Approaches to integrating genetic data into ecological networks

• Published in: Molecular ecology 2019-01, Vol.28 (2), p.503-519
• Main Authors: Clare, Elizabeth L., Fazekas, Aron J., Ivanova, Natalia V., Floyd, Robin M., Hebert, Paul D. N., Adams, Amanda M., Nagel, Juliet, Girton, Rebecca, Newmaster, Steven G., Fenton, M. Brock
• Format: Article
• Language: English
• Subjects: Bats; Chiroptera; Data processing; Deoxyribonucleic acid; DNA; DNA barcoding; Ecological monitoring; Ecology; food webs; high‐throughput sequencing; Insects; interaction networks; metabarcoding; Network analysis; Networks; Nodes; Nucleotide sequence; Parasites; Pulp; Trophic relationships
• ISSN: 0962-1083; 1365-294X
• DOI: 10.1111/mec.14941

As molecular tools for assessing trophic interactions become common, research is increasingly focused on the construction of interaction networks. Here, we demonstrate three key methods for incorporating DNA data into network ecology and discuss analytical considerations using a model consisting of plants, insects, bats and their parasites from the Costa Rica dry forest. The simplest method involves the use of Sanger sequencing to acquire long sequences to validate or refine field identifications, for example of bats and their parasites, where one specimen yields one sequence and one identification. This method can be fully quantified and resolved and these data resemble traditional ecological networks. For more complex taxonomic identifications, we target multiple DNA loci, for example from a seed or fruit pulp sample in faeces. These networks are also well resolved but gene targets vary in resolution and quantification is difficult. Finally, for mixed templates such as faecal contents of insectivorous bats, we use DNA metabarcoding targeting two sequence lengths (157 and 407 bp) of one gene region and a MOTU, BLAST and BIN association approach to resolve nodes. This network type is complex to generate and analyse, and we discuss the implications of this type of resolution on network analysis. Using these data, we construct the first molecular‐based network of networks containing 3,304 interactions between 762 nodes of eight trophic functions and involving parasitic, mutualistic and predatory interactions. We provide a comparison of the relative strengths and weaknesses of these data types in network ecology.