Population response to climate change: linear vs. non-linear modeling approaches View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2004-12

AUTHORS

Alicia M Ellis, Eric Post

ABSTRACT

BACKGROUND: Research on the ecological consequences of global climate change has elicited a growing interest in the use of time series analysis to investigate population dynamics in a changing climate. Here, we compare linear and non-linear models describing the contribution of climate to the density fluctuations of the population of wolves on Isle Royale, Michigan from 1959 to 1999. RESULTS: The non-linear self excitatory threshold autoregressive (SETAR) model revealed that, due to differences in the strength and nature of density dependence, relatively small and large populations may be differentially affected by future changes in climate. Both linear and non-linear models predict a decrease in the population of wolves with predicted changes in climate. CONCLUSIONS: Because specific predictions differed between linear and non-linear models, our study highlights the importance of using non-linear methods that allow the detection of non-linearity in the strength and nature of density dependence. Failure to adopt a non-linear approach to modelling population response to climate change, either exclusively or in addition to linear approaches, may compromise efforts to quantify ecological consequences of future warming. More... »

PAGES

2

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/1472-6785-4-2

DOI

http://dx.doi.org/10.1186/1472-6785-4-2

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1012800138

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/15056394


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"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Age Factors", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Animals", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Climate", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Deer", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Linear Models", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Michigan", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Nonlinear Dynamics", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Population Dynamics", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Predatory Behavior", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Snow", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Temperature", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Wolves", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Dartmouth College", 
          "id": "https://www.grid.ac/institutes/grid.254880.3", 
          "name": [
            "Department of Biological Science, Dartmouth College, 03755, Hanover, NH, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Ellis", 
        "givenName": "Alicia M", 
        "id": "sg:person.0726667364.48", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0726667364.48"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Pennsylvania State University", 
          "id": "https://www.grid.ac/institutes/grid.29857.31", 
          "name": [
            "Department of Biology, The Pennsylvania State University, 208 Mueller Lab, University Park, 16803, PA, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Post", 
        "givenName": "Eric", 
        "id": "sg:person.01141223243.35", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01141223243.35"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1186/1472-6785-1-5", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1001170064", 
          "https://doi.org/10.1186/1472-6785-1-5"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1073/pnas.231391598", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1003900720"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1139/z91-044", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1010974508"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1046/j.1523-1739.1997.95366.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1011391748"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/416389a", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1015998984", 
          "https://doi.org/10.1038/416389a"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/416389a", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1015998984", 
          "https://doi.org/10.1038/416389a"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0169-5347(99)01764-4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1020811344"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/29291", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1022872460", 
          "https://doi.org/10.1038/29291"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/29291", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1022872460", 
          "https://doi.org/10.1038/29291"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/j.1523-1739.1990.tb00268.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1024261813"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s004420100655", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1028078805", 
          "https://doi.org/10.1007/s004420100655"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1890/0012-9658(2002)083[2997:pdapci]2.0.co;2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1036119733"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/44814", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1043186794", 
          "https://doi.org/10.1038/44814"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/44814", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1043186794", 
          "https://doi.org/10.1038/44814"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1098/rspb.2000.1071", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1048793338"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1086/282863", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1058592986"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/biomet/76.2.297", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1059419926"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1126/science.224.4655.1350", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1062528820"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1126/science.266.5190.1555", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1062549272"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1126/science.269.5224.676", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1062550625"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2307/1381751", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1069462285"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2307/1383091", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1069463436"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2307/1939357", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1069663215"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2307/3535", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1070364527"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2307/3546809", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1070368587"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2004-12", 
    "datePublishedReg": "2004-12-01", 
    "description": "BACKGROUND: Research on the ecological consequences of global climate change has elicited a growing interest in the use of time series analysis to investigate population dynamics in a changing climate. Here, we compare linear and non-linear models describing the contribution of climate to the density fluctuations of the population of wolves on Isle Royale, Michigan from 1959 to 1999.\nRESULTS: The non-linear self excitatory threshold autoregressive (SETAR) model revealed that, due to differences in the strength and nature of density dependence, relatively small and large populations may be differentially affected by future changes in climate. Both linear and non-linear models predict a decrease in the population of wolves with predicted changes in climate.\nCONCLUSIONS: Because specific predictions differed between linear and non-linear models, our study highlights the importance of using non-linear methods that allow the detection of non-linearity in the strength and nature of density dependence. Failure to adopt a non-linear approach to modelling population response to climate change, either exclusively or in addition to linear approaches, may compromise efforts to quantify ecological consequences of future warming.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1186/1472-6785-4-2", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": true, 
    "isPartOf": [
      {
        "id": "sg:journal.1028425", 
        "issn": [
          "1472-6785"
        ], 
        "name": "BMC Ecology", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "1", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "4"
      }
    ], 
    "name": "Population response to climate change: linear vs. non-linear modeling approaches", 
    "pagination": "2", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "3cfa9c35b36784a106be423582d8e85de81a919f622f3e6dd8b9b0eacb060b2c"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "15056394"
        ]
      }, 
      {
        "name": "nlm_unique_id", 
        "type": "PropertyValue", 
        "value": [
          "101088674"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1186/1472-6785-4-2"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1012800138"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1186/1472-6785-4-2", 
      "https://app.dimensions.ai/details/publication/pub.1012800138"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-11T09:49", 
    "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/v90/0000000347_0000000347/records_89785_00000000.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://link.springer.com/10.1186%2F1472-6785-4-2"
  }
]
 

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.10.1186/1472-6785-4-2'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1186/1472-6785-4-2'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1186/1472-6785-4-2'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1186/1472-6785-4-2'


 

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

197 TRIPLES      21 PREDICATES      63 URIs      33 LITERALS      21 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1186/1472-6785-4-2 schema:about N2234834f1364429d8cf73dd137f7738d
2 N2a8906a8086146f38fad7445b9b1ef3a
3 N4ff9a4175acc48c4bcc4945d0494e7ca
4 N522ad97b9d08449287e64306f701f2a0
5 N6fa38e5ee8cb4dbaabb8790f71de06bf
6 N959a3bbeab9d46648650fbefed41e9e6
7 Nbfaabbf7a1894bdaa08f79b62f283ab2
8 Nd47a04c88b384f8ba7c5c095a42fd7b2
9 Ne97458e5e7634be5a84e4a1a15694d6d
10 Neeacf4c05d5c44ca9c739938737ef307
11 Nf60e91d8bb8d4eddbc23ff27aa062ff1
12 Nfada6e5d636a43558ca883b6f45679b1
13 anzsrc-for:06
14 anzsrc-for:0602
15 schema:author N7bd64fde0a3b4b4a9a81c620ef10a4dc
16 schema:citation sg:pub.10.1007/s004420100655
17 sg:pub.10.1038/29291
18 sg:pub.10.1038/416389a
19 sg:pub.10.1038/44814
20 sg:pub.10.1186/1472-6785-1-5
21 https://doi.org/10.1016/s0169-5347(99)01764-4
22 https://doi.org/10.1046/j.1523-1739.1997.95366.x
23 https://doi.org/10.1073/pnas.231391598
24 https://doi.org/10.1086/282863
25 https://doi.org/10.1093/biomet/76.2.297
26 https://doi.org/10.1098/rspb.2000.1071
27 https://doi.org/10.1111/j.1523-1739.1990.tb00268.x
28 https://doi.org/10.1126/science.224.4655.1350
29 https://doi.org/10.1126/science.266.5190.1555
30 https://doi.org/10.1126/science.269.5224.676
31 https://doi.org/10.1139/z91-044
32 https://doi.org/10.1890/0012-9658(2002)083[2997:pdapci]2.0.co;2
33 https://doi.org/10.2307/1381751
34 https://doi.org/10.2307/1383091
35 https://doi.org/10.2307/1939357
36 https://doi.org/10.2307/3535
37 https://doi.org/10.2307/3546809
38 schema:datePublished 2004-12
39 schema:datePublishedReg 2004-12-01
40 schema:description BACKGROUND: Research on the ecological consequences of global climate change has elicited a growing interest in the use of time series analysis to investigate population dynamics in a changing climate. Here, we compare linear and non-linear models describing the contribution of climate to the density fluctuations of the population of wolves on Isle Royale, Michigan from 1959 to 1999. RESULTS: The non-linear self excitatory threshold autoregressive (SETAR) model revealed that, due to differences in the strength and nature of density dependence, relatively small and large populations may be differentially affected by future changes in climate. Both linear and non-linear models predict a decrease in the population of wolves with predicted changes in climate. CONCLUSIONS: Because specific predictions differed between linear and non-linear models, our study highlights the importance of using non-linear methods that allow the detection of non-linearity in the strength and nature of density dependence. Failure to adopt a non-linear approach to modelling population response to climate change, either exclusively or in addition to linear approaches, may compromise efforts to quantify ecological consequences of future warming.
41 schema:genre research_article
42 schema:inLanguage en
43 schema:isAccessibleForFree true
44 schema:isPartOf N702efe7fd8234f538f0130b4d852f50b
45 Ne7cdd99906704ca2a1e76533de0b84cf
46 sg:journal.1028425
47 schema:name Population response to climate change: linear vs. non-linear modeling approaches
48 schema:pagination 2
49 schema:productId N83a9a29d9795417391124e74fbd19e6c
50 N84a686e1fd8f416785a8201f531ca10b
51 N87dd93873ed64a13aec653e227c59437
52 N895ebe3581864b9d8d8a9b949c1ee883
53 Nd63aaf4adece4007b9bb941b73488dfe
54 schema:sameAs https://app.dimensions.ai/details/publication/pub.1012800138
55 https://doi.org/10.1186/1472-6785-4-2
56 schema:sdDatePublished 2019-04-11T09:49
57 schema:sdLicense https://scigraph.springernature.com/explorer/license/
58 schema:sdPublisher N3813fc2c4b724ae4828f37ae7542a175
59 schema:url https://link.springer.com/10.1186%2F1472-6785-4-2
60 sgo:license sg:explorer/license/
61 sgo:sdDataset articles
62 rdf:type schema:ScholarlyArticle
63 N2234834f1364429d8cf73dd137f7738d schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
64 schema:name Michigan
65 rdf:type schema:DefinedTerm
66 N2a8906a8086146f38fad7445b9b1ef3a schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
67 schema:name Deer
68 rdf:type schema:DefinedTerm
69 N3813fc2c4b724ae4828f37ae7542a175 schema:name Springer Nature - SN SciGraph project
70 rdf:type schema:Organization
71 N4ff9a4175acc48c4bcc4945d0494e7ca schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
72 schema:name Predatory Behavior
73 rdf:type schema:DefinedTerm
74 N522ad97b9d08449287e64306f701f2a0 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
75 schema:name Animals
76 rdf:type schema:DefinedTerm
77 N6fa38e5ee8cb4dbaabb8790f71de06bf schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
78 schema:name Climate
79 rdf:type schema:DefinedTerm
80 N702efe7fd8234f538f0130b4d852f50b schema:issueNumber 1
81 rdf:type schema:PublicationIssue
82 N7bd64fde0a3b4b4a9a81c620ef10a4dc rdf:first sg:person.0726667364.48
83 rdf:rest Nb1fe0253303b4edf95566e4bac6b2907
84 N83a9a29d9795417391124e74fbd19e6c schema:name doi
85 schema:value 10.1186/1472-6785-4-2
86 rdf:type schema:PropertyValue
87 N84a686e1fd8f416785a8201f531ca10b schema:name nlm_unique_id
88 schema:value 101088674
89 rdf:type schema:PropertyValue
90 N87dd93873ed64a13aec653e227c59437 schema:name pubmed_id
91 schema:value 15056394
92 rdf:type schema:PropertyValue
93 N895ebe3581864b9d8d8a9b949c1ee883 schema:name readcube_id
94 schema:value 3cfa9c35b36784a106be423582d8e85de81a919f622f3e6dd8b9b0eacb060b2c
95 rdf:type schema:PropertyValue
96 N959a3bbeab9d46648650fbefed41e9e6 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
97 schema:name Linear Models
98 rdf:type schema:DefinedTerm
99 Nb1fe0253303b4edf95566e4bac6b2907 rdf:first sg:person.01141223243.35
100 rdf:rest rdf:nil
101 Nbfaabbf7a1894bdaa08f79b62f283ab2 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
102 schema:name Wolves
103 rdf:type schema:DefinedTerm
104 Nd47a04c88b384f8ba7c5c095a42fd7b2 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
105 schema:name Snow
106 rdf:type schema:DefinedTerm
107 Nd63aaf4adece4007b9bb941b73488dfe schema:name dimensions_id
108 schema:value pub.1012800138
109 rdf:type schema:PropertyValue
110 Ne7cdd99906704ca2a1e76533de0b84cf schema:volumeNumber 4
111 rdf:type schema:PublicationVolume
112 Ne97458e5e7634be5a84e4a1a15694d6d schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
113 schema:name Nonlinear Dynamics
114 rdf:type schema:DefinedTerm
115 Neeacf4c05d5c44ca9c739938737ef307 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
116 schema:name Temperature
117 rdf:type schema:DefinedTerm
118 Nf60e91d8bb8d4eddbc23ff27aa062ff1 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
119 schema:name Age Factors
120 rdf:type schema:DefinedTerm
121 Nfada6e5d636a43558ca883b6f45679b1 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
122 schema:name Population Dynamics
123 rdf:type schema:DefinedTerm
124 anzsrc-for:06 schema:inDefinedTermSet anzsrc-for:
125 schema:name Biological Sciences
126 rdf:type schema:DefinedTerm
127 anzsrc-for:0602 schema:inDefinedTermSet anzsrc-for:
128 schema:name Ecology
129 rdf:type schema:DefinedTerm
130 sg:journal.1028425 schema:issn 1472-6785
131 schema:name BMC Ecology
132 rdf:type schema:Periodical
133 sg:person.01141223243.35 schema:affiliation https://www.grid.ac/institutes/grid.29857.31
134 schema:familyName Post
135 schema:givenName Eric
136 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01141223243.35
137 rdf:type schema:Person
138 sg:person.0726667364.48 schema:affiliation https://www.grid.ac/institutes/grid.254880.3
139 schema:familyName Ellis
140 schema:givenName Alicia M
141 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0726667364.48
142 rdf:type schema:Person
143 sg:pub.10.1007/s004420100655 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028078805
144 https://doi.org/10.1007/s004420100655
145 rdf:type schema:CreativeWork
146 sg:pub.10.1038/29291 schema:sameAs https://app.dimensions.ai/details/publication/pub.1022872460
147 https://doi.org/10.1038/29291
148 rdf:type schema:CreativeWork
149 sg:pub.10.1038/416389a schema:sameAs https://app.dimensions.ai/details/publication/pub.1015998984
150 https://doi.org/10.1038/416389a
151 rdf:type schema:CreativeWork
152 sg:pub.10.1038/44814 schema:sameAs https://app.dimensions.ai/details/publication/pub.1043186794
153 https://doi.org/10.1038/44814
154 rdf:type schema:CreativeWork
155 sg:pub.10.1186/1472-6785-1-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1001170064
156 https://doi.org/10.1186/1472-6785-1-5
157 rdf:type schema:CreativeWork
158 https://doi.org/10.1016/s0169-5347(99)01764-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020811344
159 rdf:type schema:CreativeWork
160 https://doi.org/10.1046/j.1523-1739.1997.95366.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1011391748
161 rdf:type schema:CreativeWork
162 https://doi.org/10.1073/pnas.231391598 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003900720
163 rdf:type schema:CreativeWork
164 https://doi.org/10.1086/282863 schema:sameAs https://app.dimensions.ai/details/publication/pub.1058592986
165 rdf:type schema:CreativeWork
166 https://doi.org/10.1093/biomet/76.2.297 schema:sameAs https://app.dimensions.ai/details/publication/pub.1059419926
167 rdf:type schema:CreativeWork
168 https://doi.org/10.1098/rspb.2000.1071 schema:sameAs https://app.dimensions.ai/details/publication/pub.1048793338
169 rdf:type schema:CreativeWork
170 https://doi.org/10.1111/j.1523-1739.1990.tb00268.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1024261813
171 rdf:type schema:CreativeWork
172 https://doi.org/10.1126/science.224.4655.1350 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062528820
173 rdf:type schema:CreativeWork
174 https://doi.org/10.1126/science.266.5190.1555 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062549272
175 rdf:type schema:CreativeWork
176 https://doi.org/10.1126/science.269.5224.676 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062550625
177 rdf:type schema:CreativeWork
178 https://doi.org/10.1139/z91-044 schema:sameAs https://app.dimensions.ai/details/publication/pub.1010974508
179 rdf:type schema:CreativeWork
180 https://doi.org/10.1890/0012-9658(2002)083[2997:pdapci]2.0.co;2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036119733
181 rdf:type schema:CreativeWork
182 https://doi.org/10.2307/1381751 schema:sameAs https://app.dimensions.ai/details/publication/pub.1069462285
183 rdf:type schema:CreativeWork
184 https://doi.org/10.2307/1383091 schema:sameAs https://app.dimensions.ai/details/publication/pub.1069463436
185 rdf:type schema:CreativeWork
186 https://doi.org/10.2307/1939357 schema:sameAs https://app.dimensions.ai/details/publication/pub.1069663215
187 rdf:type schema:CreativeWork
188 https://doi.org/10.2307/3535 schema:sameAs https://app.dimensions.ai/details/publication/pub.1070364527
189 rdf:type schema:CreativeWork
190 https://doi.org/10.2307/3546809 schema:sameAs https://app.dimensions.ai/details/publication/pub.1070368587
191 rdf:type schema:CreativeWork
192 https://www.grid.ac/institutes/grid.254880.3 schema:alternateName Dartmouth College
193 schema:name Department of Biological Science, Dartmouth College, 03755, Hanover, NH, USA
194 rdf:type schema:Organization
195 https://www.grid.ac/institutes/grid.29857.31 schema:alternateName Pennsylvania State University
196 schema:name Department of Biology, The Pennsylvania State University, 208 Mueller Lab, University Park, 16803, PA, USA
197 rdf:type schema:Organization
 




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


...