Determination of the gas exchange phenology in an evergreen coniferous forest from 7 years of eddy covariance flux data using ... View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2013-05

AUTHORS

Yoshiko Kosugi, Satoru Takanashi, Masahito Ueyama, Shinjiro Ohkubo, Hiroki Tanaka, Kazuho Matsumoto, Natsuko Yoshifuji, Mioko Ataka, Ayaka Sakabe

ABSTRACT

We defined gas exchange phenology as the seasonality of the gas exchange characteristics of a forest canopy, and investigated how the gas exchange phenology could be directly detected from an eddy covariance (EC) dataset and its influence on the canopy fluxes within an evergreen Japanese cypress forest. For the detection of gas exchange phenology, we derived three bulk parameters of the extended big-leaf model (Kosugi et al. 2005) inversely from EC flux data over a 7-year period: surface conductance (gc), maximum rate of carboxylation of the “big leaf” (VCMAX), and intercellular CO2 concentration of the “big leaf” (CI). The relationship between gc and the vapor pressure deficit declined in winter and spring. The relationship between the daily ecosystem respiration and air temperature was greater in the spring than in the other seasons. The temperature dependence curve of VCMAX decreased substantially in the winter and was different from that of an evergreen broadleaved forest. A decrease in CI was occasionally coupled with the decrease in canopy gross primary production during April and August, indicating that stomatal closure was responsible for a decline in canopy photosynthesis. Gas exchange phenology should be quantified when understanding the determining factors of the seasonality of canopy fluxes at evergreen coniferous forests. More... »

PAGES

373-385

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s11284-012-1019-4

DOI

http://dx.doi.org/10.1007/s11284-012-1019-4

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1001765519


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0602", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Ecology", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/06", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Biological Sciences", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Kyoto University", 
          "id": "https://www.grid.ac/institutes/grid.258799.8", 
          "name": [
            "Laboratory of Forest Hydrology, Division of Environmental Science and Technology, Graduate School of Agriculture, Kyoto University, 606-8502, Kyoto, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Kosugi", 
        "givenName": "Yoshiko", 
        "id": "sg:person.010704751171.47", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010704751171.47"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Forestry and Forest Products Research Institute", 
          "id": "https://www.grid.ac/institutes/grid.417935.d", 
          "name": [
            "Department of Meteorological Environment, Forestry and Forest Products Research Institute, 305-8687, Tsukuba, Ibaraki, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Takanashi", 
        "givenName": "Satoru", 
        "id": "sg:person.016453736331.82", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016453736331.82"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Osaka Prefecture University", 
          "id": "https://www.grid.ac/institutes/grid.261455.1", 
          "name": [
            "Graduate School of Life and Environmental Sciences, Osaka Prefecture University, 599-8351, Sakai, Osaka, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Ueyama", 
        "givenName": "Masahito", 
        "id": "sg:person.014313347444.56", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014313347444.56"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "National Agricultural Research Center for Hokkaido Region", 
          "id": "https://www.grid.ac/institutes/grid.419106.b", 
          "name": [
            "NARO Hokkaido Agricultural Research Center, 062-8555, Sapporo, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Ohkubo", 
        "givenName": "Shinjiro", 
        "id": "sg:person.014405650065.32", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014405650065.32"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Kyoto University", 
          "id": "https://www.grid.ac/institutes/grid.258799.8", 
          "name": [
            "Laboratory of Forest Hydrology, Division of Environmental Science and Technology, Graduate School of Agriculture, Kyoto University, 606-8502, Kyoto, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Tanaka", 
        "givenName": "Hiroki", 
        "id": "sg:person.016275666367.04", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016275666367.04"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of the Ryukyus", 
          "id": "https://www.grid.ac/institutes/grid.267625.2", 
          "name": [
            "Faculty of Agriculture, University of the Ryukyus, 903-0213, Okinawa, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Matsumoto", 
        "givenName": "Kazuho", 
        "id": "sg:person.016525425431.05", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016525425431.05"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Kyoto University", 
          "id": "https://www.grid.ac/institutes/grid.258799.8", 
          "name": [
            "Laboratory of Forest Hydrology, Division of Environmental Science and Technology, Graduate School of Agriculture, Kyoto University, 606-8502, Kyoto, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Yoshifuji", 
        "givenName": "Natsuko", 
        "id": "sg:person.015323333233.09", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015323333233.09"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Kyoto University", 
          "id": "https://www.grid.ac/institutes/grid.258799.8", 
          "name": [
            "Laboratory of Forest Hydrology, Division of Environmental Science and Technology, Graduate School of Agriculture, Kyoto University, 606-8502, Kyoto, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Ataka", 
        "givenName": "Mioko", 
        "id": "sg:person.0703417254.79", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0703417254.79"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Kyoto University", 
          "id": "https://www.grid.ac/institutes/grid.258799.8", 
          "name": [
            "Laboratory of Forest Hydrology, Division of Environmental Science and Technology, Graduate School of Agriculture, Kyoto University, 606-8502, Kyoto, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Sakabe", 
        "givenName": "Ayaka", 
        "id": "sg:person.013356615571.30", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013356615571.30"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1111/j.1365-2486.2008.01795.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1000912948"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/treephys/19.7.407", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1004514420"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0168-1923(91)90010-n", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1006523103"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0168-1923(91)90010-n", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1006523103"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1023/a:1002473906184", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1007005731", 
          "https://doi.org/10.1023/a:1002473906184"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1175/1520-0442(2004)017<2281:atmfct>2.0.co;2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1007014357"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0168-1923(97)00072-5", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1013339853"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.agrformet.2006.11.004", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1013570605"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.agrformet.2006.02.011", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1013692759"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0168-1923(02)00103-x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1014506885"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0168-1923(02)00103-x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1014506885"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1034/j.1600-0889.2003.00010.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1016862602"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1046/j.1365-2486.2003.00597.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1017755861"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jhydrol.2007.01.039", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1022217379"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1046/j.1365-3040.2003.00960.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1022405622"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.agrformet.2007.12.006", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1023616194"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11284-005-0047-8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1025558089", 
          "https://doi.org/10.1007/s11284-005-0047-8"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1046/j.1466-822x.2001.00268.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1028119517"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1046/j.1365-2486.2002.00530.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1028577220"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf00386231", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1032536189", 
          "https://doi.org/10.1007/bf00386231"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf00386231", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1032536189", 
          "https://doi.org/10.1007/bf00386231"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf00120530", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1032754782", 
          "https://doi.org/10.1007/bf00120530"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf00120530", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1032754782", 
          "https://doi.org/10.1007/bf00120530"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0065-2504(08)60018-5", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1036865609"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.agrformet.2005.08.010", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1037621041"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0168-1923(99)00088-x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1039922433"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/j.1600-0889.2007.00321.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1042105574"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/j.1365-3040.1992.tb00974.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1042434947"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0022-5193(77)90265-x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1043292082"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0168-1923(95)02248-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1043409059"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1098/rstb.2010.0102", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1043916837"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.agrformet.2006.06.009", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1044095351"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/treephys/26.9.1173", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1044699411"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.13101/ijece.4.10", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1045387882"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jhydrol.2006.05.025", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1045697097"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1046/j.1365-2486.2001.00434.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1048652654"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/j.1365-3040.1997.00094.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049489457"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10310-012-0346-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1050661111", 
          "https://doi.org/10.1007/s10310-012-0346-1"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.agrformet.2005.08.016", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1052800527"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.ecolmodel.2011.05.006", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1053110182"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.5751/es-00530-070207", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1073096082"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2013-05", 
    "datePublishedReg": "2013-05-01", 
    "description": "We defined gas exchange phenology as the seasonality of the gas exchange characteristics of a forest canopy, and investigated how the gas exchange phenology could be directly detected from an eddy covariance (EC) dataset and its influence on the canopy fluxes within an evergreen Japanese cypress forest. For the detection of gas exchange phenology, we derived three bulk parameters of the extended big-leaf model (Kosugi et al. 2005) inversely from EC flux data over a 7-year period: surface conductance (gc), maximum rate of carboxylation of the \u201cbig leaf\u201d (VCMAX), and intercellular CO2 concentration of the \u201cbig leaf\u201d (CI). The relationship between gc and the vapor pressure deficit declined in winter and spring. The relationship between the daily ecosystem respiration and air temperature was greater in the spring than in the other seasons. The temperature dependence curve of VCMAX decreased substantially in the winter and was different from that of an evergreen broadleaved forest. A decrease in CI was occasionally coupled with the decrease in canopy gross primary production during April and August, indicating that stomatal closure was responsible for a decline in canopy photosynthesis. Gas exchange phenology should be quantified when understanding the determining factors of the seasonality of canopy fluxes at evergreen coniferous forests.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/s11284-012-1019-4", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isFundedItemOf": [
      {
        "id": "sg:grant.6050305", 
        "type": "MonetaryGrant"
      }, 
      {
        "id": "sg:grant.5815309", 
        "type": "MonetaryGrant"
      }
    ], 
    "isPartOf": [
      {
        "id": "sg:journal.1312370", 
        "issn": [
          "0912-3814", 
          "1440-1703"
        ], 
        "name": "Ecological Research", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "3", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "28"
      }
    ], 
    "name": "Determination of the gas exchange phenology in an evergreen coniferous forest from 7 years of eddy covariance flux data using an extended big-leaf analysis", 
    "pagination": "373-385", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "086886093dfb177c67f78b263efbaf6fe0c1d5904631db0f6b58edce605e9e54"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s11284-012-1019-4"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1001765519"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s11284-012-1019-4", 
      "https://app.dimensions.ai/details/publication/pub.1001765519"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-10T19:59", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000001_0000000264/records_8681_00000519.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "http://link.springer.com/10.1007%2Fs11284-012-1019-4"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s11284-012-1019-4'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s11284-012-1019-4'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s11284-012-1019-4'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s11284-012-1019-4'


 

This table displays all metadata directly associated to this object as RDF triples.

249 TRIPLES      21 PREDICATES      64 URIs      19 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s11284-012-1019-4 schema:about anzsrc-for:06
2 anzsrc-for:0602
3 schema:author Ncfc4a36b4b474dfc9de7c45545631d68
4 schema:citation sg:pub.10.1007/bf00120530
5 sg:pub.10.1007/bf00386231
6 sg:pub.10.1007/s10310-012-0346-1
7 sg:pub.10.1007/s11284-005-0047-8
8 sg:pub.10.1023/a:1002473906184
9 https://doi.org/10.1016/0022-5193(77)90265-x
10 https://doi.org/10.1016/0168-1923(91)90010-n
11 https://doi.org/10.1016/0168-1923(95)02248-1
12 https://doi.org/10.1016/j.agrformet.2005.08.010
13 https://doi.org/10.1016/j.agrformet.2005.08.016
14 https://doi.org/10.1016/j.agrformet.2006.02.011
15 https://doi.org/10.1016/j.agrformet.2006.06.009
16 https://doi.org/10.1016/j.agrformet.2006.11.004
17 https://doi.org/10.1016/j.agrformet.2007.12.006
18 https://doi.org/10.1016/j.ecolmodel.2011.05.006
19 https://doi.org/10.1016/j.jhydrol.2006.05.025
20 https://doi.org/10.1016/j.jhydrol.2007.01.039
21 https://doi.org/10.1016/s0065-2504(08)60018-5
22 https://doi.org/10.1016/s0168-1923(02)00103-x
23 https://doi.org/10.1016/s0168-1923(97)00072-5
24 https://doi.org/10.1016/s0168-1923(99)00088-x
25 https://doi.org/10.1034/j.1600-0889.2003.00010.x
26 https://doi.org/10.1046/j.1365-2486.2001.00434.x
27 https://doi.org/10.1046/j.1365-2486.2002.00530.x
28 https://doi.org/10.1046/j.1365-2486.2003.00597.x
29 https://doi.org/10.1046/j.1365-3040.2003.00960.x
30 https://doi.org/10.1046/j.1466-822x.2001.00268.x
31 https://doi.org/10.1093/treephys/19.7.407
32 https://doi.org/10.1093/treephys/26.9.1173
33 https://doi.org/10.1098/rstb.2010.0102
34 https://doi.org/10.1111/j.1365-2486.2008.01795.x
35 https://doi.org/10.1111/j.1365-3040.1992.tb00974.x
36 https://doi.org/10.1111/j.1365-3040.1997.00094.x
37 https://doi.org/10.1111/j.1600-0889.2007.00321.x
38 https://doi.org/10.1175/1520-0442(2004)017<2281:atmfct>2.0.co;2
39 https://doi.org/10.13101/ijece.4.10
40 https://doi.org/10.5751/es-00530-070207
41 schema:datePublished 2013-05
42 schema:datePublishedReg 2013-05-01
43 schema:description We defined gas exchange phenology as the seasonality of the gas exchange characteristics of a forest canopy, and investigated how the gas exchange phenology could be directly detected from an eddy covariance (EC) dataset and its influence on the canopy fluxes within an evergreen Japanese cypress forest. For the detection of gas exchange phenology, we derived three bulk parameters of the extended big-leaf model (Kosugi et al. 2005) inversely from EC flux data over a 7-year period: surface conductance (gc), maximum rate of carboxylation of the “big leaf” (VCMAX), and intercellular CO2 concentration of the “big leaf” (CI). The relationship between gc and the vapor pressure deficit declined in winter and spring. The relationship between the daily ecosystem respiration and air temperature was greater in the spring than in the other seasons. The temperature dependence curve of VCMAX decreased substantially in the winter and was different from that of an evergreen broadleaved forest. A decrease in CI was occasionally coupled with the decrease in canopy gross primary production during April and August, indicating that stomatal closure was responsible for a decline in canopy photosynthesis. Gas exchange phenology should be quantified when understanding the determining factors of the seasonality of canopy fluxes at evergreen coniferous forests.
44 schema:genre research_article
45 schema:inLanguage en
46 schema:isAccessibleForFree false
47 schema:isPartOf N5fe56dde16e84ee8b0c4f8a0972cdff7
48 Ne45ea889344d4989a7f3750273f325fc
49 sg:journal.1312370
50 schema:name Determination of the gas exchange phenology in an evergreen coniferous forest from 7 years of eddy covariance flux data using an extended big-leaf analysis
51 schema:pagination 373-385
52 schema:productId N0525426c5c5544aabca2c7c77c3dc99b
53 N5a0ae25907414daeb9a7338fba7df433
54 N939df4c921ce4b5aaa88f97811f0778c
55 schema:sameAs https://app.dimensions.ai/details/publication/pub.1001765519
56 https://doi.org/10.1007/s11284-012-1019-4
57 schema:sdDatePublished 2019-04-10T19:59
58 schema:sdLicense https://scigraph.springernature.com/explorer/license/
59 schema:sdPublisher N89f742876eb946bbbbfff6157a26efb7
60 schema:url http://link.springer.com/10.1007%2Fs11284-012-1019-4
61 sgo:license sg:explorer/license/
62 sgo:sdDataset articles
63 rdf:type schema:ScholarlyArticle
64 N0525426c5c5544aabca2c7c77c3dc99b schema:name readcube_id
65 schema:value 086886093dfb177c67f78b263efbaf6fe0c1d5904631db0f6b58edce605e9e54
66 rdf:type schema:PropertyValue
67 N5a0ae25907414daeb9a7338fba7df433 schema:name doi
68 schema:value 10.1007/s11284-012-1019-4
69 rdf:type schema:PropertyValue
70 N5f4cd68533b244e4a640d49e51c47578 rdf:first sg:person.014313347444.56
71 rdf:rest Ndc87a567ccdb47799646c2e9f47484af
72 N5fe56dde16e84ee8b0c4f8a0972cdff7 schema:volumeNumber 28
73 rdf:type schema:PublicationVolume
74 N83f8b27501254ff99a402a5095275f15 rdf:first sg:person.015323333233.09
75 rdf:rest Neb5452a4c56749cd8fd67cd47cdbc948
76 N89f742876eb946bbbbfff6157a26efb7 schema:name Springer Nature - SN SciGraph project
77 rdf:type schema:Organization
78 N939df4c921ce4b5aaa88f97811f0778c schema:name dimensions_id
79 schema:value pub.1001765519
80 rdf:type schema:PropertyValue
81 Naedb453cac304d4e8a146572d73c1282 rdf:first sg:person.016453736331.82
82 rdf:rest N5f4cd68533b244e4a640d49e51c47578
83 Nb59ad473752f4eff9a6afa10b63199ec rdf:first sg:person.013356615571.30
84 rdf:rest rdf:nil
85 Nbb3096a5638e4d60a56faa1f7f90fb7c rdf:first sg:person.016525425431.05
86 rdf:rest N83f8b27501254ff99a402a5095275f15
87 Nc2d47f04d45d4fbcb3aa28dd0a6dc752 rdf:first sg:person.016275666367.04
88 rdf:rest Nbb3096a5638e4d60a56faa1f7f90fb7c
89 Ncfc4a36b4b474dfc9de7c45545631d68 rdf:first sg:person.010704751171.47
90 rdf:rest Naedb453cac304d4e8a146572d73c1282
91 Ndc87a567ccdb47799646c2e9f47484af rdf:first sg:person.014405650065.32
92 rdf:rest Nc2d47f04d45d4fbcb3aa28dd0a6dc752
93 Ne45ea889344d4989a7f3750273f325fc schema:issueNumber 3
94 rdf:type schema:PublicationIssue
95 Neb5452a4c56749cd8fd67cd47cdbc948 rdf:first sg:person.0703417254.79
96 rdf:rest Nb59ad473752f4eff9a6afa10b63199ec
97 anzsrc-for:06 schema:inDefinedTermSet anzsrc-for:
98 schema:name Biological Sciences
99 rdf:type schema:DefinedTerm
100 anzsrc-for:0602 schema:inDefinedTermSet anzsrc-for:
101 schema:name Ecology
102 rdf:type schema:DefinedTerm
103 sg:grant.5815309 http://pending.schema.org/fundedItem sg:pub.10.1007/s11284-012-1019-4
104 rdf:type schema:MonetaryGrant
105 sg:grant.6050305 http://pending.schema.org/fundedItem sg:pub.10.1007/s11284-012-1019-4
106 rdf:type schema:MonetaryGrant
107 sg:journal.1312370 schema:issn 0912-3814
108 1440-1703
109 schema:name Ecological Research
110 rdf:type schema:Periodical
111 sg:person.010704751171.47 schema:affiliation https://www.grid.ac/institutes/grid.258799.8
112 schema:familyName Kosugi
113 schema:givenName Yoshiko
114 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010704751171.47
115 rdf:type schema:Person
116 sg:person.013356615571.30 schema:affiliation https://www.grid.ac/institutes/grid.258799.8
117 schema:familyName Sakabe
118 schema:givenName Ayaka
119 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013356615571.30
120 rdf:type schema:Person
121 sg:person.014313347444.56 schema:affiliation https://www.grid.ac/institutes/grid.261455.1
122 schema:familyName Ueyama
123 schema:givenName Masahito
124 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014313347444.56
125 rdf:type schema:Person
126 sg:person.014405650065.32 schema:affiliation https://www.grid.ac/institutes/grid.419106.b
127 schema:familyName Ohkubo
128 schema:givenName Shinjiro
129 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014405650065.32
130 rdf:type schema:Person
131 sg:person.015323333233.09 schema:affiliation https://www.grid.ac/institutes/grid.258799.8
132 schema:familyName Yoshifuji
133 schema:givenName Natsuko
134 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015323333233.09
135 rdf:type schema:Person
136 sg:person.016275666367.04 schema:affiliation https://www.grid.ac/institutes/grid.258799.8
137 schema:familyName Tanaka
138 schema:givenName Hiroki
139 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016275666367.04
140 rdf:type schema:Person
141 sg:person.016453736331.82 schema:affiliation https://www.grid.ac/institutes/grid.417935.d
142 schema:familyName Takanashi
143 schema:givenName Satoru
144 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016453736331.82
145 rdf:type schema:Person
146 sg:person.016525425431.05 schema:affiliation https://www.grid.ac/institutes/grid.267625.2
147 schema:familyName Matsumoto
148 schema:givenName Kazuho
149 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016525425431.05
150 rdf:type schema:Person
151 sg:person.0703417254.79 schema:affiliation https://www.grid.ac/institutes/grid.258799.8
152 schema:familyName Ataka
153 schema:givenName Mioko
154 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0703417254.79
155 rdf:type schema:Person
156 sg:pub.10.1007/bf00120530 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032754782
157 https://doi.org/10.1007/bf00120530
158 rdf:type schema:CreativeWork
159 sg:pub.10.1007/bf00386231 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032536189
160 https://doi.org/10.1007/bf00386231
161 rdf:type schema:CreativeWork
162 sg:pub.10.1007/s10310-012-0346-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050661111
163 https://doi.org/10.1007/s10310-012-0346-1
164 rdf:type schema:CreativeWork
165 sg:pub.10.1007/s11284-005-0047-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025558089
166 https://doi.org/10.1007/s11284-005-0047-8
167 rdf:type schema:CreativeWork
168 sg:pub.10.1023/a:1002473906184 schema:sameAs https://app.dimensions.ai/details/publication/pub.1007005731
169 https://doi.org/10.1023/a:1002473906184
170 rdf:type schema:CreativeWork
171 https://doi.org/10.1016/0022-5193(77)90265-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1043292082
172 rdf:type schema:CreativeWork
173 https://doi.org/10.1016/0168-1923(91)90010-n schema:sameAs https://app.dimensions.ai/details/publication/pub.1006523103
174 rdf:type schema:CreativeWork
175 https://doi.org/10.1016/0168-1923(95)02248-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1043409059
176 rdf:type schema:CreativeWork
177 https://doi.org/10.1016/j.agrformet.2005.08.010 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037621041
178 rdf:type schema:CreativeWork
179 https://doi.org/10.1016/j.agrformet.2005.08.016 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052800527
180 rdf:type schema:CreativeWork
181 https://doi.org/10.1016/j.agrformet.2006.02.011 schema:sameAs https://app.dimensions.ai/details/publication/pub.1013692759
182 rdf:type schema:CreativeWork
183 https://doi.org/10.1016/j.agrformet.2006.06.009 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044095351
184 rdf:type schema:CreativeWork
185 https://doi.org/10.1016/j.agrformet.2006.11.004 schema:sameAs https://app.dimensions.ai/details/publication/pub.1013570605
186 rdf:type schema:CreativeWork
187 https://doi.org/10.1016/j.agrformet.2007.12.006 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023616194
188 rdf:type schema:CreativeWork
189 https://doi.org/10.1016/j.ecolmodel.2011.05.006 schema:sameAs https://app.dimensions.ai/details/publication/pub.1053110182
190 rdf:type schema:CreativeWork
191 https://doi.org/10.1016/j.jhydrol.2006.05.025 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045697097
192 rdf:type schema:CreativeWork
193 https://doi.org/10.1016/j.jhydrol.2007.01.039 schema:sameAs https://app.dimensions.ai/details/publication/pub.1022217379
194 rdf:type schema:CreativeWork
195 https://doi.org/10.1016/s0065-2504(08)60018-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036865609
196 rdf:type schema:CreativeWork
197 https://doi.org/10.1016/s0168-1923(02)00103-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1014506885
198 rdf:type schema:CreativeWork
199 https://doi.org/10.1016/s0168-1923(97)00072-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1013339853
200 rdf:type schema:CreativeWork
201 https://doi.org/10.1016/s0168-1923(99)00088-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1039922433
202 rdf:type schema:CreativeWork
203 https://doi.org/10.1034/j.1600-0889.2003.00010.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1016862602
204 rdf:type schema:CreativeWork
205 https://doi.org/10.1046/j.1365-2486.2001.00434.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1048652654
206 rdf:type schema:CreativeWork
207 https://doi.org/10.1046/j.1365-2486.2002.00530.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1028577220
208 rdf:type schema:CreativeWork
209 https://doi.org/10.1046/j.1365-2486.2003.00597.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1017755861
210 rdf:type schema:CreativeWork
211 https://doi.org/10.1046/j.1365-3040.2003.00960.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1022405622
212 rdf:type schema:CreativeWork
213 https://doi.org/10.1046/j.1466-822x.2001.00268.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1028119517
214 rdf:type schema:CreativeWork
215 https://doi.org/10.1093/treephys/19.7.407 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004514420
216 rdf:type schema:CreativeWork
217 https://doi.org/10.1093/treephys/26.9.1173 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044699411
218 rdf:type schema:CreativeWork
219 https://doi.org/10.1098/rstb.2010.0102 schema:sameAs https://app.dimensions.ai/details/publication/pub.1043916837
220 rdf:type schema:CreativeWork
221 https://doi.org/10.1111/j.1365-2486.2008.01795.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1000912948
222 rdf:type schema:CreativeWork
223 https://doi.org/10.1111/j.1365-3040.1992.tb00974.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1042434947
224 rdf:type schema:CreativeWork
225 https://doi.org/10.1111/j.1365-3040.1997.00094.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1049489457
226 rdf:type schema:CreativeWork
227 https://doi.org/10.1111/j.1600-0889.2007.00321.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1042105574
228 rdf:type schema:CreativeWork
229 https://doi.org/10.1175/1520-0442(2004)017<2281:atmfct>2.0.co;2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1007014357
230 rdf:type schema:CreativeWork
231 https://doi.org/10.13101/ijece.4.10 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045387882
232 rdf:type schema:CreativeWork
233 https://doi.org/10.5751/es-00530-070207 schema:sameAs https://app.dimensions.ai/details/publication/pub.1073096082
234 rdf:type schema:CreativeWork
235 https://www.grid.ac/institutes/grid.258799.8 schema:alternateName Kyoto University
236 schema:name Laboratory of Forest Hydrology, Division of Environmental Science and Technology, Graduate School of Agriculture, Kyoto University, 606-8502, Kyoto, Japan
237 rdf:type schema:Organization
238 https://www.grid.ac/institutes/grid.261455.1 schema:alternateName Osaka Prefecture University
239 schema:name Graduate School of Life and Environmental Sciences, Osaka Prefecture University, 599-8351, Sakai, Osaka, Japan
240 rdf:type schema:Organization
241 https://www.grid.ac/institutes/grid.267625.2 schema:alternateName University of the Ryukyus
242 schema:name Faculty of Agriculture, University of the Ryukyus, 903-0213, Okinawa, Japan
243 rdf:type schema:Organization
244 https://www.grid.ac/institutes/grid.417935.d schema:alternateName Forestry and Forest Products Research Institute
245 schema:name Department of Meteorological Environment, Forestry and Forest Products Research Institute, 305-8687, Tsukuba, Ibaraki, Japan
246 rdf:type schema:Organization
247 https://www.grid.ac/institutes/grid.419106.b schema:alternateName National Agricultural Research Center for Hokkaido Region
248 schema:name NARO Hokkaido Agricultural Research Center, 062-8555, Sapporo, Japan
249 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...