Evaluation of management procedures for a length-frequency data-limited fishery. View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2019-04-08

AUTHORS

Richard Kindong, Jiangfeng Zhu, Feng Wu, Libing Dai, Xiaojie Dai, Siquan Tian, Yong Chen, Meng Xia

ABSTRACT

Management procedures (MPs) based on data-limited methods (DLMs) recently developed to give management advices for data-limited stocks worldwide are scarce or yet to be implemented on freshwater species. In this study, case studies (CSs) were developed using length-frequency data (LFD) of common carp species harvested from Dianshan Lake to estimate life-history parameters from existing methods. These CSs were later used to examine their influences when tested with various MPs under scenarios when operating models (OMs) were subjected to observation and estimation uncertainties. The results after management strategy evaluation (MSE) was run for various defined OMs showed that three MPs emerged best for providing managing advice. For high yield to be maintained during short-term periods, MinlenLopt1 suggested the smallest length at full retention (sLFR) to be 42.11 cm; while Slotlim and matlenlim2 suggested that to maintain biomass and stable spawning biomass (SBMSY) and also avoid overfishing from occurring in this fishery, sLFR should be 56.1 cm. Values given by these MPs allowed the removal of species that spawned at least once. Also, life-history parameters derived from CS4 presented the best results, being more reliable in presenting better inputs for effective management of the said fishery. More... »

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s11356-019-04521-7

DOI

http://dx.doi.org/10.1007/s11356-019-04521-7

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1113301166

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/30963434


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": "Shanghai Ocean University", 
          "id": "https://www.grid.ac/institutes/grid.412514.7", 
          "name": [
            "College of Marine Sciences, Shanghai Ocean University, Shanghai, China."
          ], 
          "type": "Organization"
        }, 
        "familyName": "Kindong", 
        "givenName": "Richard", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Shanghai Ocean University", 
          "id": "https://www.grid.ac/institutes/grid.412514.7", 
          "name": [
            "College of Marine Sciences, Shanghai Ocean University, Shanghai, China."
          ], 
          "type": "Organization"
        }, 
        "familyName": "Zhu", 
        "givenName": "Jiangfeng", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Shanghai Ocean University", 
          "id": "https://www.grid.ac/institutes/grid.412514.7", 
          "name": [
            "College of Marine Sciences, Shanghai Ocean University, Shanghai, China."
          ], 
          "type": "Organization"
        }, 
        "familyName": "Wu", 
        "givenName": "Feng", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Shanghai Ocean University", 
          "id": "https://www.grid.ac/institutes/grid.412514.7", 
          "name": [
            "College of Marine Sciences, Shanghai Ocean University, Shanghai, China."
          ], 
          "type": "Organization"
        }, 
        "familyName": "Dai", 
        "givenName": "Libing", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Shanghai Ocean University", 
          "id": "https://www.grid.ac/institutes/grid.412514.7", 
          "name": [
            "College of Marine Sciences, Shanghai Ocean University, Shanghai, China. xjdai@shou.edu.cn."
          ], 
          "type": "Organization"
        }, 
        "familyName": "Dai", 
        "givenName": "Xiaojie", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Shanghai Ocean University", 
          "id": "https://www.grid.ac/institutes/grid.412514.7", 
          "name": [
            "College of Marine Sciences, Shanghai Ocean University, Shanghai, China."
          ], 
          "type": "Organization"
        }, 
        "familyName": "Tian", 
        "givenName": "Siquan", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of Maine", 
          "id": "https://www.grid.ac/institutes/grid.21106.34", 
          "name": [
            "School of Marine Sciences, University of Maine, Orono, ME, 04469, USA."
          ], 
          "type": "Organization"
        }, 
        "familyName": "Chen", 
        "givenName": "Yong", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Shanghai Ocean University", 
          "id": "https://www.grid.ac/institutes/grid.412514.7", 
          "name": [
            "College of Marine Sciences, Shanghai Ocean University, Shanghai, China."
          ], 
          "type": "Organization"
        }, 
        "familyName": "Xia", 
        "givenName": "Meng", 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1139/er-2015-0044", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1003419315"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/jai.12389", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1008113654"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/icesjms/fsu034", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1009214146"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/icesjms/fsu136", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1015544913"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1080/23308249.2015.1051214", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1015554878"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1098/rstb.2004.1582", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1015921675"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/icesjms/fst235", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1020421060"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10530-005-0231-3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1023670193", 
          "https://doi.org/10.1007/s10530-005-0231-3"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10530-005-0231-3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1023670193", 
          "https://doi.org/10.1007/s10530-005-0231-3"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1073/pnas.1520420113", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1026509098"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.fishres.2013.12.014", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1031792645"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/faf.12104", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1044378622"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/eff.12141", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1048378751"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/j.1467-2979.2010.00387.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1051829447"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1006/jmsc.1999.0532", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1054490722"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1051/kmae/2015042", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1057019417"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/icesjms/39.2.175", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1059656838"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/icesjms/fsv212", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1059657547"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1126/science.1223389", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1062466766"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1577/c08-025.1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1068060245"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/2041-210x.12791", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1084742324"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/fme.12232", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1090851671"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1139/cjfas-2017-0143", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1091396030"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1139/cjfas-2017-0325", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1093138967"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s41208-017-0062-x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1100425085", 
          "https://doi.org/10.1007/s41208-017-0062-x"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11160-018-9514-5", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1101056826", 
          "https://doi.org/10.1007/s11160-018-9514-5"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/nafm.10047", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1103842658"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/2041-210x.13081", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1106362582"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2019-04-08", 
    "datePublishedReg": "2019-04-08", 
    "description": "Management procedures (MPs) based on data-limited methods (DLMs) recently developed to give management advices for data-limited stocks worldwide are scarce or yet to be implemented on freshwater species. In this study, case studies (CSs) were developed using length-frequency data (LFD) of common carp species harvested from Dianshan Lake to estimate life-history parameters from existing methods. These CSs were later used to examine their influences when tested with various MPs under scenarios when operating models (OMs) were subjected to observation and estimation uncertainties. The results after management strategy evaluation (MSE) was run for various defined OMs showed that three MPs emerged best for providing managing advice. For high yield to be maintained during short-term periods, MinlenLopt1 suggested the smallest length at full retention (sLFR) to be 42.11\u00a0cm; while Slotlim and matlenlim2 suggested that to maintain biomass and stable spawning biomass (SBMSY) and also avoid overfishing from occurring in this fishery, sLFR should be 56.1\u00a0cm. Values given by these MPs allowed the removal of species that spawned at least once. Also, life-history parameters derived from CS4 presented the best results, being more reliable in presenting better inputs for effective management of the said fishery.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/s11356-019-04521-7", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1113424", 
        "issn": [
          "0944-1344", 
          "1614-7499"
        ], 
        "name": "Environmental Science and Pollution Research", 
        "type": "Periodical"
      }
    ], 
    "name": "Evaluation of management procedures for a length-frequency data-limited fishery.", 
    "productId": [
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s11356-019-04521-7"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1113301166"
        ]
      }, 
      {
        "name": "nlm_unique_id", 
        "type": "PropertyValue", 
        "value": [
          "9441769"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "30963434"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s11356-019-04521-7", 
      "https://app.dimensions.ai/details/publication/pub.1113301166"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-15T08:47", 
    "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/v90/0000000374_0000000374/records_119713_00000001.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "http://link.springer.com/10.1007/s11356-019-04521-7"
  }
]
 

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/s11356-019-04521-7'

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/s11356-019-04521-7'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s11356-019-04521-7'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s11356-019-04521-7'


 

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

187 TRIPLES      20 PREDICATES      51 URIs      16 LITERALS      6 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s11356-019-04521-7 schema:about anzsrc-for:05
2 anzsrc-for:0502
3 schema:author N50c4c0d36d4a4d33b02ee9eb6b86d7a0
4 schema:citation sg:pub.10.1007/s10530-005-0231-3
5 sg:pub.10.1007/s11160-018-9514-5
6 sg:pub.10.1007/s41208-017-0062-x
7 https://doi.org/10.1002/nafm.10047
8 https://doi.org/10.1006/jmsc.1999.0532
9 https://doi.org/10.1016/j.fishres.2013.12.014
10 https://doi.org/10.1051/kmae/2015042
11 https://doi.org/10.1073/pnas.1520420113
12 https://doi.org/10.1080/23308249.2015.1051214
13 https://doi.org/10.1093/icesjms/39.2.175
14 https://doi.org/10.1093/icesjms/fst235
15 https://doi.org/10.1093/icesjms/fsu034
16 https://doi.org/10.1093/icesjms/fsu136
17 https://doi.org/10.1093/icesjms/fsv212
18 https://doi.org/10.1098/rstb.2004.1582
19 https://doi.org/10.1111/2041-210x.12791
20 https://doi.org/10.1111/2041-210x.13081
21 https://doi.org/10.1111/eff.12141
22 https://doi.org/10.1111/faf.12104
23 https://doi.org/10.1111/fme.12232
24 https://doi.org/10.1111/j.1467-2979.2010.00387.x
25 https://doi.org/10.1111/jai.12389
26 https://doi.org/10.1126/science.1223389
27 https://doi.org/10.1139/cjfas-2017-0143
28 https://doi.org/10.1139/cjfas-2017-0325
29 https://doi.org/10.1139/er-2015-0044
30 https://doi.org/10.1577/c08-025.1
31 schema:datePublished 2019-04-08
32 schema:datePublishedReg 2019-04-08
33 schema:description Management procedures (MPs) based on data-limited methods (DLMs) recently developed to give management advices for data-limited stocks worldwide are scarce or yet to be implemented on freshwater species. In this study, case studies (CSs) were developed using length-frequency data (LFD) of common carp species harvested from Dianshan Lake to estimate life-history parameters from existing methods. These CSs were later used to examine their influences when tested with various MPs under scenarios when operating models (OMs) were subjected to observation and estimation uncertainties. The results after management strategy evaluation (MSE) was run for various defined OMs showed that three MPs emerged best for providing managing advice. For high yield to be maintained during short-term periods, MinlenLopt1 suggested the smallest length at full retention (sLFR) to be 42.11 cm; while Slotlim and matlenlim2 suggested that to maintain biomass and stable spawning biomass (SBMSY) and also avoid overfishing from occurring in this fishery, sLFR should be 56.1 cm. Values given by these MPs allowed the removal of species that spawned at least once. Also, life-history parameters derived from CS4 presented the best results, being more reliable in presenting better inputs for effective management of the said fishery.
34 schema:genre research_article
35 schema:inLanguage en
36 schema:isAccessibleForFree false
37 schema:isPartOf sg:journal.1113424
38 schema:name Evaluation of management procedures for a length-frequency data-limited fishery.
39 schema:productId N0b4983df99374c2b91a601afd528ea27
40 N61bbbc9b12bd4f339fdb5baaa5b6c6a6
41 Nad3756a575a84a12b85a467f02f4f754
42 Ne0c9b0bff2bc417dbe84a2a975a57dd9
43 schema:sameAs https://app.dimensions.ai/details/publication/pub.1113301166
44 https://doi.org/10.1007/s11356-019-04521-7
45 schema:sdDatePublished 2019-04-15T08:47
46 schema:sdLicense https://scigraph.springernature.com/explorer/license/
47 schema:sdPublisher Nbef7bf5a2a3b49b4b181d69fce1b98d5
48 schema:url http://link.springer.com/10.1007/s11356-019-04521-7
49 sgo:license sg:explorer/license/
50 sgo:sdDataset articles
51 rdf:type schema:ScholarlyArticle
52 N0b4983df99374c2b91a601afd528ea27 schema:name pubmed_id
53 schema:value 30963434
54 rdf:type schema:PropertyValue
55 N1f891c0c095c425cbd09725d6f7e2de5 schema:affiliation https://www.grid.ac/institutes/grid.412514.7
56 schema:familyName Zhu
57 schema:givenName Jiangfeng
58 rdf:type schema:Person
59 N237415f4b0474411b5960e72da933b5a rdf:first Nb11772b170c94d3d99670cbc5ff5e995
60 rdf:rest N70ef5863d0e24a589c1adffb5806a84a
61 N3adbd8e085764a53aa95e3bdbd31a1f2 rdf:first N60b87abf9ada4f9bb45a4c054460a297
62 rdf:rest Ne643917f06534601a20fca0a12f46b15
63 N4945b6677be34b8a8e1e17ec97fa2b86 schema:affiliation https://www.grid.ac/institutes/grid.412514.7
64 schema:familyName Wu
65 schema:givenName Feng
66 rdf:type schema:Person
67 N4b79efe34b3d408481cb2ed69aea6bfd schema:affiliation https://www.grid.ac/institutes/grid.412514.7
68 schema:familyName Dai
69 schema:givenName Xiaojie
70 rdf:type schema:Person
71 N50c4c0d36d4a4d33b02ee9eb6b86d7a0 rdf:first N5ce7f6ceeb274e5486063e9484531af8
72 rdf:rest Na09e9d880a534a84b340be4986baf753
73 N5ce7f6ceeb274e5486063e9484531af8 schema:affiliation https://www.grid.ac/institutes/grid.412514.7
74 schema:familyName Kindong
75 schema:givenName Richard
76 rdf:type schema:Person
77 N60b87abf9ada4f9bb45a4c054460a297 schema:affiliation https://www.grid.ac/institutes/grid.412514.7
78 schema:familyName Dai
79 schema:givenName Libing
80 rdf:type schema:Person
81 N61bbbc9b12bd4f339fdb5baaa5b6c6a6 schema:name nlm_unique_id
82 schema:value 9441769
83 rdf:type schema:PropertyValue
84 N70ef5863d0e24a589c1adffb5806a84a rdf:first Na1f61ca0baf04efe88d835291c3e4c36
85 rdf:rest rdf:nil
86 N9542f7ab2ee0466883f856759b82f9c9 rdf:first Nbad3006473e647ae8ac56d7d0886b3c5
87 rdf:rest N237415f4b0474411b5960e72da933b5a
88 Na09e9d880a534a84b340be4986baf753 rdf:first N1f891c0c095c425cbd09725d6f7e2de5
89 rdf:rest Ne877c412ce424808af8fd2b19562d863
90 Na1f61ca0baf04efe88d835291c3e4c36 schema:affiliation https://www.grid.ac/institutes/grid.412514.7
91 schema:familyName Xia
92 schema:givenName Meng
93 rdf:type schema:Person
94 Nad3756a575a84a12b85a467f02f4f754 schema:name dimensions_id
95 schema:value pub.1113301166
96 rdf:type schema:PropertyValue
97 Nb11772b170c94d3d99670cbc5ff5e995 schema:affiliation https://www.grid.ac/institutes/grid.21106.34
98 schema:familyName Chen
99 schema:givenName Yong
100 rdf:type schema:Person
101 Nbad3006473e647ae8ac56d7d0886b3c5 schema:affiliation https://www.grid.ac/institutes/grid.412514.7
102 schema:familyName Tian
103 schema:givenName Siquan
104 rdf:type schema:Person
105 Nbef7bf5a2a3b49b4b181d69fce1b98d5 schema:name Springer Nature - SN SciGraph project
106 rdf:type schema:Organization
107 Ne0c9b0bff2bc417dbe84a2a975a57dd9 schema:name doi
108 schema:value 10.1007/s11356-019-04521-7
109 rdf:type schema:PropertyValue
110 Ne643917f06534601a20fca0a12f46b15 rdf:first N4b79efe34b3d408481cb2ed69aea6bfd
111 rdf:rest N9542f7ab2ee0466883f856759b82f9c9
112 Ne877c412ce424808af8fd2b19562d863 rdf:first N4945b6677be34b8a8e1e17ec97fa2b86
113 rdf:rest N3adbd8e085764a53aa95e3bdbd31a1f2
114 anzsrc-for:05 schema:inDefinedTermSet anzsrc-for:
115 schema:name Environmental Sciences
116 rdf:type schema:DefinedTerm
117 anzsrc-for:0502 schema:inDefinedTermSet anzsrc-for:
118 schema:name Environmental Science and Management
119 rdf:type schema:DefinedTerm
120 sg:journal.1113424 schema:issn 0944-1344
121 1614-7499
122 schema:name Environmental Science and Pollution Research
123 rdf:type schema:Periodical
124 sg:pub.10.1007/s10530-005-0231-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023670193
125 https://doi.org/10.1007/s10530-005-0231-3
126 rdf:type schema:CreativeWork
127 sg:pub.10.1007/s11160-018-9514-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1101056826
128 https://doi.org/10.1007/s11160-018-9514-5
129 rdf:type schema:CreativeWork
130 sg:pub.10.1007/s41208-017-0062-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1100425085
131 https://doi.org/10.1007/s41208-017-0062-x
132 rdf:type schema:CreativeWork
133 https://doi.org/10.1002/nafm.10047 schema:sameAs https://app.dimensions.ai/details/publication/pub.1103842658
134 rdf:type schema:CreativeWork
135 https://doi.org/10.1006/jmsc.1999.0532 schema:sameAs https://app.dimensions.ai/details/publication/pub.1054490722
136 rdf:type schema:CreativeWork
137 https://doi.org/10.1016/j.fishres.2013.12.014 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031792645
138 rdf:type schema:CreativeWork
139 https://doi.org/10.1051/kmae/2015042 schema:sameAs https://app.dimensions.ai/details/publication/pub.1057019417
140 rdf:type schema:CreativeWork
141 https://doi.org/10.1073/pnas.1520420113 schema:sameAs https://app.dimensions.ai/details/publication/pub.1026509098
142 rdf:type schema:CreativeWork
143 https://doi.org/10.1080/23308249.2015.1051214 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015554878
144 rdf:type schema:CreativeWork
145 https://doi.org/10.1093/icesjms/39.2.175 schema:sameAs https://app.dimensions.ai/details/publication/pub.1059656838
146 rdf:type schema:CreativeWork
147 https://doi.org/10.1093/icesjms/fst235 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020421060
148 rdf:type schema:CreativeWork
149 https://doi.org/10.1093/icesjms/fsu034 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009214146
150 rdf:type schema:CreativeWork
151 https://doi.org/10.1093/icesjms/fsu136 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015544913
152 rdf:type schema:CreativeWork
153 https://doi.org/10.1093/icesjms/fsv212 schema:sameAs https://app.dimensions.ai/details/publication/pub.1059657547
154 rdf:type schema:CreativeWork
155 https://doi.org/10.1098/rstb.2004.1582 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015921675
156 rdf:type schema:CreativeWork
157 https://doi.org/10.1111/2041-210x.12791 schema:sameAs https://app.dimensions.ai/details/publication/pub.1084742324
158 rdf:type schema:CreativeWork
159 https://doi.org/10.1111/2041-210x.13081 schema:sameAs https://app.dimensions.ai/details/publication/pub.1106362582
160 rdf:type schema:CreativeWork
161 https://doi.org/10.1111/eff.12141 schema:sameAs https://app.dimensions.ai/details/publication/pub.1048378751
162 rdf:type schema:CreativeWork
163 https://doi.org/10.1111/faf.12104 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044378622
164 rdf:type schema:CreativeWork
165 https://doi.org/10.1111/fme.12232 schema:sameAs https://app.dimensions.ai/details/publication/pub.1090851671
166 rdf:type schema:CreativeWork
167 https://doi.org/10.1111/j.1467-2979.2010.00387.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1051829447
168 rdf:type schema:CreativeWork
169 https://doi.org/10.1111/jai.12389 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008113654
170 rdf:type schema:CreativeWork
171 https://doi.org/10.1126/science.1223389 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062466766
172 rdf:type schema:CreativeWork
173 https://doi.org/10.1139/cjfas-2017-0143 schema:sameAs https://app.dimensions.ai/details/publication/pub.1091396030
174 rdf:type schema:CreativeWork
175 https://doi.org/10.1139/cjfas-2017-0325 schema:sameAs https://app.dimensions.ai/details/publication/pub.1093138967
176 rdf:type schema:CreativeWork
177 https://doi.org/10.1139/er-2015-0044 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003419315
178 rdf:type schema:CreativeWork
179 https://doi.org/10.1577/c08-025.1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1068060245
180 rdf:type schema:CreativeWork
181 https://www.grid.ac/institutes/grid.21106.34 schema:alternateName University of Maine
182 schema:name School of Marine Sciences, University of Maine, Orono, ME, 04469, USA.
183 rdf:type schema:Organization
184 https://www.grid.ac/institutes/grid.412514.7 schema:alternateName Shanghai Ocean University
185 schema:name College of Marine Sciences, Shanghai Ocean University, Shanghai, China.
186 College of Marine Sciences, Shanghai Ocean University, Shanghai, China. xjdai@shou.edu.cn.
187 rdf:type schema:Organization
 




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


...