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Linear filtering reveals false negatives in species interaction data

View Article: PubMed Central - PubMed

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.

No MeSH data available.


Histogram of the imputed values for the positive interactions, forbidden interactions and negative interactions, which are potential false positives.The positive interactions are on average imputed with a higher score than both kinds of negative interactions.
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f5: Histogram of the imputed values for the positive interactions, forbidden interactions and negative interactions, which are potential false positives.The positive interactions are on average imputed with a higher score than both kinds of negative interactions.

Mentions: Finally, we performed a small experiment where true negatives or forbidden links are known. To this end, we use the 25-by-25 seed-dispersal network of Olesen and coauthors30. It consists of 156 observed positive interactions and 228 forbidden interactions due to phenological uncoupling or morphological constraints. We used the linear filter to perform LOO imputation on the interaction matrix. Figure 5 shows the distributions of the imputed values for the positive interactions, true negative interactions and negative interactions that are potential false positives. The AUC for discriminating between positive and negative interactions (both true negatives and false negatives) using LOO imputation was found to be 0.8270. When only trying to discriminate between true positives and true negatives, the AUC was 0.7981. Upon removing the true negatives, the AUC improved slightly to 0.8543. For this dataset, it seems that the true negatives are somewhat harder to identify than the negatives in general. When true negatives are known, it is best to only search for false negatives within the potentially positive interactions.


Linear filtering reveals false negatives in species interaction data
Histogram of the imputed values for the positive interactions, forbidden interactions and negative interactions, which are potential false positives.The positive interactions are on average imputed with a higher score than both kinds of negative interactions.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5382893&req=5

f5: Histogram of the imputed values for the positive interactions, forbidden interactions and negative interactions, which are potential false positives.The positive interactions are on average imputed with a higher score than both kinds of negative interactions.
Mentions: Finally, we performed a small experiment where true negatives or forbidden links are known. To this end, we use the 25-by-25 seed-dispersal network of Olesen and coauthors30. It consists of 156 observed positive interactions and 228 forbidden interactions due to phenological uncoupling or morphological constraints. We used the linear filter to perform LOO imputation on the interaction matrix. Figure 5 shows the distributions of the imputed values for the positive interactions, true negative interactions and negative interactions that are potential false positives. The AUC for discriminating between positive and negative interactions (both true negatives and false negatives) using LOO imputation was found to be 0.8270. When only trying to discriminate between true positives and true negatives, the AUC was 0.7981. Upon removing the true negatives, the AUC improved slightly to 0.8543. For this dataset, it seems that the true negatives are somewhat harder to identify than the negatives in general. When true negatives are known, it is best to only search for false negatives within the potentially positive interactions.

View Article: PubMed Central - PubMed

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.

No MeSH data available.