Laboratory Protocol for Genetic Gut Content Analyses of Aquatic Macroinvertebrates Using Group-specific rDNA Primers

Analyzing food webs is essential for a better understanding of ecosystems. For example, food web interactions can undergo severe changes caused by the invasion of non-indigenous species. However, an exact identification of field predator-prey interactions is difficult in many cases. These analyses are often based on a visual evaluation of gut content or the analysis of stable isotope ratios (δ¹⁵N and δ¹³C). Such methods require comprehensive knowledge about, respectively, morphologic diversity or isotopic signature from individual prey organisms, leading to obstacles in the exact identification of prey organisms. Visual gut content analyses especially underestimate soft bodied prey organisms, because maceration, ingestion and digestion of prey organisms make identification of specific species difficult. Hence, polymerase chain reaction (PCR) based strategies, for example the use of group-specific primer sets, provide a powerful tool for the investigation of food web interactions. Here, we describe detailed protocols to investigate the gut contents of macroinvertebrate consumers from the field using group-specific primer sets for nuclear ribosomal deoxyribonucleic acid (rDNA). DNA can be extracted either from whole specimens (in the case of small taxa) or out of gut contents of specimens collected in the field. Presence and functional efficiency of the DNA templates need to be confirmed directly from the tested individual using universal primer sets targeting the respective subunit of DNA. We also demonstrate that consumed prey can be determined further down to species level via PCR with unmodified group-specific primers combined with subsequent single strand conformation polymorphism (SSCP) analyses using polyacrylamide gels. Furthermore, we show that the use of different fluorescent dyes as labels enables parallel screening for DNA fragments of different prey groups from multiple gut content samples via automated fragment analysis.