Biology subjects

Clare, E. L.

Publications and source records attributed to Clare, E. L..

2 recordsLinked to original sources

bioRxiv2018

Assessing the impact of taxon resolution on network structure, with implication for comparative ecology

Constructing networks has become an indispensable approach in understanding how different taxa interact. However, methodologies vary widely among studies, potentially limiting our ability to meaningfully compare results. In particular, how network architecture is influenced by the extent to which nodes are resolved to either taxa or taxonomic units is poorly understood. To address this, here we collate nine datasets of ecological interactions, from both observations and DNA metabarcoding, and construct networks under a range of commonly-used node resolutions. We demonstrate that small changes in node resolution can cause wide variation in almost all key metric values, including robustness and nestedness. Moreover, relative values of metrics such as robustness were seen to fluctuate continuously with node resolution, thereby potentially confounding comparisons of networks, as well as interpretations concerning their constituent ecological interactions. These findings highlight the need for care when comparing networks, especially where these differ with respect to node resolution.\n\nStatement of authorshipDRHB, SJR and ELC conceived of the project, DRHB facilitated fieldwork in Malaysia, DRHB, HFMO and ELC undertook field collections, DRHB, SCLC and HFMO analysed the data, and DRHB wrote the manuscript with input from all authors.\n\nData Accessibility StatementAll data used in this analysis will be archived in Dryad and made available by DOI. Specific analysis scripts are available on GitHub with links given in the manuscript.

ecology↗

bioRxiv2018

Counting with DNA in metabarcoding studies: how should we convert sequence reads to dietary data?

Advances in DNA sequencing technology have revolutionised the field of molecular analysis of trophic interactions and it is now possible to recover counts of food DNA barcode sequences from a wide range of dietary samples. But what do these counts mean? To obtain an accurate estimate of a consumers diet should we work strictly with datasets summarising the frequency of occurrence of different food taxa, or is it possible to use the relative number of sequences? Both approaches are applied in the dietary metabarcoding literature, but occurrence data is often promoted as a more conservative and reliable option due to taxa-specific biases in recovery of sequences. Here, we point out that diet summaries based on occurrence data overestimate the importance of food consumed in small quantities (potentially including low-level contaminants) and are sensitive to the count threshold used to define an occurrence. Our simulations indicate that even with recovery biases incorporated, using relative read abundance (RRA) information can provide a more accurate view of population-level diet in many scenarios. The ideas presented here highlight the need to consider all sources of bias and to justify the methods used to interpret count data in dietary metabarcoding studies. We encourage researchers to continue to addressing methodological challenges, and acknowledge unanswered questions to help spur future investigations in this rapidly developing area of research.

ecology↗