Mind the gap—optimizing satellite tag settings for time series analysis of foraging dives in Cuvier’s beaked whales (Ziphius cavirostris) View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2019-12

AUTHORS

Nicola J. Quick, William R. Cioffi, Jeanne Shearer, Andrew J. Read

ABSTRACT

Studies of deep-diving beaked whales using Argos satellite-linked location-depth tags frequently return data with large gaps in the diving record. We document the steps taken to eliminate these data gaps and collect weeks of continuous time series data for a behavioral response study that took place in 2017. We used baseline data collected from 2014 to 2016 to analyze message diagnostics, and assess our current programming schedule using a multiple criteria decision making matrix (MCDM), as a robust way to develop a new sampling regime. The MCDM approach suggested animal behavior and the quantity of data collected were the main causes of gaps in our baseline tag records. We implemented a new sampling regime to sample only long-duration, presumed foraging dives, simultaneously increasing temporal coverage of each individual message and reducing the number of messages by 50%. The reduction of gaps increased the data available for continuous time series analysis from an average of just over 2 days and 13.5 sequential presumed foraging dives in our baseline tags to just over 19 days and 118 sequential presumed foraging dives in tags deployed during the 2017 behavioral response study. We demonstrate that a critical approach, based on analysis of baseline data and question-driven weighted criteria, enabled the reduction and even elimination of gaps in the diving records of these tags. This approach enabled us to develop specific settings for our tags to ensure that our data collection was optimized for statistical analysis of the specific hypotheses we were testing. More... »

PAGES

5

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/s40317-019-0167-5

DOI

http://dx.doi.org/10.1186/s40317-019-0167-5

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1112779598


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/2008/1117", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Public Health and Health Services", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/11", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Medical and Health Sciences", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "name": [
            "Duke Marine Lab, Division of Marine Science and Conservation, Nicholas School of the Environment, 135 Duke Marine Lab Road, 28516, Beaufort, NC, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Quick", 
        "givenName": "Nicola J.", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "name": [
            "University Program in Ecology, Duke Marine Lab, 135 Duke Marine Lab Road, 28516, Beaufort, NC, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Cioffi", 
        "givenName": "William R.", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "name": [
            "University Program in Ecology, Duke Marine Lab, 135 Duke Marine Lab Road, 28516, Beaufort, NC, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Shearer", 
        "givenName": "Jeanne", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "name": [
            "Duke Marine Lab, Division of Marine Science and Conservation, Nicholas School of the Environment, 135 Duke Marine Lab Road, 28516, Beaufort, NC, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Read", 
        "givenName": "Andrew J.", 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1046/j.1365-2907.2001.00080.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1009764484"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.rser.2016.11.191", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1011977399"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1890/es10-00021.1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1014284915"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/j.2007.0906-7590.05236.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1016981530"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s13253-013-0158-6", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1018315682", 
          "https://doi.org/10.1007/s13253-013-0158-6"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1371/journal.pone.0017009", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1023943427"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1890/07-1032.1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1026819845"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1371/journal.pone.0092633", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1026917578"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00300-008-0487-z", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1029216760", 
          "https://doi.org/10.1007/s00300-008-0487-z"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00300-008-0487-z", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1029216760", 
          "https://doi.org/10.1007/s00300-008-0487-z"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00300-008-0487-z", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1029216760", 
          "https://doi.org/10.1007/s00300-008-0487-z"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/j.1461-0248.2008.01249.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1031672484"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1098/rsbl.2013.0223", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1034974768"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.cub.2014.06.041", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1044907196"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1242/jeb.02505", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1051018651"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/j.1748-7692.2008.00211.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1052609634"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/2041-210x.12109", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1052803143"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2989/1814232x.2014.976655", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1070959833"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3354/meps08255", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1071168295"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/srep45765", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1084133376", 
          "https://doi.org/10.1038/srep45765"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/springerreference_26640", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1089584201", 
          "https://doi.org/10.1007/springerreference_26640"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/s40317-017-0132-0", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1090603385", 
          "https://doi.org/10.1186/s40317-017-0132-0"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/s40317-017-0132-0", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1090603385", 
          "https://doi.org/10.1186/s40317-017-0132-0"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1098/rsos.170629", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1091389441"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/mms.12460", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1093026174"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1098/rsos.181728", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1111952425"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1098/rsos.181728", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1111952425"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1098/rsos.181728", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1111952425"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1098/rsos.181728", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1111952425"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1098/rsos.181728", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1111952425"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2019-12", 
    "datePublishedReg": "2019-12-01", 
    "description": "Studies of deep-diving beaked whales using Argos satellite-linked location-depth tags frequently return data with large gaps in the diving record. We document the steps taken to eliminate these data gaps and collect weeks of continuous time series data for a behavioral response study that took place in 2017. We used baseline data collected from 2014 to 2016 to analyze message diagnostics, and assess our current programming schedule using a multiple criteria decision making matrix (MCDM), as a robust way to develop a new sampling regime. The MCDM approach suggested animal behavior and the quantity of data collected were the main causes of gaps in our baseline tag records. We implemented a new sampling regime to sample only long-duration, presumed foraging dives, simultaneously increasing temporal coverage of each individual message and reducing the number of messages by 50%. The reduction of gaps increased the data available for continuous time series analysis from an average of just over 2 days and 13.5 sequential presumed foraging dives in our baseline tags to just over 19 days and 118 sequential presumed foraging dives in tags deployed during the 2017 behavioral response study. We demonstrate that a critical approach, based on analysis of baseline data and question-driven weighted criteria, enabled the reduction and even elimination of gaps in the diving records of these tags. This approach enabled us to develop specific settings for our tags to ensure that our data collection was optimized for statistical analysis of the specific hypotheses we were testing.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1186/s40317-019-0167-5", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": true, 
    "isPartOf": [
      {
        "id": "sg:journal.1051155", 
        "issn": [
          "2050-3385"
        ], 
        "name": "Animal Biotelemetry", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "1", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "7"
      }
    ], 
    "name": "Mind the gap\u2014optimizing satellite tag settings for time series analysis of foraging dives in Cuvier\u2019s beaked whales (Ziphius cavirostris)", 
    "pagination": "5", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "c962038828815477f8bb895853714a06d0057ec9dd67aa3ab53495da41cf4b39"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1186/s40317-019-0167-5"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1112779598"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1186/s40317-019-0167-5", 
      "https://app.dimensions.ai/details/publication/pub.1112779598"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-11T11:58", 
    "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/v90/0000000359_0000000359/records_29222_00000004.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://link.springer.com/10.1186%2Fs40317-019-0167-5"
  }
]
 

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/s40317-019-0167-5'

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/s40317-019-0167-5'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1186/s40317-019-0167-5'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1186/s40317-019-0167-5'


 

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

156 TRIPLES      21 PREDICATES      50 URIs      19 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1186/s40317-019-0167-5 schema:about anzsrc-for:11
2 anzsrc-for:1117
3 schema:author Nd9fbb34496ef43ae8fb29f0fd443184a
4 schema:citation sg:pub.10.1007/s00300-008-0487-z
5 sg:pub.10.1007/s13253-013-0158-6
6 sg:pub.10.1007/springerreference_26640
7 sg:pub.10.1038/srep45765
8 sg:pub.10.1186/s40317-017-0132-0
9 https://doi.org/10.1016/j.cub.2014.06.041
10 https://doi.org/10.1016/j.rser.2016.11.191
11 https://doi.org/10.1046/j.1365-2907.2001.00080.x
12 https://doi.org/10.1098/rsbl.2013.0223
13 https://doi.org/10.1098/rsos.170629
14 https://doi.org/10.1098/rsos.181728
15 https://doi.org/10.1111/2041-210x.12109
16 https://doi.org/10.1111/j.1461-0248.2008.01249.x
17 https://doi.org/10.1111/j.1748-7692.2008.00211.x
18 https://doi.org/10.1111/j.2007.0906-7590.05236.x
19 https://doi.org/10.1111/mms.12460
20 https://doi.org/10.1242/jeb.02505
21 https://doi.org/10.1371/journal.pone.0017009
22 https://doi.org/10.1371/journal.pone.0092633
23 https://doi.org/10.1890/07-1032.1
24 https://doi.org/10.1890/es10-00021.1
25 https://doi.org/10.2989/1814232x.2014.976655
26 https://doi.org/10.3354/meps08255
27 schema:datePublished 2019-12
28 schema:datePublishedReg 2019-12-01
29 schema:description Studies of deep-diving beaked whales using Argos satellite-linked location-depth tags frequently return data with large gaps in the diving record. We document the steps taken to eliminate these data gaps and collect weeks of continuous time series data for a behavioral response study that took place in 2017. We used baseline data collected from 2014 to 2016 to analyze message diagnostics, and assess our current programming schedule using a multiple criteria decision making matrix (MCDM), as a robust way to develop a new sampling regime. The MCDM approach suggested animal behavior and the quantity of data collected were the main causes of gaps in our baseline tag records. We implemented a new sampling regime to sample only long-duration, presumed foraging dives, simultaneously increasing temporal coverage of each individual message and reducing the number of messages by 50%. The reduction of gaps increased the data available for continuous time series analysis from an average of just over 2 days and 13.5 sequential presumed foraging dives in our baseline tags to just over 19 days and 118 sequential presumed foraging dives in tags deployed during the 2017 behavioral response study. We demonstrate that a critical approach, based on analysis of baseline data and question-driven weighted criteria, enabled the reduction and even elimination of gaps in the diving records of these tags. This approach enabled us to develop specific settings for our tags to ensure that our data collection was optimized for statistical analysis of the specific hypotheses we were testing.
30 schema:genre research_article
31 schema:inLanguage en
32 schema:isAccessibleForFree true
33 schema:isPartOf N2e68d76a67474497b1bedf765df07bf8
34 N8f6f2f119f144b32ac8a9414ad3ef38c
35 sg:journal.1051155
36 schema:name Mind the gap—optimizing satellite tag settings for time series analysis of foraging dives in Cuvier’s beaked whales (Ziphius cavirostris)
37 schema:pagination 5
38 schema:productId N21df3bf8eeae49bc8cb365725dd85c1f
39 N9cd0aa9be29740958bd880ccb0882c4f
40 Nad98a6264262435b8ef0db22e7cbd811
41 schema:sameAs https://app.dimensions.ai/details/publication/pub.1112779598
42 https://doi.org/10.1186/s40317-019-0167-5
43 schema:sdDatePublished 2019-04-11T11:58
44 schema:sdLicense https://scigraph.springernature.com/explorer/license/
45 schema:sdPublisher Ne46713a84aa34fa38757546d60a448ff
46 schema:url https://link.springer.com/10.1186%2Fs40317-019-0167-5
47 sgo:license sg:explorer/license/
48 sgo:sdDataset articles
49 rdf:type schema:ScholarlyArticle
50 N07f5c23a93ec4db5bef0d94e60e8a18b schema:affiliation Ne38d2409c98444f3a1a53a3d3811fc55
51 schema:familyName Cioffi
52 schema:givenName William R.
53 rdf:type schema:Person
54 N21df3bf8eeae49bc8cb365725dd85c1f schema:name readcube_id
55 schema:value c962038828815477f8bb895853714a06d0057ec9dd67aa3ab53495da41cf4b39
56 rdf:type schema:PropertyValue
57 N2e68d76a67474497b1bedf765df07bf8 schema:volumeNumber 7
58 rdf:type schema:PublicationVolume
59 N388e2a83f86c48d1a1d7b5d6f89177c9 schema:name Duke Marine Lab, Division of Marine Science and Conservation, Nicholas School of the Environment, 135 Duke Marine Lab Road, 28516, Beaufort, NC, USA
60 rdf:type schema:Organization
61 N3b4210bf09214a16b54a7442d318bce3 schema:affiliation Nad0ec80b10b64756a374dfa3c88ec597
62 schema:familyName Shearer
63 schema:givenName Jeanne
64 rdf:type schema:Person
65 N489146a352394fcd89ec63f0f2f11913 rdf:first N3b4210bf09214a16b54a7442d318bce3
66 rdf:rest N82f91824b375487abfff39b57bc69705
67 N82f91824b375487abfff39b57bc69705 rdf:first Nf740bc90fe4244ec8451a2472c789d14
68 rdf:rest rdf:nil
69 N8f6f2f119f144b32ac8a9414ad3ef38c schema:issueNumber 1
70 rdf:type schema:PublicationIssue
71 N985c5854343c495ab8fdb60288f831e4 schema:name Duke Marine Lab, Division of Marine Science and Conservation, Nicholas School of the Environment, 135 Duke Marine Lab Road, 28516, Beaufort, NC, USA
72 rdf:type schema:Organization
73 N9cd0aa9be29740958bd880ccb0882c4f schema:name doi
74 schema:value 10.1186/s40317-019-0167-5
75 rdf:type schema:PropertyValue
76 Nad0ec80b10b64756a374dfa3c88ec597 schema:name University Program in Ecology, Duke Marine Lab, 135 Duke Marine Lab Road, 28516, Beaufort, NC, USA
77 rdf:type schema:Organization
78 Nad98a6264262435b8ef0db22e7cbd811 schema:name dimensions_id
79 schema:value pub.1112779598
80 rdf:type schema:PropertyValue
81 Nd9fbb34496ef43ae8fb29f0fd443184a rdf:first Ndec5c4f06e7e42ecac6a52df660d4a32
82 rdf:rest Nf2d35deab62b4b15b176dc115f4adccf
83 Ndec5c4f06e7e42ecac6a52df660d4a32 schema:affiliation N985c5854343c495ab8fdb60288f831e4
84 schema:familyName Quick
85 schema:givenName Nicola J.
86 rdf:type schema:Person
87 Ne38d2409c98444f3a1a53a3d3811fc55 schema:name University Program in Ecology, Duke Marine Lab, 135 Duke Marine Lab Road, 28516, Beaufort, NC, USA
88 rdf:type schema:Organization
89 Ne46713a84aa34fa38757546d60a448ff schema:name Springer Nature - SN SciGraph project
90 rdf:type schema:Organization
91 Nf2d35deab62b4b15b176dc115f4adccf rdf:first N07f5c23a93ec4db5bef0d94e60e8a18b
92 rdf:rest N489146a352394fcd89ec63f0f2f11913
93 Nf740bc90fe4244ec8451a2472c789d14 schema:affiliation N388e2a83f86c48d1a1d7b5d6f89177c9
94 schema:familyName Read
95 schema:givenName Andrew J.
96 rdf:type schema:Person
97 anzsrc-for:11 schema:inDefinedTermSet anzsrc-for:
98 schema:name Medical and Health Sciences
99 rdf:type schema:DefinedTerm
100 anzsrc-for:1117 schema:inDefinedTermSet anzsrc-for:
101 schema:name Public Health and Health Services
102 rdf:type schema:DefinedTerm
103 sg:journal.1051155 schema:issn 2050-3385
104 schema:name Animal Biotelemetry
105 rdf:type schema:Periodical
106 sg:pub.10.1007/s00300-008-0487-z schema:sameAs https://app.dimensions.ai/details/publication/pub.1029216760
107 https://doi.org/10.1007/s00300-008-0487-z
108 rdf:type schema:CreativeWork
109 sg:pub.10.1007/s13253-013-0158-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018315682
110 https://doi.org/10.1007/s13253-013-0158-6
111 rdf:type schema:CreativeWork
112 sg:pub.10.1007/springerreference_26640 schema:sameAs https://app.dimensions.ai/details/publication/pub.1089584201
113 https://doi.org/10.1007/springerreference_26640
114 rdf:type schema:CreativeWork
115 sg:pub.10.1038/srep45765 schema:sameAs https://app.dimensions.ai/details/publication/pub.1084133376
116 https://doi.org/10.1038/srep45765
117 rdf:type schema:CreativeWork
118 sg:pub.10.1186/s40317-017-0132-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1090603385
119 https://doi.org/10.1186/s40317-017-0132-0
120 rdf:type schema:CreativeWork
121 https://doi.org/10.1016/j.cub.2014.06.041 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044907196
122 rdf:type schema:CreativeWork
123 https://doi.org/10.1016/j.rser.2016.11.191 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011977399
124 rdf:type schema:CreativeWork
125 https://doi.org/10.1046/j.1365-2907.2001.00080.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1009764484
126 rdf:type schema:CreativeWork
127 https://doi.org/10.1098/rsbl.2013.0223 schema:sameAs https://app.dimensions.ai/details/publication/pub.1034974768
128 rdf:type schema:CreativeWork
129 https://doi.org/10.1098/rsos.170629 schema:sameAs https://app.dimensions.ai/details/publication/pub.1091389441
130 rdf:type schema:CreativeWork
131 https://doi.org/10.1098/rsos.181728 schema:sameAs https://app.dimensions.ai/details/publication/pub.1111952425
132 rdf:type schema:CreativeWork
133 https://doi.org/10.1111/2041-210x.12109 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052803143
134 rdf:type schema:CreativeWork
135 https://doi.org/10.1111/j.1461-0248.2008.01249.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1031672484
136 rdf:type schema:CreativeWork
137 https://doi.org/10.1111/j.1748-7692.2008.00211.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1052609634
138 rdf:type schema:CreativeWork
139 https://doi.org/10.1111/j.2007.0906-7590.05236.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1016981530
140 rdf:type schema:CreativeWork
141 https://doi.org/10.1111/mms.12460 schema:sameAs https://app.dimensions.ai/details/publication/pub.1093026174
142 rdf:type schema:CreativeWork
143 https://doi.org/10.1242/jeb.02505 schema:sameAs https://app.dimensions.ai/details/publication/pub.1051018651
144 rdf:type schema:CreativeWork
145 https://doi.org/10.1371/journal.pone.0017009 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023943427
146 rdf:type schema:CreativeWork
147 https://doi.org/10.1371/journal.pone.0092633 schema:sameAs https://app.dimensions.ai/details/publication/pub.1026917578
148 rdf:type schema:CreativeWork
149 https://doi.org/10.1890/07-1032.1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1026819845
150 rdf:type schema:CreativeWork
151 https://doi.org/10.1890/es10-00021.1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1014284915
152 rdf:type schema:CreativeWork
153 https://doi.org/10.2989/1814232x.2014.976655 schema:sameAs https://app.dimensions.ai/details/publication/pub.1070959833
154 rdf:type schema:CreativeWork
155 https://doi.org/10.3354/meps08255 schema:sameAs https://app.dimensions.ai/details/publication/pub.1071168295
156 rdf:type schema:CreativeWork
 




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


...