DOI: 10.25338/b81w7d
title: Data from: Feeding en route: Prey availability and traits influence prey selection by an avian predator on migration

description: During animal migration, ephemeral communities of taxa at all trophic
 levels co-occur over space and time. The interactions between predators
 and prey along migration corridors are ecologically and evolutionarily
 significant. However, these interactions remain understudied in
 terrestrial systems and warrant further investigations using novel
 approaches. We investigated the predator-prey interactions
 between a migrating avivorous predator and ephemeral avian prey community
 in the fall migration season. We tested for associations between avian
 traits and prey selection and hypothesized that prey traits (i.e.,
 relative size, flocking behavior, habitat, migration tendency, and
 availability) would influence prey selection by a sexually dimorphic
 raptor on migration. To document prey consumption, we sampled
 trace prey DNA from beaks and talons of migrating sharp-shinned hawks
 Accipiter striatus (n=588). We determined prey availability in the
 ephemeral avian community by extracting weekly abundance indices from
 eBird Status and Trends data. We used discrete choice models to assess
 prey selection and visualized frequency of prey in diet and availability
 on the landscape over the fall migration season. Using eDNA
 metabarcoding, we detected prey species on 94.1% of the hawks sampled
 (n=525/588) comprising 1396 prey species detections from 65 prey species.
 Prey frequency in diet and eBird relative abundance of prey species were
 correlated over the migration season for top selected prey species,
 suggesting prey availability is an important component of raptor-songbird
 interactions during fall. Prey size, flocking behavior, and non-breeding
 habitat association were prey traits that significantly influenced
 predator choice. We found differences between female and male hawk prey
 selection, suggesting that sexual size dimorphism has led to distinct
 foraging strategies on migration. This research integrated field
 data collected by a volunteer-powered raptor migration monitoring station
 and public-generated data from eBird to reveal elusive predator-prey
 dynamics occurring in an ephemeral raptor-songbird community during fall
 migration. Understanding dynamic raptor-songbird interactions along
 migration routes remains a relatively unexplored frontier in animal
 ecology and is necessary for conservation and management efforts of
 migratory and resident communities.
description: <strong>Predator Diet Sampling</strong>
 The Marin Headlands hosts the most significant raptor migration
 flight along the Pacific Coast of North America where migrating raptors
 funnel and converge before gaining altitude to cross the San Francisco Bay
 (Goodrich & Smith, 2008). We collected diet data from raptors
 banded at a raptor migration monitoring station located in the Marin
 Headlands operated by the Golden Gate Raptor Observatory/Golden Gate
 National Parks Conservancy in cooperation with the US National Park
 Service (37.8262°N, 122.4997°W; Fig. 1). Sharp-shinned hawks were lured
 into the sampling site with rock doves Columba livia, European starlings
 Sturnus vulgaris, and house sparrows Passer domesticus, then captured in
 dho-ghazzas, mist-nets, or bownets, and released after banding and
 processing (Hull & Bloom, 2001). We collected
 eDNA samples from the exterior surfaces of beaks and talons of
 sharp-shinned hawks (n= 588; Fig. 2) during fall migration in 2015 (n=282)
 and 2016 (n=276) from September through November. To sample prey DNA, we
 moistened a sterile histobrush (#25‐2188 Puritan Medical Products Company)
 in 0.7ml ultrapure water contained in 1.5ml pop-top centrifuge tube. Next,
 we gently swabbed the entire exterior surface of the upper and lower
 mandible, targeting any visible prey blood or tissue if present. We
 avoided contact with interior mouthparts and saliva to minimize collection
 of predator DNA. We then swabbed the entire surface of each talon,
 targeting visible prey blood, tissue, or feathers if present. Toe pads or
 scales were additionally swabbed if visible prey remains were present.
 After swabbing the raptor, we cut off the nylon brush tip into a 1.5ml
 screw‐top centrifuge tube containing 0.7ml Longmire's lysis buffer
 (100mM Tris pH8.0, 100mM EDTA, 10mM NaCl, 0.5% SDS, 0.2% sodium azide) or
 Queen’s lysis buffer (100mM Tris, 100mM NaCl, 100mM sodium EDTA and 10%
 n-Lauroylsarcosine, pH 8.0; Suetin et al., 1991) and stored at −20°C.
 Sampling occurred even if beaks and talons appeared clean (Bourbour et
 al., 2019, 2021). We conducted all aspects of this
 research in accordance with Institutional Animal Care and Use Committee
 (IACUC; permit #: CA_GOGA_Ely_Raptors_2020.A3), California Department of
 Fish & Wildlife (California State Permit #: SCP 13739), National
 Parks Service (study #: GOGA-00004; permit #: GOGA-2022-SCI-0019), and
 United States Geological Service guidelines (federal bird banding permit
 #: 21827). <strong>Prey DNA Extraction,
 Amplification, and Sequencing</strong> We used
 QIAmp DNA Mini Kit (QIAGEN Inc.) to extract prey DNA from swab tips. We
 conducted lab work in house at the UC Davis Genomic Variation Laboratory
 genetics lab that does not process songbird DNA to minimize risk of
 contamination. We targeted a 464-base pair (bp) amplicon region of the
 cytochrome c oxidase subunit I (COI) gene using primers COI-fsdF and
 COI-fsdR which have been demonstrated to have a high-resolution power for
 identifying species across Passeriformes and Columbiformes (González‐Varo
 et al., 2014; Bourbour et al., 2021). We modified the primers to have an
 overhang sequence that would anneal to indexed Illumina adapters
 (Illumina, 2013; Supplementary Materials Table 3). We tested primers using
 avian tissue samples from the Museum of Wildlife & Fish Biology at
 UC Davis. We used orange-crowned warbler Vermivora celata and Swainson’s
 thrush Catharus ustulatus DNA as positive controls alongside negative
 controls (PCR-grade water) during library preparation to confirm detection
 of probable prey species and assess potential contamination or
 misassignment of reads. We followed the two-step PCR amplification
 protocol outlined in Illumina (2013), see Supplementary Materials 1 for
 detailed PCR protocols. After library preparation, we quantified DNA using
 Quant-iT PicoGreen dsDNA Reagent (Thermo Fisher Scientific) with an FLx800
 Fluorescence Reader (BioTek Instruments). We then normalized each sample
 post-Index PCR to 5ng/µl individually and pooled two libraries at a
 concentration of 5nM in 15µl of EB buffer (10mM TRIS; pH=8.0-8.4). We
 sequenced the libraries on two lanes using Illumina’s MiSeq PE300 (v3)
 platform. <strong>Reference Library and
 Bioinformatics</strong> Developing custom
 reference sequence libraries can create more robust results by reducing
 overrepresented species and representing cryptic diversity (Elbrecht
 & Leese, 2017; Macheriotou et al., 2019). To start, we compiled a
 custom reference library of bird species (n=205) that range in the Pacific
 Flyway according to species account range maps (Billerman et al., 2022;
 see Supplementary Materials Tables 1,5). We used the R package PrimerMiner
 (Elbrecht & Leese, 2017) to download all publicly available COI
 barcode and mitochondrial genome sequences from NCBI and BOLD databases to
 take full advantage of full and partial sequences available for each
 species. Sequences were clustered into operational taxonomic units using a
 3% sequence similarity to reduce overrepresentation of sequences while
 capturing sequence variants representing cryptic diversity (Elbrecht
 & Leese, 2017). Out of 205 avian species, we were able to compile
 199 species’ barcode sequences for our reference library. We then manually
 reformatted the datafiles to be compatible with the reference database
 format used by the R package DADA2 (Callahan et al., 2016; Supplementary
 Materials 2). We filtered out low quality scores
 (&lt;30) and reads below 250bp using the program Cutadapt (Martin,
 2011) and used DADA2 to filter out samples with &gt;2 erroneous base
 calls, remove chimeras, and merge forward and reverse reads. We matched
 all barcode sequences to our custom reference library with &gt;99%
 bootstrap support using the ‘assignTaxonomy’ command in DADA2. Based on
 assessment of positive and negative controls, we removed prey species
 detections with &lt;100 total assigned reads and we removed prey
 species if they represented 0.5% or less of the total number of reads in
 each individual sample. <strong>Prey
 availability</strong> To obtain an index of
 weekly prey availability, we extracted abundances of prey species from the
 eBird Status and Trends data from August–November using the R package
 ebirdst (Fink et al., 2020; Strimas-Mackey et al., 2021; eBird Status and
 Trend 2005 to 2020). The persistence of eDNA in the environment is
 variable and system dependent (e.g., Andruszkiewicz et al., 2017; Barnes
 et al., 2014; Strickler, 2015) and more studies are needed to determine
 the rate of degradation of eDNA in terrestrial ecosystems (Beng &
 Corlett, 2020). However, because prey DNA on beaks and talons represents
 diet from previous meals and may be detectable for multiple days on a
 migrating raptor (Bourbour et al., 2019; Bourbour et al., 2021; Valentin
 et al., 2021), prey abundance data was extracted from several counties
 north of the sampling site where sharp-shinned hawks likely occurred prior
 to their capture at the monitoring station as they follow the coastal
 mountains (Marin, Napa, Sonoma, Lake, and Mendocino; Fig. 1; Goodrich
 & Smith, 2008). For this process, we confined eBird data
 extraction in 2.96km2 spatial cells (which is the native resolution from
 eBird Status and Trends products; Fink et al., 2020) that also contained
 sharp-shinned hawk occurrence data according to eBird checklists in the
 defined study region. Next, we summed weekly relative abundances of prey
 species across those spatial cells to represent an index of prey
 availability in the study area.
 <strong>Statistical analyses</strong>
 We performed all statistical analyses using R version 4.1.0 (R
 Core Team, 2021) in RStudio version 2022.2.3 (RStudio Team, 2022). Because
 the eBird Status and Trends data uses data from 2005-2020 (Fink et al.,
 2020), we combined both diet sampling years together. We excluded European
 starling and house sparrow detections from statistical analyses because we
 cannot confidently rule out contamination at the sampling site as the
 cause of their detection (rock dove DNA was not detected; Bourbour et al.,
 2021). We calculated rarefaction and Coverage-based R/E (Type 3) curves
 with a 95% confidence interval using the R package iNext (Chao et al.,
 2014) to assess sampling effort. Because sharp-shinned hawk females are
 30-40% larger than males (Bildstein et al., 2020), we first tested for
 differential prey size selection. We used a linear mixed-effects model
 with average prey mass (extracted from Tobias et al., 2022) as the
 dependent variable, sex as a categorical explanatory variable, and sample
 ID (individual hawk) as a random effect using the R packages lme4 (Bates
 et al., 2015), lmerTest (Kuznetsova et al., 2017), and afex (Singmann et
 al., 2022), and visualized the model using the R packages effects (Fox
 & Weisberg, 2019). <strong>Prey
 traits</strong> We categorized each species
 detected according to relative size, flocking behavior, and migratory
 tendency (Supplementary Table 1). For prey size, we used average mass
 (extracted from Tobias et al., 2022) to classify each species as small
 (&lt;30g), medium (between 30g and 60 g), or large (&gt;60g). For
 non-breeding flocking behavior, we used Billerman et al., (2022) to
 classify species as gregarious if they are described as highly social with
 conspecifics throughout fall migration, aggregate if described as joining
 small flocks during the non-breeding season, or solitary if the species is
 described as solitary and/or seldomly joining mixed-species flocks during
 migration. For migratory tendency, we used Billerman et al., (2022) to
 classify species as resident or migratory (i.e., which included both
 complete and partial migrants) in the Pacific Flyway.
 <strong>Prey selection</strong> We
 fitted separate models for male and female hawks given distinct body size
 differences and statistically significant differential prey size
 selection. For the models for each sex, we used a set of multinomial
 logistic regressions (discrete choice models) using the R package mlogit
 (Croissant, 2013). For each model, we computed the variance-covariance
 matrix of the parameters to account for repeated measures using the R
 package sandwich (Zeileis, 2006). These models were used to predict the
 probability that a species was detected on the migrating predator as a
 function of species availability and traits. We used prey detection as the
 dependent variable and included the following as explanatory variables:
 index of prey availability (from eBird Status and Trends Data),
 non-breeding flocking behavior, non-breeding habitat association, and
 migratory tendency. To visualize the diet composition and co-occurrence of
 selected prey species, we plotted the weekly relative abundances of prey
 species selected by both males and females over time along with the weekly
 proportion detected in the diet. We used the scale function in R to
 normalize (z-score) both eBird relative abundances and proportions in
 diet. Using the z-scores, we then calculated the Pearson’s R for each of
 the top seven prey species to measure the correlation between the weekly
 proportions in diet and eBird prey availability indices (Freedman et al.,
 2007).
description: # Prey availability and traits influence prey selection by a sexually
 dimorphic avian predator on migration During animal migration, transient
 communities of taxa at all trophic levels co-occur over space and time.
 The interactions between predators and prey along migration corridors are
 ecologically and evolutionarily significant. However, these interactions
 remain understudied in terrestrial systems and warrant further
 investigations using novel approaches. We investigated the predator-prey
 interactions between a migrating avivorous predator and ephemeral avian
 prey community in the fall migration season. We tested for associations
 between avian traits and prey selection and hypothesized that prey traits
 (*i.e.*, relative size, flocking behavior, habitat, migration tendency,
 and availability) would influence prey selection by a sexually dimorphic
 raptor on migration. To document prey consumption, we swabbed trace prey
 DNA from beaks and talons of migrating Sharp-shinned Hawks (*Accipiter
 striatus*; n=588). We determined prey availability in the transient avian
 community by extracting weekly abundance indices from eBird Status and
 Trends data. We used discrete choice models to assess prey selection and
 visualized frequency of prey in diet and availability on the landscape
 over the fall migration season. Using eDNA metabarcoding, we detected prey
 species on 94.1% of the hawks swabbed (n=525/588) comprising 1396 prey
 species detections from 65 prey species. Prey frequency in diet and eBird
 relative abundance of prey species were correlated over the migration
 season for top selected prey species, suggesting prey availability is an
 important component of raptor-songbird interactions during fall. Prey
 size, flocking behavior, and non-breeding habitat association were prey
 traits that significantly influenced predator choice. We found differences
 between female and male hawk prey selection, suggesting that sexual size
 dimorphism has led to distinct foraging strategies on migration. This
 research integrated field data collected by a volunteer-powered raptor
 migration monitoring station and public-generated data from eBird to
 reveal elusive predator-prey dynamics occurring in an ephemeral
 raptor-songbird community during fall migration. Understanding dynamic
 raptor-songbird interactions along migration routes remains a relatively
 unexplored frontier in animal ecology and is necessary for conservation
 and management efforts of migratory and resident communities. ##
 Description of the data and file structure 1_Bourbour_2023_Metadata.csv
 Metadata associated with each environmental DNA (eDNA) sample. Header
 info: **sample_id** = unique sample identifier, **date** = date sample was
 taken, **species** = USGS bird code (SSHA=sharp shined hawk),  **sex** =
 Male or female,  **SSHA_weigh**= weight measured in grams, **prey** = prey
 item detected through eDNA denoted USGS bird code), **prey_name** = common
 name of prey item detected through eDNA, **prey_size** = prey species
 categorized into average mass (Tobias et al. 2022) to classify each
 species as small (&lt;30 g), medium (between 30 and 60 g), or large
 (&gt;60 g), **prey_mass =** average prey mass (extracted from Tobias et
 al. 2022)**, Migratory =** For migratory tendency, 200 we used Billerman
 et al. (2022) to classify species as resident or migratory (i.e., which
 included 201 both complete and partial migrants) in the Pacific Flyway.
 2_Bourbour_2023_demultiplexed_reads_2015_2.zip Demultiplexed reads from
 generated from Illumina’s MiSeq PE300 (v3) platform from samples collected
 in 2015. Sample ID at the beginning of each file name.
 3_Bourbour_2023_demultiplexed_reads_2016.zip Demultiplexed reads from
 generated from Illumina’s MiSeq PE300 (v3) platform from samples collected
 in 2016. Sample ID at the beginning of each file name.
 4_Bourbour_2023_referencelibrary.csv Custom reference library of bird
 species (n = 205) that range in the Pacific Flyway according to species
 account range maps (Billerman et al. 2022). The  R package PrimerMiner
 (Elbrecht & Leese 2017) to download all publicly available COI barcode
 sequences from NCBI and BOLD databases for each species and manually
 reformatted the data files to be compatible with the reference database
 format used by the R package DADA2 (Callahan et al. 2016).
 5_Bourbour_2023_Prey_detections_2015.csv Prey detections obtained using
 the COI-fsdF and COI-fsdR (González‐Varo et al. 2014) from samples
 collected in 2015. Header info: Species = Prey items detected from eDNA
 samples excluding lure species, Total = total count per prey item across
 all beak eDNA Samples, Sample ID = Columns C-JR, Negative Controls =
 Columns JS-JT. 6_Bourbour_2023_Prey_detections_2016.csv Prey detections
 obtained using the COI-fsdF and COI-fsdR (González‐Varo et al. 2014) from
 samples collected in 2016. Header info: Species = Prey items detected from
 eDNA samples ex ## Sharing/Access information Repository for eBIRD data
 and analysis: Tim Mehan. 2023.
 [https://github.com/tmeeha/ggro\\_sharpie\\_prey.git](https://github.com/tmeeha/ggro\\_sharpie\\_prey.git)