Classifying forest inventory data into species-based forest community types at broad extents: exploring tradeoffs among supervised and unsupervised approaches View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2018-12

AUTHORS

Jennifer K. Costanza, Don Faber-Langendoen, John W. Coulston, David N. Wear

ABSTRACT

Knowledge of the different kinds of tree communities that currently exist can provide a baseline for assessing the ecological attributes of forests and monitoring future changes. Forest inventory data can facilitate the development of this baseline knowledge across broad extents, but they first must be classified into forest community types. Here, we compared three alternative classifications across the United States using data from over 117,000 U.S. Department of Agriculture Forest Service Forest Inventory and Analysis (FIA) plots. Each plot had three forest community type labels: (1) “FIA” types were assigned by the FIA program using a supervised method; (2) “USNVC” types were assigned via a key based on the U.S. National Vegetation Classification; (3) “empirical” types resulted from unsupervised clustering of tree species information. We assessed the degree to which analog classes occurred among classifications, compared indicator species values, and used random forest models to determine how well the classifications could be predicted using environmental variables. The classifications generated groups of classes that had broadly similar distributions, but often there was no one-to-one analog across the classifications. The longleaf pine forest community type stood out as the exception: it was the only class with strong analogs across all classifications. Analogs were most lacking for forest community types with species that occurred across a range of geographic and environmental conditions, such as loblolly pine types. Indicator species metrics were generally high for the USNVC, suggesting that USNVC classes are floristically well-defined. The empirical classification was best predicted by environmental variables. The most important predictors differed slightly but were broadly similar across all classifications, and included slope, amount of forest in the surrounding landscape, average minimum temperature, and other climate variables. The classifications have similarities and differences that reflect their differing approaches and objectives. They are most consistent for forest community types that occur in a relatively narrow range of environmental conditions, and differ most for types with wide-ranging tree species. Environmental variables at a variety of scales were important for predicting all classifications, though strongest for the empirical and FIA, suggesting that each is useful for studying how forest communities respond to of multi-scale environmental processes, including global change drivers. More... »

PAGES

8

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/s40663-017-0123-x

DOI

http://dx.doi.org/10.1186/s40663-017-0123-x

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1100514524


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/2008/0502", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Environmental Science and Management", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/05", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Environmental Sciences", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "North Carolina State University", 
          "id": "https://www.grid.ac/institutes/grid.40803.3f", 
          "name": [
            "Department of Forestry and Environmental Resources, North Carolina State University, 3041 Cornwallis Rd., Research Triangle Park, 27709, Raleigh, NC, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Costanza", 
        "givenName": "Jennifer K.", 
        "id": "sg:person.010737600647.81", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010737600647.81"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "NatureServe", 
          "id": "https://www.grid.ac/institutes/grid.422378.8", 
          "name": [
            "NatureServe, 4600 N. Fairfax Dr., 7th Floor, 22203, Arlington, VA, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Faber-Langendoen", 
        "givenName": "Don", 
        "id": "sg:person.012344130267.85", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012344130267.85"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Southern Research Station", 
          "id": "https://www.grid.ac/institutes/grid.497399.9", 
          "name": [
            "Southern Research Station, USDA Forest Service, Blacksburg, VA, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Coulston", 
        "givenName": "John W.", 
        "id": "sg:person.011301774607.76", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011301774607.76"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Southern Research Station", 
          "id": "https://www.grid.ac/institutes/grid.497399.9", 
          "name": [
            "Southern Research Station, USDA Forest Service, Raleigh, NC, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Wear", 
        "givenName": "David N.", 
        "id": "sg:person.011355442753.12", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011355442753.12"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1016/s0099-1112(15)30100-2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1004271421"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/ele.12717", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1005154671"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/ecs2.1569", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1005175919"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-1-4419-9326-7_5", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1005838324", 
          "https://doi.org/10.1007/978-1-4419-9326-7_5"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1126/science.aaf8957", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1006504357"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1890/13-2334.1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1007368211"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.14358/pers.74.11.1379", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1009759294"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/gcb.12382", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1013683234"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/jvs.12186", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1014976457"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/geb.12501", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1015435741"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/jwmg.676", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1018292097"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/jwmg.676", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1018292097"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10021-005-0054-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1018647537", 
          "https://doi.org/10.1007/s10021-005-0054-1"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10021-005-0054-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1018647537", 
          "https://doi.org/10.1007/s10021-005-0054-1"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.foreco.2013.06.040", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1020997765"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-1-4419-7390-0_8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1022031286", 
          "https://doi.org/10.1007/978-1-4419-7390-0_8"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-1-4419-7390-0_8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1022031286", 
          "https://doi.org/10.1007/978-1-4419-7390-0_8"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1890/0012-9615(1997)067[0345:saaist]2.0.co;2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1023223983"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/gcb.13585", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1023982949"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-0-387-30687-2_2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1024550600", 
          "https://doi.org/10.1007/978-0-387-30687-2_2"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-0-387-30687-2_2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1024550600", 
          "https://doi.org/10.1007/978-0-387-30687-2_2"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1023/a:1010933404324", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1024739340", 
          "https://doi.org/10.1023/a:1010933404324"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1890/11-1730.1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1025936719"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/1365-2745.12239", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1026269329"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/j.1365-294x.2010.04726.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1027608145"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/j.1365-294x.2010.04726.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1027608145"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/joc.1276", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1032895020"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/joc.1276", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1032895020"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/j.1654-109x.2011.01150.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1033084000"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2747/0272-3646.30.5.383", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1035110452"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://app.dimensions.ai/details/publication/pub.1035613449", 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-0-387-21706-2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1035613449", 
          "https://doi.org/10.1007/978-0-387-21706-2"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-0-387-21706-2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1035613449", 
          "https://doi.org/10.1007/978-0-387-21706-2"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1890/130017", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1037343620"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.foreco.2015.03.016", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1037382308"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/j.1654-1103.2011.01265.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1037484463"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/jvs.12193", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1038104985"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1177/001316446002000104", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1039619716"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1177/001316446002000104", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1039619716"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0269-7491(01)00255-x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1039672534"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1097/ss.0b013e3182446c88", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1041166696"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1097/ss.0b013e3182446c88", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1041166696"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/gcb.13309", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1042191869"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1890/07-0539.1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1042705916"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/joc.3413", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1042953498"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.foreco.2007.07.023", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1044859890"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/srep00653", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1045579895", 
          "https://doi.org/10.1038/srep00653"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1890/15.wb.006", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1045887985"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10021-001-0003-6", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1047787587", 
          "https://doi.org/10.1007/s10021-001-0003-6"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1641/b580207", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049038896"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/j.1365-2486.2010.02162.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1050577986"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/j.1365-2486.2010.02162.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1050577986"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1890/070176", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1051569361"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1890/07-1053.1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1053597703"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1126/science.1188528", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1062461718"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1126/science.1188528", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1062461718"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.18637/jss.v020.i03", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1068672298"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2307/2845026", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1070098202"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2307/3672589", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1070404241"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.5751/es-05443-180220", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1073097028"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.5751/es-05443-180220", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1073097028"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.5849/jof.15-147", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1073274363"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.7809/b-e.00080", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1074065483"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/eap.1527", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1084011219"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1126/sciadv.1603055", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1085432803"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nature22898", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1085722158", 
          "https://doi.org/10.1038/nature22898"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nature22898", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1085722158", 
          "https://doi.org/10.1038/nature22898"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.ecolind.2017.06.049", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1090577693"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1371/journal.pone.0184062", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1091523714"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2737/nrs-gtr-112", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1103746574"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2018-12", 
    "datePublishedReg": "2018-12-01", 
    "description": "Knowledge of the different kinds of tree communities that currently exist can provide a baseline for assessing the ecological attributes of forests and monitoring future changes. Forest inventory data can facilitate the development of this baseline knowledge across broad extents, but they first must be classified into forest community types. Here, we compared three alternative classifications across the United States using data from over 117,000 U.S. Department of Agriculture Forest Service Forest Inventory and Analysis (FIA) plots. Each plot had three forest community type labels: (1) \u201cFIA\u201d types were assigned by the FIA program using a supervised method; (2) \u201cUSNVC\u201d types were assigned via a key based on the U.S. National Vegetation Classification; (3) \u201cempirical\u201d types resulted from unsupervised clustering of tree species information. We assessed the degree to which analog classes occurred among classifications, compared indicator species values, and used random forest models to determine how well the classifications could be predicted using environmental variables. The classifications generated groups of classes that had broadly similar distributions, but often there was no one-to-one analog across the classifications. The longleaf pine forest community type stood out as the exception: it was the only class with strong analogs across all classifications. Analogs were most lacking for forest community types with species that occurred across a range of geographic and environmental conditions, such as loblolly pine types. Indicator species metrics were generally high for the USNVC, suggesting that USNVC classes are floristically well-defined. The empirical classification was best predicted by environmental variables. The most important predictors differed slightly but were broadly similar across all classifications, and included slope, amount of forest in the surrounding landscape, average minimum temperature, and other climate variables. The classifications have similarities and differences that reflect their differing approaches and objectives. They are most consistent for forest community types that occur in a relatively narrow range of environmental conditions, and differ most for types with wide-ranging tree species. Environmental variables at a variety of scales were important for predicting all classifications, though strongest for the empirical and FIA, suggesting that each is useful for studying how forest communities respond to of multi-scale environmental processes, including global change drivers.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1186/s40663-017-0123-x", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": true, 
    "isPartOf": [
      {
        "id": "sg:journal.1284769", 
        "issn": [
          "2095-6355", 
          "2197-5620"
        ], 
        "name": "Forest Ecosystems", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "1", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "5"
      }
    ], 
    "name": "Classifying forest inventory data into species-based forest community types at broad extents: exploring tradeoffs among supervised and unsupervised approaches", 
    "pagination": "8", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "899e55fc45ffaa0495d2282ac4c4bf80d7daae7c3df29e79a1d3a3e7fd55a1fa"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1186/s40663-017-0123-x"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1100514524"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1186/s40663-017-0123-x", 
      "https://app.dimensions.ai/details/publication/pub.1100514524"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-11T00:30", 
    "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_8695_00000603.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "http://link.springer.com/10.1186/s40663-017-0123-x"
  }
]
 

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.10.1186/s40663-017-0123-x'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1186/s40663-017-0123-x'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1186/s40663-017-0123-x'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1186/s40663-017-0123-x'


 

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

268 TRIPLES      21 PREDICATES      84 URIs      19 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1186/s40663-017-0123-x schema:about anzsrc-for:05
2 anzsrc-for:0502
3 schema:author Nbe236f41ed7d49779d144da87f41be54
4 schema:citation sg:pub.10.1007/978-0-387-21706-2
5 sg:pub.10.1007/978-0-387-30687-2_2
6 sg:pub.10.1007/978-1-4419-7390-0_8
7 sg:pub.10.1007/978-1-4419-9326-7_5
8 sg:pub.10.1007/s10021-001-0003-6
9 sg:pub.10.1007/s10021-005-0054-1
10 sg:pub.10.1023/a:1010933404324
11 sg:pub.10.1038/nature22898
12 sg:pub.10.1038/srep00653
13 https://app.dimensions.ai/details/publication/pub.1035613449
14 https://doi.org/10.1002/eap.1527
15 https://doi.org/10.1002/ecs2.1569
16 https://doi.org/10.1002/joc.1276
17 https://doi.org/10.1002/joc.3413
18 https://doi.org/10.1002/jwmg.676
19 https://doi.org/10.1016/j.ecolind.2017.06.049
20 https://doi.org/10.1016/j.foreco.2007.07.023
21 https://doi.org/10.1016/j.foreco.2013.06.040
22 https://doi.org/10.1016/j.foreco.2015.03.016
23 https://doi.org/10.1016/s0099-1112(15)30100-2
24 https://doi.org/10.1016/s0269-7491(01)00255-x
25 https://doi.org/10.1097/ss.0b013e3182446c88
26 https://doi.org/10.1111/1365-2745.12239
27 https://doi.org/10.1111/ele.12717
28 https://doi.org/10.1111/gcb.12382
29 https://doi.org/10.1111/gcb.13309
30 https://doi.org/10.1111/gcb.13585
31 https://doi.org/10.1111/geb.12501
32 https://doi.org/10.1111/j.1365-2486.2010.02162.x
33 https://doi.org/10.1111/j.1365-294x.2010.04726.x
34 https://doi.org/10.1111/j.1654-109x.2011.01150.x
35 https://doi.org/10.1111/j.1654-1103.2011.01265.x
36 https://doi.org/10.1111/jvs.12186
37 https://doi.org/10.1111/jvs.12193
38 https://doi.org/10.1126/sciadv.1603055
39 https://doi.org/10.1126/science.1188528
40 https://doi.org/10.1126/science.aaf8957
41 https://doi.org/10.1177/001316446002000104
42 https://doi.org/10.1371/journal.pone.0184062
43 https://doi.org/10.14358/pers.74.11.1379
44 https://doi.org/10.1641/b580207
45 https://doi.org/10.18637/jss.v020.i03
46 https://doi.org/10.1890/0012-9615(1997)067[0345:saaist]2.0.co;2
47 https://doi.org/10.1890/07-0539.1
48 https://doi.org/10.1890/07-1053.1
49 https://doi.org/10.1890/070176
50 https://doi.org/10.1890/11-1730.1
51 https://doi.org/10.1890/13-2334.1
52 https://doi.org/10.1890/130017
53 https://doi.org/10.1890/15.wb.006
54 https://doi.org/10.2307/2845026
55 https://doi.org/10.2307/3672589
56 https://doi.org/10.2737/nrs-gtr-112
57 https://doi.org/10.2747/0272-3646.30.5.383
58 https://doi.org/10.5751/es-05443-180220
59 https://doi.org/10.5849/jof.15-147
60 https://doi.org/10.7809/b-e.00080
61 schema:datePublished 2018-12
62 schema:datePublishedReg 2018-12-01
63 schema:description Knowledge of the different kinds of tree communities that currently exist can provide a baseline for assessing the ecological attributes of forests and monitoring future changes. Forest inventory data can facilitate the development of this baseline knowledge across broad extents, but they first must be classified into forest community types. Here, we compared three alternative classifications across the United States using data from over 117,000 U.S. Department of Agriculture Forest Service Forest Inventory and Analysis (FIA) plots. Each plot had three forest community type labels: (1) “FIA” types were assigned by the FIA program using a supervised method; (2) “USNVC” types were assigned via a key based on the U.S. National Vegetation Classification; (3) “empirical” types resulted from unsupervised clustering of tree species information. We assessed the degree to which analog classes occurred among classifications, compared indicator species values, and used random forest models to determine how well the classifications could be predicted using environmental variables. The classifications generated groups of classes that had broadly similar distributions, but often there was no one-to-one analog across the classifications. The longleaf pine forest community type stood out as the exception: it was the only class with strong analogs across all classifications. Analogs were most lacking for forest community types with species that occurred across a range of geographic and environmental conditions, such as loblolly pine types. Indicator species metrics were generally high for the USNVC, suggesting that USNVC classes are floristically well-defined. The empirical classification was best predicted by environmental variables. The most important predictors differed slightly but were broadly similar across all classifications, and included slope, amount of forest in the surrounding landscape, average minimum temperature, and other climate variables. The classifications have similarities and differences that reflect their differing approaches and objectives. They are most consistent for forest community types that occur in a relatively narrow range of environmental conditions, and differ most for types with wide-ranging tree species. Environmental variables at a variety of scales were important for predicting all classifications, though strongest for the empirical and FIA, suggesting that each is useful for studying how forest communities respond to of multi-scale environmental processes, including global change drivers.
64 schema:genre research_article
65 schema:inLanguage en
66 schema:isAccessibleForFree true
67 schema:isPartOf N082bc60b31d24f2ab5f27276fcbaf0ad
68 N3247883b8b5c428a9bed5575db4a7032
69 sg:journal.1284769
70 schema:name Classifying forest inventory data into species-based forest community types at broad extents: exploring tradeoffs among supervised and unsupervised approaches
71 schema:pagination 8
72 schema:productId N2aaf34119add4fbb94f0b8da8f45950d
73 N77382a366bc14f4daa500da75c372d22
74 Ndb28f1939091485da39c58b88ff79f8f
75 schema:sameAs https://app.dimensions.ai/details/publication/pub.1100514524
76 https://doi.org/10.1186/s40663-017-0123-x
77 schema:sdDatePublished 2019-04-11T00:30
78 schema:sdLicense https://scigraph.springernature.com/explorer/license/
79 schema:sdPublisher N0721ec3893fd47d2a68ee58a282dec9a
80 schema:url http://link.springer.com/10.1186/s40663-017-0123-x
81 sgo:license sg:explorer/license/
82 sgo:sdDataset articles
83 rdf:type schema:ScholarlyArticle
84 N0721ec3893fd47d2a68ee58a282dec9a schema:name Springer Nature - SN SciGraph project
85 rdf:type schema:Organization
86 N082bc60b31d24f2ab5f27276fcbaf0ad schema:issueNumber 1
87 rdf:type schema:PublicationIssue
88 N2355cb048d764365ad5afb8137ab0c79 rdf:first sg:person.011355442753.12
89 rdf:rest rdf:nil
90 N2aaf34119add4fbb94f0b8da8f45950d schema:name readcube_id
91 schema:value 899e55fc45ffaa0495d2282ac4c4bf80d7daae7c3df29e79a1d3a3e7fd55a1fa
92 rdf:type schema:PropertyValue
93 N3247883b8b5c428a9bed5575db4a7032 schema:volumeNumber 5
94 rdf:type schema:PublicationVolume
95 N48167e83e7864470822030d7116f13e8 rdf:first sg:person.011301774607.76
96 rdf:rest N2355cb048d764365ad5afb8137ab0c79
97 N77382a366bc14f4daa500da75c372d22 schema:name doi
98 schema:value 10.1186/s40663-017-0123-x
99 rdf:type schema:PropertyValue
100 Nbe236f41ed7d49779d144da87f41be54 rdf:first sg:person.010737600647.81
101 rdf:rest Nc0e1dfd571cc49b2b476f40d44bad889
102 Nc0e1dfd571cc49b2b476f40d44bad889 rdf:first sg:person.012344130267.85
103 rdf:rest N48167e83e7864470822030d7116f13e8
104 Ndb28f1939091485da39c58b88ff79f8f schema:name dimensions_id
105 schema:value pub.1100514524
106 rdf:type schema:PropertyValue
107 anzsrc-for:05 schema:inDefinedTermSet anzsrc-for:
108 schema:name Environmental Sciences
109 rdf:type schema:DefinedTerm
110 anzsrc-for:0502 schema:inDefinedTermSet anzsrc-for:
111 schema:name Environmental Science and Management
112 rdf:type schema:DefinedTerm
113 sg:journal.1284769 schema:issn 2095-6355
114 2197-5620
115 schema:name Forest Ecosystems
116 rdf:type schema:Periodical
117 sg:person.010737600647.81 schema:affiliation https://www.grid.ac/institutes/grid.40803.3f
118 schema:familyName Costanza
119 schema:givenName Jennifer K.
120 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010737600647.81
121 rdf:type schema:Person
122 sg:person.011301774607.76 schema:affiliation https://www.grid.ac/institutes/grid.497399.9
123 schema:familyName Coulston
124 schema:givenName John W.
125 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011301774607.76
126 rdf:type schema:Person
127 sg:person.011355442753.12 schema:affiliation https://www.grid.ac/institutes/grid.497399.9
128 schema:familyName Wear
129 schema:givenName David N.
130 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011355442753.12
131 rdf:type schema:Person
132 sg:person.012344130267.85 schema:affiliation https://www.grid.ac/institutes/grid.422378.8
133 schema:familyName Faber-Langendoen
134 schema:givenName Don
135 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012344130267.85
136 rdf:type schema:Person
137 sg:pub.10.1007/978-0-387-21706-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035613449
138 https://doi.org/10.1007/978-0-387-21706-2
139 rdf:type schema:CreativeWork
140 sg:pub.10.1007/978-0-387-30687-2_2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024550600
141 https://doi.org/10.1007/978-0-387-30687-2_2
142 rdf:type schema:CreativeWork
143 sg:pub.10.1007/978-1-4419-7390-0_8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1022031286
144 https://doi.org/10.1007/978-1-4419-7390-0_8
145 rdf:type schema:CreativeWork
146 sg:pub.10.1007/978-1-4419-9326-7_5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005838324
147 https://doi.org/10.1007/978-1-4419-9326-7_5
148 rdf:type schema:CreativeWork
149 sg:pub.10.1007/s10021-001-0003-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047787587
150 https://doi.org/10.1007/s10021-001-0003-6
151 rdf:type schema:CreativeWork
152 sg:pub.10.1007/s10021-005-0054-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018647537
153 https://doi.org/10.1007/s10021-005-0054-1
154 rdf:type schema:CreativeWork
155 sg:pub.10.1023/a:1010933404324 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024739340
156 https://doi.org/10.1023/a:1010933404324
157 rdf:type schema:CreativeWork
158 sg:pub.10.1038/nature22898 schema:sameAs https://app.dimensions.ai/details/publication/pub.1085722158
159 https://doi.org/10.1038/nature22898
160 rdf:type schema:CreativeWork
161 sg:pub.10.1038/srep00653 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045579895
162 https://doi.org/10.1038/srep00653
163 rdf:type schema:CreativeWork
164 https://app.dimensions.ai/details/publication/pub.1035613449 schema:CreativeWork
165 https://doi.org/10.1002/eap.1527 schema:sameAs https://app.dimensions.ai/details/publication/pub.1084011219
166 rdf:type schema:CreativeWork
167 https://doi.org/10.1002/ecs2.1569 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005175919
168 rdf:type schema:CreativeWork
169 https://doi.org/10.1002/joc.1276 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032895020
170 rdf:type schema:CreativeWork
171 https://doi.org/10.1002/joc.3413 schema:sameAs https://app.dimensions.ai/details/publication/pub.1042953498
172 rdf:type schema:CreativeWork
173 https://doi.org/10.1002/jwmg.676 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018292097
174 rdf:type schema:CreativeWork
175 https://doi.org/10.1016/j.ecolind.2017.06.049 schema:sameAs https://app.dimensions.ai/details/publication/pub.1090577693
176 rdf:type schema:CreativeWork
177 https://doi.org/10.1016/j.foreco.2007.07.023 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044859890
178 rdf:type schema:CreativeWork
179 https://doi.org/10.1016/j.foreco.2013.06.040 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020997765
180 rdf:type schema:CreativeWork
181 https://doi.org/10.1016/j.foreco.2015.03.016 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037382308
182 rdf:type schema:CreativeWork
183 https://doi.org/10.1016/s0099-1112(15)30100-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004271421
184 rdf:type schema:CreativeWork
185 https://doi.org/10.1016/s0269-7491(01)00255-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1039672534
186 rdf:type schema:CreativeWork
187 https://doi.org/10.1097/ss.0b013e3182446c88 schema:sameAs https://app.dimensions.ai/details/publication/pub.1041166696
188 rdf:type schema:CreativeWork
189 https://doi.org/10.1111/1365-2745.12239 schema:sameAs https://app.dimensions.ai/details/publication/pub.1026269329
190 rdf:type schema:CreativeWork
191 https://doi.org/10.1111/ele.12717 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005154671
192 rdf:type schema:CreativeWork
193 https://doi.org/10.1111/gcb.12382 schema:sameAs https://app.dimensions.ai/details/publication/pub.1013683234
194 rdf:type schema:CreativeWork
195 https://doi.org/10.1111/gcb.13309 schema:sameAs https://app.dimensions.ai/details/publication/pub.1042191869
196 rdf:type schema:CreativeWork
197 https://doi.org/10.1111/gcb.13585 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023982949
198 rdf:type schema:CreativeWork
199 https://doi.org/10.1111/geb.12501 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015435741
200 rdf:type schema:CreativeWork
201 https://doi.org/10.1111/j.1365-2486.2010.02162.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1050577986
202 rdf:type schema:CreativeWork
203 https://doi.org/10.1111/j.1365-294x.2010.04726.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1027608145
204 rdf:type schema:CreativeWork
205 https://doi.org/10.1111/j.1654-109x.2011.01150.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1033084000
206 rdf:type schema:CreativeWork
207 https://doi.org/10.1111/j.1654-1103.2011.01265.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1037484463
208 rdf:type schema:CreativeWork
209 https://doi.org/10.1111/jvs.12186 schema:sameAs https://app.dimensions.ai/details/publication/pub.1014976457
210 rdf:type schema:CreativeWork
211 https://doi.org/10.1111/jvs.12193 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038104985
212 rdf:type schema:CreativeWork
213 https://doi.org/10.1126/sciadv.1603055 schema:sameAs https://app.dimensions.ai/details/publication/pub.1085432803
214 rdf:type schema:CreativeWork
215 https://doi.org/10.1126/science.1188528 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062461718
216 rdf:type schema:CreativeWork
217 https://doi.org/10.1126/science.aaf8957 schema:sameAs https://app.dimensions.ai/details/publication/pub.1006504357
218 rdf:type schema:CreativeWork
219 https://doi.org/10.1177/001316446002000104 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039619716
220 rdf:type schema:CreativeWork
221 https://doi.org/10.1371/journal.pone.0184062 schema:sameAs https://app.dimensions.ai/details/publication/pub.1091523714
222 rdf:type schema:CreativeWork
223 https://doi.org/10.14358/pers.74.11.1379 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009759294
224 rdf:type schema:CreativeWork
225 https://doi.org/10.1641/b580207 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049038896
226 rdf:type schema:CreativeWork
227 https://doi.org/10.18637/jss.v020.i03 schema:sameAs https://app.dimensions.ai/details/publication/pub.1068672298
228 rdf:type schema:CreativeWork
229 https://doi.org/10.1890/0012-9615(1997)067[0345:saaist]2.0.co;2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023223983
230 rdf:type schema:CreativeWork
231 https://doi.org/10.1890/07-0539.1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1042705916
232 rdf:type schema:CreativeWork
233 https://doi.org/10.1890/07-1053.1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1053597703
234 rdf:type schema:CreativeWork
235 https://doi.org/10.1890/070176 schema:sameAs https://app.dimensions.ai/details/publication/pub.1051569361
236 rdf:type schema:CreativeWork
237 https://doi.org/10.1890/11-1730.1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025936719
238 rdf:type schema:CreativeWork
239 https://doi.org/10.1890/13-2334.1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1007368211
240 rdf:type schema:CreativeWork
241 https://doi.org/10.1890/130017 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037343620
242 rdf:type schema:CreativeWork
243 https://doi.org/10.1890/15.wb.006 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045887985
244 rdf:type schema:CreativeWork
245 https://doi.org/10.2307/2845026 schema:sameAs https://app.dimensions.ai/details/publication/pub.1070098202
246 rdf:type schema:CreativeWork
247 https://doi.org/10.2307/3672589 schema:sameAs https://app.dimensions.ai/details/publication/pub.1070404241
248 rdf:type schema:CreativeWork
249 https://doi.org/10.2737/nrs-gtr-112 schema:sameAs https://app.dimensions.ai/details/publication/pub.1103746574
250 rdf:type schema:CreativeWork
251 https://doi.org/10.2747/0272-3646.30.5.383 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035110452
252 rdf:type schema:CreativeWork
253 https://doi.org/10.5751/es-05443-180220 schema:sameAs https://app.dimensions.ai/details/publication/pub.1073097028
254 rdf:type schema:CreativeWork
255 https://doi.org/10.5849/jof.15-147 schema:sameAs https://app.dimensions.ai/details/publication/pub.1073274363
256 rdf:type schema:CreativeWork
257 https://doi.org/10.7809/b-e.00080 schema:sameAs https://app.dimensions.ai/details/publication/pub.1074065483
258 rdf:type schema:CreativeWork
259 https://www.grid.ac/institutes/grid.40803.3f schema:alternateName North Carolina State University
260 schema:name Department of Forestry and Environmental Resources, North Carolina State University, 3041 Cornwallis Rd., Research Triangle Park, 27709, Raleigh, NC, USA
261 rdf:type schema:Organization
262 https://www.grid.ac/institutes/grid.422378.8 schema:alternateName NatureServe
263 schema:name NatureServe, 4600 N. Fairfax Dr., 7th Floor, 22203, Arlington, VA, USA
264 rdf:type schema:Organization
265 https://www.grid.ac/institutes/grid.497399.9 schema:alternateName Southern Research Station
266 schema:name Southern Research Station, USDA Forest Service, Blacksburg, VA, USA
267 Southern Research Station, USDA Forest Service, Raleigh, NC, USA
268 rdf:type schema:Organization
 




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


...