DOI: 10.5061/dryad.ns1rn8pz2
title: Ungrazed seminatural habitats around farms benefit bird conservation without enhancing infectious disease risks

description: Context   Conservation in working landscapes is critical for
 halting biodiversity declines and ensuring farming system sustainability.
 However, concerns that wildlife may carry foodborne pathogens has created
 pressure on farmers to remove habitat and reduce biodiversity, undermining
 farmland conservation. Nonetheless, simplified farming landscapes may host
 bird communities that carry higher foodborne disease risks.  
 Objectives   We analyzed the effects of local farming practices
 and surrounding landscapes on bird communities and food-safety risks
 across 30 California lettuce farms. Specifically, we sought to determine
 how farmland diversification affects bird diversity, fecal contamination,
 and foodborne pathogen incidences, thereby identifying potential tradeoffs
 between managing farms for bird conservation versus food safety.
   Methods   We surveyed birds at 227 point-count
 locations, quantified fecal contamination along 120 transects, and assayed
 601 bird feces for pathogenic E. coli, Campylobacter spp., and Salmonella
 spp. We then used hierarchical models to quantify effects of farm
 management and landscape context on bird communities and food-safety
 risks.   Results   Surrounding ungrazed seminatural
 areas were associated with higher bird diversity, more species of
 conservation concern, and fewer flocks that may increase risks from
 foodborne pathogens. In contrast, on-farm diversification practices and
 surrounding grazing lands offered weaker bird conservation benefits.
 Surrounding grazed lands were associated with more potentially pathogenic
 bird feces in crop fields.     Conclusions
   Our results suggest that habitat conservation around produce
 farms could support bird conservation without increasing foodborne
 pathogens, especially on farms further from grazing lands. Thus,
 interventions that diversify farming systems offer potential to
 simultaneously conserve biodiversity and provide safe food for human
 consumption.
description: <em>Study region</em> Our work
 focused on three counties in the California Central Coast (<em>i.e.,
 </em>Santa Cruz, San Benito, and Monterey Counties), one of the most
 productive and economically-important agricultural regions in the United
 States, especially for fresh produce [1]. Across this region, we selected
 30 organic farms as study sites, with farms defined as contiguous lands
 managed by a single grower or operation. Though farmers often grew many
 crops (see Table S1 for farm summary statistics), all study sites included
 lettuce. Lettuce was chosen as our focal crop because prominent foodborne
 disease outbreaks have been linked to leafy greens, making them a focus of
 food-safety regulations [2]. In addition, lettuce ranked as the most
 important agricultural commodity in Monterey and San Benito Counties [3,4]
 and the seventh most important agricultural commodity in California in
 2020 (total value, production, and acreage: ~US$2.3 billion, ~3.3 million
 tons, and ~200,000 acres, respectively), with California leading the
 nation in its production (75.8% of U.S. receipts, [1].
 The Central Coast region experiences a temperate Mediterranean
 climate and exists as a landscape mosaic of large monoculture farms, small
 diversified farms, grazing lands, and other seminatural habitats
 (<em>e.g., </em>grasslands, shrublands, forest, riparian
 habitat, and wetlands). To study the effects of on-farm management
 practices and landscape context, we selected farms that independently
 varied in local diversification, the proportion of surrounding grazed
 land, and the proportion of surrounding ungrazed semi-natural habitats,
 leveraging aerial imagery from the National Agricultural Imagery Project
 (NAIP, 30 m resolution). We limited our study to organic farms because
 organic farmers (1) are constrained in which agrochemicals can be applied
 and thus often rely on diversification practices such as crop rotations
 and preserving non-crop vegetation to maintain soil fertility and control
 pests, (2) are subject to intense scrutiny regarding food-safety
 requirements, and (3) represent a growing share of the lettuce market,
 with ~22% of California lettuce acreage currently in organic production,
 approximately half of which occurs in the Central Coast [1,5].
 <em>Bird point count surveys</em>
 We surveyed birds on each farm using 10 minute, 50 m fixed-radius
 point count surveys, repeated three times over consecutive days from
 May-July in 2019 and 2020. Each year we surveyed 20 farms. Some farms were
 surveyed both years (N= 10) and others (N= 20) in only one year due to
 lettuce crop rotations. Point count locations were separated by at least
 100m (range: 100 m-1514 m, mean=459 m; [6]). Thus, the number of point
 counts per farm varied by farm size (point counts: range: 3-6, mean=5.7;
 point counts per 10 hectares: range: 0.1-11.4, mean=3.1). At least half of
 the count locations on each farm were centered in lettuce; the other half
 were located in other dominant crops (<em>e.g.,</em>
 strawberry, squash, broccoli).  All surveys were conducted by the same
 skilled observer (T. Glaser), primarily between sunrise and 10:30 am and
 always in the absence of rain or heavy fog. All individuals seen or heard
 within the survey radius were identified to species and recorded,
 alongside key covariates that may influence bird detectability
 (<em>e.g., </em>time of day, day of year, wind speed,
 temperature, presence of loud noises, <em>etc.</em>). We also
 noted the substrate (<em>e.g.,</em> crop field, tree, fence,
 <em>etc.</em>) associated with each bird
 observation. <em>Flocking birds and species-level
 traits</em> Flocking birds could increase
 food-safety risks by leaving concentrated deposits of fecal contamination
 on farms. We thus created a binary response variable to indicate whether
 flocks were observed during each survey. To reflect food-safety risks, we
 excluded observations of birds in trees (which were less likely to
 interact with the crops) and auditory detections when an individual’s
 exact location was unknown (<em>e.g.</em>, crop field vs.
 tree). We also excluded swallows because they are usually observed flying
 above crop fields but seldom contact crops. Then, we defined flocks as a
 group of 7 or more individuals of the same species observed during a
 survey. We also collected several species-level traits
 that represent the relative food-safety risk or conservation value of each
 species. First, we noted whether we observed flocking behavior in each
 species (using the criteria listed above). Second, we assigned pathogen
 prevalence traits to each species using data from [7]. These traits
 reflect the likelihood that each species on farms in the western U.S.
 would test positive for <em>Campylobacter</em> spp.,
 <em>E.coli </em>virulence genes, and
 <em>Salmonella</em> spp. Finally, we collected conservation
 scores for each species from the 2016 State of North America’s Birds
 report [8], which incorporates information on population size,
 distribution, and other components of vulnerability. Because this report
 focused on native species only, we assigned the lowest conservation score
 possible to non-native species. <em>Local farm
 management practices and landscape context</em>
 We quantified the level of local (on-farm) diversification
 associated with each 50 m radius point-count location by building a
 composite index from measurements of crop diversity, non-crop vegetation
 cover, and vegetation complexity (Supplemental methods). We also
 documented the total length of fencing, where birds often perch, in each
 point count radius. Next, we manually digitized seminatural habitat
 (forest, shrubland, grassland, pasture, and wetlands) from NAIP 2016
 imagery within a 1km radius of each sampling location using ArcMap 10.3.1
 (ESRI, Redlands, CA, USA). To assess the effects of different types of
 seminatural habitat, we overlaid spatial grazeable land data from the
 Farmland Mapping and Monitoring Program [9] on top of our land-cover map.
 Grazeable land, or land where vegetation is suitable for grazing livestock
 in California, was dominated by grasslands and pastures. We thus further
 subdivided our maps into grazed seminatural habitat (areas of overlap
 between our seminatural habitat map and grazeable lands) versus ungrazed
 seminatural habitat. <em>Bird fecal transects and
 pathogen testing</em> We surveyed bird fecal
 contamination along three parallel, 20 m transects in lettuce crops on 20
 farms per year from May-July of 2019 and 2020 (10 farms sampled in 2019 or
 2020 only, 10 farms sampled in both years). Transects were located at the
 farm edge with the most seminatural habitat, as far from a farm edge as
 possible (up to 500 m from the edge), and halfway in between. We recorded
 the number of bird feces within 20, 1m<sup>2</sup>, adjacent
 quadrats centered along each transect. In 2019 only, we also collected 10
 fecal samples from each transect, or extended sample collection beyond the
 transect as needed to obtain 10 samples. We placed samples in sterile
 cryotubes filled with 100% ethanol, immediately froze them in a liquid
 nitrogen dewar, and kept samples frozen until DNA extraction. We screened
 bird fecal samples for <em>E.coli</em> virulence genes,
 <em>Campylobacter</em> spp., and
 <em>Salmonella</em> spp. using multiplex polymerase chain
 reactions. Although Shiga-toxin producing <em>E. coli</em>
 that carries the <em>stx1</em> and/or
 <em>stx2</em> genes is responsible for causing disease in
 humans, other ‘virulence genes’ can contribute to pathogenesis.
 <em>E.coli</em> virulence genes carried by birds can be
 transferred between bacterial strains, and when combined with
 Shiga-toxins, can result in pathogenic <em>E.coli</em> strains
 that cause severe disease in humans ([10,11]; see Supplemental
 methods). <em>Statistical
 analyses</em> We used occupancy and N-mixture
 models that account for variation in detection probability to estimate
 species presence/absence and abundance, respectively, and to quantify
 changes in bird communities among sites [12–14]. Specifically, we created
 three types of N-mixture and occupancy models to (1) estimate the
 abundance/occupancy of each species at each site, (2) understand how local
 and landscape diversification affects species- and community-level
 abundance/occupancy, and (3) measure how species traits interact with
 diversification variables to affect abundance/occupancy (Supplemental
 methods). We considered community-level parameters to be statistically
 significant when their 95% Bayesian credible interval did not overlap zero
 (BCI; the range between the 2.5 and 97.5<sup>th</sup>
 percentiles of the posterior distribution). In contrast, we considered
 species-level parameters to be statistically significant when their 90%
 BCI did not overlap zero, as species-level effects are estimated with
 lower sample sizes and thus less power [15]. We also determined whether
 species varied in their responses to local diversification, landscape
 context, and/or their interactions by examining the variation (s
 parameter) associated with each slope term (Supplemental methods).
 Responses were considered to vary significantly among species when the 90%
 highest posterior density interval of s did not overlap 0 (as in
 [15]). To quantify bird conservation metrics, we
 extracted the number of individuals (and occupancy state) for each species
 at each site across 3000 posterior iterations of the N-mixture and
 occupancy models. We then calculated the species richness, Shannon
 diversity, and total bird abundance for each point-count location and each
 posterior iteration. To quantify the “conservation value” of each
 community, we extracted posteriors from the occupancy model and then
 calculated the average conservation score across all species estimated to
 occur at each site. Finally, we calculated the median and inverse
 interquartile range of each metric across all posteriors (see Supplemental
 methods for more information). We measured pathogen
 risk in several ways. First, we quantified the number of feces detected
 within each 20 m transect (<em>i.e.,</em> fecal density).
 Next, we created binary responses to indicate whether each of the assayed
 fecal samples tested positive for any pathogen. Finally, we quantified
 pathogen risk as the product of the total number of feces per 20 m
 transect and the fraction of feces testing positive for
 <em>Campylobacter </em>spp., <em>Salmonella
 </em>spp., or any <em>E.coli</em> virulence gene. We
 divided this number by 20 to ultimately arrive at an estimate of
 ‘potentially pathogenic fecal density’ or the number of potentially
 pathogenic feces per m<sup>2</sup>. We used
 generalized linear mixed models (GLMM) to test the effects of local
 diversification and surrounding seminatural habitat on bird conservation
 and pathogen risk metrics. All models included fixed effects of local
 diversification, grazed and ungrazed seminatural habitat within 1 km, and
 two interactions between local diversification and grazed and ungrazed
 seminatural habitat. Fecal density and pathogen risk models also included
 distance from the fecal transect to the nearest non-crop edge as a fixed
 effect to account for spatial variation in bird activity. Pathogen
 prevalence, fecal density, and pathogen risk models included ‘day of year’
 to account for seasonal effects that may impact pathogen exposure. Bird
 conservation models included the inverse of the interquartile range of
 richness, abundance, diversity, or conservation score across posteriors as
 model ‘weights.’ All models included a random intercept of farm to account
 for spatial dependence of individuals captured on the same farm.
 We used linear mixed models for diversity, species richness, and
 abundance estimated from N-mixture models; conservation score estimated
 from occupancy models; fecal density; and pathogen risk. We used binomial
 GLMMs with a log link function for the probability of flocks occurring and
 pathogen prevalence. We transformed some variables (fourth-root: richness,
 abundance; log: fecal density +1, pathogen risk + 0.1) to meet model
 assumptions, scaled covariates by subtracting by the mean and dividing by
 the standard deviation, and verified that models did not display
 multicollinearity (Pearson correlation coefficient &lt;0.6). We ran
 models with the glmmTMB package [16] and performed model selection with
 the MuMIn package [17] in R. To do so, we first identified the
 best-supported models within 2 AIC of the top model and then used a model
 averaging approach to assess variable significance within these top models
 [18]. Finally, we visualized and analyzed community
 turnover between sites by first extracting the median abundance of each
 species at each site across all 3000 posteriors from N-mixture models and
 then calculating the community dissimilarity between each pair of sites
 (Bray-Curtis dissimilarity). We visualized differences in community
 composition between sites via Non-Metric Multidimensional Scaling and then
 used Permutational Multiple Analysis of Variance (PERMANOVA) with the
 ‘adonis’ function in the ‘vegan’ library [19], with farm as a blocking
 factor, to assess the influence of diversification on species
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description: # Ungrazed seminatural habitats around farms benefit bird conservation
 without enhancing infectious disease risks All data for associated
 publication (title above) are included. Specific data include: (1) raw
 point count data, including birds observed and associated information; (2)
 point count covariates, including site descriptors; (3) species traits
 information for every detected species; (4) estimated occupancy data that
 resulted from applying occupancy models to point count data; (5) estimated
 abundance data that resulted from applying N mixture models to point count
 data; (6) derived bird diversity data, obtained after analyzing the
 abundance results from N mixture models; (7) raw pathogen occurence and
 fecal density data from transects; (8) aggregated pathogen prevalence
 data. Descriptions of the procedures involved are included in the methods
 description and in the resulting manuscript. ## Description of the Data
 and file structure All data are stored in CVS or RDS files. Each file has
 an associated README csv, that provides a verbal description of each
 column. RDS files are used for 3 dimensional arrays, with cells detailing
 bird occurance or abundance. Dimensions include: species, site, and
 posterior from the abundance or N mixture model.