DOI: 10.5061/dryad.m63xsj42m
title: A trait-based framework for predicting foodborne pathogen risk from wild birds

description: Recent foodborne illness outbreaks have heightened pressures on growers to
 deter wildlife from farms, jeopardizing conservation efforts. However, it
 remains unclear which species, particularly birds, pose the greatest risk
 to food safety. Using &gt;11,000 pathogen tests and 1,565 bird surveys
 covering 139 bird species from across the western U.S.A., we examined the
 importance of 11 traits in mediating wild bird risk to food safety. We
 tested whether traits associated with pathogen exposure (e.g., habitat
 associations, movement, and foraging strategy) and pace-of-life (clutch
 size and generation length) mediated foodborne pathogen prevalence and
 proclivities to enter farm fields and defecate on crops. Campylobacter
 spp. were the most prevalent enteric pathogen (8.0%), while Salmonella and
 Shiga-toxin producing E. coli (STEC) were rare (0.46% and 0.22%
 prevalence, respectively). We found that several traits related to
 pathogen exposure predicted pathogen prevalence. Specifically,
 Campylobacter and STEC-associated virulence genes were more often detected
 in species associated with cattle feedlots and bird feeders, respectively.
 Campylobacter was also more prevalent in species that consumed plants and
 had longer generation lengths. We found that species associated with
 feedlots were more likely to enter fields and defecate on crops. Our
 results indicated that canopy-foraging insectivores were less likely to
 deposit foodborne pathogens on crops, suggesting growers may be able to
 promote pest-eating birds and birds of conservation concern (e.g., via
 nest boxes) without necessarily compromising food safety. As such,
 promoting insectivorous birds may represent a win-win-win for bird
 conservation, crop production, and food safety. Collectively, our results
 suggest that separating crop production from livestock farming may be the
 best way to lower food safety risks from birds. More broadly, our
 trait-based framework suggests a path forward for co-managing wildlife
 conservation and food safety risks in farmland by providing a strategy for
 holistically evaluating the food safety risks of wild animals, including
 under-studied species.
description: <b>Data acquisition </b> We
 compiled enteric pathogen prevalence data from studies that tested for
 <i>Campylobacter </i>spp., <i>Salmonella </i>spp.,
 and/or STEC: 1) in at least 5 species of free-ranging birds
 (<i>e.g.,</i> no single species studies and no captive birds),
 2) using feces and/or cloacal swabs (<i>e.g.,</i> no necropsy
 studies), and 3) from samples collected on farms that grow produce in the
 United States. We omitted trace-back studies investigating particular
 outbreaks because they would inflate apparent prevalence
 (<i>e.g.,</i> Gardner <i>et al.</i> (2011), who
 investigated a <i>Campylobacter </i>outbreak in Alaska). We
 began by searching the reference list of the recent meta-analysis of
 foodborne pathogen prevalence in North American breeding birds by Smith
 <i>et al.</i> (2020c) for suitable studies, then expanded
 their list to include several as-of-then unpublished studies and grey
 literature, ultimately yielding pathogen data from 5 studies (Fig. 3A; see
 Appendix S1: Table S3 for included study meta-data). Three of the five
 studies are fully published (Rivadeneira et al. 2016, Navarro-Gonzalez et
 al. 2020, Smith et al. 2020a). One study was previously unpublished
 (Olimpi et al. sub-data (a) in this Dryad dataset). Finally, we used data
 from a Center for Produce Safety final grant report (Gordus et al. 2011)
 that is partially published in Cooley et al. (2007) and Gorski et al.
 (2011). Published studies focused on reporting pathogen prevalence and
 possible transmission across wildlife and in the environment (Cooley et
 al. 2007, Gorski et al. 2011), prevalence in a variety of bird species
 sampled across seasons (Navarro-Gonzalez et al. 2020), or identifying
 landscape/farm-level pathogen risk factors (Rivadeneira et al. 2016, Smith
 et al. 2020a).   Studies that tested for STEC were
 diverse in methodology (Appendix S1: Table S3). Three studies performed
 the necessary steps for bacterial culture, isolation, and identification,
 then confirmed presumptive colonies by PCR. In contrast, two studies
 directly tested feces for STEC-associated virulence genes by extracting
 DNA and then using PCR but did not culture for bacteria first. Due to
 diverse methodologies, we considered samples to be positive for STEC if
 <i>stx1 </i>and/or <i>stx2 </i>(Shiga-toxin
 producing genes) were detected. Because these genes were rarely detected
 (0.22% of samples), we conducted additional analyses on data from the
 PCR-only studies, counting samples as positive if any proven or putative
 STEC-associated virulence gene(s) was/were detected (referred to as
 “STEC-associated virulence genes” for simplicity). The interest of this
 additional analysis resides in potential for horizontal transmission of
 virulence genes between <i>E. coli</i> strains (Bryan et al.
 2015). Thus, from a broader public health perspective, it is important to
 know whether birds frequently carry <i>E. coli </i>possessing
 any virulence factor (<i>sensu lato</i>) typically found in
 STEC. We note that the bacteria referred to here as positive for
 STEC-associated virulence genes are not necessarily pathogenic to
 humans. Second, we combined bird point-count data
 collected by the authors in three prior projects to estimate bird
 abundance in crop fields (“crop contact rates”; Fig. 3B; see Appendix S1:
 Table S4 for included study meta-data; Smith <i>et al.</i>
 (2020b, 2021)). Briefly, Smith <i>et al.</i> (2020b) conducted
 100-m radius, 10 min point-count surveys during the breeding season twice
 per year over two years on 52 highly diversified farms across the U.S.A.
 states of Washington, Oregon, and California. The other two projects
 (Olimpi et al. sub-data (b) and Garcia et al. sub-data published in this
 Dryad dataset) each conducted 50-m radius, 10 min point-count surveys
 during the breeding season three times per year over two years across 20
 organic farms in the Central Coast of California. Five of their farms
 overlapped and were only counted once in analyses. We only used data from
 survey points conducted in crop fields, and birds were only counted as
 “contacts” if they were in fields (we included aerial foraging as contacts
 but excluded flyovers). We excluded tree fruit contacts because of the
 structural similarity to non-crop trees and due to the focus of included
 studies on leafy greens, brassicas, and strawberries.
 Finally, to identify which species were most likely to defecate
 in crop fields, we leveraged a dataset of 1215 fecal samples collected by
 Smith <i>et al. </i>(2020a) from brassica fields and food
 wash/packing areas across 37 farms in Washington, Oregon, and California,
 U.S.A. These samples were subsequently attributed to bird species through
 COI gene testing. Smith et al. (2020a) determined the bird species
 responsible for defecating 463 of the 1215 (38.1%) fecal samples, which
 were traced back to 35 species (Appendix S1: Fig. S1). Here, we examined
 traits that predicted the number of feces identified back to a bird
 species from the entire bird species pool (<i>n </i>= 106)
 observed while conducting point counts at the collection
 locations. Although we sought studies on foodborne
 pathogen prevalence in birds from any farms that grow produce throughout
 the United States, we only found data collected from the West Coast that
 met our inclusion criteria (Fig. 3). Briefly, studies in our analysis
 surveyed farms that spanned a gradient of types and diversity of crops
 grown, sizes, and landscape contexts. For example, Smith et al. (2020a,b)
 surveyed highly diversified farms ranging from 0.38 to 272.2 ha that
 spanned a wide range of landscape contexts from 0–100% seminatural.
 Similarly, one of the previously unpublished studies (Olimpi et al.
 sub-data (a) in this Dryad dataset) surveyed farms ranging from 1.3 to
 100.3 ha, spanning a wide range of landscape contexts from 0–85.4%
 seminatural. Farms ranged from highly diversified with many crop types to
 strawberry monocultures. The farm and regional contexts for included
 studies are described in full detail in Appendix S1: Tables
 S3-S4. <b>Species traits</b>
 We took a barriers-to-spillover approach meaning that we
 considered how species traits influence alignment of a series of
 hierarchical barriers that must align to enable transmission of foodborne
 pathogens from birds to crops (Fig. 1; Plowright et al. 2017). Thus, we
 first generated <i>a priori</i> hypotheses about traits that
 might affect foodborne pathogen prevalence in birds (Appendix S1: Table
 S1). We then examined how the same traits we hypothesized would impact
 foodborne pathogen prevalence impacted the “downstream layers” of contact
 with crops and fecal deposition in fields (Fig. 1). Our hypotheses broadly
 covered aspects of exposure/habitat preferences (diet guild, foraging
 strata, migratory strategy, sociality, high use of bird feeders, feedlot
 association, synanthropy, large daily movements [proxied by hand-wing
 index due to limited data available quantifying home range size or actual
 daily movement (Sheard et al. 2020)]), pace of life (clutch size and
 generation length), and nonnative status (<i>i.e.</i>,
 nonnative birds possess several traits that may increase reservoir
 competence) (Waldenstrom et al. 2002, Altizer et al. 2011, Ostfeld et al.
 2014, Daversa et al. 2017, Smith et al. 2020c, 2020a).
 We first sought existing databases with relevant traits and were
 successful for diet guild, foraging strata, hand-wing index, and
 generation length (De Graaf et al. 1985, Wilman et al. 2014, Barnagaud et
 al. 2017, Bird et al. 2020, Sheard et al. 2020, Smith et al. 2020b). We
 did not find robust estimates for several traits of interest (migratory
 strategy in the study region, sociality/gregariousness, clutch size, bird
 feeder use, feedlot association, and synanthropy). For these traits, we
 generated novel databases from secondary and primary sources. We used
 Birds of the World Online (Billerman et al. 2020) to classify mean clutch
 size and migratory strategy for populations in our study region and
 sociality during the breeding season (<i>i.e.,</i> when most
 produce is grown). We classified birds as highly associated with bird
 feeders if they were on one or more Project FeederWatch top 25 lists for
 Washington, Oregon, and California for 2016–2017
 (https://feederwatch.org/pfw/top25; last accessed May 2020).
 To classify feedlot association (highly associated, somewhat
 associated, not associated), we conducted a literature review, searched
 Birds of the World Online(Billerman et al. 2020), and consulted eBird
 checklists (www.eBird.org). Because of discrepancies in data availability,
 methods, and reporting, we used the review to guide expert elicitation
 because we were unable to use specific numerical guidelines to classify
 species (see Appendix S1: Section S1 and Data S1). To quantify
 synanthropy, we extracted citizen-science data from eBird and filtered
 checklists according to “best practices” (Strimas-Mackey et al. 2020). We
 used the 500-m resolution MODIS MCD12Q1 v006 land cover product to
 calculate the proportion of anthropogenic land-cover within a 700-m radius
 of each checklist location (Friedl and Sulla-Menashe 2015). We then used
 generalized additive models to quantify species’ responses to
 anthropogenic land cover while accounting for nuisance variables and
 spatial autocorrelation (Wood 2006). We first modelled the effect of each
 type of natural and anthropogenic habitat on occupancy and then used our
 model to identify the most preferred natural habitat type. The synanthropy
 index was calculated as the relative log-fold increase (or decrease) of
 occurrence probability in anthropogenic habitat versus the natural habitat
 where the species was most abundant. Specifically, this quantity was
 calculated as the slope of a species’ response to anthropogenic land
 covers minus the slope of their response to their most preferred “natural”
 land cover (see Appendix S1: Section S1 and Data S2 for full
 details). Our efforts resulted in 18 traits that we
 then narrowed down to 11 to represent key hypotheses. We selected traits
 that were least correlated with metrics representing other hypotheses by
 examining pairwise correlations. For example, mass, wing chord, and
 hand-wing index may all represent dispersal ability (Sutherland et al.
 2000, Sheard et al. 2020), but mass is also highly correlated with
 generation length (Bird et al. 2020). Therefore, we only used hand-wing
 index and generation length since they were the least correlated with each
 other. Nevertheless, some traits remained correlated after our selection
 (the highest Pearson’s correlation for traits used in models was 0.48
 between synanthropy and bird feeder association; Appendix S1: Fig.
 S2). <b>Statistical analysis</b>
 We modeled pathogen prevalence (<i>Campylobacter
 </i>spp., <i>Salmonella </i>spp., and STEC) and carriage
 of STEC-associated virulence genes as a function of bird traits using
 generalized linear mixed-effects models (GLMMs) with a binomial error
 distribution and logit link function (glmmTMB package in R) (Brooks et al.
 2017). Additionally, we used GLMMs to examine the impact of species traits
 on 1) the total number of individuals detected in crops within each point
 using a negative binomial distribution to account for overdispersion and
 2) the total number of environmental fecal samples attributed to each
 species via COI gene testing, again using a negative binomial distribution
 to account for overdispersion. We modeled (1) above using both the
 relative abundance (<i>i.e</i>., number of individuals counted
 in crop fields) of species across all sites within each study as well as
 their relative abundances per survey point (see Appendix S1: Section
 S1). We first determined the optimal random effects
 structure for each of the 7 response variables described above (Appendix
 S1: Table S5). To account for variation in methods between studies, we
 included “study” as a random effect in models using data from 3 or more
 studies and as a fixed effect in models using data from 2 studies. To
 account for multiple bird surveys on the same farm and multiple visits to
 each point-count location, we included point nested within farm in
 analyses of crop contact per survey point. Finally, to account for
 non-independence in phylogenetic relationships, we also included order,
 family, genus, and species as random effects. Including order, family, and
 genus, rather than a continuous measure of phylogenetic relatedness,
 allowed us to identify the taxonomic level of non-independence.
 Specifically, we constructed models with all combinations of order,
 family, genus, and species, and then used AIC to determine which taxonomic
 levels to include. Our final candidate model sets included a random effect
 of family for all response variables except <i>Campylobacter
 </i>spp. (genus only) and crop contact (both family and
 species). After determining the optimal random effects
 structures, we constructed 44 candidate models that tested the relative
 importance of each of the 11 traits for the 7 response variables (Appendix
 S1: Table S5). The 44 models included the null model, 11 models that
 tested a single trait, 16 additive models that tested one exposure and one
 pace of life trait, and 16 models that tested for an interaction between
 one exposure and one pace of life trait. All models that included
 hand-wing index also included an intercept for aerial foraging to account
 for the higher hand-wing index observed in species specialized in foraging
 in flight (Sheard et al. 2020). Continuous physiological traits were log
 transformed to reduce leverage of high values. We then
 ranked models based on AIC<sub>c</sub> and identified those
 that were most supported (∆AIC<sub>c</sub> &lt; 2.0)
 (Burnham and Anderson 2002). We assessed if variables improved model fit
 using likelihood ratio tests for all models with weights &gt; 0.05. We
 assessed multicollinearity for candidate models using the performance
 package in R (Ludecke et al. 2020), and found it not to be an issue in our
 models (VIF &lt; 5). We used generalized Tukey HSD tests in the
 multcomp package in R (Hothorn et al. 2008) to examine differences in
 categorical predictor variables that had high support (were included in
 models with &lt; 2 ∆AIC<sub>c</sub>) and improved model
 fit (likelihood ratio tests). We predicted pathogen prevalence and crop
 contact rates per species from the best-supported models using the
 predict() function in R, then model-averaged predictions.
description: Data S1: Feedlot trait assignment – this file contains
 information on how we generated the feedlot association trait for bird
 species in our study. The file contains tabs pertaining to (1) source
 meta-data (i.e., studies we gathered feedlot association data from), (2)
 counts (i.e., number of individuals per species observed in feedlots
 during standardized counts (e.g., point count surveys) per study), (3)
 captures (i.e., number of individuals per species captured at feedlots by
 study), (4) marginally suitable literature (i.e., studies conducted in
 feedlots that didn't provide robust data), (5) Birds of the World
 (formerly Birds of North America) Online mentions of feedlot association
 by species, and (6) final expert elicitation (i.e., how each of three
 ornithologists classified species and their final classifications). Tab 7
 contains full definitions of what each column represents. 
 Data S2: Synanthropy trait – this file contains statistics
 regarding the synanthropy trait calculations. Synanthropy indices are an
 index of relative preference for anthropogenic habitat types (agriculture
 and urban) compared to natural habitat types with greatest use.
 "Synanthropy index" is the trait we used in our final
 analyses. Tab 2 contains full definitions of what each column
 represents.  Data S3: Predictions and traits – this
 file contains pathogen prevalence and crop contact estimates for each of
 the 139 species in our study. It also contains their trait and taxonomic
 data used in analyses. Tab 2 contains full definitions of what each column
 represents.  Data S4: Meta-data – this file again
 contains the pathogen prevalence and crop contact estimates for each of
 the 139 species in our study in addition to the raw pathogen prevalence
 and crop contact data used in our analyses from original studies. Tab 2
 contains full definitions of what each column represents. 
 Data S5: Data used in analyses – this file contains the data
 analyzed in "A trait-based framework for predicting foodborne
 pathogen risk from wild birds." Tabs 1-4 contain the pathogen
 prevalence and species traits data for each bird species for each study
 included in the meta-analysis. Tab 5 contains crop contact data and
 species traits data for each bird species for each study included in the
 meta-analysis. Tab 6 includes the fecal deposition and traits data for
 each species included in the meta-analysis. Tab 7 contains full defintions
 of what each column represents in each tab.