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Linear filtering reveals false negatives in species interaction data

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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.
Keywords
MUTUALISTIC NETWORKS, ECOLOGICAL NETWORKS, COMMUNITY, PARASITES, LINKS

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Please use this url to cite or link to this publication:

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.
@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},
}

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