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Linear filtering reveals false negatives in species interaction data

Michiel Stock UGent, Timothée Poisot, Willem Waegeman UGent and Bernard De Baets UGent (2017) SCIENTIFIC REPORTS. 7.
abstract
Species interaction datasets, often represented as sparse matrices, are usually collected through observation studies targeted at identifying species interactions. Due to the extensive required sampling effort, species interaction datasets usually contain many false negatives, often leading to bias in derived descriptors. We show that a simple linear filter can be used to detect false negatives by scoring interactions based on the structure of the interaction matrices. On 180 different datasets of various sizes, sparsities and ecological interaction types, we found that on average in about 75% of the cases, a false negative interaction got a higher score than a true negative interaction. Furthermore, we show that this filter is very robust, even when the interaction matrix contains a very large number of false negatives. Our results demonstrate that unobserved interactions can be detected in species interaction datasets, even without resorting to information about the species involved.
Please use this url to cite or link to this publication:
author
organization
year
type
journalArticle (original)
publication status
published
subject
keyword
MUTUALISTIC NETWORKS, ECOLOGICAL NETWORKS, COMMUNITY, PARASITES, LINKS
journal title
SCIENTIFIC REPORTS
Sci. Rep.
volume
7
article number
45908
pages
8 pages
Web of Science type
Article
Web of Science id
000398597800001
ISSN
2045-2322
DOI
10.1038/srep45908
language
English
UGent publication?
yes
classification
A1
copyright statement
Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
id
8517561
handle
http://hdl.handle.net/1854/LU-8517561
date created
2017-04-12 14:26:55
date last changed
2017-05-16 11:47:17
@article{8517561,
  abstract     = {Species interaction datasets, often represented as sparse matrices, are usually collected through observation studies targeted at identifying species interactions. Due to the extensive required sampling effort, species interaction datasets usually contain many false negatives, often leading to bias in derived descriptors. We show that a simple linear filter can be used to detect false negatives by scoring interactions based on the structure of the interaction matrices. On 180 different datasets of various sizes, sparsities and ecological interaction types, we found that on average in about 75\% of the cases, a false negative interaction got a higher score than a true negative interaction. Furthermore, we show that this filter is very robust, even when the interaction matrix contains a very large number of false negatives. Our results demonstrate that unobserved interactions can be detected in species interaction datasets, even without resorting to information about the species involved.},
  articleno    = {45908},
  author       = {Stock, Michiel and Poisot, Timoth{\'e}e and Waegeman, Willem and De Baets, Bernard},
  issn         = {2045-2322},
  journal      = {SCIENTIFIC REPORTS},
  keyword      = {MUTUALISTIC NETWORKS,ECOLOGICAL NETWORKS,COMMUNITY,PARASITES,LINKS},
  language     = {eng},
  pages        = {8},
  title        = {Linear filtering reveals false negatives in species interaction data},
  url          = {http://dx.doi.org/10.1038/srep45908},
  volume       = {7},
  year         = {2017},
}

Chicago
Stock, Michiel, Timothée Poisot, Willem Waegeman, and Bernard De Baets. 2017. “Linear Filtering Reveals False Negatives in Species Interaction Data.” Scientific Reports 7.
APA
Stock, M., Poisot, T., Waegeman, W., & De Baets, B. (2017). Linear filtering reveals false negatives in species interaction data. SCIENTIFIC REPORTS, 7.
Vancouver
1.
Stock M, Poisot T, Waegeman W, De Baets B. Linear filtering reveals false negatives in species interaction data. SCIENTIFIC REPORTS. 2017;7.
MLA
Stock, Michiel, Timothée Poisot, Willem Waegeman, et al. “Linear Filtering Reveals False Negatives in Species Interaction Data.” SCIENTIFIC REPORTS 7 (2017): n. pag. Print.