DOI: 10.5061/dryad.xwdbrv1jt
title: Data from: DNA metabarcoding reveals rangewide variation in aquatic diet of a riparian avian insectivore, the Prothonotary warbler

description: Riparian avian insectivores not only depend on terrestrial insect prey but
 also benefit from the inclusion of aquatic prey during critical life
 history periods. Diets identified herein show that Prothonotary Warbler
 (Protonotaria citrea) nestlings were provisioned with aquatic prey
 throughout the breeding season across their range, but with variation in
 prey frequency of occurrence and taxonomy. Anthropogenic activity and
 climate change may impact the trophic link especially between aquatic and
 riparian habitats by altering the presence, abundance, and timing of prey
 availability. Thus, we used DNA metabarcoding of fecal samples to quantify
 the frequency of occurrence of nestling diet items at nine sites across
 their breeding range that differed in expected aquatic prey consumption.
 We analyzed spatial and temporal differences in the occurrence and
 multivariate diet assemblages of each prey source. Lepidoptera was the
 predominant terrestrial prey occurring in diets across space and
 time, whereas emergent aquatic insects and freshwater mollusks in aquatic
 diet exhibited greater variation. The frequency of emergent aquatic prey
 occurrence in nestling diets ranged from 61-100% across sites and was
 greater for early-season nestlings. The seasonal decrease in aquatic prey
 consumption indicates a potential temporal shift in the nutritional
 landscape from aquatic to terrestrial prey sources and a possible
 nutritional phenological mismatch for early nestlings as climate change
 advances the timing of insect emergence. Our findings also suggest that
 Prothonotary Warblers respond to environmental variability by consuming
 alternative prey and argue for future research investigating the extent to
 which shifting diets have nutritional consequences for riparian nestlings.
description: During the 2018 and 2019 breeding seasons, we sampled 9
 Prothonotary Warbler populations at sites monitored annually throughout
 their breeding range. Local nest monitors collected fecal sacs from
 nestlings aged 6-11 days during routine nest visits. Nestlings readily
 defecate when handled during banding, enabling collection of fresh fecal
 samples. We stored fecal sacs in glass vials with 96% ethanol at room
 temperature to preserve DNA until extraction. <br><br>We
 extracted DNA from samples using the Qiagen Fast DNA Mini Stool kit,
 modifying the manufacturer’s instructions to increase yield of degraded
 DNA (Trevelline et al. 2016). A two-stage targeted amplicon sequencing
 protocol was used to amplify prey DNA from our DNA extractions (Naqib et
 al. 2018). The first PCR stage amplified sequences with the primers
 LCO1490 and CO1-CFMRa (hereafter, ANML; Jusino et al. 2019). The second
 PCR stage added unique 10-bp barcodes to each sample, allowing samples to
 be pooled during sequencing while retaining sample information for
 downstream analysis. Sequencing was performed using a combination of
 Illumina MiniSeq and Illumina MiSeq (2x150 paired-end reads). Additional
 details regarding laboratory methods are provided in the main article.
 <br><br>We completed all steps for trimming and quality
 filtering to obtain amplicon sequence variants (ASVs) from demultiplexed
 sequences using the dada2 package (1.18; Callahan et al. 2016) in R
 (4.0.3; R Core Team 2020). Sequences were trimmed at the 5′ end to remove
 primers and at the 3′ end to remove lower quality segments (Phred Scores
 &lt;30) as much as possible while also retaining a 12-bp overlap
 between forward and reverse sequences for merging paired sequences
 (Callahan et al. 2016). <br><br>We retrieved matching
 reference sequences for representative ASVs and taxonomy from NCBI BLAST
 (National Center for Biotechnology Information Basic Local Alignment
 Search Tool; Sayers et al. 2022) using Biopython (1.79; Cock et al. 2009),
 then performed sequence identification consensus with the statistics
 provided by BLAST. Consensus details are described in the main
 article. <br><br>We attempted to account for read variation by
 rarefying our dataset using the phyloseq R package (rarefy_even_depth;
 McMurdie and Holmes 2013). We performed separate rarefaction simulations
 for sequence depths between 100-2,000 reads/sample, at 100-read intervals.
 The rarefied sequence depth of 1,000 reads/sample was chosen as the best
 tradeoff between an increase in diet identifications and a decrease in the
 number of samples meeting the required depth. Above 1000 reads, we did not
 detect new diet taxa at the levels of analysis (i.e., taxonomic order or
 family). All downstream data analyses were performed with the rarefied
 dataset.
description: Most data processing and analyses were performed in R (4.0.3; R
 Core Team 2020). R scripts are available from the corresponding author
 upon reasonable request. Biopython was used to perform
 the NCBI BLAST and sequences identification consensus. Python scripts are
 available from the corresponding author upon reasonable request.