Spatial sampling on streams: principles for inference on aquatic networks
Identifieur interne : 002A81 ( Istex/Corpus ); précédent : 002A80; suivant : 002A82Spatial sampling on streams: principles for inference on aquatic networks
Auteurs : Nicholas A. Som ; Pascal Monestiez ; Jay M. Ver Hoef ; Dale L. Zimmerman ; Erin E. PetersonSource :
- Environmetrics [ 1180-4009 ] ; 2014-08.
Abstract
For ecological and environmental data, prior inquiries into spatial sampling designs have considered two‐dimensional domains and have shown that design optimality depends on the characteristics of the target spatial domain and intended inference. The structure and water‐driven continuity of streams prompted the development of spatial autocovariance models for stream networks. The unique properties of stream networks, and their spatial processes, warrant evaluation of sampling design characteristics in comparison with their two‐dimensional counterparts. Common inference scenarios in stream networks include spatial prediction, estimation of fixed effects parameters, and estimation of autocovariance parameters, with prediction and fixed effects estimation most commonly coupled with autocovariance parameter estimation. We consider these inference scenarios under a suite of network characteristics and stream‐network spatial processes. Our results demonstrate, for parameter estimation and prediction, the importance of collecting samples from specific network locations. Additionally, our results mirror aspects from the prior two‐dimensional sampling design inquiries, namely, the importance of collecting some samples within clusters when autocovariance parameter estimation is required. These results can be applied to help refine sample site selection for future studies and further showcase that understanding the characteristics of the targeted spatial domain is essential for sampling design planning. Published 2014. This article has been contributed to by US Government employees and their work is in the public domain in the USA.
Url:
DOI: 10.1002/env.2284
Links to Exploration step
ISTEX:E3A33EC32098216458729EFAAB2B2C90EEB80DAELe document en format XML
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Peterson</name><affiliation><mods:affiliation>CSIRO Computational Informatics, Dutton Park, QLD, Australia</mods:affiliation></affiliation></author></analytic><monogr></monogr><series><title level="j" type="main">Environmetrics</title><title level="j" type="alt">ENVIRONMETRICS</title><idno type="ISSN">1180-4009</idno><idno type="eISSN">1099-095X</idno><imprint><biblScope unit="vol">25</biblScope><biblScope unit="issue">5</biblScope><biblScope unit="page" from="306">306</biblScope><biblScope unit="page" to="323">323</biblScope><biblScope unit="page-count">18</biblScope><date type="published" when="2014-08">2014-08</date></imprint><idno type="ISSN">1180-4009</idno></series></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">1180-4009</idno></seriesStmt></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract">For ecological and environmental data, prior inquiries into spatial sampling designs have considered two‐dimensional domains and have shown that design optimality depends on the characteristics of the target spatial domain and intended inference. The structure and water‐driven continuity of streams prompted the development of spatial autocovariance models for stream networks. The unique properties of stream networks, and their spatial processes, warrant evaluation of sampling design characteristics in comparison with their two‐dimensional counterparts. Common inference scenarios in stream networks include spatial prediction, estimation of fixed effects parameters, and estimation of autocovariance parameters, with prediction and fixed effects estimation most commonly coupled with autocovariance parameter estimation. We consider these inference scenarios under a suite of network characteristics and stream‐network spatial processes. Our results demonstrate, for parameter estimation and prediction, the importance of collecting samples from specific network locations. Additionally, our results mirror aspects from the prior two‐dimensional sampling design inquiries, namely, the importance of collecting some samples within clusters when autocovariance parameter estimation is required. These results can be applied to help refine sample site selection for future studies and further showcase that understanding the characteristics of the targeted spatial domain is essential for sampling design planning. Published 2014. This article has been contributed to by US Government employees and their work is in the public domain in the USA.</div></front></TEI><istex><corpusName>wiley</corpusName><author><json:item><name>Nicholas A. Som</name><affiliations><json:string>US Fish & Wildlife Service, Arcata FWO, CA, Arcata, U.S.A.</json:string><json:string>Correspondence to: Nicholas A. Som, US Fish & Wildlife Service, Arcata FWO, Arcata, CA, U.S.A. E‐mail:</json:string><json:string>E-mail: nicholas_som@fws.gov</json:string></affiliations></json:item><json:item><name>Pascal Monestiez</name><affiliations><json:string>Biostatistique et Processus Spatiaux, INRA, Avignon, France</json:string></affiliations></json:item><json:item><name>Jay M. Ver Hoef</name><affiliations><json:string>National Marine Mammal Laboratory, NOAA Fisheries, WA, Seattle, U.S.A.</json:string></affiliations></json:item><json:item><name>Dale L. Zimmerman</name><affiliations><json:string>Department of Statistics and Actuarial Science, University of Iowa, IA, Iowa City, U.S.A.</json:string></affiliations></json:item><json:item><name>Erin E. 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The structure and water‐driven continuity of streams prompted the development of spatial autocovariance models for stream networks. The unique properties of stream networks, and their spatial processes, warrant evaluation of sampling design characteristics in comparison with their two‐dimensional counterparts. Common inference scenarios in stream networks include spatial prediction, estimation of fixed effects parameters, and estimation of autocovariance parameters, with prediction and fixed effects estimation most commonly coupled with autocovariance parameter estimation. We consider these inference scenarios under a suite of network characteristics and stream‐network spatial processes. Our results demonstrate, for parameter estimation and prediction, the importance of collecting samples from specific network locations. Additionally, our results mirror aspects from the prior two‐dimensional sampling design inquiries, namely, the importance of collecting some samples within clusters when autocovariance parameter estimation is required. These results can be applied to help refine sample site selection for future studies and further showcase that understanding the characteristics of the targeted spatial domain is essential for sampling design planning. Published 2014. 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The structure and water‐driven continuity of streams prompted the development of spatial autocovariance models for stream networks. The unique properties of stream networks, and their spatial processes, warrant evaluation of sampling design characteristics in comparison with their two‐dimensional counterparts. Common inference scenarios in stream networks include spatial prediction, estimation of fixed effects parameters, and estimation of autocovariance parameters, with prediction and fixed effects estimation most commonly coupled with autocovariance parameter estimation. We consider these inference scenarios under a suite of network characteristics and stream‐network spatial processes. Our results demonstrate, for parameter estimation and prediction, the importance of collecting samples from specific network locations. Additionally, our results mirror aspects from the prior two‐dimensional sampling design inquiries, namely, the importance of collecting some samples within clusters when autocovariance parameter estimation is required. These results can be applied to help refine sample site selection for future studies and further showcase that understanding the characteristics of the targeted spatial domain is essential for sampling design planning. Published 2014. 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<lineatedText><line>Correspondence to: Nicholas A. Som, US Fish & Wildlife Service, Arcata FWO, Arcata, CA, U.S.A. E‐mail: <email>nicholas_som@fws.gov</email></line></lineatedText> </correspondenceTo><selfCitationGroup><citation type="self" xml:id="env2284-cit-1000"><author><familyName>Som</familyName><givenNames>N. A.</givenNames></author>,
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<author><familyName>Peterson</familyName><givenNames>E. E.</givenNames></author> (<pubYear year="2014">2014</pubYear>), <articleTitle>Spatial sampling on streams: principles for inference on aquatic networks</articleTitle>, <journalTitle>Environmetrics</journalTitle>, <vol>25</vol>, pages <pageFirst>306</pageFirst>–<pageLast>323</pageLast>, doi: <accessionId ref="info:doi/10.1002/env.2284">10.1002/env.2284</accessionId></citation></selfCitationGroup><linkGroup> <link type="toTypesetVersion" href="file:ENV.ENV2284.pdf"></link> </linkGroup></publicationMeta><contentMeta><titleGroup><title type="main">Spatial sampling on streams: principles for inference on aquatic networks</title><title type="shortAuthors">N. A. SOM <i>ET AL.</i></title><title type="short">SPATIAL SAMPLING FOR STREAM NETWORKS</title></titleGroup><creators><creator creatorRole="author" xml:id="env2284-cr-0001" affiliationRef="#env2284-aff-0001" corresponding="yes"><personName><givenNames>Nicholas A.</givenNames><familyName>Som</familyName></personName></creator><creator creatorRole="author" xml:id="env2284-cr-0002" affiliationRef="#env2284-aff-0002"><personName><givenNames>Pascal</givenNames><familyName>Monestiez</familyName></personName></creator><creator creatorRole="author" xml:id="env2284-cr-0003" affiliationRef="#env2284-aff-0003"><personName><givenNames>Jay M.</givenNames><familyName>Ver Hoef</familyName></personName></creator><creator creatorRole="author" xml:id="env2284-cr-0004" affiliationRef="#env2284-aff-0004"><personName><givenNames>Dale L.</givenNames><familyName>Zimmerman</familyName></personName></creator><creator creatorRole="author" xml:id="env2284-cr-0005" affiliationRef="#env2284-aff-0005"><personName><givenNames>Erin E.</givenNames><familyName>Peterson</familyName></personName></creator></creators><affiliationGroup><affiliation xml:id="env2284-aff-0001" countryCode="US" type="organization"><orgName>US Fish & Wildlife Service, Arcata FWO</orgName><address><city>Arcata</city><countryPart>CA</countryPart><country>U.S.A.</country></address></affiliation><affiliation xml:id="env2284-aff-0002" countryCode="FR" type="organization"><orgDiv>Biostatistique et Processus Spatiaux</orgDiv><orgName>INRA</orgName><address><city>Avignon</city><country>France</country></address></affiliation><affiliation xml:id="env2284-aff-0003" countryCode="US" type="organization"><orgDiv>National Marine Mammal Laboratory</orgDiv><orgName>NOAA Fisheries</orgName><address><city>Seattle</city><countryPart>WA</countryPart><country>U.S.A.</country></address></affiliation><affiliation xml:id="env2284-aff-0004" countryCode="US" type="organization"><orgDiv>Department of Statistics and Actuarial Science</orgDiv><orgName>University of Iowa</orgName><address><city>Iowa City</city><countryPart>IA</countryPart><country>U.S.A.</country></address></affiliation><affiliation xml:id="env2284-aff-0005" countryCode="AU" type="organization"><orgName>CSIRO Computational Informatics</orgName><address><city>Dutton Park, QLD</city><country>Australia</country></address></affiliation></affiliationGroup><keywordGroup type="author"><keyword xml:id="env2284-kwd-0001">sampling design</keyword><keyword xml:id="env2284-kwd-0002">spatial statistics</keyword><keyword xml:id="env2284-kwd-0003">stream networks</keyword></keywordGroup><supportingInformation><supportingInfoItem><mediaResource alt="supplementary data" mimeType="application/pdf" rendition="webOriginal" href="urn-x:wiley:11804009:media:env2284:env2284-sup-0001-Appendix"></mediaResource><caption>Supporting info item</caption></supportingInfoItem></supportingInformation><abstractGroup><abstract type="main" xml:id="env2284-abs-0001"><title type="main">Abstract</title><p>For ecological and environmental data, prior inquiries into spatial sampling designs have considered two‐dimensional domains and have shown that design optimality depends on the characteristics of the target spatial domain and intended inference. The structure and water‐driven continuity of streams prompted the development of spatial autocovariance models for stream networks. The unique properties of stream networks, and their spatial processes, warrant evaluation of sampling design characteristics in comparison with their two‐dimensional counterparts. Common inference scenarios in stream networks include spatial prediction, estimation of fixed effects parameters, and estimation of autocovariance parameters, with prediction and fixed effects estimation most commonly coupled with autocovariance parameter estimation. We consider these inference scenarios under a suite of network characteristics and stream‐network spatial processes. Our results demonstrate, for parameter estimation and prediction, the importance of collecting samples from specific network locations. Additionally, our results mirror aspects from the prior two‐dimensional sampling design inquiries, namely, the importance of collecting some samples within clusters when autocovariance parameter estimation is required. These results can be applied to help refine sample site selection for future studies and further showcase that understanding the characteristics of the targeted spatial domain is essential for sampling design planning. Published 2014. This article has been contributed to by US Government employees and their work is in the public domain in the USA.</p></abstract></abstractGroup></contentMeta></header>
</component></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>Spatial sampling on streams: principles for inference on aquatic networks</title></titleInfo><titleInfo type="abbreviated" lang="en"><title>SPATIAL SAMPLING FOR STREAM NETWORKS</title></titleInfo><titleInfo type="alternative" contentType="CDATA" lang="en"><title>Spatial sampling on streams: principles for inference on aquatic networks</title></titleInfo><name type="personal"><namePart type="given">Nicholas A.</namePart><namePart type="family">Som</namePart><affiliation>US Fish & Wildlife Service, Arcata FWO, CA, Arcata, U.S.A.</affiliation><affiliation>Correspondence to: Nicholas A. Som, US Fish & Wildlife Service, Arcata FWO, Arcata, CA, U.S.A. E‐mail:</affiliation><affiliation>E-mail: nicholas_som@fws.gov</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Pascal</namePart><namePart type="family">Monestiez</namePart><affiliation>Biostatistique et Processus Spatiaux, INRA, Avignon, France</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Jay M.</namePart><namePart type="family">Ver Hoef</namePart><affiliation>National Marine Mammal Laboratory, NOAA Fisheries, WA, Seattle, U.S.A.</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Dale L.</namePart><namePart type="family">Zimmerman</namePart><affiliation>Department of Statistics and Actuarial Science, University of Iowa, IA, Iowa City, U.S.A.</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Erin E.</namePart><namePart type="family">Peterson</namePart><affiliation>CSIRO Computational Informatics, Dutton Park, QLD, Australia</affiliation><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><genre type="article" displayLabel="article" authority="ISTEX" authorityURI="https://content-type.data.istex.fr" valueURI="https://content-type.data.istex.fr/ark:/67375/XTP-6N5SZHKN-D">article</genre><originInfo><publisher>Blackwell Publishing Ltd</publisher><dateIssued encoding="w3cdtf">2014-08</dateIssued><dateCreated encoding="w3cdtf">2014-05-12</dateCreated><dateCaptured encoding="w3cdtf">2013-09-27</dateCaptured><dateValid encoding="w3cdtf">2014-04-21</dateValid><edition>SomN. A., MonestiezP., Ver HoefJ. M., ZimmermanD. L. and PetersonE. E. (2014), Spatial sampling on streams: principles for inference on aquatic networks, Environmetrics, 25, pages 306–323, doi: 10.1002/env.2284</edition><copyrightDate encoding="w3cdtf">2014</copyrightDate></originInfo><language><languageTerm type="code" authority="rfc3066">en</languageTerm><languageTerm type="code" authority="iso639-2b">eng</languageTerm></language><abstract>For ecological and environmental data, prior inquiries into spatial sampling designs have considered two‐dimensional domains and have shown that design optimality depends on the characteristics of the target spatial domain and intended inference. The structure and water‐driven continuity of streams prompted the development of spatial autocovariance models for stream networks. The unique properties of stream networks, and their spatial processes, warrant evaluation of sampling design characteristics in comparison with their two‐dimensional counterparts. Common inference scenarios in stream networks include spatial prediction, estimation of fixed effects parameters, and estimation of autocovariance parameters, with prediction and fixed effects estimation most commonly coupled with autocovariance parameter estimation. We consider these inference scenarios under a suite of network characteristics and stream‐network spatial processes. Our results demonstrate, for parameter estimation and prediction, the importance of collecting samples from specific network locations. Additionally, our results mirror aspects from the prior two‐dimensional sampling design inquiries, namely, the importance of collecting some samples within clusters when autocovariance parameter estimation is required. These results can be applied to help refine sample site selection for future studies and further showcase that understanding the characteristics of the targeted spatial domain is essential for sampling design planning. Published 2014. This article has been contributed to by US Government employees and their work is in the public domain in the USA.</abstract><note type="additional physical form">Supporting info item</note><subject><genre>keywords</genre><topic>sampling design</topic><topic>spatial statistics</topic><topic>stream networks</topic></subject><relatedItem type="host"><titleInfo><title>Environmetrics</title></titleInfo><titleInfo type="abbreviated"><title>Environmetrics</title></titleInfo><genre type="journal" authority="ISTEX" authorityURI="https://publication-type.data.istex.fr" valueURI="https://publication-type.data.istex.fr/ark:/67375/JMC-0GLKJH51-B">journal</genre><subject><genre>article-category</genre><topic>Research Article</topic></subject><identifier type="ISSN">1180-4009</identifier><identifier type="eISSN">1099-095X</identifier><identifier type="DOI">10.1002/(ISSN)1099-095X</identifier><identifier type="PublisherID">ENV</identifier><part><date>2014</date><detail type="volume"><caption>vol.</caption><number>25</number></detail><detail type="issue"><caption>no.</caption><number>5</number></detail><extent unit="pages"><start>306</start><end>323</end><total>18</total></extent></part></relatedItem><identifier type="istex">E3A33EC32098216458729EFAAB2B2C90EEB80DAE</identifier><identifier type="ark">ark:/67375/WNG-3STLLW0P-D</identifier><identifier type="DOI">10.1002/env.2284</identifier><identifier type="ArticleID">ENV2284</identifier><accessCondition type="use and reproduction" contentType="copyright">Copyright © 2014 John Wiley & Sons, Ltd.Published 2014. 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