Sampling procedure for the foliar analysis of deciduous trees
Identifieur interne : 000614 ( Istex/Corpus ); précédent : 000613; suivant : 000615Sampling procedure for the foliar analysis of deciduous trees
Auteurs : Sebastiaan Luyssaert ; Hannu Raitio ; Pieter Vervaeke ; Jan Mertens ; Noël LustSource :
- Journal of Environmental Monitoring [ 1464-0325 ] ; 2002.
Abstract
Sampling can be the source of the greatest errors in the overall results of foliar analysis. This paper reviews the variability in heavy metal concentrations in tree crowns, which is a feature that should be known and understood when designing a suitable leaf sampling procedure. The leaf sampling procedures applied in 75 articles were examined. Most of the environmental studies used a closely related form of the UN/ECE-EC leaf sampling procedure, which was developed for the long-term monitoring of forest condition. Studies with objectives outside the UN/ECE-EC field of application should utilize a sampling procedure that is in accordance with the objectives of the study and based on the observed variation in pilot and similar studies. The inherent sources of heavy metal variability inside the stand, i.e. the crown class, stand management, site properties, crown dimensions, infections, seasons, etc. were discussed, but the underlying causes of this variability are rarely understood. The inherent variability in tree crowns is the reason for using leaf sampling as a tool in pollution studies. The objectives of a pollution study determine which sources of variability are utilized by the researcher.
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DOI: 10.1039/b208404j
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Environ. Monit.</title><idno type="ISSN">1464-0325</idno><idno type="eISSN">1464-0333</idno><imprint><publisher>The Royal Society of Chemistry.</publisher><date type="published" when="2002">2002</date><biblScope unit="volume">4</biblScope><biblScope unit="issue">6</biblScope><biblScope unit="page" from="858">858</biblScope><biblScope unit="page" to="864">864</biblScope></imprint><idno type="ISSN">1464-0325</idno></series><idno type="istex">AD0377972D7C86CF68A35423EDC26EAAE15A6C53</idno><idno type="DOI">10.1039/b208404j</idno><idno type="ms-id">b208404j</idno></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">1464-0325</idno></seriesStmt></fileDesc><profileDesc><textClass></textClass><langUsage><language ident="en">en</language></langUsage></profileDesc></teiHeader><front><div type="abstract">Sampling can be the source of the greatest errors in the overall results of foliar analysis. This paper reviews the variability in heavy metal concentrations in tree crowns, which is a feature that should be known and understood when designing a suitable leaf sampling procedure. The leaf sampling procedures applied in 75 articles were examined. Most of the environmental studies used a closely related form of the UN/ECE-EC leaf sampling procedure, which was developed for the long-term monitoring of forest condition. Studies with objectives outside the UN/ECE-EC field of application should utilize a sampling procedure that is in accordance with the objectives of the study and based on the observed variation in pilot and similar studies. The inherent sources of heavy metal variability inside the stand, i.e. the crown class, stand management, site properties, crown dimensions, infections, seasons, etc. were discussed, but the underlying causes of this variability are rarely understood. The inherent variability in tree crowns is the reason for using leaf sampling as a tool in pollution studies. The objectives of a pollution study determine which sources of variability are utilized by the researcher.</div></front></TEI><istex><corpusName>rsc-journals</corpusName><author><json:item><name>Sebastiaan Luyssaert</name><affiliations><json:string>Parkano Research Station, Finnish Forest Research Institute, FIN-39700, Kaironiementie 54, Parkano, Finland</json:string><json:string>Department of Forest and Water Management, Ghent University, B-9090, Geraardsbergsesteenweg 267, Gontrode, Belgium</json:string><json:string>E-mail: Sebastiaan.Luyssaert@metla.fi</json:string></affiliations></json:item><json:item><name>Hannu Raitio</name><affiliations><json:string>Parkano Research Station, Finnish Forest Research Institute, FIN-39700, Kaironiementie 54, Parkano, Finland</json:string><json:string>E-mail: Sebastiaan.Luyssaert@metla.fi</json:string></affiliations></json:item><json:item><name>Pieter Vervaeke</name><affiliations><json:string>Department of Forest and Water Management, Ghent University, B-9090, Geraardsbergsesteenweg 267, Gontrode, Belgium</json:string></affiliations></json:item><json:item><name>Jan Mertens</name><affiliations><json:string>Department of Forest and Water Management, Ghent University, B-9090, Geraardsbergsesteenweg 267, Gontrode, Belgium</json:string></affiliations></json:item><json:item><name>Noël Lust</name><affiliations><json:string>Department of Forest and Water Management, Ghent University, B-9090, Geraardsbergsesteenweg 267, Gontrode, Belgium</json:string></affiliations></json:item></author><language><json:string>eng</json:string></language><originalGenre><json:string>CRV</json:string></originalGenre><abstract>Sampling can be the source of the greatest errors in the overall results of foliar analysis. This paper reviews the variability in heavy metal concentrations in tree crowns, which is a feature that should be known and understood when designing a suitable leaf sampling procedure. The leaf sampling procedures applied in 75 articles were examined. Most of the environmental studies used a closely related form of the UN/ECE-EC leaf sampling procedure, which was developed for the long-term monitoring of forest condition. Studies with objectives outside the UN/ECE-EC field of application should utilize a sampling procedure that is in accordance with the objectives of the study and based on the observed variation in pilot and similar studies. The inherent sources of heavy metal variability inside the stand, i.e. the crown class, stand management, site properties, crown dimensions, infections, seasons, etc. were discussed, but the underlying causes of this variability are rarely understood. The inherent variability in tree crowns is the reason for using leaf sampling as a tool in pollution studies. The objectives of a pollution study determine which sources of variability are utilized by the researcher.</abstract><qualityIndicators><score>9.232</score><pdfVersion>1.2</pdfVersion><pdfPageSize>595 x 842 pts (A4)</pdfPageSize><refBibsNative>true</refBibsNative><keywordCount>0</keywordCount><abstractCharCount>1213</abstractCharCount><pdfWordCount>7095</pdfWordCount><pdfCharCount>42184</pdfCharCount><pdfPageCount>7</pdfPageCount><abstractWordCount>186</abstractWordCount></qualityIndicators><title>Sampling procedure for the foliar analysis of deciduous trees</title><refBibs><json:item><host><pages><first>pp. 1–30</first></pages><author></author><title>Manual on methods and criteria for harmonized sampling, assessment, monitoring and analysis of the effects of air pollution on forests</title></host></json:item><json:item><host><pages><first>pp. 1–30</first></pages><author></author><title>Manual on methods and criteria for harmonized sampling, assessment, monitoring and analysis of the effects of air pollution on forests</title></host></json:item><json:item><host><volume>20</volume><pages><last>22</last><first>13</first></pages><author></author><title>Tree Physiol.</title></host></json:item><json:item><host><volume>176</volume><pages><last>61</last><first>45</first></pages><author></author><title>Sci. 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Franç.</title></host></json:item></refBibs><genre><json:string>review-article</json:string></genre><host><volume>4</volume><pages><total>7</total><last>864</last><first>858</first></pages><issn><json:string>1464-0325</json:string></issn><issue>6</issue><genre><json:string>journal</json:string></genre><language><json:string>unknown</json:string></language><eissn><json:string>1464-0333</json:string></eissn><title>Journal of Environmental Monitoring</title></host><categories><wos><json:string>science</json:string><json:string>environmental sciences</json:string><json:string>chemistry, analytical</json:string></wos><scienceMetrix><json:string>natural sciences</json:string><json:string>earth & environmental sciences</json:string><json:string>environmental sciences</json:string></scienceMetrix></categories><publicationDate>2002</publicationDate><copyrightDate>2002</copyrightDate><doi><json:string>10.1039/b208404j</json:string></doi><id>AD0377972D7C86CF68A35423EDC26EAAE15A6C53</id><score>1</score><fulltext><json:item><extension>pdf</extension><original>true</original><mimetype>application/pdf</mimetype><uri>https://api.istex.fr/document/AD0377972D7C86CF68A35423EDC26EAAE15A6C53/fulltext/pdf</uri></json:item><json:item><extension>zip</extension><original>false</original><mimetype>application/zip</mimetype><uri>https://api.istex.fr/document/AD0377972D7C86CF68A35423EDC26EAAE15A6C53/fulltext/zip</uri></json:item><istex:fulltextTEI uri="https://api.istex.fr/document/AD0377972D7C86CF68A35423EDC26EAAE15A6C53/fulltext/tei"><teiHeader><fileDesc><titleStmt><title level="a">Sampling procedure for the foliar analysis of deciduous trees</title></titleStmt><publicationStmt><authority>ISTEX</authority><publisher>The Royal Society of Chemistry.</publisher><availability><p>This journal is © The Royal Society of Chemistry</p></availability><date>2002</date></publicationStmt><sourceDesc><biblStruct type="inbook"><analytic><title level="a">Sampling procedure for the foliar analysis of deciduous trees</title><author xml:id="author-1"><persName><forename type="first">Sebastiaan</forename><surname>Luyssaert</surname></persName><email>Sebastiaan.Luyssaert@metla.fi</email><affiliation>Parkano Research Station, Finnish Forest Research Institute, FIN-39700, Kaironiementie 54, Parkano, Finland</affiliation><affiliation>Department of Forest and Water Management, Ghent University, B-9090, Geraardsbergsesteenweg 267, Gontrode, Belgium</affiliation></author><author xml:id="author-2"><persName><forename type="first">Hannu</forename><surname>Raitio</surname></persName><email>Sebastiaan.Luyssaert@metla.fi</email><affiliation>Parkano Research Station, Finnish Forest Research Institute, FIN-39700, Kaironiementie 54, Parkano, Finland</affiliation></author><author xml:id="author-3"><persName><forename type="first">Pieter</forename><surname>Vervaeke</surname></persName><affiliation>Department of Forest and Water Management, Ghent University, B-9090, Geraardsbergsesteenweg 267, Gontrode, Belgium</affiliation></author><author xml:id="author-4"><persName><forename type="first">Jan</forename><surname>Mertens</surname></persName><affiliation>Department of Forest and Water Management, Ghent University, B-9090, Geraardsbergsesteenweg 267, Gontrode, Belgium</affiliation></author><author xml:id="author-5"><persName><forename type="first">Noël</forename><surname>Lust</surname></persName><affiliation>Department of Forest and Water Management, Ghent University, B-9090, Geraardsbergsesteenweg 267, Gontrode, Belgium</affiliation></author></analytic><monogr><title level="j">Journal of Environmental Monitoring</title><title level="j" type="abbrev">J. Environ. Monit.</title><idno type="pISSN">1464-0325</idno><idno type="eISSN">1464-0333</idno><imprint><publisher>The Royal Society of Chemistry.</publisher><date type="published" when="2002"></date><biblScope unit="volume">4</biblScope><biblScope unit="issue">6</biblScope><biblScope unit="page" from="858">858</biblScope><biblScope unit="page" to="864">864</biblScope></imprint></monogr><idno type="istex">AD0377972D7C86CF68A35423EDC26EAAE15A6C53</idno><idno type="DOI">10.1039/b208404j</idno><idno type="ms-id">b208404j</idno></biblStruct></sourceDesc></fileDesc><profileDesc><creation><date>2002</date></creation><langUsage><language ident="en">en</language></langUsage><abstract><p>Sampling can be the source of the greatest errors in the overall results of foliar analysis. This paper reviews the variability in heavy metal concentrations in tree crowns, which is a feature that should be known and understood when designing a suitable leaf sampling procedure. The leaf sampling procedures applied in 75 articles were examined. Most of the environmental studies used a closely related form of the UN/ECE-EC leaf sampling procedure, which was developed for the long-term monitoring of forest condition. Studies with objectives outside the UN/ECE-EC field of application should utilize a sampling procedure that is in accordance with the objectives of the study and based on the observed variation in pilot and similar studies. The inherent sources of heavy metal variability inside the stand, i.e. the crown class, stand management, site properties, crown dimensions, infections, seasons, etc. were discussed, but the underlying causes of this variability are rarely understood. The inherent variability in tree crowns is the reason for using leaf sampling as a tool in pollution studies. The objectives of a pollution study determine which sources of variability are utilized by the researcher.</p></abstract></profileDesc><revisionDesc><change when="2002">Published</change></revisionDesc></teiHeader></istex:fulltextTEI><json:item><extension>txt</extension><original>false</original><mimetype>text/plain</mimetype><uri>https://api.istex.fr/document/AD0377972D7C86CF68A35423EDC26EAAE15A6C53/fulltext/txt</uri></json:item></fulltext><metadata><istex:metadataXml wicri:clean="corpus rsc-journals not found" wicri:toSee="no header"><istex:xmlDeclaration>version="1.0" encoding="UTF-8"</istex:xmlDeclaration><istex:document><article type="CRV" xsi:schemaLocation="http://www.rsc.org/schema/rscart38 http://www.rsc.org/schema/rscart38/rscart38.xsd" dtd="RSCART3.8"><metainfo><articletype code="CRV" pubmedForm="review-article" headingForm="Critical Reviews" group="Review" fullForm="Critical Review"></articletype></metainfo><art-admin><ms-id>b208404j</ms-id><doi>10.1039/b208404j</doi><received><date><year>2002</year><month>August</month><day>29</day></date></received><date role="accepted"><year>2002</year><month>October</month><day>24</day></date></art-admin><published type="web"><journalref><link>EM</link></journalref><volumeref><link>Unassigned</link></volumeref><issueref><link>Advance Articles</link></issueref><pubfront><fpage></fpage><lpage></lpage><no-of-pages></no-of-pages><date><year>2002</year><month>October</month><day>31</day></date></pubfront></published><published type="print"><journalref><link>EM</link></journalref><volumeref><link>4</link></volumeref><issueref><link>6</link></issueref><pubfront><fpage>858</fpage><lpage>864</lpage><no-of-pages>7</no-of-pages><date><year>2002</year><month>11</month><day>26</day></date></pubfront></published><published type="subsyear"><journalref><title type="abbreviated">J. Environ. Monit.</title><title type="full">Journal of Environmental Monitoring</title><title type="journal">Journal of Environmental Monitoring</title><title type="display">Journal of Environmental Monitoring</title><title type="pubmed">J Environ Monit</title><sercode>EM</sercode><publisher><orgname><nameelt>The Royal Society of Chemistry</nameelt></orgname></publisher><issn type="print">1464-0325</issn><issn type="online">1464-0333</issn><coden>JEMOFW</coden><cpyrt>This journal is © The Royal Society of Chemistry</cpyrt></journalref><volumeref><link>4</link></volumeref><issueref><link>6</link></issueref><pubfront><fpage>858</fpage><lpage>864</lpage><no-of-pages>7</no-of-pages><date><year>2002</year><month>Unassigned</month><day>Unassigned</day></date></pubfront></published><art-front><titlegrp><title>Sampling procedure for the foliar analysis of deciduous trees</title></titlegrp><authgrp><author aff="affa affb" role="corres"><person><persname><fname>Sebastiaan</fname><surname>Luyssaert</surname></persname></person></author><author aff="affa"><person><persname><fname>Hannu</fname><surname>Raitio</surname></persname></person></author><author aff="affb"><person><persname><fname>Pieter</fname><surname>Vervaeke</surname></persname></person></author><author aff="affb"><person><persname><fname>Jan</fname><surname>Mertens</surname></persname></person></author><author aff="affb"><person><persname><fname>Noël</fname><surname>Lust</surname></persname></person></author><aff id="affa"><org><orgname><nameelt>Parkano Research Station</nameelt><nameelt>Finnish Forest Research Institute</nameelt></orgname></org><address><addrelt>Kaironiementie 54</addrelt><postcode>FIN-39700</postcode><city>Parkano</city><country>Finland</country></address><email>Sebastiaan.Luyssaert@metla.fi</email><fax>+(358) 3 4435200</fax><phone>+(358) 3 44351</phone></aff><aff id="affb"><org><orgname><nameelt>Department of Forest and Water Management</nameelt><nameelt>Ghent University</nameelt></orgname></org><address><addrelt>Geraardsbergsesteenweg 267</addrelt><postcode>B-9090</postcode><city>Gontrode</city><country>Belgium</country></address></aff></authgrp><abstract><p>Sampling can be the source of the greatest errors in the overall results of foliar analysis. This paper reviews the variability in heavy metal concentrations in tree crowns, which is a feature that should be known and understood when designing a suitable leaf sampling procedure. The leaf sampling procedures applied in 75 articles were examined. Most of the environmental studies used a closely related form of the UN/ECE-EC leaf sampling procedure, which was developed for the long-term monitoring of forest condition. Studies with objectives outside the UN/ECE-EC field of application should utilize a sampling procedure that is in accordance with the objectives of the study and based on the observed variation in pilot and similar studies. The inherent sources of heavy metal variability inside the stand, <it>i.e.</it> the crown class, stand management, site properties, crown dimensions, infections, seasons, <it>etc.</it> were discussed, but the underlying causes of this variability are rarely understood. The inherent variability in tree crowns is the reason for using leaf sampling as a tool in pollution studies. The objectives of a pollution study determine which sources of variability are utilized by the researcher.</p></abstract></art-front><art-body><section><no>1.</no><title>Introduction</title><p>Leaf analysis is an important tool for monitoring forest condition because the nutritional status of trees is often indicative of processes occurring at the ecosystem level.<citref idrefs="cit1 cit2">1,2</citref> Leaf analysis can be divided into the following steps: <it>planning, representative sampling, sample preparation, instrumental analysis</it> and <it>data evaluation</it>.<citref idrefs="cit3">3</citref> Each of these steps contains errors, but the <it>representative sampling</it> step is the source of the greatest errors in the overall results of leaf analysis.<citref idrefs="cit3 cit4 cit5">3–5</citref></p><p>A sample that is representative in terms of the chemical composition of tree leaves means that a small portion of the leaf material has similar concentrations to the elements or compounds under study as the whole population. Many errors, totalling as much as 1000%, can be made during the process of representative sample selection, ranging from all the leaves in the studied stand down to the small number of leaves used for the determination.<citref idrefs="cit6">6</citref> When under-funding or such errors occur, they can counteract all the efforts undertaken to assure quality during the subsequent steps in the laboratory, and hamper final extrapolation of the results to describe forest condition in the study area.<citref idrefs="cit7">7</citref></p><p>The majority of the publications in this field pay attention to <it>planning, sample preparation, instrumental analysis</it> and <it>data evaluation</it> (see for example <citref idrefs="cit3 cit4 cit8 cit9 cit10 cit11 cit12 cit13 cit14 cit15" position="baseline">ref. 3, 4, 8–15</citref>), but only very few have concentrated on the selection of representative leaf samples for environmental studies.<citref idrefs="cit15 cit16">15,16</citref></p><p>Subsequently, from the early thirties<citref idrefs="cit17 cit18 cit19">17–19</citref> up until the late eighties,<citref idrefs="cit20 cit21 cit22">20–22</citref> research focused on the spatial and temporal distribution of nutrients, mainly N, P and K, in tree crowns. The results were used to develop a general sampling procedure for determining nutrient levels in trees.<citref idrefs="cit1 cit23 cit24">1,23,24</citref> In the early seventies heavy metals were found to have clear effects on forest ecosystems<citref idrefs="cit25 cit26 cit27">25–27</citref> and the idea of using tree leaves as a bio-indicator gained wide acceptance.<citref idrefs="cit28">28</citref> Consequently, the field of application of leaf sampling extended from nutrients alone to cover both nutrients and pollutants.<citref idrefs="cit29 cit30">29,30</citref> However, by the time leaves started to be used as an index in environmental studies, interest in leaf sampling methodology reached its lowest level. As a consequence, environmental studies often determine the heavy metal concentrations in trees using sampling procedures that were not originally developed for this purpose (see Section 3).</p><p>In general, a sampling procedure depends on the objectives of the study, the variation in the parameters being studied, the aimed precision, and the accepted chance of wrongly rejecting the research hypothesis. Whereas the researcher decides on the objectives, the precision and the accepted chance, the variability is an inherent feature of the tree crown. This paper aims to review the heavy metal variability in tree crowns, which is a feature that should be understood when designing a suitable leaf sampling procedure for determining the heavy metal concentrations in tree crowns.</p></section><section><no>2.</no><title>Materials and methods</title><p>Leaf sampling procedures were reviewed on the basis of 75 articles in which different sampling procedures were used and described. We classified the research topic of these studies into <it>soil pollution, atmospheric pollution</it> and <it>forest growth and management</it>. A wide range of studies, which included explorative, fertilisation, thinning, site-quality, growth and health experiments, were categorized as <it>forest growth and management</it>. The 75 publications comprised 12 articles on the effects of soil pollution on the element status of tree leaves,<citref idrefs="cit31 cit32 cit33 cit34 cit35 cit36 cit37 cit38 cit39 cit40 cit41 cit42">31–42</citref> 24 articles on the effects of atmospheric deposition,<citref idrefs="cit2 cit30 cit43 cit44 cit45 cit46 cit47 cit48 cit49 cit50 cit51 cit52 cit53 cit54 cit55 cit56 cit57 cit58 cit59 cit60 cit61 cit62 cit63 cit64">2,30,43-64</citref> and 39 articles on the effects of <it>forest growth and management</it> experiments on the element status of tree leaves.<citref idrefs="cit20 cit22 cit65 cit66 cit67 cit68 cit69 cit70 cit71 cit72 cit73 cit74 cit75 cit76 cit77 cit78 cit79 cit80 cit81 cit82 cit83 cit84 cit85 cit86 cit87 cit88 cit89 cit90 cit91 cit92 cit93 cit94 cit95 cit96 cit97 cit98 cit99 cit100 cit101">20,22,65-101</citref></p><p>The leaf sampling procedures in the 75 articles were tabulated in a database with the following fields: author, year of publication, research topic, tree species, social crown class, number of heights sampled, if bulked then the number of aspects bulked, number of trees, time of sampling, temporal variation and spatial variation. When a study compared <it>e.g.</it> leaf element concentrations between two species, an entry was made for the results of the leaves of the first species, and one for the results of the second species. The results of articles investigating different social classes, different pollution regimes <it>etc.</it> were entered separately as different cases in the database. This resulted in a database containing 167 cases of leaf sampling in deciduous trees. The contents of the database are partly summarized in <tableref idrefs="tab1 tab2">Tables 1 and 2</tableref>.</p><table-entry id="tab1"><title>Relative standard deviation of heavy metal concentration between trees from the same species</title><table><tgroup cols="5"><colspec colname="1" colwidth="7.38pi"></colspec><colspec colname="2" colwidth="9.45pi"></colspec><colspec colname="3" colwidth="3.54pi"></colspec><colspec colname="4" colwidth="3.55pi"></colspec><colspec colname="5" colwidth="4.14pi"></colspec><thead><row><entry colname="1">Author</entry><entry colname="2">Tree species</entry><entry colname="3" namest="3" nameend="5">Relative standard deviation(%)</entry></row><row><entry colname="1"></entry><entry colname="2"></entry><entry colname="3">Cu</entry><entry colname="4">Zn</entry><entry colname="5">Cd</entry></row></thead><tfoot><row><entry namest="1" nameend="5"><footnote id="tab1fna">Trees from the same plot.</footnote></entry></row></tfoot><tbody><row><entry colname="1">Ellis<fnoteref idrefs="tab1fna"></fnoteref><citref idrefs="cit70">70</citref></entry><entry colname="2"><it>Acer saccharum</it> Marsh.</entry><entry colname="3"></entry><entry colname="4">20</entry><entry colname="5"></entry></row><row><entry colname="1"></entry><entry colname="2"><it>Fraxinus americana</it> L.</entry><entry colname="3"></entry><entry colname="4">15</entry><entry colname="5"></entry></row><row><entry colname="1"></entry><entry colname="2"><it>Prunus serotina</it> Ehrh.</entry><entry colname="3"></entry><entry colname="4">16</entry><entry colname="5"></entry></row><row><entry colname="1">McLennan<citref idrefs="cit22">22</citref></entry><entry colname="2"><it>Populus trichocarpa</it> Torr. and Gray ex Hook</entry><entry colname="3">27</entry><entry colname="4">30</entry><entry colname="5"></entry></row><row><entry colname="1">Loppi <it>et al.</it><citref idrefs="cit60">60</citref></entry><entry colname="2"><it>Quercus pubescens</it> Willd. and <it>Quercus cerris</it> L.</entry><entry colname="3">8</entry><entry colname="4">26</entry><entry colname="5"></entry></row><row><entry colname="1">Bargagli<citref idrefs="cit5">5</citref></entry><entry colname="2"><it>Quercus ilex</it> L.</entry><entry colname="3">25</entry><entry colname="4">29</entry><entry colname="5">45</entry></row><row><entry colname="1">Luyssaert <fnoteref idrefs="tab1fna"></fnoteref><citref idrefs="cit37">37</citref></entry><entry colname="2"><it>Salix fragilis</it> L.</entry><entry colname="3"></entry><entry colname="4"></entry><entry colname="5">39–48</entry></row></tbody></tgroup></table></table-entry><table-entry id="tab2"><title>Patterns of heavy metal distribution within tree crowns; (I) uniform along the vertical axis within the crown, (II) increasing towards the top of the crown, (III) decreasing towards the top of the crown, (IV) maximum in the middle of the crown, and (V) minimum in the middle of the crown.<citref idrefs="cit24">24</citref> Seasonal changes in metal concentration in leaves; (I) continuous decrease in leaf concentration, (II) continuous increase, (III) gradual decrease, followed by a period with a constant concentration, (IV) slight variation without any seasonal trend, (V) strong variation without any seasonal trend, and (VI) strong variation with a seasonal trend that is either increasing or decreasing</title><table><tgroup cols="10"><colspec colname="1" colwidth="12.70pi"></colspec><colspec colname="2" colwidth="15.95pi"></colspec><colspec colname="3" colwidth="3.69pi"></colspec><colspec colname="4" colwidth="3.69pi"></colspec><colspec colname="5" colwidth="3.69pi"></colspec><colspec colname="6" colwidth="3.69pi"></colspec><colspec colname="7" colwidth="3.52pi"></colspec><colspec colname="8" colwidth="3.52pi"></colspec><colspec colname="9" colwidth="3.52pi"></colspec><colspec colname="10" colwidth="3.52pi"></colspec><thead><row><entry colname="1"></entry><entry colname="2"></entry><entry colname="3" namest="3" nameend="6">Spatial pattern</entry><entry colname="7" namest="7" nameend="10">Seasonal pattern</entry></row><row><entry colname="1">Author</entry><entry colname="2">Tree species</entry><entry colname="3">Cd</entry><entry colname="4">Cu</entry><entry colname="5">Pb</entry><entry colname="6">Zn</entry><entry colname="7">Cd</entry><entry colname="8">Cu</entry><entry colname="9">Pb</entry><entry colname="10">Zn</entry></row></thead><tfoot><row><entry namest="1" nameend="10"><footnote id="tab2fna">Samples taken as a part of a nutrient study.</footnote></entry></row></tfoot><tbody><row><entry colname="1"> Guha and Mitchell<citref idrefs="cit74">74</citref><fnoteref idrefs="tab2fna"></fnoteref></entry><entry colname="2"><it>Acer pseudoplatanus</it> L.</entry><entry colname="3"></entry><entry colname="4">I</entry><entry colname="5">III</entry><entry colname="6">I</entry><entry colname="7"></entry><entry colname="8">III</entry><entry colname="9">II</entry><entry colname="10">III</entry></row><row><entry colname="1"></entry><entry colname="2"><it>Aesculus hippocastanum</it> L.</entry><entry colname="3"></entry><entry colname="4">I</entry><entry colname="5">III</entry><entry colname="6">I</entry><entry colname="7"></entry><entry colname="8">III</entry><entry colname="9">II</entry><entry colname="10">III</entry></row><row><entry colname="1"></entry><entry colname="2"><it>Fagus sylvatica</it> L.</entry><entry colname="3"></entry><entry colname="4">I</entry><entry colname="5">III</entry><entry colname="6">I</entry><entry colname="7"></entry><entry colname="8">III</entry><entry colname="9">II</entry><entry colname="10">III</entry></row><row><entry colname="1"> Ellis<citref idrefs="cit70">70</citref><fnoteref idrefs="tab2fna"></fnoteref></entry><entry colname="2"><it>Acer saccharum</it> Marsh.</entry><entry colname="3"></entry><entry colname="4"></entry><entry colname="5"></entry><entry colname="6">III</entry><entry colname="7"></entry><entry colname="8"></entry><entry colname="9"></entry><entry colname="10"></entry></row><row><entry colname="1"></entry><entry colname="2"><it>Fraxinus americana</it> L.</entry><entry colname="3"></entry><entry colname="4"></entry><entry colname="5"></entry><entry colname="6">I</entry><entry colname="7"></entry><entry colname="8"></entry><entry colname="9"></entry><entry colname="10"></entry></row><row><entry colname="1"></entry><entry colname="2"><it>Prunus serotina</it> Ehrh.</entry><entry colname="3"></entry><entry colname="4"></entry><entry colname="5"></entry><entry colname="6">I</entry><entry colname="7"></entry><entry colname="8"></entry><entry colname="9"></entry><entry colname="10"></entry></row><row><entry colname="1"> Verry and Timmons<citref idrefs="cit98">98</citref><fnoteref idrefs="tab2fna"></fnoteref></entry><entry colname="2"><it>Populus tremuloides</it> Michx.</entry><entry colname="3"></entry><entry colname="4">I</entry><entry colname="5"></entry><entry colname="6">I</entry><entry colname="7"></entry><entry colname="8">VI</entry><entry colname="9"></entry><entry colname="10">VI</entry></row><row><entry colname="1"> Lea <it>et al.</it><citref idrefs="cit82">82</citref><fnoteref idrefs="tab2fna"></fnoteref></entry><entry colname="2"><it>Acer saccharum</it> Marsh.</entry><entry colname="3"></entry><entry colname="4"></entry><entry colname="5"></entry><entry colname="6"></entry><entry colname="7"></entry><entry colname="8">I</entry><entry colname="9"></entry><entry colname="10">V</entry></row><row><entry colname="1"></entry><entry colname="2"><it>Betula alleghaniensis</it> Britton</entry><entry colname="3"></entry><entry colname="4"></entry><entry colname="5"></entry><entry colname="6"></entry><entry colname="7"></entry><entry colname="8">I</entry><entry colname="9"></entry><entry colname="10">II</entry></row><row><entry colname="1"> Morrison<citref idrefs="cit87">87</citref><fnoteref idrefs="tab2fna"></fnoteref></entry><entry colname="2"><it>Acer saccharum</it> Marsh.</entry><entry colname="3"></entry><entry colname="4">I</entry><entry colname="5"></entry><entry colname="6">III</entry><entry colname="7"></entry><entry colname="8"></entry><entry colname="9"></entry><entry colname="10"></entry></row><row><entry colname="1"></entry><entry colname="2"><it>Betula alleghaniensis</it> Britton</entry><entry colname="3"></entry><entry colname="4">I</entry><entry colname="5"></entry><entry colname="6">I</entry><entry colname="7"></entry><entry colname="8"></entry><entry colname="9"></entry><entry colname="10"></entry></row><row><entry colname="1">Capelli <it>et al.</it><citref idrefs="cit47">47</citref></entry><entry colname="2"><it>Populus nigra</it> L. cv. Italica</entry><entry colname="3"></entry><entry colname="4"></entry><entry colname="5"></entry><entry colname="6"></entry><entry colname="7"></entry><entry colname="8"></entry><entry colname="9">II/VI</entry><entry colname="10"></entry></row><row><entry colname="1"> McLennan<citref idrefs="cit22">22</citref><fnoteref idrefs="tab2fna"></fnoteref></entry><entry colname="2"><it>Populus trichocarpa</it> Torr. and Gray ex Hook.</entry><entry colname="3"></entry><entry colname="4">II</entry><entry colname="5"></entry><entry colname="6">III</entry><entry colname="7"></entry><entry colname="8"></entry><entry colname="9"></entry><entry colname="10"></entry></row><row><entry colname="1">Kim and Fergusson<citref idrefs="cit54">54</citref></entry><entry colname="2"><it>Aesculus hippocastanum</it> L.</entry><entry colname="3"></entry><entry colname="4"></entry><entry colname="5"></entry><entry colname="6"></entry><entry colname="7">IV</entry><entry colname="8">I</entry><entry colname="9">II</entry><entry colname="10">I</entry></row><row><entry colname="1">Riddell-Black<citref idrefs="cit41">41</citref></entry><entry colname="2"><it>Salix viminalis</it> L.</entry><entry colname="3"></entry><entry colname="4"></entry><entry colname="5"></entry><entry colname="6"></entry><entry colname="7">II</entry><entry colname="8">II</entry><entry colname="9">II</entry><entry colname="10">II</entry></row><row><entry colname="1"></entry><entry colname="2"><it>Salix triandra</it> L.</entry><entry colname="3"></entry><entry colname="4"></entry><entry colname="5"></entry><entry colname="6"></entry><entry colname="7">II</entry><entry colname="8">I</entry><entry colname="9">II</entry><entry colname="10">II</entry></row><row><entry colname="1"></entry><entry colname="2"><it>Salix dasyclados</it> Wimm.</entry><entry colname="3"></entry><entry colname="4"></entry><entry colname="5"></entry><entry colname="6"></entry><entry colname="7">II</entry><entry colname="8">II</entry><entry colname="9">II</entry><entry colname="10">II</entry></row><row><entry colname="1">Duncan <it>et al.</it><citref idrefs="cit33">33</citref></entry><entry colname="2"><it>Betula pendula</it> Roth.</entry><entry colname="3"></entry><entry colname="4"></entry><entry colname="5"></entry><entry colname="6"></entry><entry colname="7"></entry><entry colname="8"></entry><entry colname="9"></entry><entry colname="10">II</entry></row><row><entry colname="1">Alfani <it>et al.</it><citref idrefs="cit44">44</citref></entry><entry colname="2"><it>Quercus ilex</it> L.</entry><entry colname="3"></entry><entry colname="4"></entry><entry colname="5"></entry><entry colname="6"></entry><entry colname="7"></entry><entry colname="8">VI</entry><entry colname="9">VI</entry><entry colname="10"></entry></row><row><entry colname="1">Dinelli and Lombini<citref idrefs="cit32">32</citref></entry><entry colname="2"><it>Salix</it> spp.</entry><entry colname="3"></entry><entry colname="4"></entry><entry colname="5"></entry><entry colname="6"></entry><entry colname="7"></entry><entry colname="8">I</entry><entry colname="9"></entry><entry colname="10">I</entry></row><row><entry colname="1"></entry><entry colname="2"><it>Populus nigra</it> L.</entry><entry colname="3"></entry><entry colname="4"></entry><entry colname="5"></entry><entry colname="6"></entry><entry colname="7"></entry><entry colname="8">I</entry><entry colname="9"></entry><entry colname="10">I</entry></row><row><entry colname="1">Bargagli<citref idrefs="cit5">5</citref></entry><entry colname="2"><it>Robinia pseudoacacia</it> L.</entry><entry colname="3"></entry><entry colname="4"></entry><entry colname="5">III</entry><entry colname="6"></entry><entry colname="7"></entry><entry colname="8"></entry><entry colname="9"></entry><entry colname="10"></entry></row><row><entry colname="1">Luyssaert <it>et al.</it><citref idrefs="cit37">37</citref></entry><entry colname="2"><it>Salix fragilis</it> L.</entry><entry colname="3">III</entry><entry colname="4"></entry><entry colname="5"></entry><entry colname="6"></entry><entry colname="7"></entry><entry colname="8"></entry><entry colname="9"></entry><entry colname="10"></entry></row></tbody></tgroup></table></table-entry><p>The database was submitted to a hierarchical clustering method in which clustering was based on two fields: sampling height and time. Only 53 of the 167 cases contained information about both fields and could be clustered. The clustering algorithm divided the range of sampling procedures into five different procedural groups. These five procedures were then linked to tree species, year of publication and research topic. The results of this explorative statistical analysis were used to organise the review.</p></section><section><no>3.</no><title>The most widely used sampling procedures</title><p>Clustering the sampling procedures used in the studies resulted in five groups, each group representing a different procedure. The choice of sampling procedure was found to be independent of the studied tree species (<it>p</it> = 0.974, <it>ρ</it> = 0.01) and the age of the study, expressed by the year of publication (<it>p</it> = 0.07, <it>ρ</it> = −0.25). Thus, similar sampling procedures have been used during the last few decades to study different deciduous tree species. However, the procedure used was related to the research topic of the study (<it>p</it> = 0.00, <it>ρ</it> = 0.39). Unlike <it>forest growth and treatment</it> studies, <it>soil</it> and <it>atmospheric pollution studies</it> most frequently used the UN/ECE-EC<citref idrefs="cit1">1</citref> sampling procedure, <it>i.e.</it> sampling only fully developed leaves on the upper third of the crown of a predominant or dominant tree well before the beginning of senescence.</p><p>Although the UN/ECE-EC<citref idrefs="cit1">1</citref> procedure was designed for long-term monitoring of the condition of forests, it is often used in different types of environmental study. The problems associated with using the UN/ECE-EC<citref idrefs="cit1">1</citref> procedure outside its original field of application are discussed.</p></section><section><no>4.</no><title>Sources of variability in the stand</title><subsect1><no>4.1.</no><title>Crown class</title><p>In general, foliar nutrient concentrations decrease as the social position of the crown increases, <it>e.g.</it> the N, P, K, and S concentrations in <it>Betula alleghaniensis</it> Britton and <it>Acer saccharum</it> Marsh. were higher (<it>p</it> = 0.1) in the foliage of intermediate trees than in that of predominant trees.<citref idrefs="cit87">87</citref> In addition to lower concentrations, the concentrations in dominant trees also show less variation than those in suppressed trees.<citref idrefs="cit102">102</citref> However, these observations are not supported by any physiological explanation.</p><p>Before sampling the stand is stratified into sub-populations of suppressed, intermediated and predominant and dominant trees. The nutrient concentration in the leaves of each sub-population is expected to be more homogeneous than that in the stand as a whole. Due to the major economic importance of predominant and dominant trees, they are better representative of the site than suppressed trees.<citref idrefs="cit23 cit24">23,24</citref> Sampling for nutrients analysis is therefore restricted to the sub-population of predominant and dominant trees in the stand.<citref idrefs="cit1">1</citref> Many of the environmental studies adopted this procedure and limited the sampling to predominant and dominant trees.</p><p>Stand stratification in order to determine heavy metal concentrations is supported by the observation that Fe concentrations in <it>Betula alleghaniensis</it> Britton and <it>Acer saccharum</it> Marsh. were higher in the foliage of intermediate than in that of co-dominant trees.<citref idrefs="cit87">87</citref> Although not supported by empirical evidence, the social class and stand density in the vicinity of a tree are expected to influence its capacity to intercept deposition and thus the element concentration of the leaves. Leaves are major interception sites for particles,<citref idrefs="cit103">103</citref> and the efficiency of particle deposition on plant surfaces is proportional to wind speed,<citref idrefs="cit104 cit105 cit106">104–106</citref> tree species<citref idrefs="cit107">107</citref> and stand density.<citref idrefs="cit108">108</citref></p><p>In environmental studies the assessment of heavy metal deposition is not the only possible objective. Mass balance, metabolic turnover, or accumulation in food webs can also be investigated. The objectives of an environmental study should determine how the stand is stratified and which sub-population is sampled. Stratification could be based on crown class, as well as on diameter class,<citref idrefs="cit57 cit60 cit90">57,60,90</citref> tree height<citref idrefs="cit56">56</citref> or biomass.<citref idrefs="cit92">92</citref> Stratification reduces the number of samples, but the results are specific to the sampled sub-population. The error due to mistakes in stand stratification can amount to 300%.<citref idrefs="cit14">14</citref></p></subsect1><subsect1><no>4.2.</no><title>Inter-tree variability</title><p>The foliage of individual trees in the same stand often shows considerable differences in nutrient concentrations. The relative standard deviations for nutrients between trees from the same species are reported to range from 8 to 31%, with an average of 16% .<citref idrefs="cit20 cit22 cit70 cit87">20,22,70,87</citref> If we assume a probability of 0.05 of incorrectly rejecting the null hypothesis, and an aimed precision of 10%, then an average of nine samples from different trees have to be bulked in order to eliminate the variation in nutrient concentrations. Three to five trees, as recommended by the UN/ECE-EC, would be sufficient to eliminate the variability of N and P, but it is probably too small a set of trees to account for the variability of K, Ca and Mg.<citref idrefs="cit20 cit22 cit70 cit87">20,22,70,87</citref></p><p>Heavy metals tend to have even higher variation than nutrient elements (<tableref idrefs="tab1">Table 1</tableref>). This higher variation is, at least partly, due to the analytical method. Due to matrix interference and the risk of sample contamination, it is more difficult to obtain reproducible results for samples with low concentrations. As a consequence, the variation in the analytical results increases. If we accept a 0.05 probability of incorrectly rejecting the null hypothesis and an aimed precision of 10%, then the average heavy metal variation (<tableref idrefs="tab1">Table 1</tableref>) can be eliminated by bulking 22 samples from different trees. The need to sample 22 individuals is in agreement with the experience of the Environmental Specimen Banking project, which recommends sampling at least 12 to 20 individuals.<citref idrefs="cit7">7</citref> However, as the heavy metal concentrations in tree leaves are related to the species and region, sampling 22 trees is a recommendation rather than a standard. For instance, in order to obtain results with the same precision and accepted risk in regions with different sources and levels of pollution, a different number of trees of the same species may be needed. However, sampling three to five trees as recommended by UN/ECE-EC<citref idrefs="cit1">1</citref> is unlikely to account for the variation in heavy metal concentrations between trees. Sampling an insufficient number of trees introduces errors of up to 50% in the final results.<citref idrefs="cit14">14</citref></p><p>The number of trees needed to determine the foliar element concentration with an accepted risk and an assumed precision is best decided from the results of a pilot study.<citref idrefs="cit7 cit109">7,109</citref> However, even a pilot study cannot completely prevent arbitrariness, because the precisions used in environmental studies are still arbitrary. A precision of 5 or 10% is usually assumed when determining the element status of forest stands,<citref idrefs="cit20 cit22 cit70 cit87">20,22,70,87</citref> but no papers have yet been published on the physiological justification for these values. A physiologically based precision would be expected to depend on the element in question and on the observed element concentration.</p><p>The sampling strategy, <it>i.e.</it> random, systematic, stratified-random, transect <it>etc.</it>, used to select the trees in a stand is a part of the so-called <it>representative sampling</it> step (see Section 1). However, the sampling strategy is the choice of the researcher, not an inherent feature of tree crowns, and has therefore been excluded from this review. Several articles and handbooks do in fact pay attention to sampling strategies.<citref idrefs="cit16 cit110 cit111 cit112 cit113">16,110–113</citref></p></subsect1><subsect1><no>4.3.</no><title>Stand management</title><p>Stand management changes the growing conditions and alters the efficiency of particle deposition. Depending on the initial K concentration, removal of a <it>Betula pubescens</it> Ehrh. shelterwood decreased<citref idrefs="cit114">114</citref> or increased<citref idrefs="cit115">115</citref> the K concentration in the foliage of the <it>Picea abies</it> (L.) Karst. understorey. At the same time the understorey N and P concentrations increased. Pruning has been reported to be associated with an increase in B, Ca, N, S, Na and Cu concentrations in <it>Pinus sylvestris</it> needles,<citref idrefs="cit116">116</citref> and heavy thinning altered the leaf S concentrations in needles of <it>Picea abies</it> (L.) Karst.<citref idrefs="cit108">108</citref> The effects of stand management on heavy metal concentrations in conifer needles were thought to demonstrate, but not prove, the possible effects on deciduous tree leaves. Stand management should be avoided during a sampling regime, or at least accurately documented.</p></subsect1><subsect1><no>4.4.</no><title>Site variation</title><p>The supply of soil nutrients affects foliar nutrient levels.<citref idrefs="cit22 cit76 cit117 cit118 cit119 cit120 cit121">22,76,117–121</citref><it>Populus tremuloides</it> Michx. grown on sites with a high site index had higher foliar N, B, and Mn concentrations than individuals grown on less fertile sites.<citref idrefs="cit69">69</citref> The uptake of heavy metals by plants is affected by the availability of the metals, which is mainly determined by the total heavy metal concentrations in the soil and the soil pH.<citref idrefs="cit122">122</citref> The sampling sites should therefore be selected on the basis of physical and chemical site characterisation of the soil.</p></subsect1></section><section><no>5.</no><title>Sources of variability within the tree</title><subsect1><no>5.1</no><title>Crown dimensions</title><p>As a result of variation in the dimension of the crown, the growing conditions within the crown are considerably different from one location to another. The existence of different nutrient concentrations in leaves in the sun and leaves in the shade has been reported.<citref idrefs="cit83 cit123">83,123</citref> Leaves in the shade have higher N, P, Mg, and Ca concentrations than leaves in the sun.<citref idrefs="cit83">83</citref> These differences have been attributed to growth-optimising translocation of nutrients and carbohydrates between individual leaves.<citref idrefs="cit124">124</citref></p><p>Farago<citref idrefs="cit125">125</citref> postulated that leaves in the sun accumulate more heavy metals than leaves in the shade because of differences in transpiration rates. Due to differences in the exposure time, leaf aging results in different metal concentrations within the same crown. Increasing Cu, Zn, Mn concentrations have been reported along the age gradient of a twig of <it>Ulmus Scabra</it> Mill.<citref idrefs="cit16">16</citref> Higher Pb, Cd, Zn concentrations were found in the older leaves of <it>Populus nigra ‘Italica’</it> L.<citref idrefs="cit126">126</citref> Differences in deposition efficiency between the top and bottom part of the crown are assumed to contribute to these differences in element concentration. As a result of the combined effect of the spatial distribution of sun and shade leaves, young and old leaves, and top and bottom leaves, the element concentrations in a tree crown are distributed in accordance with distinct vertical and horizontal patterns.</p><subsect2><no>5.1.1.</no><title>Vertical patterns</title><p>Various patterns have been reported for the distribution of nutrient concentrations in a vertical direction through the crown: uniform along the vertical axis within the crown, a decrease towards the top of the crown, an increase towards the top of the crown, and minimum or maximum concentrations in the middle of the crown.<citref idrefs="cit24">24</citref> Despite a physiological foundation for this—trees try to maintain favourable growing conditions for their metabolically active leaves—opposite distribution patterns have been reported. Le Tacon and Toutain,<citref idrefs="cit80">80</citref> Ellis,<citref idrefs="cit70">70</citref> Morrison,<citref idrefs="cit87">87</citref> Erdmann <it>et al.</it><citref idrefs="cit20">20</citref> and McLennan<citref idrefs="cit22">22</citref> found all these different distribution patterns when they compared the distribution of potassium. Even within the same species, <it>e.g. Acer saccharum</it> Marsh or <it>Quercus alba</it> L., different distribution patterns have been found for the same element (see Wallihan<citref idrefs="cit100">100</citref><it>versus</it> Ellis<citref idrefs="cit70">70</citref>; McVickar<citref idrefs="cit117">117</citref> and Morrison<citref idrefs="cit87">87</citref> McVickar<citref idrefs="cit117">117</citref> and Morrison<citref idrefs="cit87">87</citref><it>versus</it> Auchmoody and Hammack<citref idrefs="cit65">65</citref>). All sampling procedures should take the variations in nutrient concentrations along the vertical axis within the crown into account. One way to do this is to exclude the distribution pattern from the sampling procedure. This means that all samples are taken from a defined vertical position, for instance the lower, middle or upper crown. Although upper-crown foliage has a more variable nutrient composition<citref idrefs="cit20 cit87">20,87</citref> and is difficult to reach, the UN/ECE-EC<citref idrefs="cit1">1</citref> procedure restricts sampling to the upper-crown foliage. The reason for sampling the upper third of the crown is that the nutrient concentrations relevant for tree growth are to be found in the metabolically active leaves, and these are mainly located in the upper third of the crown.</p><p>Furthermore, there are differences in heavy metal concentrations between the upper and lower crown (<tableref idrefs="tab2">Table 2</tableref>). Thus, a sampling procedure is needed that takes into account the distribution patterns of heavy metals. Environmental studies have frequently used the UN/ECE-EC<citref idrefs="cit1">1</citref> procedure, and thus restricted sampling to the upper third of the crown. Because descriptions of heavy metal distribution patterns are scarce (exceptions to this include <citref idrefs="cit5 cit37 cit127" position="baseline">ref. 5, 37 and 127</citref>), we do not know how the concentration at a defined crown height relates to the metal distribution pattern in the crown. This knowledge is essential for interpreting heavy metal concentrations; for example, Luyssaert <it>et al.</it><citref idrefs="cit37">37</citref> found that the Cd concentrations in the top were four times lower than those in the bottom part of a <it>Salix fragilis</it> L. crown. Restricting sampling to the upper third of the crown would result in a serious underestimation of the average Cd concentration in the tree crown. This supports the idea of adjusting, whenever the objective of the study so requires, the sampling procedure to the distribution patterns of the heavy metals. Therefore samples should be taken from foliage in the upper and lower crown at the least. This will double the amount of work required in sampling, as well as the number of chemical analyses, but it does allow quantification of the distribution pattern. A sampling procedure that does not take into account the heavy metal distribution pattern can introduce errors of up to 200% <citref idrefs="cit14">14</citref> or even 400% <citref idrefs="cit37">37</citref>.</p><p>Even within the upper, middle and lower third of the crown, the variation in heavy metal concentrations has to be taken into account. For example, the Cd concentration in the upper third of a <it>Salix fragilis</it> L. crown ranged from 2.37 to 7.58 mg kg<sup>−1</sup> dry mass.<citref idrefs="cit37">37</citref> Sufficient sample support should be used when estimating the heavy metal concentration in a specific part of the crown. Sample support is the largest volume for which the element concentration is considered homogeneous,<citref idrefs="cit128">128</citref> and depends on the processes that are studied. The sample support determines the number of samples to be taken within a crown, the variation of the results, and practical considerations for in-field sampling, sample preparation and analysis. An inadequate sample support is considered to introduce errors of only a few percent in the final leaf concentration.<citref idrefs="cit14">14</citref> No reports were found in the literature presenting an adequate sample support for leaf sampling that is based on observations.</p><p>Although heavy metal variation along the crown height is an inherent feature of tree crowns, and is most probably caused by differences in growth, age and deposition along the crown height, its magnitude is related to the unit used to express the heavy metal concentration. For instance, expressing the Cd concentrations in willow on a dry ash mass basis reduced the relative standard deviation from 26 to 17%.<citref idrefs="cit127">127</citref> Using dry ash mass as a unit of mass may reduce the RSD significantly, but the way in which the results are to used must also be taken into consideration.</p></subsect2><subsect2><no>5.1.2.</no><title>Horizontal patterns</title><p>The nutrient distribution of leaves in a horizontal plane through the crown has been reported to follow a uniform pattern. Leaves from crown positions with different aspects had similar concentrations.<citref idrefs="cit20 cit65 cit77 cit78 cit97 cit100">20,65,77,78,97,100</citref> In the northern hemisphere, however, the K concentrations in birch were slightly higher in southerly exposed leaves, apart from in autumn when northerly exposed leaves had slightly higher K concentrations.<citref idrefs="cit97">97</citref></p><p>No publications were found reporting horizontal, heavy metal distribution patterns that diverged from the uniform pattern (<it>e.g.</it><citref idrefs="cit127" position="baseline">ref. 127</citref>). However, a uniform distribution pattern does not imply the absence of variation. According to a study by Luyssaert <it>et al.</it><citref idrefs="cit127">127</citref> in which the crown of a <it>Salix fragilis</it> L. was divided into 27 horizontal planes, the average relative standard deviation of Cd in each plane was 20%.</p><p>Because the vertical relative standard deviation is larger than the horizontal relative standard deviation, leaf sampling procedures that involve sampling at several locations along the tree height most probably take into account the vertical and horizontal variation in heavy metal concentrations in the same sampling effort. On the other hand, sampling procedures that are limited to sampling at a defined height in the crown should bulk leaves from several locations at the specified height in order to account for the heavy metal variation in the horizontal plane. The number of samples that should be bulked depends on the sample support (see Section 5.1.1).</p></subsect2></subsect1><subsect1><no>5.2.</no><title>Infected leaves</title><p>Because infections by plant pathogens can destroy foliar tissue or stimulate the growth of infected foliar tissues, they are suspected of having an effect on the element concentration of foliage. Compared with uninfected trees, the B, Ca, N, Mn and S concentrations in <it>Pinus sylvestris</it> L. needles were higher, but the Fe and Mg concentrations were lower, in <it>scleroderris</it>-canker-diseased trees.<citref idrefs="cit116">116</citref> Spruce needles infected with rust had higher K, Zn, B, Fe and Al concentrations than uninfected needles.<citref idrefs="cit129">129</citref> Infection with <it>Adelges laricis</it> decreased the Ca concentration of spruce shoots, whereas its effect on the K concentration depended on the sampling time.<citref idrefs="cit130">130</citref> The effects of infection by pathogens on the heavy metal concentration in conifer needles were thought to demonstrate, but not prove, the possible effects on deciduous tree leaves.</p><p>In general, sampling procedures exclude infected leaves. When the majority of the trees are healthy, the analysis of diseased needles gives an incorrect estimate of the nutrient and heavy metal status of the whole stand. The objectives of the environmental study should determine whether infected leaves are analysed or not.</p></subsect1><subsect1><no>5.3.</no><title>Genetic variation</title><p>As a consequence of species-specific uptake and demand for nutrients, leaves of different tree species and clones that are grown in the same conditions contain different amounts of nutrients. Even the provenance of the trees has been shown to have an effect on nutrient concentrations in <it>Pinus sylvestris</it> L. needles.<citref idrefs="cit131">131</citref> Due to this species-specific uptake, a similar pollution regime can also result in different leaf heavy metal concentrations in different tree species<citref idrefs="cit34 cit132">34,132</citref> and clones.<citref idrefs="cit39 cit41 cit133">39,41,133</citref> Incorrect determination of the tree species or clone can result in errors of up to 500%.<citref idrefs="cit14">14</citref> The objectives of the sampling programme should determine whether samples from the same species, clones or provenances are used.</p></subsect1></section><section><no>6.</no><title>Sources of variability in time</title><subsect1><no>6.1.</no><title>Seasonal changes</title><p>As a consequence of leaf development, the nutrient concentrations in tree leaves change during the growing season.<citref idrefs="cit94 cit96 cit99">94,96,99</citref> Ricklefs and Matthew<citref idrefs="cit93">93</citref> reported that the changes during the growing season were not uniform among species, despite the general findings that confirm the existence of similar seasonal N, P, K, Ca and Mg changes among species.</p><p>The sampling procedure has to take into account changes in nutrient concentrations during the growing season in order to ensure that the most relevant concentration is analysed.<citref idrefs="cit134">134</citref> An optimal sampling period is determined by the objectives of the study and characterised by relatively stable nutrient levels, which reflect the overall nutrient status of the tree at that time. The stability of nutrient levels is not solely determined by physiological processes, but is also influenced by the methods used to express foliar levels, <it>e.g.</it> concentration per unit dry mass, concentration per surface unit, amount per 100 leaves <it>etc.</it><citref idrefs="cit81 cit101">81,101</citref> Spring and early summer represent the physiologically important period of <it>nutrient stress</it><citref idrefs="cit135">135</citref> that is characterised by a high demand for nutrients in order to develop the leaves. During this period the variation between trees in the same stand has been reported to be minimal,<citref idrefs="cit91 cit117">91,117</citref> but rapid changes in nutrient concentrations and leaf mass,<citref idrefs="cit20">20</citref> the flow of nutrients from perennial tissues to leaves,<citref idrefs="cit43">43</citref> and the undefined time of <it>nutrient stress</it>,<citref idrefs="cit85">85</citref> make spring and early summer sampling problematic.<citref idrefs="cit20">20</citref> Autumn is also unsuitable for leaf sampling: N, P, K and Mg are resorbed in the branches in preparation for the following year's growth, and Ca preferentially moves to the leaves.<citref idrefs="cit23 cit136">23,136</citref> Although late summer is not, from a physiological point of view, the best time to determine the status of N, P, and K, the period is characterised by relatively stable concentrations of most nutrients. Therefore leaf samples should be taken to determine the nutrient status when the new leaves are fully developed, and well before the onset of autumnal yellowing and senescence.<citref idrefs="cit1">1</citref></p><p>The Cd, Cu, Zn, Pb, Ni and Fe concentrations in tree leaves have been reported to change during the growing season. The patterns of these changes (<tableref idrefs="tab2">Table 2</tableref>) suggested that heavy metal deposition had accumulated during the growing season,<citref idrefs="cit54">54</citref> growth dilution and/or metal shunting had occurred in plant tissues,<citref idrefs="cit26">26</citref> precipitation had removed surface deposition of metals,<citref idrefs="cit53">53</citref> and the availability of metals in the soil had shown seasonal changes.<citref idrefs="cit26">26</citref> The intensity of the seasonal changes was reported to be related to the amount of metals present in the environment. Pronounced seasonal changes in Ni, Pb, Cu, and Fe concentrations were found in foliage at polluted sites. Moderate seasonal changes were also found at less polluted sites, but not at the control sites.<citref idrefs="cit44 cit57">44,57</citref></p><p>A sampling procedure should also take into account the seasonal changes in heavy metal concentrations over time so that the most relevant metal concentration can be determined.<citref idrefs="cit134">134</citref> The criteria used to select the optimal sampling time for nutrients are not necessarily the same for heavy metals. The physiological basis for seasonal changes is not known and this is necessary for determining the optimal sampling time for heavy metals. Failing to sample during the optimal time can result in errors of up to 30%.<citref idrefs="cit14">14</citref></p><p>The UN/ECE-EC<citref idrefs="cit1">1</citref> recommends, by not mentioning a different period, the same sampling period for the monitoring of heavy metal concentrations in leaves as for nutrients. This recommendation is based on practical considerations; samples for nutrients and heavy metals are obtained with the same sampling effort.</p></subsect1><subsect1><no>6.2.</no><title>Annual changes</title><p>As the growing environment of trees varies from year to year, this variation is expected to be reflected in the nutrient concentrations in the leaves. Mader and Thompson<citref idrefs="cit84">84</citref> concluded that N availability, uptake and foliar concentrations were restricted during drought. On the other hand, increasing rainfall and high mean temperatures during the growing season increased foliar concentrations of N, P, Ca and Mg in <it>Pinus sylvestris</it> L.<citref idrefs="cit137">137</citref> During four growing seasons the foliar concentrations of K, Ca and Mg remained very stable, but the N concentration varied considerably from one year to another.<citref idrefs="cit43 cit78 cit91">43,78,91</citref> Within a five-year period, relative standard deviations of 5% for N, 7% for P, 20% for Mn and 0% for Mg were reported.<citref idrefs="cit2">2</citref> In long-term environmental monitoring, changes in tree age have a strong impact on the element composition;<citref idrefs="cit138">138</citref> older trees had lower N, P, Ca and S concentrations in their leaves than younger trees.<citref idrefs="cit75 cit139 cit140">75,139,140</citref> The decline in leaf concentrations with age was attributed to a decreasing supply of nutrients owing to increasing nutrient sequestration in the stem wood and litter layer.<citref idrefs="cit141">141</citref> During maturation of the trees and changes in stand structure, changes in the mycorrhiza population, which are associated with many broadleaved trees, are likely to result in changing nutrient concentrations in the roots and leaves.<citref idrefs="cit142">142</citref> Trees in the same stand that were sampled at an interval of 27 years showed differences in their leaf concentrations of +12% for N, −23% for P, −16% for Ca, −38% for Mg and −6% for K.<citref idrefs="cit2">2</citref> The long-term variation in element concentrations may have been associated with several changes in the growing environment during the 27 year period: tree and stand aging, increased atmospheric CO<inf>2</inf> concentrations, forest soil acidification, and increased nitrogen deposition.<citref idrefs="cit2">2</citref> As annual changes in the growing environment contribute to the imprecision of foliar analysis as a diagnostic tool, the UN/ECE-EC<citref idrefs="cit1">1</citref> calls for a long-term approach using a standardised sampling procedure.</p><p>Changes in the growing environment did not affect the background heavy metal concentrations in leaves: Cd, Co, Ni and Pb concentrations were stable at five sampling sites over a period of nine years.<citref idrefs="cit48">48</citref> In contrast, in regions with a high pollution stress, the Ni, Cu, Fe and Zn concentrations in leaves varied by up to plus or minus 300% from one year to another.<citref idrefs="cit57">57</citref> Changing growing conditions and stand aging were also connected to the changes in leaf metal concentrations; the annual changes in <it>Picea abies</it> (L.) Karst. needle mass contributed to the variability of the Pb concentration in needles. Over the years, the content of Pb in the needles varied less than the Pb concentration.<citref idrefs="cit143">143</citref> Older trees had lower Fe and Al concentrations in their leaves than younger trees.<citref idrefs="cit75 cit140">75,140</citref> Assuming that there is no new pollution input, heavy metal sequestration in the stem wood and the litter layer<citref idrefs="cit59 cit144 cit145">59,144,145</citref> could explain the decreasing metal concentrations in the leaves. However, the changes in leaf metal concentrations were most often attributed to changes in pollution emissions, <it>e.g.</it> the use of unleaded petrol resulting in lower Pb concentrations, and the closure of a steel mill resulting in lower Fe concentrations in tree leaves.<citref idrefs="cit30 cit48">30,48</citref></p></subsect1></section><section><no>7.</no><title>Discussion</title><p>A sampling procedure depends on the objectives of the study, the variation in the parameters under study, the aimed precision, and the accepted chance of incorrectly rejecting the research hypothesis. The researcher chooses the objectives, the precision and the accepted chance; the variation in heavy metal concentrations in leaves is an inherent feature of the tree crown. Many sources of heavy metal variability are known and have been discussed in this review, but the underlying causes of the variability are rarely understood. Gaining an insight of the causes of metal concentration variability, and thus the ability to predict the <it>expected</it> variation in the leaves, are prerequisites for developing a sampling procedure for selecting, in accordance with the objectives of the study, a representative sample. In the meantime, however, sampling procedures have to be developed on the basis of the variation <it>observed</it> in pilot and similar earlier studies.</p><p>It is not possible to sample trees that are fully comparable because the natural dynamics of forests over space and time cause inherent variability in each tree crown. However, the unique and therefore not standardizable state of the leaf sample can, whenever necessary, be described by means of biometric parameters.<citref idrefs="cit143">143</citref> The important point is not the need to evaluate all the sources of variability in depth, but rather to determine, in accordance with the objectives, whether or not a specific source will influence the scientific value of the data.<citref idrefs="cit4">4</citref> The sampling procedure should be thoroughly described, justified by the objectives of the study, and carried out in accordance with quality control and quality assurance guidelines. In contrast to most of the articles covered by this review, publication of the results should include a description of, at the least, the sampled species, sub-population, crown position, number of trees and sampling time. The description should be completed with clone and provenance of the tree, health state of the leaves, site conditions, and recent management practices.</p><p>The UN/ECE-EC<citref idrefs="cit1">1</citref> provides a comprehensive leaf sampling procedure, which is so often used by other researchers that it appears to have become accepted as a standard leaf sampling procedure. The sampling procedure was originally developed to detect time trends and spatial patterns in the state of health of forests. When the UN/ECE-EC procedure is used for other objectives, the following points should be considered:</p><p>(1) The objectives of the study should determine which sub-population is sampled. Sampling the sub-population of predominant and dominant trees is just one of many possibilities.</p><p>(2) Heavy metal concentrations in the upper third of the crown are not always meaningful. A sampling strategy for heavy metals should attempt to describe the distribution of metal concentrations along the crown height; at least the lower and upper crown should be sampled.</p><p>(3) Sample support should be based on the variability between leaves, and be in accordance with the objectives of the study.</p><p>(4) In order to account for the variation between trees, samples from 20 trees should be taken. Sampling 5 trees (the recommended number) is likely to be insufficient to accurately determine the heavy metal concentration in the stand.</p><p>(5) Sample collection can be simplified by sampling a single aspect instead of bulking leaves from different aspects.</p><p>(6) It is not known whether late summer sampling is of physiological relevance for metals.</p></section></art-body><art-back><ack><p>This research was funded by BOF, Ghent University and the EC Quality of Life and Management of Living Resources Programme, Marie Curie Individual Fellowship contract number QLK-CT-2001-51941. 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Franç.</title><year>1997</year><volumeno>2</volumeno><pages><fpage>115</fpage><lpage>130</lpage></pages></journalcit></citgroup></biblist><compoundgrp></compoundgrp><annotationgrp></annotationgrp><datagrp></datagrp><resourcegrp></resourcegrp></art-back></article></istex:document></istex:metadataXml><mods version="3.6"><titleInfo><title>Sampling procedure for the foliar analysis of deciduous trees</title></titleInfo><titleInfo type="alternative" contentType="CDATA"><title>Sampling procedure for the foliar analysis of deciduous trees</title></titleInfo><name type="personal"><namePart type="given">Sebastiaan</namePart><namePart type="family">Luyssaert</namePart><affiliation>Parkano Research Station, Finnish Forest Research Institute, FIN-39700, Kaironiementie 54, Parkano, Finland</affiliation><affiliation>Department of Forest and Water Management, Ghent University, B-9090, Geraardsbergsesteenweg 267, Gontrode, Belgium</affiliation><affiliation>E-mail: Sebastiaan.Luyssaert@metla.fi</affiliation></name><name type="personal"><namePart type="given">Hannu</namePart><namePart type="family">Raitio</namePart><affiliation>Parkano Research Station, Finnish Forest Research Institute, FIN-39700, Kaironiementie 54, Parkano, Finland</affiliation><affiliation>E-mail: Sebastiaan.Luyssaert@metla.fi</affiliation></name><name type="personal"><namePart type="given">Pieter</namePart><namePart type="family">Vervaeke</namePart><affiliation>Department of Forest and Water Management, Ghent University, B-9090, Geraardsbergsesteenweg 267, Gontrode, Belgium</affiliation></name><name type="personal"><namePart type="given">Jan</namePart><namePart type="family">Mertens</namePart><affiliation>Department of Forest and Water Management, Ghent University, B-9090, Geraardsbergsesteenweg 267, Gontrode, Belgium</affiliation></name><name type="personal"><namePart type="given">Noël</namePart><namePart type="family">Lust</namePart><affiliation>Department of Forest and Water Management, Ghent University, B-9090, Geraardsbergsesteenweg 267, Gontrode, Belgium</affiliation></name><typeOfResource>text</typeOfResource><genre type="review-article" displayLabel="CRV"></genre><originInfo><publisher>The Royal Society of Chemistry.</publisher><dateIssued encoding="w3cdtf">2002</dateIssued><copyrightDate encoding="w3cdtf">2002</copyrightDate></originInfo><language><languageTerm type="code" authority="iso639-2b">eng</languageTerm><languageTerm type="code" authority="rfc3066">en</languageTerm></language><physicalDescription><internetMediaType>text/html</internetMediaType></physicalDescription><abstract>Sampling can be the source of the greatest errors in the overall results of foliar analysis. This paper reviews the variability in heavy metal concentrations in tree crowns, which is a feature that should be known and understood when designing a suitable leaf sampling procedure. The leaf sampling procedures applied in 75 articles were examined. Most of the environmental studies used a closely related form of the UN/ECE-EC leaf sampling procedure, which was developed for the long-term monitoring of forest condition. Studies with objectives outside the UN/ECE-EC field of application should utilize a sampling procedure that is in accordance with the objectives of the study and based on the observed variation in pilot and similar studies. The inherent sources of heavy metal variability inside the stand, i.e. the crown class, stand management, site properties, crown dimensions, infections, seasons, etc. were discussed, but the underlying causes of this variability are rarely understood. The inherent variability in tree crowns is the reason for using leaf sampling as a tool in pollution studies. The objectives of a pollution study determine which sources of variability are utilized by the researcher.</abstract><relatedItem type="host"><titleInfo><title>Journal of Environmental Monitoring</title></titleInfo><titleInfo type="abbreviated"><title>J. Environ. Monit.</title></titleInfo><genre type="journal">journal</genre><originInfo><publisher>The Royal Society of Chemistry.</publisher></originInfo><identifier type="ISSN">1464-0325</identifier><identifier type="eISSN">1464-0333</identifier><identifier type="coden">JEMOFW</identifier><identifier type="RSC sercode">EM</identifier><part><date>2002</date><detail type="volume"><caption>vol.</caption><number>4</number></detail><detail type="issue"><caption>no.</caption><number>6</number></detail><extent unit="pages"><start>858</start><end>864</end><total>7</total></extent></part></relatedItem><identifier type="istex">AD0377972D7C86CF68A35423EDC26EAAE15A6C53</identifier><identifier type="DOI">10.1039/b208404j</identifier><identifier type="ms-id">b208404j</identifier><accessCondition type="use and reproduction" contentType="copyright">This journal is © The Royal Society of Chemistry</accessCondition><recordInfo><recordContentSource>RSC Journals</recordContentSource><recordOrigin>The Royal Society of Chemistry</recordOrigin></recordInfo></mods></metadata><serie></serie></istex></record>Pour manipuler ce document sous Unix (Dilib)
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