DOI: 10.5061/dryad.7h44j104p
title: Spatio-temporal variation in diet among age and sex cohorts of a model generalist bird species, the Great Tit Parus major: new insights revealed by DNA metabarcoding

description: Dietary variation among cohorts can have a major impact on how populations
 adapt to environmental variation. Although variation in diet between
 cohorts and across habitats has been studied in many taxa, this is not
 true for most birds, especially smaller generalist passerines whose
 feeding habits are predominantly cryptic. Here we used DNA metabarcoding
 with next-generation sequencing to assess spatio-temporal dietary
 variation among age and sex cohorts of the Great Tit Parus major, a model
 species in avian ecology. Most dietary species were rare but nevertheless
 collectively made up 30% of the diet, as expected of a generalist. Winter
 moth Operophtera brumata, a major focus in tit breeding phenology
 research, was the most prevalent dietary item, but the next ten most
 prevalent Lepidoterans were collectively four times more important. There
 was considerable variation in dietary richness and composition among
 seasons and years. In winter, natural plant and invertebrate species were
 extensively represented in the diet, despite the constant availability of
 supplemental food. Diet composition varied with woodland type: in conifer
 woodlands, birds fed on species adapted to conifer plantations, as
 expected, but they also fed on many species adapted to deciduous species.
 In winter, birds in conifers used peanut feeders more than they did in
 mixed woodlands, where beech was more prevalent in the diet. In winter,
 first-year birds consumed more invertebrate species than adults,
 presumably because they were less selective, and beech (Fagus) was almost
 twice as prevalent in the first-year diet. Our results suggest
 considerable spatio-temporal variation in diet and variation among
 cohorts, and provide insight into the diet of a key model species in avian
 ecology. Such variation is rarely considered, even though it is likely to
 have important consequences for our understanding of how populations
 respond to environmental change.
description: Dietary data was obtained from Great Tit faecal samples in 2017
 and 2018. DNA was extracted from faecal samples, and DNA metabarcoding
 carried out to identify invertebrate and plant dietary taxa. The data
 underwent bioinformatic processing to obtain dietary taxa. Lab and
 bioinformatic work was carried out at Cardiff University in the School of
 Biosciences. Sampling completeness analysis was conducted. Multivariate
 generalised linear models and generalised linear mixed models were carried
 out to examine dietary composition between different age classes, sexes,
 habitat types, and seasons.
description: # Spatio-temporal variation in diet among age and sex cohorts of a model
 generalist bird species, the Great Tit *Parus major*, revealed by DNA
 metabarcoding
 [https://doi.org/10.5061/dryad.7h44j104p](https://doi.org/10.5061/dryad.7h44j104p) ## Description of the data and file structure Dietary data was obtained from Great Tit faecal samples in 2017 and 2018.  DNA was extracted from faecal samples, and DNA metabarcoding carried out to identify invertebrate and plant dietary taxa. The data underwent bioinformatic processing to obtain dietary taxa. Lab and bioinformatic work was carried out at Cardiff University in the School of Biosciences. ### Files and variables #### File: bipartitedata-winter-ageandsex.csv **Description:** Dataset to create the winter bipartite plots (Figure 6a and 6b). ##### Variables * taxa: Prey species Latin name * type: Plant or invertebrate All other columns from 'adult' to 'decid' are the percentage frequency of occurrence of each prey species for each demographic variable or habitat type. * adult: adult Great Tits * juve: first year Great Tits * female: female Great Tits * male: male Great Tits * conifer: coniferous habitat * decid: deciduous habitat #### File: fullcoi\_species\_splitseasons.csv **Description:** Invertebrate (COI) dietary dataset for composition analysis, which was split into separate seasons in the code. It contains duplicate samples from the same bird if there is one sample from winter and one from spring, as when the dataset gets split into seasons, they will no longer be duplicates. ##### Variables * SampleID: Unique Great Tit ID. A combination of the ring number and the DNA extraction number that the sample had in the lab. * Ring.number: Great Tit ring number * Extraction: The extraction number that the sample was in, in the lab * datecollected: Date that the faecal sample was collected * season: breeding (spring) and winter. Spring samples were collected from May to July. Winter samples from October to February.  * year: 2017 or 2018 * year.alt: alternative year variable for either first (2017) or second (2018). Necessary because winter spans the end of 2017 and the beginning of 2018. * sex: female or male. * age: BTO age code 3, 4, 5, or 6. * age2: adult or juvenile (first year) * site: code for the local site where the sample was collected * habitat: D = deciduous habitat, C = coniferous habitat. All subsequent columns are all the invertebrate prey species and whether they were present in the sample (1) or not (0) - presence/absence #### File: fullcoi\_species\_tidied\_containsdupes.csv **Description:** Invertebrate (COI) dietary dataset (winter and spring) that included duplicate samples from those birds that had them. Used in the richness analysis, which can handle duplicate samples from the same bird. ##### Variables * All variables as above: SampleID, Ring.number, Extraction, datecollected, season, year, year.alt, sex, age, age2, site, habitat * Followed by the invertebrate species (presence/absence) #### File: fullits2.specieslevel\_tidied\_complete.csv **Description:** Plant (ITS2) dietary dataset (winter only). Used for creating Figure S2b. ##### Variables * All variables as above: Sample, Ring, Extraction, datecollected, season, sex, age, age2, site, habitat * Followed by the plant species (presence/absence) #### File: fullcoi\_species\_tidied\_nodupes.csv **Description:** Invertebrate (COI) dietary dataset (winter and spring) that does not include duplicate samples from the same bird. Used for the composition analysis to examine dietary differences between seasons. ##### Variables * All variables as above: SampleID, Ring.number, Extraction, datecollected, season, year, year.alt, sex, age, age2, site, habitat * Followed by the invertebrate species (presence/absence) #### File: spring\_lessthan10.csv **Description:** Spring invertebrate dataset as above, but only includes species that were present in more than 10% of total samples. ##### Variables * All variables as above: SampleID, Ring.number, Extraction, datecollected, season, year, year.alt, sex, age, age2, site, habitat * Richness: Number of invertebrate species present in each sample * Followed by the invertebrate species (presence/absence) #### File: totalrich.allcategories.csv **Description:** Dietary species richness (number of species in each individual sample) dataset to be used for creation of Figure 2a. Each line is an individual bird. ##### Variables * type: categories of dietary data * invert.spr = Spring invertebrates, invert.wint = Winter invertebrates, plant = Winter plants, tot.wint = Winter invertebrates and plants * Not used in the figure: plant.comb = plant combined, inv.comb = invertebrate combined, invert = total invertebrate * richness: dietary species richness (number of prey species in each sample) #### File: pla.inv.specieslevel.csv **Description:** Full winter dietary data (plants and invertebrates) for richness and composition analysis. ##### Variables * All variables as above: Ring number, age, age2, sex, site, habitat * Followed by the plant and invertebrate species (presence/absence) #### File: winter\_lessthan10.csv **Description:** Winter dataset (invertebrates and plants), but only includes species that were present in more than 10% of total samples. ##### Variables * All variables as above: Ring number, age, age2, sex, site, habitat, richness * Followed by the plant and invertebrate species (presence/absence) #### File: bipartitedataforinverts.csv **Description:** Dataset to create the bipartite plot (Figure 3a) ##### Variables * taxa: Prey species Latin name All other columns from 'winter' to 'winter.juv' are the percentage frequency of occurrence of each prey species for each variable. * spring.F and spring.M are spring data for female and spring data for male, respectively. * spring.ad and spring.juv are spring data for adults and spring data for juveniles (first years), respectively. #### File: season and age invert bipartite GROUPED.csv **Description:** Dataset to create the bipartite plot of invertebrate guilds (Figure 3b) ##### Variables * group: The invertebrate species are grouped into guilds  All other columns from 'spring.ad' to 'winter' are the percentage frequency of occurrence of each of the guilds for each variable. * spring.ad and spring.juv are spring data for adults and spring data for juveniles (first years), respectively. * winter.ad and winter.juv are winter data (inverts and plants) for adults and juveniles (first years), respectively. ## Code/software Two R scripts are included. "Main analysis - JCoomes - Adult metab" is the analysis script. It includes code for preparatory organising and filtering of the data, the richness analysis, and the composition analysis.  "Graph creation - JCoomes - Adult metab" is the script to create the graphs. It also includes the sampling completeness analysis. Packages required are listed at the beginning of the scripts.