title: Linear filtering reveals false negatives in species interaction data.
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.
authors.affiliation: KERMIT, Department of Mathematical Modelling, Statistics and Bioinformatics, Ghent University, Coupure links 653, Ghent B-9000, Belgium.
authors.forename: Michiel
authors.initials: M
authors.lastname: Stock
authors.affiliation: Université de Montréal, Département des Sciences Biologiques, 90 Avenue Vindent d'Indy, Montréal, QC, H2V 3S9, Canada.
authors.forename: Timothée
authors.initials: T
authors.lastname: Poisot
authors.affiliation: KERMIT, Department of Mathematical Modelling, Statistics and Bioinformatics, Ghent University, Coupure links 653, Ghent B-9000, Belgium.
authors.forename: Willem
authors.initials: W
authors.lastname: Waegeman
authors.affiliation: KERMIT, Department of Mathematical Modelling, Statistics and Bioinformatics, Ghent University, Coupure links 653, Ghent B-9000, Belgium.
authors.forename: Bernard
authors.initials: B
authors.lastname: De Baets