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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Main Authors: Sander, Elizabeth L., Wootton, J. Timothy, Allesina, Stefano
Format: Dataset
Language:English
Published: Dryad 2018
Subjects:
Dy
Online Access:https://dx.doi.org/10.5061/dryad.8m11n
http://datadryad.org/stash/dataset/doi:10.5061/dryad.8m11n
id ftdatacite:10.5061/dryad.8m11n
record_format openpolar
institution Open Polar
collection DataCite Metadata Store (German National Library of Science and Technology)
op_collection_id ftdatacite
language 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)