspots: Statistically localizing the high-activity areas on soil and rhizosphere images

Abstract

The topic of microbial hotspots in soil requires not only visualizing their spatial distribution and biochemical analyses, but also statistical approaches to identify these hotspots and separate them from the surrounding activities (background). We hypothesized that each hotspot type (e.g. enzyme activities in the rhizosphere, root exudation, localization of herbicide accumulation) is a result of local process driven by biotic and/or abiotic factors, and the process rates in the hotspots are much faster than those in the soil background. We further hypothesized that the background and hotspot activities in soil belong to different statistical distributions. Consequently, hotspot determination should be based on statistical separation of activities significantly higher than the background. We analyzed for the statistical distributions of grey values on three groups of published images: 1) 14C images of carbon input by roots into the rhizosphere, 2) 14C glyphosate accumulation in the plant, and 3) zymogram of leucine aminopeptidase activity in rooted soil. The two Gaussian distributions were fit (the first representing the background, the second the hotspots) to the distribution of grey values in the images, the parameters (means and standard deviations, SD) of the fitted distributions were calculated, and the background was removed. Thus, we identified hotspotsmore » as areas outside of the Mean+2SD image intensity (corresponding to the upper ~ 2.5% of activity, being over 97.5% of background values) and finally, visualized images of solely hotspot locations. Finally, these results were compared with previously used decisions on hotspot intensity thresholding (i.e. Top-25% and 17 standard thresholding approaches in ImageJ) and discussed the advantages of the Mean+2SD as well as Mean+3SD approaches. These advantages include: i) simple unification of the thresholding approach for several imaging methods with various principles of activity distribution, ii) identification of hotspots with various activity levels, iii) analysis of “time-specific” hotspots in temporal sequences of images. Compared with 17 standard thresholding methods, we concluded that objectively elucidating and separating the hotspots should be based on statistical distribution analysis, e.g. using the Mean+2SD or Mean+3SD approaches. Furthermore, this simple Mean+2SD approach delivered suitable results for three groups of images and so, helps to understand the processes responsible for the highest activities and elucidate hotspots.« less

Authors:
ORCiD logo [1];  [2];  [3];  [1];  [4]
+ Show Author Affiliations
  1. Christian-Albrechts- Univ. of Kiel (Germany)
  2. Univ. of Gottingen (Germany)
  3. Michigan State Univ., East Lansing, MI (United States)
  4. Univ. of Gottingen (Germany); Russian Academy of Sciences (RAS), Pushchino (Russian Federation)
Publication Date:
Research Org.:
Great Lakes Bioenergy Research Center (GLBRC), Madison, WI (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Biological and Environmental Research (BER)
OSTI Identifier:
1764398
Grant/Contract Number:  
SC0018409
Resource Type:
Accepted Manuscript
Journal Name:
Rhizosphere
Additional Journal Information:
Journal Volume: 16; Journal ID: ISSN 2452-2198
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES; Rhizosphere; Microbial hotspots quantification; Statistical analyses; Visualization approaches

Citation Formats

Bilyera, Nataliya, Kuzyakova, Irina, Guber, Andrey, Razavi, Bahar S., and Kuzyakov, Yakov. How “hot” are hotspots: Statistically localizing the high-activity areas on soil and rhizosphere images. United States: N. p., 2020. Web. doi:10.1016/j.rhisph.2020.100259.
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Bilyera, Nataliya, Kuzyakova, Irina, Guber, Andrey, Razavi, Bahar S., & Kuzyakov, Yakov. How “hot” are hotspots: Statistically localizing the high-activity areas on soil and rhizosphere images. United States. https://doi.org/10.1016/j.rhisph.2020.100259
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Bilyera, Nataliya, Kuzyakova, Irina, Guber, Andrey, Razavi, Bahar S., and Kuzyakov, Yakov. Thu . "How “hot” are hotspots: Statistically localizing the high-activity areas on soil and rhizosphere images". United States. https://doi.org/10.1016/j.rhisph.2020.100259. https://www.osti.gov/servlets/purl/1764398.
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@article{osti_1764398,
title = {How “hot” are hotspots: Statistically localizing the high-activity areas on soil and rhizosphere images},
author = {Bilyera, Nataliya and Kuzyakova, Irina and Guber, Andrey and Razavi, Bahar S. and Kuzyakov, Yakov},
abstractNote = {The topic of microbial hotspots in soil requires not only visualizing their spatial distribution and biochemical analyses, but also statistical approaches to identify these hotspots and separate them from the surrounding activities (background). We hypothesized that each hotspot type (e.g. enzyme activities in the rhizosphere, root exudation, localization of herbicide accumulation) is a result of local process driven by biotic and/or abiotic factors, and the process rates in the hotspots are much faster than those in the soil background. We further hypothesized that the background and hotspot activities in soil belong to different statistical distributions. Consequently, hotspot determination should be based on statistical separation of activities significantly higher than the background. We analyzed for the statistical distributions of grey values on three groups of published images: 1) 14C images of carbon input by roots into the rhizosphere, 2) 14C glyphosate accumulation in the plant, and 3) zymogram of leucine aminopeptidase activity in rooted soil. The two Gaussian distributions were fit (the first representing the background, the second the hotspots) to the distribution of grey values in the images, the parameters (means and standard deviations, SD) of the fitted distributions were calculated, and the background was removed. Thus, we identified hotspots as areas outside of the Mean+2SD image intensity (corresponding to the upper ~ 2.5% of activity, being over 97.5% of background values) and finally, visualized images of solely hotspot locations. Finally, these results were compared with previously used decisions on hotspot intensity thresholding (i.e. Top-25% and 17 standard thresholding approaches in ImageJ) and discussed the advantages of the Mean+2SD as well as Mean+3SD approaches. These advantages include: i) simple unification of the thresholding approach for several imaging methods with various principles of activity distribution, ii) identification of hotspots with various activity levels, iii) analysis of “time-specific” hotspots in temporal sequences of images. Compared with 17 standard thresholding methods, we concluded that objectively elucidating and separating the hotspots should be based on statistical distribution analysis, e.g. using the Mean+2SD or Mean+3SD approaches. Furthermore, this simple Mean+2SD approach delivered suitable results for three groups of images and so, helps to understand the processes responsible for the highest activities and elucidate hotspots.},
doi = {10.1016/j.rhisph.2020.100259},
journal = {Rhizosphere},
number = ,
volume = 16,
place = {United States},
year = {Thu Oct 15 00:00:00 EDT 2020},
month = {Thu Oct 15 00:00:00 EDT 2020}
}
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