Data from: Ecological network inference from long-term presence-absence data
Ecological communities are characterized by complex networks of trophic and nontrophic interactions, which shape the dy-namics of the community. Machine learning and correlational methods are increasingly popular for inferring networks from co-occurrence and time series data, particularly in microbi...
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Language: | English |
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Dryad
2018
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Online Access: | https://dx.doi.org/10.5061/dryad.8m11n http://datadryad.org/stash/dataset/doi:10.5061/dryad.8m11n |
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openpolar |
institution |
Open Polar |
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DataCite Metadata Store (German National Library of Science and Technology) |
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English |
topic |
Scardinius erythrophthalmus Microcladia HenriciaAbramis brama Henricia Hedophyllum Mastocarpus Porphyra Ulva Acrosiphonia Hallosaccion Katharina tunicata Amphipoda Thymallus thymallus Corallina vancouvriensis Lepidochitona Littorina sitkana Oedignathus Archidoris montereyensis Telestes souffia Balanus glandula Alburnus alburnus Parachondrostoma toxostoma Esox lucius Leathesia Onchidella borealis Sipunculid Barbatula barbatula Chondrostoma nasus Cirolana Gobio gobio Lottia pelta interaction web Littorina scutulata Alburnoides bipunctatus Notoplana Lepomis gibbosus Lottia scutum Calliostoma ligatum Phyllospadix Ocenebra interfossa Pinnotheres pisum Perca fluviatilis Ameiurus melas network inference Nemertea Idotea Barbus meridionalis Mytilus trossulus Abramis brama LASSO regression Petrocelis Ralfsia Chironomidae Gymnocephalus cernua Lota lota Cucumaria Halichondria Haliclona Anguilla anguilla Mytilus californianus Callithamnion Iridea Pungitius pungitius Semibalanus cariosus Dynamic Bayesian networks Lottia paradigitalis Amphissa columbiana Leuciscus leuciscus Polysiphonia Blicca bjoerkna Squalius cephalus Lepasterias hexactis Phoxinus phoxinus Anthopleura Cyprinus carpio Pollicipes presence-absence Fucus Sander lucioperca Salmo salar Chthamalus Nucella canalicalata Lottia digitalis Cottus gobio Mopalia Tonicella lineata Endocladia Rutilus rutilus Prionitis Gasterosteus aculeatus Tinca tinca Nucella ostrina |
spellingShingle |
Scardinius erythrophthalmus Microcladia HenriciaAbramis brama Henricia Hedophyllum Mastocarpus Porphyra Ulva Acrosiphonia Hallosaccion Katharina tunicata Amphipoda Thymallus thymallus Corallina vancouvriensis Lepidochitona Littorina sitkana Oedignathus Archidoris montereyensis Telestes souffia Balanus glandula Alburnus alburnus Parachondrostoma toxostoma Esox lucius Leathesia Onchidella borealis Sipunculid Barbatula barbatula Chondrostoma nasus Cirolana Gobio gobio Lottia pelta interaction web Littorina scutulata Alburnoides bipunctatus Notoplana Lepomis gibbosus Lottia scutum Calliostoma ligatum Phyllospadix Ocenebra interfossa Pinnotheres pisum Perca fluviatilis Ameiurus melas network inference Nemertea Idotea Barbus meridionalis Mytilus trossulus Abramis brama LASSO regression Petrocelis Ralfsia Chironomidae Gymnocephalus cernua Lota lota Cucumaria Halichondria Haliclona Anguilla anguilla Mytilus californianus Callithamnion Iridea Pungitius pungitius Semibalanus cariosus Dynamic Bayesian networks Lottia paradigitalis Amphissa columbiana Leuciscus leuciscus Polysiphonia Blicca bjoerkna Squalius cephalus Lepasterias hexactis Phoxinus phoxinus Anthopleura Cyprinus carpio Pollicipes presence-absence Fucus Sander lucioperca Salmo salar Chthamalus Nucella canalicalata Lottia digitalis Cottus gobio Mopalia Tonicella lineata Endocladia Rutilus rutilus Prionitis Gasterosteus aculeatus Tinca tinca Nucella ostrina Sander, Elizabeth L. Wootton, J. Timothy Allesina, Stefano Data from: Ecological network inference from long-term presence-absence data |
topic_facet |
Scardinius erythrophthalmus Microcladia HenriciaAbramis brama Henricia Hedophyllum Mastocarpus Porphyra Ulva Acrosiphonia Hallosaccion Katharina tunicata Amphipoda Thymallus thymallus Corallina vancouvriensis Lepidochitona Littorina sitkana Oedignathus Archidoris montereyensis Telestes souffia Balanus glandula Alburnus alburnus Parachondrostoma toxostoma Esox lucius Leathesia Onchidella borealis Sipunculid Barbatula barbatula Chondrostoma nasus Cirolana Gobio gobio Lottia pelta interaction web Littorina scutulata Alburnoides bipunctatus Notoplana Lepomis gibbosus Lottia scutum Calliostoma ligatum Phyllospadix Ocenebra interfossa Pinnotheres pisum Perca fluviatilis Ameiurus melas network inference Nemertea Idotea Barbus meridionalis Mytilus trossulus Abramis brama LASSO regression Petrocelis Ralfsia Chironomidae Gymnocephalus cernua Lota lota Cucumaria Halichondria Haliclona Anguilla anguilla Mytilus californianus Callithamnion Iridea Pungitius pungitius Semibalanus cariosus Dynamic Bayesian networks Lottia paradigitalis Amphissa columbiana Leuciscus leuciscus Polysiphonia Blicca bjoerkna Squalius cephalus Lepasterias hexactis Phoxinus phoxinus Anthopleura Cyprinus carpio Pollicipes presence-absence Fucus Sander lucioperca Salmo salar Chthamalus Nucella canalicalata Lottia digitalis Cottus gobio Mopalia Tonicella lineata Endocladia Rutilus rutilus Prionitis Gasterosteus aculeatus Tinca tinca Nucella ostrina |
description |
Ecological communities are characterized by complex networks of trophic and nontrophic interactions, which shape the dy-namics of the community. Machine learning and correlational methods are increasingly popular for inferring networks from co-occurrence and time series data, particularly in microbial systems. In this study, we test the suitability of these methods for inferring ecological interactions by constructing networks using Dynamic Bayesian Networks, Lasso regression, and Pear-son’s correlation coefficient, then comparing the model networks to empirical trophic and nontrophic webs in two ecological systems. We find that although each model significantly replicates the structure of at least one empirical network, no model significantly predicts network structure in both systems, and no model is clearly superior to the others. We also find that networks inferred for the Tatoosh intertidal match the nontrophic network much more closely than the trophic one, possibly due to the challenges of identifying trophic interactions from presence-absence data. Our findings suggest that although these methods hold some promise for ecological network inference, presence-absence data does not provide enough signal for models to consistently identify interactions, and networks inferred from these data should be interpreted with caution. : Food web adjacency list for France river datasetAdjacency list representing all feeding interactions for the France river dataset, as identified by a literature review of gut content analyses. 'Parent' column represents the source of the interaction. 'Child' column represents the target of the interaction. 'Sign' represents the effect of the Parent species on the Child species. Since feeding interactions are +/-, each predator-prey pair will be represented by two rows: one for the negative effect of predator on prey, and one for the positive effect of prey on predator.Comte-adjlist.csvList of species for the France river network datasetComteSpNames-final.txtList of species for the Tatoosh middle intertidal datasettatoosh-control-exp-cv-spnames.txtPresence-Absence data for Tatoosh middle intertidalPresence-absence data for species in the Tatoosh middle intertidal (space-separated data frame). First column is a site identifier, second column is the census year, and all other columns are presence/absence for a species in a given site-year. Data for different sites are separated with a row of NAs.tatoosh-site-year-all.txtTrophic interaction adjacency list for Tatoosh data setOrganized as for the France adjacency list, but with trophic (feeding) interactions for the Tatoosh data set.tatoosh-trophic-adjlist.csvNontrophic interaction adjacency list for Tatoosh data setOrganized identically to the France adjacency list, but contains nontrophic (non-feeding) interactions for the Tatoosh data set.tatoosh-nontrophic-adjlist.csv |
format |
Dataset |
author |
Sander, Elizabeth L. Wootton, J. Timothy Allesina, Stefano |
author_facet |
Sander, Elizabeth L. Wootton, J. Timothy Allesina, Stefano |
author_sort |
Sander, Elizabeth L. |
title |
Data from: Ecological network inference from long-term presence-absence data |
title_short |
Data from: Ecological network inference from long-term presence-absence data |
title_full |
Data from: Ecological network inference from long-term presence-absence data |
title_fullStr |
Data from: Ecological network inference from long-term presence-absence data |
title_full_unstemmed |
Data from: Ecological network inference from long-term presence-absence data |
title_sort |
data from: ecological network inference from long-term presence-absence data |
publisher |
Dryad |
publishDate |
2018 |
url |
https://dx.doi.org/10.5061/dryad.8m11n http://datadryad.org/stash/dataset/doi:10.5061/dryad.8m11n |
long_lat |
ENVELOPE(-58.467,-58.467,-62.208,-62.208) ENVELOPE(11.369,11.369,64.834,64.834) |
geographic |
Brama Dy |
geographic_facet |
Brama Dy |
genre |
Anguilla anguilla Lota lota Salmo salar lota |
genre_facet |
Anguilla anguilla Lota lota Salmo salar lota |
op_relation |
https://dx.doi.org/10.1038/s41598-017-07009-x |
op_rights |
Creative Commons Zero v1.0 Universal https://creativecommons.org/publicdomain/zero/1.0/legalcode cc0-1.0 |
op_rightsnorm |
CC0 |
op_doi |
https://doi.org/10.5061/dryad.8m11n https://doi.org/10.1038/s41598-017-07009-x |
_version_ |
1766403938259566592 |
spelling |
ftdatacite:10.5061/dryad.8m11n 2023-05-15T13:28:24+02:00 Data from: Ecological network inference from long-term presence-absence data Sander, Elizabeth L. Wootton, J. Timothy Allesina, Stefano 2018 https://dx.doi.org/10.5061/dryad.8m11n http://datadryad.org/stash/dataset/doi:10.5061/dryad.8m11n en eng Dryad https://dx.doi.org/10.1038/s41598-017-07009-x Creative Commons Zero v1.0 Universal https://creativecommons.org/publicdomain/zero/1.0/legalcode cc0-1.0 CC0 Scardinius erythrophthalmus Microcladia HenriciaAbramis brama Henricia Hedophyllum Mastocarpus Porphyra Ulva Acrosiphonia Hallosaccion Katharina tunicata Amphipoda Thymallus thymallus Corallina vancouvriensis Lepidochitona Littorina sitkana Oedignathus Archidoris montereyensis Telestes souffia Balanus glandula Alburnus alburnus Parachondrostoma toxostoma Esox lucius Leathesia Onchidella borealis Sipunculid Barbatula barbatula Chondrostoma nasus Cirolana Gobio gobio Lottia pelta interaction web Littorina scutulata Alburnoides bipunctatus Notoplana Lepomis gibbosus Lottia scutum Calliostoma ligatum Phyllospadix Ocenebra interfossa Pinnotheres pisum Perca fluviatilis Ameiurus melas network inference Nemertea Idotea Barbus meridionalis Mytilus trossulus Abramis brama LASSO regression Petrocelis Ralfsia Chironomidae Gymnocephalus cernua Lota lota Cucumaria Halichondria Haliclona Anguilla anguilla Mytilus californianus Callithamnion Iridea Pungitius pungitius Semibalanus cariosus Dynamic Bayesian networks Lottia paradigitalis Amphissa columbiana Leuciscus leuciscus Polysiphonia Blicca bjoerkna Squalius cephalus Lepasterias hexactis Phoxinus phoxinus Anthopleura Cyprinus carpio Pollicipes presence-absence Fucus Sander lucioperca Salmo salar Chthamalus Nucella canalicalata Lottia digitalis Cottus gobio Mopalia Tonicella lineata Endocladia Rutilus rutilus Prionitis Gasterosteus aculeatus Tinca tinca Nucella ostrina dataset Dataset 2018 ftdatacite https://doi.org/10.5061/dryad.8m11n https://doi.org/10.1038/s41598-017-07009-x 2022-02-08T12:53:43Z Ecological communities are characterized by complex networks of trophic and nontrophic interactions, which shape the dy-namics of the community. Machine learning and correlational methods are increasingly popular for inferring networks from co-occurrence and time series data, particularly in microbial systems. In this study, we test the suitability of these methods for inferring ecological interactions by constructing networks using Dynamic Bayesian Networks, Lasso regression, and Pear-son’s correlation coefficient, then comparing the model networks to empirical trophic and nontrophic webs in two ecological systems. We find that although each model significantly replicates the structure of at least one empirical network, no model significantly predicts network structure in both systems, and no model is clearly superior to the others. We also find that networks inferred for the Tatoosh intertidal match the nontrophic network much more closely than the trophic one, possibly due to the challenges of identifying trophic interactions from presence-absence data. Our findings suggest that although these methods hold some promise for ecological network inference, presence-absence data does not provide enough signal for models to consistently identify interactions, and networks inferred from these data should be interpreted with caution. : Food web adjacency list for France river datasetAdjacency list representing all feeding interactions for the France river dataset, as identified by a literature review of gut content analyses. 'Parent' column represents the source of the interaction. 'Child' column represents the target of the interaction. 'Sign' represents the effect of the Parent species on the Child species. Since feeding interactions are +/-, each predator-prey pair will be represented by two rows: one for the negative effect of predator on prey, and one for the positive effect of prey on predator.Comte-adjlist.csvList of species for the France river network datasetComteSpNames-final.txtList of species for the Tatoosh middle intertidal datasettatoosh-control-exp-cv-spnames.txtPresence-Absence data for Tatoosh middle intertidalPresence-absence data for species in the Tatoosh middle intertidal (space-separated data frame). First column is a site identifier, second column is the census year, and all other columns are presence/absence for a species in a given site-year. Data for different sites are separated with a row of NAs.tatoosh-site-year-all.txtTrophic interaction adjacency list for Tatoosh data setOrganized as for the France adjacency list, but with trophic (feeding) interactions for the Tatoosh data set.tatoosh-trophic-adjlist.csvNontrophic interaction adjacency list for Tatoosh data setOrganized identically to the France adjacency list, but contains nontrophic (non-feeding) interactions for the Tatoosh data set.tatoosh-nontrophic-adjlist.csv Dataset Anguilla anguilla Lota lota Salmo salar lota DataCite Metadata Store (German National Library of Science and Technology) Brama ENVELOPE(-58.467,-58.467,-62.208,-62.208) Dy ENVELOPE(11.369,11.369,64.834,64.834) |