An empirical regression model of soluble phosphorus retention for small pristine streams evaluating tracer experiments View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2008-05

AUTHORS

Marcus Schulz, Maik Bischoff, Jörg Klasmeier, Jürgen Berlekamp, Michael Matthies

ABSTRACT

Models of in-stream phosphorus retention either lack high spatial and temporal resolution, or need a high number of input parameters.We provide a simple new approach that deals with these deficits. Soluble reactive phosphorus (SRP) tracer studies based on the nutrient spiralling concept were evaluated to derive a simple model that explains SRP retention. SRP uptake length (SRP-Sw) was considered to be a measure of transient SRP storage and was transformed to load-weighted retention (R) using an exponential relationship. Stream order (so) and flow velocity (u) were considered as input parameters to explain SRP uptake length. Model validation showed significant correlation with measured uptake lengths. The model explained 46% of SRP retention, and simulated and measured retention were in the same order of magnitude. Our model may act in concert with emission models to account for lateral SRP sources within the catchment. Although our empirical model does not describe biological processes and is not a substitute for detailed biogeochemical studies, it provides an efficient tool to predict load-weighted soluble nutrient retention and nutrient transport to downstream systems and is applicable in most small pristine streams. More... »

PAGES

115-122

Journal

TITLE

Aquatic Sciences

ISSUE

2

VOLUME

70

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00027-008-8046-9

DOI

http://dx.doi.org/10.1007/s00027-008-8046-9

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1040241476


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": "Osnabr\u00fcck University", 
          "id": "https://www.grid.ac/institutes/grid.10854.38", 
          "name": [
            "Institute of Environmental Systems Research (USF), University of Osnabrueck, Barbarastrasse 12, D-49069, Osnabrueck, Germany"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Schulz", 
        "givenName": "Marcus", 
        "id": "sg:person.01150144150.34", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01150144150.34"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Osnabr\u00fcck University", 
          "id": "https://www.grid.ac/institutes/grid.10854.38", 
          "name": [
            "Institute of Environmental Systems Research (USF), University of Osnabrueck, Barbarastrasse 12, D-49069, Osnabrueck, Germany"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Bischoff", 
        "givenName": "Maik", 
        "id": "sg:person.013433426043.65", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013433426043.65"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Osnabr\u00fcck University", 
          "id": "https://www.grid.ac/institutes/grid.10854.38", 
          "name": [
            "Institute of Environmental Systems Research (USF), University of Osnabrueck, Barbarastrasse 12, D-49069, Osnabrueck, Germany"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Klasmeier", 
        "givenName": "J\u00f6rg", 
        "id": "sg:person.01254521302.48", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01254521302.48"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Osnabr\u00fcck University", 
          "id": "https://www.grid.ac/institutes/grid.10854.38", 
          "name": [
            "Institute of Environmental Systems Research (USF), University of Osnabrueck, Barbarastrasse 12, D-49069, Osnabrueck, Germany"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Berlekamp", 
        "givenName": "J\u00fcrgen", 
        "id": "sg:person.07510126157.32", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07510126157.32"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Osnabr\u00fcck University", 
          "id": "https://www.grid.ac/institutes/grid.10854.38", 
          "name": [
            "Institute of Environmental Systems Research (USF), University of Osnabrueck, Barbarastrasse 12, D-49069, Osnabrueck, Germany"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Matthies", 
        "givenName": "Michael", 
        "id": "sg:person.01240243345.17", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01240243345.17"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1007/bf00007414", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1002322038", 
          "https://doi.org/10.1007/bf00007414"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-94-017-3405-9_6", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1002350244", 
          "https://doi.org/10.1007/978-94-017-3405-9_6"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1139/f81-114", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1013067757"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1029/2000wr000115", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1016209277"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0043-1354(02)00276-2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1024777596"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0043-1354(02)00276-2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1024777596"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1029/2001wr000878", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1030800900"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1023/a:1009947907811", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1031999539", 
          "https://doi.org/10.1023/a:1009947907811"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1029/2006gl025845", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1033640103"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1029/2006gl025845", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1033640103"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2307/1940562", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1034547984"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10750-006-0027-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1037536704", 
          "https://doi.org/10.1007/s10750-006-0027-1"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf00007171", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1038712711", 
          "https://doi.org/10.1007/bf00007171"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1890/0012-9615(2000)070[0471:nciafs]2.0.co;2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1040153430"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1046/j.1365-2427.2002.00776.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1044404122"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s004420050783", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1050524708", 
          "https://doi.org/10.1007/s004420050783"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1139/f98-071", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1050681508"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1126/science.1056874", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1062444317"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2307/1467596", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1069504168"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2307/1468461", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1069504931"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2307/1938621", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1069662521"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1080/03680770.1995.11900860", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1093111086"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2008-05", 
    "datePublishedReg": "2008-05-01", 
    "description": "Models of in-stream phosphorus retention either lack high spatial and temporal resolution, or need a high number of input parameters.We provide a simple new approach that deals with these deficits. Soluble reactive phosphorus (SRP) tracer studies based on the nutrient spiralling concept were evaluated to derive a simple model that explains SRP retention. SRP uptake length (SRP-Sw) was considered to be a measure of transient SRP storage and was transformed to load-weighted retention (R) using an exponential relationship. Stream order (so) and flow velocity (u) were considered as input parameters to explain SRP uptake length. Model validation showed significant correlation with measured uptake lengths. The model explained 46% of SRP retention, and simulated and measured retention were in the same order of magnitude. Our model may act in concert with emission models to account for lateral SRP sources within the catchment. Although our empirical model does not describe biological processes and is not a substitute for detailed biogeochemical studies, it provides an efficient tool to predict load-weighted soluble nutrient retention and nutrient transport to downstream systems and is applicable in most small pristine streams.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/s00027-008-8046-9", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1313377", 
        "issn": [
          "1015-1621", 
          "1420-9055"
        ], 
        "name": "Aquatic Sciences", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "2", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "70"
      }
    ], 
    "name": "An empirical regression model of soluble phosphorus retention for small pristine streams evaluating tracer experiments", 
    "pagination": "115-122", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "bfb2069799787c92d240382aa0754477cfa62d7af4b44c749ad273f4f4d69996"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s00027-008-8046-9"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1040241476"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s00027-008-8046-9", 
      "https://app.dimensions.ai/details/publication/pub.1040241476"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-10T21:33", 
    "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_8687_00000496.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "http://link.springer.com/10.1007/s00027-008-8046-9"
  }
]
 

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/s00027-008-8046-9'

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/s00027-008-8046-9'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s00027-008-8046-9'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s00027-008-8046-9'


 

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

155 TRIPLES      21 PREDICATES      47 URIs      19 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s00027-008-8046-9 schema:about anzsrc-for:06
2 anzsrc-for:0602
3 schema:author N140b356cdd25453cb9560335741e0c16
4 schema:citation sg:pub.10.1007/978-94-017-3405-9_6
5 sg:pub.10.1007/bf00007171
6 sg:pub.10.1007/bf00007414
7 sg:pub.10.1007/s004420050783
8 sg:pub.10.1007/s10750-006-0027-1
9 sg:pub.10.1023/a:1009947907811
10 https://doi.org/10.1016/s0043-1354(02)00276-2
11 https://doi.org/10.1029/2000wr000115
12 https://doi.org/10.1029/2001wr000878
13 https://doi.org/10.1029/2006gl025845
14 https://doi.org/10.1046/j.1365-2427.2002.00776.x
15 https://doi.org/10.1080/03680770.1995.11900860
16 https://doi.org/10.1126/science.1056874
17 https://doi.org/10.1139/f81-114
18 https://doi.org/10.1139/f98-071
19 https://doi.org/10.1890/0012-9615(2000)070[0471:nciafs]2.0.co;2
20 https://doi.org/10.2307/1467596
21 https://doi.org/10.2307/1468461
22 https://doi.org/10.2307/1938621
23 https://doi.org/10.2307/1940562
24 schema:datePublished 2008-05
25 schema:datePublishedReg 2008-05-01
26 schema:description Models of in-stream phosphorus retention either lack high spatial and temporal resolution, or need a high number of input parameters.We provide a simple new approach that deals with these deficits. Soluble reactive phosphorus (SRP) tracer studies based on the nutrient spiralling concept were evaluated to derive a simple model that explains SRP retention. SRP uptake length (SRP-Sw) was considered to be a measure of transient SRP storage and was transformed to load-weighted retention (R) using an exponential relationship. Stream order (so) and flow velocity (u) were considered as input parameters to explain SRP uptake length. Model validation showed significant correlation with measured uptake lengths. The model explained 46% of SRP retention, and simulated and measured retention were in the same order of magnitude. Our model may act in concert with emission models to account for lateral SRP sources within the catchment. Although our empirical model does not describe biological processes and is not a substitute for detailed biogeochemical studies, it provides an efficient tool to predict load-weighted soluble nutrient retention and nutrient transport to downstream systems and is applicable in most small pristine streams.
27 schema:genre research_article
28 schema:inLanguage en
29 schema:isAccessibleForFree false
30 schema:isPartOf N8bc5e3847f3341678dfb29eec84d5a78
31 Nc1f7b128526a41fc8180c56861b682ac
32 sg:journal.1313377
33 schema:name An empirical regression model of soluble phosphorus retention for small pristine streams evaluating tracer experiments
34 schema:pagination 115-122
35 schema:productId N4b3e58963913420d886963258d81a142
36 N537c51f604904ed6ae52606c14d1dafe
37 N74592a275b0c4542879ef6dcf45c7c8a
38 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040241476
39 https://doi.org/10.1007/s00027-008-8046-9
40 schema:sdDatePublished 2019-04-10T21:33
41 schema:sdLicense https://scigraph.springernature.com/explorer/license/
42 schema:sdPublisher N0b8c52560aa948cf86acbc1ac9606f7d
43 schema:url http://link.springer.com/10.1007/s00027-008-8046-9
44 sgo:license sg:explorer/license/
45 sgo:sdDataset articles
46 rdf:type schema:ScholarlyArticle
47 N0b8c52560aa948cf86acbc1ac9606f7d schema:name Springer Nature - SN SciGraph project
48 rdf:type schema:Organization
49 N140b356cdd25453cb9560335741e0c16 rdf:first sg:person.01150144150.34
50 rdf:rest N52f328a85513430088da6d8d731673cb
51 N32102ec31d74464db1e46855bb7aea49 rdf:first sg:person.07510126157.32
52 rdf:rest N873ee88938ea42b9836b8247a02a579f
53 N4b3e58963913420d886963258d81a142 schema:name doi
54 schema:value 10.1007/s00027-008-8046-9
55 rdf:type schema:PropertyValue
56 N52f328a85513430088da6d8d731673cb rdf:first sg:person.013433426043.65
57 rdf:rest N7f97acf332c54ae3a783d361dc0e889e
58 N537c51f604904ed6ae52606c14d1dafe schema:name dimensions_id
59 schema:value pub.1040241476
60 rdf:type schema:PropertyValue
61 N74592a275b0c4542879ef6dcf45c7c8a schema:name readcube_id
62 schema:value bfb2069799787c92d240382aa0754477cfa62d7af4b44c749ad273f4f4d69996
63 rdf:type schema:PropertyValue
64 N7f97acf332c54ae3a783d361dc0e889e rdf:first sg:person.01254521302.48
65 rdf:rest N32102ec31d74464db1e46855bb7aea49
66 N873ee88938ea42b9836b8247a02a579f rdf:first sg:person.01240243345.17
67 rdf:rest rdf:nil
68 N8bc5e3847f3341678dfb29eec84d5a78 schema:issueNumber 2
69 rdf:type schema:PublicationIssue
70 Nc1f7b128526a41fc8180c56861b682ac schema:volumeNumber 70
71 rdf:type schema:PublicationVolume
72 anzsrc-for:06 schema:inDefinedTermSet anzsrc-for:
73 schema:name Biological Sciences
74 rdf:type schema:DefinedTerm
75 anzsrc-for:0602 schema:inDefinedTermSet anzsrc-for:
76 schema:name Ecology
77 rdf:type schema:DefinedTerm
78 sg:journal.1313377 schema:issn 1015-1621
79 1420-9055
80 schema:name Aquatic Sciences
81 rdf:type schema:Periodical
82 sg:person.01150144150.34 schema:affiliation https://www.grid.ac/institutes/grid.10854.38
83 schema:familyName Schulz
84 schema:givenName Marcus
85 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01150144150.34
86 rdf:type schema:Person
87 sg:person.01240243345.17 schema:affiliation https://www.grid.ac/institutes/grid.10854.38
88 schema:familyName Matthies
89 schema:givenName Michael
90 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01240243345.17
91 rdf:type schema:Person
92 sg:person.01254521302.48 schema:affiliation https://www.grid.ac/institutes/grid.10854.38
93 schema:familyName Klasmeier
94 schema:givenName Jörg
95 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01254521302.48
96 rdf:type schema:Person
97 sg:person.013433426043.65 schema:affiliation https://www.grid.ac/institutes/grid.10854.38
98 schema:familyName Bischoff
99 schema:givenName Maik
100 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013433426043.65
101 rdf:type schema:Person
102 sg:person.07510126157.32 schema:affiliation https://www.grid.ac/institutes/grid.10854.38
103 schema:familyName Berlekamp
104 schema:givenName Jürgen
105 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07510126157.32
106 rdf:type schema:Person
107 sg:pub.10.1007/978-94-017-3405-9_6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002350244
108 https://doi.org/10.1007/978-94-017-3405-9_6
109 rdf:type schema:CreativeWork
110 sg:pub.10.1007/bf00007171 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038712711
111 https://doi.org/10.1007/bf00007171
112 rdf:type schema:CreativeWork
113 sg:pub.10.1007/bf00007414 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002322038
114 https://doi.org/10.1007/bf00007414
115 rdf:type schema:CreativeWork
116 sg:pub.10.1007/s004420050783 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050524708
117 https://doi.org/10.1007/s004420050783
118 rdf:type schema:CreativeWork
119 sg:pub.10.1007/s10750-006-0027-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037536704
120 https://doi.org/10.1007/s10750-006-0027-1
121 rdf:type schema:CreativeWork
122 sg:pub.10.1023/a:1009947907811 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031999539
123 https://doi.org/10.1023/a:1009947907811
124 rdf:type schema:CreativeWork
125 https://doi.org/10.1016/s0043-1354(02)00276-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024777596
126 rdf:type schema:CreativeWork
127 https://doi.org/10.1029/2000wr000115 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016209277
128 rdf:type schema:CreativeWork
129 https://doi.org/10.1029/2001wr000878 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030800900
130 rdf:type schema:CreativeWork
131 https://doi.org/10.1029/2006gl025845 schema:sameAs https://app.dimensions.ai/details/publication/pub.1033640103
132 rdf:type schema:CreativeWork
133 https://doi.org/10.1046/j.1365-2427.2002.00776.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1044404122
134 rdf:type schema:CreativeWork
135 https://doi.org/10.1080/03680770.1995.11900860 schema:sameAs https://app.dimensions.ai/details/publication/pub.1093111086
136 rdf:type schema:CreativeWork
137 https://doi.org/10.1126/science.1056874 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062444317
138 rdf:type schema:CreativeWork
139 https://doi.org/10.1139/f81-114 schema:sameAs https://app.dimensions.ai/details/publication/pub.1013067757
140 rdf:type schema:CreativeWork
141 https://doi.org/10.1139/f98-071 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050681508
142 rdf:type schema:CreativeWork
143 https://doi.org/10.1890/0012-9615(2000)070[0471:nciafs]2.0.co;2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040153430
144 rdf:type schema:CreativeWork
145 https://doi.org/10.2307/1467596 schema:sameAs https://app.dimensions.ai/details/publication/pub.1069504168
146 rdf:type schema:CreativeWork
147 https://doi.org/10.2307/1468461 schema:sameAs https://app.dimensions.ai/details/publication/pub.1069504931
148 rdf:type schema:CreativeWork
149 https://doi.org/10.2307/1938621 schema:sameAs https://app.dimensions.ai/details/publication/pub.1069662521
150 rdf:type schema:CreativeWork
151 https://doi.org/10.2307/1940562 schema:sameAs https://app.dimensions.ai/details/publication/pub.1034547984
152 rdf:type schema:CreativeWork
153 https://www.grid.ac/institutes/grid.10854.38 schema:alternateName Osnabrück University
154 schema:name Institute of Environmental Systems Research (USF), University of Osnabrueck, Barbarastrasse 12, D-49069, Osnabrueck, Germany
155 rdf:type schema:Organization
 




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


...