Background: Food webs summarise trophic interactions of the biotic components within an ecosystem, which can influence nutrient dynamics and energy flows, ultimately affecting ecosystem functions and services. Food webs represent the hypothesised trophic links between predators and prey and can be presented as empirical food webs, in which the relative strength/importance of the respective links are quantified. Some common methods used in food web research include gut content analysis (GCA) and stable isotope analysis (SIA). We combine both methods to construct empirical food web models as a basis for monitoring and studying ecosystem-level outcomes of natural (e.g. species turnover in fish assemblage) and intentional environmental change (e.g. biomanipulation).
New information: We present 12 food webs from tropical reservoir communities in Singapore and summarise the topology of each with widely-used network indices (e.g. connectance, link density). Each reservoir was surveyed over 4-6 sampling occasions, during which, representative animal groups (i.e. fish species and taxonomic/functional groups of zooplankton and benthic macroinvertebrates) and all likely sources of primary production (i.e. macrophytes, periphyton, phytoplankton and riparian terrestrial plants) were collected. We analysed gut content in fishes and bulk isotope (d13C and d15N) profiles of all animals (i.e. fishes and invertebrates) and plants collected. Both sets of information were used to estimate the relative strength of trophic relationships using Bayesian mixing models. We document our protocol here, alongside a script in the R programming language for executing data management/analyses/visualisation procedures used in our study. These data can be used to glean insights into trends in inter- and intra-specific or guild interactions in analogous freshwater lake habitats.
Keywords: freshwater communities; gut content; reservoirs; stable isotope analysis; trophic interactions.
Clare Wilkinson, Rayson B H Lim, Jia Huan Liew, Jeffrey T B Kwik, Claudia L Y Tan, Tan Heok Hui, Darren C J Yeo.