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Gradients in richness and turnover of a forest passerine’s diet prior to breeding: a mixed model approach applied to faecal metabarcoding data

Shutt, Jack D and Nicholls, James A and Trivedi, Urmi H and Burgess, Malcolm D and Stone, Graham N and Hadfield, Jarrod D and Phillimore, Albert B (2020) Gradients in richness and turnover of a forest passerine’s diet prior to breeding: a mixed model approach applied to faecal metabarcoding data. Molecular Ecology. ISSN 0962-1083

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Abstract

Little is known about the dietary richness and variation of generalist insectivorous species, including birds, due primarily to difficulties in prey identification. Using faecal metabarcoding we provide the most comprehensive analysis of a passerine’s diet to date, identifying the relative magnitudes of biogeographic, habitat and temporal trends in the richness and turnover in diet of Cyanistes caeruleus (blue tit) along a 39-site, 2° latitudinal transect in Scotland. Faecal samples were collected in 2014-15 from adult birds roosting in nestboxes prior to nest building. DNA was extracted from 793 samples and we amplified COI and 16S minibarcodes. We identified 432 molecular operational taxonomic units (MOTUs) that correspond to putative dietary items. Most dietary items were rare, with Lepidoptera being the most abundant and taxon-rich prey order. We present a statistical approach for estimation of gradients and inter-sample variation in taxonomic richness and turnover using a generalised linear mixed model. We discuss the merits of this approach over existing tools and present methods for model-based estimation of repeatability, taxon richness and Jaccard indices. We find that dietary richness increases significantly as spring advances, but changes little with elevation, latitude or local tree composition. In comparison, dietary composition exhibits significant turnover along temporal and spatial gradients and among sites. Our study shows the promise of faecal metabarcoding for inferring the macroecology of food webs, but we also highlight the challenge posed by contamination and make recommendations of laboratory and statistical practices to minimise its impact on inference.

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