DOI: 10.5061/dryad.nzs7h450x
title: Data from: Assessing foodborne pathogen survival in bird feces to co-manage farms for bird conservation, production, and food safety

description: Because birds can carry foodborne pathogens, widespread concern exists
 that birds impose food-safety risks to farms. Growers are thus often
 encouraged to deter birds and forego harvesting crops near bird feces
 (e.g., no-harvest buffers). Developing a holistic understanding of the
 pathogen spillover process– from individual infection to pathogen
 persistence on crops– is essential to understand and manage food-safety
 risks associated with birds. Here, we coupled field and greenhouse
 experiments to understand the relative risks associated with different
 bird species on California farms. We first compared E. coli survival in
 bird feces on lettuce, soil, and plastic mulch to identify where pathogens
 are likely to persist. Next, we quantified pathogen survival in feces from
 10 bird species to identify higher-risk species. Finally, we combined
 pathogen survival data with bird surveys and fecal transects to assess
 food-safety risks across 29 California lettuce farms. E. coli abundance
 rapidly declined in bird feces, but rates varied across substrates. E.
 coli survival was higher on lettuce compared to soil and plastic mulch,
 likely because of lettuce’s cooler and wetter microclimate. E. coli
 persistence was also much higher in feces from larger birds—which produce
 larger feces—than small songbirds. Importantly, minimal differences in
 persistence among species were observed after all feces were standardized
 to the same mass. Though bird feces were common on farms, most birds
 entering farms, contacting crops, and defecating on crops were small
 songbirds that defecate small feces. Coupled with our finding that ~90% of
 feces were deposited on soil, these results suggest that most birds on
 farms present relatively low food-safety risks. Synthesis and
 applications. Growers are often encouraged to deter all bird species and
 forego harvesting crops near all bird feces, but our work suggests not all
 birds and feces pose the same food-safety risks. If growers ignored small
 bird feces on soil, then we estimate that the area of California lettuce
 farms affected by no-harvest buffers could decrease from ~10.3% to ~2.7%.
 More broadly, our results suggest farmers could promote small,
 insect-eating birds by erecting nest-boxes or preserving habitat without
 necessarily compromising food safety.
description: Our methods include two distinct field operations: first, we
 collected fresh fecal materials directly from birds in or near Davis,
 California for our experiments. Second, we collected point count and fecal
 density data directly on leafy-green farms in the Central Coast of
 California. The following methods and portions of the results have been
 included in a grant report for The Center for Produce Safety (Karp
 & McGarvey, 2024). <em>2.1 Field
 experiment:</em> To quantify <em>E. coli
 </em>persistence, we implemented a field experiment on the
 University of California Davis Student Farm – an organic vegetable farm in
 Davis, California, USA. The experiment focused on two species that are
 frequently observed on farms but differ in size: Wild Turkey (5,791g;
 <em>Meleagris gallopavo</em>) and Western Bluebird (26g;
 <em>Sialis mexicanus</em>) (Tobias et al., 2022). Throughout
 the spring and summer of 2022, we collected turkey feces by following
 individuals on the UC Davis campus and picking up samples after defecation
 with sterile forceps. We obtained bluebird feces by hand-capturing adult
 birds from nest boxes along Putah Creek in California’s Central Valley and
 placing birds in sterilized cotton bags, where they often defecated. We
 then collected feces from bags with sterilized tweezers.
 After collection, sample containers were placed on ice and
 combined into composite samples by species. The composite samples were
 inoculated with <em>E. coli </em>RM14721NR – a non-pathogenic,
 spontaneous nalidixic acid and rifampicin resistant mutant derived from
 <em>E. coli </em>RM14721 (<em>E. coli</em>,
 hereafter) – which is an isolate from a lettuce field in Yuma, AZ (Carter
 & Pham, 2018). This mutant strain of <em>E. coli
 </em>is not naturally occurring in avian or soil fecal samples. The
 inoculum was prepared by growing <em>E. coli </em>in 5mL of
 tryptic soy broth (TSB) for 18h at 37°C with shaking (200 RPM) the day
 prior to fecal collection. The inoculum was mixed into Wild Turkey or
 Western Bluebird feces at a ratio of 1:20 (v/m) to achieve a final
 <em>E. coli </em>concentration of ~ 1×10<sup>7
 </sup>CFU/g feces, which is within the range of <em>E. coli
 </em>concentrations found in wild bird feces (Smith 2020). We
 quantified the inoculation broth’s <em>E. coli
 </em>concentration by plating and counting CFUs on TSA agar plates
 that were incubated at 37°C for 24 h. We then calculated the concentration
 of <em>E. coli</em> within each fecal sample (fecal
 <em>E. coli </em>concentration<em> =</em>
 inoculation concentration × total fecal mass). After
 inoculation, Western Bluebird aggregated fecal matter was subdivided into
 two size classes (0.03g and 0.06 g; N = 32 and 41, respectively), and Wild
 Turkey aggregated fecal matter was subdivided into four classes (0.03g,
 0.06g, 2.00g, and 4.75g; N = 52, 48, 52, and 52, respectively). Masses
 were chosen to correspond to the average fecal mass of (1) a small
 songbird (e.g., Yellow-rumped Warbler, <em>Setophaga
 coronata</em>), (2) a Western Bluebird, (3) a juvenile Wild Turkey,
 and (4) an adult Wild Turkey, based on samples obtained from 30 warblers,
 15 bluebirds, and 27 turkeys. Feces were then transported to the UC Davis
 Student Farm – an organic, experimental, and teaching farm— and placed in
 the open environment on one of three substrates within four hours of
 collection: lettuce (<em>Lactuca sativa</em>; butterhead
 variety), organic soil, or black plastic mulch (commonly used to manage
 weeds and decrease soil water loss in produce fields). Three temperature
 and humidity loggers were placed on the lettuce, soil, and plastic mulch
 (HOBO Data Logger, Onset, MA, USA) to measure abiotic conditions. We
 collected samples after 1–30 days (64 samples after 1–2 days; 63 after 3–5
 days; 66 after 6–8 days; 32 after 14–15 days; and 52 after 21–30 days;
 total= 277 samples). If fecal samples had not disintegrated, we collected
 the sample and surrounding substrate with sterile tweezers and transported
 them on ice to the USDA Western Regional Research Center in Albany,
 California. To quantify the amount of <em>E.
 coli</em> remaining, we combined ~10g of feces and substrate
 (lettuce leaves, plastic, or soil) with 90ml phosphate buffered saline
 (PBS) in a blender jar and blended them with an Osterizer Beehive blender
 on high speed for 60 seconds. The solution was serially diluted in PBS and
 plate counted on MacConkey agar plates containing 50mg/l nalidixic acid
 and 100mg/l rifampicin after incubation at 30°C for 24h. This assay only
 allowed growth of the selected for the nalidixic acid and rifampicin
 resistant specific mutant strain of <em>E. coli</em> added at
 the point of fecal inoculation, but inhibited the growth of any indigenous
 <em>E. coli </em>present in the fecal samples. Any other
 naturally-occurring <em>E. coli</em> that may have been
 initially present in bird feces would not be able to grow on the nalidixic
 acid/rifampicin media and thus was not included in final <em>E.
 coli</em> counts. We enriched samples that showed no growth by
 adding 1ml of the blended sample into 9ml TSB containing 50mg/l nalidixic
 acid and 100mg/l rifampicin. Samples were then incubated at 30˚C for 24h
 (shaking 200 rpm), plated onto MacConkey agar plates containing 50mg/l
 nalidixic acid and 100mg/l rifampicin, and incubated for 18h and examined
 for growth. If the sample showed <em>E. coli </em>growth after
 enrichment, it was assigned a value of 1x10<sup>3</sup> CFUs
 (i.e., the limit of detection). Otherwise, it was designated as
 <em>E. coli </em>negative. 2.2
 <em>Greenhouse experiments</em> We
 implemented greenhouse experiments to quantify <em>E.
 coli</em> survival in feces from different bird species. Greenhouse
 experiments were necessary as feces from different bird species were
 available in different seasons and abiotic conditions needed to be
 standardized as much as possible. Specifically, we used feces from 10 bird
 species collected from December 2022 through May 2023 (Table S1; See
 supplementary methods for fecal collection protocols). Species varied in
 sample sizes due to the unpredictable nature of sample collection.
 Together, our species have geographic wide ranges, represent six taxonomic
 orders, vary in body size and thus fecal deposit size, are found in
 farming environments, and exhibit a variety of migratory patterns. The
 average fecal mass produced naturally by each species was determined by
 placing feces in airtight containers (sterile vials or plastic bags)
 within 30 minutes of defecation and weighing feces within 5 hours of
 collection. Captured birds were also weighed to relate fecal mass to bird
 mass. For uncaptured species (<em>e.g.,</em> Wild Turkeys), we
 obtained an estimate of bird mass from the literature (Tobias et al.,
 2022). Samples were transported to the USDA Western
 Regional Research Center on ice. As above, we aggregated samples by
 species and then inoculated feces with <em>E. coli</em>, using
 the same methods described above. To disentangle whether fecal mass or
 species identity <em>per </em>se influenced <em>E.
 coli</em> survival, we subdivided feces into the average natural
 size for the species (0.03 – 9.8g) or a standardized mass (0.08g; or the
 average mass of a common songbird: the White-crowned Sparrow;
 <em>Zonotrichia leucophrys</em>). Within six hours of fecal
 collection, we placed the samples on lettuce from the same seed stock in a
 greenhouse, alongside a temperature/humidity logger. After three days, we
 collected the samples and the amount of <em>E. coli</em>
 remaining was quantified as described above. All methods were approved
 under University of California, Davis Institutional Animal Care and Use
 Committee Protocol #22562, California Permit S-212650004-21266-001, and
 Federal permit 24033. <em>2.3 Statistical
 analyses of pathogen survival experiments</em>
 First, we modeled temperature and humidity as a function of
 substrate to determine whether microclimates differed between lettuce,
 soil, and plastic mulch. Both variables were modeled with Gaussian
 distributions, including day of year as a covariate.
 Next, we implemented Generalized Linear Mixed-effect Models
 (GLMMs) to analyze <em>E. coli</em> survival. For the field
 experiment, we modeled <em>E. coli</em> survival (presence or
 absence of <em>E. coli</em>; binomial distribution) and the
 percent of original <em>E. coli</em> remaining (final divided
 by initial <em>E. coli</em> concentrations; Gaussian
 distribution with a log-link) as response variables. Both response
 variables were modeled as a function of the number of days in the field,
 fecal mass category, substrate (lettuce, soil, or plastic), and species
 (Western Bluebird or Wild Turkey). We did not include temperature or
 humidity in the field models to avoid collinearity (as a result, all
 models had variance inflation factors less than 1.3). Interactions between
 mass and substrate as well as mass and days in the field were initially
 tested, but then omitted as interactions were not significant. Higher
 <em>E. coli</em> survival could reflect variation in initial
 <em>E. coli</em> abundances. We thus additionally explored
 including either initial <em>E. coli</em> concentration or
 total initial <em>E. coli</em> abundance as covariates in
 survival models (i.e., <em>E. coli</em> presence/absence
 models). Finally, we included ‘sampling batch’ as a random
 intercept. For the greenhouse experiment, we modeled
 the percent of original <em>E. coli</em> remaining as a
 function of fecal mass, fecal mass squared (<em>i.e.,</em> a
 quadratic term), species identity, temperature (°C), and relative
 humidity, using a Gaussian distribution with a log-link to meet
 assumptions of normality. To test for the effect of explanatory power of
 species identity versus fecal mass, we created two additional models: one
 modeling the percent of original <em>E. coli</em> remaining as
 a function of fecal mass, temperature, and relative humidity only and
 another as a function of species, temperature, and relative humidity. We
 included batch as a random intercept. We compared models using deviance
 information criterion (DIC) and considered models better if ∆DIC &gt;
 2. All models were implemented in a Bayesian framework
 using JAGS in R (R Core Team, 2019). For each model, three chains of
 50,000 iterations were thinned by 10 with a burn-in of 10,000, resulting
 in a posterior sample of 12,000. Parameter convergence was confirmed via
 visual inspection of traceplots as well as requiring a Gelman–Rubin
 statistic &lt;1.1 (Gelman et al., 2013). Parameters were considered
 strong predictors (i.e., significant) if the 95% Bayesian Credible
 Intervals (BCI) did not overlap zero.
 <em>2.4</em> <em>Avian point counts and fecal
 transects</em> We conducted avian censuses and
 fecal contamination surveys to quantify and assess bird intrusion and
 fecal contamination across 29 organic farms growing leafy greens. We
 completed the point counts in the California Central Coast as it is one of
 the largest leafy-green producing regions in the U.S. (Karp et al., 2015).
 In total, 18, 18, and 11 farms were visited three times in summer
 (5/26/2022-7/23/2022), three times in fall (9/14/2022-11/18/2022), and
 three times in winter (2/15/2023-4/20/2023), respectively. At each farm,
 we established point-count sites in fields growing lettuce (N= 274 total),
 with 3-8 sites per farm, depending on farm size (mean 5.8 sites). Within
 farms, point-count sites were established a median of 220m apart from one
 another (inter-quartile range: 120 – 447m). The same
 expert observer conducted all points counts within the season. Surveys
 occurred near harvest (<em>i.e., </em>the highest risk period
 for fecal contamination), except in winter (when surveys occurred at the
 seedling stage). Surveys began at sunrise, with 1-2 farms surveyed per
 day. Observers waited 5 minutes before beginning the count, and then,
 during each 10-minute point count, recorded all birds heard or seen within
 the 50 m survey radius, noting whether birds were physically contacting
 crops. To quantify fecal contamination, observers
 established three 25m transects on each farm in the same fields as the
 point counts. Transects were located at a field edge, in a field interior
 (up to 500 m into a crop field), and halfway in between. Observers visited
 the transects once per season (N= 141 transects). Transects were divided
 into 25, 1m<sup>2</sup> adjacent quadrats, and the number and
 location (<em>i.e., </em>on crop or soil) of all encountered
 bird feces were noted for each quadrat. To estimate the amount of area
 that would fall within 1m<sup>2</sup> no-harvest buffers, we
 calculated the fraction of quadrats with fecal samples. Observers also
 collected up to 10 feces per transect. We then used molecular methods to
 identify birds that defecated on lettuce (see supplementary methods). If
 10 feces could not be located, then observers scoured adjacent areas until
 they collected 10 samples. In total, 1075 samples were collected (N= 373
 from fecal transects and 702 collected from other areas), 277 of which
 were identified to the originating wild bird species.
description: # Assessing foodborne pathogen survival in bird feces to co-manage farms
 for bird conservation, production, and food safety
 [https://doi.org/10.5061/dryad.nzs7h450x](https://doi.org/10.5061/dryad.nzs7h450x) ## Description of the data and file structure All data for associated publication are included. Specific data include (1) experimental data (experiment_field.csv) from the field experiment; (2) experimental data (experimentlab.csv) from the laboratory/greenhouse experiment; (3) raw point count (pointcount.csv) data, including birds observed and associated information; (4) bird occurrence found from sequencing analysis (fecal_sequencing.csv); and (5) fecal mass (fecal_mass.csv) and associated bird mass data. Descriptions of the procedures involved are included in the methods description and the resulting manuscript. ### Files and variables #### File: Spence\_Dryad\_Submission.zip **Description:** All data are stored in CVS files. Each file has an associated README csv, that provides a verbal description of each column. Missing data: NA