Inferring ecosystem networks as information flows
The detection of causal interactions is of great importance when inferring complex ecosystem functional and structural networks for basic and applied research. Convergent cross mapping (CCM) based on nonlinear state-space reconstruction made substantial progress about network inference by measuring...
Saved in:
| Published in | Scientific reports Vol. 11; no. 1; pp. 7094 - 22 |
|---|---|
| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
London
Nature Publishing Group UK
29.03.2021
Nature Publishing Group Nature Portfolio |
| Subjects | |
| Online Access | Get full text |
Cover
Loading…
| Abstract | The detection of causal interactions is of great importance when inferring complex ecosystem functional and structural networks for basic and applied research. Convergent cross mapping (CCM) based on nonlinear state-space reconstruction made substantial progress about network inference by measuring how well historical values of one variable can reliably estimate states of other variables. Here we investigate the ability of a developed optimal information flow (OIF) ecosystem model to infer bidirectional causality and compare that to CCM. Results from synthetic datasets generated by a simple predator-prey model, data of a real-world sardine-anchovy-temperature system and of a multispecies fish ecosystem highlight that the proposed OIF performs better than CCM to predict population and community patterns. Specifically, OIF provides a larger gradient of inferred interactions, higher point-value accuracy and smaller fluctuations of interactions and
α
-diversity including their characteristic time delays. We propose an optimal threshold on inferred interactions that maximize accuracy in predicting fluctuations of effective
α
-diversity, defined as the count of model-inferred interacting species. Overall OIF outperforms all other models in assessing predictive causality (also in terms of computational complexity) due to the explicit consideration of synchronization, divergence and diversity of events that define model sensitivity, uncertainty and complexity. Thus, OIF offers a broad ecological information by extracting predictive causal networks of complex ecosystems from time-series data in the space-time continuum. The accurate inference of species interactions at any biological scale of organization is highly valuable because it allows to predict biodiversity changes, for instance as a function of climate and other anthropogenic stressors. This has practical implications for defining optimal ecosystem management and design, such as fish stock prioritization and delineation of marine protected areas based on derived collective multispecies assembly. OIF can be applied to any complex system and used for model evaluation and design where causality should be considered as non-linear predictability of diverse events of populations or communities. |
|---|---|
| AbstractList | The detection of causal interactions is of great importance when inferring complex ecosystem functional and structural networks for basic and applied research. Convergent cross mapping (CCM) based on nonlinear state-space reconstruction made substantial progress about network inference by measuring how well historical values of one variable can reliably estimate states of other variables. Here we investigate the ability of a developed optimal information flow (OIF) ecosystem model to infer bidirectional causality and compare that to CCM. Results from synthetic datasets generated by a simple predator-prey model, data of a real-world sardine-anchovy-temperature system and of a multispecies fish ecosystem highlight that the proposed OIF performs better than CCM to predict population and community patterns. Specifically, OIF provides a larger gradient of inferred interactions, higher point-value accuracy and smaller fluctuations of interactions and
α
-diversity including their characteristic time delays. We propose an optimal threshold on inferred interactions that maximize accuracy in predicting fluctuations of effective
α
-diversity, defined as the count of model-inferred interacting species. Overall OIF outperforms all other models in assessing predictive causality (also in terms of computational complexity) due to the explicit consideration of synchronization, divergence and diversity of events that define model sensitivity, uncertainty and complexity. Thus, OIF offers a broad ecological information by extracting predictive causal networks of complex ecosystems from time-series data in the space-time continuum. The accurate inference of species interactions at any biological scale of organization is highly valuable because it allows to predict biodiversity changes, for instance as a function of climate and other anthropogenic stressors. This has practical implications for defining optimal ecosystem management and design, such as fish stock prioritization and delineation of marine protected areas based on derived collective multispecies assembly. OIF can be applied to any complex system and used for model evaluation and design where causality should be considered as non-linear predictability of diverse events of populations or communities. The detection of causal interactions is of great importance when inferring complex ecosystem functional and structural networks for basic and applied research. Convergent cross mapping (CCM) based on nonlinear state-space reconstruction made substantial progress about network inference by measuring how well historical values of one variable can reliably estimate states of other variables. Here we investigate the ability of a developed optimal information flow (OIF) ecosystem model to infer bidirectional causality and compare that to CCM. Results from synthetic datasets generated by a simple predator-prey model, data of a real-world sardine-anchovy-temperature system and of a multispecies fish ecosystem highlight that the proposed OIF performs better than CCM to predict population and community patterns. Specifically, OIF provides a larger gradient of inferred interactions, higher point-value accuracy and smaller fluctuations of interactions and $$\alpha$$ α -diversity including their characteristic time delays. We propose an optimal threshold on inferred interactions that maximize accuracy in predicting fluctuations of effective $$\alpha$$ α -diversity, defined as the count of model-inferred interacting species. Overall OIF outperforms all other models in assessing predictive causality (also in terms of computational complexity) due to the explicit consideration of synchronization, divergence and diversity of events that define model sensitivity, uncertainty and complexity. Thus, OIF offers a broad ecological information by extracting predictive causal networks of complex ecosystems from time-series data in the space-time continuum. The accurate inference of species interactions at any biological scale of organization is highly valuable because it allows to predict biodiversity changes, for instance as a function of climate and other anthropogenic stressors. This has practical implications for defining optimal ecosystem management and design, such as fish stock prioritization and delineation of marine protected areas based on derived collective multispecies assembly. OIF can be applied to any complex system and used for model evaluation and design where causality should be considered as non-linear predictability of diverse events of populations or communities. The detection of causal interactions is of great importance when inferring complex ecosystem functional and structural networks for basic and applied research. Convergent cross mapping (CCM) based on nonlinear state-space reconstruction made substantial progress about network inference by measuring how well historical values of one variable can reliably estimate states of other variables. Here we investigate the ability of a developed optimal information flow (OIF) ecosystem model to infer bidirectional causality and compare that to CCM. Results from synthetic datasets generated by a simple predator-prey model, data of a real-world sardine-anchovy-temperature system and of a multispecies fish ecosystem highlight that the proposed OIF performs better than CCM to predict population and community patterns. Specifically, OIF provides a larger gradient of inferred interactions, higher point-value accuracy and smaller fluctuations of interactions and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\alpha$$\end{document} α -diversity including their characteristic time delays. We propose an optimal threshold on inferred interactions that maximize accuracy in predicting fluctuations of effective \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\alpha$$\end{document} α -diversity, defined as the count of model-inferred interacting species. Overall OIF outperforms all other models in assessing predictive causality (also in terms of computational complexity) due to the explicit consideration of synchronization, divergence and diversity of events that define model sensitivity, uncertainty and complexity. Thus, OIF offers a broad ecological information by extracting predictive causal networks of complex ecosystems from time-series data in the space-time continuum. The accurate inference of species interactions at any biological scale of organization is highly valuable because it allows to predict biodiversity changes, for instance as a function of climate and other anthropogenic stressors. This has practical implications for defining optimal ecosystem management and design, such as fish stock prioritization and delineation of marine protected areas based on derived collective multispecies assembly. OIF can be applied to any complex system and used for model evaluation and design where causality should be considered as non-linear predictability of diverse events of populations or communities. The detection of causal interactions is of great importance when inferring complex ecosystem functional and structural networks for basic and applied research. Convergent cross mapping (CCM) based on nonlinear state-space reconstruction made substantial progress about network inference by measuring how well historical values of one variable can reliably estimate states of other variables. Here we investigate the ability of a developed optimal information flow (OIF) ecosystem model to infer bidirectional causality and compare that to CCM. Results from synthetic datasets generated by a simple predator-prey model, data of a real-world sardine-anchovy-temperature system and of a multispecies fish ecosystem highlight that the proposed OIF performs better than CCM to predict population and community patterns. Specifically, OIF provides a larger gradient of inferred interactions, higher point-value accuracy and smaller fluctuations of interactions and [Formula: see text]-diversity including their characteristic time delays. We propose an optimal threshold on inferred interactions that maximize accuracy in predicting fluctuations of effective [Formula: see text]-diversity, defined as the count of model-inferred interacting species. Overall OIF outperforms all other models in assessing predictive causality (also in terms of computational complexity) due to the explicit consideration of synchronization, divergence and diversity of events that define model sensitivity, uncertainty and complexity. Thus, OIF offers a broad ecological information by extracting predictive causal networks of complex ecosystems from time-series data in the space-time continuum. The accurate inference of species interactions at any biological scale of organization is highly valuable because it allows to predict biodiversity changes, for instance as a function of climate and other anthropogenic stressors. This has practical implications for defining optimal ecosystem management and design, such as fish stock prioritization and delineation of marine protected areas based on derived collective multispecies assembly. OIF can be applied to any complex system and used for model evaluation and design where causality should be considered as non-linear predictability of diverse events of populations or communities. Abstract The detection of causal interactions is of great importance when inferring complex ecosystem functional and structural networks for basic and applied research. Convergent cross mapping (CCM) based on nonlinear state-space reconstruction made substantial progress about network inference by measuring how well historical values of one variable can reliably estimate states of other variables. Here we investigate the ability of a developed optimal information flow (OIF) ecosystem model to infer bidirectional causality and compare that to CCM. Results from synthetic datasets generated by a simple predator-prey model, data of a real-world sardine-anchovy-temperature system and of a multispecies fish ecosystem highlight that the proposed OIF performs better than CCM to predict population and community patterns. Specifically, OIF provides a larger gradient of inferred interactions, higher point-value accuracy and smaller fluctuations of interactions and $$\alpha$$ α -diversity including their characteristic time delays. We propose an optimal threshold on inferred interactions that maximize accuracy in predicting fluctuations of effective $$\alpha$$ α -diversity, defined as the count of model-inferred interacting species. Overall OIF outperforms all other models in assessing predictive causality (also in terms of computational complexity) due to the explicit consideration of synchronization, divergence and diversity of events that define model sensitivity, uncertainty and complexity. Thus, OIF offers a broad ecological information by extracting predictive causal networks of complex ecosystems from time-series data in the space-time continuum. The accurate inference of species interactions at any biological scale of organization is highly valuable because it allows to predict biodiversity changes, for instance as a function of climate and other anthropogenic stressors. This has practical implications for defining optimal ecosystem management and design, such as fish stock prioritization and delineation of marine protected areas based on derived collective multispecies assembly. OIF can be applied to any complex system and used for model evaluation and design where causality should be considered as non-linear predictability of diverse events of populations or communities. The detection of causal interactions is of great importance when inferring complex ecosystem functional and structural networks for basic and applied research. Convergent cross mapping (CCM) based on nonlinear state-space reconstruction made substantial progress about network inference by measuring how well historical values of one variable can reliably estimate states of other variables. Here we investigate the ability of a developed optimal information flow (OIF) ecosystem model to infer bidirectional causality and compare that to CCM. Results from synthetic datasets generated by a simple predator-prey model, data of a real-world sardine-anchovy-temperature system and of a multispecies fish ecosystem highlight that the proposed OIF performs better than CCM to predict population and community patterns. Specifically, OIF provides a larger gradient of inferred interactions, higher point-value accuracy and smaller fluctuations of interactions and α-diversity including their characteristic time delays. We propose an optimal threshold on inferred interactions that maximize accuracy in predicting fluctuations of effective α-diversity, defined as the count of model-inferred interacting species. Overall OIF outperforms all other models in assessing predictive causality (also in terms of computational complexity) due to the explicit consideration of synchronization, divergence and diversity of events that define model sensitivity, uncertainty and complexity. Thus, OIF offers a broad ecological information by extracting predictive causal networks of complex ecosystems from time-series data in the space-time continuum. The accurate inference of species interactions at any biological scale of organization is highly valuable because it allows to predict biodiversity changes, for instance as a function of climate and other anthropogenic stressors. This has practical implications for defining optimal ecosystem management and design, such as fish stock prioritization and delineation of marine protected areas based on derived collective multispecies assembly. OIF can be applied to any complex system and used for model evaluation and design where causality should be considered as non-linear predictability of diverse events of populations or communities. The detection of causal interactions is of great importance when inferring complex ecosystem functional and structural networks for basic and applied research. Convergent cross mapping (CCM) based on nonlinear state-space reconstruction made substantial progress about network inference by measuring how well historical values of one variable can reliably estimate states of other variables. Here we investigate the ability of a developed optimal information flow (OIF) ecosystem model to infer bidirectional causality and compare that to CCM. Results from synthetic datasets generated by a simple predator-prey model, data of a real-world sardine-anchovy-temperature system and of a multispecies fish ecosystem highlight that the proposed OIF performs better than CCM to predict population and community patterns. Specifically, OIF provides a larger gradient of inferred interactions, higher point-value accuracy and smaller fluctuations of interactions and [Formula: see text]-diversity including their characteristic time delays. We propose an optimal threshold on inferred interactions that maximize accuracy in predicting fluctuations of effective [Formula: see text]-diversity, defined as the count of model-inferred interacting species. Overall OIF outperforms all other models in assessing predictive causality (also in terms of computational complexity) due to the explicit consideration of synchronization, divergence and diversity of events that define model sensitivity, uncertainty and complexity. Thus, OIF offers a broad ecological information by extracting predictive causal networks of complex ecosystems from time-series data in the space-time continuum. The accurate inference of species interactions at any biological scale of organization is highly valuable because it allows to predict biodiversity changes, for instance as a function of climate and other anthropogenic stressors. This has practical implications for defining optimal ecosystem management and design, such as fish stock prioritization and delineation of marine protected areas based on derived collective multispecies assembly. OIF can be applied to any complex system and used for model evaluation and design where causality should be considered as non-linear predictability of diverse events of populations or communities.The detection of causal interactions is of great importance when inferring complex ecosystem functional and structural networks for basic and applied research. Convergent cross mapping (CCM) based on nonlinear state-space reconstruction made substantial progress about network inference by measuring how well historical values of one variable can reliably estimate states of other variables. Here we investigate the ability of a developed optimal information flow (OIF) ecosystem model to infer bidirectional causality and compare that to CCM. Results from synthetic datasets generated by a simple predator-prey model, data of a real-world sardine-anchovy-temperature system and of a multispecies fish ecosystem highlight that the proposed OIF performs better than CCM to predict population and community patterns. Specifically, OIF provides a larger gradient of inferred interactions, higher point-value accuracy and smaller fluctuations of interactions and [Formula: see text]-diversity including their characteristic time delays. We propose an optimal threshold on inferred interactions that maximize accuracy in predicting fluctuations of effective [Formula: see text]-diversity, defined as the count of model-inferred interacting species. Overall OIF outperforms all other models in assessing predictive causality (also in terms of computational complexity) due to the explicit consideration of synchronization, divergence and diversity of events that define model sensitivity, uncertainty and complexity. Thus, OIF offers a broad ecological information by extracting predictive causal networks of complex ecosystems from time-series data in the space-time continuum. The accurate inference of species interactions at any biological scale of organization is highly valuable because it allows to predict biodiversity changes, for instance as a function of climate and other anthropogenic stressors. This has practical implications for defining optimal ecosystem management and design, such as fish stock prioritization and delineation of marine protected areas based on derived collective multispecies assembly. OIF can be applied to any complex system and used for model evaluation and design where causality should be considered as non-linear predictability of diverse events of populations or communities. |
| ArticleNumber | 7094 |
| Author | Convertino, Matteo Li, Jie |
| Author_xml | – sequence: 1 givenname: Jie surname: Li fullname: Li, Jie organization: Nexus Group, Faculty and Graduate School of Information Science and Technology, Hokkaido University, GI-CORE Global Station for Big Data and Cybersecurity, Hokkaido University – sequence: 2 givenname: Matteo surname: Convertino fullname: Convertino, Matteo email: matteo@ist.hokudai.ac.jp organization: Nexus Group, Faculty and Graduate School of Information Science and Technology, Hokkaido University, GI-CORE Global Station for Big Data and Cybersecurity, Hokkaido University, Graduate School of Information Science and Technology, Hokkaido University, Institute of Environment and Ecology, Tsinghua Shenzhen International Graduate School, Tsinghua University |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/33782461$$D View this record in MEDLINE/PubMed |
| BookMark | eNp9Uk1v1DAUtFARLaV_gAOKxIVLwN-xL0io4mOlSlzgbDnO8-Ilay92tlX_Pc6mhbaHfT7YsmdG8_zmJTqJKQJCrwl-TzBTHwonQqsWU9IqyTvZ6mfojGIuWsooPXlwPkUXpWxwLUE1J_oFOmWsU5RLcobIKnrIOcR1Ay6V2zLBtokw3aT8uzS2NCH6lLd2Cik2fkw35RV67u1Y4OJuP0c_v3z-cfmtvfr-dXX56ap1ssNT62ldurOas57RjmDfSdsrcB4L3DEmLKe99HaAoccCahHuvaKODa53jrNztFp0h2Q3ZpfD1uZbk2wwh4uU18bmKbgRjMCYCkKYAME46XnfA3fKWUkpONzNWh8Xrd2-38LgIE7Zjo9EH7_E8Mus07VRGHeSzgLv7gRy-rOHMpltKA7G0UZI-2Jo7YlwSaWu0LdPoJu0z7F-VUUJKbgWkhxH4fqDWmlRUW8e-v5n-H5-FaAWgMuplAzeuDAdhlXbCKMh2MxpMUtaTE2LOaTFzD7pE-q9-lESW0hlN2cG8n_bR1h_Ac6N0I8 |
| CitedBy_id | crossref_primary_10_1016_j_ecolind_2022_109681 crossref_primary_10_1016_j_ecolind_2023_110297 crossref_primary_10_1016_j_ecolind_2024_112277 crossref_primary_10_3390_biom14121549 crossref_primary_10_1080_15481603_2024_2387385 crossref_primary_10_1016_j_ecolind_2024_112174 crossref_primary_10_1007_s10531_024_02779_z crossref_primary_10_1371_journal_pcsy_0000024 crossref_primary_10_1016_j_ecolind_2022_109825 crossref_primary_10_1029_2022WR032008 crossref_primary_10_1038_s41598_021_88309_1 crossref_primary_10_1016_j_ecoinf_2022_101955 crossref_primary_10_3390_e26010013 crossref_primary_10_3390_w16030398 crossref_primary_10_1016_j_ecoinf_2023_102204 crossref_primary_10_1016_j_ecolind_2022_108917 crossref_primary_10_1073_pnas_2412220121 crossref_primary_10_1016_j_ecolind_2024_111704 crossref_primary_10_1016_j_ecolmodel_2024_110722 crossref_primary_10_1016_j_ecolind_2023_111523 crossref_primary_10_1016_j_ecolind_2024_112417 crossref_primary_10_1016_j_ecolind_2024_112218 crossref_primary_10_1016_j_ecolind_2024_112117 crossref_primary_10_3390_su16052101 crossref_primary_10_1098_rstb_2023_0170 crossref_primary_10_1093_femsec_fiab167 crossref_primary_10_1016_j_ecolind_2022_109536 crossref_primary_10_3389_fenvs_2024_1431645 crossref_primary_10_1016_j_ecoinf_2023_102319 crossref_primary_10_1186_s40168_022_01298_9 crossref_primary_10_3390_land11010106 crossref_primary_10_3390_e26010046 crossref_primary_10_1016_j_ecolmodel_2023_110450 crossref_primary_10_14489_vkit_2022_04_pp_033_041 crossref_primary_10_3390_e26080641 crossref_primary_10_3390_rs16132478 crossref_primary_10_1360_SSTe_2023_0005 crossref_primary_10_1016_j_ecolind_2024_112848 crossref_primary_10_1016_j_ecolmodel_2022_110254 crossref_primary_10_1029_2022JF006665 crossref_primary_10_1080_15481603_2023_2301275 crossref_primary_10_1007_s11430_023_1148_7 crossref_primary_10_1016_j_ecoinf_2023_102192 crossref_primary_10_1016_j_ecolind_2023_110326 crossref_primary_10_1016_j_ecolind_2023_110963 crossref_primary_10_1016_j_ecolmodel_2023_110433 |
| Cites_doi | 10.1109/TCST.2012.2233476 10.1371/journal.pone.0096732 10.1126/science.286.5439.509 10.1155/2016/8313272 10.1093/bioinformatics/bth463 10.1038/s41598-019-41614-2 10.1098/rspb.2013.2641 10.1080/01621459.1986.10478354 10.1890/14-1479.1 10.1016/j.paid.2006.09.018 10.1093/bib/4.3.228 10.1063/1.5025050 10.1371/journal.pone.0099462 10.1126/science.aag0863 10.1038/ncomms11180 10.1137/140956166 10.1038/srep14750 10.1098/rsif.2018.0695 10.1111/j.2006.0030-1299.14714.x 10.1140/epjb/e2010-00034-5 10.1038/srep12652 10.1103/PhysRevLett.103.238701 10.1038/nature25504 10.1029/2019WR024940 10.1007/s12080-015-0279-3 10.1038/s41593-018-0226-x 10.3390/e21050506 10.1038/nclimate3382 10.1371/journal.pone.0157945 10.1890/ES12-00048.1 10.1126/sciadv.aau4996 10.1371/journal.pone.0109462 10.1016/j.physrep.2006.12.004 10.1016/j.ecolind.2019.105510 10.1214/aos/1176344064 10.1073/pnas.0903682106 10.1139/f95-057 10.3389/frobt.2014.00011 10.1038/s41396-019-0407-y 10.1103/PhysRevLett.85.461 10.1145/1882471.1882476 10.1126/sciadv.1701088 10.1126/sciadv.aao6652 10.1126/science.1227079 10.1073/pnas.1215506110 10.1073/pnas.0702059104 10.1007/s00382-014-2182-9 10.1111/1440-1703.12164 10.1242/jcs.02714 10.1007/s11284-017-1469-9 10.1137/S003614450342480 10.1007/s00382-011-1135-9 10.1109/CBMS.2007.60 10.1162/netn_a_00092 10.1007/978-1-4614-2299-0_4 10.1126/science.aac6284 10.1007/BFb0091924 10.1007/978-3-030-05411-3_34 10.1007/978-94-017-9514-2_7 10.1017/CBO9780511617799 10.2307/1912791 10.1007/11795131_58 10.1038/s41598-018-37186-2 |
| ContentType | Journal Article |
| Copyright | The Author(s) 2021. corrected publication 2021 The Author(s) 2021. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. The Author(s) 2021. corrected publication 2021. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. The Author(s) 2021, corrected publication 2021 |
| Copyright_xml | – notice: The Author(s) 2021. corrected publication 2021 – notice: The Author(s) 2021. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. – notice: The Author(s) 2021. corrected publication 2021. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. – notice: The Author(s) 2021, corrected publication 2021 |
| DBID | C6C AAYXX CITATION NPM 3V. 7X7 7XB 88A 88E 88I 8FE 8FH 8FI 8FJ 8FK ABUWG AEUYN AFKRA AZQEC BBNVY BENPR BHPHI CCPQU DWQXO FYUFA GHDGH GNUQQ HCIFZ K9. LK8 M0S M1P M2P M7P PHGZM PHGZT PIMPY PJZUB PKEHL PPXIY PQEST PQGLB PQQKQ PQUKI PRINS Q9U 7X8 5PM DOA |
| DOI | 10.1038/s41598-021-86476-9 |
| DatabaseName | Springer Nature OA Free Journals CrossRef PubMed ProQuest Central (Corporate) ProQuest Health & Medical Collection ProQuest Central (purchase pre-March 2016) Biology Database (Alumni Edition) Medical Database (Alumni Edition) Science Database (Alumni Edition) ProQuest SciTech Collection ProQuest Natural Science Collection Hospital Premium Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central (Alumni) ProQuest One Sustainability (subscription) ProQuest Central UK/Ireland ProQuest Central Essentials Biological Science Collection ProQuest Central Natural Science Collection ProQuest One Community College ProQuest Central Korea Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Central Student SciTech Premium Collection ProQuest Health & Medical Complete (Alumni) ProQuest Biological Science Collection Health & Medical Collection (Alumni) PML(ProQuest Medical Library) Science Database Biological Science Database ProQuest Central Premium ProQuest One Academic Publicly Available Content Database ProQuest Health & Medical Research Collection ProQuest One Academic Middle East (New) ProQuest One Health & Nursing ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic ProQuest One Academic UKI Edition ProQuest Central China ProQuest Central Basic MEDLINE - Academic PubMed Central (Full Participant titles) DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef PubMed Publicly Available Content Database ProQuest Central Student ProQuest One Academic Middle East (New) ProQuest Central Essentials ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ProQuest One Health & Nursing ProQuest Natural Science Collection ProQuest Central China ProQuest Biology Journals (Alumni Edition) ProQuest Central ProQuest One Applied & Life Sciences ProQuest One Sustainability ProQuest Health & Medical Research Collection Health Research Premium Collection Health and Medicine Complete (Alumni Edition) Natural Science Collection ProQuest Central Korea Health & Medical Research Collection Biological Science Collection ProQuest Central (New) ProQuest Medical Library (Alumni) ProQuest Science Journals (Alumni Edition) ProQuest Biological Science Collection ProQuest Central Basic ProQuest Science Journals ProQuest One Academic Eastern Edition ProQuest Hospital Collection Health Research Premium Collection (Alumni) Biological Science Database ProQuest SciTech Collection ProQuest Hospital Collection (Alumni) ProQuest Health & Medical Complete ProQuest Medical Library ProQuest One Academic UKI Edition ProQuest One Academic ProQuest One Academic (New) ProQuest Central (Alumni) MEDLINE - Academic |
| DatabaseTitleList | CrossRef PubMed Publicly Available Content Database MEDLINE - Academic Publicly Available Content Database |
| Database_xml | – sequence: 1 dbid: C6C name: Springer Nature OA Free Journals url: http://www.springeropen.com/ sourceTypes: Publisher – sequence: 2 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 3 dbid: NPM name: PubMed url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 4 dbid: BENPR name: ProQuest Central url: https://www.proquest.com/central sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Biology |
| EISSN | 2045-2322 |
| EndPage | 22 |
| ExternalDocumentID | oai_doaj_org_article_500251135e5341b4bbe4c8ca622ec074 PMC8007624 33782461 10_1038_s41598_021_86476_9 |
| Genre | Journal Article |
| GroupedDBID | 0R~ 3V. 4.4 53G 5VS 7X7 88A 88E 88I 8FE 8FH 8FI 8FJ AAFWJ AAJSJ AAKDD ABDBF ABUWG ACGFS ACSMW ACUHS ADBBV ADRAZ AENEX AEUYN AFKRA AJTQC ALIPV ALMA_UNASSIGNED_HOLDINGS AOIJS AZQEC BAWUL BBNVY BCNDV BENPR BHPHI BPHCQ BVXVI C6C CCPQU DIK DWQXO EBD EBLON EBS ESX FYUFA GNUQQ GROUPED_DOAJ GX1 HCIFZ HH5 HMCUK HYE KQ8 LK8 M0L M1P M2P M48 M7P M~E NAO OK1 PIMPY PQQKQ PROAC PSQYO RNT RNTTT RPM SNYQT UKHRP AASML AAYXX AFPKN CITATION PHGZM PHGZT NPM 7XB 8FK AARCD K9. PJZUB PKEHL PPXIY PQEST PQGLB PQUKI PRINS Q9U 7X8 5PM PUEGO |
| ID | FETCH-LOGICAL-c670t-f2f2f97a943b32710f76ab8ecf0507335a42b6fadedb05eeee14ff82c3dcbcc43 |
| IEDL.DBID | M48 |
| ISSN | 2045-2322 |
| IngestDate | Wed Aug 27 01:27:47 EDT 2025 Thu Aug 21 14:06:46 EDT 2025 Fri Jul 11 15:11:03 EDT 2025 Wed Aug 13 05:30:04 EDT 2025 Wed Aug 13 07:37:12 EDT 2025 Thu Jan 02 22:57:24 EST 2025 Tue Jul 01 01:07:49 EDT 2025 Thu Apr 24 23:12:48 EDT 2025 Fri Feb 21 02:39:31 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 1 |
| Language | English |
| License | Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c670t-f2f2f97a943b32710f76ab8ecf0507335a42b6fadedb05eeee14ff82c3dcbcc43 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| OpenAccessLink | http://journals.scholarsportal.info/openUrl.xqy?doi=10.1038/s41598-021-86476-9 |
| PMID | 33782461 |
| PQID | 2506709895 |
| PQPubID | 2041939 |
| PageCount | 22 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_500251135e5341b4bbe4c8ca622ec074 pubmedcentral_primary_oai_pubmedcentral_nih_gov_8007624 proquest_miscellaneous_2507146269 proquest_journals_2556549561 proquest_journals_2506709895 pubmed_primary_33782461 crossref_citationtrail_10_1038_s41598_021_86476_9 crossref_primary_10_1038_s41598_021_86476_9 springer_journals_10_1038_s41598_021_86476_9 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 2021-03-29 |
| PublicationDateYYYYMMDD | 2021-03-29 |
| PublicationDate_xml | – month: 03 year: 2021 text: 2021-03-29 day: 29 |
| PublicationDecade | 2020 |
| PublicationPlace | London |
| PublicationPlace_xml | – name: London – name: England |
| PublicationTitle | Scientific reports |
| PublicationTitleAbbrev | Sci Rep |
| PublicationTitleAlternate | Sci Rep |
| PublicationYear | 2021 |
| Publisher | Nature Publishing Group UK Nature Publishing Group Nature Portfolio |
| Publisher_xml | – name: Nature Publishing Group UK – name: Nature Publishing Group – name: Nature Portfolio |
| References | Runge (CR48) 2018; 28 Villaverde, Ross, Morán, Banga (CR31) 2014; 9 Batushansky, Toubiana, Fait (CR13) 2016 Lizier (CR42) 2014; 1 Ye, Sugihara (CR63) 2016; 353 Mamet (CR28) 2019 Rubin (CR15) 1978; 6 Ye, Deyle, Gilarranz, Sugihara (CR38) 2015; 5 CR36 CR35 CR33 CR30 Hlavackova-Schindler, Palus, Vejmelka, Bhattacharya (CR44) 2007; 441 Dai, Hu, Tian, Xie, Hu (CR6) 2016; 11 Holland (CR11) 1986; 81 Newman (CR2) 2003; 45 CR72 CR70 Baumgartner (CR61) 1992; 33 Montalto, Faes, Marinazzo (CR43) 2014; 9 Chang, Ushio, Hsieh (CR64) 2017; 32 Albert (CR8) 2005; 118 Jost (CR50) 2006; 113 CR7 CR9 Lizier, Prokopenko (CR41) 2010; 73 Barnett, Barrett, Seth (CR45) 2009; 103 Comte, Olden (CR67) 2017; 7 Feldhoff (CR5) 2015; 44 Li, Convertino (CR39) 2019; 21 Sugihara (CR29) 2012; 338 Abdul Razak, Jensen (CR46) 2014; 9 Servadio, Convertino (CR49) 2018 Hein, Öhlund, Englund (CR66) 2014; 281 Gellner, McCann (CR56) 2016; 7 Convertino, Valverde (CR12) 2019; 107 Ruelle, Takens (CR69) 1971; 12 Ushio (CR37) 2018; 554 Nakazawa (CR51) 2020; 35 Hooper, Mullen, Coughlan (CR25) 2008; 6 Schreiber (CR40) 2000; 85 Brander (CR65) 2007; 104 Wootton, Stouffer (CR57) 2016; 9 CR19 Steinhaeuser, Chawla, Ganguly (CR3) 2010; 12 Kadoya, McCann (CR55) 2015; 5 CR17 CR59 Goodwell, Jiang, Ruddell, Kumar (CR10) 2020; 56 CR14 Cenci, Saavedra (CR68) 2018; 15 Duan, Yang, Chen, Shah (CR47) 2013; 21 Deyle (CR53) 2013; 110 Grace (CR26) 2012 Runge, Nowack, Kretschmer, Flaxman, Sejdinovic (CR34) 2019; 5 Jacobson, MacCall (CR62) 1995; 52 Sun, Taylor, Bollt (CR32) 2014 Barrett (CR24) 2007; 42 Tu, Suweis, Grilli, Formentin, Maritan (CR52) 2019; 9 Kim, Imoto, Miyano (CR16) 2003; 4 Yang, Yang, Zhang, Connor, Liu (CR27) 2018 Barabási, Albert (CR1) 1999; 286 Mongillo, Rumpel, Loewenstein (CR58) 2018; 21 CR23 CR22 CR21 CR20 Steinhaeuser, Ganguly, Chawla (CR4) 2012; 39 O’Gorman, Emmerson (CR54) 2009; 106 CR60 Zou, Conzen (CR18) 2004; 21 Clark (CR71) 2015; 96 JL Servadio (86476_CR49) 2018 86476_CR72 86476_CR30 A-L Barabási (86476_CR1) 1999; 286 86476_CR7 86476_CR70 JB Grace (86476_CR26) 2012 86476_CR36 C-W Chang (86476_CR64) 2017; 32 H Yang (86476_CR27) 2018 86476_CR33 S Cenci (86476_CR68) 2018; 15 JT Lizier (86476_CR41) 2010; 73 86476_CR35 J Sun (86476_CR32) 2014 ER Deyle (86476_CR53) 2013; 110 C Tu (86476_CR52) 2019; 9 M Newman (86476_CR2) 2003; 45 M Ushio (86476_CR37) 2018; 554 L Comte (86476_CR67) 2017; 7 G Sugihara (86476_CR29) 2012; 338 86476_CR20 P Duan (86476_CR47) 2013; 21 86476_CR60 CL Hein (86476_CR66) 2014; 281 86476_CR21 J Li (86476_CR39) 2019; 21 86476_CR22 SD Mamet (86476_CR28) 2019 A Batushansky (86476_CR13) 2016 86476_CR23 H Ye (86476_CR38) 2015; 5 SY Kim (86476_CR16) 2003; 4 AF Villaverde (86476_CR31) 2014; 9 JH Feldhoff (86476_CR5) 2015; 44 AE Goodwell (86476_CR10) 2020; 56 T Nakazawa (86476_CR51) 2020; 35 X Dai (86476_CR6) 2016; 11 86476_CR19 EJ O’Gorman (86476_CR54) 2009; 106 AT Clark (86476_CR71) 2015; 96 JT Lizier (86476_CR42) 2014; 1 86476_CR14 86476_CR59 86476_CR17 F Abdul Razak (86476_CR46) 2014; 9 G Gellner (86476_CR56) 2016; 7 PW Holland (86476_CR11) 1986; 81 K Wootton (86476_CR57) 2016; 9 R Albert (86476_CR8) 2005; 118 T Schreiber (86476_CR40) 2000; 85 G Mongillo (86476_CR58) 2018; 21 M Zou (86476_CR18) 2004; 21 K Hlavackova-Schindler (86476_CR44) 2007; 441 LD Jacobson (86476_CR62) 1995; 52 T Kadoya (86476_CR55) 2015; 5 DB Rubin (86476_CR15) 1978; 6 K Steinhaeuser (86476_CR3) 2010; 12 P Barrett (86476_CR24) 2007; 42 J Runge (86476_CR34) 2019; 5 T Baumgartner (86476_CR61) 1992; 33 A Montalto (86476_CR43) 2014; 9 K Steinhaeuser (86476_CR4) 2012; 39 L Jost (86476_CR50) 2006; 113 D Hooper (86476_CR25) 2008; 6 H Ye (86476_CR63) 2016; 353 KM Brander (86476_CR65) 2007; 104 L Barnett (86476_CR45) 2009; 103 M Convertino (86476_CR12) 2019; 107 D Ruelle (86476_CR69) 1971; 12 86476_CR9 J Runge (86476_CR48) 2018; 28 |
| References_xml | – ident: CR70 – ident: CR22 – volume: 21 start-page: 2052 year: 2013 end-page: 2066 ident: CR47 article-title: Direct causality detection via the transfer entropy approach publication-title: IEEE Trans. Control Syst. Technol. doi: 10.1109/TCST.2012.2233476 – volume: 9 start-page: 1 year: 2014 end-page: 15 ident: CR31 article-title: Mider: Network inference with mutual information distance and entropy reduction publication-title: PLoS ONE doi: 10.1371/journal.pone.0096732 – volume: 286 start-page: 509 year: 1999 end-page: 512 ident: CR1 article-title: Emergence of scaling in random networks publication-title: Science doi: 10.1126/science.286.5439.509 – year: 2016 ident: CR13 article-title: Correlation-based network generation, visualization, and analysis as a powerful tool in biological studies: A case study in cancer cell metabolism publication-title: BioMed Res. Int. doi: 10.1155/2016/8313272 – volume: 21 start-page: 71 year: 2004 end-page: 79 ident: CR18 article-title: A new dynamic Bayesian network (DBN) approach for identifying gene regulatory networks from time course microarray data publication-title: Bioinformatics doi: 10.1093/bioinformatics/bth463 – volume: 9 start-page: 1 year: 2019 end-page: 10 ident: CR52 article-title: Reconciling cooperation, biodiversity and stability in complex ecological communities publication-title: Sci. Rep. doi: 10.1038/s41598-019-41614-2 – volume: 281 start-page: 20132641 year: 2014 ident: CR66 article-title: Fish introductions reveal the temperature dependence of species interactions publication-title: Proc. R. Soc. B Biol. Sci. doi: 10.1098/rspb.2013.2641 – volume: 81 start-page: 945 year: 1986 end-page: 960 ident: CR11 article-title: Statistics and causal inference publication-title: J. Am. Stat. Assoc. doi: 10.1080/01621459.1986.10478354 – volume: 96 start-page: 1174 year: 2015 end-page: 1181 ident: CR71 article-title: Spatial convergent cross mapping to detect causal relationships from short time series publication-title: Ecology doi: 10.1890/14-1479.1 – ident: CR35 – volume: 42 start-page: 815 year: 2007 end-page: 824 ident: CR24 article-title: Structural equation modelling: Adjudging model fit publication-title: Person. Individ. Differ. doi: 10.1016/j.paid.2006.09.018 – volume: 4 start-page: 228 year: 2003 end-page: 235 ident: CR16 article-title: Inferring gene networks from time series microarray data using dynamic Bayesian networks publication-title: Brief. Bioinform. doi: 10.1093/bib/4.3.228 – volume: 28 start-page: 075310 year: 2018 ident: CR48 article-title: Causal network reconstruction from time series: From theoretical assumptions to practical estimation publication-title: Chaos Interdiscip. J. Nonlinear Sci. doi: 10.1063/1.5025050 – volume: 9 start-page: 1 year: 2014 end-page: 14 ident: CR46 article-title: Quantifying causality in complex systems: Understanding transfer entropy publication-title: PLoS ONE doi: 10.1371/journal.pone.0099462 – volume: 353 start-page: 922 year: 2016 end-page: 925 ident: CR63 article-title: Information leverage in interconnected ecosystems: Overcoming the curse of dimensionality publication-title: Science doi: 10.1126/science.aag0863 – ident: CR21 – ident: CR19 – volume: 7 start-page: 1 year: 2016 end-page: 7 ident: CR56 article-title: Consistent role of weak and strong interactions in high-and low-diversity trophic food webs publication-title: Nat. Commun. doi: 10.1038/ncomms11180 – year: 2014 ident: CR32 article-title: Causal network inference by optimal causation entropy publication-title: SIAM J. Appl. Dyn. Syst. doi: 10.1137/140956166 – volume: 5 start-page: 14750 year: 2015 ident: CR38 article-title: Distinguishing time-delayed causal interactions using convergent cross mapping publication-title: Sci. Rep. doi: 10.1038/srep14750 – volume: 15 start-page: 20180695 year: 2018 ident: CR68 article-title: Uncertainty quantification of the effects of biotic interactions on community dynamics from nonlinear time-series data publication-title: J. R. Soc. Interface doi: 10.1098/rsif.2018.0695 – volume: 113 start-page: 363 year: 2006 end-page: 375 ident: CR50 article-title: Entropy and diversity publication-title: Oikos doi: 10.1111/j.2006.0030-1299.14714.x – ident: CR9 – volume: 73 start-page: 605 year: 2010 end-page: 615 ident: CR41 article-title: Differentiating information transfer and causal effect publication-title: Eur. Phys. J. B doi: 10.1140/epjb/e2010-00034-5 – ident: CR60 – ident: CR36 – volume: 5 start-page: 1 year: 2015 end-page: 7 ident: CR55 article-title: Weak interactions and instability cascades publication-title: Sci. Rep. doi: 10.1038/srep12652 – volume: 12 start-page: 1 year: 1971 end-page: 44 ident: CR69 article-title: On the nature of turbulence publication-title: Les R Phys. Math. Strasbourg-RCP25 – volume: 6 start-page: 53 year: 2008 end-page: 60 ident: CR25 article-title: Structural equation modelling: Guidelines for determining model fit publication-title: Electron. J. Bus. Res. Methods – volume: 103 start-page: 238701 year: 2009 ident: CR45 article-title: Granger causality and transfer entropy are equivalent for gaussian variables publication-title: Phys. Rev. Lett. doi: 10.1103/PhysRevLett.103.238701 – volume: 554 start-page: 360 year: 2018 end-page: 363 ident: CR37 article-title: Fluctuating interaction network and time-varying stability of a natural fish community publication-title: Nature doi: 10.1038/nature25504 – volume: 33 start-page: 24 year: 1992 end-page: 40 ident: CR61 article-title: Reconstruction of the history of the pacific sardine and northern anchovy populations over the past two Millenia from sediments of the Santa Barbara Basin, California publication-title: CalCOFI Rep – volume: 56 start-page: e2019WR024940 year: 2020 ident: CR10 article-title: Debates? Does information theory provide a new paradigm for earth science? Causality, interaction, and feedback publication-title: Water Resour. Res. doi: 10.1029/2019WR024940 – volume: 9 start-page: 185 year: 2016 end-page: 195 ident: CR57 article-title: Many weak interactions and few strong; food-web feasibility depends on the combination of the strength of species? interactions and their correct arrangement publication-title: Theor. Ecol. doi: 10.1007/s12080-015-0279-3 – ident: CR72 – volume: 21 start-page: 1463 year: 2018 end-page: 1470 ident: CR58 article-title: Inhibitory connectivity defines the realm of excitatory plasticity publication-title: Nat. Neurosci. doi: 10.1038/s41593-018-0226-x – ident: CR14 – ident: CR30 – volume: 21 start-page: 506 year: 2019 ident: CR39 article-title: Optimal microbiome networks: Macroecology and criticality publication-title: Entropy doi: 10.3390/e21050506 – volume: 7 start-page: 718 year: 2017 end-page: 722 ident: CR67 article-title: Climatic vulnerability of the world’s freshwater and marine fishes publication-title: Nat. Clim. Change doi: 10.1038/nclimate3382 – ident: CR33 – volume: 11 start-page: 1 year: 2016 end-page: 11 ident: CR6 article-title: Application of epidemiology model on complex networks in propagation dynamics of airspace congestion publication-title: PLoS ONE doi: 10.1371/journal.pone.0157945 – year: 2012 ident: CR26 article-title: Guidelines for a graph-theoretic implementation of structural equation modeling publication-title: Ecosphere doi: 10.1890/ES12-00048.1 – volume: 5 start-page: eaau4996 year: 2019 ident: CR34 article-title: Detecting and quantifying causal associations in large nonlinear time series datasets publication-title: Sci. Adv. doi: 10.1126/sciadv.aau4996 – volume: 9 start-page: 1 year: 2014 end-page: 13 ident: CR43 article-title: Mute: A matlab toolbox to compare established and novel estimators of the multivariate transfer entropy publication-title: PLoS ONE doi: 10.1371/journal.pone.0109462 – volume: 441 start-page: 1 year: 2007 end-page: 46 ident: CR44 article-title: Causality detection based on information-theoretic approaches in time series analysis publication-title: Phys. Rep. doi: 10.1016/j.physrep.2006.12.004 – volume: 107 start-page: 105510 year: 2019 ident: CR12 article-title: Toward a pluralistic conception of resilience publication-title: Ecol. Indic. doi: 10.1016/j.ecolind.2019.105510 – ident: CR23 – volume: 6 start-page: 34 year: 1978 end-page: 58 ident: CR15 article-title: Bayesian inference for causal effects: The role of randomization publication-title: Ann. Stat. doi: 10.1214/aos/1176344064 – volume: 106 start-page: 13393 year: 2009 end-page: 13398 ident: CR54 article-title: Perturbations to trophic interactions and the stability of complex food webs publication-title: Proc. Natl. Acad. Sci. doi: 10.1073/pnas.0903682106 – volume: 52 start-page: 566 year: 1995 end-page: 577 ident: CR62 article-title: Stock-recruitment models for pacific sardine (sardinops sagax) publication-title: Can. J. Fish. Aquat. Sci. doi: 10.1139/f95-057 – volume: 1 start-page: 11 year: 2014 ident: CR42 article-title: Jidt: An information-theoretic toolkit for studying the dynamics of complex systems publication-title: Front. Robot. AI doi: 10.3389/frobt.2014.00011 – year: 2019 ident: CR28 article-title: Structural equation modeling of a winnowed soil microbiome identifies how invasive plants re-structure microbial networks publication-title: ISME J. doi: 10.1038/s41396-019-0407-y – volume: 85 start-page: 461 year: 2000 ident: CR40 article-title: Measuring information transfer publication-title: Phys. Rev. Lett. doi: 10.1103/PhysRevLett.85.461 – volume: 12 start-page: 25 year: 2010 end-page: 32 ident: CR3 article-title: An exploration of climate data using complex networks publication-title: SIGKDD Explor. Newsl. doi: 10.1145/1882471.1882476 – year: 2018 ident: CR49 article-title: Optimal information networks: Application for data-driven integrated health in populations publication-title: Sci. Adv. doi: 10.1126/sciadv.1701088 – year: 2018 ident: CR27 article-title: Revealing pathways from payments for ecosystem services to socioeconomic outcomes publication-title: Sci. Adv. doi: 10.1126/sciadv.aao6652 – ident: CR17 – volume: 338 start-page: 496 year: 2012 end-page: 500 ident: CR29 article-title: Detecting causality in complex ecosystems publication-title: Science doi: 10.1126/science.1227079 – volume: 110 start-page: 6430 year: 2013 end-page: 6435 ident: CR53 article-title: Predicting climate effects on pacific sardine publication-title: Proc. Natl. Acad. Sci. doi: 10.1073/pnas.1215506110 – volume: 104 start-page: 19709 year: 2007 end-page: 19714 ident: CR65 article-title: Global fish production and climate change publication-title: Proc. Natl. Acad. Sci. doi: 10.1073/pnas.0702059104 – volume: 44 start-page: 1567 year: 2015 end-page: 1581 ident: CR5 article-title: Complex networks for climate model evaluation with application to statistical versus dynamical modeling of south american climate publication-title: Clim. Dyn. doi: 10.1007/s00382-014-2182-9 – volume: 35 start-page: 1106 year: 2020 end-page: 1113 ident: CR51 article-title: Species interaction: Revisiting its terminology and concept publication-title: Ecol. Res. doi: 10.1111/1440-1703.12164 – ident: CR7 – ident: CR59 – volume: 118 start-page: 4947 year: 2005 end-page: 4957 ident: CR8 article-title: Scale-free networks in cell biology publication-title: J. Cell Sci. doi: 10.1242/jcs.02714 – volume: 32 start-page: 785 year: 2017 end-page: 796 ident: CR64 article-title: Empirical dynamic modeling for beginners publication-title: Ecol. Res. doi: 10.1007/s11284-017-1469-9 – volume: 45 start-page: 167 year: 2003 end-page: 256 ident: CR2 article-title: The structure and function of complex networks publication-title: SIAM Rev. doi: 10.1137/S003614450342480 – ident: CR20 – volume: 39 start-page: 889 year: 2012 end-page: 895 ident: CR4 article-title: Multivariate and multiscale dependence in the global climate system revealed through complex networks publication-title: Clim. Dyn. doi: 10.1007/s00382-011-1135-9 – volume: 7 start-page: 1 year: 2016 ident: 86476_CR56 publication-title: Nat. Commun. doi: 10.1038/ncomms11180 – ident: 86476_CR35 – ident: 86476_CR60 – volume: 110 start-page: 6430 year: 2013 ident: 86476_CR53 publication-title: Proc. Natl. Acad. Sci. doi: 10.1073/pnas.1215506110 – volume: 1 start-page: 11 year: 2014 ident: 86476_CR42 publication-title: Front. Robot. AI doi: 10.3389/frobt.2014.00011 – volume: 85 start-page: 461 year: 2000 ident: 86476_CR40 publication-title: Phys. Rev. Lett. doi: 10.1103/PhysRevLett.85.461 – volume: 12 start-page: 25 year: 2010 ident: 86476_CR3 publication-title: SIGKDD Explor. Newsl. doi: 10.1145/1882471.1882476 – volume: 21 start-page: 2052 year: 2013 ident: 86476_CR47 publication-title: IEEE Trans. Control Syst. Technol. doi: 10.1109/TCST.2012.2233476 – volume: 554 start-page: 360 year: 2018 ident: 86476_CR37 publication-title: Nature doi: 10.1038/nature25504 – volume: 33 start-page: 24 year: 1992 ident: 86476_CR61 publication-title: CalCOFI Rep – volume: 5 start-page: eaau4996 year: 2019 ident: 86476_CR34 publication-title: Sci. Adv. doi: 10.1126/sciadv.aau4996 – ident: 86476_CR21 – volume: 103 start-page: 238701 year: 2009 ident: 86476_CR45 publication-title: Phys. Rev. Lett. doi: 10.1103/PhysRevLett.103.238701 – volume: 338 start-page: 496 year: 2012 ident: 86476_CR29 publication-title: Science doi: 10.1126/science.1227079 – ident: 86476_CR30 doi: 10.1109/CBMS.2007.60 – ident: 86476_CR33 doi: 10.1162/netn_a_00092 – volume: 281 start-page: 20132641 year: 2014 ident: 86476_CR66 publication-title: Proc. R. Soc. B Biol. Sci. doi: 10.1098/rspb.2013.2641 – volume: 12 start-page: 1 year: 1971 ident: 86476_CR69 publication-title: Les R Phys. Math. Strasbourg-RCP25 – volume: 11 start-page: 1 year: 2016 ident: 86476_CR6 publication-title: PLoS ONE doi: 10.1371/journal.pone.0157945 – volume: 96 start-page: 1174 year: 2015 ident: 86476_CR71 publication-title: Ecology doi: 10.1890/14-1479.1 – volume: 6 start-page: 34 year: 1978 ident: 86476_CR15 publication-title: Ann. Stat. doi: 10.1214/aos/1176344064 – volume: 81 start-page: 945 year: 1986 ident: 86476_CR11 publication-title: J. Am. Stat. Assoc. doi: 10.1080/01621459.1986.10478354 – volume: 4 start-page: 228 year: 2003 ident: 86476_CR16 publication-title: Brief. Bioinform. doi: 10.1093/bib/4.3.228 – ident: 86476_CR20 doi: 10.1007/978-1-4614-2299-0_4 – ident: 86476_CR72 doi: 10.1126/science.aac6284 – year: 2019 ident: 86476_CR28 publication-title: ISME J. doi: 10.1038/s41396-019-0407-y – volume: 9 start-page: 185 year: 2016 ident: 86476_CR57 publication-title: Theor. Ecol. doi: 10.1007/s12080-015-0279-3 – volume: 42 start-page: 815 year: 2007 ident: 86476_CR24 publication-title: Person. Individ. Differ. doi: 10.1016/j.paid.2006.09.018 – year: 2014 ident: 86476_CR32 publication-title: SIAM J. Appl. Dyn. Syst. doi: 10.1137/140956166 – ident: 86476_CR70 doi: 10.1007/BFb0091924 – volume: 6 start-page: 53 year: 2008 ident: 86476_CR25 publication-title: Electron. J. Bus. Res. Methods – year: 2018 ident: 86476_CR49 publication-title: Sci. Adv. doi: 10.1126/sciadv.1701088 – volume: 107 start-page: 105510 year: 2019 ident: 86476_CR12 publication-title: Ecol. Indic. doi: 10.1016/j.ecolind.2019.105510 – year: 2012 ident: 86476_CR26 publication-title: Ecosphere doi: 10.1890/ES12-00048.1 – ident: 86476_CR17 – volume: 113 start-page: 363 year: 2006 ident: 86476_CR50 publication-title: Oikos doi: 10.1111/j.2006.0030-1299.14714.x – ident: 86476_CR59 – volume: 7 start-page: 718 year: 2017 ident: 86476_CR67 publication-title: Nat. Clim. Change doi: 10.1038/nclimate3382 – ident: 86476_CR7 doi: 10.1007/978-3-030-05411-3_34 – ident: 86476_CR14 – volume: 32 start-page: 785 year: 2017 ident: 86476_CR64 publication-title: Ecol. Res. doi: 10.1007/s11284-017-1469-9 – volume: 104 start-page: 19709 year: 2007 ident: 86476_CR65 publication-title: Proc. Natl. Acad. Sci. doi: 10.1073/pnas.0702059104 – volume: 44 start-page: 1567 year: 2015 ident: 86476_CR5 publication-title: Clim. Dyn. doi: 10.1007/s00382-014-2182-9 – volume: 21 start-page: 71 year: 2004 ident: 86476_CR18 publication-title: Bioinformatics doi: 10.1093/bioinformatics/bth463 – volume: 441 start-page: 1 year: 2007 ident: 86476_CR44 publication-title: Phys. Rep. doi: 10.1016/j.physrep.2006.12.004 – ident: 86476_CR9 doi: 10.1007/978-94-017-9514-2_7 – volume: 73 start-page: 605 year: 2010 ident: 86476_CR41 publication-title: Eur. Phys. J. B doi: 10.1140/epjb/e2010-00034-5 – ident: 86476_CR23 doi: 10.1017/CBO9780511617799 – ident: 86476_CR22 doi: 10.2307/1912791 – ident: 86476_CR19 doi: 10.1007/11795131_58 – volume: 21 start-page: 1463 year: 2018 ident: 86476_CR58 publication-title: Nat. Neurosci. doi: 10.1038/s41593-018-0226-x – volume: 52 start-page: 566 year: 1995 ident: 86476_CR62 publication-title: Can. J. Fish. Aquat. Sci. doi: 10.1139/f95-057 – volume: 45 start-page: 167 year: 2003 ident: 86476_CR2 publication-title: SIAM Rev. doi: 10.1137/S003614450342480 – volume: 118 start-page: 4947 year: 2005 ident: 86476_CR8 publication-title: J. Cell Sci. doi: 10.1242/jcs.02714 – volume: 39 start-page: 889 year: 2012 ident: 86476_CR4 publication-title: Clim. Dyn. doi: 10.1007/s00382-011-1135-9 – volume: 15 start-page: 20180695 year: 2018 ident: 86476_CR68 publication-title: J. R. Soc. Interface doi: 10.1098/rsif.2018.0695 – volume: 35 start-page: 1106 year: 2020 ident: 86476_CR51 publication-title: Ecol. Res. doi: 10.1111/1440-1703.12164 – volume: 28 start-page: 075310 year: 2018 ident: 86476_CR48 publication-title: Chaos Interdiscip. J. Nonlinear Sci. doi: 10.1063/1.5025050 – volume: 106 start-page: 13393 year: 2009 ident: 86476_CR54 publication-title: Proc. Natl. Acad. Sci. doi: 10.1073/pnas.0903682106 – volume: 9 start-page: 1 year: 2014 ident: 86476_CR46 publication-title: PLoS ONE doi: 10.1371/journal.pone.0099462 – ident: 86476_CR36 – volume: 5 start-page: 14750 year: 2015 ident: 86476_CR38 publication-title: Sci. Rep. doi: 10.1038/srep14750 – volume: 9 start-page: 1 year: 2014 ident: 86476_CR31 publication-title: PLoS ONE doi: 10.1371/journal.pone.0096732 – volume: 56 start-page: e2019WR024940 year: 2020 ident: 86476_CR10 publication-title: Water Resour. Res. doi: 10.1029/2019WR024940 – volume: 286 start-page: 509 year: 1999 ident: 86476_CR1 publication-title: Science doi: 10.1126/science.286.5439.509 – volume: 9 start-page: 1 year: 2019 ident: 86476_CR52 publication-title: Sci. Rep. doi: 10.1038/s41598-018-37186-2 – volume: 5 start-page: 1 year: 2015 ident: 86476_CR55 publication-title: Sci. Rep. doi: 10.1038/srep12652 – year: 2016 ident: 86476_CR13 publication-title: BioMed Res. Int. doi: 10.1155/2016/8313272 – volume: 21 start-page: 506 year: 2019 ident: 86476_CR39 publication-title: Entropy doi: 10.3390/e21050506 – volume: 353 start-page: 922 year: 2016 ident: 86476_CR63 publication-title: Science doi: 10.1126/science.aag0863 – volume: 9 start-page: 1 year: 2014 ident: 86476_CR43 publication-title: PLoS ONE doi: 10.1371/journal.pone.0109462 – year: 2018 ident: 86476_CR27 publication-title: Sci. Adv. doi: 10.1126/sciadv.aao6652 |
| SSID | ssj0000529419 |
| Score | 2.5537791 |
| Snippet | The detection of causal interactions is of great importance when inferring complex ecosystem functional and structural networks for basic and applied research.... Abstract The detection of causal interactions is of great importance when inferring complex ecosystem functional and structural networks for basic and applied... |
| SourceID | doaj pubmedcentral proquest pubmed crossref springer |
| SourceType | Open Website Open Access Repository Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 7094 |
| SubjectTerms | 631/57/2266 639/705/1042 704/158/1144 704/158/1745 704/158/2445 704/158/2446/1491 704/158/2451 704/158/2463 704/158/851 704/158/853 Anthropogenic factors Biodiversity Causality Computer applications Divergence Ecological function Ecosystem management Ecosystem models Ecosystems Fluctuations Humanities and Social Sciences Marine protected areas multidisciplinary Prey Protected areas Science Science (multidisciplinary) Structure-function relationships Synchronization Timing |
| SummonAdditionalLinks | – databaseName: DOAJ Directory of Open Access Journals dbid: DOA link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1LS8QwEA4iCF7Et-uLCt602CZpmh5VFBX05IK3kKQJCtIVuyL-e2eS7rrr82J7a5oy_TLJfCHJN4Tsa6DFFsa91HqqU66tSCtqWUqpN5oaxmlo6esbcdHnV3fF3USqL9wTFuWBI3BHRWTBrHAFDLiGG-O4lVYLSp2F-IejL8S8iclUVPWmFc-r7pRMxuRRC5EKT5PRPJWCl2DSVCQKgv3fscyvmyU_rZiGQHS-SBY6BpkcR8uXyIxrlslczCn5tkLySzzCh1UTmFlGoeakiZu920S3SSeVig2S-MfBa7tK-udnt6cXaZcXIbWizIapp3BXpa44M4wCRfCl0EY667MCczAWmlMjvK5dbbLCwZVz7yU0QW2NtZytkdlm0LgNksja5aUXUhpecWuAjcAUsM4ZwOsgcIkeyUcYKduJhmPuikcVFq-ZVBFXBbiqgKuqeuRgXOcpSmb8-vYJQj9-E-WuwwNwAtU5gfrLCXpke9RwquuDrQJyh-J0sip-KAYui9PDvEf2xsXQuXDFRDdu8BI-UUIooQLMXI9uMDaUMSBXHGuXUw4y9SfTJc3DfRDwlrj-ScHqw5ErfZj1M1Kb_4HUFpmn2AcyzOC3TWaHzy9uB2jV0OyGHvQOi2IbKA priority: 102 providerName: Directory of Open Access Journals – databaseName: ProQuest Health & Medical Collection dbid: 7X7 link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV1LT9wwELYoqFIviEeBLQ-lEjew2NiO45yqgkCA1J6KtDfLdmyKhBIgixD_vjOON2ihsHtbx6vJzNjz2WN_Q8i-AVjsYN6jLjBDhXGSVsxxyliwhlkuWLT0r9_y_EpcTopJ2nDr0rHK2ZwYJ-q6dbhHfgShGqnGVFX8uLunWDUKs6uphMYnsoTUZejV5aQc9lgwiyXyKt2VGXN11EG8wjtlLKdKihIEm4tHkbb_f1jz7ZHJV3nTGI7OVshywpHZz97wq2TBN2vkc19Z8nmd5Bd4kQ-7ZrC-7Omas6Y_8t1lpssSYSqaJQu37VP3lVydnf45OaepOgJ1oIgpDQy-VWkqwS1nABRCKY1V3oVxgZUYCyOYlcHUvrbjwsMnFyEoMETtrHOCb5DFpm38FslU7fMySKWsqISzgElgIVjn3EjmIXzJEclnOtIuUYdjBYtbHVPYXOlerxr0qqNedTUiB0Ofu54448Onj1H1w5NIeh1_aB-udRpDuugXRLzwBcReK6z1wikHUjLvAAqNyM7McDqNxE6_-M07zYBocZGYj8j3oRmGGOZNTOPbx_gXJQQUJkHMzd4NBkE5B4glsHc55yBzbzLf0tz8jTTeCrOgDKQ-nLnSi1jva-rbxy-5Tb4w9O4xVujbIYvTh0e_C7Bpavfi2PgHlEITnA priority: 102 providerName: ProQuest – databaseName: Springer Nature HAS Fully OA dbid: AAJSJ link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1Lb9QwEB71IaReEIVSFtoqSNzaiMR2HOe4IFbtSnCBSnuzbMeGSlW2arZC_HtmnAfasotEcovtaDIeez5n7G8A3hmExQ7nvdQFZlJhnEwr5njKWLCGWS5Y7OnPX-TltZgvisUOsOEsTNy0Hykt4zQ97A5736KjocNgLE-VFCW-cRf2iaodbXt_Op1_nY9_Vih2JfKqPyGTcbWh8ZoXimT9mxDm3xslH0VLoxOaPYOnPXpMpp28h7Djm-fwpMsn-esF5Fd0fI-aJriq7Eiak6bb6N0mpk16mlTqjCTcLn-2R3A9-_Tt42Xa50RInSyzVRoY3lVpKsEtZwgPQimNVd6FrKD8i4URzMpgal_brPB45SIEheqvnXVO8Jew1ywb_woSVfu8DFIpKyrhLCIRXP7VOTeSeXRacgL5oCPtesJwyltxq2Pgmivd6VWjXnXUq64mcD62uevoMv5Z-wOpfqxJVNfxwfL-u-67XhfdMogXvkCPa4W1XjjlUErmHQKgCZwMHaf78ddqBHZETKeqYksx4lhaGuYTeDsW48CiaIlp_PIhvqJEN8IkinncmcEoKOcIrAS1LtcMZO1L1kuamx-RvFtR7JOh1BeDKf0Ra7umXv9f9TdwwMjaM8rTdwJ7q_sHf4rgaWXP-tHyG3cGEtE priority: 102 providerName: Springer Nature |
| Title | Inferring ecosystem networks as information flows |
| URI | https://link.springer.com/article/10.1038/s41598-021-86476-9 https://www.ncbi.nlm.nih.gov/pubmed/33782461 https://www.proquest.com/docview/2506709895 https://www.proquest.com/docview/2556549561 https://www.proquest.com/docview/2507146269 https://pubmed.ncbi.nlm.nih.gov/PMC8007624 https://doaj.org/article/500251135e5341b4bbe4c8ca622ec074 |
| Volume | 11 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV3fixMxEB7uB4Iv4m-rZ1nBN13tJtkk-yDSK3echTtELfQtJNlEhbLVbg-9_95Jslup9sT2obDZbKeTyc43zeb7AJ5rhMUW73u59UTnTFueV8TSnBBvNDGUkTjS5xf8bMam83K-B73cUefAdmdpF_SkZqvFq5_fr97ihH-TtozL1y0mobBRjBS55Ezgt-3DIWYmERQNzju4n7i-ScWi1kcgYc8RTJBuH83uy2zlqkjpvwuH_v045R9rqjFVnd6GWx3GzMYpKO7Anmvuwo2kOnl1D4p3YZNf6Jph7ZmonLMmPQ7eZrrNOjLVMGSZXyx_tPdhdnryaXKWd8oJueVitM49wXcldMWooQRBhBdcG-msH5VBpbHUjBjude1qMyodvgrmvcRBqq2xltEHcNAsG_cIMlm7QngupWEVswbxChaJdUE1Jw5TGx9A0ftI2Y5WPKhbLFRc3qZSJb8q9KuKflXVAF5s-nxLpBr_PPs4uH5zZiDEjgeWq8-qm1-qTMUSLV2JedkwYxyz0qKVxFmESQM46gdO9UGmEP4F-jpZldc0I9oNBWQxgGebZpx-YU1FN255GS8hMNkQjmY-TGGwMZRShF8s9BZbAbL1S7Zbmq9fIsW3DCukBK1-2YfSb7Ou99Tj_zDzCdwkIcRHQcLvCA7Wq0v3FHHV2gxhX8zFEA7H4-nHKX4en1y8_4BHJ3wyjP9VDON0-gWMgSCA |
| linkProvider | Scholars Portal |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1Lb9QwEB6VIgQXxJuFAkGCE0RNbCdxDqgqj2qXPk6ttDdjOzatVCWl2arqn-I3MuM8qgXaWze3dWyNxzP-ZjL2DMA7jWaxxX0vtp7pWGibxyWzPGbMG80MFyys9O5ePj0Q3-fZfAV-D3dh6FjlsCeGjbpqLH0jX0eoplRjssw2Tn7FVDWKoqtDCY1OLLbdxTm6bO2n2Vdc3_eMbX3b_zKN-6oCscUBFrFn-JSFLgU3nCHA-iLXRjrrk4wqGGZaMJN7XbnKJJnDXyq8lziByhprBcdxb8FtBN6EnL1iXozfdChqJtKyv5uTcLneIj7SHTaWxjIXBTJiCf9CmYD_2bb_HtH8K04b4G_rAdzv7dZosxO0h7Di6kdwp6tkefEY0hldHKSuEfqzXXroqO6OmLeRbqM-QSuJQeSPm_P2CRzcCN-ewmrd1O45RLJyaeFzKY0ohTVoA6HjWaVc58whXOYTSAceKdunKqeKGccqhMy5VB1fFfJVBb6qcgIfxj4nXaKOa9_-TKwf36Qk2-GP5vSn6nVWZZ0DxjOXIdYbYYwTVlqkkjmLptcE1oaFU73mt-pSTq9oRguanNJ0Am_HZlRpitPo2jVnYYgCAYzlSOazTgxGQjlHk05Q72JJQJZmstxSHx2GtOGSoq4Mqf44iNIlWVdz6sX1k3wDd6f7uztqZ7a3_RLuMZL0hKoDrsHq4vTMvUKTbWFeBz2J4MdNK-Yfsi5SyQ |
| linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtR1db9QwzBo3gXhBfHMwoEjwBNFdk7RNHxBibKcdg9OEmLS3kKQJIE3tWG-a9tf4ddj9mg7Y3ta-NU3kOHZsx7EN8NKgWuxw32MucMOkcSnLuROM82ANt0LyZqU_L9KdffnxIDlYg999LAxdq-z3xGajLipHZ-QTFNWUakzlySR01yL2tmbvjn4xqiBFnta-nEZLIrv-7BTNt_rtfAvX-hXns-2vH3ZYV2GAORxsyQLHN89MLoUVHIVtyFJjlXdhmlA1w8RIbtNgCl_YaeLxiWUICidTOOucFDjuNVjPyCoawfrm9mLvy3DCQz40GeddpM5UqEmN0pIi2njMVCozRMuKNGyKBvxP0_33wuZfXttGGM5uw61Oi43et2R3B9Z8eReut3Utz-5BPKcwQuoaoXXbJouOyvbCeR2ZOurStRJRROGwOq3vw_6VYO4BjMqq9I8gUoWPs5AqZWUunUWNCM3QIhYm5R6FZzqGuMeRdl3icqqfcagbB7pQusWrRrzqBq86H8Proc9Rm7bj0r83CfXDn5Ryu_lQHX_XHQfrpDXHROITlPxWWuulUw6h5N6hIjaGjX7hdLcP1Pqcai9oRn2aTNR4DC-GZmRw8tqY0lcnzRAZijOeIpgPWzIYABUCFTxJvbMVAlmZyWpL-fNHk0RckQ-WI9RvelI6B-tiTD2-fJLP4QYypf40X-w-gZucCH1KpQI3YLQ8PvFPUX9b2mcdo0Tw7ap58w9Mf1hk |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Inferring+ecosystem+networks+as+information+flows&rft.jtitle=Scientific+reports&rft.au=Li%2C+Jie&rft.au=Convertino%2C+Matteo&rft.date=2021-03-29&rft.issn=2045-2322&rft.eissn=2045-2322&rft.volume=11&rft.issue=1&rft.spage=7094&rft_id=info:doi/10.1038%2Fs41598-021-86476-9&rft.externalDBID=NO_FULL_TEXT |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2045-2322&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2045-2322&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2045-2322&client=summon |