An integrated method for high-resolution definition of new diameter-based fine root sub-classes of Fagus sylvatica L
Key message Compared to the traditional approach, applying micrometric image analysis to fine root samples of Fagus sylvatica with subsequent data treatment through principal component and cluster analysis yielded specific diameter sizes for fine root sub-classes having better resolution of the corr...
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Veröffentlicht in: | Annals of forest science. 2018, Vol.75 (3), p.1-13, Article 76 |
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creator | Montagnoli, Antonio Terzaghi, Mattia Giussani, Barbara Scippa, Gabriella S. Chiatante, Donato |
description | Key message
Compared to the traditional approach, applying micrometric image analysis to fine root samples of
Fagus sylvatica
with subsequent data treatment through principal component and cluster analysis yielded specific diameter sizes for fine root sub-classes having better resolution of the corresponding branching orders, and a more coherent relationship with the values of annual production and turnover rate.
Context
Fine root traits are poorly understood, impeding an accurate representation of terrestrial biogeochemical models. Traditionally used, arbitrary diameter thresholds lead to a misestimation of fine root traits such as branching order, environmental relationship, annual production, and turnover rate.
Aims
Here, we present, as modification of the traditional method, an integrated approach to segregate, at high-resolution, fine root populations of
Fagus sylvatica
into new diameter sub-classes that better correspond with the traits mentioned above.
Methods
Samples, collected with a sequential soil coring method, were subjected to a micrometric image analysis, and resultant data were treated with principal component and cluster analysis.
Results
Results showed that fine roots were distributed into diameter-size sub-classes (0–0.3 mm, 0.3–1 mm, and 1–2 mm) different from those determined by traditional methods (0–0.5 mm, 0.5–1 mm, and 1–2 mm). New sub-classes provided a better resolution of the corresponding branching-orders, and the values of annual production and turnover rate were more coherent with diameter class and soil depth. Moreover, new sub-classes provided a more precise match with soil temperature than traditional methods.
Conclusion
Our method may help to unveil fine root dynamics and development, reduce data analysis time, and make the diameter-based classification more precise and trustworthy even in the case of non-intact samples. |
doi_str_mv | 10.1007/s13595-018-0758-y |
format | Article |
fullrecord | <record><control><sourceid>proquest_C6C</sourceid><recordid>TN_cdi_hal_primary_oai_HAL_hal_02196539v1</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2075609116</sourcerecordid><originalsourceid>FETCH-LOGICAL-c393t-a573ea491ce8dc1c80c07da743b1954d5ab44a271926e1ba3ebb804d960a458d3</originalsourceid><addsrcrecordid>eNp1kUFr3DAQhU1pIWnSH5CboKce1GhsSbaOS2iSwkIvLeQmxtZ4V8GxUklO2X9fuS7NqacZZr73GOZV1RWIzyBEe52gUUZxAR0Xrer46U11DrVpudH64e3ad5pLpcVZ9T6lRyHKQMJ5lXcz83OmQ8RMjj1RPgbHxhDZ0R-OPFIK05J9mJmj0c_-TxtGNtMv5jwWniLvMRVtWROLIWSWlp4PE6ZEaWVv8bAklk7TC2Y_INtfVu9GnBJ9-Fsvqh-3X77f3PP9t7uvN7s9HxrTZI6qbQilgYE6N8DQiUG0DlvZ9GCUdAp7KbFuwdSaoMeG-r4T0hktUKrONRfVp833iJN9jv4J48kG9PZ-t7frTNRgtGrMCxT248Y-x_BzoZTtY1jiXM6zdfmoFgZAFwo2aoghpUjjP1sQdg3CbkHYEoRdg7Cnoqk3TSrsfKD46vx_0W-z5Ixo</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2075609116</pqid></control><display><type>article</type><title>An integrated method for high-resolution definition of new diameter-based fine root sub-classes of Fagus sylvatica L</title><source>Springer Nature OA Free Journals</source><creator>Montagnoli, Antonio ; Terzaghi, Mattia ; Giussani, Barbara ; Scippa, Gabriella S. ; Chiatante, Donato</creator><creatorcontrib>Montagnoli, Antonio ; Terzaghi, Mattia ; Giussani, Barbara ; Scippa, Gabriella S. ; Chiatante, Donato</creatorcontrib><description>Key message
Compared to the traditional approach, applying micrometric image analysis to fine root samples of
Fagus sylvatica
with subsequent data treatment through principal component and cluster analysis yielded specific diameter sizes for fine root sub-classes having better resolution of the corresponding branching orders, and a more coherent relationship with the values of annual production and turnover rate.
Context
Fine root traits are poorly understood, impeding an accurate representation of terrestrial biogeochemical models. Traditionally used, arbitrary diameter thresholds lead to a misestimation of fine root traits such as branching order, environmental relationship, annual production, and turnover rate.
Aims
Here, we present, as modification of the traditional method, an integrated approach to segregate, at high-resolution, fine root populations of
Fagus sylvatica
into new diameter sub-classes that better correspond with the traits mentioned above.
Methods
Samples, collected with a sequential soil coring method, were subjected to a micrometric image analysis, and resultant data were treated with principal component and cluster analysis.
Results
Results showed that fine roots were distributed into diameter-size sub-classes (0–0.3 mm, 0.3–1 mm, and 1–2 mm) different from those determined by traditional methods (0–0.5 mm, 0.5–1 mm, and 1–2 mm). New sub-classes provided a better resolution of the corresponding branching-orders, and the values of annual production and turnover rate were more coherent with diameter class and soil depth. Moreover, new sub-classes provided a more precise match with soil temperature than traditional methods.
Conclusion
Our method may help to unveil fine root dynamics and development, reduce data analysis time, and make the diameter-based classification more precise and trustworthy even in the case of non-intact samples.</description><identifier>ISSN: 1286-4560</identifier><identifier>EISSN: 1297-966X</identifier><identifier>DOI: 10.1007/s13595-018-0758-y</identifier><language>eng</language><publisher>Paris: Springer Paris</publisher><subject>Biomedical and Life Sciences ; Cluster analysis ; Core analysis ; Coring ; Data analysis ; Data processing ; Environment ; Fagus sylvatica ; Forestry ; Forestry Management ; High resolution ; Image analysis ; Image processing ; Integrated approach ; Life Sciences ; Methods ; Research Paper ; Soil depth ; Soil temperature ; Terrestrial environments ; Tree Biology ; Trustworthiness ; Turnover rate ; Wood Science & Technology</subject><ispartof>Annals of forest science., 2018, Vol.75 (3), p.1-13, Article 76</ispartof><rights>INRA and Springer-Verlag France SAS, part of Springer Nature 2018</rights><rights>Copyright Springer Nature B.V. 2018</rights><rights>Distributed under a Creative Commons Attribution 4.0 International License</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c393t-a573ea491ce8dc1c80c07da743b1954d5ab44a271926e1ba3ebb804d960a458d3</citedby><cites>FETCH-LOGICAL-c393t-a573ea491ce8dc1c80c07da743b1954d5ab44a271926e1ba3ebb804d960a458d3</cites><orcidid>0000-0002-8921-0754</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s13595-018-0758-y$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s13595-018-0758-y$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>230,314,780,784,885,27924,27925,41120,41488,42189,42557,51319,51576</link.rule.ids><linktorsrc>$$Uhttps://doi.org/10.1007/s13595-018-0758-y$$EView_record_in_Springer_Nature$$FView_record_in_$$GSpringer_Nature</linktorsrc><backlink>$$Uhttps://hal.science/hal-02196539$$DView record in HAL$$Hfree_for_read</backlink></links><search><creatorcontrib>Montagnoli, Antonio</creatorcontrib><creatorcontrib>Terzaghi, Mattia</creatorcontrib><creatorcontrib>Giussani, Barbara</creatorcontrib><creatorcontrib>Scippa, Gabriella S.</creatorcontrib><creatorcontrib>Chiatante, Donato</creatorcontrib><title>An integrated method for high-resolution definition of new diameter-based fine root sub-classes of Fagus sylvatica L</title><title>Annals of forest science.</title><addtitle>Annals of Forest Science</addtitle><description>Key message
Compared to the traditional approach, applying micrometric image analysis to fine root samples of
Fagus sylvatica
with subsequent data treatment through principal component and cluster analysis yielded specific diameter sizes for fine root sub-classes having better resolution of the corresponding branching orders, and a more coherent relationship with the values of annual production and turnover rate.
Context
Fine root traits are poorly understood, impeding an accurate representation of terrestrial biogeochemical models. Traditionally used, arbitrary diameter thresholds lead to a misestimation of fine root traits such as branching order, environmental relationship, annual production, and turnover rate.
Aims
Here, we present, as modification of the traditional method, an integrated approach to segregate, at high-resolution, fine root populations of
Fagus sylvatica
into new diameter sub-classes that better correspond with the traits mentioned above.
Methods
Samples, collected with a sequential soil coring method, were subjected to a micrometric image analysis, and resultant data were treated with principal component and cluster analysis.
Results
Results showed that fine roots were distributed into diameter-size sub-classes (0–0.3 mm, 0.3–1 mm, and 1–2 mm) different from those determined by traditional methods (0–0.5 mm, 0.5–1 mm, and 1–2 mm). New sub-classes provided a better resolution of the corresponding branching-orders, and the values of annual production and turnover rate were more coherent with diameter class and soil depth. Moreover, new sub-classes provided a more precise match with soil temperature than traditional methods.
Conclusion
Our method may help to unveil fine root dynamics and development, reduce data analysis time, and make the diameter-based classification more precise and trustworthy even in the case of non-intact samples.</description><subject>Biomedical and Life Sciences</subject><subject>Cluster analysis</subject><subject>Core analysis</subject><subject>Coring</subject><subject>Data analysis</subject><subject>Data processing</subject><subject>Environment</subject><subject>Fagus sylvatica</subject><subject>Forestry</subject><subject>Forestry Management</subject><subject>High resolution</subject><subject>Image analysis</subject><subject>Image processing</subject><subject>Integrated approach</subject><subject>Life Sciences</subject><subject>Methods</subject><subject>Research Paper</subject><subject>Soil depth</subject><subject>Soil temperature</subject><subject>Terrestrial environments</subject><subject>Tree Biology</subject><subject>Trustworthiness</subject><subject>Turnover rate</subject><subject>Wood Science & Technology</subject><issn>1286-4560</issn><issn>1297-966X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><recordid>eNp1kUFr3DAQhU1pIWnSH5CboKce1GhsSbaOS2iSwkIvLeQmxtZ4V8GxUklO2X9fuS7NqacZZr73GOZV1RWIzyBEe52gUUZxAR0Xrer46U11DrVpudH64e3ad5pLpcVZ9T6lRyHKQMJ5lXcz83OmQ8RMjj1RPgbHxhDZ0R-OPFIK05J9mJmj0c_-TxtGNtMv5jwWniLvMRVtWROLIWSWlp4PE6ZEaWVv8bAklk7TC2Y_INtfVu9GnBJ9-Fsvqh-3X77f3PP9t7uvN7s9HxrTZI6qbQilgYE6N8DQiUG0DlvZ9GCUdAp7KbFuwdSaoMeG-r4T0hktUKrONRfVp833iJN9jv4J48kG9PZ-t7frTNRgtGrMCxT248Y-x_BzoZTtY1jiXM6zdfmoFgZAFwo2aoghpUjjP1sQdg3CbkHYEoRdg7Cnoqk3TSrsfKD46vx_0W-z5Ixo</recordid><startdate>2018</startdate><enddate>2018</enddate><creator>Montagnoli, Antonio</creator><creator>Terzaghi, Mattia</creator><creator>Giussani, Barbara</creator><creator>Scippa, Gabriella S.</creator><creator>Chiatante, Donato</creator><general>Springer Paris</general><general>Springer Nature B.V</general><general>Springer Nature (since 2011)/EDP Science (until 2010)</general><scope>AAYXX</scope><scope>CITATION</scope><scope>1XC</scope><scope>VOOES</scope><orcidid>https://orcid.org/0000-0002-8921-0754</orcidid></search><sort><creationdate>2018</creationdate><title>An integrated method for high-resolution definition of new diameter-based fine root sub-classes of Fagus sylvatica L</title><author>Montagnoli, Antonio ; Terzaghi, Mattia ; Giussani, Barbara ; Scippa, Gabriella S. ; Chiatante, Donato</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c393t-a573ea491ce8dc1c80c07da743b1954d5ab44a271926e1ba3ebb804d960a458d3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Biomedical and Life Sciences</topic><topic>Cluster analysis</topic><topic>Core analysis</topic><topic>Coring</topic><topic>Data analysis</topic><topic>Data processing</topic><topic>Environment</topic><topic>Fagus sylvatica</topic><topic>Forestry</topic><topic>Forestry Management</topic><topic>High resolution</topic><topic>Image analysis</topic><topic>Image processing</topic><topic>Integrated approach</topic><topic>Life Sciences</topic><topic>Methods</topic><topic>Research Paper</topic><topic>Soil depth</topic><topic>Soil temperature</topic><topic>Terrestrial environments</topic><topic>Tree Biology</topic><topic>Trustworthiness</topic><topic>Turnover rate</topic><topic>Wood Science & Technology</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Montagnoli, Antonio</creatorcontrib><creatorcontrib>Terzaghi, Mattia</creatorcontrib><creatorcontrib>Giussani, Barbara</creatorcontrib><creatorcontrib>Scippa, Gabriella S.</creatorcontrib><creatorcontrib>Chiatante, Donato</creatorcontrib><collection>CrossRef</collection><collection>Hyper Article en Ligne (HAL)</collection><collection>Hyper Article en Ligne (HAL) (Open Access)</collection><jtitle>Annals of forest science.</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Montagnoli, Antonio</au><au>Terzaghi, Mattia</au><au>Giussani, Barbara</au><au>Scippa, Gabriella S.</au><au>Chiatante, Donato</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>An integrated method for high-resolution definition of new diameter-based fine root sub-classes of Fagus sylvatica L</atitle><jtitle>Annals of forest science.</jtitle><stitle>Annals of Forest Science</stitle><date>2018</date><risdate>2018</risdate><volume>75</volume><issue>3</issue><spage>1</spage><epage>13</epage><pages>1-13</pages><artnum>76</artnum><issn>1286-4560</issn><eissn>1297-966X</eissn><abstract>Key message
Compared to the traditional approach, applying micrometric image analysis to fine root samples of
Fagus sylvatica
with subsequent data treatment through principal component and cluster analysis yielded specific diameter sizes for fine root sub-classes having better resolution of the corresponding branching orders, and a more coherent relationship with the values of annual production and turnover rate.
Context
Fine root traits are poorly understood, impeding an accurate representation of terrestrial biogeochemical models. Traditionally used, arbitrary diameter thresholds lead to a misestimation of fine root traits such as branching order, environmental relationship, annual production, and turnover rate.
Aims
Here, we present, as modification of the traditional method, an integrated approach to segregate, at high-resolution, fine root populations of
Fagus sylvatica
into new diameter sub-classes that better correspond with the traits mentioned above.
Methods
Samples, collected with a sequential soil coring method, were subjected to a micrometric image analysis, and resultant data were treated with principal component and cluster analysis.
Results
Results showed that fine roots were distributed into diameter-size sub-classes (0–0.3 mm, 0.3–1 mm, and 1–2 mm) different from those determined by traditional methods (0–0.5 mm, 0.5–1 mm, and 1–2 mm). New sub-classes provided a better resolution of the corresponding branching-orders, and the values of annual production and turnover rate were more coherent with diameter class and soil depth. Moreover, new sub-classes provided a more precise match with soil temperature than traditional methods.
Conclusion
Our method may help to unveil fine root dynamics and development, reduce data analysis time, and make the diameter-based classification more precise and trustworthy even in the case of non-intact samples.</abstract><cop>Paris</cop><pub>Springer Paris</pub><doi>10.1007/s13595-018-0758-y</doi><tpages>13</tpages><orcidid>https://orcid.org/0000-0002-8921-0754</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Biomedical and Life Sciences Cluster analysis Core analysis Coring Data analysis Data processing Environment Fagus sylvatica Forestry Forestry Management High resolution Image analysis Image processing Integrated approach Life Sciences Methods Research Paper Soil depth Soil temperature Terrestrial environments Tree Biology Trustworthiness Turnover rate Wood Science & Technology |
title | An integrated method for high-resolution definition of new diameter-based fine root sub-classes of Fagus sylvatica L |
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