Metabarcoding for the parallel identification of several hundred predators and their prey: Application to bat species diet analysis

Abstract

Assessing diet variability is of main importance to better understand the biology of bats and design conservation strategies. Although the advent of metabarcoding has facilitated such analyses, this approach does not come without challenges. Biases may occur throughout the whole experiment, from fieldwork to biostatistics, resulting in the detection of false negatives, false positives or low taxonomic resolution. We detail a rigorous metabarcoding approach based on a short COI minibarcode and two-step PCR protocol enabling the "all at once" taxonomic identification of bats and their arthropod prey for several hundreds of samples. Our study includes faecal pellets collected in France from 357 bats representing 16 species, as well as insect mock communities that mimic bat meals of known composition, negative and positive controls. All samples were analysed using three replicates. We compare the efficiency of DNA extraction methods, and we evaluate the effectiveness of our protocol using identification success, taxonomic resolution, sensitivity and amplification biases. Our parallel identification strategy of predators and prey reduces the risk of mis-assigning prey to wrong predators and decreases the number of molecular steps. Controls and replicates enable to filter the data and limit the risk of false positives, hence guaranteeing high confidence results for both prey occurrence and bat species identification. We validate 551 COI variants from arthropod including 18 orders, 117 family, 282 genus and 290 species. Our method therefore provides a rapid, resolutive and cost-effective screening tool for addressing evolutionary ecological issues or developing "chirosurveillance" and conservation strategies.

An appetite for pests: Synanthropic insectivorous bats exploit cotton pest irruptions and consume various deleterious arthropods

“…Previous studies that monitored its foraging activity suggest that it possibly consumes arthropod pests of field crops, pine forests and livestock (Ancillotto et al, 2017; Charbonnier, Barbaro, Theillout, & Jactel, 2014; Kahnonitch et al, 2018). In accordance, Galan et al (2018) found few species of agricultural pests in its diet but only processed three faecal samples. Therefore, despite the widespread distribution of P. kuhlii (Juste & Paunović, 2016) and persistent prevalence in anthropogenic foraging grounds, the potential services that it provides are still underexplored (Russo et al, 2018).…”

“…The high sample coverage (a testament to the sampling efficiency) obtained in the study implies that we identified the majority of detectable diversity in the bat diet. Previous studies, which used morphological methods to describe the diet of P. kuhlii (Beck, 1995; Feldman et al, 2000; Goiti et al, 2003), were incapable of detecting species consumed by P. kuhlii , and only a few species were identified in its diet with DNA metabarcoding (Galan et al, 2018). Thus, this metabarcoding study enhances the scope of P. kuhlii's potential contribution to pest control.…”

“…Yet, conventional methods to study their diets hardly deliver the taxonomic resolution required for this task (Boyles et al, 2013; Clare, Fraser, Braid, Fenton, & Hebert, 2009; Kunz et al, 2011; Whitaker, 1988). DNA metabarcoding (Pompanon et al, 2012; Taberlet, Coissac, Pompanon, Brochmann, & Willerslev, 2012) overcomes this limitation and is applied to amplify and sequence taxonomically informative DNA markers from traces of arthropod prey in bat faeces while processing multiple samples simultaneously to eventually identify various bat prey taxa to species (Bohmann et al, 2011; Razgour et al, 2011; Zeale, Butlin, Barker, Lees, & Jones, 2011)—thus, demonstrating that certain bat species consume numerous pest species (Aizpurua et al, 2018; Galan et al, 2018; Krauel, Brown, Westbrook, & Mccracken, 2018). The rapid decrease in sequencing costs and the improved methodologies for conducting metabarcoding diet studies (Alberdi, Aizpurua, Gilbert, & Bohmann, 2018) constitute a unique tool with which to assess potential ecosystem services by various species of bats and address their relevance to CBC (Gurr & You, 2016).…”

Vertebrate diversity revealed by metabarcoding of bulk arthropod samples from tropical forests

“…Given the difficulty of their identification, molecular analyses are increasingly applied to identify the taxonomic contents of bulk arthropod samples (Ashfaq et al, 2018;Elbrecht et al, 2017;Gibson et al, 2014;Kocher, Gantler et al, 2017;Liu et al, 2018;Morinière et al, 2016;Oliverio, Gan, Wickings, & Fierer, 2018;Shokralla et al, 2015;Yu et al, 2012). The targeted nature of metabarcoding means that it is a cost-effective and efficient method for identifying the taxonomic contents of hundreds to thousands of samples in parallel (Galan et al, 2018;Taberlet et al, 2012). Metabarcoding principally relies on PCR amplification of DNA extracts using primers that are universal for a selected taxonomic group targeted by a taxonomically informative "barcode marker."…”

DNA metabarcoding in diet studies: Unveiling ecological aspects in aquatic and terrestrial ecosystems

“…This latter approach has been possible due to previous good knowledge of the predator and prey diets (from conventional field observations), and/or through the use of abundance thresholds below which putative prey are eliminated (DNA of secondary prey should appear at a much lower quantity compared with primary prey), or through the identification of a consistent association with a specific prey item, the primary consumer (Hardy et al, ; Jakubavičiūtė et al, ; McInnes, Jarman, et al, ). However, accounting for and even targeting secondary preyed items, for instance by complementing the sampling of top predator(s) with that of potential prey(s), brings a broader view on species interactions and consequently a better understanding of food webs (Bowser et al, ; Galan et al, ). In this regard, dDNA approaches simultaneously targeting multiple species (both top predators and prey) (e.g., Bowser et al, ; Carroll et al, ), combined with environmental samples (e.g., samples from water column and/or soil) to determine prey availability (e.g., Willerslev et al, ), can be tremendous robust in providing comprehensive species inventories and provide insights regarding their interactions.…”